diff --git a/23-049/23-049.err.html b/23-049/23-049.err.html index 29fa486..5baef59 100644 --- a/23-049/23-049.err.html +++ b/23-049/23-049.err.html @@ -10,30 +10,30 @@

Metanorma XML Style Warning

<th valign="middle" align="left">Role</th> </tr> </thead> <tbody> <tr> <td valign="middle" align="left">Chris Little</td> -000399_40462012-5d3c-c456-b1a6-563c45d68392Figure should have title
<figure id="_40462012-5d3c-c456-b1a6-563c45d68392">
-  <image src="data:image/png;base64,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" mimetype="image/png" id="_ade677f5-ded8-22c7-3f73-e8f76193739c" height="auto" width="auto"/>
+000430fig-interval-relationsFigure should have title
<figure id="fig-interval-relations">
+  <image src="data:image/jpeg;base64,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" mimetype="image/jpeg" id="_a393d3c6-74b6-8a8a-5178-e3c221d76a41" height="auto" width="auto"/>
 </figure>

-000425fig-interval-relationsFigure should have title
<figure id="fig-interval-relations">
-  <image src="data:image/jpeg;base64,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" mimetype="image/jpeg" id="_df317d07-3c79-7508-6a9c-82370d23d83a" height="auto" width="auto"/>
-</figure>

-000485_other_​regimesHanging paragraph in clause
<clause id="_other_regimes" obligation="normative">
+000497_other_​regimesHanging paragraph in clause
<clause id="_other_regimes" obligation="normative">
 <title>Other Regimes</title>
-<p id="_6eba5fe8-c4f0-696b-ffbe-d947d740123e">There are other regimes, which are out of scope of this Abstract Specification. This could include local solar time, which is useful, for example, for the calculation of illumination levels and the length of shadows on aerial photography, or relativistic time.</p>
+<p id="_341e36d4-df29-66c3-b089-44f6f1764c96">There are other regimes, whose detailed description are out of scope of this Abstract Specification. This could include local solar time, which is useful, for example, for the calculation of illumination levels and the length of shadows on aerial photography, or relativistic time for very fast moving features.</p>
 
 <clause id="_accountancy" obligation="normative">

-000556ordinal_​rs_sectionHanging paragraph in clause
<clause id="ordinal_rs_section" obligation="normative">
+000561_3ab1a470-6b77-4525-8f00-dc9cace6bc08Figure should have title
<figure id="_3ab1a470-6b77-4525-8f00-dc9cace6bc08">
+  <image src="data:image/png;base64,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" mimetype="image/png" id="_41c8889a-7458-f7f0-43b2-05b27e725d9f" height="auto" width="auto"/>
+</figure>

+000579ordinal_​rs_sectionHanging paragraph in clause
<clause id="ordinal_rs_section" obligation="normative">
 <title>Ordinal Temporal Reference Systems</title>
-<p id="_74bf19e9-46f3-445f-76ec-8ea02e894d04">An OrdinalTemporal Reference System has a well-ordered finite sequence of events against which other events can be compared.</p>
+<p id="_4d55e6d8-1edc-14a2-74c9-dc4ccaec2e89">An ordinal temporal reference system has a well-ordered finite sequence of events against which other events can be compared.</p>
 
-<p id="_031fe69b-001a-1380-b4e6-559805b3573b">An Ordinal Temporal Reference System is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:</p>

-000610calendar_​sectionHanging paragraph in clause
<clause id="calendar_section" obligation="normative">
+<p id="_4e6a38e5-578a-6a83-b81e-a983e7432b0d">An ordinal temporal reference system is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:</p>

+000636calendar_​sectionHanging paragraph in clause
<clause id="calendar_section" obligation="normative">
 <title>Calendar Reference Systems</title>
-<p id="_c82b1fc1-e70a-fa8e-d5c1-245f17707aff">Calendars combine different timescales and their clocks and units of measure, and other events, to make a complex timeline against which events can be compared. Calculated algorithms are used to determine which instants of intervals on the compound timeline are identified and labeled.</p>
+<p id="_17f894cb-7728-f82b-fbdc-d9018fb79f28">Calendars combine different timescales and their clocks and units of measure, and other events, to make a complex timeline against which events can be compared. Calculated algorithms are used to determine which instants or intervals on the compound timeline are identified and labeled.</p>
 
-<p id="_b6a05f73-eaae-90e0-b5ee-4a42981b6a89">A Calendar is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:</p>

-000684_discrete_​and_​continuous_time_​scalesHanging paragraph in clause
<clause id="_discrete_and_continuous_time_scales" obligation="normative">
+<p id="_06787164-8d7b-6f11-3805-25004fab70dc">A calendar is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:</p>

+000714_discrete_​and_​continuous_time_​scalesHanging paragraph in clause
<clause id="_discrete_and_continuous_time_scales" obligation="normative">
 <title>Discrete and Continuous Time Scales</title>
-<p id="_8b2683c9-3654-a989-b556-61fab295d98c">A <xref target="clock_section">clock</xref> may be a regular, repeating, physical event, or ‘tick’, that can be counted. The sequence of tick counts form a discrete (counted) <xref target="timescale_section">timescale</xref>.</p>
+<p id="_e305256b-7c72-2d66-7bd1-1e211b723e64">A <xref target="clock_section">clock</xref> may be a regular, repeating, physical event, or tick, that can be counted. The sequence of tick counts form a discrete (counted) <xref target="timescale_section">timescale</xref>.</p>
 
 <p id="_5f0f8ace-9218-8cf3-5bcf-53de0b0a489c">Some <xref target="clock_section">clocks</xref>  allow the measurement of intervals between ticks, such as the movement of the sun across the sky. Alternatively, the ticks may not be completely distinguishable, but are still stable enough over the time of applicability to allow measurements rather than counting to determine the passage of time. These clocks generate a continuous (measured) <xref target="timescale_section">timescale</xref>.</p>

 
diff --git a/23-049/23-049.html b/23-049/23-049.html
index 271c85b..fbe6567 100644
--- a/23-049/23-049.html
+++ b/23-049/23-049.html
@@ -1322,10 +1322,10 @@
 
     

       Submission Date:  2023-05-23
-      Approval Date:  2023-05-23
-      Publication Date:  2023-12-13
+      Approval Date:  2024-02-14
+      Publication Date:  2024-02-14
       External identifier of this OGC® document: http://www.opengis.net/doc/AS/temporal-conceptual-model/1.0
-      Internal identifier of this OGC® document: 23-049
+      Internal identifier of this OGC® document: 23-049r1
       
       
     

@@ -1367,7 +1367,7 @@
 
 

   
-    
+    
@@ -1412,39 +1412,39 @@
 
 
@@ -1459,118 +1459,126 @@
 

 
 

-

I.  Abstract
The primary goal of the Abstract Conceptual Model for Time is to establish clear concepts, their relationships, and terminology.
The fundamental concepts of events, clocks, timescales, coordinates and calendars have been long established, but there is no clear, straightforward defining document. This Abstract Specification provides clear consistent definitions of the fundamental concepts and terminology. The conceptual model enables advantages and disadvantages of adopting a particular technological approach to be identified and provides an opportunity for communities to build consistent, interoperable representations regardless of implementation.
Traditionally, geospatial communities used 2D coordinates and the vertical (third dimension) and temporal aspects were considered attributes rather than valid components of coordinate systems. In an increasingly dynamic, faster and multidimensional world, much confusion and lack of interoperability has occurred because of inconsistent approaches to defining and expressing time. Various international bodies expended considerable effort to establish the Gregorian Calendar as a consistent timeline. The Gregorian Calendar suffices for low precision applications, such as to the nearest minute, but not so when second or sub-second accuracy is required. For example, there have been differing practices and no consensus on whether leap seconds should be part of the Gregorian timeline.
This document is consistent with ISO 19111:2019 and W3C Time Ontology in OWL.
II.  Keywords
The following are keywords to be used by search engines and document catalogues.
ogcdoc, OGC document, abstract specification, conceptual model, time, temporal referencing, referencing by coordinates, calendar, clock, timescale

III.  Preface
When OGC standards involve time, they generally refer to the ISO documents such as ISO 19108:2002 (now largely superseded), ISO 19111:2019, ISO 8601, and their freely available OGC equivalents, such as OGC 18-005r4 (the equivalent to ISO 19111:2019).
Much effort over decades has gone into establishing complex structures to represent calendar based time, such as the ISO 8601 notation, and many date-time schemas. A consequence of this effort is that many end-users and developers of software use calendar based “coordinates”, with the associated ambiguities about underlying algorithms, imprecision and inappropriate scope.
The aim of this Abstract Specification is to establish clear concepts and terminology, so that people are well aware of the advantages and disadvantages of adopting a particular technological approach to time and then perhaps build better, more appropriate, interoperable systems for their use cases.
IV.  Security Considerations
This Abstract Specification does not place any constraints on application, platform, operating system level, or network security.
V.  Submitting Organizations
The following organizations submitted this Document to the Open Geospatial Consortium (OGC):
  • U.K. Met Office
    • 
+


I.  Abstract
The primary goal of the Abstract Conceptual Model for Time is to establish clear concepts, their relationships, and terminology.
The fundamental concepts of events, clocks, timescales, coordinates and calendars have been long established, but there is no clear, straightforward defining document. This Abstract Specification provides clear consistent definitions of the fundamental concepts and terminology. The conceptual model enables advantages and disadvantages of adopting a particular technological approach to be identified and provides an opportunity for communities to build consistent, interoperable representations regardless of implementation.
Traditionally, geospatial communities used 2D coordinates and the vertical (third dimension) and temporal aspects were considered attributes rather than valid components of coordinate systems. In an increasingly dynamic, faster and multidimensional world, much confusion and lack of interoperability has occurred because of inconsistent approaches to defining and expressing time. Various international bodies expended considerable effort to establish the Gregorian Calendar as a consistent timeline. The Gregorian Calendar suffices for low precision applications, such as to the nearest minute, but not so when second or sub-second accuracy is required. For example, there have been differing practices and no consensus on whether leap seconds should be part of the Gregorian timeline.
This document is consistent with ISO 19111:2019 and W3C Time Ontology in OWL.
II.  Keywords
The following are keywords to be used by search engines and document catalogues.
ogcdoc, OGC document, abstract specification, conceptual model, time, temporal referencing, referencing by coordinates, calendar, clock, timescale

III.  Preface
When OGC Standards involve time, they generally refer to the ISO documents such as Geographic information Temporal schema ISO 19108:2002 (now largely superseded), Geographic information Referencing by coordinates ISO 19111:2019, Date and Time Format ISO 8601, and their freely available OGC equivalents, such as OGC Abstract Specification Topic 2: Referencing by coordinates  OGC 18-005r8 (the equivalent to ISO 19111:2019).
Over decades, much effort has gone into establishing complex structures to represent calendar based time, such as the ISO 8601 notation, and many date-time schemas. A consequence of this effort is that many end-users and developers of software use calendar based “coordinates”, with the associated ambiguities about underlying algorithms, imprecision and inappropriate scope.
The aim of this OGC Temporal Abstract Specification is to establish clear concepts and terminology. This is necessary so that people are aware of the advantages and disadvantages of specifying or adopting a particular technological approach to time and then perhaps build better, more appropriate, interoperable systems for their use cases.
IV.  Security Considerations
This Abstract Specification does not place any constraints on application, platform, operating system level, or network security.
V.  Submitting Organizations
The following organizations submitted this Document to the Open Geospatial Consortium (OGC):
  • U.K. Met Office
    • 
 
  • HeazelTech
    • 
-
  • Ribose Inc.
VI.  Submitters
All questions regarding this submission should be directed to the editor or the
-submitters:
Document number:23-049
Document number:23-049r1

     
Document type:OGC Abstract Specification Topic

     
Document subtype:

     
Document stage:Candidate SWG Draft

 
 
NameOrganizationRole
Chris LittleU.K. Met OfficeEditor
Chuck HeazelHeazelTechContributor
Ronald TseRibose Inc.Contributor
1.  Scope
This document defines the major underlying concepts regarding time. It does not define any concrete temporal reference systems or give detailed guidance on implementations.
2.  Conformance
According to OGC Policy “The detail of the Abstract Specification shall be sufficient to provide normative references, including models, and technical guidelines as a foundation for Standards. Each Topic, to the extent possible, provides unambiguous normative and informative information that allows for implementation of Standards in software.
The level of detail of the Abstract Specification is at the discretion of the TC as reflected by the actual content that is approved for inclusion in the document itself.”
This Abstract Specification does not include any specific requirements or conformance classes. However, it does include normative references and a normative Unified Modeling Language (UML) model. Conformance is demonstrated through inclusion of the normative references in any derivative specification and by basing any derived conceptual model on the abstract model provided in this Standard.
The Clause 7 of this Abstract Specification uses UML to present conceptual schemas for describing the higher level classes of time and temporal reference systems. These schemas define conceptual classes that:
  1. may be considered to comprise a cross-domain application schema, or
    
+
  2. Ribose Inc.
VI.  Submitters
All questions regarding this submission should be directed to the editor or the
+submitters:
NameOrganizationRole
Chris LittleU.K. Met OfficeEditor
Chuck HeazelHeazelTechContributor
Ronald TseRibose Inc.Contributor
1.  Scope
This document defines the major underlying concepts regarding time. It does not define any concrete temporal reference systems or give detailed guidance on implementations.
2.  Conformance
According to OGC Policy “The detail of the Abstract Specification shall be sufficient to provide normative references, including models, and technical guidelines as a foundation for Standards. Each Topic, to the extent possible, provides unambiguous normative and informative information that allows for implementation of Standards in software.
The level of detail of the Abstract Specification is at the discretion of the TC as reflected by the actual content that is approved for inclusion in the document itself.”
This Abstract Specification does not include any specific requirements or conformance classes. However, it does include normative references and a normative Unified Modeling Language (UML) model. Conformance is demonstrated through inclusion of the normative references in any derivative specification and by basing any derived conceptual model on the abstract model provided in this Standard.
The Clause 8 of this Abstract Specification uses UML to present conceptual schemas for describing the higher level classes of time and temporal reference systems. These schemas define conceptual classes that:
  1. may be considered to comprise a cross-domain application schema, or
    
 
    2. 
-
  2. may be used in application schemas, profiles and implementation specifications.
    
+
  3. may be used in application schemas, profiles and implementation specifications.
    
 
    4. 
 
This flexibility is controlled by a set of UML types that can be implemented in a variety of manners. Use of
 alternative names that are more familiar in a particular application is acceptable, provided that there is a
 one-to-one mapping to classes and properties in this Abstract Specification.
The UML model in this Abstract Specification defines conceptual classes. Various software systems define
 implementations or data structures. All of these reference the same information content. The same
 name may be used in implementation classes as in the model, so that types defined in the UML model may be used
-directly in application schemas.
3.  Normative references

+directly in application schemas.
3.  Normative references

 
The following documents are referred to in the text in such a way that some or all of their content constitutes requirements of this document. For dated references, only the edition cited applies. For undated references, the latest edition of the referenced document (including any amendments) applies.

 
G. Klyne, C. Newman: IETF RFC 3339, Date and Time on the Internet: Timestamps. RFC Publisher (2002). https://www.rfc-editor.org/info/rfc3339.

 
ISO: ISO 8601, Data elements and interchange formats — Information interchange — Representation of dates and times. International Organization for Standardization, Geneva https://www.iso.org/standard/15903.html.

 
ISO: ISO 19111:2019, Geographic information — Referencing by coordinates. International Organization for Standardization, Geneva (2019). https://www.iso.org/standard/74039.html.

 
ISO/IEC: ISO/IEC 22989:2022, Information technology — Artificial intelligence — Artificial intelligence concepts and terminology. International Organization for Standardization, International Electrotechnical Commission, Geneva (2022). https://www.iso.org/standard/74296.html.

 
Allen, J. F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26, pp832-843 (1983).

-
Roger Lott: OGC 18-005r4, Topic 2 — Referencing by coordinates. Open Geospatial Consortium (2019). http://www.opengis.net/doc/AS/topic-2/5.0.

-
Cox, S., Little, C.: W3C Time Ontology in OWL. World Wide Web Consortium (2022) https://www.w3.org/TR/owl-time/

-
4.  Terms, definitions and abbreviated terms
This document uses the terms defined in OGC Policy Directive 49, which is based on the ISO/IEC Directives, Part 2, Rules for the structure and drafting of International Standards. In particular, the word “shall” (not “must”) is the verb form used to indicate a requirement to be strictly followed to conform to this document and OGC documents do not use the equivalent phrases in the ISO/IEC Directives, Part 2.
This document also uses terms defined in the OGC Standard for Modular specifications (OGC 08-131r3), also known as the ‘ModSpec’. The definitions of terms such as standard, specification, requirement, and conformance test are provided in the ModSpec.
For the purposes of this document, the following additional terms and definitions apply.
4.1.  Terms and definitions

+
Roger Lott: OGC 18-005r8, Topic 2 — Referencing by coordinates (Including corrigendum 1 and corrigendum2). Open Geospatial Consortium (2023). http://www.opengis.net/doc/AS/topic-2/6.0.

+
Cox, S., Little, C.: W3C Time Ontology in OWL. World Wide Web Consortium (2022) https://www.w3.org/TR/owl-time/.

+
4.  Terms, definitions and abbreviated terms
This document uses the terms defined in OGC Policy Directive 49, which is based on the ISO/IEC Directives, Part 2, Rules for the structure and drafting of International Standards. In particular, the word “shall” (not “must”) is the verb form used to indicate a requirement to be strictly followed to conform to this document and OGC documents do not use the equivalent phrases in the ISO/IEC Directives, Part 2.
This document also uses terms defined in the OGC Standard for Modular specifications (OGC 08-131r3), also known as the ‘ModSpec’. The definitions of terms such as standard, specification, requirement, and conformance test are provided in the ModSpec.
For the purposes of this document, the following additional terms and definitions apply.
4.1.  Terms and definitions

 
-
4.1.1. conceptual model

-
description of common concepts and their relationships, particularly in order to facilitate exchange of information between parties within a specific domain

+
4.1.1. clock

+
regularly repeating physical phenomenon that can be counted

 
 
- 
Note 1 to entry: A conceptual model is explicitly chosen to be independent of design or implementation concerns.

+
4.1.2. conceptual model

+
description of common concepts and their relationships, particularly in order to facilitate exchange of information between parties within a specific domain

 
-
4.1.2. coordinate

-
one of a sequence of numbers designating the position of a point

 
+ 
Note 1 to entry: A conceptual model is explicitly chosen to be independent of design or implementation concerns.

 
+
4.1.3. coordinate

+
one of a sequence of numbers designating the position of a point

 
 
- 
Note 1 to entry: In many coordinate reference systems, the coordinate numbers are qualified by units.
[SOURCE: ISO 19111:2019]

 
-
4.1.3. coordinate reference system; CRS

 
+ 
Note 1 to entry: In many coordinate reference systems, the coordinate numbers are qualified by units.
[SOURCE: ISO 19111:2019]

 
+
4.1.4. coordinate reference system; CRS

 
-
coordinate system (Clause 4.1.4) that is related to an object by a datum (Clause 4.1.7)

 
 
+
coordinate system (Clause 4.1.5) that is related to an object by a datum (Clause 4.1.8)

 
 
 
 
- 
Note 1 to entry: Geodetic and vertical datums are referred to as reference frames.
Note 2 to entry: For geodetic and vertical reference frames, the object will be the Earth. In planetary applications, geodetic and vertical reference frames may be applied to other celestial bodies.
[SOURCE: ISO 19111:2019]

 
-
4.1.4. coordinate system

-
set of mathematical rules for specifying how coordinates are to be assigned to points

+
+ 
Note 1 to entry: Geodetic and vertical datums are referred to as reference frames.
Note 2 to entry: For geodetic and vertical reference frames, the object will be the Earth. In planetary applications, geodetic and vertical reference frames may be applied to other celestial bodies.
[SOURCE: ISO 19111:2019]

+
+
4.1.5. coordinate system

+
set of mathematical rules for specifying how coordinates are to be assigned to points

 
 
  
[SOURCE: ISO 19111:2019]

 
-
4.1.5. datum
reference frame ADMITTED

+
4.1.6. datum
reference frame ADMITTED

 
 
 
-
parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate system (Clause 4.1.4)

+
parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate system (Clause 4.1.5)

 
 
  
[SOURCE: ISO 19111:2019]

 
-
4.1.6. epoch

+
4.1.7. epoch

 
 
-
<geodesy> point in time

+
<geodesy> point in time

 
 
 
 
 
 
- 
Note 1 to entry: In ISO 19111:2019, an epoch is expressed in the Gregorian calendar as a decimal year.
Example
2017-03-25 in the Gregorian calendar is epoch 2017.23. Other notations or reference systems are options.

+ 
Note 1 to entry: In ISO 19111:2019, an epoch is expressed in the Gregorian calendar as a decimal year.
Example
2017-03-25 in the Gregorian calendar is epoch 2017.23. Other notations or reference systems are options.

 
[SOURCE: ISO 19111:2019]

 
-
4.1.7. reference frame
datum ADMITTED

+
4.1.8. reference frame
datum ADMITTED

 
 
 
-
parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate system (Clause 4.1.4)

+
parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate system (Clause 4.1.5)

 
 
  
[SOURCE: ISO 19111:2019]

 
-
4.1.8. temporal coordinate reference system; TRS

+
4.1.9. temporal coordinate reference system; TRS

 
 
 
-
coordinate reference system (Clause 4.1.3) based on a temporal datum (Clause 4.1.10)

+
coordinate reference system (Clause 4.1.4) based on a temporal datum (Clause 4.1.11)

 
 
  
[SOURCE: ISO 19111:2019]

 
-
4.1.9. temporal coordinate system

+
4.1.10. temporal coordinate system

 
 
-
<geodesy> one-dimensional coordinate system (Clause 4.1.4) where the axis is time

+
<geodesy> one-dimensional coordinate system (Clause 4.1.5) where the axis is time

 
 
  
[SOURCE: ISO 19111:2019]

 
-
4.1.10. temporal datum

-
datum (Clause 4.1.7) describing the relationship of a temporal coordinate system (Clause 4.1.9) to an object

+
4.1.11. temporal datum

+
datum (Clause 4.1.8) describing the relationship of a temporal coordinate system (Clause 4.1.10) to an object

+
 
 
 
+ 
Note 1 to entry: The object is normally time on the Earth.
[SOURCE: ISO 19111:2019]

 
- 
Note 1 to entry: The object is normally time on the Earth.
[SOURCE: ISO 19111:2019]

-
4.2.  Abbreviated terms

+
4.1.12. tick

+
event which is a single occurrence of the regularly repeating physical phenomenon of a clock

+
+
4.2.  Abbreviated terms

 
 
BIPM
International Bureau of Weights and Measures

 
TAI
International Atomic Time

@@ -1578,128 +1586,135 @@ 
4.1.10. <
 
2D
2-dimensional

 
3D
3-dimensional

 

-
5.  Conventions
The normative provisions in this standard are denoted by the URI:
http://www.opengis.net/doc/AS/temporal-conceptual-model/1.0
All requirements and conformance tests that appear in this document are denoted by partial URIs which are relative to this base.
6.  Characteristics of an Abstract Conceptual Model
The terms and definitions clause in this Abstract Specification provides a short definition for “conceptual model”. This clause provides additional information on the OGC use of “conceptual model”.
A conceptual model organizes the vocabulary needed to communicate consistently and thoroughly about the know-how of a problem domain. The aim of a conceptual model is to express the meaning of terms and concepts used by domain experts to discuss the problem, and to find the correct relationships between different concepts.
A conceptual model:
  1. is a representation of a system, made of the composition of concepts which are used to help people know, understand, or simulate a subject the model represents. A documented conceptual model represents ‘concepts’ (entities), the relationships between them, and a vocabulary;
    
+
5.  Conventions
The normative provisions in this standard are denoted by the URI:
http://www.opengis.net/doc/AS/temporal-conceptual-model/1.0
All requirements and conformance tests that appear in this document are denoted by partial URIs which are relative to this base.
6.  Characteristics of an Abstract Conceptual Model
The terms and definitions clause in this Abstract Specification provides a short definition for “conceptual model”. This clause provides additional information on the OGC use of “conceptual model”.
A conceptual model organizes the vocabulary needed to communicate consistently and thoroughly about the know-how of a problem domain. The aim of a conceptual model is to express the meaning of terms and concepts used by domain experts to discuss the problem, and to find the correct relationships between different concepts.
A conceptual model:
  1. is a representation of a system, made of the composition of concepts which are used to help people know, understand, or simulate a subject the model represents. A documented conceptual model represents ‘concepts’ (entities), the relationships between them, and a vocabulary;
    
 
    2. 
-
  2. is explicitly defined to be independent of design or implementation concerns;
    
+
  3. is explicitly defined to be independent of design or implementation concerns;
    
 
    4. 
-
  4. organizes the vocabulary needed to communicate consistently and thoroughly about the know-how of a problem domain;
    
+
  5. organizes the vocabulary needed to communicate consistently and thoroughly about the know-how of a problem domain;
    
 
    6. 
-
  6. starts with a glossary of terms and definitions. There is a very high premium on high-quality, design-independent definitions, free of data or implementation biases; the model also emphasizes rich vocabulary; and
    
+
  7. contains the definitions of the concepts that it organizes. There is a high premium on high-quality, design-independent definitions, free of data or implementation biases; the model also emphasizes rich vocabulary; and
    
 
    8. 
-
  8. is always about identifying the correct choice of terms to use in communications, including statements of rules and requirements, especially where high precision and subtle distinctions need to be made. The core concepts of a temporal geospatial problem domain are typically quite stable over time.
    
+
  9. is always about identifying the correct choice of terms to use in communications, including statements of rules and requirements, especially where high precision and subtle distinctions need to be made. The core concepts of a temporal geospatial problem domain are typically quite stable over time.
    
 
    10. 
-
7.  Abstract Conceptual Model for Time
This Temporal Abstract Conceptual Model follows ISO 19111:2019, which is the ISO adoption of OGC 18-005r4.
The model is also informed by the W3C Time Ontology in OWL.
Figure 1
8.  Temporal regimes
8.1.  General

+
7.  Temporal regimes
7.1.  General

+
+
To enable more clear reasoning about time, this Abstract Specification uses the term “Regime” to describe the fundamentally different types of time and its measurement. This is a pragmatic approach that allows the grouping of recommendations and best practices in a practical way, but without obscuring the connection to the underlying theoretical components.

 
-
To enable more clear reasoning about time, this Abstract Specification uses the term “Regime” to describe the fundamentally different types of time and its measurement. This is a pragmatic approach that allows the grouping of recommendations and best practices in a practical way, but without obscuring the connection to the underlying theoretical components.

+
The first three regimes, described below, have deep underlying physical and mathematical foundations which cannot be legislated away. The fourth regime, calendars, concerns social constructs using seemingly random mixtures of ad hoc algorithms, arithmetic, numerology and measurements. Paradoxically, the calendar regime has historically driven advances in mathematics and physics. See the article A Chronicle Of Timekeeping.

 
-
The first three regimes, described below, have deep underlying physical and mathematical foundations which cannot be legislated away. The fourth regime, calendars, concerns social constructs using seemingly random mixtures of ad hoc algorithms, arithmetic, numerology and measurements. Paradoxically, the calendar regime has historically driven advances in mathematics and physics. See the article A Chronicle Of Timekeeping.

+
With due consideration, the regimes are applicable to other planets and outer space.

+
7.2.  Events and Operators

 
-
With due consideration, the regimes are applicable to other planets and outer space.

-
8.2.  Events and Operators

+
The simplest way of relating entities in time is by events that can be ordered and established in a sequence, and this sequence is used as an approximate measure of the passage of time.

 
-
The simplest way of relating entities in time is by events that can be ordered and established in a sequence, and this sequence is used as an approximate measure of the passage of time.

+
In this regime, no clocks or time measurements are defined, only events, that are ordered in relation to each other. Examples are geological layers, sediment or ice core layers, archaeological sequences, sequential entries in computer logs without coordinated time.

 
-
In this regime, no clocks or time measurements are defined, only events, that are ordered in relation to each other. Examples are geological layers, sediment or ice core layers, archaeological sequences, sequential entries in computer logs without coordinated time.

+
One set of events may be completely ordered with respect to each other, but another set of similar internally consistent ordered events cannot be cross-referenced to the first set unless extra information is available. Even then, only partial orderings may be possible.

 
-
One set of events may be completely ordered with respect to each other, but another set of similar internally consistent ordered events cannot be cross-referenced to each other unless extra information is available. Even then, only partial orderings may be possible.

+
In this regime, the Allen Operators (see Figure 1) can be used. If A occurs before B and B occurs before C, then it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So in our example, B occurs in the interval (A,C). However, arithmetic operations like (B-A) or (C-A) cannot be performed as any timescale or measurements are not defined. For example, in geology, ‘subtracting’ Ordovician from Jurassic is meaningless. In archeology, ‘subtracting’ a layer with a certain type of pottery remains from the layer containing burnt wood and bones is again not meaningful. Only the ordering can be deduced.

 
-
In this regime, the Allen Operators (see Figure 2) can be used. If A occurs before B and B occurs before C, then it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So in our example, B occurs in the interval (A,C). However, arithmetic operations like (B-A) or (C-A) cannot be performed as any timescale or measurements are not defined. For example, in geology, ‘subtracting’ Ordovician from Jurassic is meaningless. In archeology, ‘subtracting’ a layer with a certain type of pottery remains from the layer containing burnt wood and bones is again not meaningful. Only the ordering can be deduced.

+
This regime constitutes an ordinal temporal reference system, with discrete enumerated ordered events.

 
-
This regime constitutes an Ordinal Temporal Reference System, with discrete enumerated ordered events.

+
Figure 1

+
7.3.  Simple Clocks and Discrete Timescales

 
-
Figure 2

-
8.3.  Simple Clocks and Discrete Timescales

+
In this regime, a clock is defined as any regularly repeating physical phenomenon, such as a pendulum swing, earth’s rotation about the sun, earth’s rotation about its axis, heart beat, vibrations of electrically stimulated quartz crystals or the resonance of the unperturbed ground-state hyperfine transition frequency of the cesium-133 atom. Each occurrence of the repeating phenomenon is, of course, an event, but as there are usually very many that can only be distinguished by counting, they are considered a separate class of ticks.

 
-
In this regime, a clock is defined as any regularly repeating physical phenomena, such as pendulum swings, earth’s rotation about the sun, earth’s rotation about its axis, heart beats, vibrations of electrically stimulated quartz crystals or the resonance of the unperturbed ground-state hyperfine transition frequency of the cesium-133 atom. In terms of the number of repetitions possible, some phenomena make better clocks than others, because of the consistency of each repetition and the precision of each ‘tick’. A mechanism for counting, or possibly measuring, the ticks is desirable.

+
In terms of the number of repetitions possible, some phenomena make better clocks than others, because of the consistency of each repetition and the precision of each tick. A mechanism for counting, or possibly measuring, the ticks is desirable.

 
-
An assumption is that the ticks are regular and homogeneous.

+
An assumption is that the ticks are regular and homogeneous.

 
-
There is no sub-division between two successive clock ticks. Measuring time consists of counting the complete number of repetitions of ticks since the clock started, or since some other event at a given clock count.

+
There is no sub-division between two successive clock ticks. Measuring time consists of counting the complete number of repetitions of ticks since the clock started, or since some other event at a given clock tick count.

 
-
There is no time measurement before the clock starts, or after it stops.

+
There is no time measurement before the clock starts, or after it stops.

 
-
It may seem that time can be measured between ‘ticks’ by interpolation, but this needs another clock, with faster ticks. This process of devising more precise clocks continues down to the atomic scale. At that scale the deterministic process of physically trying to interpolate between ticks is not possible.

+
It may seem that time can be measured between ticks by interpolation, but this needs another clock, with faster ticks. This process of devising more precise clocks continues down to the atomic scale. At that scale the deterministic process of physically trying to interpolate between ticks is not possible.

 
-
The internationally agreed atomic time, TAI, is an example of a timescale with an integer count as the measure of time. However in practice, TAI is an arithmetic compromise across about two hundred separate atomic clocks, corrected for differing altitudes and temperatures.

+
The internationally agreed atomic time, TAI International Atomic Time is an example of a timescale with an integer count as the measure of time. However in practice, TAI is an arithmetic compromise across about two hundred separate atomic clocks, corrected for differing altitudes and temperatures.

 
-
In this regime, Allen Operators (see Figure 2) also can be used. If L occurs before M and M occurs before N, it can be correctly deduced that L occurs before N. The full set of operators also covers pairs of intervals. So if M occurs in the interval (L,N),  integer arithmetic operations such as (M-L) or (N-L) can be performed. This is because an integer timescale or measurement is defined.

+
In this regime, Allen Operators (see Figure 1) also can be used. If L occurs before M and M occurs before N, it can be correctly deduced that L occurs before N. The full set of operators also covers pairs of intervals. So if M occurs in the interval (L,N),  integer arithmetic operations such as (M-L) or (N-L) can be performed. This is because an integer timescale or measurement is defined by the count of ticks.

 
-
This regime constitutes a Temporal Coordinate Reference System, with discrete integer units of measure which can be subject to integer arithmetic.

-
8.4.  CRS and Continuous Timescales

+
This regime constitutes a temporal coordinate reference system, with discrete integer units of measure which can be subject to integer arithmetic.

+
7.4.  CRS and Continuous Timescales

 
-
This regime takes a clock from the previous regime and assumes that between any two adjacent ticks, it is possible to interpolate indefinitely to finer and finer precision, using ordinary arithmetic, rather than any physical device. Units of Measure may be defined that are different from the ‘ticks’. For example, a second may be defined as 9,192,631,770 vibrations of the ground-state hyperfine transition of the cesium-133 atom. Alternatively and differently, a second may be defined as 1/86400th of the rotation of the earth on its axis with respect to the sun. The count of rotations is the ‘ticks’ of an earth-day clock. This latter definition is not precise enough for many uses, as the rotation of the earth on its axis varies from day to day.

+
This regime takes a clock from the previous regime and assumes that between any two adjacent ticks, it is possible to interpolate indefinitely to finer and finer precision, using ordinary arithmetic, rather than any physical device. Units of measure may be defined that are different from the ticks. For example, a second may be defined as 9,192,631,770 vibrations of the ground-state hyperfine transition of the cesium-133 atom. Alternatively and differently, a second may be defined as 1/86400th of the rotation of the earth on its axis with respect to the sun. The count of rotations is the ticks of an earth-day clock. This latter definition is not precise enough for many uses, as the rotation of the earth on its axis varies from day to day.

 
-
Alternatively, it may be that the ticks are not counted but measured, and the precision of the clock is determined by the precision of the measurements, such as depth in an ice core, or angular position of an astronomical body, such as the sun, moon or a star.

+
Alternatively, it may be that the ticks are not counted but measured, and the precision of the clock is determined by the precision of the measurements, such as depth in an ice core subject to seasonal depositions of snow, or angular position of an astronomical body, such as the sun, moon or a star.

 
-
It is also assumed that time can be extrapolated to before the time when the clock started and into the future, possibly past when the clock stops.

+
It is also assumed that time can be extrapolated before the time when the clock started and into the future, possibly past when the clock stops.

 
-
This gives us a continuous number line to perform theoretical measurements. This is a coordinate system. With a datum/origin/epoch, a unit of measure (a name for the ‘tick marks’ on the axis), positive and negative directions and the full range of normal arithmetic. This is a Coordinate Reference System (CRS).

+
This gives us a continuous number line to perform theoretical measurements. With a datum/origin/epoch, a unit of measure (a name for the ticks on the axis), positive and negative directions and the full range of normal arithmetic, this is a coordinate reference system (CRS).

 
-
In this regime, the Allen Operators (see Figure 2) also can be used. If A occurs before B and B occurs before C, it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So if B occurs in the interval (A,C), real number arithmetic operations like (B-A) or (C-A) can be performed. This is because a timescale or measurement has been defined, and between any two instants, an infinite number of other instants can be found.

+
In this regime, the Allen Operators (see Figure 1) also can be used. If A occurs before B and B occurs before C, it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So if B occurs in the interval (A,C), real number arithmetic operations like (B-A) or (C-A) can be performed. This is because a timescale or measurement has been defined, and between any two instants, an infinite number of other instants can be found.

 
-
Example
Some examples are:

+
Example
Some examples are:

 
-
  • Unix milliseconds since 1970-01-01T00:00:00.0Z
    
+
    • Unix milliseconds since 1970-01-01T00:00:00.0Z
      
 
      • 
-
    • Julian Days, and fractions of a day, since noon on 1st January, 4713 BCE.
      
+
    • Julian Days, and fractions of a day, since noon on 1st January, 4713 BCE.
      
 
      • 
 
    
 

 
-
This regime constitutes a Temporal Coordinate Reference System, with a continuous number line and units of measure, which can be subject to the full range of real or floating-point arithmetic.

-
8.5.  Calendars

+
This regime constitutes a temporal coordinate reference system, with a continuous number line and units of measure, which can be subject to the full range of real, or floating-point, arithmetic.

+
7.5.  Calendars

 
-
In this regime, counts and measures of time are related to the various combinations of the rotations of the earth, moon and sun or other astronomical bodies.
+
In this regime, counts and measures of time are related to the various combinations of the rotations of the earth, moon and sun or other astronomical bodies.
 Typically there is no simple arithmetic connecting calendar systems and timescales. For example, the current civil year count of years in the Current Era (CE) and Before Current Era (BCE) is a very simple calendar, as there is no year zero. That is, Year 14CE – Year 12CE is a duration of 2 years, and Year 12BCE — Year 14BCE is also two years. However Year 1CE — Year 1BCE is one year, not two as there is no year 0CE or 0BCE.

 
-
In this regime, the use of the Allen Operators (see Figure 2) is not straightforward. If A occurs before B and B occurs before C, then correctly deducing that A occurs before C is not always easy. The full set of Allen Operators also covers pairs of intervals. So in the example, B occurs in the interval (A,C). However, simple arithmetic operations like (B-A) or (C-A) cannot usually be done simply because of the vagaries of the calendar algorithms, multiple timescales, and multiple Units of Measure.

+
In this regime, the use of the Allen Operators (see Figure 1) is not straightforward. If A occurs before B and B occurs before C, then correctly deducing that A occurs before C is not always easy. This is because the calendar’s timeline may contain gaps, changes of units of measure, or even duplicated times.

 
-
Calendars are social constructs made by combining several clocks and their associated timescales.

+
The full set of Allen Operators also covers pairs of intervals. So in the example, B occurs in the interval (A,C). However, simple arithmetic operations like (B-A) or (C-A) cannot usually be done simply because of the vagaries of the calendar algorithms, multiple timescales, and multiple units of measure.

 
-
This Abstract Specification only addresses the internationally agreed Gregorian calendar. The book Calendrical Calculations by Nachum Dershowitz and Edward M. Reingold provides overwhelming detail for conversion to numerous other calendars that have developed around the world and over the millennia and to meet the various social needs of communities, whether agricultural, religious or other. The reference is comprehensive but not exhaustive, as there are calendars that have been omitted.

+
Example
For example, in the Gregorian calendar, calculating the number of days between the 1st February and the 30th March depends on whether the year is a leap year or not, which also depends on the century and millenium. Calculating the precise number of seconds between two dates in the Gregorian calendar also depends on whether leap seconds have been declared between the dates. There have been 27 leap seconds added between 1972 and 2022.

+

+
+
Calendars are social constructs made by combining several clocks and their associated timescales. Calendars may also have local or regional variations at different times of the year or season, such as for ‘day-light saving’ in mid-latitudes. Again, this makes calculations more complicated and prone to change.

+
+
This Abstract Specification only addresses the internationally agreed Gregorian calendar. The book Calendrical Calculations by Nachum Dershowitz and Edward M. Reingold provides overwhelming detail for conversion to numerous other calendars that have developed around the world and over the millennia and to meet the various social needs of communities, whether agricultural, religious or other. The reference is comprehensive but not exhaustive, as there are calendars that have been omitted.

 
-
A Calendar is a Temporal Reference System, but it is not a Temporal Coordinate Reference System nor an Ordinal Temporal Reference System.

-
8.6.  Other Regimes

+
A calendar is a temporal reference system, but it is not a temporal coordinate reference system nor an ordinal temporal reference system.

+
7.6.  Other Regimes

 
-
There are other regimes, which are out of scope of this Abstract Specification. This could include local solar time, which is useful, for example, for the calculation of illumination levels and the length of shadows on aerial photography, or relativistic time.

+
There are other regimes, whose detailed description are out of scope of this Abstract Specification. This could include local solar time, which is useful, for example, for the calculation of illumination levels and the length of shadows on aerial photography, or relativistic time for very fast moving features.

 
-
8.6.1.  Accountancy

+
7.6.1.  Accountancy

 
-
The financial and administrative domains often use weeks, quarters, and other calendrical measures. These may be convenient for the requisite tasks, but are usually inappropriate for scientific or technical purposes.

+
The financial and administrative domains often use weeks, quarters, and other calendrical measures. These may be convenient for the requisite tasks, but are usually inappropriate for scientific or technical purposes. This Abstract Conceptual Model for Time can support this regime.

 

 
-
8.6.2.  Agents and Agency

+
7.6.2.  Agents and Agency

 
-
Agents require a different concept of time from regimes where time is a coordinate axis or measured by clocks. An agent is an entity that senses, responds, and maintains a model of its environment, while performing actions to achieve its goals. See ISO/IEC 22989:2022, Artificial intelligence concepts and terminology. For an agent, the conceptual model of time is about flow and continuity including a sense of now, a memory of past events, and a speculation about future events. This regime addresses how the agent has awareness of the flow of events:

+
Agents require a different concept of time from regimes where time is a coordinate axis or measured by clocks. An agent is an entity that senses, responds, and maintains a model of its environment, while performing actions to achieve its goals. See ISO/IEC 22989:2022, Artificial intelligence concepts and terminology. For an agent, the conceptual model of time is about flow and continuity including a sense of now, a memory of past events, and a speculation about future events. This regime addresses how the agent has awareness of the flow of events:

 
-
  • Temporal awareness integrates impression, retention, and protention, representing the continuous movement of time;
    
+
    • Temporal awareness integrates impression, retention, and protention, representing the continuous movement of time;
      
 
      • 
-
    • Agents continuously revise their models of the environment by integrating new observations with existing models;
      
+
    • Agents continuously revise their models of the environment by integrating new observations with existing models;
      
 
      • 
-
    • Observations are used to update an agent’s model, leading to a more accurate understanding of the environment and enabling effective goal-directed behavior.
      
+
    • Observations are used to update an agent’s model, leading to a more accurate understanding of the environment and enabling effective goal-directed behavior.
      
 
      • 
 
    
 
-
    This Agent regime of time is relevant to any feature which has agency.
    
+
    This regime of time is relevant to any feature which has agency. This Abstract Conceptual Model for Time can support this regime.
    
 

 
-
8.6.3.  Astronomical Time

+
7.6.3.  Astronomical Time

 
-
Astronomers have traditionally measured the apparent locations of stars, planets and other heavenly bodies by measuring angular separations from reference points or lines and the timing of transits across a meridian. Generally astronomers use time determined by earth’s motion relative to the distant stars rather than the sun. This is called sidereal time. Times are usually measured from an epoch in daylight, such as local midday, rather than midnight. Accurate measurements of positions of stars, planets and moons were and are essential for navigation on Earth. See the book Astronomical Algorithms by Jean Meeus for examples of the calculations involved.

+
Astronomers have traditionally measured the apparent locations of stars, planets and other heavenly bodies by measuring angular separations from reference points or lines and the timing of transits across a meridian. Generally astronomers use time determined by earth’s motion relative to the distant stars rather than the sun. This is called sidereal time. Times are usually measured from an epoch in daylight, such as local midday, rather than midnight. Accurate measurements of positions of stars, planets and moons were and are essential for navigation on Earth. See the book Astronomical Algorithms by Jean Meeus for examples of the calculations involved. This Abstract Conceptual Model for Time can support this regime.

 

 
-
8.6.4.  Local Solar Time

+
7.6.4.  Local Solar Time

 
-
Local solar time may or may not correspond to the local statutory or legal time in a country. Local solar time can be construed as a clock and timescale, with an angular measure of the apparent position of the sun along the ecliptic (path through the sky) as the basic physical principle. But the sun does not appear to progress evenly along the ecliptic throughout the days and year. There may be variations of up to 15 minutes compared to an even angular speed.

+
Local solar time may or may not correspond to the local statutory or legal time in a country. Local solar time can be construed as a clock and timescale, with an angular measure of the apparent position of the sun along the ecliptic (path through the sky) as the basic physical principle. But the sun does not appear to progress evenly along the ecliptic throughout the days and year. There may be variations of up to 15 minutes compared to an even angular speed. This Abstract Conceptual Model for Time can support this regime.

 

 
-
8.6.5.  Space-time

+
7.6.5.  Space-time

 
-
When dealing with moving objects, the location of the object in space depends on its location in time. That is to say, location is an event in space and time.

+
When dealing with moving objects, the location of the object in space depends on its location in time. That is to say, location is an event in space and time.

 
-
Originally developed by Hermann Minkowski to support work in Special Relativity, the concept of space-time is useful whenever the location of an object in space is dependent on its location in time.

+
Originally developed by Hermann Minkowski to support work in Special Relativity, the concept of space-time is useful whenever the location of an object in space is dependent on its location in time.

 
-
Since the speed of light, 
+
Since the speed of light, 
   
     c
   
@@ -1775,46 +1790,55 @@ 
4.1.10. <
  axis of the full space.

 

 
-
8.6.6.  Relativistic

+
7.6.6.  Relativistic

 
-
A regime may be needed for ‘space-time’, off the planet Earth, such as for recording and predicting space weather approaching from the sun, where the speed of light and relativistic effects such as gravity may be relevant.

+
A regime may be needed for ‘space-time’, off the planet Earth, such as for recording and predicting space weather approaching from the sun, where the speed of light and relativistic effects such as gravity may be relevant.

 
-
Once off planet Earth, distances and velocities can become very large. The speed of light becomes a limiting factor in measuring both where and when an event takes place. Special Relativity deals with the accurate measurement of space-time events as measured between two moving objects. The core concepts are the Lorentz Transforms. These transforms allow one to calculate the degree of “contraction” a measurement undergoes due to the relative velocity between the observing and observed object.

+
Once off planet Earth, distances and velocities can become very large. The speed of light becomes a limiting factor in measuring both where and when an event takes place. Special Relativity deals with the accurate measurement of space-time events as measured between two moving objects. The core concepts are the Lorentz Transforms. These transforms allow one to calculate the degree of “contraction” a measurement undergoes due to the relative velocity between the observing and observed object.

 
-
The key to this approach is to ensure each moving feature of interest has its own local clock and time, known as its ‘proper time’. This example can be construed as a fitting into the clock and timescale regime. The relativistic effects are addressed through the relationships between the separate clocks, positions and velocities of the features.

+
The key to this approach is to ensure each moving feature of interest has its own local clock and time, known as its ‘proper time’. This example can be construed as a fitting into the clock and timescale regime of this Abstract Specification. The relativistic effects are addressed through the relationships between the separate clocks, positions and velocities of the features.

 
-
Relativistic effects may need to be considered for satellites and other spacecraft because of their relative speed and position in Earth’s gravity well.

+
Relativistic effects may need to be considered for satellites and other spacecraft because of their relative speed and position in Earth’s gravity well.

 
-
The presence of gravitational effects requires special relativity to be replaced by general relativity, and it can no longer be assumed that space (or space-time) is Euclidean. That is, Pythagoras’ Theorem does not hold except locally over small areas. This is somewhat familiar territory for geospatial experts.

+
The presence of gravitational effects requires special relativity to be replaced by general relativity, and it can no longer be assumed that space (or space-time) is Euclidean. That is, Pythagoras’ Theorem does not hold except locally over small areas, or that the circumference of a circle is not precisely 
+  
+    2
+    :
+    π
+    r
+  
+. This is somewhat familiar territory for geospatial experts. This Abstract Conceptual Model for Time can support this regime, providing each feature has its own clock.

 

-
9.  Attributes of the Classes
9.1.  Reference Systems

+
8.  Abstract Conceptual Model for Time
This Temporal Abstract Conceptual Model follows ISO 19111:2019, which is the ISO adoption of OGC 18-005r8.
The model is also informed by the W3C Time Ontology in OWL.
Figure 2
9.  Classes and their Attributes and Properties
9.1.  Reference Systems

 
-
The top level ‘ReferenceSystem’ is an abstract super-class and does not have many attributes or properties. Only the total dimension of the reference system and the Location, Time or Domain of Applicability have been identified as essential.

+
The top level Reference System class is an abstract super-class and does not have many attributes or properties. Only the total dimension of the reference system and the location, time or domain of applicability have been identified as essential.

 
-
The ‘ReferenceSystem’ has two abstract sub-classes: ‘SpatialReferenceSystem’, which is defined in ISO 19111:2019, and ‘TemporalReferenceSystem’, each with the attributes of Dimension and Domains of Applicability.

+
The reference system class has two abstract sub-classes: Spatial Reference System, which is defined in ISO 19111:2019, and Temporal Reference System, each with the inherited attributes of dimension and domains of applicability.

 
-
The value for Dimension is one for time, or a vertical reference system, but may be as high as 6 for spatial location with orientation as in the GeoPose Implementation Standard.

+
The value for dimension is one for time, or a vertical reference system, but may be as high as six for spatial location with orientation as in the GeoPose Implementation Standard.

 
-
Besides the conventional space and time, there may be other reference systems, such as wavelength or frequency, that could be addressed by future additions to this Abstract Conceptual Model.

-
9.2.  Ordinal Temporal Reference Systems

+
For example, a reference system may be applicable to Mars, rather than Earth, or perhaps to a specific building site with local coordinates, or a specific time range while coordinate errors are within acceptable bounds.

 
-
An OrdinalTemporal Reference System has a well-ordered finite sequence of events against which other events can be compared.

+
Besides the conventional space and time, there may be other reference systems, such as wavelength or frequency, that could be addressed by future additions to this Abstract Conceptual Model.

+
9.2.  Ordinal Temporal Reference Systems

 
-
An Ordinal Temporal Reference System is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:

+
An ordinal temporal reference system has a well-ordered finite sequence of events against which other events can be compared.

 
-
  1. applicableLocationTimeOrDomain: the location, time or domain of applicability;
    
+
    An ordinal temporal reference system is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:
    
+
+
    1. applicable location time or domain: the location, time or domain of applicability;
      
 
      2. 
-
    2. dimension: the number of dimensions in this reference system. For Ordinal Temporal Reference Systems this value is fixed at 1.
      
+
    3. dimension: the number of dimensions in this reference system. For ordinal temporal reference systems this value is fixed at 1.
      
 
      4. 
 
    
 
-
    An Ordinal Temporal Reference System does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
    
+
    An ordinal temporal reference system does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
    
 
-
    1. Epoch: An Ordinal Temporal Reference System ‘has a’ one optional Epoch
      
+
      1. Epoch: An ordinal temporal reference system may be ‘anchored by’ one optional Epoch
        
 
        2. 
-
      2. Notation: An Ordinal Temporal Reference System ‘can use’ one or more Notations to represent itself.
        
+
      3. Notation: An ordinal temporal reference system ‘is represented by’ one or more Notations to represent itself.
        
 
        4. 
-
      4. Event: An Ordinal Temporal Reference System ‘consists of’ an ordered set of Events. These events are identifiable temporal instances.
        
+
      5. Event: An ordinal temporal reference system ‘consists of’ an ordered set of Events. These events are identifiable temporal instances.
        
 
        6. 
 
      
 
@@ -1823,71 +1847,78 @@ 
      4.1.10. <
 
 
      9.2.1.  Events
      
 
-
      The Events class is an ordered list of temporal events. The events can be instances, such as the ascension of a King to a throne, or intervals, such as the complete reign of each king.
      
+
      An event is an identifiable happening or occurence of something. The events can be instants, such as the ascension of a king to a throne, or intervals, such as the complete reign of each king.
      
 
 
      Other documents may enable two such ‘king lists’ to be related, though usually not completely.
      
+
+
      Example
      Several archeological layers may be identified as containing broken pottery, overlaid with a layer of burnt wood and brick, then followed by another layer with a different style of pottery and including a coin embossed with a king’s name. Thus the pottery styles can be classified as ‘early’ and ‘late’ and the late pottery associated with the named king, who may be identified from a separate inscribed ‘king list’.
      
+
      
 
      
-
9.3.  Temporal Coordinate Reference Systems

+
9.3.  Temporal Coordinate Reference Systems

 
-
A Temporal Coordinate Reference System is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:

+
A temporal coordinate reference system is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:

 
-
  1. applicableLocationTimeOrDomain: the location, time or domain of applicability;
    
+
    1. applicable location time or domain: the location, time or domain of applicability;
      
 
      2. 
-
    2. dimension: the number of dimensions in this reference system. For Temporal Coordinate Reference Systems this value is fixed at 1.
      
+
    3. dimension: the number of dimensions in this reference system. For temporal coordinate reference systems this value is fixed at 1.
      
 
      4. 
 
    
 
-
    A Temporal Coordinate Reference System does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
    
+
    A temporal coordinate reference system does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
    
 
-
    1. Epoch: A Temporal CRS ‘has a’ one optional Epochs
      
+
      1. Epoch: A temporal coordinate reference system ‘anchored by’ one optional Epochs
        
 
        2. 
-
      2. Notation: A Temporal CRS ‘can use’ one or more Notations to represent itself.
        
+
      3. Notation: A temporal coordinate reference system ‘represented by’ one or more Notations to represent itself.
        
 
        4. 
-
      4. Timescale: A Temporal CRS ‘has a’ one Timescale which is used to represent the values along its single axis. This Timescale can be either discrete or continuous.
        
+
      5. Timescale: A temporal coordinate reference system ‘must have’ one Timescale which is used to represent the values along its single axis. This timescale can be either discrete or continuous.
        
 
        6. 
 
      
-
9.4.  Calendar Reference Systems

+
9.4.  Calendar Reference Systems

 
-
Calendars combine different timescales and their clocks and units of measure, and other events, to make a complex timeline against which events can be compared. Calculated algorithms are used to determine which instants of intervals on the compound timeline are identified and labeled.

+
Calendars combine different timescales and their clocks and units of measure, and other events, to make a complex timeline against which events can be compared. Calculated algorithms are used to determine which instants or intervals on the compound timeline are identified and labeled.

 
-
A Calendar is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:

+
A calendar is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:

 
-
  1. applicableLocationTimeOrDomain: the location, time or domain of applicability
    
+
    1. applicable location time or domain: the location, time or domain of applicability
      
 
      2. 
-
    2. dimension: the number of dimensions in this reference system. For Calendars this value is fixed at 1.
      
+
    3. dimension: the number of dimensions in this reference system. For calendars this value is fixed at 1.
      
 
      4. 
 
    
 
-
    A Calendar does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
    
+
    A calendar does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
    
 
-
    1. Algorithm: A Calendar ‘has a’ one or more Algorithms. These Algorithms specify how the multiple Time Scales are aggregated into a single Timeline.
      
+
      1. Timeline: A calendar ‘has a’ one Timeline which serves to aggregate a number of Timescales into a single coherent measure of date and time.
        
 
        2. 
-
      2. Epoch: A calendar ‘has a’ one optional Epoch
        
+
      3. Algorithm: A timeline ‘is defined by’ one or more Algorithms. These algorithms specify how the multiple timescales are aggregated into a single Timeline.
        
 
        4. 
-
      4. Notation: A calendar ‘can use’ one or more Notations to represent itself.
        
+
      5. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a Timeline.
        
 
        6. 
-
      6. Timeline: A Calendar ‘has a’ one Timeline which serves to aggregate a number of Timescales into a single coherent measure of date and time.
        
+
      7. Epoch: A calendar is ‘anchored by’ one optional Epoch
        
 
        8. 
-
      8. Timescale: A Calendar ‘has a’ two or more Timescales which are used to construct a Timeline.
        
+
      9. Notation: A calendar  is ‘represented by’ one or more Notations to represent itself.
        
 
        10. 
 
      
 
 
      9.4.1.  Timeline
      
 
-
      The timeline is usually a set of instants from the past to the future and is compounded from multiple timescales, with multiple units of measures, and complicated arithmetic determined by the calendar algorithm(s). The timeline is usually not even continuous, having gaps or even multiple simultaneous representations.
      
+
      The timeline is usually a set of instants from the past to the future and is compounded from multiple timescales, with multiple units of measures, and complicated arithmetic determined by the calendar algorithm(s). The timeline is usually not even continuous, having gaps or even duplicated times or multiple simultaneous representations.
      
 
-
      A Timeline does not have any attributes of its own. Nor does it inherit any attributes from a parent class. However, it does use associations with other classes to fully describe itself.
      
+
      A timeline does not have any attributes of its own. Nor does it inherit any attributes from a parent class. However, it does use associations with other classes to fully describe itself.
      
 
-
      1. Algorithm: A Timeline ‘has a’ one or more Algorithms. These Algorithms specify how the multiple Time Scales are aggregated into a single Timeline.
        
+
        1. Algorithm: A timeline is ‘defined by’ one or more Algorithms. These algorithms specify how the multiple timescales are aggregated into a single timeline.
          
 
          2. 
-
        2. Timescale: A Timeline ‘has a’ two or more Timescales which are used to construct the Timeline.
          
+
        3. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
          
 
          4. 
 
        
 
      
 
 
      9.4.2.  Algorithm
      
 
-
      An Algorithm specifies the logic used to construct a Timeline from its constituent Timescales. An Algorithm does not have any attributes of its own. Nor does it make use of any other classes from this Temporal model.
      
+
      An algorithm specifies the logic used to construct a timeline from its constituent Timescales. An algorithm does not have any attributes of its own. It does make use of timescales to construct the timeline of a calendar.
      
+
+
      1. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
        
+
        2. 
+
      
 
      
 
 
      9.4.3.  Calendar Examples
      
@@ -1918,40 +1949,40 @@ 
      4.1.10. <
 
      Example  8
      The International Fixed Calendar was a solar calendar with 13 months of 28 days, with an extra day at the year’s end after the thirteenth month and leap days inserted at the end of the sixth month. Months all started on the same day of the week, Sunday, and ended on a Saturday. The year-end day and leap days are not part of any week. The IFC was considered for global introduction by the League of Nations but finally rejected in 1937, though it formed the basis for some financial accounting systems for many years.
      
 
      
 
      
-
9.5.  Discrete and Continuous Time Scales

+
9.5.  Discrete and Continuous Time Scales

 
-
A clock may be a regular, repeating, physical event, or ‘tick’, that can be counted. The sequence of tick counts form a discrete (counted) timescale.

+
A clock may be a regular, repeating, physical event, or tick, that can be counted. The sequence of tick counts form a discrete (counted) timescale.

 
 
Some clocks  allow the measurement of intervals between ticks, such as the movement of the sun across the sky. Alternatively, the ticks may not be completely distinguishable, but are still stable enough over the time of applicability to allow measurements rather than counting to determine the passage of time. These clocks generate a continuous (measured) timescale.

 
-
The duration of a tick is a constant. The length of a tick is specified using a Unit Of Measure.

+
The duration of a tick is assumed constant. The duration of a tick is specified using a Unit Of Measure.

 
 
9.5.1.  Timescale

 
-
A Timescale is a linear measurement (one dimension) used to measure or count monotonic events. Timescale has three attributes:

+
A timescale is a linear measurement (one dimension) used to measure or count monotonic events. Timescale has three attributes:

 
-
  1. Arithmetic: an indicator of whether this Timescale contains counted integers or measured real/floating point numbers.
    
+
    1. Arithmetic: an indicator of whether this timescale contains counted integers or measured real/floating point numbers.
      
 
      2. 
-
    2. StartCount: the lowest value in a Timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
      
+
    3. StartCount: the lowest value in a timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
      
 
      4. 
-
    4. EndCount: the greatest value in a Timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
      
+
    5. EndCount: the greatest value in a timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
      
 
      6. 
 
    
 
-
    In addition to the attributes, the Timescale class maintains associations with two other classes to complete its definition.
    
+
    In addition to the attributes, the timescale class maintains associations with two other classes to complete its definition.
    
 
-
    1. Clock: A Timescale ‘has a’ one clock. This is the process which generates the ‘tick’ which is counted or measured for the Timescale.
      
+
      1. Clock: A timescale is ‘determined by’ one clock. This is the process which generates the ticks which are counted or measured for the timescale.
        
 
        2. 
-
      2. UnitOfMeasure: A timescale ‘has a’ one UnitOfMeasure. This class specifies the units of the clock measurement as well as the direction of increase of that measurement.
        
+
      3. UnitOfMeasure: A timescale ‘has a’ one UnitOfMeasure. This class specifies the units of the clock measurement or count as well as the direction of increase of that measurement or count.
        
 
        4. 
 
      
 

 
 
9.5.2.  Clock

 
-
A Clock represents the process which generates the ‘tick’ which is counted or measured for a Timescale. Clock has one attribute:

+
A clock represents the process which generates the ticks which are counted or measured for a timescale. Clock does not have any attributes of its own. Nor does it inherit any attributes from a parent class. However, it does use an association with another class to fully describe itself.

 
-
  1. Tick definition: a description of the process which is being used to generate monotonic events.
    
+
    1. Ticks: a description of the process which is being used to generate monotonic events.
      
 
      2. 
 
    
 
@@ -1962,11 +1993,11 @@ 
    4.1.10. <
 

 

 
-
9.5.3.  UnitOfMeasure

+
9.5.3.  Unit of Measure

 
-
The Direction attribute indicates whether counts or measures increase in the positive (future) or negative (past) direction. The attribute could be part of ‘Timescale’ or ‘TemporalCoordinateReferenceSystem’ rather than a separate class ‘UnitOfMeasure’, but on balance, it seems better here, as the names often imply directionality, such as fathoms increasing downwards, MYA (Millions of Years Ago) increasing earlier, Atmospheric Pressure in hPa (Hectopascals) decreasing upwards, and FL (FlightLevel) increasing upwards.

+
The direction attribute indicates whether counts or measures increase in the positive (future) or negative (past) direction. The attribute could be part of timescale or temporal coordinate reference system rather than a separate class of measure, but on balance, it seems better here, as the names often imply directionality, such as fathoms increasing downwards, MYA (Millions of Years Ago) increasing earlier, atmospheric pressure in hPa (hectopascals) decreasing upwards, and FL (flight level) increasing upwards.

 
-
  1. Direction: indicates the direction in which a timescale progresses as new ‘ticks’ are counted or measured.
    
+
    1. Direction: indicates the direction in which a timescale progresses as new ticks are counted or measured.
      
 
      2. 
 
    
 
@@ -1985,29 +2016,33 @@ 
    4.1.10. <
 
    Example  3
    A well preserved fossilized log is recovered and the tree rings establish an annual ‘tick’. The start and end times may be known accurately by comparison and matching with other known tree ring sequences, or perhaps only dated imprecisely via Carbon Dating, or its archaeological or geological context.
    
 
    
 
-
    Example  4
    A clock is started, but undergoes a calibration process against some standard clock, so the initial, reliable Start Time does not start at Count Zero. The clock is accidentally knocked so that it is no longer correctly calibrated, but is still working. The End Time is not the last time that the clock ticks.
    
+
    Example  4
    A clock is started, but undergoes a calibration process against some standard clock, so the initial, reliable start time does not start at a count of zero. The clock is accidentally knocked so that it is no longer correctly calibrated, but is still working. The end time is not the last time that the clock ticks.
    
 
    
 
-
    Example  5
    TAI (International Atomic Time, Temps Atomique International) is coordinated by the BIPM (International Bureau of Weights and Measures, Bureau International de Poids et Measures) in Paris, France. TAI is based on the average of hundreds of separate atomic clocks around the world, all corrected to be at mean sea level and standard pressure and temperature. The epoch is defined by Julian Date 2443144.5003725 (1 January 1977 00:00:32.184).
    
+
    Example  5
    TAI International Atomic Time (or Temps Atomique International) is coordinated by the BIPM (International Bureau of Weights and Measures, Bureau International de Poids et Measures) in Paris, France. TAI is based on the average of hundreds of separate atomic clocks around the world, all corrected to be at mean sea level and standard pressure and temperature. The epoch is defined by Julian Date 2443144.5003725 (1 January 1977 00:00:32.184).
    
 
    
 
-
    Example  6
    The Julian Day is the continuous count of days (rotations of the Earth with respect to the Sun) since the beginning of the year 4173 BCE and will terminate at the end of the year 3267 CE. The count then starts again as “Period 2”. Many computer based timescales, such as Unix Time, are based on the Julian Day timescale, but with different epochs, to fit the numbers into the limited computer words.
    
+
    Example  6
    The Julian Day is the continuous count of days (rotations of the Earth with respect to the Sun) since the beginning of the year 4173 BCE and will terminate at the end of the year 3267 CE. The count then starts again as “Period 2”. Many computer based timescales, such as Unix Time, are based on the Julian Day timescale, but with different epochs, to fit the large numbers into computer words of limited size.
    
 
    
 
    
-
    9.6.  Supporting Classes
    
+
    9.6.  Supporting Classes
    
 
 
    9.6.1.  Epoch
    
 
-
    The Epoch class provides a origin or datum for a Temporal Reference System.
    
+
    The epoch class provides an origin or datum for a temporal reference system.
    
 
    
 
 
    9.6.2.  Notation
    
 
-
    The Notation class identifies a widely agreed, commonly accepted, notation for representing values in accordance with a temporal reference system.
    
+
    The notation class identifies a widely agreed, commonly accepted, notation for representing values in accordance with a temporal reference system.
    
 
    
-
    10.  Notation
    There are often widely agreed, commonly accepted, notations used for temporal reference systems, but few have been standardized. Any particular notation may be capable of expressing a wider range of times than are valid for the reference system.
    Example
    The IETF RFC 3339 timestamp notation, a restrictive profile of ISO 8601, can express times before 1588CE, when the Gregorian calendar was first introduced in some parts of the world.
    
-
    11.  Synchronization of clocks
    If there are two or more clocks, stationary with respect to each other, and a practical method of communicating their times to each other, the clocks can be perfectly synchronized.
    However, if the clocks are moving with respect to each other, perfect synchronization would require communication to be instantaneous. As communication speed is limited by the finite constant speed of light, perfect synchronization is not possible, though repetitive protocols can be used to reduce the synchronization error to any practical desired level.
    See the book A Brief History of Timekeeping, pages 187-191.
    12.  Temporal Geometry
    The geospatial community has often used analogies between space and time to construct ‘temporal-geometry’. This analogy is useful but can be misleading and must not be taken too far. For example, taken from A Treatise on Time and Space by J R Lucas, and assuming a thing has classical rather than quantum properties:
    1.1 A thing cannot be in two places at one time;
    1.2 A thing can be in one place at two times;
    2.1 Two things cannot be in the same place at the same time;
    2.2 Two things can be in the same place at different times.
    These are not symmetrical in space and time.
    Temporal constructs such as instants, durations or intervals, multi-instants (a set of instants), and multi-intervals are not included in this conceptual model. These do have strongly analogous equivalents in space, such as points and multi-points, especially in a single dimension, such as vertical. The temporal constructs are well described in Maintaining Knowledge about Temporal Intervals by J. F. Allen (see Figure 2) and apply across all of the regimes, so do not need to be in this Abstract Conceptual Model.

    Bibliography
    
-
    [1]  Carl Stephen Smyth: OGC 21-056r10, OGC GeoPose 1.0 Data Exchange Draft Standard. Open Geospatial Consortium (2022). http://www.opengis.net/doc/DIS/geopose/1.0.
    [2]  ISO: ISO 19108:2002, Geographic information — Temporal schema. International Organization for Standardization, Geneva (2002). https://www.iso.org/standard/26013.html.
    [3]  Orzell, C.: A Brief History of Timekeeping. Oneworld Publications (2022). ISBN-13:978-0-86154-321-2.
    [4]  Andrewes, W.H.J.: A Chronicle Of Timekeeping. Scientific American (2006). https://www.scientificamerican.com/article/a-chronicle-of-timekeeping-2006-02
    [5]  Meeus, J.: Astronomical Algorithms. https://www.agopax.it/Libri_astronomia/pdf/Astronomical%20Algorithms.pdf
    [6]  Dershowitz, N., Reingold, N.M.: Calendrical Calculations — The Ultimate Edition. Cambridge University Press (2018). ISBN-13:978-1107683167. http://emr.cs.iit.edu/home/reingold/calendar-book/third-edition [last accessed 2023-01]
    [7]  Bureau International des Poids et Mesures (BIPM): Establishment of International Atomic Time and Coordinated Universal Time. https://www.bipm.org/documents/20126/59466374/6_establishment_TAR20.pdf/5b18b648-0d5a-ee02-643d-a60ed6c148fc
    [8]  Wikipedia. International Fixed Calendar. https://en.wikipedia.org/wiki/International_Fixed_Calendar [last accessed 2023-12]
    [9]  Wikipedia. List of Calendars. https://en.wikipedia.org/wiki/List_of_calendars [last accessed 2023-12]
    [10]  Lorentz Transform. Wolfram MathWorld. https://mathworld.wolfram.com/LorentzTransformation.html
    [11]  Allen, J.F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26 pp832-843 (1983).
    [12]  Minkowski, H.: Space and Time, Minkowski’s Papers on Relativity. Minkowski Institute Press, Montreal (2012) https://minkowskiinstitute.org/ebookstore
    [13]  Juan Diego Bogotá, Zakaria Djebbara: Time-consciousness in computational phenomenology: a temporal analysis of active inference. Neuroscience of Consciousness, Volume 2023, Issue 1 (2023). https://doi.org/10.1093/nc/niad004
    [14]  Lucas, J.R.: A Treatise on Time and Space. Methuen and Co. Ltd (1973) ISBN 0-416-84190-2.
    [15]  The Open Group: UNIX Time. https://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap04.html#tag_04_16 [last accessed 2023-01]
    
+
10.  Notation
There are often widely agreed, commonly accepted, notations used for temporal reference systems, but few have been standardized. Any particular notation may be capable of expressing a wider range of times than are valid for the reference system.
Example
The IETF RFC 3339 timestamp notation, a restrictive profile of ISO 8601, can express times before 1582CE, when the Gregorian calendar was first introduced in some parts of the world.

+
11.  Synchronization of clocks
If there are two or more clocks, stationary with respect to each other, and a practical method of communicating their times to each other, the clocks can be perfectly synchronized.
However, if the clocks are moving with respect to each other, perfect synchronization would require communication to be instantaneous. As communication speed is limited by the finite constant speed of light, perfect synchronization is not possible, though repetitive protocols can be used to reduce the synchronization error to any practical desired level.
See the book A Brief History of Timekeeping, pages 187-191.
12.  Temporal Geometry
The geospatial community has often used analogies between space and time to construct ‘temporal-geometry’. This analogy is useful but can be misleading and must not be taken too far. For example, taken from A Treatise on Time and Space by J R Lucas, and assuming a thing has classical rather than quantum properties:
1.1 A thing cannot be in two places at one time;
1.2 A thing can be in one place at two times;
2.1 Two things cannot be in the same place at the same time;
2.2 Two things can be in the same place at different times.
These statements are not symmetrical between space and time.
Temporal constructs such as instants, durations or intervals, multi-instants (a set of instants), and multi-intervals (a set of intervals) are not included in this conceptual model. These do have strongly analogous equivalents in space, such as points and multi-points, especially in a single dimension, such as vertical. The temporal constructs are well described in Maintaining Knowledge about Temporal Intervals by J. F. Allen (see Figure 1) and apply across all of the regimes, so do not need to be in this Abstract Conceptual Model.

Bibliography

+
[1]  Carl Stephen Smyth: OGC 21-056r11, OGC GeoPose 1.0 Data Exchange Standard. Open Geospatial Consortium (2023). http://www.opengis.net/doc/IS/geopose/1.0.
[2]  ISO: ISO 19108:2002, Geographic information — Temporal schema. International Organization for Standardization, Geneva (2002). https://www.iso.org/standard/26013.html.
[3]  Orzell, C.: A Brief History of Timekeeping. Oneworld Publications (2022). ISBN-13:978-0-86154-321-2.
[4]  Andrewes, W.H.J.: A Chronicle Of Timekeeping. Scientific American (2006). https://www.scientificamerican.com/article/a-chronicle-of-timekeeping-2006-02.
[5]  Meeus, J.: Astronomical Algorithms. https://www.agopax.it/Libri_astronomia/pdf/Astronomical%20Algorithms.pdf.
[6]  Dershowitz, N., Reingold, N.M.: Calendrical Calculations — The Ultimate Edition. Cambridge University Press (2018). ISBN-13:978-1107683167. http://emr.cs.iit.edu/home/reingold/calendar-book/third-edition. [last accessed 2023-01]
[7]  Bureau International des Poids et Mesures (BIPM): Establishment of International Atomic Time and Coordinated Universal Time. https://www.bipm.org/documents/20126/59466374/6_establishment_TAR20.pdf/5b18b648-0d5a-ee02-643d-a60ed6c148fc.
[8]  The Library of Congress: Extended Date/Time Format (EDTF) Specification. (2019). https://www.loc.gov/standards/datetime. [last accessed 2024-02]
[9]  Wikipedia. International Atomic Time. https://en.wikipedia.org/wiki/International_Atomic_Time. [Last accessed 2024-02]
[10]  Wikipedia. International Fixed Calendar. https://en.wikipedia.org/wiki/International_Fixed_Calendar. [last accessed 2023-12]
[11]  Wikipedia. List of Calendars. https://en.wikipedia.org/wiki/List_of_calendars. [last accessed 2023-12]
[12]  Lorentz Transform. Wolfram MathWorld. https://mathworld.wolfram.com/LorentzTransformation.html.
[13]  Allen, J.F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26, no. 11, pp 832-843 (1983). https://doi.org/10.1145/182.358434.
[14]  Minkowski, H.: Space and Time, Minkowski’s Papers on Relativity. Minkowski Institute Press, Montreal (2012). https://minkowskiinstitute.org/ebookstore.
[15]  IETF: Date and Time on the Internet: Timestamps with additional information. Internet Engineering Task Force (2023). https://datatracker.ietf.org/doc/draft-ietf-sedate-datetime-extended. [last accessed 2024-02]
[16]  Juan Diego Bogotá, Zakaria Djebbara: Time-consciousness in computational phenomenology: a temporal analysis of active inference. Neuroscience of Consciousness, Volume 2023, Issue 1 (2023). https://doi.org/10.1093/nc/.
[17]  Lucas, J.R.: A Treatise on Time and Space. Methuen and Co. Ltd (1973) ISBN 0-416-84190-2.
[18]  The Open Group: UNIX Time. https://pubs.opengroup.org/onlinepubs/9699919799/basedefs/V1_chap04.html#tag_04_16. [last accessed 2023-01]
[19]  W3C. Working with Time Zones, W3C Working Group Note 5 July 2011. https://www.w3.org/TR/timezone.

+
+
+
+
 
 
 
diff --git a/23-049/23-049.pdf b/23-049/23-049.pdf
index 50101d7..e1ba65d 100644
Binary files a/23-049/23-049.pdf and b/23-049/23-049.pdf differ
diff --git a/23-049/23-049.presentation.xml b/23-049/23-049.presentation.xml
index 947177c..3db47bc 100644
--- a/23-049/23-049.presentation.xml
+++ b/23-049/23-049.presentation.xml
@@ -2,7 +2,7 @@
 
 
 Topic 25 - Abstract Conceptual Model for Time
-http://www.opengis.net/doc/AS/temporal-conceptual-model/1.0http://docs.opengeospatial.org/as/23-049/23-049.html23-04923-0492023-12-132023-05-232023-05-23
+http://www.opengis.net/doc/AS/temporal-conceptual-model/1.0http://docs.opengeospatial.org/as/23-049/23-049.html23-049r123-049r12024-02-142024-02-142023-05-23
 U.K. Met Office
 
 HeazelTech
@@ -38,7 +38,7 @@
 (%)%1 and %2%1, and %2%1 or %2%1, or %2%1 and %2%1 or %2%1 from %2%1 to %2%1, %2%1 %2spellout-ordinalNOTENoteNote % to entryListDefinition ListFigureDiagramFormulaFormulaTableRequirementRecommendationPermissionBoxRecommendation testRequirement testPermission testRecommendations classRequirements classPermissions classAbstract testConformance classKeyExampleExamplewherewhereWhole of textdraftinformativenormativemodifiedadaptedDEPRECATEDSOURCEandAll Parts%Spellout editioneditionversionList of FiguresList of TablesList of RecommendationsJanuaryFebruaryMarchAprilMayJuneJulyAugustSeptemberOctoberNovemberDecemberObligationDangerWarningCautionImportantSafety PrecautionsEditorial NoteSectionClausePartParagraphChapterPageTableAnnexFigureExampleNoteFormulamfncommonsgdualplpreppartadjadvnounverbdeprecatessupersedesnarrowerbroaderequivalentcomparecontrastseesee alsoClauseClausesAnnexAnnexesAppendixAppendixesNoteNotesNote % to entryNotes % to entryListListsFigureFiguresFormulaFormulasTableTablesRequirementRequirementsRecommendationRecommendationsPermissionPermissionsExampleExamplesPartPartsSectionSectionsParagraphParagraphsChapterChaptersPagePagesADMITTEDSubmittersContributorsTable of FiguresNo security considerations have been made for this document.Submission DateApproval DatePublication DateAuthorEditorContributorDraftWork Item DraftCandidate SWG DraftCandidate OAB Review DraftCandidate Public RFC DraftCandidate TC Vote DraftApprovedPublishedDeprecatedRescindedRetiredenLatnTOC Heading Levels2HTML TOC Heading Levels2DOC TOC Heading Levels2
 
 Topic 25 - Abstract Conceptual Model for Time
-http://www.opengis.net/doc/AS/temporal-conceptual-model/1.0http://docs.opengeospatial.org/as/23-049/23-049.html23-04923-0492023-12-132023-05-232023-05-23
+http://www.opengis.net/doc/AS/temporal-conceptual-model/1.0http://docs.opengeospatial.org/as/23-049/23-049.html23-049r123-049r12024-02-142024-02-142023-05-23
 U.K. Met Office
 
 HeazelTech
@@ -58,7 +58,7 @@
 
 Traditionally, geospatial communities used 2D coordinates and the vertical (third dimension) and temporal aspects were considered attributes rather than valid components of coordinate systems. In an increasingly dynamic, faster and multidimensional world, much confusion and lack of interoperability has occurred because of inconsistent approaches to defining and expressing time. Various international bodies expended considerable effort to establish the Gregorian Calendar as a consistent timeline. The Gregorian Calendar suffices for low precision applications, such as to the nearest minute, but not so when second or sub-second accuracy is required. For example, there have been differing practices and no consensus on whether leap seconds should be part of the Gregorian timeline.
 
-This document is consistent with  and W3C Time Ontology in OWL.
+This document is consistent with  and W3C Time Ontology in OWL.
 swg-draft2024
 Open Geospatial Consortium
 ogcdocOGC documentabstract specificationconceptual modeltimetemporal referencingreferencing by coordinatescalendarclocktimescaleabstract-specification-topictechnicalTOC Heading Levels2HTML TOC Heading Levels2DOC TOC Heading Levels2
@@ -106,14 +106,14 @@ To obtain additional rights of use, visit
 
 Traditionally, geospatial communities used 2D coordinates and the vertical (third dimension) and temporal aspects were considered attributes rather than valid components of coordinate systems. In an increasingly dynamic, faster and multidimensional world, much confusion and lack of interoperability has occurred because of inconsistent approaches to defining and expressing time. Various international bodies expended considerable effort to establish the Gregorian Calendar as a consistent timeline. The Gregorian Calendar suffices for low precision applications, such as to the nearest minute, but not so when second or sub-second accuracy is required. For example, there have been differing practices and no consensus on whether leap seconds should be part of the Gregorian timeline.
 
-This document is consistent with  and W3C Time Ontology in OWL.
+This document is consistent with  and W3C Time Ontology in OWL.
 
 Preface
-When OGC standards involve time, they generally refer to the ISO documents such as  (now largely superseded), , , and their freely available OGC equivalents, such as  (the equivalent to ).
+When OGC Standards involve time, they generally refer to the ISO documents such as Geographic information Temporal schema  (now largely superseded), Geographic information Referencing by coordinates , Date and Time Format , and their freely available OGC equivalents, such as OGC Abstract Specification Topic 2: Referencing by coordinates   (the equivalent to ).
 
-Much effort over decades has gone into establishing complex structures to represent calendar based time, such as the  notation, and many date-time schemas. A consequence of this effort is that many end-users and developers of software use calendar based “coordinates”, with the associated ambiguities about underlying algorithms, imprecision and inappropriate scope.
+Over decades, much effort has gone into establishing complex structures to represent calendar based time, such as the  notation, and many date-time schemas. A consequence of this effort is that many end-users and developers of software use calendar based “coordinates”, with the associated ambiguities about underlying algorithms, imprecision and inappropriate scope.
 
-The aim of this Abstract Specification is to establish clear concepts and terminology, so that people are well aware of the advantages and disadvantages of adopting a particular technological approach to time and then perhaps build better, more appropriate, interoperable systems for their use cases.
+The aim of this OGC Temporal Abstract Specification is to establish clear concepts and terminology. This is necessary so that people are aware of the advantages and disadvantages of specifying or adopting a particular technological approach to time and then perhaps build better, more appropriate, interoperable systems for their use cases.
 
 Security Considerations
 This Abstract Specification does not place any constraints on application, platform, operating system level, or network security.
@@ -182,26 +182,33 @@ directly in application schemas.
 
 
 Terms and definitions
+
+clock
+
+
+regularly repeating physical phenomenon that can be counted
+ 
+
 
 conceptual model
 
 
-description of common concepts and their relationships, particularly in order to facilitate exchange of information between parties within a specific domain
+description of common concepts and their relationships, particularly in order to facilitate exchange of information between parties within a specific domain
 
 
- A conceptual model is explicitly chosen to be independent of design or implementation concerns.
+ A conceptual model is explicitly chosen to be independent of design or implementation concerns.
 
 
 
 coordinate
 
 
-one of a sequence of numbers designating the position of a point
+one of a sequence of numbers designating the position of a point
 
 
 
 
- In many coordinate reference systems, the coordinate numbers are qualified by units.
+ In many coordinate reference systems, the coordinate numbers are qualified by units.
 
 
 
@@ -214,22 +221,22 @@ directly in application schemas.
 
 
 
-coordinate systemcoordinate system that is related to an object by a reference framedatum
+coordinate systemcoordinate system that is related to an object by a reference framedatum
 
 
 
 
 
 
- Geodetic and vertical datums are referred to as reference frames.
-For geodetic and vertical reference frames, the object will be the Earth. In planetary applications, geodetic and vertical reference frames may be applied to other celestial bodies.
+ Geodetic and vertical datums are referred to as reference frames.
+For geodetic and vertical reference frames, the object will be the Earth. In planetary applications, geodetic and vertical reference frames may be applied to other celestial bodies.
 
 
 
 coordinate system
 
 
-set of mathematical rules for specifying how coordinates are to be assigned to points
+set of mathematical rules for specifying how coordinates are to be assigned to points
 
 
  
@@ -244,7 +251,7 @@ directly in application schemas.
 
 
 
-parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate systemcoordinate system
+parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate systemcoordinate system
 
 
  
@@ -255,15 +262,15 @@ directly in application schemas.
 
 
 
-geodesypoint in time
+geodesypoint in time
 
 
 
 
 
 
- In , an epoch is expressed in the Gregorian calendar as a decimal year.
-2017-03-25 in the Gregorian calendar is epoch 2017.23. Other notations or reference systems are options.
+ In , an epoch is expressed in the Gregorian calendar as a decimal year.
+2017-03-25 in the Gregorian calendar is epoch 2017.23. Other notations or reference systems are options.
 
 
 
@@ -276,7 +283,7 @@ directly in application schemas.
 
 
 
-parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate systemcoordinate system
+parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate systemcoordinate system
 
 
  
@@ -291,7 +298,7 @@ directly in application schemas.
 
 
 
-coordinate reference systemcoordinate reference system based on a temporal datumtemporal datum
+coordinate reference systemcoordinate reference system based on a temporal datumtemporal datum
 
 
  
@@ -302,7 +309,7 @@ directly in application schemas.
 
 
 
-geodesyone-dimensional coordinate systemcoordinate system where the axis is time
+geodesyone-dimensional coordinate systemcoordinate system where the axis is time
 
 
  
@@ -311,13 +318,20 @@ directly in application schemas.
 temporal datum
 
 
-reference framedatum describing the relationship of a <geodesy> temporal coordinate systemtemporal coordinate system to an object
+reference framedatum describing the relationship of a <geodesy> temporal coordinate systemtemporal coordinate system to an object
 
 
 
 
- The object is normally time on the Earth.
+ The object is normally time on the Earth.
 
+
+
+tick
+
+
+event which is a single occurrence of the regularly repeating physical phenomenon of a clock
+ 
 
 
 
@@ -351,152 +365,150 @@ directly in application schemas.
 
 A conceptual model:
 
-is a representation of a system, made of the composition of concepts which are used to help people know, understand, or simulate a subject the model represents. A documented conceptual model represents ‘concepts’ (entities), the relationships between them, and a vocabulary;
+is a representation of a system, made of the composition of concepts which are used to help people know, understand, or simulate a subject the model represents. A documented conceptual model represents ‘concepts’ (entities), the relationships between them, and a vocabulary;
 
 is explicitly defined to be independent of design or implementation concerns;
 
 organizes the vocabulary needed to communicate consistently and thoroughly about the know-how of a problem domain;
 
-starts with a glossary of terms and definitions. There is a very high premium on high-quality, design-independent definitions, free of data or implementation biases; the model also emphasizes rich vocabulary; and
+contains the definitions of the concepts that it organizes. There is a high premium on high-quality, design-independent definitions, free of data or implementation biases; the model also emphasizes rich vocabulary; and
 
 is always about identifying the correct choice of terms to use in communications, including statements of rules and requirements, especially where high precision and subtle distinctions need to be made. The core concepts of a temporal geospatial problem domain are typically quite stable over time.
 
 
 
 
-
-Abstract Conceptual Model for Time
-This Temporal Abstract Conceptual Model follows , which is the ISO adoption of .
-
-The model is also informed by the W3C Time Ontology in OWL.
-
-
-
-
 
 Temporal regimes
 
 General
-To enable more clear reasoning about time, this Abstract Specification uses the term “Regime” to describe the fundamentally different types of time and its measurement. This is a pragmatic approach that allows the grouping of recommendations and best practices in a practical way, but without obscuring the connection to the underlying theoretical components.
+To enable more clear reasoning about time, this Abstract Specification uses the term “Regime” to describe the fundamentally different types of time and its measurement. This is a pragmatic approach that allows the grouping of recommendations and best practices in a practical way, but without obscuring the connection to the underlying theoretical components.
 
-The first three regimes, described below, have deep underlying physical and mathematical foundations which cannot be legislated away. The fourth regime, calendars, concerns social constructs using seemingly random mixtures of ad hoc algorithms, arithmetic, numerology and measurements. Paradoxically, the calendar regime has historically driven advances in mathematics and physics. See the article A Chronicle Of Timekeeping.
+The first three regimes, described below, have deep underlying physical and mathematical foundations which cannot be legislated away. The fourth regime, calendars, concerns social constructs using seemingly random mixtures of ad hoc algorithms, arithmetic, numerology and measurements. Paradoxically, the calendar regime has historically driven advances in mathematics and physics. See the article A Chronicle Of Timekeeping.
 
-With due consideration, the regimes are applicable to other planets and outer space.
+With due consideration, the regimes are applicable to other planets and outer space.
 
 
 
 Events and Operators
-The simplest way of relating entities in time is by events that can be ordered and established in a sequence, and this sequence is used as an approximate measure of the passage of time.
+The simplest way of relating entities in time is by events that can be ordered and established in a sequence, and this sequence is used as an approximate measure of the passage of time.
 
-In this regime, no clocks or time measurements are defined, only events, that are ordered in relation to each other. Examples are geological layers, sediment or ice core layers, archaeological sequences, sequential entries in computer logs without coordinated time.
+In this regime, no clocks or time measurements are defined, only events, that are ordered in relation to each other. Examples are geological layers, sediment or ice core layers, archaeological sequences, sequential entries in computer logs without coordinated time.
 
-One set of events may be completely ordered with respect to each other, but another set of similar internally consistent ordered events cannot be cross-referenced to each other unless extra information is available. Even then, only partial orderings may be possible.
+One set of events may be completely ordered with respect to each other, but another set of similar internally consistent ordered events cannot be cross-referenced to the first set unless extra information is available. Even then, only partial orderings may be possible.
 
-In this regime, the Allen Operators (see ) can be used. If A occurs before B and B occurs before C, then it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So in our example, B occurs in the interval (A,C). However, arithmetic operations like (B-A) or (C-A) cannot be performed as any timescale or measurements are not defined. For example, in geology, ‘subtracting’ Ordovician from Jurassic is meaningless. In archeology, ‘subtracting’ a layer with a certain type of pottery remains from the layer containing burnt wood and bones is again not meaningful. Only the ordering can be deduced.
+In this regime, the Allen Operators (see ) can be used. If A occurs before B and B occurs before C, then it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So in our example, B occurs in the interval (A,C). However, arithmetic operations like (B-A) or (C-A) cannot be performed as any timescale or measurements are not defined. For example, in geology, ‘subtracting’ Ordovician from Jurassic is meaningless. In archeology, ‘subtracting’ a layer with a certain type of pottery remains from the layer containing burnt wood and bones is again not meaningful. Only the ordering can be deduced.
 
-This regime constitutes an Ordinal Temporal Reference System, with discrete enumerated ordered events.
+This regime constitutes an ordinal temporal reference system, with discrete enumerated ordered events.
 
-
+
 
 
 
 Simple Clocks and Discrete Timescales
-In this regime, a clock is defined as any regularly repeating physical phenomena, such as pendulum swings, earth’s rotation about the sun, earth’s rotation about its axis, heart beats, vibrations of electrically stimulated quartz crystals or the resonance of the unperturbed ground-state hyperfine transition frequency of the cesium-133 atom. In terms of the number of repetitions possible, some phenomena make better clocks than others, because of the consistency of each repetition and the precision of each ‘tick’. A mechanism for counting, or possibly measuring, the ticks is desirable.
+In this regime, a clock is defined as any regularly repeating physical phenomenon, such as a pendulum swing, earth’s rotation about the sun, earth’s rotation about its axis, heart beat, vibrations of electrically stimulated quartz crystals or the resonance of the unperturbed ground-state hyperfine transition frequency of the cesium-133 atom. Each occurrence of the repeating phenomenon is, of course, an event, but as there are usually very many that can only be distinguished by counting, they are considered a separate class of ticks.
+
+In terms of the number of repetitions possible, some phenomena make better clocks than others, because of the consistency of each repetition and the precision of each tick. A mechanism for counting, or possibly measuring, the ticks is desirable.
 
-An assumption is that the ticks are regular and homogeneous.
+An assumption is that the ticks are regular and homogeneous.
 
-There is no sub-division between two successive clock ticks. Measuring time consists of counting the complete number of repetitions of ticks since the clock started, or since some other event at a given clock count.
+There is no sub-division between two successive clock ticks. Measuring time consists of counting the complete number of repetitions of ticks since the clock started, or since some other event at a given clock tick count.
 
-There is no time measurement before the clock starts, or after it stops.
+There is no time measurement before the clock starts, or after it stops.
 
-It may seem that time can be measured between ‘ticks’ by interpolation, but this needs another clock, with faster ticks. This process of devising more precise clocks continues down to the atomic scale. At that scale the deterministic process of physically trying to interpolate between ticks is not possible.
+It may seem that time can be measured between ticks by interpolation, but this needs another clock, with faster ticks. This process of devising more precise clocks continues down to the atomic scale. At that scale the deterministic process of physically trying to interpolate between ticks is not possible.
 
-The internationally agreed atomic time, TAI, is an example of a timescale with an integer count as the measure of time. However in practice, TAI is an arithmetic compromise across about two hundred separate atomic clocks, corrected for differing altitudes and temperatures.
+The internationally agreed atomic time, TAI International Atomic Time is an example of a timescale with an integer count as the measure of time. However in practice, TAI is an arithmetic compromise across about two hundred separate atomic clocks, corrected for differing altitudes and temperatures.
 
-In this regime, Allen Operators (see ) also can be used. If L occurs before M and M occurs before N, it can be correctly deduced that L occurs before N. The full set of operators also covers pairs of intervals. So if M occurs in the interval (L,N),  integer arithmetic operations such as (M-L) or (N-L) can be performed. This is because an integer timescale or measurement is defined.
+In this regime, Allen Operators (see ) also can be used. If L occurs before M and M occurs before N, it can be correctly deduced that L occurs before N. The full set of operators also covers pairs of intervals. So if M occurs in the interval (L,N),  integer arithmetic operations such as (M-L) or (N-L) can be performed. This is because an integer timescale or measurement is defined by the count of ticks.
 
-This regime constitutes a Temporal Coordinate Reference System, with discrete integer units of measure which can be subject to integer arithmetic.
+This regime constitutes a temporal coordinate reference system, with discrete integer units of measure which can be subject to integer arithmetic.
 
 
 
 CRS and Continuous Timescales
-This regime takes a clock from the previous regime and assumes that between any two adjacent ticks, it is possible to interpolate indefinitely to finer and finer precision, using ordinary arithmetic, rather than any physical device. Units of Measure may be defined that are different from the ‘ticks’. For example, a second may be defined as 9,192,631,770 vibrations of the ground-state hyperfine transition of the cesium-133 atom. Alternatively and differently, a second may be defined as 1/86400th of the rotation of the earth on its axis with respect to the sun. The count of rotations is the ‘ticks’ of an earth-day clock. This latter definition is not precise enough for many uses, as the rotation of the earth on its axis varies from day to day.
+This regime takes a clock from the previous regime and assumes that between any two adjacent ticks, it is possible to interpolate indefinitely to finer and finer precision, using ordinary arithmetic, rather than any physical device. Units of measure may be defined that are different from the ticks. For example, a second may be defined as 9,192,631,770 vibrations of the ground-state hyperfine transition of the cesium-133 atom. Alternatively and differently, a second may be defined as 1/86400th of the rotation of the earth on its axis with respect to the sun. The count of rotations is the ticks of an earth-day clock. This latter definition is not precise enough for many uses, as the rotation of the earth on its axis varies from day to day.
 
-Alternatively, it may be that the ticks are not counted but measured, and the precision of the clock is determined by the precision of the measurements, such as depth in an ice core, or angular position of an astronomical body, such as the sun, moon or a star.
+Alternatively, it may be that the ticks are not counted but measured, and the precision of the clock is determined by the precision of the measurements, such as depth in an ice core subject to seasonal depositions of snow, or angular position of an astronomical body, such as the sun, moon or a star.
 
-It is also assumed that time can be extrapolated to before the time when the clock started and into the future, possibly past when the clock stops.
+It is also assumed that time can be extrapolated before the time when the clock started and into the future, possibly past when the clock stops.
 
-This gives us a continuous number line to perform theoretical measurements. This is a coordinate system. With a datum/origin/epoch, a unit of measure (a name for the ‘tick marks’ on the axis), positive and negative directions and the full range of normal arithmetic. This is a Coordinate Reference System (CRS).
+This gives us a continuous number line to perform theoretical measurements. With a datum/origin/epoch, a unit of measure (a name for the ticks on the axis), positive and negative directions and the full range of normal arithmetic, this is a coordinate reference system (CRS).
 
-In this regime, the Allen Operators (see ) also can be used. If A occurs before B and B occurs before C, it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So if B occurs in the interval (A,C), real number arithmetic operations like (B-A) or (C-A) can be performed. This is because a timescale or measurement has been defined, and between any two instants, an infinite number of other instants can be found.
+In this regime, the Allen Operators (see ) also can be used. If A occurs before B and B occurs before C, it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So if B occurs in the interval (A,C), real number arithmetic operations like (B-A) or (C-A) can be performed. This is because a timescale or measurement has been defined, and between any two instants, an infinite number of other instants can be found.
 
-Some examples are:
+Some examples are:
 
-Unix milliseconds since 1970-01-01T00:00:00.0Z
+Unix milliseconds since 1970-01-01T00:00:00.0Z
 
-Julian Days, and fractions of a day, since noon on 1st January, 4713 BCE.
+Julian Days, and fractions of a day, since noon on 1st January, 4713 BCE.
 
 
 
 
-This regime constitutes a Temporal Coordinate Reference System, with a continuous number line and units of measure, which can be subject to the full range of real or floating-point arithmetic.
+This regime constitutes a temporal coordinate reference system, with a continuous number line and units of measure, which can be subject to the full range of real, or floating-point, arithmetic.
 
 
 
 Calendars
-In this regime, counts and measures of time are related to the various combinations of the rotations of the earth, moon and sun or other astronomical bodies.
+In this regime, counts and measures of time are related to the various combinations of the rotations of the earth, moon and sun or other astronomical bodies.
 Typically there is no simple arithmetic connecting calendar systems and timescales. For example, the current civil year count of years in the Current Era (CE) and Before Current Era (BCE) is a very simple calendar, as there is no year zero. That is, Year 14CE – Year 12CE is a duration of 2 years, and Year 12BCE — Year 14BCE is also two years. However Year 1CE — Year 1BCE is one year, not two as there is no year 0CE or 0BCE.
 
-In this regime, the use of the Allen Operators (see ) is not straightforward. If A occurs before B and B occurs before C, then correctly deducing that A occurs before C is not always easy. The full set of Allen Operators also covers pairs of intervals. So in the example, B occurs in the interval (A,C). However, simple arithmetic operations like (B-A) or (C-A) cannot usually be done simply because of the vagaries of the calendar algorithms, multiple timescales, and multiple Units of Measure.
+In this regime, the use of the Allen Operators (see ) is not straightforward. If A occurs before B and B occurs before C, then correctly deducing that A occurs before C is not always easy. This is because the calendar’s timeline may contain gaps, changes of units of measure, or even duplicated times.
 
-Calendars are social constructs made by combining several clocks and their associated timescales.
+The full set of Allen Operators also covers pairs of intervals. So in the example, B occurs in the interval (A,C). However, simple arithmetic operations like (B-A) or (C-A) cannot usually be done simply because of the vagaries of the calendar algorithms, multiple timescales, and multiple units of measure.
+
+For example, in the Gregorian calendar, calculating the number of days between the 1st February and the 30th March depends on whether the year is a leap year or not, which also depends on the century and millenium. Calculating the precise number of seconds between two dates in the Gregorian calendar also depends on whether leap seconds have been declared between the dates. There have been 27 leap seconds added between 1972 and 2022.
+
 
-This Abstract Specification only addresses the internationally agreed Gregorian calendar. The book Calendrical Calculations by Nachum Dershowitz and Edward M. Reingold provides overwhelming detail for conversion to numerous other calendars that have developed around the world and over the millennia and to meet the various social needs of communities, whether agricultural, religious or other. The reference is comprehensive but not exhaustive, as there are calendars that have been omitted.
+Calendars are social constructs made by combining several clocks and their associated timescales. Calendars may also have local or regional variations at different times of the year or season, such as for ‘day-light saving’ in mid-latitudes. Again, this makes calculations more complicated and prone to change.
 
-A Calendar is a Temporal Reference System, but it is not a Temporal Coordinate Reference System nor an Ordinal Temporal Reference System.
+This Abstract Specification only addresses the internationally agreed Gregorian calendar. The book Calendrical Calculations by Nachum Dershowitz and Edward M. Reingold provides overwhelming detail for conversion to numerous other calendars that have developed around the world and over the millennia and to meet the various social needs of communities, whether agricultural, religious or other. The reference is comprehensive but not exhaustive, as there are calendars that have been omitted.
+
+A calendar is a temporal reference system, but it is not a temporal coordinate reference system nor an ordinal temporal reference system.
 
 
 
 Other Regimes
-There are other regimes, which are out of scope of this Abstract Specification. This could include local solar time, which is useful, for example, for the calculation of illumination levels and the length of shadows on aerial photography, or relativistic time.
+There are other regimes, whose detailed description are out of scope of this Abstract Specification. This could include local solar time, which is useful, for example, for the calculation of illumination levels and the length of shadows on aerial photography, or relativistic time for very fast moving features.
 
 
 Accountancy
-The financial and administrative domains often use weeks, quarters, and other calendrical measures. These may be convenient for the requisite tasks, but are usually inappropriate for scientific or technical purposes.
+The financial and administrative domains often use weeks, quarters, and other calendrical measures. These may be convenient for the requisite tasks, but are usually inappropriate for scientific or technical purposes. This Abstract Conceptual Model for Time can support this regime.
 
 
 
 Agents and Agency
-Agents require a different concept of time from regimes where time is a coordinate axis or measured by clocks. An agent is an entity that senses, responds, and maintains a model of its environment, while performing actions to achieve its goals. See ISO/IEC 22989:2022, Artificial intelligence concepts and terminology. For an agent, the conceptual model of time is about flow and continuity including a sense of now, a memory of past events, and a speculation about future events. This regime addresses how the agent has awareness of the flow of events:
+Agents require a different concept of time from regimes where time is a coordinate axis or measured by clocks. An agent is an entity that senses, responds, and maintains a model of its environment, while performing actions to achieve its goals. See ISO/IEC 22989:2022, Artificial intelligence concepts and terminology. For an agent, the conceptual model of time is about flow and continuity including a sense of now, a memory of past events, and a speculation about future events. This regime addresses how the agent has awareness of the flow of events:
 
-Temporal awareness integrates impression, retention, and protention, representing the continuous movement of time;
+Temporal awareness integrates impression, retention, and protention, representing the continuous movement of time;
 
-Agents continuously revise their models of the environment by integrating new observations with existing models;
+Agents continuously revise their models of the environment by integrating new observations with existing models;
 
-Observations are used to update an agent’s model, leading to a more accurate understanding of the environment and enabling effective goal-directed behavior.
+Observations are used to update an agent’s model, leading to a more accurate understanding of the environment and enabling effective goal-directed behavior.
 
 
 
-This Agent regime of time is relevant to any feature which has agency.
+This regime of time is relevant to any feature which has agency. This Abstract Conceptual Model for Time can support this regime.
 
 
 
 Astronomical Time
-Astronomers have traditionally measured the apparent locations of stars, planets and other heavenly bodies by measuring angular separations from reference points or lines and the timing of transits across a meridian. Generally astronomers use time determined by earth’s motion relative to the distant stars rather than the sun. This is called sidereal time. Times are usually measured from an epoch in daylight, such as local midday, rather than midnight. Accurate measurements of positions of stars, planets and moons were and are essential for navigation on Earth. See the book Astronomical Algorithms by Jean Meeus for examples of the calculations involved.
+Astronomers have traditionally measured the apparent locations of stars, planets and other heavenly bodies by measuring angular separations from reference points or lines and the timing of transits across a meridian. Generally astronomers use time determined by earth’s motion relative to the distant stars rather than the sun. This is called sidereal time. Times are usually measured from an epoch in daylight, such as local midday, rather than midnight. Accurate measurements of positions of stars, planets and moons were and are essential for navigation on Earth. See the book Astronomical Algorithms by Jean Meeus for examples of the calculations involved. This Abstract Conceptual Model for Time can support this regime.
 
 
 
 Local Solar Time
-Local solar time may or may not correspond to the local statutory or legal time in a country. Local solar time can be construed as a clock and timescale, with an angular measure of the apparent position of the sun along the ecliptic (path through the sky) as the basic physical principle. But the sun does not appear to progress evenly along the ecliptic throughout the days and year. There may be variations of up to 15 minutes compared to an even angular speed.
+Local solar time may or may not correspond to the local statutory or legal time in a country. Local solar time can be construed as a clock and timescale, with an angular measure of the apparent position of the sun along the ecliptic (path through the sky) as the basic physical principle. But the sun does not appear to progress evenly along the ecliptic throughout the days and year. There may be variations of up to 15 minutes compared to an even angular speed. This Abstract Conceptual Model for Time can support this regime.
 
 
 
 Space-time
-When dealing with moving objects, the location of the object in space depends on its location in time. That is to say, location is an event in space and time.
+When dealing with moving objects, the location of the object in space depends on its location in time. That is to say, location is an event in space and time.
 
-Originally developed by Hermann Minkowski to support work in Special Relativity, the concept of space-time is useful whenever the location of an object in space is dependent on its location in time.
+Originally developed by Hermann Minkowski to support work in Special Relativity, the concept of space-time is useful whenever the location of an object in space is dependent on its location in time.
 
-Since the speed of light, 
+Since the speed of light, 
 
   
     c
@@ -582,51 +594,71 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 Relativistic
-A regime may be needed for ‘space-time’, off the planet Earth, such as for recording and predicting space weather approaching from the sun, where the speed of light and relativistic effects such as gravity may be relevant.
+A regime may be needed for ‘space-time’, off the planet Earth, such as for recording and predicting space weather approaching from the sun, where the speed of light and relativistic effects such as gravity may be relevant.
 
-Once off planet Earth, distances and velocities can become very large. The speed of light becomes a limiting factor in measuring both where and when an event takes place. Special Relativity deals with the accurate measurement of space-time events as measured between two moving objects. The core concepts are the Lorentz Transforms. These transforms allow one to calculate the degree of “contraction” a measurement undergoes due to the relative velocity between the observing and observed object.
+Once off planet Earth, distances and velocities can become very large. The speed of light becomes a limiting factor in measuring both where and when an event takes place. Special Relativity deals with the accurate measurement of space-time events as measured between two moving objects. The core concepts are the Lorentz Transforms. These transforms allow one to calculate the degree of “contraction” a measurement undergoes due to the relative velocity between the observing and observed object.
 
-The key to this approach is to ensure each moving feature of interest has its own local clock and time, known as its ‘proper time’. This example can be construed as a fitting into the clock and timescale regime. The relativistic effects are addressed through the relationships between the separate clocks, positions and velocities of the features.
+The key to this approach is to ensure each moving feature of interest has its own local clock and time, known as its ‘proper time’. This example can be construed as a fitting into the clock and timescale regime of this Abstract Specification. The relativistic effects are addressed through the relationships between the separate clocks, positions and velocities of the features.
 
-Relativistic effects may need to be considered for satellites and other spacecraft because of their relative speed and position in Earth’s gravity well.
+Relativistic effects may need to be considered for satellites and other spacecraft because of their relative speed and position in Earth’s gravity well.
 
-The presence of gravitational effects requires special relativity to be replaced by general relativity, and it can no longer be assumed that space (or space-time) is Euclidean. That is, Pythagoras’ Theorem does not hold except locally over small areas. This is somewhat familiar territory for geospatial experts.
+The presence of gravitational effects requires special relativity to be replaced by general relativity, and it can no longer be assumed that space (or space-time) is Euclidean. That is, Pythagoras’ Theorem does not hold except locally over small areas, or that the circumference of a circle is not precisely 
+
+  
+    2
+    :
+    π
+    r
+  
+
+2 :pi r. This is somewhat familiar territory for geospatial experts. This Abstract Conceptual Model for Time can support this regime, providing each feature has its own clock.
 
 
 
 
-
-Attributes of the Classes
+
+Abstract Conceptual Model for Time
+This Temporal Abstract Conceptual Model follows , which is the ISO adoption of .
+
+The model is also informed by the W3C Time Ontology in OWL.
+
+
+
+
+
+Classes and their Attributes and Properties
 
 Reference Systems
-The top level ‘ReferenceSystem’ is an abstract super-class and does not have many attributes or properties. Only the total dimension of the reference system and the Location, Time or Domain of Applicability have been identified as essential.
+The top level Reference System class is an abstract super-class and does not have many attributes or properties. Only the total dimension of the reference system and the location, time or domain of applicability have been identified as essential.
 
-The ‘ReferenceSystem’ has two abstract sub-classes: ‘SpatialReferenceSystem’, which is defined in , and ‘TemporalReferenceSystem’, each with the attributes of Dimension and Domains of Applicability.
+The reference system class has two abstract sub-classes: Spatial Reference System, which is defined in , and Temporal Reference System, each with the inherited attributes of dimension and domains of applicability.
 
-The value for Dimension is one for time, or a vertical reference system, but may be as high as 6 for spatial location with orientation as in the GeoPose Implementation Standard.
+The value for dimension is one for time, or a vertical reference system, but may be as high as six for spatial location with orientation as in the GeoPose Implementation Standard.
 
-Besides the conventional space and time, there may be other reference systems, such as wavelength or frequency, that could be addressed by future additions to this Abstract Conceptual Model.
+For example, a reference system may be applicable to Mars, rather than Earth, or perhaps to a specific building site with local coordinates, or a specific time range while coordinate errors are within acceptable bounds.
+
+Besides the conventional space and time, there may be other reference systems, such as wavelength or frequency, that could be addressed by future additions to this Abstract Conceptual Model.
 
 
 
 Ordinal Temporal Reference Systems
-An OrdinalTemporal Reference System has a well-ordered finite sequence of events against which other events can be compared.
+An ordinal temporal reference system has a well-ordered finite sequence of events against which other events can be compared.
 
-An Ordinal Temporal Reference System is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:
+An ordinal temporal reference system is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:
 
-applicableLocationTimeOrDomain: the location, time or domain of applicability;
+applicable location time or domain: the location, time or domain of applicability;
 
-dimension: the number of dimensions in this reference system. For Ordinal Temporal Reference Systems this value is fixed at 1.
+dimension: the number of dimensions in this reference system. For ordinal temporal reference systems this value is fixed at 1.
 
 
 
-An Ordinal Temporal Reference System does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
+An ordinal temporal reference system does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
 
-Epoch: An Ordinal Temporal Reference System ‘has a’ one optional Epoch
+Epoch: An ordinal temporal reference system may be ‘anchored by’ one optional Epoch
 
-Notation: An Ordinal Temporal Reference System ‘can use’ one or more Notations to represent itself.
+Notation: An ordinal temporal reference system ‘is represented by’ one or more Notations to represent itself.
 
-Event: An Ordinal Temporal Reference System ‘consists of’ an ordered set of Events. These events are identifiable temporal instances.
+Event: An ordinal temporal reference system ‘consists of’ an ordered set of Events. These events are identifiable temporal instances.
 
 
 
@@ -635,75 +667,82 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 Events
-The Events class is an ordered list of temporal events. The events can be instances, such as the ascension of a King to a throne, or intervals, such as the complete reign of each king.
+An event is an identifiable happening or occurence of something. The events can be instants, such as the ascension of a king to a throne, or intervals, such as the complete reign of each king.
 
 Other documents may enable two such ‘king lists’ to be related, though usually not completely.
+
+Several archeological layers may be identified as containing broken pottery, overlaid with a layer of burnt wood and brick, then followed by another layer with a different style of pottery and including a coin embossed with a king’s name. Thus the pottery styles can be classified as ‘early’ and ‘late’ and the late pottery associated with the named king, who may be identified from a separate inscribed ‘king list’.
+
 
 
 
 
 Temporal Coordinate Reference Systems
-A Temporal Coordinate Reference System is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:
+A temporal coordinate reference system is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:
 
-applicableLocationTimeOrDomain: the location, time or domain of applicability;
+applicable location time or domain: the location, time or domain of applicability;
 
-dimension: the number of dimensions in this reference system. For Temporal Coordinate Reference Systems this value is fixed at 1.
+dimension: the number of dimensions in this reference system. For temporal coordinate reference systems this value is fixed at 1.
 
 
 
-A Temporal Coordinate Reference System does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
+A temporal coordinate reference system does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
 
-Epoch: A Temporal CRS ‘has a’ one optional Epochs
+Epoch: A temporal coordinate reference system ‘anchored by’ one optional Epochs
 
-Notation: A Temporal CRS ‘can use’ one or more Notations to represent itself.
+Notation: A temporal coordinate reference system ‘represented by’ one or more Notations to represent itself.
 
-Timescale: A Temporal CRS ‘has a’ one Timescale which is used to represent the values along its single axis. This Timescale can be either discrete or continuous.
+Timescale: A temporal coordinate reference system ‘must have’ one Timescale which is used to represent the values along its single axis. This timescale can be either discrete or continuous.
 
 
 
 
 
 Calendar Reference Systems
-Calendars combine different timescales and their clocks and units of measure, and other events, to make a complex timeline against which events can be compared. Calculated algorithms are used to determine which instants of intervals on the compound timeline are identified and labeled.
+Calendars combine different timescales and their clocks and units of measure, and other events, to make a complex timeline against which events can be compared. Calculated algorithms are used to determine which instants or intervals on the compound timeline are identified and labeled.
 
-A Calendar is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:
+A calendar is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:
 
-applicableLocationTimeOrDomain: the location, time or domain of applicability
+applicable location time or domain: the location, time or domain of applicability
 
-dimension: the number of dimensions in this reference system. For Calendars this value is fixed at 1.
+dimension: the number of dimensions in this reference system. For calendars this value is fixed at 1.
 
 
 
-A Calendar does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
+A calendar does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
 
-Algorithm: A Calendar ‘has a’ one or more Algorithms. These Algorithms specify how the multiple Time Scales are aggregated into a single Timeline.
+Timeline: A calendar ‘has a’ one Timeline which serves to aggregate a number of Timescales into a single coherent measure of date and time.
 
-Epoch: A calendar ‘has a’ one optional Epoch
+Algorithm: A timeline ‘is defined by’ one or more Algorithms. These algorithms specify how the multiple timescales are aggregated into a single Timeline.
 
-Notation: A calendar ‘can use’ one or more Notations to represent itself.
+Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a Timeline.
 
-Timeline: A Calendar ‘has a’ one Timeline which serves to aggregate a number of Timescales into a single coherent measure of date and time.
+Epoch: A calendar is ‘anchored by’ one optional Epoch
 
-Timescale: A Calendar ‘has a’ two or more Timescales which are used to construct a Timeline.
+Notation: A calendar  is ‘represented by’ one or more Notations to represent itself.
 
 
 
 
 Timeline
-The timeline is usually a set of instants from the past to the future and is compounded from multiple timescales, with multiple units of measures, and complicated arithmetic determined by the calendar algorithm(s). The timeline is usually not even continuous, having gaps or even multiple simultaneous representations.
+The timeline is usually a set of instants from the past to the future and is compounded from multiple timescales, with multiple units of measures, and complicated arithmetic determined by the calendar algorithm(s). The timeline is usually not even continuous, having gaps or even duplicated times or multiple simultaneous representations.
 
-A Timeline does not have any attributes of its own. Nor does it inherit any attributes from a parent class. However, it does use associations with other classes to fully describe itself.
+A timeline does not have any attributes of its own. Nor does it inherit any attributes from a parent class. However, it does use associations with other classes to fully describe itself.
 
-Algorithm: A Timeline ‘has a’ one or more Algorithms. These Algorithms specify how the multiple Time Scales are aggregated into a single Timeline.
+Algorithm: A timeline is ‘defined by’ one or more Algorithms. These algorithms specify how the multiple timescales are aggregated into a single timeline.
 
-Timescale: A Timeline ‘has a’ two or more Timescales which are used to construct the Timeline.
+Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
 
 
 
 
 
 Algorithm
-An Algorithm specifies the logic used to construct a Timeline from its constituent Timescales. An Algorithm does not have any attributes of its own. Nor does it make use of any other classes from this Temporal model.
+An algorithm specifies the logic used to construct a timeline from its constituent Timescales. An algorithm does not have any attributes of its own. It does make use of timescales to construct the timeline of a calendar.
+
+Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
+
+
 
 
 
@@ -738,38 +777,38 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 Discrete and Continuous Time Scales
-A clock may be a regular, repeating, physical event, or ‘tick’, that can be counted. The sequence of tick counts form a discrete (counted) timescale.
+A clock may be a regular, repeating, physical event, or tick, that can be counted. The sequence of tick counts form a discrete (counted) timescale.
 
 Some clocks  allow the measurement of intervals between ticks, such as the movement of the sun across the sky. Alternatively, the ticks may not be completely distinguishable, but are still stable enough over the time of applicability to allow measurements rather than counting to determine the passage of time. These clocks generate a continuous (measured) timescale.
 
-The duration of a tick is a constant. The length of a tick is specified using a Unit Of Measure.
+The duration of a tick is assumed constant. The duration of a tick is specified using a Unit Of Measure.
 
 
 Timescale
-A Timescale is a linear measurement (one dimension) used to measure or count monotonic events. Timescale has three attributes:
+A timescale is a linear measurement (one dimension) used to measure or count monotonic events. Timescale has three attributes:
 
-Arithmetic: an indicator of whether this Timescale contains counted integers or measured real/floating point numbers.
+Arithmetic: an indicator of whether this timescale contains counted integers or measured real/floating point numbers.
 
-StartCount: the lowest value in a Timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
+StartCount: the lowest value in a timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
 
-EndCount: the greatest value in a Timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
+EndCount: the greatest value in a timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
 
 
 
-In addition to the attributes, the Timescale class maintains associations with two other classes to complete its definition.
+In addition to the attributes, the timescale class maintains associations with two other classes to complete its definition.
 
-Clock: A Timescale ‘has a’ one clock. This is the process which generates the ‘tick’ which is counted or measured for the Timescale.
+Clock: A timescale is ‘determined by’ one clock. This is the process which generates the ticks which are counted or measured for the timescale.
 
-UnitOfMeasure: A timescale ‘has a’ one UnitOfMeasure. This class specifies the units of the clock measurement as well as the direction of increase of that measurement.
+UnitOfMeasure: A timescale ‘has a’ one UnitOfMeasure. This class specifies the units of the clock measurement or count as well as the direction of increase of that measurement or count.
 
 
 
 
 
 Clock
-A Clock represents the process which generates the ‘tick’ which is counted or measured for a Timescale. Clock has one attribute:
+A clock represents the process which generates the ticks which are counted or measured for a timescale. Clock does not have any attributes of its own. Nor does it inherit any attributes from a parent class. However, it does use an association with another class to fully describe itself.
 
-Tick definition: a description of the process which is being used to generate monotonic events.
+Ticks: a description of the process which is being used to generate monotonic events.
 
 
 
@@ -781,10 +820,10 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 
-UnitOfMeasure
-The Direction attribute indicates whether counts or measures increase in the positive (future) or negative (past) direction. The attribute could be part of ‘Timescale’ or ‘TemporalCoordinateReferenceSystem’ rather than a separate class ‘UnitOfMeasure’, but on balance, it seems better here, as the names often imply directionality, such as fathoms increasing downwards, MYA (Millions of Years Ago) increasing earlier, Atmospheric Pressure in hPa (Hectopascals) decreasing upwards, and FL (FlightLevel) increasing upwards.
+Unit of Measure
+The direction attribute indicates whether counts or measures increase in the positive (future) or negative (past) direction. The attribute could be part of timescale or temporal coordinate reference system rather than a separate class of measure, but on balance, it seems better here, as the names often imply directionality, such as fathoms increasing downwards, MYA (Millions of Years Ago) increasing earlier, atmospheric pressure in hPa (hectopascals) decreasing upwards, and FL (flight level) increasing upwards.
 
-Direction: indicates the direction in which a timescale progresses as new ‘ticks’ are counted or measured.
+Direction: indicates the direction in which a timescale progresses as new ticks are counted or measured.
 
 
 
@@ -803,13 +842,13 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 A well preserved fossilized log is recovered and the tree rings establish an annual ‘tick’. The start and end times may be known accurately by comparison and matching with other known tree ring sequences, or perhaps only dated imprecisely via Carbon Dating, or its archaeological or geological context.
 
 
-A clock is started, but undergoes a calibration process against some standard clock, so the initial, reliable Start Time does not start at Count Zero. The clock is accidentally knocked so that it is no longer correctly calibrated, but is still working. The End Time is not the last time that the clock ticks.
+A clock is started, but undergoes a calibration process against some standard clock, so the initial, reliable start time does not start at a count of zero. The clock is accidentally knocked so that it is no longer correctly calibrated, but is still working. The end time is not the last time that the clock ticks.
 
 
-TAI (International Atomic Time, Temps Atomique International) is coordinated by the BIPM (International Bureau of Weights and Measures, Bureau International de Poids et Measures) in Paris, France. TAI is based on the average of hundreds of separate atomic clocks around the world, all corrected to be at mean sea level and standard pressure and temperature. The epoch is defined by Julian Date 2443144.5003725 (1 January 1977 00:00:32.184).
+TAI International Atomic Time (or Temps Atomique International) is coordinated by the BIPM (International Bureau of Weights and Measures, Bureau International de Poids et Measures) in Paris, France. TAI is based on the average of hundreds of separate atomic clocks around the world, all corrected to be at mean sea level and standard pressure and temperature. The epoch is defined by Julian Date 2443144.5003725 (1 January 1977 00:00:32.184).
 
 
-The Julian Day is the continuous count of days (rotations of the Earth with respect to the Sun) since the beginning of the year 4173 BCE and will terminate at the end of the year 3267 CE. The count then starts again as “Period 2”. Many computer based timescales, such as Unix Time, are based on the Julian Day timescale, but with different epochs, to fit the numbers into the limited computer words.
+The Julian Day is the continuous count of days (rotations of the Earth with respect to the Sun) since the beginning of the year 4173 BCE and will terminate at the end of the year 3267 CE. The count then starts again as “Period 2”. Many computer based timescales, such as Unix Time, are based on the Julian Day timescale, but with different epochs, to fit the large numbers into computer words of limited size.
 
 
 
@@ -818,12 +857,12 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 Supporting Classes
 
 Epoch
-The Epoch class provides a origin or datum for a Temporal Reference System.
+The epoch class provides an origin or datum for a temporal reference system.
 
 
 
 Notation
-The Notation class identifies a widely agreed, commonly accepted, notation for representing values in accordance with a temporal reference system.
+The notation class identifies a widely agreed, commonly accepted, notation for representing values in accordance with a temporal reference system.
 
 
 
@@ -832,7 +871,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 Notation
 There are often widely agreed, commonly accepted, notations used for temporal reference systems, but few have been standardized. Any particular notation may be capable of expressing a wider range of times than are valid for the reference system.
 
-The  timestamp notation, a restrictive profile of , can express times before 1588CE, when the Gregorian calendar was first introduced in some parts of the world.
+The  timestamp notation, a restrictive profile of , can express times before 1582CE, when the Gregorian calendar was first introduced in some parts of the world.
 
 
 
@@ -857,9 +896,9 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 2.2 Two things can be in the same place at different times.
 
-These are not symmetrical in space and time.
+These statements are not symmetrical between space and time.
 
-Temporal constructs such as instants, durations or intervals, multi-instants (a set of instants), and multi-intervals are not included in this conceptual model. These do have strongly analogous equivalents in space, such as points and multi-points, especially in a single dimension, such as vertical. The temporal constructs are well described in Maintaining Knowledge about Temporal Intervals by J. F. Allen (see ) and apply across all of the regimes, so do not need to be in this Abstract Conceptual Model.
+Temporal constructs such as instants, durations or intervals, multi-instants (a set of instants), and multi-intervals (a set of intervals) are not included in this conceptual model. These do have strongly analogous equivalents in space, such as points and multi-points, especially in a single dimension, such as vertical. The temporal constructs are well described in Maintaining Knowledge about Temporal Intervals by J. F. Allen (see ) and apply across all of the regimes, so do not need to be in this Abstract Conceptual Model.
 
 
 
@@ -953,28 +992,28 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
                         ISO/IEC 22989:2022/AWI Amd 1      ISO/IEC 22989:2022/AWI Amd 1              2022-07-19          
     Geneva
 Allen, J. F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26, pp832-843 (1983).Maintaining Knowledge about Temporal Intervals
-  2024-02-09  
+  2024-02-13  
 Topic 2
   
-Referencing by coordinates
+Referencing by coordinates (Including corrigendum 1 and corrigendum	2)
   
-Topic 2 — Referencing by coordinates
-  http://www.opengis.net/doc/AS/topic-2/5.0  https://docs.ogc.org/as/18-005r4/18-005r4.html  18-005r4      2019-02-08                  
+Topic 2 — Referencing by coordinates (Including corrigendum 1 and corrigendum	2)
+  http://www.opengis.net/doc/AS/topic-2/6.0  https://docs.ogc.org/as/18-005r8/18-005r8.pdf  18-005r8      2023-09-05                  
         Roger Lott      
                       
 Open Geospatial Consortium
-        4  en  Latn  This document is identical in normative content with the latest edition (2019) of ISO 19111, Geographic Information — Spatial referencing by coordinates [ISO 19111:2019].  
-Cox, S., Little, C.: W3C Time Ontology in OWL. World Wide Web Consortium (2022) W3C TR owl-time3C TR owl-time
+        8  en  Latn  This document is consistent with the third edition (2019) of ISO 19111, Geographic Information — Referencing by coordinates including its amendments 1 and 2. ISO 19111:2019 was prepared by Technical Committee ISO/TC 211, Geographic information/Geomatics, in close collaboration with the Open Geospatial Consortium (OGC). It replaces the second edition, ISO 19111:2007 and also ISO 19111-2:2009, OGC documents 08-015r2 and 10-020. This OGC document, 18-005r5, incorporates three editorial corrections made in ISO 19111:2019 amendment 1 of 2021.  
+Cox, S., Little, C.: W3C Time Ontology in OWL. World Wide Web Consortium (2022) .W3C Time Ontology in OWL3C Time Ontology in OWL
 
-Bibliography  2024-02-09  
-OGC GeoPose 1.0 Data Exchange Draft Standard
+Bibliography  2024-02-13  
+OGC GeoPose 1.0 Data Exchange Standard
   
-OGC GeoPose 1.0 Data Exchange Draft Standard
-  http://www.opengis.net/doc/DIS/geopose/1.0  https://docs.ogc.org/dis/21-056r10/21-056r10.html  21-056r10      2022-11-28                  
+OGC GeoPose 1.0 Data Exchange Standard
+  http://www.opengis.net/doc/IS/geopose/1.0  https://docs.ogc.org/is/21-056r11/21-056r11.html  21-056r11      2023-09-08                  
         Carl Stephen Smyth      
                       
 Open Geospatial Consortium
-        10  en  Latn  GeoPose 1.0 is an OGC Implementation Standard for exchanging the location and orientation of real or virtual geometric objects (“Poses”) within reference frames anchored to the earth’s surface (“Geo”) or within other astronomical coordinate systems.  The standard specifies two Basic forms with no configuration options for common use cases, an Advanced form with more flexibility for more complex applications, and five composite GeoPose structures that support time series plus chain and graph structures.  These eight Standardization Targets are independent. There are no dependencies between Targets and each may be implemented as needed to support a specific use case.  The Standardization Targets share an implementation-neutral Logical Model which establishes the structure and relationships between GeoPose components and also between GeoPose data objects themselves in composite structures. Not all of the classes and properties of the Logical Model are expressed in individual Standardization Targets nor in the specific concrete data objects defined by this standard. Those elements that are expressed are denoted as implementation-neutral Structural Data Units (SDUs). SDUs are aliases for elements of the Logical Model, isolated to facilitate specification of their use in encoded GeoPose data objects for a specific Standardization Target.  For each Standardization Target, each implementation technology and corresponding encoding format defines the encoding or serialization specified in a manner appropriate to that technology.  GeoPose 1.0 specifies a single encoding in JSON format (IETF RFC 8259). Each Standardization Target has a JSON Schema (Internet-Draft draft-handrews-json-schema-02) encoding specification. The key standardization requirements specify that concrete JSON-encoded GeoPose data objects must conform to the corresponding JSON Schema definition. The individual elements identified in the encoding specification are composed of SDUs, tying the specifications back to the Logical Model.  The GeoPose 1.0 Standard makes no assumptions about the interpretation of external specifications, for example, of reference frames. Nor does it assume or constrain services or interfaces providing conversion between GeoPoses of difference types or relying on different external reference frame definitions.    2024-02-09  
+        11  en  Latn  GeoPose 1.0 is an OGC Implementation Standard for exchanging the location and orientation of real or virtual geometric objects (“Poses”) within reference frames anchored to the earth’s surface (“Geo”) or within other astronomical coordinate systems.  The standard specifies two Basic forms with no configuration options for common use cases, an Advanced form with more flexibility for more complex applications, and five composite GeoPose structures that support time series plus chain and graph structures.  These eight Standardization Targets are independent. There are no dependencies between Targets and each may be implemented as needed to support a specific use case.  The Standardization Targets share an implementation-neutral Logical Model which establishes the structure and relationships between GeoPose components and also between GeoPose data objects themselves in composite structures. Not all of the classes and properties of the Logical Model are expressed in individual Standardization Targets nor in the specific concrete data objects defined by this standard. Those elements that are expressed are denoted as implementation-neutral Structural Data Units (SDUs). SDUs are aliases for elements of the Logical Model, isolated to facilitate specification of their use in encoded GeoPose data objects for a specific Standardization Target.  For each Standardization Target, each implementation technology and corresponding encoding format defines the encoding or serialization specified in a manner appropriate to that technology.  GeoPose 1.0 specifies a single encoding in JSON format (IETF RFC 8259). Each Standardization Target has a JSON Schema (Internet-Draft draft-handrews-json-schema-02) encoding specification. The key standardization requirements specify that concrete JSON-encoded GeoPose data objects must conform to the corresponding JSON Schema definition. The individual elements identified in the encoding specification are composed of SDUs, tying the specifications back to the Logical Model.  The GeoPose 1.0 Standard makes no assumptions about the interpretation of external specifications, for example, of reference frames. Nor does it assume or constrain services or interfaces providing conversion between GeoPoses of difference types or relying on different external reference frame definitions.    2024-02-09  
 Geographic information
   
 Temporal schema
@@ -990,43 +1029,55 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
   A Brief History of Timekeeping
   
 
-  Andrewes, W.H.J.: A Chronicle Of Timekeeping. Scientific American (2006). 
+  Andrewes, W.H.J.: A Chronicle Of Timekeeping. Scientific American (2006). .
   A Chronicle Of Timekeeping
   
 
-  Meeus, J.: Astronomical Algorithms. 
+  Meeus, J.: Astronomical Algorithms. .
   Astronomical Algorithms
   
 
-  Dershowitz, N., Reingold, N.M.: Calendrical Calculations — The Ultimate Edition. Cambridge University Press (2018). ISBN-13:978-1107683167.  [last accessed 2023-01]
+  Dershowitz, N., Reingold, N.M.: Calendrical Calculations — The Ultimate Edition. Cambridge University Press (2018). ISBN-13:978-1107683167. . [last accessed 2023-01]
   Calendrical Calculations
   
 
-  Bureau International des Poids et Mesures (BIPM): Establishment of International Atomic Time and Coordinated Universal Time. 
+  Bureau International des Poids et Mesures (BIPM): Establishment of International Atomic Time and Coordinated Universal Time. .
   Establishment of International Atomic Time and Coordinated Universal Time
   
+
+  The Library of Congress: Extended Date/Time Format (EDTF) Specification. (2019). . [last accessed 2024-02]
+  Extended Date Time Format
+  
+
+  Wikipedia. International Atomic Time. . [Last accessed 2024-02]
+  International Atomic Time
+  
 
-  Wikipedia. International Fixed Calendar.  [last accessed 2023-12]
+  Wikipedia. International Fixed Calendar. . [last accessed 2023-12]
   International Fixed Calendar
   
 
-  Wikipedia. List of Calendars.  [last accessed 2023-12]
+  Wikipedia. List of Calendars. . [last accessed 2023-12]
   List of Calendars
   
 
-  Lorentz Transform. Wolfram MathWorld. 
+  Lorentz Transform. Wolfram MathWorld. .
   Lorentz Transforms
   
 
-  Allen, J.F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26 pp832-843 (1983).
+  Allen, J.F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26, no. 11, pp 832-843 (1983). .
   Maintaining Knowledge about Temporal Intervals
   
 
-  Minkowski, H.: Space and Time, Minkowski’s Papers on Relativity. Minkowski Institute Press, Montreal (2012) 
+  Minkowski, H.: Space and Time, Minkowski’s Papers on Relativity. Minkowski Institute Press, Montreal (2012). .
   Minkowski Space and Time
   
+
+  IETF: Date and Time on the Internet: Timestamps with additional information. Internet Engineering Task Force (2023). . [last accessed 2024-02]
+  Serialising Extended Data About Times and Events
+  
 
-  Juan Diego Bogotá, Zakaria Djebbara: Time-consciousness in computational phenomenology: a temporal analysis of active inference. Neuroscience of Consciousness, Volume 2023, Issue 1 (2023). 
+  Juan Diego Bogotá, Zakaria Djebbara: Time-consciousness in computational phenomenology: a temporal analysis of active inference. Neuroscience of Consciousness, Volume 2023, Issue 1 (2023). .
   Time-consciousness in computational phenomenology
   
 
@@ -1034,9 +1085,13 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
   Treatise on Time and Space
   
 
-  The Open Group: UNIX Time.  [last accessed 2023-01]
+  The Open Group: UNIX Time. . [last accessed 2023-01]
   UNIX Time
   
+
+  W3C. Working with Time Zones, W3C Working Group Note 5 July 2011. .
+  Working with Time Zones
+  
 
 
 
@@ -1053,6 +1108,10 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 
+
+
+
+
 
 
   
@@ -1144,7 +1203,7 @@ To obtain additional rights of use, visit
 
 
 
-Contents
+Contents
 I.<tab/>Abstract
The primary goal of the Abstract Conceptual Model for Time is to establish clear concepts, their relationships, and terminology.

 
 
The fundamental concepts of events, clocks, timescales, coordinates and calendars have been long established, but there is no clear, straightforward defining document. This Abstract Specification provides clear consistent definitions of the fundamental concepts and terminology. The conceptual model enables advantages and disadvantages of adopting a particular technological approach to be identified and provides an opportunity for communities to build consistent, interoperable representations regardless of implementation.

@@ -1152,21 +1211,21 @@ To obtain additional rights of use, visit
 
Traditionally, geospatial communities used 2D coordinates and the vertical (third dimension) and temporal aspects were considered attributes rather than valid components of coordinate systems. In an increasingly dynamic, faster and multidimensional world, much confusion and lack of interoperability has occurred because of inconsistent approaches to defining and expressing time. Various international bodies expended considerable effort to establish the Gregorian Calendar as a consistent timeline. The Gregorian Calendar suffices for low precision applications, such as to the nearest minute, but not so when second or sub-second accuracy is required. For example, there have been differing practices and no consensus on whether leap seconds should be part of the Gregorian timeline.

 
 
This document is consistent with ISO 19111:2019 and W3C Time Ontology in OWL.

-
+
 II.<tab/>Keywords
 
The following are keywords to be used by search engines and document catalogues.

 
ogcdoc, OGC document, abstract specification, conceptual model, time, temporal referencing, referencing by coordinates, calendar, clock, timescale

 
 III.<tab/>Preface
-
When OGC standards involve time, they generally refer to the ISO documents such as ISO 19108:2002 (now largely superseded), ISO 19111:2019, ISO 8601, and their freely available OGC equivalents, such as OGC 18-005r4 (the equivalent to ISO 19111:2019).

+
When OGC Standards involve time, they generally refer to the ISO documents such as Geographic information Temporal schema ISO 19108:2002 (now largely superseded), Geographic information Referencing by coordinates ISO 19111:2019, Date and Time Format ISO 8601, and their freely available OGC equivalents, such as OGC Abstract Specification Topic 2: Referencing by coordinates  OGC 18-005r8 (the equivalent to ISO 19111:2019).

 
-
Much effort over decades has gone into establishing complex structures to represent calendar based time, such as the ISO 8601 notation, and many date-time schemas. A consequence of this effort is that many end-users and developers of software use calendar based “coordinates”, with the associated ambiguities about underlying algorithms, imprecision and inappropriate scope.

+
Over decades, much effort has gone into establishing complex structures to represent calendar based time, such as the ISO 8601 notation, and many date-time schemas. A consequence of this effort is that many end-users and developers of software use calendar based “coordinates”, with the associated ambiguities about underlying algorithms, imprecision and inappropriate scope.

 
-
The aim of this Abstract Specification is to establish clear concepts and terminology, so that people are well aware of the advantages and disadvantages of adopting a particular technological approach to time and then perhaps build better, more appropriate, interoperable systems for their use cases.

+
The aim of this OGC Temporal Abstract Specification is to establish clear concepts and terminology. This is necessary so that people are aware of the advantages and disadvantages of specifying or adopting a particular technological approach to time and then perhaps build better, more appropriate, interoperable systems for their use cases.

 
 IV.<tab/>Security Considerations
 
This Abstract Specification does not place any constraints on application, platform, operating system level, or network security.

-
+
 V.<tab/>Submitting Organizations
 
The following organizations submitted this Document to the Open Geospatial Consortium (OGC):

  
  • U.K. Met Office
    • 
@@ -1211,11 +1270,11 @@ submitters:
    
 
 
    This Abstract Specification does not include any specific requirements or conformance classes. However, it does include normative references and a normative Unified Modeling Language (UML) model. Conformance is demonstrated through inclusion of the normative references in any derivative specification and by basing any derived conceptual model on the abstract model provided in this Standard.
    
 
-
    The Clause 7 of this Abstract Specification uses UML to present conceptual schemas for describing the higher level classes of time and temporal reference systems. These schemas define conceptual classes that:
    
+
    The Clause 8 of this Abstract Specification uses UML to present conceptual schemas for describing the higher level classes of time and temporal reference systems. These schemas define conceptual classes that:
    
 
-
    1. may be considered to comprise a cross-domain application schema, or
      
+
      1. may be considered to comprise a cross-domain application schema, or
        
 
        2. 
-
      2. may be used in application schemas, profiles and implementation specifications.
        
+
      3. may be used in application schemas, profiles and implementation specifications.
        
 
        4. 
 
      
 
@@ -1238,100 +1297,108 @@ directly in application schemas.
      
 
 
 4.1.<tab/>Terms and definitions
-4.1.1.conceptual model
-
      description of common concepts and their relationships, particularly in order to facilitate exchange of information between parties within a specific domain
      
+4.1.1.clock
+
      regularly repeating physical phenomenon that can be counted
      
+       
+
+4.1.2.conceptual model
+
      description of common concepts and their relationships, particularly in order to facilitate exchange of information between parties within a specific domain
      
 
 
- Note 1 to entry
      A conceptual model is explicitly chosen to be independent of design or implementation concerns.
      
+ Note 1 to entry
      A conceptual model is explicitly chosen to be independent of design or implementation concerns.
      
       
 
-4.1.2.coordinate
-
      one of a sequence of numbers designating the position of a point
      
+4.1.3.coordinate
+
      one of a sequence of numbers designating the position of a point
      
 
 
 
 
- Note 1 to entry
      In many coordinate reference systems, the coordinate numbers are qualified by units.
      
+ Note 1 to entry
      In many coordinate reference systems, the coordinate numbers are qualified by units.
      
       [SOURCE: ISO 19111:2019]      
 
-4.1.3.coordinate reference system; CRS
+4.1.4.coordinate reference system; CRS
 
 
 
-
      coordinate system (Clause 4.1.4) that is related to an object by a datum (Clause 4.1.7)
      
+
      coordinate system (Clause 4.1.5) that is related to an object by a datum (Clause 4.1.8)
      
 
 
 
 
 
 
- Note 1 to entry
      Geodetic and vertical datums are referred to as reference frames.
      
-      Note 2 to entry
      For geodetic and vertical reference frames, the object will be the Earth. In planetary applications, geodetic and vertical reference frames may be applied to other celestial bodies.
      
+ Note 1 to entry
      Geodetic and vertical datums are referred to as reference frames.
      
+      Note 2 to entry
      For geodetic and vertical reference frames, the object will be the Earth. In planetary applications, geodetic and vertical reference frames may be applied to other celestial bodies.
      
       [SOURCE: ISO 19111:2019]      
 
-4.1.4.coordinate system
-
      set of mathematical rules for specifying how coordinates are to be assigned to points
      
+4.1.5.coordinate system
+
      set of mathematical rules for specifying how coordinates are to be assigned to points
      
 
 
  [SOURCE: ISO 19111:2019]      
 
-4.1.5.datumreference frame ADMITTED
+4.1.6.datumreference frame ADMITTED
 
 
 
-
      parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate system (Clause 4.1.4)
      
+
      parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate system (Clause 4.1.5)
      
 
 
  [SOURCE: ISO 19111:2019]      
 
-4.1.6.epoch
+4.1.7.epoch
 
 
-geodesy
      point in time
      
+geodesy
      point in time
      
 
 
 
 
 
 
- Note 1 to entry
      In ISO 19111:2019, an epoch is expressed in the Gregorian calendar as a decimal year.
      
-      Example
      2017-03-25 in the Gregorian calendar is epoch 2017.23. Other notations or reference systems are options.
      
+ Note 1 to entry
      In ISO 19111:2019, an epoch is expressed in the Gregorian calendar as a decimal year.
      
+      Example
      2017-03-25 in the Gregorian calendar is epoch 2017.23. Other notations or reference systems are options.
      
       [SOURCE: ISO 19111:2019]      
 
-4.1.7.reference framedatum ADMITTED
+4.1.8.reference framedatum ADMITTED
 
 
 
-
      parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate system (Clause 4.1.4)
      
+
      parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate system (Clause 4.1.5)
      
 
 
  [SOURCE: ISO 19111:2019]      
 
-4.1.8.temporal coordinate reference system; TRS
+4.1.9.temporal coordinate reference system; TRS
 
 
 
-
      coordinate reference system (Clause 4.1.3) based on a temporal datum (Clause 4.1.10)
      
+
      coordinate reference system (Clause 4.1.4) based on a temporal datum (Clause 4.1.11)
      
 
 
  [SOURCE: ISO 19111:2019]      
 
-4.1.9.temporal coordinate system
+4.1.10.temporal coordinate system
 
 
-geodesy
      one-dimensional coordinate system (Clause 4.1.4) where the axis is time
      
+geodesy
      one-dimensional coordinate system (Clause 4.1.5) where the axis is time
      
 
 
  [SOURCE: ISO 19111:2019]      
 
-4.1.10.temporal datum
-
      datum (Clause 4.1.7) describing the relationship of a temporal coordinate system (Clause 4.1.9) to an object
      
+4.1.11.temporal datum
+
      datum (Clause 4.1.8) describing the relationship of a temporal coordinate system (Clause 4.1.10) to an object
      
 
 
 
 
- Note 1 to entry
      The object is normally time on the Earth.
      
+ Note 1 to entry
      The object is normally time on the Earth.
      
       [SOURCE: ISO 19111:2019]      
+
+4.1.12.tick
+
      event which is a single occurrence of the regularly repeating physical phenomenon of a clock
      
+       
       
 
 
@@ -1365,152 +1432,150 @@ directly in application schemas.
      
 
 
      A conceptual model:
      
 
-
      1. is a representation of a system, made of the composition of concepts which are used to help people know, understand, or simulate a subject the model represents. A documented conceptual model represents ‘concepts’ (entities), the relationships between them, and a vocabulary;
        
+
        1. is a representation of a system, made of the composition of concepts which are used to help people know, understand, or simulate a subject the model represents. A documented conceptual model represents ‘concepts’ (entities), the relationships between them, and a vocabulary;
          
 
          2. 
-
        2. is explicitly defined to be independent of design or implementation concerns;
          
+
        3. is explicitly defined to be independent of design or implementation concerns;
          
 
          4. 
-
        4. organizes the vocabulary needed to communicate consistently and thoroughly about the know-how of a problem domain;
          
+
        5. organizes the vocabulary needed to communicate consistently and thoroughly about the know-how of a problem domain;
          
 
          6. 
-
        6. starts with a glossary of terms and definitions. There is a very high premium on high-quality, design-independent definitions, free of data or implementation biases; the model also emphasizes rich vocabulary; and
          
+
        7. contains the definitions of the concepts that it organizes. There is a high premium on high-quality, design-independent definitions, free of data or implementation biases; the model also emphasizes rich vocabulary; and
          
 
          8. 
-
        8. is always about identifying the correct choice of terms to use in communications, including statements of rules and requirements, especially where high precision and subtle distinctions need to be made. The core concepts of a temporal geospatial problem domain are typically quite stable over time.
          
+
        9. is always about identifying the correct choice of terms to use in communications, including statements of rules and requirements, especially where high precision and subtle distinctions need to be made. The core concepts of a temporal geospatial problem domain are typically quite stable over time.
          
 
          10. 
 
        
 
 
-
-7.<tab/>Abstract Conceptual Model for Time
-
        This Temporal Abstract Conceptual Model follows ISO 19111:2019, which is the ISO adoption of OGC 18-005r4.
        
-
-
        The model is also informed by the W3C Time Ontology in OWL.
        
-
-
        Figure 1
        
-        
-
-
-8.<tab/>Temporal regimes
+
+7.<tab/>Temporal regimes
 
-8.1.<tab/>General
-
        To enable more clear reasoning about time, this Abstract Specification uses the term “Regime” to describe the fundamentally different types of time and its measurement. This is a pragmatic approach that allows the grouping of recommendations and best practices in a practical way, but without obscuring the connection to the underlying theoretical components.
        
+7.1.<tab/>General
+
        To enable more clear reasoning about time, this Abstract Specification uses the term “Regime” to describe the fundamentally different types of time and its measurement. This is a pragmatic approach that allows the grouping of recommendations and best practices in a practical way, but without obscuring the connection to the underlying theoretical components.
        
 
-
        The first three regimes, described below, have deep underlying physical and mathematical foundations which cannot be legislated away. The fourth regime, calendars, concerns social constructs using seemingly random mixtures of ad hoc algorithms, arithmetic, numerology and measurements. Paradoxically, the calendar regime has historically driven advances in mathematics and physics. See the article A Chronicle Of Timekeeping.
        
+
        The first three regimes, described below, have deep underlying physical and mathematical foundations which cannot be legislated away. The fourth regime, calendars, concerns social constructs using seemingly random mixtures of ad hoc algorithms, arithmetic, numerology and measurements. Paradoxically, the calendar regime has historically driven advances in mathematics and physics. See the article A Chronicle Of Timekeeping.
        
 
-
        With due consideration, the regimes are applicable to other planets and outer space.
        
+
        With due consideration, the regimes are applicable to other planets and outer space.
        
         
 
 
-8.2.<tab/>Events and Operators
-
        The simplest way of relating entities in time is by events that can be ordered and established in a sequence, and this sequence is used as an approximate measure of the passage of time.
        
+7.2.<tab/>Events and Operators
+
        The simplest way of relating entities in time is by events that can be ordered and established in a sequence, and this sequence is used as an approximate measure of the passage of time.
        
 
-
        In this regime, no clocks or time measurements are defined, only events, that are ordered in relation to each other. Examples are geological layers, sediment or ice core layers, archaeological sequences, sequential entries in computer logs without coordinated time.
        
+
        In this regime, no clocks or time measurements are defined, only events, that are ordered in relation to each other. Examples are geological layers, sediment or ice core layers, archaeological sequences, sequential entries in computer logs without coordinated time.
        
 
-
        One set of events may be completely ordered with respect to each other, but another set of similar internally consistent ordered events cannot be cross-referenced to each other unless extra information is available. Even then, only partial orderings may be possible.
        
+
        One set of events may be completely ordered with respect to each other, but another set of similar internally consistent ordered events cannot be cross-referenced to the first set unless extra information is available. Even then, only partial orderings may be possible.
        
 
-
        In this regime, the Allen Operators (see Figure 2) can be used. If A occurs before B and B occurs before C, then it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So in our example, B occurs in the interval (A,C). However, arithmetic operations like (B-A) or (C-A) cannot be performed as any timescale or measurements are not defined. For example, in geology, ‘subtracting’ Ordovician from Jurassic is meaningless. In archeology, ‘subtracting’ a layer with a certain type of pottery remains from the layer containing burnt wood and bones is again not meaningful. Only the ordering can be deduced.
        
+
        In this regime, the Allen Operators (see Figure 1) can be used. If A occurs before B and B occurs before C, then it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So in our example, B occurs in the interval (A,C). However, arithmetic operations like (B-A) or (C-A) cannot be performed as any timescale or measurements are not defined. For example, in geology, ‘subtracting’ Ordovician from Jurassic is meaningless. In archeology, ‘subtracting’ a layer with a certain type of pottery remains from the layer containing burnt wood and bones is again not meaningful. Only the ordering can be deduced.
        
 
-
        This regime constitutes an Ordinal Temporal Reference System, with discrete enumerated ordered events.
        
+
        This regime constitutes an ordinal temporal reference system, with discrete enumerated ordered events.
        
 
-
        Figure 2
        
+
        Figure 1
        
         
 
 
-8.3.<tab/>Simple Clocks and Discrete Timescales
-
        In this regime, a clock is defined as any regularly repeating physical phenomena, such as pendulum swings, earth’s rotation about the sun, earth’s rotation about its axis, heart beats, vibrations of electrically stimulated quartz crystals or the resonance of the unperturbed ground-state hyperfine transition frequency of the cesium-133 atom. In terms of the number of repetitions possible, some phenomena make better clocks than others, because of the consistency of each repetition and the precision of each ‘tick’. A mechanism for counting, or possibly measuring, the ticks is desirable.
        
+7.3.<tab/>Simple Clocks and Discrete Timescales
+
        In this regime, a clock is defined as any regularly repeating physical phenomenon, such as a pendulum swing, earth’s rotation about the sun, earth’s rotation about its axis, heart beat, vibrations of electrically stimulated quartz crystals or the resonance of the unperturbed ground-state hyperfine transition frequency of the cesium-133 atom. Each occurrence of the repeating phenomenon is, of course, an event, but as there are usually very many that can only be distinguished by counting, they are considered a separate class of ticks.
        
+
+
        In terms of the number of repetitions possible, some phenomena make better clocks than others, because of the consistency of each repetition and the precision of each tick. A mechanism for counting, or possibly measuring, the ticks is desirable.
        
 
-
        An assumption is that the ticks are regular and homogeneous.
        
+
        An assumption is that the ticks are regular and homogeneous.
        
 
-
        There is no sub-division between two successive clock ticks. Measuring time consists of counting the complete number of repetitions of ticks since the clock started, or since some other event at a given clock count.
        
+
        There is no sub-division between two successive clock ticks. Measuring time consists of counting the complete number of repetitions of ticks since the clock started, or since some other event at a given clock tick count.
        
 
-
        There is no time measurement before the clock starts, or after it stops.
        
+
        There is no time measurement before the clock starts, or after it stops.
        
 
-
        It may seem that time can be measured between ‘ticks’ by interpolation, but this needs another clock, with faster ticks. This process of devising more precise clocks continues down to the atomic scale. At that scale the deterministic process of physically trying to interpolate between ticks is not possible.
        
+
        It may seem that time can be measured between ticks by interpolation, but this needs another clock, with faster ticks. This process of devising more precise clocks continues down to the atomic scale. At that scale the deterministic process of physically trying to interpolate between ticks is not possible.
        
 
-
        The internationally agreed atomic time, TAI, is an example of a timescale with an integer count as the measure of time. However in practice, TAI is an arithmetic compromise across about two hundred separate atomic clocks, corrected for differing altitudes and temperatures.
        
+
        The internationally agreed atomic time, TAI International Atomic Time is an example of a timescale with an integer count as the measure of time. However in practice, TAI is an arithmetic compromise across about two hundred separate atomic clocks, corrected for differing altitudes and temperatures.
        
 
-
        In this regime, Allen Operators (see Figure 2) also can be used. If L occurs before M and M occurs before N, it can be correctly deduced that L occurs before N. The full set of operators also covers pairs of intervals. So if M occurs in the interval (L,N),  integer arithmetic operations such as (M-L) or (N-L) can be performed. This is because an integer timescale or measurement is defined.
        
+
        In this regime, Allen Operators (see Figure 1) also can be used. If L occurs before M and M occurs before N, it can be correctly deduced that L occurs before N. The full set of operators also covers pairs of intervals. So if M occurs in the interval (L,N),  integer arithmetic operations such as (M-L) or (N-L) can be performed. This is because an integer timescale or measurement is defined by the count of ticks.
        
 
-
        This regime constitutes a Temporal Coordinate Reference System, with discrete integer units of measure which can be subject to integer arithmetic.
        
+
        This regime constitutes a temporal coordinate reference system, with discrete integer units of measure which can be subject to integer arithmetic.
        
         
 
 
-8.4.<tab/>CRS and Continuous Timescales
-
        This regime takes a clock from the previous regime and assumes that between any two adjacent ticks, it is possible to interpolate indefinitely to finer and finer precision, using ordinary arithmetic, rather than any physical device. Units of Measure may be defined that are different from the ‘ticks’. For example, a second may be defined as 9,192,631,770 vibrations of the ground-state hyperfine transition of the cesium-133 atom. Alternatively and differently, a second may be defined as 1/86400th of the rotation of the earth on its axis with respect to the sun. The count of rotations is the ‘ticks’ of an earth-day clock. This latter definition is not precise enough for many uses, as the rotation of the earth on its axis varies from day to day.
        
+7.4.<tab/>CRS and Continuous Timescales
+
        This regime takes a clock from the previous regime and assumes that between any two adjacent ticks, it is possible to interpolate indefinitely to finer and finer precision, using ordinary arithmetic, rather than any physical device. Units of measure may be defined that are different from the ticks. For example, a second may be defined as 9,192,631,770 vibrations of the ground-state hyperfine transition of the cesium-133 atom. Alternatively and differently, a second may be defined as 1/86400th of the rotation of the earth on its axis with respect to the sun. The count of rotations is the ticks of an earth-day clock. This latter definition is not precise enough for many uses, as the rotation of the earth on its axis varies from day to day.
        
 
-
        Alternatively, it may be that the ticks are not counted but measured, and the precision of the clock is determined by the precision of the measurements, such as depth in an ice core, or angular position of an astronomical body, such as the sun, moon or a star.
        
+
        Alternatively, it may be that the ticks are not counted but measured, and the precision of the clock is determined by the precision of the measurements, such as depth in an ice core subject to seasonal depositions of snow, or angular position of an astronomical body, such as the sun, moon or a star.
        
 
-
        It is also assumed that time can be extrapolated to before the time when the clock started and into the future, possibly past when the clock stops.
        
+
        It is also assumed that time can be extrapolated before the time when the clock started and into the future, possibly past when the clock stops.
        
 
-
        This gives us a continuous number line to perform theoretical measurements. This is a coordinate system. With a datum/origin/epoch, a unit of measure (a name for the ‘tick marks’ on the axis), positive and negative directions and the full range of normal arithmetic. This is a Coordinate Reference System (CRS).
        
+
        This gives us a continuous number line to perform theoretical measurements. With a datum/origin/epoch, a unit of measure (a name for the ticks on the axis), positive and negative directions and the full range of normal arithmetic, this is a coordinate reference system (CRS).
        
 
-
        In this regime, the Allen Operators (see Figure 2) also can be used. If A occurs before B and B occurs before C, it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So if B occurs in the interval (A,C), real number arithmetic operations like (B-A) or (C-A) can be performed. This is because a timescale or measurement has been defined, and between any two instants, an infinite number of other instants can be found.
        
+
        In this regime, the Allen Operators (see Figure 1) also can be used. If A occurs before B and B occurs before C, it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So if B occurs in the interval (A,C), real number arithmetic operations like (B-A) or (C-A) can be performed. This is because a timescale or measurement has been defined, and between any two instants, an infinite number of other instants can be found.
        
 
-Example
        Some examples are:
        
+Example
        Some examples are:
        
 
-
        • Unix milliseconds since 1970-01-01T00:00:00.0Z
          
+
          • Unix milliseconds since 1970-01-01T00:00:00.0Z
            
 
            • 
-
          • Julian Days, and fractions of a day, since noon on 1st January, 4713 BCE.
            
+
          • Julian Days, and fractions of a day, since noon on 1st January, 4713 BCE.
            
 
            • 
 
          
 
 
-
          This regime constitutes a Temporal Coordinate Reference System, with a continuous number line and units of measure, which can be subject to the full range of real or floating-point arithmetic.
          
+
          This regime constitutes a temporal coordinate reference system, with a continuous number line and units of measure, which can be subject to the full range of real, or floating-point, arithmetic.
          
 
 
 
-8.5.<tab/>Calendars
-
          In this regime, counts and measures of time are related to the various combinations of the rotations of the earth, moon and sun or other astronomical bodies.
+7.5.<tab/>Calendars
+
          In this regime, counts and measures of time are related to the various combinations of the rotations of the earth, moon and sun or other astronomical bodies.
 Typically there is no simple arithmetic connecting calendar systems and timescales. For example, the current civil year count of years in the Current Era (CE) and Before Current Era (BCE) is a very simple calendar, as there is no year zero. That is, Year 14CE – Year 12CE is a duration of 2 years, and Year 12BCE — Year 14BCE is also two years. However Year 1CE — Year 1BCE is one year, not two as there is no year 0CE or 0BCE.
          
 
-
          In this regime, the use of the Allen Operators (see Figure 2) is not straightforward. If A occurs before B and B occurs before C, then correctly deducing that A occurs before C is not always easy. The full set of Allen Operators also covers pairs of intervals. So in the example, B occurs in the interval (A,C). However, simple arithmetic operations like (B-A) or (C-A) cannot usually be done simply because of the vagaries of the calendar algorithms, multiple timescales, and multiple Units of Measure.
          
+
          In this regime, the use of the Allen Operators (see Figure 1) is not straightforward. If A occurs before B and B occurs before C, then correctly deducing that A occurs before C is not always easy. This is because the calendar’s timeline may contain gaps, changes of units of measure, or even duplicated times.
          
+
+
          The full set of Allen Operators also covers pairs of intervals. So in the example, B occurs in the interval (A,C). However, simple arithmetic operations like (B-A) or (C-A) cannot usually be done simply because of the vagaries of the calendar algorithms, multiple timescales, and multiple units of measure.
          
+
+Example
          For example, in the Gregorian calendar, calculating the number of days between the 1st February and the 30th March depends on whether the year is a leap year or not, which also depends on the century and millenium. Calculating the precise number of seconds between two dates in the Gregorian calendar also depends on whether leap seconds have been declared between the dates. There have been 27 leap seconds added between 1972 and 2022.
          
+          
 
-
          Calendars are social constructs made by combining several clocks and their associated timescales.
          
+
          Calendars are social constructs made by combining several clocks and their associated timescales. Calendars may also have local or regional variations at different times of the year or season, such as for ‘day-light saving’ in mid-latitudes. Again, this makes calculations more complicated and prone to change.
          
 
-
          This Abstract Specification only addresses the internationally agreed Gregorian calendar. The book Calendrical Calculations by Nachum Dershowitz and Edward M. Reingold provides overwhelming detail for conversion to numerous other calendars that have developed around the world and over the millennia and to meet the various social needs of communities, whether agricultural, religious or other. The reference is comprehensive but not exhaustive, as there are calendars that have been omitted.
          
+
          This Abstract Specification only addresses the internationally agreed Gregorian calendar. The book Calendrical Calculations by Nachum Dershowitz and Edward M. Reingold provides overwhelming detail for conversion to numerous other calendars that have developed around the world and over the millennia and to meet the various social needs of communities, whether agricultural, religious or other. The reference is comprehensive but not exhaustive, as there are calendars that have been omitted.
          
 
-
          A Calendar is a Temporal Reference System, but it is not a Temporal Coordinate Reference System nor an Ordinal Temporal Reference System.
          
+
          A calendar is a temporal reference system, but it is not a temporal coordinate reference system nor an ordinal temporal reference system.
          
           
 
 
-8.6.<tab/>Other Regimes
-
          There are other regimes, which are out of scope of this Abstract Specification. This could include local solar time, which is useful, for example, for the calculation of illumination levels and the length of shadows on aerial photography, or relativistic time.
          
+7.6.<tab/>Other Regimes
+
          There are other regimes, whose detailed description are out of scope of this Abstract Specification. This could include local solar time, which is useful, for example, for the calculation of illumination levels and the length of shadows on aerial photography, or relativistic time for very fast moving features.
          
 
 
-8.6.1.<tab/>Accountancy
-
          The financial and administrative domains often use weeks, quarters, and other calendrical measures. These may be convenient for the requisite tasks, but are usually inappropriate for scientific or technical purposes.
          
+7.6.1.<tab/>Accountancy
+
          The financial and administrative domains often use weeks, quarters, and other calendrical measures. These may be convenient for the requisite tasks, but are usually inappropriate for scientific or technical purposes. This Abstract Conceptual Model for Time can support this regime.
          
           
 
 
-8.6.2.<tab/>Agents and Agency
-
          Agents require a different concept of time from regimes where time is a coordinate axis or measured by clocks. An agent is an entity that senses, responds, and maintains a model of its environment, while performing actions to achieve its goals. See ISO/IEC 22989:2022, Artificial intelligence concepts and terminology. For an agent, the conceptual model of time is about flow and continuity including a sense of now, a memory of past events, and a speculation about future events. This regime addresses how the agent has awareness of the flow of events:
          
+7.6.2.<tab/>Agents and Agency
+
          Agents require a different concept of time from regimes where time is a coordinate axis or measured by clocks. An agent is an entity that senses, responds, and maintains a model of its environment, while performing actions to achieve its goals. See ISO/IEC 22989:2022, Artificial intelligence concepts and terminology. For an agent, the conceptual model of time is about flow and continuity including a sense of now, a memory of past events, and a speculation about future events. This regime addresses how the agent has awareness of the flow of events:
          
 
-
          • Temporal awareness integrates impression, retention, and protention, representing the continuous movement of time;
            
+
            • Temporal awareness integrates impression, retention, and protention, representing the continuous movement of time;
              
 
              • 
-
            • Agents continuously revise their models of the environment by integrating new observations with existing models;
              
+
            • Agents continuously revise their models of the environment by integrating new observations with existing models;
              
 
              • 
-
            • Observations are used to update an agent’s model, leading to a more accurate understanding of the environment and enabling effective goal-directed behavior.
              
+
            • Observations are used to update an agent’s model, leading to a more accurate understanding of the environment and enabling effective goal-directed behavior.
              
 
              • 
 
            
 
-
            This Agent regime of time is relevant to any feature which has agency.
            
+
            This regime of time is relevant to any feature which has agency. This Abstract Conceptual Model for Time can support this regime.
            
 
 
 
-8.6.3.<tab/>Astronomical Time
-
            Astronomers have traditionally measured the apparent locations of stars, planets and other heavenly bodies by measuring angular separations from reference points or lines and the timing of transits across a meridian. Generally astronomers use time determined by earth’s motion relative to the distant stars rather than the sun. This is called sidereal time. Times are usually measured from an epoch in daylight, such as local midday, rather than midnight. Accurate measurements of positions of stars, planets and moons were and are essential for navigation on Earth. See the book Astronomical Algorithms by Jean Meeus for examples of the calculations involved.
            
+7.6.3.<tab/>Astronomical Time
+
            Astronomers have traditionally measured the apparent locations of stars, planets and other heavenly bodies by measuring angular separations from reference points or lines and the timing of transits across a meridian. Generally astronomers use time determined by earth’s motion relative to the distant stars rather than the sun. This is called sidereal time. Times are usually measured from an epoch in daylight, such as local midday, rather than midnight. Accurate measurements of positions of stars, planets and moons were and are essential for navigation on Earth. See the book Astronomical Algorithms by Jean Meeus for examples of the calculations involved. This Abstract Conceptual Model for Time can support this regime.
            
             
 
 
-8.6.4.<tab/>Local Solar Time
-
            Local solar time may or may not correspond to the local statutory or legal time in a country. Local solar time can be construed as a clock and timescale, with an angular measure of the apparent position of the sun along the ecliptic (path through the sky) as the basic physical principle. But the sun does not appear to progress evenly along the ecliptic throughout the days and year. There may be variations of up to 15 minutes compared to an even angular speed.
            
+7.6.4.<tab/>Local Solar Time
+
            Local solar time may or may not correspond to the local statutory or legal time in a country. Local solar time can be construed as a clock and timescale, with an angular measure of the apparent position of the sun along the ecliptic (path through the sky) as the basic physical principle. But the sun does not appear to progress evenly along the ecliptic throughout the days and year. There may be variations of up to 15 minutes compared to an even angular speed. This Abstract Conceptual Model for Time can support this regime.
            
             
 
 
-8.6.5.<tab/>Space-time
-
            When dealing with moving objects, the location of the object in space depends on its location in time. That is to say, location is an event in space and time.
            
+7.6.5.<tab/>Space-time
+
            When dealing with moving objects, the location of the object in space depends on its location in time. That is to say, location is an event in space and time.
            
 
-
            Originally developed by Hermann Minkowski to support work in Special Relativity, the concept of space-time is useful whenever the location of an object in space is dependent on its location in time.
            
+
            Originally developed by Hermann Minkowski to support work in Special Relativity, the concept of space-time is useful whenever the location of an object in space is dependent on its location in time.
            
 
-
            Since the speed of light, 
+
            Since the speed of light, 
 
   
     c
@@ -1595,52 +1660,72 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 
-8.6.6.Relativistic
-
            A regime may be needed for ‘space-time’, off the planet Earth, such as for recording and predicting space weather approaching from the sun, where the speed of light and relativistic effects such as gravity may be relevant.
            
+7.6.6.<tab/>Relativistic
+
            A regime may be needed for ‘space-time’, off the planet Earth, such as for recording and predicting space weather approaching from the sun, where the speed of light and relativistic effects such as gravity may be relevant.
            
 
-
            Once off planet Earth, distances and velocities can become very large. The speed of light becomes a limiting factor in measuring both where and when an event takes place. Special Relativity deals with the accurate measurement of space-time events as measured between two moving objects. The core concepts are the Lorentz Transforms. These transforms allow one to calculate the degree of “contraction” a measurement undergoes due to the relative velocity between the observing and observed object.
            
+
            Once off planet Earth, distances and velocities can become very large. The speed of light becomes a limiting factor in measuring both where and when an event takes place. Special Relativity deals with the accurate measurement of space-time events as measured between two moving objects. The core concepts are the Lorentz Transforms. These transforms allow one to calculate the degree of “contraction” a measurement undergoes due to the relative velocity between the observing and observed object.
            
 
-
            The key to this approach is to ensure each moving feature of interest has its own local clock and time, known as its ‘proper time’. This example can be construed as a fitting into the clock and timescale regime. The relativistic effects are addressed through the relationships between the separate clocks, positions and velocities of the features.
            
+
            The key to this approach is to ensure each moving feature of interest has its own local clock and time, known as its ‘proper time’. This example can be construed as a fitting into the clock and timescale regime of this Abstract Specification. The relativistic effects are addressed through the relationships between the separate clocks, positions and velocities of the features.
            
 
-
            Relativistic effects may need to be considered for satellites and other spacecraft because of their relative speed and position in Earth’s gravity well.
            
+
            Relativistic effects may need to be considered for satellites and other spacecraft because of their relative speed and position in Earth’s gravity well.
            
 
-
            The presence of gravitational effects requires special relativity to be replaced by general relativity, and it can no longer be assumed that space (or space-time) is Euclidean. That is, Pythagoras’ Theorem does not hold except locally over small areas. This is somewhat familiar territory for geospatial experts.
            
+
            The presence of gravitational effects requires special relativity to be replaced by general relativity, and it can no longer be assumed that space (or space-time) is Euclidean. That is, Pythagoras’ Theorem does not hold except locally over small areas, or that the circumference of a circle is not precisely 
+
+  
+    2
+    :
+    π
+    r
+  
+
+2 :pi r. This is somewhat familiar territory for geospatial experts. This Abstract Conceptual Model for Time can support this regime, providing each feature has its own clock.
            
             
 
 
 
-
-9.<tab/>Attributes of the Classes
+
+8.<tab/>Abstract Conceptual Model for Time
+
            This Temporal Abstract Conceptual Model follows ISO 19111:2019, which is the ISO adoption of OGC 18-005r8.
            
+
+
            The model is also informed by the W3C Time Ontology in OWL.
            
+
+
            Figure 2
            
+            
+
+
+9.<tab/>Classes and their Attributes and Properties
 
 9.1.<tab/>Reference Systems
-
            The top level ‘ReferenceSystem’ is an abstract super-class and does not have many attributes or properties. Only the total dimension of the reference system and the Location, Time or Domain of Applicability have been identified as essential.
            
+
            The top level Reference System class is an abstract super-class and does not have many attributes or properties. Only the total dimension of the reference system and the location, time or domain of applicability have been identified as essential.
            
+
+
            The reference system class has two abstract sub-classes: Spatial Reference System, which is defined in ISO 19111:2019, and Temporal Reference System, each with the inherited attributes of dimension and domains of applicability.
            
 
-
            The ‘ReferenceSystem’ has two abstract sub-classes: ‘SpatialReferenceSystem’, which is defined in ISO 19111:2019, and ‘TemporalReferenceSystem’, each with the attributes of Dimension and Domains of Applicability.
            
+
            The value for dimension is one for time, or a vertical reference system, but may be as high as six for spatial location with orientation as in the GeoPose Implementation Standard.
            
 
-
            The value for Dimension is one for time, or a vertical reference system, but may be as high as 6 for spatial location with orientation as in the GeoPose Implementation Standard.
            
+
            For example, a reference system may be applicable to Mars, rather than Earth, or perhaps to a specific building site with local coordinates, or a specific time range while coordinate errors are within acceptable bounds.
            
 
-
            Besides the conventional space and time, there may be other reference systems, such as wavelength or frequency, that could be addressed by future additions to this Abstract Conceptual Model.
            
+
            Besides the conventional space and time, there may be other reference systems, such as wavelength or frequency, that could be addressed by future additions to this Abstract Conceptual Model.
            
             
 
 
 9.2.<tab/>Ordinal Temporal Reference Systems
-
            An OrdinalTemporal Reference System has a well-ordered finite sequence of events against which other events can be compared.
            
+
            An ordinal temporal reference system has a well-ordered finite sequence of events against which other events can be compared.
            
 
-
            An Ordinal Temporal Reference System is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:
            
+
            An ordinal temporal reference system is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:
            
 
-
            1. applicableLocationTimeOrDomain: the location, time or domain of applicability;
              
+
              1. applicable location time or domain: the location, time or domain of applicability;
                
 
                2. 
-
              2. dimension: the number of dimensions in this reference system. For Ordinal Temporal Reference Systems this value is fixed at 1.
                
+
              3. dimension: the number of dimensions in this reference system. For ordinal temporal reference systems this value is fixed at 1.
                
 
                4. 
 
              
 
-
              An Ordinal Temporal Reference System does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
              
+
              An ordinal temporal reference system does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
              
 
-
              1. Epoch: An Ordinal Temporal Reference System ‘has a’ one optional Epoch
                
+
                1. Epoch: An ordinal temporal reference system may be ‘anchored by’ one optional Epoch
                  
 
                  2. 
-
                2. Notation: An Ordinal Temporal Reference System ‘can use’ one or more Notations to represent itself.
                  
+
                3. Notation: An ordinal temporal reference system ‘is represented by’ one or more Notations to represent itself.
                  
 
                  4. 
-
                4. Event: An Ordinal Temporal Reference System ‘consists of’ an ordered set of Events. These events are identifiable temporal instances.
                  
+
                5. Event: An ordinal temporal reference system ‘consists of’ an ordered set of Events. These events are identifiable temporal instances.
                  
 
                  6. 
 
                
 
@@ -1649,75 +1734,82 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 9.2.1.<tab/>Events
-
                The Events class is an ordered list of temporal events. The events can be instances, such as the ascension of a King to a throne, or intervals, such as the complete reign of each king.
                
+
                An event is an identifiable happening or occurence of something. The events can be instants, such as the ascension of a king to a throne, or intervals, such as the complete reign of each king.
                
 
 
                Other documents may enable two such ‘king lists’ to be related, though usually not completely.
                
+
+Example
                Several archeological layers may be identified as containing broken pottery, overlaid with a layer of burnt wood and brick, then followed by another layer with a different style of pottery and including a coin embossed with a king’s name. Thus the pottery styles can be classified as ‘early’ and ‘late’ and the late pottery associated with the named king, who may be identified from a separate inscribed ‘king list’.
                
+                
                 
 
 
 
 9.3.<tab/>Temporal Coordinate Reference Systems
-
                A Temporal Coordinate Reference System is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:
                
+
                A temporal coordinate reference system is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:
                
 
-
                1. applicableLocationTimeOrDomain: the location, time or domain of applicability;
                  
+
                  1. applicable location time or domain: the location, time or domain of applicability;
                    
 
                    2. 
-
                  2. dimension: the number of dimensions in this reference system. For Temporal Coordinate Reference Systems this value is fixed at 1.
                    
+
                  3. dimension: the number of dimensions in this reference system. For temporal coordinate reference systems this value is fixed at 1.
                    
 
                    4. 
 
                  
 
-
                  A Temporal Coordinate Reference System does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
                  
+
                  A temporal coordinate reference system does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
                  
 
-
                  1. Epoch: A Temporal CRS ‘has a’ one optional Epochs
                    
+
                    1. Epoch: A temporal coordinate reference system ‘anchored by’ one optional Epochs
                      
 
                      2. 
-
                    2. Notation: A Temporal CRS ‘can use’ one or more Notations to represent itself.
                      
+
                    3. Notation: A temporal coordinate reference system ‘represented by’ one or more Notations to represent itself.
                      
 
                      4. 
-
                    4. Timescale: A Temporal CRS ‘has a’ one Timescale which is used to represent the values along its single axis. This Timescale can be either discrete or continuous.
                      
+
                    5. Timescale: A temporal coordinate reference system ‘must have’ one Timescale which is used to represent the values along its single axis. This timescale can be either discrete or continuous.
                      
 
                      6. 
 
                    
 
 
 
 9.4.<tab/>Calendar Reference Systems
-
                    Calendars combine different timescales and their clocks and units of measure, and other events, to make a complex timeline against which events can be compared. Calculated algorithms are used to determine which instants of intervals on the compound timeline are identified and labeled.
                    
+
                    Calendars combine different timescales and their clocks and units of measure, and other events, to make a complex timeline against which events can be compared. Calculated algorithms are used to determine which instants or intervals on the compound timeline are identified and labeled.
                    
 
-
                    A Calendar is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:
                    
+
                    A calendar is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:
                    
 
-
                    1. applicableLocationTimeOrDomain: the location, time or domain of applicability
                      
+
                      1. applicable location time or domain: the location, time or domain of applicability
                        
 
                        2. 
-
                      2. dimension: the number of dimensions in this reference system. For Calendars this value is fixed at 1.
                        
+
                      3. dimension: the number of dimensions in this reference system. For calendars this value is fixed at 1.
                        
 
                        4. 
 
                      
 
-
                      A Calendar does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
                      
+
                      A calendar does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
                      
 
-
                      1. Algorithm: A Calendar ‘has a’ one or more Algorithms. These Algorithms specify how the multiple Time Scales are aggregated into a single Timeline.
                        
+
                        1. Timeline: A calendar ‘has a’ one Timeline which serves to aggregate a number of Timescales into a single coherent measure of date and time.
                          
 
                          2. 
-
                        2. Epoch: A calendar ‘has a’ one optional Epoch
                          
+
                        3. Algorithm: A timeline ‘is defined by’ one or more Algorithms. These algorithms specify how the multiple timescales are aggregated into a single Timeline.
                          
 
                          4. 
-
                        4. Notation: A calendar ‘can use’ one or more Notations to represent itself.
                          
+
                        5. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a Timeline.
                          
 
                          6. 
-
                        6. Timeline: A Calendar ‘has a’ one Timeline which serves to aggregate a number of Timescales into a single coherent measure of date and time.
                          
+
                        7. Epoch: A calendar is ‘anchored by’ one optional Epoch
                          
 
                          8. 
-
                        8. Timescale: A Calendar ‘has a’ two or more Timescales which are used to construct a Timeline.
                          
+
                        9. Notation: A calendar  is ‘represented by’ one or more Notations to represent itself.
                          
 
                          10. 
 
                        
 
 
 9.4.1.<tab/>Timeline
-
                        The timeline is usually a set of instants from the past to the future and is compounded from multiple timescales, with multiple units of measures, and complicated arithmetic determined by the calendar algorithm(s). The timeline is usually not even continuous, having gaps or even multiple simultaneous representations.
                        
+
                        The timeline is usually a set of instants from the past to the future and is compounded from multiple timescales, with multiple units of measures, and complicated arithmetic determined by the calendar algorithm(s). The timeline is usually not even continuous, having gaps or even duplicated times or multiple simultaneous representations.
                        
 
-
                        A Timeline does not have any attributes of its own. Nor does it inherit any attributes from a parent class. However, it does use associations with other classes to fully describe itself.
                        
+
                        A timeline does not have any attributes of its own. Nor does it inherit any attributes from a parent class. However, it does use associations with other classes to fully describe itself.
                        
 
-
                        1. Algorithm: A Timeline ‘has a’ one or more Algorithms. These Algorithms specify how the multiple Time Scales are aggregated into a single Timeline.
                          
+
                          1. Algorithm: A timeline is ‘defined by’ one or more Algorithms. These algorithms specify how the multiple timescales are aggregated into a single timeline.
                            
 
                            2. 
-
                          2. Timescale: A Timeline ‘has a’ two or more Timescales which are used to construct the Timeline.
                            
+
                          3. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
                            
 
                            4. 
 
                          
 
 
 
 9.4.2.<tab/>Algorithm
-
                          An Algorithm specifies the logic used to construct a Timeline from its constituent Timescales. An Algorithm does not have any attributes of its own. Nor does it make use of any other classes from this Temporal model.
                          
+
                          An algorithm specifies the logic used to construct a timeline from its constituent Timescales. An algorithm does not have any attributes of its own. It does make use of timescales to construct the timeline of a calendar.
                          
+
+
                          1. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
                            
+
                            2. 
+
                          
                           
 
 
@@ -1752,38 +1844,38 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 9.5.<tab/>Discrete and Continuous Time Scales
-
                          A clock may be a regular, repeating, physical event, or ‘tick’, that can be counted. The sequence of tick counts form a discrete (counted) timescale.
                          
+
                          A clock may be a regular, repeating, physical event, or tick, that can be counted. The sequence of tick counts form a discrete (counted) timescale.
                          
 
 
                          Some clocks  allow the measurement of intervals between ticks, such as the movement of the sun across the sky. Alternatively, the ticks may not be completely distinguishable, but are still stable enough over the time of applicability to allow measurements rather than counting to determine the passage of time. These clocks generate a continuous (measured) timescale.
                          
 
-
                          The duration of a tick is a constant. The length of a tick is specified using a Unit Of Measure.
                          
+
                          The duration of a tick is assumed constant. The duration of a tick is specified using a Unit Of Measure.
                          
 
 
 9.5.1.<tab/>Timescale
-
                          A Timescale is a linear measurement (one dimension) used to measure or count monotonic events. Timescale has three attributes:
                          
+
                          A timescale is a linear measurement (one dimension) used to measure or count monotonic events. Timescale has three attributes:
                          
 
-
                          1. Arithmetic: an indicator of whether this Timescale contains counted integers or measured real/floating point numbers.
                            
+
                            1. Arithmetic: an indicator of whether this timescale contains counted integers or measured real/floating point numbers.
                              
 
                              2. 
-
                            2. StartCount: the lowest value in a Timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
                              
+
                            3. StartCount: the lowest value in a timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
                              
 
                              4. 
-
                            4. EndCount: the greatest value in a Timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
                              
+
                            5. EndCount: the greatest value in a timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
                              
 
                              6. 
 
                            
 
-
                            In addition to the attributes, the Timescale class maintains associations with two other classes to complete its definition.
                            
+
                            In addition to the attributes, the timescale class maintains associations with two other classes to complete its definition.
                            
 
-
                            1. Clock: A Timescale ‘has a’ one clock. This is the process which generates the ‘tick’ which is counted or measured for the Timescale.
                              
+
                              1. Clock: A timescale is ‘determined by’ one clock. This is the process which generates the ticks which are counted or measured for the timescale.
                                
 
                                2. 
-
                              2. UnitOfMeasure: A timescale ‘has a’ one UnitOfMeasure. This class specifies the units of the clock measurement as well as the direction of increase of that measurement.
                                
+
                              3. UnitOfMeasure: A timescale ‘has a’ one UnitOfMeasure. This class specifies the units of the clock measurement or count as well as the direction of increase of that measurement or count.
                                
 
                                4. 
 
                              
 
 
 
 9.5.2.<tab/>Clock
-
                              A Clock represents the process which generates the ‘tick’ which is counted or measured for a Timescale. Clock has one attribute:
                              
+
                              A clock represents the process which generates the ticks which are counted or measured for a timescale. Clock does not have any attributes of its own. Nor does it inherit any attributes from a parent class. However, it does use an association with another class to fully describe itself.
                              
 
-
                              1. Tick definition: a description of the process which is being used to generate monotonic events.
                                
+
                                1. Ticks: a description of the process which is being used to generate monotonic events.
                                  
 
                                  2. 
 
                                
 
@@ -1795,10 +1887,10 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 
-9.5.3.<tab/>UnitOfMeasure
-
                                The Direction attribute indicates whether counts or measures increase in the positive (future) or negative (past) direction. The attribute could be part of ‘Timescale’ or ‘TemporalCoordinateReferenceSystem’ rather than a separate class ‘UnitOfMeasure’, but on balance, it seems better here, as the names often imply directionality, such as fathoms increasing downwards, MYA (Millions of Years Ago) increasing earlier, Atmospheric Pressure in hPa (Hectopascals) decreasing upwards, and FL (FlightLevel) increasing upwards.
                                
+9.5.3.<tab/>Unit of Measure
+
                                The direction attribute indicates whether counts or measures increase in the positive (future) or negative (past) direction. The attribute could be part of timescale or temporal coordinate reference system rather than a separate class of measure, but on balance, it seems better here, as the names often imply directionality, such as fathoms increasing downwards, MYA (Millions of Years Ago) increasing earlier, atmospheric pressure in hPa (hectopascals) decreasing upwards, and FL (flight level) increasing upwards.
                                
 
-
                                1. Direction: indicates the direction in which a timescale progresses as new ‘ticks’ are counted or measured.
                                  
+
                                  1. Direction: indicates the direction in which a timescale progresses as new ticks are counted or measured.
                                    
 
                                    2. 
 
                                  
 
@@ -1817,13 +1909,13 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 Example  3
                                  A well preserved fossilized log is recovered and the tree rings establish an annual ‘tick’. The start and end times may be known accurately by comparison and matching with other known tree ring sequences, or perhaps only dated imprecisely via Carbon Dating, or its archaeological or geological context.
                                  
                                   
 
-Example  4
                                  A clock is started, but undergoes a calibration process against some standard clock, so the initial, reliable Start Time does not start at Count Zero. The clock is accidentally knocked so that it is no longer correctly calibrated, but is still working. The End Time is not the last time that the clock ticks.
                                  
+Example  4
                                  A clock is started, but undergoes a calibration process against some standard clock, so the initial, reliable start time does not start at a count of zero. The clock is accidentally knocked so that it is no longer correctly calibrated, but is still working. The end time is not the last time that the clock ticks.
                                  
                                   
 
-Example  5
                                  TAI (International Atomic Time, Temps Atomique International) is coordinated by the BIPM (International Bureau of Weights and Measures, Bureau International de Poids et Measures) in Paris, France. TAI is based on the average of hundreds of separate atomic clocks around the world, all corrected to be at mean sea level and standard pressure and temperature. The epoch is defined by Julian Date 2443144.5003725 (1 January 1977 00:00:32.184).
                                  
+Example  5
                                  TAI International Atomic Time (or Temps Atomique International) is coordinated by the BIPM (International Bureau of Weights and Measures, Bureau International de Poids et Measures) in Paris, France. TAI is based on the average of hundreds of separate atomic clocks around the world, all corrected to be at mean sea level and standard pressure and temperature. The epoch is defined by Julian Date 2443144.5003725 (1 January 1977 00:00:32.184).
                                  
                                   
 
-Example  6
                                  The Julian Day is the continuous count of days (rotations of the Earth with respect to the Sun) since the beginning of the year 4173 BCE and will terminate at the end of the year 3267 CE. The count then starts again as “Period 2”. Many computer based timescales, such as Unix Time, are based on the Julian Day timescale, but with different epochs, to fit the numbers into the limited computer words.
                                  
+Example  6
                                  The Julian Day is the continuous count of days (rotations of the Earth with respect to the Sun) since the beginning of the year 4173 BCE and will terminate at the end of the year 3267 CE. The count then starts again as “Period 2”. Many computer based timescales, such as Unix Time, are based on the Julian Day timescale, but with different epochs, to fit the large numbers into computer words of limited size.
                                  
                                   
 
 
@@ -1832,12 +1924,12 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 9.6.<tab/>Supporting Classes
 
 9.6.1.<tab/>Epoch
-
                                  The Epoch class provides a origin or datum for a Temporal Reference System.
                                  
+
                                  The epoch class provides an origin or datum for a temporal reference system.
                                  
                                   
 
 
 9.6.2.<tab/>Notation
-
                                  The Notation class identifies a widely agreed, commonly accepted, notation for representing values in accordance with a temporal reference system.
                                  
+
                                  The notation class identifies a widely agreed, commonly accepted, notation for representing values in accordance with a temporal reference system.
                                  
                                   
 
 
@@ -1846,7 +1938,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 10.<tab/>Notation
 
                                  There are often widely agreed, commonly accepted, notations used for temporal reference systems, but few have been standardized. Any particular notation may be capable of expressing a wider range of times than are valid for the reference system.
                                  
 
-Example
                                  The IETF RFC 3339 timestamp notation, a restrictive profile of ISO 8601, can express times before 1588CE, when the Gregorian calendar was first introduced in some parts of the world.
                                  
+Example
                                  The IETF RFC 3339 timestamp notation, a restrictive profile of ISO 8601, can express times before 1582CE, when the Gregorian calendar was first introduced in some parts of the world.
                                  
                                   
 
 
@@ -1871,9 +1963,9 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
                                  2.2 Two things can be in the same place at different times.
                                  
 
-
                                  These are not symmetrical in space and time.
                                  
+
                                  These statements are not symmetrical between space and time.
                                  
 
-
                                  Temporal constructs such as instants, durations or intervals, multi-instants (a set of instants), and multi-intervals are not included in this conceptual model. These do have strongly analogous equivalents in space, such as points and multi-points, especially in a single dimension, such as vertical. The temporal constructs are well described in Maintaining Knowledge about Temporal Intervals by J. F. Allen (see Figure 2) and apply across all of the regimes, so do not need to be in this Abstract Conceptual Model.
                                  
+
                                  Temporal constructs such as instants, durations or intervals, multi-instants (a set of instants), and multi-intervals (a set of intervals) are not included in this conceptual model. These do have strongly analogous equivalents in space, such as points and multi-points, especially in a single dimension, such as vertical. The temporal constructs are well described in Maintaining Knowledge about Temporal Intervals by J. F. Allen (see Figure 1) and apply across all of the regimes, so do not need to be in this Abstract Conceptual Model.
                                  
 
 
 
@@ -1884,62 +1976,82 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 ISO: ISO 19111:2019, Geographic information — Referencing by coordinates. International Organization for Standardization, Geneva (2019). https://www.iso.org/standard/74039.html.https://www.iso.org/standard/74039.htmlhttps://www.iso.org/obp/ui/en/#!iso:std:74039:enhttps://www.iso.org/contents/data/standard/07/40/74039.detail.rssISO 19111:2019iso-reference ISO 19111:2019(E)URN urn:iso:std:iso:19111:stage-90.20:ed-3    90    20  
 ISO/IEC: ISO/IEC 22989:2022, Information technology — Artificial intelligence — Artificial intelligence concepts and terminology. International Organization for Standardization, International Electrotechnical Commission, Geneva (2022). https://www.iso.org/standard/74296.html.https://www.iso.org/standard/74296.htmlhttps://www.iso.org/obp/ui/en/#!iso:std:74296:enhttps://www.iso.org/contents/data/standard/07/42/74296.detail.rsshttps://standards.iso.org/ittf/PubliclyAvailableStandards/index.htmlISO/IEC 22989:2022iso-reference ISO/IEC 22989:2022(E)URN urn:iso:std:iso-iec:22989:stage-60.60:ed-1    60    60  
 Allen, J. F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26, pp832-843 (1983).Maintaining Knowledge about Temporal Intervals
-Roger Lott: OGC 18-005r4, Topic 2 — Referencing by coordinates. Open Geospatial Consortium (2019). http://www.opengis.net/doc/AS/topic-2/5.0.http://www.opengis.net/doc/AS/topic-2/5.0https://docs.ogc.org/as/18-005r4/18-005r4.htmlOGC 18-005r4
-Cox, S., Little, C.: W3C Time Ontology in OWL. World Wide Web Consortium (2022) W3C TR owl-time3C TR owl-time
+Roger Lott: OGC 18-005r8, Topic 2 — Referencing by coordinates (Including corrigendum 1 and corrigendum2). Open Geospatial Consortium (2023). http://www.opengis.net/doc/AS/topic-2/6.0.http://www.opengis.net/doc/AS/topic-2/6.0https://docs.ogc.org/as/18-005r8/18-005r8.pdfOGC 18-005r8
+Cox, S., Little, C.: W3C Time Ontology in OWL. World Wide Web Consortium (2022) .W3C Time Ontology in OWL3C Time Ontology in OWL
 
-BibliographyCarl Stephen Smyth: OGC 21-056r10, OGC GeoPose 1.0 Data Exchange Draft Standard. Open Geospatial Consortium (2022). http://www.opengis.net/doc/DIS/geopose/1.0.http://www.opengis.net/doc/DIS/geopose/1.0https://docs.ogc.org/dis/21-056r10/21-056r10.html[1]OGC 21-056r10[1]ISO: ISO 19108:2002, Geographic information — Temporal schema. International Organization for Standardization, Geneva (2002). https://www.iso.org/standard/26013.html.https://www.iso.org/standard/26013.htmlhttps://www.iso.org/obp/ui/en/#!iso:std:26013:enhttps://www.iso.org/contents/data/standard/02/60/26013.detail.rss[2]ISO 19108:2002iso-reference ISO 19108:2002(E)URN urn:iso:std:iso:19108:stage-90.93:ed-1    90    93  [2]
+BibliographyCarl Stephen Smyth: OGC 21-056r11, OGC GeoPose 1.0 Data Exchange Standard. Open Geospatial Consortium (2023). http://www.opengis.net/doc/IS/geopose/1.0.http://www.opengis.net/doc/IS/geopose/1.0https://docs.ogc.org/is/21-056r11/21-056r11.html[1]OGC 21-056r11[1]ISO: ISO 19108:2002, Geographic information — Temporal schema. International Organization for Standardization, Geneva (2002). https://www.iso.org/standard/26013.html.https://www.iso.org/standard/26013.htmlhttps://www.iso.org/obp/ui/en/#!iso:std:26013:enhttps://www.iso.org/contents/data/standard/02/60/26013.detail.rss[2]ISO 19108:2002iso-reference ISO 19108:2002(E)URN urn:iso:std:iso:19108:stage-90.93:ed-1    90    93  [2]
   Orzell, C.: A Brief History of Timekeeping. Oneworld Publications (2022). ISBN-13:978-0-86154-321-2.[3]
   A Brief History of Timekeeping
   
 [3]
-  Andrewes, W.H.J.: A Chronicle Of Timekeeping. Scientific American (2006). [4]
+  Andrewes, W.H.J.: A Chronicle Of Timekeeping. Scientific American (2006). .[4]
   A Chronicle Of Timekeeping
   
 [4]
-  Meeus, J.: Astronomical Algorithms. [5]
+  Meeus, J.: Astronomical Algorithms. .[5]
   Astronomical Algorithms
   
 [5]
-  Dershowitz, N., Reingold, N.M.: Calendrical Calculations — The Ultimate Edition. Cambridge University Press (2018). ISBN-13:978-1107683167.  [last accessed 2023-01][6]
+  Dershowitz, N., Reingold, N.M.: Calendrical Calculations — The Ultimate Edition. Cambridge University Press (2018). ISBN-13:978-1107683167. . [last accessed 2023-01][6]
   Calendrical Calculations
   
 [6]
-  Bureau International des Poids et Mesures (BIPM): Establishment of International Atomic Time and Coordinated Universal Time. [7]
+  Bureau International des Poids et Mesures (BIPM): Establishment of International Atomic Time and Coordinated Universal Time. .[7]
   Establishment of International Atomic Time and Coordinated Universal Time
   
-[7]
-  Wikipedia. International Fixed Calendar.  [last accessed 2023-12][8]
+[7]
+  The Library of Congress: Extended Date/Time Format (EDTF) Specification. (2019). . [last accessed 2024-02][8]
+  Extended Date Time Format
+  
+[8]
+  Wikipedia. International Atomic Time. . [Last accessed 2024-02][9]
+  International Atomic Time
+  
+[9]
+  Wikipedia. International Fixed Calendar. . [last accessed 2023-12][10]
   International Fixed Calendar
   
-[8]
-  Wikipedia. List of Calendars.  [last accessed 2023-12][9]
+[10]
+  Wikipedia. List of Calendars. . [last accessed 2023-12][11]
   List of Calendars
   
-[9]
-  Lorentz Transform. Wolfram MathWorld. [10]
+[11]
+  Lorentz Transform. Wolfram MathWorld. .[12]
   Lorentz Transforms
   
-[10]
-  Allen, J.F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26 pp832-843 (1983).[11]
+[12]
+  Allen, J.F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26, no. 11, pp 832-843 (1983). .[13]
   Maintaining Knowledge about Temporal Intervals
   
-[11]
-  Minkowski, H.: Space and Time, Minkowski’s Papers on Relativity. Minkowski Institute Press, Montreal (2012) [12]
+[13]
+  Minkowski, H.: Space and Time, Minkowski’s Papers on Relativity. Minkowski Institute Press, Montreal (2012). .[14]
   Minkowski Space and Time
   
-[12]
-  Juan Diego Bogotá, Zakaria Djebbara: Time-consciousness in computational phenomenology: a temporal analysis of active inference. Neuroscience of Consciousness, Volume 2023, Issue 1 (2023). [13]
+[14]
+  IETF: Date and Time on the Internet: Timestamps with additional information. Internet Engineering Task Force (2023). . [last accessed 2024-02][15]
+  Serialising Extended Data About Times and Events
+  
+[15]
+  Juan Diego Bogotá, Zakaria Djebbara: Time-consciousness in computational phenomenology: a temporal analysis of active inference. Neuroscience of Consciousness, Volume 2023, Issue 1 (2023). .[16]
   Time-consciousness in computational phenomenology
   
-[13]
-  Lucas, J.R.: A Treatise on Time and Space. Methuen and Co. Ltd (1973) ISBN 0-416-84190-2.[14]
+[16]
+  Lucas, J.R.: A Treatise on Time and Space. Methuen and Co. Ltd (1973) ISBN 0-416-84190-2.[17]
   Treatise on Time and Space
   
-[14]
-  The Open Group: UNIX Time.  [last accessed 2023-01][15]
+[17]
+  The Open Group: UNIX Time. . [last accessed 2023-01][18]
   UNIX Time
   
-[15]
+[18]
+  W3C. Working with Time Zones, W3C Working Group Note 5 July 2011. .[19]
+  Working with Time Zones
+  
+[19]
+
+
+
+
 
 
 
diff --git a/23-049/23-049.xml b/23-049/23-049.xml
index ac469f7..544be50 100644
--- a/23-049/23-049.xml
+++ b/23-049/23-049.xml
@@ -2,7 +2,7 @@
 
 
 Topic 25 - Abstract Conceptual Model for Time
-http://www.opengis.net/doc/AS/temporal-conceptual-model/1.0http://docs.opengeospatial.org/as/23-049/23-049.html23-04923-0492023-12-132023-05-232023-05-23
+http://www.opengis.net/doc/AS/temporal-conceptual-model/1.0http://docs.opengeospatial.org/as/23-049/23-049.html23-049r123-049r12024-02-142024-02-142023-05-23
 U.K. Met Office
 
 HeazelTech
@@ -22,7 +22,7 @@
 
 
                                  Traditionally, geospatial communities used 2D coordinates and the vertical (third dimension) and temporal aspects were considered attributes rather than valid components of coordinate systems. In an increasingly dynamic, faster and multidimensional world, much confusion and lack of interoperability has occurred because of inconsistent approaches to defining and expressing time. Various international bodies expended considerable effort to establish the Gregorian Calendar as a consistent timeline. The Gregorian Calendar suffices for low precision applications, such as to the nearest minute, but not so when second or sub-second accuracy is required. For example, there have been differing practices and no consensus on whether leap seconds should be part of the Gregorian timeline.
                                  
 
-
                                  This document is consistent with  and W3C Time Ontology in OWL.
                                  
+
                                  This document is consistent with  and W3C Time Ontology in OWL.
                                  
 swg-draft2024
 Open Geospatial Consortium
 ogcdocOGC documentabstract specificationconceptual modeltimetemporal referencingreferencing by coordinatescalendarclocktimescaleabstract-specification-topictechnical                                  TOC Heading Levels2HTML TOC Heading Levels2DOC TOC Heading Levels2
@@ -70,14 +70,14 @@ To obtain additional rights of use, visit
 
 
                                  Traditionally, geospatial communities used 2D coordinates and the vertical (third dimension) and temporal aspects were considered attributes rather than valid components of coordinate systems. In an increasingly dynamic, faster and multidimensional world, much confusion and lack of interoperability has occurred because of inconsistent approaches to defining and expressing time. Various international bodies expended considerable effort to establish the Gregorian Calendar as a consistent timeline. The Gregorian Calendar suffices for low precision applications, such as to the nearest minute, but not so when second or sub-second accuracy is required. For example, there have been differing practices and no consensus on whether leap seconds should be part of the Gregorian timeline.
                                  
 
-
                                  This document is consistent with  and W3C Time Ontology in OWL.
                                  
+
                                  This document is consistent with  and W3C Time Ontology in OWL.
                                  
 
 Preface
-
                                  When OGC standards involve time, they generally refer to the ISO documents such as  (now largely superseded), , , and their freely available OGC equivalents, such as  (the equivalent to ).
                                  
+
                                  When OGC Standards involve time, they generally refer to the ISO documents such as Geographic information Temporal schema  (now largely superseded), Geographic information Referencing by coordinates , Date and Time Format , and their freely available OGC equivalents, such as OGC Abstract Specification Topic 2: Referencing by coordinates   (the equivalent to ).
                                  
 
-
                                  Much effort over decades has gone into establishing complex structures to represent calendar based time, such as the  notation, and many date-time schemas. A consequence of this effort is that many end-users and developers of software use calendar based “coordinates”, with the associated ambiguities about underlying algorithms, imprecision and inappropriate scope.
                                  
+
                                  Over decades, much effort has gone into establishing complex structures to represent calendar based time, such as the  notation, and many date-time schemas. A consequence of this effort is that many end-users and developers of software use calendar based “coordinates”, with the associated ambiguities about underlying algorithms, imprecision and inappropriate scope.
                                  
 
-
                                  The aim of this Abstract Specification is to establish clear concepts and terminology, so that people are well aware of the advantages and disadvantages of adopting a particular technological approach to time and then perhaps build better, more appropriate, interoperable systems for their use cases.
                                  
+
                                  The aim of this OGC Temporal Abstract Specification is to establish clear concepts and terminology. This is necessary so that people are aware of the advantages and disadvantages of specifying or adopting a particular technological approach to time and then perhaps build better, more appropriate, interoperable systems for their use cases.
                                  
                                   
 Security Considerations
 
                                  This Abstract Specification does not place any constraints on application, platform, operating system level, or network security.
                                  
@@ -146,26 +146,33 @@ directly in application schemas.
                                  
 
 
 Terms and definitions
+
+clock
+
+
+
                                  regularly repeating physical phenomenon that can be counted
                                  
+                                   
+
 
 conceptual model
 
 
-
                                  description of common concepts and their relationships, particularly in order to facilitate exchange of information between parties within a specific domain
                                  
+
                                  description of common concepts and their relationships, particularly in order to facilitate exchange of information between parties within a specific domain
                                  
 
 
- 
                                  A conceptual model is explicitly chosen to be independent of design or implementation concerns.
                                  
+ 
                                  A conceptual model is explicitly chosen to be independent of design or implementation concerns.
                                  
                                   
 
 
 coordinate
 
 
-
                                  one of a sequence of numbers designating the position of a point
                                  
+
                                  one of a sequence of numbers designating the position of a point
                                  
 
 
 
 
- 
                                  In many coordinate reference systems, the coordinate numbers are qualified by units.
                                  
+ 
                                  In many coordinate reference systems, the coordinate numbers are qualified by units.
                                  
                                   
 
 
@@ -178,22 +185,22 @@ directly in application schemas.
                                  
 
 
 
-
                                  coordinate systemcoordinate system that is related to an object by a reference framedatum
                                  
+
                                  coordinate systemcoordinate system that is related to an object by a reference framedatum
                                  
 
 
 
 
 
 
- 
                                  Geodetic and vertical datums are referred to as reference frames.
                                  
-
                                  For geodetic and vertical reference frames, the object will be the Earth. In planetary applications, geodetic and vertical reference frames may be applied to other celestial bodies.
                                  
+ 
                                  Geodetic and vertical datums are referred to as reference frames.
                                  
+
                                  For geodetic and vertical reference frames, the object will be the Earth. In planetary applications, geodetic and vertical reference frames may be applied to other celestial bodies.
                                  
                                   
 
 
 coordinate system
 
 
-
                                  set of mathematical rules for specifying how coordinates are to be assigned to points
                                  
+
                                  set of mathematical rules for specifying how coordinates are to be assigned to points
                                  
 
 
                                    
@@ -208,7 +215,7 @@ directly in application schemas.
                                  
 
 
 
-
                                  parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate systemcoordinate system
                                  
+
                                  parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate systemcoordinate system
                                  
 
 
  
@@ -219,15 +226,15 @@ directly in application schemas.
                                  
 
 
 
-geodesy
                                  point in time
                                  
+geodesy
                                  point in time
                                  
 
 
 
 
 
 
- 
                                  In , an epoch is expressed in the Gregorian calendar as a decimal year.
                                  
-
                                  2017-03-25 in the Gregorian calendar is epoch 2017.23. Other notations or reference systems are options.
                                  
+ 
                                  In , an epoch is expressed in the Gregorian calendar as a decimal year.
                                  
+
                                  2017-03-25 in the Gregorian calendar is epoch 2017.23. Other notations or reference systems are options.
                                  
                                   
 
 
@@ -240,7 +247,7 @@ directly in application schemas.
                                  
 
 
 
-
                                  parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate systemcoordinate system
                                  
+
                                  parameter or set of parameters that realize the position of the origin, the scale, and the orientation of a coordinate systemcoordinate system
                                  
 
 
                                    
@@ -255,7 +262,7 @@ directly in application schemas.
                                  
 
 
 
-
                                  coordinate reference systemcoordinate reference system based on a temporal datumtemporal datum
                                  
+
                                  coordinate reference systemcoordinate reference system based on a temporal datumtemporal datum
                                  
 
 
  
@@ -266,7 +273,7 @@ directly in application schemas.
                                  
 
 
 
-geodesy
                                  one-dimensional coordinate systemcoordinate system where the axis is time
                                  
+geodesy
                                  one-dimensional coordinate systemcoordinate system where the axis is time
                                  
 
 
  
@@ -275,13 +282,20 @@ directly in application schemas.
                                  
 temporal datum
 
 
-
                                  reference framedatum describing the relationship of a <geodesy> temporal coordinate systemtemporal coordinate system to an object
                                  
+
                                  reference framedatum describing the relationship of a <geodesy> temporal coordinate systemtemporal coordinate system to an object
                                  
 
 
 
 
- 
                                  The object is normally time on the Earth.
                                  
+ 
                                  The object is normally time on the Earth.
                                  
                                   
+
+
+tick
+
+
+
                                  event which is a single occurrence of the regularly repeating physical phenomenon of a clock
                                  
+                                   
                                   
 
 
@@ -315,152 +329,150 @@ directly in application schemas.
                                  
 
 
                                  A conceptual model:
                                  
 
-
                                  1. is a representation of a system, made of the composition of concepts which are used to help people know, understand, or simulate a subject the model represents. A documented conceptual model represents ‘concepts’ (entities), the relationships between them, and a vocabulary;
                                    
+
                                    1. is a representation of a system, made of the composition of concepts which are used to help people know, understand, or simulate a subject the model represents. A documented conceptual model represents ‘concepts’ (entities), the relationships between them, and a vocabulary;
                                      
 
                                      2. 
 
                                    2. is explicitly defined to be independent of design or implementation concerns;
                                      
 
                                      3. 
 
                                    3. organizes the vocabulary needed to communicate consistently and thoroughly about the know-how of a problem domain;
                                      
 
                                      4. 
-
                                    4. starts with a glossary of terms and definitions. There is a very high premium on high-quality, design-independent definitions, free of data or implementation biases; the model also emphasizes rich vocabulary; and
                                      
+
                                    5. contains the definitions of the concepts that it organizes. There is a high premium on high-quality, design-independent definitions, free of data or implementation biases; the model also emphasizes rich vocabulary; and
                                      
 
                                      6. 
 
                                    6. is always about identifying the correct choice of terms to use in communications, including statements of rules and requirements, especially where high precision and subtle distinctions need to be made. The core concepts of a temporal geospatial problem domain are typically quite stable over time.
                                      
 
                                      7. 
 
                                    
 
 
-
-Abstract Conceptual Model for Time
-
                                    This Temporal Abstract Conceptual Model follows , which is the ISO adoption of .
                                    
-
-
                                    The model is also informed by the W3C Time Ontology in OWL.
                                    
-
-
                                    
-                                    
-
 
 Temporal regimes
 
 General
-
                                    To enable more clear reasoning about time, this Abstract Specification uses the term “Regime” to describe the fundamentally different types of time and its measurement. This is a pragmatic approach that allows the grouping of recommendations and best practices in a practical way, but without obscuring the connection to the underlying theoretical components.
                                    
+
                                    To enable more clear reasoning about time, this Abstract Specification uses the term “Regime” to describe the fundamentally different types of time and its measurement. This is a pragmatic approach that allows the grouping of recommendations and best practices in a practical way, but without obscuring the connection to the underlying theoretical components.
                                    
 
-
                                    The first three regimes, described below, have deep underlying physical and mathematical foundations which cannot be legislated away. The fourth regime, calendars, concerns social constructs using seemingly random mixtures of ad hoc algorithms, arithmetic, numerology and measurements. Paradoxically, the calendar regime has historically driven advances in mathematics and physics. See the article A Chronicle Of Timekeeping.
                                    
+
                                    The first three regimes, described below, have deep underlying physical and mathematical foundations which cannot be legislated away. The fourth regime, calendars, concerns social constructs using seemingly random mixtures of ad hoc algorithms, arithmetic, numerology and measurements. Paradoxically, the calendar regime has historically driven advances in mathematics and physics. See the article A Chronicle Of Timekeeping.
                                    
 
-
                                    With due consideration, the regimes are applicable to other planets and outer space.
                                    
+
                                    With due consideration, the regimes are applicable to other planets and outer space.
                                    
                                     
 
 
 Events and Operators
-
                                    The simplest way of relating entities in time is by events that can be ordered and established in a sequence, and this sequence is used as an approximate measure of the passage of time.
                                    
+
                                    The simplest way of relating entities in time is by events that can be ordered and established in a sequence, and this sequence is used as an approximate measure of the passage of time.
                                    
 
-
                                    In this regime, no clocks or time measurements are defined, only events, that are ordered in relation to each other. Examples are geological layers, sediment or ice core layers, archaeological sequences, sequential entries in computer logs without coordinated time.
                                    
+
                                    In this regime, no clocks or time measurements are defined, only events, that are ordered in relation to each other. Examples are geological layers, sediment or ice core layers, archaeological sequences, sequential entries in computer logs without coordinated time.
                                    
 
-
                                    One set of events may be completely ordered with respect to each other, but another set of similar internally consistent ordered events cannot be cross-referenced to each other unless extra information is available. Even then, only partial orderings may be possible.
                                    
+
                                    One set of events may be completely ordered with respect to each other, but another set of similar internally consistent ordered events cannot be cross-referenced to the first set unless extra information is available. Even then, only partial orderings may be possible.
                                    
 
-
                                    In this regime, the Allen Operators (see ) can be used. If A occurs before B and B occurs before C, then it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So in our example, B occurs in the interval (A,C). However, arithmetic operations like (B-A) or (C-A) cannot be performed as any timescale or measurements are not defined. For example, in geology, ‘subtracting’ Ordovician from Jurassic is meaningless. In archeology, ‘subtracting’ a layer with a certain type of pottery remains from the layer containing burnt wood and bones is again not meaningful. Only the ordering can be deduced.
                                    
+
                                    In this regime, the Allen Operators (see ) can be used. If A occurs before B and B occurs before C, then it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So in our example, B occurs in the interval (A,C). However, arithmetic operations like (B-A) or (C-A) cannot be performed as any timescale or measurements are not defined. For example, in geology, ‘subtracting’ Ordovician from Jurassic is meaningless. In archeology, ‘subtracting’ a layer with a certain type of pottery remains from the layer containing burnt wood and bones is again not meaningful. Only the ordering can be deduced.
                                    
 
-
                                    This regime constitutes an Ordinal Temporal Reference System, with discrete enumerated ordered events.
                                    
+
                                    This regime constitutes an ordinal temporal reference system, with discrete enumerated ordered events.
                                    
 
-
                                    
+
                                    
                                     
 
 
 Simple Clocks and Discrete Timescales
-
                                    In this regime, a clock is defined as any regularly repeating physical phenomena, such as pendulum swings, earth’s rotation about the sun, earth’s rotation about its axis, heart beats, vibrations of electrically stimulated quartz crystals or the resonance of the unperturbed ground-state hyperfine transition frequency of the cesium-133 atom. In terms of the number of repetitions possible, some phenomena make better clocks than others, because of the consistency of each repetition and the precision of each ‘tick’. A mechanism for counting, or possibly measuring, the ticks is desirable.
                                    
+
                                    In this regime, a clock is defined as any regularly repeating physical phenomenon, such as a pendulum swing, earth’s rotation about the sun, earth’s rotation about its axis, heart beat, vibrations of electrically stimulated quartz crystals or the resonance of the unperturbed ground-state hyperfine transition frequency of the cesium-133 atom. Each occurrence of the repeating phenomenon is, of course, an event, but as there are usually very many that can only be distinguished by counting, they are considered a separate class of ticks.
                                    
+
+
                                    In terms of the number of repetitions possible, some phenomena make better clocks than others, because of the consistency of each repetition and the precision of each tick. A mechanism for counting, or possibly measuring, the ticks is desirable.
                                    
 
-
                                    An assumption is that the ticks are regular and homogeneous.
                                    
+
                                    An assumption is that the ticks are regular and homogeneous.
                                    
 
-
                                    There is no sub-division between two successive clock ticks. Measuring time consists of counting the complete number of repetitions of ticks since the clock started, or since some other event at a given clock count.
                                    
+
                                    There is no sub-division between two successive clock ticks. Measuring time consists of counting the complete number of repetitions of ticks since the clock started, or since some other event at a given clock tick count.
                                    
 
-
                                    There is no time measurement before the clock starts, or after it stops.
                                    
+
                                    There is no time measurement before the clock starts, or after it stops.
                                    
 
-
                                    It may seem that time can be measured between ‘ticks’ by interpolation, but this needs another clock, with faster ticks. This process of devising more precise clocks continues down to the atomic scale. At that scale the deterministic process of physically trying to interpolate between ticks is not possible.
                                    
+
                                    It may seem that time can be measured between ticks by interpolation, but this needs another clock, with faster ticks. This process of devising more precise clocks continues down to the atomic scale. At that scale the deterministic process of physically trying to interpolate between ticks is not possible.
                                    
 
-
                                    The internationally agreed atomic time, TAI, is an example of a timescale with an integer count as the measure of time. However in practice, TAI is an arithmetic compromise across about two hundred separate atomic clocks, corrected for differing altitudes and temperatures.
                                    
+
                                    The internationally agreed atomic time, TAI International Atomic Time is an example of a timescale with an integer count as the measure of time. However in practice, TAI is an arithmetic compromise across about two hundred separate atomic clocks, corrected for differing altitudes and temperatures.
                                    
 
-
                                    In this regime, Allen Operators (see ) also can be used. If L occurs before M and M occurs before N, it can be correctly deduced that L occurs before N. The full set of operators also covers pairs of intervals. So if M occurs in the interval (L,N),  integer arithmetic operations such as (M-L) or (N-L) can be performed. This is because an integer timescale or measurement is defined.
                                    
+
                                    In this regime, Allen Operators (see ) also can be used. If L occurs before M and M occurs before N, it can be correctly deduced that L occurs before N. The full set of operators also covers pairs of intervals. So if M occurs in the interval (L,N),  integer arithmetic operations such as (M-L) or (N-L) can be performed. This is because an integer timescale or measurement is defined by the count of ticks.
                                    
 
-
                                    This regime constitutes a Temporal Coordinate Reference System, with discrete integer units of measure which can be subject to integer arithmetic.
                                    
+
                                    This regime constitutes a temporal coordinate reference system, with discrete integer units of measure which can be subject to integer arithmetic.
                                    
                                     
 
 
 CRS and Continuous Timescales
-
                                    This regime takes a clock from the previous regime and assumes that between any two adjacent ticks, it is possible to interpolate indefinitely to finer and finer precision, using ordinary arithmetic, rather than any physical device. Units of Measure may be defined that are different from the ‘ticks’. For example, a second may be defined as 9,192,631,770 vibrations of the ground-state hyperfine transition of the cesium-133 atom. Alternatively and differently, a second may be defined as 1/86400th of the rotation of the earth on its axis with respect to the sun. The count of rotations is the ‘ticks’ of an earth-day clock. This latter definition is not precise enough for many uses, as the rotation of the earth on its axis varies from day to day.
                                    
+
                                    This regime takes a clock from the previous regime and assumes that between any two adjacent ticks, it is possible to interpolate indefinitely to finer and finer precision, using ordinary arithmetic, rather than any physical device. Units of measure may be defined that are different from the ticks. For example, a second may be defined as 9,192,631,770 vibrations of the ground-state hyperfine transition of the cesium-133 atom. Alternatively and differently, a second may be defined as 1/86400th of the rotation of the earth on its axis with respect to the sun. The count of rotations is the ticks of an earth-day clock. This latter definition is not precise enough for many uses, as the rotation of the earth on its axis varies from day to day.
                                    
 
-
                                    Alternatively, it may be that the ticks are not counted but measured, and the precision of the clock is determined by the precision of the measurements, such as depth in an ice core, or angular position of an astronomical body, such as the sun, moon or a star.
                                    
+
                                    Alternatively, it may be that the ticks are not counted but measured, and the precision of the clock is determined by the precision of the measurements, such as depth in an ice core subject to seasonal depositions of snow, or angular position of an astronomical body, such as the sun, moon or a star.
                                    
 
-
                                    It is also assumed that time can be extrapolated to before the time when the clock started and into the future, possibly past when the clock stops.
                                    
+
                                    It is also assumed that time can be extrapolated before the time when the clock started and into the future, possibly past when the clock stops.
                                    
 
-
                                    This gives us a continuous number line to perform theoretical measurements. This is a coordinate system. With a datum/origin/epoch, a unit of measure (a name for the ‘tick marks’ on the axis), positive and negative directions and the full range of normal arithmetic. This is a Coordinate Reference System (CRS).
                                    
+
                                    This gives us a continuous number line to perform theoretical measurements. With a datum/origin/epoch, a unit of measure (a name for the ticks on the axis), positive and negative directions and the full range of normal arithmetic, this is a coordinate reference system (CRS).
                                    
 
-
                                    In this regime, the Allen Operators (see ) also can be used. If A occurs before B and B occurs before C, it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So if B occurs in the interval (A,C), real number arithmetic operations like (B-A) or (C-A) can be performed. This is because a timescale or measurement has been defined, and between any two instants, an infinite number of other instants can be found.
                                    
+
                                    In this regime, the Allen Operators (see ) also can be used. If A occurs before B and B occurs before C, it can be correctly deduced that A occurs before C. The full set of operators also covers pairs of intervals. So if B occurs in the interval (A,C), real number arithmetic operations like (B-A) or (C-A) can be performed. This is because a timescale or measurement has been defined, and between any two instants, an infinite number of other instants can be found.
                                    
 
-
                                    Some examples are:
                                    
+
                                    Some examples are:
                                    
 
-
                                    • Unix milliseconds since 1970-01-01T00:00:00.0Z
                                      
+
                                      • Unix milliseconds since 1970-01-01T00:00:00.0Z
                                        
 
                                        • 
-
                                      • Julian Days, and fractions of a day, since noon on 1st January, 4713 BCE.
                                        
+
                                      • Julian Days, and fractions of a day, since noon on 1st January, 4713 BCE.
                                        
 
                                        • 
 
                                      
 
 
-
                                      This regime constitutes a Temporal Coordinate Reference System, with a continuous number line and units of measure, which can be subject to the full range of real or floating-point arithmetic.
                                      
+
                                      This regime constitutes a temporal coordinate reference system, with a continuous number line and units of measure, which can be subject to the full range of real, or floating-point, arithmetic.
                                      
 
 
 
 Calendars
-
                                      In this regime, counts and measures of time are related to the various combinations of the rotations of the earth, moon and sun or other astronomical bodies.
+
                                      In this regime, counts and measures of time are related to the various combinations of the rotations of the earth, moon and sun or other astronomical bodies.
 Typically there is no simple arithmetic connecting calendar systems and timescales. For example, the current civil year count of years in the Current Era (CE) and Before Current Era (BCE) is a very simple calendar, as there is no year zero. That is, Year 14CE – Year 12CE is a duration of 2 years, and Year 12BCE — Year 14BCE is also two years. However Year 1CE — Year 1BCE is one year, not two as there is no year 0CE or 0BCE.
                                      
 
-
                                      In this regime, the use of the Allen Operators (see ) is not straightforward. If A occurs before B and B occurs before C, then correctly deducing that A occurs before C is not always easy. The full set of Allen Operators also covers pairs of intervals. So in the example, B occurs in the interval (A,C). However, simple arithmetic operations like (B-A) or (C-A) cannot usually be done simply because of the vagaries of the calendar algorithms, multiple timescales, and multiple Units of Measure.
                                      
+
                                      In this regime, the use of the Allen Operators (see ) is not straightforward. If A occurs before B and B occurs before C, then correctly deducing that A occurs before C is not always easy. This is because the calendar’s timeline may contain gaps, changes of units of measure, or even duplicated times.
                                      
+
+
                                      The full set of Allen Operators also covers pairs of intervals. So in the example, B occurs in the interval (A,C). However, simple arithmetic operations like (B-A) or (C-A) cannot usually be done simply because of the vagaries of the calendar algorithms, multiple timescales, and multiple units of measure.
                                      
+
+
                                      For example, in the Gregorian calendar, calculating the number of days between the 1st February and the 30th March depends on whether the year is a leap year or not, which also depends on the century and millenium. Calculating the precise number of seconds between two dates in the Gregorian calendar also depends on whether leap seconds have been declared between the dates. There have been 27 leap seconds added between 1972 and 2022.
                                      
+                                      
 
-
                                      Calendars are social constructs made by combining several clocks and their associated timescales.
                                      
+
                                      Calendars are social constructs made by combining several clocks and their associated timescales. Calendars may also have local or regional variations at different times of the year or season, such as for ‘day-light saving’ in mid-latitudes. Again, this makes calculations more complicated and prone to change.
                                      
 
-
                                      This Abstract Specification only addresses the internationally agreed Gregorian calendar. The book Calendrical Calculations by Nachum Dershowitz and Edward M. Reingold provides overwhelming detail for conversion to numerous other calendars that have developed around the world and over the millennia and to meet the various social needs of communities, whether agricultural, religious or other. The reference is comprehensive but not exhaustive, as there are calendars that have been omitted.
                                      
+
                                      This Abstract Specification only addresses the internationally agreed Gregorian calendar. The book Calendrical Calculations by Nachum Dershowitz and Edward M. Reingold provides overwhelming detail for conversion to numerous other calendars that have developed around the world and over the millennia and to meet the various social needs of communities, whether agricultural, religious or other. The reference is comprehensive but not exhaustive, as there are calendars that have been omitted.
                                      
 
-
                                      A Calendar is a Temporal Reference System, but it is not a Temporal Coordinate Reference System nor an Ordinal Temporal Reference System.
                                      
+
                                      A calendar is a temporal reference system, but it is not a temporal coordinate reference system nor an ordinal temporal reference system.
                                      
                                       
 
 
 Other Regimes
-
                                      There are other regimes, which are out of scope of this Abstract Specification. This could include local solar time, which is useful, for example, for the calculation of illumination levels and the length of shadows on aerial photography, or relativistic time.
                                      
+
                                      There are other regimes, whose detailed description are out of scope of this Abstract Specification. This could include local solar time, which is useful, for example, for the calculation of illumination levels and the length of shadows on aerial photography, or relativistic time for very fast moving features.
                                      
 
 
 Accountancy
-
                                      The financial and administrative domains often use weeks, quarters, and other calendrical measures. These may be convenient for the requisite tasks, but are usually inappropriate for scientific or technical purposes.
                                      
+
                                      The financial and administrative domains often use weeks, quarters, and other calendrical measures. These may be convenient for the requisite tasks, but are usually inappropriate for scientific or technical purposes. This Abstract Conceptual Model for Time can support this regime.
                                      
                                       
 
 
 Agents and Agency
-
                                      Agents require a different concept of time from regimes where time is a coordinate axis or measured by clocks. An agent is an entity that senses, responds, and maintains a model of its environment, while performing actions to achieve its goals. See ISO/IEC 22989:2022, Artificial intelligence concepts and terminology. For an agent, the conceptual model of time is about flow and continuity including a sense of now, a memory of past events, and a speculation about future events. This regime addresses how the agent has awareness of the flow of events:
                                      
+
                                      Agents require a different concept of time from regimes where time is a coordinate axis or measured by clocks. An agent is an entity that senses, responds, and maintains a model of its environment, while performing actions to achieve its goals. See ISO/IEC 22989:2022, Artificial intelligence concepts and terminology. For an agent, the conceptual model of time is about flow and continuity including a sense of now, a memory of past events, and a speculation about future events. This regime addresses how the agent has awareness of the flow of events:
                                      
 
-
                                      • Temporal awareness integrates impression, retention, and protention, representing the continuous movement of time;
                                        
+
                                        • Temporal awareness integrates impression, retention, and protention, representing the continuous movement of time;
                                          
 
                                          • 
-
                                        • Agents continuously revise their models of the environment by integrating new observations with existing models;
                                          
+
                                        • Agents continuously revise their models of the environment by integrating new observations with existing models;
                                          
 
                                          • 
-
                                        • Observations are used to update an agent’s model, leading to a more accurate understanding of the environment and enabling effective goal-directed behavior.
                                          
+
                                        • Observations are used to update an agent’s model, leading to a more accurate understanding of the environment and enabling effective goal-directed behavior.
                                          
 
                                          • 
 
                                        
 
-
                                        This Agent regime of time is relevant to any feature which has agency.
                                        
+
                                        This regime of time is relevant to any feature which has agency. This Abstract Conceptual Model for Time can support this regime.
                                        
 
 
 
 Astronomical Time
-
                                        Astronomers have traditionally measured the apparent locations of stars, planets and other heavenly bodies by measuring angular separations from reference points or lines and the timing of transits across a meridian. Generally astronomers use time determined by earth’s motion relative to the distant stars rather than the sun. This is called sidereal time. Times are usually measured from an epoch in daylight, such as local midday, rather than midnight. Accurate measurements of positions of stars, planets and moons were and are essential for navigation on Earth. See the book Astronomical Algorithms by Jean Meeus for examples of the calculations involved.
                                        
+
                                        Astronomers have traditionally measured the apparent locations of stars, planets and other heavenly bodies by measuring angular separations from reference points or lines and the timing of transits across a meridian. Generally astronomers use time determined by earth’s motion relative to the distant stars rather than the sun. This is called sidereal time. Times are usually measured from an epoch in daylight, such as local midday, rather than midnight. Accurate measurements of positions of stars, planets and moons were and are essential for navigation on Earth. See the book Astronomical Algorithms by Jean Meeus for examples of the calculations involved. This Abstract Conceptual Model for Time can support this regime.
                                        
                                         
 
 
 Local Solar Time
-
                                        Local solar time may or may not correspond to the local statutory or legal time in a country. Local solar time can be construed as a clock and timescale, with an angular measure of the apparent position of the sun along the ecliptic (path through the sky) as the basic physical principle. But the sun does not appear to progress evenly along the ecliptic throughout the days and year. There may be variations of up to 15 minutes compared to an even angular speed.
                                        
+
                                        Local solar time may or may not correspond to the local statutory or legal time in a country. Local solar time can be construed as a clock and timescale, with an angular measure of the apparent position of the sun along the ecliptic (path through the sky) as the basic physical principle. But the sun does not appear to progress evenly along the ecliptic throughout the days and year. There may be variations of up to 15 minutes compared to an even angular speed. This Abstract Conceptual Model for Time can support this regime.
                                        
                                         
 
 
 Space-time
-
                                        When dealing with moving objects, the location of the object in space depends on its location in time. That is to say, location is an event in space and time.
                                        
+
                                        When dealing with moving objects, the location of the object in space depends on its location in time. That is to say, location is an event in space and time.
                                        
 
-
                                        Originally developed by Hermann Minkowski to support work in Special Relativity, the concept of space-time is useful whenever the location of an object in space is dependent on its location in time.
                                        
+
                                        Originally developed by Hermann Minkowski to support work in Special Relativity, the concept of space-time is useful whenever the location of an object in space is dependent on its location in time.
                                        
 
-
                                        Since the speed of light, 
+
                                        Since the speed of light, 
 
   
     c
@@ -546,51 +558,71 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 Relativistic
-
                                        A regime may be needed for ‘space-time’, off the planet Earth, such as for recording and predicting space weather approaching from the sun, where the speed of light and relativistic effects such as gravity may be relevant.
                                        
+
                                        A regime may be needed for ‘space-time’, off the planet Earth, such as for recording and predicting space weather approaching from the sun, where the speed of light and relativistic effects such as gravity may be relevant.
                                        
 
-
                                        Once off planet Earth, distances and velocities can become very large. The speed of light becomes a limiting factor in measuring both where and when an event takes place. Special Relativity deals with the accurate measurement of space-time events as measured between two moving objects. The core concepts are the Lorentz Transforms. These transforms allow one to calculate the degree of “contraction” a measurement undergoes due to the relative velocity between the observing and observed object.
                                        
+
                                        Once off planet Earth, distances and velocities can become very large. The speed of light becomes a limiting factor in measuring both where and when an event takes place. Special Relativity deals with the accurate measurement of space-time events as measured between two moving objects. The core concepts are the Lorentz Transforms. These transforms allow one to calculate the degree of “contraction” a measurement undergoes due to the relative velocity between the observing and observed object.
                                        
 
-
                                        The key to this approach is to ensure each moving feature of interest has its own local clock and time, known as its ‘proper time’. This example can be construed as a fitting into the clock and timescale regime. The relativistic effects are addressed through the relationships between the separate clocks, positions and velocities of the features.
                                        
+
                                        The key to this approach is to ensure each moving feature of interest has its own local clock and time, known as its ‘proper time’. This example can be construed as a fitting into the clock and timescale regime of this Abstract Specification. The relativistic effects are addressed through the relationships between the separate clocks, positions and velocities of the features.
                                        
 
-
                                        Relativistic effects may need to be considered for satellites and other spacecraft because of their relative speed and position in Earth’s gravity well.
                                        
+
                                        Relativistic effects may need to be considered for satellites and other spacecraft because of their relative speed and position in Earth’s gravity well.
                                        
 
-
                                        The presence of gravitational effects requires special relativity to be replaced by general relativity, and it can no longer be assumed that space (or space-time) is Euclidean. That is, Pythagoras’ Theorem does not hold except locally over small areas. This is somewhat familiar territory for geospatial experts.
                                        
+
                                        The presence of gravitational effects requires special relativity to be replaced by general relativity, and it can no longer be assumed that space (or space-time) is Euclidean. That is, Pythagoras’ Theorem does not hold except locally over small areas, or that the circumference of a circle is not precisely 
+
+  
+    2
+    :
+    π
+    r
+  
+
+2 :pi r. This is somewhat familiar territory for geospatial experts. This Abstract Conceptual Model for Time can support this regime, providing each feature has its own clock.
                                        
                                         
 
 
 
-
-Attributes of the Classes
+
+Abstract Conceptual Model for Time
+
                                        This Temporal Abstract Conceptual Model follows , which is the ISO adoption of .
                                        
+
+
                                        The model is also informed by the W3C Time Ontology in OWL.
                                        
+
+
                                        
+                                        
+
+
+Classes and their Attributes and Properties
 
 Reference Systems
-
                                        The top level ‘ReferenceSystem’ is an abstract super-class and does not have many attributes or properties. Only the total dimension of the reference system and the Location, Time or Domain of Applicability have been identified as essential.
                                        
+
                                        The top level Reference System class is an abstract super-class and does not have many attributes or properties. Only the total dimension of the reference system and the location, time or domain of applicability have been identified as essential.
                                        
 
-
                                        The ‘ReferenceSystem’ has two abstract sub-classes: ‘SpatialReferenceSystem’, which is defined in , and ‘TemporalReferenceSystem’, each with the attributes of Dimension and Domains of Applicability.
                                        
+
                                        The reference system class has two abstract sub-classes: Spatial Reference System, which is defined in , and Temporal Reference System, each with the inherited attributes of dimension and domains of applicability.
                                        
 
-
                                        The value for Dimension is one for time, or a vertical reference system, but may be as high as 6 for spatial location with orientation as in the GeoPose Implementation Standard.
                                        
+
                                        The value for dimension is one for time, or a vertical reference system, but may be as high as six for spatial location with orientation as in the GeoPose Implementation Standard.
                                        
 
-
                                        Besides the conventional space and time, there may be other reference systems, such as wavelength or frequency, that could be addressed by future additions to this Abstract Conceptual Model.
                                        
+
                                        For example, a reference system may be applicable to Mars, rather than Earth, or perhaps to a specific building site with local coordinates, or a specific time range while coordinate errors are within acceptable bounds.
                                        
+
+
                                        Besides the conventional space and time, there may be other reference systems, such as wavelength or frequency, that could be addressed by future additions to this Abstract Conceptual Model.
                                        
                                         
 
 
 Ordinal Temporal Reference Systems
-
                                        An OrdinalTemporal Reference System has a well-ordered finite sequence of events against which other events can be compared.
                                        
+
                                        An ordinal temporal reference system has a well-ordered finite sequence of events against which other events can be compared.
                                        
 
-
                                        An Ordinal Temporal Reference System is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:
                                        
+
                                        An ordinal temporal reference system is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:
                                        
 
-
                                        1. applicableLocationTimeOrDomain: the location, time or domain of applicability;
                                          
+
                                          1. applicable location time or domain: the location, time or domain of applicability;
                                            
 
                                            2. 
-
                                          2. dimension: the number of dimensions in this reference system. For Ordinal Temporal Reference Systems this value is fixed at 1.
                                            
+
                                          3. dimension: the number of dimensions in this reference system. For ordinal temporal reference systems this value is fixed at 1.
                                            
 
                                            4. 
 
                                          
 
-
                                          An Ordinal Temporal Reference System does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
                                          
+
                                          An ordinal temporal reference system does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
                                          
 
-
                                          1. Epoch: An Ordinal Temporal Reference System ‘has a’ one optional Epoch
                                            
+
                                            1. Epoch: An ordinal temporal reference system may be ‘anchored by’ one optional Epoch
                                              
 
                                              2. 
-
                                            2. Notation: An Ordinal Temporal Reference System ‘can use’ one or more Notations to represent itself.
                                              
+
                                            3. Notation: An ordinal temporal reference system ‘is represented by’ one or more Notations to represent itself.
                                              
 
                                              4. 
-
                                            4. Event: An Ordinal Temporal Reference System ‘consists of’ an ordered set of Events. These events are identifiable temporal instances.
                                              
+
                                            5. Event: An ordinal temporal reference system ‘consists of’ an ordered set of Events. These events are identifiable temporal instances.
                                              
 
                                              6. 
 
                                            
 
@@ -599,75 +631,82 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 Events
-
                                            The Events class is an ordered list of temporal events. The events can be instances, such as the ascension of a King to a throne, or intervals, such as the complete reign of each king.
                                            
+
                                            An event is an identifiable happening or occurence of something. The events can be instants, such as the ascension of a king to a throne, or intervals, such as the complete reign of each king.
                                            
 
 
                                            Other documents may enable two such ‘king lists’ to be related, though usually not completely.
                                            
+
+
                                            Several archeological layers may be identified as containing broken pottery, overlaid with a layer of burnt wood and brick, then followed by another layer with a different style of pottery and including a coin embossed with a king’s name. Thus the pottery styles can be classified as ‘early’ and ‘late’ and the late pottery associated with the named king, who may be identified from a separate inscribed ‘king list’.
                                            
+                                            
                                             
 
 
 
 Temporal Coordinate Reference Systems
-
                                            A Temporal Coordinate Reference System is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:
                                            
+
                                            A temporal coordinate reference system is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:
                                            
 
-
                                            1. applicableLocationTimeOrDomain: the location, time or domain of applicability;
                                              
+
                                              1. applicable location time or domain: the location, time or domain of applicability;
                                                
 
                                                2. 
-
                                              2. dimension: the number of dimensions in this reference system. For Temporal Coordinate Reference Systems this value is fixed at 1.
                                                
+
                                              3. dimension: the number of dimensions in this reference system. For temporal coordinate reference systems this value is fixed at 1.
                                                
 
                                                4. 
 
                                              
 
-
                                              A Temporal Coordinate Reference System does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
                                              
+
                                              A temporal coordinate reference system does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
                                              
 
-
                                              1. Epoch: A Temporal CRS ‘has a’ one optional Epochs
                                                
+
                                                1. Epoch: A temporal coordinate reference system ‘anchored by’ one optional Epochs
                                                  
 
                                                  2. 
-
                                                2. Notation: A Temporal CRS ‘can use’ one or more Notations to represent itself.
                                                  
+
                                                3. Notation: A temporal coordinate reference system ‘represented by’ one or more Notations to represent itself.
                                                  
 
                                                  4. 
-
                                                4. Timescale: A Temporal CRS ‘has a’ one Timescale which is used to represent the values along its single axis. This Timescale can be either discrete or continuous.
                                                  
+
                                                5. Timescale: A temporal coordinate reference system ‘must have’ one Timescale which is used to represent the values along its single axis. This timescale can be either discrete or continuous.
                                                  
 
                                                  6. 
 
                                                
 
 
 
 Calendar Reference Systems
-
                                                Calendars combine different timescales and their clocks and units of measure, and other events, to make a complex timeline against which events can be compared. Calculated algorithms are used to determine which instants of intervals on the compound timeline are identified and labeled.
                                                
+
                                                Calendars combine different timescales and their clocks and units of measure, and other events, to make a complex timeline against which events can be compared. Calculated algorithms are used to determine which instants or intervals on the compound timeline are identified and labeled.
                                                
 
-
                                                A Calendar is a type of temporal reference system. Therefore, it inherits the following attributes from the TemporalReferenceSystem class:
                                                
+
                                                A calendar is a type of temporal reference system. Therefore, it inherits the following attributes from the temporal reference system class:
                                                
 
-
                                                1. applicableLocationTimeOrDomain: the location, time or domain of applicability
                                                  
+
                                                  1. applicable location time or domain: the location, time or domain of applicability
                                                    
 
                                                    2. 
-
                                                  2. dimension: the number of dimensions in this reference system. For Calendars this value is fixed at 1.
                                                    
+
                                                  3. dimension: the number of dimensions in this reference system. For calendars this value is fixed at 1.
                                                    
 
                                                    4. 
 
                                                  
 
-
                                                  A Calendar does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
                                                  
+
                                                  A calendar does not have any attributes of its own. However, it does use associations with other classes to fully describe itself.
                                                  
 
-
                                                  1. Algorithm: A Calendar ‘has a’ one or more Algorithms. These Algorithms specify how the multiple Time Scales are aggregated into a single Timeline.
                                                    
+
                                                    1. Timeline: A calendar ‘has a’ one Timeline which serves to aggregate a number of Timescales into a single coherent measure of date and time.
                                                      
 
                                                      2. 
-
                                                    2. Epoch: A calendar ‘has a’ one optional Epoch
                                                      
+
                                                    3. Algorithm: A timeline ‘is defined by’ one or more Algorithms. These algorithms specify how the multiple timescales are aggregated into a single Timeline.
                                                      
 
                                                      4. 
-
                                                    4. Notation: A calendar ‘can use’ one or more Notations to represent itself.
                                                      
+
                                                    5. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a Timeline.
                                                      
 
                                                      6. 
-
                                                    6. Timeline: A Calendar ‘has a’ one Timeline which serves to aggregate a number of Timescales into a single coherent measure of date and time.
                                                      
+
                                                    7. Epoch: A calendar is ‘anchored by’ one optional Epoch
                                                      
 
                                                      8. 
-
                                                    8. Timescale: A Calendar ‘has a’ two or more Timescales which are used to construct a Timeline.
                                                      
+
                                                    9. Notation: A calendar  is ‘represented by’ one or more Notations to represent itself.
                                                      
 
                                                      10. 
 
                                                    
 
 
 Timeline
-
                                                    The timeline is usually a set of instants from the past to the future and is compounded from multiple timescales, with multiple units of measures, and complicated arithmetic determined by the calendar algorithm(s). The timeline is usually not even continuous, having gaps or even multiple simultaneous representations.
                                                    
+
                                                    The timeline is usually a set of instants from the past to the future and is compounded from multiple timescales, with multiple units of measures, and complicated arithmetic determined by the calendar algorithm(s). The timeline is usually not even continuous, having gaps or even duplicated times or multiple simultaneous representations.
                                                    
 
-
                                                    A Timeline does not have any attributes of its own. Nor does it inherit any attributes from a parent class. However, it does use associations with other classes to fully describe itself.
                                                    
+
                                                    A timeline does not have any attributes of its own. Nor does it inherit any attributes from a parent class. However, it does use associations with other classes to fully describe itself.
                                                    
 
-
                                                    1. Algorithm: A Timeline ‘has a’ one or more Algorithms. These Algorithms specify how the multiple Time Scales are aggregated into a single Timeline.
                                                      
+
                                                      1. Algorithm: A timeline is ‘defined by’ one or more Algorithms. These algorithms specify how the multiple timescales are aggregated into a single timeline.
                                                        
 
                                                        2. 
-
                                                      2. Timescale: A Timeline ‘has a’ two or more Timescales which are used to construct the Timeline.
                                                        
+
                                                      3. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
                                                        
 
                                                        4. 
 
                                                      
 
 
 
 Algorithm
-
                                                      An Algorithm specifies the logic used to construct a Timeline from its constituent Timescales. An Algorithm does not have any attributes of its own. Nor does it make use of any other classes from this Temporal model.
                                                      
+
                                                      An algorithm specifies the logic used to construct a timeline from its constituent Timescales. An algorithm does not have any attributes of its own. It does make use of timescales to construct the timeline of a calendar.
                                                      
+
+
                                                      1. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
                                                        
+
                                                        2. 
+
                                                      
                                                       
 
 
@@ -702,38 +741,38 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 Discrete and Continuous Time Scales
-
                                                      A clock may be a regular, repeating, physical event, or ‘tick’, that can be counted. The sequence of tick counts form a discrete (counted) timescale.
                                                      
+
                                                      A clock may be a regular, repeating, physical event, or tick, that can be counted. The sequence of tick counts form a discrete (counted) timescale.
                                                      
 
 
                                                      Some clocks  allow the measurement of intervals between ticks, such as the movement of the sun across the sky. Alternatively, the ticks may not be completely distinguishable, but are still stable enough over the time of applicability to allow measurements rather than counting to determine the passage of time. These clocks generate a continuous (measured) timescale.
                                                      
 
-
                                                      The duration of a tick is a constant. The length of a tick is specified using a Unit Of Measure.
                                                      
+
                                                      The duration of a tick is assumed constant. The duration of a tick is specified using a Unit Of Measure.
                                                      
 
 
 Timescale
-
                                                      A Timescale is a linear measurement (one dimension) used to measure or count monotonic events. Timescale has three attributes:
                                                      
+
                                                      A timescale is a linear measurement (one dimension) used to measure or count monotonic events. Timescale has three attributes:
                                                      
 
-
                                                      1. Arithmetic: an indicator of whether this Timescale contains counted integers or measured real/floating point numbers.
                                                        
+
                                                        1. Arithmetic: an indicator of whether this timescale contains counted integers or measured real/floating point numbers.
                                                          
 
                                                          2. 
-
                                                        2. StartCount: the lowest value in a Timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
                                                          
+
                                                        3. StartCount: the lowest value in a timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
                                                          
 
                                                          4. 
-
                                                        4. EndCount: the greatest value in a Timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
                                                          
+
                                                        5. EndCount: the greatest value in a timescale. The data type of this attribute is specified by the ‘arithmetic’ attribute.
                                                          
 
                                                          6. 
 
                                                        
 
-
                                                        In addition to the attributes, the Timescale class maintains associations with two other classes to complete its definition.
                                                        
+
                                                        In addition to the attributes, the timescale class maintains associations with two other classes to complete its definition.
                                                        
 
-
                                                        1. Clock: A Timescale ‘has a’ one clock. This is the process which generates the ‘tick’ which is counted or measured for the Timescale.
                                                          
+
                                                          1. Clock: A timescale is ‘determined by’ one clock. This is the process which generates the ticks which are counted or measured for the timescale.
                                                            
 
                                                            2. 
-
                                                          2. UnitOfMeasure: A timescale ‘has a’ one UnitOfMeasure. This class specifies the units of the clock measurement as well as the direction of increase of that measurement.
                                                            
+
                                                          3. UnitOfMeasure: A timescale ‘has a’ one UnitOfMeasure. This class specifies the units of the clock measurement or count as well as the direction of increase of that measurement or count.
                                                            
 
                                                            4. 
 
                                                          
 
 
 
 Clock
-
                                                          A Clock represents the process which generates the ‘tick’ which is counted or measured for a Timescale. Clock has one attribute:
                                                          
+
                                                          A clock represents the process which generates the ticks which are counted or measured for a timescale. Clock does not have any attributes of its own. Nor does it inherit any attributes from a parent class. However, it does use an association with another class to fully describe itself.
                                                          
 
-
                                                          1. Tick definition: a description of the process which is being used to generate monotonic events.
                                                            
+
                                                            1. Ticks: a description of the process which is being used to generate monotonic events.
                                                              
 
                                                              2. 
 
                                                            
 
@@ -745,10 +784,10 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 
-UnitOfMeasure
-
                                                            The Direction attribute indicates whether counts or measures increase in the positive (future) or negative (past) direction. The attribute could be part of ‘Timescale’ or ‘TemporalCoordinateReferenceSystem’ rather than a separate class ‘UnitOfMeasure’, but on balance, it seems better here, as the names often imply directionality, such as fathoms increasing downwards, MYA (Millions of Years Ago) increasing earlier, Atmospheric Pressure in hPa (Hectopascals) decreasing upwards, and FL (FlightLevel) increasing upwards.
                                                            
+Unit of Measure
+
                                                            The direction attribute indicates whether counts or measures increase in the positive (future) or negative (past) direction. The attribute could be part of timescale or temporal coordinate reference system rather than a separate class of measure, but on balance, it seems better here, as the names often imply directionality, such as fathoms increasing downwards, MYA (Millions of Years Ago) increasing earlier, atmospheric pressure in hPa (hectopascals) decreasing upwards, and FL (flight level) increasing upwards.
                                                            
 
-
                                                            1. Direction: indicates the direction in which a timescale progresses as new ‘ticks’ are counted or measured.
                                                              
+
                                                              1. Direction: indicates the direction in which a timescale progresses as new ticks are counted or measured.
                                                                
 
                                                                2. 
 
                                                              
 
@@ -767,13 +806,13 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
                                                              A well preserved fossilized log is recovered and the tree rings establish an annual ‘tick’. The start and end times may be known accurately by comparison and matching with other known tree ring sequences, or perhaps only dated imprecisely via Carbon Dating, or its archaeological or geological context.
                                                              
                                                               
 
-
                                                              A clock is started, but undergoes a calibration process against some standard clock, so the initial, reliable Start Time does not start at Count Zero. The clock is accidentally knocked so that it is no longer correctly calibrated, but is still working. The End Time is not the last time that the clock ticks.
                                                              
+
                                                              A clock is started, but undergoes a calibration process against some standard clock, so the initial, reliable start time does not start at a count of zero. The clock is accidentally knocked so that it is no longer correctly calibrated, but is still working. The end time is not the last time that the clock ticks.
                                                              
                                                               
 
-
                                                              TAI (International Atomic Time, Temps Atomique International) is coordinated by the BIPM (International Bureau of Weights and Measures, Bureau International de Poids et Measures) in Paris, France. TAI is based on the average of hundreds of separate atomic clocks around the world, all corrected to be at mean sea level and standard pressure and temperature. The epoch is defined by Julian Date 2443144.5003725 (1 January 1977 00:00:32.184).
                                                              
+
                                                              TAI International Atomic Time (or Temps Atomique International) is coordinated by the BIPM (International Bureau of Weights and Measures, Bureau International de Poids et Measures) in Paris, France. TAI is based on the average of hundreds of separate atomic clocks around the world, all corrected to be at mean sea level and standard pressure and temperature. The epoch is defined by Julian Date 2443144.5003725 (1 January 1977 00:00:32.184).
                                                              
                                                               
 
-
                                                              The Julian Day is the continuous count of days (rotations of the Earth with respect to the Sun) since the beginning of the year 4173 BCE and will terminate at the end of the year 3267 CE. The count then starts again as “Period 2”. Many computer based timescales, such as Unix Time, are based on the Julian Day timescale, but with different epochs, to fit the numbers into the limited computer words.
                                                              
+
                                                              The Julian Day is the continuous count of days (rotations of the Earth with respect to the Sun) since the beginning of the year 4173 BCE and will terminate at the end of the year 3267 CE. The count then starts again as “Period 2”. Many computer based timescales, such as Unix Time, are based on the Julian Day timescale, but with different epochs, to fit the large numbers into computer words of limited size.
                                                              
                                                               
 
 
@@ -782,12 +821,12 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 Supporting Classes
 
 Epoch
-
                                                              The Epoch class provides a origin or datum for a Temporal Reference System.
                                                              
+
                                                              The epoch class provides an origin or datum for a temporal reference system.
                                                              
                                                               
 
 
 Notation
-
                                                              The Notation class identifies a widely agreed, commonly accepted, notation for representing values in accordance with a temporal reference system.
                                                              
+
                                                              The notation class identifies a widely agreed, commonly accepted, notation for representing values in accordance with a temporal reference system.
                                                              
                                                               
 
 
@@ -796,7 +835,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 Notation
 
                                                              There are often widely agreed, commonly accepted, notations used for temporal reference systems, but few have been standardized. Any particular notation may be capable of expressing a wider range of times than are valid for the reference system.
                                                              
 
-
                                                              The  timestamp notation, a restrictive profile of , can express times before 1588CE, when the Gregorian calendar was first introduced in some parts of the world.
                                                              
+
                                                              The  timestamp notation, a restrictive profile of , can express times before 1582CE, when the Gregorian calendar was first introduced in some parts of the world.
                                                              
                                                               
 
 
@@ -821,9 +860,9 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
                                                              2.2 Two things can be in the same place at different times.
                                                              
 
-
                                                              These are not symmetrical in space and time.
                                                              
+
                                                              These statements are not symmetrical between space and time.
                                                              
 
-
                                                              Temporal constructs such as instants, durations or intervals, multi-instants (a set of instants), and multi-intervals are not included in this conceptual model. These do have strongly analogous equivalents in space, such as points and multi-points, especially in a single dimension, such as vertical. The temporal constructs are well described in Maintaining Knowledge about Temporal Intervals by J. F. Allen (see ) and apply across all of the regimes, so do not need to be in this Abstract Conceptual Model.
                                                              
+
                                                              Temporal constructs such as instants, durations or intervals, multi-instants (a set of instants), and multi-intervals (a set of intervals) are not included in this conceptual model. These do have strongly analogous equivalents in space, such as points and multi-points, especially in a single dimension, such as vertical. The temporal constructs are well described in Maintaining Knowledge about Temporal Intervals by J. F. Allen (see ) and apply across all of the regimes, so do not need to be in this Abstract Conceptual Model.
                                                              
 
 
 
@@ -917,28 +956,28 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
                         ISO/IEC 22989:2022/AWI Amd 1      ISO/IEC 22989:2022/AWI Amd 1              2022-07-19          
     Geneva
 Allen, J. F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26, pp832-843 (1983).Maintaining Knowledge about Temporal Intervals
-  2024-02-09  
+  2024-02-13  
 Topic 2
   
-Referencing by coordinates
+Referencing by coordinates (Including corrigendum 1 and corrigendum	2)
   
-Topic 2 — Referencing by coordinates
-  http://www.opengis.net/doc/AS/topic-2/5.0  https://docs.ogc.org/as/18-005r4/18-005r4.html  18-005r4      2019-02-08                  
+Topic 2 — Referencing by coordinates (Including corrigendum 1 and corrigendum	2)
+  http://www.opengis.net/doc/AS/topic-2/6.0  https://docs.ogc.org/as/18-005r8/18-005r8.pdf  18-005r8      2023-09-05                  
         Roger Lott      
                       
 Open Geospatial Consortium
-        4  en    This document is identical in normative content with the latest edition (2019) of ISO 19111, Geographic Information — Spatial referencing by coordinates [ISO 19111:2019].  
-Cox, S., Little, C.: W3C Time Ontology in OWL. World Wide Web Consortium (2022) W3C TR owl-time3C TR owl-time
+        8  en    This document is consistent with the third edition (2019) of ISO 19111, Geographic Information — Referencing by coordinates including its amendments 1 and 2. ISO 19111:2019 was prepared by Technical Committee ISO/TC 211, Geographic information/Geomatics, in close collaboration with the Open Geospatial Consortium (OGC). It replaces the second edition, ISO 19111:2007 and also ISO 19111-2:2009, OGC documents 08-015r2 and 10-020. This OGC document, 18-005r5, incorporates three editorial corrections made in ISO 19111:2019 amendment 1 of 2021.  
+Cox, S., Little, C.: W3C Time Ontology in OWL. World Wide Web Consortium (2022) .W3C Time Ontology in OWL3C Time Ontology in OWL
 
-Bibliography  2024-02-09  
-OGC GeoPose 1.0 Data Exchange Draft Standard
+Bibliography  2024-02-13  
+OGC GeoPose 1.0 Data Exchange Standard
   
-OGC GeoPose 1.0 Data Exchange Draft Standard
-  http://www.opengis.net/doc/DIS/geopose/1.0  https://docs.ogc.org/dis/21-056r10/21-056r10.html  21-056r10      2022-11-28                  
+OGC GeoPose 1.0 Data Exchange Standard
+  http://www.opengis.net/doc/IS/geopose/1.0  https://docs.ogc.org/is/21-056r11/21-056r11.html  21-056r11      2023-09-08                  
         Carl Stephen Smyth      
                       
 Open Geospatial Consortium
-        10  en    GeoPose 1.0 is an OGC Implementation Standard for exchanging the location and orientation of real or virtual geometric objects (“Poses”) within reference frames anchored to the earth’s surface (“Geo”) or within other astronomical coordinate systems.  The standard specifies two Basic forms with no configuration options for common use cases, an Advanced form with more flexibility for more complex applications, and five composite GeoPose structures that support time series plus chain and graph structures.  These eight Standardization Targets are independent. There are no dependencies between Targets and each may be implemented as needed to support a specific use case.  The Standardization Targets share an implementation-neutral Logical Model which establishes the structure and relationships between GeoPose components and also between GeoPose data objects themselves in composite structures. Not all of the classes and properties of the Logical Model are expressed in individual Standardization Targets nor in the specific concrete data objects defined by this standard. Those elements that are expressed are denoted as implementation-neutral Structural Data Units (SDUs). SDUs are aliases for elements of the Logical Model, isolated to facilitate specification of their use in encoded GeoPose data objects for a specific Standardization Target.  For each Standardization Target, each implementation technology and corresponding encoding format defines the encoding or serialization specified in a manner appropriate to that technology.  GeoPose 1.0 specifies a single encoding in JSON format (IETF RFC 8259). Each Standardization Target has a JSON Schema (Internet-Draft draft-handrews-json-schema-02) encoding specification. The key standardization requirements specify that concrete JSON-encoded GeoPose data objects must conform to the corresponding JSON Schema definition. The individual elements identified in the encoding specification are composed of SDUs, tying the specifications back to the Logical Model.  The GeoPose 1.0 Standard makes no assumptions about the interpretation of external specifications, for example, of reference frames. Nor does it assume or constrain services or interfaces providing conversion between GeoPoses of difference types or relying on different external reference frame definitions.    2024-02-09  
+        11  en    GeoPose 1.0 is an OGC Implementation Standard for exchanging the location and orientation of real or virtual geometric objects (“Poses”) within reference frames anchored to the earth’s surface (“Geo”) or within other astronomical coordinate systems.  The standard specifies two Basic forms with no configuration options for common use cases, an Advanced form with more flexibility for more complex applications, and five composite GeoPose structures that support time series plus chain and graph structures.  These eight Standardization Targets are independent. There are no dependencies between Targets and each may be implemented as needed to support a specific use case.  The Standardization Targets share an implementation-neutral Logical Model which establishes the structure and relationships between GeoPose components and also between GeoPose data objects themselves in composite structures. Not all of the classes and properties of the Logical Model are expressed in individual Standardization Targets nor in the specific concrete data objects defined by this standard. Those elements that are expressed are denoted as implementation-neutral Structural Data Units (SDUs). SDUs are aliases for elements of the Logical Model, isolated to facilitate specification of their use in encoded GeoPose data objects for a specific Standardization Target.  For each Standardization Target, each implementation technology and corresponding encoding format defines the encoding or serialization specified in a manner appropriate to that technology.  GeoPose 1.0 specifies a single encoding in JSON format (IETF RFC 8259). Each Standardization Target has a JSON Schema (Internet-Draft draft-handrews-json-schema-02) encoding specification. The key standardization requirements specify that concrete JSON-encoded GeoPose data objects must conform to the corresponding JSON Schema definition. The individual elements identified in the encoding specification are composed of SDUs, tying the specifications back to the Logical Model.  The GeoPose 1.0 Standard makes no assumptions about the interpretation of external specifications, for example, of reference frames. Nor does it assume or constrain services or interfaces providing conversion between GeoPoses of difference types or relying on different external reference frame definitions.    2024-02-09  
 Geographic information
   
 Temporal schema
@@ -954,43 +993,55 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
   A Brief History of Timekeeping
   
 
-  Andrewes, W.H.J.: A Chronicle Of Timekeeping. Scientific American (2006). 
+  Andrewes, W.H.J.: A Chronicle Of Timekeeping. Scientific American (2006). .
   A Chronicle Of Timekeeping
   
 
-  Meeus, J.: Astronomical Algorithms. 
+  Meeus, J.: Astronomical Algorithms. .
   Astronomical Algorithms
   
 
-  Dershowitz, N., Reingold, N.M.: Calendrical Calculations — The Ultimate Edition. Cambridge University Press (2018). ISBN-13:978-1107683167.  [last accessed 2023-01]
+  Dershowitz, N., Reingold, N.M.: Calendrical Calculations — The Ultimate Edition. Cambridge University Press (2018). ISBN-13:978-1107683167. . [last accessed 2023-01]
   Calendrical Calculations
   
 
-  Bureau International des Poids et Mesures (BIPM): Establishment of International Atomic Time and Coordinated Universal Time. 
+  Bureau International des Poids et Mesures (BIPM): Establishment of International Atomic Time and Coordinated Universal Time. .
   Establishment of International Atomic Time and Coordinated Universal Time
   
+
+  The Library of Congress: Extended Date/Time Format (EDTF) Specification. (2019). . [last accessed 2024-02]
+  Extended Date Time Format
+  
+
+  Wikipedia. International Atomic Time. . [Last accessed 2024-02]
+  International Atomic Time
+  
 
-  Wikipedia. International Fixed Calendar.  [last accessed 2023-12]
+  Wikipedia. International Fixed Calendar. . [last accessed 2023-12]
   International Fixed Calendar
   
 
-  Wikipedia. List of Calendars.  [last accessed 2023-12]
+  Wikipedia. List of Calendars. . [last accessed 2023-12]
   List of Calendars
   
 
-  Lorentz Transform. Wolfram MathWorld. 
+  Lorentz Transform. Wolfram MathWorld. .
   Lorentz Transforms
   
 
-  Allen, J.F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26 pp832-843 (1983).
+  Allen, J.F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26, no. 11, pp 832-843 (1983). .
   Maintaining Knowledge about Temporal Intervals
   
 
-  Minkowski, H.: Space and Time, Minkowski’s Papers on Relativity. Minkowski Institute Press, Montreal (2012) 
+  Minkowski, H.: Space and Time, Minkowski’s Papers on Relativity. Minkowski Institute Press, Montreal (2012). .
   Minkowski Space and Time
   
+
+  IETF: Date and Time on the Internet: Timestamps with additional information. Internet Engineering Task Force (2023). . [last accessed 2024-02]
+  Serialising Extended Data About Times and Events
+  
 
-  Juan Diego Bogotá, Zakaria Djebbara: Time-consciousness in computational phenomenology: a temporal analysis of active inference. Neuroscience of Consciousness, Volume 2023, Issue 1 (2023). 
+  Juan Diego Bogotá, Zakaria Djebbara: Time-consciousness in computational phenomenology: a temporal analysis of active inference. Neuroscience of Consciousness, Volume 2023, Issue 1 (2023). .
   Time-consciousness in computational phenomenology
   
 
@@ -998,9 +1049,13 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
   Treatise on Time and Space
   
 
-  The Open Group: UNIX Time.  [last accessed 2023-01]
+  The Open Group: UNIX Time. . [last accessed 2023-01]
   UNIX Time
   
+
+  W3C. Working with Time Zones, W3C Working Group Note 5 July 2011. .
+  Working with Time Zones
+  
 
 
 
@@ -1017,5 +1072,9 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 
+
+
+
+
 
 
diff --git a/23-049/sections/images/Figure1Mermaid.adoc b/23-049/images/Figure1Mermaid.adoc
similarity index 100%
rename from 23-049/sections/images/Figure1Mermaid.adoc
rename to 23-049/images/Figure1Mermaid.adoc
diff --git a/23-049/sections/images/IntervalRelations(CTL).jpg b/23-049/images/IntervalRelations.jpg
similarity index 100%
rename from 23-049/sections/images/IntervalRelations(CTL).jpg
rename to 23-049/images/IntervalRelations.jpg
diff --git a/23-049/images/MISB_Figure_25.pptx b/23-049/images/MISB_Figure_25.pptx
new file mode 100644
index 0000000..a903816
Binary files /dev/null and b/23-049/images/MISB_Figure_25.pptx differ
diff --git a/23-049/images/MISB_Figure_35.png b/23-049/images/MISB_Figure_35.png
new file mode 100644
index 0000000..69bb86d
Binary files /dev/null and b/23-049/images/MISB_Figure_35.png differ
diff --git a/23-049/images/MISB_Figure_36.png b/23-049/images/MISB_Figure_36.png
new file mode 100644
index 0000000..a6f27de
Binary files /dev/null and b/23-049/images/MISB_Figure_36.png differ
diff --git a/23-049/images/diag-plantuml-md5-41df639b848fff68ea88ba94874a2485.png b/23-049/images/diag-plantuml-md5-41df639b848fff68ea88ba94874a2485.png
new file mode 100644
index 0000000..df00c84
Binary files /dev/null and b/23-049/images/diag-plantuml-md5-41df639b848fff68ea88ba94874a2485.png differ
diff --git a/23-049/images/diag-plantuml-md5-bec2594dba7d9242c2abc9cbb62bd7f5.png b/23-049/images/diag-plantuml-md5-bec2594dba7d9242c2abc9cbb62bd7f5.png
new file mode 100644
index 0000000..6c4e752
Binary files /dev/null and b/23-049/images/diag-plantuml-md5-bec2594dba7d9242c2abc9cbb62bd7f5.png differ
diff --git a/23-049/sections/images/readme.md b/23-049/images/readme.md
similarity index 100%
rename from 23-049/sections/images/readme.md
rename to 23-049/images/readme.md
diff --git a/23-049/plantuml/plantuml20240213-1-7qv577.png b/23-049/plantuml/plantuml20240213-1-7qv577.png
new file mode 100644
index 0000000..d3a200c
Binary files /dev/null and b/23-049/plantuml/plantuml20240213-1-7qv577.png differ
diff --git a/23-049/plantuml/plantuml20240213-1-yyhrmj.png b/23-049/plantuml/plantuml20240213-1-yyhrmj.png
new file mode 100644
index 0000000..d3a200c
Binary files /dev/null and b/23-049/plantuml/plantuml20240213-1-yyhrmj.png differ
diff --git a/23-049/sections/08-temporal-regimes.adoc b/23-049/sections/08-temporal-regimes.adoc
index f4c34ef..3f03d37 100644
--- a/23-049/sections/08-temporal-regimes.adoc
+++ b/23-049/sections/08-temporal-regimes.adoc
@@ -21,7 +21,7 @@ In this regime, the <> (see <