From a108a3286712b2aa21049040ddeeaeaef399bdc6 Mon Sep 17 00:00:00 2001 From: Chuck Heazel Date: Wed, 6 Mar 2024 19:14:57 -0500 Subject: [PATCH 1/4] Metanorma debugging Converted Figure 2 into an image import. Converted examples into an annex Still working on the bibliography. --- 23-049/23-049.err.html | 27 ++- 23-049/23-049.html | 216 +++++++++++--------- 23-049/23-049.pdf | Bin 3038325 -> 3125285 bytes 23-049/23-049.presentation.xml | 230 +++++++++++++++------- 23-049/23-049.xml | 87 +++++--- 23-049/sections/07-cm.adoc | 86 +------- 23-049/sections/12-temporal-geometry.adoc | 1 + 23-049/sections/annex-bibliography.adoc | 1 - 23-049/sections/annex-examples.adoc | 2 +- 9 files changed, 367 insertions(+), 283 deletions(-) diff --git a/23-049/23-049.err.html b/23-049/23-049.err.html index 5baef599..37286c12 100644 --- a/23-049/23-049.err.html +++ b/23-049/23-049.err.html @@ -10,32 +10,43 @@

Metanorma XML Style Warning

<th valign="middle" align="left">Role</th> </tr> </thead> <tbody> <tr> <td valign="middle" align="left">Chris Little</td> -000430fig-interval-relationsFigure should have title
<figure id="fig-interval-relations">
-  <image 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" 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+000432fig-interval-relationsFigure should have title
<figure id="fig-interval-relations">
+  <image 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 </figure>

-000497_other_​regimesHanging paragraph in clause
<clause id="_other_regimes" obligation="normative">
+000499_other_​regimesHanging paragraph in clause
<clause id="_other_regimes" obligation="normative">
 <title>Other Regimes</title>
 <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">

-000561_3ab1a470-6b77-4525-8f00-dc9cace6bc08Figure should have title
<figure id="_3ab1a470-6b77-4525-8f00-dc9cace6bc08">
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" mimetype="image/png" id="_41c8889a-7458-f7f0-43b2-05b27e725d9f" height="auto" width="auto"/>
+000563fig-UML-diagramFigure should have title
<figure id="fig-UML-diagram">
+  <image 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mimetype="image/jpeg" id="_41c8889a-7458-f7f0-43b2-05b27e725d9f" height="auto" width="auto"/>
 </figure>

-000579ordinal_​rs_sectionHanging paragraph in clause
<clause id="ordinal_rs_section" obligation="normative">
+000581ordinal_​rs_sectionHanging paragraph in clause
<clause id="ordinal_rs_section" obligation="normative">
 <title>Ordinal Temporal Reference Systems</title>
 <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="_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">
+000638calendar_​sectionHanging paragraph in clause
<clause id="calendar_section" obligation="normative">
 <title>Calendar Reference Systems</title>
 <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="_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
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+000716_discrete_​and_​continuous_time_​scalesHanging paragraph in clause
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 <title>Discrete and Continuous Time Scales</title>
 <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>

+000842annex-examplesHanging paragraph in clause
<annex id="annex-examples" obligation="informative">
+<title>Examples</title>
+<p id="_46c2816a-761a-6212-b222-ba0098438f14">These show how the concepts of the Abstract Cocenptual Model for Time can be applied to realistic use cases. Of course, the logical and implementation details are outside the scope of this standard.</p>
+
+<clause id="_ordinal_temporal_reference_system" obligation="informative">

+000854fig-geological-ordinal-exampleFigure should have title
<figure id="fig-geological-ordinal-example">
+  <image 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mimetype="image/jpeg" id="_d7fae0dc-2191-950d-eac7-e099474e436e" height="auto" width="auto"/>
+</figure>

+000865fig-differing-timecalesFigure should have title
<figure id="fig-differing-timecales">
+  <image 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mimetype="image/png" id="_a18a08d5-690d-8f18-afd3-ee87ef3ee099" height="auto" width="auto"/>
+</figure>

 
 
Metanorma XML Syntax

 
diff --git a/23-049/23-049.html b/23-049/23-049.html
index fbe65677..381ebccb 100644
--- a/23-049/23-049.html
+++ b/23-049/23-049.html
@@ -1412,39 +1412,43 @@
 
 
@@ -1459,19 +1463,19 @@
 

 
 

-

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
    • 
+


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 the 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:

 
 
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. 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 [Technical Committee] 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
+
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
+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.

@@ -1480,7 +1484,7 @@
 
Allen, J. F.: Maintaining Knowledge about Temporal Intervals. Communications of the ACM, vol. 26, pp832-843 (1983).

 
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.  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. clock

 
regularly repeating physical phenomenon that can be counted

@@ -1578,7 +1582,7 @@ 
4.1.11. <
 
4.1.12. tick

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

 
-
4.2.  Abbreviated terms

+
4.2.  Abbreviated terms

 
 
BIPM
International Bureau of Weights and Measures

 
TAI
International Atomic Time

@@ -1586,24 +1590,24 @@ 
4.1.12. tick
 
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. 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
    
+
  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.  Temporal regimes
7.1.  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.

 
 
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

+
7.2.  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.

 
@@ -1615,8 +1619,8 @@ 
4.1.12. tick
 
 
This regime constitutes an ordinal temporal reference system, with discrete enumerated ordered events.

 
-
Figure 1

-
7.3.  Simple Clocks and Discrete Timescales

+
Figure 1

+
7.3.  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.

 
@@ -1635,7 +1639,7 @@ 
4.1.12. tick
 
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.

-
7.4.  CRS and Continuous Timescales

+
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.

 
@@ -1657,7 +1661,7 @@ 
4.1.12. tick
 

 
 
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

+
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.
 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.

@@ -1674,7 +1678,7 @@ 
4.1.12. tick
 
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.

-
7.6.  Other Regimes

+
7.6.  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.

 
@@ -1800,16 +1804,15 @@ 
4.1.12. tick
 
 
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, or that the circumference of a circle is not precisely 
+
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.

 

-
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

+
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 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.

 
@@ -1820,25 +1823,25 @@ 
4.1.12. tick
 
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.

-
9.2.  Ordinal Temporal Reference Systems

+
9.2.  Ordinal Temporal Reference Systems

 
 
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 temporal reference system class:

 
-
  1. applicable location time or domain: 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.
    
 
-
    1. Epoch: An ordinal temporal reference system may be ‘anchored by’ 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 ‘is represented by’ 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. 
 
      
 
@@ -1854,48 +1857,48 @@ 
      4.1.12. tick
 
      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 temporal reference system class:

 
-
  1. applicable location time or domain: 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.
    
 
-
    1. Epoch: A temporal coordinate reference system ‘anchored by’ one optional Epochs
      
+
      1. Epoch: A temporal coordinate reference system ‘anchored by’ one optional Epochs
        
 
        2. 
-
      2. Notation: A temporal coordinate reference system ‘represented by’ 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 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.
        
+
      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 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 temporal reference system class:

 
-
  1. applicable location time or domain: 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.
    
 
-
    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.
      
+
      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. Algorithm: A timeline ‘is defined by’ one or more Algorithms. These algorithms specify how the multiple timescales are aggregated into a single Timeline.
        
+
      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. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a Timeline.
        
+
      5. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a Timeline.
        
 
        6. 
-
      6. Epoch: A calendar is ‘anchored by’ one optional Epoch
        
+
      7. Epoch: A calendar is ‘anchored by’ one optional Epoch
        
 
        8. 
-
      8. Notation: A calendar  is ‘represented by’ one or more Notations to represent itself.
        
+
      9. Notation: A calendar  is ‘represented by’ one or more Notations to represent itself.
        
 
        10. 
 
      
 
@@ -1905,9 +1908,9 @@ 
      4.1.12. tick
 
 
      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 is ‘defined by’ one or more Algorithms. These algorithms specify how the multiple timescales 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: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
          
+
        3. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
          
 
          4. 
 
        
 

@@ -1916,7 +1919,7 @@ 
4.1.12. tick
 
 
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.
    
+
    1. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
      
 
      2. 
 
    
 
@@ -1949,7 +1952,7 @@ 
    4.1.12. tick
 
    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.
    
 
@@ -1961,19 +1964,19 @@ 
    4.1.12. tick
 
 
    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.
      
 
-
      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.
        
+
        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 or count as well as the direction of increase of that measurement or count.
          
+
        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. 
 
        
 
    
@@ -1982,7 +1985,7 @@ 
    4.1.12. tick
 
 
    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. Ticks: 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. 
 
      
 
@@ -1997,7 +2000,7 @@ 
      4.1.12. tick
 
 
      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. 
 
        
 
@@ -2025,7 +2028,7 @@ 
        4.1.12. tick
 
        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
        
 
@@ -2036,9 +2039,38 @@ 
        4.1.12. tick
 
 
        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 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.
        
+
        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.

        Annex A
        (informative)
        Examples
        These show how the concepts of the Abstract Cocenptual Model for Time can be applied to realistic use cases. Of course, the logical and implementation details are outside the scope of this standard.
        A.1.  Ordinal Temporal Reference System
        
+
+
        Geological eras and periods are forms of compound ordinal reference systems. A consistent sequence of rock strata in a region form an ordinal temporal reference system with the events being ordered by changes from one type of rock stratum to another immediately above it, or by the appearance of distinctly different embedded fossils in the rock layers.
        
+
+
        Another consistent sequence of strata from another region can also form another ordinal temporal reference system. It may be possible to relate the two systems to each other because of layers that may share specific varieties of fossils, or a specific distinctive stratum type.
        
+
+
        Figure 2 shows how the four different geological ordinal temporal reference systems of sequences of rocks and fossils, called Periods, from west Wales (Ordovician), south Wales (Silurian), Devon (Devonian), and coal-bearing rocks (Carboniferous) have been combined to define a longer geological ordinal temporal reference system called the Paleozoic Era.
        
+
+
        Figure A.1
        
+
        A.2.  Temporal Coordinate Reference System
        
+
+
        1. A remote autonomous underwater drone, known as a ‘glider’ is making regular measurements of temperature and salinity deep in the Atlantic Ocean. The measurements are time-stamped by an on-board computer clock. The clock was synchronized to a satellite’s atomic clock when the drone was launched. When the drone surfaces to report its findings, or to be picked up by a research vessel, it is found that the computer clock as ‘drifted’ compared to time from the satellite. The drone’s clock is assumed to have ‘drifted’ in a consistent, linear, fashion, and the error correction is distributed proportionately along the time series of measurements.
          
+
          2. 
+
        2. Several timescales have been defined using the same atomic clocks. For various reasons, such as the year of starting, or the need to store numbers in limited length computer words, different epochs have been chosen. This is illustrated in Figure 3. The figure also illustrates how UTC is not a timescale, but a timeline, as it has been adjusted with leap seconds to correspond to the Gregorian calendar and not deviate more than 0.6 seconds from Earth’s actual day length. This is because UTC is based on the atomic definition of a second, the SI second, whereas the Gregorian calendar assumes that a day, based on Earth’s rotation with respect to the sun, is 86,400 seconds, but this daily rotation varies in duration every day throughout the year for a variety of reasons.
          
+
          3. 
+
        
+
+
        Figure A.2
        
+
        A.3.  Calendar
        
+
+
        A remote and partially autonomous ‘rover’ is on Mars. To manage activities, a Mars calendar is needed. The year is determined by Mars’s orbit around the Sun with respect to a distant fixed point (usually in the constellation of Aries, as used for Earth’s year). This is one timescale, with a unit of measure “Mars Year” to avoid confusion with Earth years.
        
+
+
        Months are not useful as there are two small fast moving moons. One orbits three times per Mars day, the other about every 1 1/2 Mars days, so they do not supply a useful intermediate duration between years and days.
        
+
+
        The ‘day’, the rotation of Mars on its axis with respect to the Sun, is the other timescale that comprises the Mars calendar. To avoid confusion with Earth’s days, they are called ‘Sols’. This solar day, with a similar definition to an Earth day, would be useful for planning day time and night time activities, pehaps requiring solar power generation.
        
+
+
        Other definitions of a day could have been adopted:
+1. A sidereal Mars day, the rotation of Mars with respect to the distant stars, like the sidereal day on Earth. This could be useful if the rover was performing astronomical measurements, such as for navigating using the equivalent of a sextant;
+2. An Earth orientated day, the rotation of Mars with respect to Earth in its orbit. This could be useful for planning activities needing extended communication periods with direct line-of-sight with Earth.
        
+

        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/1.0.0.
        [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.
        
 
 
 
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diff --git a/23-049/23-049.presentation.xml b/23-049/23-049.presentation.xml
index 3db47bc5..5d64e062 100644
--- a/23-049/23-049.presentation.xml
+++ b/23-049/23-049.presentation.xml
@@ -109,7 +109,7 @@ To obtain additional rights of use, visit
 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 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 ).
+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 the freely available OGC equivalents, such as OGC Abstract Specification Topic 2: Referencing by Coordinates   (the equivalent to ).
 
 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.
 
@@ -149,13 +149,15 @@ submitters:
 
 
 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.
+According to OGC Policy:
 
-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.”
+“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.
 
-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 level of detail of the Abstract Specification is at the discretion of the TC [Technical Committee] as reflected by the actual content that is approved for inclusion in the document itself.”
 
-The  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:
+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  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:
 
 may be considered to comprise a cross-domain application schema, or
 
@@ -163,11 +165,11 @@ submitters:
 
 
 
-This flexibility is controlled by a set of UML types that can be implemented in a variety of manners. Use of
+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
+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.
@@ -401,7 +403,7 @@ directly in application schemas.
 
 This regime constitutes an ordinal temporal reference system, with discrete enumerated ordered events.
 
-
+
 
 
 
@@ -602,16 +604,15 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 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, or that the circumference of a circle is not precisely 
+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.
+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.
 
 
 
@@ -622,7 +623,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 The model is also informed by the W3C Time Ontology in OWL.
 
-
+
 
 
 
@@ -902,9 +903,49 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 
-
+
+
+
+Examples
+These show how the concepts of the Abstract Cocenptual Model for Time can be applied to realistic use cases. Of course, the logical and implementation details are outside the scope of this standard.
+
+
+Ordinal Temporal Reference System
+Geological eras and periods are forms of compound ordinal reference systems. A consistent sequence of rock strata in a region form an ordinal temporal reference system with the events being ordered by changes from one type of rock stratum to another immediately above it, or by the appearance of distinctly different embedded fossils in the rock layers.
+
+Another consistent sequence of strata from another region can also form another ordinal temporal reference system. It may be possible to relate the two systems to each other because of layers that may share specific varieties of fossils, or a specific distinctive stratum type.
+
+Figure 2 shows how the four different geological ordinal temporal reference systems of sequences of rocks and fossils, called Periods, from west Wales (Ordovician), south Wales (Silurian), Devon (Devonian), and coal-bearing rocks (Carboniferous) have been combined to define a longer geological ordinal temporal reference system called the Paleozoic Era.
+
+
+
+
+
+Temporal Coordinate Reference System
+A remote autonomous underwater drone, known as a ‘glider’ is making regular measurements of temperature and salinity deep in the Atlantic Ocean. The measurements are time-stamped by an on-board computer clock. The clock was synchronized to a satellite’s atomic clock when the drone was launched. When the drone surfaces to report its findings, or to be picked up by a research vessel, it is found that the computer clock as ‘drifted’ compared to time from the satellite. The drone’s clock is assumed to have ‘drifted’ in a consistent, linear, fashion, and the error correction is distributed proportionately along the time series of measurements.
+
+Several timescales have been defined using the same atomic clocks. For various reasons, such as the year of starting, or the need to store numbers in limited length computer words, different epochs have been chosen. This is illustrated in Figure 3. The figure also illustrates how UTC is not a timescale, but a timeline, as it has been adjusted with leap seconds to correspond to the Gregorian calendar and not deviate more than 0.6 seconds from Earth’s actual day length. This is because UTC is based on the atomic definition of a second, the SI second, whereas the Gregorian calendar assumes that a day, based on Earth’s rotation with respect to the sun, is 86,400 seconds, but this daily rotation varies in duration every day throughout the year for a variety of reasons.
+
+
+
+
+
+
+
+Calendar
+A remote and partially autonomous ‘rover’ is on Mars. To manage activities, a Mars calendar is needed. The year is determined by Mars’s orbit around the Sun with respect to a distant fixed point (usually in the constellation of Aries, as used for Earth’s year). This is one timescale, with a unit of measure “Mars Year” to avoid confusion with Earth years.
+
+Months are not useful as there are two small fast moving moons. One orbits three times per Mars day, the other about every 1 1/2 Mars days, so they do not supply a useful intermediate duration between years and days.
+
+The ‘day’, the rotation of Mars on its axis with respect to the Sun, is the other timescale that comprises the Mars calendar. To avoid confusion with Earth’s days, they are called ‘Sols’. This solar day, with a similar definition to an Earth day, would be useful for planning day time and night time activities, pehaps requiring solar power generation.
+
+Other definitions of a day could have been adopted:
+1. A sidereal Mars day, the rotation of Mars with respect to the distant stars, like the sidereal day on Earth. This could be useful if the rover was performing astronomical measurements, such as for navigating using the equivalent of a sextant;
+2. An Earth orientated day, the rotation of Mars with respect to Earth in its orbit. This could be useful for planning activities needing extended communication periods with direct line-of-sight with Earth.
+
+
 Normative referencesThe 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.
-  2024-02-09  
+  2024-03-06  
 Date and Time on the Internet: Timestamps
   https://www.rfc-editor.org/info/rfc3339  RFC 3339  10.17487/RFC3339  RFC3339      2002-07                  
         G. Klyne      
@@ -920,7 +961,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
     3339        
 IETF
     Timestamps  gregorian calendar  iso  International Organization for Standardization
-  2024-02-09  
+  2024-03-06  
 Data elements and interchange formats
   
 Information interchange
@@ -939,7 +980,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
               ISO 4031:1978      ISO 4031:1978    
               ISO 8601:2000      ISO 8601:2000              2000-12-21          
               ISO 8601:1988/Cor 1:1991      ISO 8601:1988/Cor 1:1991              2000-12-21          
-              2024-02-09      
+              2024-03-06      
 Data elements and interchange formats
       
 Information interchange
@@ -960,7 +1001,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
                               ISO 8601:1988/Cor 1:1991          ISO 8601:1988/Cor 1:1991                      2000-12-21                  
             Geneva    
     Geneva
-  2024-02-09  
+  2024-03-06  
 Geographic information
   
 Referencing by coordinates
@@ -975,7 +1016,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
               ISO 19111:2019/Amd 1:2021      ISO 19111:2019/Amd 1:2021              2024-01-15          
               ISO 19111:2019/Amd 2:2023      ISO 19111:2019/Amd 2:2023              2024-01-15          
     Geneva
-  2024-02-09  
+  2024-03-06  
 Information technology
   
 Artificial intelligence
@@ -992,7 +1033,7 @@ 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
-  2024-02-13  
+  2024-03-06  
 Topic 2
   
 Referencing by coordinates (Including corrigendum 1 and corrigendum	2)
@@ -1004,16 +1045,16 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 Open Geospatial Consortium
         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  2024-02-13  
+
+Bibliography  2024-03-06  
 OGC GeoPose 1.0 Data Exchange Standard
   
 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                  
+  http://www.opengis.net/doc/IS/geopose/1.0.0  https://docs.ogc.org/is/21-056r11/21-056r11.html  21-056r11      2023-09-08                  
         Carl Stephen Smyth      
                       
 Open Geospatial Consortium
-        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  
+        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.    2024-03-06  
 Geographic information
   
 Temporal schema
@@ -1203,7 +1244,7 @@ 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.
            
@@ -1211,13 +1252,13 @@ 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 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).
            
+
            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 the 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.
            
 
@@ -1225,7 +1266,7 @@ To obtain additional rights of use, visit
             
 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
              • 
@@ -1264,25 +1305,27 @@ submitters:
              
 
 
 2.<tab/>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.
              
+
              According to OGC Policy:
              
 
-
              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.”
              
+
              “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.
              
 
-
              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 level of detail of the Abstract Specification is at the discretion of the TC [Technical Committee] as reflected by the actual content that is approved for inclusion in the document itself.”
              
 
-
              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:
              
+
              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.
              
 
-
              1. may be considered to comprise a cross-domain application schema, or
                
+
                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
+
                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
+
                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.
                
@@ -1432,15 +1475,15 @@ 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. 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
                    
+
                  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. 
 
                  
 
@@ -1468,7 +1511,7 @@ directly in application schemas.
                  
 
 
                  This regime constitutes an ordinal temporal reference system, with discrete enumerated ordered events.
                  
 
-
                  Figure 1
                  
+
                  Figure 1
                  
 
 
 
@@ -1669,16 +1712,15 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
                  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, or that the circumference of a circle is not precisely 
+
                  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.
                  
+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.
                  
                   
 
 
@@ -1689,7 +1731,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
                  The model is also informed by the W3C Time Ontology in OWL.
                  
 
-
                  Figure 2
                  
+
                  Figure 2
                  
 
 
 
@@ -1713,19 +1755,19 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
                  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;
                    
+
                    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.
                    
 
-
                    1. Epoch: An ordinal temporal reference system may be ‘anchored by’ 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 ‘is represented by’ 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. 
 
                      
 
@@ -1747,19 +1789,19 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 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 temporal reference system class:
                      
 
-
                      1. applicable location time or domain: 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.
                        
 
-
                        1. Epoch: A temporal coordinate reference system ‘anchored by’ one optional Epochs
                          
+
                          1. Epoch: A temporal coordinate reference system ‘anchored by’ one optional Epochs
                            
 
                            2. 
-
                          2. Notation: A temporal coordinate reference system ‘represented by’ 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 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.
                            
+
                          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. 
 
                          
 
@@ -1770,23 +1812,23 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
                          A calendar 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
                            
+
                            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.
                            
 
-
                            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.
                              
+
                              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. Algorithm: A timeline ‘is defined by’ one or more Algorithms. These algorithms specify how the multiple timescales are aggregated into a single Timeline.
                                
+
                              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. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a Timeline.
                                
+
                              5. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a Timeline.
                                
 
                                6. 
-
                              6. Epoch: A calendar is ‘anchored by’ one optional Epoch
                                
+
                              7. Epoch: A calendar is ‘anchored by’ one optional Epoch
                                
 
                                8. 
-
                              8. Notation: A calendar  is ‘represented by’ one or more Notations to represent itself.
                                
+
                              9. Notation: A calendar  is ‘represented by’ one or more Notations to represent itself.
                                
 
                                10. 
 
                              
 
@@ -1796,9 +1838,9 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
                              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 is ‘defined by’ one or more Algorithms. These algorithms specify how the multiple timescales 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: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
                                  
+
                                3. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
                                  
 
                                  4. 
 
                                
 
@@ -1807,7 +1849,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 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. 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.
                                  
+
                                  1. Timescale: An algorithm ‘uses’ two or more Timescales which are used to construct a timeline.
                                    
 
                                    2. 
 
                                  
 
@@ -1854,19 +1896,19 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 9.5.1.<tab/>Timescale
 
                                  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.
                                    
 
-
                                    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.
                                      
+
                                      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 or count as well as the direction of increase of that measurement or count.
                                        
+
                                      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. 
 
                                      
 
@@ -1875,7 +1917,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 9.5.2.<tab/>Clock
 
                                      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. Ticks: 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. 
 
                                        
 
@@ -1890,7 +1932,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 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. 
 
                                          
 
@@ -1969,6 +2011,8 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 
+
+
 
 3.<tab/>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.https://www.rfc-editor.org/info/rfc3339IETF RFC 3339DOI 10.17487/RFC3339
@@ -1978,8 +2022,46 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 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 18-005r8, 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 21-056r11, 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]
+
+<strong>Annex A</strong><br/>(informative)<br/><strong>Examples</strong>
+
                                          These show how the concepts of the Abstract Cocenptual Model for Time can be applied to realistic use cases. Of course, the logical and implementation details are outside the scope of this standard.
                                          
+
+
+A.1.<tab/>Ordinal Temporal Reference System
+
                                          Geological eras and periods are forms of compound ordinal reference systems. A consistent sequence of rock strata in a region form an ordinal temporal reference system with the events being ordered by changes from one type of rock stratum to another immediately above it, or by the appearance of distinctly different embedded fossils in the rock layers.
                                          
+
+
                                          Another consistent sequence of strata from another region can also form another ordinal temporal reference system. It may be possible to relate the two systems to each other because of layers that may share specific varieties of fossils, or a specific distinctive stratum type.
                                          
+
+
                                          Figure 2 shows how the four different geological ordinal temporal reference systems of sequences of rocks and fossils, called Periods, from west Wales (Ordovician), south Wales (Silurian), Devon (Devonian), and coal-bearing rocks (Carboniferous) have been combined to define a longer geological ordinal temporal reference system called the Paleozoic Era.
                                          
+
+
                                          Figure A.1
                                          
+                                          
+
+
+A.2.<tab/>Temporal Coordinate Reference System
+
                                          1. A remote autonomous underwater drone, known as a ‘glider’ is making regular measurements of temperature and salinity deep in the Atlantic Ocean. The measurements are time-stamped by an on-board computer clock. The clock was synchronized to a satellite’s atomic clock when the drone was launched. When the drone surfaces to report its findings, or to be picked up by a research vessel, it is found that the computer clock as ‘drifted’ compared to time from the satellite. The drone’s clock is assumed to have ‘drifted’ in a consistent, linear, fashion, and the error correction is distributed proportionately along the time series of measurements.
                                            
+
                                            2. 
+
                                          2. Several timescales have been defined using the same atomic clocks. For various reasons, such as the year of starting, or the need to store numbers in limited length computer words, different epochs have been chosen. This is illustrated in Figure 3. The figure also illustrates how UTC is not a timescale, but a timeline, as it has been adjusted with leap seconds to correspond to the Gregorian calendar and not deviate more than 0.6 seconds from Earth’s actual day length. This is because UTC is based on the atomic definition of a second, the SI second, whereas the Gregorian calendar assumes that a day, based on Earth’s rotation with respect to the sun, is 86,400 seconds, but this daily rotation varies in duration every day throughout the year for a variety of reasons.
                                            
+
                                            3. 
+
                                          
+
+
                                          Figure A.2
                                          
+                                          
+
+
+A.3.<tab/>Calendar
+
                                          A remote and partially autonomous ‘rover’ is on Mars. To manage activities, a Mars calendar is needed. The year is determined by Mars’s orbit around the Sun with respect to a distant fixed point (usually in the constellation of Aries, as used for Earth’s year). This is one timescale, with a unit of measure “Mars Year” to avoid confusion with Earth years.
                                          
+
+
                                          Months are not useful as there are two small fast moving moons. One orbits three times per Mars day, the other about every 1 1/2 Mars days, so they do not supply a useful intermediate duration between years and days.
                                          
+
+
                                          The ‘day’, the rotation of Mars on its axis with respect to the Sun, is the other timescale that comprises the Mars calendar. To avoid confusion with Earth’s days, they are called ‘Sols’. This solar day, with a similar definition to an Earth day, would be useful for planning day time and night time activities, pehaps requiring solar power generation.
                                          
+
+
                                          Other definitions of a day could have been adopted:
+1. A sidereal Mars day, the rotation of Mars with respect to the distant stars, like the sidereal day on Earth. This could be useful if the rover was performing astronomical measurements, such as for navigating using the equivalent of a sextant;
+2. An Earth orientated day, the rotation of Mars with respect to Earth in its orbit. This could be useful for planning activities needing extended communication periods with direct line-of-sight with Earth.
                                          
+                                          
+                                          
+BibliographyCarl Stephen Smyth: OGC 21-056r11, OGC GeoPose 1.0 Data Exchange Standard. Open Geospatial Consortium (2023). http://www.opengis.net/doc/IS/geopose/1.0.0.http://www.opengis.net/doc/IS/geopose/1.0.0https://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
   
diff --git a/23-049/23-049.xml b/23-049/23-049.xml
index 544be504..1a40f3e3 100644
--- a/23-049/23-049.xml
+++ b/23-049/23-049.xml
@@ -73,7 +73,7 @@ To obtain additional rights of use, visit
 
                                          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 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 ).
                                          
+
                                          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 the freely available OGC equivalents, such as OGC Abstract Specification Topic 2: Referencing by Coordinates   (the equivalent to ).
                                          
 
 
                                          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.
                                          
 
@@ -113,13 +113,15 @@ submitters:
                                          
 
 
 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.
                                          
+
                                          According to OGC Policy:
                                          
 
-
                                          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.”
                                          
+
                                          “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.
                                          
 
-
                                          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 level of detail of the Abstract Specification is at the discretion of the TC [Technical Committee] as reflected by the actual content that is approved for inclusion in the document itself.”
                                          
 
-
                                          The  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:
                                          
+
                                          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  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. 
@@ -127,11 +129,11 @@ submitters:
                                            
 
 
                                          
 
-
                                          This flexibility is controlled by a set of UML types that can be implemented in a variety of manners. Use of
+
                                          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
+
                                          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.
                                          
@@ -365,7 +367,7 @@ directly in application schemas.
                                          
 
 
                                          This regime constitutes an ordinal temporal reference system, with discrete enumerated ordered events.
                                          
 
-
                                          
+
                                          
                                           
 
 
@@ -566,16 +568,15 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
                                          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, or that the circumference of a circle is not precisely 
+
                                          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.
                                          
+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.
                                          
                                           
 
 
@@ -586,7 +587,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
                                          The model is also informed by the W3C Time Ontology in OWL.
                                          
 
-
                                          
+
                                          
 
 
 
@@ -866,9 +867,49 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 
 
 
-
+
+
+
+Examples
+
                                          These show how the concepts of the Abstract Cocenptual Model for Time can be applied to realistic use cases. Of course, the logical and implementation details are outside the scope of this standard.
                                          
+
+
+Ordinal Temporal Reference System
+
                                          Geological eras and periods are forms of compound ordinal reference systems. A consistent sequence of rock strata in a region form an ordinal temporal reference system with the events being ordered by changes from one type of rock stratum to another immediately above it, or by the appearance of distinctly different embedded fossils in the rock layers.
                                          
+
+
                                          Another consistent sequence of strata from another region can also form another ordinal temporal reference system. It may be possible to relate the two systems to each other because of layers that may share specific varieties of fossils, or a specific distinctive stratum type.
                                          
+
+
                                          Figure 2 shows how the four different geological ordinal temporal reference systems of sequences of rocks and fossils, called Periods, from west Wales (Ordovician), south Wales (Silurian), Devon (Devonian), and coal-bearing rocks (Carboniferous) have been combined to define a longer geological ordinal temporal reference system called the Paleozoic Era.
                                          
+
+
                                          
+                                          
+
+
+Temporal Coordinate Reference System
+
                                          1. A remote autonomous underwater drone, known as a ‘glider’ is making regular measurements of temperature and salinity deep in the Atlantic Ocean. The measurements are time-stamped by an on-board computer clock. The clock was synchronized to a satellite’s atomic clock when the drone was launched. When the drone surfaces to report its findings, or to be picked up by a research vessel, it is found that the computer clock as ‘drifted’ compared to time from the satellite. The drone’s clock is assumed to have ‘drifted’ in a consistent, linear, fashion, and the error correction is distributed proportionately along the time series of measurements.
                                            
+
                                            2. 
+
                                          2. Several timescales have been defined using the same atomic clocks. For various reasons, such as the year of starting, or the need to store numbers in limited length computer words, different epochs have been chosen. This is illustrated in Figure 3. The figure also illustrates how UTC is not a timescale, but a timeline, as it has been adjusted with leap seconds to correspond to the Gregorian calendar and not deviate more than 0.6 seconds from Earth’s actual day length. This is because UTC is based on the atomic definition of a second, the SI second, whereas the Gregorian calendar assumes that a day, based on Earth’s rotation with respect to the sun, is 86,400 seconds, but this daily rotation varies in duration every day throughout the year for a variety of reasons.
                                            
+
                                            3. 
+
                                          
+
+
                                          
+                                          
+
+
+Calendar
+
                                          A remote and partially autonomous ‘rover’ is on Mars. To manage activities, a Mars calendar is needed. The year is determined by Mars’s orbit around the Sun with respect to a distant fixed point (usually in the constellation of Aries, as used for Earth’s year). This is one timescale, with a unit of measure “Mars Year” to avoid confusion with Earth years.
                                          
+
+
                                          Months are not useful as there are two small fast moving moons. One orbits three times per Mars day, the other about every 1 1/2 Mars days, so they do not supply a useful intermediate duration between years and days.
                                          
+
+
                                          The ‘day’, the rotation of Mars on its axis with respect to the Sun, is the other timescale that comprises the Mars calendar. To avoid confusion with Earth’s days, they are called ‘Sols’. This solar day, with a similar definition to an Earth day, would be useful for planning day time and night time activities, pehaps requiring solar power generation.
                                          
+
+
                                          Other definitions of a day could have been adopted:
+1. A sidereal Mars day, the rotation of Mars with respect to the distant stars, like the sidereal day on Earth. This could be useful if the rover was performing astronomical measurements, such as for navigating using the equivalent of a sextant;
+2. An Earth orientated day, the rotation of Mars with respect to Earth in its orbit. This could be useful for planning activities needing extended communication periods with direct line-of-sight with Earth.
                                          
+                                          
+                                          
 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.
                                          
-  2024-02-09  
+  2024-03-06  
 Date and Time on the Internet: Timestamps
   https://www.rfc-editor.org/info/rfc3339  RFC 3339  10.17487/RFC3339  RFC3339      2002-07                  
         G. Klyne      
@@ -884,7 +925,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
     3339        
 IETF
     Timestamps  gregorian calendar  iso  International Organization for Standardization
-  2024-02-09  
+  2024-03-06  
 Data elements and interchange formats
   
 Information interchange
@@ -903,7 +944,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
               ISO 4031:1978      ISO 4031:1978    
               ISO 8601:2000      ISO 8601:2000              2000-12-21          
               ISO 8601:1988/Cor 1:1991      ISO 8601:1988/Cor 1:1991              2000-12-21          
-              2024-02-09      
+              2024-03-06      
 Data elements and interchange formats
       
 Information interchange
@@ -924,7 +965,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
                               ISO 8601:1988/Cor 1:1991          ISO 8601:1988/Cor 1:1991                      2000-12-21                  
             Geneva    
     Geneva
-  2024-02-09  
+  2024-03-06  
 Geographic information
   
 Referencing by coordinates
@@ -939,7 +980,7 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
               ISO 19111:2019/Amd 1:2021      ISO 19111:2019/Amd 1:2021              2024-01-15          
               ISO 19111:2019/Amd 2:2023      ISO 19111:2019/Amd 2:2023              2024-01-15          
     Geneva
-  2024-02-09  
+  2024-03-06  
 Information technology
   
 Artificial intelligence
@@ -956,7 +997,7 @@ 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
-  2024-02-13  
+  2024-03-06  
 Topic 2
   
 Referencing by coordinates (Including corrigendum 1 and corrigendum	2)
@@ -968,16 +1009,16 @@ Typically there is no simple arithmetic connecting calendar systems and timescal
 Open Geospatial Consortium
         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  2024-02-13  
+
+Bibliography  2024-03-06  
 OGC GeoPose 1.0 Data Exchange Standard
   
 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                  
+  http://www.opengis.net/doc/IS/geopose/1.0.0  https://docs.ogc.org/is/21-056r11/21-056r11.html  21-056r11      2023-09-08                  
         Carl Stephen Smyth      
                       
 Open Geospatial Consortium
-        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  
+        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.    2024-03-06  
 Geographic information
   
 Temporal schema
diff --git a/23-049/sections/07-cm.adoc b/23-049/sections/07-cm.adoc
index a38926fe..4e6efdf6 100644
--- a/23-049/sections/07-cm.adoc
+++ b/23-049/sections/07-cm.adoc
@@ -5,87 +5,5 @@ This Temporal Abstract Conceptual Model follows <>, which is the ISO a
 
 The model is also informed by the <>.
 
-[plantuml]
-....
-@startuml
-abstract class ReferenceSystem {
-        dimension "dimension >= 1"
-        locationOfApplicability = 0..1
-        timeOfApplicability = 0..1
-        domainOfApplicability = 0..1
-}
-
-note right of ReferenceSystem
-Note: Has at least one of:
-* SpatialReferenceSystem, or
-* TemporalReferenceSystem
-end note
-
-abstract class SpatialReferenceSystem {
-}
-
-abstract class TemporalReferenceSystem {
-  dimension = 1
-}
-
-note left of TemporalReferenceSystem
-Note: Consists of one only of:
-* TemporalCoordinateReferenceSystem,
-* Calendar, or
-* OrdinalTemporalReferenceSystem
-end note
-
-ReferenceSystem <|-- SpatialReferenceSystem : is a
-ReferenceSystem <|-- TemporalReferenceSystem : is a
-
-class OrdinalTemporalReferenceSystem {
-}
-class TemporalCoordinateReferenceSystem {
-}
-class Calendar {
-}
-class Timeline {
-}
-TemporalReferenceSystem <|-- OrdinalTemporalReferenceSystem : is a
-TemporalReferenceSystem <|-- TemporalCoordinateReferenceSystem : is a
-TemporalReferenceSystem <|-- Calendar : is a
-
-OrdinalTemporalReferenceSystem "1" o-- "(ordered)" Events : consists of
-OrdinalTemporalReferenceSystem "1" o-- "0..1" Epoch : anchored by
-OrdinalTemporalReferenceSystem "1" --> "1..*" Notation : is represented by
-TemporalCoordinateReferenceSystem "1" o-- "1" Epoch : anchored by
-TemporalCoordinateReferenceSystem "1" --> "1..*" Notation : is represented by
-TemporalCoordinateReferenceSystem "1" o-- "1" Timescale : must have
-
-Calendar "1" o-- "0..1" Epoch : anchored by
-Calendar "1" --> "1..*" Notation : is represented by
-Calendar "1" o-- "1" Timeline : has a
-
-Timeline "1" o-- "1..*" Algorithm : defined by
-
-class Timescale {
-  StartCount
-  EndCount
-  arithmetic
-}
-
-Timescale "1" o-- "1" Clock : determined by
-Timescale "1" o-- "1" UnitOfMeasure : has a
-
-class Clock {
-}
-Clock "1" o-- "1..*" Ticks : counts
-class Ticks {
-    }
-
-class UnitOfMeasure {
-  Direction
-}
-
-class Algorithm {
-} 
-Algorithm "1" o-- "2..*" Timescale : uses
-
-@enduml
-
-....
+[[fig-UML-diagram]]
+image::./images/Figure1Mermaid.JPG[align="center"]
diff --git a/23-049/sections/12-temporal-geometry.adoc b/23-049/sections/12-temporal-geometry.adoc
index 049eaae6..e6e37d04 100644
--- a/23-049/sections/12-temporal-geometry.adoc
+++ b/23-049/sections/12-temporal-geometry.adoc
@@ -13,3 +13,4 @@ The geospatial community has often used analogies between space and time to cons
 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 <> (see <>) and apply across all of the regimes, so do not need to be in this Abstract Conceptual Model.
+
diff --git a/23-049/sections/annex-bibliography.adoc b/23-049/sections/annex-bibliography.adoc
index 43d0a17b..db62a3e2 100644
--- a/23-049/sections/annex-bibliography.adoc
+++ b/23-049/sections/annex-bibliography.adoc
@@ -1,6 +1,5 @@
 
 [appendix,obligation="informative"]
-[[annex-bibliography]]
 [bibliography]
 == Bibliography
 
diff --git a/23-049/sections/annex-examples.adoc b/23-049/sections/annex-examples.adoc
index 30241af1..9e212bcd 100644
--- a/23-049/sections/annex-examples.adoc
+++ b/23-049/sections/annex-examples.adoc
@@ -1,7 +1,7 @@
 [appendix,obligation="informative"]
 [[annex-examples]]
-[examples]
 == Examples
+
 These show how the concepts of the Abstract Cocenptual Model for Time can be applied to realistic use cases. Of course, the logical and implementation details are outside the scope of this standard.
 
 === Ordinal Temporal Reference System

From faccb547371620f3d190df54754025f9e90a7e22 Mon Sep 17 00:00:00 2001
From: Chris Little 
Date: Thu, 7 Mar 2024 16:44:48 +0000
Subject: [PATCH 2/4] Update 23-049.adoc

Updated release to 023-049r2
---
 23-049/23-049.adoc | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/23-049/23-049.adoc b/23-049/23-049.adoc
index 86b567d5..a564c4d0 100644
--- a/23-049/23-049.adoc
+++ b/23-049/23-049.adoc
@@ -4,10 +4,10 @@
 :status: swg-draft
 :committee: technical
 :edition: 1.0
-:docnumber: 23-049r1
+:docnumber: 23-049r2
 :received-date: 2023-05-23
-:issued-date: 2024-02-14
-:published-date: 2024-02-14
+:issued-date: 2024-03-07
+:published-date: 2024-03-07
 :external-id: http://www.opengis.net/doc/AS/temporal-conceptual-model/1.0
 :referenceURLID: http://docs.opengeospatial.org/as/23-049/23-049.html
 :fullname: Chris Little

From 5d3addc5f56d5b29ccc64451e5bb1549660468fe Mon Sep 17 00:00:00 2001
From: Chris Little 
Date: Thu, 7 Mar 2024 16:53:11 +0000
Subject: [PATCH 3/4] Update annex-bibliography.adoc

---
 23-049/sections/annex-bibliography.adoc | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/23-049/sections/annex-bibliography.adoc b/23-049/sections/annex-bibliography.adoc
index db62a3e2..2cc1b3c1 100644
--- a/23-049/sections/annex-bibliography.adoc
+++ b/23-049/sections/annex-bibliography.adoc
@@ -103,3 +103,8 @@ https://datatracker.ietf.org/doc/draft-ietf-sedate-datetime-extended[https://dat
 The Library of Congress: 
 _Extended Date/Time Format (EDTF) Specification_. (2019).
 https://www.loc.gov/standards/datetime[https://www.loc.gov/standards/datetime]. [last accessed 2024-02]
+
+* [[[mih, Motion Imagery Handbook]]]
+Motion Imagery Standards Board:
+_Motion Imagery Standards Profile-2023.2: Motion Imagery Handbook, Chapter 6_. (March 2023), 
+https://nsgreg.nga.mil/doc/view?i=5470[https://nsgreg.nga.mil/doc/view?i=5470]. [last accessed 2024-03]

From 6de96b0a7dab65150ce2a7e42e943f7ee6c8bfa9 Mon Sep 17 00:00:00 2001
From: Chris Little 
Date: Thu, 7 Mar 2024 16:59:19 +0000
Subject: [PATCH 4/4] Update annex-examples.adoc

Add entry to Bibliography, corrected Figure labels and a couple of typos
---
 23-049/sections/annex-examples.adoc | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/23-049/sections/annex-examples.adoc b/23-049/sections/annex-examples.adoc
index 9e212bcd..e7f3a3c7 100644
--- a/23-049/sections/annex-examples.adoc
+++ b/23-049/sections/annex-examples.adoc
@@ -10,15 +10,15 @@ Geological eras and periods are forms of compound ordinal reference systems. A c
 
 Another consistent sequence of strata from another region can also form another ordinal temporal reference system. It may be possible to relate the two systems to each other because of layers that may share specific varieties of fossils, or a specific distinctive stratum type.
 
-Figure 2 shows how the four different geological ordinal temporal reference systems of sequences of rocks and fossils, called Periods, from west Wales (Ordovician), south Wales (Silurian), Devon (Devonian), and coal-bearing rocks (Carboniferous) have been combined to define a longer geological ordinal temporal reference system called the Paleozoic Era.
+Figure A.1 shows how the four different geological ordinal temporal reference systems of sequences of rocks and fossils, called Periods, from west Wales (Ordovician), south Wales (Silurian), Devon (Devonian), and coal-bearing rocks (Carboniferous) have been combined to define a longer geological ordinal temporal reference system called the Paleozoic Era.
 
 [[fig-geological-ordinal-example]]
 image::images/GeologicalOrdinalExample.jpg[]
 
 === Temporal Coordinate Reference System
-1. A remote autonomous underwater drone, known as a 'glider' is making regular measurements of temperature and salinity deep in the Atlantic Ocean. The measurements are time-stamped by an on-board computer clock. The clock was synchronized to a satellite's atomic clock when the drone was launched. When the drone surfaces to report its findings, or to be picked up by a research vessel, it is found that the computer clock as 'drifted' compared to time from the satellite. The drone's clock is assumed to have 'drifted' in a consistent, linear, fashion, and the error correction is distributed proportionately along the time series of measurements.
+1. A remote autonomous underwater drone, known as a 'glider' is making regular measurements of temperature and salinity deep in the Atlantic Ocean. The measurements are time-stamped by an on-board computer clock. The clock was synchronized to a satellite's atomic clock when the drone was launched. When the drone surfaces to report its findings, or to be picked up by a research vessel, it is found that the computer clock has 'drifted' compared to time from the satellite. The drone's clock is assumed to have 'drifted' in a consistent, linear, fashion, and the error correction is distributed proportionately along the time series of measurements.
 
-2. Several timescales have been defined using the same atomic clocks. For various reasons, such as the year of starting, or the need to store numbers in limited length computer words, different epochs have been chosen. This is illustrated in Figure 3. The figure also illustrates how UTC is not a timescale, but a timeline, as it has been adjusted with leap seconds to correspond to the Gregorian calendar and not deviate more than 0.6 seconds from Earth's actual day length. This is because UTC is based on the atomic definition of a second, the SI second, whereas the Gregorian calendar assumes that a day, based on Earth's rotation with respect to the sun, is 86,400 seconds, but this daily rotation varies in duration every day throughout the year for a variety of reasons. 
+2. Several timescales have been defined using the same atomic clocks. For various reasons, such as the year of starting, or the need to store numbers in limited length computer words, different epochs have been chosen. This is illustrated in Figure A.2. The figure also illustrates how UTC is not a timescale, but a timeline, as it has been adjusted with leap seconds to correspond to the Gregorian calendar and not deviate more than 0.6 seconds from Earth's actual day length. This is because UTC is based on the atomic definition of a second, the SI second, whereas the Gregorian calendar assumes that a day, based on Earth's rotation with respect to the sun, is 86,400 seconds, but this daily rotation varies in duration every day throughout the year for a variety of reasons. 
 
 [[fig-differing-timecales]]
 image::images/MISB_Figure_36.png[]
@@ -31,5 +31,7 @@ Months are not useful as there are two small fast moving moons. One orbits three
 The 'day', the rotation of Mars on its axis with respect to the Sun, is the other timescale that comprises the Mars calendar. To avoid confusion with Earth's days, they are called 'Sols'. This solar day, with a similar definition to an Earth day, would be useful for planning day time and night time activities, pehaps requiring solar power generation.
 
 Other definitions of a day could have been adopted:
+
 1. A sidereal Mars day, the rotation of Mars with respect to the distant stars, like the sidereal day on Earth. This could be useful if the rover was performing astronomical measurements, such as for navigating using the equivalent of a sextant;
+
 2. An Earth orientated day, the rotation of Mars with respect to Earth in its orbit. This could be useful for planning activities needing extended communication periods with direct line-of-sight with Earth.