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9.2.5 Continuous Assessment
Even if a device is certified as secure, new threats and vulnerabilities may still emerge over time, potentially compromising its security and invalidating the certificate. The time required for certification can be reduced if detailed and up-to-date information from the manufacturer is available, allowing for a more e...
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9.2.6 Secure Update
The CRA places significant emphasis on software upgrading to ensure the long-term security of products with digital elements. It mandates that manufacturers provide regular security updates to address vulnerabilities, ensuring that devices remain protected against emerging cyber threats. Updates should be delivered in ...
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9.2.7 Repurposing and Decommissioning
As cybersecurity threats evolve, devices may require updates, reconfigurations, or upgrades to maintain security standards. However, some changes might exceed the device's capabilities, such as requiring more storage than available or hardware modifications beyond its design. When a device can no longer meet security r...
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9.3 CRA in ERATOSTHENES and CERTIFY pilots
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9.3.0 Introduction
Clause 9.3 develops further the applicability of ERATOSTHENES, CERTIFY and their pilots as a reference for the CRA. As the CRA recently came into force and its obligations are still further away in the future, even though the text is stable, many aspects will be delegated to implementing acts. Thus, the analysis presen...
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9.3.1 ERATOSTHENES illustrative use case
To illustrate the implications of the CRA in the project, this present document uses the Intelligent Transport System pilot as a reference. The CRA is a very horizontal piece of legislation with common cybersecurity requirements for all products, regardless of sector or field of application. Most of the devices involve...
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9.3.2 CERTIFY illustrative use case
To illustrate the implications of the CRA in the project, this present document uses the Intelligent Transport System pilot as a reference. In a similar vein to the previous clause, most of the devices involved in the pilot would fall under the CRA scope according to [i.20], Article 2 (e.g. IoT nodes, central controlle...
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1 Scope
The present document provides guidelines and recommendations for documentation schemes that support the continuous and consistent documentation of quality and quality related attributes for AI-enabled systems. This includes an analysis of current documentation schemes and Use case examples. It also defines a process ho...
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2 References
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2.1 Normative references
Normative references are not applicable in the present document.
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2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. NOTE: While any hyperlinks i...
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3 Definition of terms, symbols and abbreviations
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3.1 Terms
For the purposes of the present document, the following terms apply: affected person: individuals with limited technical knowledge who may be impacted by AI systems NOTE: They require protection from arbitrary decisions and risks, and may need to take legal measures if they feel unfairly treated. They can be represente...
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3.2 Symbols
Void.
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3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply: CE Conformité Européenne (European Conformity) CRISP-DM CRoss-Industry Standard Process for Data Mining DSL Domain-Specific Language GDPR General Data Protection Regulation GPAI General-Purpose AI (System) HIPAA Health Insurance Portability an...
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4 Purpose of Documentation
The motivation to create effective documentation for AI-enabled systems became very high, since those systems are used so widely and regulation has been set up to limit potential risks that may arise from those system developed and put on the market with inappropriate characteristics. One goal of mitigating those risks...
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5 Motivation for a Harmonized Documentation Scheme
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5.1 Current AI Documentation Schemes
In the field of AI documentation, several approaches have been developed to support transparency, ethics, quality, reproducibility, discoverability, trust and accountability throughout the various stages of data and AI model life cycles. The present clause gives an overview of existing works grouped by the focus the do...
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5.2 Gaps to EU AI Act Requirements
The analysis in clause D.5 demonstrates that while current state-of-the-art AI documentation approaches generally fulfil many of the data-related documentation requirements outlined in the EU AI Act, significant gaps remain. For instance, while Data Cards, DescribeML, Factsheets, and the Dataset Development Life Cycle ...
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5.3 Summary
Based on the inconsistencies and gaps identified, the development a unified AI documentation scheme is needed that ensures thorough coverage of all documentation requirements mandated by the EU AI Act. Such a unified scheme would streamline documentation processes, provide clarity, and facilitate compliance, ultimately...
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6 Approach for Documenting AI-enabled Systems
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6.1 Overview
Documentation, in general, supports a large variety of needs and is always to be tailored to specific situations: there is no one-fits-all format or method. This flexibility is particularly crucial for AI systems, as they often operate in dynamic environments and serve diverse stakeholders with varying technical expert...
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6.2 Documentation items (what to document)
In the context of AI system documentation, a documentation item defines what is being documented to ensure transparency, accountability, and regulatory alignment across the AI system life cycle. Crucially, the documentation item is not the document itself but the subject of documentation, i.e. something that requires f...
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6.3 Documentation stakeholders (roles)
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6.3.1 General
Different stakeholders involved in AI development, deployment, and regulation have specific responsibilities that should be supported by transparent, clear documentation. These stakeholders range from those who create and provide AI technologies to those who integrate, use, or are impacted by them. ETSI ETSI TR 104 119...
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6.3.2 Audience analysis
In ISO/IEC/IEEE 26514 [i.24], Audience analysis is the process of determining who will use the information included in the documentation. The standard requires this process to be conducted taking into consideration factors such as users' background, experience, and education, familiarity with technical language, the wa...
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6.3.3 Stakeholder categories and documentation requirements
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6.3.3.1 AI Provider
Documentation Requirements: For AI providers, documentation should ensure transparency about the AI technologies being offered, including detailed descriptions of the AI models, algorithms, and data processing techniques used. Additionally, this documentation should include comprehensive records of testing methods, val...
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6.3.3.2 AI Producer
Documentation Requirements: AI producers require highly detailed documentation throughout the development life cycle. This includes technical specifications, design documents, testing protocols, and deployment records. This documentation should cover the entire development pipeline, from data preprocessing techniques t...
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6.3.3.3 AI Customer
Documentation Requirements: AI customers typically do not create their own documentation but they should ensure that the AI provider has supplied sufficient guidance and trustworthiness assurances. This should include instructions for deployment and use, certification reports, compliance assessments, or summaries of th...
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6.3.3.4 AI Partner
Documentation Requirements: For AI partners, the documentation should be detailed and precise to support their specialized tasks. AI auditors, for example, require extensive documentation on AI system design, data integrity, and quality and risk management systems, often in a machine-readable format like JSON, to perfo...
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6.3.3.5 AI Subject
For instance, a data subject might require documentation that explains how their personal data is processed by an AI system, including information on data retention policies, consent mechanisms, and privacy safeguards, presented in clear, non-technical language. In this case, it is also essential for the documentation ...
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6.3.3.6 Relevant Authorities
Documentation Requirements: Relevant authorities, such as regulators and notified bodies, do not generate their own documentation but instead receive and review documentation to ensure regulatory compliance and policy enforcement. This includes reports on AI system transparency, accountability measures, and ethical con...
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6.4 AI-system life cycle
An AI system life cycle encompasses the comprehensive series of stages involved in the creation, deployment, and maintenance of an AI-based system. This life cycle helps in structuring the development process to ensure effective, reliable, and ethical AI-based solutions. The life cycle typically includes several phases...
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6.5.1 General
An AI documentation approach serves as a high-level strategy or framework that outlines how documentation is created, organized, and maintained. It defines the what (the content and scope) and why (the purpose and goals) of documentation. In contrast, a documentation technique refers to the specific methods or tools us...
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6.5.2 (Motivation and) Overview
A documentation technique is a combination of a specific technique to represent information (e.g. text) and a specific format (e.g. a list). Documentation techniques are the foundation of specific documentation approaches and methodologies (e.g. Model Cards). Each approach is based on at least one documentation techniq...
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6.5.3 Questionnaires
Questionnaires are structured sets of questions designed to gather specific information in a systematic way. They are often used to collect metadata, feedback, or details about datasets, models, or processes. • Advantages: - Ensures consistency in data collection. - Easy to distribute and analyse. - Useful for large te...
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6.5.4 Information Sheets
Information sheets are static documents that provide detailed information in a narrative or report-like style. They are often used to communicate key details about a system, model, or dataset. • Advantages: - Comprehensive and detailed. - Highly customizable for specific audiences. - Useful for regulatory compliance an...
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6.5.5 Checklists
Checklists are lists of items, tasks, or requirements that need to be completed or verified. They ensure consistency and completeness in processes. • Advantages: - Simple and easy to use. - Ensures no steps are missed. - Useful for compliance and quality assurance. • Challenges: - May oversimplify complex processes. - ...
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6.5.5 Templates
Templates are predefined structures or formats for documenting information. They ensure consistency across documents and make it easier to create new documentation. • Advantages: - Saves time and effort. - Ensures uniformity across documents. - Easy to customize for different use cases. • Challenges: - May not fit all ...
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6.5.6 White Papers
White papers are authoritative reports that provide in-depth information on a specific topic, often used to explain methodology, results, and implications. • Advantages: - Highly detailed and formal. - Useful for communicating complex ideas to a technical audience. - Builds credibility and authority. • Challenges: - Ti...
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6.5.7 Knowledge Graphs
Knowledge graphs are network representations of information that show relationships between different entities. They help in understanding complex systems and their interconnections. • Advantages: - Provides a holistic view of complex systems. - Can be queried programmatically for insights. - Useful for organizing and ...
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6.5.8 Visual Techniques (Diagrams, Flowcharts, Infographics)
Visual documentation uses elements like diagrams, flowcharts, and infographics to communicate complex information in an intuitive way. • Advantages: - Simplifies complex systems and processes. - Improves accessibility for non-expert stakeholders. - Enhances understanding through visual representation. • Challenges: - M...
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6.5.9 Interactive Techniques
Documentation that allows users to interact with the content, such as running code, exploring data, or navigating through dynamic elements. • Advantages: - Provides hands-on learning experiences. - Encourages exploration and experimentation. - Supports real-time updates and collaboration. • Challenges: - Requires techn...
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6.5.10 Domain-Specific Language (DSL)
A specialized programming or markup language designed for a particular application domain. Used to create structured, machine-readable documentation. • Advantages: - Ensures standardization and precision. ETSI ETSI TR 104 119 V1.1.1 (2025-09) 29 - Easy to integrate into automated pipelines. - Tailored to the specific n...
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6.5.11 Summary
A documentation approach and documentation techniques work hand in hand. The approach defines the overall strategy and goals, while the techniques provide the practical tools and methods to implement that strategy. For example, a Model Card (approach) might use questionnaires, templates, and visual documentation (techn...
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6.6 Quality aspects of documentation
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6.6.1 General
The information contained in the technical documentation should follow established principles of information quality. ISO/IEC/IEEE 26514 [i.24], clause 7, identifies six key principles: correctness, consistency, comprehensibility, conciseness, minimalism, and accessibility.
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6.6.2 Correctness
The information provided in the technical documentation should accurately reflect the AI systems' actions and expected results for the specific version being documented. This includes details on functionalities, limitations, and behaviour. Any updates or changes made to the AI system (e.g. new features, bug fixes) shou...
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6.6.3 Consistency
The technical documentation should maintain a consistent structure and layout. Consistency applies to all elements including screens, pages, text formatting (headings, spacing, fonts), graphics, icons, colours, signal words, and audio-visual elements. Additionally, consistent terminology should be used for user interfa...
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6.6.4 Comprehensibility
The technical documentation should be easily understood by all relevant stakeholders. Information should be readily understood by the least experienced stakeholder within the expected audience. This is particularly important when serving a diverse user base with varying levels of experience, skills, training and knowle...
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6.6.5 Conciseness
Information within the technical documentation should be presented concisely, both in terms of format and media, avoiding unnecessary repetition or duplication. While repetition can be a useful tool for educational purposes, technical documentation should prioritize clarity and efficiency.
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6.6.6 Minimalism
The technical documentation should be minimal, containing only essential information needed for stakeholders to understand concepts, perform tasks, and troubleshoot issues. Technical documentation should avoid including content that is not strictly necessary for accomplishing these objectives. A minimalist approach ens...
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6.6.7 Accessibility
The technical documentation should be accessible to all expected stakeholder groups, considering factors like language, format, and accessibility needs and regardless of their abilities or environments. This includes ensuring technical availability, legibility, and findability of the information. For example, documenta...
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6.6.8 Systematic understanding
A systematic understanding refers to a structured and comprehensive approach to comprehending complex systems, processes, or subjects. In the context of AI systems, it involves an organized knowledge of how various components such as data, algorithms, and the corresponding infrastructure is in interaction.
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6.7 Documentation Approach
To derive a structured documentation approach, the present document describes the basic understandings and prerequisite considerations for this, as described in clause 4, 6 and 7. Already existing approaches are discussed in clause 5. From this foundation the following structured documentation approach is compiled in t...
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7 Guidance for EU AI Act Compliant Documentation
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7.1 Introduction to the EU AI Act
The EU Artificial Intelligence Act (AI Act), officially Regulation (EU) 2024/1689 [i.2] , is a comprehensive regulatory framework aimed at ensuring trustworthy, human-centered AI in Europe. Its goal is to protect health, safety, fundamental rights, and EU values while encouraging AI innovation and implementation. The A...
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7.2 Mapping of EU AI Act Stakeholder
The EU AI Act introduces a legal framework that closely maps to the roles given in clause 6.3, assigning specific responsibilities to each stakeholder to ensure AI systems meet safety, transparency, and ethical standards. Both the ISO and the EU AI Act frameworks aim to clarify who is accountable for different aspects ...
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7.3 Documentation Guidance for High-Risk AI Systems
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7.3.1 General
High-risk AI systems are permitted on the EU market only if they comply with a series of essential context requirements set out in Chapter III, Section 2 of the AI Act [i.2]. These requirements span risk management, data governance, technical documentation, record-keeping, transparency, human oversight, and performance...
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7.3.2 Risk Management System (Art. 9)
Context Requirement: According to EU AI Act providers of high-risk AI systems establish a risk management system and operate it throughout the AI system's life cycle. This is a continuous, iterative process of identifying, analysing, and mitigating risks. The provider document the risk management and keep it up-to-date...
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7.3.3 Data and Data Governance (Art. 10)
Context Requirement: According to EU AI Act high-risk AI systems that use data for training, validation, or testing meet strict data quality and governance requirements as under Art. 10. The used datasets are relevant, representative, complete, and as accurate as possible for the AI system's intended purpose. They have...
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7.3.4 Record-Keeping (Art. 12)
Context Requirement: High-risk AI systems which comply with the EU AI Act are designed to facilitate the recording of events ("logs") during operation, as appropriate for their intended purpose. The logs facilitate traceability, allowing for the reconstruction of system functionality, especially during instances of fai...
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7.3.5 Transparency and Information to Deployers (Art. 13)
Context Requirement: High-risk AI systems which comply with the EU AI Act are transparent enough to allow deployers to understand and use their outputs correctly. This includes clear, accurate, and accessible instructions for use and information to interpret systems output and behaviour. Providers also ensure deployers...
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7.3.6 Human Oversight (Art. 14)
Context Requirement: High-risk AI systems which comply with the EU AI Act are designed to allow effective human oversight to prevent or reduce risks to health, safety, or fundamental rights. When designed appropriate human overseers are able to understand the system, interpret its output, and intervene or shut it down ...
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7.3.7 Accuracy, Robustness, and Cybersecurity (Art. 15)
Context Requirement: Art. 15 of the EU AI Act mandates that high-risk AI systems achieve and maintain an appropriate level of accuracy, robustness, and cybersecurity throughout their life cycle. These characteristics are essential to ensure the system operates reliably under expected conditions, withstands disturbances...
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7.3.8 Technical Documentation (Art. 11)
High-risk AI systems which comply with the EU AI Act have a comprehensive and up-to-date technical documentation prior to market placement. The technical documentation builds upon the contextual requirements described above, operationalizing them into structured evidence required by Art. 11 and Annex IV of the AI Act. ...
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7.4 Documentation Requirements for GPAI models
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7.4.1 General
Under the AI Act, General-Purpose AI (GPAI) models are defined as models that display significant generality and are capable of competently performing a wide range of distinct tasks (Art. 3(63)). All GPAI providers should comply with obligations in Art. 53, including keeping up-to-date technical documentation, publishi...
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7.4.2 GPAI Models without Systemic Risk
Under Art. 53 of the AI Act, all GPAI model providers, regardless of systemic risk status, should maintain the following technical documentation and provide sufficient information to deployers, at a minimum [i.2]: • Technical Documentation: The document describes the model's architecture, intended tasks, training proce...
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7.4.3 GPAI Models with Systemic Risk
In addition to the baseline requirements listed in clause 7.3.2 GPAI models with systemic risk should meet the following requirements [i.2]: • Model Evaluation and Adversarial Testing: The document includes test and evaluation results, including adversarial testing. (Art. 55(1)(a)) • Systemic-Risk: The document describ...
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7.4.4 Documentation
Figure 8 gives an overview of information elements for GPAI models without and with systemic risk: Figure 8: GPAI models: Documentation Items and information Elements required by the AI Act Recommended Documentation Approaches: Providers are encouraged to adopt structured, standardized documentation methods to efficien...
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1 Scope
The present document describes the structure of the Indoor Fibre Distribution Network (IFDN) Hybrid Cabling System, main functional elements and their characteristics, deployment details and acceptance items.
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2 References
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2.1 Normative references
Normative references are not applicable in the present document.
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2.2 Informative references
References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. NOTE: While any hyperlinks i...
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3 Definition of terms, symbols and abbreviations
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3.1 Terms
For the purposes of the present document, the terms given in Recommendation ITU-T G.9940 [i.51] (ex G.fin-SA) and the following apply: hybrid cabling system: system that supports the connection of information technology equipment and transmission of optical signal and power supply and usually consist of active distribu...
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3.2 Symbols
Void.
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3.3 Abbreviations
For the purposes of the present document, the following abbreviations apply: ADU Active Distribution Unit CE Conducted Emission CS Injected currents EMC ElectroMagnetic Compatibility ESD Electro-Static Discharge FDT Fibre Distribution Terminal IFDN Indoor Fibre Distribution Network LSZH Low Smoke Zero Halogen material ...
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4 Structure of IFDN Hybrid Cabling System
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4.1 General
An Indoor Fibre Distribution Network (IFDN) is mainly composed of optical splitter or optical & electrical splitter, optical cable or optical &electrical hybrid cable, fibre connector or optical & electrical hybrid connector. The position of the IFDN cabling system is shown in Figure 4.1. Traditional optical fibre netw...
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4.2 Power over Hybrid Cable (PoHC)
Power over Hybrid Cable (PoHC) is a method of providing both power and data to a device by hybrid cable. Figure 4.6: PoHC system Figure 4.7: Common power and optical cabling system To solve the problem of compatibility between power supply and power receiving devices from different manufacturers and provide safety of u...
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5 Hybrid cable
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5.1 Structure
The hybrid cable is typically composed of optical fibre elements and current carrying elements, strength member (possible), filler (possible), yarn (possible) tape (possible), ripcord (possible), sheath, etc. The hybrid cable should meet the requirements of the application and operating environment. Typical FTTR (IFDN)...
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5.2 Optical fibre elements
The optical fibre elements can be composed of one or more optical fibres, tight or semi-tight buffered fibres, fibre ribbons, buffer tubes, or other optical core structures, or independent optical fibre cables (such as loose tube cable). The optical fibre elements should be in accordance with the following: a) For ease...
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5.3 Optical transmission performance
Optical transmission performance for cabled optical fibre elements need to conform to Table 5.1. Table 5.1: Optical transmission performance for cabled optical fibre elements Parameter Test procedure Requirements/Remarks Attenuation coefficient at 1 550 nm IEC 60793-1-40 [i.17] ≤0,30 dB/km for B-652.D ≤0,30 dB/km for B...
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5.4 Electrical performance
Electrical performance for current carrying elements need to conform to Table 5.2. Table 5.2: Electrical performance for current carrying elements No Test Severity Requirement 1 Conductor DC resistance IEC 60228 [i.1], Annex A Test equipment: A current source in conjunction with a voltmeter Sample length under test: No...
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5.6 Environmental performance
Environmental performance tests should conform to Table 5.4. Table 5.4: Environmental performance tests No. Test Test Method Severity Criteria 1 Temperature cycling IEC 60794-1-201 [i.22] Length under test: finished cable length, not less than 1 000 m - 10 °C~60°C Duration at extreme temperatures: 8 h Rate of temperatu...
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6 Hybrid Connector
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6.1 Structure
A Hybrid connector in the IFDN Hybrid Cabling System is a miniaturized plug-in connector that consists of a single-core plug and an adapter, with optical and electrical connection characteristics. It is used to transmit signals and remotely supply power to low-power terminals such as WLAN devices and cameras. At presen...
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6.3 Electrical performance
Electrical performance for Hybrid connectors needs to conform to Table 6.6. Table 6.6: Electrical performance of hybrid connectors Test Requirement Insulation resistance IEC 60512-3-1 [i.7] ≥ 500 MΩ Voltage proof IEC 60512-4-1 [i.8] Between contacts r.m.s. withstand voltage DC 1 000 V Between contacts and housing DC 1 ...
5b2fbb7f9f34ec451504359adb0cbf12
104 097
6.5 Environmental performance
Environmental performance for Hybrid connectors needs to conform to Table 6.8. Table 6.8: Environmental performance of hybrid connectors No. Test Severity Criteria 1 Cold IEC 60512-11-10 [i.13] Temperature: -10 °C Duration: 96 h O, E, V 2 Dry heat - High temperature endurance IEC 60512-11-9 [i.12] Temperature: +60 °C D...
5b2fbb7f9f34ec451504359adb0cbf12
104 097
7 ADU
5b2fbb7f9f34ec451504359adb0cbf12
104 097
7.1 Structure
The structure of the ADU usually consist of the active electrical port (for power supply), optical input port (receive optical signals from the upper level), hybrid output port (transmit optical signal and electrical power together with the connected SFU), and the optional cascading optical port (transmit optical signa...
5b2fbb7f9f34ec451504359adb0cbf12
104 097
7.2 Operating Environment
ADU for indoor cabling system should be able to operate at following environment, according to IEC 61753-1 [i.42] category OP (Outdoor protected environment): • Operating temperature: -25 ℃ ~ + 70 ℃ • Relative Humidity:5 % ~ 95 %; • Atmospheric pressure:86 kPa ~ 106 kPa.
5b2fbb7f9f34ec451504359adb0cbf12
104 097
7.3 Optical Transmission Performance
Optical transmission performance for ADU is advised to conform to Table 7.1. Table 7.1: Optical Transmission performance of ADU Item Splitter Ratio 1:5 1:9 1:4 1:8 1:16 Wavelength 1 310 nm and 1 550 nm Insertion Loss Cascading optical port:≤ 11 dB Hybrid output port: ≤ 11 dB Cascading optical port:≤ 2,4 dB Hybrid outpu...
5b2fbb7f9f34ec451504359adb0cbf12
104 097
7.4 Electrical Performance
7.4.1 Input power The ADU operates properly with an input voltage of 45 V ~ 57 V with DC power supply. ETSI ETSI TR 104 097 V1.1.1 (2025-10) 24 7.4.2 Output power The ADU output power should comply with the following requirements according to IEEE 802.3af [i.48], IEEE 802.3at [i.49], or IEEE 802.3bt [i.50] standard: • ...
5b2fbb7f9f34ec451504359adb0cbf12
104 097
7.6 Electrical performance
Electrical performance for ADU need to conform to Table 7.3. Table 7.3: Electrical performance for current carrying elements No Test Severity Requirement 1 Temperature Rise Test IEC 62368-1 [i.44] Ambient temperature: 40 °C ≤ 30 °C 2 Insulation resistance IEC 62368-1 [i.44] Between contacts r.m.s. withstand voltage:500...