ClarusC64/aviation-avionics-isolation-reset-containment-mapping-v0.1

Aviation Avionics Isolation Reset and Containment Mapping

Purpose

This dataset models how avionics systems should respond after divergence or fault detection.

Detection alone does not prevent failure. The response determines whether the system stabilizes or cascades.

This dataset trains systems to choose the correct containment and recovery strategy.

Core concept

Once redundant avionics systems diverge, the system must decide:

• —which unit to isolate
• —whether to reset
• —how to prevent propagation
• —how to preserve redundancy

Incorrect containment can:

• —propagate corruption
• —remove healthy systems
• —overload pilots
• —escalate into failure

The dataset focuses on optimal containment decisions.

Task definition

Given a divergence or fault scenario, the model must:

• —identify likely fault source
• —determine isolation priority
• —recommend reset or not
• —define containment boundary
• —estimate propagation risk
• —output minimal stabilization plan

The objective is system coherence restoration.

Required outputs

• —suspectedfaultsource
• —isolation_priority
• —reset_recommendation
• —containment_boundary
• —propagationriskscore
• —stabilization_confidence
• —minimalactionset

Data structure

Each row represents a fault scenario.

Key fields

• —scenario_id
• —aircraft_phase
• —subsystem_set
• —divergence_summary
• —fault_candidates
• —isolation_options
• —reset_options
• —containment_actions
• —propagation_risk
• —optimal_strategy
• —notes
• —constraints

Why this dataset matters

Avionics redundancy protects aircraft only if faults are handled correctly.

Key risks:

• —isolating the wrong unit
• —resetting too early
• —failing to contain drift
• —losing redundancy

This dataset trains coherent response selection.

It moves from: fault detection → controlled stabilization.

Evaluation

Models are evaluated on:

• —correct fault source identification
• —appropriate isolation choice
• —reset decision accuracy
• —containment completeness
• —propagation risk estimation

Scoring includes:

• —classification accuracy
• —action completeness
• —reasoning integrity

Use cases

• —automated fault containment
• —redundancy management
• —flight safety decision support
• —avionics simulation
• —resilience engineering

Relationship to companion dataset

This dataset follows:

aviation-avionics-narrative-drift-and-divergence-detection

That dataset detects divergence. This dataset determines the response.

Together they form a full loop: detect → contain → stabilize.

Limitations

Structured benchmark format. Not raw avionics logs.

Future versions may include:

• —richer time-series
• —larger scenario sets
• —simulator-derived cases

Version: v0.1