language:
- en
license: mit
pretty_name: Aviation Avionics Isolation Reset and Containment Mapping v0.1
dataset_name: aviation-avionics-isolation-reset-containment-mapping-v0.1
tags:
- clarusc64
- aviation
- avionics
- containment
- recovery
- redundancy-management
- safety
task_categories:
- tabular-classification
- tabular-regression
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: train
path: data/train.csv
- split: test
path: data/test.csv
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
- suspected_fault_source
- isolation_priority
- reset_recommendation
- containment_boundary
- propagation_risk_score
- stabilization_confidence
- minimal_action_set
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