Datasets:

ClarusC64
/

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

id string	flight_phase string	system_cluster string	anomaly_summary string	redundancy_state string	observed_instability string	isolation_target string	reset_required int64	containment_level string	propagation_risk float64	stability_recovery_score float64	action_priority string	notes string	constraints string	gold_checklist string
IRC-001	cruise	ADIRU	altitude drift growing	triple redundant	slow divergence	ADIRU1	1	monitor	0.42	0.6	medium	Isolate if growth continues	Under 250 words	isolate+reset+risk
IRC-002	climb	airspeed	ADC lag vs others	dual redundant	lag increasing	ADC1	1	partial	0.48	0.55	high	Reset channel	Under 250 words	isolate+reset+risk
IRC-003	descent	FCC	pitch mismatch	dual redundant	oscillation forming	FCC-B	1	full	0.62	0.5	high	Immediate containment	Under 250 words	isolate+reset+risk
IRC-004	cruise	IRS	minor heading drift	dual redundant	stable	none	0	none	0.1	0.9	low	Within tolerance	Under 250 words	isolate+reset+risk
IRC-005	approach	hydraulic	aileron delay	dual redundant	control delay	hydraulic-B	1	full	0.7	0.45	critical	Isolate immediately	Under 250 words	isolate+reset+risk
IRC-006	takeoff	ADC	speed disagreement	dual redundant	rapid divergence	ADC2	1	full	0.8	0.4	critical	Reset and isolate	Under 250 words	isolate+reset+risk

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

Downloads last month: 21

Size of downloaded dataset files:

1.68 kB

Size of the auto-converted Parquet files:

15.2 kB

Number of rows: