MétaCan
Menu
Back to cohort
Record W3120291445 · doi:10.2514/6.2021-0665

Attitude Dynamics and Control Anomaly Detection Using an Autonomous Ground Station

2021· article· en· W3120291445 on OpenAlex
Yujia Huang, Philip Ferguson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIAA Scitech 2021 Forum · 2021
Typearticle
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsSpacecraftAnomaly detectionSatelliteComputer scienceGround stationAnomaly (physics)Real-time computingRemote sensingArtificial intelligenceEngineeringAerospace engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

View Video Presentation: https://doi.org/10.2514/6.2021-0665.vid Unlike a conventional satellite ground station, an autonomous satellite ground station is introduced to detect failures in a satellite’s attitude determination and control subsystem. This paper presents the application of an anomaly detection approach on an autonomous ground station which is capable of monitoring, tracking and diagnosing subtle failures of a spacecraft. The autonomous ground station is designed to be capable of learning the behaviours of a spacecraft over time without having any prior knowledge or model of the spacecraft, and to report subtle deviations and anomalies when detected. This paper also describes the development of a binary-class classifier between the “normal” and “abnormal” behaviours of the satellite. The whole analysis contains four primary stages, including model simulation, data learning, failure detection and accuracy determination.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.546

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.222
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it