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Record W2895838676 · doi:10.2514/1.g003647

Long-Term Rotational Motion Analysis and Comparison to Observations of the Inoperative Envisat

2018· article· en· W2895838676 on OpenAlexafffund
Luc Sagnières, Inna Sharf

Bibliographic record

VenueJournal of Guidance Control and Dynamics · 2018
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsMcGill University
FundersHydro-QuébecNatural Sciences and Engineering Research Council of CanadaFaculty of Engineering, McGill University
KeywordsNutationAngular momentumSpacecraftPhysicsAngular velocityTorqueRotation around a fixed axisGeodesySatelliteOrbit (dynamics)Term (time)Aerospace engineeringGeologyMechanicsClassical mechanicsAstronomyEngineering

Abstract

fetched live from OpenAlex

A new model for the analysis and prediction of the rotational motion of large space debris over a long term is outlined. A formulation for a high-order gravity-gradient torque is derived as well as the aerodynamic torque for a spinning spacecraft defined by a general surface geometry model. The comprehensive model is exercised for a coupled orbit-attitude propagation of the defunct satellite Envisat. An in-depth analysis of the simulation results reveals that, unlike what has been assumed previously, the likelihood of Envisat’s spin axis remaining stable is small. A distinct nutation of the spacecraft’s angular momentum vector about the orbit normal is predicted, having a period on the order of a few days. Although a decay of the angular rate is expected, the nutation motion of the spin axis is expected to become larger and faster with time, underlining a potential difficulty for a future active debris removal mission. A critical review of observational studies reported on Envisat’s rotational state is performed, in light of the knowledge gained from the simulations and with a comparison to the simulation results. Finally, recommendations for future observation campaigns to provide better estimates of Envisat’s angular motion are put forward.

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.

How this classification was reachedexpand

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.278
Threshold uncertainty score0.224

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 head0.010
GPT teacher head0.236
Teacher spread0.227 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations19
Published2018
Admission routes2
Has abstractyes

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