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Record W2326945679 · doi:10.2514/6.2008-6469

A Bang-Bang Control Approach to Maneuver Spacecraft in a Formation with Differential Drag

2008· article· en· W2326945679 on OpenAlex

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 Guidance, Navigation and Control Conference and Exhibit · 2008
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsCanadian Space Agency
Fundersnot available
KeywordsSpacecraftDragControl theory (sociology)Differential (mechanical device)Aerospace engineeringComputer scienceControl (management)PhysicsAeronauticsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The idea of Formation keeping and maneuvering with diff erential drag was first introduced by C.L.Leonard in the late nineteen eighties. The method is based on simultaneously solving a double integrator and a harmonic oscillator that represent the relative states of motion. Although the results of the paper sho w that the method is feasible, it is only true for the Keplerian case of motion. In this paper, we elaborate on this method by considering the factors that were ignored in the original paper; natural perturbations, changing values of drag acceleration, err oneous measurements, attitude errors and large separation distances. Also the practical implementation issues of this controller for the upcoming JC2Sat -FF mission are discussed .

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 categoriesMeta-epidemiology (narrow)
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 score0.874
Threshold uncertainty score1.000

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 head0.179
Teacher spread0.172 · 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