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Record W2325548384 · doi:10.2514/6.2010-7960

Multiple Satellite Formation Flying Using Differential Solar Radiation Pressure

2010· article· en· W2325548384 on OpenAlexaff
Surjit Varma, Krishna Dev Kumar

Bibliographic record

VenueAIAA/AAS Astrodynamics Specialist Conference · 2010
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSatelliteRadiation pressureRemote sensingEnvironmental scienceRadiationAerospace engineeringDifferential (mechanical device)AstrobiologyMeteorologyComputer sciencePhysicsGeologyOpticsEngineering

Abstract

fetched live from OpenAlex

*† In this paper we propose the use of differential solar radiation pressure for multiple satellite formation flying. The nonlinear dynamics describing the motion of the follower satellite relative to the leader satellite is considered for the case where the leader satellite is in an unperturbed reference orbit, and the stability of such a formation in the presence of external perturbations is investigated. Several cases are considered to examine the performance of the proposed control strategy to maintain the relative motion of the follower satellites by correcting for any initial offset errors and external perturbation effects that tend to disturb the formation system. Numerical simulation results confirm that the suggested methodology using differential solar radiation pressure yields reasonable formation keeping precision and its effectiveness in ensuring formation maneuvering .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 science0.0010.001
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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2010
Admission routes1
Has abstractyes

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