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Record W2326702008 · doi:10.2514/6.2013-4683

Nano-Satellite Deorbit by Bare Electrodynamic Tether

2013· article· en· W2326702008 on OpenAlexafffund
Rui Zhong, Zheng Zhu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNano-SatelliteAerospace engineeringPhysicsMaterials scienceEngineeringComposite material

Abstract

fetched live from OpenAlex

This paper studies the dynamics of nano-satellite deorbit by bare electrodynamic tether (EDT). The IGRF2000 model of Earth’s magnetic field up to 11th order term is considered as well as the detailed gravity and aerodynamic models. The results find that the higher order terms of the Earth’s magnetic field model play a significant role in determining the dynamic characteristics of satellite with EDT, especially in the polar orbit or orbits with high inclination angles where the orbit of satellite will become elliptical due to these high order terms of Earth’s magnetic field. This is beneficial for the deorbit of satellite in the near polar orbits where the electrodynamic force is not as effective as the equatorial orbit, because the denser atmosphere at a lower perigee will provide larger atmosphere drag to dissipate the orbital kinetic energy of the satellite faster. Moreover, the analysis shows the kinetic energy dissipated by the current induced electrodynamic force is always negative, which implies the force is always against the motion of satellite even the induced voltage/current across the EDT revers their polarity in near polar orbits. Compared the decay rate by atmosphere drag only, the orbit decay rate of a satellite with EDT will be increased by several orders of magnitudes in both equatorial and polar orbits. Finally, the results indicate that the effect of Earth’s oblateness is negligible in deorbiting satellites.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.999

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)0.0020.003

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 head0.001
GPT teacher head0.147
Teacher spread0.146 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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

Citations0
Published2013
Admission routes2
Has abstractyes

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