MétaCan
Menu
Back to cohort
Record W4237190573 · doi:10.1504/ijspacese.2019.097438

Satellite orbit decay due to atmospheric drag

2019· article· en· W4237190573 on OpenAlexaff
G. Vukovich, You Gwang Kim

Bibliographic record

VenueInternational Journal of Space Science and Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicSpace Satellite Systems and Control
Canadian institutionsAdvanced Micro Devices (Canada)York University
Fundersnot available
KeywordsOrbital decaySatelliteSun-synchronous orbitPhysicsRadiation pressureDragAtmosphere (unit)Orbit (dynamics)Frozen orbitEarth's magnetic fieldGravitational fieldElliptic orbitAstronomyAstrobiologyGeodesyGeologyMagnetic fieldMeteorologyMechanicsGeostationary orbitAerospace engineering

Abstract

fetched live from OpenAlex

In the absence of disturbances, an Earth orbiting satellite will follow a Keplerian orbit, which is a regular ellipse with Earth at a focus. However, in reality, there are many additional factors such as gravity field irregularities, Earth magnetic field interactions with satellite magnetic residual and induced magnetic field, solar radiation pressure, the gravitational influence of other celestial bodies and atmospheric drag, which disturb satellite orbits and deflect them from the classic Kepler ellipse fixed in inertial space. Generally, these orbital disturbances are relatively minor over the short-term of a few orbits. However, for low Earth orbiting satellites, atmospheric drag is the dominant factor, causing a satellite to gradually lose altitude (orbital decay) and eventually enterer the dense lower layers of the Earth's atmosphere, where is burned up. Even for fairly high altitudes, this decay can be fairly rapid. This effect can also be used for planned destruction of defunct satellites so as not to add to the space debris problem. This study develops simple models and simulators for satellite atmospheric drag orbital decay prediction. The simulator can be used for satellite orbital decay assessment and studying its effects.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.456
Threshold uncertainty score0.410

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 head0.003
GPT teacher head0.192
Teacher spread0.189 · 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 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

Citations1
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Space Science and EngineeringSame topicSpace Satellite Systems and ControlFrench-language works237,207