Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".