Electromagnetic Vector Sensor Satellite for Detection of Harmful Space Debris
Bibliographic record
Abstract
The number of satellites launched into low Earth orbit (LEO) is increasing at an exponential rate. Currently, there are about 27,000 known space objects and over 100 million of unknown pieces of space debris. Collision avoidance requires precise knowledge of the positions for all space objects. New techniques are being developed to detect the small, < 10 cm, objects by the plasma waves they generate in space. The bases for these techniques are that all space objects in orbit around the Earth (1) pass through a magnetized plasma, (2) become electrically charged, and thus (3) produce detectable plasma waves. Field aligned irregularities (FAIs) in the path of orbiting space objects are monitored by the HF radar backscatter and by in situ electron density probes. Space debris and satellites moving through these irregularities and can excite plasma emissions such as whistler, compressional Alfven, or lower hybrid waves. Orbital kinetic energy is the source of lower hybrid waves which is converted into an electromagnetic plasma oscillation when a charged space object encounters a field aligned irregularity (FAI). Such compressional Alfven wavs and whistlers propagate undamped at between 400 and 9000 km/s from the source regions and can be detected at ranges up to 50 km.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".