Observations of plasma waves generated by charged space objects
Bibliographic record
Abstract
A sampling of the environment around inert space objects has determined, which linear and nonlinear waves are created by moving space debris of all sizes. Plasma waves excited by satellites and space debris moving through the Earth's plasma in low earth orbit have been measured with in situ electric field sensors on other satellites. These orbit driven plasma waves are of interest for proximity detection of space debris and sources of electrostatic and electromagnetic noise on spacecraft. Satellites and other space objects moving through the near-earth ionosphere between 200 and 1000 km altitude become electrically charged by both electron collection and photo emission in sunlight. These hypersonic, charged objects can excite a wide range of plasma waves. Measurements with the Radio Receiver Instrument (RRI) on the Swarm-E satellite have shown that electromagnetic plasma waves from known objects can be observed out to ranges of tens of kilometers. The amplitude, spectral, and polarization changes of the RRI data are consistent with electromagnetic, compressional Alfvén waves launched by charged space objects traveling across magnetic field lines. In addition, electrostatic lower hybrid waves or nonlinear ion acoustic pinned oscillations may have been self-generated and measured on the Swarm-E satellite. It is proposed that measurements of these waves with local electric field measurements or remote electromagnetic wave scatter may be useful to design systems for the location of orbiting objects. Spatial and temporal details of spacecraft charging are key to understanding the extent of waves associated with the object motion in space plasmas.
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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 | 0.000 |
| 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) | 0.001 | 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".