Track After Detection of Inert Space Objects in Conjunction with Satellite-Based Plasma Wave Sensors
Bibliographic record
Abstract
Techniques have been developed to track small objects in space using plasma waves produced by orbital debris as they pass through the ionosphere. Computer simulations and laboratory measurements provide predictions and validation of this technique to detect objects that are not seen by optical and radar systems. In situ observations that confirm the presence of these plasma waves have been made during space sensor conjunctions with known space objects. Small space objects, when they pass through a structured environment, can also be detected with ground sensors and remote satellite instruments. Space debris and satellites moving through plasma irregularities excite electric and magnetic emissions such as whistler, compressional Alfvén, or lower hybrid waves. A whistler wave disturbance is generated by conversion of orbital kinetic energy into an electromagnetic plasma oscillation when a charged space object encounters a region of field aligned irregularities (FAIs). Whistlers propagate undamped at around 9000 km/s from the source regions and can be detected at large ranges over 100 km. Fast magnetosonic waves have been detected out to these ranges of 100 km in situ electric field probes on the Canadian CASSIOPE/Swarm-E spacecraft. After detection, space debris geolocation is required to update orbit prediction models. In situ measurements from host sensors can provide both range and angle-of-arrival from measurements of electromagnetic (EM) plasma waves in space. The angle of arrive needs a vector sensor of the EM fields to give both the electric (E) and magnetic (H) vector components of the incident signal from the space debris. Forming the E × B Poynting flux from the target object, yielding its source direction. A time history of this direction allows estimation of the target trajectory has it passes by the host sensor platform. When charged target debris crosses a field aligned irregularity, it launches a dispersive waveform as a whistler down-chirp or a magnetosonic up-chirp. Propagation from the source point causes temporal dispersion in these signals that stretch in both time and spatial extent. Matched filter processing of the measured signals with wavelet-like, plasma waveforms allows determination of the range to the source at specific generation time.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".