Avoidance of Satellite Damage by Collisions in Space with theUse of VLF Plasma Waves in Space to Detect the Location of Harmful Space Debris
Bibliographic record
Abstract
The number of satellites launched into low Earth orbit (LEO) is increasing at an exponential rate. Launches support deployment of multi-satellite constellations for many applications. Experiments with electric field sensors on Swarm-E and with HAARP in Alaska have been conducted to (a) better locate the positions of satellites and space debris for prevention of collisions 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, currently “invisible” objects by the plasma waves they generate in space. The basis for this technique is that all space objects in orbit around the Earth (1) pass through a magnetized plasma, (2) become electrically charged, and thus (3) produce an electric current that excites electrostatic lower hybrid waves. Orbital kinetic energy is the power source for the lower hybrid waves. When the debris moves through field aligned irregularities (FAIs), the lower hybrid waves are converted into whistler, and compressional Alfven waves. Such whistlers propagate undamped at around 9000 km/s from the source regions and can be detected at ranges of several earth-radii. This space debris detection process has been tested with the Canadian Swarm-E satellite using the Radio Receiver Instrument (RRI) that measures electric fields in the 10 Hz to 30 kHz frequency range. The RRI makes measurements of plasma waves when near known space objects such as Starlink. An example of these data typically shows an enhancement in electric fields in a band below the local value of lower hybrid frequency. This spacecraft signature are whistler waves in a band between the ion cyclotron and lower hybrid frequencies with an upper frequency cutoff not observed for natural whistler waves. These signals can be used to both detect and track unknown space objects by computing their propagation direction and establishing an orbital state vector of the object. The goal of these measurements is to collect a catalog of space debris with sizes less than 10 cm for collision avoidance. ReferencesP.A. Bernhardt, M. K. Griffin, W. C. Bougas, A. D. Howarth, H. G. James, C. L. Siefring, and S. J. Briczinski, (2020) Satellite Observations of Strong Plasma Wave Emissions with Frequency Shifts Induced by an Engine Burn from the Cygnus Spacecraft, Radio Science, 56.P.A. Bernhardt, R.L Scott, A Howarth, George. J. Morales (2023) Observations of Plasma Waves Generated by Charged Space Objects, Phys. Plasmas 30, 092106, https://doi.org/10.1063/5.0155454 Eliasson, B., & Bernhardt, P. A. (2025). The generation of whistler, lower hybrid and magnetosonic waves by satellites passing through ionospheric magnetic field aligned irregularities. Physics of Plasmas, 32(1), Article 012103. https://doi.org/10.1063/5.0225399
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".