Analysis of 24/7 Remote Arctic Monitoring by Low Earth Orbit Satellites
Bibliographic record
Abstract
This paper investigates the feasibility and optimization of 24/7 remote monitoring in the Arctic using Low Earth Orbit (LEO) satellite constellations, specifically Starlink. Through rigorous link analysis incorporating Equivalent Isotropic Radiated Power (EIRP), Gain-to-Noise Temperature ratio (G/T), free space path loss, and rain attenuation, we assess the Carrier-to-Noise Ratio (CNR) for a specified Arctic location. To enhance system reliability, Particle Swarm Optimization (PSO) is applied to optimize satellite constellation parameters, substantially improving visibility from an initial 92.2% to 96.67%. Further optimization via the Walker-Star constellation achieves an unprecedented 100% visibility for a three-month duration. Results highlight the critical impact of atmospheric conditions, satellite configuration, and elevation angles on the overall communication performance, demonstrating a robust methodology for ensuring continuous, reliable Arctic communication. MATLAB (R2024a), MATLAB Satellite Communications Toolbox ver. 24.1, and Aerospace Toolbox ver. 24.1 were used for the analysis in this paper. Some analysis in this paper was done using the computing resources provided by BC DRI Group and Digital Research Alliance of Canada. The satellite data was retrieved from space-track.org.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".