A Norwegian Satellite for Space-based Observations of AIS in the High North
Bibliographic record
Abstract
The Automatic Identification System (AIS) for maritime vessels introduced by the International Maritime Organization (IMO) is basically an anti collision system for vessels at sea. Vessels are broadcasting messages on two channels in the maritime VHF band on regular basis to neighboring vessels for collision avoidance, and to shore stations for vessel traffic services (VTS). AIS messages can also be received by a VHF receiver in space for wide area observation of maritime activity.Norway is about to build its first dedicated satellite (AISSat-1) for such space-based observation of AIThe justification for the mission is based on careful modeling of the global AIS detection probability, with particular emphasis on observation of Norwegian ocean areas in the High North (and High South).AISSat-1 is based on a dedicated low cost high-performance nano-satellite platform (just 20×20×20cm) with three-axis attitude control. The platform will be built by the Space Flight Laboratory at the University of Toronto (UTIAS/SFL), Canada. The AIS sensor is a software defined radio developed by Kongsberg Seatex (KSX), Trondheim Norway.This paper will in some detail discuss AIS detection probability modeling results, mission architecture, satellite, payload, and AIS data distribution on ground. It is believed that AISSat-1 currently is one of the most advanced nano-satellites being developed and is possibly the only nano-satellite dedicated to demonstrate a much needed and future oriented national maritime situational awareness service.
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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 | 0.001 |
| 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".