Operator and User Perspective of Fractionated AIS Satellite Systems
Bibliographic record
Abstract
In 2013 AISSat-2 will join AISSat-1 in providing Norwegian authorities, and their partners, with the extended maritime situational awareness that satellite based AIS systems provides. Since neither satellite has propulsion and both will end up in very similar orbits, there will be times the satellites are covering the same area at the same time, and times where the satellites are separated up to half an orbit. Such a configuration gives rise to some interesting opportunities for the users and challenges for the operators. This paper investigates if an operator can use the variability to the users’ advantage in fulfilling the mission objectives – extending and improving the maritime situational awareness. Intuitively it stands to reason that the user would prefer the satellites to be spaced as far apart as possible, to minimize the mean time between updated information about vessel traffic in an area of interest. However, it is well known that in many areas of interest it is unlikely that a satellite AIS system detects every ship in the area in a single pass. With multiple systems covering the same area at the same time, the probability of vessel detection increases, and this is shown to improve value added products such as fused data products and verification of the AIS reported vessel position.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".