Remote monitoring of military assets using commercial LEO satellites
Bibliographic record
Abstract
On April 3, 1995 Orbital Communications Corporation (ORBCOMM), a subsidiary of Orbital Sciences Corporation (OSC), launched the first two of what will be a constellation of 36 low Earth orbit satellites. Working with OSC, a leading developer of small satellite technology and the low-cost launch systems (the air launched booster Pegasus), ORBCOMM has designed a system to provide low-cost mobile two-way data communications for worldwide commercial markets. The primary application areas are data communications and messaging. The project was fully funded in 1993 when Orbital Sciences and Teleglobe (Canada) entered a financing and marketing strategic alliance. US Armed Forces will be able to utilize this unique global commercial communication system without the high costs associated with developing a system solely for use by the military. Use of the ORBCOMM System by the US Armed Forces would be in-accordance with the "dual-use" policy (use of commercial assets by the military) supported by the DoD and the Commercial Satellite Communications Initiative (CSCI) directed by Congress. The US Armed Forces can effectively use the ORBCOMM System for remote asset monitoring, logistics tracking, search and rescue operations, and two-way messaging and paging.
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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 | 0.001 |
| 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 | 0.000 |
| 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".