Broadband LEO Constellations for Navigation
Bibliographic record
Abstract
There has been resurgent interest in building low Earth orbiting (LEO) constellations of satellites on a new scale. Their aim is Internet for the world with plans for potentially thousands of satellites. Here, we explore how these LEO constellations can be utilized for navigation. Closer to Earth, LEO offers stronger signals, strengthening us against jamming and aiding in indoor and urban environments. Proximity is also its weakness, where satellites have a small Earth footprint requiring many to provide global coverage. We show that the strength of the Broadband LEO constellations is their numbers, where they offer threefold improvement in satellite geometry compared to navigation core-constellations today. This allows for relaxation of the signal-in-space user range error, while still matching the position accuracy of GPS. Coupled with the more benign radiation environment in LEO compared to GPS in medium Earth orbit, this enables a navigation payload designed using commercial-off-the-shelf components. © 2018 Institute of Navigation
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".