Prospects of multiple global navigation satellite system tracking for formation flying in highly elliptical earth orbits
Bibliographic record
Abstract
Two formation flying missions are currently planned for highly elliptical orbit, NASA’s Magnetosphere Multi Scale Mission and ESA’s PROBA-3; however, neither of these missions will take advantage of the new positioning opportunities offered by multi-constellation GNSS and their modernised signal structures. This paper investigates the potential benefits through a detailed visibility simulation which includes GPS, Galileo, GLONASS, BeiDou, QZSS, WAAS, EGNOS, GAGAN, SDCM and MSAS. Results based on the PROBA-3 orbit demonstrate that the GNSS signals are marginally detectable by a standard GNSS receiver, and therefore the output of any visibility simulation is highly dependent on the input simulation parameters. Because small changes to the mission and receiver or unexpected GNSS signal levels can significantly impact the visibility, investing in weak tracking and multi-constellation GNSS is particularly advantageous to mitigate the impact of uncertainty in the HEO environment. Under the right conditions, regional systems are shown to be particularly advantageous.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".