Orbit determination for space situational awareness: A survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapidly growing number of objects encircling our planet is an increasing concern. Collisions between these objects have already occurred and pose a potential threat in the future, resulting in the creation of countless debris fragments. In particular, the Low Earth Orbit (LEO) region is densely populated and highly contested. This underscores the critical importance of space surveillance in this area. Moreover, the utilization of Medium Earth Orbit (MEO) and Geosynchronous Earth Orbit (GEO) is also on the rise. To ensure the safety and functionality of operational satellites, it is paramount to accurately determine and continuously monitor the orbits of space objects, mitigating the risk of collisions. Precise and timely predictions of future trajectories are essential for this purpose. In response to these challenges, this survey paper provides a comprehensive review of various methods proposed in the literature for Orbit Determination (OD). It also identifies research gaps and suggests potential directions for future studies, emphasizing the pressing need for adequate Space Situational Awareness (SSA).
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it