Orbital Transfer Process and Analysis of Small Satellite for Capturing Korean Satellite as Active Debris Removal (ADR) Mission
Bibliographic record
Abstract
Active debris removal, a technology that approaches and removes space debris in orbit, and the on-orbit service, a technology for extending the mission life of satellites by fuel charging or by exchanging the battery, are gaining interest with the growth of the space community. SaTReC plans to develop a satellite capable of capturing and removing Korean satellites orbiting in space after the end of their missions. In contrast to the previously launched satellites by Korea, which were mainly intended to observe Earth and the space environment, rendezvous/docking technologies, as required in the future during, for instance, space exploration missions, will be implemented and demonstrated. In this paper, an orbital transition method for next-generation small satellites that will capture and remove space debris will be introduced. It is assumed that a small satellite with a mass of approximately 200 kg will be injected into the mission orbit through Korea Space Launch Vehicle-Ⅱ in 2027. Because the satellite must access the target using a minimum amount of fuel, an approaching technology using Earth's J2 perturbation force has been developed. This method is expected to enable space debris removal missions for relatively lightweight satellites and to serve as the basis for carrying out a new type of space exploration in what is termed the 'Newspace' era.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".