An Overview of Ground Semi-Physical Verification Technology for On-Orbit Services
Bibliographic record
Abstract
On-orbit servicing is generally referred to as space assembly, maintenance, and service for spacecraft life extension of capacity enhancement in space by humans, robots, or both. The United States, Japan, Canada and ESA have included on-orbit service technologies in their space development plans. In order to make the on-orbit service system developed to adapt to space environment effects such as microgravity, thermal vacuum, and irradiation, full test verification on the ground is an effective measure to improve the success rate of space flight. In the orbital service task, docking and arresting are the key links which need to be verified on the ground. For such verification, semi-physical simulation methods are generally used abroad, and the motion of the space mechanism in microgravity environment is caculated by a precise dynamic model, and then the motion is realized by a prototype in three dimensional space. Compared with other microgravity simulation methods, the semi-physical simulation method has the advantages of low cost, good flexibility and scalability, can simulate three dimensional motion in microgravity environment, and has no time limit, which is a important test method for future on-orbit service technology. At present, the major space agencies and companies in United States and ESA all adopt semi-physical simulation methods. This paper investegates and summarizes the related projects, systems, key technologies and application methods, which can provide reference for relevat work.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".