Hypervelocity Impact Tests on Ejecta and its International Stabdarization
Bibliographic record
Abstract
Although a large spacecraft such as the International Space Station and other artificial satellites arelaunched in the earth orbit thanks to the remarkable progress in the space development, their collisions with orbital debris are an increasing concern. To examine the impact protection performance of spacecraft against orbital debris, hypervelocity impact experiments using a two-stage light gas gun and so on are necessary. There has been an active facility cross calibration program between space agencies, where tests were performed using identical targets and test conditions for each pair of tests, to assure that the results were comparable. Six distinct facility cross calibration testing campaigns have been performed between NASA and gun ranges in Germany, Russia, Japan, France, China and Canada. The test conditions for individual campaigns were negotiated at different times, so the target configuration varied between different campaigns. Projectiles with a diameter of 1 mm were used to simulate orbital debris impacting a target at velocity of 5 km/s. Copper witness plates were used as witness plates to catch the secondary debris, namely ejecta, generated due to hypervelocity impacts. The size distributions of diameter of craters made by ejecta were measured on the witness plates, and they are compared one another among a solar array coupon, CFRP honeycomb and Aluminum honeycomb in this study.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".