New Materials for LEO, GEO and Planetary Environments: Preliminary Results from MISSE-17 Experiment
Bibliographic record
Abstract
Abstract ITL developed coatings and surface treatments that include prototypes of multifunctional coatings with charge-dissipative and dust mitigation properties, including Lunar dust Work Function Matching Coating (WFMC) that can be applied to surfaces of various space materials. Selected samples were flown on MISSE-17 Material Experiment, to evaluate their resistance to space environmental factors like thermal cycling, vacuum, VUV and atomic oxygen. Two thermal control materials, i. e. a 1 mil KaptonHN with a doped diamond-like coating (DLC) on it and an aluminized 1 mil KaptonHN with a DLC film on it were selected for the ram direction. For the wake position, a doped DLC on a 1 mil aluminized KaptonHN and a doped DLC on a silver-coated Teflon FEP were selected, to check the influence of both the thermal cycling and UV exposure. In addition, two samples of Lunar dust Work Function Matching Coating (WFMC) on Kapton HN (WFMC/Kapton) were prepared and added, one in ram direction and one in wake direction. The MISSE-17 Experiment was launched from Kennedy Space Centre on March 15, 2023 and returned on SpX-29 flight in December 2023. Samples faced atomic oxygen fluence of ~8.0E+20 atoms/cm2 in ram direction and 3.76E+18 atoms/cm2 in wake direction over 145 days and 157 days, respectively. The results discussed in this paper include weight and surface morphology changes, the thermal optical properties and surface analysis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".