Regolith adherence characterization (RAC) experiment on the Moon and it’s ground-based simulation: Materials Issues
Bibliographic record
Abstract
Abstract The interaction of Lunar dust and other environmental factors, like vacuum, temperatures, solar radiation, ultraviolet irradiation, and electron (e-) and proton (p+) irradiation with structures on the Moon and the outside of the future Gateway Lunar station may lead to permanent change or complete loss of thermal, optical, and other functionalities that could potentially lead to catastrophic failures. Among these factors, Lunar regolith dust is the most aggressive, causing the main problems. ITL recently initiated a program to evaluate, further develop, and enhance its unique cornerstone Lunar Dust Mitigation Technology (DMT) for sensitive materials on external space structures. In the framework of this program, ITL prepared a set of DMT-treated samples for inclusion in the “Regolith Adherence Characterization (RAC) Payload” funded by NASA and developed and built by Aegis. Regolith Adherence Characterization (RAC) Payload mission will determine how lunar regolith dust sticks to a range of space materials and coatings (collected from NASA, academia, and industry) exposed to the Moon’s environment at different phases of flight caused by 1) landing, and 2) during routine lander operations. To understand the results of the Lunar exposure experiment, an extensive program was initiated at ITL where a set of experiments on interaction of Lunar dust simulants with samples, similar to the RAC Payload experiments on the Moon, will be conducted in ITL’s Vacuum Lunar Dusty Environment Simulator that will be upgraded for this project. This paper will discuss mainly the material problems and their preparation for experiments. The detailed description of the Vacuum Lunar Dusty Environment Simulator and the results obtained in it will be published elsewhere in upcoming publications.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".