Design of a Simulated Human-Rover Interaction Spacewalk Experiment in the LUNA Facility
Bibliographic record
Abstract
With the Artemis Program, NASA intends to return to the Moon with humans. This time, however, with partners such as Canada, Japan, and Europe, and this time to stay. While Stage 1 of the program focuses on constructing the Lunar Gateway, a mini space station in a lunar orbit whose modules are already in production in the US and Europe, Stage 2 aims at returning to the lunar surface. This time for more extended periods, as seen in the Apollo missions. \nThe focus of attention is on the lunar south pole, where water ice is to be expected in regions called Permanent Shadowed Regions (PSR), usually found inside craters. Water ice is an essential element to establish a long-term presence on lunar soil since it can be used for various applications such as drinking water, creating oxygen, and hydrogen for rocket fuel. \nTo detect regions where increased amounts of water ice is to be found, rovers can be used to collect samples and retrieve them from the PSR, which might be too cold for explorations by astronauts. However, astronauts could be used to sort the collected samples, and analyze them, if they contain water ice and, therefore, be sent back to Earth for further inspection. \nThis thesis looks at the possibility of such an inspection done by astronauts by: First analyzing the thermal environment of such an inspection and secondly by computing the maximum allowed time an astronaut has for such an inspection before the potential water ice in the sample starts to sublimate. With this objective in mind, a MATLAB script was implemented with multiple variable parameters, which can be adjusted if conditions deviate from those assumed in this thesis. With the assumed conditions described in this thesis, the heat-up time of the samples from 90K to 130K is computed to be approximately 20min. \nIn the second part of this thesis, an EVA procedure was created to simulate such a scenario inside LUNA facility, a Moon analogue facility currently under construction at the DLR campus in Porz-Wahn. With that first-of-its-kind prototype EVA procedure for the LUNA facility, that analogue environment could be tested on its operational capabilities during commissioning. \nFinally, this thesis recommends adjustments and enhancements to the LUNA facility that would ease the execution of simulated EVAs inside. Additionally, it names preconditions and requisites, such as tools needed to perform the proposed EVA.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".