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Record W6981028374

Design of a Simulated Human-Rover Interaction Spacewalk Experiment in the LUNA Facility

2023· dissertation· en· W6981028374 on OpenAlexaboutno aff

Bibliographic record

Venueelib (German Aerospace Center) · 2023
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtease and Inhibitor Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsWater iceMoon landingRocket (weapon)International Space StationSpace researchStage (stratigraphy)HabitabilityLiquid waterEarth's orbitSpace explorationScientific instrument
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.025
GPT teacher head0.325
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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