MétaCan
Menu
Back to cohort
Record W4410929992 · doi:10.1007/s42496-025-00276-2

Space-Based Solar Power: Implications for Operational Robustness in Lunar EVAs and Exploration Architectures

2025· article· en· W4410929992 on OpenAlexaff
Madelyn MacRobbie, Anna Tretiakova, Vanessa Chen, Chi Ma

Bibliographic record

VenueAerotecnica Missili & Spazio · 2025
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsUniversity of Waterloo
FundersMassachusetts Institute of Technology
KeywordsRobustness (evolution)Aerospace engineeringSpace explorationComputer scienceAstrobiologyEnvironmental scienceSystems engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Human exploration of the lunar surface has large power requirements for both the lunar base and for rover exploration. NASA’s recent contract awards indicate a reliance on fission surface power. While nuclear options provide reliable power to lunar base locations, they have a limited reach that restricts exploration capacity. The Space Exploration Vehicle’s 125-mile range only allows coverage of 0.34% of the lunar surface. A constellation of space-based solar power (SBSP) satellites paired with pressurized rovers allows 24-h, full-surface coverage on excursions from the lunar base. A case study is conducted of the constellation design, system cost, operational lifetime, and power provided using SBSP. Results of the case study demonstrate that SBSP provides an additional 20 kW/h of emergency power and extends EVA range from 125 to 1000 km to cover 26 of the lunar geologic units, at an added lifecycle cost of less than 1% of the baseline mission cost. Addition of a SBSP constellation for rovers provides operational flexibility, safety, and robustness to enable multiple lunar exploration architectures beyond that enabled by surface power infrastructures, and should be further explored for lunar missions.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.784
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.251
Teacher spread0.236 · 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 teacher head, 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAerotecnica Missili & SpazioSame topicSpacecraft Design and TechnologyFrench-language works237,207