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Lunar Volatile and Mineralogy Mapping Orbiter 12U Cubesat

2020· article· en· W3120774211 on OpenAlexaff
Roman V. Kruzelecky, Piotr Murzionak, Qi-Yang Peng, Paul Burbulea, Ian Sinclair, Gregory W. Schinn, Yang Gao, Craig Underwood, Christopher Bridges, Roberto Armellin, Andrea Luccafabris, E. A. Cloutis, Alexis Parkinson, Brynn Dagdick, M. G. Daly, Amélie St-Amour, Jean de Lafontaine

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsNGC Aerospace (Canada)York UniversityUniversity of WinnipegMPB Technologies & Communications (Canada)
Fundersnot available
KeywordsPayload (computing)OrbiterCubeSatAstrobiologyPhysicsAerospace engineeringSatelliteComputer scienceEngineering

Abstract

fetched live from OpenAlex

Finding suitable quantities of key resources for life-support and refueling is vital to future sustained lunar manned bases and commercial activities. There are large uncertainties in the lunar near-surface distribution of water ice volatiles and relevant in-situ resources, such as ilmenite (FeTiO3). Moreover, planned future lunar orbiter missions have relatively limited spatial resolution, in the km range, for the volatile mappings relative to typical lander and rover range capabilities, especially for operations within the lunar Permanently Shadowed Regions (PSRs) that could shelter accumulated water ice deposits. VMMO, for Volatile and Mineralogy Mapping Orbiter, is a low-cost 20 kg 12U Cubesat that comprises the Lunar Volatile and Mineralogy Mapper (LVMM) multi-wavelength chemical lidar science payload, the Compact LunAr Ionizing Radiation Environment(CLAIRE) monitoring payload, a COTS electronics test bed, and the supporting 12U Cubesat bus with propulsion, direct to Earth S-band and 1560 nm optical communications, on board data processing and a suite of altitude and pointing sensors for semiautonomous vision-assisted navigation from lunar orbit. VMMO will most likely be deployed from a commercial lunar transportation provider, such as Astrobotics, into a suitable near-polar injection orbit. The on-board propulsion will be used to achieve a stable lunar frozen orbit for the subsequent science operations with a perilune over the south pole under 100 km to assist the LVMM volatile and mineralogy mappings. The compact LVMM is a multi-wavelength Chemical Lidar (<6.1 kg) which will use single-mode (SM) fiber lasers emitting simultaneously at 532 nm, 1064 nm and 1560 nm. This will permit stand-off mapping of the lunar water ice distribution using active laser illumination, with a focus on selected permanently-shadowed craters in the lunar south pole;Shackleton, Faustini and Cabeus. This combination of selected laser spectral channels can provide very sensitive discrimination of water/ice in various types of Mare and Highland regolith, based on breadboard validation. The use of the SM fiber lasers enables a small laser beam divergence to provide high spatial resolution in the 10 m range at the lunar surface. There is some relevant flight heritage as part of the Fiber Sensor Demonstrator (FSD) payload on ESA’s Proba-2 spacecraft that is still operational after more than 10 years in low earth orbit. LVMM can also be used in a passive multispectral mode at 300 nm, 532 nm, 1064 nm and 1560 nm to map the lunar ilmenite in-situ resource distribution during the lunar day using the characteristic surface-reflected solar illumination. By combining the passive lunar day measurements with the active lunar night measurements, some new insights into the lunar diurnal water cycle should be possible. This paper discusses the VMMO science requirements and the supporting 12U Cubesat platform and LVMM multiwavelength chemical lidar payload and some of the associated design trade-offs.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0440.027

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 head0.023
GPT teacher head0.200
Teacher spread0.177 · 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
Published2020
Admission routes1
Has abstractyes

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