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Record W2998520424 · doi:10.2514/6.2020-1205

A CubeSat-based Robotic Asteroid Sampling Mission

2020· article· en· W2998520424 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAIAA Scitech 2020 Forum · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCubeSatTouchdownSpacecraftSampling (signal processing)Aerospace engineeringComputer scienceAsteroidSimulationEngineeringAstrobiologyPhysicsComputer visionSatellite

Abstract

fetched live from OpenAlex

In this paper, a novel approach is developed for the orbital design and control of a touch-and-go sampling maneuver on a tumbling asteroid, utilizing a small spacecraft launched from a mothership. The approach considers a mothership performing operations and hovering in the vicinity of a target asteroid. After sufficiently mapping the asteroid surface, a safe sampling location of scientific interest is determined. The mothership launches a CubeSat equipped with a deployable sampling mechanism. Utilizing its onboard thrusters and reaction wheels, a simultaneous attitude and orbital controller is implemented to perform the orbital approach, descent, and sampling maneuvers. The controller maneuvers the CubeSat into the vicinity of the target touchdown location, while simultaneously aligning the CubeSats attitude such that the deployable sample collection system can be used. After the touchdown is completed, the CubeSat, employing the same controller, would perform a return maneuver to the mothership. Simulation scenarios for the proposed approach are performed on a target asteroid for a predetermined sampling location and the results are presented and discussed.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.791
Threshold uncertainty score0.924

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)0.0010.000

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 head0.024
GPT teacher head0.243
Teacher spread0.220 · 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