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Record W7132903641

Drift Recovery and Station Keeping for the CANX-4 and CANX-5 Nanosatellite Formation Flying Mission

2015· dissertation· W7132903641 on OpenAlexaboutno aff
Josh Zachary Newman

Bibliographic record

VenueTSpace · 2015
Typedissertation
Language
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsnot available
Fundersnot available
KeywordsSpacecraftController (irrigation)Control systemSpacecraft designControl (management)Component (thermodynamics)
DOInot available

Abstract

fetched live from OpenAlex

Canadian Advanced Nanospace eXperiments 4 5 (CanX-45) are a pair of formation flying nanosatellites that demonstrated autonomous sub-metre formation control at ranges of 1000 to 50 m. To facilitate the autonomous formation flight mission, it is necessary that the two spacecraft be brought within a few kilometres of one another, with a low relative velocity. Therefore, a system to calculate fuel-efficient recovery trajectories and produce the corresponding spacecraft commands was required. This system was also extended to provide station keeping capabilities In this thesis, the overall drift recovery strategy is outlined, and the design of the controller is detailed. A method of putting the formation into a passively safe state, where the spacecraft cannot collide, is also presented. Monte-Carlo simulations are used to estimate the fuel losses associated with navigational and attitude errors. Finally, on-orbit results are presented, validating both the design and the error expectations.

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)0.000
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: Methods · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

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 head0.017
GPT teacher head0.295
Teacher spread0.279 · 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
GenreMethods

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
Published2015
Admission routes1
Has abstractyes

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