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Record W7132895258

The design and development of a propulsion system for the CanX-2 and CanX-4/-5 nanosatellite missions

2006· dissertation· W7132895258 on OpenAlexaboutno aff
Stephen Mauthe

Bibliographic record

VenueTSpace · 2006
Typedissertation
Language
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsPropulsionPayload (computing)In-space propulsion technologiesSpacecraftSpacecraft designSatelliteGlobal Positioning SystemKey (lock)
DOInot available

Abstract

fetched live from OpenAlex

At the University of Toronto's Space Flight Laboratory, two satellites, CanX-4 and CanX-5, are currently in development for the purpose of demonstrating autonomous formation flying using nanosatellite buses. The use of a nanosatellite bus offers a cost-effective means to investigate the possibility of future satellite missions involving multiple spacecraft that together act as a single mission spacecraft for coordinated observations, in situ measurements, or virtual instrumentation. In order for formation flying to take place, a novel cold-gas propulsion system called the Canadian Nanosatellite Advanced Propulsion System (CNAPS), is also being developed to provide the thrust needed for formation maintenance and augmentation. The technologies and design schemes used in CNAPS stem from the Nanosatellite Propulsion System (NANOPS), which was built as a technology demonstrator on the CanX-2 nanosatellite. While NANOPS is a key payload on CanX-2, several other key enabling technologies are being tested in an attempt to mitigate risk and improve the reliability of the critical components intended for the CanX-4/-5 mission such as a GPS receiver and antenna, attitude control system, and CMOS imaging system. The details of the design and testing as well as recommendations for further development for both NANOPS and CNAPS are 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.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.024

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.021
GPT teacher head0.269
Teacher spread0.249 · 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 designBench or experimental
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
Published2006
Admission routes1
Has abstractyes

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