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Record W4241145834 · doi:10.32920/ryerson.14664909.v1

Hardware in the Loop Testing of Microsatellite Components

2021· preprint· en· W4241145834 on OpenAlexaffabout
Sarah Hardacre

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMagnetometerSatelliteSpacecraftKalman filterComputer scienceAttitude controlAerospace engineeringSimulationControl theory (sociology)EngineeringPhysicsControl (management)Magnetic field

Abstract

fetched live from OpenAlex

The desire to bring space travel to a wider range of missions and uses has driven the market to using smaller and thus more affordable satellite systems. The Canadian Space Agency is completing the design and construction of a small satellite named QuickSat, which will utilize a magnetometer as one of its attitude and orbit determination instruments. A test bed comprised of three pairs of Helmholtz coils was used for hardware in the loop testing of the magnetometer. Testing was initially completed to prove the capabilities of the test bed, and then was completed to demonstrate the capabilities of the flight qualified magnetometer. The three pairs of Helmholtz coils were driven by data calculated from a spherical harmonic model of the Earth's magnetic field The coils were controlled using a robust controller and the magnetometer was used to drive the B-dot control law in the QuickSat simulation. The Ryerson Attitude and Control Experiment (RACE), which is a small satellite sized platform, free to spin about one axis on a near frictionless air bearing, was utilized to develop and test a system to deal with redundancy of satellite sensors. The possibility of missing, noisy or erroneous output during flight requires that a filter be applied to a satellite's flight code to determine with accuracy the attitude and orbit of the spacecraft. It was thus decided that a Kalman Filter be applied to RACE. The Kalman filter was applied to the RACE simulation successfully and initial hardware testing was carried out.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.342

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 head0.037
GPT teacher head0.240
Teacher spread0.203 · 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 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
Published2021
Admission routes2
Has abstractyes

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