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Record W2557222598 · doi:10.31274/ahac.5602

Design of an Attitude Control System for a High-Altitude Balloon Payload

2013· article· en· W2557222598 on OpenAlexaffabout
David Evan Zlotnik, James Richard Forbes, Nguyen Khoi Tran

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicScientific Research and Discoveries
Canadian institutionsMcGill University
Fundersnot available
KeywordsPayload (computing)Attitude controlAerospace engineeringObservatoryInertial measurement unitComputer scienceCosmic microwave backgroundSimulationPhysicsEngineeringAstronomyOptics

Abstract

fetched live from OpenAlex

Physicists seek to uncover expansion history of the universe by examining polarization patterns in the cosmic microwave background (CMB). Observatories in the South Pole are used to examine these polarization patterns, but no sucient celestial source exists to properly calibrate these observatories. A method of calibrating these observatories would be to point a tuned microwave source to the ground-based observatory from a far distance. The purpose of this paper is to outline how the McGill High-Altitude Balloon (McHAB) team intends to use a high-altitude balloon with a reaction wheel actuator to slew and point the payload at altitudes of 15km to 20km to within an accuracy of ±2°. A complementary filter with an inertial measurement unit (IMU) will be used to estimate the attitude of the payload. A proportional-derivative controller will be used to control the reaction wheel, and hence point the payload to a desired angle. The main processor will be a Raspberry Pi, a single-board computer using a single-core 700MHz ARMv6 CPU. The team has previously own a payload, McHAB-1, containing the Raspberry Pi and an IMU to test the attitude estimator and collect attitude data. The team has own a complete prototype of the system, McHAB-2, on April 28th, 2013. Data from this flight is presented showing that the reaction wheel can better point the system as the platform reaches higher altitudes.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.756
Threshold uncertainty score0.956

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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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

Citations2
Published2013
Admission routes2
Has abstractyes

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