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Record W2564990470

The Frequency and Modal System Identification of the Balloon-borne Imaging Testbed

2015· dissertation· en· W2564990470 on OpenAlexaboutno aff
Lun Steven Li

Bibliographic record

VenueTSpace (University of Toronto) · 2015
Typedissertation
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsnot available
Fundersnot available
KeywordsTestbedModalTelescopeRemote sensingBalloonAerospace engineeringEngineeringModal analysisPhysicsComputer scienceAcousticsOpticsGeography
DOInot available

Abstract

fetched live from OpenAlex

The Balloon-borne Imaging Testbed (BIT) is a stratospheric ballooning astronomy project that was successfully launched on September 18, 2015 from Timmins, Ontario to an altitude of 36 km over the period of one night. The design of the BIT gondola must allow the on-board telescope to maintain a constant position for an extended period of time during which the structure is subjected to external forces. Oscillations created by resonances of the gondola can prove detrimental to the resulting image quality. Therefore, the development of a modal analysis method for the gondola is explored in this thesis. This method combines simulation with measurement results to identify the modes of the system. For the gondola's external frame, the four lowest natural frequencies and their corresponding mode shapes are identified. BIT represents a new generation of balloon-borne telescopes that can produce images comparable to those of space telescopes but at significantly lower costs.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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