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Record W3163988716 · doi:10.11159/ffhmt21.137

Thermal Analysis of a 3U-Cubesat, a Case Study of Pakal Satellite

2021· article· en· W3163988716 on OpenAlexvenueno aff
Gabriel Salazar-Salinas, Estefanía Botello-Ramírez, Edgar Avalos-Gauna

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2021
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsCubeSatSatelliteComputer scienceRemote sensingSatellite trackingEnvironmental scienceAerospace engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

The current study presents the thermal control design of the 3U Cubesat "Pakal" from "Misión Colibrí".As part of the design requirements, a thermal analysis was carried out to study the effects of the space environment over the spacecraft.This was achieved by making a lumped parameter approach in the critical thermal cases and studying the effects of environmental heat fluxes on each critical component.The model was verified using Thermal Desktop software.Then a Passive Thermal Control (PTC) was proposed to keep critical components at their operating temperature.The proposed PTC consists of two types of passive control: thermal coatings and single-layer insulation.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.236
Teacher spread0.214 · 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 designNot applicable
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

Citations4
Published2021
Admission routes1
Has abstractyes

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