MétaCan
Menu
Back to cohort
Record W3127495034 · doi:10.1051/e3sconf/202123900006

Solar Power Generation System for the Canadian Space Agency STRATOS Program

2021· article· en· W3127495034 on OpenAlexafffundabout
Amine Doulfikar, Ian Cabales, Akash Hossan, Jeff Bloemink, Pooya Taheri

Bibliographic record

VenueE3S Web of Conferences · 2021
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsBritish Columbia Institute of Technology
FundersCanadian Space Agency
KeywordsPhotovoltaic systemMaximum power point trackingPhotovoltaicsModular designSolar powerElectricity generationAutomotive engineeringEngineeringElectrical engineeringBespokePower (physics)Computer scienceAerospace engineeringVoltageOperating systemPhysics

Abstract

fetched live from OpenAlex

This paper discusses the design and application of solar photovoltaics (PV) under aerospace conditions. The application of solar PV that is addressed is the Power Distribution Unit (PDU) for the Canadian Space Agency’s (CSA) stratospheric balloon (STRATOS) program. The PDU utilizes four 1 kWh Battery Unit (BUs) that have been sized with volume and weight restrictions in mind. Without the capacity to provide enough energy to support multi-day missions, they are thus supplemented by the solar power generation subsystem presented in this paper. The power generation sub-system includes a bespoke solar panel design and a centralized Maximum Power Point Tracking (MPPT) power conversion unit to maximize the power output of solar panels. The centralized unit can accommodate up to eight solar panels, each consisting of nine individual C60 SunPower solar cells. The centralized MPPT unit consists of two MPPT controllers, each controller supporting up to four solar panels. The MPPT unit is modular and can be easily integrated to the CSA’s existing intelligent Battery Management System (BMS).

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.254
Threshold uncertainty score0.511

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0290.004

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 head0.226
Teacher spread0.205 · 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

Citations1
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueE3S Web of ConferencesSame topicsolar cell performance optimizationFrench-language works237,207