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Record W2991052802 · doi:10.1142/s2301385020500077

Self-Powered Solar Aerial Vehicles: Towards Infinite Endurance UAVs

2019· article· en· W2991052802 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueUnmanned Systems · 2019
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Dynamics and Control
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTrajectoryComputer scienceLift (data mining)Solar energySolar powerPower (physics)Automotive engineeringAerospace engineeringEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

A self-powered scheme is explored for achieving long-endurance operation, with the use of solar power and buoyancy lift. The end goal is the capability of “infinite” endurance while complying with the Unmanned Aerial Vehicle (UAV) dynamics and the required control performance, maneuvering, and duty cycles. Nondimensional power terms related to the UAV power demand and solar energy input are determined in a framework of Optimal Uncertainty Quantification (OUQ). OUQ takes uncertainties and incomplete information in the dynamics and control, available solar energy, and the electric power demand of a solar UAV model into account, and provides an optimal solution for achieving a self-sustained system in terms of energy. Self-powered trajectory tracking, speed and control are discussed. Aerial vehicles of this class can overcome the flight time limitations of current electric UAVs, thereby meeting the needs of many applications. This paper serves as a reference in providing a generalized approach in design of self-powered solar electric multi-rotor UAVs.

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.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score1.000

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)0.0000.001

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 head0.004
GPT teacher head0.176
Teacher spread0.173 · 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