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Record W4409968474 · doi:10.1155/ijae/7452955

Conceptualization of a Small, Uncrewed, Microwave‐Powered Aircraft

2025· article· en· W4409968474 on OpenAlexafffund
S. S. Jagtap, Maya Rahaman-Noronha, William D. Kemp, Marco A. Antoniades, Goetz Bramesfeld

Bibliographic record

VenueInternational Journal of Aerospace Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsToronto Metropolitan University
FundersMitacs
KeywordsConceptualizationMicrowaveAeronauticsEngineeringAerospace engineeringComputer scienceTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

This work conceptualizes wireless power transfer (WPT) between a tracking stationary ground transmitter and a mobile aircraft. Initial ground tests are performed to determine the viability of the Friis equation, using rectennas developed for operation at 2.45 GHz at 4 dBm of optimal input power. An achieved rectifier operation efficiency of 59% is reached at this power level. A model for analyzing received power during WPT tests in flight conditions is then developed and applied for various flight paths possible with a small, electric UAV, using the designed rectenna for power conversion on the aircraft. A maximum of 429 mW is received when the aircraft is flown in a circular path centered around the ground transmitter, with a radiated power of 7 kW. Various other loiters are also analyzed, including racetrack, elliptical, and infinity‐shaped loiters. The racetrack and elliptical loiters performed similarly with an approximate transmitted power of 4.5 kW and received power of 300 mW. The infinity loiter performed the worst with only 150 mW of received power with nearly 8.2 kW transmitted, which occurred primarily due to polarization mismatch between the transmitter and rectenna.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.005
GPT teacher head0.201
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations0
Published2025
Admission routes2
Has abstractyes

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