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Record W4309786429 · doi:10.3390/photonics9110882

Optically Powered and Controlled Drones Using Optical Fibers for Airborne Base Stations

2022· article· en· W4309786429 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

VenuePhotonics · 2022
Typearticle
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsBroadcom (Canada)
Fundersnot available
KeywordsDroneBase stationComputer scienceRadio frequencyGround stationTransmission (telecommunications)Real-time computingRemote sensingTelecommunicationsEnvironmental scienceEngineeringAerospace engineeringSatelliteGeography

Abstract

fetched live from OpenAlex

Mobile communication services are crucial during emergency disasters and temporary events, and future mobile communication systems should be able to provide such services. Airborne base stations using drones are highly effective as stand-in base stations in areas where the ground base stations are inoperable or at temporary event sites. However, it is difficult for conventional drones to provide mobile communication services without interruption due to flight time limitations caused by their limited battery capacity. Thus, a drone drive with a non-interrupted power supply is urgently needed. In this study, we developed an airborne base station that enables drones to be driven and maneuvered by optical fibers. We simultaneously transmitted radio frequency (RF) data signals for the airborne base station and control signals for the drone and evaluated the transmission performances of the RF signals and the controllability of the drone. Furthermore, we conducted a flight experiment on a medium-sized drone powered by optical fibers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.298
Threshold uncertainty score0.358

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)0.0000.000

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 head0.012
GPT teacher head0.227
Teacher spread0.216 · 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