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Record W4241279723 · doi:10.1109/nrsc.2017.7926476

Overview of some future trends in antenna research

2017· article· en· W4241279723 on OpenAlexaff
Yahia M. M. Antar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsQueen's UniversityRoyal Military College of Canada
Fundersnot available
KeywordsGNSS applicationsComputer scienceWirelessAntenna (radio)TelecommunicationsCommunications satelliteWidebandGlobal Positioning SystemSatelliteEngineeringElectrical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

The growing of wireless communications has been an important additional driving force for the advancement of antenna technology. Antennas are the "eyes" and "ears" of communications systems, radars, satellites and other sensors. They are used in wide range of applications from terminal devices (such as mobile phones), to advanced communication systems on aircrafts, ships, vehicles, medical applications, remote sensing, global navigation satellite systems(GNSS), and so on. Strong demands like small physical size, low weight, low cost, wideband and multiband, reconfigurable capabilities, medical or even aesthetic considerations are increasingly required as essential for modern antenna designs. Furthermore, the promise of 5G will expand the possibilities of what mobile networks can do and will drive the future evolution of the internet itself. All of these aspects will put more demands for new innovations in antennas.

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)0.002
metaresearch head score (Gemma)0.003
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: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0120.011

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 head0.111
GPT teacher head0.366
Teacher spread0.255 · 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
GenreReview

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
Published2017
Admission routes1
Has abstractyes

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