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Record W2946868489 · doi:10.1109/lawp.2019.2910255

60 GHz Substrate Integrated Waveguide-Fed Monolithic Grid Dielectric Resonator Antenna Arrays

2019· article· en· W2946868489 on OpenAlexafffund
Waqas Mazhar, David M. Klymyshyn, Garth Wells, Aqeel A. Qureshi, Michael Jacobs

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationCanadian Light Source
KeywordsMaterials scienceOptoelectronicsDielectric resonator antennaResonatorAntenna (radio)Bandwidth (computing)OpticsElectrical engineeringEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Artificial grid dielectric resonator antenna (GDRA) arrays integrated with longitudinal slot-fed substrate integrated waveguide networks are demonstrated at V-band for wideband and high-gain applications. The effective permittivity of a 500 μm thick, low-permittivity polymethyl methacrylate substrate is locally increased by a factor of 22.4 by embedding groups of micro-sized rectangular metal inclusions, each group functioning as antenna elements and collectively functioning as an antenna array. The monolithic GDRA array mitigates the problem of individual element misalignment over the feed slots while providing precise interelement spacing. GDRA prototypes with 4 × 4 and 8 × 8 array elements fabricated and tested at 60 GHz demonstrate a -10 dB impedance bandwidth of 10.4% and 12.0% with broadside peak realized gain of 15.2 and 19.4 dBi.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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

Citations8
Published2019
Admission routes2
Has abstractyes

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