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Record W2162011043 · doi:10.1109/lawp.2011.2109930

Modeling and Design of Millimeter-Wave High $Q$-Factor Parallel Feeding Scheme for Dielectric Resonator Antenna Arrays

2011· article· en· W2162011043 on OpenAlexaff
Wael M. Abdel‐Wahab, Safieddin Safavi‐Naeini, Dan Busuioc

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsDielectric resonator antennaHFSSAntenna (radio)Reflection coefficientRadiation patternExtremely high frequencyAntenna efficiencyAntenna measurementOpticsResonatorAcousticsElectronic engineeringComputer sciencePhysicsEngineeringMicrostrip antennaTelecommunications

Abstract

fetched live from OpenAlex

A planar, high-Q-factor, low-cost, and low-profile waveguide feeding scheme, based upon the substrate integrated waveguide (SIW) concept, for a rectangular dielectric resonator antenna (RDRA) at the millimeter wave (mmW) band is presented. It helps to enhance the overall antenna radiation efficiency and avoid any disturbance caused by conventional feeding schemes. Furthermore, a simple transmission line (T.L.) circuit model is proposed as an easy method to calculate the antenna reflection coefficient and radiation pattern (gain). As an example, a 1 × 8 linear antenna array is used to validate the usefulness of the feeding scheme and the proposed T.L. circuit model. The simulated results obtained by the circuit model are presented in this letter and compared to those calculated by the full-wave numerical (HFSS) solver.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.037
GPT teacher head0.204
Teacher spread0.167 · 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

Citations14
Published2011
Admission routes1
Has abstractyes

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