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Record W3205182480 · doi:10.1109/tap.2021.3118816

Dual-Polarized Patch Antenna Excited Concurrently by a Dual-Mode Substrate Integrated Waveguide

2021· article· en· W3205182480 on OpenAlexafffund
Amir Afshani, Ke Wu

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsDual modeElectrical impedanceWaveguidePhysicsExcited stateAntenna (radio)Bandwidth (computing)Patch antennaOpticsComputer scienceElectronic engineeringTelecommunicationsEngineeringAtomic physics

Abstract

fetched live from OpenAlex

In this work, we propose and demonstrate an integrated dual linearly polarized cavity-backed patch antenna, which is excited by a concurrent dual-mode substrate integrated waveguide (SIW) structure. For the first time, we have exploited the TE10and TE20modes of a traveling wave in a dual-mode SIW configuration to excite the horizontal and vertical polarizations of the patch antenna. The proposed methodology yields a low-profile structure with a high interport isolation thanks to the inherent symmetry of the structure. Measurement results indicate an isolation performing better than 42 dB over a 600 MHz impedance bandwidth of the antenna. Moreover, a methodology is developed to implement transitions from the dual-mode SIW to the two different TEM transmission lines for separate but concurrent excitations of the device. Detailed field and impedance analyses are presented to explain the operation principle of the proposed antenna, which are supported by an excellent agreement between the obtained measurement and simulation results.

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 score0.002

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 science0.0010.000
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 head0.011
GPT teacher head0.222
Teacher spread0.211 · 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

Citations15
Published2021
Admission routes2
Has abstractyes

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