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Record W4395028420 · doi:10.1109/ojap.2024.3392628

Fully Embedded Dual-Element Dielectric-Based Antenna for Sub- and Terahertz Applications

2024· article· en· W4395028420 on OpenAlexafffund
Ehsan Rahmati, Pascal Burasa, Elham Baladi, Mohammad S. Sharawi

Bibliographic record

VenueIEEE Open Journal of Antennas and Propagation · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTerahertz radiationDielectricAntenna (radio)Dual (grammatical number)Element (criminal law)Materials scienceElectronic engineeringOptoelectronicsComputer scienceTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

A completely embedded, planar, dual-element dielectric based antenna directly fed by a substrate integrated insulated guide within the same layer in Sub-THz band is presented in this paper. A dielectric layer is employed to make the structure stable. The proposed structure is compatible with the standard planar millimeter-wave and terahertz manufacturing technologies. To minimize the reflection loss, matching air holes inside the guiding channel of the waveguide and air holes with a smaller perforation radius surrounding the antenna are created. The proposed compact antenna, which has been successfully tested, covers the frequency range of 234.5-278.1 GHz with a measured impedance bandwidth of 17.01%, a proper simulated average radiation efficiency of 93.6%, and a maximum gain as well as average gain of 16.08 dBi and 12.56 dBi from measurement results, respectively. Because of these features, the suggested antenna would be a great candidate for short-range wireless applications in Sub-THz frequency bands.

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: none
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.014
GPT teacher head0.251
Teacher spread0.238 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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