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Record W2465171079 · doi:10.1109/lawp.2016.2588440

Pencil-Beam Single-Point-Fed Dirac Leaky-Wave Antenna on a Transmission-Line Grid

2016· article· en· W2465171079 on OpenAlexaff
Ayman H. Dorrah, George V. Eleftheriades

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLeaky wave antennaOpticsPencil (optics)Transmission lineAntenna (radio)ScatteringBeam (structure)PhysicsGridDipole antennaMathematicsElectrical engineeringEngineeringMicrostrip antennaGeometry

Abstract

fetched live from OpenAlex

Leaky-wave antennas (LWAs) are widely used as single-point-fed linear antenna arrays. The extension of LWAs to two dimensions (2-D) implies that they can be used as single-point-fed 2-D antenna arrays without requiring a complex feeding network. However, generating a pencil beam from 2-D LWAs is not straightforward, and due care has to be taken for the design of the LWA. On the other hand, transmission-line (TL) grids have demonstrated interesting behaviors, such as an effective negative refractive index and growing of evanescent waves. In this letter, a single-point-fed TL-grid 2-D Dirac leaky-wave antenna (DLWA) design is proposed that generates a pencil beam at both broadside and slightly tilted angles. The TL-grid unit cell is analytically treated in light of its scattering and impedance matrices. The optimized TL-grid unit cell is shown to exhibit a closed bandgap in the dispersion relation that is also linearly varying with frequency (hence, it is a DLWA). The proposed 2-D DLWA design is fabricated, and the experimental results are presented.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.022
GPT teacher head0.215
Teacher spread0.193 · 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

Citations16
Published2016
Admission routes1
Has abstractyes

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