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Record W2563319616 · doi:10.1109/lawp.2016.2639586

A Dual-Layer Planar Leaky-Wave Antenna Designed for Linear Scanning Through Broadside

2016· article· en· W2563319616 on OpenAlexaff
Davide Comite, Symon K. Podilchak, Paolo Baccarelli, Paolo Burghignoli, Alessandro Galli, A.P. Freundorfer, Yahia M. M. Antar

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsRoyal Military College of CanadaRoyal Ottawa Mental Health CentreQueen's University
Fundersnot available
KeywordsLeaky wave antennaBroadsideOpticsAntenna (radio)Radiation patternMicrostrip antennaPlanarGratingAcousticsPhysicsMaterials scienceComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

A low-cost planar leaky-wave antenna (LWA) offering directive antenna beam patterns as well as linear beam scanning through broadside is proposed. The design is based on a one-sided annular slot grating placed on a dual-layer grounded dielectric slab with an integrated TM <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> antenna feed system in the bottom ground plane. By appropriate selection of the width and the period of the top radiating annular slots, as well as the substrates, the structure is optimized to excite and perturb the dominant TM mode for the generation of directive beam patterns that can scan with frequency in the far field. In particular, the antenna parameters are chosen to obtain a narrow open stopband frequency range at broadside as well as control the dispersive scanning behavior. A method-of-moments dispersion analysis has also been developed to assist in the design and fully characterize the proposed antenna. These results are complemented by full-wave simulations and measurements of a LWA prototype offering realized gain values in excess of 14 dBi with scanning through broadside at 23.5 GHz.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.534
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.024
GPT teacher head0.228
Teacher spread0.203 · 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 teacher head, 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

Citations32
Published2016
Admission routes1
Has abstractyes

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