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Record W2593835572

A high-gain planar array using parallel Yagi-Uda antenna

2006· article· en· W2593835572 on OpenAlexaff
Mahyar Modaresi, L. Shafai

Bibliographic record

VenueInternational Symposium on Antenna Technology and Applied Electromagnetics · 2006
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsBeamwidthAntenna (radio)Antenna gainRadiation patternAntenna measurementDirectivityDipole antennaAcousticsOpticsCoaxial antennaAntenna factorPhysicsElectrical engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

In this paper the design of a high-gain planar antenna is presented. The antenna is designed to operate at 15GHz and has 24dB directivity. This design basically consists of four high-gain Yagi-Uda arrays, which are placed in parallel on a broad-side configuration and are excited uniformly through a feed network. The feed network and the antenna elements are all printed on the same substrate with the dimensions of 14cm × 20cm. The excitation is through a SMA connector with a 50ω coaxial cable. The 3dB beamwidth of the radiation pattern, in the E-plane, is 5 degrees while the H-plane beamwidth is 20 degrees. The system is impedance matched to 50ω at 15GHz.

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)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.005
GPT teacher head0.193
Teacher spread0.188 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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