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Record W2552986729 · doi:10.1109/lawp.2016.2627009

Broadband Performance of Novel Closely Spaced Elements in Designing Ka-Band Circularly Polarized Reflectarray Antennas

2016· article· en· W2552986729 on OpenAlexaff
Muhammad M. Tahseen, Ahmed A. Kishk

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsAxial ratioKa bandPhysicsOpticsBroadbandBandwidth (computing)Circular polarizationMicrowaveMicrostripComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Circularly polarized Ka-band reflectarray (RA) antennas are designed and fabricated using novel broadband closely spaced elements. We present the broadband element study for the Ka-band. Three different circularly polarized RA antennas are developed and fabricated from a single dielectric substrate using three distinct elements, which are optimized. All antennas are made of 625 elements that occupy 6.25 × 6.25λ2at 30 GHz. A circularly polarized Ka-band conical horn antenna is used to excite the RA. The measured results exhibit good agreement with the simulations. Each of the proposed antennas provides an overall 30% bandwidth that represents the measured bandwidth of -10-dB reflection coefficients, 1-dB gain variation, and the 3-dB axial ratio. The estimated maximum aperture efficiency is 55% at 30 GHz. Two larger RAs of size 25.25 × 25.25 λ2(10 201 elements) are designed with focalto-diameter (F/D) ratio 0.59 and 2.74, respectively, to show that the path loss is the main reason affecting the gain bandwidth.

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)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.018
GPT teacher head0.229
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

Citations27
Published2016
Admission routes1
Has abstractyes

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