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Record W4388774101 · doi:10.1515/freq-2023-0174

Profile reduction of folded transmitarray antenna using multiple feeders

2023· article· en· W4388774101 on OpenAlexaff
Guowei Li, Yuehe Ge, Zhizhang Chen

Bibliographic record

VenueFrequenz · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsDalhousie University
FundersNational Natural Science Foundation of China
KeywordsReduction (mathematics)Antenna (radio)Computer scienceElectronic engineeringPhysicsAcousticsTelecommunicationsEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract In this paper, we introduce a novel design for a high-gain, low-profile quad-feed folded transmitarray antenna (FTA) to enable a more compact system. The proposed antenna system consists of a transmitarray, a reflectarray, and four identical planar microstrip U-slot patch antennas placed on the same surface of the reflectarray with an adjacent distance greater than 1 λ . To compare the effectiveness of our design, we developed three different antenna array systems with the same aperture size: a single-feed transmitarray antenna (TA) system, the proposed quad-feed FTA system, and a single-feed FTA system. Our experimental results demonstrate that employing four symmetrical feeders with an adjacent distance of 3.2 λ effectively reduces the height by about 76.7 % in comparison to the height of a single-feed TA, and by 30 % when compared to the height of a single-feed FTA. We also present the design, fabrication, and testing of a prototype of the proposed quad-feed FTA operating in the Ku-band. The measured results of the prototype confirm the effectiveness of our design.

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 score0.003

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.248
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

Citations0
Published2023
Admission routes1
Has abstractyes

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