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Record W4225272863 · doi:10.36227/techrxiv.19657509.v1

Wideband Reconfigurable Reflectarray based on Reflector-backed Second-order Bandpass Frequency Selective Surface

2022· preprint· en· W4225272863 on OpenAlexaff
Huan Li, Xiaokang Qi, Tianyi zhou, Zhiwei Xu, Tayeb A. Denidni

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Science Foundation of Zhejiang ProvinceNational Natural Science Foundation of China
KeywordsWidebandReflector (photography)Center frequencyCapacitive sensingBandwidth (computing)Materials scienceBand-pass filterOpticsMicrowaveWirelessOptoelectronicsElectrical engineeringEngineeringTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

In this communication, a wideband reconfigurable reflectarray based on reflector-backed active second-order bandpass frequency selective surface (FSS) is presented. The reflector is composed of periodic short-circuited parallel plate waveguide (PPW) and the FSS is composed of stacked non-resonant metallic elements separated by thin dielectric substrates. By integrating microwave varactors in the capacitive layers of FSS, more than 270 o continuous phase tunability is achieved within a factional bandwidth of 14%. A one-dimensional reflectarray prototype operating at C band is fabricated and measured. The experimental results show that it can achieve ±55 o beam scanning coverage. Symmetric beam steering is observed due to the center-fed configuration. With advantages of low cost and simple structure, the proposed reflectarray can be potentially used in wideband wireless communication and radar systems.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
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)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.022
GPT teacher head0.264
Teacher spread0.242 · 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 designSimulation or modeling
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

Citations1
Published2022
Admission routes1
Has abstractyes

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