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Record W4390690723 · doi:10.1155/2024/1804375

A High-Gain Wideband Fabry-Pérot Antenna Employing a Water-Based Frequency Selective Surface for Polarization- and RCS-Reconfigurability

2024· article· en· W4390690723 on OpenAlexaff
Ying Wang, Zhiming Liu, Huilin Zhou, Jens Børnemann, Yuhao Wang, Kong Xiang

Bibliographic record

VenueInternational Journal of RF and Microwave Computer-Aided Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Victoria
FundersNatural Science Foundation of Jiangxi ProvinceHuaqiao UniversityMinistry of Education of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsWidebandReconfigurabilityOpticsMaterials scienceRadomePolarization (electrochemistry)Antenna (radio)OptoelectronicsPhysicsElectrical engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

A Fabry-Pérot (FP) antenna with wideband dual-polarization and radar cross section- (RCS-) reconfigurability adopting water-based frequency selective surface (FSS) is presented. The feed antenna is designed to switch between horizontal polarization (HP) and vertical polarization (VP) by controlling the on/off states of two PIN diodes. By optimizing the height of the FP resonant cavity, the proposed FP antenna achieves wideband and high-gain properties. In addition, the incident wave can be absorbed when the polymethyl methacrylate (PMMA) container is filled with water, which reduces the monostatic RCS. This design can be selected to be in stealth mode or radiation mode by injecting water into or extracting water from the water container. The measured results indicate that in stealth mode, the 10-dB RCS reduction band is from 4.5 GHz to 6.4 GHz, and the peak RCS reduction is 31.5 dB at 5.2 GHz. In the radiation mode, the realized gains of the FP antenna in both HP and VP at 5.2 GHz reach 16.6 dBi. Simulated and measured results verify the realizability and operational concept of the designed FP antenna.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.504
Threshold uncertainty score0.808

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 head0.008
GPT teacher head0.220
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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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