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Record W3215852392 · doi:10.3390/electronics10222845

Air and Metal Vias Combined Metamaterial-Based Lens for Radiation Performance Enhancement of Short-Pulse Tapered Slot Antenna

2021· article· en· W3215852392 on OpenAlexaff
Yifan Yin, Qiuyi Zhang, Shunli Li

Bibliographic record

VenueElectronics · 2021
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsPolytechnique Montréal
FundersNational Natural Science Foundation of China
KeywordsMain lobeOpticsMetamaterialMaterials scienceRadiation patternSide lobeSlot antennaLens (geology)Antenna (radio)OptoelectronicsAntenna gainAntenna efficiencyPhysicsTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

A post-wall-structured tapered slot antenna (PWTSA) loaded with an embedded metamaterial-based lens (metalens) is proposed and investigated for short-pulse applications. The proposed embedded metalens consists of not only a metallic delay lens, but also an airy acceleration region surrounding the lens, which is different from the conventional metalenses used in various tapered slot antennas and can exhibit a small equivalent permittivity. Therefore, the embedded metalens has a large usable range of permittivity and does not increase the size of the original prototype PWTSA. The post-wall structure and metalens of the proposed antenna can help it achieve a comprehensive and balanced performance, including a high fidelity, low cross-polarization, stable main-lobe direction and gain enhancement. The simulated and measured results show that without any increase in antenna size, the proposed antenna enhances the realized gain by 6.4 dB over the frequency range from 9 to 26 GHz and achieves a stable radiation with the offset of the main-lobe direction being confined to 2° up to 24.9 GHz. Furthermore, the cross-polarization levels are less than −20 dB and the fidelity is kept high for the short-pulse radiation with the frequency spectrum up to 30 GHz.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.392
Threshold uncertainty score0.538

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 head0.010
GPT teacher head0.210
Teacher spread0.199 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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