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Record W2807215465 · doi:10.1038/s41598-018-23179-8

Towards Millimeter-wavelength: Transmission-Mode Fresnel-Zone Plate Lens Antennas using Plastic Material Porosity Control in Homogeneous Medium

2018· article· en· W2807215465 on OpenAlexafffund
Javad Pourahmadazar, Tayeb A. Denidni

Bibliographic record

VenueScientific Reports · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersInstitut national de la recherche scientifique
KeywordsMillimeterHomogeneousZone plateMaterials scienceOpticsTransmission (telecommunications)WavelengthFresnel zone antennaMode (computer interface)Fresnel lensPorosityOptoelectronicsLens (geology)Composite materialTelecommunicationsComputer sciencePhysicsDirectional antennaDiffraction

Abstract

fetched live from OpenAlex

Abstract We present two transmission-mode dielectric Fresnel-Zone Plate Lens (FZPL) antennas for use within the V-band spectrum. The proposed FZPs are realized via pure plastic material using two different additive manufacturing processes. The proposed FZP lenses are designed with half (λ/2) and quarter (λ/4) phase correction rings at 60-GHz with 30λ0 diameter, where λ0 is the free-space wavelength. The permittivity effect for lens sub-zones is controlled by material porosity in cube-shaped structures. The 3D printed zone plate lenses are built using additive manufacturing plastic materials with a thickness of λ0 and constant relative permittivities equal to 2.76 and 3.6. Different types of antenna with cosn-like radiation patterns as lens illuminators are analyzed on the vertical plane of the flat lenses to have a high efficiency over the considered operating band. Simulations and experimental measurements show a reasonably close match, therefore allowing for a reliable predictability.

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 score0.003

Distilled classifier scores by category (both heads)

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 communication0.0010.000
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 head0.018
GPT teacher head0.242
Teacher spread0.224 · 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

Citations26
Published2018
Admission routes2
Has abstractyes

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