MétaCan
Menu
Back to cohort
Record W4380686093 · doi:10.1038/s41598-023-35707-2

Millimeter-wave planar antenna array augmented with a low-cost 3D printed dielectric polarizer for sensing and internet of things (IoT) applications

2023· article· en· W4380686093 on OpenAlexaff
Yazan Al-Alem, Syed M. Sifat, Yahia M. M. Antar, Ahmed A. Kishk

Bibliographic record

VenueScientific Reports · 2023
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsConcordia UniversityRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsAntenna arrayAntenna measurementCoaxial antennaMicrostrip antennaAntenna efficiencyComputer sciencePlanar arrayPatch antennaArray gainDipole antennaAntenna factorRadiation patternOptoelectronicsMaterials scienceAntenna (radio)OpticsTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

A unique high gain antenna array with a 3D-printed dielectric polarizer is proposed. The packaging of the antenna array feeding structure is eliminated by aggregating the feeding network in between the antenna elements. This has a significant advantage in maintaining neat and symmetric radiation characteristics with low cross-polarization levels. The proposed structure combines two elements in one feeding point to reduce the array distribution feeding points of a 4 × 4 antenna array from 16 to 8 points. The proposed antenna array structure is extremely low in cost and can be used as either a linearly or circularly polarized one. The antenna array achieves a gain of 20 dBi/dBiC in both scenarios. The matching bandwidth is 4.1%, and the 3-dB Axial Ratio (AR) bandwidth is 6%. The antenna array uses a single substrate layer without the need for any vias. The proposed antenna array suits well various applications at 24 GHz, while maintaining high performance metrics, and low cost. The antenna array can be easily integrated with transceivers due to the use of printed microstrip line technology.

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 score0.884
Threshold uncertainty score0.500

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.012
GPT teacher head0.210
Teacher spread0.198 · 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

Citations10
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueScientific ReportsSame topicAntenna Design and AnalysisFrench-language works237,207