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Record W4383200088 · doi:10.1109/ojap.2023.3292390

A Dual Wide-Band Mushroom-Shaped Dielectric Antenna for 5G Sub-6-GHz and mm-Wave Bands

2023· article· en· W4383200088 on OpenAlexafffund
Reza Shamsaee Malfajani, H. Niknam, Sampada Bodkhe, Daniel Therriault, Jean‐Jacques Laurin, Mohammad S. Sharawi

Bibliographic record

VenueIEEE Open Journal of Antennas and Propagation · 2023
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsPolytechnique Montréal
FundersFonds de recherche du Québec – Nature et technologies
KeywordsReturn lossDielectricOpticsBandwidth (computing)Materials scienceMulti-band deviceOptoelectronicsExtremely high frequencyWidebandAntenna (radio)PhysicsTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

The design and implementation of a dual wide-band mushroom-shaped antenna are presented in this paper. The antenna consists of a cylindrical dielectric resonator (cDRA), a cylindrical dielectric rod as a waveguide (cDR), and a dielectric lens (DL). The cDRA in conjunction with the DL acts as a sub-6-GHz antenna. At the mm-wave band, the small cDR acts as a waveguide, which transfers the wave from the feed toward the larger cDRA and the DL in order to produce a high gain. A dual wide-band antenna is designed and fabricated with different dielectric constants for the cDRA/cDR and the DL. 3D printing is utilized to precisely control the dielectric constant of printed component. Measurement results show a maximum gain of 6.4 dB at 5.3 GHz with a 21% 3-dB gain bandwidth and 12.7 dB at 31.5 GHz with a 26.2% 3-dB gain bandwidth. The sub-6-GHz band exhibits a measured 10-dB return loss bandwidth of 21% (centered at 5.15 GHz), and the mm-wave frequency band demonstrates a measured 10-dB return loss bandwidth of 26.2% (centered at 30.5 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)0.001
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: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.594

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.029
GPT teacher head0.247
Teacher spread0.217 · 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

Citations14
Published2023
Admission routes2
Has abstractyes

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