MétaCan
Menu
Back to cohort
Record W7152693876 · doi:10.64897/ieet.2025v1i1.001

Highly Isolated Dual Circularly Polarized Metallic ME-dipole Antenna Array Design

2025· article· W7152693876 on OpenAlexaff
Oludayo Sokunbi, Ahmed Kishk

Bibliographic record

VenueInsights of Electrical Engineering and Technology · 2025
Typearticle
Language
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsMicrostripCircular polarizationBandwidth (computing)Decoupling (probability)Microstrip antennaMulti-band deviceDipole antennaDipoleBalunCoupling (piping)

Abstract

fetched live from OpenAlex

A novel dual circularly polarized metallic magneto-electric dipole (D-CP-M-ME-dipole) for the 30 GHz band is presented. The D-CP-M-ME-dipole uses solid metallic aluminum bulk without surrounding it with metallic cavities. It is fed by crossed narrow slots coupled to air-filled microstrip lines packaged by a bed of printed periodic mushrooms, achieving a bandwidth of 26–41 GHz (46%). An 8×8 array is constructed and fed by a carefully designed dual-polarized cooperative feeding network, which is optimized considering the mutual coupling influence. Hence, the need for an external decoupling mechanism to isolate the two polarizations is eliminated. The feed network is on Printed gap waveguide (PGW) to prevent leakage. Furthermore, parasitic elements are added to the 8×8 array to realize a 9×9 element array that improves the overall gain over the bandwidth. The 9×9 dual CP metallic ME- dipole is fabricated and measured to verify this procedure, with an ultrawide impedance bandwidth of 26–42 GHz (47%), 26.6 dBic gain, and 90% total efficiency, with less than 40 dBi isolation between the two polarizations throughout the band.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueInsights of Electrical Engineering and TechnologySame topicAntenna Design and AnalysisFrench-language works237,207