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Record W2536601924 · doi:10.1109/tap.2016.2620522

Wideband Low-Loss Magnetoelectric Dipole Antenna for 5G Wireless Network With Gain Enhancement Using Meta Lens and Gap Waveguide Technology Feeding

2016· article· en· W2536601924 on OpenAlex
Abdolmehdi Dadgarpour, Milad Sharifi Sorkherizi, Ahmed A. Kishk

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsWidebandDipole antennaAntenna (radio)OptoelectronicsMaterials scienceWirelessWaveguideOpticsAntenna gainAntenna efficiencyPhysicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

In this paper, a wideband magnetoelectric (ME) dipole antenna excited by a slot coupled to a fork-shaped printed ridge gap waveguide is proposed. To enhance the antenna gain, a meta lens consists of three layers of split ring resonators (SRRs) are integrated horizontally in front of the ME dipole antenna. Each slab contains a 3×7 array of SRR unit cells. The matching bandwidth is improved as a secondary effect of the lens. A 1 × 4 array is designed to provide more gain by adjusting the lens between the edge elements of the four-element array. The antenna is fabricated and tested. The measured results show that -10 dB S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sub> is obtained between 26.5 and 38.3 GHz. The measure realized gain is better than 15 dBi over the frequency range of 28-38 GHz. A 90% radiation efficiency at 30 GHz is achieved.

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.

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: none
Teacher disagreement score0.838
Threshold uncertainty score0.726

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 head0.018
GPT teacher head0.215
Teacher spread0.197 · 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