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Developing an Electromagnetic Vortex Beam Structure Based on Advanced Plasma Transmit-array

2022· article· en· W4296559019 on OpenAlexaff
Alireza Ghayekhloo, Halim Boutayeb, Larbi Talbi

Bibliographic record

Venue2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (AP-S/URSI) · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsPlasmaVortexElectromagnetic radiationPhysicsPhase (matter)Antenna (radio)OpticsScatteringSurface waveMicrowaveWave propagationEngineeringTelecommunicationsMechanics

Abstract

fetched live from OpenAlex

Electromagnetic (EM) vortex waves in microwave regime offer a great venue for new communication links. Comparatively to multi-band and multi-antenna mechanisms, they can provide greater channel diversity and capacity. A plasma transmitting surface is proposed based on EM scattering wave theory to produce vortex beams. As a complex medium, plasma can be modeled by the Drude dispersive dielectric model. Plasma cells are to be housed in a multilayered medium and covered in glass. In the lower K frequency band, a 360-degree circle of transmission phase is achieved using plasma advanced structures. Physical parameters of the plasma can be tailored to alter the phase front of an incident wave. The analytical proposal for the plasma phase engineering surface is verified with full wave simulations. Using the proposed transmit-array, several vortex beams (0, ±1, ±2, …) can be generated with a single structure without any blockage of the modes. Plasma arrays with reconfigurable designs can be used in future generations of mobile networks.

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.240
Teacher spread0.230 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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