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Record W3000622669 · doi:10.1049/iet-map.2019.0380

Far‐field cancellation of cross‐polarisation based on mirroring subarrays of densely arranged PDM apertures

2020· article· en· W3000622669 on OpenAlexaff
Hao Wang, Huan Li, Kuiwen Xu, Dexin Ye, Jiangtao Huangfu, Changzhi Li, Tayeb A. Denidni, Lixin Ran

Bibliographic record

VenueIET Microwaves Antennas & Propagation · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNational Natural Science Foundation of China
KeywordsMirroringField (mathematics)PhysicsNear and far fieldOpticsComputer scienceMathematicsCommunication

Abstract

fetched live from OpenAlex

Achieving highly directional planar array antennas with sufficient cross‐polarisation discriminations (XPDs) is crucial for satellite communications based on the polarisation‐division multiplexing (PDM). In this work, the authors propose a robust three‐step approach that can be used to significantly enhance the XPD of such array antennas in the normal and nearby directions based on mirroring subarrays, especially when the radiation elements are densely arranged. Theoretical, simulation and experimental results show that by dividing an in‐phase feed array into two or four identical subarrays and properly mirroring them with a differentially feeding, the XPD can be remarkably enhanced by means of far‐field cancellation, regardless of the scale and the XPD of the original array. It provides a practical and robust solution to obtain compact PDM antennas satisfying strict XPD requirements.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.021
GPT teacher head0.247
Teacher spread0.226 · 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 designSimulation or modeling
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

Citations1
Published2020
Admission routes1
Has abstractyes

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