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Record W2032828875 · doi:10.1155/2013/102925

Overview on Multipattern and Multipolarization Antennas for Aerospace and Terrestrial Applications

2013· article· en· W2032828875 on OpenAlexaff
Aixin Chen, Weiwei Jiang, Zhizhang Chen, Jiaheng Wang

Bibliographic record

VenueInternational Journal of Antennas and Propagation · 2013
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsDalhousie University
FundersNational Safety Academic FundNational Natural Science Foundation of ChinaNational Science Foundation
KeywordsPolarization (electrochemistry)Computer scienceBeam patternAerospaceElectronic engineeringMIMOReconfigurable antennaDirectional antennaRadiation patternElectrical engineeringEngineeringTelecommunicationsAntenna (radio)Aerospace engineeringBeamformingAntenna efficiency

Abstract

fetched live from OpenAlex

In recent years, reconfigurable antennas, with the ability to radiate wave in more than one pattern and polarization, play a great role in modern telecommunication systems. Compared with conventional antennas, multipattern and multipolarization antennas have more advantages and better prospects. They can be used to improve system gain and security, satisfy system requirements, avoid noisy environment, and adapt to the environment flexibly. This paper discusses different patterns and polarizations of reconfigurable antennas according to current research work in this area. In the opinion of this paper, the radiation pattern states of antennas include beam direction, shape, and gain. The polarization states of antennas include horizontal/vertical linear, ±slant 45° linear, left-hand or right-hand circular polarized. Different multipattern and multipolarization antennas with various structures and working mechanisms are compared and discussed. Multipattern and multipolarization antennas have been well applied for aerospace and terrestrial applications, such as dynamic scenarios, adaptive beam scanning, and multiple-input-multiple-output (MIMO) systems.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.918
Threshold uncertainty score0.371

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 head0.017
GPT teacher head0.254
Teacher spread0.237 · 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 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

Citations15
Published2013
Admission routes1
Has abstractyes

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