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Record W2847480339 · doi:10.1109/access.2018.2854189

Low Loss and Ultra Flat Rectangular Waveguide Harmonic Coupler

2018· article· en· W2847480339 on OpenAlexaff
Mohamed Mamdouh M. Ali, Shoukry I. Shams, Abdel-Razik Sebak

Bibliographic record

VenueIEEE Access · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsLinearityBandwidth (computing)HarmonicCoupling (piping)Electronic engineeringHybrid couplerAcousticsMaterials scienceHarmonic analysisPower dividers and directional couplersElectrical engineeringComputer scienceTopology (electrical circuits)PhysicsTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

Recently, communication networks are evolving dramatically to meet the human dynamic needs as well as provide the required support for the massive expansion in future applications. This fosters the research in the mm-wave components to create a new infrastructure for these applications. As a result, the electrical characteristics of the designed components in terms of the bandwidth and the linearity have to be evaluated in an accurate way. The linearity of the mm-wave components is evaluated through the assessment of the inter-modulation of these components, especially at the second harmonic band. In this paper, a -17-dB harmonic coupler is designed to pick a strong sample at the second harmonic, while suppressing the fundamental signal at the coupled port. A design procedure for the proposed harmonic coupler is presented and illustrated. The fabricated unit is silver plated to minimize the losses, where the measured coupling at the second harmonic band shows an excellent agreement with the simulated ones. In addition, the measured coupling level at the fundamental band is below -75 dB.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.866

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 head0.014
GPT teacher head0.244
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 teacher head, 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

Citations14
Published2018
Admission routes1
Has abstractyes

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