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Record W4392620621 · doi:10.1002/mop.34107

Electrically tunable substrate integrated waveguide equalizer based on PIN diodes

2024· article· en· W4392620621 on OpenAlexaff
Hao Peng, Chen Zhang, Yu Liu, Serioja Ovidiu Tatu, Tao Yang

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsPIN diodeDiodeVoltageMicrowaveElectronic engineeringMaterials sciencePlanarElectronic circuitEqualizerEqualization (audio)Flatness (cosmology)OptoelectronicsElectrical engineeringEngineeringComputer scienceTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Abstract In this letter, a novel design of electrically tunable substrate integrated waveguide (SIW) equalizer, based on PIN diodes, is proposed. Compared to previous SIW equalizers, this one introduces PIN diodes that exhibit varying impedance under different direct current (DC) voltages. The equalizing values of the SIW equalizer are directly linked to the DC voltage applied to the PIN diode within a specific voltage range. This feature allows it to be considered as an electrically tunable SIW equalizer. The experimental results demonstrate that the equalizing values are consistently monotonic and adjustable within the range of 1.59–7.56 dB in Ku‐band. This adjustment is attained through a DC voltage step of −0.01 V, applied within the range of 1.23–1.11 V. Additionally, the microwave equalizer is a planar structure that can be conveniently implemented on traditional printed circuit board, resulting in lower costs, easier realization, and integration. This SIW equalizer can be applied in amplitude compensation circuits and networks to compensate flatness dynamically.

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 categoriesMeta-epidemiology (narrow)
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 score0.302
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.203
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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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