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Record W4311861470 · doi:10.1063/5.0128856

Theory and practice of pulse compression in hybrid and gyromagnetic non-linear transmission lines

2022· article· en· W4311861470 on OpenAlexaff
MuhibUr Rahman, Ke Wu

Bibliographic record

VenueJournal of Applied Physics · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsSharpeningGyromagnetic ratioPulse compressionWaveformParametric statisticsUltrashort pulseElectronic engineeringComputational physicsComputer scienceMaterials scienceAcousticsPhysicsEngineeringOpticsMathematicsMechanical engineeringMagnetic momentTelecommunicationsCondensed matter physics

Abstract

fetched live from OpenAlex

This paper presents a comprehensive analysis of pulse compression capability in hybrid and gyromagnetic non-linear transmission lines (NLTLs). The corresponding theoretical analysis in the hybrid NLTLs is derived and discussed in detail with the generation and sharpening aspects of leading and trailing pulse edges. The parameters responsible for pulse sharpening are examined and their corresponding pulse compression capability is modelled by providing the output waveform while varying these parameters. A holistic overview and mathematical development of gyromagnetic NLTLs are also conducted, which are modeled and validated through an equivalent lumped element model. Important parameters such as saturation magnetization, gyromagnetic NLTL sections, and damping parameter are elaborated and their influences on pulse sharpening and compression capability are studied. Validation of the theoretical and parametric analysis is performed by an experimental demonstration. The results achieved from hybrid and gyromagnetic NLTLs are briefly summarized, and their corresponding advantages and disadvantages are highlighted. This research is set to provide a fresh and successful debut for investigating gyromagnetic and hybrid NLTLs and their inherent effects on pulse compression for future ultrafast electronic systems and interconnects.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.351

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 head0.007
GPT teacher head0.238
Teacher spread0.231 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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