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Record W3173948972 · doi:10.1002/lpor.202100051

Time‐Domain Integration of Broadband Terahertz Pulses in a Tapered Two‐Wire Waveguide

2021· article· en· W3173948972 on OpenAlexaff
Giacomo Balistreri, Alessandro Tomasino, Junliang Dong, Aycan Yurtsever, Salvatore Stivala, José Azaña, Roberto Morandotti

Bibliographic record

VenueLaser & Photonics Review · 2021
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsTerahertz radiationOpticsBroadbandTaperingIntegratorWaveformWaveguideTime domainOptoelectronicsMaterials sciencePhysicsBandwidth (computing)TelecommunicationsComputer scienceVoltage

Abstract

fetched live from OpenAlex

Abstract In this work, the time‐domain integration of broadband terahertz (THz) pulses via a tapered two‐wire waveguide (TTWWG) is reported. Such a guiding structure consists of two metallic wires separated by a variable air gap that shrinks down to a subwavelength size as the movement takes from the waveguide input to its output. It is shown that while an input THz pulse propagates toward the subwavelength output gap, it is reshaped into its first‐order time integral waveform. In order to prove the TTWWG time integration functionality, the THz pulse is detected directly within the output gap of the waveguide, so as to prevent the outcoupling diffraction from altering the shape of the time‐integrated THz transient. Since the time‐domain integration is due to the tight geometrical confinement of the THz radiation in a subwavelength gap volume, the TTWWG operational spectral range can easily be tuned by judiciously changing both the output gap size and the tapering angle. The results lead to the physical realization of a broadband, analog THz time integrator device, which is envisioned to serve as a key building block for the implementation of complex and ultrahigh‐speed analog signal processing operations in THz communication 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.563

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 head0.010
GPT teacher head0.253
Teacher spread0.243 · 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 designNot applicable
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

Citations25
Published2021
Admission routes1
Has abstractyes

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