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Record W2597046580 · doi:10.1109/tcpmt.2017.2671518

Determining the Stopband of a Periodic Bed of Nails From the Dispersion Relation Measurements Prediction

2017· article· en· W2597046580 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Components Packaging and Manufacturing Technology · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsStopbandSolverDispersion relationDispersion (optics)WaveguideReflection (computer programming)ScatteringOpticsAcousticsScattering parametersPropagation constantPhysicsMathematical analysisMathematicsComputer scienceMathematical optimizationResonator

Abstract

fetched live from OpenAlex

It is useful to determine the stopband of a bed of nails that can be used for packaging applications. The traditional methodology to identify the cell characteristics is to use the eigenmode solver, which is a numerical method that cannot be validated using a measurement setup. Here, we introduce a mathematical procedure to extract the dispersion relation out of the scattering parameters. The scattering parameters express the transmission and the reflection at the ports, which are functions of the phase constant of the propagating modes inside the device under test. A measurement setup is established by placing several successive cell rows inside a Ku -band rectangular waveguide. The proposed algorithm is validated through examples of well-known dispersion relations. The extracted dispersion relation with the introduced methodology is in good agreement with the one obtained from the eigenmode solver. The ride gap waveguide is used as an application example.

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.

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 score0.184
Threshold uncertainty score0.413

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 head0.024
GPT teacher head0.217
Teacher spread0.194 · 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