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Record W3128967060 · doi:10.5515/kjkiees.2021.32.1.56

Design and Fabrication of Ka-Band 50-W Class Solid-State Power Amplifier

2021· article· en· W3128967060 on OpenAlexaff
Jae-Sub Han, Jooyong Jung, Seong-Min Park, Kyung-Deok Yu, Bo-Gyun Kim, Hyochul Kim

Bibliographic record

VenueThe Journal of Korean Institute of Electromagnetic Engineering and Science · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsNexen (Canada)
FundersMinistry of Science and ICT, South Korea
KeywordsAmplifierDuty cycleInsertion lossElectrical engineeringPower (physics)Ka bandEngineeringMicrowaveWaveguideMaterials scienceElectronic engineeringOptoelectronicsPhysicsVoltageTelecommunicationsCMOS

Abstract

fetched live from OpenAlex

This paper presents a 50-W class solid-state power amplifier (SSPA) operating in the Ka-band for use in radar, seeker, and synthetic aperture radar applications. We first designed a unit high-power amplifier (HPA) module utilizing a 10-W class monolithic microwave integrated circuit based on GaN technology and then combined eight of these HPA modules to generate more than 50 W of output power. A waveguide radial combiner was developed to combine all the HPAs such that the insertion loss was minimized; the measured insertion loss of the waveguide radial combiner was less than −0.4 dB within the pass band. The SSPA is designed to operate at a maximum duty cycle of 20% in the pulsed mode and produces at least 75 W of output power at maximum duty with an efficiency of 12.3%, including a DC-DC converter and a drive amplifier module. CuW-based housing was introduced for the HPA modules to minimize the temperature of the bottom surface of the HPA, which was measured to reach 62.4 °C at most without the use of an additional cooling device.

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)0.001
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 score0.164
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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