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Record W4404040473 · doi:10.3390/rs16214108

Efficient Phase Step Determination Approach for Four-Quadrant Wind Imaging Interferometer

2024· article· en· W4404040473 on OpenAlexaff
Tingyu Yan, W. E. Ward, Chunmin Zhang, Shiping Guo

Bibliographic record

VenueRemote Sensing · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsUniversity of New Brunswick
FundersChengdu Science and Technology ProgramSichuan Province Science and Technology Support ProgramMinistry of Education, IndiaNational Natural Science Foundation of ChinaChengdu UniversityChengdu University of Information Technology
KeywordsInterferometryRemote sensingOpticsThermosphereCalibrationWavelengthAstronomical interferometerPhysicsGeologyIonosphere

Abstract

fetched live from OpenAlex

A four-quadrant wind imaging interferometer is a new generation of wind imaging interferometer with the valuable features of being monolithic, compact, light, and insensitive to temporal variations in the source. Its applications include remote sensing of the wind field of the upper atmosphere and observing important dynamical processes in the mesosphere and lower thermosphere. In this paper, we describe a new phase step determination approach based on the Lissajous figure, which provides an efficient, accurate, and visual method for the characterization and calibration of this type of instrument. Using the data from wavelength or thermal fringe scanning, the phase steps, relative intensities, and instrument visibilities of four quadrants can be retrieved simultaneously. A general model for the four-quadrant wind imaging interferometer is described and the noise sensitivity of this method is analyzed. This approach was successfully implemented with four-quadrant wind imaging interferometer prototypes, and its feasibility was experimentally verified.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.569

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 head0.016
GPT teacher head0.277
Teacher spread0.261 · 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 designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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