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Record W4298384415 · doi:10.1117/12.2313238

The hardware control system for WEAVE at the William Herschel Telescope

2018· article· en· W4298384415 on OpenAlexaff
José Miguel Delgado Hernández, José Miguel Herreros Linares, J. A. L. Aguerri, Kevin Dee, Sergio Picó, Diego Cano, Carlos Martı́n, J. Skvarč, Kevin Middleton, S. C. Trager, P. Bonifacio, A. Vallenari, Saúl Menéndez Mendoza, Gavin Dalton

Bibliographic record

VenueGround-based and Airborne Telescopes VII · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstronomy and Astrophysical Research
Canadian institutionsImpact
FundersScience and Technology Facilities Council
KeywordsTelescopeComputer scienceAstronomyComputer hardwareEngineeringPhysics

Abstract

fetched live from OpenAlex

When an alt-azimuth telescope is tracking at a specific field, it is necessary to use a de-rotator system to compensate the Earth’s rotation of the field of view. In order, to keep the telescope tracking the field of view selected, the instrument will need to a rotation system for compensating it [1]. The new WEAVE [2] two degrees field of view requires a new field de-rotator on the top-end of the telescope. The rotator system has been designed with a direct drive motor which eliminates the need for mechanical transmission elements such as gearboxes, speed reducers, and worm gear drives. This design is a huge advantage for the system performance and lifetime because it eliminates undesirable characteristics such as long-time drift, elasticity, and backlash. The hardware control system has been developed with a Rockwell servo-drive and controller. The rotator has to be controlled by the high-level software which is also responsible for the telescope control. This paper summarizes the model developed for simulating and the software which will be used to accept the rotator system. A performance study is also carried out to test the CIP (Common Industrial Protocol) for communications between the high-level software and the rotator hardware.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.999

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.249
Teacher spread0.240 · 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.

Study designOther design
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

Citations0
Published2018
Admission routes1
Has abstractyes

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