dataesr/scientific-paragraphs-categorization huggingface dataset:
Inventory and Quality Control We had a preproduction phase for robot assembly, during which 440 positioners were built at UM. During this period, we developed and refined our quality control (QC) protocols, procedures, tooling, and workspaces. We built and tested a Structured Query Language (SQL) database for parts QC and inventory and to provide continuous production statistics. The Figure  29. The fiber alignment fixture (left) has a camera and microscope mounted at one end on a manual micrometer stage and a motorized fiber adjustment stage at the other. Between them, the robot mounts in a vee-and-flange, which we designed for functional similarity to the true focal plate interface, while allowing installation and removal using a simple toggle clamp, with no rotation of the assembly required along its screw threads.
At the motorized stage, the fiber is gripped by another toggle clamp actuating a pivot arm with a soft foam grip. Temporary ø 0. 9 mm furcation tubing is used to assist us in sliding the fiber through the mechanical assembly. At the upper right is a closer view of the spring-loaded vee-and-flange clamping interface. At the lower right, a calibration rod is loaded in the clamp. database allowed us to identify production problems in a timely manner, and to mitigate these issues with the supplier or the assembly teams as soon as possible. With 32 parts per positioner (22 custom parts + 10 fasteners), and seven subassembly steps, the database helped maintain production rates with a reliable part count and clear QC interfaces. In addition to the SQL database, we made extensive use of Apple iOS applications and Google Sheets.
8). For most of this testing, the telescope was set at zenith so that the positioners pointed downward, parallel to gravity. It was found that some positioners that had performed well in the laboratory were no longer performing well enough for use on-sky. All positioners that did not meet requirements were parked and identified in a database. The majority of the positioners (>85%) worked as expected and were tested in a variety of dome conditions. We measured significant in-dome turbulence due to temperature gradients during the day, exacerbated by various fans in the dome. The most stable conditions occurred in the evening with the dome open, after the telescope had come into equilibrium with the ambient air. The final characterization of the positioners was completed in this configuration.
With this information in hand, a second positioning loop cycle is initiated for the "correction" move. A "handle fvc feedback" function analyzes positioner performance of the recent blind move, which takes on average 3 s. Position tracking data is updated and any highly deviant positioner is disabled for the night. Thereafter the correction move is performed. These motion paths are generally much simpler than the blind move, since corrections are small. The average times for the planning and the move execution steps are 3 and 20 s, respectively. Acquisition and processing of the FVC image after the correction move is done in parallel with other activities so that no further overhead is accrued. The combination of accuracy and speed of the robot system has thus allowed DESI to achieve the fast reconfiguration times (<2 minutes) between exposures needed to complete the survey on schedule.
GFAs On-sky Performance GFA cameras link astrometric data to the instrument alignment. Their performance is thus an essential element in achieving accurate on-sky fiber positioning. They also measure both guide and wave front correction stars. These measurements require low detector noise to maximize the signal-tonoise ratio and good stability for high-quality throughput measurements. The six guide GFAs (Section 2. 4) each acquire one 5 s integration at an ∼8 s cadence during standard on-sky spectroscopic exposures. The typical 10σ point-source depth achieved by the guider cameras during such a 5 s integration under "normal" observing conditions (transparency >0. 9, 1″ < FWHM < 1 2, r-band AB sky mag per square arcsecond between 20. 3 and 20. 4) is r = 18. 35 mag AB. This depth is sufficient for DESI guiding purposes, as it means that there are on average more than three Gaia stars per guider camera with S/N > 10, even at the highest Galactic latitudes.
Summary The DESI Focal Plane System, with 5020 robotically mounted optical fibers, is enabling a survey of extragalactic redshifts which over the next several years will exceed all previous data sets by an order of magnitude. The focal plane incorporates over 675,000 individual parts Figure  41  and can rapidly position thousands of fibers in parallel to <10 μm accuracy. It is constructed of 10 identical petals, each supporting 502 robots. Each fiber robot has two rotational axes, driven by independent ø 4 mm DC brushless gearmotors. We operate these in two speed modes, a fast mode of 176°. 07 s -1 for general repositioning, always followed by a short, fine precision move at 2°. 67 s -1. The motors do not have encoders. The robots have precision at the μm scale, and when operated "blind" are accurate to ∼50 μm rms, a number which reflects mostly our limits of calibration.
Survey operations began thereafter. In the first 12 months of science operations, from 2020 November through 2021 October, the DESI focal plane assumed 1840 field configurations, resulting in 9. 2 million spectra. Excluding calibration targets and repeat observations, this yielded 4. 2 million high-confidence, unique redshifts of galaxies and quasars, and 1. 0 million unique stellar spectra. By 2022 January DESI had measured 7. 5 million galaxies, delivering them at a rate of ∼1 million per month. We eagerly anticipate the scientific bounties that will be discovered in this unprecedented data set.
The DESI collaboration is indebted to the late  John Donaldson ,  Robin Lafever ,  Tammie Lavoie , and  Glenn Roberts  for their many contributions to the success of this project and mourns their passing. The authors in the Barcelona-Madrid Regional Participation Group (BCN-MAD RPG) wish to acknowledge support by  MICIN  under grants  PID2019-111317 GB ,  PGC2018-094773 ,  PGC2018-102021 ,  SEV-2016-0588 ,  SEV-2016-0597 ,  MDM-2015-0509 , and  CEX2020-001058-M , some of which include ERDF funds from the  European Union. IFAE is partially funded by the  CERCA program  of the  Generalitat de Catalunya. M. A. , S. B. , N. F. , F. P. , G. G. R. , and J. S. acknowledge support from the  Spanish MICINN  grants MultiDark  CSD2009-00064 ,  IFT-UAM/CSIC Severo Ochoa Award   SEV-2012-0249  and  AYA2014-60641-C2-1-P ,  CSIC-AVS  contract through MICINN grant  AYA2010-21231-C02-01  and  CDTI  grant  IDC-20101033 , and support from the  Campus of International Excellence UAM+CSIC.
desi. lbl. gov/collaboratinginstitutions. The authors are honored to be permitted to conduct scientific research on Iolkam Du'ag ( Kitt Peak ), a mountain with particular significance to the  Tohono O'odham Nation. For more information, visit https://desi. lbl. gov/. Source data files for all plots are archived at https://doi. org/10. 5281/zenodo. 6286817. Facility: Mayall (DESI).
Forward FE model Once the theoretical formulation is defined, the next step is to implement it into the FE software to generate CA vs time curves from given sets of parameters. Equation 5 is implemented into ANSYS diffusion module  [33] , including the extravasation term and the contribution of the blood plasma fraction as non-linear generation terms. The numerical formulation obtained for Eq. 5 is defined as: Where, for an element e: • c e is the nodal concentration vector and ċe is its temporal derivative • n are the element shape functions • C d e is the element diffusion damping matrix and it is defined as: • K d e is the element diffusion conductivity matrix and it is defined as: where D is the diffusion coefficient matrix, further defined • r g e is the element diffusing substance generation load vector and it is defined as: where g is the generation load vector.
5. We tested this analytical computation in small-sized models, comparing it to the numerically computed Jacobian. Results were promising, as both matrix and convergence were similar between the two methods. Further information about these results can be found in Appendix B. The optimization algorithm developed takes as inputs an initial set of values of the unknowns, as well as the curves to be fitted. The model proposed defines a set of three parameters (K Trans , v e and v p ) per node. This implies that the algorithm needs to fit three times the number of nodes simultaneously. Starting from the initial seed provided, a forward simulation of the D-ETM is executed, obtaining a first set of curves that are then used to initialize the cost function. Then, the numerical matrices needed for the Jacobian computation ( C d , R and K d (v e ) ) are exported by running several scripts on ANSYS.
Both methods retrieved accurately the reference values and the number of iterations needed was similar in both cases. The proposed curve generation and curve fitting processes were executed in a cluster composed of 480 CPUs and 1088 GB of RAM. Specifically, the resources employed for the benchmark problem and the real tumour geometry were: 16 CPUs and 16GB RAM and 24 CPUs and 32GB RAM, respectively. The optimization time of the benchmark problem was 1. 5h on average (fitting 360 time points on 339 nodes) and for the tumour geometry was around 10h on average (fitting 360 time points on 955 nodes). The optimization algorithm was developed in Python and APDL and the forward simulations were generated on ANSYS 2019R2. orders of magnitude) to parallelize several 2D simulations rather than studying all of them as a 3D case.
Acknowledgements This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 826494  (PRIMAGE). The work was also supported by the  Ministry of Science, Education and Universities, Spain  ( FPU18/04541 ). Authors would like to acknowledge the  Spanish Ministry of Economy and Competitiveness  through the project  PID2020-113819RB-I00. The authors also would like to thank  Silvia Hervas-Raluy ,  Francisco Merino-Casallo  and  Dr. Alberto Badias  for their technical help and support.
Cluster environments from IllustrisTNG simulation The large cosmological magneto-hydrodynamical Illus-trisTNG simulations  (Nelson et al. 2019)  provide the spatial and dynamical evolution of dark matter, gas, stars, and black holes on a moving-mesh code  (Springel 2010) , and assume cosmological parameters from the Planck 2015 results  (Planck Collaboration et al. 2016). Considering the series of IllustrisTNG simulation boxes, we focus here on IllustrisTNG300-1 at z = 0; the cubic box has a length of 302. 6Mpc and the mass resolution is about m DM = 4. 0 × 10 7 M /h. This large and high-resolution simulation box is ideal to accurately describe matter distribution around galaxy clusters up to their large scale environments at z = 0. Our sample of galaxy clusters is based on the halo catalog provided by IllustrisTNG and identified with a friendsof-friends (FoF) algorithm  (Davis et al.
Acknowledgements. This research was supported by the funding for the  ByoPiC  project from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  grant agreement  ERC-2015-AdG 695561  (ByoPiC, https://byopic. eu). The authors thank the very useful comments and discussions with all the members of the  ByoPiC team. We thank the  IllustrisTNG collaboration  for providing free access to the data used in this work. CG is supported by a  KIAS Individual Grant  ( PG085001 ) at  Korea Institute for Advanced Study (KIAS). She is grateful to  Professor Changbom Park  for stimulating discussions.
Data acquisition and reduction We observed VX Sgr at its visible photometric minimum over three nights (Table  1 ) with the MATISSE instrument with the aim of reconstructing images across the different bands. The data collection spanned 2. 5 months: the compact and medium configurations within 15 days in June, while the large configuration could only be observed late at the end of August 2019. The calibrators used are reported in Table  2 , and the UV coverage is displayed in Fig. 1. We used the low spectral resolution mode (R = 35) in the L, M, and N bands. For the data reduction, we applied the MATISSE data reduction software (matisse-drs,  Millour et al. 2016). It adopts the classical Fourier transforms scheme  (Perrin 2003) , with photometric calibration in the L and N bands  (Coudé du Foresto et al.
The calibrated OIFITS2 files of the four BCD configurations are merged to produce a single calibrated OIFITS2 file for each CAL-SCI pair. Figure  2  shows a representative example of the L, M, and N bands' calibrated squared visibilities and closure phases taken with the small AT configuration (A0-B2-D0-C1) on 15 June 2019. The data taken at low spatial frequencies are of good quality, except for the ranges of the atmospheric absorption, and they already show a spatially resolved emission in all the bands, with strong brightness asymmetries in the N band indicated by the significant non-null closure phases. Figure  3  displays the calibrated MATISSE spectrum of VX Sgr for one particular night (25 June 2019) overplotted on the telluric spectrum at the observing site. For each observation block of VX Sgr, the absolute flux calibration was performed by dividing the raw spectrum of VX Sgr by the raw spectrum of the associated calibrator and then multiplying this ratio by the calibrator synthetic spectrum.
2009 ) to compute synthetic intensity maps in the same observed spectral channels as MATISSE and using as input the RHD simulations snapshots. This code takes into account the Doppler shifts caused by the convective motions. The radiative transfer is computed in detail using pre-tabulated extinction coefficients per unit mass, as for the hydrostatic model atmosphere code MARCS  (Gustafsson et al. 2008) , as functions of temperature, density, and wavelength for the solar composition  (Asplund et al. 2009). The temperature and density distributions are optimised to cover the values encountered in the outer layers of the RHD simulations. The MATISSE observations cover the L, M, and N bands. While the L and M bands are mostly characterised by molecular features, the N-band spectrum may display several dust features (e.
25 and ∼4. 00 µm, before increasing at longer wavelengths where SiO and CO dominate. In addition to these molecules, H 2 O also contributes across the full spectral range. However, abrupt discontinuities are visible for RSG and UD diameters at (i) ∼3. 15 µm and (ii) ∼4. 27 µm. The strong variation in UD and RSG at ∼3. 15 µm indicates that these models are not adapted to describe the observed data since they underestimate the increase in size with the wavelength. This is not surprising because Arroyo-Torres et al. (  2015 ) already pointed out that in 3D RSG simulations the dynamical pressure is not sufficient to enlarge the stellar atmosphere to the observed sizes in the K band. On the other hand, the very extended atmospheres of the 3D AGB simulation shows a more regular increase of the radius from shorter to longer wavelengths, denoting that this simulation is more adapted to represent the data.
It should be noted that the observed data in the spectral range between 4. 27 and 4. 44 µm are partially contaminated by the presence of noise (Fig. 2 ) that makes the determination of the visibility fit less precise, and, as a consequence, there is an abrupt change in the diameter values at precisely 4. 27 µm. In the N band, Fig. 8  (bottom panel) shows the increase of the angular diameter with the wavelength as already visible in Figs. 5  and 6. However, this result is purely indicative since either the UD or the AGB simulation lack dedicated dust opacities, where the dust features prevail.
2009 ). The optical constants were obtained from the Jena database  8. We set the stellar angular diameter to 8. 82 mas  (Chiavassa et al. 2010 , the near infrared value derived by  Chiavassa et al. 2010)  to scale the synthetic stellar flux. To account for the extension of the object across the N band (Fig. 5  and Table  4 ), we computed SEDs with a flux contribution coming from a diameter of 130 mas. Figure  12  (top) displays an example where we set a grain size, a, ranging between [0. 5-5] µm, with a size distribution of n(a) ∝ a -3. 5  (Mathis et al. 1977) , for all the three species. We set the inner edge of the dust density distribution at 3. 5 R to match the reconstructed images in Fig. 5. The dust mass-loss rate is set at 2 × 10 -7 M yr -1 for each of the three species. Moreover, Fig.
Conclusions Our MATISSE observations unveil the photospheric extent for the first time, as well as the circumstellar environment of VX Sgr across the L, M, and N bands. We reconstructed monochromatic images using the MIRA software. They show a complex morphology with brighter areas, whose characteristics depend on the wavelength probed. In the N band, the central object is still visible at shorter wavelengths, but then the circumstellar brightness grows at longer wavelengths from 10 up to 40% of the central region. We employed 3D RHD simulations of AGB and RSG stars, carried out with CO 5 BOLD, to calculate synthetic intensity maps in the same observed spectral channels of the MATISSE observations. As a first step, we measured VX Sgr monchromatic angular diameters using a UD model fit and the azimuthally averaged intensity profiles from AGB and RSG simulations applied to the MATISSE interferometric data.
The general trend of MATISSE data is in qualitative agreement with the presence of melilite and olivine but not corundum. Nevertheless, it is not possible to firmly conclude on the interpretation of the current data because of the difficulty in contrasting the model parameters using the limited accuracy of our absolute flux calibration. Even if the MATISSE observations are extremely useful to characterise and resolve the stellar surface as well as its circumstellar environment, they are not suitable to firmly conclude on the nature of VX Sgr. The reason is twofold: on one hand, the spectral resolving power used here (R = 35) is not sufficient to observe the spectral lines in detail, which could provide crucial information on the velocity fields and atmospheric stratification of the observed object  (Chiavassa et al.
Acknowledgements. This work was granted access to the  HPC resources of Observatoire de la Côte d'Azur -Mésocentre SIGAMM. This research has also benefited from the help of SUV (http://www. jmmc. fr/suv. htm), the VLTI user support service of the  Jean-Marie Mariotti Center  (http://www. jmmc. fr). Moreover, this work was supported by the  "Programme National de Physique Stellaire" (PNPS)  of  CNRS/INSU  co-funded by  CEA  and  CNES. BF  acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  Grant agreement no. 883867 , project  EXWINGS ) and the  Swedish Research Council (Vetenskapsrådet , grant number  2019-04059 ). The computations of 3D models were enabled by resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at  UPPMAX.
They also may contain substances harmful to the staff and the environment. It is therefore necessary to implement protective and isolation measures to reduce the risk of chemical contamination  [3, 4]. In addition, identification and traceability of the different batches and vial numbers must be carried out. Currently, at the central pharmacy of Lille University Hospital (LUH), CDP requires two pharmaceutical technicians: one who prepares the drug, and another who ensures a second control of the procedure. LUH central pharmacy is not equipped with the methodologies and tools needed to support decision-making and management that are adapted to the requirements of its operating environment. The only information system the operators use during CDP is the software CHIMIO® of Computer Egineering company.
It contains information about chemotherapy circuit including preparation protocols. In this paper, we present an evaluation of the current CDP circuit in the central pharmacy of LUH in order to highlight the difficulties encountered. Then, we propose a preparation and control assistance tool "Smart Prep" using AR, co-developed by the Faculty of Pharmacy in Lille, Ecole Centrale de Lille and the company Computer Engineering. "Smart Prep" is an Android application that can guide the pharmacist in the preparation of chemotherapy drugs: it fetches the preparation instructions from the CHIMIO® webservice database, then displays them on the screen of the glasses during the preparation. The glasses must ensure real-time control of CDP as well as traceability.
Discussion The implementation of "Smart Prep" allows a gain in security. However, it requires a financial investment for the purchase of computer equipment (touch tablets for the preparation of baskets, labelling machines, barcodes and datamatrix scanners, computer workstations on each workstation) and for the evolution of the CHIMIO® software. It also imposes a total "dependence" on computer equipment and wifi, which may lead to a daily management of problems that may be encountered by the operator. "Smart Prep" was implemented and connected to CHIMIO® on July 22 nd , 2020. As after every implementation, a learning phase is necessary. During this period, data collection cannot be performed to avoid introducing a bias in our data which would make the results unusable. It is preferable to collect data at a distance from the beginning of the project, which explains why the data collection could not be integrated in this work.
Administration is done manually in the software. A greater level of security could be achieved by implementing an interconnected identification system. This system allows the computerized verification at the patient's bed, of the adequacy between the patient's identity (by an identification bracelet given to the patient upon arrival) and the preparation administered (via the datamatrix present on the preparation's label) as well as the correspondence with the cycle and the day of administration within the preparation's validity period. In addition, this system enables the administration of the preparation to be validated in real time at the patient's bed, in the CHIMIO® software  [16, 17]. The ONCO SAFETY system  [17]  commercialized by BBraun goes further. It is an administration assistance software for chemotherapy which, after validation of the patient's identity and the preparation, imports the administration protocol directly from the CHIMIO® software and programs the pumps (flow rate, administration time).
Building models and their connected subsystems are often simulated as standalone entities. However, in order to monitor a system s reactions to changing parameters and to assess its energy efficiency, it must be exposed to the actual dynamic context of the building under study. Hence, frameworks assessing co-operative simulation of buildings and their subsystems should be used. In this study, the Building Control Virtual Test Bed (BCVTB) framework was used for co-simulation of a small-scale building (EEBLab) connected to an Earth-to-air heat exchanger (EAHE). The EnergyPlus tool was used to simulate the indoor air temperature variations within the EEBLab, and MATLAB was used to model the EAHE system and to calculate its performance based on various parameters. The HOLSYS internet of things platform was deployed to monitor and collect the experimental data from the sensors to validate the simulations.