dataesr/scientific-paragraphs-categorization huggingface dataset:
The model was then sub- tracted to the spaxels to create a cube of residuals where both the continuum emission of the star and of any companion is removed. This step was repeated onto each datacube for the sequence once. We then cross-correlated each spaxel with a template spectrum (see Section 3. 1) smoothed to the spectral resolution of SINFONI and for which the continuum had been removed beforehand using a gaussian boxcare of 20Å too. The crosscorrelation function (CCF) was performed with the Python module crosscorrRV from the PyAstronomy package 7  customized to include a normalization of the CCF following  Briechle & Hanebeck (2001). The CCF explores velocities from -100 to +100 km s -1 in steps of 8 km s -1 (∼4 times the original sampling of the data in velocity). The individual cubes of crosscorrelation function were then corrected from the barycentric velocity using the Python module barycorrpy 8   (Kanodia & Wright 2018b,a)  and combined once phased in velocity.
BT-SETTL15 BT-SETTL15 includes a 1D cloud model where the abundance and size distribution of 55 types of grain are determined comparing the timescale of the condensation, the coalescence, the gravitational settling and the mixing of these solids supposed spherical. The details of each solids and chemical elements included in the model are described in  Rajpurohit et al. (2018). The opacities in the spectral energy distribution (hereafter SED) are calculated line by line and the overall radiative transfer is carried on by the PHOENIX code  (Hauschildt et al. 1997; Allard et al. 2001 ). The convection is handled following the mixing-length theory, and works at hydrostatic and chemical equilibrium. The model also accounts for non-equilibrium chemistry between CO, CH 4 , CO 2 , N 2 , and NH 3.
Bayesian inference of the spectro-photometry We used the ForMoSA code presented in  Petrus et al. (2020)  to compare the synthetic spectra to the data following the forwardmodelling approach. ForMoSA relies on the Nested Sampling algorithm  (Skilling 2006)  to determine the posterior distribution function of a set of free parameters in the models. This method performs a global exploration of the model parameter space to look for local maxima of likelihoods following an iterative method which isolates progressively restrained area of isolikelihood while converging toward the maximum values. That avoids missing local maxima of likelihood and allows to evaluate the Bayesian evidence that can be used for performing model selection  (Trotta 2008). Because the Bayesian inference implies to generate random points with continuous distribution inside the parameter space, we chose to reduce the step of each model grids by interpolating them with the N-dimensional linear interpolation python package griddata which is triangulating the input data with Qhull 10 before applying barycentric interpolation.
Extraction of the planet's correlation signal We ran the molecular mapping steps with the TExTRIS package (Section 2. 4) on the molecular templates (see Section 3. 3. 2) and the grids of Exo-REM and BT-SETTL15 model spectra (see Section 3. 3. 3). The tool was parallelized to gain computation time and produced as many cross-correlation maps as input model spectra. The planet is detected with a radial velocity of 20±8 km s -1 (see Figures  1 ). We extracted the cross-correlation signal in the correlation maps by averaging the cross-correlation function over the FWHM of the auto-correlation, centered on the planet radial velocity to produce averaged correlation maps and within a circular aperture centered on the planet position of 4 pixels radius (∼FWHM of the PSF) to extract the correlation signal of the planet.
Comparison of the characterization approaches In this study, we compare three different approaches to determine the physical parameters of HIP 65426 b: a standard χ 2 minimization, a Bayesian inference with the code ForMoSA, and the molecular mapping with the code TExTRIS. We show in the  Figures 5 and 6, and   The coherency between the χ 2 minimization and the Bayesian inference stems from our assumption of Gaussian distribution of uncertainties on the observed spectra and the lack of correlations between the data-points. The likelihood and the cross-correlation function are related assuming the same underlying hypothesis on the data  (Brogi & Line 2019; Ruffio et al. 2019). That can explain the overall similar trends between the results from the Bayesian inference (and the χ 2 minimization) and the molecular mapping.
Interpreting the radial velocity of HIP 65426 b An interesting result coming from the SINFONI K-band spectrum of HIP 65426 b is the determination of the radial velocity of the planet itself. As described in Section 2, great care has been taken to calibrate the wavelength dispersion of the cubes using the various telluric absorption lines, and cross-checked at the end with solution found for the OH lines present in the data (centered on zero km s -1 , see Figure A. 1). Using both independent approaches, the Bayesian inference and the molecular mapping to measure the radial velocity of HIP 65426 b in the SINFONI datacubes, we find compatible values that give a radial velocity estimate of 21 ± 7 km s -1. Similar results are obtained with the molecular mapping approach using the observation acquired on May 25 th , 2018 (20 ± 8 km s -1 ).
Summary and conclusions We analysed new K-band medium-resolution IFS data obtained with SINFONI at VLT of the HIP 65426 b exoplanet, discovered by SPHERE, to further characterize its atmosphere. Our TExTRIS python analysis toolkit for IFS data was able to retrieve the star position outside the field of view and to optimise the data processing allowing for the extraction of the planet emission spectrum. We interpreted this spectrum following a Bayesian inference with self-consistent atmospheric forward models with the ForMoSA code  (Petrus et al. 2020 ) and compared the inferred atmospheric properties of the planet to those obtained from the recent "molecular mapping" technique based on crosscorrelation between the IFS data elements and grid of models. We can summarize the main results as follows: 1.
We find a T eff =1560±100K, log g≤4. 40 dex, [M/H]=0. 05 +0. 24 -0. 22 dex, and C/O ≤0. 55 from ForMoSA. The accuracy is limited by the systematic uncertainties induced by the use of different models and by the bi-modal distribution of solutions found with the BT-SETTL15 models. That composition is consistent with a formation of HIP 65426 b closer to the host-star by core-accretion. We can not exclude a formation by gravitational instability at large separation, but the latter would imply the accretion of a substantial amount of solids. 2. The molecular mapping as performed with TExTRIS yields results consistent with the ones from ForMoSA that validates the ability of the molecular mapping to characterize atmospheres. Nevertheless, this alternative method is limited by the loss of the spectral continuum information in the data that can shift the estimates of T eff , and degrades the constraints on the other parameters (log(g), [M/H], C/O).
Acknowledgements. We dedicate this work to the memory of  Dr. France Allard , a great source of inspiration for our community. We hope that this paper will honor her career, her research and her huge contribution in the domain of the physics of atmospheres of stars, brown dwarfs and exoplanets. S. Pe ,  M. Bo ,  G. Ch ,  B. Ch. , A-M. La,  P. De  and  A. Bo , acknowledge support of the  Programme National de Planétologie  through project grants " ISEP " and " EXO-SPEC " and the  Agence Nationale de la Recherche  through grant  ANR-14-CE33-0018. This research has benefited from the support of the  IDEX cross-disciplinary project Origin of Life , and the visiting program of the  French Chilean Lab for Astronomy (FCLA ,  UMI-3886 ). A. Vi  and  M. Ho  acknowledge funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No.
757561 ). A. Wy acknowledges the financial support of the  SNSF  by grant number  P400P2_186765. G. -D. Ma  acknowledges the support of the  DFG priority program SPP 1992 "Exploring the Diversity of Extrasolar Planets " ( KU 2849/7-1 ) and from the  Swiss National Science Foundation  under grant  BSSGI0_155816  "PlanetsInTime". Part of this work has been carried out within the framework of the  National Centre of Competence in Research PlanetS  supported by the  Swiss National Science Foundation. F. Al acknowledges financial support from the  "Programme National de Physique Stellaire" (PNPS)  and the  "Programme National de Planétologie" of CNRS/INSU, France. The computations of brown dwarf and exoplanet models were performed at the  Pôle Scientifique de Modélisation Numérique (PSMN) at the École Normale Supérieure (ENS) in Lyon.
Benchtop nuclear magnetic resonance (NMR) spectrometers are versatile analytic instruments with low acquisition and operation cost. However, in the basic version, samples must be manually measured one after the other. We herein describe the open-source autosampler RotoMate that allows the automated operation of such instruments. The hardware is easily assembled from 3D-printed and inexpensive off-the-shelf parts, and is controlled by an Arduino Uno. A software package interlinks the operation of the autosampler with the software of the NMR spectrometer and the software for the processing of the spectra. Experiments for up to 30 samples can be inserted into an interactive sample list. The autosampler automatically inserts and ejects the samples, initiates measurements on the spectrometer according to parameters specified in the sample list, and interacts with a common NMR software in the processing and visualization of the obtained spectroscopic raw data.
In our laboratories, we routinely use Magritek Spinsolve benchtop NMRs for the analysis of reaction mixtures. When we purchased the instruments, no autosampler system was available, so that the students had to operate it manually. This turned out to be a time-consuming and unpopular task, since we sometimes analysed hundred samples per day, and every few minutes, a new sample had to be inserted. In order to relieve us from this repetitive task, we constructed RotoMate, a low-budget autosampler, and programmed a software to control it. It has been in service for several years and has greatly simplified the operation of the instrument. Meanwhile, commercial autosamplers have become available, but RotoMate still compares favourably. It has space for 30 queued samples, whereas the leading commercial autosampler can only handle 20 samples.
For safety reasons, this process is monitored with light barriers. The presence of a sample is checked by moving it up in a stream of compressed air towards a light barrier installed at the ''tower" above the trapdoor. The ''manual control module" allows the operator to control the device without connection to a PC. Detailed descriptions of the functions of each part can be found in the build instructions (Section 5, ''Build Instructions"). All subunits are controlled by an Arduino Uno microcontroller, which is connected to a PC via USB. The Arduino continuously sends the current status of the autosampler to the PC. The software that controls the NMR instrument must be installed on this PC (in our case the Spinsolve software package). Similar to most other software packages for analytic instruments, this NMR program can be conFig.
d to accept commands and send status messages, in this case via a socket. This is described in detail in the manual of the spectrometer. A PHP-capable webserver and a MySQL database must also be installed. Adding, editing, and removing samples, as well as starting and stopping the queue, are handled by a webinterface, which also allows to remotely monitor the status of the autosampler and the currently running measurement with any browser. The sample queue is stored in the MySQL database along with the settings and other parameters. A python software with access to the virtual serial port of the autosampler, the socket of the Spinsolve software, and the MySQL database, continuously runs in the background and coordinates all components. The python software also provides a graphical user interface (GUI), allowing to manually control the autosampler and access the webinterface with an integrated browser.
If a queue is started, the python script will 1) read the parameters for the next sample from the MySQL database, 2) send a command to the autosampler to insert the sample, 3) wait until the autosampler reports successful insertion, and if no error is encountered, 4) start the measurement in the Spinsolve software, 5) wait until the spectrometer reports that the measurement is done without error, 6. ) then send a command to the autosampler to return the sample to the rotor. This process is repeated until all samples have been measured or an error occurs (See Fig. 2 ). As an optional feature, automatic processing of spectra is implemented in the RotoMate's software. It is set up to interact with ACD NMR Processor, which was available as a free academic edition. The automatic sample processing includes baseline correction, picking a reference peak, automatic integration against an internal standard, and generation of a PDF and JDX  [9]  file.
The commands are sent to ACD via a macro file. Due to the open-source nature of the RotoMate, it should be possible to adapt it to any other NMR processing software that is capable of running pre-defined macros. Since the RotoMate is fully open-source in both hardware and software, it should also be possible to adapt our design to other models of benchtop NMR spectrometers. This would mainly involve adjusting the mounting bracket, the size of the spinners, and the connection to the compressed air system. In addition, the measurement methods ( 19 F, 1 H, Shim), as well as the communication of the Python software with the spectrometer would also have to be reimplemented for that particular model of spectrometer. Overall, the RotoMate autosampler simplifies NMR analysis as follows: its web interface allows to insert an entire list of spectroscopic experiments up to 30 different samples can be placed in the spectrometer the spectroscopic measurements are automatically performed for all samples the spectra are automatically processed the amounts of specified components against an internal standard can be optionally calculated.
Manual control The autosampler can be manually controlled from the matrix keypad, as described in Table  9. Additionally, the autosampler can be controlled using the buttons in the Python GUI. More advanced commands can be sent directly to the autosampler by entering the command into the text field, and then clicking the ''Yell!" button. A list of commands is shown in Table  10. The commands can be chained, e. g. ''hm20z" will run a homing, move the rotor to position 20, and then make a beeping noise.
Automatic processing Optionally, the autosampler software can use the ACD NMR Processor to automatically process 1 H-and 19 F NMR spectra, generate JDX and PDF files, and calculate conversions and yields. If not yet done, specify the location of your installation of ACD (e. g. C:/ACDFREE12/) by clicking on the link ''conFig. autosampler" and entering it under ''ACD folder". The autosampler software is able to automatically send commands to the ACD NMR Processor via its built-in macro function. If an internal standard is specified, the autosampler software can then read the integrals from the JDX file generated by the software. This can for example be used to automatically determine conversions and yields in an in-situ analysis of a reaction mixture, or to determine the purity of a given sample.
As an example, the determination of the EtOH content of EtOH/ H 2 O mixtures will be used in the following section. To set up a processing method, measure a reference spectrum of a sample containing all analytes and internal standard and adjust the parameters in the autosampler software accordingly (Fig. 47 ). After setting the LB and Baseline parameters in the autosampler's processing method, click on ''Save & continue". Overview of the ''yell" commands which can be sent from the PC to manually control the autosampler. Command Description E Send error to autosampler. This command will cause the autosampler to cease operation and display an error. M Measure a sample. An integer number from 1 to 32 must be added to the command to specify the holder number. Before measuring the sample, the autosampler will go to holder 32 and try to eject a sample, to ensure that there is no NMR tube already in the spectrometer.
The form requires two additional parameters: ''Equiv. " are the equivalents of the given compound relative to the other ones, and ''Number of atoms" denotes the number of hydrogen or fluorine atoms per signal. The software will then automatically set the reference value of the spectrum to your internal standard, integrate the areas corresponding to starting materials and products, and calculate conversions and yields. Keep in mind that changing the solvent will result in strong shifts of the signals, so the automatic integration will only give reliable results if the solvent does not change. A realistic application of a benchtop NMR spectrometer is to continuously monitor the content of an organic material in a medium, for example when checking the amount of an organic contaminant in waste samples, the content of a product synthesized in a student lab, or the ratio of ingredients in a solvent mixture.
Restarting the autosampler If the autosampler is not responding, the easiest solution is to restart it. This can be done in the following ways: On the PC, go to the ''Autosampler Satan" software. Under the autosampler control, press the ''Disconnect" and then the ''Connect" button. If that did not help, use a non-conductive stick (e. g. wooden cotton swab) to press the ''reset" button on the motor shield, which is located right above the hole in the lid of the electronics case. During the reset, the solenoid valve will open, and the LED lights and LCD display will briefly flash. 6. 4. 4. Spinner is stuck on the NMR tube If a spinner is fit too tightly around the NMR tube, it might be difficult to remove it. In this case, it is possible to loosen the spinner by pressing a thumbnail into the slit of the spinner (Fig.
Validation and characterization The autosampler is able to insert, change, or eject samples in less than 30 s under optimal conditions, making its speed comparable to manual changing of samples (Table  11 ). However, rather than staying next to the NMR to manually operate it for the entire duration of the measurements, the rotor is charged once with all samples and can then be left unattended until all measurements are finished. The workload for manually inserting and ejecting a sample, entering the measurement parameters in the Spinsolve software, and starting a measurement, is around one minute. Measuring 30 samples with 3 min of measurement time plus sample handling would keep the operator busy for around 2 h. During this time, the operator would have to manually process the finished samples.
3, ''Calibration of the airflow regulator"). Rotor Alignment Video: A video showing the alignment of the rotor (Section 5. 3. 2, ''Fine-tuning the air pressure, slider, and rotor placement"). Pressure Video: A video showing adjustment of the air pressure (Section 5. 3. 2, ''Fine-tuning the air pressure, slider, and rotor placement"). Pictures: A folder containing high-resolution versions of the images and Fig. s in this article. nmr_autosampler_arduino: The Arduino [11] code for the autosampler. nmr_autosampler_python: The Python code for the autosampler. nmr_autosampler_webapp:.
K. Zhao et al. ,  [16]  studied the effect of solar radiation on the cooling capacity of the cooling floor. A calculation method has been proposed to study the effect of the different emissivity values of the wall surface on the cooling capacity. They reported that the cooling capacity of the cooling floor increased significantly when exposed to a solar beam of 40-100 Wm -2. Tian et al. ,  [17]  et Feng et al. ,  [18]  have established a new model to calculate the cooling capacity as a function of solar radiation transmitted by the window. However, the models developed impose that the heat solar flux is uniform on cooling floor. Moreover, Michel de carli et al. ,  [19]  have shown that we can't have a precise simulations using a simplified model. In this context, there are several simplified models in literature for the study of solar radiation effect on cooling floor such as RC-network model, software simulation programs as TRNSYS and heat conduction transfer function.
Experimental results We present in this section the experimental data for the period of July 2014. We follow for 10 days, the inlet temperature, cooling surface temperature and outdoor air temperature as shown in figure  8. The results show that the temperature difference beteween the inlet temperature and outdoor temperature is around 12 °C. This difference in temperature can supply the heat energy required to ensure a best thermal comfort. The cooling floor surface temperature does not exceed 25 °C which corresponds to the criteria of thermal comfort  [37]. Figure  9  shows the indoor air temperature evolution at two different zones. In the study of M. H. Benzaama et al. ,  [11]  the authors presented only the temperature of the shaded zone (Position 3). The area near the window which is influenced by solar radiation is neglected.
SYNCSMART TM software developed by Breas Medical (Mölnycke, Sweden) provides an automatic detection and scoring of patient-ventilator asynchrony to help physicians in their daily clinical practice. This study was designed to assess performance of the automatic scoring by the SYNCSMART software using expert clinicians as a reference in patient with chronic respiratory failure receiving NIV. Methods: From nine patients, 20 min data sets were analyzed automatically by SYNCSMART software and reviewed by nine expert physicians who were asked to score auto-triggering (AT), double-triggering (DT), and ineffective efforts (IE). The study procedure was similar to the one commonly used for validating the automatic sleep scoring technique. For each patient, the asynchrony index was computed by automatic scoring and each expert, respectively.
An automatic analysis of the pressure and flow waveforms is required to substantially shorten the long duration spent performing visual scoring  (23). It was shown that detection of IEs by the means of an algorithm applied to the flow and pressure in the ventilation circuit was possible  (24) (25) (26) (27) (28) (29). All studies, but two, were performed in invasive ventilation where the leak is not relevant. Moreover, either only IEs were often considered  (24, 25, 27)  or only AEs without any further specifications  (26, 28, 30). DT has only been considered in two studies  (29, 31). ATs were never investigated with an automatic detection. It is therefore important to develop an algorithm to automatically detect the three main AEs (IE, DT, and AT) for NIV. The SyncSmart TM software was developed for such purpose, which works from the pressure and airflow waveforms sampled at the device rate (here 64 Hz).