dataesr/scientific-paragraphs-categorization huggingface dataset:
FIRST-PRINCIPLES CALCULATIONS The first-principles DFT calculations were performed using the projector augmented wave (PAW) method  39, 40  as implemented in the VASP  41, 42  software. Six different approximations to the XC functional were used. The LDA and the two constraint-based semilocal generalized gradient approximations (GGAs), PBE  43  and PBEsol,  44  where the latter is specifically designed for solids, were also used. 8] [49]  It captures truly nonlocal correlations and balances exchange and correlation by use of the Lindhard screening logic. 48  fraction of Fock exchange is included in two hybrid functionals. The vdW-DF-cx0p,  50  henceforth abbreviated as CX0p, is a hybrid extension of the CX functional  51  and includes both nonlocal correlations and nonlocal Fock exchange. The fraction of Fock exchange is set at 20%, following a coupling constant scaling analysis  52  of binding in sparse matter.
56 The theoretical spectra were obtained by powderaveraging of the coherent and incoherent, elastic, and onephonon emission cross sections, using phonon eigenvectors and eigenvalues from DFT based phonon calculations. The PowderTAS code performs, for a series of Q-values, the powder-average of the neutron scattering cross sections calculated for a large number of randomly oriented Q-vectors, hence forming a S(Q, ℏω) scattering map.
RESULTS AND DISCUSSION 4. 1. Neutron Powder Diffraction Measurements. The NPD data of BaZrO 3 (Figure  2 ) show that the material is Chemistry of Materials monophasic and can be indexed in the cubic Pm3̅ m space group at all three measured temperatures, T = 5, 100, and 300 K, in accordance with the results from previous NPD experiments. 20 The corresponding parameters from the Rietveld refinement are reported in Table  1. We note, however, that the refined occupancy factors correspond to a deviation from the BaZrO 3 stoichiometry, an excess of BaO or equivalently, a loss of ZrO 2 (Table  1 ). The lack of superstructure and secondary phase peaks indicates that deviation from the BaZrO 3 stoichiometry is accommodated by randomly distributed defects, which is consistent with the pronounced Lorentzian component of the peak shape (cf.
Further, the analysis of variable temperature inelastic neutron scattering data shows that the low frequency R 25 mode is quite temperature independent and stable down to at least T = 5 K. This is accurately predicted by the two hybrid functionals, HSE, which is based on the constraint-based, semilocal PBE and CX0p based on a current-conserving implementation of the vdW-DF nonlocal-correlation method. Both functionals predict a stable AFD mode but CX0p performs better for the value of the R 25 -mode frequency and the ZPE-corrected lattice spacing. The result from CX0p compares excellently with the corresponding experimental data. Furthermore, the R 25 -mode shows a weak temperature dependence, which is predicted by both hybrid functionals. As will be detailed elsewhere, the CX0p functional also performs better than HSE for predictions of thermal expansion and of extended X-ray absorption fine structure data for BaZrO 3 , while the two functionals have matching performances for the dielectric constant of BaZrO 3.
The authors acknowledge financial support from the  French Research National Agency  (through  ANR   THE-SEUS grant ), from the  center National de la Recherche Scientifique  (through  CNRS   TEMPO grant ) and from the  European Commission  (through  H2020 program 's ASTERIQS, TSAR and ThermoQuant grants). This work was also partly supported by the french  RENAT-ECH network.
The OASYS suite and its powerful integration features are used to implement a ray-tracing algorithm to accurately calculate the thermal load in any component of an undulator-based synchrotron beamline. This is achieved by sampling and converting the SRW source of a given energy into a Shadow source and using the latter code to ray trace the full beamline. The accuracy of the algorithm is proved by reconstructing the full undulator radiation distribution through an aperture and comparing the result with direct calculaton of the total power using SRW. The algorithm is particularly suited to analyze cases with complex beamline layouts and optical elements, such as crystals, multilayers, and compound refractive lenses. Examples of its use to calculate the power load on elements of two of the feature beamlines at the Advanced Photon Source Upgrade Project and a comparison of the results with analytical calculations are presented.
The power distribution of undulator radiation and its propagation through simple optical components can be calculated analytically with XOP (Sanchez del  Rio & Dejus, 2011)  or similar software, and by combining analytical calculations with ray tracings as in IDPower  (Reininger, 2001). However, existing tools are less accurate when considering elements with a complex reflectivity or absorption profile (e. g. crystal/multilayer monochromators and compound refractive lens) or when strong diffraction effects are present. Here we approach the problem by creating an ab initio procedure where no assumptions must be made and the behavior of the optical elements is accurately reproduced by robust simulation tools. The OASYS (ORange SYnchrotron Suite) environment (Sanchez del  Rio & Rebuffi, 2019)  is chosen to integrate all elements of the procedure, as well as to collect, display, and store the results.
ISSN 1600-5775 1. 1. The OASYS integrated environment Since 2013, OASYS has been developed as a versatile, userfriendly, and open-source graphical environment for modeling X-ray experiments by optical simulations  (Rebuffi & Sanchez del Rio, 2017a). Its concept stems from the need of modern software tools to satisfy the demand of performing more and more sophisticated analysis and design of optical systems for fourth-generation synchrotron and free-electron laser (FEL) facilities. The OASYS workflow mechanism describes a beamline by representing sources and optical elements as active visual elements (widgets) and the photon beam as the data content passing through their connections (wires). An example of the OASYS user interface is shown in Fig. 1. The ultimate purpose of OASYS is to integrate in a synergetic way the most powerful calculation engines available to perform virtual experiments in a synchrotron beamline, from the electron emission to the sample interaction.
For X-ray optics, OASYS integrates diverse strategies via the implementation of different simulation tools (e. g. ray-tracing and wave optics packages). It provides a language to make them communicate by sending and receiving encapsulated data  (Rebuffi & Sanchez del Rio, 2017b). OASYS itself is an empty container, while application program interfaces (APIs) are released as distinct groups of widgets called 'add-ons', which can be installed individually by users. Two tools relevant for this study are the ray-tracing program Shadow (Sanchez del  Rio et al. , 2011; Rebuffi & Sanchez del Rio, 2016)  and the wave optics program SRW  (Chubar & Elleaume, 1998; Chubar et al. , 2002 Chubar et al. , , 2013)). Moreover, OASYS makes the availability of different programs to calculate the individual response of each optical element and the characteristics (e.
g. emitted flux and power) of a source. This concept of toolbox is imported from XOP (Sanchez del Rio & Dejus, 2011), developed since 1996, which became very popular in synchrotron facilities. All applications in XOP have been ported to Python and integrated into XOPPY. XOPPY interfaces undulator simulation codes such as US, URGENT  (Walker & Diviacco, 1992) , and SRW. For crystals, the add-on XRayServer implements a front-end of X-ray diffraction and scattering routines available in its widely used web server  (Stepanov, 2004). OASYS contains a framework providing the glossary for the definition of light sources and optical components as a common layer beyond APIs. This framework separates the physical description of elements from details of the calculation algorithm. It allows users to easily benchmark calculations by using different codes for the same simulation  (Rebuffi & Sanchez del Rio, 2017b).
3. 2. 3. 1. Smoothing the results. The ray-tracing results are intrinsically noisy because of the user-selected finite number of random rays. Therefore, the accumulated power density distribution (histogram) has to be smoothed to remove the unphysical high-spatial-frequency noise. In our implementation, the package scipy. ndimage (Scipy, 2019), a well known and consolidated software library that provides several options to apply smoothing filters, is used Example of the power density implementation in OASYS: the looping mechanism is extended by a new looping point capable of sampling the energy spectrum calculated by XOPPY and sending the obtained energy intervals to the new undulator widget. The undulator widget generates rays based on the spatial and angular probability distribution of the radiation calculated by SRW.
It can calculate the incident, absorbed, and transmitted power density distribution at any point of the beamline, from the source to the sample. The tool takes full advantage of these software to accurately calculate the power propagation along the beamline, taking into account the physical behavior of optical elements. The validation and accuracy of the program were demonstrated by comparing the reconstructed power density distribution of the emission of an insertion device through a front-end mask with the reference results from SRW. Two examples taken for our design of the APS-U beamlines illustrated the capabilities made available by the new tool. The ISN beamline features multiple optical elements, secondary focusing geometry, and diffraction-limited coherence focusing. Using the new tool, we were able to analyze the incident and absorbed power density distribution at critical points along the beamline up to the sample position.
In the second case, we studied the accidental focusing of the pink beam on the radiation safety component. This ab initio algorithm is particularly suitable for calculating complicated optics (e. g. compound refractive lenses) under broad bandwidth radiation and even pink or white beam. Furthermore, the new algorithm can provide more accurate and detailed results which allow the study of extreme cases otherwise could not be calculated. Finally, the algorithm is fully integrated into the OASYS environment with graphic interfaces that are easy to configure and use. The aim of the new tool is to help the thermal analysis of optical and safety components at next-generation synchrotron facilities. It will be beneficial for the many members of the OASYS users' community, that often offer feedback to improve the software and to understand the most critical needs and trends on optics simulation tools for synchrotrons.
Acknowledgements We thank our colleagues at  APS ,  Dana Capatina ,  Jonathan Knopp ,  Tim Graber  and  Jo ¨rg Maser  for the collaboration on the design of XPCS and ISN beamlines and for the numerous fruitful discussions on the content of this paper. We acknowledge  Dr Howard Padmore  ( Lawrence Berkeley National Laboratory ) for sharing the idea of coupling SRW and  Shadow  to provide an accurate ray tracing of the undulator source. We warmly thank  Laura E. Atseff  for proofreading this paper. This work was supported by the  US Department of Energy, Office of Science, Office of Basic Energy Sciences , under contract  DE-AC02-06CH11357.
The quantity of bacteria was determined on the doublechannel RGB images using ImageJ® 1. 47V software. Bacteria with active green GFP metabolism or damaged membrane as demonstrated by PI staining are called "Live" and "Damaged" in the following text, respectively. For each experiment, 7 locations were observed on each surface sample, and two identical substrates of each type were analyzed. Experiments were repeated three times. Results are expressed as average ± standard deviation. Significance of differences was tested by Student t-test.
EPR data were plotted as I/I0 vs. illumination time. Noteworthy, without the presence of paramagnetic probe, no EPR signal is detected. Illumination of the capillaries was performed outside the EPR cavity with an UV neon-based labmade illumination chamber. Each neon presents a lmax = 365 nm (hn = 3. 4 eV) and E » 1. 5 mW. cm -at ca. 1 cm distance measured with a powermeter 1936-C and a 918D-UV-OD3 detector from Newport). The emission spectra of the neons and a schematic description of the experimental setup are depicted above. As TEMPOL does not absorb at this wavelength, there is no absorption variation during measurement. Electronic photo-generation processes occur only during the irradiation procedure and are assumed to stop as soon as light is off. All experiments were performed at room temperature (295 ± 1 K).
ACKNOWLEDGMENT This work is part of the  PLASMOTEX  project funded by the  CARNOT MICA (Materials Institute Carnot Alsace). A. Rach  and  M. Wolf  (ICPEES) are thanked for their contributions on the photocatalytic test setup. F. Courtier  and  M. Basler  ( ICS ) are both acknowledged for the implementation of the illumination chamber for EPR characterization of the films.
All the intensities values, proportional to the TEMPOL concentration (I µ [T•]), were obtained after baseline correction and double integrations. EPR data were plotted as I/I0 vs. illumination time. Noteworthy, without the presence of paramagnetic probe, no EPR signal is detected. Illumination of the capillaries was performed outside the EPR cavity with an UV neon-based labmade illumination chamber. Each neon presents a lmax = 365 nm (hn = 3. 4 eV) and E » 1. 5 mW. cm -at ca. 1 cm distance measured with a powermeter 1936-C and a 918D-UV-OD3 detector from Newport). The emission spectra of the neons and a schematic description of the experimental setup are depicted above. As TEMPOL does not absorb at this wavelength, there is no absorption variation during measurement. Electronic photo-generation processes occur only during the irradiation procedure and are assumed to stop as soon as light is off.
Acknowledgements: We acknowledge financial support for this work from the  Asociación Española Contra el Cáncer (AECC) , project  PS16163811PORR , the  Spanish MINECO  under contract  FIS2015-69941-C2-1-P ,  Junta de Andalucía (regional government)  under contract  P11-FQM-8229 , and the  Spanish Fundación ACS , sponsor of the University Chair Neutrons for Medicine. M. P. acknowledges a grant under the  program Becas de Iniciación a la Investigación  from the  Universidad de Granada (Plan Propio de Investigación). We also thank very valuable and useful comments from the referees of this manuscript.
Acknowledgements We thank  Maxim Fatkullin ,  Vladimir Bogoslovskiy , and  Sergey Kim  for help with the LMod-G electrodes;  Antonkin Nikita  for the IR measurements; and  Elizaveta Sviridova  for the wettability measurements. We are thankful to  Mekhman Yusubov  for essential support that made this work possible. This research is supported by the  Tomsk Polytechnic University  within the framework of the  Tomsk Polytechnic University Competitiveness Enhancement Program ,  5-100 , and by the  Sichuan Science and Technology Program  (Grant  2018HH0152 ).
EPR Characterization of Solar Cells Before performing the EPR analysis of the complete DSSC, it was necessary to measure the solar cell electrical performance in the absence and the presence of the spin probe. Indeed, only cells showing well-measurable and reproducible electrical performances (not reported) were tested by EPR. After measuring their electrical functionality, the cells were placed on the EPR cavity where a static magnetic field is applied while a source of microwaves is also applied to the sample. This allows the resonance condition that is necessary to promote the probe (4C-T) signal. EPR spectra were recorded with an EMX-Bruker spectrometer operating at the X band (9. 5 GHz) and interfaced with a PC (software from Bruker for handling and recording the EPR spectra). The EPR spectra were simulated with the NLSL software  [58].
Acknowledgments: The authors thank the  Université de Montpellier  for financial support. S. R and S. C. also thanks Phyco-biotech for providing spirulina. Research in Mons is supported by  FNRS-FRFC  (through the  Excellence of Science  '2Dto3D' project and  Consortium des Equipements de Calcul Intensif-CECI)  and the  European Commission  and  Région Wallonne  (through the  FEDER   BIORG-EL  project). M. S and D. B. are FNRS senior research associate and research director, respectively. J. B. acknowledges the PLATINOM technologic platform at XLIM (and common facility of the  University of Limoges) , as well as the  SIGMALIM Laboratory of Excellence.
Salem conducted a systematic study of the mechanical properties of transparent AlON and MgAl 2 O 4 ceramics, and thoroughly evaluated whether they can be used for spacecraft windows  [809]. The window materials used in spacecrafts (e. g. , the NASA space shuttle) must be able to meet numerous requirements, including maintaining cabin pressure according to the design of the vehicle, withstanding the thermal shock caused by alternative rapid heating and cooling, resisting high impact at different velocities, retaining high optical performance in various harsh environments, in order to avoid catastrophic failures. Therefore, the optical, thermal, and mechanical properties of the window materials must be well-characterized to ensure that they satisfy the stringent requirements. A database for the design and characterization of transparent ceramics for such special applications should include the following characteristic parameters: (i) elastic constants, (ii) texture and grain sizes, (iii) fracture toughness, (iv) strength and slow crack growth, (v) failure sources, and (vi) residual stress  [838].
Acknowledgement This work was supported by the  National Natural Science Foundation of China  ( 51762023  and  51962013 ), the  Natural Science Foundation of Jiangxi, China  ( 20192ACB20018 ), and  Key R&D Program of Jiangxi Province  ( 20171BBE50006 ,  20192ACB80007 , and  20192ACB80004 ). Ling Bing Kong would like acknowledge  Shenzhen Technology University (SZTU)  for financial support through a  Start-up Grant  ( 2018 ) and grant from the  Natural Science Foundation of Top Talent of SZTU  (grant no. 2019010801002 ). Dr Zhuohao Xiao  received his PhD degree from  Central South University, China , in 2009. He is currently a Professor in  Jingdezhen Ceramic Institute. His research interests include optical materials, glass-ceramics, glass materials and other functional materials. He serves as referees for various international and domestic journals.
This paper tackles the problem of the classification of partial discharge (PD) and noise signals by applying unsupervised and semi-supervised learning methods. The first step in the proposed methodology is to prepare a set of classification features from the statistical moments of the distribution of the Wavelet detail coefficients extracted from a dataset of signals acquired from a test cell under 40 kVDC. In a second step, an unsupervised learning framework that implements the k-means algorithm is applied to reduce the dimensionality of this initial feature set. The Silhouette index is used to evaluate the number of natural clusters in the dataset while the Dunn index is used to determine which subset of features produces the best clustering quality. Since the unsupervised learning does not provide any method for result validation, then the third step in the methodology of this paper consists of applying a semi-supervised learning framework that implements Transductive Support-Vector Machines.
The NN which separately learned both PD and noise patterns discriminated unknown PD patterns from accompanying external noise with a correct response rate of only 52% in average. The correct responses of the NN rose to 89% in average when the NN learned PD patterns inclusive of external noise instead of those without noise. The NN could correctly discriminate all unknown input patterns for a signal to noise ratio greater than or equal to unity. However, these techniques require a previous manual labeling of the data by the user. In many classification problems with large datasets, the manual labeling of data is a labor-intensive task. Moreover, it can lead to human errors, especially when signals are not easily distinguishable, resulting in identification problems. In order to increase the unsupervised character of PD monitoring, there have been strong efforts to develop and improve PD and noise separation techniques using different unsupervised clustering methods.
In  [10] , a PD and noise identification method based on TF (Time-Frequency) map is used. The data are obtained from measurements relevant to cable models having an artificial defect made by knife cut. The TF map allows an effective pulse separation and noise rejection under DC. In  [11]  the wavelet-decomposition and PCA method was applied to pulses produced by known noise and PD sources during experiment. The three main energies of the signals associated with each decomposition level were selected using PCA and used to form a 3-D plot. Three different clusters were obtained on the 3D map. One cluster corresponds to pulses produced by micro-voids within the test samples and the two other ones are due to noise signals. The application of DBSCAN allowed the optimum separation of the different groups minimizing the losses of isolated data.
This proposed algorithm proved to be effective at separating different PD sources and noise and the analysis of the PRPD patterns confirmed the quality of separation. As mentioned in these previous studies, the clustering results were verified using a database of well-known phase-resolved (PRPD) or timeresolved PD patterns (TRPD) for AC or DC respectively. These typical patterns are commonly used as a reference for visual verification. In addition, the waveforms can also serve the purpose of validating the results. Nonetheless, the visual or manual validation process may grow in complexity as the datasets become larger. Moreover, PRPD or TRPD patterns are able to identify different PD and noise sources when the noise level is low compared to the amplitudes of the PDs. However, real insulation systems usually exhibit several PD sources and the noise level is high, especially if measurements are performed on-line.
Since the waveform of each signal in the experimental dataset is available, in the second part of this paper, a semi-supervised classification technique based on Transductive Support-Vector Machines (TSVMs) is implemented. An advantage of semi-supervised learning is that a test set can be built from labeled data to evaluate the classification performance of the algorithm. This contributes to reduce the complexity of clustering results validation in unsupervised learning and in turn, the validation can be performed automatically, without the need of visual verification from an expert. Semi-supervised learning has recently become popular due to the variety of cases where a lot of unlabeled data are available, for example text classification  [12]  or image processing  [13]. However, this field has not been fully investigated for Partial Discharge monitoring and especially for PD-noise pattern classification.
This procedure exploits both labeled and unlabeled data to build the best classifier for PD-noise discrimination. Moreover, it requires only a reduced set of labeled data compared to unlabeled data. In our approach, we use the values of peak amplitude and charge from the signals  [14]  to assist the user in the labeling of test set. A dataset of 100 PD signals and 100 non-PD signals were so labeled. Finally we discussed the high classification performance achieved by labeling a small share of the dataset.