dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT , Argentina; YerPhI, Armenia;  ARC, Australia ;  BMWFW  and  FWF , Austria; ANAS, Azerbaijan; SSTC, Belarus;  CNPq  and  FAPESP, Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ; CERN; CONICYT, Chile; CAS,  MOST  and  NSFC, China ;  COLCIENCIAS, Colombia; MSMT CR, MPO CR  and  VSC CR, Czech Republic ;  DNRF  and  DNSRC, Denmark ;  IN2P3-CNRS ,  CEA-DRF/IRFU , France; SRNSFG, Georgia; BMBF,  HGF , and  MPG, Germany ;  GSRT, Greece; RGC, Hong Kong SAR, China ;  ISF and Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ; CNRST, Morocco; NWO, Netherlands; RCN, Norway;  MNiSW  and  NCN , Poland; FCT, Portugal; MNE/IFA, Romania;  MES of Russia  and  NRC KI , Russian Federation; JINR; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DST/NRF, South Africa; MINECO, Spain;  SRC and Wallenberg Foundation, Sweden ;  SERI ,  SNSF  and  Cantons of Bern and Geneva, Switzerland; MOST, Taiwan ;  TAEK, Turkey; STFC, United Kingdom ;  DOE  and  NSF ,  United States of America.
Introduction A bad fitted saddle may hurt or injure the horse's back. It is an important challenge for saddle designers to understand how a saddle interacts with different back shapes. Fitting is usually assessed by subjective observations, and if some rules exist, they remain subject to interpretations and mistakes. The use of electronic pressure mats to assess saddle fitting has started 25 years ago  (Harman 1994). It enables to provide objective measurements. It was first used to link pressure data and horse's back health problems (1, Table  1 ), then to evaluate the influence of saddle parameters on the pressure distributions  (6) (7) (8) (9) (10) (11) (12). The purpose of the current study is to review how pressure mat measurements were used in the literature to assess the influence of different saddle parameters, and thus suggest some general guidelines.
Search strategy The electronic search was performed in February 2019 in the SCOPUS, PubMed, and Web of Science databases. The keywords used were 'horse Ã ', 'saddle', 'pressure' and 'electronic or mat Ã or pad Ã or distribution or measurement or computerized'. Exclusion criteria were: conference proceeding, review, not an English saddle studied, no pressure mat/pad used, studies focusing on rider position, gait or saddle pad, and without dynamic recording. There was no language restriction.
This suggests that the topological Hall contribution has maxima near the coercive fields. In the literature this is sometimes referred to as a "hump structure. "  [10] ypical measurements of the Hall resistivity as a function of a magnetic field applied perpendicular to a 40 nm thick SRO film are shown in Figure  2a. The Hall resistivity is proportional to the magnetic field at high fields, which is attributed to the ordinary Data for the orthorhombic structure were taken from Jones et al. [27]  and for the tetragonal structure from Chakomoukos et al. [40]. The images were drawn using the software VESTA. [41] all effect. At lower fields a hysteresis loop is seen that is rather similar to the magnetization loop (compare the 10 K data for the same film in Figure  2c ). Therefore, we attribute the hysteretic contribution to the AHE.
[12] e have studied these phenomena in asymmetric [SrIrO 3 / SrRuO 3 /SrZrO 3 ] 6 SLs with six repetitions and layer thickness of 2 unit cells for SrIrO 3 and SrZrO 3 and 6 unit cells for SrRuO 3  [59]  and present data here to show the magnetic field dependence of the Hall effect in this system. The Hall effect of these SLs is very similar to that of orthorhombic films, but close to the compensation point at which the anomalous Hall constant changes sign, Hall resistivity loops with hump structure were observed (Figure  4 ). Direct evidence for the presence of magnetic skyrmions in SrRuO 3 is scarce. High-resolution magnetic force microscopy (MFM) measurements on SrIrO 3 /SrRuO 3 bilayers showed the presence of magnetic bubble domains during the remagnetization processes close to the coercive field, but also clear evidence for the existence of small circular domains with radii of about 10 nm or smaller.
[17] The stray-field contrast of the bubble domains indicated that the domain walls were Néel walls with counterclockwise orientation. Assuming that the wall characteristics of the small bubble domains are the same, the MFM imaging indeed hints at the existence of skyrmions with Néel texture in SrRuO 3. In another MFM study of a SrRuO 3 /SrIrO 3 /SrZrO 3 trilayer with 4 to 5 unit cell thick SrRuO 3 layers, the formation of skyrmions was however not observed, although the AHE resistivity loops showed the specific hump structure. [60] roenendijk et al. [11]  studied the Hall effect of STO/SRO/ STO, SIO/SRO/SIO and STO/SRO/SIO heterostructures and found a positive AHE in SIO/SRO/SIO, a negative AHE in STO/SRO/STO, and a compensation point with accompanying hump structures in STO/SRO/SIO. They interpreted the data as tuning of the Berry curvature in SRO by the adjacent SIO layer.
In the case of SL 2/8 the LSMO magnetization component is still small in the perpendicular-to-plane direction, but the SRO magnetization component is larger along the [110] direction than in the other directions. This shows that the magnetization state is not collinear but that spin canting is present, the latter being in agreement with polarized neutron reflectometry data  [66]  and with magnetization measurements on LSMO/SRO bilayer samples with rather thick layers. [67] s a new feature, the LSMO layers in SL 2/8 acquired a sizable in-plane uniaxial anisotropy; see the differences in the LSMO magnetization contribution in Figure  6a  for the ½1 10 and [001] directions. The uniaxial anisotropy might be due to the antiferromagnetic coupling but is present in the SRO paramagnetic phase. However, whereas the [001] direction is magnetically hard in the SRO layers, it is the magnetic easy axis in the LSMO layers (Figure  6a ), so it is not simply the SRO hard axis direction being transferred by antiferromagnetic coupling into the LSMO layers.
Anisotropic Magnetoresistance Measurements of the anisotropic magnetoresistance (AMR) were performed between 10 and 150 K. Here, only 10 K data are presented, as these show MR features induced by the magnetocrystalline anisotropy of the SRO layers most clearly. Generalizing the standard AMR definition, the MR was defined as AMR ¼ [R(θ) À R(0)]/R(0). Figure  14  shows the MR of sample SL 2/8 measured with the magnetic field rotating in two mutually orthogonal planes. The left panels reveal a pronounced jump at an angle of about À120°. This jump is caused by an abrupt movement of the magnetization vector when the magnetic field vector is swept across the direction of a magnetic hard axis. This feature is characteristic for orthorhombic SRO layers (compare Ziese et al. and Kan et al. [70, 71]  ).
2. The anisotropy and antiferromagnetic coupling can be further studied by magnetic field-dependent MR measurements, as shown in Figure  16  for SL 2/8 and in Figure  17  for SL 4/8. In both figures the left panels present MR data recorded in perpendicular fields (note again the different orientations of the SRO layers), the right panels in parallel fields. Although in perpendicular orientation the current density is always perpendicular to the magnetic field, SL 2/8 showed a striking MR anisotropy (Figure  16a-c ), whereas in this configuration the MR of SL 4/8 is isotropic or only weakly anisotropic (Figure  17a-c ). This is consistent with the angle-dependent MR measurements and the assignment of the SRO layer orientation with respect to the substrate. Since the interpretation of the MR data of SL 4/8 is more evident, the data in Figure  17  are discussed in detail first.
II. SuperKEKB PHASE I COMMISSIONING The SuperKEKB commissioning is split in three phases and each phase has its defined goals. The Phase I commissioning was a single beam commissioning and it took place starting from February until June 2016. During this period, BEAST II, a subgroup of the Belle II collaboration, was pursuing single beam loss studies near the IP of SuperKEKB, in order to enable assessments of the background conditions  [6]  within the Belle II inner detector volume  [7, 8]. Our luminosity monitors (diamonds, Cherenkov, and scintillators monitors) also took data during that period. This commissioning phase of SuperKEKB took place without the final focus system and without the Belle II detector. Both positron and electron beams were successfully produced, accelerated, injected and stored in the main rings (LER and HER) of SuperKEKB.
Loss rates were calculated using appropriate weights taking into account the cross section and the parameters of the circulating beams, separately for each of the three different single beam loss processes (bremsstrahlung, Touschek and Compton), at specified observation points, either all along the ring for global loss studies, or locally at our diamond sensor. This enabled us to obtain statistically significant results, including for the dynamics (energies, positions in the vacuum chamber, angular distributions, etc…) of the tracked particles corresponding to each process, at the beginning of the drift where we placed our diamond sensor, to be used later to study the signal in the DS using GEANT4. The original code was further optimized to calculate the loss rates at the position of our DS with sufficient resolution and precision.
The signal in the DS was then simulated separately by a standalone GEANT4 code, taking into account the geometry of the beam pipe, its material and thickness (6 mm Cu cylindrical beam pipe, 40 mm radius physical aperture) and the dynamics of the lost particles (energy, angular distribution, etc. ). The GEANT4 code simply defines a 3 m long Cu cylindrical beam pipe (40 mm radius ¼ physical aperture) and a 4 × 4 mm 2 , 500 μm thick diamond sensor touching the drift. The code receives the primary particles tracked until the entrance of the drift of our DS from the SAD code, the received information consists of the energies, positions, and angular distributions of the primary positrons. Depending on their initial dynamics (mainly energy and exiting angle), the lost positron particles in the drift may or may not exit the 6 mm Cu beam pipe (radiation length X 0 ¼ 1.
B. Electronic readout For Phase I, two (4 × 4 mm 2 , 500 μm) sCVDs connected to CIVIDEC charge amplifiers were used in the LER. The signals from the sCVDs were sent to the electronic rack located in the Belle II experiment electronic hut by rigid, about 100 meter long, half inch heliax cables. The electronic rack was equipped with a 10 bit Keysight oscilloscope (2. 5 GHz bandwidth and up to 20 GS/s sampling), a PC serving as gateway to the Linux operated Belle II DAQ computers, a Windows based server on which analysis using MATLAB was performed, a high voltage power supply for the diamond sensors (400 V) and a low voltage power supply for the charge amplifiers (12 V). The oscilloscope was used to record the data during Phase I commissioning, using two signal acquisition procedures, either a succession of continuous 10 ms records that could be processed on-the-fly every 5-6 seconds or up to fifty 410 ms long records spaced by few seconds, where the processing could only take place afterwards and lasted for a few hours.
VII. CONCLUSION Single beam loss measurements were performed successfully during the Phase I commissioning of SuperKEKB. Measurements were done using 500 μm diamond sensors placed 11. 9 meters downstream of the IP in the LER. Data analysis of Touschek scan studies enabled extracting the loss contributions in our DS from both beam-gas bremsstrahlung and Touschek scattering. Moreover, simulations were reasonably consistent with measurements, within statistical uncertainties. The qualitative and quantitative agreement between our simulations and the measurements enabled us to make preliminary estimates of the level of background in the luminosity monitor diamond sensors that could be expected during the subsequent phases of commissioning and operation of SuperKEKB. The background signal is expected to be two orders of magnitude smaller than the radiative Bhabha scattering signal.
Microstructure characterization The characterization of the LSCF microstructure was performed by Scanning Electron Microscopy (SEM) with a field emission gun equipment (ZEISS Ultra 55), operating at 3-10 kV voltage and 6. 4-7 mm working distance. The samples were coated with Pt to avoid electron charging. The samples were analyzed on the surface as well as on a fresh fractured section. For the porous sample, the average grain and pore sizes, as well as the porosity, were estimated using the ImageJ software. This analysis was performed on a polished LSCF surface.
The oxygen gauge was a home-made potentiometric sensor based on yttria stabilized zirconia. It used ambient air as reference atmosphere and was operating at 700°C. The electrochemical measurements were performed with a Solartron SI 1280B electrochemical equipment. Electrochemical impedance measurements at Open-Circuit Voltage (OCV) were first measured as a function of temperature, in order to check the reproducibility of the electrode and evaluate the precision of the measurement. They were carried out with an ac signal amplitude of 50 mV, over a frequency range of 20 kHz to 0. 5 Hz. Impedance spectra were fitted with electrical circuits using the EC-Lab ® software (V11. 01) as described in our previous work  [25]. This allowed the determination of the ohmic series resistance, Rs, corresponding to the highfrequency intercept with the real axis, the electrode polarization resistance, Rpol, determined from the low-frequency intercept, and the top impedance diagram frequency, f0, as defined in Fig.
Acknowledgements This work was financially supported by the  Institute of Engineering Univ. Grenoble Alpes  ( AGIR-POLE 2016 ). The authors would like to thank  Dr C. Montella  ( LEPMI, Univ. Grenoble Alpes ) for fruitful discussions on electrochemical kinetics and  Dr F. Charlot  ( CMTC ,  Univ. Grenoble Alpes) for making the SEM observations.
The automatic combination of explicit knowledge can be managed by AI methods, such as logical expert systems and machine learning methods. Applications of automatic reasoning methods are more and more numerous in the domain of engineering  (Salehi and Burgueño, 2018). While many expert systems based on logical reasoning were developed in the 90's  (Wagner, 2017) , AI reasoning methods are now massively applied to improve product design processes. These methods are able to learn from gathered data, to manage uncertainty, to recognize patterns and to analyze large amount of data (i. e. with the application of Deep Learning methods). One method must be selected to define a KBS for NDoE process. In the next sub-section, some of the main AI methods are compared to choose the most appropriate, among logical reasoning, uncertain reasoning and machine learning sub-domains (Figure  4 ).
Decision trees Decision trees are based on a hierarchical sequence of tests performs on learning data, to build a tree. Different types of decision tree algorithms exist, as CART  (Breiman, 2001) , C4. 5  (Quinlan, 1996) , CHAID  (Kass, 1980) , and also several evolutions, as random forest. Decision trees can be used for feature selection and classification, for instance, for fault diagnosis  (Sakthivel, Sugumaran and Babudevasenapati, 2010). These methods are non-parametric (no hypothesis on data distribution), non-linear, robust and easy to understand by a clear graphical representation. Over-fitting effects can be limited by corrective methods, as pruning methods  (Sahin, Tolun and Hassanpour, 2012). But, it is impossible to pre-define the structure of the tree. The structure is computed automatically.
The process-oriented ontology PARO (Le Duigou and Bernard, 2011) is also linked to the proposed ontology. For instance, PARO provides a detailed description of the concept "Resources", as human and technical resources available in a project. The knowledge base contains all data required to define the NDoE process, in connection with product and project data. More specifically, it contains the entire definition of each NDoE process, results from experiments and analysis, and performance indicators (e. g. execution time, accuracy and predictivity measures for a surrogate model, number of added experiments in case of an adaptive NDoE, etc. ). The inference engine, detailed in the next sub-section, is based on a dataset extracted from the ontology.
If these performance indicators do not match with designers' requirements, the KBS switch on the first mode to propose new and better configurations. These predictions must be proposed with some indicators to inform the designer whether these predictions are trustworthy or not. In order to set a bayesian network to assist designers in defining a NDoE process, three steps are required: (1) the selection and the definition of the network's variables; (2) the estimation of the structure and probability distributions by learning from data and/or adding expert knowledge; and (3) the validation of the bayesian network, to control the trustworthiness of its predictions. Next subsections give details about these three steps. More generally, the process to define the bayesian network follows Knowledge Discovery in Databases (KDD) methodology  (Lara et al.
Validation step The validation of the bayesian network consists in a cross-validation to assess its ability to propose good predictions. A common way is to split the dataset in two subsets. The first subset (e. g. 80% of the whole dataset) is used for the learning step, and the second subset is used to validate the learning step  (Powers, 2011). Once the structure and probabilities are completely determined, the bayesian network is assessed on the validation subset, and a series of confusion matrices is computed. Each confusion matrix is specific to a given variable. Common indicators used in machine learning are computed from this matrix (detailed in  (Powers, 2011) ), such as the precision (Predicted Positive Value, PPV), the sensitivity (True Positive Rate, TPR), the fall-out (False Positive Rate, FPR), the Negative Predicted Value (NPV) and the Specificity (True Negative Rate, TNR) of the bayesian network, for this variable.
The goal of this case-study is to illustrate the usefulness of the application of a KBS based on bayesian networks in the context of the product design process. In this context, few data are available. Thus, the case-study is based on a relatively small dataset. The use-case shows how this KBS could assist designers to configure their NDoE process faster to produce an effective surrogate model of the finite-element model of the air-blower. Then, the surrogate model is used interactively to make decision fast during meetings. Thanks to the surrogate model, the behavior of the product is stored in a lighter and more neutral form. There is no need to use the simulation software, or to keep it operational, to regenerate the results after several decades. The proposed KBS was implemented with following components: • The inference engine, a bayesian network, is based on the R package 'bnlearn'  (Scutari, 2010; Denis and Scutari, 2014 ); • The knowledge base was designed in Protégé 5 and used with Virtuoso Universal Server®; • The NDoE process was built on a set of Python scripts in connection with the software Uranie  (Gaudier, 2010).
Definition of variables and generation of the dataset The variables of NDoE process configuration were defined according to the surrogate modeling objective and the available methods in Uranie (Table  2 ). NDoE process configurations were generated randomly according to variables and event defined in Table  2. For instance, a configuration can be composed of a NDoE based on Latin Hypercube Sampling, with 17 experiments, a polynomial chaos model with a maximal degree of 2. with the Q² estimator  (Iooss and Lemaître, 2015). The FAIL event means an error occurred. The closest to 1 is the best. Based on air-blower finite-element model, 2000 NDoE processes were executed to create a first dataset to assess our proposal. 2000 configurations is a high number since the capitalization of NDoE data is rare in mechanical industries, but it covers almost 0.
5% of possible configurations only, in accordance with considered variables and events. The aim of this KBS is to help designers even with a small dataset. Each configuration had the same probability to be generated. The only exception concerns the degree of polynomial chaos models. The degree and the number of experiment are dependent. The number of experiments n, required to compute coefficients of the polynomial models, is determined by = ! ! ! , where p is the degree of the polynomial basis, and q is the number of factors of the finite element model. Thus, with 8 factors, 45 experiments are required for a degree of 2. In this usecase, mistakes were deliberately added in the dataset to simulate some human errors and to assess the ability of the KBS to discover the rule by itself. This equation was not followed to generate the dataset.
But, to generate the dataset, the degree of the polynomial basis can be 2 from 25 experiments. This dataset does not aim to be complete and perfect. Such a dataset may be considered as a large dataset in mechanical design departments. Thus, even with uniform distributions to generate NDoE process configurations, some methods actually occurs more often than others and some specific configurations were not in the dataset. Some variables of the bayesian networks may suffer from lack of data. These imperfections were deliberately kept to illustrate more realistic situations. These NDoE configurations were executed, and stored and structured with their results in the ontology  (Blondet et al. , 2018). Relevant data are extracted from the ontology with SPARQL 3  requests. These data are selected regarding the context of the current study (e.
Learning step The learning dataset is 80% of the full dataset. The structure is determined, in this case, only by learning from data. No expert knowledge was included. The hill-climbing algorithm was used to estimate the graph, as shown in Figure  8. This graph shows the dependencies between the variables of the configuration of the NDoE process. The full dataset is not supposed to be complete nor balanced. This choice aims to reproduce a realistic situation during a product design process. Thus, the graph of the bayesian network is an estimation at a given time. This graph will be improved with the progressive enrichment of the knowledge base.
These results shows a low probability of success for polynomial chaos surrogate model of degree two with 38 to 50 experiments, and near-zero probabilities for less than 37 experiments. The rule, deliberately ignored to generate the dataset, is about to be identified empirically by the KBS. Designers select one of the most efficient configurations, with the lowest number of experiments, for instance the first one. At this stage, users are free to modify the proposed configuration as they wish. The final configuration is defined, as shown in Table  6. This configuration is set and the NDoE process is executed by Uranie and Code_Aster. As a result, the optimization of the HVAC system relies on a cheaper function. The optimal geometry and material properties of dampers are computed in several minutes instead of hours, but the correctness of the optimal result depends on the quality of the surrogate model.