dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Integrity Checking and Interpolation Under real operating conditions, the integrity of the data must be checked. Indeed, failures or malfunctions can lead to missing raw measurements in the database. We therefore propose a preprocessing step of the raw data to verify the data and to complete by interpolation eventually missing data. The whole proposed preprocessing strategy is represented by Figure  4. The raw time series is extracted from the database for each day. Since a forecast of water consumption is targeted with an accuracy of one hour, the data are sampled with a resolution of minute (i. e. , 1440 mn per day). This preprocessing is achieved separately for each day. Then, periods without consumed liters, i. e. , events, are identified and corrected by interpolation. Data analysis and forecasting with ML algorithms needs to be achieved with no missing or inconsistent values.
Water Consumption Forecasting To evaluate the efficiency of the platform and the ML techniques, we focused on the water consumption of a private building. The water consumption is collected from a smart meter which is a single measurement point for the whole building. These are the only data available from the building and the users. The objective consists of forecasting the number of liters of consumed water with a horizon of one hour and to predict the instant of the next consumed liter by different ML approaches. All the algorithms have been developed with the Matlab R2018b environment on a desktop computer with 4 cores (Intel i7 processors at 3. 6 GHz) and 16 GB of memory. Experiments and tests have been carried out under the same conditions to find the values of the learning parameters by trial and error (learning rate value, number of neurons, number of hidden layers, type of activation function) to provide the smallest error.
Hourly Water Consumption Forecasting A three-month database (from October 2018 to December 2018) has been chosen to forecast the number of consumed water liters in the next coming hour. The data sequence is resampled with a resolution of one hour and is represented by Figure  5. This consumption has been recorded in a domestic house in France occupied by two people who consume on average 194 L per day (l/d). Household information will not be used by the ML approaches. Two ML approaches have been implemented for a one-hour water consumption forecasting, the LSTM and the BPNN. For this case, the series represented by Figure  5c  is the input of the forecast approaches. With the LSTM, input x(t) in Equations (  1 ), (  2 ), (  4 ) and (  5 ) is the preprocessed cumulative LC C j. We use the Adam algorithm, i.
For this purpose, we chose a dataset composed of 4321 events dated in milliseconds, each representing the time difference between two consecutive liters. Obviously, this dataset provides more detailed information about the water consumption than in the previous experiment. The dataset has been recorded between December the 2nd to the 20st, 2018 and is represented by Figure  6. The dataset is divided into three subsets for the learning of the LSTM and the BPNN, the training, validation and test subsets which are respectively distributed in a percentage of the dataset: 60%, 0. 3% and 40%. The parameters of two learning approaches are summarized in Table  2. Their input vector x(t) is composed of the time difference between two successive consumed liters, i. e. , the values of δ i represented by Figure  3.
Discussion on the Hourly and Events Water Consumption Forecasting Two forecasting tests have been experimented with the proposed water consumption collecting platform, i. e. , hourly and event forecasting. The first case consists of predicting the amount of water consumed during the first hour that follows the period of the collected dataset. In the second case, the instant of the next consumed liters is predicted. In both cases, an LSTM and a BPNN architectures have been designed. Their performance has been evaluated under the same conditions and have been compared in terms of precision, computational resources and execution time. With very close resources and approximately the same execution time, the forecasting error obtained with LSTM is 3 times lower than with the BPNN. In both experiments, the LSTM is more appropriate than the BPNN to grab the temporality of the data sting tests have been experimented with the proposed water consumption collecting platform, i.
Conclusions In this study, we presented a web-oriented platform to collect in real-time water consumption data and to predict them with machine-learning approaches. The data are issued from smart meters and are transmitted to a server to be handled as unevenly spaced time series with high resolution, i. e. , in milliseconds. Data sets are then extracted, preprocessed and eventually sampled to be used by machine-learning algorithms to predict the next consumptions. The preprocessing of the data consists of detecting missing values and in identifying abnormal consumption using a reference load curve for each day of the week. Then, machine-learning approaches such as the LSTM and BPNN have been implemented to forecast the next consumption. Two tests have been experimented for hourly and event water consumption forecasting in a private building.
The combination of two advanced controllers, the FOPID and fuzzy MPPT, applied to the DPC command with disturbance rejection to control the solar filter of a healthy and infected three-phase electrical network, has been proposed in this paper. MATLAB/Simulink software was used to evaluate our work. By comparing simulation results with those of recently published research, the proposed techniques had clear advantages and satisfactory results under transient and steady operating states. This paper is organized as follows: the description of the operating principle of the SAPF and the conventional DPC control is presented in Section 2; Section 3 deals with the operation of the SAPF coupled to the PV system, based on the proposed DPC command in the presence of the FOPID voltage regulator and a fuzzy MPPT.
PRESENTATION AND DISCUSSION OF SIMULATION RESULTS Various simulations were performed using MATLAB/Simulink to evaluate the proposed approaches. The parameters used for these tests are shown in Table  3. The simulation results of the two cases, (1) SAPF controlled by the conventional DPC, equipped with conventional PI and fuzzy MPPT, and (2) the solar SAPF controlled by the proposed DPC, equipped with a FOPID regulator and a fuzzy MPPT, operating under a balanced feed power supply, are shown in Figures  5, 6 , 8 and 9. Figures  5  and 6  show the waveforms of the voltages V s and source currents I s , the filter currents I f and charge currents I l together with their respective zooms. The filtering device was operated under different conditions to assess its effectiveness. After applying the SAPF at time t = 0.
Encapsulation has also ensured a high stability of the monolayers under 60 keV and 100 keV electron irradiation, allowing imaging of free edges (Fig. 1a  and Fig. SI2 ). Diffraction patterns were acquired for each beam position on the sample with typical convergence semi-angle between 3 and 5 mrad and the camera dwell time was 50 ms. Diffraction mapping analysis was done with the Pyxem  [50]  and Hyperspy  [51]  python libraries. The quantity calculated is the displacement gradient tensor, corresponding to the difference between the deformed vectors and a reference, where the reference is a calculated vector with the reciprocal space length and orientation for unstrained WS 2. A right-handed polar decomposition was used to separate deformation and rotation. The angle of rotation was then recovered from the rotation matrix.
A. Acknowledgments This project has been funded in part by the  National Agency for Research  under the program of future investment TEMPOS-CHROMATEM (reference no. ANR-10-EQPX-50 ) and from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  823717  ( ESTEEM3 ) and 101017720 (EBEAM). K. W. and T. T. acknowledge support from the  Elemental Strategy Initiative  conducted by the  MEXT, Japan , Grant Number  JPMXP0112101001 ,  JSPS KAKENHI  Grant Number  JP20H00354  and the  CREST ( JPMJCR15F3 ),  JST. This work has been supported by  Region Île-de-France  in.
X-ray powder diffraction X-ray powder diffraction (XRPD) data were collected at room temperature using a Bruker D8 Advance Vario 1 two-circle diffractometer (θ-2θ Bragg-Brentano mode), equipped with a Ge(111) monochromator (Johansson type) and a Lynx Eye detector, using Cu Kα1 radiation (λ = 1. 5406 Å). Synchrotron XRPD data of Cu2. 075Sn0. 925S3 sample was collected at room temperature on the endstation CX2 devoted to the high-resolution XRD measurements of MARS (Multi-Analysis On Radioactive Samples) beamline at SOLEIL synchrotron (Saint-Aubin, France). The beamline uses a bending magnet source and the X-rays are monochromatized by a double crystal monochromator (DCM) equipped with a pair of Si(220) crystals. The second crystal is sagittally bent to perform the horizontal beam focusing. Higher harmonics rejection and vertical collimation were achieved using the Pt strip of the two mirrors inserted before and after the DCM with a 3.
1 mRad incidence angle. The incident X-ray energy was calibrated using the absorption K-edge of an Y foil (E0 = 17. 038 keV) with a beam sizing about 1000´300 µm (H´V) at the sample position. The station is equipped by an angular precision two-circle goniometer for the collection of powder diffraction in Debye-Scherrer geometry. High-resolution data was carried out by the use of a set of 24 detectors equipped with crystal analyzer Ge(111). 17 Sample was finely ground and inserted in a boron glass capillary of diameter 0. 3 mm in order to avoid the necessity to correct the data for absorption. The analysis of the diffraction patterns was performed by Rietveld refinement using the FullProf and WinPlotr software packages. 18, 19 Background contribution was estimated either manually or using Accepted Manuscript/Final version a polynomial function.
Structure analysis in the Cu2+xSn1-xS3 series (0. 075 £ x £ 0. 15) In order to determine the crystal structure of the Cu2. 075Sn0. 925S3 phase, synchrotron X-ray powder diffraction (XRPD) data have been recorded. As previously mentioned, apparent high quality Rietveld refinement (Figure  S5 ) obtained with a kuramite structural model (Figure  S6 ) suggests in a first approach that the x = 0. 075 sample crystallizes in a tetragonal structure. The analysis provides (i) a cT/aT ratio of 2, (ii) a preferential occupation of the 2b site by Sn atoms (73. 4%) and of the 4d sites by Cu atoms (95. 5%), and (iii) a cationic distribution corresponding to the chemical composition of Cu3. 18Sn0. 82S4, i. e. Cu2. 38Sn0. 62S3 (x = 0. 38 in the generic formula Cu2+xSn1-xS3). This Sn content strongly differs from the nominal composition x = 0.
Samples Two sets of samples were used in this study. The first set consisting of 20 carbonate samples was analysed using atomic absorption spectroscopy (A800; Perkin Elmer, Norwalk, CT) to determine their elemental composition in Si, Mg, Ca, Mn and Fe. The elemental data were then used to calibrate the pXRF values. Among these samples, 7 were taken out of the subsequent study for at least one.
Sample preparation The samples were prepared as powders and pellets. ATR spectra were measured on the powders while Raman and portable X-Ray Fluorescence (pXRF) spectra were measured on 13 mm diameter and 2 mm thick pellets prepared by using a 10 tons hydraulic press. Furthermore, raw data files of Raman spectra and infrared spectra in ATR mode which are chemically determined and come from the RRUFF database were added to extend the dataset in this study and compare them with the results from our samples  [25].
Portable X-ray fluorescence spectroscopy XRF data were measured with a Thermo Fisher Scientific Niton XL3t GOLDD + handheld spectrometer. The instrument used an Aganode X-ray tube with a voltage of 50 kV and was equipped with a silicon drift detector (SDD). The X-ray beam covered a circular area of 8 mm in diameter on the sample. It operated the Main, Low, High, and Light filters with a duration of 30 s for each filter. Each sample was measured 5 times at the same point to obtain statistically reliable data. The spectral data was automatically interpreted using the Mining Cu/Zn mode, which is particularly suitable for measuring lighter elements such as Mg, Al, Si, P, and S. The samples from the RRUFF database were analysed by UA's modified CAMECA SX50 electron microprobe  [25]. The elemental concentrations measured by the pXRF device were corrected by drawing calibration curves built by plotting pXRF values against the corresponding values determined by atomic absorption spectroscopy (A800; Perkin Elmer, Norwalk, CT).
Portable Raman spectroscopy Raman spectra were collected using a RaPort portable spectrometer (Enhanced Spectrometry, Inc. , San José, CA, United States). This handheld device (weight c. 2 kg) was equipped with a green Nd:YAG laser (532 nm) for excitation with a maximum output power of 30 mW. The minimum spot size was around 15 lm. The spectral range was 130-4000 cm À1 at a spectral resolution of 6 cm À1. A baseline was automatically subtracted by EnSpectr soft- ware. While the total acquisition time was set to 30 s, the number of accumulations and the duration of each individual acquisition were automatically defined. The selection of the peak position was determined by the wavenumber at maximum intensity after two correction steps: i) a baseline correction of the raw spectra by EnSpectr software followed by ii) an automated background removal which was installed in the Crystal Sleuth software  [24].
Divalent cation in the carbonate minerals For the same carbonate structure, changes in the atomic constitution induce variations of chemical bond strengths and atomic masses, which have consequences on the vibration frequencies and thus on spectral band positions  [5]. As Mg# increased, the band position in the Raman spectrum shifted from 150 to 215 cm À1 and from 710 to 750 cm À1 in the infrared spectrum (Fig. 8 ). We noticed that the band positions are shifted to lower wavenumbers with respect to cation radius and atomic mass in both Raman and infrared spectra. Fisler et al. [10]  revealed a positive correlation between the ionic radius of the divalent cation, i. e. , Ca, Mg, Mn, Fe, Cd, Zn, Co, and Ni, and the calculated lattice energies in carbonate minerals. We, therefore, added an otavite (CdCO 3 ), smithsonite (ZnCO 3 ), and spherocobaltite (CoCO 3 ) sample from the RRUFF database to our data set to highlight the trend between peak position and ionic radius (Fig.
Mg:Fe ratio in ankerite-dolomite relationship Mineral identification in a solid ankerite-dolomite solution is always a difficult and complicated challenge due to a continuous variation in terms of chemical composition with Mg, Fe, and Mn. The term dolomite is restricted to the ratio Mg:Fe > 4, while ankerite is defined by the ratio Mg:Fe 4:1  [7]. Our ankerite samples, including the RRUFF database, have Mg:Fe lower than 4. 5 and the dolomite samples have Mg:Fe ratio between 4. 3 and 117. 3 (Fig. 11 ). The ankerite and dolomite samples show that an increase in Mg:Fe ratio appears to be associated with a shift of the Raman     Therefore, 170 -172 cm À1 in the Raman spectrum can be interpreted as the value corresponding to the border or transition region between ankerite and dolomite.
Principal Component analysis (PCA) To investigate the chemometric capability of the discrimination of carbonate minerals, a Principal Component Analysis (PCA) which reduces the number of variables of the input spectra was applied (Fig. 13 ). PCA analysis was carried out by FactoMinR (Factor Analysis and Data Mining with R) package in R platform  [26, 31, 35]. The spectral data for PCA consisted of the position of each band for Raman and infrared spectra, and the concentration of the four cations measured by pXRF. The two principal components (i. e. ,  PC1 and PC2) account for 81% of the variance, with 59. 3% and 21. 7%, respectively (Fig. 13 ). The PC 1/PC 2 score plot indicates a clear separation of the pure carbonate clusters from each other, except for the ankerite and dolomite that are close to each other and close to the origin in the PC 1/PC 2 field, meaning that neither of these variables is critical in distinguishing them.
However, they remain separated by positive PC1 values for dolomite and negative PC1 values for ankerite. These analogous plots can be interpreted as a result of a similar composition that results in similar band positions between the ankerite-dolomite series. The siderite samples show a linear distribution on the score plot due to the variation in composition within the siderite group as described above. Table  2  shows that Raman, IR, and Ca are strongly correlated to PC1 (coefficient close to 1). Mn and Fe are highly correlated in PC2 and PC3 with both showing coefficients above 0. 70. Thus, the PCA plot provides a well-classified graphical representation for the carbonate endmembers based on the combination of the three different spectral datasets.
e. , experiments recording the variation of the SHG intensity as a function of the laser polarization and analyzer angle). We first derive the analytic form of the SHG and its dependence on light polarization. This will include a detailed description of the non-linear optical susceptibility tensor, which is a key element in the SHG process. The modeling of the SHG polarimetry response is then obtained by assuming the symmetry of the parent material, the local symmetry reduction at the DWs, and their non-Ising character (i. e. , Bloch or Néel-type transitions). The validity of the model is finally assessed by fitting the experimental data with the as-derived analytic form of SHG. Note that, for didactic reasons, the modeling of the SHG signal presented in this Tutorial is simplified. A more detailed description of advanced numerical modeling of SHG microscopy and polarimetry can be found in the specialized literature (see, e.
Once trained according to the comprehensive generated datasets, the implemented techniques can classify new network operating points into a healthy or degraded cable condition with high accuracy in their predictions. The simulation results reveal that logistic regression and decision tree algorithms lead to a better prediction (with a 97. 9% and 99. 9% accuracy, respectively) result than the k-nearest neighbors (which reach only 76. 7%). The proposed framework offers promising perspectives for the early identification of LV cable conditions by using SM measurements.
The results of this study allowed the authors to highlight the most influencing parameters in the fault assessment process, such as the fault resistance. In the context of grid monitoring, the authors in  [25]  set up a power line modems (PLM)-based solution for the diagnostics of distribution network cable. By implemented various ML algorithms (combined with several preprocessing methods), the proposed approach ensures the employment of the best algorithm for a given diagnostic procedure. The work has been oriented through a two-stage approach from the degradation detection to the ageing and localized degradation assessment of XLPE-insulated cable. The key point of this approach relies on access to the PML database. The authors of  [27]  investigated the role of ML in integrity analysis of subsea cables.
Through the extensive analysis of cable insulation thickness variations and load flow calculations, a synthetic database is built. Then, the observations in the dataset are classified using several predictors whose impacts are studied. Indeed, the proposed work is a novel approach, which lies in the use of data from already largely deployed smart meters. From an economic point of view, it is a cost-effective approach compared to the actual costly monitoring of HV transmission lines where specific meters and communication systems are used (as implemented in France). In the LV distribution system, it is very expensive to deploy sensors and dedicated information and communication technologies in the entire electrical network. To tackle this challenge, this research project aims to take advantage of available data from smart meters and leverage the ML capabilities in order to detect the soft (early-stage) degradation of cable insulation (regardless of the type of fault).
The current paper is a step further on this direction. The objective is to detect the cable insulation degradation from the network operating point. To do so, relying on the generated database consisting of nodal voltages (associated with the load and generation profiles) as well as the cable insulation conditions, different machine learning techniques have been implemented. The latter in the training phase will learn what would be the possible nodal voltages linked to each load and generation profiles as well as the cable insulation conditions. Then, in the test phase, relying only on the nodal voltages (associated with the load and generation data), they will identify if the network working point corresponds to the normal conditions or if there is cable insulation degradation in the tested network.
Synthetic Creation of the Working Database In the first stage, a working database is created from the cable thickness distribution and the smart meter (SM) measurements data (i. e. , the load and the PV measured each quarter of an hour q). The SM inputs are used to obtain the net demand (ND). =   -   (11)  where NDi, PVi and Loadi are, respectively, the net demand, the PV production and the load demand at node i. Then a load flow is computed, for each observation (each quarter q of each day), using the Newton-Raphson load flow (NRLF) technique. In this study, the NRLF technique is Z AB = Z at Z bt + Z bt Z ct + Z ct Z at Z ct (8) Z BC = Z at Z bt + Z bt Z ct + Z ct Z at Z at (9) Z AC = Z at Z bt + Z bt Z ct + Z ct Z at Z bt (10).
Synthetic Creation of the Working Database In the first stage, a working database is created from the cable thickness distribution and the smart meter (SM) measurements data (i. e. , the load and the PV measured each quarter of an hour q). The SM inputs are used to obtain the net demand (ND). ND i = Load i -PV i (11) where ND i , PV i and Load i are, respectively, the net demand, the PV production and the load demand at node i. Then a load flow is computed, for each observation (each quarter q of each day), using the Newton-Raphson load flow (NRLF) technique. In this study, the NRLF technique is carried out for calculating the network nodal voltages. During NRLF computation, the nodal powers are expressed in nonlinear algebraic equations. Then, Taylor series are used to linearize those equations, which give the link between small variations in real and reactive powers as a function of small variations in the nodal voltage angles and magnitudes.
This is what makes the NRLF technique an iterative-based procedure. The particularity of this process is linked to the fact that the load levels are imposed for obtaining voltages of the same magnitude range as those obtained with a non-degraded cable. Figure  4  shows the flowchart of the synthetic creation of the knowledge database (the global flowchart of the proposed approach including the classification process is presented in Appendix A). Energies 2021, 14, x FOR PEER REVIEW 7 of 20 carried out for calculating the network nodal voltages. During NRLF computation, the nodal powers are expressed in nonlinear algebraic equations. Then, Taylor series are used to linearize those equations, which give the link between small variations in real and reactive powers as a function of small variations in the nodal voltage angles and magnitudes.