dataesr/scientific-paragraphs-categorization huggingface dataset:
In this case, DR methods play an essential role, as they employ various criteria and standard procedures to eliminate insignificant features in the dataset. Some of the standard methods for DR are principal component analysis (PCA), probabilistic PCA, neighborhood component analysis (NCA), multidimensional scaling, Sammon's mapping, and factor analysis (FA)  [122]. Most DR techniques work offline, i. e. , they require a static database (batch) of data, whose dimensionality is reduced. These techniques can be divided into linear and nonlinear ones, with the latter being generally slower but more accurate in real-world scenarios. For online data processing and real-time DR, it is mandatory to acquire a continuous stream of input data. Under this scenario, the data are assumed to be extracted from a stationary distribution.
Classification For fault classification and decision making, once the feature set has been developed, AI-based techniques are very useful  [120, 121]. Generally, before the classification stage, the choice of features used is of utmost importance. A useful feature set should be able to keep all the possible information contained in the primary dataset. This enables better training of classifiers and helps to rule out the true negatives and false negatives. In the literature, many feature selection and dimensionality reduction techniques have proved their capability of extracting noteworthy features from the raw/standardized dataset for the faster processing for classification. This is usually performed in either a supervised or a non-supervised fashion. Under supervised learning, the classes are labelled, while under unsupervised learning, no label is given  [48, 131].
There is also an ever-increasing literature base about the use of deep neural networks, and above all the convolutional neural networks (CNN), which only require raw signals without any feature engineering (see  [117]  for a very recent review for bearing faults and  [149, 150]  for gear faults). In  [117] , it is shown that for the CWRU dataset, composed only of vibration data, all deep learning tools require, in general, only three or four layers in order to achieve very high test classification rates. For example, the adaptive CNN (ADCNN), equipped with a Softmax classifier and three layers, has a testing accuracy of 97. 90%. However, most of these techniques stack the 1D temporal raw data, obtained from different accelerometers, into a 2D matrix form, similar to the representation of images.
This is likely "sub-contour porosity" caused by the difference in melt characteristics between the "contour" and "raster" laser scans To detect pores, MATLAB's edge detection function was used. This uses the Sobel Operator to approximate the intensity gradient at each point in the image. The function then uses a threshold to binarize the image into edges and non-edges. This threshold is initially calculated using just the part of the image containing the sample then it is manually adjusted to find the value which most realistically identifies pores. Any edges detected outside the area of the image containing the sample are removed. It is important to be aware therefore that the accuracy of pore identification is limited and that it is more useful to focus on the overall trends in porosity between the different geometries.
To reduce the uncertainty associated with the identification of pores using MATLAB, the application of a more specialised software package was investigated. AVIZO is a dedicated XCT and microscopy image analysis tool which could improve porosity measurements, particularly the comparison of non-HIPped and HIPped flexures. Figure  15  shows the difference in pore identification between MATLAB and AVIZO. While a full analysis has not yet been completed, preliminary qualitative observation of an aluminium flexure suggest a reduction in porosity post-HIP.
This new mode, in addition to the other GaMD-dih and GaMDdual modes, is now available within the Tinker-HP software. 12, 15 In the following, we first tested its GPU scalability and performance on the STMV system (≃1 066 624 atoms), and its sampling efficiency is demonstrated on simulations of the alanine dipeptide and the CD2-CD58 complex. A technical appendix is present at the end of the manuscript and provides the formalism of the method and the associated debiasing equations.
Acknowledgement This work has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement No  810367 ), project  EMC2  (JPP). FC thanks funding from the  French  state funds managed by the  CalSimLab LABEX  and the  ANR  within the  Investissements d'Avenir program  (reference  ANR11-IDEX-0004-02 ) and support from the  Direction Génerale de l'Armement (DGA) Maîtrise NRBC of the French Ministry of Defense. Computations have been performed at  GENCI (IDRIS, Orsay, France  and  TGCC, Bruyères le Chatel)  on grant no  A0070707671.
The microstructure and microtexture were characterized by scanning electron microscopy (SEM, QUENTA 450, FEI) and electron backscattered diffraction (EBSD). The macro-texture was measured using an X-ray diffractometer (XRD, D8 Discover, Bruker) with a Cu K α1 on the y-z plane. The analysis of texture for EBSD and XRD were both obtained using Matlab toolbox-MTEX  [19]. The transmission electron microscopy (TEM, FEI Talos F200X G2) was employed to characterize the dislocations structures for as-built and after-breaking samples, respectively. Ti64 samples were cut into flat tensile specimens along y-axis (longitudinal direction test) and z-axis (transverse direction) with a gauge length of 15 mm, a width of 2. 5 mm, and a thickness of 1. 5 mm. According to ASTM E8A, tensile testing was performed on three specimens at room temperature using a universal testing machine (Criterion 44, MTS) equipped with a contact extensometer with a gauge length of 12 mm on an initial strain rate of 1 mm min -1.
Sedimentological and ecological investigations allowed to describe the textural and compositional characteristics of the beach sands by interpreting their sand provenance and the physical/biological interactions within the beach. The topographic surveys carried out with a Terrestrial Laser Scanner and an Optical Total Station, aimed to quantify the variations of sediment volume over time, whereas the Delft3d software was applied to analyze the effects of the dominant wave motion on the sedimentary dynamics. Lastly, the geophysical techniques which included Sub Bottom Profiler procedures, Ground Penetrating Radar investigation, and resistivity models enabled us to calculate the sand sediment thickness above the bedrock.
Wave Simulations and Hydrodynamic Model Software Delft3D was used to analyze the correlation between sedimentological parameters and wave processes. As shown in Figure  7 , GRID, FLOW, and WAVE modules were applied to calculate the hydrodynamic flow and to simulate the wave motion. In particular, coupled simulations were performed by means of Delft3D-WAVE with Delft3D-FLOW to incorporate wave-current interaction. Delft3DFLOW was used to assess the hydrodynamic flow, and Delft3D-WAVE was used to simulate the wave propagation, based on the SWAN (Simulating WAves Nearshore) model. The main bioclast classification was carried out on the first sampling season. Successively, the bioclast evaluation was conducted by analyzing any differences in the Phylum already identified. It is essential to specify that the recognition of the nature of sands was relatively simpler than expected.
Wave Simulations and Hydrodynamic Model Software Delft3D was used to analyze the correlation between sedimentological parameters and wave processes. As shown in Figure  7 , GRID, FLOW, and WAVE modules were applied to calculate the hydrodynamic flow and to simulate the wave motion. In particular, coupled simulations were performed by means of Delft3D-WAVE with Delft3D-FLOW to incorporate wave-current interaction. Delft3DFLOW was used to assess the hydrodynamic flow, and Delft3D-WAVE was used to simulate the wave propagation, based on the SWAN (Simulating WAves Nearshore) model. The model was based on two main datasets: bathymetry and climatic information (waves and wind data). The bathymetric data were provided by the EMODnet website (https://portal. emodnet-bathymetry. eu/, accessed on 1 November 2022).
The wave data were acquired from SIT Puglia website (http:// cartografia. sit. puglia. it/download/PRC/Relazione%20Generale_allegato711. pdf, accessed on 30 October 2022) and from a wave buoy located in the Ionian Sea (Datawell Directional  The topography of the emerged beach sector was investigated using a Terrestria ser Scanner (TLS) that allows constructing 3D elevation models and quantifying the m phological changes over time. In particular, the instrument acquires the spatial coo nates of many points by measuring the distance between the TLS and the object of s in order to build point clouds and successive three-dimensional elevation models. repeated survey campaigns were carried out during the summer of 2018 and the sum of 2019 by using a Riegl VZ-400 laser scanner, which is characterized by a theoretical ra of 400 m.
The instrumental accuracy is ±0. 003 m for 50 m (example in Figure  8a ). Due to the length of both beaches, several scans were carried out from four stat with a distance of about 250 m from each other. The point clouds were processed thro different stages: scans registration; multi-scan adjustment and georeferencing; 3D p cloud cleaning; triangulation (mesh) and Digital Terrestrial Model DTM creation w size cell of 50 cm. The Riscan Pro and CloudCompare software were used for point c processing (Figure  8b ), filtering, and rasterization, while the elevation correction and comparison of the seasonal DTMs were carried out with ArcMap © 10. 1 (Figure  8c ). As the sampling procedure, the TLS acquisition was carried out every six month detect the main morphological changes in the emerged sectors of the beach.
The point clouds were processed through different stages: scans registration; multi-scan adjustment and georeferencing; 3D point cloud cleaning; triangulation (mesh) and Digital Terrestrial Model DTM creation with a size cell of 50 cm. The Riscan Pro and CloudCompare software were used for point cloud processing (Figure  8b ), filtering, and rasterization, while the elevation correction and the comparison of the seasonal DTMs were carried out with ArcMap © 10. 1 (Figure  8c ). As the sampling procedure, the TLS acquisition was carried out every six months to detect the main morphological changes in the emerged sectors of the beach.
Ground Penetrating Radar (GPR) The GPR investigation was applied for evaluating the sediment thicknesses in the emerged beach sector. The choice of this technique depends on the dynamic nature of this sub-environment that constantly changes with the amount of available material for transport and sedimentation. Generally, these variations are recorded within the sand deposits and particularly in the dune environment, whose stratigraphic horizons can be explored with the GPR technique. The instrument operation is based on the input of high frequency electromagnetic pulses deriving from an antenna, which is placed in contact with the surface to be investigated. The depth of the investigation depends on the frequency used and therefore on the type of antenna. As the signal frequency increases, there is a growth in data resolution but a decreasing in the investigation depth.
A survey campaign through a 200 Mhz antenna was carried out at Torre Guaceto and Le Dune beach (Figure  10a ). The choice of this frequency depended on some tests carried out previously with 400 and 40 Mhz in order to detect the most significant radar stratigraphy section (Figure  10c ). The data acquisition was performed along a cross-shore transect from the swash zone to the dune environment (Figure  10b ). With regards to the data processing, the Reflexw software was used to analyze and elaborate the 2D radar stratigraphic sections. A survey campaign through a 200 Mhz antenna was carried out at Torre Guaceto and Le Dune beach (Figure  10a ). The choice of this frequency depended on some tests carried out previously with 400 and 40 Mhz in order to detect the most significant radar stratigraphy section (Figure  10c ).
The investigation was organized by following the same cross-shore transect of the georadar investigation from 6-2 m depth (example in Figure  11 ). Moreover, a boat capable of reaching shallow waters was utilized for the investigation and the instrumentation was characterized by an Innomar SBP SES 2000 Compact system. The SBP transducer was installed on one side of the boat (Figure  11a ), while the hardware system and the data management laptop were positioned inside the boat. Moreover, the whole system was connected to a GPS RTK, and interfaced with the instrument management software to acquire the exact data location. Regarding data acquisition, SES WIN software was utilized to obtain seismic sections in real-time. The program produces a *. SES file which displays the width of the acoustic signal reflected at different depths; it also contains data relating to the longitude, latitude, depth of the seabed, and acquisition parameters.
All these data were transferred to Delph Seismic software to analyze the 2dimensional stratigraphic cross sections, define the bedrock location, and obtain strata thicknesses. In this study case, one marine field survey was carried out for each study area by applying low signal frequencies (85-115 kHz). The investigation was organized by following the same cross-shore transect of the georadar investigation from 6-2 m depth (example in Figure  11 ).
Geoelectric Investigation (ERT) A geoelectric investigation was applied to analyze the entire beach profile and to gain data about the foreshore subenvironment. The application of a geoelectric investigation provides information relating to the subsoil electrical resistivity distribution. As shown in Figure  12 , the ERT was carried out across the same GPR cross-shore transect. The investigation profile was extended until 3 m depth in the shoreface to reach the SBP profile (Figure  12c ). The measurements were carried out by adopting an IRIS Instruments Syscal Pro Georesistive Meter, two multi-electrode cables at 24-channels with 3 m interelectrode spacing for a total length of 141 m. Proceeding from NNE to SSW, the Moreover, a boat capable of reaching shallow waters was utilized for the investigation and the instrumentation was characterized by an Innomar SBP SES 2000 Compact system.
The SBP transducer was installed on one side of the boat (Figure  11a ), while the hardware system and the data management laptop were positioned inside the boat. Moreover, the whole system was connected to a GPS RTK, and interfaced with the instrument management software to acquire the exact data location. Regarding data acquisition, SES WIN software was utilized to obtain seismic sections in real-time. The program produces a *. SES file which displays the width of the acoustic signal reflected at different depths; it also contains data relating to the longitude, latitude, depth of the seabed, and acquisition parameters. All these data were transferred to Delph Seismic software to analyze the 2-dimensional stratigraphic cross sections, define the bedrock location, and obtain strata thicknesses.
Geoelectric Investigation (ERT) A geoelectric investigation was applied to analyze the entire beach profile and to gain data about the foreshore subenvironment. The application of a geoelectric investigation provides information relating to the subsoil electrical resistivity distribution. As shown in Figure  12 , the ERT was carried out across the same GPR cross-shore transect. The investigation profile was extended until 3 m depth in the shoreface to reach the SBP profile (Figure  12c ). The measurements were carried out by adopting an IRIS Instruments Syscal Pro Georesistive Meter, two multi-electrode cables at 24-channels with 3 m interelectrode spacing for a total length of 141 m. Proceeding from NNE to SSW, the first cable (steel electrodes 1-24) was placed on the subsoil of the emerged beach sector, while the second cable (graphite electrodes 25-48) was placed on the seabed (Figure  12a ).
The data were acquired both in Wenner-Schlumberger and dipole-dipole configurations. The former provides high vertical resolution while the latter high lateral resolution-although it is characterized by a lower signal-to-noise ratio than other devices  [100]. For this reason, reciprocal measures were also carried out to facilitate the data quality control necessary to obtain reliable and well-resolved images. After the quality control (carried out by setting a 10% threshold for repeatability errors, and standard deviation obtained from the stacks), the experimental data were inverted using the RES2DINV code (Geotomo Software,  [101] ). The resulting model (Figure  12d ) was obtained by inverting the dataset relating to the measurements in dipole-dipole configuration, including the GPS-RTK data that were acquired along the same transept for topography correction.
Conclusions This research described the processes of erosion, transport, and sedimentation of two Apulian sandy beaches through a multidisciplinary approach. Table  2  shows the methodological proposal for sandy beach analysis deriving from the multidisciplinary approach tested in this research. This procedure includes topographic, geophysical, sedimentological, and compositional analyses for monitoring and safeguarding these transitional environments that can be useful for research studies or industrial applications. As shown in Table  2 , the procedure for investigating sandy beaches was based upon four main types of analyses: topographic surveys through TLS and OTS; a hydrodynamic model using Delft3D software; a series of geophysics techniques by the use of GPR, ERT and SBP; and a beach sand analysis through sedimentological and compositional applications.
The model was based on two main datasets: bathymetry and climatic information (waves and wind data). The bathymetric data were provided by the EMODnet website (https://portal. emodnet-bathymetry. eu/, accessed on 1 November 2022). The wave data were acquired from SIT Puglia website (http://cartografia. sit. puglia. it/download/PRC/Relazione%20Generale_alle-gato711. pdf, accessed on 30 October 2022) and from a wave buoy located in the Ionian Sea (Datawell Directional Waverider -Mk-III property of Autorità di Bacino Distrettuale dell'Appennino Meridionale, located at 40,40 N, 17,18 E). The bathymetric data were provided by the EMODnet website (https://portal. emodnet-bathymetry. eu/, accessed on 25 October 2022).
Institut National de la Santé et de la Recherche Médicale (18CP128-00, PC201508); Aix-Marseille Université (A-M-AAP-ID-17-13-170228-15. 22-RIGNEAULT); Région Hauts-de-France; Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche; National Institutes of Health (NIH R21 EY029406-01); Agence Nationale de la Recherche (ANR-11-IDEX-0001-02, ANR-19-CE19-0019-03, ANR-21-ESRE-0003, ANR-21-ESRS-0002).
Acknowledgments. The fiber was made at FiberTech Lille. We acknowledge  Esben Andresen  and  Frédéric Louradour  for fruitful discussions. We acknowledge  R. Torero ,  A. Barras , and  M. Bastide  for giving access to the biological samples. The reported studies have been conducted following the EU ethical rules on animal. Colon tissue samples are coming from the  Biobank of the Assistance Publique des hôpitaux de Marseille  ( AP-HM ) -Marseille -FRANCE The AP-HM biobank is open to research projects and administrated by an executive and ethic committee. Mouse brain slices are coming from the  Institut des Neurosciences de la Méditerranée  following INMED agreement number  B13-055-19  and OGM agreement number  5817.
Funding. Institut National de la Santé et de la Recherche Médicale (18CP128-00, PC201508); Aix-Marseille Université (A-M-AAP-ID-17-13-170228-15. 22-RIGNEAULT); Région Hauts-de-France; Ministère de l'Education Nationale, de l'Enseignement Supérieur et de la Recherche; National Institutes of Health (NIH R21 EY029406-01); Agence Nationale de la Recherche (ANR-11-IDEX-0001-02, ANR-19-CE19-0019-03, ANR-21-ESRE-0003, ANR-21-ESRS-0002).
| X-ray microcomputed tomography analysis For XMT evaluation, native and compacted granular materials Bruker's NRecon ® software was used to reconstruct the scan projections into 2D images using Feldkamp algorithm. Gaussian smoothing, ring artifact reduction and beam hardening correction were applied. Volume rendered 3D images were generated using an RGBA transfer function in SkyScan CTVox ® software. Image analysis was performed using SkyScan CTAn ® software. Two image segmentations were successively carried out on the raw image (Figure  3a ): the first one to define the sample volume of interest (VOI) (Figure  3b ) and the second one to define the object volume (granular material volume) within this VOI (Figure  3c ). To avoid subjective segmentation, gray level threshold was set by successive iterations in order to respect a volume object (V) equal to: V ¼ m ρ Ã s ð3Þ where m (g), is the mass of the object subjected to acquisition and ρ Ã s is the true density of the material (g cm À3 ) measured by a helium pycnometer (Table  1 ).
Finite Element Modeling Results The finite element method is a powerful tool for the electromagnetic problem, especially in cases of complex geometry and high physical non-linearity, such as in the switched reluctance machine. Thus, by using the ANSYS software and by implementing the geometric parameters and the APDL program according to the selected studied SRG (12/8), we get high-performance results from the finite element modeling. Figure  3  gives graphical and numerical results for the flux density of the SRM 12/8, where the rotor position is at the extreme (unaligned and aligned positions), for a current equal to 12 A.
Finite Element Modeling Results The finite element method is a powerful tool for the electromagnetic problem, especially in cases of complex geometry and high physical non-linearity, such as in the switched reluctance machine. Thus, by using the ANSYS software and by implementing the geometric parameters and the APDL program according to the selected studied SRG (12/8), we get high-performance results from the finite element modeling. Figure  3  gives graphical and numerical results for the flux density of the SRM 12/8, where the rotor position is at the extreme (unaligned and aligned positions), for a current equal to 12 A. Figures  4  and 5  respectively show the numerical characteristics of the inductance and the static torque as functions of the rotor position from aligned to non-aligned, then, from non-aligned to aligned position.
Spectra analysis The FTIR spectra were pretreated using homemade functions written in MATLAB (2020a)  (Devaux et al. , 2018). All the spectra were smoothed using a moving window of size 3. The region between 1,580-910 cm -1 was selected to compare the chemical composition of different types of cells. The baselines were corrected using linear segments with 3 points at 910, 1,190, and 1,580 cm -1. Spectra at T = 0 were normalized so that the area under the spectrum was equal to 1. The changes in polysaccharide composition of the cell wall during degradation were studied in the fingerprint region between 1,180-950 cm -1. For a given point, time-lapse spectra were normalized as follows: the maximum absorbance of the spectrum acquired at T = 0 was taken as normalization factor. All the time-lapse spectra acquired at this point were divided by this maximum absorbance  (Devaux et al.