dataesr/scientific-paragraphs-categorization huggingface dataset:
To perform temporal comparisons at each site, images were taken at the different dates proposed by GSV with pre-and post-event image sets. We used the SfM-MVS program VisualSFM  (Wu, 2011)  for dense point cloud reconstruction for the print screen images from Google Maps and we used CloudCompare (Girardeau-Montaut, 2011) for point cloud visualisation and comparison. Comparison between two point clouds was made using point-to-mesh strategy. To this end, a mesh was generated from the reference point cloud (the point cloud with the oldest images for site 1 or the      lidar scans for sites 2 and 3) and then the other point cloud was compared to this reference mesh. The computed shortest distance, a signed value, between the mesh and the point cloud is the length of the 3-D vector from the mesh triangle to the 3-D point.
Thus, average distances and standard deviations for each comparison of point clouds were computed. Point density of point clouds was obtained using the "point density" function in CloudCompare with the "surface density" option. Beside the images taken from print screens as described above, we also obtained GSV images (4800 × 3500 pixels, 16. 8 Mpx) from Google Earth Pro on sites 2 and 3 with the "save image" function. This second way to get GSV allows us to get images with a higher resolution than print screen images. Unfortunately, there is no timeline (or back-in-time) function in Google Earth Pro; it is only possible to save images from the last picture acquisition, i. e. generally postevent images. GSV images from Google Earth Pro were processed with the Agisoft PhotoScan software  (Agisoft, 2015)  for dense point cloud reconstruction, which provides much better results than VisualSFM.
Results and discussion Different results are obtained depending on the software used for SfM-MVS processing. For all case studies, Visu-alSFM gave results with print screens from GSV in Google Maps while Agisoft PhotoScan could not align those print screens despite adding a series of control points measured with Google Earth Pro. Resolution of print screen images seem to be insufficient for processing with Agisoft Photo-Scan. However, with higher point density and empty areas, Agisoft PhotoScan provided better results with images from Google Earth Pro than VisualSFM.
4c ) with a standard deviation of the point-to-mesh distance of about 10 cm (Fig. 9  and Table  2 ) and on the entire cliff beside the vegetation with a standard deviation of about 25 cm (Fig. 4e ). In the collapsed area, the maximal horizontal distance between the two datasets is about 3. 9 m (indicated in red in Fig. 4d ). The collapsed volume (including a possible empty space between the cliff and the wall before the event) was estimated to be about 225 m 3 using the point cloud comparison. Based on Google Street images, we manually estimated the dimensions of this volume (15 m long × 10 m high × 1. 5 m deep), getting a similar value. The obtained point clouds at site 1 allow us to detect objects of a few decimetres. This accuracy was adequate to estimate the collapsed volume with an accuracy similar to the estimation made by hand based on the GSV photos and distances measured on Google Earth Pro and the French geoportal.
The three point clouds processed from GSV images and the lidar scan have been roughly aligned to this reference. Then the four SfM-MVS point clouds (three with GSV images and one with GoPro images) were precisely aligned and scaled on the lidar point cloud, which was considered to be the reference cloud. The analysis (Fig. 9 , Tables  1  and 2 ) shows that the 2010 model derived from GSV images processed with VisualSFM gives the least accurate results (Figs. 6a  and 7a ): we hardly perceive the wall of the tunnel entry and the wide cliff structures. The results of the 2014 point cloud from GSV images processed with the same program are slightly better (Figs. 6b  and 7b ): the right-hand tunnel entry is modelled while it was not the case on the 2010 point cloud. The point cloud processed in Agisoft PhotoScan derived from 2016 GSV images saved from Google Earth Pro displays much better quality than the previous (Figs.
In such difficult alignment cases, we tried to align the point clouds on stable parts where point density was high. Our study highlighted important differences in 3-D model reconstruction using different software, comparing with pre-vious works  (Micheletti et al. , 2015; Gómez-Gutiérrez et al. , 2015 , Niederheiser et al. , 2016). Agisoft PhotoScan performed better than VisualSFM when using both GSV images from Google Earth Pro (Fig. 5f-g ) and pictures acquired from a GoPro Hero camera (Fig. 5h ). Nevertheless, Visu-alSFM performed better than Agisoft PhotoScan on print screen captures from SVI. The only difference between these sources of information is the resolution: 2. 3 Mpx for print screens from Google Maps, 16. 8 Mpx for images saved from Google Earth Pro and 12 Mpx for GoPro camera, stressing the importance of picture resolution to the quality of the 3-D model.
The point density was evaluated according to the distance between the image point of view and the subject and the image types and processing software. The obtained results and the derived trends indicate that the use of GSV images from Google Earth Pro with VisualSFM increases the point density by a factor of 2 compared to the processing of GSV print screens with VisualSFM. The processing of GSV images from Google Earth Pro with Agisoft PhotoScan increases the point density by a factor of 10 compared to the processing of GSV print screens with VisualSFM (trend strips in Fig. 8 ). The expected point density of the 3-D point clouds from GSV print screens processed in VisualSFM of a subject located a few metres from the camera (Basse Corniche dots in Fig. 8 ) is about 300 points m -2 , about 50 points m -2 for an area located at about 100 m (Arly dots in Fig.
8 ) and about 0. 5 point m -2 for an area located at about 700 m (Séchilienne dots in Fig. 8 ). Despite the abovementioned prospects, some drawbacks were also observed. The main limitation found in this study is that SfM-MVS processing is designed to retrieve the internal orientation of standard cameras, whereas the images used in this research do not correspond to a standard camera due the construction of the panoramas. Indeed, the main problem comes from the different deformations on GSV print screens or images due to the panoramas construction. The radial deformations on each image, which are stronger than common camera lens, like on fisheye images from GoPro cameras, can be processed without limitation with SfM software like Agisoft PhotoScan. In addition, images from GSV are often over-or underexposed (case study 3) and their resolution is low for distant subjects (cases study 2 and 3), making it difficult to obtain results with decimetric accuracy with these constraints.
The quality of the final product was observed to be mainly dependent on the image quality and the distance between the studied area and image perspectives. Despite the abovementioned limitations, SfM-MVS with SVI can be a useful tool in geosciences to detect and quantify slope movements and displacements at an early stage of the research by comparing datasets taken at different time series. The main interest of the proposed approach is the possibility to use archival imagery and deriving 3-D point clouds of an area that has not been captured before the occurrence of a given event. This will allow expanding the database on rock slope failures, especially for slope changes along roads with conditions that are favourable for the proposed approach.
Acknowledgements FW, HN, VP, SH performed the experiments, TF, HN, SM, NJ contributed to the data interpretation and performed the theoretical development and simulations. MA, CS, SM and RC performed the device design and realization. LHL, EHL and AGD performed the growth of the samples. FW, HN, NJ, AGD, JM, JT, SSD wrote and developed the paper. FW, SSD contributed to the original scheme.
The great evolution of communication technologies and potential availability of network access mediums and service providers have led to the appearance of heterogeneous network concept. This paradigm refers to the seamless and ubiquitous interoperability between multi-coverage protocols with different access techniques. A heterogeneous vehicular network (HetVNet) is a heterogeneous network where a vehicle is a smart node equipped with various communication technologies such as Dedicated Short Range Communication (DSRC) and cellular network (3G/4G). The purpose of HetVNet is ensuring a wide area coverage to all vehicles in a large scale network, thus achieving the Always Best Connected (ABC) paradigm where the best continuous connectivity is offered to clients. In addition, HetVNet enables the acquisition and processing of a large amount of data from wide geographical areas via smart vehicles to offer various categories of services to drivers and passengers.
There are many challenges in HetVNet and security is one of them since, in one hand, vehicles exchange vital data (about congestions, accidents, hazards, road-works, etc. ) and in the other hand they form a specific network with particular characteristics (frequent fragmentation, dynamic topology, no centralized authority, etc. ). Intrusion detection systems (IDS) act as a second wall of defense when cryptography is broken and already proved their effectiveness against both external and internal intruders. Therefore, in this research work we propose and implement an intrusion detection and prediction scheme able to detect and especially predict the future misbehavior of a malicious vehicle. The attack prediction technique proposed in this paper is based on a game theory to prevent the occurrence of malicious vehicles.
III. NETWORK ARCHITECTURE AND INTRUSION DETECTION TECHNIQUE In this section, we present the HetVNet architecture with a focus on the application scenario that we attempt to secure. Afterwards, we give an overview of the different attack models with their corresponding detection policies. TLS has the ability to ensure data confidentiality and node authentication  [16]. The SC offers a couple of services to the vehicles through I2V and V2V communications  [17]. These services are various and range from road safety to other useful services for drivers/passengers. Most of the safety-oriented HetVNet applications rely mainly on alert messages dissemination to inform vehicles when an event (e. g. road accident) is detected. Among alert messages, we can cite for instance Post Crash Notification (PCN) and Emergency Electronic Brake lights (EEBL)  [7].
Each vehicle that wants to communicate with a neighbor or with an infrastructure, signs a message using its private key to generate an ECDSA signature as illustrated in Fig 2. In addition, the elliptic curve integrated encryption scheme (ECIES) is applied to encrypt the message and hence ensure data confidentiality. The vehicle encrypts a message with a public key of its neighbor that wants to communicate with; and the neighbor decrypts the message using its private key. Furthermore, for confidentiality of subsequent communications between the vehicles or between the vehicle and infrastructure, a session key is generated by the two parties, as illustrated in Fig 2. This session key is generated by using an elliptic curve diffie-hellman (ECDH). In safety-oriented HetVNet applications, alert messages are generated to ensure the safety of persons, vehicles and infrastructure.
A finite volume method is used and extended for the numerical simulation of impacts on elastic-viscoplastic solids with bidimensional curvilinear structured meshes. The formulation is based on the flux-difference splitting method [1], has second order accuracy through flux limiters, embeds the corner transport upwind method, and uses the second order accurate Strang splitting method to handle the right hand side of the system of balance laws. The approach is here derived with a Chaboche-type [2] elastic-viscoplastic solid within the small strain framework, and is illustrated on a problem of impact on a heterogeneous volume containing an inclusion. A comparison is performed with a finite element solution obtained with the finite element code Cast3M [3].
In this work, we use and extend a finite volume method applied to these dissipative media with bidimensional curvilinear structured meshes of quadrangles. The formulation is based on the flux-difference splitting method  [1] , has second order spatial accuracy through flux limiters, embeds the corner transport upwind method, and uses the second order accurate Strang splitting method to handle the right hand side of the system of balance laws. The approach is here derived with a Chaboche-type  [2]  elastic-viscoplastic solid within the small strain framework, and is illustrated on a problem of impact on a heterogeneous volume containing an inclusion. A comparison is performed with a finite element solution obtained with the finite element code Cast3M  [3].
The matrix is assumed to remain elastic, only the inclusion undergoes viscoplastic strains. For comparison purpose, a finite element solution is obtained with the finite element code Cast3M  [3] , computed with an implicit time discretization and bilinear quadrangular finite elements. This numerical solution is compared to the finite volume one in figures 4, 5, and 6 at times 1 × 10 -7 , 1. 9 × 10 -7 et 3 × 10 -7 seconds respectively. The Courant number has been set at 0. 9, the same time step being used for both numerical solutions. The comparison is performed on to the finite element solution. One can observe in figure  4  the slot of negative normal stress formed by the prescribed horizontal velocity on the left side. Its falling front has interacted with the front interface between the matrix and the inclusion, and has been both reflected and transmitted, generating an intermediate state of higher stress (lower in absolute value) and a higher first component of velocity since the impedance of the matrix is higher than that of the inclusion.
Note, the term traditional RVM based BCSKF method means the entries of P k in the prediction step of the BCSKF are found using the RVM optimisation method as detailed in  [16] ,  [17]. In other words this is the method detailed in Section II-B with xp = 0. All of the examples are implemented in Matlab on a computer with an Intel Xeon CPU E3-1271 (3. 60GHz) and 16GB of RAM. The performance of each method will be measured using the RM SE in the DOA estimate. This is given by RM SE = Q q=1 L l=1 |θ l -θl | 2 Q L , (36) where θ l is the actual DOA, θl is the estimated DOA and Q is the number of Monte Carlo simulations carried out, with Q = 100 being used in each case. This gives a measure of the estimation accuracy and the computation time will be used as a measure of the complexity of each method.
Operational and acquisition parameters To detect the expected Stark-effect-induced changes on the spectral line profile, we have used a high-resolution grating with 3600 lines per mm, that provides a spectral resolution of ≈ 0. 02 nm. The discharge parameters for the dedicated set of experiments are listed in Table  2. Two experimental scenarios have been performed to obtain the results presented here. The reference data were recorded without the voltage applied to the electrode, while the second set was obtained for the set of experiments with the electrode biased to a positive DC-voltage The obtained line profile corresponding to the 4 3 D -2 3 P transition in He-I were recorded over the whole duration of a discharge, with an exposure time of 0. 5 s, in a kinetic series of 24 scans per discharge.
Experimental data Nine subjects took part to this experiment: 6 males and 3 females (35±10 y. o. , 171±10 cm and 84±7 kg). The experiment consisted of a classical human motion capture experiment, to which a bodyscanning session was added. Subjects were equipped with 51 reflective skin markers, most of which located on specific anatomical landmarks. Once equipped, subjects were installed in a standard standing posture in a 3D body scanner (SYMCADTM II by Telmat,  Rennesson, 2012)  to obtain their external shape. It provided a textured mesh with a density of about a point every 3 mm (about 160000 nodes). Subjects then performed several motions and 3D trajectories of markers were collected using an optoelectronic system (Vicon). Only the data from a 10 second static trial were used in this study.
Data processing BSIP were estimated using two different approaches. At first, BSIP were estimated using anthropometric tables  (Dumas et al. 2007 ) and 3D coordinates of markers located on anatomical landmarks measured during the static posture trial. Then a volumetric approach was used. It consisted in obtaining a personalized and structured mesh of the body segments, and estimating their inertia parameter by assuming a constant density (see Figure  1 ). The first part of the method was adapted from  Beurier et al (2015). Beforehand a template mesh was created with the help of MakeHuman and Blender softwares. It was based on a 50 th percentile anthropometry and was made of about 9000 nodes and 18000 triangles. Its posture was similar to the one taken by the subjects in the Body scanner. Nodes corresponding to 15 anatomical landmarks were identified.
This mesh is further segmented in body segments, according to anatomical definitions used in AT  (Dumas et al. , 2007). Then, each individual raw mesh from the body scanner was post-treated. At first, nodes corresponding to the 15 anatomical landmarks of the template (a subset of the reflective markers located on subject's skin, i. e. easily identifiable), were manually identified. Then the raw mesh was cleaned and filtered using Meshlab's scripts to limit the number of point, remove duplicate vertices are below 2% of body weight (maximal error observed for the thorax). Similarly, mean absolute errors on CM location were below 1cm in the frontal and lateral axis (X and Z), and up to 4 cm in the longitudinal axis (Y, maximal error observed for the thorax and abdomen). On average, absolute errors on inertia terms remained below 10 -1 kg.
Conclusions This study provided encouraging results showing that the proposed method leads to a relevant estimation of BSIP from body scanner images. Once the template has been created, the method is largely automated and necessitates only minimal manual intervention (selection of ALs on the body scanner images). This step could be easily automated by automatically detecting of markers corresponding to the ALs on the textured mesh based on their colour or shape. In terms of experimental constraints, it only adds about a minute to the experiment in order to perform the body scan. The method should still be extensively evaluated, in particular on atypical populations. and faces, non-manifoldness… A non-rigid transformation tool (mHBM software,  Yamakazi et al. , 2013)  was then used to deform the template mesh onto the subject's cleaned mesh, ensuring a minimal distance between both mesh and a match between template and target landmarks.
Acknowledgments We acknowledge the  French Ministry for the Economy and Finance  and the  Aquitaine Regional Council  for support and funding via the  Femtoweld  project. Numerical simulations were performed using computing resources at  Mésocentre de Calcul Intensif Aquitain (MCIA)  and  GENCI. S. S. acknowledges support by the  QNRF  through the  National Priorities Research Program  (NPRP). We thank  C. Javaux Léger  for helping with the organization of the experiments.
Cation and anion concentrations of pore waters in perturbed claystone were deduced from these data and compared with those of unperturbed claystone. Powder X-ray diffraction and infrared spectrometry were performed on bulk concrete and OPA both at the interface and far from the interface, to identify mineralogical perturbation of both materials. Further mineralogical investigations were carried out on the sieved aliquot of the white crust (sieved 0e1 mm LAC) to pursue mineralogical and chemical investigations on the M-S-H phase already identified  (Dauz eres et al. , 2016; Jenni et al. , 2014). The chemical composition of the M-S-H phase was determined using a combination of electron microprobe and transmission electron microscope coupled with EDX analyses, whereas the crystalline structure of the M-S-H phase was investigated using powder X-ray diffraction, Fourier-transform infrared spectroscopy, Raman spectrometry, 29 Si Nuclear Magnetic resonance, and electron diffraction.
The experimental data were fitted using the calibration function proposed by  Hellmuth et al. (1993). A local mean porosity was then calculated for each pixel of the digitized autoradiograph from the calibration function and tracer activity using the relationships provided by  Sammartino et al. (2002)  and the in house autoradio software  (Prêt, 2003). Local pixel porosities include the connected micro-, meso-, and macro-porosity. For each autoradiograph, a combination of analysis methods was used to reveal the porosity distribution. Porosity frequency histograms were computed on different sub-areas of the autoradiograph by counting the number of pixels having similar porosity values. To localize the spatial heterogeneities, porosity maps were then computed by displaying the local porosity of each pixel using a linear grey level scale ranging between two porosity values identified on the histogram.
Raman spectrometry Raman measurements were performed in situ on thin polished sections with a Renishaw InVIA Reflex microspectrometer coupled to a DM Leica microscope equipped with 4 lenses with Â5, Â20,Â50 and Â100 magnification. The excitation laser was an argon laser (l 0 ¼ 514. 5 nm). Each spectrum was acquired with two accumulations and an acquisition time of 10 s, over a 200-1200 cm À1 spectral range. Calibration was done using the silicon 520. 4 cm À1 line. Instrument control and Raman measurements were performed with Renishaw WIRE TM software. Raman spectra acquired on the M-S-H phase were processed using the Fityk software  (Wojdyr, 2010).
Nuclear magnetic resonance (NMR) NMR was used to evaluate the structure of the Mg-bearing cement phase in the white crust separately. 29 Si MAS NMR spectra were acquired at 59 MHz on a Bruker AVANCE 7. 4 T (300 322 MHz) spectrometer equipped with a 4 mm double bearing MAS probe-head spinning at 12 kHz. About 16,000 scans were accumulated after a 45 pulse, using 1 s recycling delay. This delay was optimized to ensure complete magnetization relaxation. 29 Si chemical shifts were reported relative to TMS resonance. The spectra were deconvoluted in individual Gaussian-Lorentzian bands, using the Dmfit program  (Massiot et al. , 2002).
To reduce the total acquisition time (2 days), the background was not measured or subtracted from X-ray emission peaks as its contribution at short dwell times and for high concentrations was low  (Goldstein, 1992)  and its recording would have doubled the acquisition time. We then applied a PHIRHOZ matrix correction  (Merlet, 1994)  to provide a weight percentage for each element per pixel. The elemental maps were recorded by stage rastering using a stationary beam, with spatial resolution of 2 mm per pixel and containing 512 by 512 pixels. X-ray maps were then processed using the mMAPphase software  (Prêt et al. , 2010). The aim of this method is to identify the mineral phases present in the analysed area, from step by step projections of the scanned elemental composition points into ternary plots displaying the chemical compositions of all mineral phases.