dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS Precise comments and suggestions of anonymous reviewers greatly improved the quality of the paper. The authors would like to thank the  German Aerospace Center (DLR)  for providing the data, through the projects # GLAC0874  and  gourmele_XTI_GLAC0296 , and the  French Space Agency (CNES)  for supporting this research through the  TOSCA-CESTENG  and  TOSCA-TOP GLACIER  projects. This work was supported by the  Programme National de Télédétection Spatiale  (PNTS, http://www. insu. cnrs. fr/pnts),grant n°PNTS-2016-01. Pléiades images were acquired during the CNES Pléiades thematic commissioning phase. A. Dehecq  acknowledges a PhD fellowship from  CNES  and the  Assemblée des Pays de Savoie (APS).
By, considering these key aspects, the present study aims to understand the extent to which the results of numerical calculations can be influenced both by the differing compressive and tensile behavior of GSYs and by the assumption of strain softening at interfaces between GSYs. For this purpose, several numerical models are implemented by using the finitedifference code FLAC 2D on a typical PBLE that involves four GSYs and six interfaces. The present work also applies comprehensive, state-of-the-art numerical modelling to study the interactions between multiple layers of GSYs. This study also investigates the nonlinear axial stiffness of GSYs through a series of uniaxial tensile tests. The numerical results show that, if the GSY axial compressive and tensile characteristics are the same, then tensile force is minimized, which induces significant compressive force in the GSYs.
Byrne (1994) ,  Reddy et al. (1996) ,  Jones and Dixon (2005) ,  Seed et al. (1988) ,  Filz et al. (2001) , and  Connell (2002)  all considered strain softening at interfaces but did not consider multiple interfaces and GSY; thus, they could not calculate the strains and forces in the GSYs. In contrast,  Villard et al. (1999) ,  Haque (2007) ,  Wu et al. (2008) ,  Chen et al. (2009a) , and others did not consider strain softening at interfaces but used a single, constant friction angle (peak or residual). Note also that strain softening at interfaces is not included in conventional numerical modelling software. Other software, such as Geostress, Sage Crisp 2D, and FLAC 2D allow it but may require specific code to be developed. Given the relevance of strain-softening behavior at interfaces, it will be discussed in more detail in Section 5.
Configuration of numerical model The numerical model was implemented with the two-dimensional (2D) finite-difference modelling code Fast Lagrangian Analysis from Continua (FLAC 2D). Most of the authors  [(Byrne (1994) ,  Connell (2002) ,  Jones and Dixon (2005) ,  Fowmes et al. (2005a) ,  Chen et al. (2009b ), Zhu et al. (2009 ) andZamara et al. (2014) ] used this code to assess the interactions at the interfaces between multiple layers of GSYs and its large strain capabilities. The software can model forces and strains within multiple layers of GSYs constructed over several stages. In addition, the software can use a nonlinear stress-strain law to model materials, structural elements, and interfaces. The following sections detail the numerical configuration retained for this study.
Acknowledgement This work has been funded by  Horizon 2020 Marie Sk lodowska-Curie Research and Innovation Staff Exchange (RISE) Programme  ( 2015-2019 ) entitled DOMINO 645760. SJ acknowledges financial supports by the  Vice Presidency for External Relations (DRI) of the École polytechnique (EP)  through a Ph. D. fellowship. MA and TA thank  Royal Saudi Naval Forces  for financial supports for their studies in EP. GG acknowledges the Ph. D. fellowship from  Université de Bordeaux. The  Centre National de la Recherche Scientifique (CNRS) ,  Agence Nationale de la Recherche (ANR)  ( FMOCSOLE ANR-BLAN-2010-93801 ), and the  Institut Polytechnique de Bordeaux  are acknowledged for financial support on the material development. Dr. Yassar , Abderrahim and  Dr. Zucchi , Gaël are acknowledged for a fruitful discussion on c-PMMA.
3D images stacks were collected at 2 μ m intervals using multiphoton excitation at 860 nm every 5 min with FluoView FV1200 software (v4. 1. 1. 5, Olympus). Excitation light was separated from the emitted fluorescence with a dichroic mirror (< 650 nm). The different components of the light emitted from the sample were separated using a further dichroic mirror (570 nm) distributing the green and red fluorescence to two photomultiplier tubes (PMT voltage 350 V and PMT voltage 444 V) with fluorophore specific emission filters (BA 575-630 for Rhodamine B, GFP 495-540 for GFP). Image acquisition and processing. Bright field images were obtained on a MVX10 Research MacroView Microscope (Olympus) with a DP73 camera at 16-bits (Olympus). Multiphoton fluorescence images were acquired in frame scanning mode with 12 bits per pixel, x = 256, y = 256.
The z stack depth was variable between 200 and 300 samples depending on the experiment (each z step = 2 μ m) and thickness of the tumor. Image stacks were sampled at 5 minute intervals due to the time required to acquire multiple planes in the z-axis. Images stacks were segmented and rendered to 3D volumes. 3D volumes and fluorescence intensity were automatically quantified by Imaris software (Bitplane AG, Switzerland). Vessel diameters were measured manually using the «Measuring Distances» tool in Imaris, by choosing delimiting points on each side of the vessel walls. Images were background corrected and median filtered equally across treated samples and controls. Figures were prepared using Adobe Photoshop CS6 (version 13. 0) after conversion of RGB or grayscale images.
Acknowledgements We thank  Prof. John Lewis  ( University of Alberta ) and  Dr. Monika Záškvarová  ( Slovak Academy of Sciences ) for technical suggestions regarding the CAM imaging method and the shell-less quail egg protocol, respectively. The  U87 luc2 /GFP cells were kindly provided by  Dr. Sara Collins ,  Prof. Nori Kasahara  ( UCLA ) and  Mark Tangney  ( Cork Cancer Research Centre ). We are grateful to  Professor Catherine Yardin  and  Mrs. Marie-Laure Perrin  ( ERMA, University of Limoges ) for their biological technical support and  Dr. Claire Lefort  ( Photonics, XLIM ) for initial assistance with beam conditioning and alignment of the femtosecond laser. This project benefited from an  ANR Excellence Chair  provided to RPO as part of the  LabEx. Sigma-Lim program  ( Ministère de l'Enseignement Supérieur et de la Recherche ) and funds from  Région du Limousin.
Acknowledgments This study was supported by  SNF  Grant  200020-109320 ,  200020-122143  and  200021-140608  ( Guido Schreurs ), Discovery and Equipment grants from the  Natural Sciences and Engineering Research Council of Canada ,  NSERC  ( Alexander Cruden ), the  Fundação de Amparo à Pesquisa do Estado de Minas Gerais (Fapemig) ,  CRA 871/06  (Caroline J. S. Gomes), and the  Swedish Research Council , Grant  2008-3443  ( Faramarz Nilfouroushan ).
Client The AKA protocol consists of five phases. The first phase, user identification, is run by a client C and a server S on the insecure channel and it allows S to associate C to an IMSI value. A user ID request is first sent from the server to the client. The client's response is a tuple (TMSI, LAI), consisting of a temporary identifier and the local area identifier in which C received TMSI. If the LAI value corresponds to the LAI of S, then the latter searches for a tuple (TMSI, IMSI) in its own database; else S requests this tuple from the server S * associated with LAI, over an unspecified channel. If no IMSI can be found, then the server demands the IMSI in clear. This procedure is the essential vulnerability leading to IMSI catcher attacks 3. 2. The second phase is run only optionally by the server S and the client's operator Op over a secure channel; its purpose is to enable S to then run a batch of AKE sessions with C.
g. upon aborts), it encrypts with the operator's public key an evaluation of F 5 with the client key and the operator key, on input: the random value R id , the client's IMSI, and an index idx C , which is explained for the Preparation phase below. The client also appends the IMSI, R id , and idx C inside the en-cryption. For both types of responses, the client also sends the identity of its operator Op. Upon receiving a string of the form (m, Op), the server first checks whether the message m is a TMSI present in its database; if so, it retrieves the IMSI to which this value corresponds; else, it assumes that m is a ciphertext, and it sends it to the operator Op for decryption. Intuitively, encrypting the IMSI prevents IMSI catcher attacks; moreover, even if all IMSI values are known, no MiM adversary can encrypt a valid identification response for a new R id , since F 5 is a PRF whose key is unknown to the adversary.
In order to guarantee client unlinkability, however, we also require that TMSI values have the same length as the output of the PKE scheme. If this requirement is not fulfilled, then a distinguisher can use the length of the messages to single out one specific client e. g. one that moves into a new area. Whereas this type of attack seems less likely, it is easily feasible and highly usable in contexts of mass surveillance. We design the TMSI reallocation in such a way as to ensure that if the client remains in the same LAI (flag TMSI = 0), the server of that area can find the tuple (TMSI, IMSI) in its database. The fresh randomness R id prevents replays, whereas the IND-CCA security of the PKE scheme makes encryption probabilistic and indistinguishable from random. Preparation. This second phase is run over a secure channel, between the server and the operator.
By either observing such faulty behaviour (due to transmission errors or to server database problems), or by causing it, MiM attackers can easily track clients, in violation of the user-identity confidentiality requirement. This is a problem in all generations of mobile communication networks. Van den Broek et al. [9]  addressed IMSI catchers by replacing IMSI values by unlinkable pseudonyms PMSI. We discuss this variant in detail in Section 3. 2. Arapinis et al. [22]  showed that failure messages (in authentication/resynchronization) can be used to trace users. A follow-up paper  [21]  identifies ways in which a specific implementation deviates from 3GPP recommendations, enabling the linkability of client sessions. Though several improvements of AKA have been proposed, only two analyse the resulting security.
Powders were degassed for several days before sample preparation. The base pressure during the sublimation was <10 -6 mbar and the deposition rate was about 0. 2 Å/min, as measured by QCM before exposing the substrates. The thickness of each deposition was checked by AFM imaging (NT-MDT P47-PRO) on a shadow masked redundant sample. Scale factors in the simulation of the torque data provided effective thickness in reasonably good agreement (30%) with AFM evaluations. A CuBe cantilever was employed in an Oxford Instrument MAG2000 platform equipped with a 12 T magnet. The capacitance was detected with an Andeen-Hagerling 2550A automatic capacitance bridge.
• Second, we propose the TripSense scheme by taking advantages of the unique features of vehicular platooning. In this scheme, platoon head vehicles firstly authenticate all sensing vehicles inside the platoon and then select some of them according to their trust values. Later, the sensed data from sensing vehicles will be collected and aggregated by platoon head vehicles before they are finally uploaded to the server. Compared with previous works, our proposed scheme reduces the communication overhead and hence is more suitable for the dynamic and ephemeral vehicular ad hoc network. • Third, we design a privacy-preserving sensing vehicle selection scheme based on our trust system and a privacy-preserving data aggregation scheme based on the efficient commitment scheme in  [13]  such that platoon head vehicles can collect the data without leaking sensing vehicles' privacy.
System Model In our model, the service provider (SP) wants to inspect an area of interest (AoI) located near a highway where many platoons pass by. As illustrated in Figure  2 , our system model consists of three roles: the service provider (SP), a cloud server (CS), the immobile roadside units (RSUs) along the highway and mobile vehicles traveling on the highway, which are equipped with onboard units (OBUs) and powerful sensors. Service Provider (SP): The SP is fully trusted because it is normally controlled by the authority who wants to inspect an area of interest by collecting the data of this AoI. The data collected is a vector of readings regarding, for example, air pollution level, noise level, temperature, humidity and so on. The duty of SP is to initialize the whole system, and distribute key materials to RSUs and vehicles.
The Proposed TripSense Scheme Achieves Robustness Against Attacks Launched by Adversary • Resilience to malicious sensing attack: According to our proposed scheme, the selfish or malicious vehicles that submit arbitrary sensed data will get low evaluation scores in the trust system. Those low evaluation scores will be accumulated and finally lead to low trust scores if they keep behaving in that way. When their trust scores are lower than threshold TL TH , their sensed data will be excluded from data aggregation or they will be given a low weight in data aggregation due to low trust scores. In both ways, the attacker will be mitigated in our proposed scheme.
Simulation Settings We design a simulation to evaluate our proposed TripSense scheme in which only a set of key factors are considered and specified in order to validate the PM vehicles' sensing accuracy. It is worth noting that the selected factors are related to the movement of vehicles and the packet collision problems. In this case, we simulate the proposed scheme in the environment of MATLAB where there are a total number of n registered PM vehicles. To ensure the fairness, we suggest that each PM vehicle provides m times sensing report in totally m independent tasks. The detailed simulation parameter settings is in Table  1. forgetting factor parameter 1 T 0 initial trust score 0. 5.
Each region was scanned with several sweeps until a good signal to noise ratio was observed. The pressure in the analysis chamber was maintained below 10 À7 Pa. PHI ACCESS ESCA-V6. 0 F and Multipak 8. 2b soware packages were used for acquisition and data analysis. A Shirleytype background was subtracted from the signals. Recorded spectra were always tted using Gauss-Lorentz curves in order to determine the binding energy of the different element core levels more accurately. The accuracy of the binding energy (BE) values was within AE0. 1 eV. Electrochemical analyses were carried out in a three-electrode cell comprising a gold (chronoamperometry) or glassy carbon (other voltammetric characterizations) rotating disk electrode (RDE) (working electrode geometric area 0. 196 cm 2 ), a reversible hydrogen electrode (RHE, reference electrode) and a platinum wire (counter electrode), and a Pine bipotentiostat model AFCBP1.
ACKNOWLEDGMENTS The authors wish to thank  Alexandre Schmit ,  Laurent Courbin ,  Pascal Panizza  and  Hamza Chraïbi  for fruitful discussions, and  Hamid Kellay  for kindly proofreading the manuscript. Valuable experimental help from  Sébastien Cassagnère  and  Joël Plantard  is also acknowledged. This work was partly supported by the  CNRS  and the  Conseil Régional d'Aquitaine  through grant number  20061102030. The authors would also like to acknowledge networking support by the  COST Action   MP1205  (http://costmp1205. eu).
Interlocked grain and MFA B. guianensis is characterized by a high variability in grain orientation within the radial profile. The interlocked grain angle is initially low near the pith and regularly increases towards the bark. In larger specimens, the interlocked grain angle can reach 25°. Profiles observed for the 11 individuals were very similar despite the strong differences in stages of development or growing conditions between trees. Contrary to interlocked grain, the X-ray measurements revealed that MFA was mainly constant in mature wood with little variation around the mean value of 11. 6°(variance of 3. 6°obtained with 548 samples measured). MFA was slightly higher (20°) in the first 5 cm around the pith, with similar results for juvenile wood. Despites this high variability, we did not notice any significant effect of the grain angle on shrinkage for the whole dataset or for sapwood and heartwood subsamples.
B. Jitter introduced by the system: Secondly, we evaluated the jitter introduced by the system on the CPRI signal. The measured values in Unit Intervals (UI) are displayed in Table  1. We notice that there is more jitter at the receiver compared to the measured values at the transmitter, which we believe can be attributed in part to the active components used for the wavelength locking operation in the ONU. We also observe that the measured values comply with the CPRI specification for line rates 2. 45 Gbit/s and 4. 91 Gbit/s. However, the maximum value is slightly exceeded at 9. 83 Gbit/s. It is important to note here that the design of the WDM-PON prototype was not optimized for best timing performance. Resynchronization with an appropriate Clock Data Recovery (CDR), absent in this prototype, would substantially improve the jitter results and should provide specification compliance also at 9.
Acknowledgement: We thank  E. Giacobino ,  S. Vezzoli  and  M. Sondermann  for fruitful discussions. V. S. thanks  C. Sartel  for technical support. This work was supported by the  Champagne-Ardenne region project NanoGain. We thank the regional platform "Nanomat" for the nanofabrication. This work was partially supported by  C'nano IdF  within the project SOPHOPOL. W. G. acknowledges the financial support of the  China Scholarship Council (CSC). C. C. and A. B. would like to thank the  CNRS programme PEPS  for financial support and collaboration exchanges. L. C. acknowledges the  MIUR-project MAAT "Molecular Nanotechnologies for Health and Environment ". C. C. thanks the  COST programme "Nonlinear Quantum Optics-NQO ".
Illustration of the FFD RBF technique Let Form initial a 3D geometry which we seek to deform Form initial ¼ ðx 1 initial ; x 2 initial ; x 3 initial ,…. , x K initial Þ: ð7Þ First, we choose a set of M points, RBF database , from the initial geometry RBF database ¼ ðx 1initial ; x 2initial ,…. , x Minitial Þ A Form initial ðM o KÞ: ð8Þ Then we start with the deformation of these points (deformation ¼ moving points in 3D space), this deformation is represented by a vector describing the 3D displacement of u i the points chosen which has been positioned at x i initial in the initial geometry. The new position of the RBF database points is described by the vectors: RBF database newposition ¼ ðx 1 new ; x 2 new ,…. , x M new Þ ð 9Þ where x i new ¼ x i initial þ u i ði ¼ 1; …; MÞ. Let x i ¼ x i initial ¼ ðx i ; y i ; z i Þ and u i ¼ ðu x i ; u y i ; u z i Þði ¼ 1; …; MÞ.
We consider the following three linear systems: GA x ¼ ðu x 1 ; u x 2 ; …; u x M ; 0; 0; 0; 0Þ T GA y ¼ ðu y 1 ; u y 2 ; …; u y M ; 0; 0; 0; 0Þ T GA z ¼ ðu z 1 ; u z 2 ; …; u z M ; 0; 0; 0; 0Þ T 8 > < > : ð10Þ where G is assembled as described in the matrix  (6). The solutions of A x , A y and A z give us the interpolation coefficients for the predicted displacement in the three directions ðx; y and zÞ of the expression (4), F RBF x ; F RBF y and F RBF z respectively. Then the new geometry Form new of the initial geometry Form initial is defined as follows:  where Form new ¼ ðx i new Þ i ¼ 1;…;K ð11Þ x inew ¼ x i þ u i if x i A RBF database x i þðF RBF x ðx i Þ; F RBF y ðx i Þ; F RBF z ðx i ÞÞ else ( To illustrate the FFD RBF technique presented above, we present a simple example which is the deformation of a flat surface (Fig.
Presentation of the optimization framework The goal is to figure out a new design of the twist beam, which satisfies a FR 34 value bigger and a VM value smaller than the FR 34 and VM of the initial shape respectively. Our initial shape has 546 MPa for the Von Mises constraint and a torsion reaction force of 2460 N. We will use our developed approach of optimization which allows us to set the parameters of the shape by using the free form deformation technique with radial basis function (FFD RBF) and then optimizing under bound constraints. We start by the representation of the characteristics related to shape optimization (design variable, constraints, metamodel database and multicriteria optimization formula).
Design constraints and metamodel database Let φ 0 ¼ ðφ 01 ; φ 02 ; φ 03 Þ be the initial shape of the twist beam, and u x , u y and u z a positive offset respectively (Fig. 9 ). Then, we choose the bound constraints as follows: *φ lower ¼ ðφ 01 À u x ; φ 02 À u y ; φ 03 À u z Þ *φ upper ¼ ðφ 01 þ u x ; φ 02 þ u y ; φ 03 þ u z Þ Now we have the design variable with three components, and each component is a displacement, then, we choose 5 displacement values for each one (for example, the selected values for the displacement along the x-direction are ðÀu x ; À u x =2; 0; u x =2; u x Þ and similarly for the displacement along y-and z-directions (Fig. 10 ). With three components and for each variable five different positions, so we have a set of 125 points, each point representing a given shape of the twist beam.
Fig. 13  and Table  3  show the profiles (1) and (3) which had the best optimized value for the second criterion (FR 34 ) and the first criterion (VM) respectively. Our main goal was to look for new profiles for the twist beam satisfying some requirements (FR 34 higher and VM smaller than the initial shape ones value), a goal that we achieve successfully (Fig. 13  new profiles (2) and (  3 )). We eliminate all dominated points by a filter, and we remarked that all remaining solutions are almost located at the boundary of the space formed by the elements of the RBF database (Fig. 12 ). Then we can conclude that the solutions obtained are likely NBI solutions and our approach is able to solve the industrial problem with a reasonable computation time. Finally, we present a new profile of the automotive twist beam obtained by the shape optimization done by our algorithm (Fig.
Each spectrum deconvolution of K and L analytical lines was done with the WinAxil software package, which provides for any element an individual peak area converted in counts. To a first approximation, the count rates are proportional to corresponding elemental concentrations in the sediment. Characteristic S emission Kα line is 2. 3 keV close to L lines Rh anode tube. It is relatively free of spectral interferences and the main absorber elements are phosphorous and silicon which have a negligible influence on S measurements. [20] uantitative determination of total-S concentrations in selected sediment samples was obtained by fully automated WD-XRF equipment (BRUKER S8 Tiger), i. e. a sequential X-ray spectrometer using an end window Rh anode tube. Quantitative elemental total-S and sulfate-bearing sulfur (sulfate-bearing S) concentrations were performed from the S Kα line under vacuum with a voltage of 30 kV and an intensity of 135 mA, using a crystal Ge, a collimator 0.
X-ray diffraction The presence of pyrite in selected sediment samples was determined by X-ray diffractometry (XRD), using a D8 Advance BRUKER model, a device type Bragg-Brentano equipped with a Cu X-ray tube, a primary Soller slit of 0. 6 mm, a nine-position sample holder and the VANTEC-1 Position Sensitive Detector with a nickel filter (Ni 0. 5). Prior to analysis, dried sediment powders were inserted into the sample holder, and flattened. Measurements were made from 5 to 70°with a step of analysis of 0. 01°lasting 1 s. The voltage and amperage were set up to 40 kV and 30 mA, respectively. Qualitative analysis of the diffraction pattern of unknown sample was carried out using the identification phase procedure of the software. The pyrite Bragg reflections and Intensity (I/Io) occur at 1. 6332 (1), 2.