dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We acknowledge  Thierry Meylheuc ,  David Dalle  ´rac and  Charlotte Richard  for their technical assistance. Angeline Guenne ,  Pascale Mosoni ,  Fabienne Guillon  and  Romain Briandet  are acknowledged for helpful discussions. We are very grateful to  Mickae ¨l Desvaux  for its critical reading of the manuscript. We also acknowledge the anonymous reviewers for their helpful comments.
Acknowledgements The FACET E200 plasma wakefield acceleration experiment was built and has been operated with funding from the  United States Department of Energy. Work at  SLAC  was supported by  DOE  contract  DE-AC02-76SF00515  and also through the  Research Council of Norway. Work at  UCLA  was supported by  DOE  contract  DE-SC0010064  and  NSF  contract  PHY-1415386. Simulations were performed on the UCLA Hoffman2 cluster through  NSF   OCI-1036224. Simulation work at  UCLA  was supported by  DOE  contracts  DE-SC0008491  and  DE-SC0008316 , and  NSF  contracts  ACI-1339893  and  PHY-0960344. The work of A. D. , C. B. and S. C. was supported by the  European Research Council  ( M-PAC  project, contract number  715807 ) and by the  France-Stanford Center for Interdisciplinary Studies.
IV. COMPUTATIONAL RESULTS The proposed algorithm is implemented using the C++ language and executed under Windows 7 environment on a computer that has 2 GB of RAM and a 2. 26 GHz processor. Three sets of instances were considered, namely p1, p2, and p3, and proposed by Chao et al. [5]. The number V of vertices in these problem sets are 21, 32 and 33 respectively. More precisely, the name of each instance in each set is in the form "p So in fact, for the same problem that have the same vertices with the same coordinates and associated scores, parameters [number2] and [letter] serve to create several versions by modifying value of m and the value of T max. For example for problem "p1. 3. a", there are 32 vertices including the start and end points, m = 3 and T max = 1. 7. and in problem "p1.
Deformation of the synthetic core sample The DEM software, 'Grain Flow Code in Three Dimensions' (PFC3D version 4. 0, Itasca Company, USA) is used in this study to deform synthetic samples made of a collection of spherical grains. Spherical grains with different radii are used in the simulations (Fig. 5 ), and the grain radius follows a lognormal distribution with a mean value of 35. 5 µm and a standard deviation of 0. 1 (see Table 1 and Fig. 5b ). The Hertz-Mindlin model is used to model the contact normal (using Hertz's theory) and tangential force (using Mindlin's no-slip model) displacement relations between the grains (e. g. Mindlin & Deresiewicz 1953). For DEM, the implementation of this model can be found in Di Renzo & Di  Maio (2004)  and Di  Maio & Di Renzo (2005). The related parameters are summarized in Table  1.
RELATED WORK A lot of work has been done in the field of 3D reconstruction of real scenes. Some methods (  [8] ) use the symmetry of objects to restore the missing parts, others use the repetition of the objects present in a room in order to complete occlusions with the help of a 3D database (  [7]  ,  [6] ,  [5] ). When it comes to Kinect, the best-known method for detailed reconstruction is KinectFusion (  [3] ). KinectFusion is implemented to use the GPU, allowing real-time reconstruction. The user moves with the camera around the object or scene he wants to scan. The reconstruction is carried out by simultaneously tracking the camera's 6 degrees of freedom and aligning the point clouds of each frame according to the process known as SLAM (Simultaneous Localization and Mapping. Subsequently, many methods have been developed based on KinectFusion in order to overcome some of the problems raised by this method.
RESULTS Our algorithm was implemented in C++ with PCL (Point Cloud Library). This library provides a lot of functionalities for point clouds processing such as Filtering,Segmentation, Surface reconstruction, etc as well as the RANSAC method already implemented. Our algorithm was firstly tested on the three pieces represented on  These three shots make it possible to reconstitute the simplest scene possible : an empty quadrangular piece. The planes corresponding to the walls and the floor of this scene can easily be identified on these images. Each of the shots consists of a point cloud with a density between 30 and 45 thousand points. figure 5. Our program needs 24 seconds to load the data and execute the RANSAC method to find all the planes in each point cloud (shown in figure  6 ). Our algorithm can then start its work.
Magnetic Resonance Imaging Preparation and Data Acquisition Functional Magnetic Resonance Imaging was performed on a clinical 3T MRI unit (Magnetom Skyra, Siemens, Erlangen, Germany), using a standard transmitter-receiver head coil. Participants lay comfortably in the scanner, with headphones on to hear the instructions, foam blocks to keep the head still and the Korvit boots on their feet. They looked through a prism at a screen positioned at their feet, facing them. Lights were turned off during image acquisition. The participants were strictly instructed not to move during the entire protocol. Surface EMG was used to monitor activity of the tibialis anterior and soleus muscles to check that participants did not perform any voluntary muscle contractions during the protocol. A three-dimensional high-resolution T1-weighted volume covering the whole brain was acquired (192 contiguous axial slices, 256 × 256 in-plane matrix, yielding a voxel size of 1 mm × 1 mm × 1 mm) thereby providing an anatomical image for further co-registration and normalization.
Image Preprocessing Functional Magnetic Resonance Imaging data were analyzed using SPM12 1 (Wellcome Department of Imaging Neuroscience, University College, London, UK) implemented on Matlab (The MathWorks, Natick, MA, USA). First, native space images were corrected for the time delay between different slices (slice timing step). Then, they were realigned to the first volume and unwrapped to correct for head movements and susceptibility distortions. Participants were excluded from analysis if head motion was greater than 3mm or greater than 3 • during the whole fMRI session. Coregistration of images from different sessions was achieved using mean echo planar of slice-timed and motion corrected unwrapped images as reference image and 3D T1-weighted anatomical image as source image. The 3D T1 volume was segmented in native-space, using a unified segmentation approach  (Ashburner and Friston, 2005).
Typical structure of superresolution data In (f)PALM or STORM data, each emitter position is determined from its diffraction spot by a centroid-finding algorithm  [11, 18, 19]. The uncertainty in position can be calculated theoretically, and is related to the width S of the microscope point spread function (PSF) and the number n i of collected photons from the emitter through s i $ S ffiffiffiffi n i p  [12]  in an ideal microscope. In the most common case of 2D superresolution imaging, the parameter set is then simply (x i , y i , σ i ). The more photons a fluorescent probe emits and the higher its signal-to-noise ratio, the more precisely the molecule can be localized and the smaller σ i. Our aim is to use a probabilistic approach to extract global structural information about a molecular complex from datasets consisting of lists of molecular positions and uncertainties, (x i , y i , σ i ).
The aforementioned 2D procedure extends to the 3D case by simply ignoring the projection. The complete dataset is then: (x i , y i , σ i , z i , s z i Þ. The 2D calculations above still apply but with an anisotropic blurring function and no projection afterwards. The 3D localization probability density is found by convolving the structure with a gaussian of width σ i in the lateral directions and s z i in the axial one. This way, we directly obtain the 3D blurred density of emitters  [24] : d bf ½ b; s i ; s z i ðx i ; y i ; z i Þ. All that follows can be applied to this 3D localization case by substituting d bf ½ b; s i ; s z i ðx i ; y i ; z i Þ for d p ½ b; s i ðx i ; y i Þ and will not be further emphasized for the sake of readability. Note that in the projection scheme Eq 2, it was implicitely assumed that the error in location of the emitter are identical beyond the focal plane.
While the average position of these emitters would remain the same in the 2D projection, the error on their position estimate will be larger as emitters move beyond the focal plane due to the spread of their photons along larger spots. This effect is neglected in the present approach for the sake of tractability. From another perspective, given a measurement (x i , y i , σ i ), the quantity d p ½ b; s i ðx i ; y i Þ, is also the likelihood that the distribution of emitters is defined by b. Indeed, this is the conditional probability density of localizing an emitter at position (x i , y i ) given a structure defined by b and an uncertainty in measured position σ i. Since a dataset consists of ensembles of molecular positions, we need to extend the previous calculation to obtain the joint probability for the entire set of localizations.
Simulation of super resolution data Simulated superresolution data were produced with MATLAB 1 by randomly sampling simulated label positions ðx 0 i ; y 0 i ; z 0 i Þ for different 3D-density-priors for the tagged-Gag layer shape (truncated spherical shell as shown in  [15] ) or control reference states (fully complete spherical shells). An imprecision for each position σ i was then chosen following a Gaussian shape distribution that mimics the experimental PALM-data localization uncertainty distribution (μ = 20 nm, std = 5 nm). The superresolution fitted position is deduced from the sampled position ðx 0 i ; y 0 i Þ by adding a random displacement in the plane (δx i , δy i ) with normal isotropic distribution d i $ N ð0; s i Þ : ðx i ; y i Þ ¼ ðx 0 i þ dx i ; y 0 i þ dy i Þ (see Fig 1). The exponential decrease of the exciting intensity in TIRF microscopy and subsequent diminution of positioning precision with depth can be neglected since the TIRF depth is at least of the size of the VLPs ('100 nm): the effect is thus at most comparable to the stochastic variation of photon yield of the fluorophores before they bleach, and positioning precision can be assumed homogeneous at all the positions.
Furthermore, this effect being isotropic in the optical plan, it does not affect the symmetry of the image in 2D that is crucial for our centering and orientation methods. We computed Eq 2 numerically. This step is the most computationally intensive, so we used Fast Fourier Transform (FFT) to transform the Gaussian convolution into a simple product and speed up the calculation (see S1 File). Once we were able to evaluate the likelihood function for any parameter set, the optimization problem was numerically solved using the "interior-point" algorithm of MATLAB 1 "fmincon" solver. The solver was started with different initial values to check the robustness of its convergence to a maximum. The Hessian matrix at maxima positions was deduce from the fit of a quadratic form using 1000 evaluation points in the neighborhood of the maximum to assure robustness towards numerical noise at low scale (rounding errors).
Reconstruction from simulated PALM images To evaluate the performance of our maximum likelihood reconstruction (MLR) procedure, we simulated PALM images of truncated spheres of known geometrical parameters (R, θ, ϕ). We thus provided as an input 300 simulated datasets composed of 1500 points each with an uncertainty in position of 20 nm, comparable to the experimental datasets (Fig  1 ). We could therefore compare the computed optimal structure to the "ground truth" input structure and estimate the precision of our method from these differences. This also provides a test for the proposed confidence estimation calculated from the observed Fisher information matrix. The first two parameters are associated with the geometry of the virus-like-particles (radius R and completion of the sphere θ), while the last one is an orientation parameter (tilt angle ϕ of the particle with respect to the optical axis).
Acknowledgments The authors would like to acknowledge fruitful discussions at various steps of this project with  C. Favard ,  C. Faivre-Mosaklenko ,  D. Jost , and  D. Muriaux. Computations were partly performed at the  PSMN computing center at the Ecole Normale Superieure de Lyon. This research was partially supported by  Fondation Simone  and  Cino Del Duca  from  Institut de France  (MC) and by  PEPS PTI CNRS (MC).
A comparison with a three dimensional wind turbine model using the finite element method (FEM) is presented. Some researchers have been conducted into wind turbine tower structures. For instance, Avila et al. [15]  studied the dynamic response of the tower using FEM. A simplified model of the tower was inspected. The system was reduced to a single degree of freedom (SDOF) and the obtained numerical results were compared with those resulting from the analytical solution. Razak  [16]  developed a wind turbine library in Modelica language. This library provides models of wind turbine performance features such as shaft torque and power output, since Modelica is very appropriate for the establishment of model library  [17, 18]. The developed model can be applied into any engineering application implying wind turbine technology.
The One Wind Modelica library is in development at the Fraunhofer Institute for Wind Energy and Energy System Technology. This library comprises all major components of a wind turbine also models for aerodynamic and hydrodynamic load calculations. The in-house tool One Wind proves how coupled analyses of a wind turbine are realized based on the object oriented modeling language Modelica  [19] [20] [21] [22]. In an early stage of the design process, simple models are needed to make important design decisions. The main contribution of this work is to propose a method for the preliminary design of a mechatronic system based on an analytical approach, taking into account the effect of vibration. In fact, we developed compact analytical models in Modelica/ Dymola, intended to study the vibrational behavior of a mechatronic system.
These models are collected from an exploration of the literature to extract and test the models in the pre-sizing phase of a mechatronic system. In fact, system architects working with the simulation do not want to spend much time in the implementation of new libraries. Instead, they want to use existing libraries that are recognized as stable and efficient. This approach gives the designer a wide solution space allowing the choice among them. We illustrate this approach with the mechatronical wind turbine system. We established a simple model and a quick method to estimate the dynamic behavior of a mechatronic system using Modelica/Dymola. In this approach, only basic data about the particular wind turbine is required. This paper is structured as follows: the first section deals with this introduction.
a 1 depends on the value of the mass ratio r ¼ M M t : Therefore, in order to found the fundamental eigenvalue for any value of the mass ratio r ¼ M M t ; we have plotted the curve a 1 = f(r) (the different values are given in  [24] ) using Matlab and we looked for an approximate equation (a polynomial of order 8). We obtained the following equation a 1 ¼ 3:2 Â 10 À6 r 8 À 0:00014 Â r 7 þ 0:0026 Â r 6 À 0:025 Â r 5 þ 0:15 Â r 4 À 0:5 Â r 3 þ 1 Â r 2 À 13 Â r þ 1:9 ð9Þ Applying the boundary conditions: Wð0Þ ¼ 0; dWð0Þ dx x¼0 ¼ 0 À EI d 3 W ðxÞ dx 3 x¼L ¼ p 0 þ MV 2 WðLÞ ð 10Þ The deflection expression results the following relation for the beam deflection amplitude [23]: W ðxÞ ¼ À p 0 L 3 EI b 1 a 3 1 V v 1 3=2 þ b 2 ma 4 1 V v 1 2 sin kx À sinh kx À sin kL þ sinh kL cos kL þ cosh kL ðcos kx À cosh kxÞ ð11Þ with, b 1 ¼ À sin kL þ sinh kL cos kL þ cosh kL ðsin kL À sinh kLÞ Àcos kL À cosh kL ð12Þ and, b 2 ¼ sin kL À sinh kL À sin kL þ sinh kL cos kL þ cosh kL ðcos kL À cosh kLÞ ð13Þ.
The choice of the connector "Frame" makes the object "Flexible beam" compatible with other components existing in Modelica library and then, it can be inserted in different mechatronic systems, as it is allowed by the object oriented structure of the Modelica language. The rotating flexible beam has also been modeled in Modelica/Dymola creating so an objected called "Rotating flexible beam". Figure  4  shows the "Rotating flexible beam" icon developed in Modelica/ Dymola. This model was implemented with two connectors Frames (Frame a and Frame b). This element includes the geometric and the materiel properties of the beam and the load characteristics which can be changed by the user by modifying its respective parameter window. 4 Application to the wind turbine system.
From the curve, relative to the nearest strain gauge to the defect D, it is possible to see that the deformation zone passes into the plastic range around 40 bar. The data relative to second gauge (located at 4 mm from the defect) indicate that the material enters in the plastic regime at about 43 bar pressure. In summary, the material becomes more stressed in the vicinity of the defect D, while the plastic range is obtained for a pressure above 40 bar. In order to consider elastoplastic regime, cylindrical shells with outer axisymmetric notch were subjected to an internal pressure of 60 bar (value is consistent with the plastic field, Fig. 10 ). The model considered was axisymmetric. The notch stress intensity factor was computed at the bottom of defect and then analysed. Its distribution allows detection if the pressure of 60 bar in the elastoplastic domain exceeds the critical stress intensity factor which has been obtained from Charpy's test.
The temperature was measured for each experiment using a fast response pyrometer "IMPAC IS -1 2 Si" dedicated to silicon surface measurements (emissivity = 0. 67). The "IMPAC IS -1 2 Si" can only measure temperatures from 600 K. As Joule heating induces a potential drop dependent on the sample resistance, and hence a shift in the measured kinetic energy of the core level spectrum, we submitted the silicon sample to a "comb-like" voltage ramp, with current pulses of 25 ms and zero bias intervals of 25 ms. High resolution photoemission spectra are obtained at zero bias: the sample is grounded and the Fermi level is the same as that of the analyzer. The voltage comb was synchronized with the photoemission data acquisition to separate signals obtained during current flow and zero applied voltage.
The ORCHIDEE land surface model has recently been updated to improve the representation of high-latitude environments. The model now includes improved soil thermodynamics and the representation of permafrost physical processes (soil thawing and freezing), as well as a new snow model to improve the representation of the seasonal evolution of the snow pack and the resulting insulation effects. The model was evaluated against data from the experimental sites of the WSibIso-Megagrant project (www. wsibiso. ru). ORCHIDEE was applied in stand-alone mode, on two experimental sites located in the Yamal Peninsula in the northwestern part of Siberia. These sites are representative of circumpolar-Arctic tundra environments and differ by their respective fractions of shrub/tree cover and soil type. After performing a global sensitivity analysis to identify those parameters that have most influence on the simulation of energy and water transfers, the model was calibrated at local scale and evaluated against in situ measurements (vertical profiles of soil temperature and moisture, as well as active layer thickness) acquired during summer 2012.
, 2015]. Much work has been dedicated in the past to the understanding and representation of the continental feedback processes in the ORCHIDEE (Organizing Carbon and Hydrology In Dynamic Ecosystems) land surface model used in the ESM developed at the ''Institute Pierre Simon Laplace (IPSL)''  [Hourdin et al. , 2006]. Recent developments have improved the representation of snow processes with a new discretization of the snowpack into three layers  [Wang et al. , 2013]  replacing the single-layer model used previously. Previous work has demonstrated that this three-layer representation gives a better simulation of the energy transfers in the snowpack and of the insulation effects through a more accurate representation of the snow thermal conductivity. Snowmelt and sublimation processes have also clearly been improved, since the multilayer snow module can simulate refreeze-thaw events in the snowpack, water transfer between snow layers and has a realistic parameterization of the aerodynamic roughness of snow surfaces.
The new discretization scheme, which is common to both soil moisture and temperature, also models the heat transfers linked to water diffusion. These capabilities should allow it to better represent the complexity of cryoturbated soils which present multiple horizons with different soil properties resulting from the repeated freeze/thaw cycles. This paper discusses the capability of this new version of the ORCHIDEE land surface model to represent the water and heat transfers in Arctic environments, after evaluation against data from two instrumented sites located in a continuous permafrost area, especially instrumented for the WSibIso-Megagrant project (www. wsibiso. ru). The specific objectives of our study were first to identify the dominant physical processes and the corresponding model parameters at play in cold conditions through a global sensitivity analysis, and second to evaluate the model including the cryosphere processes against in situ measurements of soil humidity, soil temperature and active layer thickness (ALT, i.
This region was chosen to monitor vegetation dynamics and understand the impacts of climate change on boreal ecosystems. For that purpose, six key sites have been identified for vegetation mapping, each site is characteristic of a typical vegetation ecosystem encountered in the Arctic, i. e. , tundra (herbaceous and shrubs), wetlands, and forests  [Valdayskikh et al. , 2013]. The main data collected comprise vegetation species, abundance, community structure, phenology and productivity, soil pedology properties, and permafrost ALT. A meteorological station was installed in July 2012 and four out of the six sites were equipped with soil temperature and soil moisture sensors. In addition, for all sites except for the forest site, soil samples as well as soil moisture and swamp water were analyzed for water isotopes.
The soil Journal of Advances in Modeling Earth Systems 10. 1002/2016MS000860 presents a complex structure, characterized by low values of the thermal conductivity and low values of hydraulic diffusivity. The first 10 cm of soil are rich in organic matter with a deep layer of mosses with sphagnum on top. These two sites were equipped with automatic sensors (Decagon Devices, Pullman, WA) for monitoring soil temperature and moisture; these sensors were installed at the end of July 2012 at fixed depths of 2, 10, 20, and 50 cm, data were collected with a sampling frequency of 10 min. In the forest site, a supplementary sensor was installed at a depth of 100 cm, whereas for the tundra site, the deepest sensor was located at the top of the permafrost (70 cm). Data precision has been estimated from the analysis of the standard deviation of the high frequency measurements.