dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, OMG promotes a dedicated standard (SysML-Modelica Transformation standard) to integrate Modelica semantics into SysML. The objectives of the SysML-Modelica Transformation specification are to enable a bi-directional transformation between the both modeling languages. The specification defines an extension to SysML, called SysML4Modelica, which proposes matching semantics between the SysML4Modelica constructs and the Modelica language. The integration of Modelica concepts into SysML is based on a profiling approach. Basically, the SysML4Modelica constructs enable to stereotype elements that are part of the Block Definition Diagram (BDD) and the Internal Block Diagram (IBD) of SysML (the BDD is analogous to the UML class diagram whereas the IBD permits to represent physical or logical interactions between component instances via input and output FlowPorts).
Thus, BDD and IBD (and optionally the requirements diagram) are the SysML diagrams that are addressed by the approach. This SysML4Modelica profile is therefore used to bridge the gap between the two modeling language: SysML, which is a non executable graphical highlevel modeling language, and Modelica, which is used as simulation language for complex and heterogeneous systems. The next step of the proposed process is to perform Modelica code generation from SysML models profiled with SysML4Modelica. Thus, the next subsections describe this process and its implementation using Model Driven Architecture (MDA) approach.
From Modelica Model to Modelica Code To perform the generation of the Modelica code from the generated Modelica models, the approach is based on the Acceleo technology. Acceleo 4  , developed by the company Obeo, is an open source code generator from the Eclipse foundation. It implements the MDA approach to develop application from EMF based models. The Acceleo language is an implementation of the MOF Models to Text Transformation (MOFM2T) standard. The implementation of the OMG SysML-Modelica Transformation specification is available for the Topcased and Papyrus environments as an Eclipse plugin. It is adopted and promoted by the OMG SysML-Modelica working group. The next section discusses experiments feedback of the proposed process on a concrete case study about the Smart Blocks system.
Using SysML4Modelica At the first sight, the relevance of the proposed approach to early validate system design using SysML models to perform Modelica simulation, instead of writing directly Modelica code for simulation, is debatable. SysML is indeed a high-level modeling language, traditionally used to specify all aspects of a complex system at the earliest stage of the development, whereas Modelica is used later in the development life cycle for rapid prototyping. However, the integration of the both notations enables to fulfill validation challenges and offers new perspectives. On the one hand, software developers as well as software architects are experienced in using modeling methodology, but they usually lack knowledge to perform hardware related coding and setup of variables to support compilers and simulation platform.
The proposed approach allows them to initiate the simulation validation process (in collaboration with engineers) and therefore guarantees the continuous validation of the modeled expected requirements. On the other hand, from robotics engineers point of view and feedback, the advantages of using SysML over using Modelica directly are also significant since the proposed approach attempts to bring closer system modeling from requirements using SysML and allows rapid prototyping using Modelica simulation. Nevertheless, the gap between SysML and Modelica being quite important at business-level, the cost to perform simulation from SysML model can be also important since design teams have to learn two languages. One important key for the adoption success of this approach by the industry would be to provide reverse-engineering tools that enable to automate the generation of SysML models from existing simulation code.
Scalability of the approach The Smart Blocks case study is not very large. To evaluate the scalability of the proposed approach, we have applied it on a large and complex energy manager of a new generation of helicopter type. Basically, this system is composed of an energy source that emulates a permanent power source (alternator coupled with a turbine or a fuel cell system), an accumulators battery and a battery of super-capacitors. Each source is connected with a controller that gives managing strategies. The SysML model of this energy manager system contains 20 blocks, 30 properties, 37 constraints, 30 instances, 109 flow ports and 62 connectors. This case study allows to assess the scalability of the proposed approach regarding modeling effort and automatic Modelica code derivation (the transformation process takes less than one second to generate 23 Modelica files).
Nytsch-Geusen  [10]  also proposes to use a special format of UML, named UMLH, for the modeling of hybrid systems. Modelica code can be produced automatically from UMLH models. However, the generated code has to be manually completed with the physical equations of the system to allow simulations. A representation of Modelica models in SysML is introduced by Johnson et al. in  [11]. This work explores the definition of continuous dynamics models in SysML and the use of triple graph grammar to maintain a bidirectional mapping between SysML and Modelica constructs. A mapping between SysML and Modelica considering a smaller subset of the Modelica language has been proposed by  Vasaiely [12]. This work does not use the OMG specification, but uses its own mapping between SysML parametric diagrams and Modelica equations.
Conclusion and Future Work The present paper proposes a tooled MDE approach to validate requirements of complex systems at the earliest stages of design process. This approach consists to generate Modelica simulation code from SysML models. To address this issue, the OMG SysML-Modelica Transformation specification has been implemented as a UML profile for SysML called SysML4Modelica. The model transformations are based on ATL and Acceleo rules, and use a dedicated Modelica metamodel that verifies Modelica syntax. The constraints defined in the OMG specification are thus verified and ensure the SysML model consistency. The approach has been experimented and validated within several industrial case studies. We are now investigating a novel SysML modeling approach that could allow both to generate test cases and to simulate the system under test from a unique SysML model.
Pre-Heating Time and Noise Reduction The first study involved the necessary pre-heating time. Indeed, as shown in  [9, 51] , some RGB-D sensors need a time delay before providing reliable range measurements. In order to determine this time delay which is necessary for the Kinect v2 to reach constant measurement, the sensor was placed parallel to a white planar wall at a range of about 1. 36 m. Three hundred and sixty depthmaps were recorded during one and a half hours, which is one depthmap recorded each 15 seconds. An area of 10 × 10 pixels corresponding to the maximal intensities in IR data was then selected in each recorded depthmap. Indeed, the sensor is not placed perfectly parallel to the wall, thus it cannot be assumed that the shortest measured distances are located at the center of the depthmaps.
Geometric Calibration Since lenses are involved in the Kinect device acquisitions, geometric distortions as reported with DSLR cameras can be observed. For the first version of the Kinect sensor, some computer vision based calibration algorithms have been developed  [53, 54]. Unfortunately, these algorithms have not yet been adapted for the Kinect v2 sensor. Nevertheless, it appears in lots of works, e. g. ,  [55] , that time-of-flight cameras can be geometrically calibrated with standard methods. As for common 2D sensors, several images of a planar checkerboard have been taken under different points of view. It is worth noting that the infrared data have been used to handle this geometric calibration, because depth and infrared output streams result from the same lens. To determine the necessary intrinsic parameters, our dataset was treated with the "Camera Calibration Toolbox" proposed by  [56]  under the Matlab software.
In this tool, the Brown distortion model is implemented. This model is largely used for photogrammetric applications because of its efficiency. The "Camera Calibration Toolbox" allows the determination of radial as well as tangential distortion parameters, and also internal parameters such as focal length and principal point coordinates. With this technique applied, one should underline the fact that changing or removing some of the images causes the computed results to vary by a few pixels. This phenomenon is only due to the low sensor resolution. As a matter of fact, the best calibration results are obtained regarding the lower uncertainties calculated on the parameters. An overview of results obtained for these parameters is available in  [52] , as well as in  [57]. The direct integration of the distortion coefficients into our point clouds computation algorithm allows the obtainment of a calibrated three dimensional dataset, which is no longer affected by the actual distortions of the initial depthmap.
Survey of Local Deformations on the Whole Sensor Array The second part of our depth calibration approach consists of extending the deformations analysis to the whole sensor array. For this purpose, only the first 10 datasets acquired on the planar wall corresponding to the first 10 positions from 0. 8 to 3 m are considered. It appears that the corresponding point clouds are not planar, but rather exhibits a curved shape. On these 10 successive point clouds, planes have been fitted by means of least-squares adjustment. To realize a proper adjustment in the Kinect point clouds, some outlying points corresponding to null values in the depthmaps have to be previously removed. Then, the Euclidean distances between a point cloud and its corresponding plane are calculated. It provides a matrix of residuals, in which for each pixel the deviation of the actual measured distance with respect to the adjusted plane is stored.
Influence of Corrections All the corrections related to depth calibration are part of post-processing carried out on the depthmaps. In order to visualize the influence of the computed correction on the systematic distance measurement error, the dataset consisting of measurements realized on a planar wall at known ranges is used. To apply the calibration, an adjustment of the errors model in the form of a spline previously determined is required. Indeed, knowing the curve parameters and the distance measured allows inferring the correction values applied to the measurements. For the analysis, a central patch of 10 × 10 pixels on the corrected data is considered as before. The remaining deviations between real and corrected distances are shown in Figure  9a. It appears that the average deviations are reduced to zero through this calibration.
These good results can also be explained by the fact that the dataset investigated is the one used to determine the correction parameters. However, it enables validating the model. At the same time, the standard deviations computed over the 50 corrected measurements per station are almost the same or have slightly decreased compared to the initial data. They confirm the increase of the scattering of the measurements when the range raises, but they look promising for the investigated device. Considering the correction extended to the whole sensor array, the approach used is based on surface approximation within the residual values calculated with respect to a plane. These residuals are visible on a colorized representation for the initial data (Figure  9b ) and after correction of the local deformations (Figure  9c ) with the method proposed beforehand.
Object under Study and Reference Data The object under study is a fragment of a sandstone balustrade of about 40 × 20 centimeters, coming from the Strasbourg Cathedral Notre-Dame (France). As shown in Figure  10a , it presents almost a symmetric geometry and flat faces, except on its extremities. A measuring arm from FARO (FARO ScanArm) was used to generate a ground truth dataset of the fragment. It provides a very high metric quality thanks to its sub-millimetric accuracy (0. 04 mm according to the specifications). Such a device is often used for metrological purposes, but because of its cost, its bad portability and its fragility, other devices are often preferred in many missions. In this study, the dataset obtained with the measuring arm constitutes a reference and will be used to assess the quality of the model created with the Kinect v2 sensor.
Data Acquisition and Registration First of all, a set of data of the balustrade fragment has been acquired with the Kinect v2 sensor. The way used to carry out the acquisitions is rather inspired by the common workflow set up in image-based reconstruction methods. A circular network composed of acquisitions from eight different viewpoints was performed around the object under study. An overlap of about 80% on the object was reported between two successive range images. In order to overcome the deformations affecting the measurement on the borders of the sensor, the object was located in the central part of the sensor. As shown in Figure  10a , flat targets were also placed around the object during the acquisitions for the point clouds' registration to be done in a post processing step. The sensor was placed at about 1 m of the object.
Its metric accuracy will be assessed in the next Subsection. There is obviously a correlation between the deformations observed on the final mesh and error sources related to the raw data themselves. First of all, border effects can be observed along the line of sight of the sensor and constitute noise. A second problem is related to deformations of the geometry linked to the viewpoint. Among all error sources, these two problems which were detailed in  [33]  obviously contribute to the low visual quality of the mesh performed.
Accuracy Assessment In order to assess the accuracy of the Kinect-based model, the reference mesh previously presented was used in order to make a series of comparisons between them. The reference point cloud obtained by the measuring arm offers a very high density of points which is almost 15 times larger than the density of the point cloud obtained with the Kinect v2. This high point density variation may be an issue for the cloud-to-cloud comparison algorithms, resulting in a loss of efficiency in the distance computation. Additionally, considering meshes, a loss of geometric quality or a data simplification due to mesh creation can occur. Comparisons between different kinds of data were therefore carried out. The complete point cloud of the balustrade fragment and its corresponding mesh derived from Kinect raw data were compared to the reference mesh.
Suggested Improvements Regarding the results obtained for the mesh creation in Section 5. 3, it is obvious that the Kinect sensor presents technical constraints which are actually problematic for 3D modeling applications. Nevertheless, improvements in the reconstruction method are necessary in order to enhance the metrical quality of the models produced. Since there is no unique approach for aligning and merging Kinect raw data in order to get a three-dimensional reconstruction of a scene, several solutions can be considered. An approach would be to use the colorimetric information produced by the camera  [31]. In the methodology developed in the abovementioned contribution, SURF algorithm  [62]  was used for the localization and matching of keypoints on color images obtained from a Kinect v1 device.
A simple qualitative examination of visibility profiles allows us to identify a rapid drop-off in the visibilities at short baselines(<10Mλ) in 8 resolved disks. This is indicative of a significant contribution from an extended (R > 3 au, at 150 pc) contribution of light from the disk. We demonstrate that this component is compatible with scattered light, providing strong support to a prediction made by  Pinte et al. (2008). The amplitude of the drop-off and the amount of dust thermal emission changes from source to source suggesting that each disk is different. A by-product of the survey is the identification of a new milli-arcsec separation binary: WW Cha. Spectroscopic and interferometric data of AK Sco have also been fitted with a binary + disk model. Conclusions. The visibility data are reproduced well when thermal emission and scattering from dust are fully considered.
Summary and conclusion We observed 23 young stars with PIONIER at the VLTI, 21 T Tauri stars, and 2 Herbig Ae stars. Thirteen of these stars have a visibility profile showing the clear signature of a circumstellar disk. We fitted the data with two simple models: a thermal ring model and a more complex composite model consisting of the thermal ring model and an additional large scale component. The composite model significantly improves the quality of the fits in eight cases, especially the shape of the visibility curves at short baselines. These results strongly support the previous suggestion by  Pinte et al. (2008)  that the extended component is produced by scattered light. For the five other cases, the fits from the two models are of similar quality. All in all, the model results indicate that the amount of scattered flux differs from disk to disk, as does the amount of thermal emission from the inner rim.
Its interferometric data can be modelled with a binary system and resolved emission. The companion is seen at a different position during each observation, but further observations will be needed to fully constrain the orbital parameters of this milli-arcsecond binary. We also re-observed and spatially resolved the known binaries V1000 Sco, TWA 3A, and AK Sco. For V1000 Sco and TWA 3A, the separations, position angles, and flux ratios of the systems were derived. For AK Sco, both the binary system and the inner edge of the circumbinary disk are resolved. The orbital parameters and the distance to the system were derived. This model reproduces both the radial velocity measurements and the interferometric data. However, the exact shape of the circumbinary environment cannot be constrained yet from our observations.
6. 1. AK Sco AK Sco is a double-line spectroscopic binary. We observed it during seven nights between August 2011 and July 2013 (Table B. 1) with the VLTI configured with long (6 nights) and short (one night in July 2013) baselines. Data were spectrally dispersed over one or three spectral channels across the H band. The dataset reveals the presence of the central binary plus an extended surrounding environment. A first fit of interferometric data and radial velocities (from Alencar et al. (2003)) with a model consisting of a binary without circumstellar material leads to poor results, with the reduced χ 2 of the fit being χ 2 r = 18 and the residuals showing the signa-F. Anthonioz et al. : The VLTI / PIONIER near-infrared interferometric survey of southern T Tauri stars. I. First results.
Acknowledgements. PIONIER is funded by the  Université Joseph Fourier (UJF, Grenoble)  through its Poles TUNES and SMING, the  Institut de Planétologie et d'Astrophysique de Grenoble , the  "Agence Nationale pour la Recherche " with the programme  ANR EXOZODI , and the  Institut National des Science de l'Univers (INSU)  via the " Programme National de Physique Stellaire " and " Programme National de Planétologie ". The authors want to warmly thank all the people involved in the  VLTI  project. This work is based on observations made with the ESO telescopes. It made use of the  Smithsonian/NASA Astrophysics Data System (ADS) and of the Centre de Données astronomiques de Strasbourg (CDS). All calculations and graphics were performed with the open source software Yorick. We acknowledge funding from the  European Commission 's 7 th framework programme ( EC FP7 ) under grant agreement No.
284405  (DIANA) and contract  PERG06-GA-2009-256513  and also from  Agence Nationale pour la Recherche (ANR) of France  under contract  ANR-2010-JCJC-0504-01. FM, SC and MS acknowledge support from  Millennium Science Initiative, Chilean Ministry of Economy :  Nucleus P10-022-F. This research has made use of the Simbad database operated at the  CDS, Strasbourg, France , and the  Jean-Marie Mariotti Center ASPRO  and LITpro services co-developed by CRAL, IPAG, and  FIZEAU. We thank the anonymous referee for her/his suggestions for improvement.
We present MadAnalysis 5, an analysis package dedicated to phenomenological studies of simulated collisions occurring in high-energy physics experiments. Within this framework, users are invited, through a user-friendly Python interpreter, to implement physics analyses in a very simple manner. A C++ code is then automatically generated, compiled and executed. Very recently, the expert mode of the program has been extended so that analyses with multiple signal/control regions can be handled. Additional observables have also been included, and an interface to several fast detector simulation packages has been developed, one of them being a tune of the Delphes 3 software. As a result, a recasting of existing ATLAS and CMS analyses can be achieved straightforwardly.
MadAnalysis 5 in a nutshell While both LHC experiments are currently pushing limits on particles beyond the Standard Model to higher and higher scales, tremendous progress has been made in the development of Monte Carlo event generators and satellite programs, in particular with respect to precision predictions, new physics implementations and event analysis. The public MadAnalysis 5 package  [1, 2]  addresses this last aspect and provides a framework for analyzing events generated either at the parton-level, after hadronization or after detector simulation. The program hence allows one to efficiently design and recast LHC analyses. The user can in this way investigate any physics model and determine the LHC sensitivity to its signatures by either conceiving a novel analysis or recasting existing ATLAS and CMS studies.
Main concepts We recall in this section the main concepts of the MadAnalysis 5 package. We refer to Refs. [1, 2] for more details. • Universal: One can in the same way design new phenomenological analyses and recast existing LHC studies, and a given analysis can be equally applied on parton-level, hadronlevel or reconstructed-level events stored under the StdHep [3], HepMc  [4] , Lhe  [5, 6] , Lhco [7] or Root  [8]  format. • User friendly: In the normal mode of running, the user designs her/his analysis by interacting with a Python console through a metalanguage designed to be intuitive. The syntax is moreover easy to handle thanks to tab completion and in-line help. • Efficient: The Python module of the program takes care of exporting the analysis encoded with the MadAnalysis 5 metalanguage to an optimized C++ program readily to be compiled and executed.
• Flexible: MadAnalysis 5 is shipped with a series of basic (transverse momentum, etc. ) and more sophisticated (α T , M T 2 , etc. ) built-in observables that can be used in analysis design. Operations on particle four-momenta are also available. For more complicated selections, the user can directly write his/her analysis in C++ (the so-called expert mode of the tool) and implement the desired observables. • Interfacing: MadAnalysis 5 is interfaced to Zlib [9], Root  [8] , FastJet  [10]  (and FastJet-contrib) and Delphes  [11]. The installation of these packages can be performed automatically from the Python console. The program also contains the so-called MA5tune of Delphes  [12]  (see Section 4).