dataesr/scientific-paragraphs-categorization huggingface dataset:
T. Darras prepared the manuscript with contributions from all co-authors. A. Johannet contributed to the RNF Pro software used to simulate neural networks and extract parameters. Acknowledgements. The authors thank the METEO-France weather agency, the SPGD flood forecasting agency for providing rainfall data sets, as well as the Montpellier Office of DIREN for contributing discharge data. Our gratitude is extended to Caroline Wittwer, Bruno Janet, and Arthur Marchandise for the stimulating collaboration shared with the SCHAPI unit, and also to Roger Moussa, Pierre Roussel-Ragot, Pierre Ribstein, and Marc Vinches for the helpful discussions as well as MEDYCYSS observatory for support. The constant effort made by Dominique Bertin and the Geonosis Company to enhance and develop the neural network software RNF Pro are thereby acknowledged as well.
However, at low energies a more significant discrepancy can be seen. Beside the pulse intensity profile and cluster size uncertainties, another possible source of this deviation is that photo-and Auger electron energies for heavy elements by XATOM are less accurate. Currently, a non relativistic theory is used in the code and the omission of spin-orbit coupling introduces binding energy shifts that may be reflected in the calculated electron spectra. Better agreement may then be achieved once a relativistic version of XATOM becomes available. In deep-shell ionized xenon, intra-atomic Auger cascades are built up from many sequential Auger emission steps. In a 300-atom system experiencing the maximum fluence in the beam, 1 photoionization, 4 Auger decay and 2. 75 secondary ionization events per atom occur on average.
For the theoretical modelling, we employed XMDYN  44  , our tool developed for modelling finite systems such as clusters irradiated by XFEL. XMDYN has been successfully applied for modelling small molecules (C 60 ) irradiated by high intensity XFEL  31  and low-intensity synchrotron  45  pulses. It combines a Monte Carlo description of ionizations with the efficient classical molecular dynamics treatment of particle dynamics (atoms, atomic ions and free electrons). Photoionization, inner shell relaxations (corresponding cross sections and rates are calculated with the XATOM code  46  ) and electron impact ionizations (via the atomic binary-encounter-Bethe (BEB) cross sections  47  ) are included. XATOM  46  has been already successfully applied for theoretical modelling of rare gas atoms irradiated by SACLA  48, 49.
Acknowledgements The experiments were performed at SACLA with the approval of JASRI and the program review committee (No. 2012A8036 ). This study was supported by the X-ray  Free Electron Laser Utilization Research Project  and the X-ray  Free Electron Laser Priority Strategy Program  of the  Ministry of Education, Culture, Sports, Science and Technology of Japan (MEXT) , by the  Japan Society for the Promotion of Science (JSPS) KAKENHI  Grant No. 21244062 , No. 23241033 , by  MEXT KAKENHI  Grant No. 22740264 , by the  Proposal Program of SACLA Experimental Instruments of RIKEN , and by the  IMRAM  project. P. J. acknowledges support from the  Swedish Research Council  and the  Swedish Foundation for Strategic Research. S. M. acknowledges financial support from  JSPS. C. M. , E. R. , and X.
J. L. acknowledge a public grant from the  Laboratoire dExcellence Physics Atoms Light Matter (LabEx PALM)  overseen by the  French National Research Agency (ANR)  as part of the  Investissements dAvenir program  (reference:  ANR-10-LABX-0039 ) and support from the  European COST  action CM1204XUV/X-ray light and fast ions for ultrafast chemistry (XLIC). R. F. acknowledges the  Swedish Research Council (VR)  for financial support.
g. , JPF does  [24]. It allows the model to be represented not by a transition matrix, possibly very lengthy and still partial, but by a terse specification in the checker's native input language. The checker may then easily apply symbolic data structures like MTBDDs  [16] , which may in turn allow for a substantial reduction of the space explosion problem, inherent to an explicit transition matrix. In order to cope with time constraints when developing MR applications, practitioners rely mostly on fast response and high performance hardware, even if this contradicts other issues, like power saving (hence autonomy) and cost (hence mass production), and does not ascertain that critical constraints will always be respected. Modelling the application before testing it on the actual hardware and validating it by applying formal method techniques to prove its robustness, was the main motivation for the development of the MIRELA framework.
It aims at supporting the development process of MR set-ups, which are generally prone to various issues related to time and known to be difficult to control and to adjust. Most mixed reality frameworks, like those cited in  [5, 21, 14, 19, 15, 11]  do not concentrate on the validation of the developed applications. Some of them emphasise the use of formal descriptions of components in order to enforce a modular decomposition  [22, 17] , and ease future extensions  [18]  or substitutions of one module by another  [13, 12]. Such frameworks do not deal with software failure issues related to time. On the contrary, the main focus of the MIRELA framework is the formal analysis of software failure issues related to time, together with timing performance analyses and the development of automatic tools.
We revisit here essentially the second phase of the methodology of the MIRELA framework. Since the high level specifications of MIRELA are close to a subclass of UPPAAL  [23]  systems, it was originally considered to use the UPPAAL model-checker to analyse the properties of a MIRELA system  [8]. However, a serious problem was faced when trying to detect deadlocks limited to some components, since the UPPAAL query language does not allow nested path quantifiers. A proposed solution was then to use instead the PRISM tool  [16]  and analyse if and how it may be to detect bad behaviours of a tentative MIRELA system. The paper is organised as follows. First, we shortly recall the specification language of MIRELA and its semantics in terms of a network of TAST automata (a subclass of timed automata of UPPAAL).
Experimental results We applied this procedure to Examples 1 and 2. In order to automatically translate these examples to the PRISM language, we extended our compiler  [20]  with the emulation of urgent synchronisations, discussed in Sec. 3, and with a library with definitions of MIRELA components. The results of model checking of formulas and the status of each wait location s i are shown in Table  1 , where for each component, s i is the location added for s i in Figure  1  in order to emulate urgent communications (see Figure  2 ). For Example 2, as the model-checking times are similar, we show only locations for which we obtain a different status w. r. t. Example 1. We may observe that Example 1 presents several locations concerned with both local deadlock and starvation (in different contexts), for which starvation disappears in Example 2 due to the modified timing constraint on the rendering.
Obviously, probability may allow for much more realistic models and queries within the scope of MIRELA. We plan thus to introduce unreliable and stochastic components, to be checked using formalisms like PCTL, i. e. , probabilistic CTL. The computation times of the example models turned out to be quite reasonable, with the time constants carefully chosen in order to have a large gcd, but we should now consider more complex systems, both in terms of structure, in terms of interval bound characteristics, and in terms of a mixture of stochastic and non-deterministic components. It could also be considered to introduce several traits of actual programming languages, like variables or conditional jumps. In order to stay within the capabilities of model checkers, though, we might e. g. , divide a real computer application into functional blocks, each having, beside an actual implementation, a simplified specification for MIRELA, which could be used to e.
A possible practical application is also demonstrated using a biomedical sample with a complex internal structure. For this purpose, a tomographic set of X-ray DPC images was processed by the suggested phase retrieval algorithm. The obtained phase projections were used to perform CT reconstruction of the index of refraction inside the object. This reconstruction yields absolute magnitudes of the refractive index decrement, while typically a reference material has been used in order to normalize the reconstructed data and to obtain such quantitative information  [15, 16]. Finally, the differential equation for the phase retrieval is equivalent to the heat conduction one. There exists a number of software packages optimized to solve heat transfer problems, which means that an existing software can be adapted to perform the phase retrieval.
Our work concerned with biomedical samples and we estimated the phase at boundaries assuming a material equivalent to water. The projected object thickness can be estimated using, for instance, complementary absorption data typically obtained in PCI CT imaging. The Neumann boundary condition can be readily defined along the axis, which coincides with the phase contrast sensitivity axis. In the orthogonal direction, the phase derivative can be approximated either by zero, the periodic boundary condition, or a finite value. Several trials might be necessary in order to find Neumann boundary condition which minimizes artifacts. Note that object edges, at which boundary conditions are defined, should not necessarily coincide with the coordinate lines. It is assumed that the object (the gray area) exceeds the field of view (which is shown as a black rectangle) in the vertical direction.
Numerical data A numerical phantom was used both for the general assessment of the method and for studying the dependence of the solution on the magnitude of the regularization parameter γ. For the sake of comparison with results of  [7, 11] , the Shepp-Logan phantom was generated on a 256 × 256 grid. It was assumed that the generated projection shown in Fig. 3 (a) represents the line integral of the index of refraction decrement δ. The DPC image was calculated according to Eq. (  2 ) by a simple forward difference approximation to the 1D derivative operator. It is difficult to derive the exact noise model, since several mathematical operations are always performed to calculate the DPC image from a sequence of raw phase-contrast images. Therefore, we added a Gaussian noise with the root mean square value, which is equal to 5% of the maximum intensity value in the generated DPC image.
The appearance of the resulting noisy DPC image (Fig. 3(b) ) is similar to the typical data obtained in our experiments, where about 1000 photons contributed to the signal in each image pixel within 100 msec exposure time. Note that neither low-pass filtering (smoothing) was applied, nor values of bony tissues were decreased when modeling the DPC signal. Typical streak artifacts arising after 1D integration are seen in Fig. 3(c ). Figures  3(d ) and 3(e) present the phase projection obtained using the suggested Eq. (  6 ) with γ = 0. 001 and γ = 0. 05 correspondingly. For γ = 0. 05 the result is also presented that was obtained with the ANSYS software, Fig. 3(f ). It can be seen that when γ is increased the residual stripe pattern is vanishing. At large γ the appearance of another artifact can be noticed in the vicinity of the upper and lower vertices of the outer ellipse.
Experimental data Data was acquired with the analyzer based imaging setup located at the biomedical beamline ID17 of the European Synchrotron Radiation Facility (France). The Bragg reflection from [333] planes of a perfect Si crystal was used to prepare the monochromatic beam with a very small divergence on the sample's entrance plane. After the wave passes through the sample, the phasecontrast is generated by reflecting the wave from another Si crystal, identical to the first one. Detailed specifications of the imaging setup and acquisition procedure can be found in  [15]. Each differential phase contrast projection was calculated from a pair of raw phase-contrast images by using a non-linear extension for the diffraction enhanced imaging algorithm  [21]. All experiments were done at 51 keV photon energy.
Refraction-based CT using phase projections obtained from DPC images In this section we demonstrate a possible practical application of our phase retrieval method combined with refraction-based X-ray CT imaging. The experiment was made using a rabbit knee joint sample. The expected benefit of imaging the refractive index inside such sample is the simultaneous visualization of the bone and the soft tissue surrounding the bone. This is very important for the study of the osteoarthritis disease: a good contrast of both bony and soft tissues can not be achieved neither with MRI diagnostic nor with absorption CT imaging  [22]. To this end and to further validate the potential of the suggested phase retrieval algorithm, we applied it to a tomographic data set of DPC images of the rabbit knee sample.
The effective pixel size was 96 × 96 μm 2 , the photon energy was set to 51 keV. First, DPC projections were acquired in the in-plane geometry. Then the object rotation axis was aligned along the phase contrast sensitivity plane and the data acquisition was repeated. Phase images were retrieved using mixed boundary conditions shown in Figs. 2(a ) and 2(b) for the inplane and out-of-plane setups accordingly. The regularization term used to suppress the stripe artifacts was set to γ = 0. 05 for the in-plane and γ = 0. 02 for the out-of-plane geometry. FBP algorithm with the Ram-Lak filter was used for tomographic reconstruction. Examples of the index of refraction distribution reconstructed in the axial and sagittal planes are shown in Fig. 6. Reconstruction artifacts are clearly visible in the left upper quadrant of the axial distribution reconstructed in the out-of-plane geometry [Fig.
Discussion We have described an approach to the problem of the phase retrieval from unidirectional X-ray DPC measurements. The method largely suppresses streak artifacts, which arise in the phase image if a straightforward 1D integration of the noisy DPC data is used. Contrary to existing methods, the considered approach does not make use of a minimization problem, but it converts the phase retrieval to the second order partial differential equation. Boundary conditions should be imposed in order to find the unique solution of a differential equation. In this work we propose to use the mixed inhomogeneous boundary conditions. The Dirichlet boundary condition allowed us to retrieve absolute values of the phase, without necessity to find the reference material in the image and to re-normalize the reconstructed values.
Appendix: retrieving the phase with the ANSYS ® software There is a variety of both open source and commercial finite element software packages that can be potentially used to perform the phase retrieval using Eg. (6). Here we show how the problem can be set up and solved by means of the Workbench Mechanical component from ANSYS ® Academic Research, Release 15. 0. The procedure includes several steps. 1. Calculate the right-hand side of the Eq. (  6 ) and export the result in comma separated ASCII file as following: 1st, 2nd and, 3d columns are x,y, and z coordinates of the pixel and 4th column contain the calculated quantity. Assuming that reconstruction takes place in xy-plane, z coordinate can be set to zero everywhere. 4. In the section Engineering data it is necessary to create a new material.
Acknowledgments Authors would like to acknowledge the financial support from  Deutsche Forschungsgemeinschaft ,  Munich Center for Advanced Photonics  ( EXE158 ), and  Open Access Publishing Fund of Karlsruhe Institute of Technology ;  E. Brun ,  H. Requardt ,  T. Brochard ,  C. Nemoz  and for the technical support at the beamline, as well as  V. Gasilov  for helpful conversations about mathematical physics.
Section 5 analyzes related work. Finally, Section 6 presents the conclusions and outlines future research directions. 2 Approach: feature models for runtime adaptation An adaptive system is a system whose behavior can be changed during its execution according to the user´s needs or context changes. If the system can react to changes in the operating environment, the system is called self-adaptive  (Oreizy et al. 1999 ). These systems are usually conceptualized using a reference model for autonomic control loops called  MAPE-K (IBM 2003). In MAPE-K, there is a managed element (software or hardware resources) that is given autonomic behavior by coupling it with an autonomic manager. This manager is built in terms of four functions or steps (monitor, analyze, plan and execute) that define the autonomic control loop, an internal knowledge that represents the stakeholders´goals, the managed element, and, optionally, its execution environment.
Our approach can be viewed as an instance of the MAPE-K loop, as follows: the managed element is a video surveillance processing chain implemented in terms of software components; a feature model formalism is used for the knowledge representation (about the system and its context); and an optimization process based on an heuristic search algorithm is used for the planning step. This algorithm employs quality attribute metrics for guiding the system adaptation. The planning aspect takes into account the data from the monitor/analyze steps to produce a series of actions to be effected by the execute step on the managed element. In simple cases, event-condition-action (ECA) rules are used to directly produce adaptation plans from specific event combinations with the form "when event occurs and condition holds, then execute action".
g. , intrusion detection or other scenarios. The output results might be stored  for future processing, or might raise alerts to human observers. In the domain of computer vision and video surveillance systems, the processing chain can involve additional stages (e. g. , clustering, shadow removal, and data fusion -in case of multiple cameras), which require the deployment of different software components. These components can have variants (e. g. , algorithms, strategies, input data, etc), each one corresponding to a different configuration parameter of the component.
The modeling relies on FAMILIAR (FeAture Model scrIpt Language for manIpulation and Automatic Reasoning)  (Acher et al. 2013 ), a scripting language and tool dedicated to the management of feature models. In particular, FAMILIAR allows us to capture the variability of the software system and its possible contextual changes, and also supports the verification of consistency properties. The mapping and attribute specification are done by software engineers that are concerned with the system implementation. The mapping activity implies linking software elements (components and parameters) to features, and system events to feature selection/deselections (event rules). Currently, the approach is limited to one-to-one mappings between features and software elements, such as in feature-oriented software development  (Apel and Kästner 2009)  where features are mapped to implementation assets.
Platform architecture In this section, we describe the platform that materializes the approach presented in the previous Section. The platform was designed to accomplish the following goals: 1. Provide basic functions for monitoring and re-configuring component-based system; 2. Articulate the running system with a runtime (feature-based) model, in order to represent and manipulate configurations and execution contexts; 3. Support the specification, measurement, and optimization of quality attribute properties on feature models, in order to plan and perform system adaptation minimizing the impact on quality of service. The platform was implemented in C++. Its architecture defines three main collaborating modules, as shown in Fig. 5 , which provide a simple interface that makes the design loosely coupled: • Component Manager (CM): this part deals with the low-level aspects of software components and configuration changes.
Feature models and quality metrics are used for the knowledge representation of the system, context and stakeholders' goals. The general platform lifecycle is shown in Fig. 6. Essentially, it comprises 3 steps, namely: initialization, control loop, and finalization. During initialization, the modules (CM, CA and MM) are created, and two threads are started for executing the main control loop: one thread corresponds to the Component Manager thread that executes and monitors the managed system as a set of running components, while the other thread is the Configuration Adapter thread that handles events that may imply possible reconfigurations. When an event is triggered by a component, the CM thread dispatches it to the CA by sending a message. The event is queued to be later processed by the CA thread, so that the CM is not blocked and might continue with normal operation while next configuration is computed by the CA in background.
2010) , such as the Service Oriented Framework (SOF) 3 and the CTK Plugin Framework (CTK) 4 , but finally decided to use an ad-hoc solution loosely based on the OSGi model. Components were implemented as shared libraries (DLL files in the Microsoft Windows operating system) that can be loaded or unloaded dynamically upon request. These components are based on the computer vision components defined in  (Rocha et al. 2011 ) that use the OpenCV libraries (Opencv project 2015). OpenCV is a collection of efficient algorithms and data types for implementing image processing and computer vision systems. Each component manages its own internal state and resources, and performs a specific task within the processing chain, like image acquisition, segmentation, motion or face detection, etc. All components implement a common interface defined by specific C++ declarations, so that a given component can be replaced by another one with the same interface, if necessary.
Application system and quality attribute Properties For experimental purposes, we used the Component Manager implementation presented in Section 3. 3 that controls a pipeline of software components for processing a sequence of video frames. The corresponding feature model is depicted in Fig. 10. This model only includes deployment and component aspects of the system, showing the software elements that conforms it: platform executable, OpenCV libraries, the stages that conforms the pipeline, and the components that can be assembled in each stage. The CM implements two different execution modes for processing the pipeline of video frames: sequential and parallel execution. In the former, each video frame is processed sequentially in a single thread. It starts in the Image Adquisition stage, which captures images from a physical camera or a video file.
All measurements were performed on the same PC with processor AMD FX-6300 CPU 3. 5 GHz, 16 GB RAM, and running Windows 7 OS. For the first step, we performed a set of repetitive measurements on every components so as to compute the following values: startup time, i. e. , time required by the Component Manager to load the component; shutdown time, i. e. , time required to unload the component; and response time, i. e. , time required by the component to process a video frame. These values are summarized in Table  2. For startup and shutdown time, the components were loaded and unloaded 100 times, and then we computed their average values to validate their stability. These values involve the time required by the Component Manager for loading and unloading the library modules, allocating and de-allocating the components to the processing chain, and calling the init and release procedures of the component library.