dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, it is possible to develop an application program to simulate the deposition of coatings and the distribution of coating-thickness in the framework of RobotStudio™. In this study, an add-in program was developed in C# (programming language of Microsoft ™ Company) and the data exchange with RobotStudio™ is based on API functions provided by RobotStudio™  [34]. The program details are provided in Fig. 11  Fig. 12. It is available for users to focus on coating-thickness calculation and simulation in CS process. The CAD file of the component is needed in the process of coating simulation. The formats of CAD models such as IGES, STL, STEP, ACIS, and ASCII are available in Robotstudio™. After calibration of the component's position and the TCP, the robotic trajectory should be generated according to the operating parameters.
These signals can be recorded to generate the discrete points on the path for coating thickness simulation. However, the virtual Robotic controller system in RobotStudio™ collects the data every 24 milliseconds. The distance between two collected points is the product of nozzle traverse speed and time step. Table. 3 listed the distance between two discrete points under different TCP speed. Thus, an appropriate distance is significant for coating simulation. That is because a large distance gives rise to a less accurate outcome and a small one leads to excessive calculation. In this study, the distance between two consecutive points for calculation should not be larger than σ (the standard deviation of coating coating-profile). Therefore, it is necessary to interpolate a certain number of points between two adjacent points if their distance is greater than σ.
Variant interpretation TTN variant pathogenicity was determined according to the current ACMG guidelines. The predicted effects on transcripts and translation were based on several criteria: frequency in the general population (GnomAD [http:// gnomad. broadinstitute. org/]), alteration or not of the reading frame, the potential implication of functional domains, and bioinformatic predictions (MPA  8  and TITINdb [http://fraternalilab. kcl. ac. uk/TITINdb/]). To take into account the complexity of skeletal TTN transcripts, particularly the fact that some exons are expressed only during fetal development (metatranscript-only exons), the recent study of transcripts by Savarese et al  13  was used as a reference for this analysis. TTN variants were confirmed by Sanger sequencing.
Acknowledgments The authors thank the  Myobank-AFM  for providing control muscle biopsies. The authors would like to thank  Dr Gis ele Bonne  for her coordination of the  French Titin Consortium  and all consortium participants for fruitful discussion about these cases. We thank  Karen Gaudon ,  Corinne Th eze, Ang eline Madelaine and Ana€ ıs Chanut  for technical assistance. This work was funded by  AFM   21384  grant (The  French Muscular Dystrophy Association  [AFM-T el ethon]) and the D el egation a la  Recherche Clinique  et a l' Innovation du Groupement de Coop eration Sanitaire de la Mission d'Enseignement, de Recherche, de R ef erence et d'Innovation (DRCI-GCS-MERRI) de Montpellier-Nîmes.
• How do cost and storage affect the ranges of penetration and risk where one of the solar technologies dominates over the others? • Does technological and regional diversification (i. e. , time-space complementarity) reduce the risk for the grid? • How does TES in a PV-wind-CSP mix help reduce the adequacy risk (i. e. , variability of the aggregated RE production with respect to the electricity demand)? As a suitable tool for performing the analysis, a new Python Software called Energy for Climate Integrated Model (E4CLIM)  [47]  has been utilized. In this study, we implement new modules in the E4CLIM software to simulate the CSP solar field, thermal storage and electricity production by the CSP power block. We also adapt E4CLIM to the Moroccan case using observed production and demand data.
• Onshore wind production at each climate grid cell from wind speed at hub height, generally extrapolated from 10 m wind data using an empirical power law with exponent with default value of 1 7  [51]. The corrected wind speed is then fed to a power curve accounting for deviations of the air density from the standard density for which the power curve has been obtained. The air density is computed from the air temperature, pressure, and specific humidity at the surface using the ideal gas law for moist air. We choose the Siemens wind turbine with an electrical capacity of 2. 3 MW, hub height of 101 m, rotor area of 8000 m 2 and specific capacity of 0. 29 kW/m 2 corresponding to the medium specific capacity and medium hub height according to  [52]. The wind production variability may be sensitive to the choice of power curve.
• Parabolic-trough CSP without or with increasing storage duration (i. e. , solar multiple), specifically developed for this study, is calculated as explained in Section 2. 3. • the regional electricity demand by taking the sensitivity of the demand to temperature into account as well as the type of day (work day, Saturday, Sunday and holidays). The model is a linear Bayesian regression fitted against demand observations using a grid search with cross-validation. See (  [47] , Appendix A. 3. 4) for more information. Before the optimization step, the solar and wind capacity factors are summed over zones and bias corrected against observations. Finally, the E4CLIM software generates an electricity mix-ignoring the actual mix-by optimizing the geographical and technological distribution of solar and wind capacities using the mean-variance analysis, inspired by Markowitz' portfolio theory  [54] , as an alternative to the more traditional least-cost approach.
Solar Field In our model, the SF production, P SF , to be dispatched to the PB and/or the TES is given by Equation (  6 ): P SF = η SF × A SF × DNI, (6) where the DNI is estimated from the climate data (Section 2. 6. 2), A SF is the SF area and η SF is the overall SF efficiency. The latter is given by Equation (  7 ): η SF = η SF,geo × η SF,opt × η SF,th × η SF,avail , (7) where η SF,geo , η SF,opt , η SF,th and η SF,avail are efficiencies respectively associated with geometrical, optical, thermal and additional losses associated with the collectors' availability. These efficiencies are presented in Appendix A. 2.
For instance, here, we define the Capacity Credit (CC) as the peak-load reduction  [57]. It is thus given by the LR for h 0 = 1 and h f = 500. Similarly, the mid-load reduction (base-load reduction) is given by the LR for h 0 = 1 and h f = 4706 (resp. h 0 = 1 and h f = 8759). In addition, for each interval, the load reduction is decomposed into a contribution from each technology. 3 ). To assign the administrative regions and the climatic grid cells to the electrical zones in E4CLIM, we use the geographical data downloaded from the Global Administrative Areas (GADM) online database (https://gadm. org/). The available data for Morocco do not include Western Sahara because it is considered a non-autonomous territory by the United Nations (UN)-as mentioned in the caption of Figure  3. Yet, the Moroccan electrical zones and the corresponding data provided by the ONE cover Western Sahara.
We adapt E4CLIM to Morocco and its four electrical zones for the year 2018, as a special case, add new CSP and TES simulation modules to it, and use additional diagnostics. To take the different rental costs of PV, wind and CSP into account, we take a recommissioning approach, in which the total rental cost of a mix is constrained to be lower than that of the actual 2018 Moroccan mix. We evaluate the impact of rental cost and CSP storage duration on the optimal mixes together with the role of technological and regional diversification. To do so, we analyze mixes along Pareto fronts-from low RE penetrations and low risks to high penetrations and high risks-including: PV and wind only; PV, wind and CSP without storage; and PV, wind and CSP with four increasing levels of storage (i. e. , SM1), as measured by the Solar Multiple (SM1.
Acknowledgments: This work was conducted in the frame of the  Energy4Climate Interdisciplinary Center (E4C) of Institut Polytechnique de Paris and Ecole des Ponts ParisTech , supported by the MED-OCEAN research contract. The E4CLIM open-source software is supported by the  3rd Programme d'Investissements d'Avenir 345  [ ANR-18-EUR-0006-02 ] and by the  Foundation of Ecole polytechnique (Chaire "Défis Technologiques pour une Énergie Responsable " ). The authors would like to acknowledge the  National Electricity Office (ONE)  in Morocco for providing the regional hourly load data. We are also grateful to the reviewers for taking the time to provide valuable comments and suggestions.
The principle of I&AB consists in "summarizing" the sequences leading to failure states of a Markov chain by cut sets, each of them containing one initiating event and failures of other components, or "barriers. " Then a failure rate (usually called frequency in the PSA context) is calculated for each cut set, and the sum of these rates yields an equivalent failure rate for the whole system. What makes this method so efficient is the fact that it uses closed-form formulae for cut sets quantification  [12]. There is a prototype implementation of this method in the event/fault tree solver of RISKSPECTRUM  [25] , a commercial software suite for safety analyses. Therefore, the I&AB analysis can benefit from a stateof-the-art solver, efficiently decomposing probabilistic Boolean models into minimal cut sets.
STORM 1   [20]  is a state-of-the-art probabilistic model checker capable of model checking MA and its constituents. STORM is an open-source and freely available tool. It is quite competent and outperforms contemporary model checkers (on most of the benchmarks) as reported in QComp 2019 2   [19]. It uses numeric and symbolic methods for state-space generation. The architecture of STORM is modular and STORMPY 3  (Python bindings of STORM) can be used for reconfiguration of STORM underlying engines/solvers and thus a quick prototyping of solution according to user requirements is possible. STORM can parse models specified in various modeling languages, e. g. dynamic fault trees (DFTs), GSPNs, PRISM, pGCL, JANI and DOT (explicit) format. STORM supports DFT analysis and accepts DFT models in Galileo  [26]  or JSON format.
The first one is based on Markov Automata, and the second one on generalized stochastic Petri nets (GSPN). STORM supports the JANI format  [14]  which is a JSON based model interchange language to enable portability among different formal analysis tools. A BDMP with repairs can be transformed into Markov Automata that are written in JANI format. It can also be translated into a GSPN, another formalism natively supported by STORM. The transformation tools are of prototypical nature. Future work will focus on efficient state-space generation for repairable BDMPs.
The KB3 Workbench The theoretical concepts of BDMP could be implemented easily in powerful software tools thanks to the KB3 workbench 4   [7]  based on the Figaro modeling language  [6]. Figaro is object-oriented: a system model is made of a set of hierarchically organized classes constituting a "knowledge base" or library, and objects inheriting their characteristics (state variables, stochastic transitions and deterministic propagation of interactions) from the classes. Figure  4  shows the architecture of the workbench: 1. Knowledge bases in Figaro, containing generic models for a category of systems like thermohydraulic systems or abstract objects like the leaves, gates etc. of a BDMP are developed by knowledge management experts using a specialized editor: FigaroIDE 5  ; 2. The user loads a knowledge base in the tool KB3 which becomes a dedicated GUI and then builds a system model with this GUI; 3.
The system model is then translated into a purely textual representation, in the Figaro 0 language, a sub-language of Figaro that enables formal verifications and transformations; 4. The Figaro 0 model can either be transformed into a fault tree and sent to RISKSPECTRUM PSA or another fault tree processing tool or be directly processed by one of the two dynamic model solvers FIGSEQ or YAMS. Depending on the characteristics of the Figaro 0 model, only a sub-set of these three uses may be available. The BDMP knowledge base not only implements all concepts of  [11] : it also includes generalized stochastic Petri nets (GSPNs). It is possible to build models essentially made of a BDMP, in interaction with one or several GSPNs. The use of such extensions gives the tool KB3-BDMP (KB3 in which the BDMP knowledge base is loaded) a very high modeling power and flexibility, but it may reduce the number of possible solvers.
Since 2003, KB3-BDMP has been used with FIGSEQ and YAMS for assessing the reliability, availability, and safety of many complex reconfigurable systems of various kinds: electrical, thermohydraulic, information systems, etc. The possibility to quantify them with the I&AB method in RISKSPECTRUM PSA will widen their application domain as it will lift the scalability limits of a dynamic analysis. The I&AB method can handle full-scale PSAs of nuclear power plants  [1].
Patient Cohort Computational models were constructed from cardiac MRI data for 20 paroxysmal AF and 30 persistent AF patients treated at St Thomas' Hospital. Paroxysmal and persistent AFs were defined following HRS/EHRA/ECAS/APHRS/SOLAECE guidelines: paroxysmal AF is AF that terminates spontaneously or with intervention within 7 days; persistent AF is continuous AF that is sustained beyond 7 days  (Calkins et al. , 2018). MRI data consisted of contrast enhanced magnetic resonance strategy for each patient. It suggests that both patient-specific fibrosis properties and driver locations are important for planning ablation approaches, and the distribution of lesions is important for predicting an acute response. angiogram (CE-MRA) scans, which were used to delineate the left atrial (LA) endocardial wall, together with LGE-MRI data, which were processed for fibrosis tissue distribution.
Geometry Construction To build an anatomical model for each patient, the left atrium was segmented from the CE-MRA images using semi-automated tools within CemrgApp software 1  (Sim et al. , 2019; Razeghi et al. , 2020 , see Figure  1A ). LGE-MRI scans were registered with CE-MRA images to inform the distribution of fibrotic tissue incorporated into each virtual patient model (see Figure  1B ). Each LA segmentation mesh was post-processed using multiple steps, to create a mesh suitable for electrophysiology simulations (See Figure  1C ). To create a closed surface, the following filters were applied using Meshlab software 2 : Poisson surface reconstruction, marching cubes, and quadric edge collapse decimation  (Cignoni et al. , 2008; Kazhdan and Hoppe, 2013). Paraview software (Kitware, Clifton Park, NY, United States 3 ;  Ahrens et al.
, 2005)  was used to clip the closed surface mesh at the mitral valve and four pulmonary veins, and to label each of the four pulmonary veins and LA appendage  (Roney et al. , 2019). The clipped and labeled mesh was then re-meshed using mmgtools software 4 to create triangular elements of approximately equal average edge length (0. 34 mm). These steps are shown in Supplementary Figure  1. This endocardial surface mesh was then duplicated and projected 0. 1 mm epicardially to produce an epicardial surface, and these were coupled using linear elements to produce a bilayer model  (Labarthe et al. , 2014). The projection distance is an arbitrary value, since the atrial wall thickness is incorporated in the simulations through the choice of coupling coefficient, following  (Labarthe et al. , 2014).
Endocardial and epicardial fibers from a human atrial ex-vivo diffusion tensor MRI atlas  (Pashakhanloo et al. , 2016; Roney et al. , 2020)  were registered to each anatomical mesh using the universal atrial coordinate system  (Roney et al. , 2019). Specifically, the fiber fields corresponding to diffusion tensor MRI dataset 1 were used because these were shown to optimally predict arrhythmia properties  (Roney et al. , 2020). Fiber streamlines are shown in Figure  1D. More details on fiber field assignment are given in the Supplementary Material and Supplementary Figure  2. Meshalyzer software 5 was used to visualize simulation data.
Biophysical Modeling Details Biophysical simulations were run using Cardiac Arrhythmia Research Package CARPentry simulator  (Vigmond et al. , 2003) , with the monodomain model for excitation propagation. 1 www. cemrgapp. com 2 www. meshlab. net 3 www. paraview. org 4 www. mmgtools. org 5 https://git. opencarp. org/openCARP/meshalyzer The Courtemanche-Ramirez-Nattel human atrial ionic model  (Courtemanche et al. , 1998)  was used with the following changes: maximal I Kr conductance was multiplied by 1. 6 to represent LA tissue; maximal I Na conductance was multiplied by 2 to ensure physiological action potential upstroke velocities; and maximal I K1 conductance was multiplied by 0. 8 for a closer agreement with clinical restitution data  (Krummen et al. , 2012). To incorporate the effects of electrical heterogeneity, the cell model for the LA model was modified as follows for the LA appendage region: maximal I CaL conductance was multiplied by 1.
Three metrics were calculated in the largest connected region post-ablation as the remaining hotspot area, LA surface area, and fibrosis area (calculated as the remaining tissue area with reduced conductivity values i. e. , normalized LGE intensity value >56%). The width of the roof metric was calculated as the width of the largest connected region measured at a universal atrial coordinate value of 0. 5 (this is zero in the case of box isolation as the roof is not in the largest connected region post-ablation). The metric corresponding to the smallest channel height post-ablation was calculated as the minimum Euclidean distance between the mitral valve and ablation lesions of significant size (defined as those with area greater than 80% of the second largest ablation lesion). We split the dataset into training and test sets as a 70:30 split ensuring that all ablation types for a given anatomical model were in the same set.
To select hyperparameters for the random forest classifiers, we performed 5-fold cross validation with a balanced-accuracy criterion on the number of estimators, the maximum depth, and the minimum number of samples per leaf. The values tested are given in Table  1. These hyperparameters were then used for the random forest model that was trained using a balanced weighting. Accuracy, precision, and recall were calculated on the test set for the three random forest classifiers. To assess the importance of the input features to the trained random forest classifier, we used the SHapley Additive exPlanations (SHAP) methodology  (Štrumbelj and Kononenko, 2014). This was performed using scikit-learn in python, using the functions RandomForestClassifier and GridSearchCV  (Varoquaux et al. , 2015) , and the SHAP toolbox (available at: https://github.
Designing Patient-Specific Lesion Sets We have developed a virtual patient cohort that can be used to predict the optimal ablation strategy for a given patient. Our results (Figure  4 ) demonstrate that it is not simply the area of ablated tissue that is important in determining ablation outcome, but also the spatial location of ablation lesions in relation to the anatomy, fibrotic tissue distribution and driver positions, and necessitating patient-specific therapy. While providing mechanistic understanding, current simulation strategies take extensive time to create, simulate, and analyze the model output. We have shown that with a limited dataset we can create a classifier with accuracy of 0. 83. Increasing the dataset size or number of features may increase the classifier accuracy. Alternately, the simulation-trained classifier could be used to initialize a classifier based on clinical data to accelerate learning from smaller clinical data sets.
FUNDING CR is funded by a  Medical Research Council   Skills Development Fellowship  ( MR/S015086/1 ). SW is supported by a  British Heart Foundation  Fellowship ( FS 20/26/34952 ). SN acknowledges support from the  UK Engineering and Physical Sciences Research Council  ( EP/M012492/1 ,  NS/ A000049/1 , and  EP/P01268X/1 ), the  British Heart Foundation  ( PG/15/91/31812 ,  PG/13/37/30280 ), and  Kings Health Partners London National Institute for Health Research (NIHR) Biomedical Research Centre. This work was supported by the  Wellcome/EPSRC Centre for Medical Engineering  ( WT 203148/Z/16/Z ).
Context. The Solar Orbiter spacecraft will be equipped with a wide range of remote-sensing (RS) and in-situ (IS) instruments to record novel and unprecedented measurements of the solar atmosphere and the inner heliosphere. To take full advantage of these new datasets, tools and techniques must be developed to ease multi-instrument and multi-spacecraft studies. In particular the currently inaccessible low solar corona below two solar radii can only be observed remotely. Furthermore techniques must be used to retrieve coronal plasma properties in time and in three dimensional (3D) space. Solar Orbiter will run complex observation campaigns that provide interesting opportunities to maximise the likelihood of linking IS data to their source region near the Sun. Several RS instruments can be directed to specific targets situated on the solar disk just days before data acquisition.
Once data has been acquired, models and tools will help interpret the daily low-latency data and decide which on-board data to select or prioritise for downlink (for those instruments with internal-storage, or those IS instruments using selective downlink capabilities). This is an important step to increase the likelihood of scientific studies that require combined IS-RS datasets. We described in this article the tools and models that we have put into place to help within this complex operational context. Modeling also plays a central role for data analysis once the final data has been transmitted to the ground for the many reasons that are addressed in this article. Modeling compensates for an inherently incomplete sensing of the solar corona, can help connect different datasets and overall has become an essential tool to evaluate theories.
Heritage of the recent STEREO era The Sun-Earth Connection Coronal and Heliospheric Investigation (SECCHI;  Howard et al. 2008 ) onboard the STEREO mission has provided since 2007, unprecedented imaging of solar storms from vantage points situated outside the Sun-Earth line. STEREO offered for the first time an uninterrupted imaging of plasma flows from the Sun to the Earth-like distances. The analysis of wide-angle imaging from multiple vantage points motivated the development of many algorithms and software tools in the last 10-15 years. The tools primarily focus on deriving the 3D morphology of coronal and heliospheric structures, estimating their kinematics and direction of propagation in the corona and heliosphere. The following sections are purely intended to illustrate the power of using multiple viewpoints to infer the properties of coronal structures such as background streamers, coronal holes, CME flux ropes, shock waves.
Future opportunities to link Solar Orbiter RS with IS data to address some of the key science questions We briefly highlight here some examples of great opportunities that Solar Orbiter data will bring for future studies combining IS and RS data. This part introduces some of the technical chal-lenges that we are currently facing to prepare for the arrival of these new datasets. The sections that follow present the solutions that the MADAWG is proposing to address some of these challenges. 3. 1. Investigations on the corona and the origin(s) of the solar wind(s) As introduced in the previous section, the chromosphere-corona and solar wind are a coupled system that must be modelled as such. In order to compare these global models with RS observations forward modelling has become a powerful approach.