dataesr/scientific-paragraphs-categorization huggingface dataset:
The structure(s) will then be loaded into a customized molecular viewer for quick visualization. Importantly, the coloring of the matched chain(s) indicates the identical, similar, and gapped residues in the AlignMe alignment (Figure  4 ). If two structures were provided, they will both be colored in the same way, and will also be superposed according to the AlignMe alignment. Trying out the structure viewer is made easy by using the 'Generate example input' option on the main pair-wise alignment page (Figure  1 ). Then, on the results page, after clicking 'MutationExplorer', the PDB identifier of one or both sequences (4GD3 and 2ZT9) should be entered into the text boxes, before clicking submit. We note that this feature is available for any of the alignments computed as part of a batch, as the user can select the alignment name prior to selecting the protein structure.
ACKNOWLEDGEMENTS The web server has been continually maintained by the HPC application support team at the  Max Planck Gesellschaft  -we thank  Drs Markus Rampp  and  Mykola Petrov  for their efforts. We also thank  Drs Leone  and  Hellsberg  from  NINDS , and  Ms. Park  from  NHLBI  for testing of the Mu-tationExplorer functions.
In this article we study the potential of the MCP-PMT read-out to detect single photo-electron using transmission lines. Such a solution limits the number of read-out channels, has a uniform time resolution across the PMT surface and provides quasi-continuous measurement of the spatial coordinates. The proposed solution is designed to be used in the BOLD-PET project aiming to develop an innovative detection module for the positron emission tomography using the liquid detection media, the tri-methyl bismuth. In this study we use the commercial MCP-PMT Planacon from Photonis, with 32x32 anode structure. The PCB gathers signals from anode pads in 32 transmission lines which are read-out from both ends. Amplifier boards and SAMPIC modules, developed in our labs, allow us to perform the cost-effective, multi-channel digitization of signals with excellent precision.
This number is sufficiently small, that in the following studies we ignore the presence of events with two photons. We realized a detector scan with step of 2 mm along the lines and 0. 2 mm across the lines. The data taking time of 1. 5 s in each position allows to accumulate of about 3000 events per position. For each signal we register amplitude and CFD signal time for all channels in coincidence with the laser trigger. We use the so-called "L2 coincidence" option of  the SAMPIC module, with a 30 ns coincidence time window. The threshold value for accepting signals is 50 mV (a typical signal amplitude is about 300 -400 mV).
Conclusion In this study we demonstrate the possibility to read-out the MCP-PMT with transmission lines and obtain simultaneously an excellent time resolution on whole surface of the PMT and good spatial resolution. The obtained values fully satisfy requirements to the optical read-out in the BOLD-PET project and will be used there to detect the Cherenkov light, generated by the conversion of the 511 keV gamma in the liquid TMBi radiator. The use of amplifier boards and SAMPIC module, developed in our labs, allow us to realize the cost-effective, multi-channel digitization of signals with excellent precision. For a single photo-electron, we obtain the average time resolution of 70 ps (FWHM), and coordinates resolution of 1. 6 mm and 0. 9 mm (FWHM) along and across the line respectively.
Acknowledgments We thank professor  Henry Frisch  from the  University of Chicago  for the fruitful discussion on the readout PCB technology and for providing us a transmission lines readout board [19], which was used for first tests in our development. We thank  Irakli Mandjavidze  from  IRFU, CEA  for his studies and advises on optimizing signal propagation through transmission lines. We acknowledge the financial support by the  joint French-German  grants  ANR-18-CE92-0012-01 ,  DFG-SCHA 1447/3-1  and  WE 1843/8-1. This work is conducted in the scope of the  IDEATE International Associated Laboratory (LIA).
To this end, Safety analysis and particularly Model-Based Safety Analysis (MBSA) must make part of the whole design process. MBSA requires the use formal languages such as AltaRica  [20, 21]  in order to model and simulate the system behavior in different conditions in order to assess that the system satisfies the safety requirements. Generally, model-based approaches deal with the same system providing different models according to the design viewpoint. Different models means different data often developed by using different languages and software. The development of a unified system model or linked models to obtain consistent system models is still a challenge. Some researchers have dealt with the consistency of models through different approaches. For instance, the authors in  [22]  proposed an approach named SafeSysE which proposes both a methodological approach that specifies the sequence of exchange between system and dependability analyses and also allows the generation of (partially filled) safety-related artifacts from system models to ensure their consistency.
Nguyen et al. [23]  dealt with MBSE and MBSA integration through model transformation from SysML semiformal models into AltaRica 3. 0 formal language elements taking into account the system behaviour by using state machine elements. Berriche et al. in  [24]  dealt with model synchronization through the abstraction of the different models into a common formalism and then their comparison and synchronization if inconsistencies are detected. Although some researchers have worked on the connection of different virtual models, no one provides a useful generalized method to link models.
Then, the global system is built by instantiating three components that are respectively the AGV, the Cobot and the Gripper. A stepwise simulation allows to play many scenarios with the system. For instance, in Figure  12 , we can see that if the AGV moves inadvertently, then the final observer corresponding to EMA_position becomes false. This means that the EMA hold by the cobot is not in the intended place which means that there is a risk for it to hit or pinch the operator expressed above by the undesired event "Undesired contact with operator hands". By simulation of the AltaRica code or by generating the corresponding fault tree ( given in Figure  13 ), this event can be caused by one of the following events (or failure modes):.
Mechatronic Design and Analyses (Modelica) for Nominal and Dysfunctional Behavior A multi-physics model is built with Dymola tool and Modelica language to ensure that the system does not exceed the permissible force thresholds specified by the ISO/TS 15066:2016 technical specification. Thus, a dedicated model is built in order to evaluate the control strategy to limit the force on the operator if the undesired contact with operator hands occurs. In our example, this limit is based on the maximum permissible force of 280N for a transient contact on hands and fingers. The same modeling and simulation approach can be used to deal with the maximum contact pressure, contact speed and transferred energy. This is illustrated with a Dymola modeling in Figure  15. Figure  16  displays some results associated to a scenario of contact during the beginning of an undesired rotation of the main axis (base) of the cobot, the detection of this contact, the stopping of the dangerous movement followed by a backward rotation in order to protect the operator.
On the one hand, a large number of virtual models (as, for example, geometry, design calculations, simulations, etc. ) are usually available from the development phase, and on the other hand, a large number of data can be obtained from measurements during operation. However, this available database is not always uniform and consistent (e. g. , different software tools, data formats, completeness, accuracy). Therefore, it is not always possible to create added value in a single step. The aim of this section is to provide insights on the way of creating a digital twin, on the basis of different initial conditions and models. In the context of this article, a definition based on  [28]  is used: A digital twin is a virtual dynamic representation of a physical system, which is connected to it over the entire product life for a bidirectional data exchange.
Actions required: to define a methodology to build the digital twin on the proposed foundations (meta-model, framework, target workflow) for a given system with relevant tools and bridges, languages and models, such as Dymola (Modelica), Cameo (SysML), OpenAltarica and 3D modelers; -to define a methodology to operate the digital twin in order to perform functional and dysfunctional simulations regarding the target workflow; • Development of validation mechanisms: Thanks to the formal framework relying on mathematical principles, to elaborate some validation and qualification mechanisms to deal with the DT target workflow results and propose some corrections, adjustments or modifications on both the DT and the real system, in order to have them converge and improve.
With the present paper, we introduce our open-source software CDEF  (Deumer & Gollwitzer, 2022) , which provides efficient calculation of approximate scattering profiles I(q) for polydisperse ensembles of arbitrarily shaped nanoparticles. CDEF builds on the ideas put forward by  Hansen (1990)  and  Pedersen et al. (2012)  and enhances them with the option for quasi-random distribution of scatterers, which can improve convergence. An additional speed-up is achieved by offloading the actual calculation of the Debye formula to the opensource software DEBYER  (Wojdyr, 2020). The algorithm is detailed in Section 2. As an application, CDEF is used to evaluate scattering data from gold nanocubes with rounded edges in Section 3. Experimental details and the used nanomaterial are described in Sections 4 and 3, respectively.
We implemented a generator for the scrambled  Halton (1964)  series proposed by  Kocis & Whiten (1997)  and  Sobol's (1967)  series as provided by the SciPy package  (Virtanen et al. , 2020). Each point of the generated cloud then gets assigned a weight to account for density variations such as in heterogeneous or core-shell particles. Finally, as a computational tool to efficiently evaluate Debye's scattering formula, CDEF passes the points and the associated weights to the opensource program DEBYER  (Wojdyr, 2020) , whereas the SPONGE uses its own implementation of the Debye equation. Similar approaches to compute form factors for arbitrary shapes using Debye's scattering formula have been reported by  Pedersen (2002) ,  Pedersen et al. (2012)  and  Hansen (1990) , and are used by other fast programs, e.
Like CDEF, the SPONGE uses a surface description in the STL format to define the boundaries of a nano-object. It then leverages the fast VTK bindings in Python  (Schroeder et al. , 2006)  for point placement and determines whether the point lies inside or outside of the object. The computation of the point-to-point Euclidian distance matrix is done using a fast SciPy implementation (The SciPy Community, 2021), before the Debye equation is applied to obtain a simulated isotropic scattering curve. When a scattering length density is provided, the SPONGE-simulated data can be scaled to absolute units (i. e. to an absolute scattering cross section in m À1 sr À1 ). This procedure is repeated, resulting in a number of independently generated scattering curves, each based on their own set of random points.
For this, we turn the simulated data into a fitting model for use with the Monte Carlo approach as implemented in McSAS  (Pauw et al. , 2013; Bressler, Pauw & Thu ¨nemann, 2015). As the original McSAS is not easily adapted to support such a model description, we are here using the refactored McSAS3 implementation (currently in the last stages of development). McSAS3 works using the same methods as McSAS but has many practical improvements, such as multi-threaded optimization, a backend independent of the graphical user interface (for headless computation) and the option to rehistogram a previous optimization run (McSAS on GitHub;  Breßler & Pauw, 2022). The simulated data set can be converted into a fitting model, provided it has a Guinier region at low q and (on average) a Porod region at high q.
Then, for a given scaling factor, the q value of the simulated data is rescaled (in a manner identical to Section 2. 3. 1), and the intensity interpolated to the requested q value of the experimental data. Data points that fall outside the limits of the simulated data are extrapolated using a flat (Guinier) approximation at low q and a Porod slope at high q. Using this fitting model in McSAS3, experimental data can be fitted rapidly using the simulated scattering pattern of an elementary scatterer. From this, a form-free volume-weighted scaling factor distribution is obtained that best describes the experimental data. As with the original McSAS  (Pauw et al. , 2013) , a number of independent optimizations are performed to allow the estimation of the uncertainty of the resulting distribution.
Diverse models of cubic particles To show the versatile application of CDEF, we characterize the Au nanocubes that are described in Section 3. In doing so, we implemented three different cubic models (ideal cube, cube with truncated edges, cube with rounded edges) carrying a homogeneous electron density (Fig. 4 ). To simulate truncated edges, an advanced (i. e. point clouds are generated by user-written Python functions) algorithm based on the Hessian normal form is implemented, with which the truncation level of the cubic model with a side-to-side distance L can be adjusted. Further information is provided in the SI. Moreover, a cubic model with rounded edges is generated by introducing four cylinders for each Euclidean direction x, y, z, where each cylinder is located in one of the four corners with its axis being aligned along the corresponding edge (Fig.
Data processing at PTB, up to this point, is standardized using in-house software. Since scattering from water molecules and the walls of the glass capillary is also detected by the SAXS measurement, leading to an unwanted background signal, an additional capillary only filled with distilled water was measured during the same measurement to detect the corresponding background curve, which was eventually subtracted from I EXP. For better statistics, however, I EXP and the background curves were averaged over all y positions before subtraction. After subtraction of the background signal, it was not necessary to include an independent background in the fitting model. This also reduces the number of adjustable parameters.
Using CDEF, each model was fitted to the experimental data by varying the M free parameters, namely the numberweighted distribution of the side-to-side length L, which was assumed to be Gaussian, and the truncation or rounding parameters for the imperfect cubes. Powell's algorithm  (Fletcher & Powell, 1963)  with a maximal number of M Â 1000 function evaluations was used to minimize 2. The combined SPONGE + McSAS analysis was not confined to any particular size distribution but rather fitted the volumeweighted size distribution numerically. With CDEF, each 3D cloud initially consisted of N = 30 000 scattering points. Then for each function evaluation step N was varied according to the underlying spatial distribution of scatterers, the level of truncation T or the radius of curvature R curve such that N < 30 000.
, 2021). While the more direct SPONGE approach is not quick enough for iterative optimization methods, the coupling of the SPONGE with McSAS3 allows the determination of size distributions of odd-shaped particles when no information on the shape of the analytical size distribution is known. The coupling of CDEF with McSAS3 is, in principle, also possible since both programs are implemented as Python libraries. This would lead to superior performance compared with the SPONGE and will be considered in future versions of CDEF. Both approaches can be extended to include core-shell morphologies by varying the density of scatterers or assigning different electron densities to the individual punctiform scatterers. Further speed-up could be achieved by an implementation which runs on parallel hardware such as consumer graphics cards.
Swift-XRT The X-Ray Telescope (XRT) on the Neil Gehrels Swift Observatory is a grazing-incidence focusing X-ray telescope sensitive to photons with energies between 0. 2 and 10 keV  (Gehrels et al. 2004; Burrows et al. 2005). Swift-XRT observed PKS 1222+216 and Ton 599 during the VHE flares of those sources. The Swift-XRT data were extracted from the Swift data archive and analyzed using HEASoft v6. 24. The fluxes and flux errors were deabsorbed using the fixed total column density of Galactic hydrogen, N H = 2. 29 × 10 20 cm -2 for PKS 1222+216 and 1. 89 × 10 20 cm -2 for Ton 599  (Kalberla et al. 2005; Willingale et al. 2013) , and the photoelectric cross section σ(E) to account for the effects of neutral hydrogen absorption. The deabsorbed X-ray spectrum was fitted with a broken power-law model for PKS 1222+216 and a power-law model for Ton 599.
Swift-UVOT The Ultraviolet/Optical Telescope (UVOT) on the Neil Gehrels Swift Observatory is a photon-counting telescope sensitive to photons with energies ranging from about 1. 9 to 7. 3 eV or 170 to 550 nm  (Roming et al. 2005). Swift-UVOT observed PKS 1222+216 and Ton 599 approximately concurrently with Swift-XRT. The UVOT data were extracted from the Swift data archive and analyzed using HEASOFT v6. 28. The counts from the sources and background were extracted from regions of a radius of 5 0 centered on the position of the sources and nearby positions without any bright sources, respectively. The magnitude values of the sources were computed using uvotsource and converted to fluxes using the zero-points given by  Poole et al. (2008). Extinction corrections were applied following  Roming et al.
Steward Observatory During the first decade of the Fermi mission, the Steward Observatory of the University of Arizona obtained optical polarimetry, photometry, and spectra of the LAT-monitored blazars and Fermi targets of opportunity using the SPOL CCD Imaging/Spectropolarimeter  (Smith et al. 2009). We downloaded the spectrophotometric Johnson V-and R-band magnitudes from the Steward Observatory public archive. 47 hese magnitudes were obtained by convolving the flux spectra between 4000 and 7550 Å with a synthetic filter bandpass for the V or R band, summing the flux, and computing the magnitude difference with a comparison star. Smith et al. (2009)  gave the full details of the observations and data reduction. We then converted the magnitude for each bandpass to its equivalent energy flux.
The global spectral shapes of the three sources are similar, with an index α of ∼2. 1-2. 3 and a curvature parameter β of ∼0. 04-0. 06, and they differ primarily by their normalization. Using the data from 3C 279, we compared several methods to determine a baseline nonflaring spectrum. First, we defined a low state lasting from MJD 56,230 to 56,465 (see Figure  1 ), during which the flux was quiescent and stable in HE gamma rays, R-band optical, and X-rays. We checked publicly available Tuorla 48  data for the R-band light curve. For the  X-rays, we analyzed the Swift-XRT light curve using the online data products generator. 49 To ensure low, stable gammaray emission, we selected the interval to span the Bayesian blocks with the lowest flux while excluding intervals with the Sun in the ROI.
The low-state LAT SED is shown in Figure  6. Only one VERITAS observation occurred during this interval, on MJD 56,417, so the corresponding VERITAS upper limits are not constraining and are not shown. Next, using the algorithm proposed in this work and described in Section 4, we designated all epochs of the LAT light curve other than the flaring episodes as quiescent. From those epochs, we extracted those LAT data strictly simultaneous with the VERITAS observations, integrating a total of 43. 6 hr of observations. The resulting strictly simultaneous LAT spectrum and VERITAS spectral upper limits are shown in Figure  6. We then performed the same procedure for four flaring epochs during which a significant Fermi-LAT detection could be obtained strictly simultaneously with the VERITAS observations, which occurred on the nights of 2014  April 3, 2015 June 16, 2018  April 19, and 2018 June 3.
SED Modeling Multiwavelength SED modeling can shed light on the mechanisms of gamma-ray production during VHE flares. For  3C 279, we refer the reader to those works in the literature in which multiwavelength SED modeling of the epochs considered here has been performed, and we do not perform any additional modeling (see, for example,  Hayashida et al. 2015; Ackermann et al. 2016; Prince 2020; Yoo & An 2020). Object PKS 1222+216 was first detected at TeV energies by MAGIC during a flaring event in 2010 June  (Aleksić et al. 2011) , and multiwavelength SED modeling of this event has been performed by, e. g. ,  Tavecchio et al. (2011). We therefore restricted our SED modeling of the source to the duration of the second VHE detection by VERITAS in 2014 February and March. We considered data from all instruments taken from UT 2014 February 26 to 2014 March 10, inclusive.
Ton 599 has not been studied as extensively as the other two sources. Prince (2019)  and  Patel & Chitnis (2020)  modeled its variability characteristics and multiwavelength SED, respectively, during the high state in 2017 December but did not have access to TeV data. We therefore modeled the multiwavelength SED of Ton 599 during the VERITAS detection in 2017 December. We considered data from all instruments taken from UT 2017 December 15 to 2017 December 16, inclusive. To assemble our multiwavelength SEDs, in addition to the gamma-ray data from VERITAS and Fermi-LAT, we incorporated X-ray and ultraviolet data from the XRT and UVOT instruments on board the Swift satellite and optical observations from the Steward Observatory. We described the multiwavelength SEDs of the two FSRQs using a multicomponent SSC blob-in-jet model, implemented using the framework of the "Bjet" code, developed by  Hervet et al.
Additional support for science analysis during the operations phase is gratefully acknowledged from the  Istituto Nazionale di Astrofisica in Italy  and the  Centre National d'Études Spatiales in France. This work was performed in part under  DOE  contract  DE-AC02-76SF00515. Data from the Steward Observatory spectropolarimetric monitoring project were used. This program is supported by  Fermi Guest Investigator  grants  NNX08AW56G ,  NNX09AU10G ,  NNX12AO93G , and  NNX15AU81G. L. S. acknowledges support from the  Sloan Fellowship , the  Cottrell Scholars Award ,  DoE DE-SC0016542 ,  NASA 80NSSC18K1104 , and  NSF   PHY-1903412. This material is based upon work supported in part by the  National Science Foundation  under grant  PHY-1659528. A. B. ʼs work was supported by  NSF  grant  PHY-1806554  at  Barnard College, Columbia University.
O. H. thanks  NSF  for support under grants  PHY-1707432  and  PHY-2011420. J. V. was partially supported by the  Alliance program , a partnership between  Columbia University, NY, USA , and three major French institutions:  École Polytechnique ,  Paris 1 Panthéon-Sorbonne University , and  Science Po. This material is based upon work supported by  NASA  under award No. 80GSFC21M0002. S. B. acknowledges financial support by the  European Research Council  for the  ERC starting grant MessMapp  under contract No. 949555. This work made use of the online cosmological calculator of Wright (2006) and  NASA 's Astrophysics Data System. We would like to thank the anonymous referee for the helpful and timely comments that helped improve the quality of the paper. Software: Astropy (Astropy Collaboration et al.