dataesr/scientific-paragraphs-categorization huggingface dataset:
Because of this, for a given bending angle, the radius of curvature was smaller than in the unbroken crystal, in particular, for θ b = 2. 4 mrad, the radius of curvature was R b = 10. 4 m and the deflection efficiency was systematically reduced. Moreover, a large anticlastic reaction was observed that changed the crystal shape as a function of the vertical coordinate. This introduced serious complications in determining the influence of the radiation damage, solved by applying more sophisticated procedures in the data collection and analysis. The channeling efficiency was recorded by displacing the beam vertically. At each beam position, the crystal orientation was adjusted to compensate for the anticlastic bending. Focusing and parallel vertical beam optics were used to guarantee the optimal crystal orientation, both for small size or small divergence beam spots.
Simulations tools The SixTrack code 83 is used to provide a symplectic, fully chromatic and 6D tracking along the magnetic lattice of circular accelerators, taking into account interaction with obstacles (as collimators) and the machine aperture model. The machine optic and aperture is produced with the MAD-X code. 84 In SixTrack, a crystal routine has been developed 77 and will be described in Subsec. 7. 1. It has to be pointed out that BLM signals are produced by hadronic showers coming from lost particles interacting with the beam pipe, and all the other materials between the point where the proton is lost and the BLM position. In SixTrack it is possible to simulate the beam loss pattern, i. e. the expected number of protons lost per meter on the machine aperture. This means that the loss pattern and the BLM signals can be considered comparable to a first approximation, if one considers the loss distribution to be equal in both measurements and simulations.
2230004-73 Int. J. Mod. Phys. A 2022. 37. Downloaded from www. worldscientific. com by GERMAN ELECTRON SYNCHROTRON @ HAMBURG on 06/15/22. Re-use and distribution is strictly not permitted, except for Open Access articles. Two distinct approaches, based on independent computer codes, were applied in SPS and LHC simulations. One was giving emphasis to the accurate computation of particle trajectories inside crystal and basing on very fast six-dimensional (6D) transfer matrix method the particle coordinate propagation along a few essential azimuths of the accelerator lattice. 58, 92 The other was using fast Monte Carlo algorithm to provide the point-like description of crystal-particle interaction  93  and SIX-TRACK tracking code  94  to propagate particle trajectories along the azimuth of each accelerator element.
In Fig. 50 , the experimental data are shown as red dots and the simulation data as solid lines. Those of the Monte Carlo algorithm (CRYSCOLL) are shown in green, those of the GEANT-4 routine in light-blue, those of the routine DYNECHARM in dark-blue and those of FLUKA in dark-orange. Quantitative comparison produces the results of Table  12. Probability density of particles in channeling states as a function of the crystal entry angle, normalized to the critical angle, is shown in Fig. 51. The simulation routines overestimate the channeling population, in particular in the angular range around θ in = 0, and the average bending angle (see also the first column of Table  12 ). The curve obtained with CRYSCOLL has sharp edges, because of the compelling condition for channeling θ in < θ c. Considering thinner slices of channeled particles as a function of the entry angle, one finds, for each routine, a linear dependence of the average deflection on incident angle, very well correlated to experimental data, within the experimental errors.
It is important to consider that the experimental data are convoluted to the telescope accuracy, whilst the numerical simulations are not suffering instrumental inaccuracy. Simulation results weighted with the same inaccuracy of the experimental data, are affected by considerably reduced discrepancy. In particular, CRYSCOLL estimate of channeling efficiency becomes η CH = 0. 685±0. 005, a value fully compatible with the experimental one in Table  12. 59 At the same time, the RMS deflection of channeled particles in Fig. 52  and of the transition region in Fig. 55   code is the privileged tool to simulate long-term behavior of large sets of particles. SIXTRACK,  94  developed and upgraded for many decades, is the advanced code preferentially used at CERN  103, 104  for numerical simulation in large accelerators.
Although available too late for RD22 and E853, the U70 crystal was reused at RHIC and later in the initial collimation test of the T980 experiment at the Tevatron. 11 ending methods, better exploiting the elastic response of crystal to external flexural stress, systematically investigated from the beginning of the 2000s, could finally provide a substantial improvement. Strip crystals (see Fig. A. 2) a few cm long by a few mm thick, clamped by the ends to a C-shaped holder were assembled for the first time by Yu. Chesnokov and collaborators at IHEP. By imparting a curvature to the long side of the strip, an anticlastic reaction was produced with a saddle point in the middle of the strip and a quasi-circular curvature along the short side. Such a clever concept is illustrated in detail and experimentally demonstrated with data collected in U70 at IHEP in Ref.
Acknowledgments The effort of the  UA9 Collaboration  was strongly supported by the funding agencies. We also acknowledge partial support by the  European Community-Research Infrastructure Activity  under the  FP6 "Structuring the European Research Area" program  ( CARE , Contract No. RII3-CT-2003-506395 ), the  EuCARD program  GA  227579 , within the "Collimators and Materials for high power beams" work  2230004-84 Int. J. Mod. Phys. A 2022. 37. Downloaded from www. worldscientific. comby GERMAN ELECTRON SYNCHROTRON @ HAMBURG on  06/15/22. Re-use and distribution is strictly not permitted, except for Open Access articles.
AIS length & soma-AIS gap measurements AIS analysis was performed by immunolabeling with AnkG and SMI32 (pyramidal neuron marker) in the neocortex or with AnkG and Calbindin (Purkinje cell marker) in the cerebellum. PLP immunolabeling was also performed to further confirm the demyelination status of lesions analyzed. Neocortical SMI32-positive pyramidal neurons belonging to layer 5/6 were identified using hematoxylin-eosin staining on an adjacent section (data not shown), which allowed the identification of the typical large somata of layer 5 pyramidal neurons and the determination of.
Model description and simulations protocols To simulate the neocortical pyramidal cell activity, we used a previously published model available on modelDB (http://modeldb. yale. edu/114394), implemented for the NEURON simulation environment. To simulate the Purkinje cell activity, we used a multi-compartmental model previously developed  29  , also available on modelDB (http://modeldb. yale. edu/229585) and implemented for the NEURON simulator's Python interface. Both models are described in supplementary material.
Acknowledgments Part of this work was carried out on the Histomics and icm. Quant core facilities. We gratefully acknowledge  Annick Prigent  and  Brigitte Zeau  for their technical help in handling human tissues, and  Aurélien Dauphin ,  Dominique Langui  and  Claire Lovo  for their help in image acquisitions and quantification of AISs is in 2D and  3D. We thank  Ivan Moszer  for statistical analysis discussions.
Transport sensitivity scenarios The setup of CLaMS-2. 0/MESSy follows the CLaMS-2. 0 configuration described in  Konopka et al. (2019)  and uses the Modular Earth Submodel System (MESSy) as a software framework  (Jöckel et al. , 2010)  to run the model on the supercomputer JUWELS  (Jülich Supercomputing Centre, 2019). To simplify the notation, we omit the suffix MESSy in the following. ERA- In CLaMS-2. 0, an interplay between three types of extended transport is investigated: isentropic mixing (CLaMS-1. 0), tropospheric mixing and unresolved convective updrafts (CLaMS-2. 0). Extended transport means an extension relative to a pure trajectory-based transport that is used in this study as a reference. Interim (EI) and ERA5 (E5) reanalyses from the European Centre for Medium-Range Weather Forecasts ECMWF) provide the meteorology to drive the model  (Dee et al.
In the subpanels for N2O, details of tropospheric profiles are higher resolved and color-coded as in Fig. 4. Table  4  compares the root mean square differences and Sect. 3. 6 discusses the stratospheric performance within the tropospheric and stratospheric ranges, respectively. Mean CO 2 profiles (Fig. 5b, e ), can be understood as a result of a coupled tropospheric and stratospheric transport folded in time and space with the evolution of the CO 2 sources (and sinks) in the PBL while both, transport and sources show a pronounced seasonality  (Diallo et al. , 2017). CO 2 being almost chemically inert is a well suited tracer for validation of transport in the models if the lower boundary condition is realistically parameterized. However, the (assimilated) CarbonTracker data set used for the lower boundary of CLaMS does not reproduce well the CO 2 observations in the lowest part of the troposphere where air dominated by regional sources was sampled (Kathmandu/Nepal for StratoClim and Shannon/Ireland for WISE).
(2) The property (2) guarantees mass conservation within reactions, an information that is not always fully encoded in the stoichiometric matrix S (see SI text "Mass balance and the stoichiometric matrix S"). While external reactants have no corresponding rows in M, their concentrations x may influence the turnover times τ s of transporters. We present examples of GBA models and R code for their numerical optimization in Dataset S1 (see SI text "Examples of GBA models and R code for numerical optimization"). We are interested in the cellular physiology, defined through the concentration vectors c, p and the vector of reaction fluxes v, at balanced growth. For a given model specified by (M, τ , ρ) and a given environment specified by x, balanced growth at the instantaneous rate µ is specified by the following constraints: M i j v j = µc i (mass conserv.
The flux balance at the cell surface is enforced separately by Eq. 18 on transporters, making these fundamentally different from enzymatic and ribosome reactions. In particular, for a model with only one transporter s, Eq. 18 already determines the scaled uptake rate f s = (γ s ) -1. With two transport fluxes, one flux is uniquely determined by the other; a simple example would be a model that only has transporters for glucose uptake and CO 2 excretion (see example model "C" in SI text "Examples of GBA models and R code for numerical optimization"). More generally, Eq. 18 can be used to uniquely determine one transport flux fraction in terms of the others, reducing the number of variables by one. For clarity of presentation, however, we will keep Eq. 18 as a separate constraint and not eliminate any variable, until the introduction of growth control coefficients in the corresponding section.
In fact, many models in the literature do ignore some reactants, in particular water; this needs attention in realistic models where water is not only a medium but also a reactant in many biochemical reactions. Examples of GBA models and R code for numerical optimization. We present here 4 simple examples of GBA models. We assume for all models a simple irreversible Michaelis-Menten kinetics (72), so in each model turnover times τ are uniquely defined by a matrix K of Michaelis constants and a vector k cat of turnover numbers for each reaction. Each row of K corresponds to a reactant, and each column to a reaction, matching the order in the matrix M (the entries for external reactants are separated by a horizontal line). See the corresponding ". ods" files for a more detailed description of the models, including labels for reactions and reactants and different growth conditions (i.
e. external concentrations) for testing the model. Numerical simulations are done by running the R code in the file "GBA. R", with the variable "modelname" set to the desired model (e. g. modelname ← "A"). Here K and ρ are in units of g/L, and k cat in units of h -1 (resulting from product mass per protein mass per h). The code exports the results as a corresponding pdf file with relevant plots for visualization, and a csv file with the numerical values for the optimal cell state at different growth conditions. Figure  (5)  presents the schematics and the corresponding parameters (M, K, k cat , ρ) of each model. Metabolites are indicated with circles, and total protein with a rounded square. The numbers labeling reactants and reactions match the corresponding order of rows and columns in M, with the last row corresponding to total protein and last column the ribosome reaction by default.
(4, 21, 22) ). Note that in reality enzymes with very similar marginal values may still be expressed together due to the little selection pressure towards one of them. See model "D" on SI text "Examples of GBA models and R code for numerical optimization" for an example of a model with redundant reactions. Amino acid composition of the E. coli proteome. Table  S1  presents the amino acid frequency in the E. coli proteome, calculated from protein sequence (retrieved from genome annotation of E. coli NC_000913. 3 from RefSeq (53)) and weighted by protein abundance measured in Schmidt et al. (47)  under various growth conditions. Table  S1. Amino acid frequency in the E. coli proteome at various growth conditions. SI_files. zip. Zip file containing files for numerical optimization, as described in the SI text "Examples of GBA models and R code for numerical optimization".
ACKNOWLEDGEMENTS We thank  Stefan Müller  for discussions about KKT conditions,  Alexander Kroll  for verifying early calculations, and  Xiao-Pan Hu  for providing data in Table S1. This work was funded by a  Volkswagenstiftung "Life ?" grant to MJL, and by the  German Research Foundation  through grant  CRC 1310  to MJL, and, under Germany's  Excellence Strategy , grant  EXC 2048/1  (Project ID:  390686111 ) to OE and MJL.
Exploring planetary atmospheres uncovers important information as to how our solar system formed and evolved. While remote sensing is extensively used, some crucial observations require in situ measurements by an atmospheric probe. Given their scientific importance, probe missions to Saturn, Uranus, and Neptune are under consideration for the coming decades. In anticipation of future probe missions, the software tool Visualization of the Impact of PRobe Entry conditions on the science, mission and spacecraft design (VIPRE) was developed as proof-of-concept to facilitate selection of probe entry locations. Currently, there is no analytical way to identify which interplanetary trajectory from thousands of feasible launch opportunities is optimal for a considered mission concept. The search and decision process for that solution is complex and relies on the intuition of mission designers, who focus on a subset of trajectories to make the trade space manageable.
The idea of VIPRE is (1) to generate a multidimensional data cube showing relevant engineering and science parameters simultaneously for thousands of trajectories, and (2) to visualize the data for all entry sites over the body's envelope. VIPRE lays a foundation to make available the data for browsing in a 3D visualization to identify the best family of solutions for a given mission. This paper introduces the validated and verified core algorithms of VIPRE, published on GitHub Probst. VIPRE serves as a basic framework to be used and extended for different purposes. The paper further presents the motivation for the development and algorithms; it explains the computation and data visualization strategy; and gives a list of suggested functionalities to extend and further develop VIPRE to fully leverage its potential.
2007; Fletcher et al. 2020). Due to the complexity of the problem, mission concept studies usually focus on the investigation of only a limited number of specific trajectories and probe delivery opportunities, restricting the science to a very small, predefined latitude range, while leaving a huge trade space unexplored  (Banfield et al. 2018; Mousis et al. 2014 Mousis et al. , 2018;; Hofstadter et al. 2019). In order to address this gap, the software package Visualization of the Impact of PRobe Entry conditions on the science, mission and spacecraft design (VIPRE) is being developed to analyze conditions for different trajectories and target bodies. The VIPRE software package consists of the software tools IPED and VAPRE, to compute (IPED) and visualize (VAPRE) the entry conditions for all safe entry opportunities of a planetary entry probe being released from an interplanetary trajectory.
VIPRE allows a rapid assessment of feasible entry sites by evaluating a large number of arrival trajectories based on their hyperbolic arrival velocities with respect to parameters such as the flight path angle and the relative entry velocity of the probe at the point where the probe enters the atmosphere-which is from here on called the entry interface point. VIPRE is a validated software package as a proof of concept, and a step toward facilitating mission design processes in the future by combining the evaluation of technical feasibility and science value for the investigated scenarios to assess potential entry sites. For reference, for a recent Saturn probe mission study performed at the JPL, three to five trajectories were examined in detail to assess the mission feasibility of each trajectory.
VIPRE aims to allow the user to browse through thousands of trajectories to check the resulting mission options with respect to the entry probe site, as well as the impact of the science case on the engineering feasibility. VIPRE is a validated and verified framework that will allow scientists and engineers to rapidly assess the ability to meet science goals at different latitudes for a large number of approach trajectories, without violating engineering constraints such as the maximum feasible entry velocity. To enable this, VIPRE generates a multidimensional data cube from thousands of trajectories created with the JPL internal trajectory tool STAR by  Landau (2018)  and makes the data available for browsing in a 3D visualization tool to enable finding the overall best solution for a given mission.
VIPRE: Visualization of the Impact of PRobe Entry Conditions on the Science, Mission and Spacecraft Design The implementation of the VIPRE tool is based on two software tools, each explained in detail in the following sections: (1) IPED, the computation of the parameters and entry conditions for a given interplanetary trajectory data set is described in Section 4. 1; and (2) VAPRE, the visualization of the computed data in Section 4. 2. Based on the mission scenario defined by the destination planet and the atmospheric entry altitude of the probe, a given  data set of interplanetary trajectories for a specific launch window (step 1) as well as auxiliary data (step 2) is loaded. The interplanetary trajectory data set (step 1) was computed with the JPL internal software tool STAR by  Landau (2018)  for a given launch window and a maximum ToF.
(b) Each offspring trajectory is checked for ring avoidance; if the probe entry trajectory does not cross the rings, the parameters at atmospheric entry are calculated, such as the longitude and latitude of the entry location, the flight path angle, the orbital velocity and the relative entry velocity of the probe. For each offspring trajectory, the data is saved in a text file for future processing, visualization, and further analysis. In order to check if the trajectory does or does not cross the rings, the trajectory states are compared to the ring structure in the ring plane. If the position vector crosses in an area that is considered part of the ring structure at any point in time, the trajectory is flagged as unsafe. In this stage of the design, we are neither considering any gravitational influence other than the central planet on the trajectories nor discarding trajectories based on collisions with satellites such as moons, as both points are deemed not critical in the first order of the mission process.
VAPRE: Data Visualization VAPRE stands for the Visualization of Atmospheric PRobe Entry conditions for different bodies and trajectories, and is the visualization of the 3D data cube generated with IPED. The visualization feature has been implemented in order to faciliate scientists and engineers with a rapid evaluation and interpretation of the data generated. The visualization is based on Dash Python (Plotly Technologies Inc. 2015), a framework for creating web applications. It enables us to build dashboards that run on a web browser and are therefore easily accessible to a wide range of users, including the general public. VAPRE consists of two parts: the trajectory overview, representing the interplanetary phase, and the entry conditions, representing the approach phase. Their functionalities are described in the following two paragraphs.
Variables include the launch date, the ToF, the hyperbolic arrival velocity of the trajectories displayed, or the spacecraft mass at arrival at the destination planet. This way, the user can focus on the most relevant (parent) trajectories for their scenario. By clicking on a trajectory in the overview plot, the details about the selected interplanetary parent trajectory are displayed in a concise form and include additional information, such as the gravity assist sequence and the C 3. The entry conditions. By selecting an altitude and a parent trajectory, the entry condition data of the selected parent trajectory is loaded into the tool. It is displayed as a 3D visualization of the change of the entry parameters across the planet's globe for each of the offspring trajectories, by color-coding the entry location in the parameter value on the envelope of the planet at entry altitude above the 1 bar radius of the planet, as shown in Figure  7  for an example arrival at Saturn.
5. Evaluation of science objectives by assigning a factor of urgency or importance to each objective, and assigning those factors to latitudes and up to what level that objective can be achieved. This way, specific latitudes can be ranked for their science value with respect to specific science objectives. 6. Targeting of specific atmospheric features (fixed longitude/latitude) by implementing a time dependency. 7. Evaluation of the entry dynamics and thermal loads. 8. Switching between coordinate systems of the plotted data. 9. Evaluation of different mission scenarios such as lander, entry probes, or balloons, including multielement missions with release from orbit, and including more planets and other objects such as moons. The code of VIPRE is available on GitHub under the account @planetaryprobst (Probst 2021) for the general public or other institutions to use and extend to their needs.
Graph edit distance (GED) is a powerful tool to model the dissimilarity between graphs. However, evaluating the exact GED is NP-hard. To tackle this problem, estimation methods of GED were introduced, e. g. , bipartite and IPFP, during which heuristics were employed. The stochastic nature of these methods induces the stability issue. In this paper, we propose the first formal study of stability of GED heuristics, starting with defining a measure of these (in)stabilities, namely the relative error. Then, the effects of two critical factors on stability are examined, namely, the number of solutions and the ratio between edit costs. The ratios are computed on five datasets of various properties. General suggestions are provided to properly choose these factors, which can reduce the relative error by more than an order of magnitude.