dataesr/scientific-paragraphs-categorization huggingface dataset:
Section II. B. will deal with new calculations of cross sections, with a short description of the theories that are now included in ETACHA, and a discussion of mean charge state effects on cross sections values. Some computing aspects are given in section II. C. Finally, in section III, we compare the different versions of the ETACHA code for some test collision systems, demonstrating for the improvements that have been made but also exhibiting the validity domain of each version of the ETACHA code that depends on the number of states with noncorrelated or correlated configurations that have been included.
Cross sections of the elementary processes 1. Beyond the perturbative regime for projectile states from n=1 to 4 Our previous version of the ETACHA code  [4]  was well suited to a high velocity and low perturbation regime, the aim being to optimize the production of high charge states after the stripping solid foil. Therefore, the first (or Plane-Wave) Born Approximation (PWBA) can be safely used for ionization and excitation  [14, 15] , whereas the Continuum Distorted Wave (CDW)  [16]  reproduces very well capture cross sections. However, beside the number of states that needs to be included to handle projectile ion states up to n=4, the extensions of the ETACHA code intend also to tackle collision systems in the non-perturbative regime in which those theoretical approaches are well-known to fail in reproducing experiment.
In this respect, one can define the projectile perturbation parameter K p : p K = t e p p Z v Z v (4), where Z t and Z p are the target and projectile atomic numbers, v e the mean orbital velocity of the active electron, and v p the projectile velocity. Accordingly, the collision distortion will increase with K p , but for a given collision system (i. e. Z p impinging Z t at v p ), the K p parameter decreases, with increasing the n principal quantum number when considering ionization or inter-shell excitation of projectile state. Hence, within the new ETACHA code versions, we improved the theoretical description of the ionization and excitation processes by introducing the Continuum Distorted Wave-Eikonal Initial State (CDW-EIS) approximation  [17, 18]  and the Symmetric Eikonal (SEIK) model  [19, 20] , respectively.
FIG. 5. (color online) Capture cross sections for Ar 18+ on C calculated in the CEIK approximation  [26]  for each n level as a function of projectile energy. FIG. 6. (color online) Capture cross sections for Ar 18+ on C calculated in the CDW approximation  [16]  for each n level as a function of projectile energy. Accordingly, in every new ETACHA code versions, cross sections are calculated in the following way: -Ionization cross sections corresponding to 1s, 2s, 2p, 3s, 3p and 3d subshells and the n=4 shell are first calculated in the Plane-Wave Born Approximation  [14, 15] , using screened hydrogenic wave functions for the initial states of the projectile electrons. Screening and antiscreening effects by target electrons are also taken into account in an approximate way  [32, 33] , though better screening contribution could be calculated  [34]  at the expense of increased computational time.
C. Computing details The program (Microsoft DIGITAL Visual FORTRAN has been used) starts with projectile and target characteristics input (projectile atomic number, charge, mass, energy, and target atomic number, mass, density, maximum thickness). Optional calculation of coefficients accounting for projectile energy loss in target is also now provided in the new ETACHA versions  [42]. Then "reference" cross sections, for capture in fully stripped ions and ionization or excitation of hydrogenlike projectile are computed. The "reduction factors" (see section II. B. 3. ) for ionization and excitation processes are then calculated. At this point, results of those calculations are displayed, and it is possible to modify their values if desired. The following step is a recalculation of all cross sections for the non-hydrogenic incident charge state.
III. RESULTS AND DISCUSSION In what follows, we present comparisons of the different new versions of the ETACHA code, including the previous version of ETACHA23  [4]  labeled as ETACHA "old", together with experimental data for different collision systems. The discussion will enlighten the contribution of the different effects, i. e. the inclusion of more sophisticated ionization and excitation cross sections, the enlargement of the basis set, as well as the consideration of correlated configurations. A. Comparison of the different versions of the ETACHA code for test collision systems 1. Ar 10+ on C at 13. 6 MeV u -1 (v p =23 a. u; 8 electrons on the projectile) Figure  10  shows the evolution of selected charge states, for Ar 10+ on C at 13. 6 MeV u -1 for comparison between the uppermost version ETACHA4, ETACHA "old" and the experimental data recorded at GANIL (Grand Accélérateur National d'Ions Lourds in Caen, France).
Here of course, the goal was to maximize the projectile ions with the highest charge states. Although at this velocity, corresponding to K p (Ar 2p)≈0. 065 and K p (Ar 1s)=0. 26 (applying Eq. 4. ), there are slight differences in the behavior of the various charge states, both code versions give rather similar results, and agree quite well with the measurements within the error bars. FIG. 10. (color online) Evolution with carbon target thickness of selected charge states for 13. 6 MeV u -1 Ar 10+. Symbols: experiment; dotted lines: ETACHA "old"; full lines: ETACHA4 2. Pb 56+ on C at 28. 9 MeV u -1 (v p =34 a. u. ; 26 electrons on the projectile) In this case, one can explore not only the production of high charge states, but also the depletion of the ion incident charge that can be observed with tractable target thicknesses, unlike the previous example.
On figure  11 , the difference between the new ETACHA4 version and ETACHA "old" is visibly significant both for the diminution of the incident ion charge state and for the production of stripped ions. ETACHA4, which includes a larger basis set and more refined cross sections, leads to a clear improvement in the predictions when compared to experimental results taken from ref. [44]. To better understand the different effects that results in this improvement, data of the four new versions of the ETACHA code are presented figure  12. For this collision system, which involves a projectile initially dressed with 26 electrons (with 8 electrons on the 3d level), the new ETACHA23 and ETACHA3 versions give very similar results. Only a slight difference is observed in the prediction of the charge state 72+ that is produced at large target thickness but they both do not reproduce the experimental data.
B. Results of the new ETACHA code for other benchmark collision systems As presented in the introduction, the main motivations to extend the ETACHA code was to be able to treat collision systems entirely out of the perturbative regime, i. e. when the perturbation parameter K p (given by Eq. 4. ) is much larger than 1. Below, we illustrate the potential of the uppermost new version of the ETACHA code with two examples. First, let's consider the collision system Pb 56+ at 28. 9 MeV u -1 on silicon, a target that corresponds to an increase of the perturbation K p parameter of about 2. 2. From figure  13 , we can observe that a clear improvement is also achieved over the whole charge state distribution when using ETACHA4 instead of ETACHA23 and comparing to experimental data obtained under a random orientation of a silicon crystal of effective thickness 1.
IV. CONCLUSIONS The range of validity of the ETACHA code has been extended to lower velocities in all the different versions of the ETACHA code for projectiles with a few electrons up to dressed ions with (in principle) 60 electrons. We illustrated the different improvements that have been made, as well as the validity domain of the different available versions of the code by comparing the corresponding computed data with test collision systems. In particular we show that for a projectile initially dressed with 26 electrons, i. e. Pb 56+ with 8 electrons in the 3d level, it is mandatory to properly take into account n=4 and to then make use of the ETACHA4 version. Indeed, at the projectile velocity of 23 a. u. and for relatively light targets compared to the projectile atomic number, the capture cross section in n=4 is quite small, but the excitation from n=3 to n=4 is as large as the ionization of n=3.
In this respect, we can now predict with good accuracy the charge state distribution of 238 U 71+ at 51 MeV u -1 obtained after the third carbon stripper at the Riken RI-Beam factory  [47]. If increasing the target atomic number, or decreasing the velocity, i. e. for larger K p perturbation parameter, ETACHA4 should still be valid since the capture process is also now properly taken into account. Therefore, the ETACHA code should provide rather reliable data for some of the collision systems envisaged to cover the entire 100 Sn region with the Super Spectrometer Separator at SPIRAL2, like for 58 Ni 19+ on 40 Ca, 46 Ti, 50 Cr, or 54 Fe from 3. 5 to 4. 5 MeV u -1 [8]. Nevertheless, preliminary comparisons between ETACHA and measurements performed with 11 MeV u -1 U 38+ ions impinging carbon targets (a system of importance for the design of the RIA (Rare Isotope Accelerator) driver linac at MSU)  [48] , exhibit the requirement to even extend the ETACHA code towards the inclusion of n≥5.
ACKNOWLEDGMENTS The authors would like to thank  J. Reading  from  Texas A&M College Station ,  J. Nolen  and  J. Song  from  Argonne National Laboratory  for their very fruitful discussions. We would like also to acknowledge the work done by  Kamal Sharkas  during his internship in our group. This work has been supported by the  ECOS-Sud collaborative program  under the A98E02 contract and by the  French National Agency of Research  under the contract  ANR-13-IS04-0007.
The model is driven and validated through historical data of flight schedules in a European national airspace. While correctly reproducing actual statistics of the Air Transportation system, e. g. , the distribution of delays, the model allows for theoretical predictions. Upon an increase of the traffic load injected in the system, the model predicts a transition from a phase in which all conflicts can be successfully resolved, to a phase in which many conflicts cannot be resolved anymore. We highlight how the current flight density of the Air Transportation system is well below the transition, provided that controllers make use of a special re-routing procedure. While the congestion transition displays a universal scaling behavior, its threshold depends on the conflict solving strategy adopted.
The data set The structure of the European Airspace is multi-layered  [29] , since each national airspace is further subdivided in sectors which in turn group together a certain number of navigation points and airways. In order to study and reproduce this structure, we used information extracted from a database developed within the ELSA project  [30]. The database stores data coming from the Demand Data Repository (DDR)  [31] , which are property of EUROCONTROL, but can be accessed for research purposes on request. The data includes all the trajectories of civilian and commercial flights that have crossed the European Airspace in the year 2011. There are two kinds of trajectories stored for each flight: the last filed flight plans and the radar updated trajectories. Last filed flight plans are submitted by the air companies to the Central Flow Management Unit from six months to an hour before the departure of the flight and are the result of the planning process and agreement between these two actors.
Thus, the temporal resolution of the data set is not sufficient to spot the possible losses of separation between couples of aircraft, which can be expected to be many in the planned data and very few in the radar updated trajectories. In this paper we will consider all the scheduled flights in Europe from the 8th of June to the 21st of June 2011. Another needed source of information is about the structure of the airspace (i. e. , the structure of the sectors inside national airspaces) in each considered day. Such structure may vary according to the traffic demand, since almost every original sector can be split in two or more distinct ones in order to keep the workload assigned to controllers below a certain limit. By using additional data stored into the database, we can in principle extract the shape and boundaries of all the national airspaces and their sectors for every hour of the day, whereas for simplicity we assume that the structure is fixed at the beginning of the day and that it cannot be dynamically reshaped during the operations.
Network Analysis The topological structure of the trajectories in a National Airspace can be recovered by our data set by building a Navigation Point Network (NPN). The topology of such network has been already studied in the case of the Chinese Airspace  [32]  and recently in the European Airspace  [33] , using a similar dataset provided by EUROCONTROL [3]. This network is built by using the fixed geographical references as nodes and by linking them if at least an aircraft flew from one to another in the considered period of time. Since we have two sorts of trajectories, it is possible to build two different networks: a planned NPN, built with the last filed flight plans, and a real NPN built with the actual radar updated trajectories. We point out here that, during the planning session, the actual airways are mostly taken into account so that the study of the last filed flight plans gives hints on the real airway structure, while the real navigation point network dynamically created by controllers during their management action may differ substantially from the airway structure.
e. , the IN-OUT protocol). More information on the direct assignment and on sector capacity constraints can be found in Section C of the Supporting Information S1 File. If all these strategies fail, the aircraft goes on straight to the next node, a conflict arises and is recorded. Finally, since the vertical component of the flights cannot be disregarded in a realistic case, we assigned to each aircraft a desired flight level (also inferred from the data set) and allowed small vertical deviations from it. Details on the setup of the simulations used as validation can be found in Section C of the Supporting Information S1 File. The first check performed is that no conflicts are generated in any of the validating simulations done. This is a fundamental condition to assure that the model is reproducing correctly the real situation, where the very few conflicts recorded do not stem from the ATC management.
Acknowledgments The work was co-financed by EUROCONTROL on behalf of the  SESAR Joint Undertaking  in the context of SESAR Work Package E, project  ELSA  and  Complex World Network PhD. The paper reflects only the authors' views. EUROCONTROL is not liable for any use that may be made of the information contained therein. VDPS acknowledges the  EU FP7  Grant  611272  (project  GROWTHCOM ) and  CNR PNR Project "CRISIS Lab"  for financial support. We acknowledge  S. Pozzi ,  V. Beato  and  M. Ducci  of Deep Blue S. R. L. [38] for interesting discussions.
The work was co-financed by EUROCONTROL on behalf of the  SESAR Joint Undertaking  in the context of SESAR Work Package E, project  ELSA , and  Complex World Network PhD. VDPS  acknowledges the  EU FP7  Grant  611272  (project  GROWTHCOM ) and  CNR PNR Project "CRISIS Lab"  for financial support. The paper reflects only the authors' views. EUROCONTROL is not liable for any use that may be made of the information contained therein. EUROCONTROL had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. SONY-CSL only provided material support collection and analysis, decision to publish, or preparation of the manuscript. The authors are not employed by  SONY-CSL.
We introduce phew (Parallel HiErarchical Watershed), a new segmentation algorithm to detect structures in astrophysical fluid simulations, and its implementation into the adaptive mesh refinement (AMR) code ramses. phew works on the density field defined on the adaptive mesh, and can thus be used on the gas density or the dark matter density after a projection of the particles onto the grid. The algorithm is based on a "watershed" segmentation of the computational volume into dense regions, followed by a merging of the segmented patches based on the saddle point topology of the density field. phew is capable of automatically detecting connected regions above the adopted density threshold, as well as the entire set of substructures within. Our algorithm is fully parallel and uses the MPI library.
2011; Onions et al. 2013; Knebe et al. 2013; Pujol et al. 2014). On even larger scales, the identification and characterization of cosmic voids is an important task. Similar to haloes, the voids assemble in a hierarchical structure of voids and sub-voids which can be found in observational and simulation data likewise. Way et al. (2011)  and  Way et al. (2014)  give an overview on void finding techniques and the relation to the identification of overdensities. Automatic detection of structure is also performed at galactic scales. For example, Astronomers performing radio observations of molecular clouds entered the field when they started to identify clumps in positionposition-velocity (PPV) data cubes. Stutzki & Guesten (1990)  tried to fit the data by sums of triaxial Gaussianshaped clumps and  Williams et al.
(1994)  identified structure by contouring the dataset at evenly spaced levels without assuming an a priori shape for the clumps. More recently,  Rosolowsky et al. (2008)  showed how dendrograms can be used to exploit the hierarchy that naturally arises from contouring a PPV cube at multiple emission levels and used this technique to define substructures in molecular clouds. With such a large choice of astrophysical structure finding tools at hand, one might ask the question why there needs to be yet another one. The trigger for the development of a new analysis tool was our need for "on-the-fly" structure finding in the astrophysical simulation code  (Teyssier 2002) , in order to locate gas and/or dark matter clumps while the simulation is running. As pointed out in  Knebe et al. (2013)  there is a general trend towards "on-the-fly" analysis for many reasons: most modern astrophysical simulations are performed on large computational infrastructure with distributed memory.
In contrast to ahf, however, we do not use the AMR grid as a way of contouring the density field. A low density region which -for whatever reason -is refined to a high level does not compromise our results. Thus, in the landscape of existing halo finders, phew can be seen as filling the gap between p-hop  (Skory et al. 2010)  which does not find substructures but is a MPI-parallel version of hop, and adaptahop, a multithreaded software that does find substructures, but has not yet been MPI-parallelized. The aim of this paper is to present a new structure finding algorithm that: 1-can be applied to any density field defined on an adaptive grid, 2-is capable of detecting substructure, 3-is parallelized using the MPI library on distributed memory systems, and 4-is fast enough to be run at every time step of a simulation without significantly slowing down the calculation.
They constitute the finest structure (see Figure  1 , third panel) in our hierarchy. Using the relevance as a merging criterion results in a similar definition of a clump as it is obtained by algorithms that contour the dataset at evenly spaced levels in log-space (e. g. Williams et al. 1994). There, a peak-to-saddle ratio above a given value guarantees that a contour level will fall between peak density and key saddle density and thus the detection of the cor-responding clump as an individual object. However, a contour level can coincidentally lie between the peak density and the key saddle density of an object with a very low peak-to-saddle ratio, resulting in the detection of an irrelevant density fluctuation as a clump. Merging based on the relevance removes this randomness from the analysis.
Parallel implementation We now turn to the implementation of the previously described steps in a parallel, distributed-memory framework. Where a detailed description of an algorithmic block in words would prevent readability of the paper, we refer the interested reader to a corresponding block written in pseudocode located in Appendix B. We assume that the computational domain has been previously decomposed into non-overlapping spatial domains, each domain containing a partition of the AMR mesh on which the density field is defined. In every MPI task, the local partition of the mesh is referred to as the "active cells". They are wrapped by a thin layer of cells that belong to other tasks. These ghost cells are referred to as belonging to the "virtual boundaries". These virtual boundaries are updated through MPI communication before phew is called to make sure that the densities in the virtual boundary cells are equal to the densities in the corresponding active cells hosted by other MPI tasks.
Conclusions We have presented phew, a new structure finding algorithm and its MPI parallel implementation into the AMR code ramses. phew finds density peaks and their associated regions in a 3D density field by performing a watershed segmentation. The merging is based on the saddle point topology. We have described a two-step approach to merging. In a first step, we merge irrelevant density fluctuations which we consider as noise. In a second step we merge the finest substructure hierarchically, into large, connected regions above the adopted density threshold. This merging process naturally results in a tree-like representation of substructure simi-  shows the status after a further round of merging as it is described in Section 2. 6. lar to the dendrograms presented by  Rosolowsky et al.
Acknowledgements We want to thank  Stephane Colombi  for his advice on substructure merging. Furthermore we thank  Doug Potter  for helpful discussions about programming techniques. The computations leading to this publication have been performed at on the zBox4 and  Schroedinger Supercomputers  at the  University of Zurich  and at the  Swiss Supercomputing Centre CSCS in Lugano. This work has been supported by the  Swiss National Science Foundation SNF  under the project " Computational Astrophysics " and the  PASC co-design project  " Particles and Fields ".
This paper presents the latest advances in our research work focused on VIRMANEL and SILUMOD, a couple of tools developed for research in wireless mobile multihop networks. SILUMOD is a domain specific language dedicated to the definition of mobility models. This language contains keywords and special operators that make it easy to define a mobility model and calculate the positions of a trajectory. These positions are sent to VIRMANEL, a tool that manages virtual machines corresponding to mobile nodes, emulates their movements and the resulting connections and disconnections, and displays the network evolution to the user, thanks to its graphical user interface. The virtualization approach we take here allows to run real code and to test real protocol implementations without deploying an important experimental platform.