dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and Perspectives This paper presents a modeling and simulation method and a DMSF platform. This federative platform brings interoperability to several modeling and simulation languages and components. This work expands Papyrus to communicate with new external components through two DS standards. DMSF is now a platform master for distributed simulations made of both HLA and FMU-FMI components. On the one hand, HLA is used to communicate with JaamSim simulations, a display tool, while, on the other hand, FMI is used to communicate with the risk management tool and the Java app. For instance, Papyrus now works with a risk management tool connected with Java Database Connectivity (JDBC) to form the DMSF. Then, as demonstrated, thanks to the Java app, it can be connected to any IoT device through the calls to services outside the platform.
Also the exposure of per-domain data towards the central measurement gathering entity, in-instance measurements deployment and service instance topology changes further complicate metric composition implementation and system design. Despite the fact that metric composition has inherent fault localization capability because metrics are measured separately per domain, due to the higher reliability and accuracy of the results of end-to-end active performance measurements and lower total cost, it was chosen that the NetMon system uses hybrid approach based on active probing method. Section 4 will describe chosen NetMon strategy and what in addition to the active probing was needed for fault localization and how the scalability problem was resolved.
Monitoring agents (MA) Monitoring Agents (MAs) are Linux virtual or physical machines used as sources and sinks of the active probe traffic. MA is being used to measure key performance indicators (latency, latency variation, loss) end to end. Each MA device resides in multiple VPNs simultaneously, using separate linux network namespace for each VPN on the connection towards the network element it is connected to. This way it is possible to inject the probe traffic into multiple user VPNs and monitor the performance of the user service instances from a single MA device thus reducing the scalability problem mentioned in Section 3. 1. MAs use modified oneway ping OWAMP  [14]  code, capable to store Service identifier and other relevant information in the probe packets, as explained in the Section 4.
Monitoring Result Repository (MRR) The Monitoring Result Repository (MRR) is the main component for gathering, storing and displaying monitoring data flowing from MAs, MC and Cor. It is based on InfluxDB and Grafana visualisation platform. End-to-end network metrics (latency, latency variation, loss) obtained from MAs are collected and stored with their corresponding Service ID. Per-zone monitoring results needed for fault localization are gathered from the Cor. In both cases Advanced Message Queuing Protocol (AMQP) messaging is used as a transport method for sending these results. MC provides the MRR with SLA thresholds for all active service tests. These are required for the monitoring dashboards that depict all the relevant performance metrics and RAG indicators for threshold violation.
Packet Capturing Elements (PC) PCs are implemented as Linux virtual or physical machines. In the implementation given in Section 6 , PC interfaces are connected to the mirror ports of the network elements (in this case vMX routers). PCs filter probe packets sent by MAs from the traffic tapped at these interfaces. The capturing is based on extended Berkeley Packet Filter (eBPF) 4 using a recent Linux kernel (at least v4. 10). PC detects the probe packets and gets the timestamp at the moment of capturing. The summary information about the packets (service ID, packet identifier and timestamp at the moment of capturing) is sent to the Cor for further processing. PCs currently support the following encapsulations: pure IP, QinQ, IP over MPLS, implicit MPLS pseudowire (without control word), explicit MPLS pseudowire (with control word), some combinations of the two (if kernel 4.
The other data can be recorded once for multiple packets if the packets are being gathered in batches. If the information about the packets is being sent for each packet separately, then the amount of data recorded per packet is between 20 and 48 bytes for IPv4 and IPv6 respectively. It is important that the amount of data recorded for each packet is as low as possible because this information creates an overhead which has to be transported to the Cor. For a monitoring system which monitors n service instances which are being monitored at p zone borders where c packets are being analysed per second in each service instance, total minimal amount of application level packet information traffic towards the Cor is: T MonTraf ∼ = 96 npc bit / s. To illustrate the amount of traffic generated by the PC devices we will use an example.
Acknowledgements This work was supported by the  GN4 -phase 2 project under GÉANT 2020 Framework Partnership Agreement (FPA ). The project is co-funded by the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 731122  ( GN4-2 ) and  National Research and Education Networks in Europe , members of the GN4-2 consortium. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the GN4 -phase 2 consortium or the  European Commission. Authors want to thank to all the members of the JRA2T1 and JRA2T3 tasks of the GN4 -phase 2 project who provided us a huge help and support with setting up the laboratory and GTS testbed environments and especially  Jerry Sobieski ,  JRA2  activity leader, for all the support, helpful discussions and valuable comments during our research.
Acknowledgements We acknowledge  Dorit Aharonov ,  Leonardo Banchi ,  Sougato Bose ,  Fernando Brandão ,  Giuseppe Carleo ,  Vladimir Korepin ,  Laura Mančinska ,  Tzu-Chieh Wei  for helpful conversations. A. R. is supported by  EPSRC  and by  QinetiQ. C. C. and M. P. are supported by  EPSRC. S. S. is supported by the  Royal Society ,  EPSRC , the  National Natural Science Foundation of China , and the grant  ARO-MURI W911NF-17-1-0304  ( US DOD, UK MOD  and  UK EPSRC  under the  Multidisciplinary University Research Initiative ). L. W. is supported by the  Royal Society.
Theoretical simulations using virtual species We used the 'virtualspecies' package  (Leroy et al. 2016 ) in R ver. 3. 6. 0 (<www. r-project. org>) to generate virtual species distributions on a two-dimensional landscape of 50 × 50 grid cells, based on two hypothetic environmental variables with the same spatial extent. As environmental conditions often display spatial autocorrelation  (Legendre 1993) , we used the 'gstat' package  (Pebesma and Heuvelink 2016)  to generate the spatially correlated random field for each variable (under current and future scenarios) using sequential Gaussian simulations with an exponential variogram model (sill number = 0. 025, range parameter = 15). A total number of 120 virtual species distributions (half current, half future) were generated. Changes between current and future distributions therefore represented a wide range of species vulnerabilities to climate change.
On the distribution map, each grid cell has a value ranging from 0 to 1 indicating the probability-of-occurrence (i. e. relative habitat suitability) for the virtual species. Following the framework in the last section and defining the block size as 5 × 5 grid cells, we conducted both spatial-only and spatio-temporal connectivity analyses for three hundred virtual species, as combinations of the sixty divergent vulnerabilities to climate change and five different dispersal abilities (5, 10, 15, 25 and 35 grid cells as the median natal dispersal distance). Connectivity analyses were performed using R scripts in conjunction with the command version of Conefor (<www. conefor. org/>).
The latter two are carnivorous species that primarily prey on snowshoe hare and deer, respectively. We obtained the four species presences data  (year 1970-2018)  from Global Biodiversity Information Facility (GBIF) and their characteristics (e. g. dispersal abilities and longevity) from the literature. See Table  2  for detailed information. We downloaded current and projected bioclimatic variables, as well as current forest loss map from databases listed in Table  2. We defined the year 2030 as a near future time point, so that the temporal difference from current to future approximately fits the longevity of our focal species. For future climate scenarios, we adopted the four Representative Concentration Pathway scenarios (RCP 2. 6, 4. 5, 6. 0 and 8. 5; see IPCC AR5), in which the extent of emissions and global warming increases from RCP 2.
Ensemble forecasting for species distributions We used the 'biomod2' package  (Thuiller et al. 2009 ) to implement ensemble species distribution projections, which are considered to increase the predictive accuracy and robustness with regard to individual models in the face of model uncertainties  (Araújo and New 2007). To account for predator-prey interactions, we first predicted potential spatial distributions of snowshoe hare and white-tailed deer using the approach below, and then integrated their distributions with other environmental variables to predict potential distributions of their predators: Canada lynx and grey wolf, respectively. As a first step to the ensemble forecasting, we added an equal number of pseudo-absences to presences data of each focal species across study area using a surface range envelope model, which forces pseudo-absence candidates to be selected in condition that differs from a defined proportion (0.
2, and converted spatial points to the raster map. By overlapping raster layers of forest loss  (actual 2000-2012 and predicted 2012-2030)  with the forest cover in the year 2000, we obtained the potential forest cover in 2030. Further, we built the ensemble models for each species by combining eight algorithms available in the 'biomod2' package: GAM, GBM, GLM, CT, ANN, FDA, MARS and RF. To evaluate the predictive accuracies of these models, we conducted three-fold cross-validations by randomly splitting the dataset into two parts: 80% for calibration and 20% for evaluation. Then, we used the true skill statistics (TSS) as the model evaluation metrics following  Allouche et al. (2006). We selected models with TSS >0. 7 for the final ensemble forecasting. Finally, the ensemble models were projected to the current and future climate scenarios based on four ensemble algorithms: mean possibility, median possibility, weighted mean possibility and committee averaging, and only the one with the highest TSS was retained for further analysis.
Longevity (years): 15-20. Median dispersal distance (km) based on natal dispersal: 88. 0 (Poole 1997). Number of presences data from GBIF: 398. Order: Carnivora. Habitat types: Forest, shrubland, grassland, rocky areas. Longevity (years): 6-14. Median dispersal distance (km) based on natal dispersal: 129. 7 (Sutherland et al. 2000). Number of presences data from GBIF: 61. The nineteen bioclimatic variables used as input data for species distribution models (SDMs). The future climate projections downscaled to North America. The general circulation model (GCM) of the used dataset is MOHC_HADGEM2_ES (Collins et al. 2011). Forest loss data were used to predict future forest cover. Forest cover data were used as the input data for SDMs. Used to delineate the boundary of the study area. Five northern eco-regions were excluded, as there are few presences of our focal species, and the primary land- cover type is tundra.
Source Global Biodiversity Information Facility (GBIF; available at: <www. gbif. org/>) WorldClim (<http://worldclim. org/>; Fick and Hijmans (2017)) Climate Change, Agriculture and Food Security (CCAFS; <www. ccafs-climate. org/data/>) Hansen et al. (2013) Land Information Ontario (<www. ontario. ca/data/ecoregions>) Time captured 1970-2018 1970-2000 2030 2000 2000-2012 - Spatial resolution - 30″ 30 m - Data format csv (exported to point files) Raster datasets (tiff) Polygon and 1970-2000) (Mestre et al. 2015). Data category Name Species Snowshoe occurrence hare Lepus data americanus White-tailed deer Odocoileus virginianus Canada lynx Lynx canadensis Grey wolf Canis lupus Climate data Current bioclimatic variables Future bioclimatic variables Land cover data Forest canopy cover Forest loss Ecoregion data Ecoregion.
In this work, two different thermodynamic softwares, ANGE using the TBASE database, and OPENCALPHAD using the TAF-ID (Thermodynamics of Advanced Fuels -International Database), have been integrated into the GERMINAL V2 fuel performance code (of the PLEIADES platform) in order to evaluate the chemical state of (U,Pu)O 2 fuel and fission products in sodium cooled fast reactors. A model to calculate the composition and the thickness of the "Joint-Oxyde Gaine" (JOG) fission product layer in the fuel-clad gap has been developed. Five fuel pins with a final burnup ranging between 3. 8 and 13. 4% FIMA (Fissions per Initial Metal Atom) have been simulated, and the calculated width of the fission product layer have been compared with post irradiation examinations. The two different thermodynamic softwares have been compared in terms of computation time and predicted fuel-to-clad gap chemistry.
Name of In the past years, several groups have worked on implementing thermodynamic calculations inside fuel performance codes in order to improve predictive abilities. Baurens et al. [9]  and later Konarski et al. [10]  have coupled ANGE together with the ALCYONE (also in the PLEIADES simulation platform) in order to simulate, respectively, stress corrosion cracking and oxygen thermodiffusion. Simunovic et al. [11]  have coupled THERMOCHIMICA  [12]  to the mass and heat transport models of the BISON  [13]  fuel performance code. Both these examples have been focusing on the simulation of light water reactor fuel. Uwaba et al. [14]  at the Japan Atomic Energy Agency have recently coupled the MLCYONE  [15, 16]  caesium behavior simulation code to the CEDAR  [17]  fast reactor fuel performance code.
This has allowed for predictions on the JOG chemistry and geometry. In this work, two different thermodynamic softwares, both based on the Calphad method  [18, 19] , have been integrated into GERMINAL V2 in order to calculate the chemical state of the fuel. Full in-pile simulations have been performed on five fuel pins with different burnup ranging between 3. 8 and 13. 4 %FIMA burnup. JOG thickness has then been estimated on the basis of the predicted chemical composition of the gap and the known molar volumes of the involved phases. The two different thermodynamic solvers, ANGE  [20]  and OPENCALPHAD  [21, 22] , and their respective databases have been compared in terms time and prediction of JOG thickness and its composition. When available, results have been compared with experimental results.
Experiments The operation of the Phénix reactor between 1973 and 2010 associated with numerous post irradiation examinations (PIE) by the CEA resulted in an extensive database of fuel pin behavior under irradiation in a fast neutron spectrum. In this work, five fuel pins from the Phénix fast breeder reactor irradiated to different burnup (3. 8, 7. 0, 9. 0, 11. 2, and 13. 4 %FIMA at the maximum flux plane) have been Fig. 1. Measured JOG thickness versus final burnup in some SFR fuel pins irradiated in the Phénix reactor. For reference  [1] , the burnup values refer to the local burnup at which the JOG was measured. For reference  [8] , the burnup refers to the maximum burnup reached in the fuel pin. simulated with GERMINAL V2. More information concerning the fuel pins can be found in Table  1.
Thermodynamic software and databases For the calculations, two different software-database combinations have been used and compared: -ANGE (Advanced Numeric Gibbs Energy minimizer)  [20] , co-developed by CEA and EDF (Electricité de France), based on the SOLGASMIX  [23] [24] [25]  software. -OPENCALPHAD open source software  [21, 22]  using the TAF-ID  [26, 27]  database which is the result of the merging of several databases (including TBASE). The main advantage of OPENCALPHAD is its ability to utilize better thermodynamic models in the newer (and still growing) TAF-ID database, but comes at the price of increased computational time as will be discussed in Section 3. 2. The purpose of the TAF-ID project, coordinated by the Organization for Economic Co-operative Development Nuclear Energy Agency (OECD/NEA), is to provide a comprehensive thermodynamic database on nuclear fuel materials to perform a wide range of thermodynamic calculations for different applications of nuclear reactors.
This database can be seen as a synthesis of different databases (including TBASE) developed independently in different countries and has been progressively extended for five years by introducing either models coming from research and/or databases of the participants of the project, or coming from the open literature. It has been decided to adopt a full Calphad modeling approach for this database in order to provide both phase diagram and thermodynamic data calculations. Here, the description of the (U,Pu,Ln)O 2±x phase is based on the Compound Energy Formalism (CEF)  [28]  model of  Guéneau et al. [29]. This phase, made up by three sublattices, can be written as (Ba 2+ , Ce 3+ , Ce 4+ , Gd 3+ , La 3+ , Pu 3+ , Pu 4+ , U 3+ , U 4+ , U 5+ , Zr 2+ , Zr 4+ ) 1 (O 2-,Va) 2 (O 2-,Va) 1 where Va indicates a vacancy.
Up to now, the TAF-ID can be directly used with THERMO-CALC  [33]  or OPENCALPHAD codes and a thermodynamic database converter has recently been developed in order to be able to use it with FACTSAGE (in CHEMSAGE format). Parts of the TAF-ID was converted to this format for use in the THERMOCHIMICA-BISON coupling mentioned in the introduction  [34]. TBASE  [35, 36] , on its side, is a thermodynamic database elaborated at ECN Petten (Netherlands) in the 1990's which contains mainly stoichiometric compounds from reference  [37]. This is the case for most solid phases, and all liquid phases. The two notable exceptions concern the fluorite fuel phase and the metallic "white phase". The thermodynamic description of the fuel phase is represented by the variable stoichiometry species model of Lindemer & Besmann  [38] [39] [40].
It can be written as a solution between the following constituents: UO 2 , U 2 O 4. 5 , U 3 O 7 , MoO 2 , MoO 3 , Cs 2 O, Cs 2 O 2 , CsO 2 , Gd4 /3 O 2 , UGd 2 O 6 , La4 /3 O 2 , ZrO 2 , BaUO 4 , BaO, U1 /3 , U1 /3 Pu4 /3 O 2 , CeO 2 , Ce4 /3 O 2 , Pu4 /3 O 2 , and PuO 2. The metallic phase is defined as an ideal solution between Mo, Ru, and Pd. It can be noted that in all definitions above, only the elements used in this work has been included in the expression of the phases. Moreover, the TAF-ID, unlike the TBASE description, includes heat capacity data for most phases. While heat capacity data is not required to perform the calculations presented in this work, a future improvement of the GERMINAL V2 code could be to couple the results of the thermodynamic model to the heat transfer model.
Computation times A complete fuel pin simulation with GERMINAL V2 can require millions of equilibrium calculations, implying a huge computational cost associated to the thermodynamic software. A number of test equilibrium calculations were performed by OPENCALPHAD and ANGE over a temperature range of 500-2500 K, with a composition corresponding to a (U 0. 78 ,Pu 0. 22 )O 1. 975 fuel pin irradiated to 13. 4 %FIMA burnup. Here, in order to facilitate the performance evaluation, both solvers were used in their stand-alone mode, i. e. , not coupled to GERMINAL V2. The composition was taken from previous calculations performed by the ERANSO code  [41]  using nuclear data from the JEFF-3. 1  [42]  project library. As can be seen in Table  2 , 15 element groups representative of the FP, the actinides, and the oxygen were considered in the equilibria.
GERMINAL V2 fuel performance code The GERMINAL V2 fuel performance code is being developed by the CEA, and works under the PLEIADES simulation platform  [7]. The code implements a 1 1 /2-D approach for the discretization of the fuel pin geometry. This means that the pin is divided into axial cells, and each axial cell is then divided into radial cells by assuming cylindrical symmetry. Here, one radial cell may represent either the fuel itself, the gap, or the cladding. One simulation is then divided into different timesteps. In reality, the relevant physical phenomena, e. g. swelling, temperature distribution, cracking, actinide and oxygen redistribution etc. are all coupled to one another. In order to describe all these phenomena, GERMINAL V2 uses a scheme of nested convergence loops. In practice this means that one timestep consists of one loop over the axial cells, and within the evaluation of each axial cell another convergence loop solves the necessary equations within each radial cell.
The modeling of the thermal and mechanical behavior is treated by the finite element solver CASTEM2000  [43]. The description of clad mechanical behavior (irradiation and thermal-activated creep, irradiation-induced swelling, plasticity in transient conditions) allows to account for clad deformation when evaluating the fuel-to-clad gap width. The chemical composition at each radial node of the fuel is obtained from a simplified neutronic module implementing an isolated resolution of the Bateman equations. The coupling of GERMINAL V2 with a thermodynamic software (ANGE or OPENCALPHAD) elaborated in the frame of this work allows the thermodynamic equilibrium calculation at each node of the fuel pellet, and based on the amount of gas and liquid that is found, along with the fission gas release fraction, a corresponding amount is released into the fuel-to-clad gap.
-Zr and Nb are commonly grouped together  [45]. While this has been done in this work as well, it is of little consequence due to the low fission yield of Nb. The decision to make groups of representative elements was made due to the demand to keep computational cost, complexity, and failure rates sufficiently low while still describing a chemical system as close as possible to the real one. In addition, elements needed to be grouped when they were not described in both databases, since the comparison required that the same input was used in all cases. Among the main parameters for fuel chemistry simulation is the radial oxygen redistribution, and in GERMINAL V2 it is based on the work of Aitken  [52]. At each axial cell, the average O/M ratio is calculated by a correlation based on the burnup, and then, depending on the radial temperature profile, the O/M radial redistribution is calculated, fixing for each radial cell its local O/M ratio.
Here O/M refers the ratio between oxygen atoms and metallic atoms in the fluorite phase. An equilibrium calculation in each radial cell will tell the code how much of each element is found in a volatile phase. Once the amount of released FP has been calculated, the JOG thickness calculation can be summarized into the following steps: ). The amount of available oxygen is assumed to be sufficient to oxidize with all the released FP. From a more general point of view, choosing a Cs 2 MoO 4 like phase to represent all of the JOG is practical for the simulation of heat transfer in GERMINAL V2 since its thermal conductivity is relatively well studied  [55, 56]. -Regardless of how the JOG volume is obtained, by assuming that the JOG layer is uniform in thickness within each axial slice, JOG thickness, x JOG , can be calculated by the equation: x JOG = V JOG 2hπr fuel-outer (1) where V JOG is the JOG volume, h is the height of the axial slice, r fuel-outer is the outer radius of the fuel.