dataesr/scientific-paragraphs-categorization huggingface dataset:
• The FDSN-compliant ph5-dataselect webservice  (Résif, 2019a) , which serves large-N data (stored in PH5 archives) as miniSEED or Seismic Analysis Code (SAC) format on the fly (useful for small subsets of the PH5 datasets); • The standard fdsn-station webservice (FDSN, 2013) to retrieve station metadata; • The standard fdsn-availability webservice (FDSN, 2013) to interrogate the miniSEED data inventory; • The FDSN-compliant ph5-availability webservice  (Résif, 2019a) , to interrogate the PH5 data inventory; • The eidaws-wfcatalog webservice  (Trani et al. , 2017)    • The standard fdsn-event webservice (FDSN, 2013), operated by BCSF-RéNaSS (Bureau Central Sismologique Français, French Central Seismology Bureau -Réseau National de Surveillance Sismique, National Seismic Monitoring Network), which gives access to event parameters located by BCSF-RéNaSS  (Masson et al.
, 2021). Résif-DC also allows the retrieval of a complete dataset, by network, using rsync protocol. The user is granted read-only access to the part of the archive exposing the dataset. This is, particularly, useful to retrieve entire datasets in PH5 format. Figure  7  shows the amount of waveform data served by fdsn-dataselect and SeedLink. Built on top of these base services, the following additional webservices are available from Résif-DC: • The timeseries and timeseriesplot webservices  (Résif, 2019a)  to obtain pre-processed waveforms or plots; • The resp, sacpz and evalresp webservices  (Résif, 2019a)  to obtain or plot instrumental responses; A complete catalog of the Résif-DC webservices is available on https://ws. resif. fr. More details and usage examples are given in the Data access details and examples section.
Data fairness Data fairness refers to the Findable, Accessible, Interoperable, Reusable (FAIR) guiding principles for scientific data management and stewardship  (Wilkinson et al. , 2016). We here discuss how far we are complying to those best practices. The findability of our data and metadata is ensured by the international standard station webservice (FDSN, 2013), accessible via any web browser, http client, and the community's most popular tools like ObsPy  (Beyreuther et al. , 2010; Megies et al. , 2011; Krischer et al. , 2015) , the EIDA's fdsnws_scripts  (Heinloo, 2018) , or the webservice fetch scripts  (Hutko, 2013). In addition, most of the Résif datasets have digital object identifiers (DOIs) associated with their seismic network, according to the standard procedure approved in 2014 by the FDSN  (Evans et al.
Résif-SI is actively working, within the framework of a cooperation between ORFEUS and IRIS, to promote good citation usage to scientific users of waveform data, and to inform publishers of these progresses, as the introduction of licenses engages the liability of the journals and of the publishing scientists, even though this liability is not yet reinforced. An accompanying issue is that data centers that hold copies of the datasets need to expose the license information in a proper way. For this to be practically possible, Résif-SI has, with the agreement of the involved organizations, stopped all data copies to other data centers, which were previously put in place, and asked for the old copies to be deleted. The only exception is GEOSCOPE data archived at the IRIS Data Management Center (DMC), due to a strong user base of GEOSCOPE data through this data center.
8 ). The constraints come from the variety of data access methods, each one having their own log formats that are not always compatible with real-time processing. Another difficulty is to analyze the data served through webservices. It is easy to capture the quantity of data shipped by the web server, but it is not possible to know what it is made of (which network, station, location, or channel). Consequently, we have to analyze each request and evaluate the typology of the response. All the gathered information is stored in a PostgreSQL database, aggregated, and anonymized. The anonymization concerns the client's IP address and consists of hashing it as soon as it enters the databases. Then, when the requests are aggregated, we only keep HyperLogLog objects  (Flajolet et al. , 2007)  to compute the cardinality of the clients.
Therefore, our databases respect the European legislation (General Data Protection Regulation), because there is no way to retrieve an IP address or any kind of personal data. The statistics can be accessed through several means: we provide a webservice for end users (see the Data access details and examples section) and interactive dashboards for internal usage (e. g. , analyzing operational events in real time, usage, and performance evaluation, Fig. 9 ). Figure  10  is an example of rendering statistics in a geographical map. Recently, we published a system allowing automatic creation of plots and usage statistics in form of a report  (resif-delivery-stats-plotter, Bollard 2021 ). This program is aimed at network managers or principal investigators seeking information on data and metadata usage.
Furthermore, to host the foreseen volumes, a dedicated archive storage has been setup. The metadata, on the other hand, is created in classical StationXML format, as other miniSEED data, and submitted at Résif-DC using the standard procedures described previously. The PH5 archive is served to end users by two means: • rsync access for a restricted set of users needing to download the entire dataset; and • the ph5-dataselect webservice  (Résif, 2019c)  allowing the selection of a subset of the data in miniSEED format with the same options and syntax as the FDSN dataselect webservice specification. Résif-DC also provides the ph5-availability webservice  (Résif, 2019b)  for the users to get the time span of the available data.
The requirements were then generalized so that they fulfill not only Résif-SI needs but also a much broader international community. In 2018, IRIS-DMC contracted Instrumental Software Technologies, Inc. (ISTI), to build a tool for StationXML creation and editing, and this first software was already satisfying some of Résif-SI requirements. Résif-SI then contracted ISTI to continue the development of Yet Another Station Metadata INformation Editor (YASMINE), to satisfy our requirements. The results are two independent pieces of software: yasmine-GUI and yasmine-CLI. One of the main innovations was the introduction of the new atomic response object library (AROL,  Wolyniec et al. , 2019). This library, written in YAML Ain't Markup Language (YAML), is contributed by Résif-SI and is the conversion of the long-maintained PZ format library.
Figure5: Validated data and metadata are integrated at Résif-DC by passing through a series of workers which ensure that the data and metadata conforms to the specifications, register it in the archive and in the database, index it and create the side-products. Raw data is concentrated, registered and made available through standardized webservices with very low latency (around 10 seconds). Data, metadata and other assets are dumped on a distant tape system at the Computing Centre of the National Institute of Nuclear Physics and Particle Physics (CC-IN2P3, Lyon) to secure them from a local disaster.
Acknowledgments The authors first of all thank all the network operators and scientists who entrust their datasets to  Résif-SI , and who work with us to ensure the highest possible level of data completeness, and data and metadata quality. The authors thank for their active participation in Résif-DC: • our colleagues from the  Direction générale des services informatiques of the Université Grenoble Alpes (DGDSI)  (https://services-numeriques-personnels. univ-grenoble-alpes. fr) and the  Unité Mixte de Service GRICAD  (https://gricad. univ-grenoble-alpes. fr):  G. Bruno ,  G. Enderlé ,  V. Louvet ,  A. Monot ;.
Data and Resources All the software produced by Résif-SI is  open source and available via https://gitlab. com/resif and https://gricad-gitlab. univ-grenoble-alpes. fr/OSUG/RESIF. The International Federation of Digital Seismograph Networks (FDSN) is available at https://www. fdsn. org. The Observatories and Research Facilities for European Seismology (ORFEUS) is available at https://www. orfeus-eu. org. The European Integrated Data Archive (EIDA) is available at https://www. orfeus-eu. org/data/eida/. The Résif seismic portal is available at http://seismology. resif. fr. A SeedLink server is accessible at rtserve. resif. fr. A complete catalog of Résif webservices is available at https://ws. resif. fr. Metadata from RAP network are available at https://ws. resif. fr/fdsnws/station/1/query?network= RA&level=network.
Aims. We present a new radiation hydrodynamics code, called "ARK-RT" which uses a two-moment model with the M 1 closure relation for radiative transfer. This code aims at being ready for high-performance computing, on exascale architectures. Methods. The two-moment model is solved using a finite volume scheme. The scheme is asymptotic preserving to capture accurately both optically thick and thin regimes. We also propose a well-balanced discretization of the radiative flux source term able to capture constant flux steady states with discontinuities in opacity. We use the library Trilinos for linear algebra and the package Kokkos allows us to reach high-performance computing and portability across different architectures, such as multi-core, many-core, and GP-GPU. Results. ARK-RT is able to reproduce standard tests in both free-streaming and diffusive limits, including purely radiative tests and radiation hydrodynamics ones.
The paper is organized as follows. In the next section, we present more precisely the moment model and the M 1 closure relation. We go through our new numerical scheme, well-balanced and asymptotic preserving in the diffusive limit in Sect. 3. In Sect. 4, we give details of some implementation features of our code ARK-RT, especially the Kokkos and Trilinos libraries used for shared memory parallelism and linear algebra for highperformance computing. We also show some performance results. In Sect. 5, we present some numerical test cases to show the importance of the asymptotic preserving and well-balanced properties. Finally, in Sect. 5. 6 we present a physical application about the stability of the ionization front in H ii region dense cores.
Implementation and parallelization Our implementation has been done in the code ARK-RT 1 , a fork of the code ARK developed in  Padioleau et al. (2019). The hydrodynamics and gravity part of our solver is similar to ARK and A&A proofs: manuscript no. main is solved with a well-balanced and all-regime solver. Because the solver for the radiative transfer equations is time-implicit, we have to solve large sparse linear systems. It is done using the library Trilinos, described in the next section.
Linear algebra We use the second generation of packages of the framework Trilinos  (Heroux et al. 2005) , i. e. Kokkos  (Edwards et al. 2014)  for shared memory computation, Tpetra (Baker & Heroux 2012) for distributed vectors and matrices, Belos  (Bavier et al. 2012)  for linear solvers, Ifpack2  (Prokopenko et al. 2016)  for classical preconditioners and MueLu  (Berger-Vergiat et al. 2019a,b)  for the AMG preconditioner. Let us focus on the package Kokkos.
Shared memory computation As new architectures have more and more cores, the distributed memory model is not enough to take advantage of all the computational power available. Therefore, we need to use a shared memory model inside the nodes. Furthermore, computational nodes are more and more heterogeneous, for example, multicores, many-cores, or accelerators such as GP-GPUs. Each architecture requires its own interface, such as OpenMP or C++11 threads for multi-cores and many-cores processors and CUDA or OpenACC for NVIDIA GPUs. This raises the problem of portability and performance portability: many HPC codes are optimized for some specific architectures, so running the code on a different architecture will result in bad performance. The library Kokkos  (Edwards et al. 2014 ) tackles this issue.
Implementation For the hydrodynamics step, Kokkos is used as an independent library for shared memory computation. Communications between the nodes are handled by the Message Passing Interface (MPI) programming model through a regular domain decomposition. Following  Kestener (2017) , inside each node, the domains are endowed with ghost cells used to implement physical boundary conditions, but also to contain values from neighbor domains. The code is organized with computational kernels, each kernel is a C++ functor. See  Padioleau et al. (2019)  for more details. The second step is the time-implicit solver for radiative transfer. The values of the matrix and the right-hand side of the linear system have to be updated at each iteration of the Newton-Raphson method, but the graph of the matrix does not change.
The solver is then coupled with the hydrodynamics code that implements an all-regime and well-balanced solver for hydrodynamics with gravity. The tests performed show that ARK-RT is well suited to study many astrophysical problems. Using this code, we have been able to study the development of H ii regions in massive pre-stellar dense cores, especially the propagation of the ionization front in the presence of convection. We have shown that even with the destabilizing effect of convection, the ionization front is strongly stable against perturbations. A linear stability analysis similar to the Rayleigh-Taylor instability but including source term could give more insight into this behavior. Further work will consist of the development of a numerical scheme that preserves the admissible states while keeping the asymptotic preserving and well-balanced properties.
G. RELATED WORK AND DISCUSSION As of today, there are several blockchain simulators found in the literature, a summary of which is in Table  2. This table highlights what layers and associated metrics those simulators cover. Note that a simulator may, in some cases, cover a given layer but without necessarily providing as output a performance metric. This means that either a modification of the source code or post-processing treatments are required in order to compute the desired metric. For example, in BlockSim, the authors claim in their paper  [36]  that the simulator keeps track of the fee evolution, however, after analyzing it, we realized that it is not possible to retrieve it without modifying part of the code. Another example is HIVE  [44]  that emulates the behavior of smart contracts within the Ethereum environment, however, we found that it does not allow for analyzing the (h) metric (i.
e. , pending transactions). To make a distinction between simulators that make available a performance metric without requiring any code modification or post-processing, and the ones that require a modification/post-processing to obtain the metric, the following two symbols are respectively used in Table  2 : (does not require any code modification and/or post-processing) and (does require a code modification and/or post-processing. As a first simulator, let us mention Bitcoin-Simulator  [38] , which has been designed for educational purposes to help students to understand how the block generation rate (l 1 ) and block size (l 2 ) evolve over time. Although it is a welldesigned pedagogical tool, it is quickly limited to carry out in-depth simulation analyzes. More research-oriented simulators have been proposed, such as Ethereum Hive  [44]  that have been proposed for emulating and evaluating Ethereum's smart contracts from a validation & execution time perspective (d 1 -d 2 ).
A. APPLICATION LAYER At this layer, BlockPerf consists of a configuration module responsible for instantiating the run's parameters similar to the one used in BlockSim. BlockSim takes as input a list of parameters, including the size of blocks, statistical models for transaction validation, block validation, and node labels. Similarly, BlockPerf takes as input a JSON file that extends the parameters from BlockSim to include fork occurrences, the block size parameter, IP address of each node, including the 'main" one (corresponding to the BitcoinNode class, as summarized in Table  3 ) that tracks all the results using the CliStats, CpuTimeSnapshoot and MemorySnapshot classes. Optionally, the input file also considers the location to store all the logs and data for each node. Note that the BitcoinNode is responsible for tracking the execution time (a) of the overall run and collecting the metrics regarding the overall computational (o) Network Layer (m) (l) (k) Node/Data Layer (i) Consensus Layer (f) Incentive Layer BlockSim (b) (a) Application Layer (o) (n) Network Layer (m) (l) (k) Node/Data Layer (j) (i) (h) Consensus Layer (g) (f) (e) Incentive Layer BlockPerf (b) (a) Application Layer Figure  3 : Illustration of how BlockPerf extends BlockSimsymbol indicates that the simulator makes available the performance metric without any code modification and/or post-processing, while symbol indicates that such a modification and/or post-processing is needed to obtained the desired performance metric resource usage (b) of the nodes.
E. NETWORK LAYER The network layer of BlockPerf differs significantly from BlockSim, and many other simulators reported in  BlockPerf implements a P2P protocol similar to the ones used by real blockchain systems using advertisement messages (e. g. , GETADDR and ADDR) to discover neighboring nodes. BlockPerf also integrates: • a wallet management module: in charge of generating wallet addresses to uniquely identify all nodes. This module also integrates the protocol to broadcast walletrelated information in the network (via WALLADD messages) to make all nodes aware of existing addresses; • a reputation score module: in charge of assigning negative scores from the connections where malformed messages were received (e. g. , if the transaction correctness fails, nodes keep receiving outdated block information).
One may wonder whether the number of nodes used in our experiments is not too small. This parameter is not critical in this study as the objective is not about showing how efficient a given blockchain is (e. g. , from a scalability standpoint), but rather on showing that the proposed simulator provides more realistic evaluation results than an existing one (BlockSim in this case). As a consequence, we limited the experiment to be large enough to capture a realistic scenario, and short enough to be manageable (in terms of costs). One may also wonder whether the size of the datasets is large enough to draw relevant conclusions about the comparison analysis. In this regard, let us note that all the graphs given in Figures 8 to 17 only report the average values of all the measurements obtained on a daily basis for the corresponding metrics.
To overcome the limitations of state-of-the-art blockchain simulators, a new one called BlockPerf is proposed in this paper, an extension to the existing BlockSim simulator. Block-Perf tries to cover as much as possible the layers mentioned above along with its metrics. A comparative analysis with BlockSim is carried, which has demonstrated that BlockPerf provides more realistic results than BlockSim (e. g. , at the Node/Data and Network layers) respectively improved by 39% and 55% in average. However, several limitations remain to be addressed the future, as discussed in the next section. The comparison analysis conducted in this paper is built upon a benchmarking bitcoin scenario.
We estimate the influence of the features on the model predictions using a widely used metric in the machine learning community: Shapely Additive Explanations (SHAP)  (Lundberg & Lee, 2017; Pedregosa et al. , 2011). SHAP values may be calculated for the feature of each sample (i. e. , unique data point provided to the model). Here, we report and compare the mean absolute value of the SHAP (mean |SHAP|) of each feature. We thus focus on the net influence of that feature on the model prediction, and not on the influence of that feature on particular samples. We perform a grid search over the hyperparameter-space to find the appropriate set of hyperparameters for each model  (Lundberg & Lee, 2017). We separate the training (80%) and testing (remaining 20%) datasets with no overlap between these two sets.
Each sample provided to the models are distinct in time (stress step) and space. We split the samples into training and testing datasets based on time, such that a particular time (differential stress) occurs in either the training or testing dataset. This method of splitting by time reduces the potential for autocorrelation in the results. We randomize the times that occur in the training and testing datasets such that times earlier and later in the experiment can occur in either dataset. For each model that predicts a given fracture characteristic, we develop ten unique models that differ only in the separation of the training and testing datasets. We develop these models in order to reduce the influence of random variations in the training and testing dataset on the calculated model performance.
To assess the influence of autocorrelation with this method of splitting the data, we also develop models using testing datasets that are continuous in time, and occur at the end of the experiment, close to macroscopic failure (Fig. S2 ). This method of splitting is expected to lower the model performance because characteristics of fracture networks evolve toward failure. Thus, the fracture characteristics near failure may be outside the range of those characteristics earlier in the experiment. Despite this natural evolution, when we split the training and testing datasets such that the testing dataset encompasses a continuous interval of time with 20% of the data at the end of the experiment, the R 2 scores of the models do not generally decrease in the models that predict the characteristics using all of the rock types, and in the models that predict the characteristics in the igneous rocks.
S2 ). The success of the models also depends on the fracture characteristic that the model predicts. For all of the characteristics that we predict except the total We train and test models ten times using different divisions of training and testing data in order to account for random variations. We report the mean AE one standard deviation of the R 2 scores of the ten models with different training and testing data sets. Thus, the orange symbol above the total volume label on the horizontal axis shows the mean AE one standard deviation of the scores for the models that predict the total volume of fractures in a subvolume volume, we predict the median value of that characteristic within a subvolume of the rock core. In particular, we measure the lengths of all the fractures within a subvolume.
5, and thus not visible on Fig. 4. This observed lower range of the scores of models that predict the orientation remains unchanged when we alter the method of splitting the training and testing datasets (Fig. S2 ). The lower range of the R 2 scores for models that predict the orientations suggest that the other fracture characteristics behave more systematically and predictably than the orientations. Although the R 2 scores are lower for the models that predict the orientations, the models that predict the other characteristics could use the orientations to make their predictions. In the next section, we examine this question by comparing the importance of features in each model.