dataesr/scientific-paragraphs-categorization huggingface dataset:
Eruption Source Parameters The definition of ESP together with the other specific volcanic parameters, namely the volcano location in the grid domain, start and end of each single paroxysm (UTC time), and TGSD, was calculated at the pre-processing level utilizing an NCAR Command Language script. The resulting ASCII file contained all the volcanic information needed at runtime by WRF-Chem. Future releases will consider a unique pre-processing environment also including the elaboration of the raw V2B radar data. Figure  3  displays the V2B radar echo power and maximum radial velocity records in the two bins above New SE Crater (3 and 4, i. e. , at 3135 and 3285 m) on 23 November, integrated over 1. 1 s. The onset of continuous tephra emission is detected by the radar with emergent signals at 07:13 UTC, although both echoes and velocities increase strongly after 08:30.
According to the default WRF-Chem configuration  [8] , it was assumed that 75% of the erupted mass is constrained in the umbrella cloud and 25% below, with a linear distribution from the umbrella base to the vent (Figure  1  of Stuefer et al. [8] ). The elaboration of plume height and V2B data reported in Figure  4a ,b suggests that in the paroxysmal phase (09:55-10:14 UTC), the eruption reached an altitude of 11 km (a. s. l. ) and then rapidly decreased to around 5 km (a. s. l. ) after 10:30 UTC, causing different cloud layers being transported at different altitudes. These results are in agreement (within 1 km) with the analysis of Corradini et al. [35]  on the same Etna eruption. The input parameters for SO 2 were obtained from the MSVOLSO2L4 database (https://disc. gsfc. nasa. gov/datasets/MSVOLSO2L4_4/summary (accessed on 13 May 2021)).
Geopotential ERA5 vs. WRF In order to analyze the synoptic conditions in the days following the eruption of Etna, we considered the ERA5 reanalysis  [52] , which have 137 vertical levels and about 30 km of horizontal resolution. Geopotential fields were obtained by downloading ERA5 hourly data on pressure levels (available from 1979 to present) in netCDF format (https://www. unidata. ucar. edu/software/netcdf/) from the Copernicus Climate datastore (https://cds. climate. copernicus. eu/#!/home (accessed on 13 February 2021). To compare ERA5 with the WRF-Chem model output fields, we focused on the geopotential height at 500 and 200 hPa. These levels were chosen since they are representative of the upper tropospheric conditions, which determine the long-range transport of the volcanic plumes.
Conclusions In this work, we investigated the effects of variable eruption source parameters on volcanic plume transport of the Mount Etna paroxysm of 23 November 2013. It represents a further application of the WRF-Chem model for Mount Etna following Rizza et al. [32]. Concerning the model setup, the raw data obtained from the V2B Doppler radar system were elaborated at the WRF-Chem pre-processing level. This allowed the inclusion of the transient and fluctuating nature of the volcanic emissions to accurately model atmospheric dispersion of ash/gas, pointing to the exact definition of the most critical parameters, such as paroxysm onset and end, column height, total grain size distribution, and mass eruption rate (MER). This joint approach between an online coupled chemical transport model (CTM) like WRF-Chem and the data from the V2B Doppler radar system represents an innovative development of current volcanic ash transport and dispersion models as it considers the two-way feedback for all the processes (meteo, chemistry, and physics) at the time step level, in a context of time-varying eruption source parameters (ESPs).
Acknowledgments: The authors gratefully acknowledge  M. Gouhier  and the HOTVOLC observing system, hosted by OPGC, for the provision of MSG-SEVIRI satellite imagery and/or READY products via http://hotvolc. opgc. fr/(accessed on 17 May 2021) and  Yannick Guéhenneux  for processing the HOTVOLC 'RGB product' images. Measurements at Etna using the VOLDORAD-2B radar are carried out in the frame of a collaborative research agreement between the  Observatoire de Physique du Globe de Clermont-Ferrand (OPGC, Université Clermont Auvergne, Clermont-Ferrand, France) , the  French CNRS , and the  Instituto Nazionale di Geofisica e Vulcanologia ,  Osservatorio Etneo , sezione di Catania (INGV-OE). We thank  M. Coltelli ,  M. Prestifilippo  and  INGV-OE  staff for their support in.
https://github. com/JohnKendrick/PDielec, last access: 15 July 2021). https://doi. org/10. 5194/ejm-33-389-2021 Eur. J. Mineral. , 33, 389-400, 2021 3 Results and discussion 3. 1 Theoretical structural properties of antigorite The unit cell of the antigorite m = 17 polysome (S. G. Pm) contains 291 atoms. The relaxed cell parameters are a = 43. 86 Å, b = 9. 35 Å, c = 7. 39 Å and β = 91. 125 • , in very good agreement with the theoretical values (a = 43. 84 Å, b = 9. 33 Å, c = 7. 38 Å) reported by Ghaderi et al. (2015) with the same xc-functional. The theoretical edge lengths overestimate their experimental counterparts (a = 43. 505 Å, b = 9. 2509 Å, c = 7. 2625 Å;.
Acknowledgements. Pierre Chansigaud (IC2MP spectroscopy platform,  University of Poitiers ) is acknowledged for technical assistance in the production of the MIR spectrum. We thank  Bernard Grobéty  and two anonymous reviewers for their thoughtful suggestions. Calculations have been performed using the computing resources of  IMPMC (Sorbonne Université-CNRS-MNHN ) and the  HPC resources of IDRIS  under the allocation 2020-A0080910820 attributed by  GENCI  ( Grand Equipement National de Calcul Intensif ). Financial support. This research was supported by the  CNRS INSU  (project " Conditions of formation of Mg/Ni silicate ores from New Caledonia " (2010,  CNRS INSU CESSUR) ) and the  CNRT "Nickel and its environment"  (project " Ni/Co mineralization factors of laterites derived from ultramafic rocks of New-Caledonia ";  2010-2014 ).
Data availability. Structure drawings have been produced with the Vesta software (Momma and Izumi, 2011). PWscf and PHonon codes (Giannozzi et al. , 2009)  are available at http://www. quantum-espresso. org/ (last access: 15 March 2021). The pseudopotentials (Mg_ONCV_PBE-1. 0. upf, O_ONCV_PBE-1. 0. upf, Si_ONCV_PBE-1. 1. upf, H_ONCV_PBE-1. 0. upf) are available at http://www. quantum-simulation. org/potentials/sg15_oncv/ (last access: 7 June 2021). The structural, dielectric and vibrational properties of the model are provided as an electronic supplementary file, together with Vesta files enabling a visualization of the modes. Additional requests should be addressed to Etienne Balan (etienne. balan@sorbonne-universite. fr). Supplement. The supplement related to this article is available online at: https://doi.
Experimental Section Physical Characterizations: XRD data was collected using an X-ray diffractometer (X'Pert Pro, PANalytical). The morphology was analyzed by field-emission scanning electron microscopy (FEI Nova 450 Nano) and transmission electron microscopy with energy-dispersive X-ray spectroscopy (Tecnai G2 20 U-Twin and Titan G2 60-300). XPS (ESCALab 250) was used to determine the electronic state of the elements and to get information on the nature of compounds. TGA (Setaram TGDTA 92) was performed using an alumina crucible as sample holder, in Ar atmosphere with a ramping rate of 5 °C min -1. For the in-operando XRD test, 2-electrode Swagelok cells were assembled in air, using KMO as the working electrode deposited onto platinum disk as the current collector, and Zn metal disk as both counter electrode and reference electrodes.
Acknowledgements L. L. was supported by  ERC  Synergy Grant MoMa-Stor # 951513. P. S. and P. -L. T. acknowledge the support from  Agence Nationale de la Recherche  ( Labex Store-ex ) and  ERC  Synergy Grant MoMa-Stor # 951513. The authors acknowledge  Sandrine Dulard  and  Barbara Daffos  for their kind help in TEM and BET test.
Introduction Since the spread of information technologies, data produced by humanity as a whole increased sharply. These data are accessed by people and companies, for entertainment and debates, to fuel economic and scientific productions. Data have to be accessed at anytime and from everywhere as some data are accessed a lot in a short period of time. This became more relevant during the pandemic lockdown as everyone had to stay home even for working, implying a high raise of data access for IT companies, and for entertainment providers  [1]. One way to answer this increasing number of accesses is to replicate data. Data replication is a well studied technique for several years and contexts. It has been commonly used in traditional systems like parallel databases systems  [2]  and more recently Cloud systems  [3]  on which we are focusing in this paper.
This strategy considers a 3-tier fat tree data center architecture with a database at each level and one across the internet. A replication is triggered when the number of data accesses is higher than a threshold and occurs only if the energy and the bandwidth consumed by the database at a lower level are lower than the ones consumed by the database at the current level. Another data replication proposed in  [21]  takes into account availability and performance issues through the energy consumption prism. For this aim, it classifies both data and servers, the first based on their popularity and the second based on their power consumption. Then, hot data are replicated in hot servers (that consumes more) and cold data are replicated in cold servers. [22]  tries to reduce providers carbon footprint by reducing energy consumption and choosing the replication placement based on the energy mix of each data center.
Models and Objective functions In this part, models are proposed to estimate the provider's expenditure and energy consumption used in the optimization algorithm. These models are divided into 3 kinds of events: replicating, storing, reading. The expenditure model used in the following estimations are based on the work of  [6]. To estimate the energy consumption, we modeled each component that is used in the data management of a server. At first, disks energy consumption is modeled based on  [25]  that is also used in  [26]  to model Solid-State Drive. Then the memory is modeled, and more precisely the Random Access Memory, based on  [27]  which is regularly updated by technical reports of a founder like  [28]. Finally, the network energy consumption is considered through the Network Interface Card for the nodes, based on  [29] , and switches, from  [30].
Data replication between data centers (lines 1-5) The first step is the most important because it has to choose between highly different data centers. It has to replicate and place data where the trade-off between expenditure and energy consumption is the most balanced from the database administrators (and all the hierarchy above them) perspective. To consider both objectives, we could use one objective as a constraint and minimize the other or use a weighted objective to mix them into one. However, we choose Final Individual[tempNodesInDCList] = tempDCIndividual // replace in the subset of nodes from the current DC in Final Individual the result of FirstFitNoBottleneck 11: end for to find the Pareto front to let the administrator choose if this is more important to reduce energy consumption, reduce expenditure, or make a more balanced choice.
Also, the more there is data on a node, the longer the response time would be if the workload is very high. To consider this issue, we choose to wake and store those replicas on a small proportion of nodes in the data center so that those nodes are not fully loaded. This small proportion of nodes includes nodes that store the original files if those files are planned to be replicated in this data center. Otherwise, these files will be deleted. Based on our experiments, we chose to use 6. 25% of the data center nodes as it is a good trade off between performance and energy consumption. If this number of nodes cannot hold all the data, we increase this proportion by 6. 25% steps. The second optimization algorithm (Algorithm 2) takes as input, the set of files F, the set of files that will be stored in the chosen data center FilesInDC and the set of nodes in the current data center Nodes DC.
As control replication strategy, we used a strategy that places 3 replicas randomly called 3Rand. From the literature, we compare our strategy with a static replication strategy called MORM  [20]  that occurs before the experiment starts, with a multi-objective algorithm that considers availability, latency, load balancing, service time and energy consumption. We also compare our strategy with 2 dynamic replication strategies: (i) PEPR  [6]  and (ii) the one proposed by Boru et al. [5]. PEPR is a strategy that takes into account the provider profit (with an income of 0. 0205$ per cloudlet) and performance. It is triggered by each violation, replicates if it is still profitable. Boru et al. considers a hierarchical topology with 3 level of databases (central, data center, rack). In this strategy, the replication is triggered by the number of reads reaching a threshold, and the replication occurs on lower level databases (data center, rack) if they consume less energy and bandwidth than the higher level databases (central, data center).
In fact, 3Rand, E2ARS, MORM and PEPR are considered using an architecture following a peer to peer topology. In this case, all nodes can process tasks and store data. Boru et al. 's architecture is based on a three tier fat tree topology where each level has a database. In order to take its architecture into account inside a peer to peer one, we add a data center per region which corresponds to the data center database, and a region which represents the central database. However, these added structures are not taken into account to the global energy consumption, because those nodes can not process tasks, and only store data. This permits us to compare those strategies for a given amount of storage and processing resources. For the workload,  [34]  shows that when there is a post on a social media with a link to Wikipedia, there is an increasing interest about the topic, increasing the number of views on the linked Wikipedia page.
6 seconds. In the dynamic group, PEPR starts without replicas, and it adapts dynamically based on violations and profit. Implying that the stop of the increasing number of replicas can be either due to the lack of violations or the lack of profit. Boru et al. starts without replicas either, as all data are stored in the central database, and then if the number of requests is higher than a threshold it triggers the replication, and it replicates if the energy consumption and the bandwidth of the level below is lower. This is why there is a sharp increase of data replication, then it gets slower, as long as most of the highly requested data in the central database are already replicated. However, on Figure  2b , we can see that even with the 2 nd largest number of replicas, it as the highest number of violations (reaching up to 83% at the end.
Number of SLO violations per experiment -Mean (Standard -Deviation) Standardized Value of energy consumption and expenditure of experiment 1 between strategiesas it needs to keep all nodes awaken while other strategies can put them in sleep mode. Boru et al. is the one that costs and consumes the most with expenditure increase of 33% and an energy consumption that is equal to more than 3 times 3Rand. This is mostly explained by the bottleneck created by the topology increasing the execution time, implying an increase in terms of energy consumption. Also, due to its topology, Boru et al. has to retrieve data from the central database, as it is considered as another region, it increases the cost to transfer data. Compared to 3Rand, PEPR slightly reduces its energy consumption by 4% while keeping the same cost.
Participants Participants were the same included in our previous study  [13]. The sample included 46 participants (mean age = 22. 8 years, SD = 1. 12, range 20-27, 8 males). For 40 of these 46 participants, we had both the resting state and the EEG data registered during the task. This implies that the prediction of task-dependent ERP modulations was possible only for this subset of 40 participants, whereas all participants' data were used for behavioral performance prediction. All participants reported normal or corrected-to-normal vision and had no history of neurological and/or psychiatric disorders. All participants gave their informed consent before the experiment. All experimental procedures were approved by the Ethics Committee of the School of Psychology at the University of Padua (protocol n° 2536) and were conducted according to the principles expressed in the Declaration of Helsinki.
EEG Resting State Recording For each participant, the RS HD-EEG activity was recorded before the task. We used a Geodesic high-density EEG System (EGI ® GES-300) with a pre-cabled 128-channels HydroCel Geodesic Sensor Net (HCGSN-128) and electrical reference to the vertex. EEG data were recorded during the entire experiment. The sampling rate was 500 Hz. The impedance was kept below 60 kΩ for each sensor. Continuous EEG resting state activity was recorded for two minutes while participants were passively watching the movie Inscapes, which has been specifically conceived and validated as a reliable tool to avoid drowsiness or sleepiness during task-free neuroimaging data collection  [20].
None of the participants reported having noticed these changes, supporting that the change in the probabilistic context had an implicit impact on the behavior. Before starting the experimental session, participants were presented with a block of 20 training trials for each condition to ensure they understood task instructions. During training, all participants received feedback at every trial according to their performance. Specifically, a neutral yellow smile was displayed in cases in which either anticipatory (before target onset) or premature (<150 ms before target onset) responses were provided. A yellow smile was displayed if the RT was between 1000 and 1500 ms from target onset. Finally, a green smile was displayed if the RT was between 150 and 1000 ms. E-prime 2 software (Psychology Software Tools, Pittsburgh, PA, USA) was used to create and administer the stimuli.
Predicted Measures The behavioral and ERP data used in the present study were derived from a previously published database  [13]  and are publicly available (https://doi. org/10. 6084/m9. figshare. 12248933, 14 November 2021, https://doi. org/10. 6084/m9. figshare. 12246218, 14 November 2021). In the present study, we measured the mean accuracy and speed of response to the imperative stimulus for each participant. Only responses between 150 ms and 1500 ms from the target onset were considered correct responses and included in the RT analyses. To measure the implicit flexibility of motor control, we calculated the delta global index for each participant, which was the difference in his or her performance in the fast minus the slow blocks. We computed this measure for response speed (RT) and accuracy (non-premature responses).
Support Vector Regression In order to predict both behavioral performance and neural modulations (i. e. , ERP activity) at the single-subject level, we used a machine learning approach by applying a support vector regression (SVR) model to the obtained resting state connectivity patterns. The SVR, defined by  [50] , was derived from the support vector machine (SVM) that was first introduced by  [51]  as a supervised learning model for classification problems. Thus, SVR can be considered a generalization of the SVM model for regression problems. For each of the three networks of interest and frequency bands, we created a dataset of connectivity patterns, one per subject. In this case, we have only one sample (i. e. , the PLV value) per subject for the entire RS activity, thus our regression problem was implemented at the population level.
In more detail, each subject-connectivity pattern was obtained by extracting the inferior triangular part of the PLV adjacency matrix. The target parameters to be predicted from the population-connectivity data were the following: task performance (delta-global RT/Accuracy/IES) at the behavioral level and ERP effects (CNV, ODP, P3) at the neural level. A leave-one-subject-out cross-validation (i. e. , a leave-one-out cross validation scheme, implemented across population) was used to estimate the test generalization prediction accuracy. Specifically, training and test were performed a number of times (runs) equal to the population size (e. g. , 46 subjects). For each run, we randomly selected and used data from all the subjects but one (e. g. ,  45)  for training and the data from the discarded subject as a test sample.
As suggested by  [52] , leave-one-out is preferred with dataset ≤ to 100 samples. By contrast, with colossal dataset it could increase the overfitting probability, and therefore other cross-validation approaches, as kfolds, can be used  [52, 53]. For these reasons we selected this cross-validation approach amongst other possible methods given our sample size dimension. Each target parameter that had to be predicted by the model was standardized (z-scored) in order to have zero mean and standard deviation one. Moreover, the connectivity data matrix for training was also standardized across subjects. We used the Matlab functions fitrsvm and predict, respectively, to train a linear SVR model (used with default parameters) and predict the target parameter of the test subject starting with its connectivity pattern.
Matlab function cvpartition was used, at each run, for implementing the "leave one subject out" crossvalidation scheme. The prediction accuracy was computed at the end of the crossvalidation loop on the predicted parameter values for the test subjects. Specifically, we computed the Bayesian correlation between the vector containing the concatenation of the target value to be predicted at each run (i. e. , one for each test subject) and the vector containing the concatenation of the corresponding predicted values. Thus, the prediction accuracy was expressed in terms of a correlation coefficient (see  [54] , for a similar procedure applied to fMRI data). We used Bayesian correlation since it provides a measure of the likelihood of the alternative hypothesis. Only positive correlations that were significant were reported as an index of a good-quality fitting.
Note that negative correlations are an index of a very bad fitting and were just not considered. For the sake of completeness, the full results are given in an additional file on Open Science Framework (https://osf. io/xw8nq/, 14 November 2021). The analysis has been performed using the software JASP (https://jasp-stats. org/, 14 November 2021), reporting the Bayesian Factor (BF) in favor of the alternative Hypothesis H1 (positively correlated), according to which there is a correlation between the considered variables. Note that the reported BF values correspond to the assumption of a beta prior width = 0. 5, but contextually, a BF robustness check will be reported in the following pictures in order to estimate the strength of the results. Considering the scale of interpretation of the Bayes factor (BF) given  [55] , here we only reported the BFs above √10, namely 3.
In particular, we performed Bayesian correlation analysis using the software JASP (https://jasp-stats. org/, 14 November 2021), reporting the Bayesian Factor (BF) in favor of the alternative Hypothesis H1, according to which there is a correlation between the considered variables. Note that the reported BF values correspond to the assumption of a beta prior width = 0. 5, but contextually, a BF robustness check was also reported.