dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The observational datasets used in the present study can be accessed freely from their original source: SMOS L1C (MIR SCLF1C, version 505): https://smos-ds-02. eo. esa. int. The post-processing performed before comparison to CMEM output is described in Sect. 2. 1. E-OBS: http://www. ecad. eu/download/ensembles/download. php; LandSAF LST: http://landsaf. ipma. pt/; ERA-Interim: http://www. ecmwf. int/ en/research/climate-reanalysis/era-interim. The simulations were produced specifically for this study but can be reproduced using the corresponding release of the models: ORCHIDEE: https://forge. ipsl. jussieu. fr/orchidee (release no. 3400); CMEM: https://software. ecmwf. int/wiki/display/LDAS/CMEM (version4. 1); HTES-SEL: http://www. ecmwf. int (cycle38r2).
Acknowledgement. The authors would like to thank  Filipe Aires  and  Jean-Pierre Wigneron  for their helpful comments. We are also very thankful to one anonymous reviewer whose comments have greatly improved the document. This work contributes to the  FP7 Earth2Observe project  under grant agreement no. 603608. Edited by: A. Weerts Reviewed by:  R. S. Westerhoff  and  Y. Zeng.
Related Work A number of research efforts have recently been focusing on redesigning HPC applications to use dynamic runtime systems. The dense linear algebra community has massively adopted this modular approach over the past few years  [14, 2, 7]  and delivered production-quality software relying on it. For example, the MAGMA library  [2] , provides Linear Algebra algorithms over heterogeneous hardware and can optionally use the StarPU runtime system to perform dynamic scheduling between CPUs and GPUs, illustrating the trend toward delegating scheduling to the underlying runtime system. Moreover, such libraries often exhibit state-ofthe-art performance, resulting from heavy tuning and strong optimization efforts. However, these approaches require that accelerators process a large share of the total workload to ensure a fair load balancing between resources.
Background We integrate our solution to the StarPU runtime system as it provides a flexible platform to deal with heterogeneous architectures. StarPU  [4]  is a C library that provides programmers with a portable interface for scheduling dynamic graphs of tasks onto a heterogeneous set of processing units called workers in StarPU (i. e. CPUs and GPUs). The two basic principles of StarPU are firstly that tasks can have several implementations, for some or each of the various heterogeneous processing units available in the machine, and secondly that necessary data transfers to these processing units are handled transparently by the runtime system. StarPU tasks are defined as multi-version kernels, gathering the different implementations available for CPUs and GPUs, associated to a set of input/output data.
The master worker joins the team of inner threads while the other StarPU threads are paused. Depending on the inner scheduling engine, the set of computing resources assigned to a cluster may be dynamically adjusted during the execution of a parallel task. This obviously requires the inner scheduler (resp. runtime system) to be able to support such an operation. For instance, parallel kernels implemented on top of runtime systems like OpenMP will not allow removing a computing resource during the execution of the parallel task. In this case we refer to the corresponding parallel task as a moldable one and we consider resizing the corresponding cluster only at the end of the task or before starting a new one. From a practical point of view, we integrate in a callback function the specific code required to force the inner runtime to run on the selected set of resources.
Local Ensemble Transform Kalman Filter The Local Ensemble Transform Kalman Filter (LETKF) is an efficient data assimilation scheme for spatiotemporal chaotic systems  (Hunt et al. , 2007). Its orginal source code is available at https://github. com/ takemasa-miyoshi/letkf. It is an Ensemble Kalman Filter  (Evensen, 1994) , which means that it uses an ensemble of N model forecasts to estimate both the mean state and the standard deviation of the atmospheric state. The underlying assumption is that the background and the observations are considered as Gaussian random variables. Therefore, knowing only the first two moments, the mean and the standard deviation, is sufficient to completely know the atmospheric state. By comparing the mean and standard deviation of the background and observations, an analysis is computed, which serves as an initial state for a new model forecast.
LETKF Configuration The LETKF is used with an ensemble of 16 members, as in  Greybush et al. (2012)  and  Navarro, Forget et al. (2014). Initial states are constructed from the same atmospheric state taken from the climatology of the Mars Climate Database  (Lewis et al. , 1999)  30 sols before the date of the start of the assimilation. This initial state is then integrated for 30 sols with a global total dust opacity that is different for each member of the ensemble, with values varying from 0. 05 to 1. 5. The cycling of the assimilation setup is the same as in  Greybush et al. (2012) , with a time window of six Martian hours. Observations are considered 3 h before and 3 h after the time of the analysis. The model variables are linearly interpolated to the locations of observations in space and time.
It could be a simple forcing of the ice by temperature, but also scavenging of dust by water ice or vertical transport of ice and vapor that follows the vertical diurnal transport of dust could play a role. Instead of injecting a special dust that is not vertically transported by winds, another possibility could have been to change the optical properties of dust. The LMD GCM uses the latest available data for optical properties, well tested, and producing results in agreement with TES measurements  (Madeleine et al. , 2011). However,  Forget et al. (1999)  points out that a realistic uncertainty of 5% in the value of the single-scattering albedo induces a change of 50% in the heating rate, a leverage that could be considered for modifying Earth and Space Science 10. 1002/2017EA000274 temperature below 100 Pa.
Finding an Atmospheric State That Is Stable for the Model Forcing a GCM to a given temperature field appears to be a complex problem. The TuT experiment shows how improving temperature in the analysis can turn in fact to a worsening of the background because of the global wave response. An example of this is well shown in  Zhao et al. (2015) , where the influence of the assimilation time window on the excitation of the Kelvin wave modifies the assimilation products. A path to solve the problem would be to start considering the atmosphere of Mars as a global dynamical system. Assimilation usually considers the local approach for atmospheric fields to be the efficient one. This is true on Earth, Earth and Space Science 10. 1002/2017EA000274 but should be questioned for Mars. The current observational data sets for Mars provide local measurements and are easier assimilated with a local scheme.
Computational Setup The numerical simulations are performed with the code elsA developed by ONERA  [15]. This solver is a multidisciplinary code object-oriented, specialized in both internal and external §ows. This code solves both Euler and Navier Stokes equations. It is based on a cell-centered ¦nite volume formulation on multiblock structured meshes. A wide panel of turbulence models and numerical schemes is available. The study reported in this paper relies on a steady-state RANS approach. Boundary layers are assumed to be fully turbulent due to the large Reynolds number, based on the rotor chord (Re > 10 6 ). As reported in  [16] , there are 4 main sources of errors when comparing numerical predictions with experimental data: (1) accuracy of the simulated geometry (and di©culty to represent small details such as technological e¨ects); (2) boundary conditions (e.
As a primary laboratory in the field of ionising radiation metrology, the Laboratoire National Henri Becquerel (LNE-LNHB), CEA Saclay, is involved in measurements, evaluations and dissemination of radioactive decay data. Data measurements undertaken by various laboratories are evaluated by an international commission of experts (Decay Data Evaluation Project) coordinated by LNHB staff in order to establish a set of recommended decay scheme data. New nuclide evaluations are regularly added to our website, the NUCL ÉIDE database, published in the BIPM-5 Monographie series and uploaded to our web application LARAWEB, a dedicated tool for alpha and gamma spectrometry. The Mini Table  of  Radionuclides is produced from time-to-time with data extracted from our database. Various publications are described, along with new search criteria and decay scheme visualisation in LARAWEB.
Introduction Evaluation of nuclear decay data at the Laboratoire National Henri Becquerel (LNHB), CEA, began in 1974 and many publications on this topic have been released. In order to facilitate the management and distribution of these data, a computerized database called NUCL ÉIDE was developed, as well as many tools for both evaluators and users. All necessary data are stored in one single database, NUCL ÉIDE, which is used as an entry point and has allowed the development of specific software to maintain and update this database. The LNHB is therefore able to offer a variety of tools related to reference nuclear decay data for users in many fields such as nuclear medicine, reactor monitoring and shielding, spectrometry, environmental studies, etc. We present some of the publications and tools we distribute, and more specifically the latest enhancements of LARAWEB which is our web application for alpha and gamma spectrometry.
Data evaluation New or updated nuclide evaluations are regularly added to the NUCL ÉIDE database through the work of evaluators from the Decay Data Evaluation Project (DDEP)  [1]. These evaluations are undertaken when new measurements are published or special interest for a new nuclide emerges. All published measurements and data are analysed and compiled by a DDEP evaluator and a set of recommended decay data is established. After a review procedure among evaluators, these data are inserted in the NUCL ÉIDE a e-mail: christophe. dulieu@cea. fr database and made available to the users through multiple media: electronic files on our website, printed publications, and our web applications.
LNHB website In the atomic and nuclear data section of our website, one can find a variety of information concerning evaluated nuclides, mainly in the form of PDF or text files. Thus, the recommended data and decay scheme relative to a nuclide, and information concerning the way the evaluation was achieved, are available as PDF files. Decay data are also available in the ENSDF file format  [2]  and as a simple text file. A specific PenNuc text file, intended to be used for Penelope Monte Carlo simulation, has recently been added that describes the complete decay scheme of a nuclide to be used as a radioactive source in a simulation process. As of today, nearly 220 nuclides are available, and the website is regularly updated when new evaluations become available. The main LNHB website can be found at: http://www.
LARAWEB/NUCL ÉIDE -LARA LARAWEB is a web-based application designed for spectrometry purposes. It contains information relating to ≈ 450 nuclides of particular interest in the field of alpha, X-and gamma-ray spectrometry. The main data presented  are half-life, decay constant, daughters, Q values, possible parents, specific activity and, most importantly, alpha, X and gamma emissions for each selected nuclide.
Decay data display Energy and intensity thresholds can be set to restrict the displayed data to the most intense emissions. Multiple nuclides can be selected at the same time for comparison. When available, starting and ending levels of each emission are also displayed. Such information now allows the user to interactively display the nuclide decay scheme,   according to the selected thresholds. Thus, only the most intense or energetic emissions are displayed, offering a simplified view of the decay scheme. LARAWEB can display the possible coincidences between to cascading nuclides, based on their respective intensities. This feature, combined with the possibility to plot a simplified decay scheme, makes it a convenient tool for spectrometrists.
Advanced search engine An integrated search engine allows all nuclides to be found which correspond to a range of chosen search criteria, such as; decay mode (β + /ε, β -, IT, α), emission type (alpha, X or gamma), emission energies (up to three individual energies or ranges may be specified), emission intensity, mass number and half-life. The user can then select one or more nuclides amongst the resulting list in order to display the complete information. Emissions meeting the search criteria are then highlighted, along with other significantly intense emissions. This tool can be used to quickly identify the originating radionuclide of an alpha or gamma ray in a spectrum when the energies of the peaks are known. LARAWEB is available through the Atomic and Nuclear Data section of the LNHB website: http://www.
Conclusions Data evaluation and dissemination is a continuous process in which the LNHB is deeply involved. We are permanently updating data and tools and developing new ones to fulfil the needs of decay data users and evaluators. Over a period of 10 years, about 100 new nuclide evaluations have been added to our database and published in six volumes of the Monographie BIPM-5, and an updated pocket-sized reference guide entitled the Mini Table of Radionuclides issued in 2015. Many new developments have been implemented in the online tool LARAWEB, including information on γ -γ coincidences and the ability to plot decay schemes. In particular, the use of intensity thresholds allows the main decay radiations to be displayed and a simplified decay scheme to be plotted, which is unique to LARAWEB.
We further determine the number of jamming attackers through the coverage rate of beaconing circles and utilize weight-based fuzzy c-means to classify the data set. When the data set is classified as acceptable, we further explore the means of using particle swarm optimization algorithm to calculate the positional coordinates of each attacker. We simulate our techniques in MATLAB, and both urban traffic area and open area are considered in our simulation. The experimental results suggest that the proposed algorithm can achieve high precision when determining the number of attackers while the result of the classified performance is always satisfying. Our localization results lead to higher accuracy than other existing solutions. Also, when the data set is limited, the chances of taking accurate localization are higher than other measures.
We focus on using the range-free algorithms to find the location of the jamming attacker in VANET. Such solutions can be effective by eliminating the attackers from the network and guiding the routing protocol to avoid the jammed region. The vehicles record their locating information and combine the information with if-jammed signal in every fixed time interval. The collected information of each vehicle is transmitted to the RSU when the vehicle moves into the communication range of this RSU. Then, RSUs forward their collected information to the application server. At last, the application sever localizes jammers using corresponding algorithms. The main contributions of our work are as follows: we use the incorrectly classified negative area to measure the distance between samples and centroids in fuzzy c-means (FCM) algorithm, then use the weightbased FCM to classify the data set.
We provide two new definitions named Beaconing circle and Coverage rate. We also determine the number of adversaries using the Coverage rate of Beaconing circles; we use particle swarm optimization (PSO) algorithm to find the minimum cover circle in each classified cluster gained from the weight-based FCM. Moreover, the weight-based PSO algorithm is invited to deal with the partly distributed data set; we finally propose a general-purpose localization framework to perform jamming source positioning in VANET. In weight-based FCM, the calculation formula of the distances between points and centers was redefined with incorrectly classified negative area. At the same time, the possibility of the point being classified into incorrect cluster went down. We then applied the Beaconing circle and Coverage rate to evaluate the performance of classification when the cluster number was confirmed.
We further formulated the problem of determining the number of attackers as an automated segmentation of the input data set, and the algorithm iterated with the increasing cluster number until an acceptable classified performance was obtained. Moreover, we utilized the clusters returned by weight-based FCM to further localize multiple Pang et al. adversaries. We also used minimum cover circle to define the evaluation function in PSO. Then, the PSObased localization algorithms were developed to calculate more accurate coordinates of jamming attacker in each cluster. As we demonstrated through our experiments in MATLAB, our approach is highly effective in finding and localizing multiple attackers in VANET. Furthermore, we proposed a general-purpose localization framework to perform multiple jamming source positioning.
Determining the number of attackers and classifying the data set The inaccurate estimation of the attacker number and unsatisfactory segmentation of data set will eventually result in failure in localizing the multiple adversaries. To address this issue, we use a weight-based FCM model. We first define the beaconing circle and the coverage rate. Then, we further use them to design a evolutional procedure to determine the attacker number and classify the data set. The data set gathered from vehicles can be seen as S = fs i g, i = 1, 2, 3, :::, m, where s i is ith threedimensional sample, m is the total number of samples. The data set has been divided into two sets through the if-jammed value; they are expressed as X = fx k g, k = 1, 2, 3, :::, p and Y = fy j g, j = 1, 2, 3, :::, n, respectively, where x k is a two-dimensional (2D) sample representing the coordinates of the kth point in jammed region, and y j is a 2D sample representing the coordinates of the jth point in no-jammed region and m = p + n.
It is a clustering process which maps each instance s i to one element of the set fX , Y g, and the ifjammed signal is used as class label. The confusion matrix and common performance metrics that can be calculated are shown in Figure  3. It is obvious that this classifier makes perfect clustering that the true-positive rate is 1 and the false-positive rate is 0. Then, the mission of determining the number of attackers and classifying the data set converts to find some circle regions which include all elements of X and no element of Y. The reason is that any element of Y will increase the fp rate if it is included in the jammed regions. It also indicates that some no-jammed areas are placed in jammed region.
Experimental environment. We implemented our approach in MATLAB. Both the open map and the urban traffic map were used. As being in the 500 3 500 open map, vehicles followed Markov movement. The urban traffic map was generated by the Simulation of Urban Mobility (SUMO) simulator. The map is a part of the area from Shanghai, one of the biggest cities in China as shown in Figure  5. Vehicles were only allowed on road and must choose a random direction at each corner. The weight-based FCM algorithm used fuzzy index m = 2 and Dt = 1. The results indicate that there are three different types of scenarios. The main focus of the first scenario is to test the correctness and the accuracy of our estimated method. In this scenario, we test every coverage rate when the possible number of attackers changes from 2 to 5.
E m (k + i). In another word, the energy-based method has a little chance to fail in local optimum. Due to the longer runtime, energy-based method is hard to be applied in some real-time requirements, and it is also inappropriate to process the bigger data set which is collected by nodes in VANET. However, the energybased method is a more generalized method, and it can solve almost all the multiclass detection problems. The reason why our method can work better is that it follows the geographic features of jamming attackers and the mobile features of vehicles.
Using these two types of algorithms, some critical points are always misclassified into an individual cluster, and it makes our estimation method failure. The grid-based algorithms are always used in data search because of its high efficiency. Yet, the gird structures are built with many attributes, and it is inoperative in our work. The reason is that we only consider the positional attribute in our work. Finally, the efficiency of neural network-based algorithm is too low to be accepted. Based on these facts, we choose the partition-based clustering algorithms in our work; some popular partition-based algorithms such as k-means, PAM, and traditional FCM are performed to make a comparison with our proposed weight-based FCM in this experiment. Due to the city-wide applied range of VANET, the data set used in our work is very big.
Thus, the accuracy of clustering is a significant standard to demonstrate the effectiveness of the weightbased FCM algorithm. In this experiment, both open map and urban traffic map were selected. The actual number of jamming attackers was three, and four during the experiment. Corresponding to the actual number, the data X gathered from vehicles were classified into three, and four clusters using weight-based FCM, respectively. We used n to represent the vehicle number we have deployed and t to represent the simulation time. n 3 t is an important parameter which is used to change the size of the collected data set fX , Y g. With the different values of n and t, the multiclass ROC matrixes and the true-/false-positive rates computed through the clustering results are shown in Tables  1  and 2.
The ROC matrix is a very practical and intuitive way of seeing such a distribution. These matrixes can be illustrated with the following example: there are three actual jamming attackers on open map, and data set fX , Y g is generated by n = 40, t = 200. From this data set X , the sum of one column represents that the data size belongs to each class (actual attacker). In another word, 565 elements in X are of class ''1,'' 469 elements in X are of class ''2,'' and 708 elements in X are of class ''3''; the sum of one row represents the data size of a class which is classified by weight-based FCM. The value of ath row and bth column (a, b = 1, 2, 3, a 6 ¼ b) represents the number of elements in class b that have been misclassified into class a. It is clear that all elements in X are correctly classified through our proposed weight-based FCM algorithm.
Experimental environment. We implemented our approach in MATLAB as well. The maps, motion model of vehicles we used in this section are the identical ones that we used in the last section. The particle size m = 50, simulation time t = 200, and the PSO effective parameters e = 0:1, u 1 = 0:0001 3 rand(1), and u 2 = 0:0003 3 rand(1), where rand(1) represents a random number from 0 to 1, and it will make the trajectory of every particle as the sine wave. The localization errors have been defined as the following: the Euclidean distance between the estimated location of the jammer and the true location of the jammer in the network. And the localization error can be seen as an important metric to evaluate the accuracy of our PSO algorithm in comparison to the others. The results show three different scenarios.