dataesr/scientific-paragraphs-categorization huggingface dataset:
It is a self-organized P2P wireless network. It has several features compared to WSN: first, the node size and applied regions of VANET are much bigger, the hardware-based methods are hard to be deployed; second, nodes in VANET are more powerful on energy and computational ability, and most of them have an on-board GPS receiver. But, nodes in WSN are always energy constrained and computational ability constrained, and the positional information only can be computed with sensor data without any calibration; Third, every node in VANET has complete autonomy, and they are uncontrolled and unpredictable. All nodes can be used as information collectors instead of processors, and the RSUs can be easily reused as repeater stations; Fourth, the control channel of VANET is completely accessible for any user; some interference avoidance algorithms are not useful.
Conclusion In this article, we proposed to use a range-free solution to localize multiple jamming attackers in VANET. Vehicles only need to record their positions and ifjammed signal in every fixed time interval. This method does not rely on cryptography and additional hardware infrastructures as the basis. We formulate the problem of localizing multiple jamming attackers as a process of determining cluster number, classifying data, and searching circle center on the data set which consists of the information collected by vehicles. First, we provide two new definitions of Beaconing circle and Coverage rate, and we give a theoretical analysis of using them for determining the number of attackers; Then, we provide a weighted distance to improve FCM algorithm named weight-based FCM and use it for clustering the collected data set.
Furthermore, we use PSO algorithm to localize the jammer source in each classified cluster, and a weight-based PSO algorithm is also developed to estimate the jammer location in the partly distributed data set. Finally, we provide a useful general-purpose localization framework to apply our approach in practical applications. To validate our approach, we conducted many simulation experiments in MATLAB associated with the data set generated by the SUMO simulator. The experimental results provide strong evidence of the effectiveness of our proposed approach. We discover that the Coverage rate can determine the number of adversaries correctly, and weight-based FCM always performs an accurate classification which is better than the other clustering algorithms. We also observe that PSO can always achieve a higher precision than the other existing range-free algorithms, while weightbased PSO can still make a more effective estimation when the distribution of data set is partial.
Simulation-based experiments are one of the alternative testing approaches that can cover multiple domains  [6]. However, the development of smart grid solutions and technologies has increased the need for a more integrated simulation approach covering all targeted areas  [7] ,  [8]. A general framework for smart grid validation and roll-out is necessary. One of the main barriers to this has been the lack of design approaches and corresponding software tools that are capable of simulating power and ICT systems holistically  [5]. The lack of such system validation for smart grids is especially addressed by the European ERIGrid project  [9]. By providing a Pan-European research infrastructure ERIGrid supports the technology development as well as the roll-out of smart grid solutions and concepts.
It tackles a holistic, CPESbased approach by integrating European research centres and institutions with outstanding research infrastructures and jointly develops common methods, concepts, and procedures. The aim of this paper is to discuss advanced modeling approaches, a formal specification of corresponding validation scenarios, and co-simulation based testing methods for CPES that are being developed in ERIGrid. The rest of this paper is organized as follows: Section II gives an overview of the proposed methodology for a holistic test description, while Section III discusses the co-simulation of CPES. Necessary software and component interfaces which are being used in the ERIGrid co-simulation environment are introduced in Section IV. A proof-of-concept example of both the test specification and the co-simulation based experiments is described in Section V.
B. System Configurations (SC) The specification of system configurations is central to the test description, similar to other related specification work (e. g. , smart grid use cases with reference to the Smart Grid Architecture Model (SGAM), information modeling for power system ICT via the Common Information Model (CIM), or other applications of the Unified Modeling Language (UML) or the Systems Modeling Language (SysML)). To integrate the SC description methods used in smart grid disciplines (e. g. , electrical, ICT, or thermal systems), ERIGrid adopted and generalized the basic system description concepts that are employed in the power systems CI). To avoid overly complex specification details, the following upper ontology model has been identified: Systems are composed of Components and are themselves components.
III. CO-SIMULATION OF CPES USING MOSAIK AND FMI The software co-simulation in ERIGrid is realized by employing the framework mosaik as a co-simulation master and interfacing all simulators via the Functional Mock-up Interface (FMI) standard. In the following, the components of this cosimulation platform are briefly introduced. Subsequently, the coupling between mosaik and FMI is elucidated, as well as the utilization of the platform in the project. A. Framework and Interfaces 1) Mosaik: Mosaik is a co-simulation framework that is designed for easy integration of simulators and flexible creation of co-simulation scenarios. These design goals are achieved via two Application Programming Interfaces (API). The Component-API allows integration of simulators via description of the accessible data and a set of interface functions.
The API has been ported to different programming languages to provide a broad user support. The Scenario-API provides a set of functions to establish data flow connections between simulators and execute the co-simulation. The execution is managed by a discretely timed scheduling algorithm. Two versions of mosaik have been developed so far. Mosaik 1  [16]  incorporates a domain specific language for the two API types with the goal of automatic consistency checking of cosimulation scenarios. It ultimately proved to be too inflexible for practical work. Mosaik 2 (e. g. [17] ,  [18] ) is an enhanced, streamlined version of the software that is purely based on Python and provides more concise and flexible APIs. 2) Functional Mock-up Interface: FMI  [19]  is a standard for the interfacing of simulators and simulation models that is supported by various modeling and co-simulation tools.
Separate interfaces are established for FMI-ME and FMI-CS. Each of them is kept as generic as possible so that integration of several FMUs of the same type requires only an adjustment of parameter sets. As shown in  [21] , a mapping has to be established between the mosaik API functions and the FMI++ functions. ERIGrid employs a mapping similar to the one presented in that work, although using updated function references. Similarly, the XML-based description of model variables has to be mapped onto the mosaik model description. FMI includes a complex variable description with data fields like "variability" and "causality", many of which do not possess a counterpart in mosaik. Parameter-type variables are also parameters in mosaik. Variables with the causality "input" or "output" are summarized as "attributes".
C. Application in ERIGrid Mosaik fullfills two roles in ERIGrid. On the one hand, it acts as an environment for exemplary co-simulation test cases, serving its usual purpose. This setup utilizes the coupling between mosaik and FMI-CS to allow integration of complex tools like POWERFACTORY. On the other hand, mosaik is also used as a testbed for a library of newly developed smart grid component models. These models are supplied following the FMI-ME standard to enable versatile future use. The developed models should be capable of being integrated into a variety of different simulation environments like MATLAB or OPENMODELICA. Therefore, FMI-ME is the preferred standard since it provides more freedom in the model usage. Due to the interface presented above, mosaik is still able to integrate these FMUs and serve as an environment for integration testing.
For testing, an FMU is integrated and presented with pre-defined input data from a generic data source component. The output may be stored in a database component and analyzed for unexpected behavior. As a matter of fact, mosaik's flexible Scenario-API allows a convenient way to automatically test sensitivities towards different parameter values, temporal resolutions, integrator types, and so forth. Mosaik's capabilities for such a type of black box analysis have already been demonstrated in  [18].
IV. SOFTWARE AND COMPONENT INTERFACES APPLIED IN ERIGRID Challenges in transferring R&D results into real-world applications often arise already in the design phase, due to incompatible simulation-based approaches adopted by researchers and industry that prevent a consolidated solution for validation. Hence, one goal of the ERIGrid project is the demonstration of the feasibility of FMI-based co-simulation and model exchange for validating Smart Grid applications, in order to promote tool and model interoperability and stress the importance of this topic in the FMI community. To this end, the proof-of-concept studies performed in ERIGrid need to be representative and demonstrate the added value of such an approach. In the following, the rationale behind the choice of tools and models in this context is explained.
For ERIGrid, MATLAB/SIMULINK 4  will be primarily used to provide the needed controller functionality for simulations. Furthermore, a dedicated library of models will be developed using OPENMODELICA and MATLAB/SIMULINK, which will complement and expand the functionality provided by the domain-specific tools. The selection of the tools and models was driven by several factors, such as previous experience of partners, availability of FMI-compliant interfaces or details regarding licenses.
Even though POWERFACTORY does not offer the functionality to export models in a way that is compliant with FMI-ME, it provides an API that allows to interact with it  [22]. The functionality of this API has been successfully mapped to the that specified for FMI-CS  5. Communication network modeling: Simulators for this domain use abstractions of the deployed hardware and software that allows the representation of communication processes as a sequential processing and transmission of (virtual) messages and signals. Hence, communication network simulators commonly implement discrete event-based simulation paradigms, where each event marks a significant step of message processing or transmission. The network simulator NS-3 has already been successfully used for co-simulation, see for instance  [23] , but no FMI-compliant interface is available so far.
Based on previous theoretical work dealing with the capability of FMI to handle discrete event semantics  [24] ,  [25] , an interface according to the FMI-CS specification is being developed in ERIGrid for NS-3. Automation and control: For implementing control algorithms MATLAB/SIMULINK has been chosen, due to its versatility as well as its popularity and widespread use for this purpose. The capability of FMI to encapsulate the functionality of discrete event-driven controller models has been already discussed in  [26]. SIMULINK implements a continuous timedriven paradigm and allows to translate models into FMUs for ME (discussed next in Section IV-C). MATLAB on the other hand is a multi-purpose computational environment, which adheres to no specific modeling paradigm and provides by itself no notion of time.
All the models will be developed either in MATLAB/SIMULINK or OPENMODEL-ICA, which allow to export models as FMUs for ME. Power system components: A number of models specific to power systems are currencly under development for FMI-ME, including a reduced-order distribution dynamic equivalent model, FlexHouse thermal model, PV and battery models, and aggregated wind turbine dynamic model. This set of tool-independent models can potentially be used for different simulation studies and proof of concepts. For example, the reduced-order dynamic equivalent model represents an actual 115 kVA smart grid laboratory at the University of Strathclyde. Although simulation tools such as POWERFACTORY provide many component models, this reduced-order equivalent model will allow for the dynamics of a real smart grid laboratory to be incorporated in simulation studies.
This enables large-scale system studies to be undertaken with the least amount of computational requirements. Communication network modeling: The difficulty of incorporating the effects of communication within a power system simulation tool (due to its discrete event-based nature) often leads to simplistic fixed-time delay models being utilized. However, within ERIGrid, a representative communications model that can be easily utilized within a power system simulation is under development. It will rely on various communication network parameters as inputs and will calculate 6 See http://matlab-fmu. sourceforge. net delays based on stochastic equations taking into consideration a set of defined uncertainties  [27]. By employing the IEC 61850 GOOSE protocol, the model will delay a signal based on a value chosen by means of a Gaussian distribution.
A. Modelling and Simulation needs Generally speaking, software experiments can be subdivided into monolithic software experiments (one domain, one tool), multi-domain tools (multiple domains, one tool), hybrid models (multiple tools, one domain), and heterogeneous modeling (i. e. , multiple domains and tools). The latter is predominantly realized with co-simulation and will be the main focus of the proof-of-concept of the above proposed holistic test case formalization  [8] ,  [10]. The application of the mosaik framework and the FMI opens up a massive spectrum of co-simulation possibilities, but gives also rise to development and implementation challenges. It has been decided to define three exemplary test cases that at least contain one co-simulation experiment, each of which treats one specific development need: 1) interfacing software tools with physical controllers 2) signal-based synchronization 3) cyclic dependencies between simulation federates Software/hardware interfacing will be studied using an onload tap changer and its control.
Storing and Querying the Data Persistently storing data and executing declarative queries over it are among the most important aspects of managing data. Systems that provide such functionality for RDFbased semantic data either use an existing database management system (DBMS), for instance based on the relational model, e. g. PostgreSQL, or are designed from scratch, usually as a graph store. For data sets that can be handled on a single machine, a centralized architecture is usually preferred (e. g. , RDF-3X  [25] , Hexastore  [35] , SW-Store  [1] ), but a distributed architecture can be adopted as well (e. g. ,  [15] , TriAD  [12] ). Such systems are usually called triple stores, and the standard declarative query language for RDF that they support is SPARQL  [13]. In the case of a relational database management system storage back-end these queries are automatically translated into SQL queries.
Automated Reasoning A distinguishing feature of semantic data is its accompanying meta-data that describes the meaning of the data. This meta-data enables automated reasoning processes to derive data that is given implicitly by a semantic data set and its meaning, but that has not been expressed directly. In order to obtain a complete answer set to a given query, the reasoning processing can be performed either at the data loading or query run-times. In the former, all logical consequences are materialized in the data set. This impacts negatively the loading time and the size of the persisted database but ensures fast query processing. In the latter, all the reasoning machinery is performed at query run-time to produce a rewriting of the original query. Compared to the materialization approach, the query rewriting solution is thus characterized by a slower query processing by a faster data set loading time and a smaller persisted database.
PRESENTERS Olaf Hartig is an Assistant Professor at the Department of Computer and Information Science (IDA) of Linköping University. Olaf holds a Ph. D. in Computer Science from the Humboldt-Universität zu Berlin, Germany. His research interests are related to various areas of data management with a particular focus on Web data, graph data, and semantic data management. He has published 1 book  [14] , 2 book chapters, 5 journal articles, and 15 research papers in top international conferences in the fields of the Semantic Web and Databases. Moreover, Olaf presented 7 tutorials at such conferences including WWW 2010, WWW 2013, and ICDE 2014; and he was lecturer at the 2011 Indian-Summer School on Linked Data. Olivier Curé is a tenured associate professor in Computer Science at the University of Paris-Est Marne la Vallée (UPEM) in France.
ACKNOWLEDGMENTS We would like to thank a number of people with whom we discussed various aspects of the topics covered in the tutorial. These discussions have been tremendously helpful for designing the tutorial. Our thanks go to:  Badre Belabbess ,  Eva Blomqvist ,  Jeremy Lhez ,  Robin Keskisärkkä ,  Valentina Ivanova , and  Xiangnan Ren. Olaf Hartig 's work on this tutorial has been funded partially by the  CENIIT program  at  Linköping University  (project no. 17. 04 ). Olivier Curé's work on this tutorial has been funded partially by the  FUI (Fonds Unique Interministériel ) 17  Waves  project.
2  PDT is based on the activation of a photosensitizer (PS) by light at an appropriate wavelength to generate reactive oxygen species, especially singlet oxygen. 3 Besides, the radius of interaction for singlet oxygen is inferior to 0. 2 µm and the half life is short, from 0. 03 µs to 0. 18 ms, according to the medium, inducing cell damages in close proximity of the activated PS. 4  Owing to the localization of the optical fiber into the tumor tissue, an interstitial PDT (iPDT) can be suggested. 5 7] [8]  For Phase II clinical studies, we can mention the one of the Muragaki group on patients with primary malignant brain tumors including 13 GBM cases  9  or the one of the Whelan group (NCT01966809) for recurrent high-grade gliomas using intraoperative PDT to eradicate cancer cells after resection as local recurrences in the surgical bed after tumor resection may be due to residual tumor cells.
First, they display a hydrodynamic diameter ,10 nm, even after functionalization, which is small enough to provide exclusive renal excretion and well adapted for further clinical development. They can also accumulate in the tumor by the EPR effect. So far, no toxicity has been noticed during regulatory toxicity tests on rodents and monkeys, and AGuIX has been recently accepted for a Phase I clinical assay (EudraCT no: 2015-004259-30). The presence of gadolinium enables the NPs to be used as a positive MRI contrast agent; it will therefore be possible to detect the tumor tissue, to determine an accurate position for the PDT fiber and to follow up by noninvasive imaging photodynamic efficiency. In this study, it was decided not to graft separately the PS and the targeting peptide on the particle but to graft directly the compound PS@KDKPPR.
Flash floods are natural phenomena with environmental, social and economic impacts. To date, few numerical models are able to simulate hydrological processes at catchment scale at a reasonable time scale to describe flash events with accurate details. Considering a ~810 km 2 Mediterranean river coastal basin (southwestern France) as a study case, the objective of the present study was to assess the ability of the sub-daily module of the lumped Soil and Water Assessment Tool (SWAT) to simulate discharge (1) time-continuously, by testing two sub-basin delineation schemes, two catchment sizes, and two output time-steps; and (2) at flood time-scale, by comparing the performances of SWAT to the performances of the event-based fully distributed MARINE model when simulating flash flood events. We showed that there was no benefit of decreasing the size of the minimum drainage area (e.
Considering the Têt Mediterranean river basin (810 km 2 , southwestern France) as a study case, the objective of the present study was to assess the ability of the sub-daily module of the SWAT model to simulate discharge (1) time-continuously, by testing two sub-basin delineation schemes (1 and 15 km 2 minimum drainage areas), two catchment sizes (90 and 810 km 2 ), and two output time-steps (daily and hourly); and (2) at flood time-scale, by comparing the performance of SWAT to the performance of the process-oriented, event-based, fully-distributed MARINE model when simulating flash flood events.
For the time-continuous SWAT model, additional daily temperature, humidity, wind, and radiation data were gathered from the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) meteorological model, which data are organized in an 8 km × 8 km grid  [53] [54] [55]. At the time of the study, SAFRAN outputs were available from January 2005 to July 2014, hence the last complete year for the SWAT run was 2013. Also, in the specific case of SWAT modelling, we used a typical SWAT watershed delineation scheme (15 km 2 minimum drainage area, Figure  S1 ) and a very detailed watershed delineation (1 km 2 minimum drainage area, Figure  S1 ) in order to assess the effect of the sub-basins' delineation on the quality of hourly time-step simulation results and to determine the effect of the SWAT delineation scheme on the comparison with MARINE results: on one hand, a finer delineation may capture the meteorological information from more climate stations and meteorological information may be better spatialized, since sub-basins are smaller (see Appendix A for the rainfall stations-sub-basins association); on the other hand, a finer delineation may more correctly represent the transfer dynamics at the sub-daily time-step and may approach the 500 × 500 m resolution of the MARINE calculation grid.
Sensitivity Analysis and Model Calibration and Validation Calibration and validation of the two models were performed by comparing the simulated discharge to the measured discharge. For the SWAT and SWAT-CUP SUFI-2 modelling tools, we resampled the aperiodic records to hourly data to fit the precipitation input time-step (see also Section 2. 3. 3). For both SWAT time-continuous calibration and event-scale MARINE calibration, we used the Nash-Sutcliffe model efficiency (NS) as an objective function. 2. 4. 1. Procedure for the SWAT Model SUFI-2  [56] [57] [58]  was used for calibration, validation, sensitivity, and uncertainty analysis of the SWAT model. SUFI-2 is an iterative algorithm. It maps all model uncertainties on the parameters, which are described by a multivariate uniform distribution in a parameter hypercube.