dataesr/scientific-paragraphs-categorization huggingface dataset:
The main advantage is that the resulting computational cost is polynomial. However, the constructed relations are exclusive to the number of tracks and targets and the decision criteria has the inconvenience of being optimistic. Chavez-Garcia and Aycard  [18]  developed a real-time multi-sensor fusion system devoted to road scene perception exploiting the evidence theory. By adding the object classification to the their kinematic features provided by three sensors (LiDAR, radar and camera) and by fusing them with Yager's rule (see Section 3. 4), they showed improved detection results. To achieve good performance, an important a priori knowledge of the object classes characteristics is necessary to define the bbas. In  [19] , Hachour et al. introduced a matching algorithm based on the Generalized Bayes' Theorem and evaluated its performance on synthetic data (simulated tracking scenarios).
The objectives of this article are multiple : determine the appropriate source model for the retained object attributes (cf. Section 4. 2), propose a robust multiple feature fusion strategy and choose the best combination rule (cf. Section 4. 1) and finally evaluate the approach on a large amount of realistic driving scene data (cf. Section 5). The aim of this paper is to present an original strategy to improve and extend a camera-based MOA algorithm  [21]  with the use of LiDAR data. The knowledge on which the association is applied is derived on the one hand from the object's position and size obtained from images of the scene. On the other hand, the object direction of motion are extracted from 3D Lidar point clouds. Both data represent two different similarity measures for the construction of two credal sources.
all tracks/targets. To assess the feasibility of the fusion framework in the application context of autonomous vehicles, the KITTI database  [22]  is used. A quantitative analysis performed on an important amount of data containing objects of different nature is done. To the authors knowledge, such an extensive validation (considering several hundreds of frames and pairings) of a belief function-based object association solution in real conditions is unique. The article is organized as follows: the application context and problem description are given in Section 2. Some fundamental concepts in evidence theory and their application to MOA are presented in Section 3. The modeling of the object features and the multiple feature fusion strategy are then described in Section 4 to be later tested on experimental data in Section 5.
In the context of a complex surrounding environment (urban traffic, pedestrians. ), the perception system should be robust enough to handle several objects to be detected and associated  [23]. In this paper, the input of the MOA algorithm is based on camera images. As already mentioned, from these 2D data, it is possible to determine the position and the size of the objects. In addition, the use of a LiDAR provides 3D data from the point cloud (see Figure  1 ). The LiDAR information can be used to improve the MOA algorithm by including, in addition to the position from the camera, the orientation of the object. Indeed, as one can see on Figure  1  taken from the KITTI benchmark  [22] , the camera-based image can be naturally enriched with LiDAR informa-tion. The perception of objects in cluttered environment, and the temporal association of perceived objects, is here realized through the fusion of measures from these two heterogeneous sensors.
Experimental Results This section evaluates the proposed multi-feature fusion on real data from KITTI  [22]. It largely extends  [33]  in which the authors rated the orientation model 1 from Eq. (  31 ) on a pedestrian detection and tracking sequence from KITTI. Here, tests are conducted with both orientation bbas on multiple scenarios (urban and highway traffic, etc. ). First, the dataset used for validation and the assessment criteria are presented. In a second step, the bbas parameter identification is discussed and the position and orientation bbas validity is shown. Finally, a quantitative evaluation of the fusion framework and a discussion related to conflict management are proposed.
Dataset Description and Evaluation Criteria KITTI provides a set of sequences containing labeled detections of pedestrians, cars, cyclists, vans, and trucks in various road scenes (highway, urban driving, etc. ) as ground truth. Data from several sensors (inertial measurement units, GPS, cameras, LiDAR, etc. ) mounted on a manually driven car are available. The results described here focus on the image and LiDAR data. The detections are defined by object tracklets which contain the dimensions of the 2D bounding boxes, occlusion states on the image frame, etc. For each detection, a 3D bounding box defines the set of LiDAR data that coincides with the object. The orientation angles used here correspond to the motion direction of these 3D detections. Several sequences are used due to their object and road context heterogeneity.
The second contribution relies on the selection and study of the appropriate mass models w. r. t. to the available sensor data. Hence, this paper first provides a recall of the most used mass models and discusses the specialized source model finally retained. The model identification process is also depicted. A credal fusion combines the position and orientation data. For this fusion, several rules are evaluated according to their rate of true associations on three complex sequences provided by the KITTI database. The proposed approach performed better than the individual sources as they provide different characteristics related to an object's comportment. The fusion, therefore, merges the evidence provided by each source to solve ambiguities. The third major contribution is the evaluation of the proposed strategy on an important and influential set of real data in the field of IV.
Commonly, belief functionbased MOA algorithms are validated on limited data sets. In this case, the paper stands out by the extensive objective validation it highlights. The results have also demonstrated the impact of the feature fusion in the association of dynamic objects to ensure a robust perception in the IV context. Several perspectives could be considered. In the current work, a LiDAR provides the object direction of motion and a camera issues the position and size. An interesting research direction to investigate would be to define the object position using the LiDAR data and the direction of motion based on the image frames. Some preliminary tests of this configuration show very similar results compared to those of this paper. This investigation will be pursued for comparison purposes.
Methodology The TSWH is simulated using the TRNSYS (transient system simulation tool) program. The main operating and design parameters were introduced; the design parameters include general specifications of the used thermal collector, storage tank and auxiliary system. Weather data files for the various Moroccan climatic zones are then integrated into the simulation process. A realistic load profile is as well considered for accurate prediction of thermal loads and system performances. Based on the hourly output data, namely water outlet in the solar collector and temperature inside the tank, energetic performance indexes were built. These indices are the monthly solar fractions and collector thermal efficiencies. Based on the evaluated useful energy produced by the TSWH and the total thermal loads required, a large-scale economic study is developed to examine the potential of generalizing these systems into the Moroccan residential sector.
This evaluation is based on a comparison of cumulative energy savings generated by switching from the conventional gas heaters to TSWH. Finally, it was possible to estimate the overall reductions in CO2 emissions. The assessment is believed to be sufficiently accurate as it follows a rational methodology for quantifying the full-scale economic impact of generalizing these solar systems. First of all, it was necessary to evaluate the number of capita per climatic zone, this parameter is not yet available, it is available only in a regional database according to the High Commission of Plans. Therefore, it was essential to convert the available regional data to comply with the climatic zoning. A schematic diagram of the followed methodology is shown in Figure  7. All the elements presented describe the Moroccan market as a fertile ground for the insertion of this technology and popularizing it.
This evaluation is based on a comparison of cumulative energy savings generated by switching from the conventional gas heaters to TSWH. Finally, it was possible to estimate the overall reductions in CO 2 emissions. The assessment is believed to be sufficiently accurate as it follows a rational methodology for quantifying the full-scale economic impact of generalizing these solar systems. First of all, it was necessary to evaluate the number of capita per climatic zone, this parameter is not yet available, it is available only in a regional database according to the High Commission of Plans. Therefore, it was essential to convert the available regional data to comply with the climatic zoning. A schematic diagram of the followed methodology is shown in Figure  7.
Parameter Value A c 2. 72 m 2 F R 0. 791 F R U L 24 kJ•h -1 •m -2 •k -1 G test 96 kg•h -1 •m -2 β 40 • N R 20 D U api 5. 1 kJ•h -1 •k -1 U APo 5. 1 kJ•h -1 •k -1 V 162 L (UA) s 6. 45 kJ•h -1 •k -1 Q aux 3 kW Figure  12  presents a comparison between monthly solar fractions of the TSWH investigated by Michaelides et al. study  [6]  and those predicted by the introduced TRNSYS model. •h -1 •k -1 UAPo 5. 1 Kj•h -1 •k -1 V 162 L (UA)s 6. 45 kJ•h -1 •k -1 Qaux 3 kW Figure  12  presents a comparison between monthly solar fractions of the TSWH investigated by Michaelides et al. study  [6]  and those predicted by the introduced TRNSYS model. The average relative error is about 5%, which is an acceptable value. The deviation between the results can be attributed to the update in climatic conditions caused when using the Meteonorm database that corresponded to the period 1986-1992 for the study of Michaelides et al.
Method: We conducted a systematic review by querying PubMed database for the titles of articles published up to March 2019 with the terms [virtual] AND [addictive] OR [addiction] OR [substance] OR [alcohol] OR [cocaine] OR [cannabis] OR [opioid] OR [tobacco] OR [nicotine] OR [methamphetamine] OR [gaming] OR [gambling]. Results: We screened 319 abstracts and analyzed 37 articles, dividing them into two categories, the first for assessment of cue reactivity (craving, psychophysiological response and attention to cue) and the second for intervention, each drug (nicotine, cocaine, alcohol, cannabis, gambling) being detailed within each category. Conclusions: This overview suggest that VR provide benefits in the assessment and treatment of substance use disorders and behavior addictions and achieve high levels of ecological validity.
Acknowledgements We are grateful to  Dr. Andrea Somogyi  and the scientific and administrative staff of SOLEIL for their helpful support and kind assistance. The FESEM and X-ray microscopy images (Fig. 1) were made at the  Carl Zeiss Microscopy demonstration centre of Oberkochen, Germany :  Dr. Endre Majorovits ,  Holger Blank ,  Matthew Andrew , and  Stéphane Letheux  are warmly thanked for their hospitality and expertise. We would like to thank  Alex Wilson  and  Kelly Russell  for sampling the Mount Meager tephra. We are grateful to  Dr. David Neave  and  Dr. Enik ő Bali  for their constructive reviews and supportive comments. FS thanks  Tim Druitt , scientific coordinator of ClerVolc, for his support over time. This is ClerVolc contribution number  439. Editor: Ambre Luguet.
Two important aspects of interoperability in a laboratory context are the exchange of information between technical devices, and the deployment and/or execution of test-specific third-party software on the infrastructure. In the case of external access to a laboratory, necessary software adaptations caused by deployment constraints or lack of a suitable Application Programming Interface (API) may be a major part of the effort of integrating the laboratory. In extreme cases, software may have to be rewritten to adapt to the target environment. Another potential obstacle is related to the differences in security and confidentiality policies between research infrastructures. Figure  2  describes different interfacing possibilities for integrating an external (third party) element (equipment, controller, etc.
JaNDER Communication Platform for Lab-Coupling This section provides an overview of the communication platform developed in the ERIGrid project which allows to exchange data online. This could be used for coupling different RIs: testing a software or a controller in a remote laboratory, acquiring data from several RIs or even creating a virtual research infrastructure. This platform is called "Joint Test Facility for Smart Energy Networks with Distributed Energy Resources" (JaNDER). JaNDER is a cloud platform for the exchange of information (measurements, control signals, laboratory asset descriptions) between geographically distributed labs by using a secure internet connection. This section describes the three different JaNDER levels developed.
Basic Data Sharing via JaNDER-L0 JaNDER-L0 implements the base functionalities used by all the other layers and is therefore a fundamental building block for the whole architecture. In particular, its main purpose is to allow a basic mechanism for exchanging live data (i. e. typically measurements and controls) between different RIs. The starting point for each RI is a real time repository based on Redis which is open source, in-memory data structure store, used as a database, cache and message broker. This real time repository is used to collect measurements and controls from the field (or more frequently, as shown in Fig. 3 , from an already existing SCADA system): the reason for adopting this repository is decoupling the JaNDER platform from any specific automation solution already installed in the infrastructure.
The idea is to have data points from each RI available in the same basic format by using a simple key-value repository. The remote connection of remote infrastructures is then implemented by deploying a common real time repository (which can be hosted in a cloud environment, for example) which is automatically synchronized with all the local real time databases of the partners. In other words, the common repository acts as a central broker for connecting the different local repositories of the partners and can be thought as a "virtual bus" connecting all authorized facilities. There is no handling of standardized protocols or complex interaction patterns above the exchange of data points through the virtual bus. The fully open source nature of JaNDER-L0 makes it easy to extend the virtual research infrastructure community to new participants.
IEC 61850-Based Communication Platform via JaNDER-L1 JaNDER-L1 is a software abstraction built on top of level 0 and its purpose is to provide an IEC 61850 interface on top of the very simple data structures defined in Redis. The "Mapping" and "CID" files shown as inputs in Fig. 4  are the fundamental inputs needed by the IEC 61850 server in order to work. More in detail, the CID (Configured IED Description) is the standard IEC 61850 file used for configuring a device (an IED) and contains a data model representing (a subset of) the contents of the Redis repository in terms of IEC 61850 Logical Nodes. Apart from this file, it is of course necessary to link the data attributes defined inside it with the live values stored in Redis: this is done by means of a mapping file, which is a text file where each line contains an IEC 61850 data attribute name and a corresponding Redis data point name.
CIM-based Communication Platform via JaNDER-L2 JaNDER-L2 is a software abstraction build on top of Level 0 (and not Level 1, even if this would be technically possible in principle) and its purpose is to enable the definition of a CIM-based service-oriented architecture on top of the basic live data exchange made possible by the lower JaNDER levels. The client application (RI3), as indicated in Fig. 5 , is an open source graphical interface for handling CIM network representations in conformance with the CGMES profile called CIMDraw, augmented with SCADA interfacing code developed specifically for ERIGrid. Apart from this SCADA interface, which is a different representation of the contents of the real time repository, the main interest for this level lies in the possibility of integrating with other CIM-based services like for example power flow calculation engines, state estimators, voltage control algorithms etc.
The integrated research infrastructure created helps to test components in a real system behaviour also in RIs without a HIL capable simulator. The technical possibility of conducting such joint experiments allows the application of control algorithms running in one research infrastructure for the remote control of devices which are physically located in other facilities. The advantage of the VRI concept is the possibility for one RI to access the resources located at a remote site -these resources can range from actual hardware devices to real time simulators or Supervisory Control and Data Acquisition (SCADA) systems. A typical VRI can be seen in Fig. 6. In this particular case one of the RIs (TUD) acts as a network simulator while other two RIs (DTU and VTT) participate in a closed loop experiment with their hardware resources.
Integration of a Remote OLTC Controller via IEC 61850 This test case aims at characterizing a software of a RI (ICCS) located in Greece with power system equipment at a remote RI (OCT) located in Spain. The utilization of IEC 61850 through JaNDER-L1 offers the advantage to implement the test case with a widely accepted communication protocol which is used at actual field implementations. In OCT's laboratory the OLTC controller communicates with the local Redis though Modbus protocol. The local Redis updates the cloud Redis which is synchronized also with the local Redis in ICCS RI. Where an IEC 61850 server maps the local Redis measurements and control signals to IEC 61850 logical nodes. Finally, an IEC 61850 client is used to update the control signals in the IEC 61850 server and acquires the measurements from it.
State Estimator Web Service One of the test cases performed in ERIGrid concerns the state estimation via web of a RI that publishes the measurements with JaNDER. In this case the state estimation is done using the Common Information Model (CIM) through JaNDER-L2. In particular one RI on which the web service state estimator has been demonstrated is Tecnalia's smart grid laboratory. On the RI side, JaNDER-L0 was implemented. The system architecture is shown in Fig. 9. In order to connect the physical devices in the laboratory (Inverter_1, Inverter_2, Load Bank_1, etc. ) to JaNDER-L0, a set of communication protocol gateways have been developed. These gateways are software applications in charge of translating the communications between the device specific protocol (Modbus in this case) and the JaNDER-L0 protocol (based on Redis).
The gateway applications perform basic tasks such as periodically polling devices for measurement and status values and executing commands published in JaNDER, this is accomplished by mapping Redis variables to Modbus registers and vice versa. The local Redis instance is connected to other applications such as Node Red used for data processing and Redis commander The implementation of JaNDER-L2 is based on the development of a CIM model containing the representation of the laboratory network. This file contains the link to the actual measurements as names of CIM analogue objects which are set to be the corresponding keys in JaNDER-L0. This allows an application using the CIM model to ask for needed measurements to JaNDER-L0 and updating their values on demand. A deployment example is shown in Fig.
Geographically Distributed Real-Time Simulation While the previous two examples show the hardware/software integration between two RIs, the last examples aim at demonstrating the "Virtual Research Infrastructure"  Ideal transformer method with synchronous AC coupling Demonstrated application Frequency control within a large transmission system approach. The integration of the RIs can emulate the same setup of a SIL, CHIL or PHIL implementation. An example of implementation where a large power system is split for simulation within two DRTS at two different laboratories was carried on within ERIGrid. The test setup utilized is shown in Fig. 11  and summarized in Table  1. The objective of the study was to establish the feasibility of utilising a geographically distributed real-time simulation setup to analyse power systems dynamic phenomena, such as frequency events and incorporating controls at similar time scales.
Conclusion In this chapter different kinds of RI couplings have been presented with the goal of exploiting the synergies among them and making their resources available for external users avoiding additional investment costs. In order to integrate different RIs, a suitable communication platform is necessary. The tool developed in ERIGrid, JaNDER, is able to establish a real-time communication among several RIs. The very low communication latency allows to implement steady-state analysis of the extended system under test. Moreover, JaNDER is able to use also standard such as IES 61850 or CIM. This allows each type of user (e. g. academia using custom protocol, or industries using standard) to access to remote laboratories in a very simple way. Some examples of test cases performed in ERIGrid using JaNDER concern the software/hardware integration in different RIs, a web service application and a realtime geographically separated PHIL.
000547 seconds. Example 11. In this example, we compare the results obtained with our method to those obtained with the method of El foutayeni, the method given by Yu, and the method of Chen-Harker-Kanzow-Smale (CHKS). For attending this, we adopt our MATLAB program to calculate the optimal solution z, the final values w = Mz + q, the number of iterations, and the time in seconds. Considering the following linear complementarity problem LCPðM, qÞ, where M = ðm ij Þ 1≤i,j≤n such as m ii = 4 for i = j,m i,i+1 = m i+1,i = -1 for all i = 1, ⋯, n, and it equals to 0 otherwise, and q = ðq i Þ 1≤i≤n such as q i = -1: Tables 1-4 present the summaries of the results obtained, where Iter represents the iteration numbers when the algorithm ends and Time indicates the total cost in seconds to resolve the problem.
It then follows that the study of fault slip, whether in simulated granular fault gouge, or in situ, is a rich area of application for machine learning (ML). Solid Earth geoscience and seismology are historically data-rich areas of research  [36, 37] , drawing insights from seismic, electromagetic, magenotelluric, gravity datasets among others. Indeed, some applications of what might be considered 'modern' ML techniques originated from the fields of geostatistics and geophysics. For example, Gaussian process regression was initially developed from an effort to estimate the distribution of gold from samples from boreholes  [38]  in the 1960s. Similarly, least absolute shrinkage and selection operator (LASSO) regression, was initially introduced in the geophysics literature for the linear inversion of band-limited reflection seismograms in 1986  [39] , before being independently discovered and further developed in the statistics community in 1996  [40].