dataesr/scientific-paragraphs-categorization huggingface dataset:
5. 0 open-source code (in fact, the CGR algorithm implemented in ION 3. 5. 0 includes a few parameters and procedures related to opportunistic CGR (O-CGR)  [48] ; O-CGR is an experimental CGR extension that also considers discovered contacts in addition to those in the contact plan; since in this work all contacts are scheduled, the probabilistic calculations are not discussed nor described in this section), there is no detailed and formal description of the algorithm available yet (the CCSDS documentation is still under development  [47] ). As a result, in this section, an in-depth explanation of the CGR scheme is provided. For the sake of clarity, the discussed algorithms are structured following their implementation in ION; however, they can be translated to more compact and elegant expressions without continue, break, and return statements.
Simulation Platform. There exist several tools to evaluate DTNs. Among them, the ONE simulator  [64]  has been extensively used for DTN studies; however, the platform is specifically designed to model opportunistic DTNs (social networks, vehicular networks, etc. ). On the other hand, emulation environments such as CORE  [65]  are available to directly test existing DTN implementations; but CORE has to be executed in real time, which hinders its application in extensive analysis. To tackle these limitations, a new simulator called DtnSim (DtnSim is still under development; however, the code will be made publicly available under an opensource license at https://bitbucket. org/lcd-unc-ar/dtnsim) was implemented in Omnet++  [66] , a discrete event network simulator platform. Using an event driven framework allows DtnSim to efficiently simulate scenarios at accelerated speeds.
As a result, in DtnSim, the APP layer allows generating either a large amount of punctual traffic or periodic amounts of data. The NET layer is the element in charge of providing delaytolerant multihop transmission (i. e. , routes) and is probably the most mature module of DtnSim at the moment. In this layer, each DTN node includes a local storage unit (nonvolatile memory model) in order to store in transit bundles. Also, the CGR routing algorithm described in Section 2. 1 was implemented as an exchangeable submodule of the network layer; thus, other algorithms can easily be supplied via a clearly defined interface. Independently from the routing submodule, the NET layer can take a contact plan as an input which defines the forthcoming contact opportunities among the nodes. The contact plan format is based on the format used in the ION software stack: a text file comprised of a list of contacts in the following form: contact <start> <end> <source> <destination> <rate> The MAC layer of the DtnSim node is designed to provide a reliable wireless link and to multiplex the shared medium among nodes with wireless interfaces.
Although each DtnSim node is based on a single APP and NET layer, several MAC modules can be attached to the NET module in order to mimic various transceivers on a single node. Thus, this layer sets the real bundle transmission rate which will depend on the transmitter/receiver module bit-rate as well as any possible medium arbitration mechanism (e. g. , contention). An example analysis based on this layer has been presented in  [10] , where dynamic and static channel negotiations are compared. For the sake of simplicity, the MAC layer used for the present analysis transparently sends and receives bundles without further intervention. Thus, the same module is used to communicate nodes connected through Internet on ground. Finally, DtnSim was extended with a fault-injector module based on the exponential model described in Section 2.
Simulation Scenarios. In order to assess CGR behavior under transient failures in realistic delay-tolerant satellite constellations, two appealing and realistic configurations are proposed and discussed below: a sun-synchronous alongtrack and a Walker-delta formation. Both constellations are based on 16 cross-linked LEO satellites (max. link range of 1000 Km at 500 km height), 25 ground target points, and 6 ground stations. The specific orbital parameters of the satellites are summarized in Table  3  while the ground stations and target locations are in Table  4  (the resulting contact plans used for the simulations as well as the STK  [67]  scenario of the Walker formation can be found in https://upcn. eu/icc2017. html). Systems Tool Kit (STK)  [67]  software was used to propagate these parameters for an analysis period of 24 hs.
This metric can thus be directly correlated with energy usage in the node transmitter. In this case, the along-track not only uses lower hop counts but also evidences a higher energy usage due to bundle transmissions. The last two plots on the right provide a metric on the storage used by the nodes. The upper curve depicts the overall spacecraft data recorder (SDR) memory used (outbound queue buffers occupancy) while the lower one shows the quantity of bundles that remains in the limbo at the end of the simulations because of the absence of feasible routes. In general, a significant number of bundles remains in the limbo for the along-track formation. Also, the Walker constellation evidences a lower memory utilization. This is coherent with previous analysis and confirms the inability of CGR of finding all feasible routes in the along-track topology.
Discussion In order to describe the CGR inability to find all feasible routes in the along-track constellation, a simplified alongtrack topology and its corresponding contact graph are presented in Figures  8(a ) and 8(b), respectively. In contrast to the Walker formation, the along-track formation exhibits a remarkable link redundancy since a single point on Earth is able to simultaneously reach several orbiting nodes while each satellite has simultaneous access to the front and back neighbor. In this example, a ground station (GS A) can reach a satellite (SAT B) which in turn can reach the front and back satellites in the constellation (SAT C 1 and SAT C 2 ). Eventually, the three satellites will be able to reach a ground target (GT D) when the constellation passes over that ground area (notice that contacts 4, 5, and 6 might occur later in time requiring a temporal storage of data in the satellites).
This paper presents an overview of NARMER-1, the new generation of photon point-kernel code developed by the Reactor Studies and Applied Mathematics Unit (SERMA) at CEA Saclay Center. After a short introduction giving some history points and the current context of development of the code, the paper exposes the principles implemented in the calculation, the physical quantities computed and surveys the generic features: programming language, computer platforms, geometry package, sources description, etc. Moreover, specific and recent features are also detailed: exclusion sphere, tetrahedral meshes, parallel operations. Then some points about verification and validation are presented. Finally we present some tools that can help the user for operations like visualization and pre-treatment.
Introduction Starting from the 60s, CEA launched the development of MERCURE, a gamma-ray shielding code using the pointkernel integration formalism. MERCURE-3 was published in 1967  [1] , and MERCURE-4 in 1974  [2]. Later in the 80s, EDF and COGEMA (AREVA) became sponsors of MERCURE for its development and validation. MERCURE-5v3 released in 1995  [3]  followed by MERCURE-6  [4]. MERCURE uses a Monte Carlo method for the point-kernel integration and build-up factors to take scattered photons into account. MERCURE-6 has enabled the build-up factor calculation for composed shields by using new algorithms developed by SERMA (Reactor Studies and Applied Mathematics Unit)  [5]. Then, the development of NARMER-1 has been launched to integrate both gamma-ray attenuation and reflection features (implemented by MERCURE-6 and NARCISSE-3  [6]  respectively).
For this purpose, the code has been re-written in C++ allowing a modern programming based on object oriented design, with a great care to some extra-functional properties such as modularity, extensibility, maintainability and testability. Ongoing works are related to the improvement of reflection calculations, parallel computing, interoperability with CAD geometries, verification and validation, and integrability in the EDF, CEA software platforms. The development is also based on a components sharing strategy with other SERMA codes for example for the base libraries (geometry, composition, source term, etc. ) NARMER-1 is part of a set of software developed by SERMA in the broad field of reactor studies. It is closely related to TRIPOLI-4 ®  [7]  as it uses the same geometry package allowing easier comparison to this reference Monte Carlo code for validation purpose.
Cross sections come from JEF2. 2 evaluation files  [10]. The build-up factors are available for each single chemical element from hydrogen (Z=1) to californium (Z=98), and are tabulated in 22 thicknesses (from 0. 5 to 50 mean free path) and 195 energy groups (from 15keV to10MeV). Energy groups are identical to energy groups of total microscopic cross sections. For a homogeneous material composed of many chemical elements, the build-up factors are computed on the fly by the code which determines an equivalent atomic number of the mixture (ܼ ). For a given energy group g, the total macroscopic cross-section per electron of the mixture is calculated in equation (2) while equation (3) gives the total cross section per electron. ߪ = ∑ ܰ ߪ ∑ ܰ (2) ߪ = ߪ ܼ (3) The equivalent atomic number of the mixture (ܼ , ) is obtained with respects to the corresponding computed cross-section (ߪ ).
Source integration For a source S distributed over energy and space, the response ܴ ఈ described by a response function ݂ ఈ ‫)ܧ(‬ to be calculated follows the equation  (11). ܴ ఈ ‫ݎ(‬ ⃗) = න ‫ܧ݀‬ ௦ ா ೌೣ ா න ‫ݎ݀‬ ௦ ሬሬ⃗. ೞ ‫ܧ(ܵ‬ ௦ , ‫ݎ‬ ௦ ሬሬ⃗) ݂ ఈ ‫)ܧ(‬ ‫ܭ‬ ‫ݎ([‬ ௦ ሬሬ⃗, ‫ܧ‬ ௦ ) → ‫ݎ‬ ⃗)] ‫ܤ‬ ఈ ‫ݎ([‬ ௦ ሬሬ⃗, ‫ܧ‬ ௦ ) → ‫ݎ‬ ⃗)] (11) NARMER-1 uses a Monte Carlo method to integrate points-kernel with build-up factor over the source domain. Considering the spatial and energy meshes defining the source, the code performs a stratified sampling as variance reduction technique. New sampling points are added in each cell proportionally to the variance of the cell, until the variance on the global result reaches the asked precision.
Computed physical quantities The following response functions are available in NARMER-1 calculations: uncollided flux, ambient dose equivalent rate H*(10), energy fluence, and air KERMA. Continuous energy and multi-group cross sections come from JEF2. 2. Conversion coefficients from fluence to dose equivalent rate and air KERMA come from ICRP74  [11]. 3 Generic features It is currently developed on Linux system but is maintained and available on both Linux and Windows.
Geometry package NARMER-1 comes with the TRIPOLI-4 ® native geometry package, allowing for both a pure surface-based representation, and a combinatorial representation with predefined shapes and boolean operators (any combination of these two kinds of representations can be adopted). As well as TRIPOLI-4 ® , NARMER-1 has been also made compatible with any geometry developed in the format of the ROOT software  (Brun and Rademakers, 1997). This allows the user to directly use (without recompiling) an already built ROOT geometry. More generally, NARMER-1 may be linked to any thirdparty geometry module providing an Application Programming Interface for the current particle position and the next intersection in the direction of flight. Details concerning visualization tools for building and checking the geometry will be provided in Section 4.
Tetrahedral meshes For specific uses, geometry and emitting volumes are modelled using computer-aided design software allowing easier definition of very complex 3D scenes. In this context the code has the possibility to define tetrahedral meshes for the definition of emitting volumes. Observed performances of the sampling method allow computation in good conditions. Fig. 2. A detector point at z=0 in front two emitting meshes, a tetrahedral mesh in the region z>0 and a cartesian mesh in the region z<0 As an illustration also allowing some considerations about verification and validation, the simple test case Fig. 2  shows a detector point belonging to a median plan defined by z=0 separating two identical emitting volumes having the form of a parallelepiped. The emitting volume in the region z>0 is described by a tetrahedral mesh and the one in the region z<0 is described by a cartesian mesh.
Verification and Validation Since the NARMER-1 code is attended to be used for radiation protection purposes, verification and validation are crucial issues. Verification is supported in particular by non-regression testing. Excellent results are observed about non-regression results with respect to MERCURE-6. Validation is based on comparison with calculations performed with the TRIPOLI-4 ® Monte Carlo code as well as with experimental benchmarks. Here are presented four cases illustrating the validation aspect: two basic tests and two realistic cases in which the ambient dose equivalent rate H*(  10 ) is calculated with NARMER-1 and confronted with TRIPOLI-4 ® results. The definition of the geometry is common for both types of calculation. The relative difference of H*(10) of NARMER-1 from TRIPOLI-4 ® is defined by equation (  12  The ambient dose equivalent rate H*(  10 ) is calculated at six points located at 7.
Visualization and pre-treatment Sharing the same geometry package as TRIPOLI-4 ® , NARMER-1 can be used with the pre-and-post treatment tool distributed with TRIPOLI-4 ® , known as "Visualizer T4G". The user also has possibility to check the definition of the geometry by using the last verification tools implemented in TRIPOLI-4 ® , including "Check Geom" and "Check Tracking" functionalities.
Summary and conclusion NARMER-1 is a simplified code that allows to perform fast calculations to evaluate ambient dose equivalent H*(10) and KERMA for radiation protection studies. A validation study has been done with comparison to TRIPOLI-4 ® considered as a reference code. Basic configurations and sophisticated configurations show overall a good agreement between the two codes. In some cases, we observe an overestimation of NARMER-1 with respect to TRIPOLI-4 ®. The results are conservative for radiation protection studies. NARMER-1 calculations are much faster than TRIPOLI-4 ® ones: NARMER-1 calculations last less than one minute and can reach several minutes for complicated configurations. These calculations times remain orders of magnitude below those met for TRIPOLI-4 ®.
Then, the information of the individual power loss corresponding to the wind farm capacity (increased in 1 MW steps) are stored in PLosses, n, and in, respectively. The capacity and location of wind turbines will be selected by comparing the minimum value of these losses in stored memory. Finally, the accumulated data of the minimum power loss, location, and size of the wind turbines are obtained. It is observed that each state converges sufficiently for different systems and it can predict the optimal size and location of wind farms for minimum losses. The time reduction and memory consumption required by the proposed linear model will help control centers of utilities by The figure shows that for each location (bus), curves will have similar behavior, which can be divided into two parts.
), the wireless networks communication in this band has more complexities than other bands. 2, 3 n the contrary, some authorized spectrum bands are only used in limited geographical areas or periods of time, which lead a lot of spectrum resources wasted. In order to reutilize these vacant spectrum resources, cognitive radio (CR), as a promising technology, has emerged to allow secondary users (SUs) unlicensed networks to access underutilized spectrum bands which is authorized to the primary users (PUs). 4, 5 So that, CR technology has many benefits on wireless networks' data transmission under complex environment by improving spectrum utilization, which gives out numerous novel ideas (e. g. 7] [8]  Due to the ongoing research of CR networks, new CR network standards are required to propose on TV White Spaces (TVWSs) for experiments.
15. 4 (ZigBee),  13  all of which share the same 2. 4 GHz ISM band. Due to their different physical (PHY) layers, each two networks of them cannot decode the packets of each other directly. Heterogeneity in medium access mechanisms: Coexisting networks may use different medium access mechanisms, for example, WiFi and some ZigBee networks are based on Carrier Sense Multiple Access (CSMA) mechanism, while the other types of ZigBee networks use Time Division Multiple Access (TDMA)-based medium access pattern. They are by nature conflicting with each other. Heterogeneity in network protocols: Coexisting networks may use different network protocols with different packet formats. As a result, they cannot communicate with each other directly as the packets are not interpretable by others. Heterogeneity in power level: Coexisting networks may transmit their data packets at different power levels, for example, the maximum transmission power of a WiFi node is 20 dBm compared with that of a ZigBee node which is only 0 dBm.
Motivated by this argument, the Multi-Headers (MH) mechanism is proposed, which puts several headers into the beginning of data part in ZigBee frame such that the remaining headers can pass CRC check when some headers are collided by WiFi signals. In the asymmetric scenario, due to the even distribution of bit errors across the whole packet, the MH mechanism cannot work any more. The forward error correction (FEC) approach is introduced to recover the ZigBee packets using Reed-Solomon (RS) code, a block-based linear error-correction code widely used in digital communication. The authors developed a full-featured TinyOS-compatible RS library (TinyRS) to decode the ZigBee packets. In conclusion, the advantages of BuzzBuzz include the low cost, the ease of using and the low consumption. The major disadvantage is the overhead created by the MH and the FEC components in terms of packet size and extra processing overhead.
When hearing SCH, cells can decode the channel allocation information. Only the cell to whom the channel is allocated transmits data on the allocated channel while others stay silent. ECMA 392. ECMA 392 has another homogeneous network coexistence mechanism. In ECMA 392, due to the deployment of beacon period (BP) mechanism in frame construction, frames of various devices in same network can be allocated appropriately. Each BP contains several different signal windows, which is designed for the contention and reservation, so different devices can emit their beacons in BP to compete the frames allocation after BP. Using this mechanism, intra-cell collisions can be eliminated. For inter-cell coexistence, three types of self-coexistence scenario are specified in ECMA 392: Coexistence of two master-slaver networks; Coexistence of two peer-to-peer networks; Coexistence of a master-slaver network and a peer-to-peer network.
Coexistence under presence of PUs As mentioned in the previous section, in TVWSs, a particularity that makes the coexistence of CR networks challenging is the presence of PUs, whose transmission may not necessarily follow a predictable pattern. Hence, coordination among heterogeneous secondary networks under the presence of PUs consists of an importance design. We point out that although the PUs issue is mentioned in a number of CR coexistence standards (e. g. TVWSs database in IEEE 802. 19. 1  19  ), the problem of how to react when PUs arrive has not been specified. For example, in IEEE 802. 19. 1, it is mentioned that a PU list in TVWSs database can provide PU information to CMs to manage spectrum hopping of the coexisting networks, but the problem of coexistence under the presence of PUs is not specified.
Transparent authentication on mobile phones suffers from privacy issues especially when biometric information is involved. In this paper, we propose a solution to address those two issues using the Biohashing algorithm on behavioral information extracted from a mobile phone. The authentication scenario is tested on a dataset composed of 100 users and shows promising results with a 10% EER in the worst case scenario (i. e when protection key is compromised) and a 1% EER in the best case one. In addition, privacy concerns are discussed and experimentally evaluated both in a quantitative and qualitative ways. This opens new perspectives concerning online authentication using smartphone sensing abilities.
Besides, samples can be of any type including geolocation, application usage, web browsing history, emailing and many more, therefore important privacy concerns occur. This is even more true when the authentication task is outsourced. In addition, the cancellability of biometric data is an important security risk in case of theft of a large dataset and is an important brake to the deployment of online transparent authentication services. Our contribution is a new authentication framework that is privacy preserving and cancellable. In addition, this new scheme does not add any burden to the user and can by applied on multiple samples issued from different smartphone sensors. The system is evaluated on a dataset containing data from 100 users collected during one month. The organisation of the paper is as follows.
By merging this modality with other behavioral modalities, authors are able to achieve an EER of 5% after 1 minute of user interaction with the device. Those results are obtained on a private database of 100 users. No privacy protection scheme is envisaged in this paper. The privacy problem was noted in  (Jakobsson et al. , 2009). In this article, the authors advise to (i) remove unique identifier information, (ii) use pseudonyms, (iii) use aggregated data. To the best of our knowledge, there are few proposed solutions in the literature to deal with privacy concerns. The authors in  (Safa et al. , 2014 ) use an homomorphic encryption scheme. In  (Nauman et al. , 2013)  the authors describe a protocol dedicated to keystroke analysis. In  (Chow et al. , 2010) , the authors address the problem of online authentication using implicit information and store the data directly on the mobile phone, thus delegating the autorization server role to the mobile phone.
, x n ) ∈ R n. For more protection, the key K is stored in the secure element of the mobile phone (see section 3. 2. 2). The BioHashing process is divided into two steps: • Random projection: the key K is used as a seed to generate m random vectors r j ∈ R n , j = 1,. , m and m ≤ n. After orthonormalization by the Gram-Schmidt method [37], these vectors are gathered as the column of a matrix O = (O i, j ) i, j∈[1,n]×[1,m]. A projection of the biometric feature vector ( f 1 ,. , f n ) is then computed. • Quantization: This step is devoted to the transformation in a binary-valued vector of the previous real-valued vector using a simple thresholding. More precisely, a binary vector B = (B 1 ,. , B m ) called BioCode is obtained from the previous projected vector by thresholding. The goal of this step is to guarantee the irreversibility of the whole process.