dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, as mentioned by the authors, this result a e-mail: olivier. serot@cea. fr represents an upper limit since the possible re-absorption of unbound neutrons is not taken into account in their calculations. In the present work, our aim is to calculate PFNS by using the FIFRELIN Monte Carlo code which simulates the evaporation of prompt neutrons from the fully accelerated Fission Fragment (FF) and by adding a scission neutron component. We focus on the 252 Cf spontaneous fission, for which PFNS is accurately described by the Mannhart evaluation  [9]. The first section describes our 'Reference calculation' performed with FIFRELIN without scission neutron. Then, in the second part, we explain how scission neutrons are incorporated within the 'three sources' model (following the denomination proposed by Kornilov  [1] ).
Conclusion In the present work, the 'three sources' model proposed by Kornilov has been revisited. The evaporated neutrons are simulated with the FIFRELIN Monte Carlo code and the scission neutrons are represented by a Gaussian (with an average energy of 0. 25 MeV). All components needed for the PFNS calculation are deduced in order to respect the total prompt neutron multiplicity (3. 76) and the average total excitation energy (34. 2 MeV). A very nice agreement with the Mannhart evaluation was obtained by using a low scission neutron component of 2%.
7   The accuracy is the most common metric to both monitor and evaluate an NN. It is defined as the successful classification rate reached over a dataset. The training accuracy, the validation accuracy and the test accuracy are the successful classification rates achieved respectively over the training, the validation and the test sets. At the end of each epoch it is useful to compute and to compare the training accuracy and the validation accuracy. For some trained models we will measure in this paper (see e. g. Table  1 ) the following two additional quantities: the maximal training accuracy, corresponding to the maximum of the training accuracies computed at the end of each epoch the maximal validation accuracy, corresponding to the maximum of the validation accuracies computed at the end of each epoch.
Performances over Artificial Augmented Clock Jitter In this section we present the results that we obtained over two datasets named DS low jitter and DS high jitter. Each one contains 10, 000 labelled traces, used for the training/profiling phase (more precisely, 9, 000 are used for the training, and 1, 000 for the validation), and 100, 000 attack traces. The traces are composed of 1, 860 time samples. The two datasets have been obtained by artificially adding a simulated jitter effect over some synchronized original traces. The original traces were measured on the same Atmega328P microprocessor used in the previous section. We verified that they originally encompass leakage on 34 instructions: 2 nops, 16 loads from the NVM and 16 accesses to look-up tables. For our attack experiments, it is assumed that the target is the first look-up table access, i.
e. the 19th clock cycle. As in the previous section, the target is assumed to take the form Z = HW(Sbox(P ⊕ K)). To simulate the jitter effect each clock pattern has been deformed 15  by adding r new points if r > 0 (resp. removing r points if r < 0), with r ∼ N (0, σ 2 ). 16 For the DS low jitter dataset, we fixed σ 2 = 4 and for the DS high jitter dataset we fixed σ 2 = 36. As an example, some traces of DS low jitter are depicted in the left-hand side of Fig. 6 : the cumulative effect of the jitter is observable by remarking that the desynchronization raises with time. Some traces of DS high jitter are depicted as well in the right-hand side of Fig. 6. For both datasets we did not operate any PoI selection, but entered the entire traces into our CNN. We used the same CNN architecture (9) as in previous section.
We assisted again to a strong overfitting phenomenon and we successfully reduced it by applying the DA strategy introduced in Sec. 3. 2. This time we applied both the shifting deformation SH T with T = 200 and T ∈ {0, 20, 40} and the add-remove  The results we obtained are summarized in Table  3. Case SH 0 AR 0 corresponds to a training performed without DA technique (and hence serves as a reference suffering from the overfitting phenomenon). It can be observed that as the DA parameters raise, the validation accuracy increases while the training accuracy decreases. This experimentally validates that the DA technique is efficient in reducing overfitting. Remarkably in some cases, for example in the DS low jitter dataset case with SH 100 AR 40 , the best validation accuracy is higher than the best training accuracy.
We judge this behaviour positively. Concerning the DA techniques we observe that they are efficient when applied independently and that their combination is still more efficient. According to our results in Table  3 , we selected the model issued using the SH 200 AR 40 technique for the DS low jitter dataset and the one issued using the SH 200 AR 20 technique for the DS higher jitter. In Fig. 7  we compare their performances with those of a Gaussian TA possibly combined with a realignment technique. To tune this comparison, several state-of-the-art Gaussian TA have been tested. In particular, for the selection of the PoIs, two approaches have been applied: first we selected from 3 to 20 points maximising the estimated instantaneous SNR, secondly we selected sliding windows of 3 to 20 consecutive points covering the region of interest.
The training phase has been performed using 98, 000 labelled traces; 1, 000 further traces have been used for the validation set. We performed the training phase over a desktop computer equipped with an Intel Xeon E5440 @2,83GHz processor, 24Gb of RAM and a GeForce GTS 450 GPU. Without data augmentation each epoch took about 200s. 17 The training stopped after 25 epochs. Considering that in this case we applied an early-stopping strategy that stopped training after 20 epochs without validation loss decrement, it means that the final trainable weights are obtained after 5 epochs (in about 15 minutes). The results that we obtained are summarized in Table  4. They prove not only that our CNN is robust to the misalignment caused by the jitter but also that the DA technique is effective in raising its efficiency.
A comparison between the CNN performances and the best results we obtained over the same dataset applying the realignment-TA strategy in the right part of Fig. 9. Beyond the fact that the CNN approach slightly outperforms the realignment-TA one, and considering that both case-results shown here are surely non-optimal, what is remarkable is that the CNN approach is potentially suitable even in cases where realignment methods are impracticable or not satisfying. It is of particular interest in cases where sensitive information does not lie in proximity of peaks or of easily detectable patterns, since many resynchronization techniques are based on pattern or peak detection. If the resynchronization fails, the TA approach falls out of service, while the CNN one remains a further weapon in the hands of an attacker.
The virtualisation of Radio Access Networks (RANs) is one of the goals in designing 5G mobile networks. This paper aims at presenting a proof of concept for the virtualisation of radio resources using Open Air Interface (OAI), a software-based Long-Term Evolution (LTE) eNodeB physical emulator. OAI was extended to support multi-tenancy, representing diverse Virtual mobile Network Operators (VNOs) with different Service Level Agreements (SLAs). A comprehensive analytical model for managing the virtual radio resources has been proposed, with two key parts: estimation of available radio resources and their allocation to different VNOs. The estimation is performed by the model, and the allocation is managed by OAI scheduling. Various scenarios and use cases are studied in this virtual RAN environment, network performance being evaluated for different situations, by varying guaranteed levels, serving weights, and used services.
Introduction Future mobile networks will face an even faster growth of mobile data demand  [1]. Nevertheless, drastic temporal and geographical variations of traffic, in addition to the shortage of network capacity, put a very challenging situation to operators  [2]. The common provisioning approach used in Radio Access Networks (RANs) of considering only busy hours is no longer acceptable. It leads to an inefficient resource usage with high CAPital and OPerational Expenditures (CAPEX and OPEX, respectively) costs. Thus, operators have to find a practical, flexible, and cost-efficient solution for their networks, in order to address this issue, as they have scarce radio resources available. On the other hand, the appearance of Virtual Network Operators (VNOs), which aim to provide specific services without owning an infrastructure can be seen as a definitive trend.
Base Band Unit pools (BBU-pools) data centre: a set of Virtual Machines used for baseband processing of traffic among UEs and network cores. Fronthaul transport network: it carries signals between BBU pools and Remote Radio Heads (RRHs), using Common Public Radio Interfaces, with high data rates over optical fibres. The optical equipment needs to have the lowest delay possible, since the maximum round-trip delay must be below 150 μs (i. e. a maximum of 15 km of BBU-RRH distance)  [34]. The optical switch must enable the scaling or migration of BBU pools in multiple data centres. RRHs: transceivers in charge of exchanging data and control traffic to/from UEs through the air interface, supporting multiple RATs. By comparing C-RANs with current mobile networks, it can be seen that the BS has been divided into RRHs and BBU pools (and the transmission in between them).
Playground for commercial handsets, as well as application, service, and content providers. Rapid prototyping of 3GPP compliant and noncompliant use cases, and new concepts towards 5G, from Machine-to-Machine/Internet of Things (M2M/IoT) and Software-Defined Network (SDN) to Cloud-RAN (C-RAN) and massive Multiple Input Multiple Output (MIMO). OAI is implemented in Linux, using a cellular network emulator  [32] , while VRRM is implemented in a Windows-based server, deployed on the same network domain, as depicted in Fig. 1. The VRRM server issues policies based on information and statistics received from the infrastructure emulator. The servers are connected through an internal network, managed by the cloud provider, the links carrying information and control signals among servers. A bidirectional interface between OAI and VRRM using Transmission Control Protocol (TCP) sockets was defined to: Transfer scenarios and configurations from OAI to VRRM.
Afterwards, the optimisation module is initialised. The optimisation function was developed in MATLAB  [37]  and converted to a managed Dynamic Library Link (DLL); the optimisation is based on the interior-point method, using MATLAB's linprog  [29]. The initialisation of MATLAB-based modules is more time-consuming than other elements; hence, the initialisation is done once at the beginning and used multiple times later on. After having all the pre-requisite modules loaded, the control panel appears.
Reference scenario An urban reference scenario was defined, details being provided through the Open Air Interface Scenario Descriptor (OSD) XML file. Each OSD XML file is composed of five key parts, each of them defines a subset of components: Environment/system, including fading and antenna, as specified in Table  2. The 5 MHz bandwidth corresponds to 25 RBs. From these, 11 RBs are allocated for VRRM, the remaining data rate being reserved for the physical operator. In this sense, performance results are according to the usage of 11 RBs. Topology, including area, UE/eNodeB distribution and mobility, as specified in Table  3 ; a single cell with an eNodeB is located in its centre. Application, including predefined and customised traffic profiles for downlink, a Constant Bit Rate profile being used with 32 byte packets being sent continuously.
Performance of the scenario without VRRM To demonstrate the performance of VRRM, a specific scenario was defined, consisting of a partial capacity of the 5 MHz bandwidth in the 1. 9 GHz band of an LTE eNB, emulated by OAI  [32]. To have an accurate knowledge of the emulator behaviour, the performance of the scenario without VRRM additional features is assessed. In fact, due to the needed computational resources of an LTE BS running on a general purpose platform, some options were taken, which affect the achieved throughputs, but do not affect the study and performance of the implemented VRRM policies that aim at differentiating users. For the used traffic profile, a single UE achieves a maximum download data rate of 2. 28 Mbps using 16QAM. Figure  9  presents the impact of the increase of the number of UEs on the network, showing the number of scheduled UEs per sub-frame, their data rate, and the total monitored network data rate, which are the key metrics to analyse the performance of VRRM.
Conclusions This paper addresses the virtualisation of radio resources as part of RAN virtualisation, a candidate solution for 5G networks, in order to achieve flexibility and efficiency. A virtual RAN operates over cloud data centres (known as C-RAN) as the physical infrastructure. A V-RAN serves multiple VNOs, which do not own access infrastructure and demand for connectivity to serve their subscribers. Virtualisation of radio resources proposes to aggregate all the radio resources from different RATs and provides virtual links for each of the VNOs based on their SLAs and requirements. The architecture for the V-RAN is illustrated and briefly discussed. In addition, a comprehensive analytical model for managing virtual radio resources is proposed, which has two key components: the estimation of available radio resources and their allocation to different VNOs.
The estimation of available resources is made by VRRM. Based on the data rates of different access technologies as a function of SINR, and the distribution of the SINR, the PDF of a single RRU of each RAT is calculated. After estimating the total network capacity, VRRM allocates it to the various services of the network, in order to maximise the usage efficiency, considering the priority of the services of different VNOs, based on their SLAs. Furthermore, the aforementioned concept and model were implemented in OAI, a software-based LTE eNo-deB physical emulator. The implementation guideline was to create multiple groups of subscribers, add support for different policies for each group in the scheduler, and create the communication links to/from the VRRM server. Various changes have been introduced to OAI, including new sets of variables.
Figure2presents the flowchart of the VRRM server. Before the appearance of the Graphical User Interface on screen, there are multiple initialisations to be done. At first, the server makes a connection to the SQL Server [6]. The database is used to speed up the procedure by not recalculating intermediate steps or results. Considering the VRRM procedure in specific, calculating the PDFs of RRUs, RATs, and network is the most time-consuming tasks during evaluations and simulations. By saving the intermediate results during the calculation of probability functions, it is possible to significantly decrease the processing time. Afterwards, the optimisation module is initialised. The optimisation function was developed in MATLAB [37]  and converted to a managed Dynamic Library Link (DLL); the optimisation is based on the interior-point method, using MATLAB's linprog [29].
Literature Search We used two complementary search procedures to identify research documents published since 2000 on climate change policy in relation to rural areas (including peer-reviewed articles, reports, briefs, and books produced by a diversity of writers from universities, think tanks, independent research institutions, and policy units of governments or international organisations). Firstly, we searched scientific journal papers in Web of Science (www. webofknowledge. com) and Scielo (www. scielo. org, a publication database focusing on Latin America) on 15 January 2016 with keywords related to climate change policies, rural development, rural environment (agriculture, forestry, land, ecosystem, or livelihoods), and Latin America (see Section 1 of the Supplementary Materials with details on the search).
The query resulted in more than 200 records. Seven from Scielo and 42 from Web of Science were finally selected, after abstract examination. Secondly, we searched other documents, mainly reports and policy briefs, by browsing major websites related to climate change policies (see the list in Section 2 of the Supplementary Information) and querying Google and Google Scholar with similar keywords as the ones used in the first procedure. We found 92 additional documents, which led to an overall total of 141 documents (see the full list in Section 4 of the Supplementary Information). Selected literature was analysed using the predefined framework. We built an Excel database with the following information for each document: policy research type, policy cycle stage, sector(s), climate objective(s), theoretical references, and one or multiple research question(s).
Results The analysis of research questions in the selected papers led to a typology of 19 main issues related to climate change policies, covering five types of analysis (Figure  2 ). Seven of these issues dealt with the first stage of the policy cycle, agenda setting. Seven dealt with the phases of formulation, adoption, and implementation. The final three issues referred to the evaluation phase. There was a high number of documents focusing on situation analysis (as part of information for policy making, in the stage of agenda setting) and the integration of climate in policy content (as part of the analysis of policy content in the stages of formulation to implementation) (see issues d1 and b2 in Figure  2 ). Selected literature was analysed using the predefined framework. We built an Excel database with the following information for each document: policy research type, policy cycle stage, sector(s), climate objective(s), theoretical references, and one or multiple research question(s).
Acknowledgments: The funding partners that have supported this research include the  International Climate Initiative (IKI)  of the  German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB) , the  Norwegian Agency for Development Cooperation , and the  CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA)  with financial support from the  CGIAR Fund. The authors thank the " Public Policy and rural development in Latin America (PP-AL)" network (managed by Cirad ), which organized a workshop in Brasilia in October 2015, where this study was initiated. They also thank  Gilles Massardier  for his advice on conceptual frameworks.
We have therefore chosen to reinvestigate low frequency measurements by implementing in the CODALEMA experiment low frequency antennas and using the SELFAS simulation code  [8]  to highlight a possible new emission mechanism. Figure  1  shows the simulation with SELFAS code of the electric field strength as a function of time, recorded by an antenna in vertical polarization located 300 m to the shower core (in µV • m -1 ) for a vertical shower initiated by a proton at 0. 1 EeV. On the full-band trace (blue line), the negative peak at 0 ns is due to is due to the shower development in the air. The second, positive peak around 1 µs has been interpreted as the effect of the coherent deceleration of the shower front hitting the ground: the sudden death pulse (SDP)  [9, 10]. The sudden death pulse arrives 1 µs after the normal pulse, which is consistent with the propagation time to antenna (300 m).
Instrumental setup Currently, the EXTASIS project is based on low frequency antennas triggered by scintillators: two low frequency antennas directly triggered via cables, and five antennas for which the trigger is distributed over a network (see figure  2 ), and is supported by the CODALEMA experiment  [11, 12]. We know that the particle detector (scintillator) detects only cosmic ray events, because it is sensitive only to the particles arriving at ground. When at least 5 out of the 13 scintillators are fired in a given time window, a master trigger is built by a multiplicity card and sent to a dedicated datation station which dates by GPS the event, to the compact array and to the low frequency antennas. For the two cabled antennas, the timing is made by this dedicated GPS station, and data are acquired thanks to a digital oscilloscope (PicoScope).
Deconvolution and image treatment: Confocal images of beads and biological data were deconvoluted with the Huygens 3. 7 software (Scientific Volume Imaging, Hilversum, Netherlands) using a measured PSF (supp. fig. 3B ). We tested the Classical Maximum Likelihood Estimation (CMLE) algorithm and the Good's (GMLE) algorithm. Signal-to-noise ratios, also called the R-parameter from 15 to 20 are recommended by the manufacturer for noisy confocal images R-values > 20 for low noise wide-field images. Since we used a confocal microscope with a low noise detector, we tested R-parameters of 26 to 34 and visually inspected the results. In images of figure 2 to 6 brightness and contrast were adjusted equally for all images after deconvolution and before 3D reconstruction and volume rendering. Image analysis was performed with ImageJ  [23]  and 3D-volume rendering was performed with ICY  [24].
Resolution improvement by deconvolution Firstly, we determined the resolution of the confocal microscope before and after deconvolution of confocal data of beads in the ri-mismatched and ri-matched case with the detector pinhole set at 1 Airy Unit (AU). The resolution of the optical system taken from the raw images was 225 nm ± 12 nm in x,y and 546 nm ± 18 nm in z-direction in AF1-medium and 223 nm ± 11 nm in x,y and 550 nm ± 19 nm in z-direction in AF1+-medium (Fig. 1A, table 1 ). After deconvolution, we observed a 1. 7-fold increase in lateral resolution for both mounting media, a 2. 8-fold increase in axial resolution for AF1 and a 2. 5-fold increase for AF1+ for beads imaged next to the coverslip (Fig. 1 A , table  1 ). The resolution values for AF1 and AF1+-mounted beads were comparable, corroborating previous findings, that close to the coverslip, a refractive index mismatch does not impair lateral and axial resolution since high numerical aperture objectives are corrected for this  [13, 18].
Deconvolution optimization in biological data Having established optimal image acquisition parameters, we then wanted to apply our workflow on biological samples and chose the meiotic spindle of the mouse oocyte labelled with alpha-tubulin coupled with FITC. We acquired 3D-images of meiotic spindles with a pinhole size of 0. 6 Airy units at a sampling rate of 40 nm in x,y and 83 nm in z-direction. We wanted to determine the optimal deconvolution parameters and tested the Classic Maximum Likelihood Estimation (CMLE) and the Good's roughness Maximum Likelyhood Estimation algorithm (GMLE), implemented in Huygens deconvolution software. CMLE and GMLE are iterative restoration method optimizing the likelihood of an estimate of the object given the measured image and the PSF. We also studied the influence of denoising prior to deconvolution using the Pure Denoising plugin implemented in ImageJ  [26, 27, 28].