dataesr/scientific-paragraphs-categorization huggingface dataset:
The model takes into account the energy loss of the fission products in the fissile deposit itself and the energy deposition in the gas (both using the SRIM software). Several different filling gases were considered. Comparison of the model results with experimental data gave very promising results. Fission chambers are also candidates for neutron flux monitoring in fusion reactors. To study fission chambers for divertor neutron flux monitoring, Batyunin et al. [4]  presented a simulator for data acquisition performance tests. Starting from experimental pulse shapes, the fission chamber signals in various modes (pulse, current and Campbell) were simulated as a function of fission rate and algorithms for smooth transition between the modes were established. The effect of abrupt changes in neutron flux on the signals was also investigated.
For detector physics and associated treatment and analysis, works have been presented by Guillot et al. concerning passive gamma spectrometry deconvolution software for quantifying the uncertainties associated at the gamma ray spectrum  [73]. Passive gamma ray spectrometry enables to characterize (both identify and quantify) radionuclide in mass and activity. Gamma ray spectrum exploitation, treatment and analysis are generally done in two main steps. The first step is the extraction of the raw data contained in the spectrum (peak areas) and the second step is to establish the detection efficiency of the measurement setup. Deconvolution software uses different raw data extraction methods which need to be optimized in some applications like actinide spectrum treatment and analysis. Barat et al.
A detailed description of the new sophisticated displacement system is given by the authors  [128]. It allows a wide automatic displacement range from À139 to +906 mm. The second two-superposed-cell calorimeter was integrated into a multi-sensor device called CARMEN  [20]  developed in the framework of the joint IN-CORE research program between CEA and AMU in order to quantify several key parameters simultaneously such as thermal and fast neutron fluxes, photon fluxes and nuclear heating in periphery channels of OSIRIS reactor up to 2 W/g, by coupling a differential calorimeter  [130] , a gamma thermometer, ionization chambers, fission chambers, SPNDs, and Self-Powered Gamma Detectors (SPGDs). The responses of the two-superposed-cell calorimeters were numerically simulated by a finite element method using the CAST3M code.
In a general manner, it could be useful to compare the codes that are developed in the acoustic community, and to share the validation mock-up facilities. In the context of Generation IV reactors, CEA-DEN, AREVA and EDF are studying many ultrasonic applications  [27, 139, [162] [163] [164]  and associated robotics means  [165, 166]. Some of these studies may be missing in the present short review. As will appear in the following, Civa code is intensely used to model and optimize ultrasonic methods and devices  [167, 168]. The possibility of ultrasonic telemetry measurements at full power in a sodium reactor (550 °C approx. ), with immersed TUSHT transducers, has been proved with the Sonar device in Phenix (specular reflection of flat targets, at a distance of 270 mm), with the goal to detect local subassembly head displacements  [137].
Some principles of this kind of remote inspection have been validated in Phenix reactor. The optimization studies (propagation mode, frequency, etc. ) for cracks detection in these complex structures are performed using Civa code  [168, 172] , which mixes analytical propagation models in the regular regions, and finite element models to compute the diffraction matrix (S matrix) in more complex regions, in which acoustic modes conversion can occur (branches, flaws). In this approach, S matrices are modal signatures of defects and could allow their characterization. Reference  [172]  gives some numerical and preliminary experimental results. Additional experimental work is necessary to master the selection of the input "pure" propagation mode for instance by using frequency at the maximum of group velocity, or by using phased array transducers to control the emitted wavefront, and to verify the effective capabilities for defects characterization.
To this, fragmentation mechanisms are studied by CEA with LMA-CNRS and Ecole Centrale Marseille, with the help of the associated acoustic emission  [190]. The acoustic burst signals that are emitted by the particles fragmentations and by friction in the 100 kHz-1 MHz frequency range are recorded and the amplitude, duration and temporal shape of the bursts are analyzed, as well as the cumulative number of events, versus the compact density and the applied stress. As a result, the signature of the acoustic emission makes possible the in-situ determination of the starting and the end of the granules fragmentation. This could allow monitoring of powders compaction through a multi-parameter and pattern recognition based analysis. A reproducible research paradigm is adopted in  [191] , to tackle the issue of strong links between data, treatments and the generation of documents, in data driven analyses.
Switch fabric architectures offer a much better basis for multiprocessor systems, and provide considerable performance and usability benefits. Several high-performance switch fabric standards have been developed. PCIexpress (PCIe), 10 Gigabit Ethernet, and RapidIO are the most viable choices for HA and high speed applications, offering better overall backplane throughput with low-latencies and deterministic delays. ATCA is the most promising architecture to substantially enhance the performance and capability of existing standard systems as it is designed to handle tasks such as event building, feature extraction and high level trigger processing. It is the first commercial open standard designed for high throughput as well as availability (HA). The high throughput features are of great interest to data acquisition physics, while the HA features are attractive for experiments requiring a very high up-time.
The ATCA standard was originally conceived to specify a carrier grade-based system infrastructure for telecommunications. It was built from the ground up to support a range of processors. Compared to the Versa Module Europa (VME) bus which is conventionally used in data acquisition systems, the ATCA standard offers advantages especially with respect to communication bandwidth and shelf management. The ATCA carrier-blade form factor supports well-balanced systems, delivering teraOPS of processing power in a single sub-rack. The architecture is flexible as to the types of processors that can co-exist in the system. One of the most critical aspects of implementing the ATCA architecture is the ability of high-performance blades to communicate with each other, so that vast quantities of data can be moved from board to board through the switch fabric within an ATCA system.
The ATCA platform is gaining traction in the physics community  [239]  because of its advanced communication bus architecture (serial gigabit replacing parallel buses), HA, n + 1 redundancy, variety of form factors, very high data throughput options and its suitability for real-time applications  [240]. Active programs are showing up most notably at DESY for XFEL  [241] [242] [243]  and JET  [244]  but also at other laboratories such as ILC  [245, 246] , IHEP, KEK, SLAC, FNAL, ANL, BNL, FAIR  [247, 248] , ATLAS  [249]  at CERN, AGATA  [250, 251] , large telescopes  [252]  and also Ocean Observatories  [253]. Both the CMS and ATLAS detectors are investigating ATCA solutions for future upgrades and ILC and ITER are setting up prototype experiments to test its potential. Most of these programmes put the emphasis on HA.
The system is based on in-house developed ATCA instrumentation modules À IO blades and data switch blades, establishing a PCIe network on the ATCA shelf's backplane. The data switch communicates to an external host computer through a PCIe data network. At the hardware management level, the system architecture takes advantage of ATCA native redundancy and "hot swaps" specifications to implement fail-over substitution of IO or data switch blades. A redundant host scheme is also supported by the ATCA/PCIe platform. At the software level, PCIe provides implementation of hot plug services, which translate the hardware changes to the corresponding software/operating system devices. The paper presents how the ATCA and PCIe based system can be setup to perform with the desired degree of HA, thus being suitable for advanced Fusion control and data acquisition systems.
The system was conceived to exhibit HA properties, aiming to fulfil the demands of Nuclear Fusion diagnostics. A test plan is currently underway to test the availability, setting appropriate availability goals and create fault scenarios to assess system behaviour. The ATCA specification not only contains intrinsic HA features but also provides redundancy resources that can be used to increase system availability, especially at the data transmission level on the ATCA backplane. The current solution for PCIe device hotplug and blade Hot Swap relationship is customized for the particular application and hardware components but aims to be compatible with other hardware. A demonstration of the system was presented at the ANIMMA 2015 conference, using a 100 m fiber optics link between the ATCA shelf and the host PC, allowing the computer to be placed farther away from the shelf and the effects of Single Event Upsets (SEU).
The most popular method for code transfer to local memories requires: (i) a JTAG module (IEEE 1532-2002 standard), and (ii) dedicated software (SW) code (e. g. the impact tool from XILINX) running in a near PC, capable to transfer the FPGA code to the storage device through the JTAG cable  [259, 260]. However, this FPGA code update method may be unfeasible specially in complex machines with restricted access. As example, in future fusion devices the human access to the instrumentation cubicles may not be possible due to the high-energy neutrons (MeV range) produced during discharges. This high-energy neutrons can cross the vessel wall reaching neighbour areas including the instrumentation cubicles. Thus, for safety reasons the FPGA code update must be done remotely. Even during initial test phases, before contamination, the cubicle access may be restricted requiring remote update features.
Therefore, the remote update capability is mandatory for ATCA based reconfigurable modules developed at IPFN, a frequent supplier of those type of FPGA-based modules for several nuclear fusion devices in world, including ITER  [261, 262]. One of the requirements for reconfigurable modules operating in future nuclear environments including ITER is the remote update capability. Accordingly, work has been done on the development alternative method for FPGA remote programming to be implemented in new ATCA based reconfigurable modules  [263]  for applications where for safety reasons the human access to the instrumentation cubicles should be restricted or even not allowed. The presented method is capable to store new FPGA codes in Serial Peripheral Interface (SPI) flash memories using the PCI express (PCIe) network established on the ATCA backplane, linking data acquisition endpoints and the data switch blades.
The method is based on the Xilinx Quick Boot application note, adapted to PCIe protocol and ATCA based modules. The new method allows to program SPI flashes remotely, without any dedicated hardware (e. g. JTAG) or software tool (e. g. XILINX software tools) in the remote site. Moreover, it can be considered a safety solution once the initial FPGA code is never deleted during the update process. FPGA is preferably configured with the update code however, if the remote update procedure fails the FPGA is configured with the initial FPGA code. Furthermore, even if the method was tailored for ATCA based modules, it can be adapted to any system with PCIe network established between reconfigurable modules and host PC. The method was developed with the Xilinx KC705 Evaluation Kit and successfully tested in the Advanced Mezannine Card prototype (AMC-MKX1) developed by IPFN, and installed on the ATCA-PTSW-AMC4 carrier module from the ITER fast plant Instrumentation & Control (I&C) products catalogue.
Considering other modules from the ITER catalogue, the next step will be the method extension to series-6 FPGAs. To minimize the risk of using FPGAs in radiation environment recent tests on the development of systems resilient to SEU were done and also reported at the conference  [264]. A Virtex-6 FPGA from Xilinx (XC6VLX365T-1FFG1156C) is used on one of IPFN's developed ATCA board (ATCAIO-PROCESSOR board), included in the ITER Catalog of I&C products À Fast Controllers. The Virtex-6 is a re-programmable logic device where the configuration is stored in Static RAM (SRAM), functional data stored in dedicated Block RAM (BRAM) and functional state logic in Flip-Flops. SEU due to the ionizing radiation of neutrons causes soft errors, unintended changes (bit-flips) to the values stored in state elements of the FPGA.
A reduced part of the detector, 1/256 of the total, was going to be equipped with the new electronics during 2016 to evaluate the proposed architecture in real conditions in the so called "demonstrator project". The upgraded version of the Read-out Driver will be the core element of the back-end electronics in Phase-II. This module includes two Xilinx Series 7 FPGAs for data receiving and processing and will be installed and working in an ATCA framework.
New life to VME and CAMAC architectures In spite of the growth on the use of ATCA by the community developments involving older instrumentation standards such as CAMAC or VME were still presented. The new data acquisition system for the HORUS spectrometer  [275] , at the 10 MV Tandem accelerator at the Institute for Nuclear Physics in Cologne and consisting of 14 high-purity germanium detectors. In order to process all 30 detector signals, the analog data acquisition was replaced by a digital one using the commercially available DGF-4C modules from the company XIA  [276] [277] [278]. This is a CAMAC based module that provides four complete spectroscopic channels. The DGF-4C modules have been extensively used for the data acquisition at the Miniball spectrometer  [279] , where Rev. D modules were used.
Of course, some challenges are still to be tackled such as the precise theoretical analysis of the gamma and neutron radiation effects on the fiber properties even if irradiation campaigns have already given guidelines to design optical fiber sensors able to work in nuclear environment. The conference provided an overview of the advancements in the field of control and data acquisition. New standards, such as ATCA, are gaining traction on the community due to the ability to develop HA systems. However, older standards such as VME and CAMAC still find their niche market in several applications. For NPPs dedicated hardware designed SBC are also appearing. Growing measurement requirements in the range of Mcps require a development of new measurement methods. Digital direct sampling is providing the ability to perform those measurements through implementation in reconfigurable devices such as FPGAs with the additional benefit that the systems can be reprogrammed.
Our system is implemented as a decision-making support system designed for medical staff members for patients tracking and monitoring. It helps care providers to take decisions about patients' treatment management and evaluates the performance of the PED. In fact, thanks to the proposed decision support system each MS member knows the number of patients in the PED, being treated or waiting for treatment and follows the evolution of patients' treatment process. Thus, practitioners are more likely able to satisfy patients' needs. Our software is a more flexible tool where we can modify and act directly thanks to a more ergonomic interface. For the development and simulation of results, we chose Java in eclipse for several reasons: the existence of a good support for this language, an active community and rich libraries whose Swing API offers a friendly and ergonomic graphical support since the appearance of graphic elements fits with the host operating system.
• Simulation Scenario The access to the PED Jeanne de Flandre database has followed a privacy protocol. We have recovered for each patient a certain number of information: Age, sex, mode of arrival, diagnosis, the exact entry and exit dates that enabled us to deduce the length of stay, the date of the first consultation and finally the orientation or hospitalization. To solve the MHCTS in the PED, we propose the following illustrative example. We will detail the different aspects and logic adopted by our decision support system. Our initial situation is a waiting room with patients already signed up with the registration nurse. In treatment rooms there are medical staff members executing health care operations corresponding to several patients. The table below represents the patient database.
We choose a sample from the database given above. It contains 10 patients who arrived at the PED in 27 th of November 2013. To each patient correspond a pathology, an emergency degree and an arrival date. Patients' information is requested by a nurse and processed into our system. Data received by the reception nurse passes through a pathology test in order to collect several data including emergency degree and needed resources. These data will be used to generate a priority degree giving patients positions in the waiting queue. The position is generated using the proposed scheduling algorithm.
The results also showed that the proposed approach outperforms the approach currently used by the health care practitioner. When we start the simulation the different databases are loaded. Our software includes managing interfaces covering essentially a Main Interface which allows administrator to visualize the different transactions and interactions in our system, a Theater (PED) Interface describing our PED architecture with different compartments and showing an animation representing medical staff paths while executing health care tasks and patients pathways and an Indicators Interface used to monitor the status of the different actors in the PED (figure  6 ).
Future challenges in Big Imaging problems will require that traditional, "black-box" machine learning methods, be revisited from the perspective of ongoing efforts in distributed computing. This paper proposes a distributed architecture for astrophysical imagery, which exploits the Apache Spark framework for the efficient parallelization of the learning problem at hand. The use case is related to the challenging problem of deconvolving a space variant point spread function from noisy galaxy images. We conduct benchmark studies considering relevant datasets and analyze the efficacy of the herein developed parallelization approaches. The experimental results report 58% improvement in time response terms against the conventional computing solutions, while useful insights into the computational trade-offs and the limitations of Spark are extracted.
I. INTRODUCTION Recent advances in distributed computing are causing a paradigm shift in the analysis of petascale data  [1] , with the objective to enable the accurate trends prediction, typically in the domain of retail and social networking. Empowering distributed learning over large-scale scientific datasets, is considered the next emerging challenge, e. g. ,  [2] ; Considering the astrophysics domain, the respective instrumentation (e. g. , Large Synoptic Survey Telescope  [3] ) can generate on a daily basis comparable magnitudes of data to the ones generated by popular social media platforms. At the same time, analytics over astrophysical datasets are performed at a significantly larger amount of information than the one considered in the social or Interner-based media  [4]. Current bibliography trends thus highlight the benefits of using Big Data technology in large-scale astronomic imaging.
Kira  [5] , is a representative framework, which leverages on Apache Spark  [6]  for speeding-up the source extraction process in astronomical imaging. An alternative approach  [7]  exploits the concept of Message Passing Interface  [8]  for parallelizing the cross-match between reference and sample catalogs. Finally, the Corral framework  [9]  proposes an Application Programming Interface (API) that automates the transformation of raw astronomical data into valuable information over distributed computing platforms. These representative approaches focus on essential low-level tools for extracting datasets before applying any learning or optimization algorithm. Nevertheless, very little is reported on how learning algorithms can be profiled for providing an alternative computing solution that outperforms conventional approaches in the astrophysics domain.
Specifically, it considers a set of networked computing resources, that are organized into a hierarchical cluster of computing resources, comprised of a master and M workers. The learning problem and the respective datasets, are submitted to the master of the cluster through the driver program. The initial imagery datasets can be either locally available on the master or stored in a Spark-compliant distributed file system. The partitioning of the initial datasets relies on the concept of the Resilient Distributed Dataset (RDD), which is defined as a read-only collection of N data records  [10]. Through the driver the application program can control several aspects of the distributed learning process, namely: (a) control how each individual dataset will be modeled as a RDD; (b) create RDD bundles through the RDD Data Bundle Component; (c) define the transformations and the respective actions on the bundled RDDs.
Even so, as the value of N increases the size of the individual data blocks decreases, leading to reduced load for each computing stage per task and worker. Thus, for N 32 → 128 the cluster exhibits a more stable behavior in terms of time response, and the standard deviation of the execution time decreases from 24. 66 sec to 8. 48 sec. Disk Usage. For measuring the disk usage we employed the Spark-automated log files that are generated on each worker during the execution of the application program. The disk usage for the calculation of the intermediate results with respect to the time elapsed per worker is presented in Fig. 3. We observe that as the number of partitions increases, the requirements in disk usage decrease; when N 48→ 128 the disk usage after an hour of execution drops from 9.
3 GB to 6. 6 GB. The increased disk demands illustrate that the memory allocated per worker is not sufficient for storage and computations. In addition, these results highlight that the scale of an input imaging dataset is relevant to the RAM capacity of the cluster, and the demands of the solving approach; while the initial dataset herein considered belongs to the order of MB, it unfolds to intermediate results that can rapidly reach the order of GBs. The results thus far highlight the trade off between CPU work load and memory usage. A small number of partitions results in fewer data blocks with relatively large size and increased demands in memory, which in turn increases the disk demands. On the other hand, as the number of partitions increases, the size of the data blocks decreases, and subsequently, the disk usage requirements become more relaxed.