dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction MASURCA is a zero-power mock-up facility that has been mainly operated for getting experimental neutron physics validation data on sodium-cooled fast reactor cores. Thanks to the combination of several favourable features of the reactor building and of the core layouts, it is also possible to consider the development of a new capability: MASURCA as a beam facility. In recent studies  [1, 2] , we evaluated -by means of simulations -this possibility of operating MASURCA as a fast neutron source in which neutrons are "extracted" from the critical core using a radial channel (Fig. 1 ). In order to maximise the intensity and the fast-to-thermal neutron ratio of the beam at the end of the radial channel, entering the experimental zone, we looked into different core configurations. We concluded that MASURCA has the potential for delivering a continuous neutron beam with an intensity of 1.
The objective of this paper is to give an overview of the capabilities of Eulerian bifluid approach to meet the needs of studies for nuclear safety regarding hydrogen risk, boiling crisis, and pipes and valves maintenance. The Eulerian bifluid approach has been implemented in a CFD code named NEPTUNE CFD. NEPTUNE CFD is a three-dimensional multifluid code developed especially for nuclear reactor applications by EDF, CEA, AREVA, and IRSN. The first set of models is dedicated to wall vapor condensation and spray modelling. Moreover, boiling crisis remains a major limiting phenomenon for the analysis of operation and safety of both nuclear reactors and conventional thermal power systems. The paper aims at presenting the generalization of the previous DNB model and its validation against 1500 validation cases.
The walls of the containment building and metal structures also play an important role from a thermal viewpoint. The walls, significantly cooler than the gas, condense the water vapor in the gas mixture and thus limit the pressure increase in the containment. Furthermore, the temperature difference between fluid and walls generates convection loops, enhancing the mixing of gases having different density. In this first part dedicated to the hydrogen risk, our strategy is to increase gradually the level of complexity and to validate all the physical phenomena involved separately. The first step is the analysis and the understanding of gas stratification and transport phenomena. In order to achieve this, CFD calculations are performed with two kinds of solver, namely, NEPTUNE CFD and Code Saturne, for the PANDA test 17.
NEPTUNE CFD is dedicated to the simulation of incompressible and compressible multicomponent/multiphase flows, whereas Code Saturne is dedicated to homogeneous incompressible or low Mach number compressible flows, with only one momentum equation representing the mixture of gases, liquid, and particles. NEPTUNE CFD is mainly used for nuclear engineering, whereas Code Saturne is used for nuclear and fossil energy engineering and for environment (as geophysical flows). The vapor condensation along the wall of the containment building is the next step towards increasing complexity. The modelling proposed in NEPTUNE CFD assumes that liquid droplets form along the wall. Vapor condensation on droplets makes them to grow. Once the droplet diameter reaches a critical value, gravitational forces compensate surface tension force and then droplets slide over the wall.
It can be seen that the number of moles of gas in the vessel rises as the gas temperature drops. It can be deduced from this that droplet evaporation was the main cause of gas cooling, with the droplets drawing from the gas the energy they required to evaporate. But, the air-vapor mixture was initially assumed as homogeneous in the benchmark exercise. With this assumption, thermal-hydraulics conditions of the test cannot lead to droplet evaporation. Because of mass and energy conservation, the steady state is finally correctly reproduced. We have to keep in mind that code-experiment comparisons may lead to discrepancies due to weaknesses in modelling but also because of exaggerated simplifications of initial and boundary conditions in calculations. Results obtained with NEPTUNE CFD code are detailed in  Mimouni (2010).
We assume that 𝐴 𝑔0 = 𝑛 site 𝜋𝑟 𝑤 2 , (12) which is the formulation at the CFD scale of condensation properties experimentally observed at the droplet scale  [19] ! 1. 3. PANDA Facility: Test 17. PANDA 17 is a test case which deals with the slow destruction of a stratification  [20]. Initially Vessel-1 presents an Air-He mixture layer at ambient temperature and pressure (Figure  2 ). Then an air plume of 303 K is injected inside the facility to impact the layer and lead to its slow erosion. As the process is running, over the initial layer we can see a decreasing helium concentration whereas we can observe an increase of helium concentration on lower part and then a stabilization. The vessel temperature is constant and does not allow wall condensation. This experiment aims to test CFD code capacity to model correctly buoyancy effects.
In this section, Code Saturne and NEPTUNE CFD codes are used in the calculations. Calculations obtained with Code Saturne (named "single phase" in figures) in a previous work were in good agreement with experimental data  [14] [15] [16]. Calculations performed with NEPTUNE CFD are named "two phases" in figures. In this test case, we compare the helium concentrations along time at different locations (Figure  3 ). Comparisons between experiment and calculations show a reasonable agreement qualitatively and quantitatively. We do not reproduce correctly the rude decrease of helium concentration on probe series 14 at elevation 5. 626 m which is located close to the injector and the initial layer (Figure  4 ). This decrease is representative of the plume impact on the layer. This one influences the helium concentration evolution on the probes of the series 14.
The walls, significantly cooler than the gas, condense the water vapor in the gas mixture and thus limit the pressure increase in the containment. Furthermore, the temperature difference between fluid and walls generates a convection loop, enhancing the mixing of gases of different density. The original approach presented in this work is the standard model in the code and is currently used in the simulation of the whole containment in industrial scenarios. The test 25 experiment consists of injection of steam and helium through a vertical pipe of 0. 2 m diameter in Vessel-1 (DW1)  [21]. The mixture is vented into the PANDA Wetwells (WW1 and WW2) (Figure  7 ), which are used as a buffer volume smoothing the pressure increase in vessels DW1 and DW2 due to the gas injection. The conditions of the test 25 experiments are summarized in Table  1.
Conclusion about Our Current Capabilities to Simulate Sprays. Gas stratification and vapor condensation at the walls in the containment building have been assessed with two kinds of CFD approaches. Calculations compare reasonably well with experimental data and give an idea of the degree of maturity of CFD codes. The reliability and applicability of the spray model implemented in the NEPTUNE CFD code have been also addressed in this section: heat and mass transfer (HMT) on single droplet (CARAIDAS experiment); spray behaviour under severe accident conditions in a closed cylindrical vessel with a volume of 7 m 3 (TOSQAN experiment). A step further in the understanding of the phenomena involved in an industrial geometry was achieved in the numerical simulation of two interconnected vessels using the PANDA facility built at PSI (Switzerland).
Finally, a numerical simulation of the interaction between two PWR containment sprays has been performed with a new model of polydispersion and collision of droplets. The droplet size and velocity distributions at a distance of 20 cm below the spray nozzle outlet have been precisely measured and used as input data in the calculations. The water droplet polydispersion in size has been treated with a sectional approach. The influence of collisions between droplets is taken into account with a statistical approach based on the various outcomes of binary collisions. A two-fluid multidimensional simulation, on the basis of two interacting real PWR spray nozzles, is compared to the results obtained on the CALIST facility and shows a good agreement. However, monodispersed and polydispersed approach gives similar results regarding the air entrainment caused by droplets.
Results and Generalization of the DNB Model. The sensitivity of mesh refinement has been evaluated in previous works  [38]  and was found acceptable. In contrast to empirical relations, theoretical models implemented in the CFD code that take into account the basic mechanisms involved in the CHF phenomenon should better adapt to any new flow boiling configuration and to geometry. Thus, the DNB model has been assessed in a large scope in terms of mass flux, pressure, quality, and channel diameter in  [38]  and some results are reproduced in Figures  45  and 46. As already discussed, we propose in this section an improvement of the DNB model. Indeed, regarding strongly subcooled cases with high values of liquid mass flowrate at inlet, it is quite difficult to reach the condition  crit = 0. 5.
Presentation of the Model. In the previous model, the code was using a loop on the heat flux equation to find the wall temperature by an iterative process. The novelty we proposed is to calculate the wall temperature as a function of the imposed wall heat flux; then an iterative process is carried out on the nucleation site density to solve the heat flux balance. The key idea is that the piece of experimental data used to determine a correlation for the wall temperature is much more important than that for nucleation site density. Furthermore, temperature measurements are more reliable than those of the nucleation site density. These facts lead to a correlation more general and more robust for wall temperature. The range of application covered by the Liu-Winterton correlation  [40]  is very large for pressure, heat flux, and mass flow and as a consequence has been selected in the following: 𝑞 wall = √ (𝑇 wall -𝑇 liq ) 2 (ℎ fc 𝐹) 2 + (𝑇 wall -𝑇 sat ) 2 (ℎ nb 𝑆) 2 , (28) where (i) ℎ fc is forced convective heat transfer coefficient given by Dittus-Boetler  [41] , (ii) ℎ nb is nucleate boiling heat transfer coefficient given by Cooper  [42] , (iii)  is factor to amplify the heat transfer by forced convection because of the turbulence induced by the bubble, (iv)  is reduction factor of the heat transfer by nucleate boiling because of the convective flow: 𝐹 = (1 + Pr ( 𝜌 𝑙 𝜌 V -1)) 0.
Manon  [44]  has provided experimental data on wall temperature from DEB-ORA test. In Figure  55  heat flux, mass flow rate and pressure are fixed and only the inlet temperature is varying. Results show clearly that the wall temperature is overestimated with the standard model but the new model predicts accurately the wall temperature. This is of relevant interest for critical heat flux calculation. Boiling Flow in a Vertical Annulus, with Refrigerant Fluid as Simulating Fluid. The Arizona State University (also known as ASU) experiment is described in  [45, 46]  for two-phase boiling flow measurements. The test section of the ASU experiment consists of a vertical annular channel with a heated inner wall and an insulated outer wall. The inner tube is made of 304 stainless steel (inlet diameter: 14.
Being wireless and battery-free, the sensing element of the network can be installed inside the machine and on rotating devices. Data acquisition is achieved by an electronic interrogation device, called interrogator. The sensing principle is based on the surface acoustic wave technology. Specifically, the sensing transducer is made of a piezoelectric substrate patterned with electrodes. When illuminated by an electromagnetic wave (emitted by the interrogator), the backscattered signal is characteristic of the sensor and carries a signature representative of the physical quantity being monitored. The main contribution of this work concerns the design of a network (in the computer science meaning) of battery-free sensors/interrogators which make use of the wireless communication to transmit data in several hops to an end user.
3 ). It is supported by the SPI access functionalities of the core implementation of TinyOS-2. x (configuration of the chip, either as a modem for digital communication or as a flexible RF source for interrogating SAW sensors, definition of the emitted frequency and emitted power). Having configured the radiomodem, the most common activity of digital data transfer is handled by Xe1203Uart. The hardware performs pattern matching as included in all transfer headers to validate that the received RF signal actually includes digital data. This pattern is detected at the hardware level by the radiomodem in lower power receive mode, and is used to trigger a wake up interrupt of the STM32 microcontroller (EXTI) which was left otherwise in a low power consumption mode. Interfaces between these low-level functionality and the higher level routing functionalities provided by TinyOS-2.
Because the standard deviation on multiple measurements is used as a quality of service indicator, the number of samples used to compute the standard deviation is a mandatory property of the validity of the analysis; obviously, if a single acquisition was possible before a timeout is reached when the value is transmitted to the user, the standard deviation is null but has no relevance as to the quality of service. Hence, one last indicator provided to the user is the number of measurements needed to achieve the targeted number of averages, or whether a lower number of valid measurements was acquired before a timeout was reached. Figure  5  chart exhibits the raw data prior to the application of the calibration coefficients to convert the resonance frequency measurement to temperature. On top of the figure is one sample of the frequency records: this raw dataset exhibits some significant divergence from the average trend, observed here as vertical lines.
The bottom graph in Fig. 5  shows the evolution of such an indicator, with sharp rises indicative of poor link budget, either because of jamming interferences or obstacles between the reader and the sensor. A threshold on this standard deviation efficiently removes outliers. The resulting processed dataset will be displayed in Fig. 10. On a static sensor, most sensor RADAR cross section properties will be either successful if the cooperative target is within range, or fail if the sensor is out of range, and the two scenarios are either completion of the targeted number of averages or no sample acquisition, the latter being qualified on returned signal level comparison with threshold values. The case of sensors located on rotating parts is more interesting, since the sensor is only viewed intermittently by the sample number (a.
Interrogator-based data transfer The core novelty of the hardware discussed throughout this document is the use of a radiomodem as a peripheral of the central processing unit for probing the passive sensor response in a RADAR-like strategy. Having acquired the necessary information, the radiomodem is reconfigured for its original digital communication purpose. In this point to point configuration, which does not require TinyOS support, little energy constraint lie on the data sink which is always awake, while the data source, which might be battery powered, aims at saving energy by switching to sleep mode and switching off all peripherals when interrogation or communication are not taking place. However, such a strategy requires some sort of synchronization if all nodes are to participate in a multi-hop network-beyond the basic point to point communicationas will be discussed below (Fig.
Using the MAC layer within TinyOS Since the communication part and the sensor interrogation module can create interferences in the network, both of them use the previously described MAC layer. In fact, the nodes willing to interrogate a sensor should wait for the medium availability, and so do the nodes that want to communicate through the network. TinyOS is designed to encapsulate the platform dependant components so that we can add a MAC layer at the Xe1203 level without adapting the uppers layers.
Interrogator network communication The previously described MAC layer is aimed at being used in the context of a collection network. Such a network is designed to carry measurements taken from sensors by interrogators to one collection point of the network (also named root node or sink). More than one collection point can collect the data since the routing protocol is address-free. The collection tree protocol (CTP)  (Fonseca et al. 2006a ) allows to make a tree based collection network in which data from interrogators are forwarded to collection points. We use the implementation of CTP provided by TinyOS.
Data exploitation As a demonstration of the reliability of the wireless passive sensor approach for both collecting and routing data, a 1-year long indoor and outdoor temperature measurement experiment is exhibited in Figs. 9 and 10. During this period, the interrogation unit was switched on for 5 s every 5 min, yielding the collection and transmission of 2,347,215 samples. One sensor is located indoor and was stolen while the experiment was running, yielding 1,717,415 useful data, while the other is fixed on the outer side of a window: in both cases, the distance to the interrogation RADAR unit is 10 m. Data are collected every 5 min by an automated wakeup: in this experiment, the reader was connected to the main power supply and limiting wake up duration to save battery was not considered.
This dataset is the processed version of the raw data exhibited in Fig. 5. The various quality of service indicators discussed earlier are evaluated on this dataset. Two processing steps are considered: because probing the SAW sensor is an analog measurement, the data quality is directly linked to the signal to noise ratio. Because in this experiment the aim was to maximize the interrogation range, the received signal is at the threshold of the detection limit and any obstacle (e. g. person walking between the interrogation unit and the sensing transducer) will increase the measurement standard deviation. Any active interference (e. g. external radiofrequency emitters in nearby environments or keyless entry systems for opening car doors) will induce saturation of the reception stage and feedback loop aimed at dropping the emitted pulse to its minimum.
Applying these two criteria rejects 4. 4% of all data acquired continuously (once every 5 min) over one year. The temperature is deduced from these filtered measurements by applying the calibration coefficients, and the resulting dataset is again tested for consistency: unreasonable temperature values (outliers from a reasonable indoor and outdoor temperature range) or frequency values resulting in the computation of negative square roots are rejected. The latter condition only rarely occurs following the initial data validity tests: only 0. 004% (67 out of the remaining 1,641,907 samples after the initial processing step) of the samples are rejected because they would yield in a calculation of the square root of a negative argument (Fig. 9 ). From the resulting dataset, users can then define various filters on the resulting temperature aimed at keeping only data within "reasonable" bounds to prevent false alarms (Fig.
This dataset is the processed version of the raw data exhibited in Fig. 5. The various quality of service indicators discussed earlier are evaluated on this dataset. Two processing steps are considered: because probing the SAW sensor is an analog measurement, the data quality is directly linked to the signal to noise ratio. Because in this experiment the aim was to maximize the interrogation range, the received signal is at the threshold of the detection limit and any obstacle (e. g. person walking between the interrogation unit and the sensing transducer) will increase the measurement standard deviation. Any active interference (e. g. external radiofrequency  data validity tests: only 0. 004% (67 out of the remaining 1,641,907 samples after the initial processing step) of the samples are rejected because they would yield in a calculation of the square root of a negative argument (Fig.
9 ). From the resulting dataset, users can then define various filters on the resulting temperature aimed at keeping only data within "reasonable" bounds to prevent false alarms (Fig. 10 ). This is the step of pre-processing in data exploitation in the scope of monitoring the health state of the system, detecting and diagnosing its faults, predicting its future health state and taking appropriate decisions regarding its operation  (Soualhi et al. 2015). Data should be complete, free of errors and noise and not corrupted. This step is very important as any wrong data used in the implementation of the algorithms will lead to wrong estimation, prediction and decision results. This is known as garbage in, garbage out. Indeed, the data gathered by the sensors carry relevant and useful about the system and its environment and this should be preserved.
This paper presents a simulator tool that can simulate large databases of visually realistic longitudinal MRIs with known volume changes. The simulator is based on a previously proposed biophysical model of brain deformation due to atrophy in AD. In this work, we propose a novel way of reproducing realistic intensity variation in longitudinal brain MRIs, which is inspired by an approach used for the generation of synthetic cardiac sequence images. This approach combines a deformation field obtained from the biophysical model with a deformation field obtained by a non-rigid registration of two images. The combined deformation field is then used to simulate a new image with specified atrophy from the first image, but with the intensity characteristics of the second image. This allows to generate the realistic variations present in real longitudinal time-series of images, such as the independence of noise between two acquisitions and the potential presence of variable acquisition artifacts.
(Preboske et al. , 2006; Prados et al. , 2015). Estimating and correcting the bias present in such morphometry tools is important, especially for clinical applications. In addition to tracking volumetric changes in specific brain structures, longitudinal imaging data can also be used to study the temporal inter-relationship of atrophy in different structures. For instance,  Carmichael et al. (2013)  studied the groupings of 34 cortical regions and hippocampi from the per-individual rates of atrophy estimates in these regions. In  Fonteijn et al. (2012) , authors defined AD progression as a series of discrete events. Along with other clinical events, the timings of atrophy in various brain structures were included in a set of discrete events. Without any prior to their ordering, the model finds the most probable order for these events from the data itself.
(2013)  where the authors simulate visually realistic time series of cardiac images. Intensity variation in the simulated images of a patient is obtained by resampling the intensities from the repeat scans if available, otherwise from the real images of the same patient taken at different times. Figure  1  shows a diagram of the complete framework. To implement this framework, we have developed an open-source atrophy simulator software called Simul@trophy 1. To our knowledge, Simul@trophy is the first atrophy simulator to be made open-source. Simul@trophy uses the biophysical model presented in  Khanal et al. (2016a)  but introduces a new numerical scheme to compute divergence, which removes the numerical inconsistency presented in the previous work. This is further explained in detail in Section 4.