dataesr/scientific-paragraphs-categorization huggingface dataset:
The power trends show a reduction of the maximum value when the axial reflectors are removed. The same behavior was obtained by the ATHENA calculations. The code comparisons are shown in Figure  21. The power trend is in very good agreement, as indicated in Figure  21a. The reactivity behavior, Figure  21b , is in very good agreement during the first part of the transient (in accordance with Figure  18a  static calculations), but some discrepancies are observed approaching the maximum reactivity value. The differences after 0. 1 s, namely after the end of the neutronic perturbation due to the CR extraction, may be due to the difference in thermal-hydraulic feedback treatments (multi 1D approach for APOLLO3 ® /THEDI and 3D approach for ATHENA). Other sources of differences identified between the two codes are the thermal properties adopted.
For analyzing the discrepancies, an integrated approach is proposed starting from lattice calculations up to core transient applications. This innovative integrated multiphysics sensitivity analysis supports the code robustness, ranking phenomena, and coupling validation. More investigations are expected as well as comparisons with other codes and coupling calculation options before the end of the CAMIVVER Project. The SA investigations may be improved by profiting from other methods implemented in the URANIE code and by considering a series of surrogate models representing the lattice behavior and its uncertainties to be used as input options for core calculations, as well as the inclusion of other figures of merit (e. g. , safety criteria and safety operating envelop and margins).
Acknowledgments: Special thanks to our colleagues for the exchanges during the  CAMIVVER  project. Thanks to our Framatome colleagues, including  Laurent Graziano ,  Clement Lafaurie ,  Denis Verrier ,  Alexandre Chea ,  Alberto Previti ,  Alberto Brighenti , and  Damien Raynaud , for the exchanges on APOLLO3 (r) and CATHARE3 codes. We also thank our CEA colleagues, including  Roland Lenain ,  Paolo Cattaneo ,  Daniele Tomatis ,  Simone Santandrea , and  Pietro Mosca. Special thanks also to KIT and EDF partners of the  CAMIVVER  project, including  Luigi Mercatali ,  Juan Blanco ,  Diego Ferraro , and  Adrien Willien.
Thus, operations such as adding, deleting or removing can be made to this main class to vary it, and then generate the connector directly in the pretargeted language. This kind of implementation can be achieved through delta-oriented programming with tools like DeltaJ  (Koscielny et al. , 2014). However, this would be a major constraint in terms of openness and scalability, as some technical choices such as the target language have to be made upstream. To avoid this, MDE provides platform independence and allows source code to be generated a posteriori using model-to-code transformation techniques to generate source code in a desired language.
Then, for each delta module, the difference with the metamodel is evaluated that constitute so-called delta actions for later use. It should be noted that delta actions can be calculated between two delta modules, i. e. , one delta module is considered as a core model to create another delta module. In this case, constraints on the order of application of the module deltas are created. The industrial case study proposed in Sec. 4 will illustrate the application and use of delta actions through a concrete case. To create delta modules and delta actions, we use SiPL  (Pietsch et al. , 2015) , a model-driven delta-oriented tool offered on top of the Eclipse modeling framework (EMF)  (Steinberg et al. , 2008).
Thus, in the current step, all delta actions corresponding to the configuration of the feature model are automatically proposed, while respecting constraints such as the order of application. The application of these delta actions allows to automatically obtain the expected connector model. The connector model is then instantiated to generate the source code of the connector in the desired language. The last step, last box from the left at the bottom in Fig. 1  is a sub-process that involves the use of certain Eclipse-based tools. To transform the model into code, the Eclipse GenModel tool is used to generate the model code which then instantiates the expected connector with property values provided by a end user. An example of an application showing concretely how each step of the entire process works is presented in the section.
3. The Helios analyzer gathers data from different information sources such as social network, scientific and political report and integrate these information in the Helios scrapper database. Helios Analyzer allows to make a weather forecast of the latest news. Fig. 4  shows two connectors that allow the described application to interact: an event-driven connector based on publish-subscribe  (Tarkoma, 2012)  communication pattern, and an hybrid connector that combine one event-driven consumer and one synchronous http endpoint. The objectives of the first connector are to synchronize user access between the SaaS-Console, the Helios scraper, and the Helios analyzer applications, and to synchronize data between the Helios scraper database and the Helios analyzer database. This connector accomplish event-driven communication using the publish-subscribe pattern.
We identify interactions performed by the same connector: SaaS-Console ↔ Helios scrapper, SaaS-Console ↔ Helios analyzer, and Helios scrapper database → Helios analyzer database. The directions of the arrows indicate in which direction the exchanges are made. From SaaS-Console to Helios Scraper and Helios Analyzer and conversely, when the status of a user account changes in the UserAccessTopic in which the SaaS-Console has beforehand pushed. An event is then sent to Helios Scraper and Helios Analyzer through the connector. Events are then delivered to consumers subscribed to the topic, in this case Helios scraper and Helios analyzer. In this study case, connectors source consume the event from SaaS-Console. Upon receiving the event, components subscribed to the topic can consume the message from the sink of the connector.
This is partly the same as for the flow SaaS-Console to Legibase. The difference is that for the communication from the connector to Legibase, the connector sink calls the API exposed by Legibase, upon receiving the event. The flow from Helios scraper and Helios analyzer operates in the same way, with the difference that the communication is between databases and not between business applications. A first effort has been made by our industrial partner BL with the creation of BL-MOM, a messagingbased API (Application Platform Interface) that aims to support implementing a new connector according to the publish-subscribe communication pattern and based on the RabbitMQ broker. However, to date, BL-MOM only supports the development of asynchronous connectors. Another problem with BL-MOM is that backwards compatibility is not obvious.
Indeed, our industry partner BL has found that adapting the BL-MOM API to create connectors based on a Kafka broker, for example, instead of a RabbitMQ broker requires an important development cost. In this specific case, our approach overcomes these limitations in terms of backwards compatibility, connector type and development time. The present exchange flow case study is about a new need to connect the SaaS-Console to Legibase for access contract management, connect the SaaS-Console to Helios Scrapper and Helios analyzer for user access creation and update purpose, and connecting Helios Scrapper and Helios analyzer database for synchronization.
Discussion and Lessons learned The proposed approach helps to reduce the effort required for the development of connectors by reusing their commonalities. We made sure that the analysis of variability was as complete as possible. For this purpose, we considered concrete connectors used in the industry, or open source tools such as Spring Cloud Data Flow (SCDF) 3  , and Kafka Connect 4. We also retrieved features found in the literature, such as from the EIPs. This rigorous analysis of commonalities and variability has provided us a feature model that cover a wide range of connector types. The paper also explains the transition from a feature model to a software product line architecture manually and following rules we described. We have also shown how to use the feature model and the product line architecture to easily create a desired connector.
For this purpose, deltaoriented programming was exploited using the SiPL tool and other Eclipse tools such as GenModel. The DE sub-process being set up only once with punctual evolution, by considering the steps of AE only, we noted with the presented example that the generation of source code process based on our approach take less than one hour. This is very encouraging in terms of time saving, as according to feedback from BL developers and experts, it takes about a week for a novice to develop a connector for a single exchange flow. Although feedback from our industrial partner, BL confirms that an experienced developer takes less time, about three hours, it is always advantageous to start with an existing base project, this reduces, for example, the number of copy/paste operations that need to be performed without forgetting anything.
Several models can be obtained by using different correction factors, model parameters, and assumptions, and these models are compared and validated against experimental data and modelling studies from the literature. In this study, several different stoichiometric thermodynamic models are compared to determine which are the most suitable and reliable. The analysis has been carried out for the prediction performance to predict syngas compositions. To correct some of the errors associated with thermodynamic models, correction factors are utilized to modify the equilibrium constants of methanation and water gas-shift reactions, using the data from 27 experimental values published in the literature, which allows them to better predict the real output composition of the gasification reactors. The models are formulated and optimized in MATLAB.
In the last years, many studies were carried out to study the optimal operating conditions, the overall system performance, and the limitations and the potentials of a variety of SOFC power plants integrated with biomass gasification systems. However, the current commercial process simulation software packages do not include built-in models for SOFC. In the literature, the most common SOFC system modelling approach is to integrate process simulators with a stack model in a programming language. Marcantonio et al. [5]  coupled Solid-Oxide Fuel Cells (SOFCs) to gasification technology. A steady-state model of a biomass-SOFC was developed using a process simulation software, ASPEN Plus. The present paper showed an entire plant composed by a gasification part, a gas cleaning unit, and an SOFC system.
Surface ocean dynamics play a key role in the Earth system, contributing to regulate its climate and affecting the marine ecosystem functioning. Dynamical processes occur and interact in the upper ocean at multiple scales, down to, or even less than, few kilometres. These scales are not adequately resolved by present observing systems, and, in the last decades, global monitoring of surface currents has been based on the application of geostrophic balance to absolute dynamic topography maps obtained through the statistical interpolation of along-track satellite altimeter data. Due to the cross-track distance and repetitiveness of satellite acquisitions, the effective resolution of interpolated data is limited to several tens of kilometres. At the kilometre scale, sea surface temperature pattern evolution is dominated by advection, providing indirect information on upper ocean currents.
As such, several past attempts to improve surface current retrieval have been based on the use of the sequential information provided by SST products, either through maximum cross-correlation techniques  [39]  or by directly considering tracers' advection equation  [40] [41] [42] [43]. Our work exploits the data prepared for an OSSE that was originally designed for different objectives in the framework of the European Space Agency ocean CIRculation from ocean COLour observations (CIRCOL) project  [44]. They consist of one year of synthetic, daily ADT and surface geostrophic currents data over the Mediterranean Basin. Full details explaining how we simulate the observing system geometry are reported in Section 2, where all pre-processing steps to prepare our training and test datasets are described.
Primitive Equation Model Data The Mediterranean Forecasting System (MFS) is a hydrodynamic model for the Mediterranean Basin and the Atlantic Ocean off the Strait of Gibraltar  [45]. Monthly to 15-minute instantaneous outputs of 3D horizontal currents and sea surface height (SSH), as well as monthly to hourly estimates of 3D temperature and salinity fields are available via the Copernicus Marine Service web portal (Product ID: MEDSEA-ANALYSIS-FORECAST-PHY-006-013). For the present study, we relied on daily outputs of SSH and SST, extracting information within the boundaries of the Mediterranean Basin (30 to 46 • N and -6 to 37 • E). These fields are provided on a 1/24 • regular grid and 125 unequally spaced vertical levels. The simulations are based on the NEMO model (Nucleus for European Modelling of the Ocean) used in combination with Wave Watch-III for the wave component.
Satellite Absolute Dynamic Topography The sea surface geostrophic currents were obtained from the Copernicus Marine Service and are derived from optimally interpolated absolute dynamic topography data merging observations from a constellation of radar altimeters. Such a constellation is composed of four to six altimeters in the 2008-2019 temporal range  [32]. The geostrophic currents are provided as daily fields with nominal 1/8 • horizontal resolution. The 2008-2019 time series was extracted. The corresponding Copernicus Marine Service product and dataset ID are SEALEVEL_MED_PHY_L4_REP_OBSERVATIONS_008_051/dataset-duacsrep-medsea-merged-allsat-phy-l4, respectively (accessed on 1 March 2021 and now included as part of the SEALEVEL_EUR_PHY_L4_MY_008_068/cmems_obs-sl_eur_phy-ssh_my_allsat-l4-duacs-0.
Satellite Sea Surface Temperature Data Remotely sensed SST data are taken from the European Copernicus Marine Service (https://marine. copernicus. eu/access-data, last accessed 14 January 2022). They are Level-4 (L4) products, which means they provide gap-free estimates on a regular grid and are operationally produced and freely distributed in near-real time. We have used here 11 years (2008-2019) of the ultra-high-spatial-resolution (UHR) Mediterranean dataset, reaching a nominal 0. 01 • × 0. 01 • resolution (Product ID: SST_MED_SST_L4_NRT_OBSERVATIONS_ 010_004_c_V2). This SST dataset is retrieved by first combining the night-time images collected by multi-platform infrared sensors, after specific quality control and cloudy pixel removal, and by successively running a two-step optimal interpolation algorithm  [37].
Before using satellite SST L4 data to build our predictor tensor, we had to map them on the same grid used for the model training. To obtain a consistent prediction, we preliminary assessed the effective spatial scales resolved by the model SST, and eventually filtered the UHR to remove scales that have never been seen by the network. This was achieved by applying a low-pass Lanczos 2D filter (with window size = 9 and cutoff = 1/8) directly to the UHR data before remapping on the final 1/24 • grid (through a basic bilinear interpolation). As discussed in the Results section, this pre-processing has the drawback of further smoothing the SST field in areas not covered by concurrent infrared satellite measurements due to cloud contamination or other coverage issues.
Sea Surface Drifter Data In situ measurements of sea surface currents were obtained from autonomous Lagrangian drifting buoys that are passively transported by the ocean surface currents  [46, 47]. During the drifting buoy evolution, the data on the position are interpolated at uniform intervals (~30 minutes) relying on the kriging interpolation method developed by  [47]. The velocities are finally computed via a finite-difference method of the interpolated positions and provided with six-hourly temporal resolution. The data covering the period of our study have originally been provided by the Italian Institute of Oceanography and Experimental Geophysics (OGS) for the purposes of the ESA-CIRCOL project. The timeseries are accessible via http://doi. org/10. 6092/7a8499bc-c5ee-472c-b8b5-03523d1e73e9, last accessed 14 January 2022); buoy-derived surface current values are only retained if the buoy is equipped with a drogue: a device that guarantees the buoy evolution to be driven by the ocean currents rather than by surface winds  [48].
Simulating Altimeter-like ADT Maps One year (2017) of synthetic altimeter-derived ADT maps was obtained from the outputs of the Copernicus Marine Service MFS hydrodynamic simulation, using the data unification and altimeter combination system (DUACS) mapping method. The different steps are detailed below. Firstly, sea level anomaly (SLA) was computed from model outputs by means of Equation (  1 ): SLA = SSH -(MDT -0. 344) (1) where the mean dynamic topography (MDT) is provided as a static field together with the model outputs. A 0. 344 constant (expressed in m) allows us to adjust the SLA values in the Mediterranean Sea to guarantee that the spatio-temporal average of SLA is zero during 2017. The large-scale, high-frequency variability, usually removed by applying a dynamic atmospheric correction (DAC)  [49]  is filtered out of these synthetic data by applying a Loess filter.
Preparation of Training and Test Datasets for Deep Convolutional Learning The OSSE data were simulated starting from year one of the primitive equation model daily output described above. The original input images cover the entire Mediterranean domain at 1/24 • spatial resolution, leading to an individual image size of 380 × 1000 pixels. We randomly chose 40 dates (~11% of the total) to be kept aside as independent test data, and we successively resampled the original images, extracting much smaller tiles (76 × 100), which were later used as input to the network training. Despite the random holdout strategy being a standard, different choices of the test dataset could also be done. However, the test on OSSE prediction only serves here to assess the relative performance of the different network architectures, not its absolute performance, which is not relevant per se, when looking at simulated input data.
Deep Convolutional Models Learning Strategy and Configuration All deep convolutional models considered in this work (Section 3) have been written in Python using the open-source library Keras. They are trained adopting an early stopping rule to avoid overfitting and minimize the generalization error. In practice, the original training dataset is randomly split into a proper training set (85% of data) and a validation set (15%) (not to be confused with the fully independent test dataset described above, which is never seen during the training) based on which both training (hindcast) and validation losses are updated during the network optimization. The validation loss, in particular, is used as an estimate of the generalization error, and early stopping consists in terminating the iterative learning as soon as its values start to increase.
Automatic Eddy Detection The angular momentum for eddy detection and tracking algorithm (AMEDA) is freely available software for the detection and tracking of oceanic eddies from 2D gridded fields of surface currents and/or sea surface height  [53]. It is based on the computation of eddy local normalized angular momentum (LNAM) and on the observations of closed streamlines around the LNAM extrema. The algorithm was successfully applied to remotely sensed 2D fields and model outputs, e. g. ,  [35, 53, 54] , and enables the determination of eddies' contour and trajectories as well as eddy merger/splitting events. In this study, AMEDA was used to identify eddy shapes seen by standard altimetry products (described in Section 2. 2) and the fields obtained from the combination of satellite altimetry ADT and high-resolution satellite SSTs.
The ADR-SR network implemented here employs 16 residual blocks and finally includes slightly less than 1. 8M trainable parameters. ADR-SR performance assessed on the test dataset significantly improved with respect to that of the other networks, and, for the first time, including the information on the low-resolution ADT interpolation error leads to a tangible reduction of the RMSD over most of the basin (Figure  3B, C ). Still, a lower accuracy is found close to some of the altimeter repeated tracks (visible as diamond shapes), which might be due to the limited ability either of ADR-SR and of the previously tested networks to correctly handle the information provided at different spatial scales by the input predictor variables. This specifically reflects the larger scale of ADT mapping error patterns with respect to the geophysical variables.
Applying Dilated Adaptive Residual Super-Resolution Trained on Simulated Data to Real Satellite Observations Our successive analysis is aimed to verify to what extent we can use the features learned from observing system simulations based on primitive equation modelling to improve present-day, data-driven reconstructions, but also to identify eventual limitations of the present OSSE set up and issues related to real observation-based products. We have thus applied the model trained on OSSE data to predict high-resolution ADT starting from properly pre-processed, satellite-altimeter-based, low-resolution ADT and high-resolution SST products (e. g. , Figure  6 ). For both variables, we have actually taken optimally interpolated data (also known as Level 4 (L4), see Section 2), covering the 2008-2019 period.
Assessing the accuracy of the observation-based, super-resolved maps is not trivial, as large-coverage, high-resolution observations of the surface topography/currents are not presently available. As such we followed a double approach: on the one hand, we performed a qualitative analysis of the reconstructed patterns, looking at areas of intense The dADR-SR model outperforms any of the previous networks when tested on the independent dataset, displaying a marked reduction of the RMSD over the entire basin (Figure  5 ), with only extremely few and very small spots showing a minimal degradation (Figure  4B, C ). The information captured at the different scales by including all predictors thus further enhances the accuracy of the reconstruction with respect to the model that does not consider the low-resolution ADT interpolation error.
mesoscale activity visible in the satellite SST L4 data and comparing the (sub)mesoscale eddies identified by the AMEDA automatic detection algorithm in original and superresolved data; on the other hand, we built a match-up database with the surface current estimates provided by surface drifters (see Section 2) and used it to compute the statistics of the differences with respect to the geostrophic currents estimated from standard altimeter ADT and super-resolved ADT field. The dADR-SR model reveals impressive potential to resolve mesoscale turbulent features that are generally smeared out, often misplaced or even totally missed by standard altimetry, when clear-sky thermal data are present. Figure  7  presents a wonderful example of such a turbulent field, with many mesoscale eddies, dipoles and current meanderings well visible, especially in the western Mediterranean Basin, detaching from the Algerian current towards the centre of the basin along the North Balearic front and Liguro-Provençal current.