dataesr/scientific-paragraphs-categorization huggingface dataset:
Results and Discussion Energy Dispersive X-ray Spectroscopy (EDX) EDX data have been collected on an FEI-Osiris microscope operated at 200 kV and equipped with a Brücker EDX system consisting of four silicon drift detectors which ensure a high signal-to-noise ratio even at a probe size <1 nm. The sample has been prepared as wedgeshaped by focused ion beam (FIB) to study the influence of the thickness on the composition measurements. The quantitative EDX analysis has been carried out by two methods, namely the Cliff-Lorimer method (or k-factor method)  [15]  implemented in commercial EDX software and the ζ-factor method, first proposed by Watanabe et al. [16]. The Cliff-Lorimer method relates the concentration ratio of two elements to the ratio of the intensity peaks in the spectrum through the so-called k-factors.
15  [11]. The atom positions and their time of flight were recorded, and the 3D sample volume was reconstructed using the IVAS software, which enables an iterative process in which the initial geometrical characteristics of the tip as well as the detector efficiency are varied until the thicknesses of the reconstructed layers correspond to the experimental values determined from STEM images with straight interfaces. A 3D reconstruction of the sample tip, starting from the Al 0. 05 Ga 0. 95 N buffer layer and including seven periods of the stack, is presented in Fig. 3a. A zoom corresponding to the region marked as a black rectangle is shown in Fig. 3b , whereas Fig. 3c  reports the variation of the Ga/(Ga+Al) and Al/(Ga+Al) ratios along the growth axis. These ratios have been obtained by counting the number of Al and Ga atoms in sampling boxes of 17 × 17 × 0.
Main Research Outcomes At the outset experiments were conducted with pure CO 2 to set the baseline and verify the methodology, calibration procedures, accuracy and consistency of the measuring devices. Data from the National Institute of Standards and Technology (NIST)  (Chickos, 2005)  was used as a control dataset. Subsequently tests were conducted with the selected quaternary CO 2 mixtures discussed above. The experimental data were compared with pure CO 2 measurement results, similarities in the general trends in the vapour phase were observed but more pronounced differences existed in the liquid phase. Further, quaternary CO 2 gas mixtures were prepared and tested within the targeted ranges of pressure and temperature,.
Small to medium scale pipeline releases Medium-scale experiments involving high pressure releases of CO 2 containing a range of impurities from a pipe were performed at INERIS, France. The experimental rig and the measurement techniques were developed during the CO 2 PipeHaz project (CO 2 PipeHaz, 2009;  Jamois et al. , 2014). In CO 2 QUEST, the same system was used to gain a better understanding of the influence of the presence of impurities on both the flow inside the pipe and on the external dispersion. The data were gathered for the validation of the mathematical models produced in CO 2 QUEST. The rig and equipment is shown in Figure  8. The pipe is 40 m long with a 50 mm internal diameter. Pressure (P), temperature (T) and heat flux transducers (F) were placed along its length at intervals of 10 meters.
Large scale pipeline releases The main objective of this section of the project was to conduct controlled large-scale experiments involving high pressure releases of CO 2 with a range of impurities. Experimental data recorded included near-field measurements of the dispersing jets and temperature measurements in the vicinity of a pre-designed crack geometry. This activity has provided experimental data that can be used to validate the CFD models developed as part of the project and described in the next section. A fully instrumented industrial scale CO 2 pipeline was designed and built by Dalian  uniform grids in physical space in that refined numerical solution meshes are only used at those points in a flow where significant change is taking place. Solutions are also calculated on each grid with a time step appropriate to that grid, with the whole refinement process controlled by using solutions to obtain an estimate of the truncation error.
Ductile model The present study develops a fluid-structure coupling methodology for simulation of scenarios of pipeline failure involving ductile and brittle fracture propagation. To simulate the state of the fluid in the rupturing pipeline a one-dimensional compressible CFD model is applied. The proposed methodology couples the CFD model describing the pipeline decompression  (Mahgerefeh et al. , 2006; Brown et al. , 2015)  and the Modified Bai-Wierzbicki (MBW) material damage model  (Döbereiner and Thibaux. 2013; Talemi et al. , 2016a) , which has been implemented in the Finite Element Analysis (FEA) code ABAQUS. The coupled fluid-structure ductile fracture model has been validated against experimental data reported in the literature on fracture propagation in a large-scale X70 pipeline  (Makino et al.
The movement of the gas phase, which depends on phase properties like density and viscosity, is governed by the properties of the gas mixture. In our numerical implementation we allow the composition of this gas mixture to be continuously updated with time as the different gases of the mixture dissolve into brine at different rates according to their specific solubilities. With this approach we assume that the gas mixture consisting of CO 2 and impurities does not separate within the gas phase itself and can therefore be treated as a so-called pseudo gas mixture representing the fluid properties of the mixture as calculated from the updated EOS. In practice this is implemented into the TOUGH2 code as follows: during the simulation, we first compute the mass fractions of the different gases in the gas mixture.
We then calculate the gas properties of each component gas, for example, compressibility factor, fugacity, and solubility of each gas into the brine phase by using the thermodynamic equations developed by  Duan and Mao (2006)  and  Duan et al. (2007). We then calculate density, viscosity and enthalpy of the gas mixture. These physical properties are determined by the external program REFPROP developed by U. S. National Institute of Standards and Technology (NIST). This program uses Kunz and Wagner models for gas mixtures  (Kunz et al. , 2007). After advancing a time step dissolved mass in the brine will be subtracted from the initial gas contents and the mass of each gas is updated for the next time step.
Also in this WP EoS development was supported by numerous experiments undertaken to establish the thermophysical properties of CO 2 mixtures, including tertiary and quaternary mixtures, at the typical pressures and temperatures in CCS processes. The focus of WP2 was on the fluid flow of CO 2 mixtures during "normal" CCS operations and during accidental high pressure releases of CO 2 following pipeline rupture in brittle or ductile fracture modes. At the same time, the impact of CO 2 impurities on the compression power requirements using various CO 2 compression strategies was investigated via the application of EoS. Additionally, the EoS have been coupled to a CFD code to simulate steady-state fluid flow within pipeline networks. In order to provide understanding of the types of phenomenon that can occur during a high pressure CO 2 release and to provide validation data for the associated CFD simulations, CO 2 mixture experimental releases at various scales have been performed.
research leading to the results contained in this paper received funding from the European Commission 7th Framework Programme FP7-ENERGY-2012-1 under grant number 309102. The paper reflects only the authors' views and the European Commission is not liable for any use that may be made of the information contained therein. The work by CanmetEnergy outlined in section 3. 3 was supported by PERD (Program of Energy Research and Development) and the Clean Energy Fund. The authors gratefully acknowledge the support provided by these programs.
Code availability The MetUM is available for use under licence. A number of research organizations and national meteorologi-cal services use the MetUM in collaboration with the Met Office to undertake basic atmospheric process research, produce forecasts, develop the MetUM code and build and evaluate Earth system models. For further information on how to apply for a licence please contact um_collaboration@metoffice. gov. uk. JULES is available under licence free of charge. For further information on how to gain permission to use JULES for research purposes see https://jules. jchmr. org/software-and-documentation. The model code for NEMO v3. 4 and v3. 5 is available from the NEMO website (http://www. nemo-ocean. eu). On registering, individuals can access the code using the opensource subversion software (http://subversion.
Acknowledgements. We thank the editor and the two reviewers ( Stephen Griffies and Andy Hogg ) for their constructive comments. Matt Martin  provided useful input on data assimilation for ORCA12. This work was primarily supported by the  Joint DECC/Defra Met Office Hadley Centre Climate Programme  ( GA01101 ). Part of the work was undertaken with National Capability funding from  NERC  for ocean modelling. We acknowledge use of the MONSooN system, a collaborative facility supplied under the  Met Office-NERC Joint Weather and Climate Research Programme (JWCRP). Malcolm J. Roberts  acknowledges support from the  EU   FP7 IS-ENES2  project for work on ESMF and regridding tools. We acknowledge the considerable effort on development and evaluation of ORCA12 by the  DRAKKAR community. Helene T. Hewitt  thanks  Immy Hewitt.
In the literature, most underwater dense pipelines are based on the geometric criterion  [21, 26, 32, [52] [53] [54] [55] [56]. Some studies also employed this criterion  [37, [39] [40] [41]  through the use of software developed for the air environment such as the open-source software MicMac  [57]  or the proprietary software Photoscan (Agisoft). The spatio-temporal criterion is rarely used in this context but is nonetheless often found in structure-from-motion (SFM) strategies because of its tracking capabilities  [26]. As for the spatial neighborhood criterion, this approach of quasi-dense reconstruction was mainly developed in the context of high disparity aerial imagery  [58] [59] [60]  and is practically absent from underwater work. It is used in some studies  [16, 37, 38, 61, 62]  through the open-source software PMVS (Patch-based Multi-View Stereo)  [63]  and in a recently developed pipeline for an aquatic study  [64] , both times in combination with the geometric criterion.
This ability really distinguishes it from more usual algorithms dedicated to sparse matching. Therefore, we compute the number of inliers (seeds) and outliers obtained according to the number of interest points requested (target ranges). For each range from  to [3900-4000], computations are done on the whole database. The results are shown as a graph in Figure  20. The ratio of inliers versus outliers is stable (±4% only over all the ranges) even when the number of interest points is very low or very high. In general (median), our algorithm can extract 53% of seeds from the initial interest points (73% in the best cases and 28% in the worst cases). Furthermore, only 23% of the initial matches become outliers (7% in the best cases and 46% in the worst cases) and no false inliers have been found.
Given these results, we can consider that our algorithm is robust to the number of interest point requested. Implementations of SIFT and SURF detectors-descriptors available in the OpenCV library have been used to process the image database. The results obtained are presented in the tables of Figure  20. Overall, SURF performs a little bit better than SIFT (46% versus 35% median value), but we must note that SURF is very irregular in its results and beyond 1000 points of interest the median rate falls below 40%. The filter also often lets through some outliers  (3) (4) (5). Conversely, SIFT is more stable and unfiltered outliers are less common (one or two occasionally). However, it presents a low ratio of inliers compared to the number of interest points and this ratio further decreases with the increase in the number of points.
However, numerous acquisitions in low light conditions are blurred. So, the closed-loop allowing to adapt the vehicle speed to the sensor needs has to be improved. We extracted several dense point clouds from this dataset, some of which are illustrated in Figure  29. These results were used to analyze the performance of our post-processing pipeline. Concerning the robustness to the quality of the input images, the various point clouds we produced show that the adaptive threshold step we added in the Harris detector is crucial. Indeed, on the images we acquired under quite similar conditions, the final thresholds found by our algorithm for a given target range of interest points vary in an interval of 20%. Furthermore, we acquired images with very low light conditions (close to sunset time) to test the limits of our adaptive thresholding.
Acknowledgments: Thanks to the  French Direction Générale de l'Armement (DGA)  for the funding of the thesis that lies behind the work presented in this article, to the  French University of Marne-la-Vallée  for the funding of the optical payload, to Charenton-le-Pont city for diving and swimming pool access and to Fischer Connectors company for their generous grant and advices. Thanks to  Marc Pierrot-Deseilligny  for his help with MicMac and to  Jane Jaquemet  for her time and her patience in the rereading of this article.
Developers and operators of a full range of hydroelectricity structures (500 kms of dykes, 19 in-stream structures and 12 high-head installations) for over 100 years, CNR and SHEM are required to guarantee a constant monitoring of their infrastructure to ensure hydraulic safety, meet their regulatory requirements and to be prepared to new challenges: improved efficiencies, optimised monitoring of high risks areas and information sharing between the different operators. To reach a better surveillance and improve risk management, CNR and SHEM called upon the Advitam Group to set up, in less than 12 months, the innovative software solution 'ALOHA' for the inspections and monitoring of their structures to a better knowledge of risk. This monitoring is ensured by over 15,000 sensors, allowing an analysis of historic data spread over 30 years.
ALOHA, a development based on the ScanPrint® web based software, allows the full range of operators to collect, visualise, analyse and edit all of the auscultation and monitoring data in an integrated platform. To respond to the specific needs of CNR and SHEM, innovative modules were developed for both in-office work and mobile tools. This article will detail the functional requirements of the CNR and SHEM, the solutions proposed by Advitam with the ALOHA system and the integration of the solution in the risk management.
All the works are monitored by the 15,000 monitoring measured at weekly to quarterly intervals. The Société Hydroélectrique du Midi (SHEM), operates, monitors and maintains 58 hydroelectricity plants, the majority being high-head, plus 12 dams in the Pyrenees, the Massif Central and the valley of the Lot. From the 1 st of January 2016, SHEM will carry out all examination operations on these dams internally, where some of this work has previously been outsourced; to this end, the departments concerned (the Safety Pole in the head office and on-site operators) and also the Engie experts instructed by SHEM to analyse its examination data, will use the ALOHA tool. ALOHA was initiated in November 2013 by the CNR's Centre d'Analyse Comportementale des Ouvrages Hydrauliques and relates more specifically to the redesign of the current structural examination database.
7HOHPHWU\ IURP WKH PRQLWRULQJ VHQVRUV SHEM's structures are dams that are independent of each other, each one having its own set of examination equipment, and references which differ from those of CNR (see 2. 5). Due to their situation in a highland and high mountain environment, SHEM's dams are subject to tough access constraints, particularly in winter. Thus, in order to overcome this physical isolation, much of the examination data for these dams does not have to be read at physical visits to the structures, but instead is transmitted directly to the database, via a telemetry device (collector) which forms the link between the examination equipment and ALOHA. The tool, therefore, needs to have an interface allowing the management of these telemetry data.
)DLOVRIW PRGH For structures in isolated locations, losses of network connection can occur between the examination device and the application for processing the examination data. Continuity of service must nonetheless be guaranteed so as to be able to continue to acquire and validate these data while meeting requirements in terms of timing for level 1 and 2 analyses (see 3. 1. 5). Thus, a specific module in the application which can be used on a mobile input terminal should allow the following situations to be managed: x loss of the central unit (collector) that launches the telemetry x loss of connection with the basic ALOHA tool. Allowing the following actions to be carried out in another way: x acquisition on the dam of data about the structure (on the mobile input terminal) x offline validation of acquired examination data with, to this end, access to a sufficiently long history.
6SHFLILFDWLRQ DQG VWUDWHJ\ RI IORZV Being a project carried by CNR and SHEM from the start, the flow strategy wanted by the SI CNR needed to allow exchanges between a variety of geographical sites and users. The basic premise was a dissociation in the production of the CNR and SHEM bases, although they would be hosted by CNR. SHEM and CNR users would then only be able access the base of their respective companies. In addition to internal users, the two bases needed to be open to external service providers involved in the behavioural analysis or level 2 validation (paragraph 2. 6). The tool also had to be able to communicate easily with other proprietary databases, to retrieve telemetry data, associated measurements (hydrometry) or GIS data (base maps, land data etc. ).
Non-compliance with the cut-off date for production of 24 December 2015 The launch meeting for the ALOHA project was held on 16 March 2015. One of the objectives set at this meeting was to complete the specification workshops by the end of April. By the start of May, the following works had been carried out: x Transfer of data from the Castor / SHEM / IGNHr bases with regard to the structures, equipment, sensors, photos, measurements, components, inspection routes, campaigns, scenarios and observations. x Online launch of an initial version of ALOHA from early April. Regular updates throughout the project have allowed the new developments to be tested while "going with the flow" of the overall project schedule. x Holding of user workshops for all the subjects initially planned. x The interfaces with the other systems (central unit, GIS, etc.
) had been specified. x The functional specifications had been entirely revised to ensure that the various meetings covered all the points. x The mobile ALOHA app had been specified. One of the criteria for choosing the ScanPrint IMS® solution had been the level of cover of the functionalities proposed without specific development or settings. With around 70% of functionalities covered, it was possible to make use of the AGILE development method in order to validate the modules and developments as the project progressed.
Understanding what CNR / SHEM needed The initial transfer of data allowed an initial version of ALOHA to be quickly put in place. Thus, it was possible from the start to organise user workshops on the solution under development, allowing needs to be specified and business rules to be described and adjusted in ScanPrint IMS® in concrete terms. These workshops took place over a 2-month period from March to May 2015. As the detailed specifications defined prior to the consultation were very detailed, the part analysing users' needs was thus relatively small and did not reveal any oversights or misunderstanding of what was needed.
$UFKLWHFWXUH The solution relies on: x a Microsoft SQL database server already present in the CNR's architecture. This is because the data of SHEM and CNR are split into 2 different bases. x a dedicated IIS-hosted web application service (IIS : Internet Information Services) The application server hosts the various applications for CNR and SHEM: x The ALOHA Base: This is an RIA (Rich Internet Application) based on Entity Framework. NET and RIA services. x ALOHA Mobile. This is an HTML5 app compatible with the various OS on the market (Windows, Windows Phone, Android and IOS). The app was developed on the Ionic and Angular JS frameworks. The "https" protocol was put in place in order to ensure data security. 7HOHPHWU\ For the telemetry of dams in isolated sites, a single automatic examination data collection centre, the "Collector", connects at regular intervals to the local units specific to each dam (which are in turn connected to automatic measurement units fitted in the dams) and recovers the data acquired by the latter.
The data from this collector must then be integrated in the ALOHA application. Thus a development specific to this need was carried out. ALOHA thus has a collector control interface (Figure  2 ) allowing agents to do the following directly from the application : x Retrieve data collected at local unit level. During this retrieval operation, measurements and any error messages are consolidated in the general database. The measurement retrieval process is supplemented by the on-the-fly calculation of the various components. x Control the acquisition cycle of each collector (frequency and date) x Force an acquisition on a collector +LVWRU\ UHWULHYDO The history retrieval was implemented essentially on 3 existing main bases: x An Oracle base (Castor for CNR) x An Access base (IGNHR for CNR) x A series of Excel files (extraction of Panda data for SHEM).
The retrieval of a history is not simple a transfer of data from one data model to another. Indeed, data retrieval faced numerous problems: x The various bases were managed differently from the measuring units. Thus, different mapping operations had to be performed to make the data consistent. x The databases were not self-sustaining as certain business parameters were included in the applications. Thus, certain data also had to be retrieved in the applications. x A verification of all the data was carried out during the retrieval process. x As some data was incomplete, characterisation work was carried out in parallel. Indeed, ALOHA includes a reference which contains all the assets concerned by the management cycle (powerplant, dams, locks, dikes, measuring equipment etc. ) and also all the "objects" for organising this physical estate (the Rhône, the various canals, departments and developments).
Each test is symbolised by a red or green shape; x If one of the tests is not verified, the user must confirm the measurements: A "Confirm measurements" button appears, which the user then needs to click to be able to record the measurements; x If measurements cannot be entered, the user can move on to the next sensor; x In this case, the application automatically creates an anomalous finding in which the user enters additional information; x Once a connection is possible on the ALOHA server, the reader synchronises the data, and the data is then available to everyone concerned. Inter-operability and portability of tools x Analysis tool x Specific modules for the examination of hydraulic structures ,QWHJUDWHG WRRO By its design and its various modules, ALOHA is an integrated solution in which a single interface is used to display all the information needed for the comprehensive management of the properties playing a part in hydraulic safety.