dataesr/scientific-paragraphs-categorization huggingface dataset:
This is due to an increase in pumping efficiency at the low plasma temperatures corresponding to high neutral pressure, semi-detached operation. The implication is that in the event of a loss of seeding, Ne impurity could be lost rather rapidly from the system. To provide input to GIS design and in support of the development of the ITER Plasma Control System, we have begun a series of time dependent simulations using the SOLPS-ITER code to study the response to the experimentally derived gas injection waveforms. Initial findings, are, as expected, that the high throughputs providing fastest response time will lead to overdrive of the system and thus much lower throughputs will in reality be required. The challenge is now to continue these rather time consuming and difficult simulations to establish the best compromise between total throughput and Ne concentration which provides adequate heat flux reduction on acceptable timescales.
Finally, the different methods are applied and compared on analytical test functions in Section 10. The ensuing analysis of results and presentation of related softwares should help in choosing specific gradient-enhanced techniques. All methods described in this article have been implemented and tested with the opensource matlab/octave GRENAT Toolbox  [12]. n t = ( n p + d • d • ) = (n p + d • )! n p ! d • ! (1) where n p is the number of parameters and d • is the degree of the polynomial. This is why other techniques for approximating functions such as parametric kernel-based metamodels have received much attention. The literature is already rich in contributions presenting and detailing surrogates models  [14] [15] [16] [17] [18] [19] [20] [21] [22] [23]. Hereafter, the basic steps in building and using surrogate models are summarized (see also Fig.
References Contents Original Keywords  [39]  Initial developments for taking into account gradients, application to water flow through a borehole Bayesian prediction using derivatives, Gaussian Process  [40]  Approximation of functions using gradients Kriging to model gradients  [41]  Theoretical developments and application to analytical functions First Order Kriging  [43, 44]  Gradient-based metamodel for minimizing the drag of an aerofoil (CFD) Direct and indirect cokriging  [45]  Gradient-based cokriging for optimization, infill strategy and application to structural optimization Kriging model including derivative information  [46]  Comparison with other gradient-enhanced metamodels and application to fluids Kriging, gaussian process including derivative  [21]  Developments for using gradient and hessian information (code available) Gradient-/Hessian-Enhanced Kriging  [47]  Application to aerodynamic optimization Cokriging, Gradient-Enhanced Kriging  [48]  Comparison of kriging with and without gradient, infill strategy and application to aerodynamic optimization.
Available softwares for gradient-enhanced metamodels Despite the wide use and availability of metamodels that exclusively use simulation responses, y(), the more recent gradient-enhanced metamodels are only proposed in a few codes. GRENAT  [12] , which stands for GRadient ENhanced Approximation Toolbox, is the toolbox that was used for generating all results and plots proposed in this review and in  [59, 94, 95, 131, 132]. GRENAT is written in Matlab/Octave and follows the object oriented Matlab's syntax. It allows building and exploiting response-only, and indirect and direct gradient-enhanced kriging, radial basis functions and support vector regression. It can be linked to the MultiDOE toolbox  [133]     The ooDACE Toolbox  [134, 135] , also developed in object-oriented Matlab, has an implementation of cokriging that could accomodate accounting for gradients.
Nevertheless, no HE scheme fits every application efficiently. One possible alternative is to determine the best HE scheme given an application. Criterion are bounded by limitations of the server and application constraints like, among others, execution time (complexity), number of homomorphic operations that are processed, memory usage and security strength. As HE can be very memory and time consuming, analyzing every existing HE scheme by varying their input parameters would involve intensive software simulations. In this work, we present a tool named PAn-THErS that aims to help analyzing and prototyping HE schemes. PAnTHErS workflow is illustrated in Figure  1 : it proposes to build functional models of HE schemes (step 1 ) which can be analyzed and configured regarding the application requirements (step 2 ).
Implementation PAnTHErS is implemented in Python using Sage. Each type of functions (e. g. atomic) is defined by a class (e. g. AtomicFunction class) allowing creating HE schemes executable. Moreover, each type of functions has also two associated classes corresponding to complexity and memory cost analysis (e. g. AtomicFunctionComplexity and AtomicFunctionMemory classes). A design pattern Visitor can be used to generate analysis functions automatically (e. g. Memory. addTimes and Complexity. multScalMat functions presented in Section 3). A Visitor is an operation performed on elements of an object structure of a class without changing the class itself  (Lasater, 2007). In addition, for each HE scheme that has to be model a distinct class is created. HE basic functions are implemented in each HE scheme class.
Conclusion and future works This paper presents PAnTHErS, a tool that provides a way of evaluating HE schemes. Besides, this approach offers scalability and incremental design. It dispenses with the need for software implementation and simulations of HE schemes. The schemes are modeled as sets of reusable functions that are stored Figure  5 : Evolution of memory cost in function of log 2 (q) expressed in number of 32-bit integers stored. We fix ω = log 2 (w). in the library. After the modeling phase, PAnTHErS returns valuable information about HE schemes in terms of computational complexity, memory cost and multiplicative depth. This analysis is a lightweight operation as the functions of the library have already been analyzed. Evaluating PAnTHErS results enables to determine if the scheme is an interesting candidate for a particular application using HE.
Measurements of the isotopic contents of spent nuclear fuel provide experimental data that are a prerequisite for validating computer codes and nuclear data for many spent fuel applications. Under the auspices of the Organisation for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) and guidance of the Expert Group on Assay Data of Spent Nuclear Fuel of the NEA Working Party on Nuclear Criticality Safety, a new database of expanded spent fuel isotopic compositions has been compiled. The database, Spent Fuel Compositions (SFCOMPO) 2. 0, includes measured data for more than 750 fuel samples acquired from 44 different reactors and representing eight different reactor technologies. Measurements for more than 90 isotopes are included. This new database provides data essential for establishing the reliability of code systems for inventory predictions, but it also has broader potential application to nuclear data evaluation.
Introduction Integral benchmark experiments provide global measures of data performance for applications and are a valuable resource for nuclear data testing and evaluation efforts. Integral testing of the US ENDF/B-VII. 1 nuclear data library  [1]  and several international nuclear data libraries rely heavily on testing using critical benchmarks as compiled in the International Criticality Safety Benchmark Experiment Project (ICSBEP) Handbook  [2]. Integral testing of ENDF/B-VII. 1 included more than 2,000 critical benchmarks for compounds, metals, solutions, and other mixed systems in thermal, intermediate, and fast neutron energy spectra. The use of criticality experiments as a centerpiece of data-testing efforts in the United States reflects both the investment in nuclear data development by the criticality community and the availability of high-quality and well-documented ICSBEP benchmarks.
Under the auspices of the Organisation for Economic Cooperation and Development (OECD) Nuclear Energy Agency (NEA), physics benchmarks have been compiled and documented in the International Reactor Physics Benchmark Experiment (IRPhEP) Handbook  [3]  that exercise nuclear data in broader range of applications. As of 2014, the IRPhEP Handbook contained 136 evaluations and measurements of critical buckling, spectral characteristics, reactivity coefficients, kinetics, reaction rates, and power distribution measurements. Both the ICSBEP and IRPhEP benchmarks are applied to realistic applications, including uncertainty analysis and cross section adjustment, uncertainty evaluation for reactor core design methods, criticality safety problem validation, and cross section data testing  [4]. However, an intrinsic limitation of both the ICSBEP and the IRPhEP benchmarks is that their focus, for the most part, is on static systems of unirradiated nuclear materials.
Thus, many of these benchmarks exercise data for predominantly uranium and plutonium metals and compounds, moderators/reflectors materials, and absorber (criticality control) materials. There are relatively few benchmarks for irradiated nuclear materials. Interim dry fuel storage research and licensing activities are increasing as spent fuel pools at nuclear plants in many countries are reaching capacity. In addition, geological repositories in Finland and Sweden are expected to be operational in the next decade. Recognizing the need for improved experimental benchmarks to support international activities in spent fuel management, the Expert Group on Assay Data for Spent Nuclear Fuel (EGADSNF), under the guidance of the Working Party on Nuclear Criticality Safety of the OECD/NEA, has developed and expanded a database of spent fuel isotope compositions obtained by destructive radiochemical analysis of irradiated fuel samples from commercial nuclear reactors.
Under this initiative, the Spent Fuel Compositions database, SFCOMPO 2. 0, originally developed by the Japan Atomic Energy Research Institution  [5] , has been expanded from 246 samples to more than 750 samples from 44 reactors representing eight different international reactor technologies. Because of the design complexities and uncertainties in the actual conditions of an operating reactor, applying these data to obtain realistic estimates of the accuracy of calculated results can be extremely difficult. In addition, there can be large uncertainties in the measurement data attributed to the complex radiochemical measurement processes not present in criticality and reactor physics experiments. Under the OECD/NEA, these data have been more completely documented and peer reviewed for accuracy by international experts.
2 libraries  [8]. More recently, additional assay measurements, including data made available in SFCOMPO, have been used in the validation of JEFF-3. 1  [9, 10]. Unlike the ICSBEP and IRPhEP benchmarks involving primarily steady-state systems, spent fuel experiments involve timedependent isotope depletion and decay as defined by the transmutation equations. Therefore, identifying the relationships between computational bias and nuclear data is particularly challenging. Tools to help understand and quantify the complex relationships between nuclear data and measured compositions are therefore needed in order to better utilize databases such as SFCOMPO 2. 0 for nuclear data testing and evaluation. This paper describes the current status of the new SFCOMPO 2. 0 database and benchmark development efforts, and it summarizes and demonstrates new nuclear data sensitivity and uncertainty analysis tools for depletion problems being developed and targeted for future release in the SCALE nuclear systems analysis code system  [11].
SFCOMPO database 2. 1. Database content SFCOMPO 2. 0 is a database of well-documented, peer-reviewed measurements of spent fuel assay data  [12]  that can be used by the nuclear community for validating computational systems and nuclear data in applications of reactor physics, nuclear fuel cycle, used fuel storage and disposal, as well as advanced fuels and reactor design concept evaluations. The measured assay data have been compiled and reviewed by international radiochemistry experts and modeling practitioners as coordinated through the activities of the OECD/NEA EGADSNF  [13]. Since 2007, the database has been expanded beyond pressurized water reactor (PWR) and boiling water reactor (BWR) fuel types that were included in earlier versions. The database now includes eight different reactor designs: light water PWR, BWR, waterewater energetic reactor (VVER)-440, and VVER-1000; heavy water CANDU (CANada Deuterium Uranium), graphitemoderated Magnox, AGR (advanced gas-cooled reactor), and RBMK (Reaktor Bolshoy Moshchnosti Kanalnyy) reactor designs.
Measurements have been acquired from 67 different fuel assemblies and 291 fuel rods. The updated database, SFCOMPO 2. 0, contains more than 20,000 measurement entries, with associated uncertainties as reported by the measurement laboratories. Each reactor design class includes a range of fuel assembly designs. The diversity of designs, coolants, moderators, enrichments, and fuel burnup values provides a wide range of neutron spectral conditions during irradiation that can be used to test the performance of nuclear data over different neutron energy regions. For example, the database includes spent fuel measurements for samples with enrichments from 0. 71 wt. % (natural U) to 4. 9 wt. %, and burnup values from 0. 85 GWd/tU to more than 70 GWd/tU. Coolant and moderator materials include light water, heavy water, gas, and graphite.
The reactors, country of origin, and the number of measured fuel samples available for each reactor are summarized in Table  1. The database is designed with a top-to-bottom hierarchical structure organized by reactors, fuel assemblies, fuel rods, and samples, with the measurement data usually being associated at the sample level. However, some experiments involving dissolution of entire assemblies from a reprocessing plant are included and are therefore associated with the assembly level. The database includes measured nuclide concentrations, activities, isotopic atom and mass ratios, measurement method, uncertainty, laboratory, and measurement date. Measurements for 91 isotopes are included (Fig. 1 ). Measurement data are always included as the values and units reported by the laboratory or experimental program.
Where possible, the measurement data are automatically converted by the program to a common mass basis of mg/g fuel or mg/g initial U. With the addition of newer experimental program data that include high-precision measurements and improved design and operating data documentation, the quality of the database has improved significantly compared to earlier versions. Moreover, as part of the data review process, multiple independent cross checks of the information have been performed. Also, preliminary benchmark models have been developed for many of the experiments. An important caveat on the database is that these reviews have been performed to ensure that the database accurately reflects the experimental data as reported and are traceable to primary reference reports. The reviews do not constitute a formal evaluation of the data to assess quality and accuracy of the measurements, consistency of the data, completeness of the documentation, development of benchmarks, or an analysis of uncertainties.
Database application interface SFCOMPO 2. 0 is a structured query language Java H2 relational database accessible through a graphical application interface developed by the NEA. The database has a similar interface and functionality as the IRPhEP Database and Analysis Tool  [15]  and Database for the ICSBEP  [16]  programs, also developed by the NEA. Information in the database can be accessed by browsing the data in a hierarchical organization structure by reactor technology type (e. g. , BWR, Magnox), reactor name, assembly ID, fuel rod, and measured fuel sample level. Fig. 2  illustrates the database structural organization as viewed in the application interface browser, expanded to show the data for one of the Fukushima-Daini-1 reactor data sets. Fig. 3  provides a screen shot of the database browse panel showing the organizational structure (left side) and the display of design and operating information at the assembly level.
A major advancement is the ability to store and display more complete and detailed fuel and assembly design information and time-dependent operating history data for the reactor, assembly, fuel rod, and measured sample. SFCOMPO 2. 0 also provides references for all the data it displays. These references are linked to primary reports such as radiochemistry laboratory reports, open journal papers, and assay data evaluation reports, providing full traceability of the data. Primary experimental and reference reports are accessible as electronic portable document format (PDF) versions of the reports that can be accessed through a built-in viewer.
Search capabilities Many database fields can be queried using the Basic and Advanced search panels. The Basic search query includes reactor type, measured fuel type (i. e. , U-metal, UO 2 or MOX, UO 2 -Gd 2 O 3 ), sample burnup range, and measured nuclide (see the nuclide search panel in Fig. 1  These search features allow the user to rapidly identify measurement data for isotopes, the reactor, and fuel design parameter characteristics of interest. The search panel returns the nuclide concentration values and uncertainties of experimental measurements as queried by the user for inspection and comparison purposes. SFCOMPO 2. 0 allows the user to visualize measurement data of selected samples and provides the numeric measurements data, along with other information queried. The search results list the sample level information for all samples matching the search criteria and the measurement data in separate panels.
A graphical plotter displays nuclide concentrations provided in normalized units of initial (unirradiated) fuel mass as a function of sample burnup. A more general plotting capability can be used to display most of the measurement data against other fields used as the x axis. Fig. 4  shows the results of a database query for 239 Pu measurements for all uranium-based fuels and a plot of the results and uncertainties as a function of sample burnup.
Applications of the database Destructive radiochemical assay data are one of the primary means used to validate depletion codes and nuclear data libraries for spent fuel applications. Measured isotopic data in SFCOMPO 2. 0 include many nuclides that dominate applications in nuclear criticality safety (burnup credit), gamma radiation sources, passive neutron sources, decay heat, and long-lived radiological isotopes important to geological repository disposal. A widely used approach to burnup code and data validation is to apply the code to calculate the same nuclide inventories for which measurements are available. This technique has underpinned uncertainty analysis for many spent fuel applications, including burnup credit, in nuclear criticality safety. In addition to measured nuclide concentration data, these applications also require the detailed design and reactor operating conditions needed to model the fuel assembly irradiation and predict the nuclide concentrations in the fuel.
Provided that the system description is given such that accurate estimates of fðEÞ can be obtained by transport calculations of the fuel assembly, and the nuclide content is measured with low uncertainty, spent fuel measurements can be used for nuclear data testing because the results of computation simulations of these experiments rely directly on the accuracy of the data used by the codes. Application to nuclear data evaluation is complicated by the large amount of data used by the codes for burnup simulations and time dependence of data importance in transmutation analysis. The production of any nuclides usually depends on cross sections and half-lives for many different nuclides in the production chain, and fission products also depend on their fission yields. Software tools to aid in the evaluation of nuclear data uncertainty in transmutation systems are being developed at Oak Ridge National Laboratory (ORNL).
Therefore, direct perturbation of nuclear data for these complex systems to determine the importance of nuclear data on different observables is often not efficient or practical. An efficient approach to calculating nuclear data sensitivities and uncertainties from nuclear data is to use adjoint-based perturbation theory. Adjoint techniques for criticality safety analyses have been developed and are now widely adopted for nuclear data sensitivity analysis, which is required for numerous applications including experiment similarity analysis  [18]  and nuclear data adjustment for eigenvalue problems  [19]. However, similar techniques are not widely available to analyze time-dependent transmutation or depletion systems. The development of the adjoint equations for depletion was first published by Gandini  [20]  and implemented in the ORIGEN code by Williams and Weisbin  [21].
(  2 ) to calculate sensitivity coefficients that relate nuclear data to uncertainty in nuclide concentrations after a specified irradiation and decay period. Using this approach, the importance of all 50,000 nuclear data elements in transmutation matrix on the production of any nuclide, group of nuclides, or integral response of interest can be quantified using a single forward and adjoint ORIGEN calculation. This capability provides an important tool for evaluation of spent fuel isotopic composition experiments, enabling the identification of important nuclear data responsible for formation of measured isotopic concentrations. Although sensitivities provide the importance of nuclear data, they do not reflect the uncertainties in the nuclear data. Uncertainties in the nuclear data propagate through the transmutation calculations and produce uncertainties in the calculated A prototype code for nuclear data sensitivity analysis using ORIGEN is being developed and tested.
Conclusions Increasing international interest in spent fuel isotopic data to validate computation systems extends to many scientific applications involving spent fuel, including nuclear criticality safety, fuel cycles, spent fuel storage, transportation, geological disposal, and advanced reactor and fuel design. The OECD/NEA, with support from many international organizations and experts, has led efforts to compile and expand experimental spent fuel measurement data representative of the world's current and past fleet of commercial reactors. This database, SFCOMPO 2. 0, was developed to serve as a central resource of reliable, well-documented experimental assay data, with design   specifications and operating information for use by the international scientific community, industry, and regulatory authorities.
The efforts of the EGADSNF have resulted in a 3-fold increase in the number of measurements as well as improved quality and documentation of the data through the contributions of experts in NEA member countries engaged in compiling and reviewing data. The EGADNSF plans to progressively expand the database and initiate development of benchmark models for each dataset with recommended model inputs and calculated results. Through the contributions and effort of NEA member countries, SFCOMPO 2. 0 is today's largest, freely accessible open source of experimental isotopic assay data for international reactor designs. Application of spent fuel measurements has not been universally adopted for integral nuclear data testing, and isotopic assay data are a largely underutilized resource in many data evaluation activities, including ENDF/B.