dataesr/scientific-paragraphs-categorization huggingface dataset:
Framework for integration: the MYSTIC tool Integrated OAD activities have been running at ONERA for a long time, but the existing tool suites were either based on commercial integration softwares, or not sufficiently modular to perform MDO studies. Therefore in the frame of this project, and taking benefit of on-going internal studies on MDO, a large effort was undertaken to define a new, modular, evolutive tool to conduct the configuration studies, first at L0 level. A first, simplified version of the tool called FAST (Fixed-wing Aircraft Sizing Tool) was initiated and is shared with ISAE. The description can be found in  [1]. An enhanced version calibrated and improved with ONERA internal knowledge has then been developed under the name of MYSTIC, and will be used in this study. -Loop 1: design of HTP and VTP surfaces upon trimmability and stability HQ criterions at take-off and in cruise, iterating on the CoG position, -Loop 2: iteration on the wing position to ensure a desired static margin, after calculation of aerodynamic center, -Loop 3: iteration on the maximum take-off weight (updated after OWE and mission fuel calculation) and wing size (to ensure required approach speed and accommodate the mission fuel).
The logic of the study will be to progressively replace the L0 tools pre-existing in MYSTIC by the higher fidelity modules that will be described in the subsequent section B, and to tend to the process definition described in section A. 1. In order to achieve this goal, a common input/output XML file is used, gathering the whole description of the aircraft through a dictionary of parameters. The geometrical parameterization is shared among all the modules and enables to generate an OpenVSP 8  CAD model for visualization and disciplinary computation purposes.
Cruise performance module (CANOE) This module is based on Euler-CFD computations performed by the SU²  [2]  code and on the in-house ONERA far-field drag decomposition tools ffd  [3]. This tool enables, from a CFD solution, to compute the different physical drag components (viscous, wave and induced) and eliminate the non-physical drag sources, called spurious drag. Thanks to this approach, the sensitivity of the total drag with respect to the mesh is drastically reduced compared to classical near-field integration methods. This aerodynamic module also provides the other aerodynamic coefficients (notably the lift and pitching moment coefficients). The surface meshes used for the Euler computations are created using OpenVSP  [4]  and the geometry defined by MYSTIC. The unstructured final volume meshes are generated using tetgen  [5].
Low-speed-performance module This module is based on a coupling between a VLM and 2. 5D RANS computations performed by the ONERA in-house elsA code. The objective of this coupling ([6] and  [7] ) is to integrate in the VLM, the nonlinear effects captured by the 2. 5D RANS computations (detached flows). Having 2. 5D computations instead of purely 2D computations enables to take into account the transversal effects along the wing span and provides a more accurate prediction of the maximum lift coefficient (assuming that the 2. 5D RANS computations are accurate). The coupling algorithm (also detailed in Figure  7 ) proceeds as follows. For each spanwise section of the wing,  A dedicated validation procedure has been set-up to evaluate the accuracy of the method and the results have been compared with full 3D RANS computations and experimental results.
This is highly expensive, for higher frequencies, and unpractical for eventual optimization or refurbishing processes. 9 Rapid numerical methods (BEM) for solving Helmholtz equation in presence of arbitrary scattering geometries are based on mathematical approximations of an interaction matrix, representative of a perturbed linear system written on this surface. Onera's in-house code BEMUSE solves this system using either a Brakhage-Werner or a Burton-Miller integral formulation. Sommerfeld's radiation condition applies at infinity and the scattering surface defines the boundary conditions which can be, for instance, Neumann, Dirichlet or Robin conditions. These elements are coupled with an algebraic approach of the kernel approximation (inverse matrix) based on the Adaptive Cross Approximation (ACA) method.
Aero-elasticity Today the aeroelastic analysis does not enter directly into the OAD process set-up, it is a secondary module which allows to analyze the aeroelastic behavior of the pre-designed aircraft but the final objective is to include this module in the sizing loop. The aeroelastic analysis is mainly devoted to the structure optimization of the pre-designed aircraft under dimensioning loadings (defined by the flight domain) and consequently to the flutter analysis of the optimized aircraft. The analysis is based on the NASTRAN code which has all the required modules for an aeroelastic analysis of a complex structure. It has been coupled with the hybrid approach developed at ONERA. This method couples the classical DLM approach with 3D RANS CFD computations performed by the in-house elsA code  [9]  to consider the non-linear effects which are not taken into account with DLM (zero lift coefficient, camber, thickness, stall, shocks,…).
As an illustration, the overall sizing of the aircraft with this highfidelity model in the loop leads to the results shown on Figure  20  in terms of mass breakdown and fuel consumption. Compared to the L0 results, the optimal aspect ratios for MTOW and fuel consumption are shifted towards lower ones, but result are not directly comparable due to different wing planform (with and without kink). Similar results were also obtained for other wing planform parameters (sweep angle, taper ratio, spanwise thickness and twist distributions). This exhibits that the OAD process coupled with the high-fidelity aerodynamic module can be used for wing planform optimization as wing planform parameter sensitivities are correctly captured. Figure  20 : masses of the resized aircraft 3. Structure Using the L0 modules of MYSTIC (especially for structure), it was observed that the dependency of wing weight to AR was fully linear, due to the very simple mass breakdown correlations used in this approach.
A-320-like mission The TLARs of the aircraft are given by the configuration CSR-01 from the CeRAS open database of the University of Aachen  11. The main features are recalled below: -Reverse engineering of the CeRAS configuration with the MYSTIC code, -Design of an A320-like aircraft with rear engine and T-tail features (to ease future innovative engine integration studies) -Design of the previous aircraft with more aggressive technology hypothesis (eg. SFC reduced by 20%, aerodynamic efficiency increased by 15%, airframe weights reduced by 10%) representative of a 2035 EIS. Results and illustration of the configurations are given below:  More specifically, it can be seen that the rear-engine option, after subsequent redesign of wing, HTP and VTP, provides an overall 1. 5% penalty both on MTOW and on fuel burn, which is consistent with the experience of aircraft manufacturers.
IV. Conclusions In the frame of the Clean Sky 2 -ITD Airframe, ONERA is conducting a design and downselection study on innovative aircraft architecture. In order to enable a fair comparison and a sufficient level of accuracy, the inhouse MYSTIC OAD process is used and enriched towards new configurations and higher fidelity. This paper has described the set of tools used to incorporate more high-fidelity in the OAD process, which proves to be challenging from an interface point of view on the one hand (set of data to be exchanges) and from a computational point of view on the other hand (robustness and calculation time of HF models). To improve the reliability of the overall process, a set of parametric studies have been conducted on the overall process, with respect to simple parameters such as aspect ratio.
Acknowledgments The authors would like to specially thank: -The  European Commission  and the  Clean Sky 2 Joint Undertaking  for the financial support in the frame of  Clean Sky 2 Airframe ITD ;  -Peter Schmollgruber  and  Alessandro Sgueglia  ( ONERA Information Processing and Systems Department ) for their contribution to the MYSTIC tool development. -Alexandre Sapet and  Jean-Luc Hantrais-Gervois  ( ONERA Aerodynamics, Aero-Acoustics and Aero-elasticity Department ), developers of the CANOE tool suite. - Maxime Vergez  ( ONERA Aerodynamics, Aero-Acoustics and Aero-elasticity Department ), developer of the VLM/2. 5DRANS coupling strategy. - Laurent Sanders  ( ONERA Aerodynamics, Aero-Acoustics and Aero-elasticity Department ) for his contribution to the aeroacoustics studies.
Fission chambers are operated in Campbelling mode for an efficient gamma rejection. FNDS also includes a specific software that processes measurements to compensate online the fissile material depletion and to adjust the sensitivity of the detectors, in order to produce a precise evaluation of both thermal and fast neutron flux even after long term irradiation. FNDS has been validated through a two-step experimental program. A first set of tests was performed at BR2 reactor operated by SCK•CEN in Belgium. Then a second test was recently completed at ISIS reactor operated by CEA in France. FNDS proved its ability to measure online the fast neutron flux with an overall accuracy better than 5%.
A. FNDS general description In order to compensate difficulties mentioned above, FNDS includes an acquisition system allowing measurements with fission chambers in Campbelling mode. This mode allows a very efficient rejection of the gamma contribution  [2]  while offering a very wide dynamic range. FNDS also integrates signal processing software that firstly calculate in real time the evolution of the fissile coating of the fission chamber using an evolution code DARWIN  [3] , and that secondly deduce the corresponding evolution of the neutron sensitivity of the fast fission chamber. This software extracts in real-time the value of the fast neutron flux from the signal. In order to allow a precise evaluation of thermal neutron fluence received by the 'fast' fission chamber, FNDS is equipped with a second detector that is sensitive to thermal neutron flux and positioned near the 'fast' fission chamber.
This second detector may be a fission chamber with 235 U coating or a Self-Powered Neutron Detector (SPND). FNDS thus consists in 2 detectors for 'fast' and 'thermal' spectrum domains, an acquisition system operating in Campbelling mode and a calculation module for on-line analysis of neutron fluxes (Figure  2 ). These innovations were patented for the detectors  [4]  and for the entire measurement system  [5].
D. Processing software The FNDS processing software calculates the sensitivity of each fission chamber to the thermal, epithermal and fast neutrons from specific input data (variance measurements, calibration coefficients, mass and isotopic composition of the fissile coatings, nuclear data library, etc. ) and then calculates the thermal and fast neutron fluxes versus time. Processing software includes DARWIN code  [3] , developed by the CEA, to compute fission cross sections of each isotope present in the fissile coating and to calculate the isotopic evolution of these coatings with fluence and time. The method to combine measurements and estimate fast neutron flux is detailed in  [6, 8].
ISIS reactor allows stable and reproducible irradiation conditions. Its neutron flux level is also sufficient to operate fission chambers in Campbelling mode. Moreover, its flexibility allowed to carry out all the measurements that were necessary for the qualification (online measurements and activation dosimetry measurements without external disturbance). Table  1  and Table  2  summarize the results of respectively thermal and fast neutron fluxes measured by FNDS and compared with values given the TRIPOLI4 Monte-Carlo code and activation dosimetry. These results confirm the good agreement for absolute thermal neutron flux using Campbelling mode operation and the corresponding calibration process  [10]. The deviation between FNDS fast neutron flux measurements and dosimetry results is mainly covered by the uncertainties of the activation dosimetry measurements (typically 4% for fast neutron flux evaluation).
2. 1. 1. 3. Elaboration of S3: Illkirch ORC. This scenario is based on a future project in Illkirch-Graffenstaden. The electricity capacity estimation for Illkirch is based on an assumed production temperature of 150 °C and flowrate of 82. 5 kg/s. The drilling for this plant is planned to start in 2018. The geothermal energy is dependent on the return temperature (T reinj ) whose relation with ambient temperature (T amb ) is expressed by Eq. (1), obtained by performing a regression of an existing dataset for Illkirch ORC performance provided by the appointed supplier. Additionally, another study was done outside this project and it resulted in Eq. (2) to describe electrical power (P el ) as a function of the ambient temperature for the parameters given for Illkirch. Accordingly, it will produce from 2.
b) Stimulation for Illkirch is assumed to take place to consider worst-case scenario. c) 12 Heat exchangers for S1, 3 heat exchangers for S4 and S5. d) e)g) Only applicable for S1. f)h) Only applicable for S2-S5. g) Other component consists of LSP pump, pipes, pumps, filters and valves. Except for LSP pump, the composition of its contents for S2-S5 is half of those for S1. i) Only applicable for S1, S4, S5. processes refer to the Ecoinvent 3 database. When exact materials cannot be found in this database, the USLCI and Agri-BALYSE databases are used as alternatives. Finally, some similar materials are selected when they do not exist in either database. To accommodate inaccuracy in estimation and material selection, uncertainty is introduced by using Ecoinvent Pedigree Matrix  (Weidema et al.
Each metre of drilling at Rittershoffen required 23 kWh, and this is adopted for the Illkirch study. 2. 2. 2. 2. Drilling mud. In the Rittershoffen wells bentonite was the main viscosity agent used for the drilling mud, while in Illkirch wells mainly glycol mud is expected to be used. Unfortunately, not every mud component can be precisely represented by the Ecoinvent database, and thus the generic "inorganic chemical" was chosen to characterize these components. Consequently, inorganic chemical occupies 23% and 48% of the total weight of mud for GRT-1 and GRT-2 respectively and 5% for the case of the Illkirch well. In addition, LCI for calcium carbonate is taken from Agri-BALYSE, a public LCA database of French agricultural raw products. 2. 2. 2. 3. Casing and cement. The casing installation is designed uniquely for every well programme due to the unique geological properties of each well, thus it will differ in diameter and depth.
Rittershoffen geothermal heat plant The Rittershoffen heat plant consists of an LSP Pump, heat exchangers, piping, pumps valves, electrical components and the building that houses the geothermal installation. 2. 2. 5. 1. Plant construction. The building at Rittershoffen houses the heat exchange system, the offices, and the electrical room. This inventory includes the construction materials of foundation, steel structures, enclosures, insulation, and electrical installations (electrification and transformers). During the construction, electricity and diesel were consumed. Materials include mainly concrete, galvanized steel, rock wool, and cladding. Wall cladding is  represented as MCM (Metal Composite Material) and roof cladding is represented as insulated metal panel, both taken from the USLCI database.
Uncertainty In this study, both background LCA and foreground uncertainty are taken into account. Background uncertainty is incorporated in Ecoinvent 3 database. Foreground uncertainty is developed with the help of a Ecoinvent data quality pedigree matrix; a feature embedded in OpenLCA. Data sources are assessed according to the five independent characteristics reliability, completeness, temporal correlation, geographic correlation, and further technological correlation  (Weidema et al. , 2013). Each characteristic is divided into five quality levels with a score between 1 and 5, with 1 having the best data quality. Based on these inputs a lognormal distribution will be created. As a real case, the uncertainty for S1 is mainly due to the discrepancy of integrating the Ecoinvent 3 database, which leads to having low geographic and technological correlation.
However, since the rental cost is a constraint, it requires careful planning to minimize the number of  1 Calculated by OpenLCA for the Alsace electricity mix having 50. 3% nuclear, 45% hydro, 3. 6% coal and 0. 6% PV. Ecoinvent LCI database and ILCD 2011 (midpoint) are used. 2 Reference for France Natural Gas CO 2 emission: ADEME (2014). Sum of CO 2 emissions from upstream and combustion activities = 66. 9 tCO 2 /TJ PCI. Reference for boiler efficiency is assumed to be 97% (Garcia et al. , 2012). unproductive days. When the delay between wells is too long, it is no longer economical to keep the rig onsite. Casings installed in GRT wells originated in both South Korea and the Czech Republic (approximately 25% and 75% respectively) and were partially inspected in Dubai. Procuring the casing from a closer location is an alternative that will reduce GHG emissions.
Acknowledgements The authors acknowledge the  ECOGI Company  for providing relevant information about the Rittershoffen geothermal project. This study was supported by  ADEME (French Agency for Environment and Energy)  in the framework of the  EGS Alsace  project. The authors also acknowledge the team of ÉS Géothermie and  Roquette Frères  for all their expertise and their enormous support during the undertaking of this study. Turboden is also acknowledged for their information with regards to ORC components. The authors would equally thank  Dr. Martino Lacirignola  from  ADEME  for his valuable advice and help.
Sequence of EBNA1 cDNA The sequence of EBNA1 cDNA (GenBank: MG021311. 1) is as follows (the GAr-encoding sequence which forms G4 is highlighted in cyan): ATGTCTGACGAGGGACCAGGTACAGGACCTGGAAATGGCCTAGGACAGAAGGAAGACAC ATCTGGACCAGACGGCTCCAGCGGCAGTGGACCTCAAAGAAGAGGGGGGGATAACCATGGAC GAGGACGGGGAAGAGGACGAGGACGAGGAGGCGGAAGACCAGGAGCTCCGGGCGGCTCAG GATCAGGGCCAAGACATAGAGATGGTGTCCGGAGACCCCAAAAACGTCCAAGTTGCATTGGC TGCAAAGGGGCCCACGGTGGAACAGGAGCAGGAGGAGGGGCAGGAGCAGGAGGGGCAGGA GCAGGAGGAGGGGCAGGAGCAGGAGGAGGGGCAGGAGCAGGAGGAGCAGGAGGAGGGGC AGGAGCAGGAGGAGGGGCAGGAGCAGGAGGAGGGGCAGGAGCAGGAGGAGGGGCAGGAG CAGGAGGAGGGGCAGGAGCAGGAGGAGGGGCAGGAGGAGGAGGAGGGGCAGGAGCAGGA GGAGGGGCAGGAGCAGGAGGAGGGGCAGGAGCAGGAGGAGGGGCAGGAGGGGCAGGAGC AGGAGGAGGGGCAGGAGCAGGAGGAGGGGCAGGAGCAGGAGGAGGGGCAGGAGCAGGAG GGGCAGGAGCAGGAGGAGGGGCAGGAGCAGGAGGGGCAGGAGCAGGAGGAGGGGCAGGA GCAGGAGGAGGGGCAGGAGCAGGAGGGGCAGGAGCAGGAGGGGCAGGAGCAGGAGGGGC AGGAGCAGGAGGGGCAGGAGGAGGAGGAGCAGGAGGGGCAGGAGGGGCAGGAGCAGGAG GGGCAGGAGGGGCAGGAGCAGGAGGAGGGGCAGGAGGGGCAGGAGCAGGAGGAGGGGCA GGAGGGGCAGGAGCAGGAGGGGCAGGAGGGGCAGGAGCAGGAGGGGCAGGAGGGGCAGG AGCAGGAGGGGCAGGAGGGGCAGGAGCAGGAGGAGGGGCAGGAGCAGGAGGGGCAGGAG CAGGAGGTGGAGGCCGGGGTCGAGGAGGCAGTGGAGGCCGGGGTCGAGGAGGTAGTGGA GGCCGGGGTCGAGGAGGTAGTGGAGGCCGCCGGGGTAGAGGACGTGAAAGAGCCAGGGGG GGAAGTCGTGAAAGAGCCAGGGGGAGAGGTCGTGGACGTGGTGAAAAGAGGCCCAGGAGT CCCAGTAGTCAGTCATCATCATCCGGGTCTCCACCGCGCAGGCCCCCTCCAGGTAGAAGGCC ATTTTTCCACCCTGTAGGGGAAGCCGATTATTTTGAATACCACCAAGAAGGTGGCCCAGATGG TGAGCCTGACATGCCCCCGGGAGCGATAGAGCAGGGCCCCGCAGATGACCCAGGAGAAGGC CCAAGCACTGGACCCCGGGGTCAGGGTGATGGAGGCAGGCGCAAAAAAGGAGGGTGGTTT GGAAAGCATCGTGGTCAAGGAGGTTCCAACCAGAAATTTGAGAACATTGCAGAAGGTTTAAG AACTCTCCTGGCTAGGTGTCACGTAGAAAGGACTACCGATGAAGGAACTTGGGTCGCCGGTG TGTTCGTATATGGAGGTAGTAAGACCTCCCTTTACAACCTCAGGCGAGGAATTGCCCTTGCTAT TCCACAATGTCGTCTTACACCATTGAGTCGTCTCCCCTTTGGAATGGCCCCTGGACCCGGCCCA CAACCTGGCCCACTAAGGGAGTCCATTGTCTGTTATTTCATTGTCTTTTTACAAACTCATATATTT GCTGAGGGTTTGAAGGATGCGATTAAGGACCTTGTTATGCCAAAGCCCGCTCCTACCTGCAATA TCAAGGCGACTGTGTGCAGCTTTGACGATGGAGTAGATTTGCCTCCCTGGTTTCCACCTATGGT GGAAGGGGCTGCCGCGGAGGGTGATGACGGAGATGACGGAGATGAAGGAGGTGATGGAG ATGAGGGTGAGGAAGGGCAGGAGTGA.
Microscopy and Image Analysis Samples were examined with an LSM 800 confocal laser microscope (Carl Zeiss MicroImaging, Jena, Germany). ImageJ  [14]  was used for analysis of images. PLA signals were quantified in 100 cells per sample according to the protocol provided as Supplementary materials. Data analysis was carried out by ANOVA with Tukey HSD test using GraphPad Prism 5 for Windows (GraphPad Software, San Diego, CA, USA). Data shown are mean ± SD of three independent experiments.
Of note, PhenDC3, but not PDS, reduced the NCL-EBNA1 mRNA interaction (Figure  4A ). This is surprising as both compounds bind strongly the EBNA1 mRNA G4 structure in vitro  [11, 22]. In addition, a previous report has shown that PDS suppresses EBNA1 synthesis in vitro  [15] , while PhenDC3 increases the levels of EBNA1 in cellulo  [11]. Altogether, these contrasting results indicate that different G4 ligands may induce distinct biological responses and complementary assays are required to determine the activity of these compounds in the cellular context. To refine this finding, we developed a protocol to quantify PLA results using the public domain image processing program ImageJ (Figure  4B  and Supplementary materials). As advantages, quantitative PLA generates information suitable for statistical tests, avoids subjective data interpretation, and enables the estimation of differences less evident by the comparison of single images.
This methodology has been used for calibration purposes in the ice sheet modelling community (e. g. Stone et al. , 2010; Applegate et al. , 2012). The size of the LHS consists of 600 model realisations: half of it with the flux at the grounding line of  Schoof (2007)  (hereafter AN40S) and the rest with  Tsai et al. (2015)  (hereafter AN40T). The range of explored parameters is listed in Table  2. We assume a uniform statistical distribution within this range. (Fretwell et al. , 2013). The colour shading corresponds to the root mean square error in ice thickness relative to observations. The stars outlined in red are the 12 ensemble members having the lowest RMSE. The initial ice sheet geometry, bedrock and ice thickness, is taken from the Bedmap2 dataset  (Fretwell et al. , 2013, Fig. 4 ) using a spatial bilinear interpolation to generate these data on the 40 km grid.
On the other hand, the West Antarctic ice sheet shows more contrasted responses. Whilst for some ensemble members, the Ross embayment upstream region can be well represented (e. g. AN40S004 or AN40T065), the region feeding the Filchner-Ronne ice shelf shows a quasi-systematic ice thickness underestimation. These model deficiencies can be attributed to our coarse model resolution, providing a poor representation of the complex bedrock structure in the Filchner-Ronne area. The model differences from the observations are very similar to results from the Potsdam Parallel Ice Sheet Model (PISM-PIK) shown in  Martin et al. (2011)  in terms of amplitude but also in terms of structure. They are also generally similar to  Pollard and DeConto (2009). Consistent model biases amongst these models, which use different in- put data, suggest a common source of error related to the coarse model resolution (20 to 40 km) or uncertainties in the bedrock dataset, particularly large in East Antarctica (Fig.
Acknowledgements. We thank  Michiel van den Broeke  ( IMAU, Utrecht University ) for providing the RACMO2. 3model outputs. We also warmly thank  Claire Waelbroeck  for fruitful discussions on the construction of the index for sub-shelf melting rates. This is a contribution to  ERC  project  ACCLIMATE ; the research leading to these results has received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013) / ERC  grant agreement  339108. Edited by:  Julia Hargreaves Reviewed  by:  Fuyuki Saito  and two anonymous referees.