dataesr/scientific-paragraphs-categorization huggingface dataset:
The results obtained from the implementation of the proposed algorithm indicated that it was highly effective in finding the required solution to a set of problems which it randomly generated in comparison to a set of other algorithms which existed in literature. Rezazadeh et al. [25]  presented an optimization mathematical model for a virtual cell formation problem. At the first stage, the researchers proposed a linear programing model embedded with a particle swarm optimization (PSO) algorithm, and later they used a SA-based local search engine for solving this model. The results obtained from the application of the proposed model were compared with the optimal solution generated using the LINGO software. Based on operational time required for part processing, Nouri et al. [26]  introduced the bacteria foraging algorithm (BFA) as an optimization method for operation time required for the cell formation issue.
Tunnukij and Hicks  [36]  detailed the GA and Grouping GA that were used to address cell formation problems. Comprehensive summaries and taxonomies of research dedicated to formation of part family and machine cell problems using GA were presented by Arora et al. [37]. Shiyas and Pillai  [38]  introduced a mathematical model for the design of manufacturing cells which considered two conflicting objectives such as the heterogeneity of cells and intercell moves. The model was first solved using the optimization software package LINGO and then a GA based solution methodology was developed for solving problems of any size and consequently saving the computational time. Khaksar et al. [39]  presented a model for designing a multi-floor layout of CMs using integer linear programing. A GA approach was used with multiple objectives to minimise the total cost of the intracell, new machine purchasing and machine processing time.
Machine-part incidence matrix with operational sequence were the input data to design CMs. To measure the effectiveness of CMs, both grouping efficiency and grouping efficacy were used for this purpose. Comparing the consistency and the results obtained from applying the proposed algorithm showed that it has outperformed its counterpart from previous approaches. To simultaneously solve the cell formation and layout problems, Forghani and Mohammadi  [50]  proposed an integrated approach based on developing GA to effectively sort out such complex problems in acceptable computational time. Design inputs were represented by alternative process routings. Parts' demand, capacity of machine, multi-row arrangement of machines within cells, dimensions of cell, aisle distances, etc were considered in the proposed approach to be more realistic.
The performance of the proposed GA was evaluated by applying it to several examples previously presented in literature. The results indicated that when decisions about cell formation, inter-and intracell layouts and routing of parts are simultaneously made, the total material handling costs are significantly reduced compared to the sequential design approach. Golmohammadi et al. [51]  introduced GA to form a dynamic cell that uses the rectilinear distance notation to determine the cell layout continuous space. The goal behind this was to accurately calculate the costs of inter-and intracell movements of the parts as well as calculate the cost of restructuring the cell. The results obtained from implementation of the proposed GA showed its high accuracy as well as high speed of implementation compared to the application of a mathematical non-linear programing model through LINGO package for this purpose.
| Mechanism implementation and GA model description This section aims to provide a brief description of the mechanism necessary to implement the proposed approach. As a starting point, machine cells and part families are determined. Then, the machine-part incidence matrix is constructed. Generally, '0-1' machine-part incidence matrix is used to model the manufacturing system; all elements "a ij " in the matrix are either 1 or 0 "a ij " equal to '1' if part i is processing on machine j; otherwise, 0. Then, MATLAB software is used to build a particular GA programme to identify groups of machines and parts; group efficiency measure was used to evaluate the fitness function in the GA with the weight factor (q ¼ 0:5). After forming machine cells and parts families, machine cells and parts families are matched based on a "density index", which measures the density of (1s) inside the diagonal blocks.
| COMPUTATIONAL RESULTS AND ANALYSIS The combination of GA basic parameters used to solve the CMs design problem is 0. 5 for crossover probability, 0. 2 for mutation probability, 50 for population size and 500 for the maximum number of generations. MATLAB software was used to build a GA programme. Figure  3  presents the results obtained by using GA and the comparison between fitness functions. Also, for further clarification, the attached Appendix B contains in detail the codes of the designed GA in this research as well as the results obtained for 500 generations that include values of all best, worst, and average fitness for the 500 runs. The best value of fitness function was found at generation 91 and the value is 0. 8073 and this is the maximum reachable. Machine chromosome and part chromosome at generation 91 is the best chromosome and is considered the final solution.
2003; Carlson et al. 2011). The second boundary condition on solid walls is ∇φ • n = 0, which states that there is no diffusive flux through the walls. The full system of fluid and phase-field equations (2. 2-2. 3,2. 9-2. 10) together with the boundary conditions is discretized and solved using open-source code FreeFEM  (Hecht 2012). Adaptive mesh is used near the two-phase interface to capture the variation of the function C. More details about the PF equations and simulations can be found in appendix B. The PF model has three unknown free parameters; the phase-field mobility M , the contact-line friction µ f , and the interface thickness. To provide an intuition about each parameter, we vary them one at a time. As before, θ 0 = 95 • and Ca = 0. 20. Figure  3(a)  shows ∆x for µ f = 0, = 0.
Equilibration runs First, we measure the equilibrium properties of the water drop between two static plates and generate thermodynamically consistent initial conditions. This is done through socalled equilibration runs (see appendix C. 2). During the run-time of the MD simulation, we collect flow data in regular 0. 2 nm × 0. 2 nm bins for a time interval of 12. 5 ps. This yields instantaneous density ρ i (x, y, t) and flow velocity u i x (x, y, t), u i y (x, y, t) data as functions of space and time. After the initial transient, ρ i (x, y, t) is averaged over time to reduce noise in the liquid-vapour interface shape. The interface shape is extracted based on this averaged density, i. e. ρ (x, y). The interface position is determined by seeking the location where the liquid density transitions from bulk density to very small vapour density.
2) are spatially dependent. In the PF model, we define these fluid parameters through linear combination based on phase-field variable C as ρ (C) = ρ C + 1 2 -ρ v C -1 2 and µ (C) = µ C + 1 2 -µ v C -1 2. (B 3a,b) Recall that ρ and µ are the density and the viscosity of the liquid component, and ρ v and µ v are the density and the viscosity of the vapour component. The introduced governing equations are linearised, written into the weak form, and solved using an open-source finite-element software FreeFEM (Hecht 2012), which allows easy specification of finite-element weak form. Linear elements were used for the phasefield variables, while the fluid flow was resolved using Taylor-Hood elements (quadratic for velocity and linear for pressure). Mesh resolution was refined and results were checked for a few selected simulation cases.
20, 0. 20) nm (figure  1b ), and over time by aggregating all measurements in consecutive windows of 12. 5 ps. Averaged and binned variables are saved to file "on-the-fly" concur-rently with the simulation, thus vastly reducing the output size. Saving all atomistic trajectories would not be feasible. Consequently, the output of MD simulations is a range of data files containing so-called frames, corresponding to the sequence in time of the partially averaged MD data. Each frame contains the instantaneous field outputs ρ i (x, y, t), u i x (x, y, t) and u i y (x, y, t) as defined in §3. Post-processing to obtain averages over time intervals of several ns has been performed with in-house codes based on the freely available repository (https://github. com/MicPellegrino/densmap. git). The exact scripts are available upon reasonable request.
C. 4. Polynomial extrapolation of MD interface shape In this appendix, we describe how a polynomial fit is used to determine the final drop displacement for comparison with CFM models. In addition, the usefulness of the fit for extracting dynamic contact angle is assessed. Recall that through the extraction of θ 0 values (appendix C. 3) the first reliable bins for comparison with CFM have been identified (shown with green in figure  5a-d ). Consequently, there is a gap in interface data near the walls. To illustrate this, we show the equilibrium and sheared (Ca = 0. 20) interface shapes for θ 0 = 95 • in figure  C. 3(a). With a red dashed line, we show the assumed wall position. In CFM models, however, the interface shape continues to evolve smoothly until meeting the wall. Consequently, when comparing MD with CFM models, the empty data region near the wall is undesirable.
Acknowledgments We acknowledge funding from  Swedish Research Council  ( INTERFACE centre  and grant nr. VR-2014-5680 ). U. L. acknowledge support from the  ERDF  (project No. 1. 1. 1. 1/20/A/070 ). S. Z. acknowledges funding from  ERC  (advanced grant TRUFLOW). Numerical simulations were performed on resources provided by the  Swedish National Infrastructure for Computing (SNIC)  at  PDC ,  NSC , and  HPC2N. The authors thank  Tomas Fullana  and  Dr. Petter Johansson  for fruitful discussions. The developers of the PARIS Simulator code are acknowledged for continuous advancement and support of the tool. S. Z. thanks the dean of the faculty of sciences of  Sorbonne Université ,  Stéphane Régnier , for the grant of a sabbatical leave that made his contribution to this work possible.
Image analysis Automatic image analysis was performed with the open source program ImageJ  [22]  to assess the morphological parameters. After correct conversion of true physical values to pixel size, image binarisation was performed by adjusting the grey level threshold of the area of interest so that U-7Mo kernels, coating layers and the Al matrix can be clearly distinguished. Additional filtering steps were added to eliminate noise and small objects with low pixel surface-area. All particles with a continuous boundary were modelled as ellipses having equivalent area ( A ) and perimeter ( P ), allowing the major ( d major ) and minor ( d minor ) axes to be determined, as well as the circular equivalent diameter ( dEC ) and maximum ( Maxd F ) and minimum ( Mind F ) Feret's diameters. As a brief recall, note that, d major and d mino r refer to the major and minor axes of the best fitted ellipse defining the particle.
XRD characterization Samples were characterised by powder X-ray diffraction on a Bruker AXS D8 Advance diffractometer with θθ Bragg-Brentano geometry in a θ -2 θ configuration using filtered Cu K α radiation ( α1-α2 radiations λ= 1. 540598 Å and λ= 1. 54439 Å) equipped with a 1D LynxEye detector. The patterns were measured from 15 °-100 °in 2 θ angle with a scan-step of 0. 01 °and 0. 5 s. The FullProf software suite  [23]  was used to refine the XRD data using the LeBail method.
Morphological characterisation: evolution of U-7Mo kernels with hot-rolling The evolution of particle morphology with the HR process was assessed by using an automatic procedure using ImageJ software. Comparison of the shape factor histograms for the particles within the compacts and within the fuel-plate samples are presented in the Supplementary Materials. They show that the four shape parameters ( AR, conv. , conc. , and cir. ) are greatly affected, with a spread of the distributions of the values. The three identified shapes: rounded, flattened and elongated has been converted to AR, convexity ( conv. ) and concavity ( conc. ) factors, yielding the following classification: • Rounded particles: AR < 1. 10; conv. and conc. < 1. 05 • Flattened particles or random shape: particles with at least one flat edge, 1.
Acknowledgement The authors would like to thank  KAERI  ( Korean Atomic Energy Research Institute ) for producing and supplying the  U-Mo  powders produced by centrifugal atomisation. This project has received funding from the  EURATOM  research and training program  2014-2018  under grant agreement no. 661935. Chevreul Institute (FR 2638 ),  Ministère de l'Enseignement Supérieur, de la Recherche et de l'Innovation, Hauts-de-France Region  and  FEDER  are acknowledged for supporting and partial funding of this work. E. Callarec  and  A. Rolland  are warmly thanked for their careful preparation of the samples as well as their strong involvement in the SEM images collection. The authors are grateful to  D. Troadec  ( IEMN lab ) for the preparation of the fibbed lamella.
Powders characterization All the samples were characterized by Powder X-Ray Diffraction (PXRD) using a Bruker D8 diffractometer equipped with a Lynxeye detector in the reflection geometry with Göbel mirror and using Cu K α 1,2 radiation ( λ average = 1. 54184 Å). PXRD patterns were recorded at room temperature in the 5 -100 °range (2 θ ), a step size of (2 θ ) = 0. 01 °and a total counting time of about 1. 5 hours per sample. All the PXRD patterns were refined by the Rietveld method using the Cox-Hastings pseudo-Voigt profile function  [15]  implemented in the Fullprof_suite program  [16]. Additionally, SEM observations were carried out on the pristine samples using a FEI Quanta 200 ESEM FEG microscope equipped with a Large Field Detector (LFD) and a Back-Scattered Electron Detector (BSED).
Electrical characterizations Electrical properties of the sintered samples were evaluated by impedance spectroscopy, using a Solartron 1260 impedancemeter between 0. 1 Hz and 10 MHz. The experiments were undertaken under Ar/H 2 3 % between 623 K and 973 K to reach an dioxygen pressure P(O 2 ) of about 10 -30 atm. This latter value was determined from direct measurement by a Genair apparatus (Setnag) at 923 K, and conversion at the operating temperature to take into account the displacement of the H 2 /H 2 O equilibrium. In these conditions, the ceramic samples prepared are expected to be strictly ionic conductors  [17]. Additional details regarding the preparation of the samples have been described in our previous publications  [8]. The Nyquist diagrams collected were fitted with the ZView software (Scribner Associates Incorporated).
Sintered pellets were first polished using a 1 μm diamond spray. Grain boundaries were revealed by thermal etching at 1573 K, which did not alter the microstructure of the samples. Several images (typically 4 to 8) were then recorded and processed using successively ImageJ and Gimp softwares  [25]  in order to determine the average grain size. Populations of around 10 0 0 grains were considered, leading to a relative discrepancy around 2 %  [26]. Finally, the equivalent disc diameter was determined for each operating condition with the ImageJ software, using the tetrakaidecahedral grain model established by Coble  [27]. From a general point of view, the pellets prepared from powders incorporating only one doping element reached relative densities typically in the 95-99 %TD range. As such, they mostly reached the target in terms of specifications.
In addition to corrosion tests and SEM observations performed on Si-and Zr-doped samples, thermodynamics calculations were undertaken to assess the potential formation of ternary oxides as secondary products of the corrosion reaction, such as: M O y + N a x O z → N a x M O z+y ( M = T h, Si, Zr ) (5) The Gibbs free energies were calculated using the HSC Chemistry 5. 1 software at T = 773 K, to account for the conditions of the corrosion tests. Na 2 ThO 3 data were estimated from the Gibbs free energy of formation reported by Dash et al. [38]. In all the cases considered, the G R value was found to be negative, meaning that the formation of such ternary oxide is thermodynamically favored. Moreover, adding impurities to the samples led Na 2 SiO and Na 2 ZrO 3 to form preferentially compared to Na 2 ThO 3.
Conclusion The effect of various impuritites, i. e. aluminium, zirconium and silicon, on the life-cycle of Th 0. 85 Y 0. 15 O 1. 925 electrolyte to be used in electrochemical oxygen sensors for liquid sodium was evaluated in this study. After firing the oxalate precursors at 873 K, all the oxide powders prepared were found to exhibit the single-phase fluorite-type structure expected for such materials, while the variation of the unit cell parameter confirmed the incorporation of the impurities in the lattice. Doping with 0. 2 -1 wt. % of impurities only slightly affected the morphology of the powders, for example by decreasing the grain size. After sintering, the effect of impurities over the microstructure of the prepared pellets also remained limited. Indeed, the relative density / grain size data fitted well with the sintering trajectories established previously for pure materials.
Acknowledgments The authors would like to thank  Sandra Ollivier  and  Florent Bernard  for performing the synthesis of the samples,  Adel Mesbah  for PXRD data refinement, as well as  Vincent Beau  and  Smaïl El Hannaoui  for their help during corrosion experiments and SI measurements. They are also grateful to  CEA  for its continuous financial support. Last, but not least: welcome to the world, Jeanne !.
Condensations trails (or contrails) that form behind aircraft have been of climatic interest for many years. Yet their radiative forcing is still uncertain. A number of studies estimate the radiative impact of contrails to be similar or even larger than that of CO 2 emitted by aviation. Hence, contrail mitigation may represent a significant opportunity to reduce the overall climate effect of aviation. Here we analyze an 8 year dataset of radiosonde observations from Trappes, France, in terms of the potential for contrail and induced cirrus formation. We focus on the contrail vertical and temporal distribution and test mitigation opportunities by changing flight altitudes and fuel type. Potential contrail formation is identified with the Schmidt-Appleman criterion (SAc). The uncertainty of the SAc, due to variations in aircraft type and age, is estimated by a sensitivity study and is found to be larger than the radiosonde measurement uncertainties.
g. ,  Voigt et al. , 2017; Bräuer et al. , 2021)  or from the long-term dataset processed by the In-service Aircraft for a Global Observing System (IAGOS;  Petzold et al. , 2015). In addition to airborne observations, radiosonde (RS) measurements can provide a good insight into the vertical profile of the atmosphere. RS measurements are regularly performed in space and time for the sake of NWP, which allows to derive climatologies over long periods. Furthermore, they cover the entire vertical column with a relatively good vertical resolution. As a consequence, they are not limited to the flight levels of the present-day fleet of aircraft as currently sampled by IAGOS. This is of particular interest when investigating future impacts of an alternative fleet of aircraft that rely on alternative fuels (e.
(2022)  used the Integrated Global Radiosonde Archive (IGRA) and combined vertical profiles from a broad set of stations. Even though  Agarwal et al. (2022)  applied corrections on the RS profiles, not all RS types are generally capable of providing the required measurement accuracy to detect conditions prone to contrail formation and/or ISSR, especially at the colder temperatures. Here we focus on the use of an 8 year dataset of RS performed by Météo-France from Trappes, France. The radiosonde station is located close to the Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA,  Haeffelin et al. , 2005) , which is equipped with a set of passive and active remote sensing instruments that will complement the RS profiles in future work. Most importantly, a single type of RS (Meteomodem type M10) were launched throughout the selected 8 year time period, and these RS measurements are currently being incorporated into the GCOS Reference Upper-Air Network (GRUAN  Dirksen et al.
Such atmospheric conditions are of particular interest because they are thermodynamically (and potentially spatially) close to regions where persistent contrails can form either because of colder temperature or larger RH. Contrails could therefore spread into such a regions through mixing on the vertical or horizontal direction. This study also goes beyond  Spichtinger et al. (2003) ,  Haywood et al. (2009)  and  Agarwal et al. (2022)  by investigating the role of alternative fuels on potential contrail formation and potential mitigation by flight altitude changes. By varying the flight altitude distribution (FAD) we quantify the potential of vertically shifting flights to reduce contrails. Following this introduction, we present the utilized data for this study in Section 2 and the statistical processing methods to flag the contrail formation in Section 3.
Data This study uses routine radiosonde (RS) launches made by Météo-France close to the city of Trappes, France. In follow up studies, these observations will be complemented and combined by observations from the Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA,  Haeffelin et al. , 2005). The SIRTA facility is located in Palaiseau, approximately 30 km away from Trappes, and is equipped with an extensive set of passive and active remote sensing instruments, such as an all-sky camera to track contrail development, radiometers, and a lidar.
Radiosonde observations We analyze an 8 year dataset of RS observations spanning the years 2012 to 2019. The spatial coverage and representativity of a RS station is determined by the distribution of wind direction and wind speed. Figure  1a -b shows frequency of occurrence of all RS measurements of the analyzed period separated for altitudes below 9 km and between 9 and 13 km. The average horizontal advection at 11 km altitude of an RS ascent is approximately 85 km. Occasionally, horizontal displacements of up to 200 km are possible. The distributions are characterized by an elliptical shape with the major axis in the east-west direction, following the mean westerly flow at this location. Even though we analyze RS from a single station, the observations span a significant part of northern France, which is subject to intense flight traffic.
, 2004). During daytime, direct sunlight can heat the exposed temperature and humidity sensors. Therefore, a post-processing of the radiosonde humidity measurements (RH RS,liq ) is mandatory to obtain reliable RH profiles. We have thus corrected the RS measurements for radiative heating and time lag of the RH sensor prior to our analysis. The applied corrections are detailed and evaluated in Appendix A. For quality control, RS that do not reach a minimum altitude of 15 km and that contain spurious measurements, i. e. , incomplete profiles and nonphysical temperature measurements, are screened out of the dataset, which leaves 5512 full profiles out of a total of 5773 (or 95 %). Subsequent to the applied RH correction and removal of spurious data, the RS profiles are interpolated on a uniform vertical grid ranging from 0 to 18 km with a vertical resolution of 25 m.