dataesr/scientific-paragraphs-categorization huggingface dataset:
), among other physical properties. Finally, the liquid Reynolds number FG. and the liquid Weber number HG. are also used to group together the liquid inertia (G(1-x)) and viscosity forces (I. ) and the liquid inertia and capillary forces (?), respectively. (1 ) L L G x d Re µ - = (18) [ ] 2 (1 ) L L G x d We ρ σ - = (19) The parameters are then shaped into a function that is not upper-confined, taking the following form: 0 i a h i i I a p = ∏ (20) where J represents the generic nondimensional parameter among the N considered, while a1,…aN are the corresponding exponents and a0 is a constant. The optimization algorithm is based on the multidimensional unconstrained nonlinear minimization and has been carried out with the MATLAB software. The function to be minimized is chosen as the root mean square error (RMSE): ( ) 2 1 h,exp h,pred RMSE I I N = - ∑ (21) The algorithm starts by finding the initial constant K ! and the sets of exponents K , … K M that minimize the RMSE.
Then, a sensitivity analysis of the exponent values is performed to reveal and keep only the independent parameters that affect the minimization function. As a matter of fact, the RMSE can be more or less influenced by each exponent variation during the research of its minimum value. Due to the limited diversification of the database, some of the parameters are not independent of one another and can therefore be excluded. Further details and the complete process of the optimization algorithm for the boiling and condensation databases are provided in the Appendix section. For boiling heat transfer, the RMSE is found to be mostly influenced by only the vapor Froude number, the Boiling number and the liquid-to-vapor mass quality ratio, with the Inclination effect Ih assuming the final form of Eq.
(  22 ), which returns a RMSE of 10. 8%: 0. 064 0. 35 0. 23 , 1 124 h boil V x I Fr Bo x - -   = ⋅ ⋅ ⋅     (22) As expected, the negative exponent of the Froude vapor number indicates a lower inclination effect with increasing flow velocity and with decreasing tube diameter. Surprisingly, the boiling database has led to a positive exponent of the Boiling number, showing that higher heat fluxes exhibit a more emphasized effect of the inclination angle on heat transfer during evaporation. However, the influence of gravity on nucleation in flow boiling has not been fully established yet, and no further outcomes are extrapolated at this point. More experimental data would be of great use for in achieving better understanding of this phenomena. Finally, the effect of the liquid-to-vapor quality ratio is limited and represented by a positive exponent of 0.
064. Surprisingly, the Bond number does not seem to have a major influence on the condition of the database, which tends to show that the inclination effect is mainly governed by convective and boiling heat transfer, successfully characterized by the vapor Froude and Boiling numbers only. The accuracy of the present tool for boiling heat transfer is shown in Figure  11 , where the model is first compared to several data points taken from the two evaporating heat transfer studies of Baba et al. [40]  and Kundu et al. [47]. The two data sets were chosen to show the accuracy of the developed correlations for different fluids and at explicitly dissimilar operating conditions. The first trend represents the coolant FC-72 used in a single tube of 4 mm ID with a very low mass velocity of 40 kg/m 2 s and a heat flux of 8 kW/m 2.
The latter refers to R134a in a wider channel of 7 mm, with an imposed heat flux of 4. 5 kW/m 2 and a higher mass velocity of 300 kg/m 2 s. In both cases, the agreement with the model that replicates the experimental trends and values is satisfactory. The comparison between the experimental and predicted Ih values for the entire flow boiling database is shown in Figure  11b. By excluding the points for which the experimental Ih is lower than 20%, almost all the database is predicted within an error band of ±30%. The same procedure, of which details are given in the Appendix section, has been followed for the condensation heat transfer, returning the expression of the Inclination effect Ih as shown in Eq. (  23 ): 0. 37 0. 76 0. 060 0. 28 , 1 10. 1 h cond V tt x I Fr Bd X x - - -   = ⋅ ⋅ ⋅     (23) Similar to the case for the boiling tool, the flow inertia decreases the influence of the tube orientation on the heat transfer coefficient by means of the negative exponent of the vapor Froude number.
Conclusions and future perspectives A global analysis of the effect of the tube inclination on two-phase heat transfer for boiling, condensation and heated gas-liquid flow has been performed in the present study. A total amount of 5408 data points from 3. A sensitivity analysis of the quality of the optimization relative to each exponent is performed. As stated by Layssac et al. [34] , the calculated RMSE can be more or less affected by variation of each exponent during the research of the minimum. Therefore, the third step consists of running the first two steps of the algorithm by imposing a given value of the exponent to be tested, leaving the other free to change. The evolution of the RMSE as a function of the tested exponent reflects the impact of this exponent on the global optimization.
(  27 ), whereas Figure  16a -b provides the initial comparison with the experimental database (RMSE = 14. 9%) and the sensitivity analysis for all the exponents. 0. 015 0. 01 1. 12 0. 30 0. 30 0. 937 0. 264 , 1 0. 0416 By erasing the weak parameters from the initial expression, the final form of the Ih for convective condensation is as shown in Eq. (  28 ). The remaining four parameters (8@ ; , NO, 1 -R /R and Martinelli parameter A BB ) have a similar and non-negligible slopes (Figure  17b ) in the sensitivity analysis, thus having the same importance in the evaluation of the Inclination effect. With respect to the initial form of the correlation, the RMSE becomes only slightly higher (15. 1%), with a satisfactory agreement (Figure  17a ). 0. 37 0. 76 0. 060 0. 28 , 1 10. 1 LF h cond V tt L L T x I Fr Bd X Re We x δ δ - - - -   -   = ⋅         (27) h cond V tt x I Fr Bd X x - - -   = ⋅     (28).
[17]e predicted Inclination effects are first compared with the experimental values obtained from the studies of Lips et al. [20](with the refrigerant R134a at 40 °C in a regular channel of 8. 38 mm ID and with a mass flux of 300 kg/m 2 s) and that of Del Col et al. [17](with R32 at 40 °C in a square 1. 23 mm minichannel at a mass flux of 200 kg/m 2 s). In both studies, the new model returns a fair agreement with the experimental trends and data. The comparison with the entire condensation database is shown in Figure12b. Almost all the experimental data with a significant Inclination effect (>20%) are well predicted falling into an error range of ±30%.
[17]e predicted Inclination effects are first compared with the experimental values obtained from the studies of Lips et al. [20](with the refrigerant R134a at 40 °C in a regular channel of 8. 38 mm ID and with a mass flux of 300 kg/m 2 s) and that of Del Col et al. [17](with R32 at 40 °C in a square 1. 23 mm minichannel at a mass flux of 200 kg/m 2 s). In both studies, the new model returns a fair agreement with the experimental trends and data. The comparison with the entire condensation database is shown in Figure12b. Almost all the experimental data with a significant Inclination effect (>20%) are well predicted falling into an error range of ±30%. 150 Baba et al. I h [%] 50 100 Kundu et al. R134a, d=7 mm, q=4. 5 kW/m G=300 kg/m 2 s, T sat =7. 5 °C FC-72, d=4 mm, q=8 kW/m 2 , 2 , G=40 kg/m 2 s, T sat =56 °C Predicted I h [%] 0 0 0.
These two techniques were implemented and tested in this work by usig the commercial FE software ABAQUS 6. 14  [29]. For more details concerning their implementation and numerical issues see Appendix A. Briefly speaking, the adaptive meshing technique was computationally more efficient than the remeshing technique, however, for partial slip conditions, the adaptive meshing procedure did not provide a clear stabilization of the contacting surfaces profile, which lead to overestimate wear effects. In addition, it is very memory demanding. In this setting, as this work is settled in the context of high cycle fatigue under PSR, the remeshing technique was adopted.
Experimental data Aiming to investigate the influence of wear under fretting fatigue conditions (partial slip), the experimental data provided by  [14, 16]  is assessed and used as reference for comparison with the numerical results. In her work, besides evaluating the applicability of the nonlocal intensity factors proposed by  [42]  when those are used to define crack initiation frontiers under fretting conditions, the size effect in fretting fatigue problems was also investigated. In this case, two types of experimental campaigns were evaluated. In one of them (Group-2a of tests) 1  , the aim was to investigate the effect of the volume of material being stressed under the contact. In this group-2a tests, the stress gradient inward the contact and the area of the slip zones were the same for all the experiments.
Numerical model To carry out the fretting fatigue simulations, the commercial FE software ABAQUS 6. 14 was used  [29]. It allowed a faster and simpler implementation of  the contact evolution approach assessed in this work. The FE model adopted in this work is illustrated in Figure  5. As depicted, a static normal load, P , presses a cylindrical pad of radius R against a flat specimen which is already subjected to a static mean bulk load, F b. After that, a tangential load Q is applied to the pad in phase with an alternate bulk load. Note that the mean and the alternate bulk loads give rise to the mean (σ m ) and alternate (σ a ) bulk stresses, respectively. A central refined zone composed of linear quadrilateral elements is defined in a rectangular region surrounding the contact surfaces.
Conclusions In this work, the influence of considering wear effects when estimating fret-400 ting fatigue life was investigated. Available fretting fatigue data for a Ti-6Al-4V alloy were confronted with both: life estimates taking into account the contact/stress evolution due to material removal and life estimates where these effects are neglected (simplifying approach often adopted). Numerical simulations were carried out through the FE method. Results have shown that, considering wear effects allow us to slightly improve the accuracy of life predictions, where results were less scattered when compared to estimates obtained when wear is disregarded. In order to estimate the fatigue life, the TCD has been used in association with three different multiaxial fatigue criteria (SWT, Findley and MWCM).
Life Cycle Inventory This LCA phase consists of quantifying all input/output flows associated with each elementary process for each step of each phase of the system  [20]. This inventory was based on data from the Jatropha experimental station in Teriya Bugu, supplemented by literature data. The main data and hypotheses describing the Jatropha oil based system are outlined in the following paragraphs. Extraction and emission flows related to the production and transportation of inputs such as mineral fertilizers are from the ecoinvent v3 database.
The oil is often traditionally used for making soap, but also used in small quantities as a medicine. However, all of the oil produced in this study was used as biofuel in a generator. Combustion yield for Jatropha oil considered in this study was 0. 28 kg oil/kWh electricity  [18]. Table  8  specifies the associated emissions. Emissions from diesel combustion in the diesel system were reported according to the ecoinvent v3 database.
Thus, rather than adopting an emission rate related to total decomposition of the cake, which could reach values of around 300 g of CH4 per kg of oilcake according to the data reported by Nielsen & Hauschild  [30]   [31] , an emission rate matching that related to composting was used. Data related to composting waste from palm oil mills in Malaysia was chosen because of the similar tropical conditions  [32]   [33]. Thus an emission rate of 29. 7 g of CH4 per kg of oilcake was calculated. These impacts of the discarded oilcake were attributed to the life cycle stage of oil extraction.
Life Cycle Impact Assessment The impact assessment calculations were performed using SimaPro 9. 0 software. The main impact assessment method used was ReCiPe 2016 Midpoint, Hierarchist version. An indicator of nonrenewable Cumulative Energy Demand (CED) was also considered, including non-renewable along with livestock and human energy. For interpretation, note that this indicator is extensively redundant with the fossil resource scarcity indicator from ReCiPe, but it allows investigating the contribution of livestock and human energy to the energy demand. Toxicity and ecotoxicity indicators were not taken into account due to the uncertainties on the impact of toxic compounds under tropical conditions and the lack of specific data on the contamination of soil, groundwater or food crops by toxic substances in Jatropha oilcake.
Conclusions: Perspectives for Jatropha cropping systems in Africa Using Jatropha oil as a substitute for diesel for the purpose of decentralized electricity production in Mali is an issue that should be considered from many angles: agricultural, economic, social and environmental. The present study focused on the latter. However, data availability is limited and data from projects that had been set up for some years had to be retrieved to obtain objective results. Data were thus obtained from Malian plots that had been studied for 6 years, along with Brazilian data in similar pedoclimatic conditions. The findings should then be relatively representative. While environmental criteria are not a priority for smallholders in their crop management choices, the present study aimed at highlighting this aspect.
DTA results The DTA data are most easily presented in Fig. 4 , showing the melting temperature for all of the analyzed compositions, in isoplethal sets of different constant Te-content. The dataset by Terzieff  [8]  is also included for comparison, and it is seen that the melting temperatures steadily falls from the metal-rich side (44 at. % Te) towards the Te corner. It is apparent in the diagram that the liquidus at high-Ni content increases again to 61 at. % Te before falling steeply, indicating that the congruent melting of the d phase in Ni-Te (close to 55. 5 at. % Te  [5]  ) a little into ternary compositions. The solidification temperatures are also plotted for 50 at. % and 55 at. % Te in Fig. 5. At 50 at. % Te, there is an eutectic reaction in Ni-Te,  [5]  and it seems by the proximity of the liquidus and solidus that there is an eutectic valley going in the direction of added Fe.
Phase Diagrams of the Fe-Ni-Te System Figures  6  and 7  show isothermal phase diagram sections at 700 and 800 C, respectively, including the as-prepared compositions of the samples for isothermal heat treatments, and tie-lines drawn between the selected phase boundary compositions from the WDS analyses. The Thermo-Calc software package was used to plot the blank triangular diagram and experimental data points;  [21]  overlaid in the diagram are hand-drawn tentative phase boundaries based on the results. In cases where what seemed to have been a single phase region containing several microstructures with different BSE contrast, a large grid of composition measurements was made and the average used. In such a case, one has either happened to obtain a three-phase sample with one phase being crystallographically similar to another, so that it preferentially precipitates in only one region, or there has been a decomposition on cooling.
Acknowledgments Open access funding provided by  Royal Institute of Technology. The main author, C. -M. A. , is grateful to the  Swedish Research Council (Vetenskapsra ˚det)  for the funding of the PhD project, which is part of the  SAFARI  project of the  CEA-SRC collaboration  for R&D of the ASTRID generation IV reactor. Furthermore, C. -M. A. is grateful to all the colleagues at  CEA  providing resources and guidance in all experimental methods:  Sylvie Chatain  for teaching him how to calibrate and use the DTA apparatus,  Kevin Ginestar  for teaching how to prepare samples and use the SEM,  Christophe Bonnet  for aiding with sample preparation for XRD analysis, and  Bonnie Lindahl  for performing some of those XRD analyses. He also thanks his colleagues at  CEA  and  KTH  for all the advice provided for the analysis of microstructures.
Then, they were positioned in front of a computer screen and presented instructions for how to complete trials on the MRT test, including three sample trials for practice. Participants were instructed to read and ensure they understood the instructions and to complete the sample trials. They were able to ask trials about the protocol during this time. Following the instruction and practice phase, each participant's gaze was calibrated to the computer screen using a Tobii X3-120 eye tracker and Tobii Studio (v3. 4. 8, Danderyd, Sweden) software. A 5-point calibration was used for gaze calibration and participants rested their head in a chin rest mounted to the table approximately 60 cm away from the computer screen (24 inch monitor). The chin rest was utilised throughout the testing phase of the experiment to minimize head movements and improve gaze recordings.
All participants performed the test at the same computer station, in the same lab, and under the same monitor settings. Luminance was controlled and measured to be 90. 0 ± 0. 0 lux across all trials on the MRT using a Testoterm TM 0500 Lux-Meter. After completing eye tracking calibration, participants fixated on a small black crosshair in the middle of the screen prior to reading the task instructions (See Supplementary File). Time to complete the test was calculated from the final answer for trial 20 or until 15 minutes elapsed from the time of the SPACE key press to initiate the test. Gaze and keypress event data were recorded at 120 Hz and pupillometry data were recorded at 40 Hz. Due to the low number of left handed (n = 10) and ambidextrous (n = 7) individuals and the importance of controlling analyses to include only right handed individuals  [63] [64] [65]  , left handed and ambidextrous participants were excluded from further analyses.
One further right handed individual who reported they suffered from a neurological disorder was also excluded. MRT performance. Each participant's responses were tallied and a total score (Score: All Correct) was calculated for the entire test (max score: 40), the 10 mirror foil trials (max score: 20) and the 10 structural trials (max score: 20). A secondary score (Score: 2 Correct) was also computed in an attempt to control for guessing  11  whereby participants scored a single point only when both answers for a given trial were correct (max score total = 20; max score mirror foils = 10; max score structural foils = 10). A participant's data were excluded from further analyses when they responded with keys other than F1, F2, F3 and F4 (10 participants). The time to complete every trial was recorded and from this, a time to completion (TTC) was calculated, in minutes, by summating trial completions times for the entire test, the mirror foil trials and the structural foil trials of the test.
Eye tracking. Fixation data. Gaze coordinates for the left and right eyes were averaged and applied to the I-VT (Velocity-Threshold identification) fixation filter within Tobii Studio software. The I-VT fixation filter classifies fixations according to the velocity of directional shifts of the eyes in visual °/s. First, gaps in the data recording less than 75 ms were linearly interpolated within Tobii Studio according to Komogortsev and colleagues  65  , who determined this to be the minimum blink duration. Data were then classified by the I-VT filter in Tobii Studio to a fixation event when the velocity between adjacent gaze positions was less than 30°/s, in accordance with previous research  [65] [66] [67]. When the velocity between data samples exceeded 30°/s, these data were classified under saccade events.
In this way, a ratio larger than 1 suggested a participant spent more time fixated on, or directed a larger number of fixations toward, images that were identical rotated versions of the standard image (correct responses), whereas a ratio less than 1 suggested more time fixated on, or a larger number of fixations toward, the foil images of the standard (incorrect responses). Pupillometry data. Pupil diameter data at each time frame were averaged between the left and right eyes. Where recordings were not available for both eyes the pupil diameter for a single eye was used, the pupil diameter of the right and left eye being highly correlated  68. Following Lemercier and colleague  69  , a 10-point average filter was used to filter the data and a cubic spline was used to interpolate missing data over a maximum gap of 10 frames in Visual 3D software  23.
The Use of Original Tracking Error e in the Control Law In this section, the control law will be obtained based on the original error model  (5). Solving the generalized eigenvalue minimization problem (using MATLAB and Robust Control Toolbox) given in  (17)  with σ 2 = 5 and ς 2 = 0. 01, we obtain the decay rate of γ = 0. 01 and the following gains of the TS fuzzy model: The gains of the nonlinear predictor observer  (20)  are chosen inside the stability region and manually tuned to obtain the best tracking possible (L 1 = 12, L 2 = 12, L 3 = 5). The results of the experiment are given in Figures  6  and 7. F 1 = -13 Solving the generalized eigenvalue problem given in  (28)  with τ = 66 ms and ρ 2 = 5, the gains of the TS fuzzy predictor observer  (25) , κ i = (i = 1,. , 16) (see Section 2.
Acknowledgement This work has been supported by the  French Ministry of Higher Education  through a Ph. D. grant to G. P. and by  ANR  (project  ViSCAP   ANR-17-CE08-0030 ). Interns C. Boned  and  L. Halé  are acknowledged for their help in coding. Helpful discussion with F. Rebillat ( LCTS, University of Bordeaux ) is gratefully acknowledged.
IV. EXTRACTING THE FISHER INFORMATION FROM EXPERIMENTAL DATA We can use the data shown in Fig. 1  c to get a measured value for the Fisher information of our measurement. As can be seen from Eq. 9, the Fisher information depends on the slope and the noise properties of the measurement presented before. The slope s(α i ) = d M ρ(α) dα is experimentally determined for each displacement amplitude α i by a symmetric difference quotient s(α i ) = P |↓ (α i+1 ) -P |↓ (α i-1 ) α i+1 -α i-1. ( 12 ) For the first and last measurement point is is determined by an asymmetric difference quotient s(α i ) = P |↓ (α i+1 ) -P |↓ (α i ) α i+1 -α i ( 13 ) As discussed before, the noise is dominated by quantum projection noise.
The average projected range of the ions according to SRIM simulations  [14]  is 9. 2 nm with a straggling of 5. 7 nm. The sample morphology was investigated by atomic force microscopy (AFM), with two different systems operating in the dynamic mode namely, an Agilent 5500 Picoplus, (Agilent, Santa Clara CA, USA) and a Nanoscope IIIa equipment (Bruker, Billerica Massachusetts, USA). Silicon cantilevers (Bruker, Billerica Massachusetts, USA) with a nominal radius of 8 nm and force constant of 40 N/m were employed. The root-mean-square roughness (σ), the pattern wavelength (λ) and the power spectral density (PSD) were analyzed using Gwyddion 2. 53 software (developed at Czech Metrology Institute, Czech Republic)  [15]. This software together with ImageJ  [16]  was used to assess the angle spread distribution of the ripple structures.