dataesr/scientific-paragraphs-categorization huggingface dataset:
This new database (table 1) extends available data at MCL x ∈ {0. 8, 1. 5} to higher Re τ 1000, which to the best of the authors' knowledge is the highest reported Re τ for compressible TPC flow. Pirozzoli 2011) wall turbulence. On the other hand, at constant Re τ , the coefficient of variation (CV p ) w := ( p rms /p) w scales approximately with M2 CL x in compressible TPC flow  (Gerolymos & Vallet 2014 , 2018). We concentrate in § 3 on the combined (Re τ , MCL x ) effect on p , and in § 3. 4 we attempt to identify independently the relative importance of Mach number MCL x and wall cooling T r / Tw.
9    MCLx 2. 49, and against MCLx (2. 4), for varying 66 Re τ 983, from the present database (table 1); also included are incompressible DNS data  (Kim et al. 1987; Moser et al. 1999; Hu & Sandham 2001; Hoyas & Jiménez 2006; Lee & Moser 2015)  and incompressible-flow correlation (Chen & Sreenivasan 2022) using Re τ (CS, 2022). M ¯CL x ≈ -2. 00±0. 02 M ¯CL x ≈ -0. 81±0. 02 M ¯CL x ≈ -1. 49±0. 02 M ¯CL x ≈ -0. 33±0. 02 Re τ ≈ -341±1 Re τ ≈ -341±1 Re τ ≈ -250±5 Re τ ≈ -110±5 Re τ ≈ -100±3 Re τ ≈ -100±3 Re τ ≈ -110±5 Re τ ≈ -250±5 (a) ( b) ( c) (d ) ( e) ( f ) (g) ( h) ( i) ( j) ( k) ( l).
Pressure-velocity correlations Modifications of near-wall turbulence structure  (Bradshaw 1977 ) associated with these MCL x -effects on p rms ( § 3) can be assessed by considering correlation coefficients between fluctuating pressure and velocity components c p u := p u p 2 u 2 ≡ p u p rms u rms ; c p v := p v p 2 v 2 ≡ p v p rms v rms. (3. 4a,b) In general, for all of the (Re τ , MCL x ) flow conditions in the database (table 1), c p u > 0 for all y ∈]0, δ ] (figure  8 ), whereas c p v < 0 for all y ∈]0, δ [ (figure  9 ), with [c p v ] CL = 0 because of symmetry. In the near-wall region (y 15) c p u , at constant MCL x , shows weak dependence on Re τ (figure  8 ). On the other hand, as MCL x increases above 0. 8, at constant Re τ , the near-wall level of c p u also increases indicating that compressibility effects enhance      Pressure fluctuations in compressible wall turbulence the positive correlation of p with the streamwise velocity fluctuation u near the wall (figure  8 ).
This region (y 15) corresponds precisely to the region where an increase of p + rms was observed with increasing MCL x (figure  3 ). Regarding c p v (figure  9 ) we observe near the wall (y 15) a very marked influence of MCL x , at constant Re τ , both in level and slope. For all of the (Re τ , MCL x ) flow conditions in the database (table 1) we have roughly c p v ( y y p PEAK ) -0. 05, which progressively develops to a plateau with increasing Re τ. At low MCL x 0. 33, c p v decreases monotonically for y y p PEAK towards the wall where it reaches a high negative value -[c p v ] w 0. 4 (depending on Re τ ), in line with incompressible flow data  (Hoyas & Jiménez 2006; Lee & Moser 2015). At the incompressible flow limit, which is quite well represented by the MCL x 0. 33 data  (Gerolymos et al.
This is in line with previous work  (Yu et al. 2019; Tang et al. 2020) , but the availability of the present (Re τ , MCL x )-matrix database (table 1) allows us to evaluate the influence of each of the two parameters separately. Far more than the differences in p rms (figure  3 ), the large (with increasing MCL x ) modifications in the pressure-velocity correlation coefficients c p u and c p v near the wall (figures 8, 9) are structural modifications  (Bradshaw 1977 ) suggesting significant differences in the flow field near the wall. This region (y 15) where p -statistics are strongly MCL x -dependent is precisely the region where flow structure and dynamics are dominated by the near-wall streaks  (Chernyshenko & Baig 2005a,b; Wang, Pan & Wang 2019 ) characteristic of buffer-layer turbulence  (Panton 2001; Adrian 2007; Bernard 2019 ), but which actually extend also into the viscous sublayer, practically down to the wall.
Conclusions The construction of the (Re τ , MCL x )-matrix DNS database of compressible TPC flow presented in this paper enables the precise evaluation of the effect of each of the two flow-determining parameters, the other being held constant. The present study focusses on the effects of these two parameters on the fluctuating pressure p. The wall-to-recovery temperature ratio Tw /T r is not a free parameter, but is the result of viscous heating determined by (Re τ , MCL x ). Therefore, results in the paper correspond to increasingly cold-wall conditions (relative to adiabatic-wall temperature) with increasing MCL x. The average wall shear stress τw is a very satisfactory scale for the level of pressure fluctuations, p + rms ≡ p rms (identical in (•) + wall-units or of the peak depends principally on MCL x , consistently moving away from the wall with increasing MCL x , with a much weaker dependence on Re τ.
A correlation for [p rms ] + w with respect to (Re τ ‡ , MCL x , T r / Tw ), valid for airflow within the databases range, fits reasonably well both the incompressible and compressible DNS data. Pressure-velocity correlations are strongly influenced by MCL x in the near-wall region (y 15), where, at constant MCL x , they depend weakly on Re τ. Joint p. d. f. s show that the near-wall increase of the streamwise correlation coefficient c p u is the result of increasingly intense p < -p rms events within the low-speed streaks (u -u rms ), with little influence from the higher-speed flow region between streaks. On the other hand, the decrease of -c p v in the near-wall region appears to be the result of the global modification, with increasing MCL x , of the flow structure both inside and between streaks.
Funding. The computations reported in the present work were performed using HPC resources allocated at  PRACE-JUWELS  (project  2021-240083 )  GENCI -IDRIS/TGCC/CINES  (grants  2010-022139  to  2021-022139 ) and at  ICS-UPMC  ( ANR-10-EQPX-29-01 ).
Declaration of interests. The authors report no conflict of interest. Data availability statement. Data will be made available at http://www. aerodynamics. fr/DNS_database/. Author  ORCIDs. G. A. Gerolymos https://orcid. org/0000-0003-3207-5659; I. Vallet https://orcid. org/0000 -0003-1286-831X. Author contributions. The authors are listed alphabetically and have both contributed equally to the entire work reported in the paper, and to writing the paper.
Wall-normal variation of mean thermodynamic variables {p, ρ, T, μ} with respect to their wall values, plotted against the HCB-scaled non-dimensional distance from the wall y (2. 1b), for varying centreline Mach number (2. 4) 0. 32 MCLx 2. 49 ( MCLx -based line types are used for p( y)) and HCB friction Reynolds number (2. 1b) 73 Re τ 983 (Re τ -based line types are used for { ρ( y), T( y), μ( y)}), from the present database (table 1). (z) S is practically 958 A19-5.
Re τ ∈ {100, 110, 250, 340} (also scaled by its peak value, [p rms ] PEAK (3. 1), for the different nearly constant Re τ ), from the present DNS database (table 1). Pressure fluctuations in compressible wall turbulence M ¯CL x Incomp. 0. 33±0. 02 0. 81±0. 02 1. 49±0. 02 CS (2022) 1. 63 incomp. 1. 82 1. 92 2. 00±0. 02 2. 11 2. 22 2. 49 10 2 10 3 10 4 Press University Cambridge by online https://doi. org/10. 1017/jfm. 2023. 42 Published Figure 3. Profiles of pressure-fluctuation r. m. s. p rms , in wall-units (2. 3), plotted against the HCB-scaled (2. 1b) non-dimensional distance from the wall y (logscale), for varying 73 Re τ 965 at nearly constant MCLx ∈ {0. 33, 0. 80, 1. 50, 2. 00} and for varying 0. 32 958 A19-12 MCLx 2. 49 at nearly constant.
Re τ ∈ {100, 110, 250, 340}, from the present DNS database (table 1). 75±2 Re τ 151±1 0. 33±0. 02 M ¯CL x 1. 92 102±5 173±4 0. 81±0. 02 2. 00±0. 02 113±1 250±5 1. 49±0. 02 2. 11 139±5 341±1 1. 82 2. 22 973±8 2. 49 (a) ( b) c p u c p u M ¯CL x 0. 33±0. 02 Re τ 100±3 y y (c) ( d ) c p u c p u M ¯CL x 0. 81±0. 02 Re τ 110±5 y y (e) ( f ) c p u c p u M ¯CL x 1. 49±0. 02 Re τ 250±5 y y (g) (h) c p u c p u 2 0. 3 Wall-zoom M ¯CL x 2. 00±0. 02 Re τ 341±1 y y 2 3 4 5 958 A19-17 Figure 8. Profiles of correlation coefficient c p u of streamwise pressure transport (3. 4a,b), plotted against the HCB-scaled non-dimensional distance from the wall y (logscale and linear wall-zoom), for varying HCB Reynolds numbers 73 Re τ 983 at constant centreline Mach numbers MCLx ∈ {0. 33, 0. 80, 1. 50, 2. 00}, and for varying centreline Mach numbers 0.
The collected HT-STAM-17-OEt powders after activation were characterized by XPS. The binding energies of Cu 2p 3/2 and Cu 2p 1/2 decrease by 0. 5 and 0. 4 eV, respectively (Fig. 5c ), indicating different Cu coordination after electrical activation. This means that ionization-induced oxidation state generation is also accompanied by a partial change in the Cu paddle wheel structure. Owing to the huge tendency of H 2 O adsorption on the oxygen of -C-O-Cuin the Cu paddle wheel  46  , H 2 O ionization occurs on this site. Fourier transform alternating current voltammetry (FT ac voltammetry), as a technique for studying the underlying electron transfer reaction  47, 48  , is employed to verify the structural variation. Harmonics at all levels from FT ac voltammetric data for HT-STAM-17-OEt electrocatalyst are displayed in Fig.
Acknowledgements This work is supported by the  National Key Research and Development Program of China  ( 2021YFA1501000 ), the  China Postdoctoral Science Foundation  ( 2021M701292 ), the  National Natural Science Foundation of China  ( 52171069 ,  22005109 ), the  Key Laboratory of Material Chemistry for Energy Conversion and Storage, Ministry of Education  ( 2021 ), and the  Open Project Program of Hubei Key Laboratory of Materials Chemistry and  Service Failure  ( 2020MCF02 ). We thank all the funding for supporting this work. We also acknowledge the measurement support of the  Analytical and Testing Center of Huazhong University of Science and Technology (HUST) , the  Analytical and Testing Centre of the School of Chemistry and Chemical Engineering (HUST) , and  Research Core Facilities for Life Science (HUST).
Structural characterization The structure of the biomass and hydrochars was analyzed by XRD using a D8 Advance Bruker AXS diffractometer with a copper cathode (λKα = 15,418 Å). The 2θ angle was varied from 5° to 80° using a step size of 0. 05° and a counting time of 1 s. The different organic and inorganic crystalline phases were identified with the PDF4 + database supplied by the ICDD. Phase identification was made using EVA software.
Hence, the following estimations of  are meant to demonstrate this possibility more than to state the exact quantitative evaluation of. To perform the fit of a given experimental TDBS signal using the here-developed model, the slowly-varying background of the raw signal is filtered out by using 6th-order Butterworth band-pass filter (between 10 and 150 GHz). The fit is performed using the python module lmfit for non-linear least-squares minimization and curve fitting  [69]. The model with non-dimensional parameters is used (see Eq. (  5 ) for the part associated with the incident CAP and a similar expression for that associated with the reflected part), over a time window on the experimental data spanning from -0. 2 to +0. 2 ns around the CAP arrival time at the LN/diamond interface. The following parameters are assumed to be known and fixed (see Tables  1  and A.
To fit the signals, a non-linear least square method is used with the curve_fit function from the scipy Python library  [94]. The first and second cosine parameters are constrained to the LN and water-ice VII Brillouin frequency (  ) ranges, respectively, such that   ∈  [60 -69]  GHz for the LN and   ∈  [50 -59]  GHz for the water-ice VII. To evaluate the error of the fit, the residual normalized energy is calculated, and signals with more than 0. 1 residual energy are rejected. The covariance matrix obtained from the least squares fit provides the estimation of the standard deviation for each parameter, indicating the error made on each parameter. The interval of confidence is set at two times the standard deviation. The average error on the frequency for the water-ice VII estimated from measurements done on the Ti side is ±0.
Funding The research leading to these results received funding from  French National Research Agency (ANR, France)  under Grant Agreement No  ANR-18-CE42-0017. This work was also supported by the  Région Pays de la Loire  through the  RFI Le Mans Acoustique (project title ''Pari Scientifique OPACOP 2018 , salary of T. T. when he was a Ph. D. student).
Because of the sample height, four Cellarinella sp. 1 and C. sp. 2 and five Reteporella sp. connected scans were used and the total run time for each specimen was between 16 and 20 h. From raw images projection, stacks of ~4000 to 5100 images (image size: 2000 × 2000 pixels, pixel size ~6 µm) were reconstructed, applying thermal correction, misalignment compensation, ring artefact reduction and beam hardening correction using the NRecon ® (Bruker) software package. Segmentation within a pre-selected ROI was performed with CT Analyzer ® (Bruker) software package by using a 3D-adaptive thresholding procedure (mean of minimum and maximum value within spherical kernels of eight pixels' radius) starting from a pre-determined pre-thresholding value, and a despeckle routine in the 3D space. Resulting images were saved as monochrome bitmaps (1 bit) and then transformed into 3D-renders and animations with CTVox ® software package (Bruker).
Zooid morphometrics (zooid length, primary orifice length and width of approx. 20 complete and adult zooids per band) were measured from scanning electron micrographs, whereas the high-quality micro-CT scans obtained for each species were used to measure growth check line thickness, and basal, lateral and frontal zooidal wall thickness (30 replicated measurements per growth band). Image Software (ImageJ 1. 53r) was used for morphological measurements that were conducted on adult and non-reproductive zooids.
The results (Table  1 ) were normalized and translated in percentage of reactivity by subtracting the background and considering the highest response as 100%. For each extract, the test was repeated three times. δ = (R sample /R reference -1) (1) where R is the ratio of the numbers (n) of the heavy and light isotope of an element (e. g. , n( 18 O)/n( 16 O)) in the sample and the reference material  [67]. The sample was measured on four sequential injections and raw data were corrected for sample-to-sample memory; the reported value is the mean value. Data sets were corrected for instrumental drift during the run and normalized to the VSMOW/SLAP (standard light Antarctic precipitation) scale by assigning a value of 0‰ and -55. 5‰ (δ 18 O)/0‰ and -427. 5‰ (δ 2 H) to VSMOW2 and SLAP2, respectively  [68].
In this numerical study an optimized trench film cooling design is determined using a Bayesian algorithm and neural network trained RANS model. Three objective functions were considered, the area-averaged film cooling efficiency, spatial standard deviation of film cooling efficiency and hot gas ingestion into the trench. Nine geometrical design parameters were varied to allow for a 3D trench shape and to find an optimal trench design based on the initial parametrization of the trench. Jet-engine like inflow boundary conditions with respect to turbulence intensity and length scales were applied. The investigated momentum ratios ( I) were 1 and 8 at a main flow Reynolds number ( Re D ) of 2500. For each design the steady state Reynolds Averaged Navier-Stokes (RANS) equations were solved using the commercial Computational Fluid Dynamics (CFD) code Ansys Fluent V2022 R1.
Design optimization An overview of the design optimization procedure is given in Fig. 2. First, the shape of the trench is specified by parameterizing it. Next, the optimizer, here a parallelized Bayesian optimization algorithm, decides which designs are evaluated in this generation of designs. For each generation 20 designs were evaluated in parallel. Evaluating a design means performing a CFD simulation (CAD model creation and meshing, simulation and postprocessing). Each design was simulated for two momentum ratios. Subsequently, the data of interest for each CFD simulation is collected and fed back into the optimizer algorithm. The objective functions to be optimized are three output parameters of the CFD simulations. The area-averaged film cooling efficiency of the trench walls (bottom and sides, η T ) and of the wall downstream of the trench ( η) as well as the standard deviation of the film cooling efficiency of the downstream wall σ.
It solves a secondary optimization problem to choose future design parameters. The Python package GPyOpt 1. 2. 6  [29]  is an implementation of the Bayesian optimization algorithm and was chosen in this work. Adjustments to the code were necessary to offer interfaces to the in-and outputs of the CFD simulations. The following arguments are different from GPyOpt's default settings: 1. domain was used to handle the constraints on the design parameters. 2. evaluator_type = 'thompson_sampling' was chosen to select the different hyperparameters for a batch. 3. batch_size was used to adjust the batch size, and ini-tial_design_numdata was set to the same value. To test the optimizer, it was applied to two test functions (Rosenbrock and Rastrigin). These functions are both non-linear, smooth, and non-convex.
Acknowledgments Thanks to CADFEM for supporting us with the GEKO model tuning,  CAD  and mesh generation. Thanks to our colleagues from  UniBw M: Axel Buck  for his support on the digitization of Schreivogel's experimental 2D data of the film cooling efficiency to determine correct lateral averaged plots. Junsu Shin  for his prelimary investigation of optimizer algorithms. The work was sponsored by the  Deutsche Forschungsgemeinschaft  ( DFG  project number  PF 443/7-1 ). Sillvya Jeyaseelan  thanks  STIBET III  from  UniBw M and Eramus  for support of this work. Michael Pfitzner  thanks ITIS e. V. for support of part of this work. We acknowledge the financial support by the  Universität der Bundeswehr München. The support is gratefully acknowledged.
XPS XPS is performed with a Versa probe II ULVAC-PHI in order to identify the elements present in the material before and after ablation. This spectrometer is equipped with the monochromatized source Al Kα (hν=1486. 6 eV). The source is focused on the sample surface, has a diameter of about 100 µm and power of 25 W. All measurements are carried out in an ultra-high vacuum chamber (7×10 -10 mbar). The survey spectra are recorded over the 0-1200 eV spectral range, using pass energy set to 23 eV and resolution of 0. 5 eV. All XPS spectra binding energies are corrected using the C(1s) line of C(sp 2 ) at 284. 6 eV. The curve fitting and background subtraction are accomplished using the Casa XPS software. Spectrum curve fitting is performed using Voigt function, convolution product of Gaussian (80%) and Lorentzian (20%) distributions.
13 C Nuclear Magnetic Resonance Spectroscopy A Burker Avance-300 spectrometer was employed to perform the solid-state NMR experiments using a 4 mm cross-polarization magic angle spinning (CP/MAS) at a working frequency of 76. 46 MHz. Samples were pressed into cylindrical zirconia rotors and spun at a magic angle at 7 kHz. The spectra were recorded as the sum of 4000 scans and calibrated using the methane carbon atoms of adamantane as an external standard (δ = 29. 47 ppm). Data were processed by XWinNMR software with a line broadening of 10 Hz.
Gel Permeation Chromatography Cellulosic samples were derivatized to cellulose tricarbanilate (CTC) using phenyl isocyanate. The tricarbanilation process was achieved following the procedure reported elsewhere  [30]. The derivatives were dissolved in tetrahydrofuran (THF), filtered through a 0. 45 µm Teflon membrane, and placed in a 2 mL auto-sampler vial preceding the GPC analysis. In order to avoid random error, four analyses were implemented. The measurement error is thus the standard deviation calculated from four measurements. The number-average molecular weight (Mn) and weight-average molecular weight (Mw) were determined using the Dionex Ultimate-3000 HPLC system with a UV detector with THF as the eluent. Analyses were held at 30 • C at a flow rate of 1 mL/min. The calibration curves were built using standard polystyrene samples and data collection and analysis were ensured by Chromeleon software version 6.
It is worth mentioning that acid hydrolysis leads to the fragmentation of pyranose linkages, ensuring the deterioration of amorphous regions with a decrease in fiber lengths. The dimensions of the fibers were calculated using Image J software. For MCC samples, diameters of about 11. 7 ± 1. 8 µm, 6. 12 ± 2. 9 µm, 7. 39 ± 1. 3 µm, and 6. 38 ± 2. 6 µm were reported for MCCGEKac, MCCGEKal, MCCGEOac, and MCCGEOal, respectively, whereas lengths of 61. 6 ± 6. 4 µm, 67. 1 ± 7. 2 µm, 56. 9 ± 8. 4 µm, and 60. 3 ± 7. 5 µm were found for MCCGEKac, MCCGEKal, MCCGEOac, and MCCGEOal samples, correspondingly. These findings reflect the microscale of the obtained MCC, indicating the efficiency of the employed hydrolysis methods. Moreover, the obtained results are similar and comparable to those reported in the literature.
Indeed, the reported lengths are similar to those of MCC extracted from Eucalyptus sulfate pulp  [45]  and Posidonia oceanica  [46]. of pyranose linkages, ensuring the deterioration of amorphous regions with a decrease in fiber lengths. The dimensions of the fibers were calculated using Image J software. For MCC samples, diameters of about 11. 7 ± 1. 8 μm, 6. 12 ± 2. 9 μm, 7. 39 ± 1. 3 μm, and 6. 38 ± 2. 6 μm were reported for MCCGEKac, MCCGEKal, MCCGEOac, and MCCGEOal, respectively, whereas lengths of 61. 6 ± 6. 4 μm, 67. 1 ± 7. 2 μm, 56. 9 ± 8. 4 μm, and 60. 3 ± 7. 5 μm were found for MCCGEKac, MCCGEKal, MCCGEOac, and MCCGEOal samples, correspondingly. These findings reflect the microscale of the obtained MCC, indicating the efficiency of the employed hydrolysis methods. Moreover, the obtained results are similar and comparable to those reported in the literature.
Thermal Stability The thermal stability of cellulose and MCC samples was evaluated by DSC and TGA analyses. The exploration of this property is crucial for assessing the material performance for advanced applications. Materials with high thermal stability enable large and advanced applications. Indeed, some preparation processes, such as composites preparation, occur at relatively high temperatures of around 200 • C  [34] , which make the identification of the degradation profile of the lignocellulosic materials more than necessary. Figure  4  shows the obtained TGA thermograms, the derivative thermograms (DTG) are also exhibited in the same figure. Figure  5  displays the DSC curves of all samples. The reported data are described in Table  4. analyses. The exploration of this property is crucial for assessing the material performance for advanced applications.
Wind potential in tropical climates urban environment The wind speed data is taken from a weather station located at Pierrefonds airport on the seafront, which is an open area. The presence of obstacles such as buildings or vegetation will however disturb the flow and lead to lower wind speeds. A readjustment of the meteorological wind data is necessary to adapt them to an urban area. The norm on wind action [5] gives a method taking into account the roughness of the original terrain as well as the roughness of the target site and the altitude at which the wind speed is to be known. Considering only the variation due to the modification of the terrain roughness, i. e. in the absence of altitude variation and too imposing obstacles, the expression is written: ܸ ‫)ݕ(‬ = ܸ × ‫ܥ‬ ‫,ݕ(‬ ‫ݕ‬ ) (1) C ୰ (y, y ) = 0.