dataesr/scientific-paragraphs-categorization huggingface dataset:
The data we used are based approximately on the meteorological classes in the Pasquill -Gifford scheme  (Pasquill, 1961; Gifford, 1976) , and include unstable, neutral and stable conditions. The seven classes provided by ADMS range from highly unstable (A) to highly stable (G) and are given in Table  2. Different sizes of square fields were simulated for each meteorological condition, assuming steady-state, with concentrations determined at 0. 7 and 1. 5 m heights at 1, 2 and 10 m downwind of the field edge (Fig. 1 ). Each field study was given a field size class, based on the square root of the reported area to represent an average linear dimension, of either 50, 75, 100 or 150 m, generally rounding down to provide a worst-case. An adjustment factor was determined for each study to scale the field data from the field size class and distance downwind to a 200 m square field and 10 m downwind, based on the average over the seven meteorological classes.
Meteorological data was selected for the country of the trial if available, or the default country for the same regulatory zone if not. Chemical properties of the AI and reported application rate were obtained from the database. The product application rate and concentration of AI in the product, both of which are required in BROWSE, were not provided, therefore BROWSE inputs were adjusted to ensure the AI application rate per ha was that recorded in the trial. Table  3  shows the inputs used for each simulation. All input values not included in Table  3  were left as BROWSE defaults but in any case are not relevant to the vapour exposure calculation.
Discussion Because none of the trials in the database was designed specifically to test the BROWSE model, any determination of how 'good' BROWSE is at predicting vapour concentrations and thus exposures, or how  conservative the model might be, can only be estimated. The size of the treated area was one major factor that might have reduced the measured air concentration, and we have tried to take this into account by adjusting the data. The comparison between the predicted distributions and the measured data are given in Table  5 , but this can only be an indication because of further issues with the measured data that we cannot take into account. These are outlined here. 1. 13 of the 23 studies did not measure air concentrations over a 7-day period (which is the period over which the BROWSE distributions of 24-h rolling averages were determined) and therefore there is a number of 'missing' data points.
Because these relate to time periods more than 2 days after the application, and the highest concentrations are known to occur soon after the application, these missing data are very likely to be significantly lower or zero. For 8-h monitoring periods over 7 days, there should be 21 measured values of vapour concentration, from which 19 24-h rolling averages can be calculated. For the 22 studies which measured at both adult and child breathing heights, this would give 836 data points and for the two trials that measured at only adult breathing height, another 38 data points, making a total of 874. Thus, the percentage of acute data less than the 95th percentile could be around 96% for adults, and 97% for adult and child exposures combined, for the adjusted data (for comparison see Table  5 ).
The BROWSE datasets were based on 90th percentile temperatures across each regulatory zone and so were designed to be conservative. In addition, the experimental Limits of Detection and Quantification were in many cases greater than the predicted concentration, which, together with the 'missing values' discussed above, will particularly affect the lower percentiles of the distribution. Further work, including more experimental data with improved protocols and technique, would be needed to evaluate whether the BROWSE model can reliably predict the lower percentiles. There was one study where some data needed further consideration, CEMR-8025. Data from this study for non-zero adult exposure is shown as red squares in Fig. 2. There are three values of the rolling 24-h average for adults that are higher than the predicted 95th percentile, and the maximum longer-term average is also greater than the 95th percentile of that predicted.
Out of 5 AIs included in the database, only one would be classified as 'moderate volatility' (VP 9. 00 × 10 -3 Pa) in the current exposure assessment according to EFSA et al. (  2022 ), as such a wider range of VPs would be beneficial for further research. Only three of the AIs were replicated over different sites and timings, therefore, more AIs with different physico-chemical properties would provide more detailed insights. While there were multiple sites across the central and southern regulatory zones, there was only one site in regulatory northern zone. The applications to vine and pome fruit were well replicated across sites and application timing (in total 16 studies at early and late foliage development stages), repeated experiments for other AIs and crops would be helpful for identifying the possible range of concentrations that could be achieved.
Context. The second Gaia data release, DR2, contained radial velocities of stars with effective temperatures up to T eff = 6900 K. The third data release, Gaia DR3, extends this up to T eff = 14 500 K. Aims. We derive the radial velocities for hot stars (i. e. , in the T eff = 6900-14 500 K range) from data obtained with the Radial Velocity Spectrometer (RVS) on board Gaia. Methods. The radial velocities were determined by the standard technique of measuring the Doppler shift of a template spectrum that was compared to the observed spectrum. The RVS wavelength range is very limited. The proximity to and systematic blueward offset of the calcium infrared triplet to the hydrogen Paschen lines in hot stars can result in a systematic offset in radial velocity. For the hot stars, we developed a specific code to improve the selection of the template spectrum, thereby avoiding this systematic offset.
Introduction The Gaia satellite (Gaia Collaboration 2016a) was launched in December 2013. By continuously scanning the sky, it is collecting astrometric, photometric, and spectroscopic information on a large number of stars. A number of data releases have made part of this wealth of information public (DR1 by Gaia Collaboration 2016b; DR2 by Gaia Collaboration 2018; EDR3 by Gaia Collaboration 2021). Now the third data release (Gaia DR3, Gaia Collaboration 2022) is available, which not only updates the previous releases, but also adds substantially new information, such as BP/RP spectra  (De Angeli et al. 2023) , astrophysical parameters  (Creevey et al. 2023) , and variability classification  (Eyer et al. 2023). This new information also includes the radial velocities for almost 34 million stars  (Katz et al.
2023) , and Radial Velocity Spectrometer (RVS) spectra for almost one million stars  (Seabroke et al. , in prep. ). The Gaia data are processed by the Data Processing and Analysis Consortium (DPAC; see Gaia Collaboration 2016b, for details). Within DPAC, the Coordination Unit 6 (CU6) is responsible for handling the RVS spectra. Its main tasks are the reduction of the RVS data for use by other Coordination Units and for publication in the data releases, as well as the determination of the radial velocity from these spectra. A secondary task is measuring the broadening of the spectral lines. This broadening is mainly due to the (projected) rotational velocity of the star, but may also include other effects, such as macroturbulence Corresponding author: R. Blomme, e-mail: ronny. blomme@oma. be (Frémat et al.
The previous data release DR2 did not include these, as the quality of the results could not be guaranteed. The present paper describes how these hot-star radial velocities were derived, and the caveats that have to be taken into account in their use. For stars even hotter than 14 500 K, there remain challenges that could not be solved in DR3 and will therefore be handled in the next release, DR4. Determining radial velocities for hot stars with the Gaia RVS is quite challenging. The instrument is described in detail in  Cropper et al. (2018). It is a medium-resolution spectrograph (R ≈ 11 500) covering the wavelength range 846-870 nm. This rather small wavelength range has been chosen to optimise radial velocity determination for cool stars. Figure  1  shows two examples of the RVS spectrum of a hot star.
e. , grvs_mag > 12 mag) to determine the stellar radial velocity. spectrum 1. These parameters are the effective temperature (T eff ), the gravity (log g), and the metallicity ([M/H]). In principle, the astrophysical parameters are determined by Coordination Unit 8 (CU8). However, for DR3, the CU8 processing was done after the CU6 processing. The considerable time needed for the processing and the validation of the results means that it is not feasible to iterate between the CU6 and CU8 processing. CU6 therefore used parameters from the preliminary CU8 run. This CU8 run used an earlier version of the GSP-Phot  (Andrae et al. 2023 ) and GSP-Spec  (Recio-Blanco et al. 2023)  pipelines. GSP-Phot used DR2 photometric data instead of the better-quality and more detailed DR3 BP/RP data, while GSP-Spec used RVS spectra from the preliminary CU6 run (discussed in Sect.
Improved method Because of the unsatisfactory results of the preliminary run for hot stars, we decided on a new approach, implemented as the module ReDetermineApHotStars. This module does an exhaustive brute-force search among the combination of many templates and broadening velocities to find the combination that best fits (in χ 2 sense) all transit spectra of a given star. As part of the procedure, ReDetermineApHotStars also shifts the template being explored in velocity space, and it thus also determines the transit radial velocities. However, only the updated template parameters and the broadening velocity are output from ReDe-termineApHotStars; the radial velocity determination is left to the three radial velocity modules (Sect. 2. 1. 2). The way the ReDetermineApHotStars module is included in the STA part of the pipeline is shown in Fig.
2. ReDe-termineApHotStars is called for stars with a template T eff ≥ 7000 K. It is also called for 6500 ≤ T eff < 7000 K when the atmospheric parameters are not from literature data (see Sect. 2. 1. 1). We apply this procedure only to stars that are bright enough (G RVS ≤ 12 mag) so that we have a sufficient S/N in the spectra. The G RVS value used in this procedure is not exactly the same as the grvs_mag available in the DR3 archive. The latter value is determined in the MTA part of the pipeline as the median of all valid transit G RVS values  (Sartoretti et al. 2023). During the run of STA, this median value was not yet available, therefore STA used the first valid value from the list of transits. Because of the requirement for bright enough stars for ReDe-termineApHotStars, the hot-star radial velocities in the DR3 archive are mostly limited to grvs_mag ≤ 12 mag.
Conclusions The third Gaia data release (Gaia DR3) is the first data release to contain radial velocities of stars with a template T eff (rv_template_teff) between 7000 and 14 500 K. The archive contains 543 017 stars in this range. A preliminary run of the pipeline deriving these hot-star radial velocities from the spectra of the Radial Velocity trometer gave unsatisfactory results. The problem is due to the proximity to and systematic blueward offset of the calcium infrared triplet lines to the Paschen hydrogen lines. This leads to a systematic offset in radial velocity if the template does not describe the observed spectrum correctly. For this reason, a specific module was developed that improves the choice of the template. Using this module leads to a much better agreement between the Gaia radial velocities and the reference data.
Chemin); -the National Natural Science Foundation of China (NSFC) through grants 11573054, 11703065, and 12173069, the China Scholarship Council through grant 201806040200, and the Natural Science Foundation of Shanghai through grant 21ZR1474100; -the Tenure Track Pilot Programme of the Croatian Science Foundation and the École Polytechnique Fédérale de Lausanne and the project TTP-2018-07-1171 'Mining the Variable Sky', with the funds of the Croatian-Swiss Research Programme; -the Czech-Republic Ministry of Education, Youth, and Sports through grant LG 15010 and INTER-EXCELLENCE grant LTAUSA18093, and the Czech Space Office through ESA PECS contract 98058; -the Danish Ministry of Science; -the Estonian Ministry of Education and Research through grant IUT40-1; -the European Commission's Sixth Framework Programme through the European Leadership in Space Astrometry (ELSA) Marie Curie Research Training Network (MRTN-CT-2006-033481), through Marie Curie project PIOF-GA-2009-255267 (Space AsteroSeismology & RR Lyrae stars, SAS-RRL), and through a Marie Curie Transferof-Knowledge (ToK) fellowship (MTKD-CT-2004-014188); the European Commission's Seventh Framework Programme through grant FP7-606740 (FP7-SPACE-2013-1) for the Gaia European Network for Improved data User Services (GENIUS) and through grant 264895 for the Gaia.
Anders), the  Severo Ochoa Centre of Excellence Programme  ( SEV2015-0493 ), and  MICIN/AEI/10. 13039/501100011033 (and the  European Union  through  European Regional Development Fund 'A way of making Europe')  through grant  RTI2018-095076-B-C21 , the  Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelen-cia 'María de Maeztu ') through grant  CEX2019-000918-M , the  University of Barcelona 's official doctoral programme for the development of an  R+D+i  project through an  Ajuts de Personal Investigador en Formació (APIF) grant , the  Spanish Virtual Observatory  through project  AyA2017-84089 , the  Galician Regional Government, Xunta de Galicia , through grants  ED431B-2021/36 ,  ED481A-2019/155 , and  ED481A-2021/296 , the  Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC) , funded by the  Xunta de Galicia  and the  European Union  ( European Regional Development Fund -Galicia 2014-2020 Programme ), through grant  ED431G-2019/01 , the  Red Española de Supercomputación (RES) computer resources at MareNostrum , the  Barcelona Supercomputing Centre -Centro Nacional de Supercomputación (BSC-CNS)  through activities  AECT-2017-2-0002 ,  AECT-2017-3-0006 ,  AECT-2018-1-0017 ,  AECT-2018-2-0013 ,  AECT-2018-3-0011 ,  AECT-2019-1-0010 ,  AECT-2019-2-0014 ,  AECT-2019-3-0003 ,  AECT-2020-1-0004 , and  DATA-2020-1-0010 , the  Departament d'Innovació, Universitats i Empresa de la Generalitat de Catalunya  through grant  2014-SGR-1051  for project ' Models de Programació i Entorns d'Execució Parallels ' (MPEX-PAR), and  Ramon y Cajal Fellowship   RYC2018-025968-I  funded by  MICIN/AEI/10.
Geometry extraction method Fig. 3a  shows the addition of a convolutional layer to an image of surgical mask layers. The photo's original resolution was 1600 × 1200 pixels. The top and cross-sectional views of each metal foams' top were analysed to determine the best results. Fig. 3b  shows how threedimensional geometry was converted from microscopic images. Point clouds could be generated from convolutional matrices  [34]. By using commercial CAD software (i. e. , SolidWorks), these points can be converted to solid geometry. The application of this method improves the prediction quality and enables a better understanding of the flow patterns generated by PSPM. According to Fig. 3b , the original picture was used as input data. In digital image processing, image acquisition is the first step.
In order to preserve their size, the layers that perform convolution were padded with zeros prior to passing their output to the max-pooling operation. Padding protects an image's corners from information loss and output size reduction. The second fully connected layer was given a dropout probability of 0. 217 to prevent the network from becoming overfitted. In practice, the dropout layer further reduces generalization loss. 80% to 20% of the images in this 5-image dataset are used as a training set, while the remaining images are used as a test set. The network acquires knowledge through the modification of its filters. CNN weights are defined and modified in accordance with their hyperparameters. The network contains 1126 learnable parameters or weights in total. To perform a numerical analysis of the PSPM, its exact three-dimensional geometry was required.
It is noticeable that each training iteration updates the weight parameters based on a partial derivative of the loss function against the current weight. The calculated derivatives at the front of FNN cells may be vanishingly small when the gradient of the activation function is less than unity, effectively preventing the weight from changing. Therefore, training the entire neural network is extremely slow or even completely halted, particularly for the front FNN cells. When using a large number of FNN cells to simulate a longer time sequence, gradient vanishing occurs, which affects the performance of both deep neural networks and FNNs. This is similar to the problem of multiple hidden layers in deep neural networks. A homemade Python-and Julia-based code is developed by our group to machine learning.
In the numerical simulation, a SIMPLE algorithm (https://github. com/gaabnuneses/SIMPLE-in-Julia, http s://github. com/DelNov/T-Flows) is used for flow passing through the layers. All numerical simulations are developed based on the secondorder (upwind) of accuracy. TensorFlow (https://github. com/te nsorflow/tensorflow) is used for machine learning. The geometry of  mask and its features are extracted using image processing technique, and OpenCV-Python (https://github. com/spmallick/learnopencv) is used for that purpose.
The root-mean-square error (RMSE) of the training dataset can be used as a proxy for the accuracy of the loss function in obtaining the desired flow pattern  [56]. RMSE(Θ) = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ 1 N s * N s ∑ Nc i ∑ Ns j ( Δh i ( Θ NN ( Θ, T j , S j ) , T j , S j ) -Δh t i ) 2 √. (23) A weighted sum of the outputs of the input layer is applied to the output layer, which consists of neurons m + 1. f j = g ( w1 0,j + ∑ n i=1 w1 i,j x i ). ( 24 ) A neuron's activation function determines whether it is active or not  [57]. It determines how significant the neuron's input is during the prediction process through simpler mathematical calculations. Activation of the neurons in this layer generates the model's output, just as activation of neurons in the hidden layer does.
Tracking transient vortices through a complex geometry necessitates an increase in the average resolution of the mesh. As a result, the training data set will expand. According to the results, the training dataset for layer 3 has increased by 14. 4% compared to other layers. Additionally, the prediction time was increased by 9. 84% compared to other layers. The pressure patterns at t = 0. 1 and t = 0. 5 (Fig. 13b  and c ) demonstrate that the quality of training data directly impacts the output of PIML. Fig. 14  presents a transient pressure through layer 4. According to the results, although unclassified data can detect patterns, their high standard deviation makes it impossible to predict the fluid flow behavior in this layer. The maximum variation of critical parameters such as vorticity, pressure drop, and dissipation rate occurs in the third layer.
Conclusions Flow simulation through complex geometries, such as pore-scale porous media (PSPM), is a true challenge in fluid mechanics. The grid generation for complex geometries is the primary barrier to developing numerical methods in these domains. The present study presented a novel solution for predicting flow through complex geometry. Physics-informed machine learning was developed based on transient and turbulence computational methods to predict transient flow through the microscale PSPM. To achieve this objective, a method of image processing was used to extract 3D geometry from a series of 2D microscopic images of a surgical mask. After validating the extracted geometry, commercial software (Star CCM+) was used to simulate a transient flow. For two inlet velocities (0. 1 and 0. 5 m/s), the simulated flow passed through six distinct layers of a surgical mask.
2-The fiber concentration plays an important role in pressure drop in each layer. The results indicated that layer 3 has the strongest impact on the pressure drop due to its fiber concentration. 3-According to the results, the training dataset for layer 3 has increased by 14. 4% compared to other layers. Additionally, the prediction time was increased by 9. 84% compared to other layers. The results indicated that the PIML method could reduce total calculation time (including training and prediction) by up to 22. 5 and 20. 7 times compared with standard numerical simulation, on average, for speeds of 0. 1 and 0. 5 m/s, respectively. Numerous factors, such as the inherent difference between CPU and GPU, algorithms, and software, can influence this factor.
Acknowledgements N. Karimi  acknowledges the financial support by the  Engineering and Physical Science Research Council, UK ; through the grant number  EP/V036777/1   Risk Evaluation Fast Intelligent Tool (RELIANT ) for  COVID 19. M. S. Shadloo acknowledge the access to French HPC resources provided by the  French regional computing center of Normandy (CRIANN)  (Grants No. 2017002 ).
-4 are the utilized weighting coefficients. This equation penalizes the residual of the governing equation using L PDE. In addition, L DATA is utilized to demonstrate data loss. Initial condition is represented by L IC , while boundary conditions are represented by L BC. Fig. 6a displays a diagrammatic representation of the PIML's fundamental structure. In this diagram, the unknown PDE parameters û = [u, v, p, ∅], x = [x, y], and λ are depicted along with the weights w i = 1, 2, …, 4 within the PIML procedure, PIML training is regarded as one of the most important steps. Fig. 6b provides a detailed outline of the training procedure. Here, the PIML framework involves the following steps [53]. 1 Create a dataset for training with Ns random combinations of transmissions and storability and Nb batches.
This study investigates the feasibility and performance of a missile propelled by a Hybrid Rocket-Based Combined Cycle (HRBCC) engine, which combines hybrid rocket and air-breathing engines. The HRBCC engine could enable intra and extra-atmospheric hypersonic flight in the future. Using a hybrid rocket engine could bring several advantages as reduced costs, increased reliability and simplicity. This research presents the unique feature of the hybrid rocket engine developed by HyPrSpace and explains its benefits for the conception of a HRBCC system. The performance of the missile was studied in two operational modes: pure rocket and ramjet. The results were then used to develop an inhouse code that compared the trajectory of different missiles. Our findings indicate that HRBCC engines provide several advantages over other propulsive technologies and can meet the requirements of various military applications.
Pure rocket mode To determine certain characteristics of the combustion products specific to the chosen propellant pair, the Chemical Equilibrium Application (CEA) software developed by NASA  (McBride SG, 1996)  was used. This software enables to calculate key parameters such as the molecular mass (MW), the temperature of the combustion chamber (T c ) and the ratio of thermal capacities (γ) of the combustion products. A code was developed to plot these parameters as functions of the O/F mixture ratio. With these parameters, the characteristic velocity (c*) and thrust coefficient (C F ), defined by Eq. 4. 1, have been calculated. c* γ R MW T c γ 2 γ + 1 γ+1 γ-1 C F 2γ 2 γ -1 2 γ + 1 γ+1 γ-1 1 - P e P c γ-1 γ ⎧ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎨ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎪ ⎩ (4. 1) Where R is the gas constant, P e is the exit pressure and P c the pressure in the combustion chamber.