dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions A large comparison exercise has been performed featuring three simulation cases in axial, sheared and yawed inflow conditions. Results were obtained from more than 19 simulation tools originating from 12 institutes ranging in fidelity from BEM to CFD and compared to state of the art field measurements from the DanAero turbine. More than 15 different variable types ranging from lifting line variables to pressures, loads and velocities have been compared for the different conditions, resulting in over 250 comparison plots. The result is a unique insight in the current status and accuracy of rotor aerodynamic modeling. For axial flow conditions a good agreement was found between the various code types, where a dedicated grid sensitivity study was necessary for the CFD simulations.
Statistical Analysis All statistical analysis was conducted by SAS version 9. 4. Descriptive statistics were used to analyze baseline patient demographics, pharmacokinetics, and safety profiles. The results were expressed as mean ± standard deviation (SD), except for T max , which was expressed as median (minimum and maximum values). To assess the associated DDI, a twosided 90% CI for the true mean difference between treatments for pharmacokinetic parameters (C max , AUC 0-t , and AUC 0-∞ ) on the log scale was computed from the following linear mixed effect model: log (parameter) = Sequence + Period + Treatment + Error, with fixed terms for sequence, period, and treatment and with an unstructured 3-by-3 matrix of treatment-specific variances and co-variance for subject within sequence. These confidence limits were then exponentiated to obtain the 90% CIs for the true GMRs for the pharmacokinetic parameters, including C max , AUC 0-t , and AUC 0-∞ (co-administration versus administered alone).
Acknowledgements The authors acknowledge  Dan Zhang ,  Medical Communication, Sanofi, China  for publication process coordination;  Ya Li ,  Sanofi, China  for her contribution to the implementation of clinical trials;  Haibiao Jiang ,  Sanofi, China  for the protocol design;  Jing He  and  Ran Hu ,  R&D, Sanofi, China  for study conduct; and  Boya Feng ,  Sanofi, China  for  CSR (Clinical Study Report ) writing support. The authors would like to thank all the participants of the study. In addition, the authors would like to thank  Dr. Amit Bhat ,  Fatemah Bensaheb , and  Dr. Satya Lavanya Jakki  from  Indegene Pvt Ltd, India  for their medical writing support with inputs from all the authors.
The screening model consists of a hierarchical classification of the factors, and thus to determine an initial set of factors, in our case, the most influential impurities  [17]. Model for k factors (Eq. (  1 )): Y = b 0 + b 1 X 1 + b 2 X 2 + b 3 X 3 +. + b k X k (1) Where b 0 is the average response and b 1 , b 2 …. b k are the regression coefficients. The aim of the present paper was to evaluate the impact of impurities on the phosphoric acid process and to investigate their effects, especially on crystallization and filtration stages. During the current study, four responses of interest were considered, namely: filterability F, aspect ratio L/w, thickness e, and mean diameter d. Finally, the coefficient determination R 2 and analysis of variance (ANOVA) were performed using the software package Nemrodw (trial version) to verify through experimentation the efficiency, fitness, and adequacy of the predicted models  [18].
Effect on filterability Filtrabilities were calculated using Eq. (  4 ), Table  4  depicts the obtained experimental design results for filterability; the experimental data was analyzed using free trial Nemrodw software  [20] , Fig. S1  and Table  S1  illustrate the statistical data of the 12 experiments. As filterability needs to be maximized, it was deduced that the optimal combinations are the 10th and 5th trials, corresponding to filterability values of 6. 32 and 6. 08, respectively. Thus, the design factor (impurity) levels that yield the optimum performance could be easily determined. It is also noticed from these experiments that the presence of different ionic species (cations such as Al 3+ , Fe 3+ , Mg 2+ , Na + ) and/or anions such as (F -,CO     of Fe 3+ ions during these runs is dependent probably on their co-existence with Al 3+ ions in a ratio of Fe 2 O 3 /Al 2 O 3 = 0.
Effect on aspect ratio L/w Table  5  shows the aspect ratio measurements for the 12 crystallization batches undertaken. As is well known, filterability is closely related to the size and shape of the crystals, therefore, to have a good filtration and washing rates, the aspect ratiodefined as the proportion between length L and width w of the phosphogypsum crystals-should be minimized as much as possible. Fig. S2  and Table  S5  illustrate the statistical data of the 12 experiments. During these crystallization batches, AR varied from 5. 7 to 8. 5 depending on the used formula and the percentage of impurities. The largest ratios were recorded in trials 1, 3, and 8, corresponding to the lowest filterability values of 3. 94; 4. 32, and 4. 62, respectively. Oppositely, the highest filterability values of 5.
Effect on thickness Table  6  shows the thickness measurements for the 12 crystallization batches undertaken. Given the difficulty of measuring the thickness of crystals, image analysis software ImageJ  [33]  was used for this investigation. Fig. S3  and Table  S9  illustrate the statistical data of the 12 experiments. The thickness values varied between 5. 5 and 8. 1, and the highest values were obtained in trials 2 and 10, corresponding to the highest levels of Al 2 O 3 and K 2 O. The graphical distribution of effects and the Pareto study are displayed in Fig. 7a , Fig. 7b  and 7c , respectively. From these results, it is interesting to note that Al 3+ and K + ions exhibited a significant increase in the thickness, otherwise, the effect of Si 4+ and Fe 3+ ions on this dimension appears to be limited comparatively to their effect on the aspect ratio characterized by length and width.
, 2021). Satellite imagery provides the most valuable information about the Earth with minimum cost and time on a global scale. With the coming new series of satellite imagery (i. e. Landsat-9, Sentinel-2) in addition to improving data quality, the temporal resolution of the dataset has been significantly improved  (McCorkel et al. , 2018). Accordingly, the use of remote sensing has been considered in many applications such as classification  (Ahmad et al. , 2021; Sellami and Tabbone, 2022) , active fire monitoring  (Wang et al. , 2022; Xu et al. , 2021b) , flood mapping  (Luppichini et al. , 2022; Muñoz et al. , 2021) , and damage mapping  (ElGharbawi and Zarzoura, 2021; Pilger et al. , 2020). Due to the increase in the availability of remote sensing datasets, wildfires have received significant attention in past years  (Morresi et al.
, 2022; Smith-Ramírez et al. , 2022). In this regard, the many researches have been done for burned area mapping based on medium resolution optical datasets  (Campagnolo et al. , 2021) , synthetic aperture radar images  (Zhang et al. , 2021b) , high-resolution datasets  (Gajardo et al. , 2022) , and very high resolution (VHR) datasets  (Warner et al. , 2017). Furthermore, many burned area products have been provided based on high and medium resolution datasets on the local and global scale  (Chuvieco et al. , 2016; Franquesa et al. , 2022; Hawbaker et al. , 2020; Otón et al. , 2021b). The accuracy of the Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product has been reported by some researches  (Boschetti et al. , 2016; Otón et al. , 2021a; Vetrita et al. , 2021). For example,  Boschetti et al.
(2019 reported  the globally MCD64A1 product had an estimated Commission and Omission Errors of 40. 2% and 72. 6%, respectively. Moreover, the results of the accuracy assessment for MODIS burned area products are different and deepened on characteristics of region-of-interest. However, the medium resolution sensors have a high temporal resolution but mapping burned areas in more detail and high accuracy needs a fine spatial resolution dataset and advanced methods. In past years, many burned area mapping methods have been developed based on the high-resolution dataset  (Barboza Castillo et al. , 2020; Farasin et al. , 2020; Knopp et al. , 2020). Mainly, these methods focused on change detection based on manual feature generation. These methods can be applied in two main phases: (1) feature extraction based on spatial feature extractor (i.
e. texture feature), spectral indices (i. e. normalized burned ratio index, normalized vegetation index); (2) classification based on machine learning-based methods (support vector machine, and random forest)  (Seydi et al. , 2021a). These methods provided promising results but have some limitations: (1) extraction of handcrafting features and tuning them are a time-consuming process, and (2) the strength of machine learning-based methods lie in of quality and quantity of input feature dataset and sample dataset. In other words, the feature dataset (spectral and spatial features) determine the performance of machine learning methods, and (3) is more sensitive to noise condition and low generalization  (Seydi et al. , 2021b). In past years, deep learning-based methods have been provided promising results in many applications such as classification  (Ma et al.
, 2021; Wu et al. , 2021) , change detection  (Liu et al. , 2021; Zhang et al. , 2021d) , crop mapping  (Sagan et al. , 2021; Xu et al. , 2021a). The deep learningbased methods can extract meaningful features from the input dataset, automatically. Currently, burned area detection by deep learning-based methods have been considered and changed into hot topic research areas  (Zhang et al. , 2021a; Zhao et al. , 2022). In this regard, many researches have been done for mapping the burned areas by remote sensing imagery. For example,  Zhang et al. (2021c)  proposed a deep learning-based framework for mapping burned areas by time-series Sentinel-1 and bitemporal Sentinel-2 images. The proposed deep learning is based on deep Siamese self-attention with share weight. They used the pre/post event Sentinel-2 images also, the temporal pre/post-fire backscatter, and coherence maps, and differencing them (differencing post-event and pre-event) as input data.
Pinto et al. (2021)  designed a framework for burned area mapping by the BA-Net and bi-temporal Sentinel-2 imagery. This framework is applied in four main phases: (1) generating burned area map by BA-Net and VIIRS dataset that used training deep learning model in the last phase, (2) splitting the result of burned areas into individual events, (3) preparing pre/post-event Sentinel-2 images, (4) model training based on deep learning model and resampled sample data. Farasin et al. (2020)  proposed a double step U-Net for estimating wildfire damage severity using Sentinel-2 imagery. The mentioned proposed method uses two U-Net models which the first model generates the binary severity damage amp while the second U-Net applies for regression to estimate severity damage level. They claimed the proposed method to be robust for estimating the severity of damage in similar geological aspects.
Maskouni and Seydi (2021)  proposed a deep learning framework based on a convolutional neural network (CNN) and bi-temporal Sentinel-2 imagery. This framework extracts the deep features by multi-scale convolution layers then, the high-level feature explorer by single kernel convolution. They evaluated the proposed framework by stacking pre/post-event red, green, and blue bands. They reported the proposed CNN framework has high efficiency in the detection of small fire areas on a local scale. Ban et al. (2020)  developed a near real-time wildfire progression monitoring framework by a CNN model using time series Sentinel-1 datasets. The generated the pseudo sample data by change detection by log-ratio and master and slave datasets. Next, the stacked pre/post-fire datasets and digital elevation (DEM) products are fed to the CNN model for monitoring of the wildfire progression.
Seydi et al. (2021b)  proposed a deep learning-based framework for burned area mapping by heterogeneous remote sensing datasets. They used the pre-fire Sentinel-2 imagery and post-fire hyperspectral PRecursore IperSpettrale della Missione Applicativa (PRISMA) imagery for mapping burned areas. This proposed deep learning framework is CNN architecture and inspired by a deep Siamese network. The result of burned area mapping by the abovementioned deep learning-based methods is illustrated and these methods have high potential in mapping the burned areas. Furthermore, these frameworks provided promising results compared with other state-of-the-art machine learning-based methods. Howbeit, some limitations remain that motivate presenting a new framework for burned area mapping. We refer to some limitations of the abovementioned burned area mapping studies: (1) The most method focused on burned area mapping by change detection using bi-temporal datasets or time-series datasets.
However, these methods provided promising results but extraction of burnt area information from temporal datasets is big challenging and time-consuming. Moreover, the change information originated from other factors (except for the fire) that added false pixels to burned area mapping results. (2) The spatial resolution is the most important factor in the accurate burned area mapping that some methods focused on medium resolution datasets (more than 250 m) for burned area mapping. This theme is more evident in the accurate mapping of burned areas in small areas that are very challenging by mediumresolution datasets (i. e. MODIS, VIIRS). (3) Many frameworks fused the SAR and optical datasets for burned area mapping that provided acceptable results. Although, these methods suffer high computational costs and demand for robust processing tools for extraction of burned area information.
Another limitation of the fusion framework, the pre-processing (i. e. image registration) of SAR (i. e. de-speckle, multi-looking, terrain correction) and optical (atmospheric correction) datasets are very time-consuming. (4) In image processing and computer vision fields, classification defines as the process of predicting a complete input based on its scores while semantic segmentation assigns labels for each input image pixel. Thus, the CNN-based classification frameworks need low sample data while requiring more time for classifying burned areas. Furthermore, these methods suffer low generalization comparison with semantic segmentation-based methods. (5) Some deep learning-based frameworks used additional preprocessing (i. e. coherence map generation) or feature generation (i. e. NBR) that do not apply in an end-to-end manner and demand more time and computational costs.
In this regard, this study proposes an end-to-end deep learning-based framework for mapping burned areas to minimize the above-mentioned challenges. The proposed deep learning framework is inspired by U-Net architecture but has fundamental evolution in the structure of deep feature extraction. The proposed Burnt-Net combines the quadratic morphological learnable kernel convolution and residual blocks with standard convolution for deep feature extraction. Furthermore, the Burnt-Net enhances the efficiency of deep feature extraction and speed of burned area mapping by patching the input dataset. Unlike, many change detection-based burned area mapping frameworks use monotemporal Sentinel-2 imagery for burned area mapping. The main contribution of this research is:  The remaining part of the study is outlined as follows: Section 2 the structure of Burnt-Net.
Methodology The proposed burned area mapping framework can be applied to the three main steps: (1) data preparing, (2) Burnt-Net model training, and (3) prediction based on Burnt-Net. The data preparation included preprocessing the Sentinel-2 dataset and patch-generation. Next, the labeled datasets are fed for the training Burnt-Net model. In this step, the dataset is split into three main parts such as training, validation, and testing datasets. After tuning model parameters, the model applies for mapping burned areas and then the result of burned area mapping assesses by reference datasets. The overview of burned area mapping by the proposed method is shown in Fig. 1.
Training process The error of the training model is fed to the optimizer and is updated the parameters. Due to back-propagation, the parameters are updated at each step to decrease the error of comparing the results obtained from the network with the validation dataset. This research combined the Distribution/Region-based loss functions for calculating the error of the network. To this end, a hybrid loss function has been employed that combines the weighted cross-entropy (WCE) loss and intersection-overunion (IOU) loss (Eq. (  12 )). Loss WCE (R, P) = - 1 N ∑ N i=1 ∑ 2 c=1 w c R c i logP c i ( 10 ) Loss IOU (R, P) = 1 - ∑ N i=1 ∑ 2 c=1 R c i logP c i ∑ N i=1 ∑ 2 c=1 (R c i +P c i -R c i P c i ) (11) Loss(R, P) = Loss WCE (R, P) + Loss IOU (R, P) (12) where R and P are true values and predicted values, respectively.
Description of Train/Validation areas Train Area (I) Since, August 14, 2021, a large wildfire broke out near a road in Cepeda de La Mora, Spain which had taken two days. This wildfire affected the municipalities of Robledillo, Villaviciosa, Riofrio, Sotalbo and Solosancho in Castilla y León. Finally, this fire damaged the forest areas of more than 20,000 (hectares) in this area. Train Area (II) At the same time, on August 14, 2021, a forest fire began in Azuébar, located in Sierra de Espadán Natural Park, west of Spain which had taken nearly two days. Thus, this wildfire burnt more than 490 (hectares) of valuable forests in Azuébar. The color composite of the Sentinel-2 dataset is shown in Fig. 7-(c), (d). Train Area (III) On August 16, 2021, a wildfire spread in the southern French department of VAR that has been fed by a strong mistral.
Description of Test areas Test Area (I) On July 15, 2021, a wildfire began spreading in the municipalities of Castro Marim and Tavira in the Faro District-Algarve, Portugal. This wildfire took more than many hours and caused the burning of about 6000 (hectares) of forest areas in this area. The color composite of the Sentinel-2 dataset for this study area is shown in Test Area (II) Since, July 28, 2021, a wildfire has spread in southern Turkey, Antalya. This wildfire took some days and burnt more than 54,000 (hectares) of valuable forests in Antalya. Fig. 8-(c), (d)  shows the color composite of the Sentinel-2 dataset for this study area. Test Area (III) On July 3, 2021, a wildfire broke out in the Limassol district of Cyprus, destroying not only pine forests but also other vegetation within a few hours.
This wildfire burnt more than 3200 (hectares) of forest and vegetation. Fig. 8 -(e), (f) illustrates the color composite of the Sentinel-2 dataset for this study area. Test Area (IV) On August 16, 2021, a wildfire started in the west sector of the Attica region at Pateras mountain. This fire continued for more than one day and was intensified by strong winds that lead to the burning of more than 9,000 (hectares) of the large forests. As seen, Fig. 8-(g ), (h) shows the color composite of the post-fire Sentinel-2 dataset. Test Area (V) Since, August 3, 2021, a forest fire has been raging in the municipality of East Mani in the Peloponnese region, burning down   large pine forests. This fire continued until more than three days and burnt more than 9200 (hectares) of the extent forests. The color composite of the post-fire Sentinel-2 dataset is shown in Fig.
Sentinel-2 images Sentinel-2 is a European Space Agency (ESA) Earth observation (EO) project that performs continuous multispectral observations of the Earth's land surface at high spatial resolution. In addition to completing the Landsat and Spot missions, Sentinel-2-A and Sentinel-2-B have increased data availability for remote sensing. Each of these satellites (Sentinel-2-A and Sentinel-2-B) has a 10-day resolution, and combining them gives a 5-day resolution  (Seydi et al. , 2021a). The Sentinel-2 satellite acquired data at 13 spectral bands and the spatial resolution are 10 (m), 20 (m), and 60 (m) for specific spectral bands but not for all bands. In this study, the level-2A product was used as input data to map the burned areas based on surface reflectance. Table  1  shows the details of the data sets used for both study areas.
Parameter setting This study used the only spectral bands, which have 10 (m), and (m) spatial resolutions for burned area mapping. The input patch size for the Sentinel-2 is 1024 × 1024 × 10, the number of Epochs = (epochs), weight initializer set He-normal-Initializer  (He et al. , 2015)  for convolution layers, and also, random value for morphological layers, initial learning rate 10 -4 , the mini-batch size is 1. The Adaptive Moment Estimation (Adam)  (Kingma and Ba, 2014)  optimizer is used for tuning network parameters. It is worth noticing all hyperparameters are constant during the process for the U-Net model. Furthermore, some Test Areas datasets have spatial dimensions lower than 1024 × 1024 that were filled by adding flip original dataset and after classification was changed into original size.
Comparison with MODIS MCD64A1 product The MCD64A1 product is burned area data that is generated based on MODIS images on a global scale. This product has a monthly temporal resolution and a spatial resolution of 500 m. The burned area mapping approach for MCD64A1 uses the active fire dataset and surface reflectance imagery. Thus, this algorithm utilizes the vegetation index and dynamic thresholding for generating the MCD64A1 burned area product. As seen, Fig. 16  presents the MCD64A1 burned area product for six test areas. Based on this, the results of the burned area by MCD64A1 product show the general structure of the burned area while missing  information in the details. Fig. 16  (a), (e), (g), (i), and (k) are the result of burned areas for Test areas I, II, III, IV, V, and VI, respectively which are a high number of false pixels.
Therefore, accurate burned area mapping by such products is a big challenge. Furthermore, these products were evaluated by many researchers that provided an accuracy lower than 80% while the proposed Burnt-Net provides an accuracy of more than 97% by OA index. One point to be noted is that MODIS MCD64A1 products have a monthly temporal resolution while for some analysis timely burnt mapping is the most important. The U-Net and Burnt-Net frameworks use the encoder and decoder they have provided different results. Both models trained with the same condition and sample dataset while they have provided different results in the burned area mapping. The main difference between these models is in the extraction of deep features by convolution and morphological layers. The Burnt-Net uses the MRM blocks that improve the efficiency of the network in the extraction of high-level deep features.
The proposed method provides an accuracy of more than 97% for test areas while other methods provided an accuracy lower than 95%. Besides, the LBAP has high miss detection and false pixels in the different areas. The MODIS MCD64A1 product provides the main shape of burned areas while not being able to detect burned areas in more detail. Based on numerical and visual analysis, the Burnt-Net has some advantages in comparison with other state-of-the-art methods: (1)   it provides the burned area map more accurate than other products and methods, (2) it provides robust results compared with a U-Net model, (3) utilizes only post-fire dataset, (4) can be applied in the End-to-End framework requires no pre-processing, and (5) the Burnt-Net is fast and can generate the burned area in the near real-time.