dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, the absence of structure in the transfer function indicates our data is the true sky brightness distribution convolved with our 30 Gaussian restoring with added radiometer noise. In order to fully characterize the uncertainty on our model parameters, we follow similar fitting procedures from  Koch et al. (2020) , who used the PYMC3 package  (Salvatier et al. , 2015) , to run a Markov Chain Monte Carlo (MCMC) sampling to fit our final models. Due to the large range of angular scales spanned by the H i emission, we assign uniform priors for each of our model parameters and sample the amplitude A in log 10 space: log 10 A ∼ U(-20, 20) (10) γ ∼ U(0, 5) (11) Q ∼ U(0, 10) (12) We found that the fitting routine converges quickly with these choices of priors and the final fits are not affected by reasonable changes to the range of these priors.
, 2019b). The left and right panels of Figure  14  compare the integrated intensity images of this HVC from the pilot and test observations, respectively. We utilize the SEARCH task in BBarolo, a software package for fitting 3D tilted-ring models to emission-line cubes  (Di Teodoro & Fraternali, 2015) , in order to exclude noisy pixels from the integration of the signal. We set the SNR threshold for genuine signal to any pixel with SNR > 5 over the span of two or more adjacent channels, applied within the LSRK velocity range of 95 km s -1 to 119 km s -1. The remaining task parameters use default values. The images from the pilot and test observations show similar large-scale morphology. However, the small-scale structures in the pilot data displays more pronounced density gradients  in individual clumps.
SUMMARY Utilizing a combination of ASKAP and Parkes GASS H i data to obtain a cube covering all angular scales, we presented the most sensitive and detailed H i images of the SMC ever achieved. These images were produced by our custom imaging pipeline, which is designed to optimize a joint deconvolution approach in a cluster environment in order to recover an accurate representation of the extended sky brightness distribution. In addition to a full description of the calibration procedures, imaging pipeline, and quality assessments, we demonstrated that: • the spatial power spectrum is a powerful tool in determining the correct scaling factor applied to the total power observations when combining interferometric and single dish data. For example, an overestimated scaling factor results in a decrease in power at small scales because the combination effectively washes out the small-scale structure present in an interferometer-only image.
These pilot data facilitate the study of how H i contributes to the conditions necessary for star formation, probe the hierarchical turbulent spectrum down to physical scales three times smaller than previously measured, and truly characterize the multi-phase make up of the interstellar medium. GASKAP-HI provides an unequivocal view of the nearby H i associated with the detailed structures observed in the dust and molecular gas. Additionally, the lessons learned from imaging extremely extended emission for the GASKAP-HI survey will inform the data processing of future ultra wide-field surveys to be undertaken by the Square Kilometer Array that will face identical imaging challenges. The increase in brightness temperature sensitivity, coupled with the unparalleled angular and spectral resolution, ensures the GASKAP-HI survey will provide touchstone data products for the study of H i in the Milky Way and Magellanic System for the next decade and beyond.
This research was supported by the Australian Research Council (ARC) through grant DP190101571. N. M. -G. acknowledges the support of the ARC through Future Fellowship FT150100024. G. A. and J. F. G. acknowledge support from the State Agency for Research (10. 13039/501100011033) of the Spanish MCIU, through grants AYA2017-84390-C2-1-R and PID2020-114461GB-I00 (co-funded by FEDER) and the "Center of Excellence Severo Ochoa" award for the Instituto de Astrofísica de Andalucía (SEV-2017-0709). C. E. M. is supported by an NSF Astronomy and Astrophysics Postdoctoral Fellowship under award AST-1801471.
Datasets used For the purpose of this study, we benefited from a set of bathymetric and seismic data acquired on board Research vessel Le Suroît in the period 2003-2005 during the MARADJA (https://doi. org/10. 17600/3020100) and MARADJA2/SAMRA cruises (https://doi. org/10. 17600/5020080 and https://doi. org/ 10. 17600/5020090). For bathymetry, we have used Kongsberg EM300 (for the slope and deep basin) and EM1000 (for the continental shelf) Simrad multibeam echosounders. Data processing was made with the Caraibes ® software, allowing us to produce a Digital Elevation Model (DEM) with the resolution of 25-35 m (depending on depth) for our study zone (Figure  2 ). We have also gathered coastal bathymetric data digitized from Leclaire (1970)'s bathymetric maps. During the 2003 and 2005 cruises, seismic reflection data were obtained by either six-or 24-channel streamers using a combination of two and six double chamber gas-injection air-guns.
We have used the SU (Seismic Unix) CWP/SU software (Center of Wave Phenomena, Colorado School of Mines) for the stack and migration of the seismic data, complemented by some previously published seismic lines of the Algerian Oil Company  (Déverchère et al. , FIGURE 2  Map of the Boumerdès area displaying the position of the seismic sections used in this study and the contours of the two flat surfaces PS1 and PS2 identified under GIS (see also Figure  3 ). Mean position of the 2003 May 21 rupture plane (projected in map view) is also shown as a thin black rectangle, after  Delouis et al. (2004). This fault model makes the hypothesis of a single rupture plane ~60 km long and ~24 km wide extending from the surface to a depth of ~17 km. The grey contour lines inside the rupture plane displays the coseismic slip values in centimeters.
Methods of geoscientific visualization and data analysis On land, remote sensing techniques provide since long the possibility to capture the small scale topographic roughness thanks to DEMs of high spatial resolution, thus encouraging the use of new tools such as VR for virtual geological exploration of the morphology and a quality control on structural measurements (e. g. ,  Nesbit et al. , 2020; Jaud et al. , 2022). Until now, such virtual explorations are rarely performed under the sea because data resolution is often too low and because free tools available to explore the richness of datasets by VR techniques (beyond the seafloor morphology alone) are yet limited (e. g. ,  Métois et al. , 2021; Billant et al. , 2019, and references therein). However, submarine environments appear to benefit from several advantages to preserve the record of geological events, such as low erosional rates, continuous sedimentation in time and space and limited impacts by human activities  (Perea et al.
, 2021). Furthermore, the recent development of instruments for multiscale seafloor mapping and sub-seafloor seismic imaging allows us to improve year after year the spatial coverage, the resolution and the accuracy of the data, at least in some specific zones. In our study area (Figures  1, 2 ), a first analysis has been performed using classic GIS tools (QGIS and ArcGIS), in particular to digitize canyon thalwegs and extract bathymetric profiles along these thalwegs. To complement this GIS approach, a Virtual Reality (VR) environment was built using the platform Unity (https://unity. com/) for real-time content creation. For the DEM exploration, we use the free Unity package (including ArcGIS Maps SDK plugin) and an Oculus Quest headset, with touch controllers, including GIS-like interaction capabilities, as also done by  Billant et al.
Hypothetical horizontal strain rates across the two segments of the Boumerdès margin using assumed heaves on the four main scarps and ages of growth strata in perched basins PS1 and PS2. Age of onset for Scarp b is unknown and has been assumed as equivalent to the one of Scarp d. See text for details. Frontiers inEarth Science frontiersin. org study were obtained during the 2003 MARADJA (doi: 10. 17600/ 5020080) and the 2005 MARADJA2 (doi: 0. 17600/5020080) and SAMRA (doi: 10. 17600/5020090) experiments on board of the Research Vessel "Le Suroît" and are subject to a formal agreement between Algeria and France. The data are stored and managed by SISMER (Ifremer) and are submitted to restricted access. Requests to access these datasets should be directed to Cecile. Pertuisot@ifremer. fr.
Acknowledgments This work is part of the  ISblue AISMERALDA (Ateliers Immersifs Sous-Marins et Réalité Augmentée: Littoral, Dorsales, Autres ) project aiming at building pedagogical scenarios for MsC students by using multisource data sets in selected marine and coastal virtual environments. We are indebted to  Karim Yelles  ( Centre de Recherches en Astronomie, Astrophysique et Géophysique, Algiers, Algeria ),  Rabah Bracene  ( Sonatrach, Boumerdès, Algeria ) and all the scientists involved in the Algerian-French cruises MARADJA (2003) and  MARADJA2/SAMRA (2005 ). We are grateful to  Antonio Cattaneo  (Ifremer, France),  Bernard Mercier de Lépinay  ( CNRS, UCA, Nice, France ),  Nathalie Babonneau ,  David Graindorge  ( UBO University, France ),  Stéphane Dominguez  ( Montpellier University, France ),  Anne Domzig  (Petronas,  Kuala Lumpur ,  Indonesia ) and  Pierre Strzerzynski  ( Le Mans University, France ) for methodological support at different stages of this work and for fruitful discussions.
The most efficient and beneficial mechanism to the feature of extracting data from an image, has been the Convolutional Neural Network (CNN) and it is used in many fields (Optical character recognition, image classification, object recognition and Facial recognition etc. ). In this papier, we studied the character classification problems, using pre-trained models based on Convolutional Neural Network (CNN), and how the performance can change the outcome of dataset that is given. For that, we have used five pre-trained models' such as VGG16/19, ResNet, Xception et MobileNet. The experiment shows that Xception had the best performance rate compared to other models for all datasets, VGG16/19 performance rate are variants depend on dataset. However, Experiments shows that ResNet achieve the worst accuracy rate compared to other methods.
Convolutional Neural Network (CNN) has been recognized as the most powerful and effective mechanism for feature extraction, but traditional classifiers connected to CNN do not fully grasp the extracted features. Therefore, we will present a comparative direction between proposed solutions about the image classification problem using CNN. As we present in this research paper we will discuss the use of the Deep Learning techniques, especially convolution neural network (CNN), will focus on pre-trained  [7]  models' application from Keras Library  [8] , it's a deep learning API programming in Python, that use TensorFlow  [9]  as a platform, it was developed for being fast and smooth in experimentation. In the purpose to apply the Transfer Learning  [10]  on our three different datasets, the first has 26 classes (alphabetic from a to z in lowercase), the second and third has 62 classes (alphabetic from a to z in lowercase and uppercase and numbers from 0 to 9), they have different parameters of training, testing and evaluation.
For this purpose, we have selected five modules from Keras Library (VGG16/19  [14] , Xception  [15] , MobileNet  [16]  and ResNet  [17] ) and trained them on three datasets, we got mixed results, between 80% and 100% on VGGs, Xception, ResNet models, except for MobileNet model, the results were not as good as we expected, in fact, they were poor.
Their main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small convolutional filters (3x3), showing that a significant improvement over prior art configurations can be obtained by increasing the depth at 16 -19 layers of weight. In their paper  [15] , Microsoft Research has shown that convolution and highly indivisible convolution lie at either end of a discrete spectrum, with the Inception module representing an intermediate point in between. These observations led us to propose replacing the Inception module with deep separable convolutions in the neural machine vision architecture. They presented a new architecture based on this idea, called Xception, which has a similar number of parameters to Inception V3  [11] , Xception shows small increases in sort performance for the ImageNet  [12]  dataset and large increases for the JFT dataset  [13].
D. Datasets: The first dataset has only alphabetics character in lowercase, with 26 classes, the second dataset has alphabetics character lowercase, uppercase and numbers from 0 to 9 with 64 classes and the third dataset has alphabetic characters and numbers with 64 classes, all datasets contains images with a white background and black character in the middle of the shape, for all models, we resize the images to 32x32 as an input shape parameter, except the Xception model we resize the images to 71x71, because its only accepts 71x71 input shape or higher, The figure V show a sample of dataset used.
H. Results: To conclude the experiment section, we got a variants results expend on dataset size and the used model, we note that some models may perform better in a small dataset, but in the large ones also the performance might be low or medium, as example we have VGG19, some models can have a high performance only on the large dataset, as example we have VGG16, and the others can have a good performance on any size of dataset, as example we have Xception. This variant may due to the architecture of the models or the shape of images in the dataset. The modify of the layers that contain the models, using Fine-Tuning technique, can improve the model's performance.
V. CONCULSION: As we presented in this paper, we compared between different pre-trained models' application from Keras Library, the result of Transfer Learning diverse from dataset to the other, depending on the datasets size. The efficiency of models in certain datasets didn't mean that they were efficient in all datasets. The architect of model most focus on upcoming research, so it can adapt to any kind of dataset, using Fine-Tuning  [18]. In future works, we plan to improve the character classification results, by combining several methods, and semantic information.
F. Dataset 2 : 2 Figure 22 FIGURE VIII. ACCURACY AND VALIDATION ACCURACY OF MODELS DATASET 2 Figure VIII represent how accuracy (train) and validation accuracy (test) has changed during the 12 epochs, Xception, VGG16 and VGG19 shows a high accuracy for train and test, however, Xception shows less or low validation accuracy, compared to the one in dataset 1, and the longer the go stable they get, with a slight of overfitting, while, MobileNet and ResNet shows a unsatisfying result, where we see the validation accuracy (test) is low.
The parameterization is built using local-scale simulations performed with the computational fluid dynamics (CFDs) code Code_Saturne. The twodimensional CFD simulations in an infinite street canyon are used to quantify the effect of trees, depending on the tree characteristics (leaf area index, crown volume fraction, and tree height to street height ratio) using a drag porosity approach. The tree crown slows down the flow and produces turbulent kinetic energy in the street, thus impacting the tracer dispersion. This effect increases with the leaf area index and the crown volume fraction of the trees, and the average horizontal velocity in the street is reduced by up to 68 %, while the vertical transfer coefficient by up to 23 % in the simulations performed here. A parameterization of these effects on horizontal and vertical transfers for the street model MUNICH is proposed.
Street and tree modeling setup In the present study, the tree effect is studied in three street canyons of the following different street aspect ratios: a wide street canyon (WC), an intermediate canyon (IC), and a narrow canyon (NC). Their characteristics are presented in Table  1. A color code is introduced to simplify the reading of the figures, with red symbols for the wide canyon (WC), purple ones for the intermediate canyon (IC), and blue ones for the narrow canyon (NC). Abbreviations, parameters, and variables used are listed in Appendix A. The kε linear production turbulence model is used in Code_Saturne. Stationary simulations are performed with a thermally neutral atmosphere. A 2D infinite street canyon is modeled with periodic condition on the y axis, but the flow and the wind speed vector are 3D.
1 % to 2. 8 %. Second, this new mixing length expression is also used to compute the vertical wind profile and average wind speed along the street. Only one constant is fixed in the wind attenuation coefficient to maximize the fit between Code_Saturne and MUNICH tree effect on average wind speed. The comparison of the average wind speed gives a normalized mean absolute error ranging from 3. 4 % to 6. 8 % and a normalized mean bias from -2. 8 % to -1. 1 %. MUNICH now includes a relatively simple parameterization of the tree effect on both horizontal and vertical aerodynamic processes, based on commonly used tree parameters that can be easily computed from urban databases and can reproduce the main effects obtained in much more detailed and costly CFD simulations. This parameterization can also be used in urban climate models to compute water and heat transfer in tree-lined streets.
Code availability. The last version of the MUNICH source code is available online at https://doi. org/10. 5281/zenodo. 4168984  (Kim et al. , 2022)  and https://github. com/cerea-lab/munich (last access: 5 January 2022). Data availability. For the three 2D canyons considered in the present study, the mesh, the source code, and the XML setup file allowing the reproduction of the CFD results using Code_Saturne version 6. 0 are available online at https://gitlab. enpc. fr/alice. maison/tree_parametrization (last access: 3 December 2021) and at https://doi. org/10. 17632/fzfrjsz3mv. 2  (Maison and Flageul, 2021)  under the GNU GPL2. 0 license.
INTRODUCTION Of all proteins, those that span lipid membranes are among the most challenging to compare at the primary sequence level. The reasons are two-fold: first, a membrane-spanning segment requires a high component of hydrophobic aminoacids to match the hydrophobicity of the lipid tails, which reduces the compositional complexity; second, membrane proteins are highly divergent in sequence space  (1). These factors are compounded by the fact that membrane proteins have historically been more challenging to examine with structural biology methodologies; the resultant limited coverage of structural space within the Protein Data-Bank (2) (https://blanco. biomol. uci. edu/mpstruc/) has reduced the availability of data sets for method training and testing  (3). AlignMe (for Alignment of Membrane proteins) is one of only a few methods designed specifically for, and demon-W30 Nucleic Acids Research, 2022, Vol.
Furthermore, even the most recent machine learning models are not universally accurate in all aspects, meaning that comparison of such models with one another -and with structures and sequences of related proteins -remains an important step. Consequently, web services that provide ready access to such comparison tools will continue to serve an important role. During the last 10 years, we noticed a few opportunities for enhancements to AlignMe and to the server, which led to the introduction of three new features: anchoring (26); batch processing; and mapping of alignment features onto structures using the web-based server, MutationExplorer. At the same time, we ported the AlignMe software containing these features to GitHub to enhance transparency and reproducibility and to facilitate updates (https://github.
Accord- ingly, the default value for anchor weights is 1000. However, by gradually increasing the weight in the range of 0. 1-100 and assessing the impact on the alignment (i. e. whether the two positions are matched, and what is the effect on the local region on the alignment), the optimum value can be identified for a given pair of sequences. Note that the number of anchors is not limited to one pair of matched positions. Indeed, an anticipated use of anchors will be in attaining a full-length alignment of two sequences for which large fragments have previously been obtained using structure-based alignments of structural repeats for the En-coMPASS database  (35). Obviously, using more than one anchor creates the potential for mutually incompatibility. In such a situation, computing alignments with each of the anchors separately should allow the user to assess their relative impacts and interdependence (e.
RUNNING BATCHES OF INDIVIDUAL PAIRWISE ALIGNMENTS A common situation faced by users of the AlignMe web server was the need to run multiple pairwise alignments for different sequences, for example, when comparing many different templates for a homology model. Previously, when using the web server, the user would be required to initiate each pairwise alignment individually through the main input page. Although the AlignMe software can be eas-ily accessed via GitHub for local installation in multi-use cases, usage of all its functions requires several dependencies: BLAST, PSIPRED and OCTOPUS. As some of these are complex codes, their installation imposes significant time and expertise requirements, while limiting the range of usable operating systems. Therefore, to streamline access to AlignMe and to make large-scale calculations available to all users, we introduced an option to run batches of alignments on the server with a single set of input parameters.
A combination of the two new features, i. e. batches of anchored alignments, is also possible. In this case, the user simply needs to specify the sequence number in addition to the residue number in the input file listing the requested anchors. The results page and downloadable results files highlight any applied anchors on the corresponding sequence alignments (Figure  2 ). We note that currently, to align batches of sequences using profile representations, the web server can automatically compute profiles of the following three types: (i) hydropathy profiles based on hydrophobicity scales; (ii) PSIPRED secondary structure predictions or (iii) OCTOPUS transmembrane helix predictions. User-generated profiles are not currently compatible with batches of alignments, and so for the time being, the user is encouraged to install the software locally if they require that specific advanced functionality.
To this end, the AlignMe server now provides the option to map the sequence similarities identified in the AlignMe alignment onto one or more structures. Specifically, every pairwise alignment results webpage now includes a link to a molecular structure viewer at the Mutation-Explorer web server (http://proteinformatics. uni-leipzig. de/ mutation explorer/; Figure  3 ). With this option, the user can upload a structural coordinate file that corresponds to a sequence in the alignment, or, if available in the Protein DataBank (2), provide the associated four-character identifier for automatic retrieval. A second coordinate file or PDB identifier corresponding to the other sequence can also be given. The MutationExplorer viewer will automatically relate the alignment to the structure; that is, it will find the first chain in the structure file that maps exactly onto a sequence in the alignment.