dataesr/scientific-paragraphs-categorization huggingface dataset:
Nonetheless, we were able to successfully fly the RPA (Figure  9 ). A test stationary flight was first carried out at 2. 5 m above the ice, giving a radar footprint of 1 m. Four flight lines (L1 to L4) were then followed at a mean altitude of about 5 m (not measured; note that for the IMST Radar Module, the maximum measurement range capability corresponds to = ( ⁄ ) = 30. 7 m). Eight holes were drilled through the ice to measure thickness across the studied area to validate the results. Ice thickness varied from 50 cm to 0 cm near the lake's edge. Weather conditions during the test flight were challenging, with a mean wind speed of 20 km/h (gusting up to 35 km/h) and air temperatures of -15 • C. Nonetheless, we were able to successfully fly the RPA (Figure  9 ). A test stationary flight was first carried out at 2.
5 m above the ice, giving a radar footprint of 1 m. Four flight lines (L1 to L4) were then followed at a mean altitude of about 5 m (not measured; note that for the IMST Radar Module, the maximum measurement range capability corresponds to R max = c (1024/4) B = 30. 7 m). Eight holes were drilled through the ice to measure thickness across the studied area to validate the results. Ice thickness varied from 50 cm to 0 cm near the lake's edge. An example of data recorded during the stationary flight is illustrated in Figure  8. It shows significant fluctuations in the retrievals (blue line), including strong drop-out values. These values relate to the difficulties in identifying the right echoes of the ice surface, ice bottom or both. Overlaying the synchronous viewing angle record (orange signal in Figure  10 ) with the measurements revealed the RPA's relatively important motion variations (pitch, yaw, roll) and the poor correlation between the measured fluctuations in derived ice thickness and the off-nadir viewing angle variation.
The radar footprint was 1. 3 m, but a plate size of 0. 60 × 0. 60 m was enough to enable a clear bottom echo. In this experiment, the radar time acquisition frequency was set at 6 h to optimize the power consumption from its lead battery (no solar panels were used). The radar-based SWE values, processed by the in-system processor, were automatically transferred along with other data (snow depth, radar profile, air temperature-Tair) every day via a cellular telephone connection. In order to validate the radar measurements, a gamma ray SWE sensor was installed at about 3 m from the radar (GMON-CS725 sensor from Campbell Scientific Canada ® , with a footprint of 7-10 m and a time acquisition frequency of 6 h)  [16, 65]. Snowpit and snow core measurements were also performed throughout the winter in the vicinity of the sensors.
The radar footprint was 1. 3 m, but a plate size of 0. 60 × 0. 60 m was enough to enable a clear bottom echo. In this experiment, the radar time acquisition frequency was set at 6 h to optimize the power consumption from its lead battery (no solar panels were used). The radar-based SWE values, processed by the in-system processor, were automatically transferred along with other data (snow depth, radar profile, air temperature-T air ) every day via a cellular telephone connection. In order to validate the radar measurements, a gamma ray SWE sensor was installed at about 3 m from the radar (GMON-CS725 sensor from Campbell Scientific Canada ® , with a footprint of 7-10 m and a time acquisition frequency of 6 h)  [16, 65]. Snowpit and snow core measurements were also performed throughout the winter in the vicinity of the sensors.
Results show a low 13. 5% bias and 25 mm RMSD (10%) for the dry snow period, compared to a gamma ray SWE sensor (GMON-CS725 sensor from Campbell Scientific Canada ® ). It must be noted, however, that since SWE data is biased by wet snow, a snowmelt detection algorithm is needed. Numerical simulations of SWE variations according to the liquid water content in snow show the high sensitivity of the radar to the melting process. In controlled manual operating mode, we conclude that the observed capabilities of this radar in different applications and environments, including arduous climatic conditions, make this an extremely reliable system and cost-effective instrument. However, in an autonomous and continuous operating mode, unfavorable operational conditions outside the limits of radar use may occur, such as for ice and wet snow conditions.
Acknowledgments: We thank all the students from the  Université de Sherbrooke (UdeS)  for their tremendous field contributions to all the experiments performed in this study. Alexis Lussier-Desbiens , and  John Bass ,  M. Sc. from  UdeS  assisted in the RPA experiment. We would also like to thank  Ludovic Brucker  from the  NASA Goddard Space Flight Center  for his contribution to the Arctic field campaign,  Sylvain Jutras  and  Amandine Pierre , from the  Université Laval  for their contributions to the measurements at the NEIGE-FM station, and  Ing. Laurent Arnaud  from  IGE, Grenoble  for his contribution to the Antarctic campaign. We also thank  Ing. Reinhard Kulke  and  Manfred Hägelen , IMST for their valuable help and advice on the use of the radar.
Funding: This research was financed by the  Natural Sciences and Engineering Research Council of Canada (NSERC). Logistical support for field campaigns was provided in part by the  Centre d'Études Nordiques (CEN) du Québec. The Arctic experiments were supported by the  Canadian Polar Knowledge program  at the  Canadian High Arctic Research Station at Cambridge Bay, Nu ,  Crown-Indigenous Relations and Northern Affairs Canada. The Antarctic experiment was carried out in the frame work of a  France-Quebec collaborative program  ( CFQCU ), and made possible thanks to the financial support of the  French Polar Institute (IPEV)  ( NIVO  project) and the  Government of Quebec (Fonds de recherche du Québec Nature et Technologie, FRQ-NT program ).
There are 50 clusters in the final sample. The 25 clusters for which reasonably homogeneous VLA data sets have been completed are presented in this paper; we expect to add about seven additional objects in future papers. Pilot observations for the survey were made as part of National Radio Astronomy Observatory (NRAO) programs    1  lists the observation blocks for each cluster. The observations were primarily made in the mostextended A configuration, though a subset of southern clusters were observed in BnA or BnA to A move time. The goal and median observing time per cluster was 10 hr, though a few clusters had a total observation time shorter or longer than this goal. Of this 10 hr of total observing time, the median time onsource was 7. 4 hr. Most of the off-source time was allocated to the observation of phase calibrators, with a median cycle time of about 10 minutes.
The observing efficiency was determined Note. The sources for the photometric centers, distances, and structural parameters are the same as in  Tremou et al. (2018). (This table is available in machine-readable form. ) primarily by the length of the observed blocks. Table  2  lists the total and on-source time for each cluster. We made a few observations that are not used in this paper: these were all taken in move time between A and D configurations. In the course of verifying the catalogs in this paper, we found that the flux densities of apparent point sources were lower in these data compared to nonmove time VLA data or ATCA data for the same clusters. We tried many experiments to account for these discrepancies and were unable to do so, other than to say they are consistent with substantial decorrelation (up to a factor of 2) in the A to D move time data.
Data Reduction The data for both cluster frequency bands were separately flagged and calibrated according to standard procedures with either Common Astronomy Software Application (CASA;  McMullin et al. 2007 ) version 4. 2. 2, or Astronomical Image Processing System (AIPS;  Greisen 2003). Before imaging, the reduced visibilities from each observational epoch were stacked to maximize the sensitivity in each baseband. The combined bands were then imaged separately, with minimum fields of view (diameters) of 11′ at 5. 0 GHz and 7 5 at 7. 2 GHz. These were chosen to match the FWHM of the primary beam at the lowest frequency of each baseband (4. 0 or 6. 0 GHz, respectively). Pixel sizes were set to 0 08 at 5. 0 GHz and 0 06 at 7. 2 GHz to adequately sample the synthesized beams of each frequency range.
In AIPS, the data were imaged using IMAGR, Briggs weighting with a robust parameter of one, frequencyindependent deconvolution, and facets to account for wide-field effects. With data imaged in CASA/clean, nterms=2 was used to account for nonzero spectral indices of sources in the field. A primary beam response correction was applied to each subband image assuming the average frequency in that subband. The synthesized beam size for each image, as well as the image rms flux density values inμJy per beam, are listed in Table  2. The median rms values for the low and high frequency basebands across all clusters were 2. 3 and 2. 1μJy per beam, respectively. We used self-calibration on the few clusters for which there was sufficient flux (5 mJy), typically with a single pass of phase self-calibration with a solution interval of 10 minutes.
These four GCs have an average of 18 sources detected at 5σat 5. 0 GHz in the radial range 2 5-3 7 that we take as the background region. The other VLA GCs with imaging of comparable sensitivity have an average of 18. 6 sources per GC in this region. Hence, from our data themselves there is no evidence of a population of sources that is more common at low Galactic latitude, as would be expected for essentially any Galactic population except for one within tens of parsecs of the Sun. The serendipitous discovery of a 0. 2-0. 3 mJy Galactic radio continuum source in the foreground (distance ∼2. 2 kpc) of the GC M15 (b=-27) using very long baseline interferometry  (Kirsten et al. 2014; Tetarenko et al. 2016)  shows that such sources do exist at some surface density, but evidently not a level sufficient to measurably affect our results.
Hence our excess measurements represent conservative lower limits to the radio source population. A complete analysis of the luminosity function of radio sources in the MAVERIC sample, including both VLA and ATCA data, will occur in a future paper. Since one of the central goals of MAVERIC is to constrain the population of stellar-mass black holes in GCs, we note that 12 of our sample GCs also appear in the study of  Weatherford et al. (2020) , who combine models of how black hole populations affect cluster structure and mass segregation with observed data for a sample of Galactic GCs, theoretically predicting the number of black holes in each cluster. Of the 12 overlapping GCs, the two with the largest core excesses of radio sources, M22 and M55, are both in the top quartile of predicted black hole numbers.
On the other hand, we see only mild evidence for a core radio source excess in M13, which has the largest predicted black hole population in the  Weatherford et al. (2020)  sample. Since most black holes are unlikely to be in mass-transferring binaries and hence observable as radio continuum sources, these comparisons are not straightforward to interpret. It would be valuable to obtain new, deep radio data for some of the GCs not in our current VLA sample that are suggested to host large black hole populations.
Conclusions Here we have presented the initial results of our VLA radio continuum survey of Milky Way GCs. We observed 25 relatively nearby and massive clusters for ∼10 hr each with the VLA at typical frequencies of 5. 0 and 7. 2 GHz. These data represent the first deep high-resolution radio continuum survey of GCs as well as a sensitive C band ∼0. 4 deg 2 extragalactic survey. This paper presents radio source catalogs for each GC; in most cases these catalogs will represent a mixture of cluster and background sources. Through both source counts and spectral indices we found strong evidence for a population of radio sources associated with GCs. It is likely that a subset of these sources are steepspectrum millisecond pulsars, while others represent compact or other kinds of binaries whose classification is not yet clear.
Within the core radii, a conservative bootstrapped estimate suggests that just below half (23/54) of the 5. 0 GHz sources are likely associated with the GCs. The corresponding estimate for the half-light radii is about 30% (61/201), and the fraction is likely lower at larger radii. These catalogs represent an important but incomplete step in our efforts to understand the radio continuum source populations in GCs. Future papers from our group will use the full range of multiwavelength information, including X-ray observations and optical photometry and spectroscopy, to determine and refine classifications of individual sources. New VLA data have recently been obtained for seven additional GCs not discussed here, and an analysis of these data, as well as the ATCA part of the MAVERIC sample, will allow more secure conclusions about the properties and classification of cluster radio continuum sources.
II. 3. SIMULATION ENVIRONMENT DEVELOPMENT A simulation environment was developed by using C++ programming language. Graphical user interface was designed and developed with "Visual Studio / Windows Form Application". OpenGL library  [35]  was used for the 3D visualization. A virtual transtibial residual limb was developed using the available CAD model library (free3D. com) (Figure  9 (a)). A hexahedral mesh of the model (Figure  9 (b)) was generated using the open-source tool IA-FEMesh  [36] , from which the code automatically generates the related MSS-CS model (Figure  9 (c)) and calculate its parameters from the predefined physical parameters of the soft tissues. A Young's modulus of 50KPa  [8] ,  [37]  was used. The density for soft tissue was set up as 1060Kg/m3  [38]. A geometrical process was developed to create the socket model (Figure  5 ).
The same physical properties (Young' modulus of 50KPa and a density of 1060Kg/m 3 ) were assigned to the stump soft tissues. The socket moves according to a displacement constraint. Finally, a FE model of the stump-socket interaction was developed using Abaqus for the comparison with our MSS-CS model. A C++ code was written to convert the MSS-CS model to an Abaqus model, so that the same meshes are used in both simulations. A convergence analysis was conducted to determine the optimal mesh configuration (7393 nodes of which 778 are related to the bones and are fixed, and 6270 hexahedral elements (Figure  12 )) of the involved models. The same physical properties (Young' modulus of 50KPa and a density of 1060Kg/m 3 ) were assigned to the stump soft tissues. A Poisson ratio of 0. 45 was used  [39].
B. Stump-socket interaction simulation The results shown in the Figure  16  were captured at the end of the socket donning process simulation by using Abaqus software and our MSS-CS approach. Overall, the two pressure patterns have a satisfactory correspondence in a qualitative manner. To make a quantitative evaluation, two profiles were tracked on the front vertical line as show in figure  17 (a), and those existing on the circumference of a circle as shown in figure  17(b ). The first points set has been chosen since the line cut a critical point in the stump, the patellar tendon, and the points are represented by their height, i. e. z coordinate. The second point set has been chosen in a way to evaluate the pressure on tabular flares as well as medial, lateral and posterior pressure zones.
V. CONCLUSIONS AND PERSPECTIVES A novel approach was proposed to model soft tissue deformation and stump-socket interaction toward a Computer-Aided Design (CAD) system for lower limb prostheses. A Mass-Spring System Corrective Spring (MSS-CS) model was developed and evaluated. Nonlinear "Corrective" springs were introduced to respect the non-compressibility of the soft tissues. A node-to-surface contact formulation was also integrated and evaluated. Obtained results confirmed the efficiency and accuracy of the proposed method comparing to a complex and detailed finite element model. As perspectives, this present approach will be used to simulate the stump-socket interaction in real case studies. Thus, the evaluation of the proposed approach will be enhanced with more datasets. Finally, this modeling approach will be integrated into a CAD-CAM platform providing a fast feedback to the prosthetist during the socket manufacturing process.
The proposed methods suffer from some limitations and especially in the case of occlusions and highly crowded scenes. Indeed, it is rather hard to detect and recognize multiple human bodies in crowded scene. One solution to overcome these limitations is to apply some preprocessing and learning process in order to cope with the various occlusions and crowded scenarios. Furthermore, it is worth mentioning that to the best of our knowledge there is no publicly available datasets for person detection in highly crowded scenes that could be used for the learning process. There are other alternatives to handle this problem like the use of a Kinect camera  [57] , that can be helpful for recognition of the action as well as the summarization of it. But, the occlusion and action recognition in a crowd scene still represent a challenge  [58, [62] [63] [64].
HOG based recognition For the training data, we used the Weizmann, KTH, and UCF-ARG datasets, since they involve several human actions, namely, walking, running, hand waving, jacking, jumping, bend and side. When it comes to human interactions we used videos form UT-Interaction and INRIA XMAS (IXMAS) datasets for training. Figure  5  represents the flowchart of our training analysis; Fig. 5a  represents the computation of HOGs of one subsequence. Therefore, for each action, we computed a set of HOGs that represent each action. Figure  5b  illustrates the HOGs of four categories of videos, and each video represents an action. After detecting the human's body silhouette and extracting the shots for each detected person, the histogram of oriented gradient of the temporal difference map between each pair of consecutive images is computed.
65), FC(number of identities). The input of our system is a TDMap image with a resolution of 120×120 pixels. For the training and testing we used the preprocessed data from KTH, Weizmann, and UCF-ARG datasets. The model is trained using CrossEntrpy with a batch size of 128 examples, Adam as optimizer with a learning rate of 1e-3. As an activation function we use the Parametric Rectified Linear Unit (PReLU), which is a generalized parametric formulation of ReLU. This activation function the parameters of rectifiers are learned adaptively and improves the accuracy with a negligible extra computational cost  [53]. Only positive values are fed to the ReLU activation function, while all negative values are set to be zero. PReLU assumes that a penalty should be applied for negative values, and it should be parametric.
Results and discussion In this section, we evaluate the performance of the proposed method. For the proposed background modeling technique, the evaluation is made on SBI dataset and comparing with two background-subtraction-based approaches. Also, the built multiple human action dataset is presented with a description. For evaluating the performance of the multiple action recognition approach, Weizmann and UCF-ARG human action recognition datasets utilized to train the proposed approach, when the proposed dataset as well as PET09 dataset are used for testing. The performance of the proposed action recognition methods are presented also. The recognition of detected actions is performed using two method : (1) Action classification named (CS-HOG-TDMap) using Cosine similarity measure between HOGs of TDMap in the training set and the current action to recognize.
Then, the video is summarized by recording one frame from each shot. The proposed method is implemented using MATLAB R2018a on a computer with the following configuration: An Intel Core i5 processor running at 3. 4 GHz and 8 GB of RAM. The proposed method is an action-based video summarization approach. After the recognition of multiple action of the people in the scene, the summarization of each person action within a timeline is performed. The used datasets are suitable for summarization of the human action after recognition of each one them. While the other datasets like YouTube, UCF50 or Hollywood are not. So, the used dataset are the only ones in literature that are suitable for summarization of human actions in a private or public scene monitored by a surveillance camera. Datasets including HMDB51, UCF101, YouTube, and Hollywood cannot be used for recognizing actions with the proposed algorithm owing to the complexity of the videos that are collected from movies (eg.
YouTube), videos captured by moving cameras, and video produced by a jitter camera such as the variation of the point of view, the variation of illuminations. Many approaches use the entire video to classify the action in it without analyzing the content of the videos. For that, we did not use this kind of datasets. Also, our method is to recognize and summarize multiple human actions for surveillance videos. Using the presented representation of data that divide each video into short clips of 1 second, considering some videos that contains more than 10 second. The numbers of videos used are about 1500 clips used in the training and testing parts. The summarization of the number for each action is illustrated in the Table  1.
Datasets The datasets used in the experiments, the Weizmann, KTH, UCF-ARG, UT-Interaction, IXMAS and MHAD (our dataset) are briefly reviewed in this section. The other types of video that are collected from movies (eg. YouTube), like HMDB51, UCF101, YouTube , and Hollywood cannot be used for recognizing actions with the proposed algorithm owing to the complexity of the videos  [55]. Also, the videos are captured by moving cameras, and video produced by a jitter camera such as the variation of the point of view. The Weizmann's Actions as Space-Time Shapes dataset was recorded in 2005, aiming to test new algorithms for human action recognition  [32]. Weizmann's dataset use s a space-time-based algorithm where each sequence represents only one person acting. The background is known, which makes it easy to remove.
The collected dataset contains the main human actions, including (walking, running, jumping, galloping sideways, bending, one-hand waving, and two-hands waving, jumping in place, jumping jack and skipping). The dataset contains nine actions, and each action has nine videos that represent the situations of human actions made by nine different actors. Similarly, the KTH dataset contains a set of human actions including walking, running, boxing, hand waving, hand clapping, and jogging  [33]. In this paper, the video contains four different scenarios which represent many states of objects and scenes, including outdoors and indoor videos, different scales of human body and cloth e s with different colors. This dataset contains 2391 images with a resolution of 160x120 pixels captured by a static camera.
The Multi-view Human Action dataset UCF-ARG is a set of videos recorded from different angles and classified into three categories: a ground camera, a rooftop camera at a height of 100 feet, and an aerial camera mounted into the payload platform. Each of these subsets contains 10 actions acted by 12 actors, representing most situations possible for each action, including 4 iterations by each actor in different directions. Because of the similarity between the KTH and UCF-ARG videos and UIUC dataset  [3]  videos, w e use UIUC dataset just for testing. The dataset consists of 532 highresolution video sequences of 14 human action classes, and every action is performed by eight persons. All the video sequences are recorded indoor scenes. For human-human interaction the UT-Interaction  [46]  and IXMAS  [47]  datasets are used.