esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Shot Prediction Based on Spatial-Temporal Interaction between Players in Soccer Videos
## 4 Conclusions  
In this paper, we propose a model for multimodal shot prediction by utilizing spatial-temporal interactions between players in soccer videos. The proposed model constructs a graph that considers the relationship and importance of players with various audio-visual features acquired from the video as nodes. Then, we extract latent features from the graph based on GCRNN considering both spatial and temporal interactions. Finally, we predict the shot occurrence by using BNNs, which is a probabilistic deep-learning method. The BNN provides uncertainty about the prediction results and thus users can analyze and evaluate the results with high confidence. In experiments using video clips extracted from actual professional soccer league matches, our method achieved the highest accuracy in all evaluation metrics at and around the shot moment. Specifically, the results of an AP of 0.948 and an F1 score of 0.858 indicate that it is a high-performance prediction model, demonstrating its superiority. From the experiment, we confirmed the effectiveness of the novelty of our study, that is, the audio feature, and the xG for considering the importance based on players' positions.  
This approach is expected to improve predictive accuracy in diverse scenarios and environments. We also believe our shot prediction model can be easily adapted to other sports. Given that soccer is a sport with a large number of players and high degrees of freedom for each player, it is considered arguably more challenging than other sequential sports such as basketball and rugby. Therefore, as our model has successfully captured the more complex inter-player relationships in soccer, it is highly likely to provide accurate event predictions in similar competitive sports. Consequently, our model demonstrates significant applicability and potential benefits for a range of sports, underscoring its utility in diverse sporting contexts.  
Conceptualization, R.G., Y.M., K.M., T.O. and M.H.; methodology, R.G., Y.M., K.M. and T.O.; software, R.G.; validation, R.G.; writing--original draft preparation, R.G.; writing--review and editing, Y.M., K.M., T.O. and M.H.; visualization, R.G.; funding acquisition, K.M., T.O. and M.H. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by JSPS KAKENHI Grant Numbers JP21H03456 and JP23K11211.  
Not applicable.  
The soccer video dataset used in this study is a non-public dataset provided by Hokkaido Consadole Sapporo. The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
In this research, we used the data provided by Consadole Sapporo.  
The authors declare no conflicts of interest.
# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
## 2 Related Works
### Traditional Image Processing-Based Methods  
[PERSON] et al. proposed an automated WBC detection and classification process that used a combination of several image processing techniques to separate the nucleus from target cells in 2010 [13]. Firstly, they converted images into grayscale and removed the darkest part as the nucleus from the WBCs. Then they utilized automatic contrast stretching, histogram equalization, and image arithmetic to localize the nucleus and applied an automatic global threshold to carry out the segmentation. The nucleuses would be identified with types of leukocytes after passing through a minimum filter and automatic threshold. The proposed model finally achieved accuracy between 85 and 98%.  
Another piece of research on automatic WBC recognition was proposed by [PERSON] et al. in 2011 [14]. They exploited the Gram-Schmidt method to segment nuclei in the beginning and then distinguished basophils. A Snake algorithm was used to divide the cytoplasm. The color, morphology, and texture features were extracted and fed into two classifiers: SVM (Support Vector Machine) and ANN (Artificial Neural Network). It obtained a 93% segmentation result and 90% and 96% classification accuracies, respectively.  
In addition, [PERSON] and [PERSON] proposed an imaging processing technique for RBC counting and identification in 2018 [PERSON] and [PERSON] (2018). They applied the K-medoids algorithm and Granulometric analysis to separate the RBCs from the abstracted WBCs and then counted the RBCs by labeling the algorithm and circular Hough transform. A comparison was taken, and the results demonstrated that the circular Hough transform outperformed in the counting and the labeling algorithm and was successful in recognizing whether or not the RBC was normal.  
For platelet counting, [PERSON] et al. proposed an automatic platelet counter in 2016 that employed circular Hough transform to microscopic blood images [PERSON] and [PERSON] (2018). They utilized the size and shape features of platelets and achieved an accuracy of 96%. Moreover, [PERSON] et al. proposed an image analysis system able to segment and carry out CBC counting in 2017 [PERSON] and [PERSON] (2018). Microscopic blood images would be processed by HSV thresholding, connected component labeling, and statistical analysis. The results showed that the proposed system had over 90% accuracy.
# Simulation of the Influence of Wing Angle Blades on the Performance of Counter-Rotating Axial Fan
## 4 Analysis of Optimization Results
### Full Flow Field Analysis  
In order to analyze the full flow field characteristics of the wing blade fan, the performance of the wing blade fan under full flow conditions was simulated through numerical calculation and compared with the prototype fan. Figure 11 shows the analysis of total pressure \(Pt\) and efficiency \(\eta\). The optimal wing angle blade fan has a rated flow point of 740 m\({}^{3}\)/min, and the rated flow point of the prototype fan is 730 m\({}^{3}\)/min. The efficiency of the angle blade fan at the rated flow point is 77.10%. At the rated flow point of the prototype fan, the total pressure and efficiency of the optimal angle blade fan are increased by 7.24% and 1.76%, respectively. Under full flow conditions, the total pressure and efficiency have increased by 11.32% and 3.88% on average.
## 2 Materials and Methods
### Datasets
#### 2.1.4 CMIP6 Data  
The Coupled Model Intercomparison Project (CMIP) involves climate models developed by various national and institutional entities. CMIP6, the sixth phase of CMIP, features an increased number of participating models, improved experimental design, and a larger simulation database compared with previous CMIP projects over the past two decades [63; 64]. CMIP6 data are sourced from the NASA Center for Climate Simulation ([[https://www.nccs.nasa.gov](https://www.nccs.nasa.gov)]([https://www.nccs.nasa.gov](https://www.nccs.nasa.gov))).  
This study integrates multiple CMIP6 models to estimate NDVI changes under different scenarios. Traditional multimodel ensemble (MME) averaging calculates the arithmetic mean of Gross Primary Productivity (GPP) values across all models to reduce uncertainty in multimodel simulations. A weighted MME method uses ERA5 meteorological data as a benchmark, assigning weights to each model based on its performance during the historical period (2001-2014). For each CMIP6 model, the difference between its simulation results and ERA5 data is calculated, and the weight for each model is assigned based on the Root Mean Square Error (RMSE). This study uses an inverse error weighting method to assign the weights, meaning that models with smaller errors are given higher weights. The calculation formula is as follows:  
\[\mathrm{W_{i}}=\frac{1/\mathrm{e_{i}}}{\sum_{j=1}^{n}(1/\mathrm{e_{i}})} \tag{1}\]  
where \(\mathrm{W_{i}}\) is the weight of the i-th model, \(\mathrm{e_{i}}\) is the RMSE value of the model, and n is the total number of models.  
The climate models adopted in this study, along with their relevant information and assigned weights, are presented in Table 1. Four Shared Socioeconomic Pathways (SSPs) were selected, SSP126, SSP245, SSP370, and SSP585, representing varying future economic development conditions under different carbon emission levels and radiative forcing scenarios, where SSP126 denotes a low-emission scenario, SSP245 and SSP370 indicate medium-emission scenarios, and SSP585 represents a high-emission scenario.
The objectives of the present study were to (i) validate the Dark Spectrum Fitting (DSF) atmospheric correction ([PERSON] and [PERSON], 2018) applied to Pleiades satellite data recorded over turbid estuarine waters and (ii) highlight the benefits of such very high spatial resolution for the monitoring of water turbidity dynamics in estuarine environments, including nearshore zones and ports.
## 3 Experimental Results
### Strain Results under Thermal Load
#### 3.3.3 Track Bed Strain  
Figure 15 shows the variation of the track bed temperature-strain plot according to the number of thermal loading cycles. It can be seen that with the increase of the number of thermal loading cycles, the temperature-strain plot of the track bed gradually shifts toward the strain reduction direction. In the initiation and stabilization stages of the interlayer gap, the plot distribution is dense and the strain changes at a slow rate, while in the expansion stage of the interlayer gap, the distribution of the plot is sparse and the strain changes rapidly. From comparison analysis of Figure 15a-c, it can be seen that from the fixed end section to the sliding end section, the interval between the plots of the track bed gradually closes, indicating a gradual decrease in the track bed strain.  
Figure 16 shows the variation of the track bed strain with the thermal loading cycles under the three temperature rising conditions of 10 \({}^{\circ}\)C, 20 \({}^{\circ}\)C and 30 \({}^{\circ}\)C. It can be seen from the figure that under the same temperature load, the track bed strain decreases gradually with the increase of the number of thermal loading cycles; additionally, in the three stages (initiation, expansion, stabilization) of the gap development, the strain reduction rate conforms to the slow-fast-slow variation law. Compared  
Figure 14: Variation of the CA mortar strain with the thermal loading cycles.  
Figure 15: Track bed strain variation with the thermal load.  
with the first thermal loading cycle, at the end of the 18 th thermal loading cycle, a strain reduction of approximately 50%, 30% and 10% is observed at the fixed end section, the mid-span section and at the sliding end section, respectively.  
Under the same temperature load, as the number of thermal loading cycles increases, the strain of the structural layer of the track system gradually increases or decreases. After the 18 thermal loading cycles, the strain variations in the different sections of each structural layer with respect to that of the initial condition (before thermal loading) are summarized in Table 2. Table 2 shows that under the same temperature load and at the end of the 18 th thermal loading cycle, the track slab strain has increased by approximately 100%, 35% and 3% at the fixed end section, the mid-span section and at the sliding end section, respectively. The strain of the CA mortar at the fixed end section and the mid-span section decreased by approximately 50% and 20%, respectively, while that at the sliding end section increased by approximately 100%. The strain of the track bed at the fixed end section, mid-span section and sliding end section decreased by 50%, 30% and 10%, respectively.
## 3 Results
### Comparison between Augmented Dataset and Original Dataset  
Our method used data augmentation strategy to overcome the limitation of the small and imbalanced training dataset. In this Section, we compared the performances of zebrafish egg classification with and without data augmentation. Besides the training result acquired with augmented datasets of 7864 image patches, another model was trained with the original dataset of 638 patches without augmentation. The improvement of accuracy in the case of the augmented datasets against the original datasets was significant. Validation accuracy was improved from 83.8% to 98.8% after data augmentation.  
To further analyze the effect of balancing the imbalanced datasets by augmentation, we had computed the metrics of sensitivity, specificity, precision, and accuracy between the methods with and without data augmentation (as shown in Table 2). The metrics were defined as Sensitivity = N\({}_{\text{TP}}\)/(N\({}_{\text{TP}}\) + N\({}_{\text{FN}}\)), Specificity = N\({}_{\text{TN}}\)/(N\({}_{\text{TN}}\) + N\({}_{\text{FP}}\)), Precision = N\({}_{\text{TP}}\)/(N\({}_{\text{TP}}\) + N\({}_{\text{FP}}\)), where N stands for the number of samples, TP, FP, TN, FN represent true positive, false positive, true negative, false negative, respectively. From Table 2, it can be observed that data augmentation led to evident improvements in both sensitivity and accuracy, while the specificity and precision of both methods were at the same level. We also compared the convergence performance of model training between the methods with and without data augmentation. As shown in Figure 7, data augmentation led to faster convergence speed and a smaller gap between the training accuracy curve and validation accuracy curve, implying that data augmentation yielded better specificity and generalization ability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Sensitivity** & **Specificity** & **Precision** & **Accuracy** \\ \hline with Data Augmentation & 97.3\% & 99.2\% & 99.2\% & 98.8\% \\ without Data Augmentation & 68.0\% & 99.6\& 99.4\% & 83.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the classification performance between the methods with and without data augmentation.  
Figure 6: The training accuracy curve and validation accuracy curve of both global averaged pooling method and conventional fully connected layers method.
## Interferometry processing
### Supervised Classification Algorithms  
Starting with the \(S\)2 input dataset and the training set, the supervised classifications were performed in SAGA GIS vers. 8.0.0 and the confusion matrix was computed. Given the characteristics of the \(S\)2 input dataset and the analyzed alpine territory, the best performing algorithms adopted were the k-nearest neighbors classification-KMC and the minimum distance with pre-segmentation (SNIC) by applying a distance threshold of 50. The k-nearest neighbors (k-NN) is an algorithm used in pattern recognition for the classification of objects based on the characteristics of the objects in close proximity to the ones considered. It is a non-parametric classification method. In both cases, the input is the closest k training example in the feature space. The output depends on whether the k-NN is used for classification or regression. In the k-NN classification, the output is a membership in a class. An object is classified by a plurality vote of its neighbors, with the object assigned to the most common class among its k closest neighbors (k is a positive, typically small, integer). If k = 1, the object is simply assigned to the class of that single closest neighbor. In the k-NN regression, the output is the property value for the object. This value is the average of the closest neighboring k values. On the other hand, the minimum distance classifier is used to classify unknown image data to classes which minimize the distance between the image data and the class in the multi-feature space. The distance is defined as an index of the similarity so that the minimum distance is identical to the maximum similarity. Therefore, the minimum distance technique uses the mean vectors of each endmember and calculates the Euclidean distance from each unknown pixel to the mean vector for each class. All pixels are classified to the nearest class unless a standard deviation or distance threshold is specified, in which case some pixels may be unclassified if they do not meet the selected criteria. The classification was performed following a hierarchical approach described in the analysis section.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 4 Experimental Setup  
### SDD Dataset  
There is no publicly available space debris dataset for the use of space debris detection in videos at present. Therefore, we created a space debris video saliency detection dataset named SDD ([[https://github.com/taojianggit/SDD](https://github.com/taojianggit/SDD)]([https://github.com/taojianggit/SDD](https://github.com/taojianggit/SDD)) (accessed on 27 July 2022)), which includes 45 synthetic video sequences and 2 real video sequences, to assess the quantitative and qualitative performances of the space debris detection algorithm. Each synthetic sequence contains 250 images and 2 real video sequences contain 1551 images. Finally, SDD includes 47 video sequences; 33 synthetic sequences are used for training and the remaining 14 sequences are used for testing. All synthetic video sequences are created by using the same video satellite sensor properties of Tiantuo-2 [28]. The specific parameters of video satellite sensors are provided in Table 3.  
The SDD dataset covers different SNRs, space debris diameters, space debris motion directions, and motion speeds. Here, the SNR value is defined as \(SNR=\frac{S-B}{\sigma}\), where \(S\) is the mean pixel value of the object region, \(B\) is the mean pixel value of the background region, and \(\sigma\) is the standard deviation of the background region. In general, the background region is three times the size of the object region. We simulated both the high SNR space debris and low SNR space debris with SNR = 10, 5, 1, 0.8, 0.6, 0.4, 0.2, and 0.1. The space debris with diameters = 0.01 m, 0.05 m, 0.1 m, and 0.5 m were simulated. The motion directions of space debris were changed with an interval of 20\({}^{\circ}\) from 0\({}^{\circ}\) to 340\({}^{\circ}\). The space debris with motion speeds (pixel/frame) = 0.16, 0.20, 0.27, 0.40, 0.44, 1.00, 1.33, 2.00, and 4.00 are defined in the dataset. The examples of synthetic video sequence images of space debris are shown in Figure 7.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Resolution & 960 pixels \(\times\) 576 pixels \\ Focal length & 1000 \\ Field of view & 2.5 \(\times\) 2.5 \\ Pixel dimensions & 8.33 \(\times\) 8.33 \\ Frame rate & 25 frame/s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Video satellite sensor parameters.
# Satellite Attitude Determination and Map Projection Based on Robust Image Matching
## 3 Method  
The framework of the proposed method consists of: (1) extracting feature points from a base map with known latitude and longitude and from a satellite image with unknown attitude parameters using SURF; (2) matching pairs of these feature points roughly based on SURF descriptors; (3) eliminating incorrect feature pairs, or outliers, with RANSAC or its variant utilizing the geometric constraints; and (4) deriving satellite attitude from remained inliers. Figure 2 shows a flowchart of the proposed scheme. In this section, we will provide mathematical basis of satellite attitude determination from observed image with known satellite position at first, then how we extract and match feature pairs from satellite and base map images, and finally how we complete map-projection. As an outlier rejection technique, we examine four algorithms from the RANSAC family. In this study, the position information is obtained from the two-line elements (TLEs) distributed by the North American Aerospace Defense Command (NORAD).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Wavelength (\(\mu\)m)** & **GSD (m)** & **Swath (km)** & **Bit Depth (bit)** \\ \hline OLI Band 4 & 0.64–0.67 & 30 & 185 & 16 \\ \hline \end{tabular}
\end{table}
Table 2: Specifications of base map image of OLI (Band 4) onboard Landsat-8.  
Figure 2: Overview of the proposed method for satellite attitude determination and map projection. The method combines satellite and base map images.
## 5 Numerical Analysis  
This section conducts a numerical analysis using Wuxi City, China, as an example. Based on data from the Wuxi Transportation Bureau, the \"Wuxi Statistical Yearbook\", and other materials, Wuxi currently has 4040 taxis and a total urban road mileage of 4049 km. With reference to Wuxi's per capita disposable income, the passenger's waiting time cost is set to 33 CNV per hour. According to government pricing standards, the unit price for taxi rides is 2.76 CNV per kilometer. When urban road speed limits are considered, the average driving speed of vehicles is set to 40 km per hour. Statistics show that the average commuting distance for Wuxi residents is 6.6 km, and the average daily number of ride-hailing orders in Wuxi reached 240,200 in the first half of 2023.  
The data related to ride-hailing platforms in this section refer to the Didi platform. The unit price for ride-hailing rides is 2.49 CNV per kilometer. For passenger order cancellations, the platform stipulates that, if a passenger cancels an order more than 2 min after it is assigned, the platform will charge the passenger a cancellation fee of 3 CNV per order.  
Passengers' decisions to cancel orders are influenced by the arrival time of cruise taxis, \(T_{X}\), and the pickup time of online-responding vehicles, \(T\). Based on the model established earlier, passengers' order cancellation decisions can be categorized into seven different scenarios for different vehicle arrival situations and types of vehicles assigned to orders, as shown in Figure 3.  
Intervals 1-3 belong to the range where passengers do not need to pay a penalty for canceling their orders. Passengers who cancel orders within intervals 4-6 will incur penalties. In intervals 1, 2, and 4, when passengers encounter cruise taxis, they will cancel their orders, regardless of whether the platform assigns a taxi or a ride-hailing vehicle. In intervals 3 and 5, passengers will only cancel orders assigned to taxis through the platform. Passengers in interval 6 will not cancel any orders. Interval 7 represents the situation where vehicles assigned through the platform arrive earlier than cruise taxis, and passengers in this range will directly take the vehicles assigned through the platform.
# Finite Difference Algorithm on Non-Uniform Meshes for Modeling 2D Magnetotelluric Responses
## 3 Forward Algorithm
### Benchmark with Homogeneous Half-Space  
To test the accuracy of our FD algorithm, a homogeneous half-space model is illustrated for TM-mode. The size of computational domain was designed as 20 km \(\times\) 5 km. The model was homogeneous with a conductivity of 0.1 S/m. We supposed that there is one magnetotelluric site located on the surface (\(z=0\) km). The magnetotelluric responses of the magnetic field \(H_{x}\) are computed in the frequency range 0.001 to 1000 Hz. The given domain was discretized into many meshes and nodes with non-uniform approach. According to the information of mesh generation, the number of nodes in \(y\)-axis and \(z\)-axis are set as 51 and 41, respectively. The total numbers of nodes generated were 2091.  
The numerical results of the homogeneous half-space are shown in Figure 3. It is obvious that the magnetic field \(H_{x}\) including its real part and imaginary part computed by the FD method with non-uniform meshes are good agree with the analytical solution. The numerical results indicate that our FD approach can provide very good accuracy for 2D magnetotelluric modeling.