esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Convolutional Neural Network and Candlestick Representation to Predict Sports Match Outcomes
## 4 Experiments
### Comparison of Different Approaches in the Outcome Judgment Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Approach** & **Accuracy** & **Precision** & **Recall** & **F Measure** & **Kappa** \\ \hline \multicolumn{2}{c}{**First stage**} & \multicolumn{2}{c}{**Second Stage**} & & & & \\ \hline \multirow{6}{*}{CNN classifier} & Logistic regression & 69.29\% & 0.6923 & 0.4821 & 0.5684 & 0.3417 \\  & SMO for SVM & 65.17\% & 0.6508 & 0.3661 & 0.4686 & 0.2386 \\  & Naive Bayes & 62.55\% & 0.5638 & 0.4732 & 0.5146 & 0.2135 \\  & Adaboost & 67.04\% & 0.6176 & 0.5625 & 0.5888 & 0.3148 \\  & J48 & 65.92\% & 0.6154 & 0.5000 & 0.5517 & 0.2815 \\  & Random forests & 59.93\% & 0.5253 & 0.4643 & 0.4929 & 0.1637 \\  & MOEA for fuzzy classification & 66.29\% & 0.6486 & 0.4286 & 0.5161 & 0.2737 \\  & Simple judgment model & 62.17\% & & & & \\ \hline \multirow{2}{*}{CNN classifier Logistic regression} & & 60.11\% & & & & \\  & 61.42\% & & & & \\  & Home & 58.05\% & & & & \\ \multicolumn{2}{c}{Betting} & & 63.67\% & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different approaches used in the judgment model. CNN prediction model. With the help of judgment in the second stage, the improvement of prediction accuracy is significant. The McNemar value between the logistic regression judgment model and the betting market prediction was 0.468, whereas the p value was 0.494, thereby supporting the null hypothesis that the performances of both models are the same. The logistic regression judgment model outperformed the betting market prediction but did not reach the 0.05 statistical significance level.
## 4 Speech Emotion Recognition Datasets
### English Language Datasets
#### 4.1.4 Iemocap  
IEMOCAP (Interactive Emotional Dyadic Motion Capture) [28] is a dataset designed for research on emotion recognition from speech, facial expressions, and gestures. It consists of recordings of dialogues between pairs of actors who act out scenes both improvised and according to prepared scripts. It contains audio and video data, as well as information about body movement obtained through motion capture technology, which makes. Five sessions were recorded in IEMOCAP with 10 actors (5 males and 5 females), and each session was manually annotated for emotions such as angry, excited, fear, sad, surprised, frustrated, happy, disappointed, and neutral. The recordings were divided into smaller segments corresponding to individual utterances, with assignment to specific emotions, subjectively rated by several annotators. IEMOCAP is widely used in research on emotions in speech, dialogue systems, and analysis of nonverbal communication, providing key information in both linguistic and emotional aspects.
# Micro-Focusing of Broadband High-Order Harmonic Radiation by a Double Toroidal Mirror
## 2 Experimental Setup
### Direct Focus Measurements  
In order to directly record an image of the focal spot, we used a Ce-doped yttrium aluminum garnet (Ce:YAG) scintillation crystal [17; 25; 38], which converts the XUV radiation into visible light with 550-nm central wavelength. The resulting luminescence spot was imaged onto a cooled electron multiplying charge-coupled device (EMCCD) camera by using an optical system composed of a microscope objective (Mitutoyo M Plan Apo 100X) and two lenses for relay imaging (see inset to Figure 1a). The crystal was fixed to a thin fused silica window and placed under vacuum, while the imaging system with the camera was under atmospheric pressure. The field of view  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Tangential**} & \multicolumn{2}{c}{**Sagittal**} \\ \cline{2-5}  & \(R_{T}\) **[mm]** & \(f_{T}\) **[mm]** & \(R_{S}\) **[mm]** & \(f_{S}\) **[mm]** \\ \hline First mirror & 2050 & 265.3 & 137.2 & 265.0 \\ \hline Second mirror & 4213 & 545.2 & 281.8 & 544.4 \\ \hline Combined & \multicolumn{2}{c}{Focal length = 164.2 mm} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Radii of curvature (provided by the manufacturer), focal lengths of the individual mirrors in the two planes, and the equivalent focal length of the assembly.  
and resolution were estimated to be 50 \(\upmu\)m and 2 \(\upmu\)m, respectively. A limiting factor for the measurement was the nonlinear dependence of the luminescence yield as a function of the XUV intensity [17; 39] and, in particular, its saturation. To limit this phenomenon, we used a 2-\(\upmu\)m-thick Al filter after the fused silica plate (see Figure 1), which decreased the HHG energy by two orders of magnitude. Consequently, the luminescence yield was reduced, and the detection required a single-photon-sensitive camera. Furthermore, it was crucial to record single-shot images of the focal spot, since accumulating several shots led to a seemingly larger spot size, because of the pointing instability of the incoming beam.  
These measurements confirm that the Wolter optics compensate efficiently for coma aberrations. Astigmatism could be observed in non-optimized focusing conditions. However, it was not possible to determine precisely the spot size, which was found to be systematically a factor two or three times larger than the wavefront-based measurements presented in Section 3. This probably indicates that this measurement method is still hampered by the nonlinearity of the crystal or other technical limitations.
## 2 Methods  
### Landfill Sites Surveyed  
In this study, we conducted rainwater infiltration monitoring using ERT at two landfill sites with different characteristics. The outline of two landfill sites is summarized in Table 1.  
Site A is a public landfill site in mountainous northwestern Saitama Prefecture, Japan (36\({}^{\circ}\)05\({}^{\prime}\)38.1\({}^{\prime\prime}\) N, 139\({}^{\circ}\)12\({}^{\prime}\)14.9\({}^{\prime\prime}\) E). The landfill has a capacity of approximately 620,000 m\({}^{3}\) and a surface area of 57,000 m\({}^{2}\), and is built in the form of a valley with a depth of 15 m. The bottom and outer perimeter are covered with impermeable liner sheets, and the internal structure is a semi-aerobic landfill with leachate collection pipes at the bottom and several vertical gas extraction wells. The site began landfilling in 2019, primarily with MSW non-combustible residues (38%), incinerator ash (24%), plastic residues from industrial waste (30%), and other materials (8%). The landfill is a sandwich construction with alternating layers of waste (2.5 m) and soil cover (0.5 m). The soil cover is a mixture of conglomerate rock and granite porphyry, with silty fine sand dominating. In 2022, when this study was conducted, the third layer of the landfill was underway. The landfill was 9 m deep, and the landfill progress rate was 23%.  
Site B is a public landfill located in a flat area in the southeastern Saitama Prefecture, 52 km away from Site A in a straight line (36\({}^{\circ}\)53\({}^{\prime}\)01.2\({}^{\prime\prime}\) N, 139\({}^{\circ}\)43\({}^{\prime}\)11.0\({}^{\prime\prime}\) E). The landfill has a capacity of approximately 370,000 m\({}^{3}\), a surface area of 42,000 m\({}^{2}\), and a depth of approximately 9 m. It has an impermeable system and semi-aerobic structure similar to Site A. Landfilling began in 1988. Only MSW incineration ash from the surrounding area has been landfilled. The study area has been in operation for approximately 7 years.
# Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines
## 2 Individual Methods
### Data Processing  
The traditional approach mainly uses random sampling to divide the training set and test set. However, random sampling often leads to sampling bias when the sample size is not massive. This causes the distribution pattern of the test set obtained by random sampling to deviate significantly from the original dataset, rendering the prediction results unreliable and subjective [40].  
Taking the remaining strength data as an example, the impacts of random and stratified sampling on the dataset division results were studied. According to the distribution law of the remaining strength data, the collected data were divided into three intervals. The deviations between the test sets obtained by the two sampling methods and the initial data are shown in Table 2. The Mean Absolute Percentage Error (MAPE) of random and stratified sampling is presented in Figure 9. The MAPE of random sampling is 14.89%, while that of stratified sampling is 7.17%. The results indicate that the test set obtained by stratified sampling shows better consistency with the initial samples. Therefore, this paper proposes the stratified sampling method to divide the training and test sets.  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Classification** & **Overall** & **Random** & **Stratified** & **Rand Error \%** & **Strat Error \%** \\ \hline
1 & 0.376 & 0.320 & 0.384 & 14.89 & \(-\)2.13 \\
2 & 0.369 & 0.447 & 0.335 & \(-\)21.14 & 9.21 \\
3 & 0.255 & 0.233 & 0.281 & 8.63 & \(-\)10.19 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of sample selection bias between random sampling and stratified sampling.
# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 3 Methodology
### Final Segmentation  
The proposed methodology is completed by obtaining the final segmentation after the hysteresis thresholding is applied to the decisive probability map. For the derived flood areas, an edge correction is performed via the image dilation operation. The connected components of flood and non-flood areas are calculated, and relatively small areas are removed. In particular, if a blob is considered to be flooded but does not exceed about 0.3% of the whole image pixels, then this blob is reclassified as background to reduce noise effects, e.g., small water pits which do not belong to the flood area. Furthermore, background blobs with an area of 0.05% of all the pixels in the image are attributed to the flood class. These blobs can occur in the midst of a flood, because of fluctuations in the values of the color components (e.g., shadows), disrupting the continuity, and were wrongly classified as background and, therefore, excluded at the early stages of the method.  
Figure 8 shows the final segmentation of our proposed approach (see the last column). The blue and dim gray represent the segmented flood and background, respectively. Cyan pixels from the second column, which did not pass the thresholds, and blue pixels, which are not connected to the red ones, are excluded from the segmented flood area. The same applies to the small blue blobs connected to red pixels, because they do not fit the aforementioned area criterion. In the next section, we present more results and discuss them in detail.
## 5 Research Directions
### Performance  
As we discussed in Section 4, the DT in CPS has very strict performance requirements to achieve real-time computing, real-time control, and real-time communication. We believe that the joint optimization of sensing, control, networking, computing, and data analysis is critically important for DT to meet these performance requirements. There have been some works focused on one of these optimization targets (sensing, control, networking, or data analysis), and some results have been achieved. However, if we need to maximize the overall performance of the system, we must study the interaction between all components and design a joint optimization strategy. Taking the intelligent transportation system as an example, suppose we are generating DT for moving vehicles in a certain area to better manage traffic. We use sensor data from both vehicles and cities to build DT. When we select data, simply having more data is not optimal. Instead, the pre-processing of the data should account for the quality of the data, the bandwidth of the network, the computing power of the infrastructure, and the Quality of Service (QoS) requirements of the specific application. Similarly, when we are transmitting the data, we need to consider not only the current communication environment but also the mobility of the vehicle, and the data storage space of the vehicle, among others.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 4 Methodological Approaches in Crash Research
### Real-Time Prediction and Emerging Technologies  
Several recent methodological developments focus on real-time analysis capabilities and applications in emerging vehicle technologies. [PERSON] et al. [67] developed hybrid Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) methodological frameworks for real-time crash risk prediction, combining temporal sequence modeling with spatial feature extraction capabilities and demonstrating the benefits of parallel structures for capturing both long-term dependencies and local features. [PERSON] et al. [52] presented real-time risk assessment methodological approaches for connected autonomous vehicles, utilizing HMM-based prediction methods and time-varying risk maps for continuous safety monitoring.  
[PERSON] et al. [50] incorporated temporal and spatial dependencies in modeling frameworks for urban intersection analysis, developing latent variable representations of count data models to accommodate for spatial and temporal dependence. [PERSON] et al. [51] developed methodological frameworks using Bayesian hierarchical models for analyzing crash frequencies with temporal and spatial dependencies on mountainous freeways. [PERSON] et al. [68] proposed a Monte Carlo-based microsimulation approach to estimating collision probability in real traffic conflicts, with a methodology that can simulate conflicts involving an arbitrary number of vehicles under various initial conditions, using automated detection methods and accounting for variability in driver behavior parameters.  
Specialized applications across these methodological frameworks demonstrate their practical utility. [PERSON] and [PERSON] [48] investigated factors influencing crash severity at signalized intersections by using ordered probit models. [PERSON] [49] explored pedestrian injury severity by using various disaggregate modeling approaches. [PERSON] et al. [69] presented multivariate probit modeling frameworks for the simultaneous analysis of injury severity and seat belt use. Geographic-specific applications include work by [PERSON] et al. [70], who examined factors affecting crash severity in Riyadh; [PERSON] [71], who assessed rural road safety conditions in Egypt; and [PERSON] et al. [72], who proposed enhanced frameworks to model crash frequency while accommodating zero-crash zones. Infrastructure and environmental factors have been examined by [PERSON] et al. [73], [PERSON] et al. [74], [PERSON] et al. [75], and [PERSON] et al. [76] by using various methodological approaches.
# Stability Criterion for Dynamic Gaits of Quadruped Robot
## 5 Results and Discussion  
The simplified and mechanical models of the quadruped robot used in this paper are shown in Figure 12. Each leg of the robot has three degrees of freedom.  
Figure 11: Weights for the distance between \(ZMP_{0}\) and the two support lines.  
The geometric and weight parameters are shown in Table 1.  
The moments of inertia of the body around the principal axes are \(J_{xx}=0.74\), \(J_{yy}=0.56\), and \(J_{zz}=0.22\) (kg\(\cdot\)m\({}^{2}\)).  
A simulation model of the quadruped robot was built in the dynamic-simulation software ADAMS. The contact parameters between each foot and ground are shown in Table 2 (referring to the contact parameters between dry rubber and dry asphalt).  
Trotting is the most commonly used dynamic gait and has been the focus of previous studies; thus, this study used the trot gait as the basic experimental gait. The trot-gait parameters for all simulations are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Value (Unit)** & **Definition** \\ \hline \(L\) & 0.5 (m) & Length of the body \\ \(W\) & 0.3 (m) & Width of the body \\ \(H\) & 0.48 (m) & Height of the body \\ \(l_{1}\) & 0.055 (m) & Length of the leg’s first segment \\ \(l_{2}\) & 0.2 (m) & Length of the leg’s second segment \\ \(l_{3}\) & 0.2 (m) & Length of the leg’s third segment \\ \(m_{0}\) & 15.14 (kg) & Mass of the body \\ \(m_{1}\) & 0.9 (kg) & Mass of the leg’s first segment \\ \(m_{2}\) & 1 (kg) & Mass of the leg’s second segment \\ \(m_{3}\) & 0.3 (kg) & Mass of the leg’s third segment \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometric and weight parameters of the robot.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Contact Parameters (Unit)** & **Motion on Horizontal Plane** \\ \hline Stiffness (N/mm) & 2855 \\ Damping (N*s/mm) & 0.57 \\ Force exponent & \(1.1\) \\ Penetration depth (mm) & 0.1 \\ Static coefficient & 0.5 \\ Dynamic coefficient & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Contact parameters between the feet and the ground.  
Figure 12: (a) Simplified; and (b) mechanical models of the quadruped robot.  
Three quadruped-robot offline-gait simulations were designed as follow:
* Simulation 1 verified that the quadruped robot can maintain its stable state during dynamic motion when _ZMP_ does not lie on the current support line.
* Simulation 2 verified that the stability criterion and measurement proposed in this paper can determine the robot's stability state efficiently and evaluate the stability accurately compared with the stability margins that only consider the distance between _ZMP_ and the current support line and the stability analysis method called LAR.
* Simulation 3 showed that the stability-analysis method proposed in this paper is applicable to motion in 3D space.
# Stochastic Modeling for Intelligent Software-Defined Vehicular Networks: A Survey
## 6 Open Issues
### Mobility Control  
The increasing mobility of vehicles in the context of SDVNs presents significant challenges for network stability and management. As vehicles move at high speeds and change their locations frequently, the wireless channels used by an SDVN become unstable, leading to data transmission errors and delays. This can result in difficulties in acquiring real-time data from the vehicles and networks using the controller, leading to delays in the controller's command distribution. To address this challenge, various solutions have been proposed, such as the use of predictive algorithms and mobility-aware routing protocols. However, these solutions are still in their infancy and cannot be fully implemented in SDVNs.  
Figure 9: The scope of intelligent digital-twin-based SDVN [73].  
One potential solution to the high-mobility challenge is the incorporation of vehicle movement behavior in network stability prediction. By predicting the movement of vehicles in a given network environment, it may be possible to anticipate wireless channel instability and take proactive measures to prevent data transmission errors and delays. However, incorporating vehicle movement behavior into network stability prediction is a complex and challenging task. It requires the use of sophisticated algorithms that can accurately model and predict the movement of vehicles in a given network environment. Additionally, it requires the integration of data from multiple sources, such as vehicle sensors, network infrastructure, and historical data, to create an accurate predictive model. Despite the challenges, efforts are being made to develop predictive algorithms that can accurately model and predict the movement of vehicles in high-mobility environments. These efforts hold promise for enabling the more effective control and management of SDVNs in high-mobility scenarios, improving their overall performance and reliability.
# Sparse Weighting for Pyramid Pooling-Based SAR Image Target Recognition
## 2 SAR Image Recognition with Sparse Weighting Spatial Pyramid Pooling
### Classification Procedure  
After the representation coefficient \(\hat{u}^{\omega}\) was obtained, the given weighted global query vector \(y^{\omega}\) and the global training matrix \(F\) were applied to calculate the minimum reconstruction residual; then, the query image to the subject \(c\) that had the global minimum residual [12] was allocated:  
\[c=\text{argmin}_{j}||F\delta_{j}(\hat{u}^{\omega})-y^{\omega}||_{2}. \tag{12}\]  
Figure 2: Illustration of the numeric value results of the sub-region sparsity. (**a**,**b**) are example images illustrating the distribution of sub-regions in the construction of the three-level pyramid. (**c**,**d**) show the \(\varsigma_{k}\) of the sub-regions corresponding to the images (**a**,**b**).
## 4 Simulation Results
### Physical Simulation Results
#### 4.2.1 Self-Building Multi-Small Spherical Robot Formation Simulation Platform  
ROS/Gazebo is an open-source physical simulation platform that is widely used in robot development and multi robot coordination simulation. The Gazebo-based simulation platform mainly includes small spherical robot models, unmanned aerial vehicle models, and environmental plugins. Unmanned aerial vehicles are used for the localization of the spherical robots based on the global visual location method [24]. Figure 16 shows the graphical interface loaded in the multi-robot formation simulation.  
The simulation platform adopted the ROS framework (shown in Figure 17) and was ran on the Ubuntu 14.04 system platform. The ROS-based framework mainly consists of a drone model package, a drone control package, a drone vision package, a small spherical robot model package, a spherical robot control package, and a spherical robot driving package. When the simulation platform runs, all of the models, including a drone, three small spherical robot models, a environment model, as well as control algorithms (including the control algorithms of three spherical robots, an unmanned aerial vehicle, and a vision-based positioning and path planning algorithm) needed to be loaded. Due to the heavy computation, the simulation platform was implemented using three computers, as shown in Figure 18. Each computer was in charge of the control algorithm. In addition, one of the three computers was simultaneously responsible for drone control, vision-based robot position calculation, and interface display work. The communication between the multiple computers adopted the communication software multimasters kill [25].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Robot Number** & **\(\Delta\)x (m)** & **\(\Delta\)y (m)** & **\(\Delta\)z (m)** & **\(\Delta\)\(\phi\) (rad)** \\ \hline
1 & 0.134 & 0.112 & 0.010 & 0.073 \\
2 & 0.118 & 0.071 & 0.023 & 0.134 \\
3 & 0.172 & 0.107 & 0.011 & 0.092 \\
1–2 & 0.181 & 0.112 & 0.006 & 0.177 \\
1–3 & 0.055 & 0.063 & 0.001 & 0.080 \\
2–3 & 0.212 & 0.115 & 0.006 & 0.205 \\ \hline \hline \end{tabular}