esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Evaluation Metrics  
The evaluation of the GRU model's effectiveness was conducted using a variety of widely recognized evaluation techniques. These included the use of a confusion matrix, as well as metrics such as accuracy, recall, precision, and F-score, as referenced in [42]. The confusion matrix, also known as error matrix, serves as a tool for statistical classification, visually representing the model's performance, as shown in Figure 7. This figure illustrates a binary classification scenario, distinguishing between two categories: a positive (P) class and a negative (N) class. The matrix is structured to highlight several key types of outcomes: true positive (TP), which indicates accurate predictions of positive instances, meaning the predictions and actual values both are positive; and false positive (FP) refers to instances falsely identified as positive when they are actually negative. True negative (TN) points to correct predictions of negative instances, where both the predicted and actual values are negative. Lastly, false negative (FN) describes instances where positive values are mistakenly identified as negative [42]. In addition, the accuracy for the model indicates the ratio among the numbers of correctly predicted samples to the total number of input samples. This is shown in Equation (10).  
\[\textit{Accuracy}=\textit{Sum of TruePositive}+\textit{Sum of True Negative}/\textit{Total population} \tag{10}\]  
The recall represents the amount of correct positive results divided by the amount of all relevant samples. This is represented in Equation (11).  
\[\textit{Recall}=\textit{TruePositives}/\textit{(TruePositives}+\textit{FalseNegatives}) \tag{11}\]  
The precession metric estimates the number of accurate positive results divided by the number of positive results expected through the classifier. This is represented in Equation (12).  
\[\textit{Precision}=\textit{TruePositives}/\textit{(TruePositives}+\textit{False Positives}) \tag{12}\]  
Finally, the F-score is calculated using Equation (13). This equation illustrates just one score of the equilibrium, reflecting the recall and precision in a single value. The F-score denotes a balance between two metrics: recall and precision. It is a balanced mean of two different scores; a product of 2 will become a score of 1 when both of the recall and precision equal 1.  
\[\textit{F-Measure}=2\times\textit{[(Precision}\times\textit{Recall})/\textit{( Precision}+\textit{Recall})] \tag{13}\]  
Figure 7: General structure of confusion matrix.
## 5 Numerical Examples
### Dimensional Synthesis of Stephenson III Six-Bar Slider-Crank Function Generator  
The prescribed positions for the Stephenson III six-bar slider-crank function generator are given in Table 6 and the free choices were assumed as \(r_{1x}\) = 0.12859 and \(r_{1y}\) = 1.0473. Using the [PERSON] software package, the computation time to solve the synthesis equations given in Equations (36) and (38) was 4.38 h on the same work station referred to in Section 5.1, and a total of 36,061 nonsingular solutions were obtained. Among them, 31 Stephenson III six-bar slider-cranks that have less than 0.01% structural error were found, and the number of the feasible mechanisms for each assembly configuration is tabulated in Table 7. The kinematic diagrams in the initial positions and slider displacements of two synthesized mechanisms are shown in Figure 4 and the corresponding solutions are listed in Table 8. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Precision** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \(\Delta\theta_{j}\) (deg) & 0 & 39 & 88 & 140 & 182 & 225 & 253 & 287 & 333 \\ \hline \(\Delta\eta_{j}\) & 0 & \(-\)0.16691 & \(-\)1.08488 & \(-\)2.29326 & \(-\)2.83569 & \(-\)2.59666 & \(-\)1.93088 & \(-\)0.95797 & \(-\)0.18975 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Prescribed positions for the Stephenson III slider-crank function generator. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Configuration** & **Type of** & **Solutions** & **Link Lengths** & **Orientations (Deg)** \\ \hline \multirow{8}{*}{1} & \multirow{8}{*}{Crank-rocker} & \(r_{2x}\) & 2.54452041055486 & \multirow{8}{*}{\(r_{2}\)} & 2.99784510000088 & \(\phi\) & 31.9204361635379 \\ \cline{3-6}  & & \(r_{2x}\) & \(-\)0.439101961903118 & & & \\ \cline{2-6}  & & \(r_{3x}\) & \(-\)0.439101961903118 & & & \\ \cline{2-6}  & & \(r_{3x}\) & \(2.6988583961284\) & \multirow{2}{*}{\(r_{3}\)} & 3.00115483999674 & \(\alpha\) & 98.4132035930371 \\ \cline{3-6}  & & \(r_{3x}\) & \(0.265943554102272\) & & & & \\ \cline{2-6}  & & \(r_{4y}\) & \(2.54563352441547\) & & & & \\ \cline{2-6}  & & \(r_{5x}\) & \(0.272684581643968\) & & & & \\ \cline{2-6}  & & \(r_{5y}\) & \(-\)3.32482508216621 & & & & \\ \hline \multirow{8}{*}{3} & \multirow{8}{*}{Double-crank} & \(r_{2x}\) & \(-\)0.448150890638856 & \multirow{8}{*}{\(r_{2}\)} & 0.943859211257232 & \(\phi\) & \(-\)118.34679745901 \\ \cline{3-6}  & & \(r_{3x}\) & \(0.03966289812873\) & & & & \\ \cline{1-1} \cline{2-6}  & & \(r_{3y}\) & \(0.854821906164786\) & & & & \\ \cline{1-1} \cline{2-6}  & & \(r_{4x}\) & \(-\)0.725026834630641 & & & & \\ \cline{1-1} \cline{2-6}  & & \(r_{4y}\) & \(-\)0.083645206854701 & & & & \\ \cline{1-1} \cline{2-6}  & & \(r_{5x}\) & \(0.650197621387553\) & & & & \\ \cline{1-1} \cline{2-6}  & & \(r_{5y}\) & \(-\)2.32353155551589 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The number of feasible mechanisms for each configuration.
# Real-Time Interaction for 3D Pixel Human in Virtual Environment
## 1 Introduction
If the entire video stream of a user is converted directly into a relatively low-resolution pixel-style avatar, subtle expressions and facial movements may be lost. Therefore, in the proposed framework, we only transform the user's body into a pixel style and retain a high-resolution facial video stream to facilitate efficient real-time communication in online meetings. The main contributions of this study can be summarized as follows:  
* We propose a novel pixel-style avatar to solve segmented human real-time 3D visualization problems in a virtual environment. * We use 3D pose data to generate a 3D pixel avatar arm and use high-resolution facial video to reflect facial expressions, enabling more realistic interactions and intuitive communication.
# SPinDP: A High-Speed Distributed Processing Platform for Sampling and Filtering Data Streams
## 1 Introduction
In contrast to large-scale batch data analysis, even a small amount of noise in real-time stream analysis can lead to critical errors in the results. It is also very inefficient to use large amounts of raw data for real-time analysis directly. Therefore, adopting SPinDP for sampling or filtering to reduce unnecessary data and performing tasks like anomaly detection, trend analysis, etc., with refined data proves to be effective. The contributions of this paper can be summarized as follows. Firstly, we propose a new distributed processing platform, SPinDP, to support data stream purification, which cannot be easily achieved with existing technologies. Secondly, we design and implement a high-speed distributed cluster capable of processing data streams without latency by exploiting recent InfiniBand with RDMA technologies. Thirdly, we present new sampling and filtering algorithms suitable for the distributed processing of data streams. Fourthly, we evaluate the functionality and efficiency of SPinDP through experiments, analyze the results, and demonstrate the effectiveness of the platform. The rest of the paper is organized as follows. Section 2 describes the research background and related work of this paper. In Section 3, we propose the overall architecture and key components of the proposed platform, SPinDP. Section 4 confirms the functionality and efficiency of SPinDP through various experiments. Finally, in Section 5, we summarize and conclude the paper.
## 3 Results  
Keyword searches of the Web of Science (76), Scopus (63), and Pubmed (56) databases yielded a total of 195 articles. The subsequent elimination of duplicates (74) resulted in the inclusion of 121 articles. Of these 121 studies, 78 were excluded because they deviated from the previously defined inclusion criteria, leading to 43 records being selected. Subsequently, 7 records not retrieved were excluded, and then 23 reports were excluded because they did not meet the criteria (18 were off-topic, and 5 were reviews). After their eligibility was confirmed, 13 primary studies were selected for qualitative analysis. The selection process and the summary of selected records are shown in Figure 3 and Table 2, respectively. Figure 3: The literature search of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram and database search indicators. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Authors** & **Type of Study** & **Age and Number of Participants** & **Aim of the study** & **Materials and Clinical Data** & **Results and Percentages** \\ \hline
**[PERSON]** & Randomized & 210 children & Effectiveness of silver fluoride (AgF) and stannous fluoride compared with stannatic children with caries on primary molars: AgF and control group (ART technique) & The study’s main unfavorable outcomes included dental discomfort, earth, antibiotic usage, and more involved restrictive procedures. \\
**[PERSON]** & Randomized & 210 children & (AgF) and stannous fluoride compared with stannatic children with caries on primary molars: AgF and control group (ART technique) & Findings: neither of the treatment groups experienced many unfavorable effects. When comparing children treated with AgF to those treated with ARI, the prevalence rate of negative sequelae was lower at 0.18 \\
**[PERSON], at al** & Randomized & 226 children & Comparison of glass inoner current SMART technique (silver-modified stannatic) & Children with asymptomatic caries in primary molars were randomly assessed in SMART and conventional groups were randomly assessed in SMART and conventional groups & No significant difference between SMART and conventional techniques. \\
**(2023)** [78] & Randomized & 226 children & (Silver-modified stannatic) between 4 and 8 years old & Comparison of stannatic and conventional groups & The restoration success rates for the SMART and conventional groups were 38.4\% and 45.8\%, respectively (p = 0.105). \\
**[PERSON], at al** & Randomized & 226 children & (Silver-modified stannatic) between 4 and 8 years old & Comparison of stannatic and conventional groups & 38.4\% and 45.8\%, respectively (p = 0.105). \\
**(2023)**  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
The developed algorithms were presented mainly in algebraic relations, and less often in DFT matrix factorizations. However, none of the publications known to us explains how these ratios were obtained and from what considerations the presented matrices were constructed. This is especially true for small-length real-valued DFT algorithms. Thus, the details of constructing small-length real-valued DFT algorithms are essential. Nevertheless, the solutions given in the literature do not provide a complete imagination about the organization of the small-length real-valued DFT calculation process since their corresponding signal flow graphs are not presented anywhere. In addition, it should be noted that real-valued DFT algorithms for small-length sequences are described in publications available to the authors rather poorly [14].  
Applications of short-size FFT algorithms are known from the literature [1; 3; 4; 5; 7]. The need to calculate real-valued DFTs arises, for example, in the world of the Internet of Things, smart sensors and connected devices generate a massive amount of data on petabytes per second. Due to communication costs that impact performance and energy consumption, there is an increasing need to perform a significant amount of computation closer to the edge rather than transferring large portions of raw data to the cloud. The latency and security risk of relying on the cloud is intolerable for applications deployed in drones, autonomous vehicles, robotics, and wearables. These applications are often enabled by DSP algorithms and, more specifically, by small-length real-valued DFTs, which extract meaningful information from raw data. Therefore, the efficient deployment of signal processing in embedded devices will improve near-sensor processing, avoid expensive data transmission, enable freedom from the cloud, and provide low latency and low energy consumption. This is a significant challenge due to the embedded systems resource constraints and the increased computational requirements of data processing.  
The purpose of the article is to show the details of the organization of calculations of small-length DFTs for the case of real-valued input data as well as to reduce the number of arithmetic operations needed to compute the output.  
Similar solutions for some \(N(3,5,7)\) can be found in [14]. However, there are no considerations of how the presented matrices were constructed and the data flow diagrams are not shown. We have completed the missing algorithms for the remaining \(N(4,6,8,9)\), described the way in which these algorithms were obtained, and shown the data flow diagrams for all \(N\) from 3 to 9.
# Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste
## 1 Introduction
### Objective  
The objective of this paper is to determine an effective machine learning method for predicting energy recovery from waste using a constrained dataset. The novelty of the research lies in the comparative analysis of three different machine learning approaches--ElasticNet, Decision Trees, and Neural Networks--in the specific context of energy recovery, providing insights that could be crucial for improving the efficiency and accuracy of such predictions in the waste management field. The utilitarian goal is to create an effective mathematical model that can be used in European countries with a low level of waste-to-energy conversion.
## 1 Introduction
A significantly wider voltage range was available for the SC to maximize the utilization of its energy capacity and hence reduce its size without added penalties in terms of components count, current stress, and the sizes of the corresponding inductors;
3. The ability to reduce the switching current ripple in inductors by using the interleaved phase-shifted carriers modulation technique for the different input port currents. The paper is organized into seven sections. In Section 2, the ability of the proposed converter architecture to maximize the utilization of SCs is analyzed and compared with other topologies. In Section 3, the operating principles of the proposed converter are presented. In Section 4, the proposed phase-shifted carriers interleaved modulation technique and its effect on the ripple of the inductor current was analyzed, whilst the control of the proposed converter architecture is introduced in Section 5. A detailed experimental evaluation of the proposed converter architecture is included in Sections 6 and 7 summarizes the conclusions of this work. Figure 1: Block diagram for the PV-powered UAV power system: (**a**) with separated converters; (**b**) with MPC.
# Resizer Swin Transformer-Based Classification Using sMRI for Alzheimer's Disease
## 3 Methodology
### Model Structure
#### 3.2.3 Resizer Module  
In the field of deep learning for image processing, the input image size is typically scaled to 224 \(\times\) 224, and both training and inference are carried out at that resolution. Currently, image scaling often uses both bilinear and trilinear interpolation. At the same time, the sMRI slices need to be resized to fit as input to the network. In actuality, this modification does not improve the image of the network and even somewhat reduces the performance of the model [33]. Therefore, this experiment substitutes the learnable Resizer module for the original linear interpolation after the CNN transfers the single-channel to the three-channel color space. Resizer seeks to significantly improve the classification performance by learning attributes favorable to Swin categorization by collaborative training  
Figure 2: Colorization CNN network structure. The numbers and cubes of different colors are the feature map and \(C\times H\times W\) of the feature map after a series of convolution operations; Blocks 1, 2, and 3 show the specific details of the convolution operation corresponding to the blue, yellow, and orange feature maps, respectively. Given the lightness channel \(L\) of a gray-scale image, this model predicts the corresponding a and b color channels of the image in the CIE Lab color space. Finally, it is converted to the RGB color space, and the image is output.  
with the backbone network, in contrast to other approaches that resize images to enhance human eye perception. The Resizer module is shown in Figure 3. The bilinear Resizer model allows features calculated at the original resolution to be incorporated into the model. It acts as an inverse bottleneck (up-scaling). The skip connection in Figure 3 combines the bilinearly resized image with the CNN features.
## 6 Simulation Setup  
The simulation was performed by presenting three experimental scenarios in order to verify the QoS's performance of the proposed AFB-GPSR. The scenarios are designed to assess the impact of various MANET environmental settings on AFB-GPSR performance in comparison to GPSR [23] and topology-based AODV routing protocols [59]. The first scenario examines the performance under various node densities while holding the other simulation parameters constant, and the second scenario is tested by varying the speed of nodes. On the other hand, the third scenario presents the impact of transmission power on AFB-GPSR in comparison to the conventional GPSR with 1 s and 2 s fixed beaconing intervals.  
To verify the proposed AFB-GPSR routing protocol, three extensive simulation scenarios were implemented in this section to investigate the scalability in terms of various network sizes (nodes density), the mobility metric in terms of nodes' motion speeds, and the effect of various node transmission power values on the routing efficiency through network metrics of reliability and latency.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 5 Reinforcement Learning Based Power Allocation
### Learning Process  
An agent interacts with the environment by sensing the current state and taking an action (according to a policy). This results in environment transitioning in a new state and an agent is rewarded accordingly. The process is repeated till we have an optimum policy. The learning process under Q-learning [26] is given by Equations (5)-(10).  
Figure 2: V2P user classification.  
Under a policy \(\pi\), a state is given by Equation (5):  
\[V^{\pi}(s)=\ E_{\pi}\{r_{t}|s_{t}=s\}=E_{\pi}\{\sum_{k=1}^{\infty}\eta^{k}r_{t+k+1 }|s_{t}=s\} \tag{5}\]  
where \(\eta\) is the discount factor with value ranging between 0 and 1. The discount factor helps to determine the future reward. A discount factor of 0 will make the agent strive for only current rewards, whereas, a discount factor near to 1, will make the agent strive for long term rewards. \(V\pi(s)\) is the expected discounted reward. There exists at least one optimal policy \(\pi^{*}\), such that:  
\[V^{\prime}(s)=\ V^{\pi}(s)=max_{a}\{R(s,a)+\eta\ \sum_{s^{\prime}\in S}P_{s,s^{ \prime}}(a)V^{*}(s)\} \tag{6}\]  
\(P_{s,s^{\prime}}(a)\) is a transition probability from state \(s\) to \(s^{\prime}\). The action values or the \(Q\)-values is the expected return of taking action '\(a\)' in state '\(s\)' under the policy '\(\pi\)':  
\[Q^{\pi}(s,a)=R(s,a)+\eta\ \sum_{s^{\prime}\in S}P_{s,s^{\prime}}(a)Q(s^{\prime},a) \tag{7}\]  
and the optimal policy \(Q^{*}(s,a)\) is given by:  
\[Q^{*}(s,a){\equiv}Q^{\pi^{*}}(s,a),\forall s,a \tag{8}\]  
and so, we get:  
\[V^{*}(s)=max_{a}Q^{*}(s,a) \tag{9}\]  
The update rule, that helps the Q-learning algorithm (by adjusting the Q-values) is given by:  
\[Q_{t+1}(s_{t},a_{t})=\ \left\{\begin{array}{cl}Q_{t}(s_{t},a_{t})+\alpha[r_{t+1 }+\gamma max_{a^{\prime}}Q_{t}(s_{t+1},a_{t+1})-Q_{t}(s_{t},a_{t})]&\mbox{if $s=s_{t}$ \ and $a=\ a_{t}$}\\ Q_{t}(s_{t},a_{t})&\mbox{otherwise}\end{array}\right. \tag{10}\]  
Here, \(\alpha\) denotes the learning rate. The values of learning rate are between 0 and 1. A learning rate of 0 means that the agent has learnt nothing, while a learning rate of 1 means the agent only considers the most recent information. The update rule in Equation (10) is based on the steps taken by an agent to reach a terminal point. The Q-values are adjusted based on the difference between discounted new values and the old values. The parameter gamma is used to discount the new values and the step size is adjusted using learning rate.
###### Abstract  
During the process of satellite capture by a flexible base-link-joint space robot, the base, joints, and links vibrate easily and also rotate in a disorderly manner owing to the impact torque. To address this problem, a repetitive learning sliding mode stabilization control is proposed to stabilize the system. First, the dynamic models of the fully flexible space robot and the captured satellite are established, respectively, and the impact effect is calculated according to the motion and force transfer relationships. Based on this, a dynamic model of the system after capturing is established. Subsequently, the system is decomposed into slow and fast subsystems using the singular perturbation theory. To ensure that the base attitude and the joints of the slow subsystem reach the desired trajectories, link vibrations are suppressed simultaneously, and a repetitive learning sliding mode controller based on the concept of the virtual force is designed. Moreover, a multilinear optimal controller is proposed for the fast subsystem to suppress the vibration of the base and joints. Two sub-controllers constitute the repetitive learning sliding mode stabilization control for the system. This ensures that the base attitude and joints of the system reach the desired trajectories in a limited time after capturing, obtain better control quality, and suppress the multiple flexible vibrations of the base, links and joints. Finally, the simulation results verify the effectiveness of the designed control strategy.  
flexible-base; flexible-link; flexible-joint; space robot; capturing satellite; singular perturbation theory; vibration suppression; repetitive learning sliding mode stabilization control  
26 July 202125 August 202125 August 202131 August 2021  
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**Key words:**  
* **Applied**
* **Sciences**