esa-sceva/satcom-chunk-collection huggingface dataset:
## 3. Methodology
### Model Performance Assessment and Comparison  
#### 3.4.1 Confusion Matrix  
The confusion matrix includes true positive (TP), false positive (FP), true negative (TN), and false negative (FN) categories. Using these values, various statistical indices, such as accuracy, sensitivity, specificity, threat score, equitable threat score, [PERSON]'s skill score, odds ratio, and odds ratio skill score can be calculated [55]. The value calculated from the confusion matrix provides useful information on model performance and classification accuracy.  
In this study, the sensitivity, specificity, overall accuracy, and kappa statistic were used to validate the performance of the LSMs. The percentages of landslide and non-landslide pixels classified correctly into those two categories enable the calculation of sensitivity and specificity, and the overall percentage classified correctly (in both categories together) indicates the accuracy of the LSMs [56]. In addition, the kappa statistic is used to evaluate the reliability of the landslide models. Its value ranges from \(-1\) (non-reliable) to \(1\) (reliable) [57].
## 3 Overview of OCC
### Generic OCC Architecture  
The Figure 2, shows a generalized block diagram of an OCC system. The input binary data stream is passed to Optical Signal Modulator which modulates the signal using the selected technique. The modulated signal is mapped to the LED driver which is used to control the LED. The modulated signal is transmitted over a free-space optical medium. Mobile phone cameras receive the directed light as live video. The video undergoes frame sampling and thresholding to demodulate the signal and decode the received data.
# Performance Optimization of Grounding System for Multi-Voltage Electrical Installation
## 2 Principles of Grounding Systems
### Key Parameters Influencing Grounding Performance
#### 2.3.1. Soil Resistivity and Moisture Content  
Soil resistivity \(\rho_{soil}\) is a critical factor in determining the performance of a grounding system. It varies with the moisture content, temperature, and soil composition. The resistivity of soil is typically measured in Ohm-meters (\(\Omega\cdot\)m). The total resistance of a grounding electrode in a given soil is given by the following:  
\[R_{electrode}=\frac{\rho_{soil}}{2\pi\cdot L}ln\left(\frac{D}{r_{electrode}}\right) \tag{5}\]  
where \(L\) is the length of the electrode, \(D\) is the distance from the electrode to the measurement point, and \(r_{electrode}\) is the radius of the electrode. As the moisture content increases, soil resistivity decreases, improving the performance of the grounding system. In dry conditions, additional measures such as increasing the number of electrodes or using conductive materials may be required to ensure a low-impedance grounding path.  
\begin{table}
\begin{tabular}{l l l} \hline
**NEC Article** & **Regulation Aspect** & **Description** \\ \hline Article 90 & Introduction & Defines the scope, purpose, and application of NEC grounding requirements. \\ \hline Article 100 & Definitions & Provides key definitions for grounding, bonding, and related electrical terms. \\ \hline Article 110.26 & Working Space Requirements & Specifies clearances for electrical equipment to ensure safe maintenance. \\ \hline Article 250 & Grounding and Bonding & Comprehensive guidelines for grounding and bonding of electrical systems. \\ \hline
250.4 & General Requirements & Establishes principles for effective fault current path and protection against electric shock. \\ \hline
250.52 & Grounding Electrodes & Lists acceptable grounding electrode types (e.g., ground rods, water pipes, concrete-encased electrodes). \\ \hline
250.56 & Resistance of Grounding Electrodes & Specifies that the ground electrode system should have a resistance of 25 ohms or less. \\ \hline
250.66 & Sizing of Grounding Electrode & Defines the minimum size of conductors based on service entrance conductor size. \\ \hline
250.90-250.96 & Bonding Requirements & Mandates bonding of metallic parts to eliminate potential differences. \\ \hline
250.102 & Grounding Conductor Sizing & Specifies minimum conductor sizes for grounding based on system capacity. \\ \hline
250.104 & Bonding of Piping and Structural Metal & Requires bonding of metal water piping and structural steel to the grounding system. \\ \hline
250.186 & High-Voltage Systems & Outlines special grounding rules for systems above 1 kV, including industrial applications. \\ \hline Article 690 & Solar PV System Grounding & Provides grounding rules for photovoltaic systems to prevent electrical hazards. \\ \hline \end{tabular}
\end{table}
Table 2: National Electrical Code (NEC) Standards for Grounding in the USA [11].
# A Novel Fractional Fourier Transform-Based ASK-OFDM System for Underwater Acoustic Communications
## 7 Conclusions and Future Work  
In this article, the authors proposed a novel FrFT-ASK-OFDM system for bandwidth-efficient, high data rate UWA communication. The proposed system employed the ASK modulation scheme, which was shown to be highly recommended in an underwater environment since it efficiently utilizes the limited bandwidth, which is not the case for PSK or QAM. The proposed system also employed FrFT with OFDM, which has the advantage of reducing the BER to a large extent in the absence and presence of CFO. Numerical results showed that the proposed system is very suitable for application in an underwater environment. This is due to its bandwidth saving and great BER performance in a channel characterized by scarce bandwidth, multipath fading and ICI due to Doppler spread. In particular, the system model proposed in this article can achieve a very promising BER performance and can reach higher data rates when compared to other system in the literature.  
Regarding future work, even though the ASK modulation achieves a better bandwidth efficiency in the UWA channel when combined with FrFT as compared to commonly used modulation schemes, the BER of un-coded OFDM systems is relatively high. However, the ASK FrFT-OFDM is very promising in UWA channels, and can be combined with some other techniques to enhance the BER. In addition, the PAPR issue should be addressed carefully. Coding is one of the techniques that is mentioned in this article as an enhancement for power efficiency, but this is not within the main scope of this article. The literature is full of techniques and algorithms to reduce PAPR at the expense of more system complexity. However, these approaches will require further investigation and improvements to be compatible with the UWA communication channel. Moreover, theoretical modeling and physical experiments based on field tests will be addressed in the future to support and emphasize the simulation results presented in this study. More specifically, simulations based on Watermark benchmark software will be carried out in future work [36].  
[PERSON] proposed the idea. [PERSON] and [PERSON] conceived and designed the simulations, analyzed the data, and wrote the paper. [PERSON] and [PERSON] reviewed the paper and simulation results.  
The authors declare no conflict of interest.
## 1 Introduction
At each level, a resource selection diversity approach is used, which helps to better balance the interactions and constraints between the optimization objectives. * In terms of the mission planning methodology, we propose a progressive optimization mechanism. Firstly, an initial task planning scheme is generated in the allocation optimization phase, which contains the allocation sequences of satellites and target tasks. Secondly, in the time window optimization phase, an execution time window is assigned to each target task based on the planning scheme in the allocation optimization phase. Finally, the planning results of the previous two phases are combined in the global optimization phase to generate the final mission planning scheme. Through stage-by-stage incremental decision-making, conflicts between planning schemes can be reduced, and the utilization rate of satellite resources can be effectively improved. * In terms of constructing the mission planning algorithm, a population size adaptive strategy for an improved differential evolution algorithm is proposed to address the problem that multiple satellites are prone to falling into the local optimal solution under the large-scale target mission scenario, as well as for solving the difficulty of different constraints faced by the three-layer structure of the MSIMPLTS-MLOO model. The algorithm dynamically adjusts the population size according to the evolution of the population to adapt to the needs for model solving and thus improve the solving efficiency and quality of the mission planning scheme. The rest of the work is organized as follows: Section 2 introduces the basic MSDMP scenario and the motivation for studying MSIMPLTS. Section 3 introduces the MSIMPLTS model and its objective function and constraints. Section 4 details the specifics of the POM-PSASIDEA algorithm. Section 5 presents our simulation experiments to analyze the effectiveness and stability of the POM-PSASIDEA algorithm. Section 6 concludes and offers future perspectives.
# Learning-Based Online QoE Optimization in Multi-Agent Video Streaming
## 3 System Model
### Quality of Experience  
The goal of this work is to maximize the inter-agent QoE utility for all users. In this paper, we consider the fairness utility functions in [13] and optimize the inter-agent fairness with two existing evaluations--the sigmoid-like QoE function and the logarithmic fairness function. By analyzing real-world user rating statistics, a sigmoid-like relationship between the web page loading time and the user QoE was reported in [14]. Inspired by that, we draw a similar nonlinear, sigmoid-like QoE curve to map the streaming stall time ratio, and verify that (i) reducing the stall time for users who already have a very low stall time or (ii) increasing the stall time for users who already suffer from a high stall time does not impact the QoE values, while (iii) users with a mediocre QoE are more sensitive to stall time changes:  
\[f(x)=\frac{1}{1+e^{10(x-0.35)}}. \tag{16}\]We also consider a logarithmic utility function that achieves the well-known proportional fairness [13] among the users:  
\[f(x)=\log_{2}(-x+2). \tag{17}\]  
It is easy to see that, with the unit stall time decrease, this utility function provides (i) a larger QoE increment for users experiencing a higher stall time and (ii) a smaller increment for users already enjoying a good performance with a low stall time.  
In both Equations (16) and (17), \(x\) represents the stall time ratio for playing a video. It is defined by  
\[x(L_{v},k)=\sum_{l\in L_{v}}\frac{T_{s}(l,T_{l},k)}{T_{l}}, \tag{18}\]  
where \(L_{v}\) denotes the time slots that video \(v\) has been played in, and \(T_{s}(l,T_{l},k)\) denotes the stall time for user \(k\) in time slot \(l\) with slot length \(T_{l}\).  
We note that (16) is only one representative QoE function, while other functions may be used. Suppose that, in \(L\) time slots, \(V_{k}(L)\) is the set of videos played by user \(k\). Then, the total QoE of the \(L\) time slots obtained by user \(k\) is given as:  
\[Q_{k}=\sum_{v\in V_{k}(L)}f(x(L_{v},k)), \tag{19}\]  
and, for all users, the total QoE is  
\[Q(L)=\sum_{k\in K}Q_{k}. \tag{20}\]  
Substituting (19) and (18) in (20), we have  
\[Q(L)=\sum_{k\in[K]}\sum_{v\in V_{k}(L)}f\left(\sum_{l\in L_{v}}\frac{T_{s}(l,T _{l},k)}{T_{l}}\right). \tag{21}\]  
Note that the QoE function defined in Equation (19) assigns a higher quality of experience to a lower stall time. The QoE metric remains constant for small stall times. If the stall times are lower than a certain value and are not noticeable, the QoE does not vary, as obtained in the sigmoid-like function of Equation (19). In addition, the QoE decreases rapidly with increasing stall times and remains zero if the stall times exceed a certain value, therefore ruining the viewing experience.
## 3 Audio Characterization and Recognition Tests
### SVM Classifier Training and Recognition
#### 3.3.2 Defective Audio Identification with Different Noise Components  
To study the recognition of mechanical vibration and PD with different background noise components, first, 10 frames from A1-C4 were selected and normalized; then, the background noise with different components was superimposed with mechanical vibration and PD, to generate 160 frames of mechanical vibration and 160 frames of PD with background noise. Finally, CD1 and TF1 were used to identify the defective audio. The recognition results are shown in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multirow{2}{*}{**Classifier**} & \multicolumn{4}{c}{**Recognition Accuracy (\%)**} \\ \cline{3-6}  & & **Background Noise /** & **Normal Vibration** & **Abnormal Vibration** & **PD/** & **Total/** \\  & & **66 Frames** & **19 Frames** & **/58 Frames** & **64 Frames** & **207 Frames** \\ \hline \multirow{2}{*}{Time domain} & CD1 & 100 & & 96.1 & & 96.8 & 97.8 \\  & CD2 & 100 & 0 & 94.8 & 96.8 & 88 \\  & TF1 & 100 & & 98.7 & & 100 & 99.6 \\  & TF2 & 100 & 100 & & 93.1 & 100 & 98.6 \\  & CM1 & 89.4 & & 97.7 & & 100 & 95.7 \\  & CM2 & 92.4 & 100 & 98.2 & 100 & 96.2 \\  & CT1 & 83.3 & & 96.1 & & 96.8 & 92.2 \\  & band energy & CT2 & 86.3 & 0 & 94.8 & 96.8 & 83.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: SVM classifier recognition accuracy.  
In Figure 10a, when the background noise component of mechanical vibration was at or below 50%, the recognition rates of CD1 and TF1 remained above 90%, and with the increase in the background noise component, the recognition rates of CD1 and TF1 finally decreased to 80% and 84%. In Figure 10b, TF1 maintained an 80% recognition rate when the background noise component of PD reached 80%, while the recognition rate of CD1 decreased rapidly to 54% as the background noise component increased.  
This analysis shows that, when the switchgear had mechanical defects or PD defects and the indoor background noise component was large, the proposed TF features combined with the SVM classifier could still identify the mechanical vibration and PD better; compared with the selected time domain features, the proposed TF features also showed a better classification performance.
# Ordered Subspace Clustering for Complex Non-Rigid Motion by 3D Reconstruction
## 5 Experiments and Analysis  
To evaluate performances of state-of-the-art clustering methods together with our proposed method noted by OSC-NRSfM, we conducted experiments on representative datasets. First, we performed face clustering and expression clustering experiments on the BU-4 DFE dataset [25]. Second, we widely evaluated our proposed method for complex non-rigid motion using MSR Action3D Dataset. Finally, we tested our method on the Utrecht multi-person motion (UMPM) [26] dataset, which contains 3D joint positions and 2D point tracks of real-world 2D projections out of videos. We compared the proposed OSC-NRSfM method with LRR [14], OSC [16], QOSC [17], CNRMS [10]and sparse subspace clustering (SSC) [13]. Here we used subspace clustering error (SCE) [16] and normalized mutual information (NMI) to measure clustering results, which are described as follows.  
\[SCE=\frac{\text{num.misclassified points}}{\text{total num.of points}}, \tag{18}\]  
where the denominator stands for the number of all samples and the numerator stands for the number of misclassified samples.  
The parameters \(\lambda,\lambda_{1},\lambda_{2},\lambda_{3}\) of our methods and the ones of the compared approaches were tuned experimentally according to the experimental results and the parameter setting analysis described in [10; 14; 16; 17].
# Speed Optimization in DEVS-Based Simulations: A Memoization Approach
## 5 Memoization Technique to Reduce the Search Cost in the DEVS Coupled Model
### Experiment and Evaluation  
In this sub-section, we discuss the results of simulating the same set of DEVS models that were simulated in the original DEVS engine by applying the coupled model processing method of the coordinator with the memoization technique.  
To find the cause of the problem of excessively increasing simulation time, the model performance evaluation was performed by coupling 10,000 basic atomic models in various ways. Table 4 summarizes the simulation results of the coupled model with memoization applied. By applying the memoization technique to the couple model, the overall simulation time speed was improved by about 10 times. The model configuration with the least performance improvement was {1000, 11}, which used only 11 coupled models. On the other hand, the configuration with the highest performance improvement was {10, 10, 10, 10}, which shows that the greater the number of coupled models and the more complex the hierarchical structure, the higher the performance improvement efficiency.  
Table 5 presents the simulation results of one coupled model containing 10, 100, 1000, and 10,000 atomic models, and the model processing structure of the coordinator with the proposed memoization showed performance improvement of as little as 2.1 to as much as 2.8 times even when the child node (atomic models) is increased for one coupled model. Figure 8 visually shows the comparison results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Atomic** & **Coupled Model** & **Base Simulation** & **Proposal Simulation** & **Improvement** \\  & **Model** & **Coupled Model** & **Time (s)** & **Time (s)** & **Time (s)** \\ \hline \(\{10,10,10,10\}\) & & 1111 & 188 & 16 & \(\times 11.75\) \\ \(\{10,10,100\}\) & & 1101 & 181 & 16 & \(\times 11.31\) \\ \(\{10,100,10\}\) & & 1011 & 175 & 15.6 & \(\times 11.21\) \\ \(\{100,10,10\}\) & 10,000 & 111 & 73 & 7.2 & \(\times 10.13\) \\ \(\{100,100\}\) & & 101 & 79 & 7.2 & \(\times 10.97\) \\ \(\{10,1000\}\) & & 1001 & 168 & 16.9 & \(\times 9.94\) \\ \(\{1000,10\}\) & & 11 & 56 & 7.5 & \(\times 7.46\) \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of proposal structure simulation times and the original model (coupled).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Atomic** & **Coupled Model** & **Base Simulation** & **Proposal Simulation** & **Improvement** \\  & **Model** & **Time (ms)** & **Time (ms)** & **Time (ms)** & **Time (ms)** \\ \hline \(\{10\}\) & 10 & & 0.23 & 0.09 & \(\times 2.55\) \\ \(\{100\}\) & 100 & & 7.511625 & 2.620083 & \(\times 2.86\) \\
# The Effects of L-Citrulline and Malic Acid on Substrate Utilisation and Lactate Elimination
## 3 Results
### RER and RPExHR during Exercise  
Friedman ANOVA tests revealed significant differences in RE values during the exercise protocol at 70 W (\(\chi^{2}\) = 6.889; N = 9; \(p\) < 0.05; Kendall's W = 0.383), 105 W (\(\chi^{2}\) = 10.667; N = 9; \(p\) < 0.05; Kendall's W = 0.593) and 140 W (\(\chi^{2}\) = 8.222; N = 9; \(p\) < 0.05; Kendall's W = 0.457). Additionally, Wilcoxon pairwise comparisons revealed that citrulline significantly reduced RE values at powers of 70 W, 105 W and 140 W compared to malic acid (Wilcoxon, \(Z\) = \(-\)2.31, N = 9, Bonferroni-corrected \(p\) < 0.0167; \(Z\) = \(-\)2.67, N = 9, Bonferroni-corrected \(p\) < 0.0167; and \(Z\) = \(-\)2.67, N = 9, Bonferroni-corrected \(p\) < 0.0167), and compared to the placebo ([PERSON], \(Z\) = \(-\)2.19, N = 9, Bonferroni-corrected \(p\) < 0.0167; Z = \(-\)2.43, N = 9, Bonferroni-corrected \(p\) < 0.0167) (Figure 3). Absolute mean and standard deviation values are shown in Table 2.  
Friedman ANOVA tests revealed significant differences in RPExHR values during the exercise protocol at powers of 175 W (\(\chi^{2}\) = 6.00; N = 7; \(p\) < 0.05; Kendall's W = 0.429) and 210 W (\(\chi^{2}\) = 6.348; N = 6; \(p\) < 0.05; Kendall's W = 0.529). Additionally, Wilcoxon pairwise comparisons revealed that malic acid significantly reduced RPExHR values at powers of 175 W and 210 W compared to the placebo (Wilcoxon, \(Z\) = \(-\)2.36, N = 7, Bonferroni-corrected \(p\) < 0.0167; and \(Z\) = \(-\)2.37, N = 6, Bonferroni-corrected \(p\) < 0.0167) (Figure 4). Absolute mean and standard deviation values are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Placebo RER** & **Malic Acid RE** & **Citrulline RER** \\ \hline
70 W & 0.857 \(\pm\) 0.087 & 0.860 \(\pm\) 0.064 & 0.820 \(\pm\) 0.075 \\
105 W & 0.927 \(\pm\) 0.088 & 0.928 \(\pm\) 0.069 & 0.898 \(\pm\) 0.085 \\
140 W & 0.970 \(\pm\) 0.075 & 0.972 \(\pm\) 0.067 & 0.931 \(\pm\) 0.083 \\ \hline \end{tabular}
\end{table}
Table 2: Mean and standard deviation (\(\pm\)SD) of RE values at different loads during exercise.  
Figure 2: The RE (**A**) and lactate (**B**) values before supplementation (pairwise Wilcoxon signed-rank test). The boxplot shows the median (line), lower and upper quartile (column), minimal and maximal values (whiskers) and outliers (circle).  
Figure 4: The RPExHR values of three different supplementation groups during exercise (pairwise Wilcoxon signed-rank test, *: Bonferroni-corrected \(p\) < 0.0167) The boxplot shows the median (line), lower and upper quartile (column), minimal and maximal values (whiskers) and outliers (circle).  
Figure 3: The RER values of three different supplementation groups during exercise (pairwise Wilcoxon signed-rank test, *: Bonferroni-corrected \(p\) < 0.0167) The boxplot shows the median (line), lower and upper quartile (column), minimal and maximal values (whiskers) and outliers (circle).  
Table 4 and 5 summarise the average HR and RPE values of three different supplementation groups during exercise.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 4 State-of-the-Art Real-Time Metaverse  
### Integration of the Physical and Virtual Worlds  
Figure 4 presents a detailed view of the architecture for integrating physical, virtual, and real-time metaverse layers within a digital ecosystem. This image presents a conceptual overview of the physical-virtual world ecosystem, highlighting the synchronization of the physical and virtual environments. The _Physical layer_ comprises four main components: users, IoT/sensors, virtual service providers, and physical service providers. Users engage with the virtual world using various devices, and IoT/sensors facilitate the synchronization between the physical and virtual worlds [15; 97]. Virtual service providers offer digital goods and services. In contrast, physical service providers handle tangible goods and services transactions, highlighting the importance of interaction and synchronization between physical and virtual entities for a seamless user experience.  
The _Virtual layer_ includes avatars for virtual navigation, virtual environments for constructing the virtual world, and virtual goods/services such as virtual workspace and education [21]. The virtual layer bridges the gap between the physical and virtual worlds by providing immersive and interactive experiences. At its core, the real-time metaverse leverages data from IoT devices, sensors, and service providers to continuously update the virtual world in alignment with physical events [98]. The ability to \"bring the physical world to the virtual\" lies in deploying advanced sensors. These sensors capture critical information about real-world environments, such as the spatial layout, temperature, and motion of objects [99]. The data are then transmitted to cloud processing centers via high-speed communication technologies, enabling the real-time updating of virtual spaces [100]. Once the data are collected, they undergo data fusion--a process in which inputs from different sensors are combined to create a single, cohesive representation of the physical environment in the virtual world [101; 102]. Fusing these diverse data types creates a rich, detailed, and accurate 3D model of the physical environment in the virtual world [103]. The data are processed in the cloud, allowing real-time synchronization between the two realms.