esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Output Voltage Stability of Non-Contact Excitation Motor
## 5 Simulated Analysis
### Output Voltage Stability Comparison for Stable Loads  
In the following, first, the difference between the two algorithms in stabilizing voltage and current when the load is constant is analyzed. The non-contact excitation motor can adapt to various working conditions and meet the actual requirements.  
According to Figure 9, we can compare the simulation graphs of the output voltage under the two algorithms. The red line represents the result of the system output voltage under the traditional sliding mode control: the output voltage is stable at 110 V, and the stabilization time is 0.001 s. The blue line shows the result of the output voltage under the improved sliding mode control; it can be seen that in the simulated waveform of the improved sliding mode control, the output voltage is finally stabilized at 110 V. Compared with traditional sliding mode control, the settling time is faster, which is 0.0005 s, and the time to reach stability is 50% faster. By comparing the simulation diagram of the output voltage and the time required for the output voltage to stabilize under the control of the two algorithms, it can be seen that the improved sliding mode control has obvious advantages, compared with the traditional sliding mode control. The overshoot is also more minor.  
Figure 8: Resonant circuit output simulation plot: (**a**) simulation of the voltage output from the resonant circuit; (**b**) simulation of the current output from the resonant circuit.  
Figure 7: Simulation diagram of the output of the full-bridge inverter circuit: (**a**) the voltage simulation diagram of the result of the full-bridge inverter circuit; (**b**) current simulation diagram of creating the full-bridge inverter circuit.
## 5 Experiment
### Metric  
In order to reasonably evaluate our work, we use different metrics for the closed-world and open-world settings separately. We first define \(P\) as the total number of monitored traces. True positive (\(TP\)) represents a monitored trace classified as its category. Wrong positive (\(WP\)) indicates that it is classified as other monitored categories. Especially in the open-world scenario, a false negative (\(FN\)) indicates that it is classified as the unmonitored category. If an unmonitored trace is considered a monitored category, it is a false positive (\(FP\)). Therefore, we define accuracy for closed-world evaluation as:  
\[Accuracy=\frac{TP}{P}. \tag{1}\]  
The \(F_{1}\)-score used for open-world evaluation is defined as:  
\[F_{1}=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{2}\]  
where:  
\[Precision =\frac{TP}{TP+WP+FP} \tag{3}\] \[Recall =\frac{TP}{TP+WP+FN} \tag{4}\]  
We run each experiment 10 times and use the mean and standard deviation to report the performance. Moreover, we randomly sample instances for training and testing each time to ensure more reliable and representative results.
## 5 Numerical Example  
In this section, a numerical example is presented via MATLAB to verify the effectiveness of the theoretical results in Section 4.  
Consider the time-varying impulsive positive hybrid systems based on FSM in form (3), where FSM \(G=(\Xi,\Sigma,\zeta)\) is reported in Figure 1, \(\Xi=\{q_{1},q_{2},q_{3},q_{4},q_{5}\}\), and \(\Sigma=\{\sigma_{1},\sigma_{2},\sigma_{3},\sigma_{4}\}\). The system matrices are \(A_{q_{1}}=\left(\begin{array}{cc}-1&1.8+t\\ 0.5&-1.9-t\end{array}\right)\), \(A_{q_{2}}=\left(\begin{array}{cc}-1.2-t&0.8-0.2t\\ 0.3&-2.2-1.2t\end{array}\right)\), \(A_{q_{4}}=\left(\begin{array}{cc}-1&1.5\\ 2+t&-t\end{array}\right)\), \(B_{q_{1}}=\left(\begin{array}{cc}1\\ 0\end{array}\right)\), \(B_{q_{2}}=B_{q_{4}}=\left(\begin{array}{cc}1\\ 1\end{array}\right)\), \(H_{q_{1}}=\left(\begin{array}{cc}0.1\\ 0.1\end{array}\right)\), \(H_{q_{2}}=\left(\begin{array}{cc}0.3\\ 0.1\end{array}\right)\), \(H_{q_{4}}=\left(\begin{array}{cc}0.1\\ 0.4\end{array}\right)\), \(D_{q_{1}}=\left(\begin{array}{cc}0.4t&0.4\\ 0.6&0.8\end{array}\right)\), \(D_{q_{2}}=\left(\begin{array}{cc}0&0.5\\ 0&0.6\end{array}\right)\), \(D_{q_{4}}=\left(\begin{array}{cc}0.2t&1\\ 1&0\end{array}\right)\), \(C_{q_{1}}=\left(\begin{array}{cc}0.9t&1+t\end{array}\right)\), \(C_{q_{2}}=\left(\begin{array}{cc}1&0.8+sin(t)\end{array}\right)\), \(C_{q_{4}}=\left(\begin{array}{cc}0.8+t&0.8\end{array}\right)\).  
The desired set is \(E=\{q_{1},q_{2}\}\) and particular exogenous input \(v(t)=\frac{16.3}{1+180t}\in\omega_{1}\). Let \(T=3\), \(\xi(t)=1.5\), \(\eta(t)=0.6\), \(\alpha=\lambda=0.6\), \(\mu_{1}=1.1\), \(\mu_{2}=1.2\), \(\mu_{4}=1.14\), \(x(0)=\left(\begin{array}{cc}0\\ 0\end{array}\right)\), and \(q(0)=q_{4}\). From condition (1) of Theorem 7, the evolution path of current node of FSM can be obtained as \(q_{4}\xrightarrow{\sigma_{1}}q_{2}\xrightarrow{\sigma_{2}}q_{1}\xrightarrow{ \sigma_{4}}q_{2}\xrightarrow{\sigma_{2}}\cdots\). According to the conditions of Theorem 7 and MDADT (13), applying the linear matrix inequality (LMI) Control Toolbox and the linear programming toolbox in MATLAB, the following results can be obtained: \(\tau_{1}=0.2799\), \(\tau_{2}=0.5435\), \(\tau_{4}=0.3848\), \(K_{1}=K_{4}=-1\), \(K_{2}=0.2\), \(v_{q_{1}}=\left(\begin{array}{cc}1+t\\ 1+t\end{array}\right)\), \(v_{q_{2}}=\left(\begin{array}{cc}1\\ 1+t\end{array}\right)\), and \(v_{q_{4}}=\left(\begin{array}{cc}1+t\\ 1\end{array}\right)\).  
The node \(q_{3}\) and node \(q_{5}\) are not in the evolution path. That is to say, the continuous subsystems at node \(q_{3}\) and node \(q_{5}\) are not activated, so they are not mentioned.
## 3 Results and Discussion
### Transverse Electrical Conductivity  
For fibers separated by a distance of less than 50 nm, the barrier potential energy is relatively low, resulting in a significant tunneling probability. Figure 13 depicts the trend of tunneling probability with signal frequency for various barrier sizes. It is observed that the tunneling probability rises with an increase in signal frequency. This holds true for all barrier sizes, as the particle's energy elevates with frequency according to Equation (17). As the barrier size diminishes, the charge can be conducted even at lower frequencies due to the reduced barrier potential energy required for passage. This observation underscores the closely packed fibers forming efficient conductive pathways along the transverse direction, leading to high electrical conductivity.  
Another important parameter that dictates the transverse conductivity arises due to fiber waviness. The term \(l_{c}\), as explained in Figure 8, is crucial in estimating the transverse conductivity of a lamina. In laminas with increased waviness, fibers tend to have multiple contacts with adjacent fibers along the longitudinal direction, thereby enhancing the probability of tunneling. The outcomes illustrated in Figure 13 correspond to a 20% fiber contact length along the fiber direction employed for this study. To understand the effect of fiber  
Figure 12: Overall inductance versus number of fibers on a log–log scale showing the saturation of the induction.  
Figure 13: Tunneling probability versus signal frequency for different barrier sizes.  
contact length \(l_{c}\), three varying degrees of fiber contact along the longitudinal direction were considered, and the results are plotted as shown in Figure 14.  
The quantum tunneling-based Monte Carlo model described in Section 2.1.2 is used to generate 1000 datasets for the transverse conductivity (\(\sigma_{T}\)) in the frequency range 100 MHz to 1 GHz. Utilizing the mean and standard deviation from the dataset, \(\sigma_{T}\) was plotted with a 95% confidence interval, as shown in Figure 15. A consistent finding across all datasets is the negligible conductivity up until \(\sim\)400 MHz. Beyond 450 MHz, an average transverse conductive of \(\sim\)12.67 S/m and standard deviation \(\sim\)1 S/m was observed, aligning with the broad spectrum of experimentally measured and simulated transverse conductivities as documented in existing literature. At frequencies above \(\sim\)10 GHz, as the longitudinal conductivity rapidly declines, the transverse conductivity becomes comparatively significant and plays a crucial factor in dictating the EMI SE of CFRP composites.  
Figure 14: Tunneling probability versus signal frequency for different fiber contact lengths (contact length shown as a percentage of the entire fiber length).  
Figure 15: Transverse conductivity with a 95% confidence interval obtained from the quantum tunneling-based Monte Carlo simulations.
# Similarity-Driven Edge Bundling: Data-Oriented Clutter Reduction in Graphs Layouts
###### Abstract  
Graph visualization has been successfully applied in a wide range of problems and applications. Although different approaches are available to create visual representations, most of them suffer from clutter when faced with many nodes and/or edges. Among the techniques that address this problem, edge bundling has attained relative success in improving node-link layouts by bending and aggregating edges. Despite their success, most approaches perform the bundling based only on visual space information. There is no explicit connection between the produced bundled visual representation and the underlying data (edges or vertices attributes). In this paper, we present a novel edge bundling technique, called _Similarity-Driven Edge Bundling (SDEB)_, to address this issue. Our method creates a similarity hierarchy based on a multilevel partition of the data, grouping edges considering the similarity between nodes to guide the bundling. The novel features introduced by SDEB are explored in different application scenarios, from dynamic graph visualization to multilevel exploration. Our results attest that SDEB produces layouts that consistently follow the similarity relationships found in the graph data, resulting in semantically richer presentations that are less cluttered than the state-of-the-art.  
Keywords:edge bundling; graph visualization; visual clutter reduction +
Footnote †: journal:
## 5 Results
These workflows enable continuous monitoring of model performance, wherein the measured data are read one hour later to calculate and record the actual performance of the models. The proposed framework provides a solution to research question 2 (RQ2), as it outlines a systematic approach to deploying machine learning models with self-supporting capabilities. This approach enables continuous monitoring and updating of models with minimal human intervention, effectively answering the research question. Another workflow, scheduled hourly, determines whether retraining is required for model families when systematic bias is detected. The criteria for determining bias are established based on control chart theory or when deviations exceeding 1.5 times the accepted accuracy are observed across the majority of samples in the recent period. In this application, a tolerance of 5 \({}^{\circ}\)C was adopted. When a newly created cross-validated model demonstrates performance superior to that for the existing set of parameters, the workflow replaces the old model in the repository and archives the previous one. This requires the storage of individual model predictions since these workflows facilitate continuous performance assessment, and new process data can contribute to ongoing updates of the models. Additional workflows are responsible for conducting the performance life-cycle assessment, including evaluating the number of models available for active consideration and monitoring the frequency and effectiveness of the retraining processes. These workflows, responsible for maintaining updated models and conducting a comprehensive performance assessment, play a crucial role in advancing the optimal selection of actions (strategies) for the decision-making process. The RL technique becomes helpful in this particular schema due to the presence of an environment that allows for the exploration of the rewards associated with various actions to be taken at the asset level. The policy defines the set of actions, and the agent systematically proposes them based on anticipated degradation, which is closely connected to the evolution of the variable estimated by the ensemble models continuously updated by the framework. The results obtained validate the potential of the proposed framework in improving predictive and prescriptive maintenance outcomes compared to the static solutions dominant in the literature. Unlike models suffering performance degradation over time due to concept drift and a lack of retraining mechanisms, our framework continuously tracks prediction errors and initiates automatic retraining processes, ensuring the relevance and accuracy of models. This autonomous mechanism has been shown to lead to significant improvements in maintaining high predictive accuracy over extended operating periods, as validated through the case study in a real industrial environment. In addition, the integration of RL into decision-making, along with new ensembles of models, has shown how maintenance policies can be dynamically optimized, leading to tangible operational benefits, such as reduced unplanned downtime and resource optimization.
# Two-Component Bayesian Hierarchical Models for Cost-Benefit Analysis of Traffic Barrier Crash [PERSON]
## 13 Optimization Results
\begin{table}
\begin{tabular}{l l c c c c c c c c c} \hline \hline  & & & & & & & & & & & \\ \hline ML2000B & State Highway & 4115 & 5 & 13 & 26 & Box-beam & 241 & 4810 & 1,377,961 & 1 \\ \hline ML90I & Interstate & 4319 & 11 & 4 & 26 & Box-beam & 470 & 1983 & 378,591 & 2 \\ \hline ML80I & Interstate & 5079 & 7 & 1 & 26 & concrete & 63 & 3617 & 373,292 & 3 \\ \hline ML80D & Interstate & 6545 & 7 & 17 & 26 & Box-beam & 241 & 5128 & 277,206 & 4 \\ \hline ML25D & Interstate & 6862 & 10 & 4 & 23 & W-beam & 1514 & 3815 & 261,031 & 5 \\ \hline ML34B & State Highway & 4058 & 8 & 4 & 24 & Box-beam & 152 & 2517 & 184,740 & 6 \\ \hline ML10B & State Highway & 3751 & 6 & 4 & 25 & W-beam & 1064 & 3670 & 145,149 & 7 \\ \hline ML35B & State Highway & 2077 & 7 & 5 & 26 & W-beam & 352 & 226 & 124,971 & 8 \\ \hline ML4200B & State Highway & 607 & 7 & 9 & 26 & Box-beam & 750 & 6719 & 114,334 & 9 \\ \hline ML20B & State Highway & 3054 & 17 & 4 & 25 & concrete & 165 & 719 & 110,451 & 10 \\ \hline ML10B & State Highway & 3728 & 7 & 1 & 26 & W-beam & 1227 & 3670 & 105,874 & 11 \\ \hline ML25I & Interstate & 6098 & 5 & 21 & 25 & Box-beam & 827 & 2943 & 83,728 & 12 \\ \hline ML25I & Interstate & 6884 & 10 & 18 & 26 & W-beam & 476 & 3653 & 81,578 & 13 \\ \hline ML80I & Interstate & 6475 & 6 & 11 & 26 & Box-beam & 269 & 5138 & 69,693 & 14 \\ \hline ML80I & Interstate & 6386 & 7 & 2 & 23 & Box-beam & 291 & 3617 & 67,923 & 15 \\ \hline ML80D & Interstate & 4960 & 5 & 0 & \textless{}22 & Box-beam & 96 & 5878 & 51,798 & 16 \\ \hline ML10B & State Highway & 3654 & 6 & 2 & 25 & W-beam & 465 & 3670 & 51,746 & 17 \\ \hline ML80D & Interstate & 6524 & 10 & 287 & 26 & Box-beam & 208 & 5128 & 48,250 & 18 \\ \hline ML90I & Interstate & 7550 & 7 & 6 & 24 & Box-beam & 858 & 1812 & 48,045 & 19 \\ \hline ML80D & Interstate & 5030 & 5 & 0 & 25 & Box-beam & 168 & 5138 & 46,531 & 20 \\ \hline ML80I & Interstate & 6395 & 5 & 0 & 25 & W-beam & 52 & 3617 & 45,940 & 21 \\ \hline ML80D & Interstate & 6379 & 5 & 0 & 25 & W-beam & 151 & 3902 & 45,319 & 22 \\ \hline ML25I & Interstate & 6894 & 5 & 0 & 22 & W-beam & 127 & 3653 & 44,608 & 23 \\ \hline ML25D & Interstate & 6872 & 5 & 0 & 20 & W-beam & 216 & 3815 & 43,602 & 24 \\ \hline ML25D & Interstate & 5831 & 5 & 0 & 25 & W-beam & 216 & 3815 & 43,600 & 25 \\ \hline Total & & - & - & - & - & & _84_ & _miles_ & - & _53,314,766_ & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The top critical highway-system barriers, sorted based on highest benefits.
# A Novel Heterogeneous Parallel Convolution Bi-LSTM for Speech Emotion Recognition
## 4 Experimental Evaluations
### Feature Extraction  
We conducted the following feature extraction for the three databases in this study. Each speech was segmented into frames with a 25 ms window and 10 ms shifting step size. Each frame was Z-normalized. To each frame, 32D Low-Level Descriptor (LLD) features, including 12D Chroma [23] and 20D MFCC [24], were extracted. The High-Level Statistical Functions (HSF), such as the mean of Chroma and the mean, variance, and maximum of MFCC, were calculated. Totally, 72 D acoustic features were used as the input of the model.
## 1 Introduction  
China's coastline covers 18,000 km. The development of international business has promoted the rapid development of China's sea transportation over the past three decades. Meanwhile, offshore accidents and risks increased fast because of the frequent economic activity. There is strong demand for the salvage departments of China to carry out the periodic replacement of new salvage equipment and extended service, with the development of technical equipment and management capability of salvage. Based on this, by taking optimal supplier selection for the purchasing of salvage ships as an example, this paper studied the special circumstances in optimal supplier selection, when the overall scores of several suppliers are similar and the optimal supplier is hard to identify.  
The methods of weight determination can be mainly divided into three types: subjective methods [1; 2; 3; 4], objective methods, and correspondence between subjectivity and objectivity methods [5; 6; 7]. In research, different methods of weight determination are supposed to be chosen for different objects [8; 9; 10; 11]. In the foreign and domestic research of index weight determination of supplier, at an early stage, the method of weight determination was mainly subjective method, such as Analytic Hierarchy Process (AHP) [12; 13], the MCDM-ANP (Multiple Criteria Decision Making-Analytic Network Process) method [14; 15], and Fuzzy Group Decision Making Method [16], but the evaluation of weight carried out on the basis of human experience, weight determination is overly dependent on expert judgement, leading to data features underutilization. In recent years, the method of combining subjective and objective was presented [17; 18; 19; 20; 21; 22]. Most of them using diversity factors of entropy and extra weights were endowed to the indexes with high discreteness as identifying highly recognizable indexes, and the identifying difficulty was relieved when the overall scores of several suppliers are similar. However, when using the Diversity Factors Method (DFM) in supplier selection, since numbers of the sample data of supplier are fewer, it may lead to over correction of weight in adjustment process, while the importance of index cannot be considered.  
Therefore, this paper makes an improvement on the DFM by presenting the Limited Diversity Factors Method (LDFM) in order to relieve the over correction of weight in adjustment process by using the DFM in supplier selection. The initial weight that was calculated by AHP can be adjusted through the LDFM and the final index weight of salvage ships supplier can be obtained. By comparing the LDFM and the DFM as well as validating the effectiveness and rationality of the LDFM through quantitative analysis, finding that the shortage of the DFM can be made up.
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 3 MC-BDP Reference Architecture
### MC-BDP Architectural Layers
#### 3.4.5 The Orchestration Layer  
MC-BDP's orchestration layer is responsible for launching, stopping, and managing containers in a cluster. It is therefore in charge of managing services deployed to containers, registering additional nodes from different clouds (or removing them), scaling the number of containers that a service runs on, controlling which containers run on which nodes, and monitoring the overall state of the cluster. A number of technologies exist for providing container cluster orchestration, the most popular of which is Kubernetes, developed by Google and later open-sourced to the wider community [83]. As Kubernetes is the most established orchestrator technology at present, and Docker swarms are becoming increasingly popular due to their simplicity and ease of installation, most container networking technologies are compatible with them (Flannel, Open vSwitch, Weave Net).  
Ref. [28] conducted a thorough survey of container orchestration systems whereby ten popular technologies were evaluated and classified according to three main categories (application model, scheduling, and cluster infrastructure and management), each with a number of sub-categories. MC-BDP is agnostic in terms of the orchestration technology used; provided that it is compatible with the container and container networking approach selected, orchestration could be implemented with any of the technologies reviewed in the aforementioned study.
# The Development of Teleoperated Driving to Cooperate with the Autonomous Driving Experience
## 6 Comparison with Existing Systems
Although the current system demonstrates robust performance in small-scale field trials, its scalability to large environments (e.g., urban or highway scenarios) has not been empirically tested. Challenges such as higher speeds, longer communication distances, and multi-agent interactions remain open for future investigation. Ongoing efforts will explore these aspects in extended deployments to assess the system's broader applicability.