esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Conflict Distribution Characteristics between Motor Vehicles and E-Bikes at Roundabouts
## 2 Materials and Methods  
### Data Collection  
Depending on the central island shape and the signal control way, roundabouts can be categorized as different types and different traffic rules for vehicles and e-bikes could be different at different roundabouts [34]. In China, conventional roundabouts with heavy e-bike traffic flow and more frequent conflict are usually characterized as large radius, multi-lanes, circle central island, and four-way cross signalized intersections. For these conventional roundabouts, the two-step left turn is the most used control mode. Therefore, this research targeted the conventional roundabout with a circle central island, four-way cross shape, and two-step left turn signalized intersection as shown in Figure 1.  
To reduce the physical effects of conflicts between vehicles and e-bikes, we selected three roundabouts with similar physical characteristics in Xi'an and Xianyang. The physical specifics of each roundabout are shown in Table 1. Aerial photographs are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Name** & **Diameter** & \begin{tabular}{c} **No. of** \\ **Lanes** \\ \end{tabular} & \begin{tabular}{c} **Interweave** \\ **Angle** \\ \end{tabular} & \begin{tabular}{c} **Isolation** \\ **Angle** \\ \end{tabular} & \
\begin{tabular}{c} **Exclusive** \\ **e-Bike Lanes** \\ \end{tabular} &
\begin{tabular}{c} **Signal** \\ **Control Mode** \\ \end{tabular} \\ \hline Zhuque & 70 m & 4 & 25\({}^{\circ}\) & None & None & Two-step \\ Hanguang & 72 m & 4 & 27\({}^{\circ}\) & None & None & Two-step \\ Chenyangzhai & 79 m & 5 & 30\({}^{\circ}\) & None & None & Two-step \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of detailed information about roundabouts.  
Figure 1: The conventional roundabout.  
To determine the minimum sample size, pre-surveys during peak and flat hours were separately conducted from October 14 to 18, 2019. In total, 149 min of video data were collected. After data processing, the standard deviation of the conflicts per unit time (5 min) at the three roundabouts was 6.81. According to Equation (1), the minimum sample size was figured out.  
\[N\geq\left(\frac{SK}{E}\right)^{2} \tag{1}\]  
\(N\) is the minimum sample size, \(S\) is standard deviation, and \(K\) is constant; when the confidence level is 95%, \(K\) is 1.96. \(E\) is the margin error value; when \(K\) is 1.96, \(E\) is 1.43.  
According to Equation (1), we obtained the minimum sample size of 88. The required investigation time was at least 7.33 h. Under the condition of good weather with clear vision, we conducted a formal survey from 28 October to 8 November 2019 (10 working days in total). A total of 9.5 h of video data were obtained.
# Adaptive Multi-Channel Clustering in IEEE 802.11s Wireless Mesh Networks +
## 4 The CHaChA Clustering Approach
### Online Cluster Adaptation
#### 4.4.3 Roaming between Clusters
At the same time, it adopts the mesh configuration of the new cluster (mesh ID and channel) on its secondary interface. Possible disconnections between CM and CH could cause non-delivery of the final LEAVE/JOIN messages. However, such a situation would be handled subsequently by the isolation/failure detection. The JOIN_REQ and LEAVE_REQ message carry the MAC address of the source and destination cluster's CH, respectively, to enable the mutual decision of accepting or rejecting CMs. The cluster membership of CMs can in principle also be obtained from the information in the periodic CH announcement messages. However, these are sent on the base channel as unreliable broadcasts using UDP. In contrast, roaming negotiation is done via reliable unicast messages based on TCP. This ensures the availability of up-to-date information. Figure 6: Roaming restrictions in an example network. Reprinted from ref. [18]. Figure 7: Sequence diagram for CM roaming between clusters. Reprinted from ref. [18].
# A Method for Broken Rotor Bars Diagnosis Based on Sum-Of-Squares of Current Signals
## 4 Experimental Validation and Discussion
### Results and Discussion
#### 4.2.4 Comparison with Popular Methods of Broken Rotor Bars Diagnosis  
To illustrate the effectiveness of the SS method, this paper compares the effect of the SS method with popular methods under different load conditions, including HT, [PERSON]'s vector modulus, TEO, and d-q transform. The results are shown in Figure 8. Three results can be obtained as follows.  
1. The SS method and [PERSON]'s vector modulus have the best effect, and four signature frequency components are obvious in the spectra of the SS method under three load conditions. The spectra of the SS method and [PERSON]'s vector modulus are very similar. Theoretically, the results obtained by the two methods are consistent, but the principle of the SS method is simper than [PERSON]'s vector modulus.
2. In the spectra of classical methods, including the HT, TEO, and d-q transform methods, only two signature frequency components are discernible, and there is a strong interfering frequency component near 3\(f_{\text{brb}}\) and 4\(f_{\text{brb}}\), which is caused by equipment noise.
3. For the experimental data used in this paper, the effectiveness of the d-q transform method is unsatisfying compared with other methods.
## 3 Strategy and Performance Analysis of Spread Spectrum Code Acquisition
### Signal Model  
The spread spectrum signal waveform can be expressed as \(s(t)\), and the chip period is \(T_{c}\). The receiving end uses the root raised cosine filter to match the \(s(t)\) signal, after which the channel response has the raised cosine characteristic [25]. The signal after passing through the matched filter can be expressed as \(r(t)\), assuming that the local spread spectrum code is not strictly aligned with the received signal. Then, the normalized spread spectrum code synchronization error is \(\tau T_{c}\), where \(\tau\) is the normalized signal transmission delay. \(r(t)\)  
Figure 5: Block diagram of the receiver of burst spread spectrum signal.  
is sampled at the chip rate at time \(t=kT_{c}\). Considering the complex baseband model under an ideal Gaussian channel, the sampled baseband signal can be expressed as  
\[r(k)=As(k)e^{j(2\pi fkT_{c}+\varphi)}+n(k) \tag{1}\]  
where \(A\) is the signal amplitude, \(f\) is the carrier frequency and \(\varphi\) is the carrier phase after matched filtering. \(n(k)\) is the noise after sampling, where \(n\sim N(0,\sigma_{n}^{2})\). For detailed expressions, please refer to Appendix A.
## 3 Research Methodology
### Model Training
#### 3.7.2 Model 2--LSTM-Based Deep Learning (DL)  
In this model, our aim is to design a neural network that allows us to consider both lagged features and other related features in one deep learning model. To achieve this, we propose a neural network model that includes both LSTM and dense layers. This model was implemented in Python using Tensorflow and Keras [44]. The description of the primary components of our proposed deep learning model is as follows.  
LSTM layer: LSTM is a special type of recurrent neural network (RNN) that is first introduced by [PERSON] and [PERSON] (1997). It offers powerful capabilities for capturing complex temporal patterns and has become a valuable tool for time series forecasting due to its ability to capture intricate temporal patterns. LSTM learns from a sequence ofdata points by constructing a mathematical model that represents the relationships within the input sequence. It evaluates the data at each point in the sequence, processes them, updates their internal state, and subsequently progresses to the next time step) [45]. The LSTM architecture is illustrated in Figure 2. In this architecture, \(f_{t}\) represents a forget gate, \(i_{t}\) is an input gate, \(o_{t}\) stands for an output gate, \(c_{t}\) denotes a cell state, and \(h_{t}\) signifies a hidden state. The simplified forms of the equations are described in Equation (1).  
\[\begin{array}{c}f_{t}=\sigma_{g}\Big{(}W_{f}x_{t}+U_{f}h_{t-1}+b_{f}\Big{)}\\ i_{t}=\sigma_{g}(W_{i}x_{t}+U_{i}h_{t-1}+b_{i})\\ o_{t}=\sigma_{g}(W_{o}x_{t}+U_{o}h_{t-1}+b_{o})\\ c_{t}=f_{t}\odot c_{t-1}+i_{t}\odot\sigma_{c}(W_{o}x_{t}+U_{c}h_{t-1}+b_{c})\\ h_{t}=o_{t}\odot\sigma_{h}(c_{t})\end{array} \tag{1}\]  
where \(\sigma\) is an activation function and the operator \(\odot\) denotes the Hadamard product.  
Dense layer: A dense layer, also known as a fully connected layer, is a fundamental component of neural networks. It connects every neuron from one layer to all neurons in the next layer, with each connection having a weight learned during training. The operation of a dense layer includes multiplying input values from the previous layer by their corresponding weights, summing these weighted inputs for each neuron, and optionally adding a bias term. An activation function is applied to the output of each neuron in a dense layer, introducing nonlinearity to the neural network and enabling it to capture complex, nonlinear data relationships.
# Advances in Deep Space Probe Navigation
## 3 Exploring Further Advances in Deep Space Probe Navigation
### Spacecraft Control  
Self-learning control emerges as a new approach by incorporating a self-learning process of historical control information directly into the controller [26]. Therefore, the control algorithm is simplified, consisting merely of a learning term and an updating term. The algorithm was investigated in depth in Refs. [27; 28], which also presented methods for varying learning intensity and applications on spacecraft formation control.  
Performance-adjustable prescribed performance control has more general performance tuning capabilities, and its application to nonlinear multi-agent systems was originally proposed in Ref. [29]. In the following studies, the authors further extended this method to spacecraft attitude control systems [30; 31] and spacecraft attitude-orbit control systems [32]. This performance-adjustable approach is also applied to event triggering mechanisms in Ref. [33], which implements the trade-offs of sampling rate, convergence accuracy, and convergence rate for spacecraft attitude-orbit control systems [32].  
Ref. [34] proposes a minimum-time capture control method for the test mass release phase of drag-free spacecraft, which quickly captures the test mass to the cage center of the inertial sensor. Ref. [35] proposes a novel distributed control method for surrounding a noncooperative target that has maneuverability through spacecraft formation.
## 5 Discussion of the Results  
### Comparison in Capacity from Push-Over Analysis  
Figure 9 shows the comparison in capacity between the TF, which is the benchmark, and the dissipative frames, with and without self-centering systems. In particular, the graph reports the output of the pushover analyses in terms of base shear of each frame normalized to the maximum base shear of the TF (i.e., 390 kN) vs. top displacements of the frames normalized to the maximum top displacement of the TF (i.e., 0.46 m). Moreover, colored points are introduced to indicate the yielding and the ultimate rotations of the plastic hinges, deduced from the push-over analyses.  
The comparison outlines that the frames equipped with BCCs exhibit higher initial stiffness due to the lengthened panel zones which make shorter columns. The maximum base shear is lower than in the TF because of a lower design value of the bending moment resistance of the friction devices, which is chosen in order to comply with the capacity design criteria. Therefore, the frames with friction dampers are designed assuming a behavior factor higher than in the TF. Figure 8 also shows hardening branches after the yielding for both IF-TB and IF-FD; this response is achieved thanks to the stiffness of the CBC in the post-yielding branch of the constitutive behavior; this fact helps to solve the second-order effects. Conversely, the IF without self-centering systems, exhibits a post-yielding softening branch, which is mainly due to the damaged strength of the plastic hinge at the column base and to the elastic perfectly plastic behavior of the friction connection to the beam. In this regard, it has to be noted that the TF shows an elastic perfectly plastic capacity curve because the hardening behavior of the plastic hinge of the beam compensates for the P-effects. Finally, the RC structural members of IF-TB and IF-FD do not develop plastic hinges for global drift higher than 5% (i.e., top displacement equal to 0.3 m), which corresponds to d/d\({}_{\rm TF,max}\approx 0.65\) in the graph. For drift values higher than 5%, the friction devices attain their ultimate rotation, which is consistent with the design provisions.  
Based on the observations above, it can be stated that the adoption of friction-damping devices in MRFs is useful for achieving a not-damaged response of the RC primary bearing structure for drifts up to the ultimate rotation of the system.  
Figure 9: Capacity of dissipative frames vs. benchmark frame.
## 1 Introduction
Specifically, the multi-UAV cooperative search path planning problem is decomposed into numerous single-UAV path planning subproblems, each of which is evolved by an independent particle swarm. By designing a cooperative evolution strategy among multiple swarms, it enables them to co-evolve to find the global optimal solution. In addition, the introduction of motion coding mechanism effectively changes and extends the search space, enhances the population diversity of the algorithm, and improves the probability of searching the target. The main contributions of this paper are presented as follows: (1) We have proposed a novel particle swarm optimization algorithm incorporating multiple swarm co-evolutionary and motion encoding mechanisms, which effectively changes and expands the search space, strengthens the population diversity, and raises the probability of searching for the dynamic target. (2) Based on the problem decomposition strategy, a cooperative coevolution PSO framework is proposed for dealing with complex optimization problems, which improves the convergence speed and accuracy of the algorithm by means of parallel optimization computation. (3) A mathematical derivation of a target motion model that corresponds to a Markov process is given. The conclusion is that the initial prior information of the target will be highly attenuated and the probability distribution of the target will be extremely dispersed over time, thus enhancing the search difficulty. Based on this model, we have designed six dynamic search scenarios that are more complex compared to previous studies to assess the performance of the proposed algorithm. The rest of the paper Is organized as follows: Section 2 introduces the steps to establish an objective function. The proposed CC-MPSO algorithm is specified in Section 3. Simulation results and a discussion of searching for moving targets by multi-UAVs are offered in Section 4. Finally, conclusions and plans for future research are drawn in Section 5.
# Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization
## 2 Related Works
However, the main objective of our work is different in the sense that it attempts to combine BGWO with BPSO algorithms to enhance the exploration capability of the original BGWO algorithm by leveraging the original BPSO algorithm. A multi-objective differential evolutionary algorithm for optimizing FS was proposed in [53]. This algorithm was proposed to provide a tradeoff between the diversity and convergence of non-dominating solutions. Like our proposed work, this work addressed the problem of optimizing the FS problem. However, it did not include SI in its proposed mechanism. An improved binary pigeon-inspired optimizer was proposed in [54] and applied to the FS optimization problem. This optimizer was proposed to overcome the stuck-in-local-optima problem of the original binary pigeon-inspired optimizer [55]. The objective of this work is like our proposed approaches in the sense that it tried to overcome the effect of the local optimal solution on the optimization process. Our work is different in applying the improvement to the BGWO algorithm by combining it with the BPSO algorithm. A hybrid SI approach that combined the BGWO with BPSO algorithms was proposed in [56]. This approach is very similar to our work since it combined the same two metaheuristic approaches applied in our work. Like our work, the objective of this study was to provide a balance between exploration and exploitation of the search space. However, the way of combining the two metaheuristic approaches, i.e., GWO and PSO, differs from our proposed work since the two approaches were used in an alternative fashion, where BGWO was applied in the even iterations while BPSO was applied in the odd ones. The approaches proposed in this work combined both BGWO and BPSO algorithms together in each iteration, which, logically speaking, results in lower classification errors and better fitness values. A recent hybrid swarm intelligence metaheuristic optimization approach was proposed in [57] to optimize the FS process. This work combined the BPSO, BGWO, and tournament selection [58] algorithms. Like our work, the objective of this combination was to provide a balance between exploration and exploitation of the solution space to achieve a near-global optimal solution. Another recent hybrid SI metaheuristic optimization approach was proposed in [59] to optimize the FS problem. This work combined ACO with GWO to enhance the exploration power of GWO while preserving its strong exploitation characteristic. This is the same objective of our work with the difference in combining GWO with ACO instead of PSO. In general, there does not exist an algorithm that is able to resolve all FS optimization issues for all datasets [60]. Therefore, there is always a strong requirement to design novel algorithms, approaches, and techniques to address the FS optimization problem. This emphasizes the significance of the work proposed in this paper.
## 2 Related Work  
### Cloud-Edge Collaboration ICS Architecture and Security Challenges  
Cloud-edge collaboration ICS architecture represents a paradigm shift in industrial automation, addressing the limitations of purely cloud-based or edge-only deployments through intelligent workload distribution and resource optimization. Figure 1 illustrates the cloud-edge collaboration ICS architecture, which adopts a three-tier collaborative framework comprising the cloud platform, edge nodes, and terminal equipment [14]. The cloud platform serves as the core control center, built upon distributed server clusters and storage infrastructure that enables dynamic resource allocation, data analysis, and comprehensive task management. Edge nodes are deployed at network edges near industrial devices, providing localized computing capabilities, data processing, and device control through containerized technologies. These edge nodes act as intelligent intermediaries that bridge the cloud platform with terminal equipment. The terminal equipment consists of sensing devices, smart switches, and field equipment that perform real-time data collection, environmental monitoring, and command execution.  
While cloud-edge collaboration brings significant benefits to ICSs, it simultaneously introduces substantial security and privacy challenges that demand immediate attention. The distributed nature of this architecture expands the attack surface, creating multiple potential entry points for malicious attackers [2; 15]. Edge nodes, in particular, occupy a critical position in this ecosystem as they process sensitive operational data while directly influencing physical control processes. Attackers can exploit compromised edge nodes to launch false data injection attacks, which can manipulate sensor readings, deceive control algorithms, and ultimately lead to cascading failures, industrial espionage, and even catastrophic system-wide disruptions [4; 16].  
Edge nodes deserve special consideration from security and privacy perspectives due to their unique role as critical bridges between cloud platforms and terminal devices. They not only handle substantial volumes of sensitive industrial data but also maintain direct control over physical processes. Availability attacks (such as false data injection attacks, DDoS attacks, and replay attacks) targeting edge nodes can disrupt entire industrial operations, while privacy attacks (such as confidentiality attacks, eavesdropping attacks, and side-channel attacks) may result in the exposure of proprietary manufacturing processes, operational parameters, and competitive intelligence. Therefore, enhancing the security and privacy protection mechanisms for edge nodes is paramount to ensuring the overall safety, reliability, and trustworthiness of cloud-edge collaboration ICSs.
# An Adaptive Model Predictive Voltage Control for LC-Filtered Voltage Source Inverters
## 1 Introduction
Contrary to IAC, DAC directly calculates the control gains based on a proper adaption mechanism, eliminating the identification parts, and reducing the computational burden [4; 19; 20]. Both branches provide good performance against uncertainties and varying parameters. However, the convergence of the system and control parameters is only ensured if the required level of persistent excitation (PE) of the system inputs and outputs is available [28]. Otherwise, only the estimated parameters converge bounded values and not the desired values. Moreover, these methods use many adaption gains that are usually tuned based on trial and error. Whereas, they have a significant influence on the system performance and parameters convergence. These problems would be more severe where the number of uncertain parameters and disturbances increased. Therefore, a possibility of divergence and instability due to improper control and gain tuning always exists. This paper proposes an adaptive model predictive voltage control for an LC-filtered VSI to address the aforementioned issues associated with previously presented control methods and benefit from their advantages. Firstly, in the proposed control method, an augmented state-space model is proposed, which lumps all system structured and unstructured uncertainties in two disturbances inputs, one for inductor current dynamic and the second for capacitor voltage dynamic. Therefore, it does not need to estimate each uncertain parameter and disturbance individually, which reduces the computational burden and the required PE. Afterward, two adaptive observers are presented to evaluate disturbances inputs and make augmented model implementation possible. Finally, based on the augmented state-space and estimated outputs and disturbances, an MPC scheme is employed. MPC calculates the optimum converter voltage vector, which minimizes a de fined cost function, including tracking error, switching efforts, and overcurrent protection, under the uncertain parameters and disturbances. In summary, the proposed adaptive MPC-based voltage control keeps the fast, dynamic response and ease of digital implementation of the MPC. It also simultaneously reduces the sensitivity to model uncertainties. Moreover, the load-current-sensorless operation of MPC is provided since load currents are considered in the augmented model's disturbance inputs, which are already estimated by the observers. In the following sections, the dynamics of a three-phase LC-filtered VSI are described in Section 2. Section 3 presents the conventional MPC. The proposed control method, which includes three mains subsections, an augmented state-space model, adaptive observers, and MPC-based voltage control, is discussed in Section 4. After that, to evaluate the performance of the proposed control method and the conventional one, simulation and experimental results are presented in Section 5. Finally, this paper is concluded in Section 6.