esa-sceva/satcom-chunk-collection huggingface dataset:
# The Modeling of Time Series Based on Least Square Fuzzy Cognitive Map
## 2 Preliminaries
### Fuzzy Cognitive Map  
FCM can be understood as a graphical representation of the knowledge. One FCM consists of concepts (nodes) and directed weights. An example of an FCM with three concepts is illustrated in Figure 1. Concepts, also called nodes, \(C_{1},C_{2},\cdots,C_{c}\) represent the main features of the mapped system. The directed weights \(w_{ij}\) labeled with fuzzy values show the strength of the causal conditions between the concepts. The square matrix \(W=[w_{ij}]\) is the weight matrix and \(i,j=1,2,\ldots n\), where \(w_{ij}\in[-1,1]\). If \(w_{ij}>0\), it means that the increase in value of concept \(C_{j}\) leads to an increase in concept \(C_{i}\), and vice versa. If \(w_{ij}<0\), it means that the increase in value of concept \(C_{j}\) leads to a decrease in concept \(C_{i}\) and vice versa. If \(w_{ij}=0\), it means that there is no relationship between \(C_{j}\) and \(C_{i}\).  
Given an FCM including \(c\) concepts. The activation state values \(A\) and the weight matrix \(W\) are expressed as  
Figure 1: The framework of a fuzzy cognitive map including three concepts.  
\[A=\left[\begin{array}{ccc}A_{11}&A_{12}&\cdots&A_{1c}\\ A_{21}&A_{22}&\cdots&A_{2c}\\ \vdots&\vdots&\vdots\\ A_{n1}&A_{n2}&\cdots&A_{nc}\end{array}\right]\text{, }W=\left[\begin{array}{ccc}w_{11}&w_{12}& \cdots&w_{1c}\\ w_{21}&w_{22}&\cdots&w_{2c}\\ \vdots&\vdots&\vdots\\ w_{c1}&w_{c2}&\cdots&w_{cc}\end{array}\right]\]  
with \(c\) being the number of concepts and \(n\) being the number of samples. The elements in each row of the matrix \(A\) are the state values of concepts at the corresponding time. The primary mission for constructing an FCM focuses on learning the weight matrix.  
The reasoning process of the FCM is generally described as following,  
\[A_{(t+1)i}=f\left(\sum_{j=1}^{c}A_{tj}w_{ji}\right) \tag{4}\]  
where \(A_{tj}\) is the state value of \(i\)th concept at time \(t\), \(A_{(t+1)i}\) is the state value of \(i\)th concept at time \(t+1\). In this equation, \(w_{ji}\) is the fuzzy weight which shows the value of the influential intensity from concept \(C_{j}\) to \(C_{i}\). The activation function \(f(\cdot)\) is a nonlinear monotonically increasing function which squashes the weighted sum of the concepts' states into a certain interval. One of the most widely used activation functions is the unipolar sigmoid function as given in following,  
\[f(\text{x})=\frac{1}{1+e^{-\lambda x}} \tag{5}\]  
where \(\lambda>0\) is the shape parameter of the function. The state value of the sigmoid function is affected depending on these parameters.
## 1 Introduction
The proposal presented here improves the size of the delay that can be controlled compared to some related works found in the literature, such as [14; 24; 27; 28]. It is important to highlight that the observer-predictor structure proposed in the present work allows stabilizing the kind of systems previously mentioned with a larger delay than the observer-predictor control proposed in [11; 24]. This improvement is achieved due to the fact that the new proposed observer-predictor in the present work has a different structure, as well as the fact that it has two free parameters for the design of the observer. Moreover, necessary and sufficient conditions are presented for the proposal in contrast with some strategies found in the literature, where necessary and/or sufficient conditions are not derived [19; 29]. This paper is organized as follows. Section 2 describes the kind of system to be studied. In Section 3, the necessary preliminary results are shown; these results will be used later in the main results of this paper. Section 4 presents the main results, that is, the estimation strategy and the design of the control strategy. In Section 5, the step tracking and step disturbance rejection of the proposed scheme is analyzed. In Section 6, a robustness analysis with respect to uncertainty in the time delay is presented. In Section 7, three simulation examples implemented in Matlab-Simulink software illustrate the performance of the proposed strategy and finally, conclusions are presented in Section 8.
# A Reaction Microscope for AMO Science at Shanghai Soft X-ray Free-Electron Laser Facility
## 2 Apparatus Setup
### Data Acquisition System  
To detect the fragment ions and electrons in coincidence, a fast data acquisition system is required. Two MCP detectors equipped with delay-line anodes are employed to measure the arrival time and impact position of the charged fragments [45]. For ion detection, to have a high collection efficiency for Coulomb explosion studies, a pair of larger MCPs (120 mm diameter) is employed with a square delay-line anode (DLD 120, Roentdek). For electron detection, as a result of the small time-of-flight differences, a hexagonal anode is of significant importance in reducing the dead time and increasing detection efficiency [14]. The effective detection area is limited by the diameters of the MCPs and anode about 80 mm (Hex75, Roentdek). It should be noted that a mesh with the same parameter described before is placed in front of each detector to generate a post-acceleration voltage so as to increase the MCP detection efficiency.  
The delay-line readout relies on a fast and precise timing measurement. The data-acquisition system combines the data collected from each detector into complete events, which are stored for offline analysis. The positive signals from the MCPs are inverted and then amplified by fast-timing amplifiers (ORTEC FTA820) along with signals from the delay-line anodes. The amplified signals (gain 200) are inputs to the constant-fraction discriminator (ORTEC QUAD935) with a minimum pulse-pair dead-time of 15 ns to discriminate the signal and convert it into a signal in NIM format, which can minimize the influence of changes in the size and shape of the analog signal on signal processing.  
Figure 3: Schematics of REMI including the geometric dimensions (unit: mm) of the REMI. Trajectories of ions and electrons with different emission angles are shown as colored lines.  
A time-to-digital conversion module (Roentdek TDCSHPi) containing two cards is used to convert the input NIM signal into a digital signal. Each card contains eight, high-precision acquisition channels and each channel can perform multi-hit response with a high time resolution of 35 ps RMS with 25 ps LSB (Least Significant Bit). Taking the trigger signal as the time-zero point, all time signals belonging to the same event are stored in the data packet as a whole. The data stored in event-by-event mode can reconstruct complete information of the particle momentum in the offline analysis. The analysis code is embedded in the Go4 (GSI Object Oriented On-line Off-line system) analysis environment (GSI, 2018) based on CERN's ROOT (CERN, 2018). Besides, in the testing phase, we also use the software package CoboldPC (Computer Based Online-offline List-mode Data analyzer for PC) supported by RoentDek to realize the data acquisition and data analysis. The timing resolution achieved is about 1 ns and the position resolution of these detectors is typically about 0.1 mm, which can be improved a bit via careful calibration.
# The Current State of Research of Wire Arc Additive Manufacturing (WAAM): A Review
## 2 Materials, Processes and Methods
### Materials
#### 2.4.2 Aluminum
The use of well-studied filler metals avoids the tendency of aluminum to form hot cracks, but in order to be able to use the advantages of age-hardenable aluminum alloys, new filler materials have to be developed. For example, [PERSON] et al. developed and qualified a new TiB-doped filler metal for additive manufacturing, which can be processed without cracking and shows a significant increase in strength through aging [288]. [PERSON] et al. showed the age-hardening of a novel high-strength Al-Zn-Mg-Cu alloy [252]. This alloy exhibits a light anisotropy in its mechanical properties as well, and [PERSON] et al. showed the feasibility of additive manufacturing of EN-AW6016 [254]. The aluminum WAAM-made parts exhibit anisotropy behavior in their mechanical properties [55; 58; 249; 250; 258] and meet the mechanical properties given by the wire manufacturers, and post-weld heat treatments are possible [289]. Taken together, it can be concluded that arc-based additive manufacturing is possible and that the mechanical properties are similar to those of standard welded metal. Furthermore, the results show that the typical problems of aluminum welding, such as pore formation or susceptibility to hot cracking, can be overcome.
## 2 Materials and Methods  
### Mechanical Fatigue Testing  
The test constructs were subjected to fatigue testing through dynamic bending in accordance with ASTM F1717. As shown in Figure 1a, each construct consisted of four pedicle screws (Ti-6 Al-4V, 4.0 mm diameter, 30 mm length) and two titanium rods (Ti-6 Al-4V, 5.5 mm diameter, 120 mm length) inserted into ultra-high molecular weight polyethylene (UHMWPE) test blocks to simulate a vertebctomy. Both fully threaded (FT) and partially unthreaded (PU) pedicle screws were tested, and the screws and rods had been pre-treated by sandblasting and anodization. For the fatigue test, the UWMWPE blocks were clamped in an MTS 370 machine (MTS Systems Corporation, Eden Prairie, MN, USA) and a compressive force applied.  
Figure 1: (**a**) American Society for Testing and Materials (ASTM) F1717 standard configuration. (**b**) Two different setups were evaluated: (i) pedicle screw fully inserted into the test block with an exposed length of 3.6 mm (EXP-FT-01 and EXP-PU-01) and (ii) pedicle screw inserted leaving 7.6 mm of the screw shaft exposed (EXP-FT-02 and EXP-PU-02).  
A previous study by our institute [16] determined the critical condition for pedicle screw insertion as having the threaded portion exposed by 1 or 2 threads to accommodate rod placement and ensure alignment between the tulip of the screw and the rod. Two different setups were evaluated (Figure 1b): (i) pedicle screw fully inserted into the test block with an exposed length of 3.6 mm [16] (EXP-FT-01 and EXP-PU-01) and (ii) pedicle screw inserted leaving 7.6 mm [16] of the screw shaft exposed (EXP-FT-02 and EXP-PU-02).  
Loading was applied in a cyclic sine wave at a frequency of 5 Hz with a load ratio of 0.1 (minimum load divided by maximum load). Static testing was first used to determine the ultimate load for the EXP-FT-01 model as 340 N [16]. In accordance with ASTM F1717, loading for fatigue testing should begin at 50% of the ultimate load, which is 170 N for the EXP-FT-01 construct. Therefore, for all test setups (Figure 1b), loading began at 170 N and was incrementally increased after every third sample (170 N to 190 N to 220 N) until either the construct underwent permanent deformation or failed or the number of cycles reached 5,000,000 cycles. Otherwise, the load level was decreased every 3 samples until sample run-out. The maximum and minimum loads and the number of cycles sustained were used to calculate the fatigue strength for each test setup.
## 2 Methodology
### Convergence and Termination
#### 2.2.2 Particle Swarm Optimization  
Particle Swarm Optimization (PSO) is an optimization technique inspired by the social behavior of birds or fish. In PSO, each particle, representing a potential solution, explores the search space based on its velocity and adjusts its position according to its own experience and that of its peers. This study uses PSO for inter-cluster power allocation, considering Quality-of-Service (QoS) factors and energy consumption. Additionally, Adaptive Particle Swarm Optimization (APSO) is introduced for intra-cluster resource allocation, optimizing node distance and energy load [19].  
The velocity and position updates of each particle are governed by the following equations:  
\[v_{i}^{(t+1)}=w\cdot v_{i}^{(t)}+c_{1}\cdot r_{1}\cdot\left(p_{i}^{\text{best} }-x_{i}^{(t)}\right)+c_{2}\cdot r_{2}\cdot\left(g^{\text{best}}-x_{i}^{(t)} \right), \tag{24}\]  
\[x_{i}^{(t+1)}=x_{i}^{(t)}+v_{i}^{(t+1)}, \tag{25}\]  
where  
* \(v_{i}^{(t)}\): velocity of particle \(i\) at iteration \(t\);
* \(x_{i}^{(t)}\): position of particle \(i\) at iteration \(t\);
* \(p_{i}^{\text{best}}\): personal best position of particle \(i\);
* \(g^{\text{best}}\): global best position in the swarm;
* \(w\): inertia weight, controlling the balance between exploration and exploitation;
* \(c_{1},c_{2}\): cognitive and social coefficients, respectively;
* \(r_{1},r_{2}\): random numbers uniformly distributed in \([0,1]\).  
By adjusting the parameters \(w\), \(c_{1}\), and \(c_{2}\), the PSO algorithm adapts its search behavior to balance exploration and exploitation.  
In this study, APSO modifies the inertia weight \(w\) dynamically to improve convergence. The adaptive inertia weight is defined as  
\[w=w_{\text{max}}-\frac{(w_{\text{max}}-w_{\text{min}})\cdot t}{T_{\text{max}}}, \tag{26}\]  
where \(w_{\text{max}}\) and \(w_{\text{min}}\) are the maximum and minimum inertia weights, \(t\) is the current iteration, and \(T_{\text{max}}\) is the maximum number of iterations.
## 5 Simulation Analysis
### Risk Assessment Simulation  
According to the meteorological data and power flow data at 12:00, combined with other relevant real-time data, a comprehensive risk assessment of the eight distribution lines at 13:00 is carried out. The risk assessment values are shown in Table 3 and the risk assessment value is \(L\ =\ [0.450\ 0.623\ 0.329\ 0.719\ 0.516\ 0.522\ 0.360\ 0.260]\). The comprehensive vulnerability rate and risk values of each distribution line is shown in Figure 7. The three types of risk indicators for each distribution line are shown in Figure 8, as shown by data:  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Lines** & \(P_{k}^{t}(Y_{2})\) & \(P_{k}^{t}(Y_{3})\) & \(P_{k}^{t}(Y_{4})\) & \(P_{k}^{t}(Y_{5})\) & \(P_{k}^{t}(X)\) & \(\eta_{k,1}^{t}\) & \(\eta_{k,2}^{t}\) & \(\eta_{k,3}^{t}\) & \(\eta_{k,4}^{t}\) & \(\eta_{k,5}^{t}\) & \(\eta_{k,sit}^{t}\) & **F(k,t)** \\ \hline
45–46 & 0.125 & 0.04 & 0.027 & 0.009 & 0.239 & 0.25 & 0.459 & 0.214 & 0.425 & 0.3 & 0.238 & 0.420 \\
67–68 & 0.150 & 0.045 & 0.027 & 0.004 & 0.275 & 0.625 & 0.307 & 0.214 & 0.54 & 0.525 & 0.343 & 0.518 \\ DL-B & 0.063 & 0.045 & 0.021 & 0.003 & 0.148 & 1 & 0.18 & 0.539 & 0.18 & 0.05 & 0.096 & 0.230 \\
9–12 & 0.178 & 0.045 & 0.023 & 0.0096 & 0.265 & 0.325 & 0.395 & 0.388 & 0.74 & 0.5 & 0.364 & 0.534 \\
70–71 & 0.240 & 0.039 & 0.029 & 0.0091 & 0.343 & 0.25 & 0.231 & 0.257 & 0.27 & 0.175 & 0.163 & 0.450 \\
29–39 & 0.147 & 0.034 & 0.019 & 0.0037 & 0.223 & 0.375 & 0.325 & 0.366 & 0.45 & 0.47 & 0.311 & 0.465 \\ DL-A & 0.058 & 0.020 & 0.013 & 0.007 & 0.116 & 1 & 0.187 & 0.429 & 0.18 & 0.075 & 0.103 & 0.207 \\ DL-C & 0.071 & 0.039 & 0.015 & 0.005 & 0.138 & 1 & 0.151 & 0.217 & 0.18 & 0.075 & 0.076 & 0.204 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vulnerability analysis of distribution network in ice weather.  
Figure 6: Two methods for predicting distribution line faults.
# Quantum Game Application to Recovery Problem in Mobile Database
## 4 Discussion
### Simulation Results
#### 4.2.2 Experiment Two  
Aim: The second set of experiments evaluated the suggested recovery model's performance as a function of mobility rate in terms of actual execution time. The suggested recovery model is based on the creation of a knowledge base that is created once and used to choose the best suitable procedure based on the reward matrix and dominant equilibrium method. The decision is done here on the basis of the integration of three utility functions that serve as performance and evaluation benchmarks for the candidate protocols.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Meaning** \\ \hline Channel type & Channel/Wireless Channel \\ MAC type & Mac/802\_11 \\ Radio-propagation model & Propagation/Two Ray Ground \\ Network interface type & Phy/wireless Phy \\ Interface queue type & queue/drop tail/priqueue \\ Antenna model & Antenna/Omni Antenna \\ Link layer type & LL \\ Routing protocol & Destination-Sequenced Distance Vector (DSDV) \\ Coordinate of topology & 670 m \(\times\) 670 m \\ Max packet in _ifq_ & 500 \\ Time to stop simulation & 250 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation Parameters.  
Figure 5: Execution time as a function of mobility rate.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm/Log Size** & **5 KB** & **30 KB** & **60 KB** & **90 KB** & **120 KB** & **150 KB** \\ \hline Log Management & 1 & 1.3 & 1.8 & 2.5 & 3.3 & 3.5 \\ Mobile Agent & 1.1 & 1.5 & 1.9 & 2.4 & 3 & 3.15 \\ Hybrid Method & 1.2 & 1.6 & 1.7 & 2.3 & 2.8 & 3 \\ Proposed Model & 1 & 1.4 & 1.8 & 2.3 & 2.7 & 2.75 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Execution time (Seconds) as a function of log file size.
###### Abstract  
This study examines railway network resilience through a complex network analysis of both the physical infrastructure and service operations. The objectives were to quantify the resilience characteristics of China's railway network, assess its performance under different disturbance types, and evaluate recovery strategy effectiveness. Network resilience was measured through accessibility, invulnerability, and restorability dimensions. The simulation results demonstrate that targeted disruptions cause significantly more damage than random disturbances, particularly when high-betweenness nodes are affected. Statistical analysis confirmed that betweenness-based recovery strategies outperform degree-based and random approaches. These findings provide a quantitative foundation for infrastructural planning, emergency response protocols, and resource allocation in railway systems.  
Keywords:railway transport network; network resilience; topological structure; railway simulation analysis; resilience optimization +
Footnote †: journal: Applied sciences
## 4 Overview: Signal Processing Methods
### Dimensionality Reduction Methods
#### 4.6.2 Principle Component Analysis (PCA)  
Principal Component Analysis (PCA) is a statistical technique used for dimensionality reduction and data visualization. It identifies principal components, which are orthogonal vectors that capture the maximum variance within the dataset. By mapping data onto these components, PCA transforms high-dimensional data into a lower-dimensional representation while retaining essential variability.
# Studying the Impact of Different TCP DoS Attacks on the Parameters of VoIP Streams
## 4 Results and Discussion
### Results for Only Voice Streams
#### 4.1.6 Discussion of the Obtained Results for the Voice-Stream study  
The obtained results showed something surprising. During all attacks, Asterisk could be accessed through a browser. This was proven by the results for the numbers of different TCP packets. Even during the TCP SYN attack, which is the most burdensome DoS attack, Asterisk was accessible. In terms of the aim of this work, the study showed that the different TCP DoS attacks did not have much impact on either the parameters of the voice flows or on the flows themselves. No call dropping was observed during the study. From the obtained results, it was found that during the TCP SYN attack, the value of the jitter increased, compared to the control result. This increase, however, was very far from the maximum allowed average jitter value of 30 ms. In the other three attacks, the jitter value was close to the jitter value in the control measurement.  
Figure 21: RTD between VM_3 and Asterisk.  
Figure 22: RTD between VM_4 and Asterisk.  
Measurement of the round-trip delay between Asterisk and each of the other subscribers showed that the individual TCP DoS attacks had no impact on Asterisk's performance. The expectation was that during the attacks, pings to the VoIP server should have been impossible--the expected results were either \"Destination unreachable\" or huge delay values. As can be seen from the graphs, there were no lost measurement packets or huge latency values. On the contrary, the RTD values were very far from the allowable delay value of 150 ms per direction.  
There were no packets lost because the root causes of packet loss, high jitter levels and high network delays, were not present. As can be seen from the presented graphs, the values of both parameters (jitter and delay) were low.  
It should be mentioned that during the measurement, the Asterisk \"Responsive Firewall\" feature was enabled. Because of this functionality, the effects of the various DoS attacks were neutralized. This functionality is powerful, and the results show that the \"Responsive Firewall\" protects the system very well from the various TCP DoS attacks, as they do not affect either the parameters of the voice streams or the voice streams themselves.  
The single spikes with large values above 20 ms that can be observed in the graphs showing the variation in jitter values, as well as the single spikes in the round-trip-delay measurement, are due to an increase in the delay in the modeled network. This increased delay was due to moments of high computational load on the workstation used for modeling the network and all processes in it. Such spikes are always observed in this type of research and real-time traffic modeling; the goal is for there to be as few of these spikes as possible and for them to be of small amplitude.
# LDPC Codes on Balanced Incomplete Block Designs: Construction, Girth, and Cycle Structure Analysis
## 3 Girth and Cycle Structure Analysis of BIBD-LDPC Codes  
We now consider a BIBD-LDPC code whose parity-check matrix is the incidence matrix (**I**) of a given BIBD\((\kappa,1;v)\). In this section, we analyze the cycle structure of BIBD-LDPC codes, determine their girth, and present a method for counting cycles in their Tanner graphs. Specifically, We investigate how the combinatorial properties of BIBDs influence the cycle distribution in the Tanner graphs of these codes, potentially providing valuable insights into their iterative decoding performance.
## 4 Modeling and Results
### Metrics
#### 4.2.2 Map  
MAP [32] is a comprehensive metric widely used in classification and information retrieval tasks, particularly when evaluating models in contexts with imbalanced datasets or in multilabel scenarios. Unlike accuracy, which considers only correct predictions, mAP provides a deeper insight into the quality of the model's ranking and its ability to distinguish between classes. However, it is more complex to compute and interpret compared to simpler metrics, like accuracy, and it is computationally intensive for large datasets or multiclass problems.  
MAP measures the area under the precision-recall curve, which plots precision against recall. By considering both precision (the proportion of true positive predictions among all the positive predictions) and recall (the proportion of true positives identified out of all the actual positives), mAP provides a balanced view of the model's performance. Thus, the higher the mAP value, the better the model's performance is. The process for calculating mAP involves several steps, as follows:  
1. Calculating precision [33] and recall [34] for each class;
2. Using the computed values to plot the precision-recall curve [35] for each class;
3. Determining the area under each precision-recall curve, representing the average precision for that class;
4. Finally, calculating the mean of all the average precision values across all the classes.  
Considering that mAP is commonly applied in multiclass classification tasks and scenarios with a high degree of class imbalance, we used it for the models' assessment in our research, as one of the main metrics.