esa-sceva/satcom-chunk-collection huggingface dataset:
# Hamming Code Strategy for Medical Image Sharing
## 3 Hamming Codes
\right]\]  
\[G=[Q^{T}I_{11}]=\left[ \begin{array}{cccccccccccccccc}1&1&0&0&1&0&0&0&0&0&0&0 &0&0&0\\ 1&0&1&0&0&1&0&0&0&0&0&0&0&0\\ 0&1&1&0&0&0&1&0&0&0&0&0&0&0\\ 1&0&0&1&0&0&0&1&0&0&0&0&0&0\\ 0&0&1&1&0&0&0&0&1&0&0&0&0&0\\ 1&1&1&0&0&0&0&0&0&1&0&0&0&0\\ 1&1&0&1&0&0&0&0&0&0&1&0&0&0\\ 1&0&1&1&0&0&0&0&0&0&0&1&0&0\\ 0&1&1&1&0&0&0&0&0&0&0&0&1&0\\ 1&1&1&0&0&0&0&0&0&0&0&0&1&0\\ 1&1&1&1&0&0&0&0&0&0&0&0&0&0&1\end{array} \right]\]  
An 11-bit data \(d=[d_{1}\ d_{2}\ d_{3}\ d_{4}\ d_{5}\ d_{6}\ d_{7}\ d_{8}\ d_{9}\ d_{10}\ d_{11}]\) can be encoded into a systematic 15-bit codeword \(c=d*G=[p_{1}\ p_{2}\ p_{3}\ p_{4}\ d_{1}\ d_{2}\ d_{3}\ d_{4}\ d_{5}\ d_{6}\ d_{7}\ d_{8}\ d_{9}\ d_{10}\ d_{11}]\), where the parity bits are computed as follows:  
\(p_{1}=d_{1}\oplus d_{2}\oplus d_{4}\oplus d_{7}\oplus d_{8}\oplus d_{9}\oplus d _{11}\),  
\(p_{2}=d_{1}\oplus d_{3}\oplus d_{5}\oplus d_{7}\oplus d_{8}\oplus d_{10}\oplus d _{11}\),  
\(p_{3}=d_{2}\oplus d_{3}\oplus d_{6}\oplus d_{7}\oplus d_{9}\oplus d_{10}\oplus d _{11}\),  
and \(p_{4}=d_{4}\oplus d_When an authorized doctor would like to access this patient's medical images, he/she must obtain both shadows \(S_{1}\) and \(S_{2}\). The original image reconstruction algorithm is then used to regenerate the original medical images for diagnosis. Because two shadows can be used to reproduce the two original medical images, we have to put them on two different databases. This is a security measure ensuring that any security breach happened to one server will not compromise the whole system. Without the shadow stored on the other server, the patients' health information does not leak out after the incidence. In the following sections, the shadow generation and original images reconstruction algorithms are described in detail using both (7,4) Hamming and (15,11) Hamming codes.
# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
## 4 Experiments and Results
### Results and Discussions
#### 4.2.3 Different Category Distributions Comparsion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sample** & **Attack** & **Precision** & **Recall** & **F1-Score** & **G-Mean** \\ \hline \multirow{8}{*}{Original sample} & Benign & 0.9943 & 0.9948 & 0.9945 & 0.9945 \\  & Bot & 0.9124 & 0.8363 & 0.8727 & 0.8735 \\  & DoS & 0.9111 & 0.9305 & 0.9207 & 0.9207 \\  & Infiltration & 0.8433 & 0.8028 & 0.8226 & 0.8228 \\  & DDoS & 0.9771 & 0.9765 & 0.9768 & 0.9768 \\  & Web Attack & 0.0 & 0.0 & 0.0 & 0.0 \\  & Brute Force & 0.9146 & 0.9037 & 0.9091 & 0.9091 \\ \hline \multirow{8}{*}{Hybrid sample} & Benign & 0.9983 & 0.9986 & 0.9984 & 0.9984 \\  & Bot & 0.8924 & 0.9480 & 0.9195 & 0.9198 \\  & DoS & 0.9648 & 0.9532 & 0.9590 & 0.9590 \\ \cline{1-1}  & Infiltration & 0.8469 & 0.9171 & 0.8805 & 0.8815 \\ \cline{1-1}  & DDoS & 0.9879 & 0.9827 & 0.9853 & 0.9853 \\ \cline{1-1}  & Web Attack & 0.3952 & 0.7238 & 0.5106 & 0.5347 \\ \cline{1-1}  & Brute Force & 0.9592 & 0.9438 & 0.9514 & 0.9514 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performance of the model on the CSE-CIC-IDS2018 before and after data augmentation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Test Accuracy/\%**} \\ \cline{3-6}  & & \(\alpha=0.1\) & \(\alpha=0.07\) & \(\alpha=0.05\) & \(\alpha=0.03\) & \(\alpha=0.01\) \\ \hline \multirow{4}{*}{NSL-KDD} & FedAvg & 94.13 & 86.60 & 81.00 & 76.87 & 41.21 \\  & FedProx & 94.15 & 86.37 & 80.98 & 77.19 & 41.05 \\  & FedOpt & 87.55 & 82.58 & 77.04 & 71.08 & 48.25 \\  & CGFL & **96.89** & **88.04** & **85.99** & **80.49** & **56.02** \\ \hline \multirow{4}{*}{CIC-IDS2017} & FedAvg & 89.13 & 88.00 & 86.90 & 83.21 & 70.39 \\  & FedProx & 88.37 & 88.08 & 86.45 & 83.92 & 71.21 \\ \cline{1-1}  & FedOpt & 87.19 & 83.64 & 82.50 & 77.26 & 63.27 \\ \cline{1-1}  & CGFL & **92.19** & **88.47** & **88.03** & **85.29** & **77.03** \\ \hline \multirow{4}{*}{CSE-CIC-IDS2018} & FedAvg & 87.55 & 86.51 & 80.38 & 74.74 & 57.17 \\ \cline{1-1}  & FedProx & 82.58 & 86.26 & 81.11 & 73.79 & 58.69 \\ \cline{1-1}  & FedOpt & 84.08 & 81.97 & 78.03 & 67.71 & 57.43 \\ \cline{1-1}  & CGFL & **89.08** & **88.74** & **85.28** & **79.13** & **63.43** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparison with other methods on different category distributions.
## 4 Results and Discussion  
### Experiment Settings  
Ten different test functions are employed to gauge the performance of the proposed method in order to demonstrate its usefulness. To guarantee a fair comparison, the population size in this paper is fixed at 30 and the number of iterations is set at 1000. The parameters of different algorithms that might be used are displayed in Table 1. In order to avoid random variation, tests are carried out 10 times independently.  
However, additional analysis is required to determine how well the ALGWO-BPNN-PID algorithm performs in control issues. In order to assess the superiority of the ALGWO-BPNN control technique, the four tests are also performed using the fitness value, overshoot, and settling time performances. The simulation's settings are similar. As a controller  
\begin{table}
\begin{tabular}{c c} \hline Algorithm & Values of the Parameters \\ \hline GWO & a = 2 (linear reduction during iteration) \\ SOGWO [38] & a = 2 (linear reduction during iteration) \\ PSO & \(c_{1}=2,c_{2}=2,w_{Max}=0.9,w_{Min}=0.2\) \\ DE & F = 0.5, CR = 0.5 \\ \hline \end{tabular}
\end{table}
Table 1: Parameter setups of different algorithms.  
Figure 4: The process of total algorithm.  
model, the incremental digital PID is employed. The sampling interval is set at one. The \(g(x)\) and the other function are the activation functions described above. The output control signal of the controller is restricted to [\(-\)10, 10]. Equation (21) is used to assess the algorithm's effectiveness.  
\[J=\int\limits_{0}^{\infty}{(w_{1}|e(t)|+w_{2}u^{2}(t))dt} \tag{21}\]  
where \(e(t)\) is the system error and \(u(t)\) is the controller output, and \(w_{1}\) and \(w_{2}\) are the weights, with values of 0.001 and 0.999. Furthermore, overshoot will cause the fitness to increase more quickly.
# Application of Acoustic Emission Technique in Landslide Monitoring and Early Warning: A Review
###### Abstract  
Landslides present a significant global hazard, resulting in substantial socioeconomic losses and casualties each year. Traditional monitoring approaches, such as geodetic, geotechnical, and geophysical methods, have limitations in providing early warning capabilities due to their inability to detect precursory subsurface deformations. In contrast, the acoustic emission (AE) technique emerges as a promising alternative, capable of capturing the elastic wave signals generated by stress-induced deformation and micro-damage within soil and rock masses during the early stages of slope instability. This paper provides a comprehensive review of the fundamental principles, instrumentation, and field applications of the AE method for landslide monitoring and early warning. Comparative analyses demonstrate that AE outperforms conventional techniques, with laboratory studies establishing clear linear relationships between cumulative AE event rates and slope displacement velocities. These relationships have enabled the classification of stability conditions into \"essentially stable\", \"marginally stable\", \"unstable\", and \"rapidly deforming\" categories with high accuracy. Field implementations using embedded waveguides have successfully monitored active landslides, with AE event rates linearly correlating with real-time displacement measurements. Furthermore, the integration of AE with other techniques, such as synthetic aperture radar (SAR) and pore pressure monitoring, has enhanced the comprehensive characterization of subsurface failure mechanisms. Despite the challenges posed by high attenuation in geological materials, ongoing advancements in sensor technologies, data acquisition systems, and signal processing techniques are addressing these limitations, paving the way for the widespread adoption of AE-based early warning systems. This review highlights the significant potential of the AE technique in revolutionizing landslide monitoring and forecasting capabilities to mitigate the devastating impacts of these natural disasters.  
landslides; acoustic emission; monitoring systems; early warning; development and challenges +
Footnote †: journal: Applied sciences  
1 Poly Changda Engineering Co., Ltd., Guangzhou 510620, China; [EMAIL_ADDRESS] (J.S.); [EMAIL_ADDRESS] (J.L.); [EMAIL_ADDRESS] (J.L.)  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Experimental Results
### Cross-Validation
#### 4.3.2 Stratified K-Fold  
We divided our dataset into five clusters using stratified K-fold, making sure that the proportion of each category in the training set and test set remained the same as the original data set, each cluster giving a different accuracy. Table 6 and Figure 8 show the accuracy for each cluster and the mean using stratified K-fold cross-validation. Figure 9 shows other performance metrics of the six classifiers using stratified K-fold cross-validation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Cluster 1** & **Cluster 2** & **Cluster 3** & **Cluster 4** & **Cluster 5** & **Average** \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline MLP & 79.28 & 86.42 & 88.57 & 90.00 & 93.33 & 87.52 \\ SVM & 90.00 & 88.57 & 91.42 & 92.85 & 88.14 & 90.19 \\ RF & 91.42 & 91.42 & 88.57 & 95.71 & 92.59 & 91.94 \\ Bagging & 90.00 & 87.14 & 88.57 & 94.28 & 92.59 & 90.51 \\ AdaBoost & 90.00 & 84.28 & 90.00 & 91.42 & 92.59 & 89.65 \\ Gradient & 91.42 & 87.14 & 88.57 & 94.28 & 92.59 & 90.80 \\ Boosting & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Stratified K-fold Cross-Validation Accuracy Prediction For Six Classifiers.  
Figure 6: K-fold Cross-Validation For Six Classifiers.  
Figure 7: Other performance metrics for six classifiers using K-fold cross-validation.
# Synchronization of Analog-Discrete Chaotic Systems for Wireless Sensor Network Design
## 3 Study on Synchronization Noise Immunity  
The first study on the performance of Pecora-Carroll analog-discrete and discrete-analog synchronization is synchronization noise immunity. This study aims to assess the influence of noise on the quality of chaotic synchronization in each case. The following subsections elaborate more on the simulation (using the LTspice and discrete models) and experimental (using the hardware and discrete models) setups and analyze the acquired results.
## 2 Simulation Environment and Studied Buildings
### Details of the CLT Building  
CLT construction is still not widespread in China and there are no local design standards available that can be adopted for timber office building design. Therefore, this study applies the Eurocode 5 and relevant documents [52; 53] for the CLT structure design. According to the references above, 3-layer CLT panels with the total thickness of 150 mm and 5-layer CLT panels with the total thickness of 180 mm are used for external wall design and roof design respectively. The width and length of a single panel are set to be 1200 and 18,000 mm, which satisfies the current construction module in China. When compared with the RC building, the basic dimensions of the CLT building, such as floor height, building orientation, ground areas and major functions, are the same as for the reference concrete buildings. However, the load-bearing structure and external envelope are redesigned to meet the building regulations of the different climate zones. The related design parameters for the CLT buildings in the five studied cities are presented in Tables 5-7 and Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Conductivity** \\ **(W/(mK))** \\ \end{tabular} & \begin{tabular}{c} **Density** \\ **(kg/m3)** \\ \end{tabular} & \
\begin{tabular}{c} **Specific Heat** \\ **Capacity (J/(kgK))** \\ \end{tabular} &
\begin{tabular}{c} **Resistance** \\ **(m2K/W)** \\ \end{tabular} \\ \hline Granolithic render & 6 & 0.87 & 2085 & 837 & 0.0069 \\ Concrete undried aggregate & 40 & 1.73 & 2243 & 837 & 0.0231 \\ Membrane & 0.4 & 1 & 1100 & 1000 & 0.0004 \\ Felt/Bitumen layers & 1.5 & 0.5 & 1700 & 1000 & 0.003 \\ Cement plaster & 30 & 0.72 & 1860 & 800 & 0.0417 \\ Vermiculite aggregate & 65 & 0.17 & 450 & 837 & 0.3824 \\ EPS insulation & 55 & 0.035 & 25 & 1400 & 1.5714 \\ CLT panel & 180 & 0.13 & 500 & 1500 & 1.3846 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Details of CLT roof design in Beijing.  
Figure 5: External wall and roof designs of the CLT building in the five cities.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **Thickness** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Conductivity** \\ **(W/(mK))** \\ \end{tabular} & \begin{tabular}{c} **Gas** \\ **(W/m2K)** \\ \end{tabular} & \
\begin{tabular}{c} **Convection Coefficient** \\ **(m2K/W)** \\ \end{tabular} &
\begin{tabular}{c} **Resistance** \\ **(m2K/W)** \\ \end{tabular} \\ \hline Outer pane & 8 & 1.06 & - & - & 0.0075 \\ Cavity & 6 & - & Air & 4.5382 & 0.1795 \\ Inner pane & 8 & 1.06 & - & - & 0.0075 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Details of CLT external window design in Beijing.
## 4 Conclusions  
The presented approach uses a sequential encoder-stacked decoder architecture in combination with attention (SESDA) to predict load power 24 h ahead. The training data are collected using the smallest Euclidean distance between the daily features of the forecasted day and the days in the past. For each forecast day, a fast online training is executed using the filtered most similar past data. The features included in the encoder-decoder model range from hourly weather parameters, calendar data to the load demand from the previous hour.  
The algorithm achieves the best results in comparison with the benchmark methods which include Linear Regression, a combination of Linear Regression and the usage of Neural Networks, and the Hidden Markov Model approach. Although the difference between the MAPE values of HMM and the encoder-decoder model is not as large as the difference between HMM and Linear Regression, the HMM seems less stable than the proposed architecture. However, the error reduction comes at a cost of the increased forecast execution time.  
One of the limitations of the algorithm is the requirement of the availability of pairs of consecutive daily data for the training due to the network architecture (encoder-decoder model). For data with many gaps related to whole days, the algorithm may perform less successfully. Additionally, the algorithm requires on average 96 historical time series including the previous days for the training. If the requirement is not fulfilled, the prediction error will increase depending on the number
# Flexural Wave Propagation and Defect States of Periodic Slab Track Structure in High-Speed Railway
## 1 Introduction
For the unit slab track structure, it is necessary to develop an elastic wave analysis model that integrates the multi-periodicity. The unique periodicity modifies the characteristics and formation mechanisms of wave bandgaps. The defect states within such specialized periodic structures need further investigation. Consequently, this study focuses on the slab track structure to elucidate the bandgap characteristics and formation mechanisms of flexural waves, and to investigate the defect states within the slab track structure. The paper is organized as follows: first, a model for the propagation of flexural waves in unit slab track structures is established, capable of analyzing both periodic and defective ballastless tracks. Subsequently, the bandgap characteristics, vibration transmission, and the influence of structural parameters of periodic ballastless track structures are discussed in the Section 3. The Section 4 analyzes the defect state characteristics and formation mechanisms considering local contact loss between layers of the unit slab track. Finally, conclusions are drawn in the Section 5.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 3 Algorithm Design  
This section shows how the problem can be solved using a genetic algorithm. First, we give a brief introduction to the genetic algorithm, and then define the genetic algorithm fitness function and associated operators according to the problem.
## 4 Results and Discussion
### Hydrogeochemical Simulation and Analysis
#### 4.4.2 Analysis of the Effect of pH on Minerals  
After statistical analysis, the pH variation range of the site groundwater was 7.55-9.26, so the trend of minerals in the aquifer was further determined by analyzing the effect of pH variation on the saturation index of minerals. Taking S01 as an example, the pH was taken as 7.55, 8, 8.26, 9 and 9.26, respectively, which can truly reflect the trend of dissolution and precipitation of minerals in the aquifer.  
As can be seen from Figure 9, the saturation index of dolomite, calcite, aragonite and MgCr\({}_{2}\)O\({}_{4}\) is increasing with the increase of pH, indicating that MgCr\({}_{2}\)O\({}_{4}\) was more prone to precipitation in the alkaline environment, mainly because the ionic activity product increases with the increase of pH under the condition of constant temperature, which led to the easy precipitation. With the increase of pH, the saturation index of Na\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\), K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) and CrCl\({}_{3}\) were decreasing, indicating that the dissolution of Na\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\), K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) and CrCl\({}_{3}\) was more likely to occur in the alkaline environment, mainly because the ionic activity product decreased with the increase of pH under the condition of constant temperature, which led to the dissolution to occur easily, and the saturation index of the remaining minerals was less affected by the change of pH.  
Figure 8: Comparison of saturation index (SI) at three points, (**a**) refers to chromium-containing minerals, including trivalent chromium and hexavalent chromium. (**b**) contains anhydrite, gypsum, halite, aragonite, dolomite and calcite.  
Figure 9: The influence of pH on the SI of minerals.
## 3 Results
### Zeta Potential Measurements  
Figure 9 demonstrates the streaming potential coupling coefficient (dU/dP) of Whatman filter paper and RPS. Both cellulosic sources exhibit a negative streaming potential coupling coefficient; hence, a negative zeta potential. For Whatman filter paper, there is a decrease in dU/dP with increasing pH up to pH = 6, whereas for RPS, there is a decrease in dU/dP with increasing pH up to pH = 4. In other words, RPS exhibits a larger zeta potential as its dU/dP measurements are lower than those of Whatman filter paper. The samples' isoelectric point (IEP) was estimated by extrapolation of the polynomial fit (dashed lines) of the available data, representing the pH in which the substance's water solubility is minimal. The IEP of Whatman filter paper is around pH = 3, and for RPS it is  
Figure 8: SEM imaging of the obtained NC particles (size bar is 20 \(\upmu\)m).  
Figure 7: The RFU signal increase rate for 1-5 h hydrolysis between 15–25 min of testing.  
around pH = 1. Such extrapolation allows screening of the region of interest about IEPs. Note that at pH = 3, the solution is fully titrated (0.05 M HCl); thus, it is impossible to take measurements at pH < 3. The evolution of the streaming potential coupling coefficient with pH for RPS is comparable with cotton, regenerated cellulose, and lignocellulosic fibers [75]. The contribution of the examined structure of the fiber and the tendency to swell in aqueous solutions is responsible for the differences in their zeta potential.
## 4 Overview of GNSSs
### Glonass  
GLonASS is a global satellite navigation system that provides positioning, navigation, and timing services on a continuous, worldwide basis. GLONASS receivers calculate their position in the GLONASS reference system using satellite technology, based on triangulation principles. The space segment's main functions are transmission of radio navigation signals and storage and retransmission of navigation messages sent by the control segment. The GLONASS ground segment (also referred to as the control segment or operational control system) is responsible for the proper operation of the GLONASS system [78].
# Analysis of Synchronous Generators' Local Mode Eigenvalues in Modern Power Systems
### Contribution and Paper Structure  
We can display four original contributions of the paper:  
* An estimate is made of the expected local mode eigenvalues of synchronous operating generators (the boundaries of the areas where eigenvalues for different types of synchronous generators in different operating conditions are expected).
* The impact of the operating point of synchronous generators on the local mode eigenvalue is investigated; defined are rules which help the operators to estimate under which operating conditions local oscillations are most weakly damped.
* The impact of the AVR on the local oscillations is analyzed.
* Experimental results, which are important because they confirm numerically obtained results.  
The article is organized into six sections. The linearized mathematical model of a synchronous generator connected to the infinite bus is shown in the second section. An eigenvalue analysis for synchronous generators without a control system is presented in the third section. The impact of the AVR is studied in the fourth section. Some measurement results are shown in the fifth section. In conclusion, first, a summary of the most important results is given, and second, some topics are provided for future research in the field of synchronous generator oscillations.