esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Modeling of Ropes and Large Floating Structures Based on the ANCF
### Equilibrium Equation  
A general multibody system comprises a series of rigid and flexible bodies subject to motion constraints and forces. Given that the initial end of the cable is treated as a constrained fixed point and the floating structure is suspended at this end, the Lagrange multiplier formula is employed to define generalized constraint forces [41]. The kinematic constraints among various components of the multibody system are expressed in vector form, indicated by \(\mathbf{\Phi}(\mathbf{q},t)=0\). \(\mathbf{\Phi}\) represents the vector of linearly independent constraint equations. This numerical formulation enables constraints between rigid and flexible bodies, modeled using the Absolute Nodal Coordinate Formulation [42]. Taking the second derivative of \(\mathbf{\Phi}(\mathbf{q},t)=0\) with respect to time produces \(\mathbf{\Phi}_{q}\dot{\mathbf{q}}=-\mathbf{\Phi}_{t}\), and there is the following:  
\[\mathbf{\Phi}_{q}\mathbf{\ddot{q}}=-\mathbf{\Phi}_{tt}-\left(\mathbf{\[PERSON]}_{q }\dot{\mathbf{q}}\right)_{q}\dot{\mathbf{q}}-2\mathbf{\Phi}_{qt}\dot{\mathbf{q}} \tag{46}\]  
where \(\mathbf{\Phi}_{t}\) represents the partial derivative of the constraint vector with respect to time.  
\[\mathbf{Q}_{c}=-\mathbf{\Phi}_{tt}-\left(\mathbf{\Phi}_{q}\dot{\mathbf{q}} \right)_{q}\dot{\mathbf{q}}-2\mathbf{\Phi}_{qt}\dot{\mathbf{q}} \tag{47}\]  
Subsequently, forces are applied to each constrained node to satisfy the equilibrium equation in the specified form:  
\[\begin{bmatrix}\mathbf{M}&\mathbf{\Phi}_{q}^{T}\\ \mathbf{\Phi}_{q}&\mathbf{0}\end{bmatrix}\begin{bmatrix}\mathbf{\ddot{q}} \\ \mathbf{\Lambda}\end{bmatrix}=\begin{bmatrix}\mathbf{Q}_{\text{int}}+\mathbf{Q}_ {ext}\\ \mathbf{Q}_{c}\end{bmatrix} \tag{48}\]where \(\mathbf{M}=\mathbf{M}^{i}+\mathbf{M}^{k}\) is the global assembly of the total mass matrix of the cable and floating structure; \(\mathbf{q}\) is the vector of generalized coordinates containing different sets of absolute coordinates; \(\mathbf{\Phi}_{q}(\mathbf{q},t)=0\) is the Jacobian matrix associated with the constraint equation vector \(\mathbf{\Phi}\) related to the generalized coordinates; and \(\boldsymbol{\lambda}\) is the vector of Lagrange multipliers. This equation can be utilized to solve for the acceleration vector \(\mathbf{\tilde{q}}\) and the Lagrange multipliers \(\boldsymbol{\lambda}\). With a given set of initial conditions, integrating the acceleration vector yields the velocity and position.
## 4 Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(P_{d}\)} & \multirow{2}{*}{\(T_{h}\)} & \multicolumn{4}{c}{**WGA**} & \multicolumn{4}{c}{**WAA**} & \multicolumn{4}{c}{**Proposed**} \\ \cline{3-14}  & & **MOOSPA** & **LE** & **ME** & **FE** & **MGOSPA** & **LE** & **ME** & **FE** & **MGOSPA** & **LE** & **ME** & **FE** \\ \hline \multirow{8}{*}{0.7} & 10 & 161.284 & 0.178 & 0.022 & 2.907 & 159.976 & 0.179 & 0.022 & 2.883 & **42.109** & 0.397 & 0.414 & 0.345 \\  & 50 & 89.038 & 0.169 & 0.035 & 1.580 & 87.357 & 0.171 & 0.036 & 1.549 & **42.825** & 0.310 & 0.539 & 0.234 \\  & 100 & 76.199 & 0.178 & 0.042 & 1.341 & 74.278 & 0.182 & 0.042 & 1.305 & **44.584** & 0.287 & 0.596 & 0.209 \\  & 200 & 62.837 & 0.181 & 0.050 & 1.089 & 61.656 & 0.188 & 0.056 & 1.061 & **44.348** & 0.249 & 0.609 & 0.193 \\  & inf & 68.324 & 0.960 & 1.066 & 0.159 & 67.166 & 0.429 & 1.054 & 0.159 & **45.686** & 0.278 & 0.580 & 0.245 \\ \hline \multirow{8}{*}{0.8} & 10 & 143.707 & 0.146 & 0.014 & 2.596 & 141.189 & 0.147 & 0.014 & 2.550 & **36.082** & 0.354 & 0.336 & 0.314 \\  & 50 & 76.610 & 0.138 & 0.021 & 1.370 & 74.895 & 0.139 & 0.022 & 1.337 & **36.768** & 0.270 & 0.440 & 0.224 \\  & 100 & 62.928 & 0.140 & 0.025 & 1.117 & 60.841 & 0.143 & 0.026 & 1.077 & **37.263** & 0.237 & 0.476 & 0.197 \\  & 200 & 52.996 & 0.141 & 0.034 & 0.927 & 50.991 & 0.149 & 0.041 & 0.884 & **38.128** & 0.211 & 0.521 & 0.168 \\  & inf & 64.178 & 0.939 & 1.024 & 0.125 & 60.241 & 0.357 & 0.923 & 0.166 & **36.427** & 0.237 & 0.461 & 0.197 \\ \hline \multirow{8}{*}{0.9} & 10 & 125.013 & 0.130 & 0.007 & 2.264 & 123.187 & 0.130 & 0.007 & 2.230 & **32.107** & 0.295 & 0.322 & 0.256 \\  & 50 & 63.179 & 0.127 & 0.016 & 1.131 & 61.794 & 0.128 & 0.017 & 1.104 & **34.063** & 0.238 & 0.421 & 0.194 \\ \cline{1-1}  & 100 & 50.815 & 0.127 & 0.017 & 0.905 & 49.684 & 0.129 & 0.018 & 0.883 & **34.298** & 0.198 & 0.451 & 0.169 \\ \cline{1-1}  & 200 & 44.269 & 0.126 & 0.017 & 0.785 & 43.164 & 0.132 & 0.026 & 0.757 & **32.148** & 0.182 & 0.440 & 0.141 \\ \cline{1-1}  & inf & 65.936 & 0.915 & 1.094 & 0.088 & 54.893 & 0.300 & 0.801 & 0.191 & **34.398** & 0.210 & 0.441 & 0.180 \\ \hline \hline \end{tabular} The bold values indicate the best MGOSPA result in each row.  
\end{table} 
Table 2: Tracking performance with different values of \(P_{d}\). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(\sigma_{v}\)} & \multirow{2}{*}{\(T_{h}\)} & \multicolumn{4}{c}{**WGA**} & \multicolumn{4}{c}{**WAA**} & \multicolumn{4}{c}{**Proposed**} \\ \cline{3-14}  & & **MGOSPA** & **LE** & **ME** & **FE** & **MGOSPA** & **LE** & **ME** & **FE** & **MGOSPA** & **LE** & **ME** & **FE** \\ \hline \multirow{8}{*}{0.1} & 10 & 117.961 & 0.118 & 0.002 & 2.141 & 117.082 & 0.119 & 0.002 & 2.125 & **29.261** & 0.276 & 0.310 & 0.217 \\  & 50 & 60.364 & 0.117 & 0.004 & 1.091 & 59.793 & 0.118 & 0.005 & 1.080 & **29.522** & 0.218 & 0.360 & 0.173 \\  & 100 & 51.333 &
## 5 Discussion and Future Works
Model updates are aggregated at fog nodes using secure techniques such as secure aggregation or differential privacy, guaranteeing the confidentiality of individual contributions. Subsequently, these aggregated updates are recorded on the blockchain, providing a transparent and tamper-proof record of all transactions and interactions within the network. This innovative integration of FL and blockchain technology not only mitigates the risk of data breaches but also reinforces the trustworthiness of the CIDS ecosystem. Through its commitment to privacy-preserving practices and transparent transaction recording, our proposed system stands as a testament to its dedication to maintaining trust and safeguarding user privacy in the realm of intrusion detection. In future works, our CIDS will continue to evolve to address emerging threats such as data poisoning, model inversion attacks, and model inversion attacks. To mitigate the risk of data poisoning, where adversaries inject malicious data into the training process to compromise model integrity, our system will incorporate advanced anomaly detection techniques and robust data validation mechanisms. Additionally, to counter model inversion attacks, which exploit vulnerabilities in machine learning models to infer sensitive data from model outputs, we will implement enhanced privacy-preserving techniques such as secure enclaves and differential privacy to protect against information leakage. Moreover, to innovate new consensus mechanisms that enhance the existing PoW algorithm, our future research will explore novel approaches such as proof of stake (PoS) or proof of authority (PoA) to improve scalability, energy efficiency, and overall system security. These enhanced consensus mechanisms will not only bolster the resilience of our system against adversarial attacks but also optimize resource utilization and facilitate seamless network operation. Through these future works, our CIDS aims to stay at the forefront of intrusion detection technology, ensuring robust security and privacy protection in dynamic and evolving threat landscapes.
## 3 Test Methods
### Data Acquisition and Processing  
According to the Nyquist sampling theorem, the sampling frequency should be at least twice the maximum vibration frequency. In this study, the 2D001 accelerometer sensor had a maximum sampling frequency of 500 Hz, which was significantly higher than the effective vibration frequency at the site (<250 Hz), ensuring sufficient data sampling.  
The data acquisition time was set to 5 s to capture the complete vibration process of the train passing through the tunnel, ensuring that the entire vibration wave profile was recorded.  
High-pass and low-pass filters were applied to eliminate noise and irrelevant frequency components, preserving the valid vibration signals. The raw data were normalized to eliminate systematic errors between different measurement points, ensuring consistency across the dataset. Outliers and noise were removed to maintain the integrity and accuracy of the data. A band-pass filter (0.25-100 Hz) was used to eliminate irrelevant frequency components, retaining only the valid vibration signals.
# Terrain Irregularity Sensing by Evaluating Feet Coordinate Standard Deviation
### Numerical Terrain Feature Evaluation Methods  
Another way of evaluating terrain roughness is by fitting a plane to a surface and estimating an error from the surface roughness [27]. Also, using the Fourier analysis method to obtain a vectorial evaluation of a rough surface is possible. Moreover, in the paper [28], terrain roughness is measured by using metrics like crossability, coverability, etc. Unfortunately, this method has not yet been tested on actual robots, so it is hard to tell whether or not this would be useful.  
Finally, the most widely used method to describe rough terrain is a root-mean-square (\(RMS\)) parameter [29]. The main parameter is the \(RMS\) height. Other parameters are the \(RMS\) deviation, slope, autocorrelation length, and adequate slope.  
The main and simplest parameter is the \(RMS\) height. It is given by the following:  
\[\zeta=\left[\frac{1}{n-1}\sum_{i=1}^{n}\left(z(x_{i})-\bar{z}\right)^{2}\right] ^{\frac{1}{2}}, \tag{3}\]  
where \(n\) is the number of sample points, \(z(x_{i})\) is the height of the surface at point \(x_{i}\), and \(\bar{z}\) is the mean height of the profile overall [29]. The RMS height, generally speaking, gives the terrain height from its mean profile and is the most basic and simplest terrain roughness estimate. The mean profile of the terrain can be calculated by the following:  
\[\bar{z}=\frac{1}{Area(R)}\iint\limits_{R}z(x,y)dxdy, \tag{4}\]  
where \(R\) is the rectangular base of the step field. In this case, this parameter gives an average height of the terrain in a rectangular area of size \(x\) by \(y\). This can be calculated to any other profile as necessary.  
Another parameter, root-mean-square deviation, is defined as the RMS difference in height between points separated by a lag or step and is calculated using Equation (5). This parameter shows the height difference between two points of two adjacent steps. In other words, this parameter represents terrain height variation.  
\[v(\Delta x)=\left\{\frac{1}{n}\sum_{i=1}^{n}\left[z(x_{i})-z(x_{i}+\Delta x) \right]^{2}\right\}^{\frac{1}{2}}. \tag{5}\]  
It is a function of the lag or step [29].  
Finally, another commonly reported parameter is the \(RMS\) slope, \(s_{rms}\), which is the RMS deviation divided by the step length, \(\Delta x\):\[s_{rms}=\frac{v(\Delta x)}{\Delta x}. \tag{6}\]  
This parameter gives a general representation of the terrain profile inclination. It is common for the \(RMS\) slope to be reported in degrees [29].
## 4 Validation and Evaluation
### Experimental Results and Analysis Based on Self-Collected Data Sets
#### 4.1.3 Results and Analysis of the LOS and NLOS Cases When the Distance between TX and RX Is Certain  
The experimental results of the SM-TCNNET model for human activity recognition on LOS and NLOS are shown in Tables 1 and 2. The 1 st, 2 nd, 3 rd, and 4 th are the results of the four folds, respectively, and then the average value is calculated at the end. Our SM-TCNNET model shows better classification performance for all human activities. As can be seen from Table 1, the average scores of all four metrics reached above 99 on LOS. As can be seen from Table 2, on NLOS, the average scores of all four metrics reached above 97. It indicates that our SM-TCNNET model has excellent classification performance for the human activity recognition task.  
The confusion matrix of the SM-TCNNET model proposed in this paper for the LOS case on the self-harvested dataset is shown in Figure 11, where the main diagonal line indicates the recognition accuracy of human activities. It can be seen from Figure 11 that all human activities were classified correctly, and similar actions could be distinguished perfectly. The confusion matrix in the NLOS case is shown in Figure 12; it caused reflections, scattering, and attenuation on the signal transmission path due to the presence of occlusion between TX and RX, making the signal propagation path more complex and diverse. Two of the sample actions for walking were incorrectly predicted. As a whole, the accuracy of the model can meet the requirement of identifying human activities. This indicates that the SM-TCNET model proposed in this paper can simultaneously capture the spatiotemporal characteristics within the WiFi CSI signal and accurately achieve the recognition of human activities.
# Using Machine Learning for Web Page Classification in Search Engine Optimization
## 6 Evaluation Results
\begin{table}
\begin{tabular}{c c c c c} \hline
**Classifier** & \multicolumn{2}{c}{**Contingency Table**} & \multicolumn{2}{c}{**McNemar \(X^{2}\)**} & \(p\)**-Value** \\ \hline J48 & 35 & 68 & 9.9417 & 0.001616 \\ \hline SVM & 35 & 68 & 6.75 & 0.009375 \\  & 40 & 61 & 6.75 & 0.009375 \\ \hline LogReg & 40 & 63 & 5.703 & 0.01694 \\ \hline KNN & 44 & 59 & & \\
35 & 66 & 5.6277 & 0.01768 \\ \hline NaiveBayes & 36 & 67 & & \\
60 & 41 & & & \\ \hline \end{tabular}
\end{table} 
Table 8: Results of the McNemar test for comparison of used classification algorithms with baseline classification in the majority class. Figure 3: Comparison of accuracy of classifiers using 10-folds’ cross validation with baseline accuracy (red line). Considering the results of 10-folds' cross validation of classifiers (\(n=10\)), the W value was calculated for each pair of classificatory baseline accuracy. The obtained \(p\)-values are shown in Table 9.  
The \(p\)-values in Table 9 show that all the tested classifiers scored significantly better than baseline accuracy. Somewhat worse results were obtained for Naive Bayes, but still significantly better in comparison to baseline accuracy. This confirmed our hypothesis. For analyzing the differences between classification accuracy of the built classification models, the Friedman test was used [66]. It is a non-parametric test that is known as an alternative to the parametric ANOVA test. By using the Friedman test, the existence of a significant difference in the accuracy of the built classification models was confirmed at the significance level of 0.05 (\(p=0.0051\)). The additional post hoc Nemenyi test was used to determine between which pairs of classifiers the significant differences existed [67]. The results are shown in Table 10.  
From these results one can observe that significant differences exist between Naive Bayes and j48 decision tree algorithm (\(p<0.05\)) and between Naive Bayes and KNN.
# Research on Panorama Generation from a Multi-Camera System by Object-Distance Estimation
## 3 Proposed Method
### Proposed Inverse Panoramic Imaging Model
#### 3.2.2 Inverse Panoramic Imaging Model  
Let point \(P\) be projected on the cylinder surface, which is captured from the \(i^{th}\) camera and mapped to point \(p_{i}\) in the real image \(I_{i}\). The ideal image model can be derived based on a traditional collinear equation [11], as shown in Equation (3).  
\[\begin{bmatrix}u_{i,p}\\ v_{i,p}\\ 1\end{bmatrix}=\lambda K_{i}R_{i}\begin{bmatrix}X_{P}-X_{i}\\ Y_{P}-Y_{i}\\ Z_{P}-Z_{i}\end{bmatrix} \tag{3}\]  
where \(\lambda\) is the scale factor, \(K_{i}\) is the inner orientation matrix of the \(i^{th}\) camera, \(\left(u_{i,p},v_{i,p}\right)\) is the undistorted coordinate of \(p_{i}\). \(\begin{bmatrix}X_{P}&Y_{P}&Z_{p}\end{bmatrix}^{T}\) and \(\begin{bmatrix}X_{i}&Y_{i}&Z_{i}\end{bmatrix}^{T}\) are the cylindrical  
Figure 2: Transformation from panoramic plane to cylinder.  
coordinates of points \(P\) and \(C_{i}\), respectively. In addition, the cylindrical projection point \(P\) satisfies Equation (4):  
\[X_{P}^{2}+Y_{P}^{2}=r^{2} \tag{4}\]  
where \(r\) represents the cylindrical radius.  
The panoramic cylindrical surface is unwrapped onto a panoramic plane, with \(o-xy\) being taken as its panoramic planar coordinate system, as illustrated in Figure 2. Point \(P\) is mapped to point \(p\) based on Equation (5), which is derived from Equations (3) and (4).  
\[\begin{bmatrix}u_{i,p}\\ v_{i,p}\\ 1\end{bmatrix}=\lambda K_{i}R_{i}\begin{bmatrix}rsin\frac{\pi x_{p}}{L}-X_{i} \\ rcos\frac{\pi x_{p}}{L}-Y_{i}\\ 2\pi r/L\big{(}W/2-y_{p}\big{)}-Z_{i}\end{bmatrix} \tag{5}\]  
where \(x_{p},y_{p}\) stand for the coordinate of point \(p\) in the virtual image in the x- and y-directions, respectively.
## 1 Introduction  
Compact semiconductor terahertz (THz) emitters are required for the development of portable spectroscopic THz imaging systems. Such systems are of great demand for non-harmful imaging and spectroscopy in biomedical, industrial and security fields [1]. Field-effect transistors (FETs) can operate as a compact detector [2; 3; 4] or emitter [5; 6; 7] for THz waves, when incoming radiation creates voltage between transistor channel terminals, or the opposite case where voltage applied along the channel excites THz radiation. For example, resonant THz emission of tunable frequency from 2 DEG plasma oscillations can be excited by the strong electric field along HEMT channel [8], while its frequency could be controlled by a gate bias. In this light, GaN, which is suitable for high-power electronics, is a promising material. However, strong electric field also can activate other THz generation mechanisms such as shallow impurity emission or increased thermal radiation, which would deteriorate the emission spectra of resonant plasmonic emitter [9]. Therefore, the investigation, which would help to find the way to manage the unwanted THz signal, is very important. It was shown recently that 450 V/cm electric field pulses of only 8 \(\upmu\)s in duration can elevate the temperature of AlGaN/GaN heterostructure lattice significantly even if the sample is in liquid helium [8]. On the other hand, THz electroluminescence from shallow impurities invoked by a strong electric field can interplay with thermal radiation. A local minimum of emission signal was observed for AlGaN/GaN HEMT structures on sapphire cooled down to cryogenic temperatures and biased with voltage pulses of different amplitudes and durations [10]. It was concluded that the electrical pumping power above 0.22 W causes additional heating of the device, which leads to the suppression of impurity related emission signal. However, the value of the temperature from which the thermal emission mechanism is activated and dominates the emission spectrum was not defined.  
In this work, THz emission from AlGaN/GaN structure intended for high electron mobility transistors grown on sapphire substrate was investigated. The electrically excited emission was measured at different sample temperatures and the suppression of the signal was observed at \(T_{\mathrm{s}}=155\) K (electrical power of 0.72 and 0.33 W for \(U_{\mathrm{P}}=60\) V and 90 V, respectively). To understand such a behavior, the emission spectroscopy was performed and theoretical spectra were calculated at different sample temperatures. It was found that at sample temperatures above 155 K, the thermal emission from lattice dominates the spectra, and this is caused mainly by the thermal radiation from sapphire substrate. However, at sample temperatures below 155 K the thermal radiation is weak and electroluminescence is caused by another radiative mechanisms. We measure directly, that thermal radiation from the sample contributes to the THz spectrum of the electroluminescence at much higher average electrical power than indicated in the [8] measuring hot-electron temperature and electron plasma radiation.
## 7 Architecture Validation
### Resource Consumption  
Since the previously described microservices are independently scalable, there is a need for some of the metrics that can be used to trigger a scale-out or scale-in operation. Any high-scale traffic and workloads applied to the system will be reflected directly in the CPU and memory consumption of each microservice. The measurement units used to benchmark and draw decisions are mCPU units for CPU usage and MB for memory.  
The term \"mCPU\" stands for milliCPU, representing one-thousandth (1/1000) of a CPU core. Essentially, 1000 mCPU equals one full CPU core. This unit is preferred because, depending on the hardware configuration where the system is deployed, there can be a highly dynamic set of hardware CPUs, making it complicated to determine the exact amount of CPU to reserve due to differences in hardware configurations, whether virtual or physical CPUs. Using mCPU standardizes CPU resource allocation regardless of the underlying hardware variations.  
This sub-section details the resource consumption of the proposed system inside the cluster in testing scenarios. Obtaining a baseline is mandatory for proper data analysis and performance testing. The baseline was made by averaging the CPU and memory consumption across all microservices over an observation period of 3 h while the system was in an idle state. Measurements were made at 30 min intervals.  
To have a benchmarking scale, all CPU and memory consumption metrics recorded are correlated with a limit of 250 m CPU units and 250 MBs of memory. Those hard limits on resources were imposed to observe how close to a critical state the microservices would get.