esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Conditions  
The control algorithm is designed in Automotive Simulation Models(tm) (ASM) provided by the dSPACE(r) GmbH Software 2014-B (64-bit, Paderborn, Germany) and interacted with the MATLAB(r)/Simulink(r) R2013b (64 Bit, Natick, MA, USA). The ASM allows the multibody vehicle simulation procedures. The car model has 10 DOF. An overall software interface is presented in Figure 8.  
During the simulation, the braking processes were conducted on a straight road as well as in combination with cornering maneuvers. Different complex maneuvers, such as straight or cornering braking on a split-\(\upmu\) road surface and straight or cornering with change of the tire-road friction coefficients, were simulated. The results are introduced as a comparison of the vehicle motion with and without the activated controllers. Road variations such as dry, wet, and icy surfaces as well as their combinations were designed and simulated to prove the ABS controller robustness and its ability to hold the optimal wheel slip in different road conditions.  
The reaction of the ABS controller on the wheel slip characterizes the system adaptability. The factor can be expressed in percentage and calculated by the following equation:  
\[\omega_{p-t-p}=\frac{\omega_{\max}-\omega_{\min}}{\omega_{\max}}\cdot 100. \tag{11}\]  
Furthermore, the effect of the ABS controller performance can be described with the index of performance \(ABS_{IP}\). The variable is a ratio between the vehicle deceleration with and without the controller and is found as follows:  
\[ABS_{IP}=\frac{-a_{ABS}}{-a_{skid}}. \tag{12}\]  
First, the simulation is dedicated to split-\(\upmu\) roads. When half of the road has a significantly higher friction coefficient as compared to the other half, a high yaw moment occurs. The driver is not able to compensate properly for the yaw dynamics, and the vehicle can spin around the COG. The corresponding simulation in this study is performed for cornering and straight braking maneuvers.  
For the straight road, half wet-half dry and half icy-half wet surfaces were chosen. For the curved road, half dry-half icy and half wet-half dry surface profiles were designed.  
Next, the model was simulated on a curved road line for different tire-road friction characteristics. The road friction conditions vary during the braking process from icy and dry to wet. Afterwards, the same road conditions were applied to the straight road profile. In this experimentation part the controller robustness is studied.  
Figure 8: Simulation software interface by the dSPACE® GmbH.
## 4 Results and Discussion
### Analysis of the Impacts of Abandoned Dregs on the Bridge Project in the Abandoned Dreg Site
#### 4.2.1 Analysis of the Effect of Vertical Force Intensity on the Top of the Pile  
Firstly, the calculation is carried out without vertical force intensity at the top of the pile. Then, a vertical force intensity of 4 MPa is loaded on the top of the pile in 10 stages, and the force intensity is increased by 0.4 MPa each time. The results of each calculation are analyzed and compared. The mechanical parameters are adopted from Table 1 for the storm condition. The lateral displacement of the pile top and the maximum bending moment of the pile body under different vertical force intensities are calculated and shown in Figure 12. The results of each calculation are compared and analyzed after applying vertical force intensity to the top of the pile hierarchically.  
It can be seen from Figure 12a that the minimum lateral displacement of the top of the bridge pile is 3.91 cm when no vertical force intensity is applied. The displacement increases significantly after the force intensity is applied to the top of the pile. As the vertical force intensity is applied step by step, the inclination of the pile body is increasing, so the lateral displacement of the top of the pile shows an increasing trend. From Figure 12b, it can be seen that the maximum bending moment of the pile body increases continuously with the increase in vertical force intensity within a certain range. The maximum bending moment is 641.58318 kN-m when the vertical force intensity is 1.6 MPa, and the maximum bending moment of the pile body shows a decreasing trend after exceeding this range. Because of the loose nature of the abandoned dregs, initially, the bridge piles buckle to an increasing degree as the force intensity increases. When the vertical force intensity exceeds a certain range, the ultimate bearing capacity of the pile can be increased, and the influence of soil slip force is weakened. As a result, the bending moment of the bridge pile tends to decrease again.
# Enhancing Meteor Observations with Photodiode Detectors
## 4 Preliminary Results  
On the night of 11-12 August 2023, our proprietary meteor detection device was deployed on the roof of the observatory in Pyskowice (50.33\({}^{\circ}\) N, 18.62\({}^{\circ}\) E), which is managed by the SUTO (Silesian University of Technology Observatories) research group, which specializes in technical aspects of astronomical imaging. The device was powered and connected to the Internet, which allowed continuous data transfer of both photodiode waveforms and camera images directly to the Silesian University of Technology's servers.  
Observational conditions during the experiment were rather average. Pyskowice, being a small city, unfortunately generates some sky brightening caused by nearby streetlights and other human activity, which could have affected the quality of the observations. In most cases, the fainter meteors could not be recorded due to the elevated brightness of the sky background, which contributed to the noise present in both the camera and the photodiodes.  
Data analysis was carried out after the observations were completed. It was performed manually (visual inspection) and consisted of reviewing the photodiode waveforms and analyzing the recorded images in parallel. Places where sudden jumps in brightness values were observed in the photodiode signals were carefully analyzed in the corresponding images to detect the presence of the meteor. The results of sample detections from the first and second days of the Perseid maximum are presented in Figure 8 and Figure 9, respectively. Of the 12 waveforms presented in these charts, we have selected 6 to which we attach images captured by the camera to highlight various aspects of meteor detection (see Figure 10).  
Figure 8: Examples of meteor detections registered by our photodiode detector on the first day of Perseids maximum, 11 August 2023.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 5 Improved Scheme
### Initialization  
The initialization procedure is performed by the AS when it sets up the system parameters:  
* Let G be an elliptic curve group defined by a generator \(P\) and a prime number \(p\). The AS randomly selects \(\left\{x\in Z_{p}^{*}\right\}\) as its secret key.
* The AS computes the secure key set \(\left\{PSK_{i},i=1,...,n\right\}\) using the hash-chain method as shown in Figure 2, e.g., \(h^{2}(x)=h(h(x))\).
# Activation Process of ONU in EPON/GPON/XG-PON/NG-PON2 Networks
## 5 Simulation Results
### Simulations for ITU Standards
#### 5.1.2 Xg-Pon  
In principle, activation for XG-PON is based on GPON. However, there have been several major changes such as: downstream PLOAM messages are only sent once; States O2 and O3 are merged together into a single state; some PLOAM messages are altered; and the duration of the quiet window depends on the fiber spacing distance, which is the value determined by the difference between the fiber length of the most remote and the nearest ONU from the OLT. In our simulation, the most remote unit was set by the user with the highest possible value of 40 km. At a 20-km difference, the quiet window duration was the same as for GPON, and at a distance of 40 km, the quiet window was increased by 200 us. The nearest unit was set to 1 km. The entire activation process is described in Section 4.2, and the messages along with the transmission process are shown in Figure 5.  
The basic equation for the computation of equalization delay was the same as in the case of GPON. However, the calculation of the individual components varied, as for XGPON \(EqD_{i}\) were computed as [27]:  
\[T_{eqd}\geq RspTime_{\max}+(L_{min}+D_{max})\cdot\left(\frac{n_{1577}+n_{1270} }{c}\right), \tag{5}\]  
where \(L_{min}\) represents the minimum distance, \(D_{max}\) is the maximum differential distance and the refractive index values for the wavelengths \(n_{1577}\) and \(n_{1270}\) are given as follows: \(n_{1577}=1.4686\) and \(n_{1270}=1.4677\). RTD is defined as [27]:  
\[RTD_{i}=T_{pd}\frac{n1270+1577}{n1270}-RspTime_{i}. \tag{6}\]
## 2 Materials and Methods
### Method  
#### 2.3.1 Building of Land Degradation and Development Index (LDDI)  
LDDI was built based on the Euclidean distance method [22], which has been widely used in environmental and ecological research, such as dryness monitoring [11]. In 2D space, the Euclidean distance model can be written as follows:  
\[\rho=\sqrt{\left(\mathrm{x_{2}-x_{1}}\right)^{2}+\left(\mathrm{y_{2}-y_{1}} \right)^{2}} \tag{1}\]  
where \(\rho\) represents the Euclidean distance between points \(\left(\mathrm{x_{1},\ y_{1}}\right)\) and \(\left(\mathrm{x_{2},\ y_{2}}\right)\).  
In this study, we measured LDDI using NDVI and NPP. Before building the LDDI, we normalized the NDVI and NPP to values ranging from 0 to 1 by using the following two equations:  
\[\mathrm{NNDVI}=\frac{\mathrm{NDVI}-\mathrm{NDVI}_{\mathrm{min}}}{\mathrm{ NDVI}_{\mathrm{max}}-\mathrm{NNDVI}_{\mathrm{min}}} \tag{2}\]  
\[\mathrm{NNPP}=\frac{\mathrm{NPP}-\mathrm{NPP}_{\mathrm{min}}}{\mathrm{NPP}_{ \mathrm{max}}-\mathrm{NPP}_{\mathrm{min}}} \tag{3}\]  
where \(\mathrm{NNDVI}\) and \(\mathrm{NNPP}\) represent the normalized NDVI and NPP; \(\mathrm{NDVI}_{\mathrm{max}}\), \(\mathrm{NPP}_{\mathrm{max}}\), \(\mathrm{NNDVI}_{\mathrm{min}}\) and \(\mathrm{NPP}_{\mathrm{min}}\) represent the maximum NDVI, maximum NPP, minimum NDVI and minimum NPP.  
Then, based on the Euclidean distance method, we defined LDDI as the distance to the points with minimum NDVI and minimum NPP. Longer distances represent higher LDDI values. Specifically, Equation (1) can be written as:  
\[\mathrm{LDDI}=\sqrt{\left(\mathrm{NNDVI}-\mathrm{NNDVI}_{\mathrm{min}}\right) ^{2}+\left(\mathrm{NNPP}-\mathrm{NNPP}_{\mathrm{min}}\right)^{2}} \tag{4}\]  
where \(\mathrm{NNDVI}_{\mathrm{min}}\) and \(\mathrm{NNPP}_{\mathrm{min}}\) represent the minimum \(\mathrm{NNDVI}\) and minimum \(\mathrm{NNPP}\), respectively. Because the value of \(\mathrm{NNDVI}_{\mathrm{min}}\) and \(\mathrm{NNPP}_{\mathrm{min}}\) is 0, LDDI can be simplified as:  
\[\mathrm{LDDI}=\sqrt{\left(\mathrm{NNDVI}\right)^{2}+\left(\mathrm{NNPP} \right)^{2}} \tag{5}\]
# Artificial Intelligence in Cybersecurity: A Review and a Case Study
## 6 Enhancing Security in Low-Rate Wireless Networks
### Optimizing IEEE 802.15.4 Encryption Using Genetic Algorithms
WSN and IoT devices are often constrained by low processing power, limited memory, and restricted power supplies. This poses a challenge for securing their wireless communication, as robust encryption algorithms like WPA, WPA2, and WEP are computationally expensive and energy-intensive for these devices. AI offers promising solutions for enhancing security, However, for resource-constrained devices with low processing power and limited battery life, alternative approaches like lightweight algorithms might be more practical for real-time implementation. Our proposed approach addresses this by employing AI offline to generate encryption codes. These pre-computed codes are then used within lightweight pseudo-random noise (PN) sequence communications, enabling security within the widely used IEEE 802.15.4 protocol for WSN and IoT devices.
## 4 Results and Discussion
### Soil-Column Interaction at the Slip-Surface  
The effect of geogrid modulus on shear behavior-soil movement was studied using the finite differences method for different lengths of ordinary and encased granular columns (L/D = 8, 10 and end bearing); here L = the length of the column and D represents the diameter of the column. Figures 5-7 explain the variations of the normalized shear stress (which is the shear stress after improvement divided by the shear stress of the soil (\(\tau_{\rm imp}/\tau_{\rm soil}\))), with the normalized soil movement ((\(\delta\)/D) where \(\delta\) is the lateral soil movement) behavior of sand improved with ordinary granular columns (OGC) and geogrid encased granular columns (GECs). Different values of the modulus of elasticity have been used in the analysis as shown in Table 3. It concluded that increasing the modulus of elasticity of geogrid contributes to increasing the overall resistance of the soil against lateral movement especaiilly at the shear plane as shown in Figures 5a, 6a and 7a, the reason behind that is the overall stiffness of the granular columns was increased [21; 22; 33]. The use of geogrid encased columns can also improve the load distribution in the soil, which can lead to a reduction in the shear stress on soil particles [33; 34]. It was noticed that the largest effect of geogrid occurred on end bearing geogrid encased granular columns. The values of shear strength were increased by (17.7)%. Furthermore, it was found that the geogrid has a positive effect on the value of the lateral displacement of the column. Increasing the modulus of elasticity of the geogrid led to an increase in the overall resistance to lateral displacement as shown in Figures 5b, 6b and 7b.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Unit** & **Granular Column [29]** & **Clayey Sand [30]** & **Clay [15]** \\ \hline Model & - & Mohr-Coulomb & Mohr-Coulomb & Mohr-Coulomb \\ Unit weight & kN/m3 & 20 & 18 & 17 \\ Cohesion & kPa & 1 & 5 & 20 \\ Angle of friction & Degrees & 40 & 35 & 5 \\ Angle of dilation & Degrees & 10 & 5 & 0 \\ Modulus of elasticity & MPa & 50 & 15 & 5 \\ Poisson’s ratio & - & 0.3 & 0.3 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of the used materials in the FoS analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Elastic Modulus** & **Unit** & **Poisson’s Ratio** & **Reference** \\ \hline E1 & 0.120 & GPa & 0.33 * & [31] \\ E2 & 2.625 & GPa & 0.33 * & [32] \\ E3 & 6.552 & GPa & 0.33 * & [32] \\ \hline \hline \end{tabular}  
* Poisson’s ratio has been assumed.  
\end{table}
Table 3: Geogrid properties used for numerical analysis.
## 4 Multi-Objective Parameter Tuning
### The Multi-Objective Tuning Problem  
The problem stated in Section 3.1.1 is generalised to a multi-objective setting:  
* \(\Theta\), a possibly infinite set of \(d\)-dimensional parameter configurations;
* \(I\), a possibly infinite set of problem instances;
* \(O\), a set of \(n\) performance objectives;
* \(\mathbf{c}(\theta)=\mathbf{c}(\theta,i,O)\), an \(n\)-dimensional random vector that represents the performance of the algorithm given a configuration \(\theta\in\Theta\) on problem instance \(i\in I\) based on a set of performance objectives \(O\);
* \(\mathbf{C}(\theta)=E[\mathbf{c}(i|\theta,O)]\), the expectation of the performance of parameter configuration \(\theta\in\Theta\) for each objective in \(O\), over all instances \(i\in I\).  
The vector \(\mathbf{C}(\theta)=\left[C^{1}(\theta),C^{2}(\theta),\dots,C^{n}(\theta)\right]\) consists of the expected performance of a parameter configuration \(\theta\) based on each performance objective. The goal of a multi-objective tuning algorithm is to find a set of performance vectors \(P\) such that for each vector \(\mathbf{C}_{p}\in P\), no individual objective \(C_{p}^{i}\) can be improved without worsening at least one other objective. Such a set is known as a Pareto-optimal set.
# Surface Roughness Prediction of Bearing Ring Precision Grinding Based on Feature Extraction
## 3 Grinding Surface Roughness Feature Extraction and Fusion
### Algorithm Flow  
The process for predicting the surface roughness of bearing outer ring precision grinding, based on the feature extraction method proposed in this section, is shown in Figure 1. The specific steps are as follows:  
Step 1: Through a grinding test of thin-walled bearing rings, the vibration and acoustic emission signals in the grinding process are collected. The CEEMDAN algorithm is used to decompose each vibration and acoustic emission signal into a set of modal component IMFs, and the Pearson correlation coefficient between each IMF and the original signal is calculated. According to the size of the Pearson correlation coefficient, the first eight IMFs are retained in order from largest to smallest, removing noise and fake IMFs.  
Step 2: The time-domain, frequency-domain, and entropy features were extracted from the first eight IMFs retained, and principal component analysis (PCA) was used for fusion dimensionality reduction of the extracted features. Then, the grinding parameters and the signal features after dimensionality reduction were combined into a total feature set, which was then divided into a training sample set and test sample set.  
Step 3: The SVR parameters were set, and the SVR was trained using grid search and 5x cross-validation based on the training sample set and test sample set.  
Step 4: The trained SVR fitted the surface roughness of the thin-walled bearing ring grinding and obtained the prediction results of the surface roughness.  
Figure 1: Algorithm flow chart.
## 8 Discussion
### Technical Considerations  
* _Interoperability:_ Smooth interoperability between various blockchain systems is necessary due to the varied stakeholder profiles in the agricultural supply chain. Technical factors should take into account how well different systems work together, making it possible for data and transactions to be effectively shared and comprehended across the ecosystem.
* _Data Standards:_ For blockchain technology to be used in agriculture successfully, consistent data standards must be established. All participants can accurately read the information stored on the blockchain thanks to consistency in data formats, structures, and coding rules. In order to create a consistent and unified system, this factor becomes crucial.
* _Integration with Current Systems:_ A wide range of current databases, technology, and systems are necessary for agriculture. One technological obstacle that has to be overcome is the seamless integration of blockchain with these older systems. For a seamless transition to blockchain solutions, existing agricultural technology must be compatible and work well together.
* _Scalability:_ Making sure blockchain solutions are scalable is essential since the agricultural supply chain includes a large number of transactions and data. When developing systems, technical considerations should be directed towards creating solutions that can meet the increasing needs of the agricultural sector without sacrificing functionality.
## Nomenclature  
GPM Gaussian pseudo-spectral method  
NLP Nonlinear programming  
CVP Control vector parameterization  
LG Legendre-Gauss  
SQP Sequential quadratic programming  
AGPM Adaptive Gaussian pseudo-spectral method  
\(\varphi\) Roll angle of the robot  
\(\theta\) Pitch angle of the robot  
\(\phi\) Yaw angle of the robot  
\(u^{\prime}\) Velocity at the origin in the local coordinate system  
\(u\) Velocity along the \(x\)-axis in the local coordinate system  
\(v\) Velocity along the \(y\)-axis in the local coordinate system  
\(w\) Velocity along the \(z\)-axis in the local coordinate system  
\(\Omega\) Angular velocity at the origin in the local coordinate system  
\(p\) Angular velocity along the \(x\)-axis in the local coordinate system  
\(q\) Angular velocity along the \(y\)-axis in the local coordinate system  
\(r\) Angular velocity along the \(z\)-axis in the local coordinate system  
\(T_{\varphi}\) Velocity transformation matrix  
\(T_{\omega}\) Angular velocity transformation matrix  
\(F\) The resultant force exerted on the robot  
\(\tau\) The resultant moment exerted on the robot  
\(B\) Buoyancy  
\(G\) Gravity of the industrial robot  
\(u^{\prime}_{f}\) Velocity of water flow in the local coordinate system  
\(u^{\prime}_{r}\) Velocity of water flow relative to the robot  
\(a_{x}/a_{y}\) Longitudinal/lateral accelerations of vehicle
## 3 Parameter Optimization of PRPS and CGPR
### Parameter Optimization of PRPS
#### 3.2.2 Arrangement Angle Optimization  
The arrangement area of PRPS is also a critical parameter in the design. Eight sets of numerical simulations with different arrangement angle (i.e., 0\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\), 150\({}^{\circ}\), 180\({}^{\circ}\), 210\({}^{\circ}\), 240\({}^{\circ}\)) were performed to obtain optimal scheme, and the results are shown in Figure 12.  
Figure 11: Effect of pipe diameter on mean crown settlement (MCS) and pillar plastic ratio (PPR, red elements represent plastic zone).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Scheme ID** & \(D\) **(mm)** & \(T\) **(mm)** & \(S_{0}\) **(m\({}^{2}\))** & \(I_{0}\) **(m\({}^{4}\))** & \(E_{0}\) **(GPa)** & \(\rho_{0}\) **(kg/m\({}^{3}\))** & **Sketch** \\ \hline
1 & 89 & 4 & \(6.2\times 10^{-3}\) & \(3.1\times 10^{-6}\) & 56.7 & 3253 \\
2 & 108 & 6 & \(9.2\times 10^{-3}\) & \(6.7\times 10^{-6}\) & 62.9 & 3465 \\
3 & 127 & 8 & \(1.3\times 10^{-2}\) & \(1.3\times 10^{-5}\) & 67.1 & 3610 \\
4 & 159 & 10 & \(2.0\times 10^{-2}\) & \(3.1\times 10^{-5}\) & 67.0 & 3608 \\
5 & 178 & 10 & \(2.5\times 10^{-2}\) & \(4.9\times 10^{-5}\) & 63.3 & 3477 \\
6 & 186 & 10 & \(2.7\times 10^{-2}\) & \(5.9\times 10^{-5}\) & 61.9 & 3429 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Cross-section parameters of the pipe roof.  
It could be observed that the arrangement angle had a great effect on controlling crown settlement. The MCS decreased by 46.1% when the arrangement angle increased from 0\({}^{\circ}\) to 180\({}^{\circ}\); in comparison, the PPR merely reduced by 16.5% during the same period. This phenomenon showed good agreements with pipe diameter optimization. The MCS reduced quickly until the arrangement angle reached 150\({}^{\circ}\). Thus, the arrangement angle could be optimized as 150\({}^{\circ}\).  
According to the above parameter optimization of PRPS, the MCS was well controlled with the implementation of PRPS. However, the rock pillar was still broken, and the plastic zone penetrated the rock pillar. Therefore, CGPR should also be applied to improve the mechanical properties of the rock pillar.