esa-sceva/satcom-chunk-collection huggingface dataset:
# The Security Perspectives of Vehicular Networks: A Taxonomical Analysis of Attacks and Solutions
## 4 Security Solutions
### Solutions for EV Infrastructure  
EVs are the part of the vehicular networks and future generation transportation systems; it is necessary to provide security solutions for EV-based vehicular networks. Though the amount of research is less in this direction, some of the significant contribution are mentioned here. A privacy preserving scheme for EVs is researched in [126]. It uses Randomized Anonymous Credentials (RAC). The committed hashchains are used as one of the parameters. Some blockchain-based feasibility in security solutions of EV infrastructure are studied in [127; 128]. Spatio-temporal parameters, roles, sessions, and environment of operation have been considered as parameters. A flatness control and rule-based algorithm for EVs are shown in [129]. It uses fuzzy logic to control the system and optimum value. It is more used for energy management in EV infrastructure. Use of lattice-based cryptography and SWIFFT hashing for EV communication is investigated in [130]. The use of lattice cryptography provides the less computational and communication complexity and moreover, the approach is post-quantum attack resistant. Another novel research has been found in this direction of EV-based security by using wavelet decomposition method [131]. A modified SVM is used for stability and proper classification of the security and insecurity of the incidents. The summarization of these solutions are shown in Table 9.
# An Exploratory Review of Microplastic Pollution, Associated Microbiomes and Pathogens in Water
## 2 Materials and Methods
### Statistical Analysis of Socio-Economic Correlations  
To explore the potential relationships between economic factors and microplastic (MP) contamination, we conducted a correlation analysis between the country-level gross domestic product per capita (GDP per capita, nominal, year 2023) and the average MP concentration (particles/m\({}^{3}\)). The analysis was performed separately for different aquatic environments, namely rivers, lakes, and sea/bay. Pearson's correlation coefficients were calculated using R software (version 4.4.3). Scatter plots with fitted linear regression lines and 95% confidence intervals were generated to visually illustrate the relationships. GDP data were obtained from the World Bank database.
## 2 Problem Formulation  
In the case of optimal load dispatch, the values of some or all of the control variables need to be specified to optimize (minimize or maximize) a predetermined objective function. The quality of a solution depends on the model's accuracy under study. In general, an OPF optimization problem can be defined as follows [9]:  
\[\text{Min }J(x,\ u) \tag{1}\]  
\[\text{Subject to}:\ g(x,\ u)=0 \tag{2}\]  
\[h(x,\ u)\ \leq\ 0 \tag{3}\]  
where \(x\) is the vector of the state variables (dependent variables). This vector includes the power of the reference generator, the voltage of the bus bar, the reactive power of the generators, and the load of the transmission lines. Therefore, \(x\) can be expressed as follows:  
\[x^{T}=\left[P_{G_{1}},V_{L_{1}},\ldots,V_{L_{NPQ}},Q_{G_{1}},\ldots,Q_{G_{NG}},S_{l_{1}},\ldots,S_{l_{NTL}}\right] \tag{4}\]  
where \(NPQ\), \(NTL\), \(NG\), are the number of load bus bars, the number of transmission lines, and the number of generators, respectively.  
In addition, \(u\) is the vector of the decision variables. The primary control variables commonly used for this purpose include the generators' output voltage and active power, the transformers' settings, and the capacity of the reactive power compensators, which can be expressed as Equation (5):  
\[u^{T}=\big{[}P_{G_{2}},\ldots,P_{G_{NG}},V_{G_{1}},\ldots,V_{G_{NG}},Q_{C_{1}}, \ldots,Q_{C_{NC}},T_{1},\ldots,T_{NT}\big{]} \tag{5}\]  
where \(NC\) and \(NT\) are the number of generators and the number of reactive power compensators.
# The Impact Assessment of Water Supply DMA Formation on the Monitoring System Sensitivity
## 3 Results and Discussion  
### Model Network  
#### 3.1.1 First Phase Analysis  
Figure 7 shows an exemplary contour plot of networks with the smallest (\(A\)) and the highest (\(L\)) degree of complexity and the size of the pressure head changes caused by the opening of the hydrant. The range of correct pressure readings by the installed manometers was assumed as 0.8 m H\({}_{2}\)O. The open hydrant was marked by a red dot in Figure 7. In the case of the \(A\) network, the assumed range of reduced pressure covers two nodes, and in the case of the \(L\) network, three nodes.  
Figure 6: Location of the additional pressure manometers and flow meters.  
Table 1 summarises the number of nodes located in the area of the pressure head lowered by at least 0.8 m H\({}_{2}\)O after opening the H1, H2, and H3 hydrants. Figure 8 presents graphs showing the average number of these nodes depending on the total length of the pipes, the number of loops, and the fractal dimension of the analysed network.  
In analysing the data presented in Table 1 and Figure 8, it should be stated that the range of reduced pressure area caused by a large leak (opening of a fire hydrant) is relatively small, limited to a few nodes. There is no clear dependence of the number of these nodes on the complexity of the network. In 11 cases (opening of the H3 hydrant), this number is 0,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Network** & \(A\) & \(B\) & \(C\) & \(D\) & \(E\) & \(F\) & \(G\) & \(H\) & \(I\) & \(J\) & \(K\) & \(L\) \\ \hline Total length (m) & 2400 & 2400 & 2400 & 2600 & 2600 & 2600 & 2700 & 2800 & 2900 & 3200 & 3200 & 4000 \\ \hline Number of loops (-) & 0 & 0 & 0 & 1 & 1 & 1 & 4 & 4 & 4 & 8 & 8 & 16 \\ \hline Fractal dimension (-) & 1.010 & 1.013 & 1.039 & 1.055 & 1.056 & 1.044 & 1.070 & 1.075 & 1.069 & 1.092 & 1.109 \\ \hline Open hydrant & \multicolumn{8}{c}{The number of nodes detecting a pressure drop equal to or above 0.8 m H\({}_{2}\)O} \\ \hline H1 & 2 & & & 10 & 6 & & & & & & & \\ H2 & 4 & 1 & 8 & 10 & 3 & 7 & 7 & 3 & 10 & 3 & 6 & 3 \\ H3 & 0 & 5 & 7 & 6 & 0 & 7 & 7 & 4 & 10 & 3 & 7 & 3 \\  & & 0 & 0 & & & 0 & 0 & 0 & 0 & 0 & 0 which suggests that in the conditions of using pressure gauges with a measurement error of \(\pm\)2%, an indication of a location of a leak, even as large as in the paper, will be subject to high uncertainty or even impossible. The solution to this problem may be a significant increase in the accuracy and number of pressure gauges, and the parallel use of other types of sensors, e.g., measuring the water flow rate or acoustic sensors.
# Robust Optimization for Household Load Scheduling with Uncertain Parameters
## Simulation Result
### Complete Robust Schedules  
In order to tackle the problems caused by the uncertainties, the presented robust optimization method is used and the complete robust schedules can be obtained, under which the comfort constraints will not be violated regardless of what values the uncertain parameters take. To achieve this, all the robust parameters (\(\Gamma\)) in the robust optimization formulation should be taken as the maximum values. Figure 5a,b give the actual temperature ranges of the indoor air and hot water under the complete robust schedules.  
Figure 4: **(a)** The actual temperature set of the air in room; and **(b)** the actual temperature set of the hot water in tank.  
Figure 5: **(a)** The actual temperature range of the air in room (under the complete robust schedules); and **(b)** the actual temperature range of the hot water in tank (under the complete robust schedules).  
Compared with Figure 4a, Figure 5a indicates that the AC turns on heating during the many periods during which the indoor temperature may be lower than the lower threshold, so that the comfort constraints are guaranteed. Additionally, compared with Figure 4b, Figure 5b shows that the water is intelligently preheated to a higher temperature during the period of 3 a.m. to 6 a.m. before the extra hot water usage. The effect of doing this is very obvious and attractive: firstly, it benefits from the low electricity price during such periods; secondly, the higher water temperature withstands the influence caused by extra hot water usage. Overall, the complete robust schedules absolutely satisfy all comfort constraints for both the AC and WH. This proves the feasibility and effectiveness of the proposed robust optimization in handling household load scheduling with uncertain parameters.  
The schedules for other appliances in the complete robust schedules are shown in Figure 6. From Figure 6, it can be seen that complete robust schedules optimally rearrange all schedules and redesign the charging and discharging plans for the battery. This rescheduling leads to low electricity payment (12.75 yuan) by shifting loads to the period with low electricity prices. In spite of this, to guarantee the comfort constraints, the total electricity bill has inevitably increased by 2.94 yuan when compared with that under deterministic optimization (9.81 yuan).
# Model Test Analysis of Groundwater Level Fluctuations on
## 3 Test Materials and Methods
### Physical Modelling Principles
#### 3.2.4 Monitoring Systems  
The deformation of the cover soil and the pressure of the karst cavity are monitored according to the test requirements of this paper. The displacement meter sensor model DMWY-100 is used for the determination of the cover soil deformation, with a compact size suitable for small-scale indoor model tests. The output is highly sensitive and can resolve displacement changes of less than 0.005 mm; has wide range, high accuracy, small drift, and stable performance; stainless steel full bridge housing, which is corrosion resistant and pressure resistant; technical parameters are shown in Table 1. The pressure in the karst cavity is monitored using a levelogger 5 barometric pressure sensor to monitor changes in pressure during supply and drainage. A video camera is used to record the deformation to collapse of the overburden on the karst bedrock, with a focus on capturing the deformation, intensification, and end of the karst collapse caused by rainfall and lateral recharge, water supply, and drainage.
# Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network
## 2 Materials and Methods
### Proposed Architecture
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Layer** & **Parameters** & **Activation** & **Description** \\ \hline
1. Convolution (3 \(\times\) 3) & Filter size = 64 & ReLU & This layer uses 32 filters with size 3 \(\times\) 3. It extracts local features from the input image by convolving the filters over the input. \\
2. Batch Normalization (BN) & & & \\
3. Max Pooling & & & \\
4. Convolution (5 \(\times\) 5) & Filter size = 128 & ReLU & This layer applies larger filters (5 \(\times\) 5) to capture broader features. Increasing the number of filters to 128 enhances the model’s capacity to learn detailed representations. \\
5. Batch Normalization (BN) & & & \\
6. Transposed Convolution (7 \(\times\) 7) & Filter size = 256 & ReLU & This layer further processes the data with larger receptive fields, allowing the network to capture more contextual information. \\
8. Batch Normalization (BN) & & & \\
9. Max Pooling & & & \\
10. Convolution (7 \(\times\) 7) & Filter size = 128 & ReLU & This layer continues to build on the feature extraction capabilities with a focus on larger spatial relationships. \\
11. Batch Normalization (BN) & & & \\
12. Transposed Convolution & & & \\
13. Convolution (5 \(\times\) 5) & Filter size = 256 & ReLU & This layer enhances the feature representation by applying a higher number of filters, focusing on detailed aspects of the input data. \\
14. Batch Normalization (BN) & & & \\
15. Max Pooling & & & \\
16. Convolution (5 \(\times\) 5) & Filter size = 128 & ReLU & This layer refines the features extracted in previous layers, allowing for a more focused representation. \\
17. Batch Normalization (BN) & & & \\
18. Transposed Convolution (5 \(\times\) 5) & Filter size = 256 & ReLU & The final convolution layer in this sequence applies a large number of filters to consolidate the learned features into a comprehensive representation. \\
20. Batch Normalization (BN) & & & \\
21. Max Pooling & & & \\
22. Convolution (7 \(\times\) 7) & Filter size = 64 & ReLU & This layer further processes the data with larger receptive fields, allowing the network to capture more contextual information. \\
23. Batch Normalization (BN) & & & \\
24. Transposed Convolution (3 \(\times\) 3) & Filter size = 128 & ReLU & A smaller filter size is used here to refine the output of previous layers, focusing on specific details. \\
26. Batch Normalization (BN) & & & \\
27. Max Pooling & & & \\
28. Convolution (3 \(\times\) 3) & Filter size = 256 & ReLU & This final convolution layer enhances the feature representation before attention mechanism and classification. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed layers of the proposed MSCNN model.
## 5 Experimental Results
Figure 26.0 EM sensors installed on the landmark F-3 marker and the bendable surgical tool end tip. In order to compare the rigid, straight-type endoscope and curette used in conventional TSA operations with the proposed bendable endoscope and surgical tool, a phantom-based mock tumor removal experiment was conducted. Figure 32 shows the experiment as conducted using rigid type endoscope and curette. The endoscope shown in Figure 32a was a Sinuscope Nasal Endoscope, with \(0^{\circ}\) and dimensions of \(4\) mm \(\times\) 175 mm. The curette had an outer diameter of \(4\) mm and was manufactured by SHANZA. Tumors in the observation region could be removed, while those in its blind region could not be accessed, as they were not visible. The proposed bendable endoscope and bendable surgical tool, as well as the available observation region, are shown in Figure 33b. Here, tumors in the previously blind region could be seen and removed. Finally, a phantom experiment using the surgical navigation system was conducted. Figure 34a illustrates the situation just before the surgical tool entered a nostril. At the same time, the end tip position of the surgical tool is shown by the red circle on the screen of the surgical navigation system in Figure 34b. We were able to confirm that there was very good synchronization between the navigation system and tool being operated, throughout serial surgical tool manipulations. The attached video clip demonstrates the performance of the navigation system, including its warning and tool tracking functions. Figure 26.1 Results for the proposed bendable endoscope and curette in phantom experiment. (**a**) experiment on the use of proposed bendable endoscope and bendable surgical tool. ; (**b**) blind region removal using bendable endoscope and bendable surgical tool. Figure 26.2 Results of using conventional rigid endoscope and curette in phantom experiment. (**a**) experiment on the use of conventional straight endoscope and surgical tool. ; (**b**) blind region caused by using a conventional straight endoscope and surgical tool.
## 2 Theory  
### Joint Reconstruction of an Image and a Motion Model  
Motion-compensated reconstruction was accomplished using the GRICS method [6; 14]. The algorithm solves alternatively and iteratively for both a motion-corrected image \(\rho\) and the parameters of a motion model \(\alpha\). The software corrects for either rigid or non-rigid body motion. The problem is formulated as follows:  
\[\begin{cases}\rho,\alpha=\operatorname*{arg\,min}_{\rho,\alpha}||E(u_{\alpha}) \rho-m||^{2}+\lambda||\rho||^{2}+\mu R(\alpha),\\ u_{\alpha}(x,y,z,t)=\begin{cases}[x,y,z,1]\alpha(t)\ (\text{rigid model}), \text{or}\\ \alpha(x,y,z)s(t)\ (\text{non-rigid model})\end{cases}\end{cases} \tag{1}\]  
where \(m\) stands for the acquired MRI \(k\)-space data, \(u_{\alpha}\) is the displacement field at each \(k\)-space sample time, and \(E\) is the encoding operator (a complete description of the encoding operator \(E\) can be found in [13]). In the case of a rigid motion model, \(\alpha(t)\) represents a rigid transformation matrix; it is expressed using homogeneous coordinates. In the case of a non-rigid motion model, a separable motion model is used, with a spatial component \(\alpha(x,y,z)\), and the temporal component \(s(t)\) is a motion signal, acquired from a motion sensor or navigator data. A Tikhonov regularization controlled by the hyper-parameter \(\lambda\) is employed. An additional regularization term is added in the non-rigid case, as \(R(\alpha)=||\
abla\alpha||^{2}\). In each iteration of the GRICS algorithm, two coupled least-squares problems are solved in an alternating scheme. The first problem addresses image reconstruction, where the objective is to recover the image \(\rho\) by minimizing the discrepancy between the measured k-space data and the forward model, as well as the regularization term that stabilizes the solution. Once the image is updated, the motion correction problem is tackled in the second least-squares step. Here, the residual from the reconstructed image is used to refine the motion parameters \(\alpha\) (either under rigid or non-rigid motion assumptions). By linearizing the forward model around the current estimates, a Gauss-Newton method efficiently updates \(\alpha\) to reduce motion-induced artifacts. The least-squares problems are solved using the Conjugate Gradient (CG) solver. This iterative interplay between image reconstruction and motion estimation is applied in a multi-resolution fashion: first, it is applied to the low-resolution (central) k-space data; then, it continues until the last resolution level, where only the reconstruction step is performed.
## 1 Introduction
Altitude loss is considered a metric in spin flight, requiring swift control to avert collisions with the ground. Faster convergence speed in altitude control results in smaller altitude losses. To achieve faster convergence and higher control accuracy, an extension of finite time stability, fixed time stabilization, has received much attention for providing a predefined convergence time toward equilibrium independent of initial conditions [22]. Thus, to minimize altitude loss during spin recovery, fixed-time SMC emerges as a potential candidate for automated spin recovery. The main innovations and merits of this paper are:  
1. Unlike the finite-time controller in [23], the proposed fixed-time spin recovery controller based on the nonsingular integral terminal sliding mode (NITSM) approach has a faster convergence rate, which has helped the aircraft recover from spin motions in a shorter time and with less altitude loss. In addition, the convergence time of the proposed spin recovery controller is independent of the initial states. Thus, the success rate of spin recovery has increased remarkably. 2. Different from the conventional fixed-time control methods that depend on piecewise continuous or saturation functions, this study presents an enhanced fixed-time NITSM spin recovery strategy that avoids singularity issues. It directly constructs the NITSM surface, thus simplifying fixed-time convergence analysis with reduced computational complexity. 3. The introduced fixed-time spin recovery controller is designed to effectively mitigate the undesirable chattering phenomenon associated with SMC. Moreover, the continuity of the control law presents practical advantages, thus facilitating its implementation in engineering applications.
## 3 GLS
The Gauss-Markov theorem [7] proves that the OLS estimator is the BLUE, and very similar theorems prove the same result for WLS or GLS. The ML estimate depends on the assumed distributions for the errors. For example, if we replace the Gaussian (Normal) distributions with logNormal distributions, the ML estimate will change. In the cases considered here, ML gives the same estimate as GLS, because the data distribution is Gaussian. Because the ML approach makes strong use of the assumed error distributions, the ML estimate is sensitive to the assumed error distribution. The ML method has desirable properties, including asymptotically minimum variance as the sample size increases. However, in our example, the sample size is tiny (two), so asymptotic results for ML estimates are not relevant. It still is possible that an ML estimator will be better for nonGaussian data than GLS [6]. Typically, \"better\" is defined as the mean squared error (MSE) of the estimator, which is well known to satisfy MSE = variance + bias\({}^{2}\). In some cases, biasedestimators have lower MSE than unbiased estimators because the bias introduced is more than offset by a reduction in variance [8].
## 1 Introduction  
Buried pipes are extensively used to transport oil and gas from offshore platforms. Under unfavorable loading combinations, the pipe's uplift resistance may be exceeded and may lead to excessive deformations [1; 2; 3; 4; 5] and significant disruptions. The peak uplift resistance of buried pipes is typically determined using a combination of laboratory, field tests and numerical modelling [1; 2; 3; 4; 5; 6; 7; 8; 9; 10]. It has been shown that the peak uplift resistance of buried pipes depends on the density of the surrounding soil, although the relative pipe-to-soil movement required to mobilize the peak uplift resistance is not significantly affected by soil density [11]. The increase in pipe embedment depth is attributed to the increase in the peak uplift resistance up to a critical embedment depth, beyond which no further increase in the normalized peak uplift resistance is recorded [5]. Laboratory-scale and field tests for the prediction of the peak uplift resistance of buried pipes are time-consuming and costly to perform [7] and in this context advanced numerical modelling may offer a viable alternative. A variety of artificial intelligence techniques have been employed in the literature for the prediction of the uplift resistance of buried pipes, including artificial neural network (ANN), neuro-genetic network (NGN), genetic programming and simulated annealing (GP-SA), multivariate adaptive regression spline (MARS), support vector machine (SVM), intelligent fuzzy radial basis function neural network inference method (IFRIM) and group method of data handling-harmony search (GMDH-HS). A summary of the various artificial intelligence (AI) and soft computing techniques (SC) used in the literature to predict the uplift resistance of buried pipes in various soil types is shown in Table 1. Although artificial neural networks (ANN) have been widely used for the prediction of the peak uplift resistance of buried pipes [6; 12; 13; 14], the capabilities of ANN may have not have been fully explored. For example, only a limited number of studies developed artificial network models in combination with other techniques, which may have enhanced their accuracy [13]. It has been shown that when artificial neural networks are developed with boosting and bagging ensemble techniques, the prediction accuracy of artificial intelligence models may significantly increase [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]. This paper aims to perform a series of small-scale tests on pipes buried in geogrid-reinforced sands and the measured peak uplift resistance was used to calibrate advanced numerical models employing neural networks. Therefore, multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types were used to train two neural network models, which were then developed using bagging and boosting ensemble techniques.