esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 2 Preliminary Materials  
The 'Internet of' series of technologies has been an idea that has been developing over time for many different purposes. In today's society, most of the new technological evolution is typically part of this thought process, whether its Internet of People (Human-Machine Interaction), Internet of Agents (Machine Learning and Artificial Intelligence), Internet of Content (Cloud), or Internet of Things (Machine-to-Machine Interaction) [1]. While the authors in [1] looked at the overall 'Internet of' series, we will focus on the Internet of Things. The idea of the Internet of Things is essentially when multiple devices,otherwise known as \"Things\", are connected together in some manner through the use of a wireless network of some sort. This network can be any wireless protocol such as Bluetooth, WiFi, 5G, ultra-wideband, radio frequency identification, and many more. Now, all these devices communicate with each other, send data to each other, and are typically used to make things smarter [1; 2; 3]. Some of the uses of IoT are seen in technology found in cities, homes, transportation, military, agriculture, and so many more. The idea is to make life easier for people who use technology by making the technology smarter. However, all IoT use cases generally fall within the general multilayer architecture ranging from three to six layers, with the four-layer model being the most popular. The five-layer architecture, as shown in Figure 1, is one that we will focus on in this investigation, as it goes a bit deeper compared to the four-layer model [4].  
Figure 1: Five-layer architecture. This image has been completely re-modified; however, the idea of the image was inspired by [4].  
The five-layer architecture consists of the perception layer, which is the layer that gathers the data. This can consist of sensors and other things that are used to collect information, such as barcodes [4]. We then move up a layer to the network access layer, which is essentially the means of communication. This is how the device sends out signals and communicates with other devices. This can be seen as wireless protocols such as ultra-wideband, WiFi, Bluetooth, etc., and it helps create the sensor network [4]. We then have the 'Network Transmission Layer' above that, which is how the network communicates as a whole. This can be seen as communication using satellites, mobile communication networks, or even a specific dedicated network [4]. The next layer would be the \"Application Support Layer\" which consists of middleware, cloud platforms, application host, etc. [4]. Finally, we have the \"Presentation Layer\", which is what IoT devices are solely used for, whether it is for a smart cities, agriculture, transportation, and many more [4]. In other words, the presentation layer is the output layer for IoT devices where they act upon the world or environment.
## 2 Materials and Methods
### Preparation of the Glass-Ceramic Samples  
CEL2, FaGC, and SCNA powders were uniaxially (1-D) pressed in the form of cylinders (diameter 10 mm, height 10 mm); the pressing conditions were set at 500 MPa for 60 s to obtain crack-free \"green\" bodies. Samples for flexural testing (52 mm \(\times\) 10 mm \(\times\) 4 mm) were also produced by a similar approach. Ethanol (Sigma-Aldrich, St. Louis, MO, USA) was used as a binder for the glass particles. After being compacted, the so-obtained \"greens\" were thermally treated (TT) in air to produce sintered glass-ceramic samples, hereafter referred to as TT-CEL2, TT-FaGC, and TT-SCNA. The specimens were heated at a rate of 5 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\) up to the proper sintering temperature, reported in Table 2; after being held at the desired temperature for 3 h, they were left to cool inside the furnace to room temperature at a rate of 10 \({}^{\circ}\)C\(\cdot\)min\({}^{-1}\). The sintering parameters were chosen on the basis of preliminary hot-stage microscopy results (reported elsewhere [26; 27; 28]) and DTA in order to achieve a good densification along with the minimum shrinkage of the final glass-ceramic samples.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Glass Name**} & \multicolumn{6}{c}{**Composition (mol.\%)**} & \multirow{2}{*}{**Melting Conditions**} \\ \cline{2-3} \cline{5-10}  & **SiO\({}_{\mathbf{2}}\)** & **P\({}_{\mathbf{2}}\)O\({}_{\mathbf{5}}\)** & **CaO** & **Na\({}_{\mathbf{2}}\)O** & **MgO** & **K\({}_{\mathbf{2}}\)O** & **Al\({}_{\mathbf{2}}\)O\({}_{\mathbf{3}}\)** & **CaF\({}_{\mathbf{2}}\)** \\ \hline CEL2 & 45 & 3 & 26 & 15 & 7 & 4 & - & - & 1400 \({}^{\circ}\)C for 1 h \\ FaGC & 50 & 6 & 18 & 7 & 3 & 7 & - & 9 & 1550 \({}^{\circ}\)C for 1 h \\ SCNA & 57 & - & 34 & 6 & - & - & 3 & - & 1550 \({}^{\circ}\)C for 1 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Compositions and preparation details of the starting materials.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Parent Material** & **Sintering Conditions** & \(\rho_{\mathbf{s}}\) **(g\(\cdot\)cm\({}^{-3}\))** \\ \hline TT-CEL2 & CEL2 & 1000 \({}^{\circ}\)C for 3 h & 2.46 \(\pm\) 0.10 \\ TT-FaGC & FaGC & 800 \({}^{\circ}\)C for 3 h & 2.50 \(\pm\) 0.12 \\ TT-SCNA & SCNA & 1000 \({}^{\circ}\)C for 3 h & 2.53 \(\pm\) 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sintered glass-ceramic samples: preparation details and resulting densities.
## 2 Materials and Methods
### Evaluation Metrics
#### 2.3.9 ROC Curve  
The ROC (_Receiver Operation Characteristics_) curve plots the false positive rate on the abscissa and the _recall_ rate on the ordinate. The ROC curve relates the sensitivity of the model (recall) to the number of negative samples classified as positive (optimistic failures). An increase in _recall_ (higher rate of positives predicted by the model compared to the total number of real positives) implies fewer false negatives and, therefore, more false positives, in short, a more optimistic model. The closer the ROC curve is to the upper left corner of  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Range of Values of \(\kappa\) Coefficient** & **Matching (Level of Agreement)** \\ \hline \([0.00,0.20]\) & none \\ \([0.21,0.39]\) & minimal \\ \([0.40,0.59]\) & weak \\ \([0.60,0.79]\) & moderate \\ \([0.80,0.90]\) & strong \\ \([0.91,1.00]\) & almost perfect \\ \hline \hline \end{tabular}
\end{table}
Table 3: Level of agreement according to the \(\kappa\) coefficient.  
the graph, the better the model performs. However, it must be underlined that when the data are unbalanced, with few positive samples, the ROC curve, or the value of the area of the ROC curve (ROC AUC), can be misleading, with a plot very close to the ideal but with too low a precision. In this sense, the relevance of the ROC curve is limited to contexts with balanced data or situations in which optimistic failures are intended to be highlighted. The PR curve would be more informative in the presence of unbalanced data.
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 5 Experiment and Evaluation
### Experiment Design
#### 5.3.2 Second Experiment: Query Historical Data  
The second experiment measures the response time of the proposed CFC framework and the traditional cloud architecture when querying historical data. Similar to the previous experiment, 1600 clients uploaded data in the speed of 760 Mbytes/s and 400 clients sent query requests.
# System Design and Experimental Study of a Four-Roll Bending Machine
## 1 Introduction
[PERSON] established an experimental-simulation collaborative research framework to systematically reveal the nonlinear correlations between springback rate and rolling speed, feed rate, pre-deformation, and die curvature radius during the roller bending of aluminum alloy components, providing a scientific basis for springback control [13]. [PERSON] et al. developed a three-roll push bending research framework integrating theoretical analysis and numerical simulation to systematically investigate the springback behavior and feed control mechanisms of 3003-H24 aluminum alloy tubes in CNC tube bending, with experimental validation enhancing equipment processing capabilities [14]. [PERSON] et al. created a four-roll plate bending precision control research system combining theoretical analysis and finite element simulation to elucidate plate bending springback mechanisms and validate process parameter development feasibility, while proposing a side roll bending deformation compensation scheme to improve equipment accuracy and efficiency [15]. Currently, research on the improvement and optimization of four-roll roll bending machines remains relatively limited, particularly in enhancing their bending accuracy. Present optimization efforts for roll bending machines primarily focus on mechanical structure enhancement or design, while control strategies appear relatively monolithic, predominantly adopting hydraulic drive [16] and mechanical drive [17] methods. However,these two drive modes exhibit slow dynamic responses, often leading to hysteresis errors. Furthermore, traditional roll bending machines demonstrate a low level of intelligence, lacking effective real-time monitoring and control systems, thereby struggling to meet the high precision and high efficiency requirements of advanced equipment manufacturing. To address these challenges, this study aims to design and develop a high-precision multi-axis controlled four-roll CNC bending machine, proposing a fully servo-driven four-roll bending machine system. This equipment seeks to overcome the technical bottlenecks in forming precision control of traditional rolling machines by innovatively employing high-precision servo-electric cylinders to drive all forming rolls (except the feed roller main roll) and constructing a precision control architecture to achieve high-precision forming and surface quality regulation of complex bent components. Notably, no prior research has reported the full servo-driven implementation of working rolls.
## 3 Real-time Capable Robust Maximum Power Point Tracking
### Kalman Filter-Based Online Parameter Identification  
Before results for the real-time capable maximum power point tracking procedure are summarized in the following subsection, the accuracy of the Kalman filter-based online parameter identification was validated. For that purpose, the measured data from Figure 5 were employed. Initially, the vector of expected values \(\mu_{x,0}^{\mathrm{e}}\) was chosen as  
\[\mu_{x,0}^{\mathrm{e}}=\begin{bmatrix}-501.9&286.1&-30.31\end{bmatrix}^{T}\enspace, \tag{39}\]resulting from a second-order Taylor series expansion of Equation (3) at the arbitrarily selected point \(I=4.7\,\)A. Due to the large deviation from the initial current \(I_{0}\approx 2\,\)A (cf. Figure 5a), the covariance was set to the large initial entries  
\[\mathbf{C}_{x,0}^{\mathrm{e}}=\frac{1}{4}\cdot\mathrm{diag}\left\{\left[\left( \mu_{x,1,0}^{\mathrm{e}}\right)^{2}\quad\left(\mu_{x,2,0}^{\mathrm{e}}\right)^ {2}\quad\left(\mu_{x,3,0}^{\mathrm{e}}\right)^{2}\right]\right\}\ . \tag{40}\]  
Despite this significant initial uncertainty and the measurement variance \(C_{v,k}=9\,\)W\({}^{2}\), the approximation for the current-power characteristic converged quickly to the true values, as seen by a comparison of the Kalman filter outputs (the surface in Figure 8) with the measured data as well as with the offline identification result of Figure 5e included in the same graph. Obviously, the Kalman filter provides an accurate second-order polynomial approximation of the true power characteristic at the currently measured data point.  
Additionally, the accuracy of the Kalman filter estimates can be quantified with the help of \(\sigma_{P_{\mathrm{EL}},k}\) defined in Equation (24). According to Figure 9, points with the largest uncertainty coincide with those operating conditions at which the step-like current variations depicted in Figure 5a occur. Hence, at those points, the Kalman filter directly provides a hint about the reduced approximation quality of the power curve in the respective situations. Besides a visualization of the absolute values of the estimated standard deviation \(\sigma_{P_{\mathrm{EL}},k}\) in Figure 9a, the percentage \(\frac{\sigma_{P_{\mathrm{EL}},k}}{P_{\mathrm{EL}},k}\cdot 100\%\) is shown in Figure 9b to highlight the efficiency of the proposed estimator. Even in transient phases, during which the uncertainty increases inevitably, the relative estimation uncertainty does not exceed the range of 6% from the currently measured power.  
Figure 8: Kalman filter-based online parameter identification for the measured data in Figure 5a–c; black dots represent the power measurements in Figure 5c; the red curve at the final time instant corresponds to the offline identified current–power characteristic in Figure 5e.
## 4 Methodologies and Results Analysis
### Final Phase
In the case of the first two scenarios, where the intruder has not obscured or partially obscured their face, the proposed system has enhanced system accuracy to 97.01% and 84.13% from 95% and 80.8%, to 75.6% and 65.5% from 69% and 34.5%, and to 72% and 50% from 94% and 75% resulting from the use of neural networks, a skin segmentation template, matching modified haar cascade methodologies, a two step R-CNN, higher order statistics, and faceness-net methodologies. However, in scenarios 3 and 4, where the intruder has fully obscured their face using some type of transparent, solid, paper, plastic, or leather material and the intruder has been captured in the dark using an analog camera without night vsion facility, all the existing methodologies have failed in detecting the intruder except for the neural networks and two step R-CNN method. However, the two step R-CNN method requires the usage of a thermal camera to detect an intruder in the dark night [20]. The proposed system can detect an intruder using an analog camera without night vision capability. In scenarios 3 and 4, the proposed system has enhanced accuracy to 78.19% and 66.5% from 56.70%, 44.01% and 69%, 55% as represented in Figure 15:0  
Figure 15:1 Comparision Analysis of face detection for various age groups in four scenarios: face not obscured; face partially covered; face fully covered; and captured in the dark by an analog camera.
## 4 Results and Discussion
### Developed Models
#### 4.1.3 MARS  
As shown in Table 8, the developed MARS model with the first order of interaction had the highest R values as well as the lowest MSE values on both the training and test datasets. Hence it was chosen as the optimum MARS model in this study.  
In the developmental process of the selected first order of interaction MARS model, only eight basis functions after the backward elimination stage were used out of the 20 basis functions employed in the forward selection stage. The eight basis functions of the selected MARS model and their respective equations are shown in Table 9.  
The developed optimum MARS model for predicting ground vibration as a result of blasting is provided in Equation (33).  
\[\begin{array}{c}PPV=-2.85717-(0.0211305\times BF1)+(0.0270673\times BF2)+(0.019 0881\times BF3)\\ +(0.033926\times BF5)-(0.0570272\times BF6)+\left(5.46015\times 10^{-5}\times BF7\right) \\ +\left(3.56504\times 10^{-5}\times BF10\right)+\left(2.79304\times 10^{-5}\times BF10 \right)\end{array} \tag{33}\]
## 2 Research Background
### Localization Protocols Proposed for UWSNs
Hence, ordinary sensor nodes have enough localization information to estimate their location on board. On the other hand, centralized localization computational methods preserve the energy of sensors.
## 4 Solving the Non-Linear System
\begin{array}{ccccccccc}-1&0&0&0&0&0&0&0&0\\ 1&-1&1&0&0&0&1&0&0\\ 0&1&0&-1&-1&1&1&0&0&0\\ 0&0&0&1&0&-1&0&0&0\\ 0&0&0&1&0&-1&0&0&0\\ 0&0&0&0&1&-1&0&0&0\\ 0&0&0&0&1&0&1&-1&0&0\\ 0&0&0&0&0&0&0&1&0&0\\ 0&0&1&0&0&0&0&-1&-1\\ 0&0&0&0&0&0&0&0&1&0\\ 0&0&0&0&0&0&0&0&1\\ \end{array} \right].\]  
Then, System (16) can be written in the form:  
\[\left[ \begin{array}{cc}A_{11}&0_{14,10}\\ A_{21}&A_{22}\end{array} \right]\left[ \begin{array}{c}X_{p}\\ X_{q}\end{array} \right]=\left[ \begin{array}{c}b_{p}\\ b_{q}\end{array} \right]+\left[ \begin{array}{c}f(X)\\ 0_{10,1}\end{array} \right],\]and be split into two subsystems:  
\[A_{11}X_{p}=b_{p}+f(X), \tag{17}\]  
and:  
\[A_{21}X_{p}+A_{22}X_{q}=b_{q}. \tag{18}\]  
By setting \(X_{p}=\left[ \begin{array}{c}Z_{p}\\ W_{q}\end{array} \right]\) with:  
\[Z_{p}=\left[ \begin{array}{c}P_{4}^{2}\\ P_{5}^{2}\\ P_{6}^{2}\\ P_{7}^{2}\\ P_{8}^{2}\\ P_{9}^{2}\\ P_{10}^{2}\\ P_{11}^{2}\\ P_{12}^{2}\\ P_{13}^{2}\end{array} \right]\in\mathbb{R}^{10\times 1},\quad W_{p}=\left[ \begin{array}{c}Q_{1,4}\\ Q_{1,10}\\ Q_{2,4}\\ Q_{3,5}\end{array} \right]\in\mathbb{R}^{4\times 1}.\]  
The subsystem (17) can take the form:  
\[\tilde{A}_{11}Z_{p}=b_{p}+f(X).\]  
There exists \(X^{*}\), which satisfies the solution of (16) and can be found through a standard numerical method like the Newton-Raphson, such that:  
\[f(X^{*})=-\tilde{A}_{12}W_{p}-\tilde{A}_{12}X_{q}+\tilde{b}_{p}. \tag{19}\]  
Then, by replacing (19) in the above expression:  
\[\tilde{A}_{11}Z_{p}+\tilde{A}_{12}W_{p}+\hat{A}_{12}X_{q}=b_{p}+\tilde{b}_{p},\]  
or, equivalently,  
\[\hat{A}_{11}X_{p}+\hat{A}_{12}X_{q}=\hat{b}_{p}. \tag{20}\]  
where \(\hat{A}_{11}=\left[ \begin{array}{cc}\tilde{A}_{11}&\tilde{A}_{12}\end{array} \right]\), and \(\hat{b}_{p}=b_{p}+\tilde{b}_{p}\). The system (21) with the system (18) will provide a solution for the non-linear system (16). The following Theorem is provided. **Theorem 1**. : _Consider the non-linear system (16). Then, an effective linearization of System (16) is:_  
\[\hat{A}X=\hat{B}. \tag{21}\]  
_where:_  
\[\hat{A}=\left[ \begin{array}{cc}\hat{A}_{11}&\hat{A}_{12}\\ A_{21}&A_{22}\end{array} \right],\quad\hat{B}=\left[ \begin{array}{c}\hat{b}_ {p}\\ b_{q}\end{array} \right].\]  
_Furthermore:_  
* _The matrix_ \(\hat{A}_{11}\) _is defined as:_ \[\hat{A}_{11}=\left[ \begin{array}{cc}\tilde{A}_{11}&\tilde{A}_{12}\end{array} \right].\] _where_ \(\tilde{A}_{11}\) _is given, and_ \(\tilde{A}_{12}\) _is defined in (_19_);_* _The matrix_ \(\hat{A}_{12}\) _is defined in (_19_);_
* _The column vector_ \(\hat{b}_{p}\) _is defined as:_ \[\hat{b}_{p}=b_{p}+\tilde{b}_{p}.\] _where_ \(\tilde{b}_{p}\) _is defined in (_19_)._
## 4 Grid Synchronization for the Secondary Control Unit
### Discrete Fourier Transform (DFT)  
The DFT method is considered as the first method of detecting the frequency and harmonics of the periodic signal [166]. Grid synchronization using DFT analysis is a mathematical tool, which transforms a given function from the time domain to the frequency domain. The authors in [133; 167] use recursive adaptive window DFT (RDFT) for power converter line synchronization by means of filtering the received grid voltage. This technique offers a high degree of invulnerability against noise. Still, when the DFT time window does not emulate the grid period, a phase shift arises between the filtered voltage and the grid voltage. Hence, an improved phase-detection via sliding DFT (SDFT) has been proposed in [168; 169]. This technique displays a robust performance in phase-tracking capability with fast transient response under grid contingencies. This method, which is based on a phase-detection system, is adequate as it requires a lesser amount of operation to obtain a single frequency component. As a result, computational complexity is reduced with a more straightforward implementation than conventional DFT methods. In highly polluted grid conditions, a positive-sequence phase-angle estimation method is presented in [170; 171; 172], known as interpolated DFT (IpDFT), which can realize an optimum trade-off between the estimation accuracy and the response time. The proposed technique depends on the obtained sampling rate and can be utilized in the control hierarchy to determine errors and estimate phasors by using analytical analysis, thus generating a one-cycle transient, which is invulnerable to voltage imbalances, harmonics, noises, and grid frequency variations.
## 2 Materials and Methods
### Proposed Method  
Combining the predictions obtained from various suitable models appropriately, also termed ensemble learning, has proven to be an effective approach for improving prediction accuracy [36]. It aims to achieve more reliable and accurate prediction than that of a single model. Bagging and boosting are two popular traditional ensemble learning approaches, which are found to be very effective in many applications [37]. Stacking is also a widely used ensemble approach introduced by [38]. Employing different models as base learners is always preferable, as each learner tries to learn diverse features during the training phase. In this study, three different models are chosen as base learners, and therefore, the stacking strategy which is suitable to combine the predictions is used. The primary concern with existing ensemble methods is that they give equal importance to all the base models, or the weights are assigned based on some predefined function [39]. However, all the models may not perform alike on the same dataset. Therefore, combining the performances of the base models has to be performed in a meaningful way. The base models that are chosen to obtain the prediction are not guaranteed as the best models, as there could be other possible models that may give different predictions for the same data. The typical ensemble learning approach that combines the model's prediction ignores this uncertainty about the base models. To address this, we introduce a Bayesian probability-based novel ensemble learning approach for effectively combining the base model's predictions. Our approach handles the aforementioned issue by considering the uncertainty in the model and allows us to assign different weights to the models based on their performance. The diagram shown in Figure 9 demonstrates the workflow of the proposed method where, \(f_{1}\), \(f_{2}\), and \(f_{3}\) represent the prediction obtained from WRN-40, DenseNet-121, and EfficientNet-B7. Then, performance metrics are calculated for the base models on the validation dataset, which are utilized to obtain their prior probabilities. In the next step, the posterior probability is calculated using the maximum likelihood estimation (MLE) method. Based on these findings, finally, the weights of the base learners are obtained using Bayes theorem, which are the key findings in this study, and are used to  
Figure 8: Illustration of base model’s feature map and class activation map: (**a**) WRN-40; (**b**) DenseNet-121; and (**c**) EfficientNet-B7. combine the predictions of the three fine-tuned base models. The procedure of the proposed approach is outlined in Algorithm 1, which is discussed below in detail.