esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussion
### Mineral Compositions and Micro-Morphologies  
#### 3.4.1 Mineral Compositions of Specim  
Software Jade6.0 was used to analyze the mineral compositions of shotcrete without the replacement of cement and with the partial replacement of cement with gangue powder. The results are illustrated in Figures 10 and 11.  
The analysis of Figures 10 and 11 shows that quartz, calcite, and kaolinite account for large proportions (57.2%, 23.4%, and 12.3%, respectively) of the mineral composition of shotcrete without the replacement of cement, and the proportions of other minerals including muscovite and gypsum are separately 5.6% and 1.5%. In shotcrete with the partial replacement of cement with gangue powder, minerals such as quartz, calcite, and kaolinite separately account for 59.3%, 18.7%, and 14.1%, while the proportions of other minerals including muscovite and gypsum account for 7% and 0.9%. These proportions imply that the two types of shotcrete contain similar types of minerals. The difference is that the shotcrete with the partial replacement of cement with gangue powder contains more clay minerals such as kaolinite than the gangue-based shotcrete, which results in a lower strength. However, shotcrete partially replacing cement with gangue powder contains higher proportions of clay minerals such as kaolinite. The water absorption and swelling tendency of kaolinite may induce microcracks within the shotcrete, compromising its strength. Additionally, the flaky-structured muscovite in shotcrete tends to form weak interfaces, reducing the bonding strength between aggregates and cementitious materials, thereby further lowering its mechanical performance.  
Figure 11: Mineral compositions of shotcrete with the partial replacement of cement with gangue powder.  
Figure 10: Mineral compositions of shotcrete without replacement of cement.
## 3 Methodology of the Proposed Concept
### Adaptive Histogram Equalization
**  
The histogram equalization (\(HE\)) enables areas with low local contrast to gain a boost in contrast. Equalization of the histogram accomplishes this by effectively spreading out the densely populated intensity values that previously weakened image contrast. The \"histeq\" function of MATLAB is used to transform the grayscale image. As displayed in Equation (9), the implemented histogram equalization on the \(GS_{I}\). \[HE_{I}=(GS_{I}\ ^{*}\ HE) \tag{9}\]  
where \(HE_{I}\) is the output image of the histogram equalization. Note: The operator \"_\"\" in Equation (9) performs the MATLAB function of applying the histogram equalization on the said image dataset. **Step 4: Adaptive Histogram Equalization. **  
In this paper, the adaptive histogram equalization technique is used to improve the contrast in images by computing multiple histograms, each corresponding to a different section of the image, and using them to redistribute the image's lightness values. As a result, it is well suited for increasing local contrast and sharpening the edges in each region of an image. The \"_adapthised_\" function operates on small regions in the image, called _tiles_. This function increases the contrast of each tile so that the output region's histogram approximates a specified histogram. Equation (10) illustrates the adaptive histogram equalization. \[AHE_{I}=(HE_{I}\ ^{*}\ AHE) \tag{10}\]  
where \(AHE_{I}\) is the implemented output image of the adaptive histogram equalization. Note: The operator \"_\"\" in Equation (10) performs the MATLAB function of adaptive histogram equalization which is implemented on the said image dataset. **Step 5: Gray-level thresholding. **  
In this study, the quality of gray-level thresholding of an image is a critical issue, therefore, the selection of a right grey-level threshold variance is important. In this paper, the global gray-level thresholding manages to remove the unnecessary segments of the image. The function \"_graytlresh_ (\(I\))\" of MATLAB computes a global threshold \(T\) of the image \(I\), using [PERSON]'s method. [PERSON]'s method selects a threshold that minimizes the intraclass variance of the black and white pixels that have been thresholded. With imbinarize, the global threshold T can be used to convert a grayscale image to a binary image. Equation (11) displays the implemented G=gray-level threshold (\(GLT\)). \[GLT_{D}=(AHE_{I}\ ^{*}\ GLT) \tag{11}\]  
where \(GLT_{D}\) is the output image of the gray-level threshold. Note: The operator \"_\"\" in Equation (11) performs the MATLAB function of gray-level thresholding.
## 5 Discussion  
The pile-beam-arch method is a construction form that combines the shallow-buried excavation method and the cover excavation method. It allows vehicles to drive on the upper pavement, which inevitably leads to the influence of dynamic load on soil excavation. However, this paper ignores the influence of the upper dynamic load on the construction of the pile-tunnel method. The next work can refer to [PERSON]'s research results of using the modified Cam-clay (MCC) constitutive model under dynamic load conditions to conduct a further comprehensive study of the surface settlement of the pile-beam-arch method [42].  
The hole pile subway construction technology is the first application in South China, and the opposite soft layer is relatively thick. This research can provide engineering experience for the wider promotion of the hole pile method in South China in the future. In this paper, the influence of the rise-span ratio on the surface settlement and the steel pipe pile in the construction process of the hole-pile method is investigated by three-dimensional finite element modelling and on-site monitoring. Through this study, the settlement law of the hole-pile method in the weak layer in southern China is found, which fills the research gap in the construction technology of the hole-pile in southern China. The maximum surface settlement (\(\mu\) = 0.31) calculated by the finite element method is about 59 mm, which is different from the maximum settlement value of 30 mm mentioned by [PERSON] [23]. The thickness of the weak layer of the subway station in this paper is relatively large, the bearing capacity of the foundation is obviously weak, and the hole pile method in this paper belongs to the semi-reverse construction method and [PERSON]'s reverse construction method. There are differences, which may be the cause of different surface settlement. At the same time, [PERSON]'s article also shows that, in fact, in the actual monitoring, it can be concluded that the surface settlement caused by the excavation of the hole pile method is often much larger than the standard value [43].  
In this finite element modelling analysis, the pilot tunnel stage and the buckle arch stage were found to be the main stages of surface settlement. This result is consistent with the field monitoring data of 30 Beijing subways summarised by [PERSON] [20]. However, the sum of the pilot tunnel stage and the arch stage accounts for about 80% of the total settlement, which is 10% less than that of [PERSON] [20], due to the neglected interaction between the pilot tunnel lining and the soil structure. The pit-pile method has inevitable advantages in the construction of underground stations and will certainly be used in more areas in the future. The relatively stable soil layer still causes relatively large settlements, especially in the weak layer, so it is necessary to reinforce the layer in advance, for example by using high-pressure jet grouting piles [44].  
Figure 17: Bending moment for each rise–span ratios (\(\mu\) = 0.28).
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 5 Cyber-Physical System
### Physical Asset: Smart PhotoVoltaic Station
#### 5.1.3 Service Modules  
The brain of the SPVS physical asset is implemented through ESP32 micro-controller units (MCU). The choice of this chip is motivated by its good performance, relative to the requirements posed by tasks it is intended to perform and the services it should expose, at a low price and bundled in a tiny board. The ESP32 board also includes an antenna module that allows it to establish wireless communication (either WiFi or Bluetooth) on the 2.4 GHz band.  
The device is powered using the energy generated by the PV panel and stored in the accumulation system. In particular, a Maximum Power Point Tracker (MPPT) is used to monitor the output of the PV panel and adjust the voltage and current so as to keep the system always at the point of maximum power. The output of the MPPT feeds the battery, but it is too high to power the logical components that require at most 5 volts. Additionally, it is also important to split the power sources to use for low power (sensors and control devices) and high power (servo motors) devices, respectively. Therefore, two step-down voltage converters are installed between the system's power source output (the battery) and the modules that need to be powered. These step-down voltage converters are tuned to output constant 5 volts. By keeping the power sources separated, it is possible to avoid undesired power drops when performing power intensive tasks that would result in the reboot of the MCU board or even the damage of the most sensitive components.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Maximum Power (Pmax) & 20 W \\ Open Circuit Voltage (Voc) & 22.3 V \\ Short Circuit Current (Isc) & 1.21 A \\ Voltage at Maximum Power (Vmpp) & 17.8 V \\ Current at Maximum Power (Impp) & 1.12 A \\ Module Efficiency of Voc & \(-\)0.45\% \\ Module Efficiency of Isc & \(-\)0.45\% \\ Module Efficiency of power & \(-\)0.45\% \\ Nominal Operating Cell Temperature (NOCT) & 45 (\(\pm\) 2) \({}^{\circ}\)C \\ Operating Temperature Range & \(-\)40 \({}^{\circ}\)C to \(+\)85 \({}^{\circ}\)C \\ Cells type/Array & 2 \(\times\) 18 \\ Maximum system voltage & 600 VDC \\ \hline \hline \end{tabular} Values are measured at Standard Test Conditions (STC), with Irradiance 1000 Wm\({}^{-2}\), Air Mass (AM) 1.5 Spectrum and Temperature 25 \({}^{\circ}\)C.  
\end{table}
Table 2: Offgridec Mono20W3-01-001560 PV module specifications.
# A Heat Load Prediction Method for District Heating Systems Based on the AE-GWO-GRU Model
## 3 Case Study
### AE-Based Data Augmentation Method  
#### 3.5.1 Autoencoder  
AE is an unsupervised learning network architecture used for data dimensionality reduction, feature extraction, and data reconstruction. It consists of a basic encoder and decoder three-layer structure [33]. The encoder compresses the input data into a low-dimensional coding representation, samples the low-dimensional space, and then reconstructs the data sample through the decoder. During the training process, the aim is typically to minimize the reconstruction loss. Data augmentation is performed using AE to generate new synthetic data samples, thereby expanding the original training dataset, enhancing the model's generalization ability, and reducing the risk of overfitting. The AE model is shown in Figure 12.  
In the figure, \(x_{1},x_{2},\dots,x_{m}\) represents the input data, \(h_{1},h_{2},\dots,h_{p}\) represents the feature vector, and \(\hat{x}_{1},\hat{x}_{2},\dots,\hat{x}_{m}\) represents the reconstructed data. The encoder uses a nonlinear activation function to obtain the feature vector. The feature vector is reconstructed by the decoder and mapped back to the original data space to obtain the reconstructed data. The update formulas for the AE network are as follows:  
\[h=\sigma(W_{1}x+b_{1}) \tag{7}\]  
\[\hat{x}=\sigma(W_{2}h+b_{2}) \tag{8}\]  
where \(W_{1}\) and \(W_{2}\) represent the weight matrix, \(b_{1}\) and \(b_{2}\) represent the bias term, and \(\sigma\) represents the activation function.
## 2 Materials and Methods
### Equipment and Testing  
Ferrite content in the weld metal was measured by the magnetic permeability technique (Fischer Feritscope FMP30). It has to be noted that ferrite content measured by magnetic permeability was converted to and displayed as % ferrite by Fischer Feritscope's internal correlation. The reason for this was to present ferrite content using the same unit as in the image analysis evaluation done for the HAZ, although this is not included in the current paper. Light optical microscopy (Olympus BX60M, Zeiss AxioCam MRC5) was used for microstructural characterization and image analysis. Electrolytic  
Figure 4: (**Left**) Back side of the joint showing the layout of the thermocouples inserted through drilled holes. (**Right**) Front side of the joint showing the layout of the harpooned thermocouples in the last passes. Note that the image shows the harpooned thermocouples only in the last passes because most of them have to be removed prior to welding of the following weld pass.  
Figure 3: Example of two inserted thermocouples to register temperatures in Weld Pass 2. Holes of 2 mm in diameter were used to insert the thermocouples from the backside by drilling through the root pass. After the insertion, the thermocouples were spot welded on the surface of the joint—in this case, the surface of the root pass.  
etching with 40% NaOH was selected to reveal the general microstructure and \(\sigma\)-phase, whilst 10% oxalic acid was used to reveal nitrides.  
Impact toughness testing was conducted in the weld metal and the HAZ according to ISO 9016 [15]. A total of 36 specimens were tested, 12 per experiment, which corresponds to three repetitions for each temperature and location.  
Corrosion testing was performed following ASTM G48E [16] for a total of 30 coupons, 10 per experiment, to evaluate the critical pitting temperature (CPT). This test is normally considered for surfaces in contact with the media, not for cross sections. However, in this study, the cross section was the area of interest. In such large size specimens, it was decided not to consider the weight loss criterion established by Norsok M-601 (<4 g/m\({}^{2}\)) because the results might not reflect the true corrosion resistance of the area. Instead, the criterion to pass the test was the absence of pits deeper than or equal to 0.025 mm.  
To compare the influence of the arc energy on the pitting corrosion resistance of the GMAW welds, the ASTM G150 test [17] was performed on coupons extracted 6 mm from the root. The test was stopped as soon as 0.1 mA/cm\({}^{2}\) was registered in order to find the most susceptible location to pitting corrosion. Since the 60 s holding time requested by the standard was not kept after registering the 0.1 mA/cm\({}^{2}\), the temperature at which pitting started was not strictly speaking the CPT, but it served for comparison purposes.  
Finally, weld metal oxygen content was analyzed in a LECO TC-436 furnace.
###### Abstract  
As the demand for precision positioning grows around the world and spreads across various industries, initiatives are being taken to increasingly protect Global Navigation Satellite System (GNSS) receivers from intruders of all kinds, from unintentional industrial interference to advanced GNSS spoofing systems. The timing and cost of these forthcoming satellite navigation safety efforts are difficult to decipher due to the large number of new signals and constellations being deployed. However, it is safe to say that the newly designed anti-jamming and anti-spoofing GNSS systems open up new opportunities for innovative technologies. The false acoustic signal delay is equal to the sum of the spoofer receiving antenna delay, processing delay, and propagation delay from the spoofer to the victim. The victim finds the same location as the spoofer's receiving antenna, and receivers located in different locations will have the same XYZ. The article presents classifications of terrorist attacks of this type.  
GNSS; LNSS; Local Navigation Satellite Systems; Unmanned Transport Systems; USV; unmanned surface vehicles; UUV; unmanned undersea vehicles; jamming; meaconing; spoofing 2021, 10, 401. [[https://doi.org/10.3390/electronics1004001](https://doi.org/10.3390/electronics1004001)]([https://doi.org/10.3390/electronics1004001](https://doi.org/10.3390/electronics1004001))
# Numerical Study of the Flow and Blockage Ratio of Cylindrical Pier Local Scour
## 2 Mathematical Model
### Hydrodynamic Model  
For the flow, the governing equations employed for LES are obtained by filtering the Navier-Stokes equations (Equations (1) and (2)).  
\[\frac{\partial\overline{u}_{i}}{\partial t}+\frac{\partial\overline{u}_{i} \overline{u}_{j}}{\partial x_{j}} = -\frac{1}{\rho}\frac{\partial\overline{p}}{\partial x_{i}}+\frac{ \partial}{\partial x_{j}}\left(v\frac{\partial\overline{u}_{i}}{\partial x_{j} }\right)-\frac{\partial\tau_{ij}}{\partial x_{j}}+f_{i}, \tag{1}\] \[\frac{\partial\overline{u}_{i}}{\partial x_{i}} = 0, \tag{2}\]  
where \(\overline{u}_{i}\) is the \(i\)-th filtered velocity component in Cartesian coordinates, \(\overline{p}\) is the filtered pressure, \(\rho\) is the density, and \(v\) is the kinematic viscosity. \(\tau_{ij}\) is the sub-grid stress (SGS) tensor required for the LES formulation, defined in Equation (3).  
\[\tau_{ij}=2\
u_{t}\overline{S}_{ij}+\frac{1}{3}\tau_{ij}\delta_{ij},\qquad \overline{S}_{ij}=\frac{1}{2}\left(\frac{\partial\overline{u}_{i}}{\partial x _{j}}+\frac{\partial\overline{u}_{j}}{\partial x_{i}}\right) \tag{3}\]  
where \(\
u_{t}\) is the turbulent viscosity at the subgrid scale, \(\overline{S}_{ij}\) is the rate-of strain tensor at the resolved scale, and \(\delta_{ij}\) is the Kronecker delta. The eddy viscosity is modeled using Equation (4) using the Smagorinsky model.  
\[\
u_{t}=L_{s}^{2}|S|,\qquad|S|=\sqrt{2\overline{S}_{ij}\overline{S}_{ij}}, \tag{4}\]  
where \(L_{s}\) is the sub-grid length scale. This length scale cannot be described with a constant value when it is located in the near-wall region; instead, it decreases as it approaches the wall. This effect is captured by the Function (5).  
\[\frac{1}{L_{s}^{n}}=\frac{1}{(C_{s}\Delta)^{n}}+\frac{1}{(\kappa L_{w})^{n}}, \tag{5}\]  
where \(n=2\) is the Mason wall matching power ([PERSON], 1992 [37]), \(C_{s}=0.1\) is the Smagorinsky constant, \(\Delta=V^{1/3}\) s the average spacing (where \(V\) is the volume of the cell), \(\kappa=0.4\) is the [PERSON] constant, and \(L_{w}\) is the distance from the cell center of a control volume to the wall.  
Figure 1: Sketch of the computational domain used for the numerical simulations (narrow channel case).  
The present study employs the diameter of the pier (\(D\)) and the mean velocity (\(U\)) at the inflow boundary as reference scales for the length and velocity, respectively. Using these length and velocity scales, we then define the time scale \(T=D/U\). By setting these reference scales, the governing equations become in terms of the pier Reynolds number, defined in Equation (6), which is based on the cylinder diameter, the mean flow velocity, and kinematic viscosity (\(\
u=1\times 10^{-6}\,\text{m}^{2}/\text{s}\)).  
\[\text{Re}_{D}=\frac{UD}{\
u}. \tag{6}\]
## 2 Considered State-Space Model  
Let us consider the DDFL system in the form  
\[E\Delta^{\alpha}x_{i+1}=Ax_{i}+Bu_{i},\quad 0<\alpha<1,\quad i\in\mathbb{Z}_{+}, \tag{1}\]  
where \(x_{i}\in\mathbb{R}^{n}\) is the state vector, \(u_{i}\in\mathbb{R}^{m}\) is the input vector, \(E\), \(A\in\mathbb{R}^{n\times n}\), \(B\in\mathbb{R}^{n\times m}\) and \(\Delta^{\alpha}x_{i}\) is the Grunwald-Letnikov fractional-order backward difference defined by [21]  
\[\Delta^{\alpha}x_{i}=\sum_{j=0}^{i}(-1)^{j}\binom{\alpha}{j}x_{i-j}, \tag{2}\]  
where  
\[\binom{\alpha}{j}=\left\{\begin{array}{ll}1&\quad\text{for}\quad j=0,\\ \frac{\alpha(\alpha-1)...(\alpha-j+1)}{j!}&\quad\text{for}\quad j=1,2,3,\ldots \.\end{array}\right. \tag{3}\]  
In descriptor systems \(\text{det}E=0\) and therefore the matrix \(E\) is not invertible.  
Definition 1: Let \(A\), \(B\) be some matrices of the same size. \(A\) set of such matrices of the form \(A+\lambda B\) is called a matrix pencil, where \(\lambda\) is a parameter. If \(A\), \(B\) are square matrices and \(\text{det}[A+\lambda B]\
eq 0\), then the pencil is called a regular one.  
According to Definition 1 we distinguish two subclasses of descriptor systems:  
1. with the regular matrix pencil of the pair \((E,A)\), i.e., \(\text{det}[E\lambda-A]\
eq 0\) for some \(\lambda\in\mathbb{C}\);
2. with the singular matrix pencil of the pair \((E,A)\), i.e., \(\text{det}[E\lambda-A]=0\) for some \(\lambda\in\mathbb{C}\).  
If the matrix pencil of the pair \((E,A)\) is regular, then the solution of the state equation of a descriptor system exists and it is unique for any consistent initial condition [2,7].  
There are several methods for analyzing the system (1) with the regular matrix pencil, which are based on the Drazin inverse [5], the Laurent series expansion [9] and the Weierstrass-Kronecker decomposition [11] methods.
# Internet of Things Applications as Energy Internet in Smart Grids and Smart Environments
## 5 Smart Energy Metering and Management Applications Based on IoT
The combination of AMI and DSM systems provides an important opportunity to identify instant load demand that allows service providers to control energy price and tariff according to demands and load analyses. As explained before, the dynamic price management is based on the DR vision, in which this process is separated into three types, called the CPP, TOU, and RTP. The most efficient approach among these is the RTP technique, which reduces peak consumption demand better than others. In addition, RTP can be realized by adapting the AMI system into the distribution network. [PERSON] et al. proposed a heterogeneous AMI structure in [48], in which authors considered and analyzed the AMI system containing various wireless communication systems. Authors also developed several heuristic models to figure out handicaps of the existing AMI networks. Resource numbers, universal data aggregation points (UDAPs), optimum routes between SMs, and base stations can be specified by the developed model in [48]. Another AMI system proposal, called the interface mitigated ZigBee for high-traffic AMI (HTAMI), is proposed by [PERSON] et al. in [74]. In addition to studies related to low-traffic AMI applications, HTAMI especially investigated interference problems for routing control cases, network initialization, and address distribution. The communication background of the AMI systems is very much alike to communication methods of the other IoT applications. While the most popular wired communication method is PLC, wireless technologies are LTE, LTE-A, Wi-Fi, Bluetooth, WiMAX, and so on. However, wireless communication methods are widely utilized in AMI applications due to their advantages, such as increased coverage area, low error rate, wider bandwidth opportunity, and security [73; 47].
# Analysis and Prediction of the IPv6 Traffic over Campus Networks in Shanghai
## 4 IPv6 Traffic Prediction Model
### Summary  
The proposed model LS6 combines the advantages of the deep learning model LSTM and statistical model SARIMA to predict the future IPv6 traffic volume with the information on historical IPv6 and IPv4 traffic volume. We consider LSTM and SARIMA as feature extractors and pre-train them. We feed IPv6 and IPv4 historical traffic volume information into LSTM and SARIMA, respectively, and get four intermediate values. The four intermediate values are then input into an MLP, and the contribution of each value to the final predicted value is trained. The output of the MLP is the final predicted value. We believe that the superiority of LS6 lies in exploiting the correlation between IPv4 and IPv6 and combining the advantages of deep learning and statistical methods.