esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrity Monitoring Method for Navigation Satellites Based on Multi-Source Observation Links
## 3 Design of Integrity Monitoring System with Multi-Source Observation Links
### Optimization Strategies of Observation Links
As shown in Figure 5, angle \(\alpha\) could be described as follows:  
\[\alpha=\arccos\frac{R_{OG}^{2}+R_{GS}^{2}-R_{OS}^{2}}{2R_{OG}R_{GS}}-90 \tag{3}\]  
where \(R_{OS}\) is the distance between the target satellite and the geocenter, \(R_{OG}\) is the distance between the ground monitoring receiver and the geocenter, and \(R_{GS}\) is the distance between the target satellite and ground monitoring receiver. When angle \(\alpha\) is less than angle \(\alpha_{\max}\) and more than angle \(\alpha_{\min}\), the satellite-to-ground observation link is valid. Angle \(\alpha_{\min}\) and angle \(\alpha_{\max}\) are related to the performance of monitoring receiver. Secondly, the valid number of satellite-to-ground and satellite-based observation links is respectively recorded as \(n\) and \(m\). Then, \(k\) observation links can be selected from all valid links. There are \(C_{m+n}^{k}\) options as shown below:  
\[C_{m+n}^{k}=\frac{(m+n)!}{k!\times(m+n-k)!} \tag{4}\]  
Figure 5: The chosen valid satellite-to-ground observation links. Thirdly, satellite position dilution of precision (SPDOP) under different combined observation links should be evaluated. SPDOP reflects geometric conditions of different observation links for the target satellite. The calculation method is described as follows:  
\[A^{j}_{k\times 3}=\left( \begin{array}{ccc}\frac{\partial\rho_{1}}{\partial x_{1} }&\frac{\partial\rho_{1}}{\partial y_{1}}&\frac{\partial\rho_{1}}{\partial x_{1 }}\\ \frac{\partial\rho_{2}}{\partial x_{1}}&\frac{\partial\rho_{2}}{\partial y_{1}} &\frac{\partial\rho_{2}}{\partial x_{1}}\\ \vdots&\vdots&\vdots&\vdots\\ \frac{\partial\rho_{1}}{\partial x_{1}}&\frac{\partial\rho_{2}}{\partial y_{1 }}&\frac{\partial\rho_{2}}{\partial x_{1}}\end{array} \right) \tag{5}\]  
\[Q^{j}=\left(A^{J\Gamma}PA^{j}\right)^{-1}=\left( \begin{array}{ccc}q_{11}&q_{12 }&q_{13}\\ q_{21}&q_{22}&q_{23}\\ q_{31}&q_{32}&q_{33}\end{array} \right) \tag{6}\]  
\[SPDOP^{j}=\sqrt{q_{11}+q_{22}+q_{33}}(j=1,2\ldots C_{m+n}^{k}) \tag{7}\]  
where \(A^{j}_{k\times 3}\) is the observation matrix, \(\rho\) is the range of data between the target satellite and monitoring receiver, \(\left(x_{1}\text{,}q_{1}\text{,}z_{1}\right)\) is the coordinate of the target satellite, \(P\) is the weight matrix, \(Q\) is the cofactor matrix, and \(q\) is the element of matrix \(Q\). Finally, \(k\) observation links can be chosen according to the values of \(SPDOP\). The \(SPDOP^{j}\) (\(j=1,2\ldots C_{m+n}^{k}\)) are sorted in the order from smallest to largest. The observation links related to the minimum \(SPDOP\) are the orbit determination links.
# A Framework for Constructing a Secure Domain of Sensor Nodes
## 4 Procedures for Creating a Secure Domain of Sensor Nodes
### The Procedure for Node Status Confirmation
* If the authentication procedure was successful, but the node being requested does not have the role of Master, then prepare the _status_confirm_ans_ package as follows: N_ID = N_ID gathered from _status_confirm_req_ packet; Ans = STATUS_NEW_MAD--the other node is playing the MASTER role in the domain; ADR = MAD--address of the current M node;  
Figure 19: The sequence diagram for the procedure for node status confirmation. SQ = SQ from _status_confirm_req_ increased by 1; The other fields are zeroed. * Encrypt the _status_confirm_ans_ packet using the NSK of the N_ID node and send it to the R node. **(5) Acquire node status** confirmation--on the R node, depending on the content of the _Ans_ field, do the following:  
If _Ans_ = _STATUS_OK_, in the RAM of the node, update the RN, GAD, Time, and SQ field values. Update the GAD field in the NVRAM of the node; If _Ans_ = _STATUS_NEW_MAD_ in the RAM and the NVRAM of the node, update the value of the MAD field. Perform the procedure for node status confirmation again with the new MAD address.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using GraphPad Prism 10.1.1 (GraphPad Software, Inc., San Diego, CA, USA). One-way repeated-measures ANOVA was employed to compare the conductivity and permittivity of potato tissue before and after electroporation with varying pulse parameters. Per set of experiments, conductivity and permittivity were separately compared at six logarithmically spaced frequencies between 10 Hz and 1 MHz (i.e., 10; 100; 1000; 10,000; 100,000; 1,000,000 Hz), respectively. [PERSON]'s post hoc test was used to obtain multiplicity-adjusted \(p\)-values to address the multiple-comparison problem. The family-wise alpha threshold was set to 0.05.  
\begin{table}
\begin{tabular}{l l l l l} \hline \multicolumn{2}{c}{**Variable Pulse Parameters**} & \multicolumn{3}{c}{**Fixed Pulse**} & \multicolumn{3}{c}{**Standard Conductivity Solution**} \\ \cline{2-5} \multicolumn{1}{c}{**Pulse Parameters**} & **Variable Values** & **Units** & **Parameters** & **Solution Temperature (\({}^{\circ}\)C)** & **Conductivity (S/m)** \\ \hline \multirow{3}{*}{Pulse voltage (PV)} & 100, 200, 300, 400, & \multirow{3}{*}{V} & NP = 70 & \multirow{3}{*}{20.8} & \multirow{3}{*}{0.1305} \\  & 500, 600, 700, 800, & PW = 100 \(\upmu\)s & & & \\  & 900, 1000 & & PI = 100 ms & & \\ \hline \multirow{3}{*}{Number of pulses (NP)} & 5, 10, 15, 20, 30, 40, & \multirow{3}{*}{-} & PI = 100 ms & \multirow{3}{*}{21.4} & \multirow{3}{*}{0.1305} \\  & 50, 60, 70 & & PW = 100 \(\upmu\)s & & \\  & 100, 200, 30, 50, 70, 80, & \multirow{3}{*}{\(\upmu\)s} & NP = 70 & \multirow{3}{*}{20.8} & \multirow{3}{*}{0.1305} \\  & 90, 100, 200 & & PI = 100 ms & & \\ \hline \multirow{3}{*}{Pulse interval (PI)} & 50, 100, 150, 200, 250, & \multirow{3}{*}{ms} & NP = 70 & \multirow{3}{*}{21.0} & \multirow{3}{*}{0.1305} \\  & 300, 350, 400, 500 & & PW = 1000 V & & \\ \hline \end{tabular}
\end{table}
Table 1: Overview of the four sets of experiments, which varied the number of pulses, the pulse interval, the pulse width, and the pulse voltage while keeping other fixed pulse parameters constant in each set. Alongside, the temperature of the standard conductivity solution and corresponding conductivity as measured prior to calibrating the cell constant.
## 4 Simulation Results  
Simulations were built to show the effectiveness of the suggested controller. The Euler approach was used to discretize the model and controller in Matlab 2018. The fixed step size was 0.001 s, and the simulation time was set to 20 s. The controller parameters were chosen as \(\beta_{1}=4\), \(\beta_{2}=1\), \(K=4\), \(K_{1}=3\), and \(K_{20}=2\). To test the effectiveness of the proposed FSMC, a chain feedback controller (FBC) was compared. The details about the FBC can be found in [8].  
The reference path was set as \(x-y=0\), and the relevant vehicle parameters were \(L_{1}=0.5\), \(L_{2}=1.5\), and \(L_{3}=0.5\). The vehicle speed was set as 1.5 m/s, and the initial error of the position was 8 m. The heading error and the initial articulated angle were selected as 1 deg and \(-1\) deg, respectively. The path tracking results are shown in Figures 4 and 5. Figure 4 shows the vehicle's tracking paths when following a predetermined line, and the position error, heading angle error, articulated angle error, and sliding mode variable variation are given in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{\(\dot{s}\)} & \multicolumn{8}{c}{\(K_{21}\)} \\ \cline{2-9}  & **NB** & **NM** & **NS** & **ZO** & **PS** & **PM** & **PB** \\ \hline NB & LA & ML & ML & MS & ML & ML & LA \\ NM & ML & ML & MS & Z & MS & ML & ML \\ NS & MS & MS & LE & Z & LE & MS & MS \\ ZO & LA & ML & LE & Z & LE & MS & LA \\ PS & MS & MS & LE & Z & LE & MS & MS \\ PM & ML & ML & MS & Z & MS & ML & ML \\ PB & LA & ML & ML & MS & ML & LA \\ \hline \end{tabular}
\end{table}
Table 2: The fuzzy control rules.  
Figure 3: Membership functions: (**a**) \(s\) and \(\dot{s}\); \(K_{21}\) (**b**).  
SMC and FBC. Compared with SMC, the reaching time for the sliding mode variable reduced by 50% from the beginning condition. The suggested control strategy increased the tracking accuracy by 20% when the vehicle quickly entered the steady state and went online. Compared to the traditional SMC and FBC, the FSMC proposed in this research performed noticeably better, according to the simulation outcomes.
###### Abstract  
To address the problem of the collapse of the roof of the Bailuyuan tunnel during construction, the causes of collapse were analyzed, targeted treatment measures were proposed, and the effects of the treatment measures were evaluated through on-site monitoring and three-dimensional numerical simulations. The results showed that the particular characteristics of loess and the synergy of groundwater were the internal causes of the tunnel's collapse as well as, to a certain extent, atmospheric precipitation. Therefore, the combination of multiple factors contributed to the tunnel's collapse. Untimely monitoring and measurement, as well as the low initial support parameters, reflect a lack of human understanding of the collapse. Based on the analysis of the causes of the collapse, comprehensive treatment measures for inside and outside the tunnel are proposed, which are shown to be effective and to be capable of preventing the occurrence of further collapses. After the collapse treatment, the measured maximum settlement of the tunnel vault was 65.1 mm, the maximum horizontal convergence was 25 mm, the maximum surrounding rock pressure was 0.56 MPa, and the maximum stress on the steel arch frame was 54.34 MPa. Compared with the original design plan, the vertical stress, horizontal stress, and shear stress of the surrounding rock obtained from numerical simulation after the collapse treatment were greatly reduced, the reduction rate at the vault reached 50%, and the safety factors of the initial support positions after treatment met the specification requirements. The research results can provide engineering guidance for the design and construction of large-section tunnels crossing deep-loess strata, and they are of important engineering significance.  
deep-buried large-section loess tunnel; collapse and roof fall; causes of collapse; treatment measures; effect evaluation +
Footnote †: journal: Applied sciences
## 2 Overall Analysis Approach and Implemented Methods
### Structural Sizing
#### 2.4.1 Validation of the Structural Model with FEM Analyses
azimuthal, and axial displacements are inhibited, whereas the other one differs from the previous only for the axial displacement, which is allowed. Torsion is inhibited by having imposed zero rotation with respect to the longitudinal axis at these points. The system so defined is, thus, isostatic. The obtained results are summarised in terms of safety factors for both tensile and buckling strength, with a good accordance between the model implemented and FEM analyses (see Table 6). In addition, looking at the values assumed by the safety factors, it can be observed how the design test case #1 is dominated by the strength criterion, whereas test case #2 is driven by buckling. Figure 13 shows the lowest buckling modes for both test cases; the effective length of the column is comparable to the distance between supports. For the shorter tank, the buckling eigenvalue is about 2.55, whereas for the longer one it is about 1.35; this confirms what was said previously, that the test case 1 is dominated by static sizing, whereas for test case 2 bending-induced buckling is dominant.
# Advantages of Corrosion-Resistant Overlay Welding on Steel S355J2N
## 5 Discussion
Nevertheless, given the significant differences in the properties of the base metal and the deposited layer, a buffer layer is always necessary to create a joint with a stable transition. The required wall thickness, however, is determined by the required thickness of the layer. Regarding the buffer layer, it is worth revisiting the estimated mixing ratio at the fusion zone, as shown in Figure 4. The heat input was maintained at a relatively high level via the heat input energy, while the electrode speed was at the upper limit [17]. Using a simple calculation method [39; 40] and employing Figures 9 and 11, the fusion (mixing) ratio (D) can be determined as follows, using any image editing software:  
* We approximate the entire weld cross-section using a circle. * We determine the original surface of the base metal, which will intersect the weld cross-section. * We calculate the area of the resulting circular segment (ABM) and relate it to the total weld cross-sectional area (ABM + AFM) (7). \[D\%=\frac{A_{BM}}{A_{BM}+A_{FM}}\cdot 100 \tag{7}\]  
Based on Equation (7), we performed checks at several locations, resulting in the D value fluctuating within the range of 21.5-23.5%, slightly deviating from our estimation. This can be attributed to several simplifying assumptions, and therefore, we believe that Equations (5) and (6) are still valid with a good approximation. Another question that may arise is the determination of the critical Cr or Ni content at which the phase change occurs. Using the Cr and Ni values obtained from the weld phase analyses, we performed a second-degree regression. During the investigation, we utilized all measurement results obtained from EDX phase analyses, regardless of the location. The result of the regression analysis is shown in Figure 18.  
In Figure 18, the phase-change boundary can be defined such that a Ni content of 11.29% still corresponds to delta ferrite, whereas a Ni content of 11.73% indicates austenite. This can be interpreted as follows: a Ni content of less than 11.29% always corresponds to delta ferrite, while a Ni content greater than 11.73% always corresponds to austenite. This relationship requires further analysis to determine the exact boundary. Once the relationship is established, we will be able to approximate the Ni content in a given welding seam based on the Cr content, and vice versa. Even in the absence of knowing the exact boundary, the correlation coefficient is 0.975, which is considered an excellent fit.
# Thermal Performance Analysis of a Double-Pass Solar Air Collector: A CFD Approach
## 2 Materials and Methods
### Efficiency Calculation  
The thermal efficiency of the collector is calculated as the ratio between the useful energy gained by the fluid on the collector's cavity and absorber and the net solar energy on the collector's aperture, using Equation (16):  
\[\eta=\frac{Q_{u}}{A_{c}I} \tag{16}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Boundary** & **Type of Boundary** & **Characteristics** \\ \hline Inlet & Inlet-vent & \(T_{ps1,in}\) = 298.15 K \\ \hline Outlet & Mass flow & \(\dot{m}_{1}\) = 0.01 kg/s \\  & & \(\dot{m}_{2}\) = 0.02 kg/s \\ \hline Cover & \begin{tabular}{c} Wall \\ Mixed (convection, radiation, \\ and heat generation) \\ \end{tabular} & \begin{tabular}{c} \(q_{c}\) = 37.37 kW/m\({}^{3}\) \\ HTC\({}_{\text{C-a}}\) = 8.27 W/m\(\cdot\)K \\ \(T_{a}\) = 298.15 K \\ T\({}_{\text{s}}\) = 280.05 K \\ \end{tabular} \\ \hline Bottom duct & \begin{tabular}{c} Wall \\ Convection \\ Conduction \\ \end{tabular} & HTC\({}_{\text{b}}\) = 0.6 W/m\(\cdot\)K \\ \hline Absorber plate & \
\begin{tabular}{c} Wall \\ Heat generation \\ \end{tabular} &
\begin{tabular}{c} \(I\) = 900 W/m\({}^{2}\) \\ \(\varepsilon_{p}\) = 0.35 \\ \(q_{p}\) = 13,481 kW/m\({}^{3}\) \\ \end{tabular} \\ \hline \multicolumn{2}{c}{Fluid interfaces:} & Coupled wall & - \\ - & cavity-absorber plate & \\ - & duct-absorber plate & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Boundary conditions considered in the CFD modeling of the U-shape double-pass CPC.  
The heat transfer of the analyzed solar collector resembles a counterflow heat exchanger, where the cold fluid flows through the cavity, and the hot fluid flows through the duct. Therefore, the useful energy gain of the collector is calculated using Equation (17).  
\[Q_{u}=\dot{m}C_{p,avg}\big{(}T_{ps2,out}-T_{ps1,in}\big{)} \tag{17}\]
## 4 Experiments and Results
### Network Width  
Next, we study the impact of network width, or the number of neurons per hidden layer. The performance impact of the network width is shown in Figure 13. The results are shown with six hidden layers, and the width varies between 25 and 300. We see that the modeling performance is better when the number of neurons is increased. It can also be observed that the modeling performance after 175 neurons per hidden layer improves very slowly when more neurons are added. However, the convergence speed in network width is faster than that of network depth. Doubling one hidden layer to two layers decreases the median MAE from 0.04655 to 0.03485, by 25.13%, and further doubling to four layers improves the MAE to 0.03045, by 12.62%.  
Regarding the network width, doubling the number of neurons per layer from 25 neurons to 50 reduces the MAE from 0.08045 to 0.05025, by 37.54%, and further doubling to 100 neurons achieves a 0.0372 median MAE, with 25.97% improvement. These results reflect the fact that an increase in the number of hidden layers and the number of neurons  
Figure 12: MAE vs. number of hidden layers.  
Figure 13: MAE vs. number of neurons per hidden layer.  
per hidden layer has a different impact on the deep neural network. In a fully connected neural network, every neuron in each layer of the network is connected to every other neuron in the adjacent forward layer. If such a neural network has \(n\) neurons per layer and \(m\) hidden layers, the total number of neuron links is \(\mathcal{O}\big{(}mn^{2}\big{)}\). Since the number of neuron links scales linearly with network depth but exponentially with network width [43], the network performance improves more significantly as the network widens.
###### Abstract  
To improve the stability of automatic retransmission requests, a competition control method for automatic retransmission requests of cooperative-based listening nodes is proposed. Because the number of source nodes involved in retransmission communication is reduced, the method reduces the communication energy consumption. Cooperative communication consists of two main parts of work. When the source node sends signals to the surrounding nodes, the relay nodes and other nodes receive the broadcast signals. The signals received by both nodes are the same. A node competition mechanism based on the probability of link disruption as an indicator is designed. The node with higher energy is identified as the node that retransmits the signal. An experiment on the performance comparison of multiple algorithms is organized by simulation. The results show that the proposed new strategy has lower values in BER and BER metrics. This indicates that the proposed method transmits high values of signal quality compared to other dominant methods of retransmitting signals. Because the forwarded signal is successfully transmitted and the source node is no longer involved in the retransmission effort. In cooperative communication, the number of retransmission requests is reduced, and the energy consumed by the system is reduced. Because the improved algorithm achieves low-power communication, the proposed algorithm is practical and innovative in cooperative communication.  
cooperative communication; automatic retransmission requests; listening nodes; interruption probability; bit error rate 2023132161. [[https://doi.org/10.3390/app13042161](https://doi.org/10.3390/app13042161)]([https://doi.org/10.3390/app13042161](https://doi.org/10.3390/app13042161))
# Defect Detection on Rolling Element Surface Scans Using Neural Image Segmentation
## 3 Convolutional Neural Networks  
Traditional multi-layer perceptrons (MLP) are a kind of artificial neural network that is exclusively made up of fully connected layers. When high resolution images are used as input data, these fully connected layers result in a high number of parameters, called weights. This is due to all input pixels being connected with all neurons in the following layer. This makes them prone to overfitting and computationally intensive to train. To lower the parameter count of MPLs, preprocessing is applied to the input data. The preprocessing reduces the input data dimensionality. These preprocessing steps must be carefully chosen to not discard information from the data that is important to the classification task. As such, it relies heavily on expert knowledge to minimize dimensionality while retaining as much information as possible. This has similar downsides as the classical optical inspection and machine learning approaches, as outlined in the introduction.  
CNNs are another kind of ANN. They reduce the parameter count of MLPs by only connecting neurons of ensuing layers locally. Furthermore, the weights are shared across the dimensions of the input data, usually images. The weight sharing allows translation invariant detection of, e.g., defects in the image. In practice, these features are implemented using a convolution operation. Learned filter kernels of predetermined size are slid across the image with a predetermined step-width, called stride. The network needs only to learn the parameters of the filter kernels. This reduces their parameter count compared to an MLP operating on equal sized input data. This allows CNNs to directly operate on high resolution input images, without preprocessing. At every position of the input image, the kernels are convolved with the underlying pixels. The resulting image is called an activation map. The activations of a whole layer are then stored together in a tensor and fed further downstream in the neural network. As is the case in traditional ANNs, non-linearities are applied to the resulting activations. This allows the network to approximate non-linear functions. Deep CNNs stack multiple layers of convolution operations together. Every subsequent layer extracts more and more high-level features from the input image.  
Most classification systems that rely on CNNs directly classify the input image, based on these high-level features. More recently, however, image segmentation approaches have come to the fore. Here, the pixels of an input image are individually classified into a set of classes. For defect detection on images, this not only allows the classification of the input, but also the localization of the defects. The importance of defect localization is discussed in [10].  
A decoder part is added to the network to achieve this segmentation. The decoder consists of further convolution layers. It maps the CNN's high-level features, also called latent representation, back into an image of the input image's size.