esa-sceva/satcom-chunk-collection huggingface dataset:
# A Non-Smooth Numerical Optimization Approach to the Three-Point Dubins Problem (3 PDP)
## 3 Derivation of Dubins Path with Trigonometry
### Case CSC  
This case happens when \(s_{2}=0\) and, depending on the sign of \(s_{1}\) and \(s_{3}\), gives origin to the curves LSL, RSR, LSR, and RSL. Then, system (5) simplifies to  
\[\begin{split} d&=\,s_{1}(\sin\vartheta_{1}-\sin \alpha)+\ell_{2}\cos\vartheta_{1}+s_{3}(\sin\beta-\sin\vartheta_{2})\\ 0&=\,s_{1}(\cos\alpha-\cos\vartheta_{1})+\ell_{2} \sin\vartheta_{1}+s_{3}(\cos\vartheta_{2}-\cos\beta),\end{split} \tag{8}\]  
and by imposing the continuity conditions to (8) on the angle of the line segment \(\vartheta_{1}=\vartheta_{2}=\vartheta_{S}=\alpha+s_{1}\ell_{1}=\beta-s_{3} \ell_{3}\), we have  
\[\begin{split} d&=\,(s_{1}-s_{3})\sin\vartheta_{S}-s_ {1}\sin\alpha+s_{3}\sin\beta+\ell_{2}\cos\vartheta_{S}\\ 0&=\,(s_{3}-s_{1})\cos\vartheta_{S}+s_{1}\cos\alpha-s_ {3}\cos\beta+\ell_{2}\sin\vartheta_{S}.\end{split} \tag{9}\]  
We combine these two equations in (9), multiplying by \(\sin\vartheta_{S}\) and \(\cos\vartheta_{S}\), respectively, and we obtain an explicit expression for \(\ell_{2}\) and an implicit expression for \(\vartheta_{S}\):  
\[\begin{split}\ell_{2}&=\,(d-s_{3}\sin\beta+s_{1} \sin\alpha)\cos\vartheta_{S}+(s_{3}\cos\beta-s_{1}\cos\alpha)\sin\vartheta_{S }\\ 0&=\,(d-s_{3}\sin\beta+s_{1}\sin\alpha)\sin\vartheta_{S }-(s_{3}\cos\beta-s_{1}\cos\alpha)\cos\vartheta_{S}+s_{3}-s_{1}.\end{split} \tag{10}\]  
This case can be further separated into two subcases, the first, \(CSC^{+}\), with the two circular arcs turning in the same direction (LSL and RSR), and the other, \(CSC^{-}\), with circular arcs in the opposite direction (LSR and RSL). They have two different solutions, as detailed below.
## 2 Methodology
### Increasing of Pointing Accuracy
#### 2.8.2 Bernoulli's Lemniscate Scanning  
To evaluate the possibility of further increasing the pointing accuracy, another methodology of scanning can be analyzed. The lemniscate of Bernoulli is a plane curve defined  
Figure 14: Pointing accuracy in case of helicoidal scanning.  
Figure 13: Control diagram modified to increase pointing accuracy.  
from two given points, known as foci, at a distance 2c from each other as the locus of the point P, so that PF1-PF2 = c\({}^{2}\) (Figure 16).  
Considering P = (Px,Py) as the designated point achieved during the pointing phase, and \(a\) the half-width of the [PERSON]'s lemniscate (a = c\(\cdot\sqrt{2}\)), it is possible to define the parametric equation of lemniscate scanning [30] along the \(X\)-axis (Equation (13)) and \(Y\)-axis (Equation (14)).  
\[Lx=Px+\frac{a\cdot\cos(\omega t)}{1+sin^{2}(\omega t)} \tag{13}\]  
\[Ly=Py+\frac{a\cdot\sin(\omega t)\cdot\cos(\omega t)}{1+sin^{2}(\omega t)} \tag{14}\]  
Figure 17 depicts the pointing accuracy graph in the case of [PERSON]'s lemniscate scanning:  
Figure 16: [PERSON]’s lemniscate.  
Figure 17: Pointing accuracy graph in case of lemniscate scanning.  
Figure 15: Locking area (red circle) in case of conical scanning.
# Investigation of a Precise Control Scheme for Rice Quality
## 2 Materials and Methods
### Experimental Design  
The Design-Expert software was utilized to construct five-factor experiments via RSM. Optimal experimental designs were selected to reduce the number of trials and corresponding costs compared to a full factorial design. Additionally, fitting the results with a second-order model enabled the determination of interactions between the variables under investigation. A central composite design (CCD) was chosen as the experimental design for the RSM, comprising 59 experiment groups with 32 factorial designs, 10 axial points, and 17 central points. The primary experimental variables included drying temperature (T), relative humidity (RH), initial moisture content (MC), air velocity (V), and tempering ratio (TR). Their respective levels are detailed in Table 1. The levels of the factors were determined through a combination of empirical observation and extensive literature review.  
It was assumed that the responses would be correlated with the independent variables via a second-order polynomial equation [21; 22] as follows:  
\[Y=\beta_{0}+\sum_{i}^{5}\beta_{i}x_{i}+\sum_{i}^{5}\beta_{ii}x_{ii}^{2}+\sum_{i \
eq j=1}^{5}\beta_{ij}x_{i}x_{j}+\varepsilon \tag{5}\]  
where \(Y\) is the response calculated by the model; \(\beta_{0}\), \(\beta_{i}\), \(\beta_{ii}\), and \(\beta_{ij}\) are the regression coefficients for the intercept and the linear, quadratic, and interaction terms, respectively; and \(\varepsilon\) is the random error.
# Reliability Analysis of C\({}^{4}\)ISR Systems Based on Goal-Oriented Methodology
## 2 Methodology
The \(\beta\)-factor model and the probability algorithm are primary methods handling the data correction of common cause failures in GO models. The \(\beta\)-factor model uses the \(\beta\) factor to measure the impact of common cause failures. Let \(\lambda_{1}\), \(\lambda_{2}\), and \(\lambda\) denote failure rates of unit failure, common cause failure, and system failure, respectively. It is obvious that:  
\[Q=Q_{1}+Q_{2} \tag{8}\]  
where, \(Q_{1}\), \(Q_{2}\), and \(Q\) are unit failure probability, common cause failure probability, and system failure probability, separately. Accordingly, the \(\beta\) factor can be computed by:  
\[\beta=\frac{Q_{2}}{Q}=\frac{Q_{2}}{Q_{1}+Q_{2}}=\frac{1-e^{-\lambda_{2}t}}{1-e ^{-\lambda t}}=\frac{1-e^{-\lambda_{2}t}}{(1-e^{-\lambda_{1}t})+(1-e^{-\lambda _{2}t})} \tag{9}\]  
In engineering cases, the \(\beta\) factor should be within [0, 0.25], in which, 0 represents no common cause failures. Generally, the more common cause failures involved, the larger the value of \(\beta\). And, the value can be selected based on the experience of specialists. Additionally, the probability algorithm assumes the existence of a common cause failure between units \(A\) and \(B\), thus the following formula can be obtained:  
Figure 1:0  
where, \(Q_{A}\) and \(Q_{B}\) are failure probabilities of units A and B, respectively; \(Q_{A,B}\) is the probability of units A and B fail simultaneously; The common cause failure probability of units A and B is \(C_{A,B}\), then, the failure probability of units A and B can be computed by:  
\[ \begin{array}{c}Q_{A}=Q_{AI}+C_{A,B}\\ Q_{B}=Q_{BI}+C_{A,B}\\ Q_{A,B}=Q_{AI,BI}+C_{A,B}\\ \end{array}  \tag{11}\]  
where, \(Q_{AI}\) and \(Q_{BI}\) are failure probabilities of units A and B. Accordingly, the following equation can be obtained:  
\[ \begin{array}{c}R=c_{0}+c_{1}Q_{AI}+c_{2}Q_{BI}+c_{3}Q_{AI,BI}+(c_{1}+c_{2}+ c_{3})C_{A,B}\\ R=R_{I}+(R_{00}-R_{11})C_{A,B}\\ \end{array}  \tag{12}\]  
where, \(R_{I}\) is the system success probability without common cause failures; \(R_{00}\) and \(R_{11}\) are system success probabilities under the condition that the success probabilities of units A and B with common cause failures are 0 and 1. Hence, for the situation of the system with \(M\) common cause failure units, the following formula is easy to be reached:  
\[R=R_{I}+\sum_{m=1}^{M}{(R_{00}...-R_{11}...)C_{m}} \tag{13}\]
# TSCH and RPL Joining Time Model for Industrial Wireless Sensor Networks
## 1 Introduction
However, the relationship between the TSCH medium access mechanism and the RPL protocol has not been studied as deeply as TSCH synchronization, but their relationship is particularly important, since they are part of the complete process of deploying a new node. An analytical model both for TSCH and RPL has been proposed in this article, allowing the characterization of the total time that a node takes to connect to a network based on TSCH andn RPL. Using this model it is possible to choose an optimal configuration, both for TSCH and RPL, that allows one to improve TSCH synchronization and DODAG RPL connection times. Depending on the use case of the WSN, this model allows to obtain an optimized configuration in each case, focusing on improving the network formation time or extending the whole battery lifetime of the network. The rest of the article is structured as follows. Section 2 gives a brief introduction to the TSCH access mechanism, which is part of the IEEE 802.15.4 standard, and also of the RPL dynamic routing protocol, emphasizing those aspects that are most relevant during the deployment, RPL tree formation and joining of new nodes to the network phases. In Section 3, an analysis of the current state of the art is presented, reviewing those works that have evaluated different proposals for improvements in the processes of synchronization and joining in networks based on TSCH and RPL. In Sections 4 and 5, the proposed analytical modeling is detailed, for both TSCH and RPL, respectively. Section 6 describes the different simulation scenarios that have been proposed to perform the validation of the analytical model, showing the results obtained for each of them. A series of tests with real equipment were also carried out to validate the results obtained in the simulations,the results of which are shown in Section 7. Finally, Section 8 presents the conclusions drawn from this analysis.
## 2 Theory Background and Methodology
### Compressed ESM Based on the Equivalent Redundant Dictionary
#### 2.2.2 Construction of the Equivalent Redundant Dictionary
According to [PERSON] [20], the constraints in Equation (14) can be adjusted, and the problem is described by Equation (15):  
\[\underset{\mathbf{\Upsilon}}{\mbox{\rm argmin}}||\mathbf{ \Upsilon}||_{1}\ \mbox{subject to}\ ||(\mathbf{D}\mathbf{e})^{H}(\mathbf{p}-\mathbf{D}\mathbf{e}\mathbf{\Upsilon})||_{2}\leq\delta \tag{15}\]  
where \(\delta=\sigma||(\mathbf{D}\mathbf{e})^{H}\mbox{\boldmath$p$ }||_{2}\), and \(\sigma\) is a small number. Equation (15) is the basis formulation of ERDCESM, which can be solved by CVX toolbox [32]. Once the coefficient \(\Upsilon\) is obtained, the pressure can be reconstructed via Equation (10). Since the dictionary \(D\) in ERDCESM is a combination of \(\phi\) (it is suitable for the spatially sparse source) and \(U\) (it is suitable for the spatially extended source), the ERDCESM works well for different types of sources.
# Optimization Design Study of Pipe Curtain Freezing Scheme for Sanya Estuary Submarine Channel
## 5 Optimal Design of Freezing Scheme and Analysis of Numerical Simulation Results
### Analysis of Numerical Simulation Results for Scenario 2  
The model's parameters were kept constant through the single control variable method, with the reduction in freezing pipes solely based on the optimized option 1. Given that the freezing zone of the west wall is upstream, it must serve as a water barrier and thus requires a higher freezing effect. Consequently, only the freezing pipes at the top and bottom plates and the east wall freezing zone were reduced from two to one row, resulting in a total of  
Figure 9: Diagram of \(-1\)\({}^{\circ}\)C vs. \(-10\)\({}^{\circ}\)C isotherms at different times.  
18 pipes being removed. The pipes in the weak freezing zones remained unchanged. Through numerical simulation, the changes in \(-1\)\({}^{\circ}\)C and \(-10\)\({}^{\circ}\)C isotherms at different times are illustrated in Figure 10. After 11 days of freezing, the \(-10\)\({}^{\circ}\)C isotherm filled the space between the occluded and single steel pipes, indicating that the temperature outside the pipe interval was below \(-10\)\({}^{\circ}\)C. The \(-1\)\({}^{\circ}\)C isotherm was fully developed by day 16, and the \(-10\)\({}^{\circ}\)C isotherm by day 24. After 40 days of active freezing, the permafrost curtain was well established, with the measured thickness of the curtain in the west wall freezing zone (upstream) at 4.34 m, in the east wall (downstream) at 4.17 m, in the top plate at 4.11 m, and in the bottom plate at 4.15 m. Compared to the design standard limit for the thickness of the \(-1\)\({}^{\circ}\)C permafrost curtain, the optimization program was found to be very effective. Similarly, the limit value for the \(-10\)\({}^{\circ}\)C curtain thickness was met, indicating that the model calculation results of the Freezing Optimized Scheme 2 satisfied the requirements for soil reinforcement, did not compromise construction safety, and could be implemented.
## 2 Investigation of Field Measurement Problems  
### Wheel-Rail Force Monitoring  
According to Chinese specification Testing of China railway high-speed electric multiple units on completion of construction [28], the wheel-rail lateral force limit of a high-speed train is 56 kN, and the wheel-rail vertical force limit of high-speed trains is 170 kN. The wheel-rail force limit values were obtained by Chinese railway experts and scholars through many dynamic performance tests on high-speed trains. If it exceeds this value, the trains may be dangerous.  
The wheel-rail force is monitored by railway department employees using the wheel-rail contact dynamic stress tester (Figure 1b) as the train passes through a ground turnout, and the sampling frequency is about every half hour. The wheel-rail force of 42 sets of trains through the turnout was monitored each day from 10 June 2022 to 19 June 2022. Figure 1a displays the average wheel-rail maximum force for 42 sets of trains traveling at 250 km/h on a daily basis. There were 8 days of the average wheel-rail maximum lateral force over-limit and 7 days of the average wheel-rail maximum vertical force over-limit.
# U-Net-Based Semi-Automatic Semantic Segmentation Using Adaptive Differential Evolution
## 3 Evaluations
### Results
#### 3.2.2 Segmentation Results  
Tables 1-3 show the semantic segmentation results in terms of IoU, dice, and pairwise accuracy. As shown in Table 3, we found that the proposed method had the best performance; however, the performance improvement regarding the semantic segmentation was not so apparent because the pairwise accuracy includes the accuracy regarding the background. In Tables 1 and 2, we observed that there were no clear differences between the results of the U-net, DeepLabv3, and the proposed methods, in terms of the femur. Compared to artificial bone and spine, the femur is a relatively easier-to-extract bone area because its shape is not complex. Therefore, DeepLabv3 worked well even in this experimental condition with a small number of training images. Regarding artificial and spine bones, we verified the proposed method outperformed U-net, thanks to optimizing windowing parameters with jDE. jDE is a stochastic method, but these results were stable. Moreover, we verified that the U-net-based model was better suited than DeepLabv3 under the condition of limited training data, based on these results. The architecture of DeepLabv3 has many more layers than the U-net, so DeepLabv3 needs a lot of training data. Figures 5-7 show the segmentation results, and (a), (b), (c), (d), and (e) are the input images, GT images using the proposed method, with the results of Deeplabv3, of the U-net, and of the proposed method, respectively. From Figure 7a, we can see that the white color is not unified in bone areas because the bone contours are denser than those of the inside. Considering the semantic segmentation from the input images, it makes it difficult to extract whole bone parts.  
As shown in the left figures in Figure 5 and the right figures in Figure 6, the proposed method, windowing jDE + U-net, could extract more precise areas compared to U-net alone. The proposed method could automatically generate the GT images from handwritten lines and optimize DICOM image parameters for performance improvement. From these results, we could verify that an appropriate windowing parameter generated clearer input images for the U-net model, and the proposed method only needed to solve the easier semantic segmentation problem, thanks to the input images using jDE.  
Figure 5: Artificial bone segmentation results of U-net and U-net with windowing by jDE.  
Figure 6: Spine segmentation results of U-net and U-net with windowing by jDE.
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 2 Antenna Design Methodology
### Bending Analysis of the Proposed Antenna  
In order to verify the antenna's \(S_{1I}\), it is required to have bending analysis along the horizontal and vertical axes of the antenna, which is made up of a flexible Roger RT5880 substrate. The bending of the antenna on wearable situations is critical. Antenna bend analysis in free space along the \(x\)- and y-axis is described in this section. Using a cylindrical form with a diameter of 30 mm, 60 mm, and 90 mm, the bending analysis of the antenna is checked with simulations.
# Challenges of Using the L-Band and S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems
## 4 Simulation Methodology
### Scenario 1  
In this scenario, the mobile receiving station communicating with the Omnispace satellite experienced interference from the 5G NTN constellation. The simulation was conducted over a 24 h period with a time step of one second. The mobile station was located in the Northern part of Finland. It is assumed that the Omnispace wanted link is continuously active whenever the mobile station is within the service area of the Omnispace satellite. Similarly, the 5G NTN satellites were assumed to be always active as they are likely to provide service to users within their designated service areas. Figure 8 illustratesthe simulation, depicting the interference caused by the 5G NTN system on the Omnispace system.
# Neighbor Discovery for IPv6 over BLE Mesh Networks
###### Abstract  
IPv6 over Bluetooth Low Energy (6 LoBLE) can realize the seamless connection between Bluetooth Low Energy (BLE) devices and the Internet, solve the restrictions on Bluetooth applications as short distance communication solutions, and advance the development of applications of smart cities, such as smart buildings and smart city management. Since neighbor discovery is the first step for BLE devices to establish a connection with each other, this paper proposes a novel neighbor discovery protocol for IPv6 over BLE mesh networks, including the structure of IPv6 over BLE mesh networks, the networking solutions, and IPv6 address auto-configuration and update mechanism. Furthermore, we analyze and simulate the performance of the neighbor discovery protocol utilizing the Reviewed simulator, and the results show the effectiveness of the proposed neighbor discovery protocol even in a crowded environment.  
Bluetooth Low Energy; mesh networks; IPv6 over BLE; neighbor discovery +
Footnote †: journal: Applied Sciences  
1  
Jiangsu Key Laboratory of Big Data Security & Intelligent Processing, Nanjing University of Posts and Telecommunications, Nanjing 210023, China; [EMAIL_ADDRESS] (B.L.); [EMAIL_ADDRESS] (Z.S.)  
Chongqing Key Laboratory of Photocetronic Information Sensing and Transmitting Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; [EMAIL_ADDRESS]  
Dipartimento di Informatica (DI), Universita degli Studi di Milano Via Celoria 18, 20133 Milano (MI), Italy; [PERSON]ESS]  
Institute of Software Applicaiton Technology, Guangzhou & Chinese Academy of Sciences, Guangzhou 511458, China; [EMAIL_ADDRESS] (J.W.); [EMAIL_ADDRESS] (H.Z.)  
Correspondence: [EMAIL_ADDRESS]
## 2 Methods
### Model Design  
The proposed separation model includes three parts: encoder, separation network and decoder. The block diagram of the speech separation system is shown in Figure 1.
## 2 Methods--Data Processing
### Data Collection  
Data were accessed through the infrastructure of our collaborators. For this study, the city Reading (UK) was selected due to the largest amount of available data (Figure 1). As a line of interest, bus line 17 was chosen as it runs with the highest frequency and thus generates the most data. For this line, the predictions were made based on all vehicles which interact with this particular line, see Section 2.2.1. Automatic Vehicle Location (AVL) data were collected for all vehicles within the Reading bus network. Each vehicle sends its position approximately every 40 s, and the company providing the integrated AVL system passes the data on to several third-party entities before it is recorded. Due to the handling of data by several independent companies, only limited amounts of information are transmitted and retained. The available data are as follows.  
* Timestamp
* Position (latitude and longitude)
* Line number
* Direction (eastbound or westbound)  
Based on this limited information, it is not possible to match the vehicles with the timetables for the current journey. A journey is a specific trip found in the bus line timetables, such as the 9 AM eastbound service. An additional challenge is matching a vehicle to a specific route pattern. These patterns are slightly different routes that a vehicle on the same line might take. On the basis of the available data, a vehicle cannot be matched to such a pattern. Therefore, a route pattern for each city was arbitrarily selected and used to calculate route trajectories, which is an acceptable approach, as in the selected cities the differences between patterns are negligible.  
Figure 1: Map of Reading showing both the Eastbound as well as Westbound journey patterns. The blow-out area shows the city centre where the line negotiates a one-way system and therefore, runs on two separate routes depending on the direction of travel.
## 5 Experimental Results
### Limitation  
We take an example to illustrate in which case the interpolation method yielded undesirable results. Figure 10 shows the interpolation results of the water hitting different obstacles. The obstacle in the pool led to different liquid motions for the splash near the obstacle. The surface of both the \(L_{0}\) and the \(L_{2}\) based interpolation results demonstrate that part of the water was pushed into the obstacle. Even though the \(L_{0}\) based result outperformed the \(L_{2}\) based result, both them were undesirable results. Because the large differences of the source and the target surface had difficulty finding the corresponding points between the source and target surface, the inaccuracy deformation calculation led to incorrect matching and further resulted in undesirable results and artifacts. Through our experiments, we observed that either the \(L_{0}\) or the \(L_{2}\) based interpolation method failed when the fluid had relatively complex structures and large differences between the source and the target. Therefore, there is still room to improve the fluid simulation approach.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Resolution**} & \multirow{2}{*}{**Iteration**} & \multicolumn{3}{c}{\(L_{0}\) Based**} & \multicolumn{3}{c}{\(L_{2}\) Based} \\ \cline{3-8}  & & **Error** & **Factor** & **Time** & **Error** & **Factor** & **Time** \\ \hline \multirow{4}{*}{\(16\times 16\times 16\times 16\)} & 0 & 4.1207 & 69.37\% & 0.073 s & 4.1207 & 69.37\% & 0.076 s \\ \cline{2-8}  & 1 & 3.0787 & 74.71\% & 0.084 s & 3.1301 & 75.96\% & 0.077 s \\ \cline{2-8}  & 2 & 1.9302 & 62.69\% & 0.067 s & 2.0283 & 64.8\% & 0.073 s \\ \cline{2-8}  & 3 & 1.3648 & 70.71\% & 0.068 s & 1.5698 & 77.39\% & 0.097 s \\ \hline \multirow{4}{*}{\(32\times 32\times 32\times 32\times 32\)} & 0 & 1.9837 & 62.98\% & 3.793 s & 1.8924 & 68.36\% & 3.78 s \\ \cline{2-8}  & 1 & 1.3720 & 69.16\% & 3.436 s & 1.4362 & 75.89\% & 4.434 s \\ \cline{2-8}  & 2 & 0.8306 & 60.54\% & 2.829 s & 0.8947 & 62.29\% & 4.486 s \\ \cline{2-8}  & 3 & 0.6313 & 76.01\% & 2.979 s & 0.7171 & 70.15\% & 4.484 s \\ \cline{2-8}  & 4 & 0.5498 & 87.09\% & 3.199 s & 0.6488 & 90.47\% & 4.257 s \\ \cline{2-8}  & 5 & 0.5479 & 99.67\% & 3.236 s & 0.6601 & 101.7\% & 4.331 s \\ \hline \multirow{2}{*}{\(64\times 64\times 64\times 64\)} & 0 & 0.9440 & 69.54\% & 81.41 s & 1.1113 & 77.72\% & 87.496 s \\ \cline{2-8}  & 1 & 1.0097 & 106.9\% & 75.616 s & 1.1269 & 105.7\% & 91.247 s \\ \hline total time & - & - & 176.79 s & - & - & 204.838 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Errors and timings for the registration step performed with two constraint methods.