esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
Let \(V\) be the amount of data to be delivered and \(C\) the average sustained physical layer link bit rate: then, \(T_{\text{data}}=V/C\). Then, it is \(T_{i}^{\text{c}}\) and \(N_{\text{p}}\) that should be determined. Let \(L_{i,j}\) denote the maximum continuous connected link holding time for the \(j\)-th established link of the \(i\)-th transmission phase, and we have:  
\[L_{i,j}\triangleq\max\{x:s_{i,j}(t)\geqslant s_{\text{th}},\forall t,1 \leqslant t\leqslant x\}, \tag{2}\]  
where \(s_{i,j}(t)\) denotes the RSS of the \(j\)-th link in the \(i\)-th phase at time \(t\) and \(s_{\text{th}}\) represents the minimum RSS threshold for maintaining the radio connection. Therefore, in the \(i\)-th transmission phase, we have the available time for delivering the target content, \(T_{i}^{\text{d}}\), as:  
\[T_{i}^{\text{d}}=\min\{L_{i,1},L_{i,2},\cdots,L_{i,H_{i}}\}, \tag{3}\]  
where \(H_{i}\) denotes the number of hops for the \(i\)-th transmission phase. Hence, by taking the expectation of the equation \(T_{\text{data}}=\sum_{i=1}^{N_{\text{p}}}T_{i}^{\text{d}}\), we can obtain \(V/C=\text{E}[N_{\text{p}}]\text{E}[T^{\text{d}}]\), where the subscript \(i\) of \(T_{i}^{\text{d}}\) is omitted to refer to the data transmission time for a general path. For the \(i\)-th transmission phase, the control sub-phase time \(T_{i}^{\text{c}}\) is determined by the hop count \(H_{i}\) and the control signal transmission time for each constructed link. Let \(T_{L_{i,j}}\) denote the time required to set up the \(j\)-th link in the \(i\)-th transmission phase. Then, we have:  
\[T_{i}^{\text{c}}=\sum_{j=1}^{H_{i}}T_{\text{L}_{i,j}}, \tag{4}\]  
where the expression of \(T_{\text{L}_{i,j}}\) depends on the specific dissemination scheme, and it will be derived in the following for the proposed routing protocol. Summing it all up, the overall target content transfer time can be expressed as:  
\[T_{\text{e2e}}=\frac{V}{C}\left(1+\frac{\text{E}[T^{\text{c}}]}{\text{E}[N_{ \text{p}}]\text{E}[T^{\text{d}}]}\right), \tag{5}\]  
where all of the subscripts \(i\) in \(T_{i}^{\text{c}}\) and \(T_{i}^{\text{d}}\) are omitted to refer to a general case. A summary of the main symbols used throughout the paper along with their respective definitions is provided in Table 1.  
Figure 1: Several transmission phases for the high volume content.
## 5 Discussions
### Cornering Stiffness
#### 5.1.2 Constant Radius Cornering Test: Yaw Rate Gain (Steady-State Response)  
Table 8 and Figure 12 show the simulation results obtained for the yaw rate gain at vehicle speeds in the range of 10-80 km/h and the three different tire cornering stiffness values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Velocity (km/h)** & \(\mathbf{K_{us}}\) **(Type 1) (rad/g)** & \(\mathbf{K_{us}}\) **(Type 2) (rad/g)** & \(\mathbf{K_{us}}\) **(Type 3) (rad/g)** \\ \hline
20 & 1.560 & 1.534 & 1.598 \\
30 & 0.734 & 0.705 & 0.776 \\
40 & 0.443 & 0.414 & 0.486 \\
50 & 0.306 & 0.275 & 0.348 \\
60 & 0.232 & 0.201 & 0.272 \\
70 & 0.190 & 0.158 & 0.230 \\
77 & 0.194 & 0.151 & 0.235 \\
80 & 0.204 & 0.168 & 0.255 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Understeer coefficients computed at different vehicle speeds using different cornering stiffness values.  
Figure 11: Variation of understeer coefficient with vehicle speed for three different cornering stiffness values.  
Figure 10: Variation of lateral force with tire slip angle for three different cornering stiffness values.  
As shown in Figure 12, the yaw rate gain for the Type 2 cornering stiffness was higher than that for the Type 1 (original) or Type 3 (lower) cornering stiffness. In other words, given a constant lateral force acting on the tire, a higher cornering stiffness improves the steering sensitivity (i.e., reduces the tire slip angle) and enhances the handing performance as a result.
## 2 Fundamental Models and Literature Search
### Planning Model  
In oceanic data collection, it is commonly observed that, owing to the a priori knowledge of sensor node or cluster head locations, the prevalent path planning models involves the utilization of integer programming models. Among these, the Traveling Salesman Problem (TSP) model stands out as the most extensively employed. However, unlike typical combinatorial optimization problems, the process of data transmission involves highly intricate mechanisms. These complexities include diverse factors, notably communication range, communication delay, noise perturbations, AMV and sensor energy consumption, and value of information (VoI). Consequently, it becomes evident that models for oceanic data collection are much more complex than TSP in nature. In this section, we compile various optimization models mentioned in existing literature, introducing their established optimization objectives and constraints. We aim for these contents to provide readers with a general portrayal of some representative knowledge.
# Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion
## 2 Image Data Acquisition and Processing
### Image Data Acquisition  
The images of the bottom of the sugarcane were collected from the sugarcane farm in Fusui County, Guangxi, China. The sugarcane variety was Guitang No. 49, the sugarcane was in the mature stage and the average stem diameter was about 2.5 cm. The sugarcane was grown in the open air and planted side by side according to the requirements for mechanical harvesting. In order to match the diversity of the sample environment, images were collected at 8:00, 12:00 and 18:00, and the lighting conditions include side light, forward light and back light. These were the three moments when the light intensity changed the most in the daytime. During this period, the sugarcane photos in different light directions can be obtained by adjusting the camera shooting angle. During image acquisition, the shooting direction of the camera can simulate the forward light, side light and back light by setting in the same, vertical and opposite directions as the light propagation direction. Considering that the camera's shooting angle will affect the detection performance, images were collected from multiple shooting angles during the image acquisition process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Method** & **Precision (\%)** & **Detection Time (s)** & **Remark** \\ \hline [PERSON] & Classification and & & & In a laboratory environment, sugarcane \\ et al. [2] & recognition method & 91.552 & 0.76 & leaves were stripped off. \\  & based on SVM & & & \\ [PERSON] & & & & In a laboratory environment, sugarcane \\ et al. [3] & based on Radon & 100 & 0.21 & leaves were stripped off. \\ [PERSON] & Hyperspectral imaging & 98.31 & / & In a laboratory environment, sugarcane \\ [PERSON] et al. [4] & technology & & & leaves were stripped off. \\ [PERSON] & Recognition technology & & & In a laboratory environment, sugarcane \\ et al. [5] & based on wavelet & 100 & 0.25 & leaves were stripped off. \\ [PERSON] & An improved YOLO v3 & 90.38 & 0.0228 & In a laboratory environment, sugarcane \\ [PERSON] & Sobel edge detection & & & leaves were stripped off. \\ et al. [6] & method & & & In a laboratory environment, sugarcane \\ [PERSON] & Vertical projection & & & In a laboratory environment, sugarcane \\ et al. [7] & function method & & & leaves were stripped off. \\ This paper & YOLO v4 & 95.12 & 0.0145 & In a natural environment, with \\  & & & & sugarcane leaves not stripped \\ \hline \hline \end{tabular}
\end{table}
Table 1: Related research on sugarcane stem node recognition.  
The image set collected was composed of images of one single sugarcane stem node and images of multiple sugarcane stem nodes at a ratio of 1:3 to improve the robustness of the algorithm model. These two types of images are shown in Figure 2. Then, 1600 images were expanded to 8000 images using data expansion to generate the training data set and testing data set. The training data set was 7200 images, and the testing dataset was 800 images at the ratio of 9:1.
# Durability of Fabric-Reinforced Cementitious Matrix (FRCM) Composites: A Review
## 5 Conclusions  
Inorganic-matrix composites have been increasingly used for strengthening and repairing existing masonry and concrete structures. However, limited studies are available regarding their long-term behavior. This paper presented a review of the available state of research on the durability of FRCM, SRG, and TRC composites. Various testing procedures and ageing conditions were used to investigate the durability of inorganic-matrix composites. Results indicated that their durability strongly depends on the matrix and fiber employed. Furthermore, the literature review showed that the following aspects still need to be investigated:  
1. Various conditioning environments were utilized in the literature to simulate the effect of outdoor exposure. Some of them were proposed by researchers and some others taken from standards for different materials. Therefore, the results could not be easily compared. In order to standardize specific conditioning environments for durability tests of FRCM, a thorough comparison among these conditioning environments is needed. 2. Studies reported contradictory results regarding the durability of FRCM lime-based matrices exposed to saline environments. The difference among the results was attributed to the variation in the porosity and pore radius. Further studies are required to correlate the physical properties of the matrix with its durability against various environments. 3. Limited studies are available regarding the corrosion of steel cords embedded within inorganic matrices. Studies of the performance of steel cords in corrosive environments are required to gain information on the durability of SRG composites. 4. The effect of wet-dry cycles on the durability of inorganic-matrix composites including glass fiber textiles can be influenced by the contrasting effects of hydration and densification. Therefore, more studies are needed to better understand the dominant effect. 5. The durability of the bond between matrix and fiber and composite and substrate was not thoroughly investigated yet. Studies on the effect of freeze-thaw and wet-dry cycles, alkaline environments, sulfate attack, and other exposures are needed to gain a clear and reliable understanding of the long-term behavior of externally bonded inorganic-matrix composites. Conceptualization, K.A.-L., T.D., and P.C. ; investigation, K.A.-L. and T.D. ; writing--original draft, K.A.-L.; writing--draft review, T.D. ; writing--editing T.D. and P.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research was performed with the financial support of the Politecnico di Milano. The authors declare no conflict of interest. Figure 11: Effect of sulfate and chloride attack on the load-carrying capacity of steel reinforced grout (SRG)–masonry [36] and FRCM–masonry [33] joints.
## 3 Model Definition
Figure 2: The spherical Bessel functions \(J_{l}(r)\) for \(l=0,1,2\) displaying possible charge density fluctuations in the sphere along the sphere radius \(r\) (arbitrary units) for volume plasmon modes; the angular distribution for these modes is governed by the real spherical functions \(Y_{lm}(\Omega)\) similarly as for the surface plasmon modes (cf. Figure 1 right). The exemplary temperature dependence of self-frequencies of volume plasmon modes \(\omega_{li}\), \(li=11,\ 12,\ 21\), for diluted electrolyte \(n\simeq 10^{14}\ 1/\text{m}^{3}\) and ion mass \(\sim 10^{4}m_{e}\), \(a\sim 50\) μm—right. The function \(F(\mathbf{r},t)\) describes volume plasmon oscillations, whereas \(\sigma(\Omega,t)\) describes the surface plasmon oscillations. Let us emphasize that the first term in the Equation (22) corresponds to surface self-oscillations, while the second term describes the surface oscillations induced by the volume plasmons. The frequencies of the surface self-oscillations equal to,  
\[\omega_{0l}=\omega_{p}\sqrt{\frac{l}{2l+1}}, \tag{23}\]  
which, for \(l=1\), is the dipole type surface oscillation frequency, described for metallic nanosphere by [PERSON] [6], \(\omega_{01}=\omega_{p}/\sqrt{3}\). _Ionic Surface Ion Plasmon Frequencies for the Electrolyte Nanosphere Embedded in a Dielectric Medium, with \(\varepsilon_{1}>1\)_  
One can now include the influence of a dielectric surroundings (in general, distinct from the inner one of considered ionic system) on plasmons in this system. In order to do it, let us assume that ions on the surface (\(r=a+\), i.e., \(r\geq a,\ r\to a\)) interact with Coulomb forces renormalized by the relative dielectric permittivity \(\varepsilon_{1}>1\) (distinct from \(\varepsilon\) for inner medium). Thus a small modification of Equation (19) is of order,  
\[ \begin{array}{l}\frac{\partial^{2}\delta\rho_{2}(\mathbf{r})}{\partial t^{2 }}=-\frac{2}{3m}\
abla\left\{\left[\frac{3}{5}\varepsilon_{F}n+\varepsilon_{F }\delta\rho_{2}(\mathbf{r},t)\right]\frac{\mathbf{r}}{\tau}\delta(a+ \varepsilon-r)\right\}\\ -\left[\frac{2}{3}\frac{\varepsilon_{F}}{m}\frac{\mathbf{r}}{\tau}\
abla \delta\rho_{2}(\mathbf{r},t)+\frac{\omega_{p}^{2}}{4\pi}\frac{\mathbf{r}}{r} \
abla\int d^{3}r_{1}\frac{1}{|\mathbf{r}-\mathbf{r}_{1}|}\left(\delta\rho_{1 }(\mathbf{r}_{1},t)\Theta(a-r_{1})\right.\right.\\ \left.\left.+\frac{1}{\varepsilon_{1}}\delta\rho_{2}(\mathbf{r}_{1},t)\Theta(r _{1}-a)\right)\right]\delta(a+\varepsilon-r),\end{array}  \tag{24}\]  
Note that Equation (18) is not affected by the outer medium. The solution of the above equation is of the same form as that for Equation (19), but with the renormalized surface plasmon frequencies,  
\[\omega_{0l}=\omega_{p}\sqrt{\frac{l}{2l+1}\frac{1}{\varepsilon_{1}}}. \tag{25}\]
# Energy Efficient Secure Communication Model against Cooperative Eavesdropper
###### Abstract  
In a wiretap channel system model, the jammer node adopts the energy-harvesting signal as artificial noise (jamming signal) against the cooperative eavesdroppers. There are two eavesdroppers in the wiretap channel: eavesdropper EI is located near the transmitter and eavesdropper E2 is located near the jammer. The eavesdroppers are equipped with multiple antennas and employ the iterative block decision feedback equalization decoder to estimate the received signal, i.e., information signal at E1 and jamming signal at E2. It is assumed that E1 has the channel state information (CSI) of the channel between transmitter and E1, and similarly, E2 has the CSI of channel between jammer and E2. The eavesdroppers establish communication link between them and cooperate with each other to reduce the information signal interference at E2 and jamming signal interference at E1. The performance of decoders depends on the signal to interference plus noise ratio (SINR) of the received signal. The power of information signal is fixed and the power of the jamming signal is adjusted to improve the SINR of the received signal. This research work is solely focused on optimizing the jamming signal power to degrade the performance of cooperative eavesdroppers. The jamming signal power is optimized for the given operating SINR with the support of simulated results. The jamming signal power optimization leads to better energy conservation and degrades the performance of eavesdroppers.  
simultaneous wireless information and power transmission (SWIPT); physical layer security; wiretap channel model; iterative block decision feedback equalization (IBDFE) Article
## 1 Introduction
### Motivation and Contributions  
In the proposed integrated NG-PON-FSO system, each beam with a phase profile and structured amplitude has one of \(N\) orthogonal modal basis modes. This orthogonality offers the multiplexing, demultiplexing, and spatial co-propagation of each independent data channel with low inherent crosstalk. A single LP[0,1] mode is used for the investigation, as the higher-order LP modes influence the communication compared to first-order modes. Inspired by this motivation, the proposed work contributions are described as follows:  
* An LP modal bidirectional and symmetric NG-PON-FSO system is designed.
* System performance is investigated for uplink and downlink transmission under the impact of a graded-index multimode fiber (GIMMF), link losses, and receiver noise.
* A comparative analysis of the proposed design with the existing ones is presented.  
This work is organized as follows: The proposed design, simulation parameters, generated modes, and optical spectra of various wavelengths are described in Section 2. Section 3 presents the evaluated results together with the analysis. Sections 4 and 5 illustrate a discussion and summarize the conclusion, respectively.
## 6 Closed-Loop Simulation
### Adaptability to Initial States  
This section mainly analyzes the robustness of the closed-loop algorithm under different initial states. According to the analysis in Section 4.1, if the initial state satisfies (18), the optimal landing time obtained by the DNN optimal predictor is reliable. Therefore, this section randomly selects 150 initial state combinations according to (18), and then performs a Monte Carlo simulation to analyze the landing accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\kappa\) & **0.95** & **0.98** & **1.02** & **1.05** \\ \hline position error (m) & 0.8299 & 0.3573 & 0.0025 & 0.0182 \\ velocity error (m/s) & 1.7474 & 0.5079 & 0.0201 & 0.0755 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Statistics of landing error with \(C_{\rm d}=1.0\).  
Figure 19: Comparison of the closed-loop thrust magnitude profiles between different disturbances.  
Selecting \(C_{\rm d}=1.0\) and \(\kappa=1.02\), the scatter plot of position error and velocity error in the horizontal direction can be obtained, as shown in Figure 20.  
Figure 20 shows that the rocket can achieve precision landing for all initial states. The position error of the landing in the horizontal direction is within 0.06 m, and the velocity error of the landing in the horizontal direction is within 0.4 m/s. It meets the requirement of safe landing. In addition, the excessive vertical speed at the terminal time will cause damage to the rocket structure, so it is also necessary to analyze the vertical speed of landing, as shown in Figure 21.  
The simulation results show that the vertical velocity of landing is less than 1.7 m/s, and most of the numerical examples are less than 1 m/s. The rocket trajectories under the random initial state are shown in Figure 22. The position components' profiles under the random initial state are shown in Figure 23.  
Figure 21: Errors of the vertical velocity.  
Figure 20: Errors of the horizontal position and velocity.  
The simulation results show that the rocket can accurately land under different initial conditions. Figure 23 shows that the landing time varies greatly under different initial values, with a maximum variety of nearly 40 s. The algorithm proposed in this paper can effectively and reliably solve the optimal landing time.
## 3 Results of Analysis of Ground Subsidence Risk Levels Using Machine Learning
### LightGBM (Light Gradient Boosting Machine)  
LightGBM (LGBM) is an algorithm in which a tree-based boosting technique is applied in the same manner as XGB. It has been used in solving regression and classification problems and in selecting the priority of importance of influencing factors. LGBM is advantageous for its fast operation speed because it derives a result using a method that reduces data characteristics by employing partial data only. Thus, LGBM processes bigdata quickly and with a high level of accuracy, and can derive the importance among the influencing factors used, advantages which have made it a popular choice [26].  
LightGBM calculates the loss function using cross-entropy. The equation for calculating the cross-entropy is presented in (5), where \(N\) is the number of samples, \(K\) is the number of classes, \(y_{i,j}\) refers to the binary variable indicating whether the i-th sample belongs to the j-th class, and \(p_{i,j}\) refers to the probability that the i-th sample belongs to the j-th class. LightGBM derives its results by learning to update the model while minimizing the CE received from the previous model [27].  
\[\text{CE}=\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{N}y_{i,j}\log\big{(}p_{i,j} \big{)} \tag{5}\]
# Analysis of a Tracked In-Pipe Robot's Obstacle-Crossing Performance
## 3 Results
### Prototype Testing and Analysis of the Robot
#### 3.4.2 Results of Prototype Testing  
The following presents test results for the obstacle-crossing height of the pipeline robot. As indicated in Table 1, the robot was capable of normal traversal within a horizontal pipe at a speed of approximately 4.2 m/min. When encountering obstacles below 7.5 mm in height, the robot demonstrated swift passage, characterized by a short traversal time and high speed. However, when attempting to cross a 7.8 mm obstacle, the robot's speed significantly decreased. The growth rate of passing time was highest when the obstacle height was 7.8 mm; based on Equation (10), and related to the current passing period (C) and the previous passing period (P), the traversal time markedly increased, suggesting that the robot struggled to surmount such a height. These findings corroborate the accuracy of the maximum obstacle-crossing height formula and highlight the challenges the pipeline robot faces when confronted with higher obstacles. The conclusions drawn from this simulation test are as follows: the maximum obstacle-crossing height of the pipeline robot is constrained by its dynamic performance and mechanical structure. Therefore, these factors should be thoroughly considered in future research and development efforts to enhance the robot's obstacle-crossing capability. For environments with high obstacles, optimizing the robot's structure or incorporating additional power devices could be explored to improve its traversal performance. Moreover, to more accurately assess the pipeline robot's obstacle-crossing ability, future research should incorporate a broader range of real-world scenarios for testing and analysis. Additionally, other variables, such as obstacle shape and pipe diameter, should be included in the scope of research to gain a more comprehensive understanding of the robot's performance across diverse environments.  
\[\text{Growth Rate = (C - P)/P} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Serial Number** & **Height (mm)** & **Passing Period (s)** & **Velocity (m/min)** & **Growth Rate (\%)** \\ \hline
1 & 0 & 4 & 4.2 & \(\backslash\) \\
2 & 5 & 5 & 0.24 & 25 \\
3 & 7.5 & 7 & 0.15 & 40 \\
4 & 7.8 & 15 & 0.045 & 114.29 \\
5 & 7.6 & 7.5 & 0.12 & 7.14 \\
6 & 7.7 & 8.5 & 0.08 & 13.33 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Testing results.  
Figure 14: Pipeline robot prototype for testing.