esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Controller Design
### Control of IPA System (Inner Loop)
#### 3.1.2 The Cascade Fractional-Order PID (CFOPID) Controller  
The CFOPID structure described in this paper is depicted in Figure 9. The suggested structure is made up of two controllers, PI\({}^{\lambda}\) and PD\({}^{\mu}\), which are connected in a cascade. Equation (12) expresses the system's control signal:  
\[\text{C2(s)}=\frac{\text{U(s)}}{\text{E(s)}}=\left(K_{\text{p}}+\frac{\text{K }_{\text{i}}}{\text{S}^{\lambda}}\right)\cdot\left(K_{\text{p1}}+\text{K}_{ \text{d}}\text{S}^{\mu}\right) \tag{12}\]  
Figure 8: Graphical representation of FOPID controller.  
Figure 7: The FOPID controller structure.  
This controller's performance must be optimized to improve transient responsiveness while also minimizing error. Six parameters must be modified in order to achieve this goal: \(\mathrm{K_{p}}\), \(\mathrm{K_{i}}\), \(\mathrm{K_{d}}\), \(\mathrm{K_{p1}}\), \(\lambda\), and \(\mu\).
## 3 Results and Discussion  
### Spontaneous Combustion Stage Division and Index Selection  
This section uses the LN samples as an example to select the indicator gases and divide the spontaneous combustion of coal stages while considering the existing research [24]. As shown in Figure 2, the process of the spontaneous combustion of coal is divided into six stages.  
* Stage I: oxidation stage (t < 50 \({}^{\circ}\)C). At this stage, coal begins to slowly oxidize, and every forecast indicator rises slowly. This stage can be characterized by the CO concentration, which is less than 17.225 ppm, and the spontaneous combustion of coal.
* Stage II: self-heating stage (50 \({}^{\circ}\)C < t < 0 \({}^{\circ}\)C). At this stage, the increase in CO/O\({}_{2}\) is accelerated, and the reaction rate of coal and oxygen is slowly increased. The accumulated heat can increase the temperature of coal.
* Stage III: accelerating stage (70 \({}^{\circ}\)C < t < 90 \({}^{\circ}\)C). The main indication that this stage has been entered is the appearance of C\({}_{2}\)H\({}_{4}\), which indicates that coal oxidation is beginning to accelerate, and the rapid increase in C\({}_{2}\)H\({}_{6}\) and CO/O\({}_{2}\). After this stage begins, the spontaneous combustion of coal will be difficult to control.  
Figure 1: Coal oxidation and temperature rise experimental system.  
Stage IV: intense stage (90 \({}^{\circ}\)C \(<\) t \(<\) 120 \({}^{\circ}\)C). The indication that this stage has begun is the presence of C\({}_{2}\)H\({}_{6}\)/CH\({}_{4}\) at its maximum value. At this time, the coal sample reaction is intense, and C\({}_{2}\)H\({}_{4}\) begins to increase rapidly.
* Stage V: pyrolysis stage (120 \({}^{\circ}\)C \(<\) t \(<\) 160 \({}^{\circ}\)C). This stage is marked by the maximum amount of C\({}_{2}\)H\({}_{4}\)/C\({}_{2}\)H\({}_{6}\), followed by a slower rate of increase.
* Stage VI: fission stage (t \(>\) 160 \({}^{\circ}\)C). At this stage, the formation rate of C\({}_{2}\)H\({}_{4}\) is lower than C\({}_{2}\)H\({}_{6}\), while the maximum C\({}_{2}\)H\({}_{4}\)/C\({}_{2}\)H\({}_{6}\) value appears.
## 3 Results
### Electrochemical Performance
For example, the N-doped carbon aerogel possessed a specific capacitance of 185.8 F g\({}^{-1}\) at 1 A g\({}^{-1}\), which was higher than the values for the carbon aerogel (155 F g\({}^{-1}\) at 1 A g\({}^{-1}\)), and the specific capacitance of the N-doped carbon aerogel at 10 A g\({}^{-1}\) remained 105 F g\({}^{-1}\), while the carbon aerogel only showed capacities of 84 F g\({}^{-1}\), suggesting the beneficial effects of the N heteroatoms doped into the carbon aerogels. Moreover, a specific capacitance of 105 F g\({}^{-1}\) was maintained for the N-doped carbon aerogel at 10 A g\({}^{-1}\), which demonstrates good performance stability. Furthermore, the specific capacitance of the N-doped carbon aerogel was higher than other N-doped carbon materials, such as heteroatom-doped carbon nanofiber aerogels (171.2 F g\({}^{-1}\), 0.5 A g\({}^{-1}\)) [60], sulfur and nitrogen dual-doping carbon materials (170 F g\({}^{-1}\), 1 A g\({}^{-1}\)) [61], and KOH-activated carbon aerogels (152.6 F g\({}^{-1}\), 0.5 A g\({}^{-1}\)) [62]. Figure 5f shows the electrochemical impedance analysis of the carbon aerogel and N-doped carbon aerogel at an open circuit potential (0.01 Hz to 100 kHz). The low-frequency region of the Nyquist spectrum of the N-doped carbon aerogel was much closer to vertical lines, which confirms the dominance of the electric double-layer capacitor as well as the good capacitive performance. In addition, the length of the Warburg-type lines (the 45\({}^{\circ}\) segment) was short, which indicates low resistance and high ion diffusion efficiency for ion transport. At high frequency, the appearance of the semicircle indicates that the N-doped carbon aerogel has a very low charge transfer resistance and ion diffusion resistance. The equivalent series resistance obtained from the first intersection with the real axis is only 0.5 \(\Omega\), further confirming a high charge transfer rate between the electrolyte and active materials.
## 2 Materials and Methods
### Simulation of SIF\({}_{760}\), GPP, and ET  
#### 2.3.1 The SCOPE Model  
The SCOPE model is a one-dimensional vertical integrated radiative transfer and energy balance model capable of simulating the radiative transfer processes of the vegetation-atmosphere-transport (SVAT) model, which can be applied to quantitative studies of CO\({}_{2}\), H\({}_{2}\)O, and heat flux exchanges with the terrestrial atmosphere and canopy fluorescence spectroscopy [47]. The model consists of several modules such as radiative transfer and energy balance: the radiative transfer module consists of the leaf radiative transfer model Fluspect and the canopy radiative transfer module RTMo based on the SAIL model. The Fluspect module, which can simulate the radiative transfer of reflected light, transmitted light, and fluorescence inside the leaf, is an extension of the PROSPECT model with the addition of radiative transfer of the SIF on the leaf scale [26; 48]. The energy balance module is primarily a biochemical model for calculating the photosynthetic rate and the proportion of absorbed radiation converted to fluorescence in plants. We used version 2.1 of the SCOPE model to simulate the half-hourly variations in SIF\({}_{760}\), GPP, ET, and fluorescence escape probability (f\({}_{\text{esc}}\)) in the vegetation canopy. The input parameters of the SCOPE modelinclude leaf optical parameters, leaf biochemical parameters, canopy structural parameters, and meteorological parameters. The details of the inputs are shown in Table 1.
# Weed Detection Method Based on Lightweight and Contextual Information Fusion
## 3 Experimental Validation and Results Analysis
### Comparative Experiments with Different IoU and Soft-NMS-X  
To evaluate the impact of different loss functions on the model's detection performance, experiments comparing classic loss functions were conducted. Furthermore, to assess the effectiveness of combining Soft-IoU with traditional loss functions, experiments with various combinations were performed. The experimental results are shown in the following figure. In Table 2, the following abbreviations are used: G, D, E, S, W, M represent GIoU [37], DIoU [38], EIoU, SIoU [39], WIoU [40], and MPDIoU [41] loss functions, respectively. The YOLOv5 model with the introduced Soft-NMS structure is denoted as YOLOv5-SN.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Params (M)**} & \multirow{2}{*}{**FLOPs@640 (B)**} & \multicolumn{2}{c}{**[EMAIL_ADDRESS]**} & \multirow{2}{*}{**[EMAIL_ADDRESS]:0.95(\%)**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall (\%)**} \\  & & & & & & & \\ \hline yolov5s & 7.1 & 15.8 & 76.3 & 53.5 & 76.9 & 71.5 \\ yolov5s + G & 7.1 & 15.8 & 77.7 & 53.7 & 76.3 & 72.5 \\ yolov5s + D & 7.1 & 15.8 & 77.3 & 53.7 & 76.9 & 72.4 \\ yolov5s + E & 7.1 & 15.8 & 77.8 & 54.2 & 76.9 & 73.0 \\ yolov5s + S & 7.1 & 15.8 & 77.9 & 54.0 & 76.5 & 72.7 \\ yolov5s + W & 7.1 & 15.8 & 75.8 & 52.8 & 76.2 & 71.6 \\ yolov5s + M & 7.1 & 15.8 & 75.8 & 53.0 & 76.6 & 71.2 \\ yolov5s + SN & 7.1 & 15.8 & 77.3 & 57.5 & 76.4 & 71.3 \\ yolov5s + SN + D & 7.1 & 15.8 & 77.6 & 57.5 & 76.7 & 72.4 \\ yolov5s + SN + E & 7.1 & 15.8 & 77.6 & 57.9 & 76.8 & 73.0 \\ yolov5s + SN + G & 7.1 & 15.8 & 77.7 & 57.4 & 76.2 & 72.6 \\ yolov5s + SN + S & 7.1 & 15.8 & 76.7 & 57.2 & 77.3 & 70.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative experiments of YOLOv5s with different loss functions.  
From Table 2, it is evident that the inclusion of different loss functions in YOLOv5 has a notable impact on the network's detection results. Compared to YOLOv5s, the addition of EIoU results in a 1.6% improvement in [EMAIL_ADDRESS], a 0.7% improvement in [EMAIL_ADDRESS]:0.95, and a 1.5% increase in recall. Among various IoUs, the incorporation of EIoU yields the most significant improvements.  
When Soft-NMS-EIoU is introduced into the YOLOv5s network and compared with other combinations of IoUs and Soft-NMS, it achieves the best results in [EMAIL_ADDRESS]:0.95 and recall performance metrics while maintaining similar performance in other metrics. Compared to the original YOLOv5s network, with the same parameters and computational load, it enhances [EMAIL_ADDRESS] by 1.3%, [EMAIL_ADDRESS]:0.95 by 4.4%, and recall by 1.5%, with only a minor sacrifice of 0.1% in precision. Compared to EIoU, Soft-NMS-EIoU added to YOLOv5 shows a 3.5% improvement in [EMAIL_ADDRESS]:0.95, with similar performance in other metrics.  
Overall, the addition of Soft-NMS-EIoU to YOLOv5s significantly enhances the network's detection performance.
# A Public Infrastructure for a Trusted Wireless World
## 2 Background
### Public Key Cryptography  
Public key cryptography is the same as asymmetric cryptography. The two terms can be used interchangeably. Public key cryptography began in 1976 with a paper publication by [PERSON] and [PERSON] describing a method of establishing a common key in a secure manner over an insecure channel [9].  
Each entity (person or device) that uses public key cryptography has a key pair that consists of a public key and a private key. Private keys are secret and known only to their owners. They are protected by a passphrase and can be stored on separate hardware cryptographic devices such as smart cards. Private keys are used for proving the identity of an entity. Public keys are made known openly and are distributed to all hosts with which the entity wants to securely communicate. The two keys are mathematically dependent, but the private key cannot be derived from the public key. The data encrypted with the public key can only be decrypted with the private key and vice versa. Since the public key is shared freely, a method to ensure the authenticity of the public key is created through a public key certificate. A public key certificate is an electronic document used to prove the ownership of a public key. This ensures trust that the public key belongs to the entity it is associated with. This is most commonly done using the X.509 standard. X.509 is a standard defining the format of the digital certificates used to validate the ownership of a public key. This standard allows interoperability among numerous tools and applications among vendors [10].
# Research on the Positioning Method of Steel Belt Anchor Holes Applied in Coal Mine Underground
## 2 Overall System Design  
### Hardware Platform Construction  
After laying the steel belt at the top of the coal mine tunnels, it is necessary to locate the anchor holes of the steel belt, and use the robotic arm to move the detection platform to a position close to the steel belt, and then use the mobile platform to carry inductive sensors for detecting the steel belt. The detection platform was built in the laboratory, and the schematic diagram of the platform structure is shown in Figure 1.  
The XY mobile platform consists of two slides which enable the sensor to move along the X and Y axes. Each slide is equipped with a grating ruler to record the sensor's position in real time. The controller is the central hub for system control and data processing, with a motor driver controlling the stepper motor to provide power for the detection platform.  
Figure 2 presents the overall layout of the detection system. The coordinate paper is laid out beneath the platform to verify the positioning accuracy of anchor holes on the steel belt. The detection surface of the sensor is covered with a small amount of dust to simulate the dusty environment of a coal mine tunnel.
# Numerical Modeling of 3D Slopes with Weak Zones by Random Field and Finite Elements
###### Abstract
[33] used two-phase random media to simulate the slope of soil-rock mixtures, and characterized the spatial variability of the soil [34] and rock layers of the weak zones, analyzing the failure mechanism of the soil-rock mixture slope with weak zones. A comparison of the slope stability analysis methods in the literature in presented in Table 1, including not only slope stability analysis, but also the analysis of the influence of weak zones on slope stability. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Method** &
\begin{tabular}{c} **Investigation** \\ **Approach** \\ \end{tabular} & **Remarks** \\ \hline [PERSON] et al., 1999 [35] & Limit equilibrium method & Analytical & Simple calculation method, only suitable for regular slopes \\ [PERSON] \& [PERSON], 1999 [36] & Strength reduction & Numerical & Calculate more complex models, but need to predict the slip surface in advance \\ [PERSON] \& [PERSON], 2009 [4] & Centrifuge modeling & Physical model & Show the plastic failure but cannot reflect the complex engineering geological conditions \\ [PERSON] et al., 2013 [37] & Finite element strength reduction method & Numerical & Reflect the influence of the weak zones on the stability of the slope, but cannot reflect the spatial heterogeneity of the weak zones \\ [PERSON] et al., 2018 [33] & Two-phase random media & Numerical & Reflect the characteristics of the rock-soil slope, but limited to a two-dimensional model, and cannot reflect the spatial variability of the slope shape \\ [PERSON] et al., 2020 [38] & Discrete element method & Numerical & Simulate the nonlinear large deformation characteristics, but have low solution efficiency for complex models \\ [PERSON] et al., 2021 [28] & Random finite element method & Numerical & Characterize the stratification of the soil inside the slope, but cannot reflect the slope of the soil–rock mixture. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of typical investigations on the analysis of slope stability. This research aims to estimate the stability of a 3D corner slope with weak zones, introducing a two-phase random media model to invert the spatial distribution of rock and weak zones. Based on the basic theory of random fields, a mathematical theoretical model for characterizing rock weak zones using two-phase random media and a simulation method for the spatial inclination of weak zones are proposed. In this study, using the USDFILD platform in ABAQUS software, a Fortran program corresponding to the proposed model is developed, and 3D finite element modeling of slopes with weak zones is realized. The two-phase random media model and the finite element method are combined to quantitatively evaluate the safety factor of the slope, offering technical support for stability studies of slopes with weak zones.
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 1 Introduction
### Contributions  
In this paper, we investigate FANET networking in high-mobility scenarios and propose a Kalman-filter-assisted networking algorithm. Based on the proposed method, we analyze the cluster stability and networking overhead cost. The major contributions of this paper are presented as follows.  
1. A FANET clustering algorithm is proposed in this paper. First, the Silhouette coefficient is used to determine the number of clusters for given nodes' positions and velocities. It aims to find an optimal number of clusters with the highest average coefficient value. Then, the \(k\)-means++ algorithm is used to group all nodes into clusters periodically. When calculating nodes' distance, both position and velocity differences are considered. Moreover, the CHs of the previous iteration are used to initialize the centroids of the current iteration.
2. A high-mobility FANET networking scheme is proposed based on the linear Kalman filter. To model nodes' mobility influenced by external interferences, we use the Gaussian-Markov process to characterize nodes' mobility influenced by external interferences. Specifically, we establish the state transfer relationship of position and velocity between two adjacent times and the Kalman. On this basis, the Kalman filter is utilized to predict the positions and velocities of all nodes.
3. This paper further evaluates the networking performance from three aspects, i.e., the cluster stability, ratio of changed edges based on the abstracted graph and computational complexity. The first metric measures the relative position and velocity difference within a cluster, whereas the second metric reflects the required communication overhead costs during the network evolution.
4. Extensive simulations are conducted to evaluate the performance of our proposal under different node trajectories and velocities. Thereafter, we compare it with the existing FANET networking algorithms. The simulation results show that the  
Figure 1: A diagram of the FANET: nodes are organized into clusters. The network topology varies as time proceeds.  
proposed scheme outperforms the conventional schemes marginally from the aspects of cluster stability coefficient and ratio of changed edge numbers. Nonetheless, it presents a relatively low computational complexity in contrast to other algorithms; this implies that the proposed scheme is applicable in high-mobile scenarios, which require fast and reliable networking.  
The remainder of this paper is outlined as follows. Section 2 present a review of related work. In Section 3, we describe the stochastic mobility model of the FANET node and introduce the prediction method based on the Kalman filter. In Section 4, the clustering method based on \(k\)-means++ is elaborated, and the performance evaluation metric includes cluster stability and overhead cost. The simulation and numerical results are presented and discussed in Section 5. Relevant conclusions are finally drawn in Section 6.
# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
## 3 Foundation Model-Enhanced Coastal Land Cover Recognition
### Water Area Extraction with Foundation Model  
We employ a state-of-the-art foundation model regarding semantic segmentation called the Segment Anything Model (SAM) [29] to segment the satellite image into water and non-water regions for extracting water areas. The SAM is an innovative deep learning model for image segmentation, which enables conducting new tasks without the need for prior knowledge. As a foundation model, the SAM is developed by training on the extensive SA-1B dataset, which includes over 1 billion masks distributed across 11 million well-labeled images.  
The architecture of the SAM includes the image encoder, the prompt encoder, and the lightweight mask decoder. The image encoder focuses on extracting and learning features from the training images. The prompt encoder focuses on extracting and learning features from masks labeled in sample images. The prompt includes three types: point prompt, rectangle prompt, and polygon prompt. In the proposed approach, we randomly generate the point prompts from water samples. Each point prompt is the pixel labeled as water. The lightweight mask decoder aims to fuse features from the image and the prompt to generate the segmentation results.  
Considering the powerful capability of domain transferring of the SAM, we generate 1000 point prompts to fine tune SAM and then segment satellite images into water and non-water areas.
## 2 Materials and Methods
### Reference UM-VLC Channel Model
#### 2.3.1 Geometry  
Before defining the channel components, it is convenient to define some geometrical parameters. Let us consider an underground tunnel of dimensions \(L_{x}\times L_{y}\times L_{z}\) (m), corresponding to its length (\(x\)-axis), width (\(y\)-axis), and height (\(z\)-axis), respectively. The TX or LED is positioned in the fixed coordinates \(\mathbf{T}=[x_{\text{tx}},y_{\text{tx}},z_{\text{tx}}]^{\mathsf{T}}\) (m), whereas the receiver position (RX) or the PD position, \(\mathbf{R}=[x_{\text{rx}},y_{\text{rx}},z_{\text{rx}}]^{\mathsf{T}}\) (m), can vary. Having defined these parameters, the distance between the LOS links, \(d\), is given by  
\[d=||\mathbf{R}-\mathbf{T}||\,. \tag{29}\]  
Consider \(\mathbf{n}_{\text{tx}}=[n_{\text{tx}}^{x},n_{\text{tx}}^{y},n_{\text{tx}}^{z} ]^{\mathsf{T}}\) as the TX normal vector and \(\mathbf{n}_{\text{rx}}=[n_{\text{rx}}^{x},n_{\text{rx}}^{y},n_{\text{rx}}^{z} ]^{\mathsf{T}}\) as the RX normal vector. Using the same definitions as [7], the RX tilt angle, \(\beta_{\text{rx}}\), is defined as the angle from the \(z\)-axis vector, \([0,0,1]^{\rm T}\) to \(\mathbf{n}_{\rm rx}\), whereas the TX tilt angle, \(\beta_{\rm rx}\), is defined as the angle from the negative \(z\)-axis vector, \([0,0,-1]^{\rm T}\) to \(\mathbf{n}_{\rm rx}\). Both tilt angles' definitions are illustrated in Figure 1b. Assuming that TX is higher on the \(z\)-axis than RX, then we want the tilt angles to have values \(\beta_{\rm tx},\beta_{\rm rx}\in[0,\frac{\pi}{2}]\).  
We denote \(\mathbf{n}_{\rm tx}^{\rm proj,z}=[n_{\rm tx}^{x},n_{\rm tx}^{y},0]^{\rm T}\) as the projection of \(\mathbf{n}_{\rm rx}\) onto the \(x\)-\(y\) plane and \(\mathbf{n}_{\rm rx}^{\rm proj,z}=[n_{\rm rx}^{x},n_{\rm rx}^{y},0]^{\rm T}\) as the projection of \(\mathbf{n}_{\rm rx}\) onto the \(x\)-\(y\) plane. Using the same definitions as [7], the RX rotation angle, \(\alpha_{\rm rx}\), is defined as the angle from the \(x\)-axis vector, \([1,0,0]^{\rm T}\) to \(\mathbf{n}_{\rm rx}^{\rm proj,z}\), whereas the TX rotation angle, \(\alpha_{\rm rx}\), is defined as the angle from the \(x\)-axis vector, \([1,0,0]^{\rm T}\) to \(\mathbf{n}_{\rm rx}^{\rm proj,z}\). The definitions of both rotation angles are illustrated in Figure 1c. A priori, there are no restrictions on the rotation angle values, i.e., \(\alpha_{\rm tx},\alpha_{\rm rx}\in[0,2\pi)\).  
With these definitions, we can express \(\mathbf{n}_{\text{tx}}\) and \(\mathbf{n}_{\text{rx}}\) by [7]  
\[\mathbf{n}_{\text{tx}}=\begin{bmatrix}\sin(\beta_{\text{tx}})\cos(\alpha_{\text{ tx}})\\ \sin(\beta_{\text{tx}})\cos(\alpha_{\text{tx}})\\ -\cos(\beta_{\text{tx}})\end{bmatrix}\quad\text{and}\quad\mathbf{n}_{\text{rx }}=\begin{bmatrix}\sin(\beta_{\text{rx}})\cos(\alpha_{\text{rx}})\\ \sin(\beta_{\text{rx}})\cos(\alpha_{\text{rx}})\\ \cos(\beta_{\text{rx}})\end{bmatrix}. \tag{30}\]