esa-sceva/satcom-chunk-collection huggingface dataset:
# Settlement Prediction and Differential Settlement Criterion for Heightening and Thickening Leveee
## 2 Methodology
### Generalized Settlement Prediction Model (GSPM)
#### 2.2.2 Further Explanation of Physical Meaning of Parameters  
The comparison of different prediction models has been listed in Table 1. The GSPM can describe both S-shaped and hyperbolic curves because it has four parameters. In fact, the present settlement data are sufficient to fit four parameters, and the specific fitting method is carried out using Mathematica.  
In this section, the physical meaning of parameters is explained via comparisons with different models. The following equation symbols are listed in Table 1. The explanation is as follows:  
First, the equivalence between the GSPM and logistic model will be discussed. For Equation (1), when \(b\to\infty\) then \(a(1-\mathrm{e}^{-\frac{t}{b}})\to a\).  
When \(-a=k\) and \(d=a_{2}\) and \(\frac{1}{c}=b_{1}\), the GSPM and logistic model are the same, i.e., the logistic model is a special case of the GSPM. Under these circumstances, the physical meaning of parameters from the GSPM can be explained from the perspective of the logistic model. Parameters \(a\) and \(k\) have the same physical meaning. Parameters \(d\) and \(a_{2}\) have the same physical meaning. Parameters c and \(b_{1}\) have the same physical meaning.  
Second, the equivalence between the GSPM and hyperbolic model will be discussed.  
The hyperbolic model can be written as follows:  
\[y(t)=\frac{t}{a_{1}+\mathrm{t}}S_{\infty}. \tag{2}\]  
However, when \(d=0\), the GSPM can be written as:  
\[y(t)=-a(1-e^{-\frac{t}{b}}). \tag{3}\]  
If the GSPM and the hyperbolic model are the same, then  
\[\left\{\begin{array}{c}-a=S_{\infty}\\ \frac{t}{a_{1}+t}=1-e^{-\frac{t}{b}}\end{array}\right.. \tag{4}\]  
Mathematica 11.3 will be used to solve Equation (4) and the result is shown as Equation (5):  
\[b=-\frac{t}{ln^{\frac{a_{1}}{a_{1}+1}}} \tag{5}\]  
Equation (5) can demonstrate that the hyperbolic model is a special case of the GSPM. Hence, the physical meaning of parameters from the GSPM can be explained from the perspective of the hyperbolic model. Parameters \(b\) and \(a_{1}\) have the same physical meaning in such a situation.  
Hence, equivalence between the GSPM and previous models (logistic model and hyperbolic model) has been explained. Moreover, the physical meaning of the GSPM parameters can also be explained based on the hyperbolic model and logistic model.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **GSPM** & **Logistic Model** & **Hyperbolic Model** \\  & & [29; 32; 33] & [13; 14; 34; 35] \\ \hline Expression & \(y(t)=-\frac{a(1-e^{-\frac{t}{b}})}{de^{-\frac{t}{b}}+1}\) & \(y(t)=\frac{k}{a_{2}e^{-b_{1}}+1}\) & \(y(t)=\frac{t}{a_{1}+\mathrm{t}}S_{\infty}\) \\ Descriptive ability & Both S-shaped and hyperbolic curve & Only S-shaped curve & Only hyperbolic curve \\ Number of parameters & 4 & 3 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different prediction models.
## 4 Application of Moisture Identification and Algorithm Comparison
### Comparison of Selection Algorithms
#### 4.2.3 Comparison of Identification Results  
The feature parameters processed by the Pearson coefficient method and PCA were respectively put into the SVM classifier, keeping all the parameters constant. The identification results are shown in Figure 9,c.  
The comparative results of the three feature selection methods are shown in Table 5. The running environments were the same, including the Win 7 professional system, Intel (R) Core (TM) i5-4590 CPU. The main frequency was 3.3 GHz, and the running memory was 8 G.
# Developing a Fire Monitoring System Based on MQTT, ESP-NOW, and a REM in Industrial Environments
## 3 Methodology
### The MQTT Protocol  
MQTT is a lightweight, open messaging protocol designed for machine-to-machine (M2M) communications. It operates over TCP/IP and is ideal for low-bandwidth, high-latency environments, such as industrial environments [15]. By adopting a publish-and-subscribe model, MQTT reduces unnecessary data transmissions, ensuring that only relevant information (i.e., fire alerts or environmental changes) is sent to the monitoring server. It addresses key challenges that wireless networks face in industrial environments, such as electromagnetic interference from machinery and the metal infrastructure [17].  
Studies have shown MQTT to be effective in various IoT applications, providing fast and efficient communication in a constrained environment [18]. A diagram depicting the MQTT publish-and-subscribe model, showing the flow of data, is given in Figure 2. This architecture ensures efficient communication by transmitting only relevant data (e.g., fire alerts or significant environmental changes), thereby reducing the load on the network. Studies have shown that MQTT is well-suited for high-latency, low-bandwidth environments, making it an ideal choice for fire detection systems where timely transmission of critical alerts is essential [19].
## 2 Design of the Proposed Feed Array
### 4 \(\times\) 4 Feed Array Design
Figure 8 shows the simulated cost variation according to the operating frequency. The solid line and dashed line indicate the cost variations of the proposed antenna and the half-wave dipole array, respectively. The results show that the proposed antenna can obtain a more uniform field distribution compared to the conventional half-wave dipole array. In particular, the near-field of the proposed antenna becomes closest to a uniform distribution from 2.6 GHz to 3.3 GHz with a cost of less than 5.
## 2 Basic Constructions  
The following definitions are used throughout the paper. Let  
\[\mathbf{E}=\left\{(x,y,z)\,\frac{x^{2}}{a^{2}}+\frac{y^{2}}{b^{2}}+\frac{z^{2} }{c^{2}}-1\leq 0\right\} \tag{1}\]  
be a given spheroid (ellipsoid of revolution) with \(c=b\).  
**Definition 1**.: _A set \(\mathbf{\Lambda}\subset R^{3}\) is called a cover (outer approximation) for the spheroid \(\mathbf{E}\) if \(\mathbf{\Lambda}\supset\mathbf{E}\)._  
The following extended spheroid, referred to as an \(\varepsilon\)-spheroid,  
\[\mathbf{E}(\varepsilon)=\left\{(x,y,z)\,\big{|}\,\frac{x^{2}}{\left(a+ \varepsilon\right)^{2}}+\frac{y^{2}}{\left(b+\varepsilon\right)^{2}}+\frac{z ^{2}}{\left(b+\varepsilon\right)^{2}}-1\leq 0\right\} \tag{2}\]  
can be considered as an outer approximation of the original spheroid \(\mathbf{E}\) with a given error \(\varepsilon\geq 0\).  
**Definition 2**.: _set \(\mathbf{\Lambda}(\varepsilon)\subset R^{3}\) is called an \(\varepsilon\)-cover if \(\mathbf{E}(\varepsilon)\supset\mathbf{\Lambda}(\varepsilon)\supset\mathbf{E}\), where \(\varepsilon\) is an error of the cover._  
In this paper, the \(\varepsilon\)-cover of the spheroid \(\mathbf{E}\) by spheres \(S_{k}\), \(k=1,\ldots,M\) is studied. The multi-spherical \(\varepsilon\)-cover is denoted by \(\mathbf{\Lambda}(\varepsilon,M)\), i.e., \(\mathbf{E}\subset\mathbf{\Lambda}(\varepsilon,M)=\underset{k=1}{M}S_{k}\).  
From a practical point of view, a \"good\" \(\varepsilon\)-cover \(\mathbf{\Lambda}(\varepsilon,M)\) must use a few circles \(M\) and provide a small approximation error \(\varepsilon\). However, we may expect that for sufficiently small \(\varepsilon\), decreasing \(\varepsilon\) in \(\mathbf{\Lambda}(\varepsilon,M)\) results in increasing \(M\), and vice versa. Thus, we cannot minimize \(\varepsilon\) and \(M\) simultaneously. Instead, our problem is formulated as follows:  
Find \(\mathbf{\Lambda}(\varepsilon,M)\), i.e., positions, radii and total number \(M\) of spheres, providing a reasonable balance between \(\varepsilon\) and \(M\).  
The following heuristic two-stage approach is proposed to construct the optimized \(\varepsilon\)-cover.  
**Stage 1**. For a given \(\varepsilon\), find the minimum number \(M^{*}\) of the spheres covering the given spheroid \(\mathbf{E}\); i.e., find \(\mathbf{\Lambda}(\varepsilon,M^{*})\). Here, the decision variables are the positions and radii of the circles and their total number \(M\).  
**Stage 2.** For \(M^{*}\) obtained at Stage 1, find a cover of the given spheroid \(\mathbf{E}\) with the minimal value \(\varepsilon^{*}\leq\varepsilon\); i.e., find \(\boldsymbol{\Lambda}(\varepsilon^{*},M^{*})\). Here, the decision variables are the positions and radii of the circles and the proximity parameter \(\varepsilon\).  
In what follows, constructing the cover \(\boldsymbol{\Lambda}(\varepsilon^{*},M^{*})\) is referred to as an Optimized Spherical Covering (OSC). Detailed descriptions of problems arising at Stages 1,2 and their solutions are presented in the next section.
# Performance Analysis of Cooperative Low-Power Wide-Area Network for Energy-Efficient B5G Systems
## 3 Error Performance Analysis
### Diversity Order  
In this section, we analyze the diversity order of the uplink cooperative technique with the exact BER expression (Equation (15)), which is defined as:  
\[\eta=-\lim_{\rho\rightarrow\infty}\frac{\log_{10}P_{b}}{\log_{10}\rho}, \tag{16}\]  
which is used to verify the tendency of BER performance; i.e., the slope of BER curve, in high SNR regime [17]. The diversity order is obtained through Taylor series expansion for (15), of which the first-order coefficient shows how the system parameters affect the performance in high SNR regime;e.g., the distances between ID and GWs, the number of antennas, pass-loss factor, and so on. An asymptotic expression is given by  
\[P_{b}\approx\beta_{b}\rho^{-K}=\frac{\Psi}{2^{K}}\prod_{i=1}^{N}d_{i} \alpha K_{i}\rho^{-K}, \tag{17}\]  
_Proof_: See Appendix B.  
where \(\Psi=\binom{2K-1}{K-1}\), \(K=\sum_{i=1}^{N}K_{i}\), and \(\beta_{b}\) denotes the first coefficient of Taylor series expansion for the BER. It varies according to the total number of antennas of all GWs that participate in cooperative communication. It also depends on the distances between the ID and the GWs. Plugging (17) into (16) shows that the diversity order of uplink cooperative LPWAN system with \(N\) GWs equipped with multiple antennas \(K_{i}\) is equal to \(K\); i.e.,  
\[\eta=-\lim_{\rho\to\infty}\frac{\log_{10}\left(\frac{\Psi}{2^{K}} \prod_{i=1}^{N}d_{i}\alpha K_{i}\rho^{-K}\right)}{\log_{10}\rho}=K. \tag{18}\]
## 4 Analysis of Influencing Factors
### Influence of the Horizontal Bearing Stiffness
#### 4.5.3 Tunnel Structural Stress  
Figure 19 illustrates the maximum tensile and compressive stresses experienced by the tunnel structure for each tested horizontal bearing stiffness. It can be seen from the figure that, comparing three steel bracings to five concrete bracings, the tensile stress is reduced by 2.61%, and the compressive stress is reduced by 11.28%. Increasing the horizontal bearing stiffness more effectively controls the compressive stress of the tunnel structure compared to the tensile stress.  
Figure 18: Tunnel maximum displacement for various horizontal bearing stiffnesses.
## 3 Results
### Connection with Limbic Structures  
3.4.1. Mesolimbic Projection of the SNc/Ventral Tegmental Area (VTA)  
The midbrain dopaminergic system is traditionally divided into the nigrostriatal pathway (SNc \(\rightarrow\) dorsal striatum) and the mesolimbic pathway, which leads from the VTA to limbic structures (ventral striatum, amygdala, prefrontal cortex). However, it appears that the SNc itself, particularly its dorsal region adjacent to the VTA, also contributes to limbic innervation [28]. Consequently, SN involvement in the limbic system is predominantly through projections to the ventral striatum and limbic cortex. The ventral striatum exerts its influence on circuits associated with reward and emotion. As demonstrated by [PERSON] et al. (2017) [66], the orbitofrontal cortex and the anterior cingulum receive dopaminergic inputs from the SNc and the VTA. In turn, these regions send fibers (directly or indirectly) back to the aforementioned regions, modulating the activity of dopaminergic neurons.
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 3 Proposed Method
### Loss Functions  
The network architecture of our proposed algorithm consists of two stages. In the first stage, we train the watermark embedding and extraction networks. The watermark embedding network takes the original watermark information \(W_{o}\) and the cover image \(I_{o}\) as inputs to generate the watermarked image \(I_{e}\). The watermark extraction network extracts the watermark \(W_{d}\) from the attacked image \(I_{a}\). The total loss function of this stage consists of three parts, as shown in the following formula,  
\[L_{stage1}=L_{mse}(I_{o},I_{e})+L_{pertotal}(I_{o},I_{e})+L_{mse}(LL_{2},LL_{2e}) +\lambda L_{mse}(W_{o},W_{d})+(1-\lambda)(W_{o},W_{z}) \tag{5}\]  
In addition, we use the watermark loss \(L_{mse}(W_{o},W_{d})\) to improve the accuracy of watermark extraction, the perceptual loss \(L_{perceptual}(I_{o},I_{e})\) to reduce the semantic differences between the two images, and the visual similarity loss \(L_{mse}(I_{o},I_{e})\), \(L_{mse}(LL_{2},LL_{2e})\) to enhance the visual similarity of the images. Here, \(L_{mse}\) represents the \(l_{2}\) loss, \(\lambda=1\) means contains a watermark, \(\lambda=0\) means its does not contain a watermark, and the perceptual loss is the \(l_{2}\) loss of the output features from the first three modules (\(F_{1}\), \(F_{2}\), \(F_{3}\)) of the pre-trained VGG16 network, expressed as the following equation.  
\[L_{pertotal}(x,y)=0.65L_{mse}(F_{1}(I_{o}),F_{1}(I_{e}))+0.3L_{mse}(F_{2}(I_{o} ),F_{2}(I_{e}))+0.05L_{mse}(F_{1}(I_{o}),F_{1}(I_{e})) \tag{6}\]  
The second phase focuses on embedding reversible information and training the watermark extraction network. The watermark embedding network follows the pre-trained network in the first stage, fixes the network parameters, and does not perform  
Figure 11: The proposed process of image recovery and watermark extraction.  
gradient backpropagation. Since embedding reversible information causes distortion loss for watermarked image \(I_{e}\), the watermark extraction network needs to continue training based on inheriting the training parameters from the first stage. The total loss function for this phase is shown in the following equation.  
\[L_{stage2}=\lambda L_{mse}(W_{o},W_{d})+(1-\lambda)(W_{o},W_{z}) \tag{7}\]  
There is only one watermark loss \(L_{mse}(W_{o},W_{d})\) that can improve the correct watermark rate.
## 3 MG-Based BTS Operation
### Processing and Energy Management Systems  
#### 3.4.1 Processing System  
As introduced in the general overview, the local controller is based on an SBC, which implements the local controller and an HMI (Human-Machine Interface). The local controller consists of a set of processes responsible for coordinating and controlling the different operating modes of the MG-based BTS. Among its main functions are collecting measurements from the SBC driver, the SoC estimation, the control of the energy flows through the EMS, and adding high-level features of the IEC61850 standard. A global controller in the shape of an IEC61850 client could perform control actions and monitoring tasks. Finally, the HMI provides an integrated interface, which displays the data collected from the SBC driver and control variables with the aim of facilitating the manual intervention in the MG-based BTS operation.  
The processes comprising the local controller and the HMI can be categorized in three levels: (i) the physical interaction level in which the data from the sensors and the BTS parameters is obtained; (ii) the logic level which is based on the developed IEC61850 Server and implements the EMS, the high-level functionalities, and the modification of the IEC61850 plant model; and (iii) the HMI.  
The processes are executed at fixed time intervals depending on the level they are in and the data dependency among them. Those processes in the physical interaction level and those implementing the HMI have the smallest execution interval (2 s) for efficient operation, since the HMI must display the data collected by the processes in the physical interaction level. The execution interval for the processes in the logic level is set to 5 s. It is important to note that the Raspberry PI OS does not feature real-time capabilities. Therefore, the definition of the execution intervals depends on the process execution time.
## 5 Influence of Permeability Coefficient in Aquitard
### Deformation Simulation with Waterproof Curtain  
Figure 9 shows the change of horizontal displacement of bored piles with the change of permeability coefficient, and Figure 10 shows the change of settlement amount with the change of permeability coefficient. Figures 11 and 12 are flow network diagrams of foundation pits with different permeability coefficients. All of the above are with a waterproof curtain. The research conclusion shows that the increase of the permeability coefficient of the weakly permeable layer leads to the decrease of the horizontal displacement and the increase of the surface settlement. It is easy to find out by observing the flow network diagram:  
Figure 9: Horizontal displacement of different permeability coefficients with waterproof curtain.  
(1) The interval between the iso-water head lines in the upper area of the weakly permeable layer is larger than that in the lower part, and the water head at the same position increases while the pressure head decreases. The smaller the permeability coefficient of the weakly permeable layer, the larger the interval distance between the isohydraulic lines, and the larger the water head at the same position, which is considered to be the reason for the reduction of the surface settlement outside the pit.  
(2) As the permeability coefficient of the aquitard decreases, the iso-water head line shifts at the boundary of the aquitard, the seepage curve occurs and the seepage obstruction  
Figure 11: Flow network diagram with permeability coefficient of \(5\ \times\ 10^{-2}\) m\(\cdot\)d\({}^{-1}\).  
Figure 10: Settlement of different permeability coefficients without waterproof curtain.  
increases, the curve is not dense, the smaller the permeability coefficient of the aquitard, the offset angle bigger.  
(3) After the permeability coefficient of the weakly permeable layer increases, both the horizontal and vertical seepage increase, and the increase is obvious near the bored pile. The seepage velocity in the lower part of the weakly permeable layer is obviously higher than that in the upper part. The weakly permeable layer will cause the pore water pressure on the left and right sides of the bored pile to increase, while the pore water pressure on the left side increases more, and the pore water pressure on the right side increases less. It is considered that this is the reason why the horizontal displacement of bored piles increases as the permeability coefficient decreases. In this model, the weakly permeable layer is located just near the precipitation height of the foundation pit, and has the characteristics of downward seepage in the upper part and upward seepage in the lower part.
## 2 Related Works
Moreover, many SLAM applications such as the Google self-driving car, which is based on high definition map and indoor navigation using priori map, do not require a moving platform to navigate in unknown environments. Therefore, this research aims at proposing a novel RGB-D SLAM approach that, along with deep learning, utilizes a highly accurate priori LiDAR point cloud data as guidance for indoor 3-D scene construction and camera pose estimation. The contributions of this research can be summarized as follows. 1. Using an effective combination of priori LiDAR data and deep learning, an approach for SLAM system automatic initialization and motion recovery in complex indoor environments, is provided. 2. Priori LiDAR data of the indoor scene, along with the multithreaded segment-based map double window optimization strategy, is used to eliminate cumulative error in camera pose and map construction, as well as improve the real-time performance. 3. A feasible solution is provided for automatic matching of 3-D LiDAR data and 2-D image data from the RGB-D sensor in large indoor scenes, and fusing the 2-D and 3-D data to construct an accurate environmental model of the scene, thereby compensating the deficiency of single data source. 4. To ensure accuracy, high real-time performance, and robustness, priori LiDAR data and deep learning are successfully integrated into ORB-SLAM2, creating a complete and novel SLAM framework.
# Towards Strengthening the Resilience of IoV Networks--A Trust Management Perspective
## 4 Open Research Directions
### Threshold Setting  
The purpose of trust management is to divide the vehicles in the system into the categories of trustworthy and untrustworthy, so it is usually necessary to set a trust threshold. Those above the trust threshold are trusted nodes; otherwise, they are malicious nodes. Thus, to improve accuracy, it is extremely important to set a threshold. Either too high or too low a threshold will threaten the security of the system. However, the thresholds in the existing literature are all steady, but the vehicle network is a dynamic system, and it is necessary to set an adaptive threshold. The threshold value can be adjusted in real time with the number of vehicles, environment, and other factors.
## 4 Slope Excavation Stability Analysis
### Slope Stability Analysis of Different Excavation Schemes
#### 4.1.2 Scheme No.2 Plastic Zone Analysis  
The change in the plastic area of the slope during the excavation process can reflect the stability of the slope. To further analyze the slope stability of the proposed scheme, a numerical simulation of step-by-step excavation was carried out on the slope of Scheme No.2, and the change in the plastic zone is shown in Figure 6.  
Figure 6: Changes in the plastic zone during excavation of Scheme No.2. (**a**) The first-class slope excavation; (**b**) the second-class slope excavation; (**c**) the third-class slope excavation; (**d**) the fourth-class slope excavation; (**e**) the fifth-class slope excavation; (**f**) the sixth-class slope excavation.  
Through the analysis of Figure 6a,b, we found that after the first-class excavation, the slope plastic zone calculated by Midas GTS does not belong to the slope formed by the excavation, but belongs to the original slope. Instead, the calculation of the plastic zone formed by the excavation slope starts after the second-class slope excavation. This is because Midas GTS only calculates the stability factor of the most dangerous slope when calculating the stability factor of a model with multiple slopes. However, the stability coefficient of the excavation slope formed after the first-class excavation was larger than that of the original slope, which is the most dangerous slope. The stability coefficient of the excavated slope formed after the second-class excavation was smaller than the original slope, and the excavated slope is the most dangerous.  
Based on Figure 6c-f, we found that there was an obvious relaxation deformation zone when the slope was excavated to the fourth-class slope. With the increase in the excavation depth, the scope of the relaxation deformation zone was further expanded, and the relaxation deformation zone developed into a potential plastic run-through zone after the sixth excavation. After the sixth-class excavation, the potential landslide shear exit appeared at the location of the collapsed mining face.  
Combined with the analysis of Scheme No.2 in Figure 5, the stability coefficient of the slope body gradually decreased from 3.3 to 1.04 under normal conditions with the increase in excavation depth, and the stability coefficient of the slope body decreased from 3.2 to 1.01 under rainfall conditions. The overall stability of the slope body was good when the excavation reached the fourth class. When the excavation reached the fifth class, the overall stability decreased, and the stability changed from stable to less stable, indicating that reinforcement measures needed to be taken.