esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
## 3 Path Loss and Antenna Model
Antenna gain \(G(\theta)\) is the \(A(\theta)\) normalized for unit gain, i.e., for same total radiated power as with an omni-directional antenna with \(G(\theta)=0\) dB. The BS is assumed to cover \(360^{\circ}\) with \(N\) antenna beams with 3 dB beam overlap, i.e., \(\theta_{\rm 3 dB}=360^{\circ}/N\). Examples of the omnidirectional and directive antenna patterns are illustrated in Figure 2. Only the omni-directional and 8-sector BS antennas (\(N=8\), \(\theta_{\rm 3 dB}=45^{\circ}\), \(\max(G)=9\) dB) are taken as examples in this paper. The MS has omni-directional antenna. The approximation (3) assumes that (most of) the power is near the direct line between BS and MS. More accurately, the antenna gain is applied to the multipaths that may arrive/depart at any angle [14]. Additionally, the user effect, e.g., [36; 37; 38], and polarization of antennas and the radio channel, e.g., [18], are ignored in this study. Nevertheless, the simplistic approximation is assumed, as it allows simple simulations with directive antennas to examine the influence of the noise-limited censored PL. In the simulations conducted in this study, we considered three example PL distributions that were used to create the true PL samples in both training and positioning phases. These examples were:  
* Example 1, in which, the parameter values are selected as approximate median values given in 3 GPP model [14] for various environments including both LOS and NLOS in both outdoor and indoor environments. For simplicity we do not distinguish between LOS and NLOS, nor do we specify the used radio frequency. The model (1) and (2) is used with parameters \(\alpha=4\), \(\beta=60\) dB, \(\sigma=7\) dB, and noise threshold at 140 dB [13]. * Example 2 is similar to Example 1, except that the parameter values were taken from a channel measurement campaign [10]. In [10], the parameters, when the censored PL data points are taken into account, are \(\alpha=2.2\), \(\beta=51\) dB, \(\sigma=7.6\) dB, and noise threshold at 95 dB. * Example 3, the third example is the 3 GPP path loss model for urban microcellular scenarios at 2.0 GHz and 28 GHz frequencies [14]. The model is a dual-slope model with different parameters for LOS and NLOS. The LOS-state is defined by a link distance-dependent LOS probability model. In all of the examples, we assume that the PL statistics are stationary and use the same PL model for all BSs [5; 12; 30; 39]. The BS antenna is either an omni-directional or an eight-sector directive antenna that covers \(360^{\circ}\) with \(\theta_{\rm 3 dB}=45^{\circ}\) and \(\max(G)=9\) dB. The model-fitting results are presented in Section 5 and the positioning simulations in Section 7. Figure 2: Antenna gain as a function of the offset angle \(\theta\). The directive beam patterns have beam-width of \(\theta_{\rm 3 dB}=360^{\circ}/N\).
# Physical-Layer Security in Power-Domain NOMA Based on Different Chaotic Maps
## 4 Simulation Results
### Security Analysis
#### 4.1.2 Information Entropy Analysis  
The information entropy of an image can reveal its information repeatability. The density of an eight-bit grayscale image has \(2^{8}\) possible values, so its ideal information entropy value is 8. If the encoded image's information entropy is closer to 8, it is closer to random distribution. Information entropy is described below [33]:  
\[\text{Information entropy}\left(\mathbb{T}\right)=-\sum_{i=0}^{2^{n_{\text{B }}}-1}P\left(\mathbb{T}_{i}\right)\log_{2}P\left(\mathbb{T}_{i}\right) \tag{26}\]  
where \(\mathbb{T}\) indicates the encrypted image, \(n_{b}\) indicates the total number of bits that represent the symbol \(\mathbb{T}_{i}\), and \(P(\mathbb{T}_{i})\) indicates the probability that \(\mathbb{T}_{i}\) will appear. The information entropy of the encrypted images is computed. As listed in Table 2, the encrypted images were close to a random source, and the proposed systems were sufficiently secured against entropy attacks.
## 2 Methods
### Graph Convolutional Neural Network  
Graph convolutional neural network [14] is developed from graph neural network [15]. According to the definition of graph neural network, we formally define the set of graphs \(G=(g_{1},g_{2},\ldots,g_{n})\). For each graph \(g=(V,E)\), where \(V\) is the set of points in the graph. \(E\) is the set of edges in the graph. For any point \(v\in V\), there is an edge connected to itself, that is \((v,v)\in E\). For each graph, there is a matrix \(X\in\mathbb{R}^{i\times j}\) that represents the initial feature set of each point in the graph, where \(i\) represents the number of nodes in the graph and \(j\) represents the dimension of each node's feature. We will discuss how to obtain the initial state in Section 2.2. For each graph, there is a corresponding adjacency matrix \(A\) and degree matrix \(D\). Then, the matrix \(\widetilde{A}=D^{-\frac{1}{2}}AD^{\frac{1}{2}}\) is the normalized symmetric adjacency  
Figure 1: Schematic of our method. There are five news texts for example.  
matrix of the graph. According to the definition of GCN, the convolution formula for the first layer of the graph is:  
\[H^{(1)}=ReLU\Big{(}\widetilde{A}XW_{0}\Big{)} \tag{1}\]  
In this formula, \(W_{0}\in\mathbb{R}^{j\times k}\) represents the weight matrix. \(ReLU(\cdot)\) represents the activation function, defined as \(ReLU(x)=\max(0,x)\). Iterating the number of GCN layers can get deeper features. By using the GCN hidden state update formula, our hidden state update formula can be obtained:  
\[H^{(t+1)}=ReLU\Big{(}\widetilde{A}H^{(t)}W_{0}\Big{)} \tag{2}\]  
where \(t\) represents the number of iterations. In particular, \(H^{(0)}=X\).
# Terminal Distributed Cooperative Guidance Law for Multiple UAVs Based on Consistency Theory
Considering the mutual communication among aircraft, in [28] the average estimated value of the time-to-go of each member was taken as the coordination variable to design the variation curve of range, and the control quantity was designed to track the nominal trajectory, so as to realize the impact time cooperative guidance. Based on the principle of distributed communication and network synchronization, a distributed time cooperative guidance law was designed by taking the \"lead-followers\" mode to realize the simultaneous convergence of multiple aircrafts to the target position in [29]. In [30], the desired time-to-go was directly set as the average of each member's time-to-go, so as to design a hybrid guidance law satisfying both impact time and impact angle. The research in [31] designed a guidance and control integrated guidance law satisfying the impact time constraint, in which not only the time-varying velocity, but also the constraints such as uncertainty and line-of-sight were considered. The above cooperative guidance laws based on communication adopted a centralized coordination strategy, and the coordination variable existed only in one member of the formation, which was easy to implement. However, this strategy required the information of the whole formation, and when the members were attacked and failed, the coordinated control of the entire formation would fail, which would reduce the robustness and reliability of the system. Therefore, a distributed cooperative guidance law design based on consistency theory is proposed in this paper. The main contributions of this paper are as follows:  
1. A new time-to-go estimation method is proposed, which is more accurate than the existing method in [32] when the leading angle is large. 2. A non-singular sliding mode guidance law (NSMG) of impact time control with equivalent control term and switching control term is designed, which still appears to have excellent performance even if the initial leading angle is zero, while some existing impact time control laws in [4; 8; 33] are invalid. Then the guidance law is extended to attack maneuvering targets. 3. Adopting hierarchical cooperative guidance architecture, a terminal distributed cooperative guidance law based on consistency theory is designed, which is not only applicable to different forms of communication topology, but also effective in the case of communication topology switching. The other parts of this paper are arranged as follows: In Section 2, the problem statement and motion models are given. The new time-to-go estimation method and the bottom layer guidance law based on sliding mode control theory are proposed in Section 3. The upper-level distributed coordination strategy based on the consistency theory is given in Section 4. Several numerical simulation examples are provided and compared in Section 5. The conclusions are given in the final section.
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 6 Service-Oriented Middleware (SOM) Architectures Approaches for WSNs
### MiSense  
In [42], the authors proposed MiSense, Service-Oriented, components-based middleware layers that support the distributed sensor applications with a different performance of requirements [42]. The MiSense middleware provides an abstraction layer in between an underlying network infrastructure and the application. In addition, it provides an abstract programming model to the WSN application that can maintain the balance between network lifetime and QoS requirements for the application. The content-based, publish/subscribe service, provided by MiSense, enables the designer of any application to adapt to the services. MiSense also helps break down the middleware into different layers. The layers can be self-contained, and interact with the components that address the issues of tension between the requirements' optimization, flexibility, and the ability to develop reusable WSN applications with efficient energy.  
Figure 6: MiSense Architecture [42].  
The middleware has three layers: the communication layer, common service layer, and domain layer, as shown in Figure 6. They handle data aggregation, event detection, routing, and topology management. This approach uses adapted rules for the middleware, which can increase the data accuracy and bandwidth. The energy consumption decreases by an increased data rate and changes some sensors into the sleep state mode [42]. The MiSense does not support heterogeneous data that comes from different networks. It is also dependent on TinyOS (TinyOS Alliance). This approach does not determine the standard of SOA used between the gateway and the applications [42]. This SOA has flexibility and interoperability limitation between the various platforms provided in this approach. Since binary forms are used for remote procedures, the execution of SOA applications can be slow. The results can increase the processing time and energy consumption.
# An Enhanced Feature Extraction Network for Medical Image Segmentation
## 4 Experiment
### Experimental Setup and Evaluation Metrics  
In this section, we mainly introduce the experimental settings in the process of training and testing according to the following aspects. All models were implemented using the Keras framework 2.3.0 [46] with Tensorflow 2.2.0 [47] as the backend. The training and testing were based on the Ubuntu 16.04 system with four NVidia GeForce Titan graphics cards, which have 62 gigabytes of memory. For all models, the SGD optimizer was chosen to train 300 epochs, and the batch size and learning rate were set to 8 and 1 \(\times 10^{-4}\), respectively. During training, both Early Stopping and ReduceLROnPlateau were used. In this paper, the evaluation metrics such as Sorensen-Dice Coefficient (DSC), mean Intersection over Union (mIoU), Precision, and Recall were adopted. However, we mainly focused on DSC and mIoU, which are recognized as indicators of the challenge of lesion boundary segmentation. Equations (12)-(15) are our evaluation metrics:  
\[DSC=\frac{2 TP}{2 TP+FP+FN}, \tag{12}\]  
\[mIoU=\frac{1}{k+1}\sum_{i=0}^{k}\frac{TP}{FN+FP+TP}, \tag{13}\]  
\[Precision=\frac{TP}{TP+FP}, \tag{14}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{15}\]  
where \(TP\), \(TN\), \(FP\) and \(FN\) represent the number of true positives, true negatives, false positives and false negatives, respectively. Additionally, \(k\) in Equation (13) is the number of target classes to be predicted.
## 4 Damage Constitutive Model of Deep Expansive Clay under Freeze-Thaw Cycles
### Determination of the Parameters of the Damage Constitutive Equation
The model can reasonably predict the damage evolution of soil under the combined action of loading and freeze-thaw cycles. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{ \begin{tabular}{c} **The Number of Freeze–thaw** \\ **Cycles** \\ \end{tabular}  } & \multicolumn{3}{c}{\(\sigma_{3}\) = 100 kPa} & \multicolumn{3}{c}{\(\sigma_{3}\) = 200 kPa} & \multicolumn{3}{c}{\(\sigma_{3}\) = 300 kPa} \\ \cline{2-10}  & \(E_{n}\) (MPa) & \(m\) & \(F_{0}\) & \(E_{n}\) (MPa) & \(m\) & \(F_{0}\) & \(E_{n}\) (MPa) & \(m\) & \(F_{0}\) \\ \hline
0 & 647 & 0.384 & 0.457 & 674 & 0.341 & 0.445 & 660 & 0.316 & 0.471 \\
3 & 584 & 0.324 & 0.387 & 485 & 0.387 & 0.381 & 543 & 0.384 & 0.249 \\
6 & 502 & 0.318 & 0.754 & 534 & 0.345 & 0.241 & 526 & 0.
## 4 Classification of Domain Knowledge
### Model Selection and Usage Strategies
The classification of indoor functional areas is based on the detection of key objects. If pillows are present, the functional area is likely a bedroom. If there are dining tables, plates, and spoons, the functional area is likely a dining room. Local semantic features in visual information may be more helpful for classification than texture or edge features. Appropriate pre-training methods can improve the network's ability to extract semantic features, prevent focusing on the entire space, and ultimately improve network performance. It should be note that, when computational resources are limited, the width multiplier can be reduced from 1.0 to 0.75 or 0.5 to decrease the number of parameters and computational load. For simpler detection environments, the number of certain blocks can be appropriately reduced to simplify the model structure and shorten inference time. Adjusting the dropout rate according to the size of the dataset can help prevent overfitting and improve the model's generalization ability. Setting the learning rate too high may prevent the model from finding the optimal solution, while setting it too low could slow down the convergence rate. The learning rate should be fine-tuned based on the actual dataset to determine the optimal value. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Input** & **Operator** & **Exp Size** & **\#Out** & **SE** & **NL** & **S** \\ \hline \(224^{2}\times 3\) & conv2d & - & 16 & - & HS & 2 \\ \(112^{2}\times 16\) & bneck, \(3\times 3\) & 16 & 16 & - & RE & 1 \\ \(112^{2}\times 16\) & bneck, \(3\times 3\) & 64 & 24 & - & RE & 2 \\ \(56^{2}\times 24\) & bneck, \(3\times 3\) & 72 & 24 & - & RE & 1 \\ \(56^{2}\times 24\) & bneck, \(5\times 5\) & 72 & 40 & ✓ & RE & 2 \\ \(28^{2}\times 40\) & bneck, \(5\times 5\) & 120 & 40 & ✓ & RE & 1 \\ \(28^{2}\times 40\) & bneck, \(3\times 3\) & 240 & 80 & - & HS & 2 \\ \(14^{2}\times 80\) & bneck, \(3\times 3\) & 200 & 80 & - & HS & 1 \\ \(14^{2}\times 80\) & bneck, \(3\times 3\) & 184 & 80 & - & HS & 1 \\ \(14^{2}\times 80\) & bneck, \(3\times 3\) & 184 & 80 & - & HS & 1 \\ \(14^{2}\times 80\) & bneck, \(3\times 3\) & 480 & 112 & ✓ & HS & 1 \\ \(14^{2}\times 112\) & bneck, \(3\times 3\) & 672 & 112 & ✓ & HS & 1 \\ \(14^{2}\times 112\) & bneck, \(5\times 5\) & 672 & 160 & ✓ & HS & 2 \\ \(7^{2}\times 160\) & bneck, \(5\times 5\) & 960 & 160 & ✓ & HS & 1 \\ \(7^{2}\times 160\) & conv2d, \(1\times 1\) & - & 960 & - & HS & 1 \\ \(7^{2}\times 960\) & pool, \(7\times 7\) & - & - & - & - & 1 \\ \(12^{2}\times 960\) & conv2d, \(1\times 1\), NBN & - & 1280 & - & HS & 1 \\ \(1^{2}\times 1280\) & conv2d, \(1\times 1\), NBN & - & k & - & - & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specific parameters of MobileNetV3 (large). ✓ indicates whether the SE module is used.
# A New Method to Verify the Measurement Speed and Accuracy of Frequency Modulated Interferometers
## 2 Methodology
For the EOM (4002, Newport) [30], the optical phase difference \(\Delta\Phi_{1}\) induced by applying \(V(t)\) is  
\[\Delta\Phi_{1}=\frac{\pi}{V_{\pi}}V, \tag{7}\]  
where \(V_{\pi}\) is a half-wave voltage. Because the laser beam in the measurement arm was double-phase modulated (Figure 1), by substituting Equation (6) into Equation (7), the phase \(\Delta\Phi_{EOM}=2\Delta\Phi_{1}\) is  
\[\Delta\Phi_{EOM}=2\Delta\Phi_{1}=\frac{2\pi}{V_{\pi}}V=\frac{2\pi}{V_{\pi}}V_ {EOM}\sin(\omega_{EOM}t). \tag{8}\]  
However, the total phase difference \(\Phi\) between the arms of the interferometer is  
\[\Phi=\omega_{0}\tau=\frac{4\pi n_{air}(L-l)}{\lambda}+\frac{4\pi}{\lambda}(n_{ e}+\Delta n_{e})l=\frac{4\pi n_{air}(L-l)}{\lambda}+\frac{4\pi}{\lambda}n_{e}l+ \Delta\Phi_{EOM}, \tag{9}\]  
where \(L\), \(n_{air}\), \(l\), \(n_{e}\), \(\Delta n_{e}\), and \(\lambda\) are the unbalance length of the interferometer, the refractive index of air, the length and unperturbed refractive index of the electro-optic (EO) crystal, the change in \(n_{e}\) induced by applying \(V(t)\), and the laser wavelength, respectively. Here,  
\[\Delta\Phi_{EOM}=\frac{4\pi}{\lambda}\Delta n_{e}l=\frac{4\pi}{\lambda}\Delta l, \tag{10}\]  
\(\Delta l\) is the change in the optical path in the EO crystal, which is defined as the virtual displacement of M2 due to applying \(V(t)\). Substituting Equation (8) into Equation (10), \(\Delta l\) becomes  
\[\Delta l=\frac{\lambda}{2}\frac{V_{0}}{V_{\pi}}\sin(\omega_{EOM}t). \tag{11}\]  
Substituting Equations (5) and (10) into Equation (9), \(\Delta l\) measured by the interferometer is  
\[\Delta l=\frac{\lambda}{4\pi}\left\{\arctan\biggl{(}-\frac{I_{3\omega}}{I_{ 2\omega}}\cdot\frac{I_{2}(m)}{J_{3}(m)}\biggr{)}\right\}-[(L-l)n_{air}+n_{e}l]. \tag{12}\]  
In this experiment, we compared the measurement in Equation (12) with the calculated displacement in Equation (11) at the high phase-modulation frequency \(\omega_{m}\). For the frequency modulated interferometer, the maximum measurable speed \(V_{max}\) can be given by [13]  
\[V_{\text{max}}\leq k\frac{\lambda}{4}f, \tag{13}\]  
where \(k\) and \(f\) are an integer and the modulation frequency of LD, respectively. Here, \(k\) represents the ratio of the cutoff frequency of the LIAs and the modulation frequency of LD; normally \(k\) = 0.1-0.8. In this paper, a virtual displacement was developed at a speed of tens of kHz and without hysteresis generated using an EOM.
## 6 Performance Evaluation
### Machine Learning Algorithms' Implementation and Validation  
#### 6.2.1 Experimental Setup  
The test bed for building the machine learning algorithms for anomaly detection consists of both hardware and software configurations. The proposed models were built and tested on a machine with Intel Core i78700 CPU, which operates with 12 cores at a speed of 3.20 GHz, has RAM of 16 GB, and a dedicated Graphical Processing Unit (GPU) of type NVIDIA GeForce RTX 2060 Rev. This setup helps with multitasking, management of our large dataset, and intensive computation that are parallelisable. The operating system of the test bed is Ubuntu 22.04.4 LTS 64-bit; Python3.8 is installed on the OS and Jupyter Notebook [34] is utilised as a development environment. The libraries used for building the neural network are PyTorch [35], which helps us utilise the GPU to accelerate the development process and HDBSCAN from Scikit-learn [24], which is compatible with Python and is used for clustering. In the preprocessing phase, Numpy [36] and Pandas [37] were used, while Matplotlib [38] and Seaborn [39] were used for plots.
# Deep Learning Methods in Image Matting: A Survey
## 3 Traditional Methods
### Propagation-Based Methods  
Propagation-based methods [3; 50; 51; 52; 53], also known as affinity-based methods, initially establish connections between adjacent pixels and then use a specific optimization strategy to propagate opacity information from known regions to unknown regions, leveraging this information to estimate the alpha mattes. Propagation-based methods can be divided into three categories: the one-level color smooth model, two-level alpha-color model, and three-level color mixture model. The one-level color smooth model calculates the alpha values of unknown pixels based on the assumption of color smoothness, which means that the foreground and background images of an arbitrary image are locally smooth and that each pixel in a small region of the image can be represented by a linear combination of adjacent pixels. The two-level alpha-color model estimates the alpha values based on the alpha-color assumption, which implies that alpha values can be generated by combining the corresponding color channels linearly. The three-level color mixture model is similar to the one-level color smooth model as it also relies on the color approximation of pixels in a small local region of images. However, in the three-level color mixture model, pixels that need to be predicted are represented as a linear combination of all pixels within a region. This suggests that each pixel is expressed as a mixture of several similar pixels.  
The advantages of the methods that are based on propagation are as follows. (1) The alpha information is propagated from known regions to unknown regions, ensuring that the estimated alpha matte has local smoothness characteristics to avoid the problem of discontinuous alpha mattes caused by sampling methods. (2) The propagation of alpha information between unknown and known pixels can be modeled as an optimization problem based on quadratic objective functions, which can be solved efficiently using mature mathematical optimization tools, resulting in low computational cost. However, the working principle of propagation-based methods also leads to a considerable decrease in the performance of extracting the foregrounds of discontinuous objects. Moreover, the space complexity of propagation-based methods significantly increases as the image resolution increases as they need to store the correlations between pixels. This limits their application in high-resolution images.