esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Accuracy of Impervious Surface Extraction Based on a Threshold Hierarchical Method (THM)
## 4 Results
### Range of Different Thresholds  
The abundance of impervious surface could be determined by the LSMA method; the values ranged from 0 to 1 and were divided into ten parts in the steps of 0.1, considering ten thresholds. For each threshold, areas with an abundance larger than the threshold were selected (Figure 7).  
Based on the above, the scope of the area with an abundance larger than the threshold was found to gradually increase as the threshold increased.  
### Range of Different Thresholds  
For each threshold and for areas where the abundance was larger than the threshold, considering the scope described in Figure 7, the GF-2 imagery and the OOM were selected and employed; thereafter, various outcomes for extracting impervious surfaces could be determined through the THM by adopting different thresholds.  
Considering an entire image, the distribution of ISA could be described in two parts: (1) in areas where the abundance data of ISA was smaller than the threshold, information on ISA determined through the Landsat 8 OLI and data on the abundance of ISA was obtained by adding the high-albedo and low-albedo fractions, which were unmixed by the LSMA; (2) in areas where the abundance of ISAs was larger than the threshold, the GF-2 image was adopted, and the ISA (including roads and buildings) and non-ISA (including water, vegetation, and bare soil) were classified by the OOM. The abundance of the ISAs and non-ISAs was 1 and 0, respectively.  
Ten different thresholds, ranging from 0.1 to 1, in steps of 0.1, were employed through the THM. The ISA distribution information at different thresholds could be described as Figure 8.  
Figure 6: Abundance of the impervious surfaces.  
Several aspects could be concluded based on Figure 9:  
1. The RMSE at different thresholds obtained through the THM showed a \"U\" trend. The RMSE was 0.2019 at a threshold of 0.1, and 0.2203 at a threshold of 1.0; the RMSE was the smallest (0.1871) at a threshold of 0.5.
2. The THM is suitable for improving the accuracy in extracting impervious surfaces. The RMSE value was smaller than that obtained by the LSMA, which was 0.2107 at a threshold of \(<\)0.9.
3. The optimal threshold was 0.5. As compared to the LSMA, the precision of the THM, considering the optical threshold, could be increased by 3.83%.  
Figure 7: Scope for every threshold.  
Figure 8: Information on impervious surfaces at different thresholds obtained through the threshold hierarchical method (THM).  
Figure 9: Variations in the root-mean-square error (RMSE) at different thresholds obtained through THM.
## 5 System Implementation and Field Testing
### Testing Results
#### 5.2.1 Attraction Information Displaying  
To verify the operation of communication between the mobile app subsystem and the central subsystem, we selected an attraction, and then checked the content displayed on the screen. Figure 6 shows the testing result. Compared with the content in the database, we verified that the mobile app subsystem can access the database in the central subsystem and show the result correctly.  
Figure 5: Field of the testing.  
Figure 6: Experimental results of attraction information display: (**a**) attraction list; and (**b**) results.
# Beam-Plasma Stabilizer for the New Type of Nuclear Power Energy Systems
## 1 Introduction  
One of the most important anisotropic plasma applications is plasma energetics [1, 2]. The development of space nuclear power plants (NPP) started in the beginning of the 1960s, and by the end of the 1980, the Buk [3] and Topaz [4] nuclear power plants were created, tested, and proved to be successful in terms of their orbital operation, providing energy a whole series of spacecraft.  
Today, compact and highly efficient nuclear power plants have been developed [5, 6]. Such installations have already made it possible to solve the problems of power supply for industrial facilities and social infrastructure in hard-to-reach regions around the world, where the use of imported hydrocarbon fuel greatly increases the price of electricity and heat.  
For the effective operation of such nuclear power plants, a wide range of reliable electronic devices including, but not limited to, current and voltage stabilizers, generators, rectifiers, and key elements that are capable of operating at temperatures above 1000 K and high radiation levels are required. This problem cannot be solved by traditional semiconductor devices [7]; only gas discharge devices satisfy these requirements.  
The devices that are based on nonequilibrium plasma of low-voltage beam discharges (LVBD) [8, 9, 10], in which the nonlocal nature of the distribution function of charged particles allows one to control groups of electrons of different energies and thereby influence the energy characteristics of the gas-discharge devices [11, 12] are of particular interest.  
In this study, a new method of high-voltage stabilizing and a triode that is based on a helium LVBD special design are proposed.
## 1 Introduction
### Literature Review
#### 1.2.2 Non-Contact Vibration Analysis Methods  
Non-contact vibration analysis methods have emerged as promising alternatives by leveraging the inherent frequency-rotation relationship [17; 18; 19]. These include alternator W-terminal signal analysis, which extracts rotational information from uncorrected alternator phase voltage waveforms, achieving a steady-state diagnostic accuracy within \(\pm\)1.2%, as demonstrated by [PERSON] et al. [20], and laser tachometry, a technique measuring the optical reflections from rotating shafts with a reported precision of \(\pm\)0.5% in controlled environments [21]. Adaptive noise cancelation algorithms, such as those proposed by [PERSON] et al. [22], improve robustness against mechanical interference but require computationally intensive processing. Despite these advancements, inherent limitations persist due to environmental noise sensitivity, transient-phase signal distortion during rapid acceleration, and the complex calibration requirements for optical surface optimization [23].
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 4 Simulation Results
### Case 1: ACA for M-PSK Signals  
The average classification accuracy of M-PSK signals, i.e., BPSK, QPSK, 16-PSK, and 64-PSK, is evaluated in this section. From Figure 2, it can be seen that the ACA approaches approximately 100% at an SNR of 5 dB. The different values of the SNR are in the range from \(-10\to 5\) dB. The ACA was determined for each considered class, i.e., \(\Xi=[\mathbb{C}_{1},\mathbb{C}_{2},\mathbb{C}_{3},\mathbb{C}_{4}]\), where \(\mathbb{C}_{1},\mathbb{C}_{2},\mathbb{C}_{3}\), and \(\mathbb{C}_{4}\) represent the BPSK-, QPSK-, 16-PSK-, and 64-PSK-modulated signals, respectively.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Solver & GA \\ \hline \(N_{ears}\) & 40 \\ \hline \(L_{b}\) & 4, 1, \(-3.14,0\) \\ \hline \(U_{b}\) & 6, 20, 3.14, 1 \\ \hline Fitness Limit & Infinite \\ \hline Constraint tolerance & \(1\times e^{-3}\) \\ \hline Nonlinear Constraint & Aug lag \\ \hline Creation Function & GA Uniform \\ \hline Population size & 200 \(\times\) 40 double \\ \hline No. of Generations & 100 \\ \hline Migration Direction & Forward \\ \hline Crossover Fraction & 0.8 \\ \hline Crossover & Multiple-Point \\ \hline Functional Tolerance & \(1\times e^{-6}\) \\ \hline Initial range & \([0,1]\) \\ \hline Scaling function & Rank \\ \hline Selection & Best-Fitted \\ \hline Elite count & 0.05\(\times\)_Population Size_ \\ \hline Mutation function & Adaptive Feasible \\ \hline Problem Type & Bound Constraints \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.  
The ACA values for all \(\Xi\) are comprehensively presented in Table 2, which shows the classification accuracy for all classes \(\Xi\) with a fixed SNR of **10** dB. The ACA approaches approximately 100% at an SNR of 10 dB, and as shown in Table 2, the ACA is approximately 99% for GA-based optimization. Table 2 also shows the ACA for the local search methods of GA, i.e., GA-Pattern Search (GA-PS), GA-Fmincon (GA-FM), GA-Fminsearch (GA-FS), and GA-Fminunc (GA-FU). It is evident from the results that the ACA ranges from 96% to 99% for the local search methods.  
The mean square error (MSE), as stated in \(\mathbf{R_{1,3}}\) Equation (8), is presented in Figure 3. From Figure 3, the MSE with the IRS is much better than that without the IRS for all considered classes, i.e., \(\Xi\). In addition, these modulated formats are presented with the ACA to show their performance and their discrimination with hybridized GA, GA-PS, GA-FM, GA-FS, and GA-FU. The ACA convergence tables of \(\bar{\mathbb{C}}_{1},\mathbb{C}_{2},\mathbb{C}_{3}\), and \(\bar{\mathbb{C}}_{4}\) reveal better performance than other hybridized techniques of GA for the four-class classification case of PSK signals.
# Secure Internal Data Markets
## 3 Security Issues of Internal Data Markets
### Integration  
In the case of a global data market, the whole infrastructure and the system environment is modelled in order to accommodate the market software. Contrastingly, when small enterprises or small divisions inside a large company set up a data market, the market software is integrated into an already existing ecosystem, i.e., the design decisions of the surrounding system are not tailored for the market, but the market software will need to fit in. This is not only a deployment issue, but can also cause a lot of problems from a security perspective.
## 4 Discussion

Table 3: Cross-validation AUROC values of ML algorithms. Our data reflected all the distributional characteristics of the risk factors demonstrated in previous studies. Additionally, because it is a multicenter study, it has the advantage of lowering the bias that can occur when only one institution is used for validation. However, our study had several limitations. We attempted to collect patients with BM from as many RCC patients as possible through a domestic multicenter study. Although the data were collected from 11 institutions, the probability of BM occurrence in RCC patients was only 10%, with data collected from 137 patients with BM. Generally, ML performance improves as the quantity of training data increases [31]. As a result, we collected as much data as possible for a small dataset, and it was developed at an AUROC level of 0.72. In a rough classification performance evaluation, AUROC can be interpreted as follows: 90-100 = excellent, 80-90 = good, 70-80 = fair, 60-70 = poor, and 50-60 = failure [32]. Our model performed fairly with the current data; however, in future work, we will collect more data and improve the performance using a model with good or excellent performance. Additionally, our study has the advantage of reflecting the characteristics of domestic patients and a domestic multicenter study; however, because of this, performance cannot be guaranteed when applied to other countries. Therefore, to expand and apply it to people abroad, the model must be upgraded and verified by adding samples from various countries.
## 3 Main Results
### Data Acquisition for Grinding Depth  
The three axes of the CMM are equipped with an air source brake switch and a micro-moving device, which can realize precision transmission of a single axis and adopt a high-performance data acquisition system. In this study, CMM was used to measure the material removal depth of the blades before and after grinding along the grinding path. Moreover, a confocal laser scanning microscope was used to scan the three straight trajectories (A, B and C) across the grinding path, in order to extract 2D contour of the grinding traces on the leaf surface. The depth of grinding is shown in Figure 6.  
Blade back arc removal depth is defined as the distance difference between the deepest grinding point and the surface point. Each experiment was performed three times to guarantee the validity of the results. In addition, the blade thickness was measured at the same position before and after each experiment. The data values of 9 measurement  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment No.** & **Speed (m/min)** & **Force (N)** & **Mesh** & **MRD (\(\mathbf{\eta}\)m)** & **SNR (db)** \\ \hline
1 & 350 & 20 & 120 & 94.49 & 39.45 \\
2 & 350 & 25 & 180 & 87.30 & 38.78 \\
3 & 350 & 30 & 240 & 79.94  
points were obtained. Table 3 shows the average material removal depth (MRD) and corresponding SNR. The SNR is defined as follows:  
\[SNR=-10\times log_{10}(\frac{1}{n}\sum_{i=1}^{n}\frac{1}{y_{i}^{2}}) \tag{9}\]  
where \(n\) is the number of values obtained in each Taguchi experiment and \(y_{i}\) is all values obtained in each experiment. The greater the SNR, the greater the MRD. Therefore, the ideal level of process parameter values was the most significant level of SNR.
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 3 Improved Burst OFDM Synchronization Method
### Symbol Timing Synchronization Method Based on 2-Bit Non-Uniform Quantization
#### 3.2.3 Search Method Based on Distortion Function  
According to the quantization method described in the previous section, the information rate distortion is chosen as the optimization objective. To find the optimal quantization parameters, a traversal method is used to search through all possible combinations of these parameters. The squared error distortion function of a sequence is used to represent the information distortion [31], and it is defined by  
\[d(\mathbf{s},\mathbf{\tilde{s}})=\frac{1}{L}\sum_{k=0}^{L-1}d(s_{k},\tilde{s}_ {k}), \tag{17}\]  
where \(s_{k}\) represents the original signal value and \(\tilde{s}_{k}\) represents the quantized signal value. This distortion function allows us to quantify the information loss due to quantization and find the optimal set of quantization parameters that minimize this loss. Algorithm 1 outlines the steps to find the optimal non-uniform quantization parameters.  
```
1:Input:\(\mathbf{s}=s_{k}(1\leq k\leq L)\).
2:Initialization: Set search step size \(\Delta\), and the initial threshold \(T_{th}=\Delta\).
3:Step1: Compute the maximum magnitude of the real and imaginary parts of the training sequence \(\mathbf{s}\) using \(T_{\max}=\max\Bigl{(}\max\bigl{(}\bigl{|}s_{k}^{I}\bigr{|}\bigr{)}\max\Bigl{(} \bigl{|}s_{k}^{Q}\bigr{|}\Bigr{)}\Bigr{)}\) for all \(k(1\leq k\leq L)\);
4:Step2: For all \(\{T_{0},T_{1}\}\) that \(T_{0}\in[0,T_{th})\) and \(T_{1}\in[T_{th},T_{max}]\), quantize \(\mathbf{s}\) using (10) and (11), and compute the corresponding distortion \(d(\mathbf{s},\mathbf{\tilde{s}})\);
5:Step3: Find the minimum distortion \(d_{min,T_{th}}\) and the corresponding parameter combination as \(\{T_{0}^{*},T_{1}^{*}\}=\mathop{\arg\min}_{T_{0}\in[0,T_{th}),T_{1}\in[T_{th}, T_{\max}]}d(\mathbf{\tilde{s}},T_{0},T_{1})\);
6:Step4: Repeat Steps 2 to 3 with updated threshold \(V_{th}=V_{th}+\Delta\) until \(V_{th}=T_{max}\);
7:Step5: Compute the minimum distortion value \(d_{min}\) among \(\{d_{min,T_{th}},T_{th}\in[0,T_{\max}]\}\), and return the corresponding optimal parameters \(T_{th,opt}\), \(T_{0,opt}\), and \(T_{1,opt}\).
```  
**Algorithm 1** Optimal parameter search algorithm for 2-bit non-uniform quantization
## 2 Problem Description and Modeling
### Mobile Robot Kinematics  
Ignoring the slip and deformation of the tires, the kinematic model of the skid-steer mobile robot is represented as follows:  
\[\dot{X}=\begin{bmatrix}\dot{x}\\ \dot{y}\\ \dot{\theta}\end{bmatrix}=\begin{bmatrix}v\cos\theta\\ v\sin\theta\\ \omega\end{bmatrix}=\begin{bmatrix}\cos\theta&0\\ \sin\theta&0\\ 0&1\end{bmatrix}u \tag{1}\]  
where the state vector \(X=[x,y,\theta]^{T}\) is the Cartesian coordinate and the orientation angle, and the control vector \(u=[v,\omega]^{T}\) represents the linear and angle velocity.  
According to Equation (1), the reference state vector \(X_{r}=[x_{r},y_{r},\theta_{r}]^{T}\) and the reference control vector \(u_{r}=[v_{r},\omega_{r}]^{T}\) have the following relationship:  
\[\dot{X}_{r}=\begin{bmatrix}\dot{x}_{r}\\ \dot{y}_{r}\\ \dot{\theta}_{r}\end{bmatrix}=\begin{bmatrix}v_{r}\cos\theta_{r}\\ v_{r}\sin\theta_{r}\\ \omega_{r}\end{bmatrix}=\begin{bmatrix}\cos\theta_{r}&0\\ \sin\theta_{r}&0\\ 0&1\end{bmatrix}u_{r} \tag{2}\]  
Then, the kinematic errors can be computed as  
\[X_{e}=\begin{bmatrix}x_{e}\\ y_{e}\\ \theta_{e}\end{bmatrix}=\begin{bmatrix}\cos\theta&\sin\theta&0\\ -\sin\theta&\cos\theta&0\\ 0&0&1\end{bmatrix}\begin{bmatrix}x_{r}-x\\ y_{r}-y\\ \theta_{r}-\theta\end{bmatrix} \tag{3}\]  
According to Equation (3), the error equation can be derived as  
\[\begin{split}\dot{x}_{e}&=\omega y_{e}-v+v_{r}\cos\theta_{e}\\ y_{e}&=-\omega x_{e}+v_{r}\sin\theta_{e}\\ \dot{\theta}_{e}&=\omega_{r}-\omega\end{split} \tag{4}\]  
Then, the control input is written as  
\[u_{e}=\begin{bmatrix}v_{r}\cos\theta_{e}-v\\ \omega_{r}-\omega\end{bmatrix} \tag{5}\]  
Figure 1: Four-wheel skid-steer mobile robot driven by hydraulic system.  
Substituting (5) to (4), the kinematic model about \(X_{e}\) is written as  
\[\dot{X}_{e}=\begin{bmatrix}\dot{x}_{e}\\ \dot{y}_{e}\\ \dot{\theta}_{e}\end{bmatrix}=\begin{bmatrix}0&\omega&0\\ -\omega&0&v_{r}\frac{\sin\theta_{e}}{\theta_{e}}\\ 0&0&0\end{bmatrix}\begin{bmatrix}x_{e}\\ y_{e}\\ \theta_{e}\end{bmatrix}+\begin{bmatrix}1&0\\ 0&0\\ 0&1\end{bmatrix}u_{e} \tag{6}\]  
In this study, the main purpose of introducing \(X_{e}\) is to decouple the control variables \(u=[v,\omega]^{T}\) and state variables \(X=[x,y,\theta]^{T}\), transforming the non-affine nonlinear model represented by Equation (1) into an affine nonlinear model represented by Equation (6), thereby simplifying the computation of NMPC. Similar approaches can be found in some references, such as [15; 19]. By designing an appropriate controller, the state of the system (6) can be controlled to be zero, thereby ensuring that the mobile robot tracks the desired pose.
# PGN: Progressively Guided Network with Pixel-Wise Attention for Underwater Image Enhancement
## 3 The Proposed Method
### Feature Representation Block  
In our network, we use several FRBs to progressively complete the underwater image enhancement task. This structure decomposes the whole enhancement process into several controllable stages. At each stage, we introduce supervision from clear images to the network. We aim to segment the image restoration procedure into a series of sequential sub-processes, which can dis-aggregate the intricate and severely degraded image recovery process into an aggregation of several less intensive restoration processes. The FRB consists of two parts: dilated convolution layers and a Pixel-Wise Attention Module (PAM). Several dilated convolution layers with different dilation factors are utilized to extract rich contextual features, and the PAM provides supervision for network learning at each stage, enhancing these features by generating guidance maps from clear images.  
For the \(n\)th FRB, FRB takes the output feature \(F^{n-1}\) of the previous FRB as input. We first feed the input feature into the dilated convolution layers and obtain a feature map \(F^{n}_{d}\). Meanwhile, the features \(F^{n-1}\) learned in the previous stage are fed to a convolution layer and we obtain \(f_{n}\). PAM uses clear images to supervise \(f_{n}\), and generate a guidance map \(M^{n}\) with the same size as \(F^{n}_{d}\). Then, the guidance map \(M^{n}\) is pixel-wise multiplied by feature \(F^{n}_{d}\) to enhance the features of important regions. To better preserve the learned features, we add the multiplied feature and input feature \(F^{n-1}\) as the output of the FRB. The output feature map of the FRB is formulated as follows:  
\[F^{n}=F^{n-1}+F^{n}_{d}\times M^{n}. \tag{1}\]  
To learn rich contextual information, we utilize the dilated convolution layers with different dilated factors to progressively expand the receptive field of our network. Dilated convolution increases the size of the receptive field by increasing the stride of the convolution kernel. Compared with the standard convolution, this structure can greatly expand the receptive field when the number of convolution layers is the same. As the network deepens, dilated convolution layers can obtain more context information and deepen the network's understanding of image structural features.  
Figure 2: Overview of our network. We use three Feature Representation Blocks (FRBs) after the Feature Extraction Module. The input features of each FRB are fed into a convolution layer and Pixel-Wise Attention Module (PAM), which uses clear images to supervise the features, and generate a guidance map. Then, the guidance map is pixel-wise multiplied by the features of the backbone to enhance the features of important regions, and sent to the next stage.  
In our model, each FRB has 2 dilated convolution layers that apply 3 \(\times\) 3 convolutions with different dilation factors. As the number of FRBs increases, the dilation factor starts from 1 and increases layer by layer. The dilation factors are 1, 1, 2, 4, 8 and 10.
###### Abstract  
Nanomaterials have been applied in the analysis of trace contaminants in aquatic samples; however, the adsorption affinity of heavy metal ions to metallic oxide nanoparticles is low. Meso-2,3-dimercaptosuccinic acid-polydopamine-zinc oxide (DMSA/PDA/ZnO) was synthesized through a facile self-assembly process for enhancing the adsorption capacity of ZnO to cadmium cation (Cd(II)). A method for the analysis of trace Cd(II) in water samples was developed while using dimercaptosuccinic acid/Polydopamine/zinc oxide (DMSA/PDA/ZnO) as an adsorbent and atomic absorption spectrometry (AAS) as a means of determination. The results demonstrate that the adsorption ratio of DMSA-PDA-ZnO to 1 mg/L Cd(II) is around 95% at pH 6.0-8.0 with a contact time of 31 h, and the enrichment rate can be more than 98% with the dosage of DMSA-PDA-ZnO exceeding 1.0 g/L. The adsorption isotherm of Cd(II) to DMSA/PDA/ZnO fits the Langmuir equation well, and the saturated adsorption of DMSA-PDA-ZnO to Cd(II) is 28.5 mg/g. The detection limit (3\(\sigma\)) of this method is 0.1 ug/L, with a relative standard deviation of 1%. Additionally, this method presents satisfactory performance with a recovery rate of 86-90% when applied to the detection of Cd(II) in environmental water samples.  
cadmium; dimercaptosuccinic acid; polydopamine; ZnO +
Footnote †: journal: Applied Physics Letters  
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