esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Adaptive Dynamic Surface Control with Constant Gain for Non-Affine Pure-Feedback Systems
## 5 Conclusions  
In this paper, a fuzzy adaptive dynamic surface control with constant gain was developed to tackle the tracking problem for non-affine pure-feedback systems with uncertain external disturbances. The abovementioned technique overcomes the \"explosion of complexity\" problem inherent in the backstepping design process. The difficulty of non-affine appearance in pure-feedback systems is eliminated by the mean-value theorem. The simulation results show that all in the closed-loop system signals are SGUUB, and the tracking error may be ensured to be arbitrarily small by tuning the parameters of control laws. Compared with existing fuzzy control algorithms, the RTE index of the proposed control algorithm is reduced by 39.91%. In addition, in the tracking control problem of a flexible robot, the presented control algorithm has also demonstrated excellent tracking performance, with tracking errors reduced to the range of \(-0.0204\) to \(0.007171\). Future research will introduce a filter error compensation mechanism to further improve the tracking performance of the proposed algorithm.  
Conceptualization, L.C. and Y.W.; methodology, L.C., Y.W. and D.W.; formal analysis, L.C., Y.W. and J.C.; investigation, L.C., S.L. and D.W.; writing--original draft preparation, L.C.; writing--review and editing, L.C. and Y.W.; project administration, S.L.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program (grant number 2023 YFF0611203), and the National Natural Science Foundation of China (grant number 62203041). The APC was funded by 2023 YFF0611203.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
Conflicts of Interest: Author [PERSON] was employed by the Beijing Xinqiao Technology Development Co., Ltd., China. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 7: Comparison of the tracking error of the proposed controller (red solid line) with the PID controller (solid line marked with diamond shape).
# Research on Compression Behavior of Square Thin-Walled CFST Columns with Steel-Bar Stiffeners
## 2 Experimental Program  
### Test Specimens  
Six column specimens were fabricated in the test. The square tubes of the column specimens were made of Q235 cold-rolled steel sheets with a thickness of 2 mm. The section width of a square tube was 200 mm. To unify the stress of the column sections under the axial static loads applied at the end of columns, steel sheets of 250 \(\times\) 250 \(\times\) 6 mm were welded on both ends of the column specimens. The specimen details are shown in Figure 2.  
The diameter of steel bars was determined according to the requirements of the minimum inertial moment of stiffeners. HPB235 steel bars were used to make the stiffeners of the specimens for welding convenience and better ductility.  
The specimen plan is shown in Table 1. There are two thin-walled CFST columns with steel-bar stiffeners inside the tube, two thin-walled CFST columns with steel-bar stiffeners outside the tube, one thin-walled CFST column without any stiffeners inside, and one steel tubular column without concrete and stiffeners inside the tube. The section details of the specimens are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Column Number**} & **Stiffener** & **Concrete** & **Column Height (mm)** & **Column Width** & **Tube Thickness** & **Steel-Bar** & **Cross-Section** \\  & **Setting** & **Setting** & **Height (mm)** & **(mm)** & **(mm)** & **Diameter (mm)** & **Area Ratio of** \\ \hline C1 & Empty tube & No concrete & 1200 & 200 & 2 & - & 100\% \\ \hline C2 & No stiffeners & Concrete-filled & 1200 & 200 & 2 & - & 3.96\% \\ \hline \multirow{3}{*}{C3} & Steel-bar & \multirow{3}{*}{Concrete-filled} & \multirow{3}{*}{1200} & \multirow{3}{*}{200} & \multirow{3}{*}{2} & \multirow{3}{*}{12 mm} & \multirow{3}{*}{5.07\%} \\  & & inside tube & & & & & \\ \hline \multirow{3}{*}{C4} & Steel-bar & \multirow{3}{*}{Concrete-filled} & \multirow{3}{*}{1200} & \multirow{3}{*}{200} & \multirow{3}{*}{2} & \multirow{3}{*}{16 mm} & \multirow{3}{*}{5.91\%} \\  & & inside tube & & & & & \\ \hline \multirow{3}{*}{C5} & Steel-bar & \multirow{3}{*}{Concrete-filled} & \multirow{3}{*}{1200} & \multirow{3}{*}{200} & \multirow{3}{*}{2} & \multirow{3}{*}{12 mm} & \multirow{3}{*}{5.07\%} \\  & & outside tube & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specimen plan.  
Figure 2: Details of column specimen.
## 3 Diversity-Controlled MUST Technique
### Performance Analysis of the MUST-Based UE  
Without loss of generality, the error probability of the MUST-based UE for given \(\mathbf{h}_{B.i}(i)\) is represented as:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Number of Multipliers** & **Number of Address** & **Number of Comparators** \\ \hline MUST-based UE & \(N_{r}\) & \(N_{r}-1\) & \(1\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computation complexity of the receiver at the MUST-based UE.  
Figure 3: Receiver structure at the MUST-based UE with the diversity-controlled MUST technique.  
\[\begin{split} P_{b,B}&=\Pr\left\{\hat{b}_{B,1}^{i}=0|b_{B,1}^{i}=1,\mathbf{h}_{B,i}(i)\right\}\\ &=\sum_{d\in\{d_{1}d_{2}\}}\Pr\left\{\Re(s_{i})=d|b_{B,1}^{i}=1 \right\}\Pr\left\{\Re(g_{B,i})|\Re(s_{i})=d,\mathbf{h}_{B,i}(i)\right\}\\ &=\sum_{d\in\{d_{1}d_{2}\}}\frac{1}{2}\int_{-\infty}^{0}\frac{1} {\sqrt{2\pi\left(N_{0}/2\varepsilon\left\|\mathbf{h}_{B,i}(i)\right\|^{2} \right)}}\exp\left(-\frac{(x-d)^{2}}{2\left(N_{0}/2\varepsilon\left\|\mathbf{ h}_{B,i}(i)\right\|^{2}\right)}\right)dx\\ &=\sum_{d\in\{d_{1}d_{2}\}}\frac{1}{2}\int_{-\infty}^{-\sqrt{ \frac{2\varepsilon}{N_{0}}\left\|\mathbf{h}_{B,i}(i)\right\|^{2}d}}\frac{1}{ \sqrt{2\pi}}\exp\left(-\frac{x^{2}}{2}\right)dx\\ &=\sum_{d\in\{d_{1}d_{2}\}}\frac{1}{2}Q\left(\sqrt{2\rho\left\| \mathbf{h}_{B,i}(i)\right\|^{2}d^{2}}\right),\end{split} \tag{7}\]  
where \(\rho\triangleq\frac{\mathcal{E}}{N_{0}}\) indicates the transmit SNR at the BS.  
The average BER of the MUST-based UE can be obtained by integrating Equation (7) over the random variable (RV) \(\left\|\mathbf{h}_{B,1}(i)\right\|^{2}\). Note that the RV \(\left\|\mathbf{h}_{B,1}(i)\right\|^{2}\) follows the Chi-square distribution with \(2N_{r}\) degrees-of-freedom (DoF) [11], i.e., \(Z_{i}\triangleq\left\|\mathbf{h}_{B,i}(i)\right\|^{2}\sim\chi_{2N_{r}}^{2}\).  
Then, the average BER of the MUST-based UE is given as:  
\[\begin{split}\bar{P}_{b,B}&=\int_{0}^{\infty}P_{b,B}f_{Z_{i}}(z_{i})dz_{i}\\ &=\int_{0}^{\infty}\sum_{d\in\{d_{1}d_{2}\}}\frac{1}{2}Q\left( \sqrt{2\rho z_{i}d^{2}}\right)f_{Z_{i}}(z_{i})dz_{i}\\ &=\frac{1}{2}\sum_{d\in\{d_{1}d_{2}\}}\int_{-\infty}^{\infty}Q \left(\sqrt{2\rho z_{i}d^{2}}\right)\frac{1}{(N_{r}-1)!}z_{i}^{N_{r}-1}\exp \left(-z_{i}\right)dz_{i}\\ &=\sum_{d\in\{d_{1}d_{2}\}}\left(1-\sqrt{\frac{d^{2}\rho}{1+d^{2 }\rho}}\right)^{N_{r}}\sum_{i=0}^{N_{r}-1}\binom{N_{r}-1+i}{i}\left(\frac{1}{2 }\right)^{N_{r}+1+i}\left(1+\sqrt{\frac{d^{2}\rho}{1+d^{2}\rho}}\right)^{i}, \end{split} \tag{8}\]  
where \(d_{1}\) and \(d_{2}\) are defined in Equation (5).
# Multi-Player Tracking for Multi-View Sports Videos with Improved K-Shortest Path Algorithm
## 3 Methodology  
### An Overview of the Proposed Multiple Players Tracking Framework  
Multiple players tracking system generally consists of two steps: detecting players for each frame and then linking the detections into trajectories. The former part is our previous work that has been published as a conference paper ([PERSON] et al., 2018). As shown in Figure 2, given a basketball video, we first detect players in 2D images with Mask RCNN ([PERSON] et al., 2018) frame by frame and then convert the input frames into binary images with foreground-background separation. Afterwards, a probability occupancy map (POM) ([PERSON] et al., 2018) algorithm is implemented to generate a map representing the existence probability of a player at each discrete location in the basketball court. The input of the POM consists of the above 2D player detections together with the multi-camera calibration data ([PERSON] et al., 2018). In the existing KSP tracking approach ([PERSON] et al., 2018), the output of POM is the only input of KSP tracking. In our proposed method, however, we also consider player appearance features (the jersey color, jersey number, etc.) as other inputs of the tracking model. The same as the output of the POM, the available appearance features also exert important influences on the weights of linking edges. After that, \(k\) player trajectories can be output based on KSP algorithm. Since the main difference between our proposed method and the existing KSP tracking approach is that we consider the appearance features while the original KSP does not, we thus name our method KSP-AF.
# GPSR: Gradient-Prior-Based Network for Image Super-Resolution
## 2 Related Work
### Attention-Based Networks  
The visual attention mechanism is a visual information-processing mechanism unique to the human brain, which is able to acquire crucial local regions while suppressing irrelevant information by swiftly scanning the global image [28]. In the field of machine vision, the attention mechanism can be interpreted as an efficient way to manage resources by allocating more available computational resources to critical image signals. In recent years, numerous studies [12; 15; 29; 30] have verified the effectiveness of attentional mechanisms. Considering that treating channel-wise features equally hinders the feature representation ability, [PERSON] et al. [29] proposed a very deep residual channel attention network (RCAN), which explores interdependencies among channels using the proposed Residual Channel Attention Module (RCAB) to adaptively reweight channel-wise features, thus prompting the network to focus on critical information. [PERSON] et al. [15] proposed a Second-Order Attention Network (SAN), which endeavors second-order feature statistics to fully explore the dependencies of intermediate features to improve the feature representation ability further. [PERSON] et al. [30] proposed the first Cross-Scale Non-Local (CS-NL) attention module, which combines the new CS-NL prior with local and in-scale non-local priors to reconstruct high-quality images. Considering that most attention mechanisms act only on each independent network layer and they ignore the relevance of intermediate layer features, [PERSON] et al. [12] proposed the holistic attention network (HAN), which models the interdependencies between intermediate layers and adaptively highlights significant multi-level features. The above studies [12; 15; 29; 30] have confirmed that the attention mechanism is able to adaptively highlight crucial features and effectively enhance the feature representation ability. However, existing spatial attention modules [31; 32] only capture spatial information that are beneficial for SR tasks in extremely localized ways due to the lack of a large receptive field. Although the non-local operations [13; 14; 15; 30; 33] provide more global information to the network by capturing long-range dependencies, their high computational cost is not negligible.
## 4 Conclusions  
In the present study, _in situ_ synthesis of silver-acrylate, silver-epoxy and silver-IPN nanocomposites was achieved by thermal polymerization through a simultaneous radical and/or cationic polymerization processes using a silane/silver salt couple at room temperature and under air. Remarkably, and unlike published works so far, heating is not required to activate the reaction. The new redox systems were proposed to initiate in two-component systems (DPSi/silver salt) the ring-opening polymerization (ROP) of (3,4-Epoxycyclohexane) methyl 3,4-epoxycyclohexylcarboxylate (EPOX) and the free radical polymerization (FRP) of trimethylolpropane triacrylate (TMPTA) and styrene (St).  
Figure 8: Energy-Dispersive X-ray Spectrometry (EDS) analysis recorded from Ag(0) nanoparticles in EPOX.  
Figure 9: DSC thermograms for the cured samples containing **(1)** DPSi/AgSbF\({}_{6}\) (2%/1% _w/w_) and **(2)** DPSi/NVK/AgSbF\({}_{6}\) (2%/2%/1% _w/w_) in TMPTA.  
Interestingly, the addition of \(N\)-Vinylcarbazole (NVK) to the silane/silver salt systems leads to a drastic increase of polymerization reactivity and efficiency, respectively. This three-component system generates free radicals (R\({}_{3}\)Si\({}^{\bullet}\) and NVK\({}^{\bullet}\)) or ions (R\({}_{3}\)Si\({}^{+}\), NVK\({}^{+}\) and H\({}^{+}\)). Remarkably, fast ROP and FRP are observed. Interestingly, these initiators can also be used for the synthesis of interpenetrated polymer networks (IPN), e.g., for the polymerization of TMPTA/EPOX blends. Highly crosslinked network were achieved and the nanocomposite obtained, contains near spherical geometry for most of the nanoparticles, homogeneously dispersed in the polymer network without significant agglomeration as a result of a surrounding effect of polymer chains formed by the rapid cationic and/or radical chain growth. Interestingly, an increase on Tg values was observed after addition of \(N\)-vinylcarbazole (NVK) in the formulations. This was ascribed to a decrease of free volume of the polymer network because the formation of PVK in the film of polymer leads in the impediment of the mobility crosslinked epoxide and/or an acrylate polymer network. The mechanical and optical properties of the obtained new organic polymer/silver nanoparticles will be investigated in near future.
# NDN Content Store and Caching Policies: Performance Evaluation
## 5 Simulation and Discussion
### Simulations Results
#### 5.2.2 Number of Upstream Hops  
With more CS, the probability of finding the requested content closer to consumer is high, the long routes (or high number of hops) are avoided. This situation is shown in Figure 5(a). Once again, this is true and verified for scenarios simulated with a high skewness factor, i.e., \(\alpha=1.1\). For \(\alpha=0.75\), as already verified for interest re-transmission and retrieval delay, this metric presents a relatively constant value; see Figure 5(b).
# Attention-Oriented CNN Method for Type 2 Diabetes Prediction
## 3 Materials and Methods
### Assigning Class Weights
#### 3.2.2 Convolutional Neural Network  
Convolutional neural network (CNN) is a feed-forward neural network mainly used to process data with grid structure. Different from common neural network structures, the core idea of CNN is to extract the features of input data through convolutional layers and pooling layers, and classify through fully connected layers [47; 48; 49].  
In this paper, we design a CNN model for T2 DM prediction to predict diabetes. The structure of our proposed CNN model is shown in Figure 3. First, we use a convolutional layer to process the input data. This layer has 32 filters, and the size of each filter is set to 3 \(\times\) 3. ReLU is used as the activation function of this layer to enhance the nonlinear capability of the model, the shape of the input data is (number of features, 1), where the number of features is the number of features of the preprocessed input data. Then, we use a pooling layer with the window size set to 2 to reduce the feature map size and retain key features. We add a dropout layer after the convolutional layer and set the dropout rate to 0.5 to reduce overfitting. Dropout [50], also known as random deactivation, is a commonly used regularization technology in CNN and is often used to prevent the over-fitting of network models. The dropout layer will randomly set a part of the input units to 0 to reduce their influence and improve the generalization ability of the model. We then add a second 1D convolutional layer with 64 3 \(\times\) 3 filters and a ReLU activation function. This move aims to further extract and learn higher-level features in the input data. The dropout layer is applied again, and its dropout rate is still 0.5 to enhance the robustness of the model. Finally, we convert the output of the convolutional layer into a one-dimensional vector through a flattening layer for input to the subsequent fully connected layer. The fully connected layer can combine and map the correlation between different input features. We add a fully connected layer with 128 neurons and use ReLU as the activation function. Finally, a fully connected layer is added, using softmax as the activation function, for the binary classification prediction of T2 DM. The above information is summarized in Table 4.  
Figure 3: The structure diagram of the CNN model proposed in this article.  
Figure 2: Datasets category distribution chart, where (**a**) represents the category distribution of the NHANES dataset, and (**b**) represents the category distribution of the PID dataset.
## 5 Experimental Validation and Analysis
### Experimental Design
The DK960 eddy current sensors (DEYIKE Sensing Instruments, Wuxi, China) were selected for their high-frequency response and precision in detecting small displacements. The ART-USB-2872C data acquisition card (Art Technology, Xi'an, China) provides high-speed, synchronized multi-channel sampling, which is essential for the synchronous sampling method. Figure 14: ART-USB-2872C data acquisition card. Figure 15: DK960 eddy current sensor. \begin{table}
\begin{tabular}{c c} \hline
**Parameter Category** & **Technical Indicators** \\ \hline Measuring range & 0.5–3 mm (2 mm calibration recommended) \\ Linearity & \(\pm\)0.8\% FS (typical) \\ Sensitivity & 4 V/mm (default range) \\ Output impedance & 50 \(\Omega\)\(\pm\) 5\% \\ Probe material & Polyimide insulation layer + 316 L stainless steel \\ Waterproof grade & IP67 (probe part) \\ \hline \end{tabular}
\end{table} 
Table 2: Technical parameters of the DK960 eddy current sensor. commenced. The recorded dual-channel voltage data files were saved for subsequent processing and analysis.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 3 Methodology
### Data Exploration  
The POIs dataset was divided into two categories: hospitals and ambulances. The hospital dataset exclusively comprised hospital-related information, while the ambulance dataset contained data about ambulances. As a result, separate queries were performed for each dataset type.  
Due to the significant volume of POIs resulting from the dataset split, only five cities were suitable for conducting the queries. As outlined in Table 1, specific areas exhibited a limited number of POIs, whether hospitals or ambulances, thereby potentially affecting the reliability of results. To ensure the robustness of our findings, we utilized a heatmap graph (Figure 8) illustrating the density of POIs. This visualization provided valuable insights into the spatial distribution of POIs, thereby guiding the selection of cities with a sufficient concentration. The heatmap analysis, shown in Figure 8, represents the density of point distribution through colored map areas, with a gradient from brighter to unshaded indicating higher point concentrations. This informed the inclusion of cities where the density of hospitals and ambulances met the experiment's criteria. Consequently, the experiment focused on cities with numerous POIs: Adelaide, Brisbane, Melbourne, Perth, and Sydney.  
of hospitals and ambulances introduced the necessary variance in the results, thereby allowing for a comparison of the accuracy between the Euclidean and road network distance methods. This methodological choice enhances the thoroughness and accuracy of our assessment of the Euclidean distance method's correctness in identifying the nearest hospitals or ambulances for residential addresses, thereby ensuring our experimental findings' overall comprehensiveness and accuracy.
# Numerical Modeling of Bowel Sound Propagation: Impact of Abdominal Tissue Properties
## 1 Introduction
Therefore, developing a bowel sound simulation device that can accurately mimic the structure of the abdominal cavity of different individuals is of great significance for revealing the propagation law of sound waves, optimizing the signal acquisition and analysis, and providing key technical support for the clinical application of bowel sound analysis. Accurate modeling and analysis of bowel sounds in clinical settings presents several challenges. To address these challenges, it is essential to develop a model that effectively captures the mechanisms of bowel sound generation and their propagation within the abdominal cavity. Numerical simulations approximate solutions through discretization and iterative solving, making them well suited for complex physical phenomena and boundary conditions. In contrast, analytical models simplify systems to provide exact solutions, but they can be limited in complex situations and computationally expensive. In the field of nanoelectronic device modeling, semi-analytical models have proven to be a helpful balance, reducing computational complexity while maintaining high accuracy [18]. Building on this approach, this study uses an analytical model to define the characteristics of the sound source and provide accurate initial conditions for numerical simulations. The sound wave propagation is then analyzed through numerical modeling in COMSOL Multiphysics(r) (version 6.2.) [19]. This combined approach improves both the accuracy of the results and computational efficiency, offering a practical solution for modeling the dynamics of bowel sound generation and propagation. This study aims to develop a comprehensive numerical model to simulate intestinal sound generation and propagation, focusing on the acoustic properties of abdominal tissue layers, including adipose tissue, muscle, and skin. By integrating tissue-specific acoustic parameters into a finite element model, this research establishes a proof-of-concept framework for accurately simulating sound propagation. A key objective is to investigate how variations in fat layer thickness influence sound attenuation, accounting for individual anatomical and physiological differences. The model will be personalized based on individual-specific tissue composition, enabling precise simulation of real-world abdominal conditions. By correlating these variations with an expanded bowel sound database, the model has the potential to enhance diagnostic accuracy and support personalized medicine. Additionally, this research contributes to the development of non-invasive gastrointestinal health monitoring devices, offering significant clinical value for early disease detection and management.