esa-sceva/satcom-chunk-collection huggingface dataset:
# Miniature Coil for Wireless Power and Data Transfer through Aluminum +
## 4 Through Metal Communication System
### Experimental Validation  
To validate our system, bidirectional communication was tested using the designed prototypes and a pair of coils. Each coil was located and aligned on each side of a 500 mm \(\times\) 500 mm \(\times\) 1 mm aluminum plate. The test setup is shown in Figure 18.  
Using the previously described protocol, communication began at the outside subsystem with a push of a button of the microcontroller. Then, the wireless power transfer signal, \(P_{tx}=35\) W, and data signal, 100 bps, were transmitted through the metal plate. The inside subsystem automatically harvested the power, processed the incoming data and transmitted a response. The outside subsystem processed the data and a LED lights up indicating the successful data reception. Using this procedure, successful communication outside-inside and inside-outside was confirmed. The system operated using only the harvested power through the metal plate.  
Figure 17: Built prototypes for wireless power transfer through metal communication system: (**a**) outside, and (**b**) inside subsystems.  
For further validation, a sample of inside to outside data transfer is presented in Figure 19. The transmit data corresponds to the digital output of the microcontroller of the inside subsystem, and the receive data is the demodulated data at the output of the outside subsystem. Communication is successful at a data rate of 100 bps using a 2 kHz carrier. A bit error rate analysis (BER) was calculated for the data transmission through metal using 360,000 bits during a time period of 60 min of data transmission. The results showed that the BER \(<1\times 10^{-6}\) for the wireless power transfer data link through metal.
# Comparative Study of Computational Methods for Classifying Red Blood Cell Elasticity
## 4 Statistical Analysis of Elasticity from the Perspective of Red Blood Cell Surface Optimization
### Data and Analysis Tools  
The basic data for comparison were calculated, and we recorded the surface area and volume values of individual RBCs in the simulation experiment. We are aware that the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline stretching coefficient (\(k_{s}\)) & \(5\times 10^{-6}\) N/m \\ bending coefficient (\(k_{b}\)) & \(3\times 10^{-19}\) N/m \\ coefficient of local area conservation (\(k_{al}\)) & \(2\times 10^{-5}\) N/m \\ coefficient of global area conservation (\(k_{ag}\)) & \(7\times 10^{-4}\) N/m \\ coefficient of volume conservation (\(k_{z}\)) & \(900\) N/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Elastic coefficients of the healthy red blood cell (RBC) used in simulations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Number of Classes** &
\begin{tabular}{c} **Validation and Testing** \\ **Number of Classes** \\ \end{tabular} & **Accuracy** \\ \hline
4 & 4 & 55.48\% \\
2 & 2 & 61.72\% \\
4 & 2 & 93.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results obtained by neural networks with EfficientNet_v2_B0 as the core model.  
possibilities of obtaining these data from real experiments are limited, and the following analysis is methodologically suitable, especially for in silico experiments. Study [7], however, indicates the possibility of measuring \(SA:V\) values also within in vitro experiments, including their connection to computational experiments. Study [8] also deals with the measurement of \(SA\) and \(V\) values.  
We deliberately chose the statistical methods for further data processing to be as simple as possible in order to find out whether even such an approach will already bring relevant results. This simplicity should be somewhat of a counterpoint when compared to the latest machine learning methods.
# Friction Behavior of Ceramic Materials for the Development of Bridge-Bearing Friction Materials
## 2 Properties of Ceramics  
Zirconia-based (ZrO\({}_{2}\)) ceramics supplied by DKFC (Dongpu Korea Fine Ceramics, Icheon, Republic of Korea) were selected as bridge-bearing friction materials considering their economic feasibility, performance, and applicability. The key properties of the materials previously used for bridge bearings and the characteristics of the zirconia-basedceramics used in this study are presented in Table 1. Their compositions are listed in Table 2. Zirconia ceramics exhibit significantly higher strengths than conventional materials, offering excellent wear resistance owing to their high hardness. However, their high strength results in brittleness, which makes them susceptible to vibration.  
Despite their brittleness, when they are applied as a friction material in bridge bearings subjected to continuous wheel loads, the vibrations and energy transmitted from the bridge superstructure dissipate as they reach the bearing. Moreover, the frictional behavior under high loads contributes to damping, allowing the ceramic material to overcome its inherent brittleness. This makes zirconia ceramics suitable candidates for bridge-bearing applications, where durability and wear resistance are critical.  
Ceramics, formed by sintering at high temperatures, exhibit excellent chemical and heat resistance, enabling them to withstand harsh environments and maintain durability under repeated friction. Their application in bridge bearings reduces reliance on lubricants, minimizing wear and deformation. This improvement extends the replacement cycle and lowers maintenance costs, making ceramics a promising alternative for enhancing the longevity and reliability of bridge bearings.
## 4 Numerical Studies on the Effectiveness of SA-TLGDs
### MDOF-Shear Frame Structure with Multiple SA-TLGDs Attached

Table 3: Parameters and dimensions of the two optimized SA-TLGDs attached to the 3 rd floor of the MDOF-shear frame structure. To account for the above-mentioned shift of the fundamental frequency of the MDOF-main system, the two SA-TLGDs are equipped with a total of three gas volumes \(V_{01}\), \(V_{02}\), and \(V_{03}\) at each side of the tube. The optimal eigenfrequencies of the SA-TLGDs that correspond to the shifted natural frequencies yield again by minimizing the frequency domain based quadratic performance index \(J(\
u)\) in the state space representation (see Section 3.2): \(f_{A1,opt}=2.07\) Hz, \(f_{A2,opt}=2.36\) Hz in the case of \(f_{S1}^{-5\%}=2.26\) Hz and \(f_{A1,opt}=2.29\) Hz, \(f_{A2,opt}=2.61\) Hz in the case of \(f_{S1}^{+5\%}=2.50\) Hz. The linearized viscous damping ratios of the two SA-TLGDs remain unchanged. Subsequently, the required sizes of the gas volumes for the two installed SA-TLGDs, based on the chosen absorber dimensions listed in Table 3, from Equation (9) are: \(V_{01,A1}=0.0912\) m\({}^{3}\) (\(L_{g1,A1}=1.86\) m), \(V_{02,A1}=0.0095\) m\({}^{3}\) (\(L_{g2,A1}=0.19\) m), \(V_{03,A1}=0.0110\) m\({}^{3}\) (\(L_{g3,A1}=0.22\) m) for the first installed SA-TLGD and \(V_{01,A2}=0.0702\) m\({}^{3}\) (\(L_{g1,A2}=1.43\) m), \(V_{02,A2}=0.0069\) m\({}^{3}\) (\(L_{g2,A2}=0.14\) m), \(V_{03,A2}=0.0087\) m\({}^{3}\) (\(L_{g3,A2}=0.18\) m) for the second installed SA-TLGD. The separated gas chambers are connected via controllable valves (cf. again Figure 1). To achieve the optimal absorber tuning frequency the appropriate number of valves must be opened or closed. In practical applications, the air chamber volumes \(V_{0i}\) can be redesigned with a smaller or larger cross-sectional area than \(A_{T}\), i.e., basically the geometric shape of the air chambers is arbitrary and therefore adjustable to the available installation space of  
Figure 10: Frequency response functions (FRF) of the force excited MDOF-shear frame structure with two sub-optimal tuned SA-TLGDs in parallel connection (steady state response of floor displacement \(w_{3}\); frequency shift of the fundamental natural frequency \(f_{S1}=2.38\) Hz to \(f_{S1}^{+5\%}=2.50\) Hz and \(f_{S1}^{-5\%}=2.26\) Hz assumed). Figure 9: Frequency response functions (FRF) of the force excited MDOF-shear frame structure with and without the two activated optimally tuned SA-TLGDs (steady state response of floor displacement \(w_{3}\)). the structure. Obviously, the cross-sectional area of the tube \(A_{T}\) must be kept constant at least up to the maximum dynamic vibration amplitude \(u=U_{max}\) of the fluid mass.
# Dual-Modality Transformer with Time Series Imaging for Robust Epileptic Seizure Prediction
## 3 Methodology
### Dual-Stream Feature Extraction  
#### 3.1.1 Time Series Stream  
The time series stream consists of parallel temporal and frequency paths. For temporal features, we employ a Transformer encoder:  
\[F_{s}^{t}=\text{MSA}\left(\frac{Q_{s}K_{s}^{\uparrow\top}}{\sqrt{d_{k}}} \right)V_{s}+\text{FFN}(X_{s}) \tag{1}\]  
where MSA denotes Multi-head Self-Attention, a key component that allows the model to attend to different positions of the input sequence simultaneously, where (\(X_{s}\in\mathbb{R}^{T_{s}\times d_{s}}\)) is the input time series. The scaling factor \(\sqrt{d_{k}}\) in the attention computation prevents the dot products from growing too large in magnitude, particularly for large values of \(d_{k}\), which could push the softmax function into regions with extremely small gradients. Where each attention head is calculated as:  
\[head_{i}=\text{Attention}(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V}) \tag{2}\]  
The Query (\(Q\)), Key (\(K\)), and Value (\(V\)) matrices are learned transformations of the input sequence \(X_{s}\). \(W_{i}^{Q}\), \(W_{i}^{K}\), and \(W_{i}^{V}\) are learnable parameter matrices for the i-th attention head, and \(W^{O}\) is the output projection matrix.  
FFN refers to the Feed-Forward Network, a position-wise fully connected layer that processes each position independently:  
\[\text{FFN}(x)=\max(0,xW_{1}+b_{1})W_{2}+b_{2} \tag{3}\]  
where \(W_{1}\), \(W_{2}\) are weight matrices and \(b_{1}\), \(b_{2}\) are bias vectors. The dimension of the internal layer (\(W_{1}\)) is typically set to four times the model dimension. This network allows the model to process the attended information and capture non-linear relationships.  
The frequency characteristics are captured through a two-layer neural network with non-linear activation:  
\[F_{s}^{f}=\sigma!(W_{2}\delta(W_{1}X_{s}+b_{1})+b_{2}) \tag{4}\]where \(\sigma\) is the sigmoid activation function, \(\delta\) represents the ReLU activation, and \(W_{1}\), \(W_{2}\) are learnable weight matrices that project the input into the frequency-sensitive feature space. The intermediate dimension is set to match the temporal feature dimension for balanced representation.  
The temporal-frequency features are integrated via:  
\[F_{s}=\gamma F_{s}^{t}+(1!-!\gamma)F_{s}^{f} \tag{5}\]  
where \(\gamma\) is a dynamic integration coefficient that determines the relative importance of temporal and frequency features. This adaptive weighting mechanism allows the model to emphasize different aspects of the signal based on the input characteristics. where (\(\gamma\)) is learned through:  
\[\gamma=\sigma(W_{\gamma}[F_{s}^{t};F_{s}^{f}]+b_{\gamma}) \tag{6}\]  
Here, \([F_{s}^{t};F_{s}^{f}]\) represents the concatenation of temporal and frequency features, allowing the model to consider both feature types when determining their relative importance. The sigmoid activation ensures \(\gamma\in(0,1)\), providing a natural way to balance the contribution of each feature type.
# AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals
## 4 Experiments and Analysis
### Experiment and Analysis Based on Machine Learning
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**MethodsData Sets** & **Train (70)/ Test (30)** & **Train (80)** & **5-Fold CV** & **10-Fold CV** & **20-Fold CV** \\ \hline C4.5 Decision Tree & 84.23 & 84.94 & 83.89 & 84.39 & 84.56 \\ C5.0 Decision Tree & 84.41 & 84.58 & 84.86 & 85.02 & 85.16 \\ Naive Bayes & 68.37 & 68.44 & 68.89 & 68.91 & 68.89 \\ Logistic Regression & 70.04 & 70.23 & 70.33 & 70.28 & 70.31 \\ (LR) & 75.60 & 75.58 & 76.01 & 75.98 & 75.96 \\ ANN (MLP) & 89.42 & 89.66 & 89.92 & 90.25 & 90.38 \\ C\&Rf & 81.04 & 81.07 & 81.81 & 81.75 & 81.73 \\ CHAID & 80.98 & 80.93 & 81.02 & 81.11 & 81.15 \\ Two-Class SVM & 74.55 & 74.62 & 74.55 & 74.81 & 74.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Prediction of stroke by machine learning algorithm after normalization (%). Forest algorithm based on a decision tree that has a heuristic methodology but also has analytical and analytical advantages.
## 3 Materials and Methods
### Li\({}_{2}\)S Nucleation Experiment  
Li\({}_{2}\)S\({}_{6}\) electrolyte solution (0.2 M) was prepared by dissolving S and Li\({}_{2}\)S in a molar ratio of 7:1 in lithium-air electrolyte (1 M LiTFSI dissolved in tetraethylene glycol dimethyl ether, with 1 wt% LiNO\({}_{3}\)) and stirring at room temperature for 24 h in a glovebox. Carbon fiber felt was used as the cathode, lithium foil as the anode, and MoS\({}_{2}\)-NG, MoS\({}_{2}\)-G, and MoS\({}_{2}\)/PP were used as separators. The cathode side and anode side were each treated with 25 \(\upmu\)L of Li\({}_{2}\)S\({}_{6}\) electrolyte and 20 \(\upmu\)L of lithium-air electrolyte, respectively, to assemble coin cells. The cells were discharged at a constant current of 0.112 mA to 2.06 V, followed by potentiostatic discharge at 2.05 V to allow Li\({}_{2}\)S nucleation and growth until the current dropped below 10\({}^{-5}\) A. The rate of Li\({}_{2}\)S nucleation growth was calculated using [PERSON]'s law.
# ECG Signal Classification Using Interpretable KAN: Towards Predictive Diagnosis of Arrhythmias
###### Abstract  
To address the need for accurate classification of electrocardiogram (ECG) signals, we employ an interpretable KAN to classify arrhythmia diseases. Experimental evaluation of the MIT-BIH and PTB datasets demonstrates the significant superiority of the KAN in classifying arrhythmia diseases. Specifically, preprocessing steps such as sample balancing and variance sorting effectively optimized the feature distribution and significantly enhanced the model's classification performance. In the MIT-BIH, the KAN achieved classification accuracy and precision rates of 99.08% and 99.07%, respectively. Similarly, on the PTB dataset, both metrics reached 99.11%. In addition, experimental results indicate that compared to the traditional multi-layer perceptron (MLP), the KAN demonstrates higher classification accuracy and better fitting stability and adaptability to complex data scenarios. Applying three clustering methods demonstrates that the features extracted by the KAN exhibit clearer cluster boundaries, thereby verifying its effectiveness in ECG signal classification. Additionally, convergence analysis reveals that the KAN's training process exhibits a smooth and stable loss decline curve, confirming its robustness under complex data conditions. The findings of this study validate the applicability and superiority of the KAN in classifying ECG signals for arrhythmia and other diseases, offering a novel technical approach to the classification and diagnosis of arrhythmias. Finally, potential future research directions are discussed, including the KAN in early warning and rapid diagnosis of arrhythmias. This study establishes a theoretical foundation and practical basis for advancing interpretable networks in clinical applications.  
ECG signal; KAN; arrhythmia; balance; variance sorting +
Footnote †: journal:  
Article
## 2 Research Questions and Hypotheses  
The goal of the authors with this manuscript is to see whether these display parameters and devices positively or negatively affect the interaction of the human with the computer. During the research, the authors set up seven RQs and Hs. The RQs are the following:  
* RQ1: Does the change of camera type influence the probability of correct answers on the tests?
* RQ2: Does the change of camera field of view influence the probability of correct answers on the tests?
* RQ3: Does the change of camera rotation influence the probability of correct answers on the tests?
* RQ4: Does the change of contrast ratio influence the probability of correct answers on the tests?
* RQ5: Do turning on or off the presence of shadows influence the probability of correct answers on the tests?
* RQ6: Does changing the device used influence the probability of correct answers on the tests?* RQ7: What are the optimal preferences for these factors for achieving the largest probability of correct answers on the tests? The following are the Hs:
* H1: Camera type used does not affect the probability of correct answers; opposite to: perspective type positively influence the probability of correct answers on the tests.
* H2: Changing the camera field of view has no effect on the probability of correct answers; opposite to: changing the camera field of view to a higher degree can positively influence the probability of correct answers on the tests.
* H3: Camera rotation does not affect the probability of correct answers; opposite to: changing the camera rotation increases the probability of correct answers on the tests.
* H4: Contrast ratio does not affect the ratio of correct answers; opposite to: changing the contrast ratio from higher to lower values can positively influence the probability of correct answers on the tests.
* H5: The presence of shadows does not affect the probability of correct answers on the tests; opposite to: the ratios of corrects answers are different in case of shadows and in the absence of shadows.
* H6: Using a desktop display or the Gear VR, the probabilities of correct answers are equal; opposite to: using the Gear VR the probability of correct answers is larger.
* H7: Based on the previous hypotheses, the optimal preferences are the perspective camera type, higher field of view, some rotation, lower contrast ratio while also using the Gear VR.
# A Hybrid Robust-Learning Architecture for Medical Image Segmentation with Noisy Labels
## 1 Introduction
By incorporating a 3D image segmentation module and a 2D LQAM, our hybrid architecture could predict label quality scores of the whole-volume slices in one shot. Second, we employ shape-awareness regularization to encourage the introduction of prior shape information. To be concrete, we apply the Hausdorff loss, based on distance transform maps, to boost the segmentation performance. Third, we construct our hybrid architecture based on the inspiration of the re-weighting strategy, which could adjust the contributions of relatively clean and noisy slices to improve the noise tolerance of the trained method. Finally, we experiment with two representative, publicly available datasets, i.e., liver segmentation and abdominal multi-organ segmentation. The results have shown the competitive performance of our noise-robust hybrid architecture. The main contributions are as follows:  
1. Different from previous studies, we target the challenging problem of 3D medical image segmentation with noisy labels, especially the inconsistent noisy label qualities among different slices. To address this problem, we propose a novel end-to-end hybrid robust-learning architecture to combat noisy labels from the perspective of slice-level label-quality awareness;
2. We propose a novel slice-level label-quality awareness method, which automatically generates quality scores for each slice in a set without knowing the prior noise distribution. With the help of re-weighting, our method can alleviate the negative effect of noisy labels. The design is particularly effective for 3D medical image segmentation by satisfying the constraints of noise tolerance and the capacity limitations of GPUs;
3. We propose a shape-awareness regularization loss to introduce prior shape information to provide extra performance gains. In the presence of noisy labels, we regard it as an auxiliary loss instead of the main learning targets and, further, it benefits the model training together with slice-level label-quality awareness. To our knowledge, this is the first attempt to apply prior shape information for the problem of learning with noisy labels.
# Design of a Configurable Spike-Encoding Circuit Based on Focal Plane Array
## 2 System Requirement and Coding Theory
### Spike FPA System
#### 2.2.2 Accurate Mode  
The FPA will enter accurate working mode once it receives the area coordinates and mode switching signal. In accurate mode, pixels that are not confined by region coordinates are disabled. The pixel-coding circuit in the framed portion will switch to a multi-pulse precise coding mode, and the 4-layer SNN that follows will conduct target recognition and classification, as depicted in Figure 4. This precise encoding necessitates longer encoding periods, increases pixel power consumption, and slows down image processing. However, given that only a portion of FPA pixels are active and the precision of target recognition, this trade-off is worthwhile. Precision mode replicates the gaze and object resolution mechanisms of biological visual systems. In their paper, [PERSON]  
Figure 2: Block diagram of a spike-encoding FPA system.  
and [PERSON] proposed a four-layer structure SNN that can be used for target recognition and classification [15] and discussed its hardware implementation method [16].
# An Improved SIFT Underwater Image Stitching Method
###### Abstract  
Underwater image stitching is a technique employed to seamlessly merge images with overlapping regions, creating a coherent underwater panorama. In recent years, extensive research efforts have been devoted to advancing image stitching methodologies for both terrestrial and underwater applications. However, existing image stitching methods, which do not utilize detector information, heavily rely on matching feature pairs and tend to underperform in situations where underwater images contain regions with blurred feature textures. To address this challenge, we present an improved scale-invariant feature transform (SIFT) underwater image stitching method. This method enables the stitching of underwater images with arbitrarily acquired images featuring blurred feature contours and that do not require any detector information. Specifically, we perform a coarse feature extraction between the reference and training images, and then we acquire the target image and perform an accurate feature extraction between the reference and target images. In the final stage, we propose an improved fade-in and fade-out fusion method to obtain a panoramic underwater image. The experimental results show that our proposed method demonstrates enhanced robustness, particularly in scenarios where detecting feature points is challenging, when compared to traditional SIFT methods. Additionally, our method achieves higher matching accuracy and produces higher-quality results in the stitching of underwater images.  
SIFT; image stitching; image alignment; image preprocessing; point matching +
Footnote †: journal: Applied