esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image
## 3 Methodology
### Attention Recurrent Unit  
The recurrent unit generates the hidden state at the current moment by gating the current input and the hidden state of the previous recurrent unit. As can be seen from the previous section, the recurrent unit is sensitive to local information but lacks access to multiple global information. Therefore, the proposed method makes further improvements to the recurrent unit. Referring to Figure 3d, the attention gating unit is introduced to obtain global information of the current input. The attention gating unit is shown in Figure 3e.  
Specifically, the attention circulation unit and the circulation unit have similar structures, and only the attention gating unit is introduced later. Therefore, the equation is basically the same as above, except for the hidden state. it is now expressed as Equation (6). The attention gating unit first globalizes the current input \(x_{t}\) and turns each two-dimensional feature channel into a real number \(r\in\mathbb{R}\). \(r\) has a global receptive field to some extent, and the number of channel outputs is the same as the number of channels of the original feature map. Secondly, \(R\) is rescaled to \(R^{{}^{\prime}}\) through two layers of \(1\times 1\) convolution, and then \(R^{{}^{\prime}}\) is output to \(R^{{}^{\prime\prime}}\) through the sigmoid activation function. Finally, we multiply \(x_{t}\) and \(R^{{}^{\prime\prime}}\) as the output at of the gating unit.  
\[h_{t}=(f_{t}\odot z_{t}\oplus h_{t-1}\odot(1-z_{t}))\oplus a_{t} \tag{6}\]  
This simple modification combines global information with local information to minimize the tension between semantics and location. As a result, more accurate predictions can be made, and the experiments in this article have also proven this.
# Enhanced Effective Filtering Approach (eEFA) for Improving HSR Network Performance in Smart Grids
## 2 Effective Filtering Approach (EFA)
### Issues in EFA  
EFA is one of the best traffic reduction techniques for HSR networks. However, it has the following drawbacks:  
1. It still forwards the first frame to all DANH rings; and
2. It uses a control message to discover passive QuadBox rings in both unidirectional and bidirectional communications.  
These drawbacks cause additional redundant traffic and control overheads in HSR networks. In this study, eEFA is proposed to solve these two problems, thereby improving the network traffic performance of HSR networks.
## 2 Materials and Methods
### SEM and EDX Analyses  
For EDX, four IOLs of each group were air-dried and examined with a Jeol JMC-6000 (Jeol Co., Akishima, Tokyo, Japan) SEM coupled with the EDX, recording a single spot from the intact surface of Group 1 IOLs. On the contrary, in the damaged IOLs of both Groups 2 and 3, three different spots were recorded: in particular, Spot \(\alpha\) was obtained from the undamaged surface, Spot \(\beta\) from the edge of the laser hit and Spot \(\gamma\) from the center of the laser injury.
# Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
## 5 Experimental Results  
To test the effectiveness of bearing fault identification using the FOR-adaptive cascade observer (FOR-ACO), this procedure is validated and compared with two state-of-the-art techniques: the FOR-SPIO and the FOR-PIO. Additionally, the CWRU vibration signal is used to test the power of single-type fault classification and UIAI-Lab acoustic emission signal is selected to test the power of multi-type fault identification in crack-variant and load-variant conditions. Tables 3 and 4 illustrate the window characterization for training and testing samples for CWRU and UIAI-Lab datasets, respectively.
## 6 Security Solutions
### Communication Security  
IoT devices use CoAP (Constrained Application Protocol) [71] as an application layer protocol. The CoAP implements IP-based communication protocols to achieve pervasive interactions between connected devices. The communication protocols implement various security schemes to ensure authenticity, confidentiality, and integrity of the information. Research on IoT security proposes new standards and methods for IoT systems. These security solutions introduce additional layers or modify existing layers in the IoT protocol stacks, as shown in Figure 16. In this section, we first review the cryptographic algorithms that are suitable for IoT devices. Next, we survey security methods that provide network and transport layer security for IoT systems.
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 4 Results and Discussion
### Discussion of xLSTM Results  
#### 4.2.1 Time Series Prediction Performances of xLSTM for Wind Power Prediction  
The performance assessment of the xLSTM model for wind power output prediction reveals promising capabilities across various wind speed regimes, as shown in Figure 2. The xLSTM model demonstrates varying prediction accuracy depending on wind speed conditions, as quantified in Table 5. At low wind speeds (0-5 m/s), the model achieves an \(R^{2}\) value of 0.886, indicating substantial correlation between predicted and actual values despite the inherent variability in this operational range. The MAPE in this region stands at 9.65%, with relatively low RMSE of 32.7 kW and MAE of 29.4 kW that reflect the xLSTM model's capacity to manage the stochastic nature of low wind conditions.  
In the medium wind speed range (5-12 m/s), which encompasses the steepest portion of the power curve where minor speed fluctuations produce significant power output changes, the xLSTM model maintains robust performance with an \(R^{2}\) value of 0.901. This  
Figure 4: Seasonal probabilistic distribution of power generation characteristics: comparative analysis of SCADA measurements and xLSTM predictions in (**a**) winter (December–February), (**b**) spring (March–May), (**c**) summer (June–August), and (**d**) autumn (September–November).  
range exhibits the highest absolute errors (RMSE of 35.5 kW and MAE of 47.8 kW) and MAPE (11.83%), attributable to the rapid rate of change in power output relative to wind speed in this transition region.  
The xLSTM model achieves its strongest predictive accuracy in the high wind speed range (>12 m/s), with an \(R^{2}\) value of 0.954 and a notably low MAPE of 4.21%. This enhanced performance corresponds to the plateau region of the power curve where output stabilizes, resulting in relatively modest RMSE of 34.2 kW and MAE of 42.6 kW despite the higher power magnitudes involved.  
Aggregating across all wind speed ranges, the xLSTM model achieves comprehensive performance metrics of \(R^{2}\) = 0.923, MAPE \(\epsilon\) = 8.47%, RMSE \(\xi\) = 34.6 kW, and MAE \(\sigma\) = 36.2 kW. These values indicate a strong overall predictive capability with particularly robust performance at high wind speeds where power generation reaches maximum capacity. The xLSTM model's distinct performance characteristics across different wind speed regimes highlight its adaptability to the non-linear relationship between wind speed and power output. This graduated performance profile suggests potential enhancements through regime-specific optimization strategies or ensemble approaches combining specialized models for each operational range.
## 3 Test Procedures
### Mode Tests  
In the mode tests, the corrugated panels were supported by four soft springs to mimic the free boundary conditions. The selection criteria of the soft spring are: the frequency of the vibration system formed by the soft spring and the plate is lower than the first-order natural frequency of the plate; that is to say, the vibration of the panel relative to the soft spring is the movement of the overall panel, and the simulation and experimental results can be compared to verify whether the selection of the soft spring is reasonable. As schematically shown in Figure 2a, the springs are located at the four corners of the rectangular panel. An impact hammer was used to excite the vibration of the panel at the measure points, as shown in Figure 2a. For accuracy, the average data of the three parallel tests were reported. The vibration signal was captured by an accelerometer bonded at point 19 and was finally transmitted to the dynamic signal analysis system developed by Donghua Testing Technology Co., Ltd., Taizhou, China. For the hammering mode test, the governing differential equation of a continuous system excited by a hammer force \(\{F(t)\}\) can be written as [43]:  
\[[M]\big{\{}\ddot{x}\big{\}}+[C]\big{\{}\dot{x}\big{\}}+[K]\{x\}=\{F(t)\} \tag{1}\]  
where \([M]\), \([C]\), and \([K]\) are the mass, damping, and stiffness matrices of the continuous system, respectively. \(\{F(t)\}\) is a step input force vector. The frequency response function (FRF) of the system can be written as:  
\[H(j\omega)=\frac{X(j\omega)}{F(j\omega)}=\frac{1}{-\omega^{2}[M]+j\omega[C]+[K]} \tag{2}\]  
where \(F(j\omega)\) is the impulse input force generated by the hammer, while \(X(j\omega)\) is the response of the system.  
The Poly-reference Least Square Complex Frequency (PolyLSCF) method proposed by [PERSON] [44] was used in this research to fit the frequency response function. This method has been widely used to fit the frequency response function in the frequency domain, which has a relatively high recognition accuracy and can generate a clear stable map. The frequency response characteristics diagram of points 34 and 40 is shown in Figure 2b, respectively. The vibration peaks of the two points showed good consistency in the frequency domain, which is beneficial for mode picking.  
In this paper, the accelerometer sensor and modal hammer were from Donghua Testing Technology Co., Ltd., Taizhou, China. The accuracy of the accelerometer is 1.075 mV/(m-s\({}^{2}\)  
Figure 2: (a) Experimental equipment of mode testing. (b) Typical frequency response based on PolyLSCF.  
and the frequency response range is 1-10,000 Hz. The accuracy of the modal hammer is 2.118 mV/N, and the working range is 1-5000 N. The sampling frequency for the modal test is 2000 Hz. The PolyLSCF was used to fit the frequency response function. The dynamic signal, mode shapes, mode frequencies, and damping ratios were obtained using the dynamic signal acquisition and analysis software system, DHDAS, from Donghua Testing Technology Co., Ltd., Taizhou, China.
## 3 Results
### Uncertainty Analysis  
It is important to note that the risk assessments performed here only consider fruits and vegetables consumed in the raw state. Additionally, these results indicate that the irrigation type and exposure of the various crops to reclaimed water (and the resulting microorganisms in reclaimed water) have an impact on the annual risks of infection. Sensitivity analyses were performed for all irrigation scenarios by assessing the rank order correlation of uncertainty for the variables considered in this model, specifically the pathogen concentration, time between irrigation and harvest, human body weight, the equivalent volume of water irrigated onto crops, fraction of fruits and vegetables consumed raw, as well as the time of year that harvesting is conducted. Pathogen concentration contributed the most to uncertainty for all irrigation types; however, the equivalent volume  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Scenario**} & \multicolumn{2}{c}{**DALY per Year ***} \\ \hline
**Irrigation** & **Organism** & **Average** & **Lower** & **Upper** \\
**Type** & & & **Confidence Limit** & **Confidence Limit** \\ \hline Spray (SI) & _Salmonella_ spp. & \(\mathbf{7.49\times 10^{-6}}\) & \(5.07\times 10^{-7}\) & \(\mathbf{8.93\times 10^{-5}}\) \\ \hline  & Adenovirus A–F & \(1.15\times 10^{-9}\) & \(4.98\times 10^{-10}\) & \(2.64\times 10^{-9}\) \\ \hline  & _Cryptosporidium_ spp. & \(\mathbf{8.74\times 10^{-5}}\) & \(\mathbf{1.27\times 10^{-5}}\) & \(\mathbf{4.81\times 10^{-4}}\) \\ \hline  & _Giardia_ spp. & \(\mathbf{3.44\times 10^{-4}}\) & \(\mathbf{5.38\times 10^{-5}}\) & \(\mathbf{9.98\times 10^{-4}}\) \\ \hline Drip (DI) & _Salmonella_ spp. & \(2.25\times 10^{-7}\) & \(1.16\times 10^{-8}\) & \(\mathbf{4.00\times 10^{-6}}\) \\ \hline  & Adenovirus A–F & \(3.66\times 10^{-11}\) & \(7.25\times 10^{-12}\) & \(1.28\times 10^{-10}\) \\ \hline  & _Cryptosporidium_ spp. & \(\mathbf{2.76\times 10^{-6}}\) & \(\mathbf{2.61\times 10^{-7}}\) & \(\mathbf{2.54\times 10^{-5}}\) \\ \hline  & _Giardia_ spp. & \(\mathbf{1.26\times 10^{-5}}\) & \(\mathbf{1.14\times 10^{-6}}\) & \(\mathbf{1.16\times 10^{-4}}\) \\ \hline Subsurface & _Salmonella_ spp. & \(2.28\times 10^{-8}\) & \(1.12\times 10^{-9}\) & \(4.05\times 10^{-7}\) \\ \hline  & Adenovirus A–F & \(3.63\times 10^{-12}\) & \(6.92\times 10^{-13}\) & \(1.28\times 10^{-11}\) \\ \hline  & _Cryptosporidium_ spp. & \(2.73\times 10^{-7}\) & \(2.57\times 10^{-8}\) & \(\mathbf{2.66\times 10^{-6}}\) \\ \hline  & _Giardia_ spp. & \(\mathbf{1.27\times 10^{-6}}\) & \(1.10\times 10^{-7}\) & \(\mathbf{1.25\times 10^{-5}}\) \\ \hline \hline \end{tabular}  
* Time between irrigation and consumption is assumed to be 15 days. Bolded values are greater than the acceptable limit.  
\end{table}
Table 2: Annual risk of infection for irrigation scenarios based on 10,000 Monte Carlo simulations.  
of exposure also had an important impact on the magnitude of the health outcome for the DI and SDI models.
## 3 Results and Discussion
### Comparison with Existing Models  
To compare with existing results, the leave-one-out cross-validation was performed for the MLR equation proposed by [18]; here, the same variables used in their model, used as input variables, and the coefficients were estimated by the least squares method. The results were compared with the cross-validation results using the proposed MLR equation and DNN (Figure 12).  
Figure 11: Comparisons of predicted and observed ultimate bearing capacity by the DNN considering the cross-validation error.  
In the leave-one-out cross-validation, the MLR equation, which consists of the variables proposed by [18] showed the highest MAE of 89.7 kPa and bias of 1.023. The DNN model showed a favorable performance for new data with an MAE of 74.9 kPa and bias of 0.999 as the optimal structure was selected considering the cross-validation results. The MLR equation, which consists of the variables selected in this study, showed the best performance among the three models with the lowest MAE of 70.5 kPa and bias of 1.003. The evaluation of the error through cross-validation is crucial for evaluating the performance of the prediction model because the accuracy of the prediction model is evaluated based on the independent data, and the MLR equation that uses four input variables proved to be able to predict more accurate ultimate bearing capacity. The comparison of the observed and the predicted ultimate bearing capacity by the three models established with all the load test data is shown in Figure 13, and the statistical results of these comparisons are summarized in Table 6.  
Figure 12: Comparison of the predicted and the observed ultimate bearing capacity by leave-one-out cross-validation.  
Figure 13: Comparison of the observed ultimate bearing capacity to that of the predicted using three models.  
On the basis of the MAE, the performance ranking for the final models in which all load test data were used showed the same rank as the performance that considers cross-validation. As a result, the proposed MLR demonstrates high performance in predicting the ultimate bearing capacity of aggregate pier-reinforced clay as it has the lowest MAE, bias, and COV in bias.  
Therefore, the ultimate bearing capacity could be predicted effectively with only four parameters. Moreover, it could improve the prediction accuracy and reduce the predictive variability in comparison with the existing MLR equation. The DNN was also applicable for the ultimate bearing capacity prediction by selecting the optimal structure considering the cross-validation. However, the performance was inferior to the proposed MLR, and model-updating might be a time-consuming task because it requires selecting a new optimal structure. By contrast, the MLR equation could easily improve by updating the coefficients considering the new data.
# Three-Dimensional Object Recognition Using Orthogonal Polynomials: An Embedded Kernel Approach
## 2 Preliminaries  
This section describes the OP used in this paper, as well as the process of computing moments, which will be used in the vision and control system of mobile robots to ensure safe navigation in environments. Moments have a significant role in the process of feature extraction. Feature selection is the process of choosing a subset of relevant features from a larger set for a particular task, such as classification. The length of the feature vector is important in the performance of machine learning algorithms. Forming a feature vector using too few features may not capture enough information, which affects the recognition accuracy. On the other hand, constructing a feature vector using too many features can lead to overfitting. Therefore, finding the optimal feature vector length is essential.  
Features that possess energy compaction and localization properties in the moment domain are able to simplify feature handling and have a significant impact on processing steps [46]. This is because they allow the signal to be expressed using a small number of moments, reducing computational complexity. Moreover, the localization property in space identifies the region of interest, which speeds up the computation and improves feature classification [47]. To achieve these properties, this paper uses the squared Krawtchouk-Tchebichef polynomial (SKTP) as a discrete transform. The SKTP has been shown to have robust performance in different applications, demonstrating its powerful energy compaction and localization properties [46]. Moreover, its localization property in space contributes an additional reduction in computation and feature classification by locating the region of interest (ROI) [48].
###### Abstract  
As for the fault diagnosis process of a reciprocating compressor, vibration signals are often non-stationary, nonlinear, and multi-coupled, which makes it difficult to conduct effective fault information extraction. In this paper, a method based on optimized resonance-based sparse signal decomposition (RSSD) and refined composite multiscale dispersion entropy (RCMDE) is proposed. The quality factors in RSSD are optimized by atom search optimization (ASO) primarily, then the optimal quality factors are applied to the RSSD of reciprocating compressor fault signals. The noise interference in the original vibration signal can be effectively distinguished from the low resonance component after decomposition. The genetic algorithm (GA) is employed to optimize the core parameters of RCMDE. Finally, the RCMDE of the low-resonance component is extracted as the eigenvalue for pattern recognition. The experimental study illustrates that the spring failure, valve wear, and normal valve conditions of reciprocating compressors can be effectively distinguished by the proposed method.  
fault diagnosis; reciprocating compressor; resonance-based sparse-signal decomposition; atom search optimization; refine composite multiscale dispersion entropy
## 2 Scenario of Interference Source Moving along the Satellite Trajectory
### Derivation of Relative Angle Difference \(\psi\) with Sidelobe Gain
\begin{bmatrix}X_{i}-X_{g}\\ Y_{i}-Y_{g}\\ Z_{i}-Z_{g}\end{bmatrix}  \tag{7}\]  
\[\psi(^{\circ})=\frac{180}{\pi}\cos^{-1}(\frac{\overset{\rightarrow}{P_{s}} \overset{\rightarrow}{\cdot}\overset{\rightarrow}{P_{l}}}{\left|\overset{ \rightarrow}{P_{s}}\right|\cdot\left|\overset{\rightarrow}{P_{l}}\right|} \tag{8}\]  
Figure 3 shows the radiation pattern of a ground station antenna with a parabolic reflector and rectangular feed horn. The bore-sight gain \(G_{r}^{g}\) (\(\xi=0^{\circ}\)) of the antenna is 59 dBi at 8 GHz. The blue solid line indicates the radiation pattern obtained using the GO and PO methods. The GO method is used to model the antenna, focusing inside the reflector, and the PO method is used to calculate the scattering on the reflector surface by considering the current distribution on the surface [15; 16; 17; 18; 19; 20]. By combining these two methods, the ground station antenna radiation pattern can be calculated. The parabolic diameter of the ground station antenna is 11.3 m, and a feed horn antenna with rectangular aperture is employed. Since sidelobe gains exhibit large fluctuations according to angle, we apply a regression model in order to more easily observe the tendency of the \(J/S\) ratio. The regression model is based on the exponential Equation (9):  
\[G_{r}^{s}(\xi)=a_{1}\cdot e^{b_{1}\cdot\xi}+a_{2}\cdot e^{b_{2}\cdot\xi}, \tag{9}\]  
where \(a_{1}\) (\(=-31.5817\)), \(a_{2}\) (\(=36.7337\)), \(b_{1}\)(\(=0.0046\)), and \(b_{2}\) (\(=-0.0682\)) are the coefficients that best fit the point, shown as the red solid line. To determine the sidelobe gain, we apply the \(\psi\) obtained from Equation (8) to Equation (9). All scenarios for downlink with airborne interference sources are simulated using Equations (1)-(5). Detailed simulation parameters are given in Table 1.  
Figure 3: Radiation pattern of the ground station antenna with regression model.
# Covert Surveillance Video Transmission with QAM Constellations Modulation
###### Abstract  
This study explores the covert transmission of surveillance videos using QAM (quadrature amplitude modulation) constellations. By analyzing transmission strategies used in surveillance video systems, we adopt KL divergence as the constraint for covertness performance and mutual information to characterize transmission rates. Utilizing the Taylor series expansion method, we simplify the complex integral calculations of mutual information and KL divergence into summation operations. This simplification not only reduces computational complexity but also ensures precise calculations. Theoretical derivations establish the necessary conditions for the covert transmission of surveillance videos, optimizing modulation probabilities for constellation points. Experimental results and simulation validations demonstrate the superior performance of our proposed method in terms of both information transfer and precision.  
surveillance video; covert transmission; QAM constellation; Taylor series expansion +
Footnote †: journal:  
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