esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings
## 4 Experimental Results and Analysis  
This section used the comprehensive simulation test bench for mechanical failure to perform experiments as shown in Figure 4. The experimental device included a comprehensive mechanical  
Figure 3: The process of Deep Adaptive Dynamic Kernel Principal Component Analysis (Deep ADKPCA).  
fault simulation test rig, AIC9916 FS comprehensive fault diagnosis and analysis system software, FL6816L16 channel sensor, BSZ800D-16 vibration signal acquisition instrument, and computer storage device. In the experiment, the speed was 1730 r/min.  
There were three sets of data in the experiment. The first set was the vibration signal of a normal undamaged rolling bearing to establish an offline model. The second set was the vibration signal of the outer ring fault rolling bearing with a fault diameter of 0.2 mm, and the last set was the vibration signal of the rolling body fault rolling bearing with a fault diameter of 0.2 mm. The online monitoring part used the fault data collected by the second and third sets. Each set of vibration signal data collected 1024 samples, that is \(n=1024\). The update window size \(L=3\). The first control limit was calculated through normal data in the offline modeling process, and then in the online monitoring process, the model was updated every three samples collected. The selected fault diameter was relatively small, and the fault features in the vibration signal were weak, so these faults were accounted as the incipient fault.
## 4 Experiment and Analysis
### Three-Dimensional Deformation Rates along the Weihe Fault  
In order to obtain the east-west, north-south and vertical deformation components, the three-dimensional rates were calculated by integrating GNSS and InSAR observations, as described in Section 3.2, and as shown in Figure 5. The north-south components were mainly contributed to by GNSS data as the InSAR is insensitive to the north-south motion of its near-polar-orbit observations. It is found that deformation patterns in horizontal east-west (Figure 5a) and north-south directions (Figure 5b) are not as clear as the vertical deformation patterns (Figure 5c) in the Weihe Basin. The east-west deformation, ranging from 5.6 mm/yr to 7.4 mm/yr, is dominated by the eastern component. The north-south deformation, ranging from \(-\)3.2 mm/yr to \(-\)2.4 mm/yr, is dominated by the southern component. The small spatial variations in east-west and north-south deformation suggest the small strike-slip motion rate of the Weihe Fault. The spatial variations of vertical deformation (Figure 5c) are relatively large along the Weihe Fault, where the minimum and maximum deformation rates are \(-\)9.7 mm/yr and 6.2 mm/yr, respectively. There are two main reasons to explain it. The first is the activity of normal faults in the Weihe Basin, and the second is related to anthropological activities such as groundwater extraction and recharge. Precise leveling data are used to evaluate the integrated vertical deformation rates. As shown by the gray arrows in Figure 5c, they show the similar spatial pattern: subsidence in the south and uplift in the north. The statistical results show that the standard deviation between them is 1.69 mm/yr and the mean value is 0.6 mm/y, demonstrating the reliability of the produced vertical deformation.
## 5 Conclusions  
In this work, the piezostack actuator direct-drive valve (PADDV) system was designed and its control performance was evaluated at high temperatures. An experimental apparatus was constructed using a heat chamber and high-temperature resistant sensors for testing the valve system at high temperatures. The step response of the valve system was analyzed using the return spring at high temperature with a step input voltage. It has been observed that the time constant is increased as the displacement of the spool is decreased. In order to evaluate the spool tracking performance, both a classical PID controller and PID controller integrated with a fuzzy algorithm was designed and implemented in the valve system, which was subjected to temperatures up to 150 \({}^{\circ}\)C. The results achieved from the control realization are summarized as follows.  
The PID controller can be used to control the valve system up to a frequency of 200 Hz at room temperature. However, the valve system cannot be controlled using the same gains under the frequency variation. Therefore, a fuzzy algorithm was applied to tune the gains according to the temperature and operating frequency. The performance of the system is satisfactory at low frequencies and between room temperature and 150 \({}^{\circ}\)C. The valve system exhibits the time delay at frequency of 200 Hz for the entire temperature range because the stiffness of the return spring is designed considering high-temperature conditions. Although the system exhibits a time delay, the spool performance which follows the desired displacement is satisfactory.  
The results of this study show that the PADDV with the thermal insulation using the general piezostack actuator can be practically used to control the flow rate at various high operating temperatures required in many components or systems. However, for successful practical application, the durability of the valve system using the piezostack actuator at high temperatures over long operating periods should be investigated thoroughly. Such a study will be conducted in the near future.  
Figure 12: Tracking control results with 200 Hz, (**a**) Tracking control result via PID controller at 30 \({}^{\circ}\)C, (**b**) Tracking control result via PID controller at 150 \({}^{\circ}\)C with same PID gains at 30 \({}^{\circ}\)C, (**c**) Tracking control result via fuzzy PID controller at 150 \({}^{\circ}\)C.
# An Overview of the Public Health Challenges in Diagnosing and Controlling Human Foodborne Pathogens
## 5 Antimicrobial Resistance and Food Safety  
There are numerous steps in the food production process that contribute to the prevention and control of food-borne illnesses, including transporting, storing, processing and preparing food, as well as assuring the safety and health of food for humans [223]. Food-borne illness is one of the most serious public health threats associated with antibiotic resistance. Previous studies have indicated that an increase in antibiotic-resistant bacteria has led to an increase in foodborne illnesses [224]. In recent years, numerous studies have been conducted to find solutions to antibiotic resistance, especially resistance spread from food to humans [224]. There are a number of antibiotic-resistant bacteria in foods and humans; however, some basic and easy-to-follow steps can help prevent the spread of antibacterial resistance foodborne pathogens, like appropriate hand washing, convenient vegetable washing and cooking temperatures. [PERSON] et al. [225] state that antibiotic residues can negatively affect public health in a number of ways, such as nephropathy, allergic reactions, hepatotoxic effects and carcinogenicity.  
There are four main consequences of antibiotic-resistant bacteria and infectious diseases: (1) delay or failure in treatment; (2) restrictions in antimicrobial selection; (3) survival of resistant strains in other bacterial illnesses; and (4) coexistence and increased pathogenicity of resistance genes due to selection [226; 227]. Studies conducted in the past have indicated that numerous food-borne pathogens are resistant to a variety of antibiotics. Someexamples of significant resistant foodborne bacteria include Thermotolerant _Campylobacter_, _Salmonella_, _Staphylococcus aureus_ and _Enterococci_ species [224].
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 2 Basic Theory of CNN and Capsule Network  
### Convolutional Layer  
The convolutional layer is composed of multiple feature maps, and each feature map is composed of multiple neurons. Each neuron of the feature map is connected to the local area of the feature map of the previous layer through the convolution kernel. The convolutional layer performs convolution operations on the input signal through different convolution kernels to extract corresponding features. It is generally believed that a convolution kernel defines a certain pattern. The convolution operation is used to calculate the degree of similarity between each position and the pattern. The more similar the current position to the pattern, the stronger the response. The convolution operation formula is shown below:  
\[\mathbf{y}_{i}^{l+1}(j)=\mathbf{K}_{i}^{l}\times\mathbf{x}^{l}(j)+b_{i}^{l}, \tag{1}\]  
where \(\mathbf{K}_{i}^{l}\) and \(b_{i}^{l}\) represent the weights and biases of the \(i\)-th kernel in the \(l\)-th layer, respectively, and \(\mathbf{x}^{l}(j)\) denotes the \(j\)-th local region in the \(l\)-th layer. \(\mathbf{y}_{i}^{l+1}(j)\) is output value of the convolutional operation.  
Two major features of convolutional layers are local connection and weight sharing. Local connection means that each time the convolutional layer performs a convolution operation, it only connects and operates with some nodes in the feature map of the previous layer. At the same time, the weight sharing reduces the network parameters of the convolutional layer greatly, which further reduces the risk of overfitting. In practice, a correlation operation is often used instead of a convolution operation.  
In the classic CNN structure, the convolutional layer is usually followed by the pooling layer. The pooling layer is to sample the feature map of the previous layer to reduce the size of the feature map. Common pooling functions are maximum pooling and mean pooling. By alternately stacking the convolutional layer and the pooling layer, the features in the input signal can be effectively extracted [27]. As the number of network layers deepens, the features learned by the network gradually change from simple features to complex and abstract features.
## 5 Results and Analysis
### Demand Profiles in Case Studies  
In this work, four different daily electricity consumption patterns in four seasons were investigated to validate the proposed methods. The key features of power generation and demand in a village user are summarised in Table 1 while the waveforms in Figure 8 display the demand profiles in these days.  
Figure 7: The household consumption on four seasonal days. (**a**) Spring; (**b**) Summer; (**c**) Autumn; (**d**) Winter.  
As given in Table 1, the load changes from 0 to 5.8 kW on the spring day. The power demand raises to 9.7 kW on the autumn day. The autumn day consumes the highest energy (16.1 kWh) over the next three days. The winter day also consumes significant energy as poor illuminance and cold weather increase lighting and heating requirements. In terms of the period and the percentage of the low demand, four season days display a similar trend while winter and autumn consume more power than spring and summer in general. In Figure 8, a similar trend is shown that the peak hours on four typical days were less than 1 h even on autumn and winter days. The three ranges, named the morning section, the midday section and the evening section, are found in Figure 8. Taking the spring day for example, the consumption increased from 6:30 in the morning and reached the peak at 8:15. Afterwards, the consumption fluctuated before decreasing to below 1 kW. From 13:00, the consumption increased again till 14:00. In this midday section, the energy consumption also fluctuated because different appliances were used over this period. 17:00 saw a consumption peak and the next peak occured around 19:00. The three sections were less distinctive on the selected winter day since this day was a weekend and the occupants had a different activity schedule to the weekday.
###### Abstract
[29] have investigated the VIV mechanism of a truss girder through wind tunnel tests and numerical simulations. Previous studies on box girders and \(\Pi\)-beams have demonstrated that modifying the railing design can effectively suppress VIV. [PERSON] et al. [30] found that both the installation location and shape of the railing can significantly affect bridge VIV. [PERSON] et al. [31] found that installing wavy railings on the upper surface edge of the box girder can entirely suppress vertical and torsional VIV. [PERSON] et al. [32] discovered that inclined railings could completely suppress both vertical and torsional VIV by significantly reducing the spanwise correlation of the streamwise velocity in the wake. [PERSON] et al. [33] and [PERSON] et al. [34] demonstrated that installing suppressor plates at the top of the railing or removing the maintenance rails under the girder significantly suppressed the VIV of steel box girders through wind tunnel tests. [PERSON] et al. [35] and [PERSON] et al. [36] found that reducing the railing ventilation rate can significantly suppress VIV in box girders. In conclusion, these studies demonstrate that adjusting the railing ventilation rate can effectively suppress bridge VIV. However, there is a lack of research conducted on the railings of double-deck truss girders. Additionally, the VIV of double-deck truss girders suffers from insufficient research data, incomplete understanding of vibration mechanisms, imperfect design specifications, and scarce field monitoring data. This research aims to investigate the impact of railing ventilation rate on truss girder VIV, which will fill in the shortcomings of existing research and provide valuable insights for designing truss girder suspension bridges. This research investigates the VIV characteristics of the Huangjuentuo Bridge, currently under construction in China, along with the impact of railing ventilation rates and the associated mechanisms. Wind tunnel tests and computational fluid dynamics (CFD) simulations are conducted to achieve these objectives. Section 2 focuses on calculating the structural dynamic characteristics of the bridge using the finite element method. In Section 3, wind tunnel tests are carried out to measure the VIV response of the sectional model under different railing ventilation rates during the completion stage and the VIVresponse of the construction stage cross-section. Section 4 investigates the mechanism of VIV occurrence and the influence of railing using numerical simulation. Figure 1 provides a visual representation of the technology roadmap employed in this research.
## 3 Empirical Results and Analysis
### Comparison of Vertical and Horizontal Peak Ground Motion Attenuation  
#### 3.2.1 Comparison of Vertical PGA and Horizontal PGA  
The analysis showed that the PGA of vertical strong-motion records was 0.673 times that of the horizontal PGA. Because of the randomness and variability of PGA values in  
Figure 4: (**a**) Scatter plot of PGA-to-EPA ratio for each station; (**b**) comparison of PGA and EPA attenuation curves.  
Figure 3: Fitted attenuation curves for horizontal and vertical EPA decay relationships.  
strong-motion records, there were 20 stations where the vertical PGA exceeded the recorded PGA in the NS direction, and also there were 15 stations where the vertical PGA exceeded the recorded PGA in the EW direction. This demonstrated that the probability of a vertical acceleration peak exceeding the horizontal acceleration peak was approximately 13.3%.  
In engineering practice, it is generally considered that the vertical peak is 1/2 to 2/3 times the horizontal peak. For instance, the seismic coefficient for the vertical direction in seismic design codes can be considered to be 65% of that for the horizontal direction. Therefore, the reference value of the 2/3 ratio was introduced in Figure 5. Figure 5 displays the distribution of the ratio between the vertical PGA and horizontal PGA. The green dotted line represents the computed ratio of the vertical PGA divided by the horizontal PGA, which both were predicted using different equations, and it exhibits the trend of the V/H (vertical/horizontal) PGA fitting values with respect to the fault distance. By inputting different fault distances into the PGA prediction equation at 1 km intervals, the predicted PGA values for vertical and horizontal directions were calculated. The calculated vertical PGA was almost 0.577 times that of the horizontal PGA.  
According to Figure 5, the ratio between the computed values of the vertical and horizontal PGA predicted using the equations was greater than 1/2. The ratio between the vertical and horizontal PGA decreased gradually with an increasing fault distance and was slightly greater than 2/3 in the range of fault distances from 0 to 7 km. This suggests that vertical seismic action has a significant impact on the seismic resistance of engineering structures in the near-fault area. From a statistical perspective, although there were a few cases in the near-fault range of large-magnitude earthquakes where the ratio exceeded 2/3, the difference with 2/3 did not go beyond 5%. Considering the redundancy in structural seismic design, estimating the vertical seismic action using a ratio of 2/3 in the near-fault range of large-magnitude earthquakes is still safe and feasible. This estimation scheme for vertical seismic action has also been validated based on a single earthquake event in Turkiye (Pazarcik earthquake), which further confirms its reasonableness.
# Response to Variations in River Flowrate by a Spaceborne GNSS-R River Width Estimator
## 1 Introduction  
Knowledge of river streamflow is crucial for a number of water-related applications. For example, streamflow information is needed for hydrological modeling, watershed management, flood monitoring and warning systems, and hydrodynamic power management. Despite the need for streamflow data, existing in situ streamflow gauge networks are limited in number, particularly in remote regions, and are on the decline worldwide [1]. A number of remote-sensing-based algorithms for estimating streamflow have been proposed [2; 3]; however, these algorithms require ancillary data and/or a priori knowledge of the river properties that precludes their use on a global scale. Furthermore, in order to be useful for short-term flood monitoring during extreme conditions, the algorithms require up-to-date remote sensing measurements at high spatial resolution that traditional satellite systems cannot provide. Existing Global Navigation Satellite System-Reflectometry (GNSS-R) systems present the potential for exploiting high-temporal-resolution data over large scales for use as inputs to flowrate algorithms, or as a means of developing a novel remote sensing signature for river properties (e.g., width) that correlate directly to streamflow. To investigate this potential, we used bistatic radar data from the NASA Cyclone Global Navigation Satellite System (CYGNSS) Mission to study the signal response to a range of flowrates over a segment of an uncontrolled river. Due to the high sensitivity of the CYGNSS signal to land/water boundaries, water bodies on the order of 100 m or less in width have been observed to create a well-defined signal response [4]. Our results indicate that the signal waveform width corresponding to the river is sensitive to flowrate due to the changing width with increased flows. We introduce the concept of an associated GNSS-R width (AGW) that maps approximately linearly to observed river streamflow. Our results indicate that the river's AGW is highly correlatedwith changes in its streamflow at high flowrates and stream heights (when the river can be expected to overspill its banks).
## 4 Experiments
### Ablation Experiments
impact of perceptual loss on accuracy was relatively modest, its primary function was to optimize texture alignment, enhancing the model's ability to discern fine details of defects. In the combined module experiments, the results further revealed the synergistic interactions between the components and the associated performance trade-offs. When FasterNet and MSCAM were used together, mAP rose to 0.839, and the inference frame rate reached 164 FPS. MSCAM compensated for FasterNet's shortcomings in shallow feature extraction and suppressed background noise interference, thus improving detection accuracy while maintaining high inference speed, demonstrating the synergistic effect between the two. The combination of MSCAM and perceptual loss achieved peak accuracy, with mAP reaching 0.861 and F1 at 0.79, but the inference speed dropped to 109 FPS. This combination notably enhanced the model's response strength in the central regions of defects, with reduced edge misdetections, suggesting that perceptual loss optimized MSCAM's attention focusing, improving the precision of defect edge capture. The PerMSCA-YOLO model, integrating FasterNet, MSCAM, and perceptual loss, strikes an optimal balance between mAP and FPS. FasterNet's lightweight design mitigates the computational load introduced by MSCAM and perceptual loss, while MSCAM's multi-scale feature enhancement and perceptual loss's texture alignment create a complementary synergy. This enables the model to achieve higher detection accuracy and robustness while retaining real-time performance. The model's test results are shown in Figure 9.  
To further analyze the impact of the different modules on the regions of interest, we employed Grand-CAM to generate heatmaps that illustrate the differences in response intensity in the defect regions across models. The heatmap in Figure 10 clearly demonstrates that after incorporating MSCAM and perceptual loss, the model's response to key areas of track defects became more concentrated, validating the effectiveness of the multi-scale convolutional attention module and perceptual loss in improving the model's attention precision. These results convincingly show that MSCAM enhances the model's ability to capture diverse defects, such as fine cracks and corrosion deposits on the track surface, by reinforcing the expression of features across different scales. Additionally, perceptual loss further bolstered the model's robustness against complex backgrounds and irregular defects.