esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring of Industrial Machine Using a Novel Blind Feature Extraction Approach
## 3 Proposed Signal Processing Methodology
### Feature Extraction Algorithms
However, for the defect bearing signal, a significant peak is observed. For multiple signals, manual data processing is performed to cross-validate the results obtained with the ground truth results published by the NASA authors [18]. The Weiner filter is applied to the raw data for extracting the defect vibration data and once the signal is obtained, Hilbert Transform is used to extract the envelopes of the signal component. The envelope for a defect and unhealthy bearing data set is presented in Figure 4:0 Evidence of the rolling element bearings hitting the outer race periodically is observed. For the healthy case, no non-stationary vibration components are seen which indicates the diagnosis is successful. For the IMS bearing data set with 2156 signal samples, the SK feature estimate is presented in Figure 10 below. It is evident that the SK based feature extraction clearly detects the faults and any other non-stationary behaviour in the data. Figure 4:1 SK –based wiener filter extracted from SK curve through thresholding at 20 units. Figure 4:2 Envelope of the SK filtered signal. The decision boundary is extracted using the change detection algorithm. From the change detection algorithm, it is identified that bearing 4 has been damaged on day 27 as shown in Figure 11, which is a week earlier than the final run to failure inspection day. Thus, the proposed approach has the potential to identify early fault characteristics.
## 4 Numerical Case Study--Assessment of SSI Effects on a Steel Building
### Braced Frame
#### 4.2.1 Fixed-Base Braced Frame  
Since in the observed literature [43; 44] only the moment resisting frame was analyzed, the information regarding the placement and geometry of the bracing system was not given. Therefore, the equivalent seismic force approach was used for the design of the braces. The selected braces are shown in Figure 10. It was decided to use hollow steel tubes with a square cross-section that were designed according to Eurocode 8 by fulfilling the code-based bracing criteria [45; 52]. Bracing was placed only on the external frames while the inner frames were left without bracing. This research deals with examination of one external frame. It is important to emphasize that most of the dead and live loads in the building are carried by the moment resisting frames. Braced frames are loaded with self-weight and also with nodal forces coming from seismic load combination from the slabs at the nodes connecting columns and beams. Nodal forces are calculated to be 235 kN for the middle nodes and 117 kN for external nodes for seismic combination of the forces. Plastic hinges, described in Table 4, were calculated for all cross-sections and assigned to the model. The bracing is designed only to withstand axial loads. Pushover analysis was performed for two different types of lateral load distributions as it was done in previous chapters for the moment resisting frame.  
If the bracing cross sections are observed, the hinges are compound of compressive and tensile axial limits parallel with deformations of tensile force \(\Delta t\) and compressive force \(\Delta c\).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Cross-Section** & **Story** & **My (kNm)/Mp (kNm)** & **@y/@p** & \(\chi\) \\ \hline HEB 340 & 1, 2, 3, 4 and 5 & 178/271 & 0.007/0.039 & 1 \\ \hline IPE 330 & 1, 2, 3, 4 and 5 & 196/221 & 0.013/0.08 & 1 \\ \hline Cross-section & Story & \(+\)N(kN)\(/-\)N(kN) & \(\Delta t\)(mm)\(/\Delta c\)(mm) \\ \hline \(80\times 80\times 5\) & 5 & 412.5/113.58 & 6.21/1.71 \\ \hline \(80\times 80\times 10\) & 3 and 4 & 770/189.74 & 6.21/1.53 \\ \hline \(80\times 80\times 14.2\) & 2 & 1027.68/231.38 & 6.21/1.39 \\ \hline \(95\times 95\times 14.2\) & 1 & 1262.1/406.12 & 6.21/1.99 \\ \hline \end{tabular}
\end{table}
Table 4: Braced frame plastic hinge properties.  
Figure 10: Braced frame.
# Wideband Mixed Signal Separation Based on Photonic Signal Processing
## 3 Stealth Communication
### Related Work
The reference term that only includes noise \(\cos[\omega_{c}+n(t)]\) and the signal term that includes both noise and signal \(\cos[\omega_{c}+s(t)+n(t)]\) corresponds to \(x_{1}\) and \(x_{2}\) in Section 2.1. The signal \(s(t)\) and noise \(n(t)\) corresponds to \(s_{soi}\) and \(s_{int}\) in Section 2.1. In this case, \(x_{1}\) and \(x_{2}\) is not a linear combination of \(s_{soi}\) and \(s_{int}\), therefore, the stealth method is resistant to the BSS attack. Figure 6a shows the experimentally measured spectrum of the stealth signal hidden in a 5 THz bandwidth noise. With the right key (D1 = D2), the binary stealth signal is recovered with a clear eye pattern with a bit error rate of \(1\times 10^{-6}\) (Figure 6b) [3, 71, 76, 77]. Without the right key (D1 \(\
eq\) D2), only the noise is received (Figure 6c), and the signal pattern measured from the oscilloscope is the same as pure noise without modulating stealth signal (Figure 6d). The key space depends on the optical delay applied and the accuracy to match the delay [78]. The experimental results show that the delay needs to be matched within a 0.3 mm resolution, and the delay (D1 and D2) ranges from 10 m to 25 km has been tested. The key space can also be increased and experimentally tested with other physical level  
Figure 5: Schematic diagram of optical stealth communication [73]. Figure 6: Experimental results of optical stealth communication: (**a**) spectrum of the stealth signal hidden in noise; (**b**) recovered stealth signal; (**c**) stealth signal hidden in noise without matching condition; (**d**) pure noise without stealth signal. methods, including dispersion as another orthogonal dimension [71, 77, 79], and phase mask [73, 80].
# Case Study of Underground Shield Tunnels in Interchange Piles Foundation Underpinning Construction
## 5 Field Monitoring and Data Analysis
### Data Analysis  
(1) Settlement test  
Figure 11: Monitoring sensor arrangement of the underpinning pile cap.  
After comparison, the numerical simulation model's calculated results are consistent with the field monitored settlement date. This proves the reasonability and safety of the underpinning bearing platform construction scheme.  
(2) Stress test  
In order to verify the safety of origin bridge structure during shield tunnel excavating, field stress tests are also necessary during the shield tunnel excavating. In this project, stress monitoring sensors named JMZX-212 AT are adopted to gauging the stress changes of the bridge pier and the underpinning bearing platform. The stress sensor is suitable for measuring the surface and internal stress of concrete structure. The accuracy of the stress sensor was \(1\ \mu\cdot\varepsilon\) and has an effective measuring range is \(\pm 1500\ \mu\cdot\varepsilon\), which is also suitable for the requirement of this project.  
Figure 12: Vertical displacement results of the overpass pier.  
Figure 13: Vertical displacement results of the underpinning cap.  
Bridge pier stress monitoring started from bridge foundation excavation till the shield tunnel construction completed and the stress variations are gauged by four sensors numbered 1 to 4 that placed on the bridge pier surface. As it presented in FigureComparison of monitoring data of four sections clearly indicates that the piles foundation underpinning scheme could effectively reduce the impact of shield tunneling on the  
Figure 16: Compressive stress gauging results of piles foundation underpinning.  
Figure 15: Tensile stress gauging results of piles foundation underpinning.
# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 2 MPC Implementation
### Nonlinear MPC  
Nonlinear techniques are unavoidable in complex systems because linear model predictive control applications do not always result in acceptable performance [31]. This is one of the reasons why nonlinear model predictive control (NMPC) has received a lot of attention in the last several decades, with many recent breakthroughs in both academia and industry. The term NMPC refers to MPCs that employ a nonlinear dynamic model and nonlinear constraints, resulting in increased complexity. Nonlinear model predictive control (NMPC) uses nonlinear system models to estimate the dynamic state of the plant. Nonlinear MPC, as with linear MPC, requires multiple iterations within the prescribed prediction horizon to arrive at optimal control parameters. Unlike linear MPCs, the control problems are not always convex in the case of nonlinear MPCs due to the way the nonlinear MPC solves them [32; 33]. The NMPC usually employs Newton-like optimization methods to arrive at optimal solutions viz. direct collocation and direct single/multiple shooting. The NMPC algorithms are generally designed on the basis of the inherent similarity of the successive optimal control problems. The previously obtained optimal solution can be utilised to efficiently initialise the Newton-type solution technique, saving significant time in the computation process. As a result, path-following algorithms (also known as \"real-time iterations\") capitalise ever more on the similarity of subsequent problems. Thus, such algorithms do not directly iterate towards the convergence of the given optimization problem; instead, they iterate to solve recent problems before starting with the current NMPC problem with a more effectively selected initialization [34].  
NMPC models have been typically implemented in the industrial processes involving slow sampling rates or distributed parameter systems. However, recent advancements in computational algorithms as well as in controller hardware have made it possible for large-scale implementations of NMPCs in high-sampling-rate applications such as the automotive manufacturing processes, or even when the states are distributed over a large space [35]. Recent applications of NMPC include optimal terrain path and obstacle avoidance trajectory following in varied real-time aerospace applications [36; 37].
## 1 Introduction
In flow-based methods [8; 9; 10], optical flow is mainly used for propagating feature maps between key frames and non-key frames, rather than as input information. The extracted features only contain image information and the trained network may mistakenly identify some stationary shadow regions in the background as TV. In order to solve the above problems, we propose two convolutional neural networks based on an encoder-decoder architecture. The first network has the ability to learn the difference between frames and utilizes the image and optical flow information of three consecutive frames as the input to perform rough detection of the TV. The second network corrects the coarse contours and uses the image information and the rough detection result of the current frame as the input to perform fine detection of the TV. By combining these two networks, the defocused TV can be detected effectively and the false alarms caused by the motion of formed elements or impurities can be suppressed. This article is organized as follows: Section 2 introduces our previous works on TV detection. The details of two convolutional neural networks with encoder-decoder architecture are described in Section 3. Section 4 introduces the dataset we used and the experimental results. Section 5 presents the discussion. Conclusions are provided in Section 6.
# The Rise of Quantum Information and Communication Technologies
## 4 Next Steps
### Standardization  
There is a need to develop and consolidate a standard solution enabling multi-vendor interworking and interoperability, not only at the physical level but also at the management and control ones. This is mandatory for the mature deployment of QCTs solutions. Multiple groups, such as ANSI, ITU, IETF, ETSI, CEN-CENELEC, GSMA and IEEE, are producing significant synergic efforts. The definition of standard management and control procedures is another key aspect that concerns the integration of future quantum nodes and equipment in classic infrastructure (e.g., optical networks, 5G and beyond); this requires further effort in the definition of abstractions, interfaces and protocols.
# Classification of Malicious URLs by CNN Model Based on Genetic Algorithm
## 2 Materials and Methods
### Feature Extraction
#### 2.1.2 Calculation of Fitness Function  
The result of feature selection based on a genetic algorithm mainly depends on the definition of the fitness function. This paper uses the accuracy of support vector machine (SVM) classification as the fitness function. The selected features are directly classified by the support vector machine algorithm, and the accuracy rate is obtained. The accuracy rate is used to determine whether it is the optimal feature. The support vector machine is based on the principle of structural risk minimization, which has strong generalization ability and global optimality. Let \(\varphi(x)\) be the vector of the sample \(x\) after mapping, and divide the hyperplane into Equation (1).  
\[f(x)=w^{T}\varphi(x)+b \tag{1}\]  
In Equation (1), \(w\) is the weight vector and \(b\) is the offset. Transform the problem into a quadratic programming problem,  
\[\min\left[\frac{1}{2}\|w\|^{2}+c\sum_{i=1}^{n}\xi_{i}\right] \tag{2}\]  
\[s.t.y_{i}[(w\cdot\varphi(x_{i}))+b]-1+\xi_{i}\geq 0 \tag{3}\]  
where \(\xi_{i}\geq 0\), \(\xi_{i}\) is a relaxation variable, and the data \(x_{i}\) can be correctly classified when \(0<\xi_{i}<1\); when \(\xi_{i}\geq 1\), data \(x_{i}\) are misclassified. \(i=1,2,\cdots,n\), \(C\) is the penalty parameter. The dual form of the problem is,  
\[\max\left[\sum_{i=1}^{n}\alpha_{i}-\frac{1}{2}\sum_{i,i=1}^{n}\alpha_{i} \alpha_{i}y_{i}y_{i}(\varphi(x_{i})\cdot\varphi(x_{j}))\right] \tag{4}\]  
\[s.t.\sum_{i=1}^{n}\alpha_{i}y_{i}=0,0\leq\]  
\[\alpha_{i}\leq c,i=1,2,\cdots,n \tag{5}\]  
Here, \(\alpha_{i}\) is the Lagrange multiplier and \(y_{i}\) is the target value. According to the KKT condition in optimization theory, only a small number of sample values are not zero. Solving nonlinear transformation needs to define kernel function. In this paper, the Gaussian kernel is used:  
\[k(x_{i},x_{j})=\exp(-\gamma\|x_{i}-x_{j}\|^{2}) \tag{6}\]  
Figure 1: Genetic algorithm feature extraction flow chart.  
where \(\gamma\) is the kernel parameter. When the \(\gamma\) value is too large or too small, it will produce too-small or too-large training errors. Finally, the accuracy of the classification results as a fitness function.
## 3 Centralized Lubrication and Waste Grease Recovery Test Platform Test
### Vacuum Degree Test  
#### 3.1.1 Numerical Simulation of Grease Discharge in Bearing Cavity  
Based on vacuum liposome, this study uses FLUENT to simulate the oil discharge in the bearing cavity. The grid is divided before numerical calculation, and then the parameters of grease are defined. The density of grease is 900 kg/m\({}^{3}\) and the viscosity is 1400 Pa-S. According to Formulas 1-5, the negative pressure value of the vacuum is determined to be \(-\)0.01 to \(-\)0.09 MPa. The vacuum degrees were chosen to be 0.07 MPa, 0.08 MPa and 0.09 MPa considering the actual operating requirements and bearing oil seal pressure. The number of iterations is 1000. The distribution of oil output speed of bearing grease under different vacuum degrees is shown in Figure 8.  
It can be seen from Figure 8 that, as the degree of vacuum in the bearing cavity increases, the inner and outer differential pressure of the bearing cavity increases, which advances the discharge speed of the waste grease. The higher the vacuum degree, the more favorable it is for discharge of waste grease, and the injected new grease can also enter the bearing for lubrication more smoothly. Figure 8 also demonstrates that the recovery squeezing method for the waste grease of existing wind turbines fails to collect the waste grease smoothly due to the lack of power to squeeze the grease into the oil collection bottle. Therefore, vacuum liposuction provides a theoretical basis for the design of the active waste oil recovery device for wind turbines. The grease suction and drainer device designed by vacuum liposuction can improve the vacuum degree of the bearing chamber and ensure good bearing lubrication.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Detection** & **Standard Value** & **Measured** & **Qualification** \\
**Parameters** & **Standard Value** & **Results** & **Judgment** \\ \hline Single liposuction & \(\geq\)1 mL & 1.03 mL & qualified \\  & Work cycle 10–25 times, & & \\  & Vacuum degree of & & \\ Vacuum degree & liposuction mouth is not & 0.08 MPa & qualified \\  & greater than & & \\  & \(-\)0.07 MPa & & \\ Nominal working & 4.5 MPa & 4.3 MPa & qualified \\ pressure & & & \\ \hline \end{tabular}
\end{table}
Table 1: Test standards and experimental data.  
Figure 7: The structure of the tested suction and drainer device.
## 2 Materials and Methods  
### Image Acquisition  
We referred to the maturity definition standards for strawberries from the literature [4] and divided the strawberry growth process for target detection into three stages: raw, turning, and ripe. Raw indicates strawberries exhibiting pure white or green colors; turning denotes strawberries transitioning from pink to light red, with color coverage ranging from 0% to 80%; and ripe represents deep red strawberries, with the color coverage exceeding 80%. To accurately detect strawberry maturity, we collected and constructed four different strawberry datasets to validate our algorithms:  
1. StrawDI_Db1 Strawberry Dataset: This is a high-quality open-source dataset specifically designed for strawberries, sourced from approximately 150 hectares of strawberry plantations in Huelva Province, Spain, and includes 2700 strawberry images ([[https://strawdi.github.io/](https://strawdi.github.io/)]([https://strawdi.github.io/](https://strawdi.github.io/)), accessed on 15 November 2024);
2. Straw_Det Strawberry Dataset: A total of 1500 strawberry images were collected from the Roboflow platform ([[https://robotflow.com/](https://robotflow.com/)]([https://robotflow.com/](https://robotflow.com/)), accessed on 15 November 2024), primarily from the Strawberry-Detection Dataset and Strawberry-Detection2 Dataset;
3. Straw_Cul Strawberry Dataset: A total of 1400 images of strawberries were captured using a smartphone under various lighting conditions. The shooting scenes spanned both indoor and outdoor environments, from 6:00 AM to 6:00 PM, encompassing various weather conditions such as sunny and overcast days;
4. SBDS Strawberry Dataset: This custom strawberry dataset, comprising a total of 5600 images, was created by merging the self-captured Straw_Cul dataset with the open-source datasets Straw_Det and StrawDI_Db1.  
The SBDS dataset aggregates strawberry images from various complex environments, vividly illustrating the diverse conditions of strawberry growth environments and the variability in strawberry instances. Representative samples from different environments are shown in Figure 1. Dark and humid conditions, as depicted in Figure 1(a2,a3,c1), may lead to reduced image quality, while complex and dense growth scenarios, shown in Figure 1(b1,c1), may interfere with accurate strawberry detection. Additionally, occlusions caused by surrounding leaves, as seen in Figure 1(a4,b4,c4), may result in missed detections, further reducing detection accuracy. Strong sunlight, illustrated in Figure 1(c2), can cause overexposure or deep shadows in certain areas of the image, making it difficult for the model to accurately identify object contours and features. Variations in background color contrast, as shown in Figure 1(a1), necessitate more complex detection algorithms to address these challenges. To validate the robustness of the proposed method, a comprehensive evaluation was conducted on the four different strawberry datasets.
# Experimental Study on Permeability Evolution of Sandstone during Triaxial Compression Damage
## 4 Conclusions  
To explore the deformation behavior, permeability attributes, and damage progression of sandstone in both dry and saturated conditions, a series of hydromechanical tests were conducted. The following conclusions were drawn:  
1. Different stress thresholds correspond to distinct stages of rock deformation, namely the micro-fracture closure stage, linear elastic deformation stage, stable crack expansion stage, crack non-stationary expansion stage, and post-peak strain and damage stage. Under dry conditions, with increasing confining pressure, the failure mode shifts from macroscopic multi-crack failure to shear failure. In saturation conditions, the predominant failure mode is inclined plane shear failure.
2. Throughout the rock specimen's deformation and failure process, the evolution of sandstone permeability exhibits four characteristic stages: gradual decrease, steady development, gradual increase, and rapid growth. This pattern correlates well with the micro-fracture closure, linear elastic deformation, crack development and expansion, and post-peak strain and failure stages. Key permeability values increase as pore pressure increases, but decrease as confining pressure increases. The permeability \(k_{cc}\) can be approximated as the minimum permeability \(k_{min}\).
3. The permeability adheres to a distinct four-stage pattern of gradual decrease, steady development, gradual increase, and rapid growth. This trend corresponds to the four phases of slow volumetric compression, rapid volumetric compression, rapid volumetric expansion, and slow volumetric expansion observed during rock deformation. The correlation between permeability and volumetric strain during the compression and expansion stages can be adequately described by the corresponding equations.
4. The propagation of internal cracks in sandstone under different conditions leads to evolving fracture damage. A correlation curve depicting the relationship between permeability and the damage variable was constructed, shedding light on the underlying mechanisms that govern the evolution of permeability in response to alterations in rock damage.  
Paper revision and polishing, L.W. and Z.L.; laboratory experiment, H.L.; paper writing, Z.L. and Q.Y.; literature research, Q.Y. All authors have read and agreed to the published version of the manuscript.  
The project was funded by the Science Foundation of Department of Transportation of Guangxi, grant number 2020-1-14, the Natural Science Foundation in Guangxi Province of China, grant numbers 2021 GXNSFBA196043 and 2023 GXNSFAA026185, the National Natural Science Foundation of China, grant number 51074152.  
Not applicable.  
The authors declare no conflict of interest.