esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hierarchical Decision Fusion Diagnosis Method for Rolling Bearings
## 3 Case Study
### Diagnostic Results of Rolling Bearings
#### 3.3.2 Fault Isolation Results of Rolling Bearings  
Assuming that there is a fault in the fault detection layer, it is necessary to isolate the fault of the rolling bearing. According to the testing sample set of Table 3, the location of the fault is diagnosed. The result is shown in Figure 9.  
From the diagram, when the bearing is diagnosed based on the average rectified value (feature 4), standard deviation (feature 5) and root mean square (feature 7), the fault diagnosis rate is more than 99.9%. However, for the cases based on kurtosis (feature 6),  
Figure 8: Diagnostic accuracy of fault detection for the rolling bearing. 1: _Ma_; 2: _Mi_; 3: \(P_{k}\); 4: \(A_{\varphi}\); 5: _st_; 6: \(k_{u}\); 7: _mr_; 8: \(S\); 9: \(C\); 10: \(I\); 11: \(L\); 12: decision fusion.  
Figure 9: Diagnostic accuracy of fault isolation for the rolling bearing. 1: _Ma_; 2: _Mi_; 3: \(P_{k}\); 4: \(A_{\varphi}\); 5: _st_; 6: \(k_{u}\); 7: _mr_; 8: \(S\); 9: \(C\); 10: \(I\); 11: \(L\); 12: decision fusion.  
waveform factor (feature 8), peak factor (feature 9), pulse factor (feature 10) and margin factor (feature 11), the diagnostic accuracy is lower than 70%. When using the decision fusion method (number 12) for fault isolation layer diagnosis, the diagnostic accuracy is 98.57%. Although slightly lower than the highest diagnostic accuracy under a single feature, it is a combination of multiple features, which can avoid the possible error caused by the artificial selection of features. Therefore, a small reduction in diagnostic accuracy caused by decision fusion is acceptable.
## 2 Security and Privacy Challenges in IoE
### Fundamental Security Risks in IoE
#### 2.1.2 Data Privacy and Confidentiality Risks  
The large datasets continuously collected by IoE devices pose serious threats to user privacy. In IoE systems, privacy breaches generally occur through data leaks, unauthorized access, and data manipulation [14; 42]. The major privacy risks are as follows:  
* _Lack of encryption during data storage and transmission_: Since IoE devices often operate with lightweight hardware, they face difficulties in supporting strong encryption algorithms [10; 43].
* _Lack of anonymity_: When user data is collected on centralized servers, anonymity is compromised, making user behaviors traceable [44].
* _Side-channel attacks_: By analyzing the energy consumption or processing times of IoE devices, attackers can infer user behavior patterns [45]. To mitigate these risks, techniques such as differential privacy, homomorphic encryption, and secure multi-party computation (SMPC) are recommended [46].
## 2 Previous Works
### Heterogeneous Patch Filtering  
In CNN, image patches often contain mixed land cover types, which affects the decision of the model as the output will reflect the more obvious land cover types. Studies have attempted to filter and refine the image patches or objects to achieve heterogeneous image patches or objects before classification tasks [3; 20] ([PERSON] 2019, [PERSON] 2019). [PERSON] et al. (2018) [21] presented a novel approach depending on multi-view unmanned aerial vehicle (UAV) orthophotos and using CNN-based adaptive patch windows and OBIA classification to improve the classification of wetlands in the United States. [PERSON] et al. (2018) [PERSON] et al. (2018) developed a model by integrating their models, namely, multiresolution segmentation, the center of gravity, and CNN. Their results improved the identification of irregular segmented objects from a very high-resolution remote sensing image, where their approach has successfully reduced the uncertainty associated with OBIA during classification in China. [PERSON] et al. (2018) [PERSON] et al. (2018) compared CNN-OBIA and traditional machine learning models (i.e., SVM, ANN, and RF) in wetland classification and mapping in the United States. They found that CNN-OBIA achieved an accuracy higher than traditional models. [PERSON] et al. (2019) [PERSON] et al. (2019) [PERSON] et al. (2019) proposed an object-based heterogeneous filter integrated into a CNN to overcome the limitations of jagged errors at boundaries and the expansion/shrinkage of land cover areas originating from CNN-based models. [PERSON] et al. (2019) [PERSON] et al. (2019) developed an approach based on the integration of CNN and OBIA with a majority overlapping region method to label the image segments. [PERSON] et al. (2019) [PERSON] et al. (2019) showed that the integration of OBIA and CNN can improve image classification and change detection results compared to OBIA-based classification. [PERSON] et al. (2020) [PERSON] et al. (2020) proposed adaptive patch sampling to map the object primitives into image patches along with the object primitive axes. The methods based on image patch filtering or image object filtering aim to improve the model's ability to classify the precise edge of ground objects correctly with some filtering methods that can be applied to image patches or image objects. While some studies have reported improvement in classification accuracy using this method, the challenge remains to best map image objects into image patches.
## 2 Pipeline Deformation Calculation  
### Foundation Beam Models  
When analyzing the pipe-soil interaction, the foundation beam model should be selected to maximize the true reflection of the deformation between the two.  
In 1867, [PERSON] proposed the [PERSON] foundation beam model theory [10] (single-parameter model), which is widely used because of its simplicity and clear physical significance, and the expression is as follows:\[q(x)=kw(x) \tag{1}\]  
where \(q(x)\) is the extra stress on the pipeline brought on by shield excavation; \(k\) is the foundation reaction force coefficient; \(w(x)\) is the foundation deformation.  
Then, in 1954, [PERSON] proposed the [PERSON] foundation beam model theory [11] (two-parameter model); based on the [PERSON] foundation beam model theory, the shear effect between a pipe and the soil is represented by adding a shear layer, and the continuity of soil deformation is also taken into account, as shown in Figure 1. This is more in line with actual engineering, and the expression is as follows:  
\[q(x)=kw(x)-G_{c}\frac{\partial^{2}w(x)}{\partial x^{2}} \tag{2}\]  
where \(G_{c}\) is the shear stiffness of the shear layer.  
Considering that the pipeline is buried at a certain depth in the ground, the foundation reaction coefficient can be determined according to the theoretical formula proposed by [PERSON] [12] with the following expression:  
\[k=\frac{1.3E_{s}}{D(1-v^{2})}\sqrt{\frac{v_{s}D^{4}}{EI}} \tag{3}\]  
where \(E_{s}\) is the modulus of elasticity of the soil; \(v\) is Poisson's ratio; \(D\) is the outer diameter of the pipeline; \(EI\) is the equivalent bending stiffness of the pipeline.  
The shear stiffness, \(G_{c}\), can be obtained from the following equation:  
[PERSON]G_{c}=\frac{E_{s}h_{t}}{6(1+v)} \tag{4}\]  
where \(h_{t}\) is the thickness of the shear layer, taken as 2.5 times the outer diameter of the pipeline [13].  
Compared with the Winkler foundation beam model, the Pasternak foundation beam model is widely used in projects in soft-soil areas due to the full consideration of the shear interaction between the pipe and the soil and the continuity of soil deformation, while the Pasternak foundation beam model can help to calculate and derive the surface settlement and vertical deformation of the pipeline in a more efficient and convenient way [14; 15]. This work will use the Pasternak foundation beam model theory to derive the vertical deformation formulae for pipelines.  
Figure 1: Pasternak foundation beam model.
## 3 FlexMonitor
### Overview  
As shown in Figure 2, the monitoring framework FlexMonitor can be regarded as a monitoring module plugged into the controller. Through the southbound interface, the monitoring module in the controller can deploy the monitoring strategy and collect the monitoring data. Through the northbound interface, it can also provide the network view to the upper network management applications.  
The functionalities of FlexMonitor can be mainly classified into four parts: the monitoring task interpretation part, the monitoring strategy deployment part, the monitoring data collection part and the monitoring data analysis part. The monitoring task interpretation part is responsible for interpreting the monitoring requests of the upper management applications. Effective interpretation can be helpful to choose the appropriate monitoring strategies. The monitoring strategy deployment part is responsible for carrying out the monitoring tasks through deploying the specific monitoring strategies in the network. The collection part is responsible for collecting the monitoring data accurately and in a timely manner from the network devices or end hosts using various monitoring approaches. According to the specific monitoring requirements, the analysis part is responsible for analyzing and aggregating the raw monitoring data from the collection part.  
Among the four functional parts, the monitoring strategy deployment part and the monitoring data collection part are the key parts to FlexMonitor. On the one hand, the accuracy, timeliness and incurred overhead of network monitoring depends on the adopted status or statistics collection approaches to a great extent. On the other hand, the choice of the status or statistics collection approaches depends on the adopted monitoring strategy.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Parameters** \\ **Combination** \\ \end{tabular} & \begin{tabular}{c} **_p-Value** \\ **\end{tabular} & \begin{tabular}{c} **Depth 1** \\ **\end{tabular} & \begin{tabular}{c} **Depth 2** \\ **\end{tabular} & \begin{tabular}{c} **Depth 3** \\ **\end{tabular} & \
\begin{tabular}{c} **Depth 4** \\ **\end{tabular} &
\begin{tabular}{c} **Overall** \\ **Mean** \\ \end{tabular} \\ \hline \multirow{2}{*}{\(n_{1}-v_{\mathrm{f1}}\)} & \multirow{2}{*}{0.04} & 13.84 & 12.83 & 11.05 & 10.69 & 12.10 \\  & & (0.05) & (1.67) & (1.61) & (0.77) & (1.70) \\  & & A & AB & B & B & \\ \hline \multirow{2}{*}{\(n_{1}-v_{\mathrm{f2}}\)} & \multirow{2}{*}{0.21} & 11.64 & 14.82 & 17.02 & 16.49 & 14.99 \\  & & (0.48) & (1.52) & (4.70) & (3.59) & (3.41) \\  & & A & A & A & A & \\ \hline \multirow{2}{*}{\(n_{2}-v_{\mathrm{f1}}\)} & \multirow{2}{*}{0.51} & 9.77 & 9.17 & 11.62 & 10.21 & 10.19 \\  & & (3.12) & (1.21) & (0.94) & (1.93) & (1.94) \\ \cline{1-1}  & & A & A & A & A & \\ \hline \multirow{2}{*}{\(n_{2}-v_{\mathrm{f2}}\)} & \multirow{2}{*}{0.20} & 17.91 & 13.76 & 16.06 & 16.34 & 16.02 \\  & & (1.43) & (1.66) & (2.29) & (2.84) & (2.38) \\ \cline{1-1}  & & A & A & A & A & \\ \hline \hline \end{tabular} Note: Values on the same row with the same letter do not differ statistically.  
\end{table} 
Table 13: Rk mean values at the four measured profile depths and the analysis of their statistical difference. Standard deviation in parentheses. There are different opinions in the literature regarding the effect of the cutting speed during machining processes. Some researchers found that the cutting speed has no effect on the surface quality, while others found that the cutting speed has a slight effect depending on process conditions [15]. The results regarding the effect of milling parameters on the wood surface quality are not surprising and are expected, but in comparison with other studies from the literature bring a novel approach to the evaluation of the surface quality, regarding wood as a stratified material. A stratified analysis of surface roughness parameters can not only clarify the surface complex morphology, but it is helpful to determine the contribution of processing parameters to the overall surface quality and their significance.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 4 Experiments and Conclusions
### Limitations  
This paper has two main limitations. First, our model is specifically optimized for motion blur and may not perform well with other types of blur, such as Gaussian, defocus, or composite blur. This is because the model assumes characteristics specific to motion blur, which may lead to suboptimal results in cases with multiple blur types. Second, our model uses class-specific training, where separate networks are trained for each blur type. While this works well for individual blur types, it limits flexibility, especially in dynamic scenes where the blur type can change over time. This training approach may not be suitable for real-time or rapidly changing conditions, leading to performance degradation.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{\(L_{k}\)} & \multicolumn{3}{c}{\(L_{u}\)} & \multicolumn{3}{c}{Metrics} \\ \hline  & **MAE** & **Huber** & **MSE** & **MSE** & **Gradient Difference** & **SSIM** & **PSNR**\(\uparrow\) & **SSIM**\(\uparrow\) & **MS-SSIM**\(\uparrow\) \\ \hline (a) & \(\surd\) & \(-\) & \(-\) & \(\surd\) & \(-\) & \(-\) & 29.0911 & 0.8793 & 0.9722 \\ (b) & \(\surd\) & \(-\) & \(-\) & \(-\) & \(\surd\) & \(-\) & 28.7942 & 0.8562 & 0.9693 \\ (c) & \(\surd\) & \(-\) & \(-\) & \(-\) & \(-\) & \(\surd\) & 29.2061 & 0.8786 & 0.9730 \\ \hline (d) & \(-\) & \(\surd\) & \(-\) & \(\surd\) & \(-\) & \(-\) & 28.7785 & 0.8479 & 0.9604 \\ (e) & \(-\) & \(\surd\) & \(-\) & \(-\) & \(\surd\) & \(-\) & 28.0960 & 0.8509 & 0.9572 \\ (f) & \(-\) & \(\surd\) & \(-\) & \(-\) & \(-\) & \(\surd\) & 28.4322 & 0.8575 & 0.9591 \\ \hline (g) & \(-\) & \(-\) & \(\surd\) & \(\surd\) & \(-\) & \(-\) & 29.0375 & 0.8697 & 0.9712 \\ (h) & \(-\) & \(-\) & \(\surd\) & \(-\) & \(\surd\) & \(-\) & 29.1117 & 0.8814 & 0.9630 \\ (i) & \(-\) & \(-\) & \(\surd\) & \(-\) & \(-\) & \(\surd\) & **29.3616** & **0.8827** & **0.9741** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluating various loss functions.
# Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining
###### Abstract  
The roughness of haul roads significantly impacts fuel consumption in open-pit coal mine trucks, yet there is currently a lack of quantitative road classification methods in this regard. This study proposes a fuel-efficient road classification methodology for open-pit coal mines. Using UAV-captured point cloud data of mine roads as the basis for roughness analysis and the International Roughness Index (IRI) as the evaluation metric, the research establishes linear relationships between IRI and fuel consumption for both loaded and unloaded trucks. The K-means clustering algorithm is employed to classify road quality into \"good\", \"moderate\", and \"poor\" categories, with the Haerwusu Open-pit Coal Mine serving as a case study. Results demonstrate that 150 m represents an appropriate IRI segmentation interval for Haerwusu, with IRI thresholds of 12 (15) and 20 (21) serving as critical segmentation points for loaded (unloaded) trucks. From analyzing two end-slope roads in the case study mine we found that upgrading \"poor\" roads to \"moderate\" quality could reduce fuel costs by 3% for loaded trucks and 2% for unloaded trucks. This study provides a quantitative road classification method for open-pit coal mines, offering a theoretical foundation for reducing transportation costs and promoting sustainable mining development.  
m e road; road classification; haulage road roughness; mine transportation Article
## Appendix A Challenges for Producing High-Power Radially Polarized Laser Beams  
In laser resonators, rotational polarization symmetry is usually broken by the introduction of an optical component that is not perpendicular to the beam path, like an output mirror placed at the Brewster angle [83]. Nevertheless, if the surfaces of the optical elements inside the resonator are perpendicular to the cavity axis, the radially and azimuthally polarized transverse modes survive. Although the discrimination against the fundamental (Gaussian) and high-order modes generally leaves both the \(\mathrm{TM}_{01}\) and \(\mathrm{TE}_{01}\) modes, a laser cavity can be made to selectively oscillate in only one of the two by introducing a birefringent [84; 85; 86] or diffractive element [87]. Outside the cavity, the complementary \(\mathrm{TM}_{01}\) and \(\mathrm{TE}_{01}\) modes can be converted into each other by rotating the transverse electric field component by \(90^{\mathrm{o}}\)[84; 85; 88]. Amplification of radially polarized laser beams is also possible using specially cut Ti:sapphire crystals [89]. This offers the possibility to create dedicated ultrafast and ultra-intense laser systems tailored to specific needs.  
For applications that rely on existing commercial lasers or ultra-intense laser facilities, the challenge is to develop methods to convert a linearly or circularly polarized output into radially polarized radiation with maximum conversion efficiency. The simplest approach is based on a Mach-Zehnder interferometer [90; 91]. Phase masks and diffractive phase elements can also be used for better conversion efficiency [92; 93; 94]. Special polarization converters can achieve \(>99\%\) mode purity [27; 95]. The beams obtained then are in good agreement with the most rigorous calculations [25; 28]. However, not all these techniques are practical at the high laser intensities required for electron acceleration. Nonlinear effects, dispersion, and phase stability are important issues for potential applications of RPLBs in ultrafast science. To the best of our knowledge, these questions are not fully addressed in the literature.
## 2 Materials and Methods
### Radiographic Evaluation of Knees  
Anteroposterior standing full-leg X-rays were taken from the full-extension knee. The source-to-image distance (SID) was 3 m. X-ray exposure parameters were dependent  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Clinical Characteristics/Timepoints** & **Pre-TKR** & \
\begin{tabular}{c} **3 Months** \\ **Post-TKR** \\ \end{tabular} &
\begin{tabular}{c} **12 Months** \\ **Post-TKR** \\ \end{tabular} \\ \hline Subjects, \(n\) (\%) & 86 (100) & 70 (81) & 74 (86) \\ \hline Mean age, years \(\pm\) SD & 59.9 \(\pm\) 4.7 & 60.2 \(\pm\) 4.7 & 60.9 \(\pm\) 4.7 \\ \hline Mean BMI, kg/m\({}^{2}\)\(\pm\) SD & 32.3 \(\pm\) 4.2 & 32.3 \(\pm\) 4.2 & 32.4 \(\pm\) 4.2 \\ \hline Previous TKR of opposite knee, \(n\) (\%) & 20 (23) & 16 (23) & 15 (20) \\ \hline Female, \% & 50 & 53 & 49 \\ \hline Mean age in males, years \(\pm\) SD & 59.2 \(\pm\) 5.1 & 59.5 \(\pm\) 5.1 & 60.2 \(\pm\) 5.1 \\ \hline Mean age in females, years \(\pm\) SD & 60.6 \(\pm\) 4.2 & 60.9 \(\pm\) 4.2 & 61.6 \(\pm\) 4.2 \\ \hline Mean BMI in males, kg/m\({}^{2}\)\(\pm\) SD & 31.5 \(\pm\) 4.0 & 31.4 \(\pm\) 4.0 & 31.3 \(\pm\) 4.4 \\ \hline Mean BMI in females, kg/m\({}^{2}\)\(\pm\) SD & 33.1 \(\pm\) 4.3 & 33.1 \(\pm\) 4.3 & 33.7 \(\pm\) 4.4 \\ \hline Obesity (BMI \(\geq\) 30), \(n\) (\%) & 59 (69) & 49 (70) & 50 (68) \\ \hline KL grade 2, \(n\) (\%) & 4 (4.7) & - & - \\ \hline KL grade 3, \(n\) (\%) & 39 (45.3) & - & - \\ \hline KL grade 4, \(n\) (\%) & 43 (50.0) & - & - \\ \hline gOA grade 2, \(n\) (\%) & 16 (19) & - & - \\ \hline gOA grade 3, \(n\) (\%) & 70 (81) & - & - \\ \hline \hline \end{tabular} Abbreviations: \(n\)—numbers; SD—standard deviation; BMI—body mass index; TKR—total knee replacement; KL—radiographic knee osteoarthritis as classified by the Kellgren–Lawrence system; gOA—the highest grade of knee OA changes in two knee joint compartments (tiboferomal and/or pateltofemoral joints) as classified by the Nottingham system (NSy).  
\end{table}
Table 1: Clinical characteristics of the study cohort by timepoints.  
on the patient's limb size and tissue characteristics, and were modified digitally. Three radiographs of the limb were taken at different levels and digitally fused by the software; of those radiographs, knee X-rays (tibiofemoral joints) were assessed in the present study for kOA features. In 62 out of 86 patients, additional axial views of the patellofemoral (PF) joints were obtained with the subject in a standing position, with knee joints at 60\({}^{\circ}\) of flexion, according to the technique described by [PERSON] et al. [31].  
Two radiologists independently graded the knee OA (kOA) severity using two grading systems--the Nottingham system (grades 0-3), and the KL system (grades 0-4)--for the classification of kOA [32; 33]. For the Nottingham system (NSy), joint space narrowing (JSN) and osteophyte (OF) size were classified on a 4-point scale (grades 0-3). The highest grade of OA changes (OF and/or JSN) among all knee compartments was expressed as the radiographic global grade of kOA (gOA). Any differences in scoring were resolved by a consensus reading between two radiologists.
# Optimizing Assembly in Wiring Boxes Using API Technology for Digital Twin
## 4 Proof of Concept for Optimizing Assembly in Wiring Boxes
### Creating the Digital Twin Model Using API Technology
Mate connectors in Onshape serve as local coordinate systems and are used to define the position and orientation of parts within assemblies. By aligning these connectors, you can determine the global coordinates of parts [22]. To calculate the coordinates of each fuse relative to the origin (the gripper's mate connector in the 0 position), it is necessary to consider the following:  
* The local coordinates of each mate connector on the fuse within the fuse's local coordinate system. * Transforming the local coordinates of the fuse into global coordinates (relative to the origin). The transformation formula is  
\[P_{global}=T_{3 Dfuse}\cdot P_{local} \tag{11}\]
* \(P_{local}\) is the position of a mate connector on the fuse in local coordinates. The coordinates of the point in the fuse's coordinate system (which is at the center). * \(T_{3 Dfuse}\) is the transformation matrix (including rotation and translation) that converts the fuse's local coordinates into global coordinates. * \(P_{global}\) is the position of a mate connector on the fuse in global coordinates. The transformation matrix \(T_{3 Dfuse}\) can be decomposed into two components, rotation \(R_{3 Dfuse}\) and translation \(t_{3 Dfuse}\):  
\[T_{3 Dfuse}= \begin{bmatrix}R_{3 Dfuse}&t_{3 Dfuse}\\ 0&1\end{bmatrix}  \tag{12}\]  
where \(R_{3 Dfuse}\) is a \(3\times 3\) matrix and \(t_{3 Dfuse}\) is a \(3\times 1\) vector. \[T_{3 Dfuse}=\begin{bmatrix}R_{11}&R_{12}&R_{13}&t_{x}\\ R_{21  
maximum repeatability error did not impact the assembly process. This flexibility allowed for a smooth insertion and reliable assembly even with minor positional variations. Digital twin, made by API technology, represents an advanced solution for monitoring and controlling the resistor mounting process in the wiring box. A digital twin creates a virtual replica of the entire physical process, allowing for the real-time tracking of each stage of assembly. By integrating sensors and real-time data, this digital model can detect and analyze variations and anomalies in the process, thereby optimizing performance and reducing errors. APIs facilitate interconnections between various systems and devices, ensuring the smooth and efficient communication of data to the digital twin. Thus, quick and informed decisions can be made for adjustments in real time, maintaining the quality and reliability of the assembly. Also, digital twin enables simulations and test scenarios, anticipating potential problems and implementing preventive solutions. In conclusion, using a digital twin to mount fuses in the wiring box provides increased the control and continuous monitoring, leading to a more efficient and safer process.