esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this research, the application of a TRUM in a high-overload environment (high acceleration of 10,000 g) is studied. Based on the structure of the PMR40 ultrasonic motor, the disc spring is used as the cushioning element to improve the weak rotor structure, and four new motors are designed. The FE software ADINA is used to establish the impact dynamics model of a TRUM working in a high-overload environment. A high-impact process of the four motors is simulated, and the stress of stator, rotor and friction plate of each motor and the force variation on the contact surfaces are studied. Finally, the simulation results are compared. Meanwhile, four actual TRUMs are manufactured. The high-impact test, measurement experiments for the stator and rotor webs plastic deformation, and a mechanical performance test before and after the impact test are carried out. Finally, the experimental results are compared and analyzed. It can be concluded that high overload will have different degrees of influence on the internal components of the TRUM. The occurrence of plastic deformation in the internal parts does not mean that the motor will not work immediately, but will cause a change in the motor's performance. The performance degradation is directly proportional to the degree of damage to internal parts during the impact process. Combined with the impact dynamics simulation and experimental results, this leads to the conclusion that the TRUMs with the disc spring are more capable of high-overload resistance than the TRUMs without the disc spring. Performance of TRUM-4 with the modified rotor and disc spring decreased little after the 10,000 g acceleration high-impact test, so the structure of TRUM-4 is considered to be high-overload resistant and the purpose of structural improvement is achieved. In addition, the establishment of a TRUM impact dynamics model is of great significance for guiding the optimization of ultrasonic motors to expand their application in high-overload environments.  
Conceptualization, J.Z. and L.Y.; Methodology, J.Z.; Software, J.Z., C.M and H.C.; Validation, L.Y. and H.C.; Writing--original draft preparation, J.Z., L.Y. and C.M.; Writing--review and editing, X.S. and L.C.  
This work was supported by the National Natural Science Foundation of China (grant No. 51575265), and the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
The authors are very grateful to [PERSON], [PERSON], [PERSON] and [PERSON] from the NUAA Super Control Technology Co. Ltd. of financial support and providing TRUMs machining assistance.  
The authors declare no conflict of interest.
## 4 Case Study
### Result of Classification (Highpass)  
Following the same procedure as in the preliminary study, the OC-SVM model was trained using only normal data, with the model configured to include at least 90% of the normal data within the learned boundary. The training results and fault diagnosis rates for each feature are summarized in Table 3.  
As with the Gaussian noise-based results, the training accuracy for normal data exceeded 90% across all features. The most significant improvement was observed in the kurtosis feature, where the fault detection rate increased dramatically from 26.22% to 99.5%. Although the fault detection rate for skewness decreased by approximately 27%, this reduction was relatively minor compared to the substantial gain in kurtosis. On average, models trained using high-pass noise exhibited higher fault diagnosis performance across features.
[21] employs homogenization and finite element methods to quantify how spiral-wound composite coatings mitigate crack-tip stress intensity factors under pressure, offering insights for frost-resistant coating design. Ref. [22] develops a contact mechanics model to assess additional stresses from mobile lift chains during pipeline repairs, emphasizing safety thresholds relevant to frost-affected maintenance. Ref. [23] establishes an analytical framework for pipe-support interactions in mountainous terrains, linking soil properties and mounting gaps to hoop stress variations--a key consideration for frost-heave-induced displacements. These studies collectively inform multi-physical modeling of frost-jacketing and thaw-settlement mechanisms in water pipelines, yet gaps remain in quantifying ice-water phase effects on long-term deformation. The interaction between soil and underground structures [24; 25] is a highly complex problem. Due to its mathematical complexity, obtaining analytical solutions for such problems is extremely challenging; consequently, most scholars adopt numerical methods for analysis. In existing research, although scholars have explored the frost heave and thaw settlement characteristics of the pipe-soil system during freeze-thaw cycles, most studies tend to overlook the complexity of real engineering conditions, especially regarding multilayer soil structures. The heterogeneity of complex soil layers exacerbates the impact of environmental factors on pipeline behavior, making traditional design methods insufficient in such cases. The interaction between pipelines and the surrounding seasonal frozen ground requires detailed numerical simulations for analysis. However, current models often fail to comprehensively consider the effects of the ice-water phase transition on heat conduction, moisture migration, and soil stress. To address these shortcomings, this study constructs a more precise numerical simulation platform using COMSOL Multiphysics 6.2, taking into account pipeline burial depth, radius, soil properties, and moisture migration. Through THM coupling simulations, the dynamic changes of the pipe-soil system in real engineering applications are analyzed in depth. This innovative study clarifies the deformation mechanisms caused by frost heave and thaw settlement, contributing to a better understanding of stress concentration and deformation in pipelines in seasonally frozen regions. It not only fills the gap in existing research but also provides scientific foundations and practical guidance for the design and management of frost heave resistance in water pipelines in frozen soil regions.
## 5 Studies and Mechanism about n-3 PUFAs and Melanoma
### In Vitro Studies
Within malignant melanocytes, melanogenesis is frequently upregulated, offering potential therapeutic targets. Paradoxically, this process both promotes tumor initiation and impedes vertical growth and metastasis in later stages of the disease. Despite advancements in treatment modalities, such as targeted and immune therapies, the emergence of drug resistance poses a persistent challenge. Redox biology,encompassing ROS and reactive nitrogen species, assumes a central role in the entire spectrum of melanoma pathophysiology, ranging from initiation to progression and metastatic cell development. Redox metabolic rewiring has been implicated in acquired resistance to BRAF/MEK inhibitors. The consequences of this redox rebalance appear to be dual-faceted: on the one hand, cells may exhibit less aggressive behavior or undergo apoptosis; on the other hand, they may demonstrate enhanced survival upon dissemination into the circulating system or after exposure to drug treatment, thereby facilitating metastasis promotion or fostering additional drug resistance [157, 158, 159]. Furthermore, Nrf has been acknowledged as a pivotal regulator orchestrating cellular responses to oxidative fluctuations. Upon activation, this signaling cascade induces the expression of a diverse array of antioxidant enzymes. Conversely, Nrf has been implicated in a spectrum of activities, and sustained activation of Nrf in malignant cells may expedite processes such as metastasis and chemoresistance. This duality suggests that Nrf may assume distinct roles, either acting to prevent or promote cancer, adding complexity to its potential therapeutic implications and consequently there has been a growing interest in exploring this transcription factor as a potential target for cancer treatment [160]. However, to date, there are no studies that account for this modulation by PUFAs and Nrf in melanoma. An in vitro investigation revealed a reduction in the proliferation of melanoma cells when exposed to varying concentrations of EPA and DHA (50 mM and 100 mM) across A375, A2058 and G361 human melanoma cell lines. This reduction was concomitant with observed oxidative damage to both protein and DNA [59] (Table 1).
## 4 Experiment and Discussion  
### Image Segmentation  
#### 4.1.1 Comparison with and without DC Component  
In this paper, we embedded fixed-parameter Gabor filters into shallow networks to enhance image features. This approach effectively alleviates the problem of semantic recognition difficulty in deep networks caused by insufficiently extracted features from shallow networks. Usually, \(\psi\) and \(\gamma\) are fixed at 0 and 1, respectively. To make a filter closest to that of an animal's vision system, the half-response spatial frequency bandwidth is usually set to 1. The value of \(\lambda\) is determined by the width of the veins in the image [36] and can be used to calculate the value of \(\sigma\). Based on the vein features in the image, we created three different scales and eight different orientations of filters. By removing the DC component in the filter, the impact of lighting and image gray distribution is reduced,  
Figure 7: Projection of the image on the calibration plate after correction using homography matrix. (**a**) The calibration plate, (**b**) the original projection image, and (**c**) the projection image after homography correction.  
resulting in a frequency and directional visual representation closer to that of human perception. In order to better observe the results, we applied Gabor filters both with and without DC components to process the near-infrared images of the dorsal hand. The original image and the filtered images both with and without DC components are shown in Figure 8. It can be observed that the input image has better results after the Gabor filtering process without the DC component.
# Causality Analysis with Different Probabilistic Distributions Using Transfer Entropy
## 2 Mathematical Modeling
### Review of Probability Density Functions
#### 2.2.3 The \(\alpha\)-Stable Distribution  
The \(\alpha\)-stable distribution lacks a closed-form probability density function; instead, it is defined by the characteristic function shown below in Equation (9):  
\[F_{\alpha,\beta,\delta,\gamma}^{stab}(x)=\exp\bigl{\{}i\delta x-|\gamma x|^{ \alpha}(1-i\beta l(x))\bigr{\}}, \tag{9}\]  
where  
\[l(x)=\left\{\begin{array}{ll}sgn(x)\tan\bigl{(}\frac{\pi\alpha}{2}\bigr{)}& \text{for }\alpha\
eq 1\\ -sgn(x)\frac{2}{\pi}\ln|x|&\text{for }\alpha=1\end{array}\right.,\]  
\(0<\alpha\leq 2\) is called the _stability_ index or characteristic exponent,  
\(|\beta|\leq 1\) is the _skewness_ parameter,  
\(\delta\in\mathbb{R}\) is the distribution's _location_ (mean), and  
\(\gamma>0\) is the distribution's _scale_ factor.  
The function is characterized by four parameters. However, there are certain special cases where the probability density function (PDF) has a closed form:  
* When \(\alpha=2\), it represents independent realizations; in particular, when \(\alpha=2\), \(\beta=0\), \(\gamma=1\), and \(\delta=0\) then the exact equation for normal distribution is obtained.
* When \(\alpha=1\) and \(\beta=0\), this represents the Cauchy distribution, which is discussed in detail in the following paragraph, and when
* \(\alpha=0.5\) and \(\beta=\pm 1\), this represents the L'evy distribution, which is not included in our analysis.  
The \(\alpha\)-stable distribution is defined by four factors. The shift factor \(\delta\) is responsible for the location of the function. The scale coefficient \(\gamma\) represents the broadness of the distribution function, with higher values generating larger fluctuations. It should be noted that in case of the control performance assessment task this is a good candidate for the control quality measure [33]. Finally, the stable functions have two shape coefficients, namemly, the skewness \(\beta\) and stability \(\alpha\). Interpretation of the skewness is intuitive, while the stability index is more interesting, as \(\alpha\) information about the tails. In the case of \(\alpha=2\), the tails' fatness follows the normal distribution, and as such it might be assumed that there should be no excessive outliers and the time series should behave similarly to the independent realizations. When \(\alpha<2\), the tails increase and are more significant, meaning that the time series starts to be persistent. In the case of control performance interpretations, this can be caused by the uncoupled disturbances, delayed correlations, or by human interactions with the control system. It is important to remember that such behavior is quite common in real industrial data [34].  
Moreover, it is important to remember that the moments do not exist for \(\alpha\)-stable distributions.
# Review on Quality Control Methods in Metal Additive Manufacturing
## 2 Metal AM and Welding
### Similarities between Metal AM and Welding  
Both metal AM and welding deposit metal, and the powder or wire-type metal materials are repeatedly melted and solidified. Because the manufacturing principles are similar, there are several studies reporting that the purpose of metal AM is similar to that of welding and vice versa [8; 9; 10]. For example, [PERSON] et al. [8] mentioned that both PBF and DED have the potential to repair and restore metal parts that have been damaged or are at their end-of-life. [PERSON] et al. [9] repaired damaged stainless steel parts using the DED process, and [PERSON] et al. [10] reported that laser welding can be used to manufacture 3D metal products, such as with rapid prototyping. Several studies have also been conducted to develop hybrid metal manufacturing systems that can perform the roles of both metal AM and welding simultaneously [11; 12].  
Another similarity between metal AM and welding can be found in the product quality issue. During deposition, the metal experiences repetitive melting and cooling. Thus, anomalies [13; 14; 15; 16] such as melt pools, plumes, spatters, and keyholes, and defects [17; 18; 19] such as pores, layer misalignment, dimensional errors, and residual stress generation, are commonly found during metal AM and welding processes. Such anomalies are affected by process parameters such as laser power, nozzle speed, cooling time, temperature, material feeding rate, and laser spot size. Anomalies are often unpredictable, thus, they are usually identified experimentally.  
Figure 3 shows a conceptual representation of common anomalies and the defects in metal AM and welding. The melt pool is a small pool of metallic liquids that can be generated during deposition. A small melt pool can increase the process time, and a large melt pool can cause vaporization of the material and increase the porosity of the product [20]. A plume is a mixed fume of plasma and the vaporized metal. The spatters are small splashes of molten metal or metal powders. During deposition, metal materials are boiled by the high-energy input of the concentrated beam, and the boiled metal emits a plume and spatter during the process [21]. Undesirable plume and spatter infusing into the product often result in dimensional errors, undesirable surface quality, and a decrease  
Figure 2: Schematic diagram of the DED and welding processes using (**a**) a coaxial nozzle and (**b**) a material feed nozzle.  
in tensile properties. The keyholes are caused by vapor depression. A deep keyhole causes excessive porosity and thus degrades the fatigue life of a product. The pore is a void inside the product caused by the air trapped during deposition. Large and uneven pores inside a product are known to cause cracks [22]. It was found that the major process parameters affecting the anomaly occurrence are laser power, scan speed, cooling time, temperature, material feeding rate, and laser spot size.
# Broadband Generalized Sidelobe Canceler Beamforming Applied to Ultrasonic Imaging
## 4 Results
### Plane Wave Emission Test  
Plane wave emission is one of the most commonly used emission ways in commercial ultrasonic imaging products [29]. Commercial ultrasonic imaging systems need enough emission energy to achieve sufficient system signal-to-noise ratio (SNR). Therefore, the system will obtain better imaging quality with higher investigation depth [30]. Plane wave emission uses all elements to transmit and receive the ultrasonic; the entire imaging region will be covered with one single emission. Therefore, only DAS and Broadband-GSC can be introduced to process the echo signal after the plane wave emission. Beamforming responses of scattering points with plane wave emission are shown in Figure 7.  
Beamforming responses indicate that the average FWHM of the scattering points are 1.38 mm and 0.77 mm after DAS and Broadband-GSC at z = 40.6 mm, respectively. Broadband-GSC improves the lateral resolution by 44.2% compared with DAS. Figure 8 also indicates that Broadband-GSC can reduce the sidelobe energy. Plane wave emission experiment indicates that Broadband-GSC can achieve an increase in lateral resolution with only one single emission. Consequently, Broadband-GSC will not only reduce the system's hardware complexity, but also introduce the possibility for higher investigation depth.  
Figure 6: Lateral variation of each beamforming, (a) z = 32 mm; (b) z = 46.7 mm.
# Theory of Electrical Breakdown in a Nanocomposite Capacitor
## 2 Methods  
### Model  
We consider a parallel-plate capacitor in which the gap of thickness \(d\) between the metallic electrodes is filled with a nanocomposite dielectric. The rectangular electrodes are assumed identical to each other and have the lateral dimensions \(L_{1}\) and \(L_{2}\) along the \(x\) and \(y\) axes, respectively. The nanocomposite is represented by identical spherical nanoparticles (NPs) of radius \(R\) with the dielectric permittivity \(e_{i}\) randomly dispersed in the host dielectric with the dielectric permittivity \(e_{h}\) (see Figure 1).  
We assume that the NP radius is much less than the capacitor dimensions, i.e., \(R\ll d,L_{1},L_{2}\), and treat the NPs as point dipoles which possess the polarizability of a dielectric sphere. Such an approach follows the assumption made by [PERSON] [14] which has been widely used for the modeling of the dielectric properties of composites. The applicability of this approximation is limited to relatively small volume fractions of NPs (see Section 2.3).
## 3 Results and Analysis  
### Verification of PS-InSAR Monitoring Results Accuracy  
The quality and quantity of PS-InSAR PS points depend on the selection of the PS point output threshold. The PS point threshold demonstrates a positive correlation with monitoring coherence and accuracy while exhibiting a negative correlation with the number of PS points. Therefore, to ensure optimal quality and quantity of PS points, the following thresholds have been established: 0.65 for 2015-2018, 0.75 for both 2018-2019 and 2019-2020, and 0.80 for 2020-2021. Figure 3 depicts the distribution of PS output results along with their corresponding PS points.  
For the purpose of ensuring the accuracy of monitoring results, numerous scholars [12; 27; 30; 44; 45; 53; 54; 55; 56; 57] have conducted comparisons between monitoring results and leveling measurement values, GPS, GNSS, D-InSAR, and SBAS-InSAR monitoring values,  
Figure 3: PS point distribution map for each time period.  
Figure 2: PS-InSAR technical flow chart.  
to validate their precision. If the disparities between these two sets of results are minimal and essentially consistent, it can be inferred that the monitoring is feasible. In order to guarantee the accuracy and reliability of monitoring outcomes, a comparison is made between mine-leveling data and PS-point monitoring values to verify their monitoring accuracy. Based on the acquisition time of radar data images, 16 sets of mine-leveling monitoring data from March 2019 to March 2020 are collected as presented in Appendix A.  
Table 10 is attached herewith. The average value of adjacent PS points is selected for comparison based on location information pertaining to each leveling point. The resulting error value after comparison is illustrated in Figure 4.
## 2 Energy Prediction and Fault Detection in Solar-Hydrogen Systems
### Emerging Trends in Renewable Energy Monitoring
Ensemble deep learning techniques, such as PropensityNet, DNN, and CNN-LSTM, can be applied to fused sensor data from IIoT devices for efficient fault diagnosis [100]. These developments in IoT and AI integration lead to safer and more effective industrial operations, lowering maintenance costs and downtime. The integration of AI and IoT in renewable energy systems is summed up in Table 5, which also highlights the advantages that come with it, including improved problem detection, real-time monitoring, and predictive maintenance [101; 102; 103; 104; 105; 106; 107]. AloT, or artificial intelligence and the Internet of Things, not only makes renewable energy systems more reliable and efficient but also comes with problems that must be overcome in order to reach its full potential. This tendency looks to be very important for managing renewable energy in the future as it develops. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No** & **Aspect** & **Description** & **Benefits** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Real-Time Monitoring} & Continuous data collection by IoT devices across renewable energy systems. & Enables timely detection of anomalies and optimizes system performance. \\ \cline{3-4}  & & & AI analyzes historical and real-time data to forecast equipment failures. & Reduces downtime and maintenance costs, extends equipment lifespan. \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Fault Detection} & AI models identify specific fault patterns from IoT sensor data. & High accuracy in detecting faults, leading to prompt corrective actions. \\ \cline{3-4}  & & & AI adjusts operations based on real-time conditions, like weather or demand. & Maximizes energy production efficiency and reliability. \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{Challenges} & Issues include data security, network reliability, and computational demands. & Overcoming these challenges is crucial for widespread adoption. \\ \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AI and IoT (AloT) integration in renewable energy monitoring.
## 4 Discussion
### Discussion of Major Quantitative Findings and Qualitative Feedback from the Users
#### 4.1.1 Perceived Usefulness and Perceived Ease-of-Use  
In terms of PU and PEOU, many users prefer the joystick control, as it is convenient and easy to operate. This result supports the research findings of [PERSON] et al. [45] and [PERSON] et al. [46] that compare gesture recognition and other controlling modes and find that the availability of joystick controllers is more prevalent than other interactive interfaces. As shown in Table 6, users qualitative feedback reflects the issues with gestural detection reported by [PERSON] et al. [45] and [PERSON] et al. [7].