esa-sceva/satcom-chunk-collection huggingface dataset:
# WAMs Based Eigenvalue Space Model for High Impedance Fault Detection
## 1 Introduction
### Clustering  
The principles of protection discussed in the previous sub-section are used to estimate the region were the eigenvalues reside during normal operation. In order to accommodate the unique location and drift patterns of the eigenvalues of a line, eigenvalues are separated into clusters. In this work \(k\)-means clustering was utilized to develop the eigenvalue clusters [27].  
The goal of \(k\)-means clustering is to find patterns or similarities among the members of a dataset. These patterns are then used to separate the data points into groups where the members share similar characteristics [28]. Typically, metrics such as the Squared Euclidean Distance are used as the basis of the algorithm. The Squared Euclidean Distance is expressed as a sum of squares:  
\[d^{2}(p,q)=(p_{1}-q_{1})^{2}+(p_{2}-q_{2})^{2}+\dots+(p_{n}-q_{n})^{2} \tag{18}\]  
where \(d\) is the distance between two points, while \(p\) and \(q\) are the coordinates of a point in the Euclidean Space. \(k\)-means clustering is an iterative algorithm, where the clusters are re-calculated and cluster members are reassigned to different clusters until a convergence criteria is met.
## 5 Evaluation
### Training Results  
The performance of each trained model on the test dataset and the performance of the bagged ensemble are summarized in Table 4. All values listed in the table represent the average performance across the entire test dataset. Among the evaluated models, the bagged ensemble of UNet++ achieved the highest performance across all evaluation metrics, with an IoU of 0.441, mIoU of 0.706, precision of 0.618, recall of 0.581, and accuracy of 0.971.  
An example of the prediction results for the test data using the bagged UNet++ model is shown in Figure 6. For this image, the model achieved an IoU of 0.437, mIoU of 0.698, precision of 0.720, recall of 0.526, and accuracy of 0.961. IoU was closest to the average values across the entire test dataset, making this image a suitable visual representation of a typical test case. Compared with the GT mask, few instances exist where the model makes incorrect predictions, such as falsely identifying non-weed areas as weeds. Overall, the model demonstrated the ability to accurately identify weed locations in an image. These results suggest that the model effectively learns the features of the weed pixels and successfully detects them.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **IoU** & **mIoU** & **Precision** & **Recall** & **Accuracy** \\ \hline Unet++\_1 & 0.407 & 0.687 & 0.587 & 0.545 & 0.968 \\ \hline Unet++\_2 & 0.420 & 0.694 & 0.579 & 0.591 & 0.970 \\ \hline Unet++\_3 & 0.408 & 0.688 & 0.561 & 0.580 & 0.970 \\ \hline Unet++\_bagging & 0.441 & 0.706 & 0.618 & 0.581 & 0.971 \\ \hline DeepLabV3+\_1 & 0.268 & 0.613 & 0.425 & 0.427 & 0.960 \\ \hline DeepLabV3+\_2 & 0.285 & 0.622 & 0.442 & 0.441 & 0.960 \\ \hline DeepLabV3+\_3 & 0.285 & 0.621 & 0.453 & 0.429 & 0.959 \\ \hline DeepLabV3+\_bagging & 0.292 & 0.626 & 0.471 & 0.422 & 0.962 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Training results for each model and bagging results.
# A Review of Research on Dropwise Condensation Heat Transfer
## 3 Dropwise Condensation Heat Transfer Model
### Single Drop Heat Transfer Model  
[PERSON] [25] was the first man to put forward a relatively complete model of dropwise condensation. When he established the mathematical model for the heat transfer rate of a single liquid droplet, he considered that the thermal resistance is composed of the vapor-liquid interfacial resistance \(\Delta R_{i}\), the resistance due to the conduction through the drop itself \(\Delta R_{d}\), the resistance from the promoter layer \(\Delta R_{p}\), and the resistance due to the curvature of the drop of the liquid-vapor interface \(\Delta R_{c}\), as shown in Figure 3. In this model, the convective heat transfer within the droplet is negligible. However, for the above model, each drop is rationally treated, and it is believed that the drop is hemispherical and the contact angle of the droplet is \(90^{\circ}\), which is obviously not practical. Therefore, [PERSON] [22] improved the [PERSON] model and established a brand-new mathematical model by considering the influence of surface contact angle. Considering the four thermal resistances in the heat transfer process, the heat transfer rate corresponding to a fixed droplet radius r can be expressed using the following equation.  
\[q_{d}=\frac{\Delta\Gamma\pi r^{2}(1-r_{min}/r)}{\left(\frac{\delta}{k_{out}sin^{ 2}\theta}+\frac{r\theta}{4k_{c}sin\theta}+\frac{1}{2h_{i}(1-cos\theta)}\right)} \tag{7}\]  
where \(h_{i}\) is the interface heat transfer coefficient (mainly determined by vapor pressure), W/(m\(\cdot\)K), \(k_{c}\) is the thermal conductivity of condensate, W/(m\(\cdot\)K), \(k_{coat}\) is the thermal  
Figure 3: Schematic diagram of thermal resistance network of a single droplet.  
Figure 2: Schematic diagram of three different droplet departure modes: (**a**) sliding; (**b**) rolling; (**c**) jumping.  
conductivity of the coating material, \(\mathrm{W/(m\cdot K)}\), \(\delta\) is the thickness of promoter layer, and \(r\) is the droplet radius, m.  
The model established by [PERSON] [22] is only applicable to smooth surfaces and cannot be well predicted for micro-nano structure surfaces. At present, the surface of the micro-nano structure has complex shape characteristics, so it is difficult to establish a single drop heat transfer model that suits all micro-nano structure surfaces. Therefore, it is a good choice to establish different heat transfer models for different micro-nano structure surfaces. [PERSON] [26], [PERSON] [27], [PERSON] [28], and [PERSON] [29] improved the heat transfer model of a single droplet on the surface according to the different morphology of the droplet on different surfaces. With further research, in addition to the four thermal resistances mentioned above, the thermal resistance between solid and liquid is also considered in the heat transfer process, which makes the heat transfer model more accurate [30].
## 3 Results
### Attribution Analysis of WUEc Changes in Xinjiang  
#### 3.3.1 The Response of WUEc Variation to Climate and Vegetation Factors  
The overall negative correlation between T and WUEc in Xinjiang is observed in 60% of the region, with the spatial distribution of SHAP values showing a decreasing trend from south to north (Figure 6a). Kashi and Kezhou have higher SHAP values, ranging from 0.22 to 0.65 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\), covering 26% of the region. Urumqi and Changji, on the other hand, exhibit lower SHAP values, ranging from \(-\)0.59 to \(-\)0.18 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\), covering 18% of the region. Pre showed a positive correlation with WUEc in Xinjiang overall (68% of positively correlated regions), and the SHAP values displayed a spatial upward trend from south to north, which contrasts with the spatial distribution of SHAP values for T and WUEc (Figure 6b). In Kashi and Hetian, Pre was negatively correlated with WUEc, with SHAP values ranging from \(-\)0.61 to \(-\)0.19 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\), accounting for 18% of the region. The higher SHAP values for Pre and WUEc were concentrated in northern Yili and Bozhou, with SHAP values ranging from 0.07 to 0.52 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\), covering 37% of the region. The SHAP values for VPD and WUEc were more evenly distributed and showed weak correlation overall in the study area. Sixty percent of the area exhibited smaller SHAP values (\(-\)0.15 to 0.00 gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\)), while the areas with higher SHAP values were mainly concentrated in Yili (Figure 6c).  
Figure 5: Spatial distribution of WUEc sustainability and stability in Xinjiang, 2002–2022, (**a**) spatial distribution of the Hurst index of WUEc in Xinjiang, (**b**) future evolutionary trend of WUEc in Xinjiang, and (**c**) spatial distribution of the CV coefficient of WUEc in Xinjiang.  
Figure 6: Spatial distribution patterns of SHAP values for meteorological factors: (**a**) SHAP value distribution for temperature (T), (**b**) SHAP value distribution for precipitation (Pre), and (**c**) SHAP value distribution for vapor pressure deficit (VPD). Positive SHAP values indicate a positive effect on WUEc, while negative values indicate a negative effect.  
The correlation between EVI and WUEc in Xinjiang was weak, whereas LAI exhibited a relatively strong negative correlation with WUEc. Specifically, the SHAP values of EVI and WUEc are relatively evenly distributed across the study area, with generally smaller values. Areas with SHAP values ranging from \(-0.05\) to \(0.13\) gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\) account for 67% of the region. The SHAP values for LAI and WUEc exhibit a decreasing trend from south to north. The areas with lower SHAP values (\(-0.42\) to \(-0.06\) gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\)) are mainly concentrated in Bozhou, Changji, and most parts of Yili, accounting for 39% of the region. Meanwhile, areas with higher SHAP values (0.43 to \(1.28\) gC\(\cdot\)mm\({}^{-1}\cdot\)m\({}^{-2}\)) account for only 3% and are primarily located in a small portion of the eastern part of Yili (Figure 7).
# Gradient-Based Aerodynamic Optimization of an Airfoil with Morphing Leading and Trailing Edges
## 3 Results and Discussion
### Minimization of Drag by Morphing Both Leading and Trailing Edge
#### 3.4.2 Starting from Optimized mLE  
Another optimization case has been conducted, taking the optimized airfoil with mLE as initial. The drag coefficient converges to 105.97 counts, a corresponding reduction of 18.1%, which is larger than the optimization result that starts from the baseline. The shapes of baseline and optimized airfoil with morphing leading and trailing edge and the corresponding pressure coefficient distributions on the surface are compared in Figure 12. Similarly to the initial airfoil, the optimization results in a thicker leading edge by enlarging the upper leading edge radius. The leading edge slight drops around 4.3 degrees. The chord decreases from 1 to 0.99689 m, and the chord-wise position of leading edge point moves around 0.142% of chord length--primarily to satisfy the constant skin length constraints. The trailing edge moves upward, and the corresponding deflection angle is \(-1.09\) degrees. Additionally, the boat tail angle is enlarged, and hence, the camber near the trailing edge increases, resulting in a local lift increment at after 30% of the chord. An analogical pressure coefficient distribution is obtained in the front 50% comparing with the pressure coefficient distribution only using mLE in Figure 8b, and two smooth pressure recoveries replace the pressure drop around 50% chord on the upper surface. Thus a lower shock wave drag is achieved.  
Figure 11: Comparison of results for the RAE 2822 optimization using morphing leading and trailing edges. (**a**) \(C_{p}\) distribution. (**b**) Leading edge shape. (**c**) Trailing edge shape.  
The optimization results are summarized in Table 1. The moment coefficient \(C_{m}\) is presented here for completeness, although it was not included in the optimization problem.
## 5 Performance Analysis
### Overall Results  
The simulation results for ABCP-descriptor, \(LBP_{8,1}\) and GP-descriptor [30] are presented in Table 3. In [30] is tested the performance of GP-criptor against nine of the widely used classical methods in machine learning. The performances of the methods are significantly low compared to that of GP-criptor. For this reason, we compared the results with the GP-criptor [30] by proposing the ABCP-criptor, which is a new method that can rival the GP-criptor, which has a higher result than the classical methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & \(N_{classes}\) & \(N_{instances}\) & \(N_{train\_in}\) & \(N_{test\_in}\) & **Instance Dimensions** \\ \hline Broaddrz & 20 & 100 & 50 & 50 & 64*64 \\ Kylberg & 20 & 160 & 80 & 80 & 115*115 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Datasets.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Artificial Bee Colony Programming-Descriptor (ABCP-Descriptor)** \\ \hline Colony Size & 100 \\ Iteration Number & 30 \\ Maximum Tree Depth & 6 \\ Number of Branches in the _Code_ node & 7 \\ Initial Method & Ramped Half and Half \\ Functions & + (_plus_), \(-(minus)\), \(*\) (_times_), / (_rdivide_) \\ Limit & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters.  
When the results were evaluated, successful results were obtained with ABCP-descriptor and LBP. The average test success of the methods in the DS-Broadtz is higher than the DS-Kylberg. The highest test accuracy was found in the ABCP-descriptor as 97.3% for the DS-Broadtz and 95.188 for the DS-Kylberg. The highest standard deviation has the ABCP-descriptor in the DS-Kylberg data set with 6.38. The higher the deviation, the greater the difference between the maximum and the minimum test accuracy compared to LBP. The GP-descriptor [30] has the highest mean as 93.21% in DS-Kylberg. The ABCP-descriptor is considered to be comparable to the GP-descriptor because its classification success is high.
## 6 Conclusions  
In practice, when large fleets were driving on the road, the collected images often encountered multiple resolutions. To avoid having personnel adjust images based on individual experience, this study incorporated fuzzy logic into image pre-processing, like integrating expert experience into fuzzy inference. The proposed fuzzy method provided consistent parameter adjustments for images. The contribution of this paper proposed fuzzy enhancement that made pre-processing more flexible to handle multiple resolutions. This proposed fuzzy enhancement with RGB and CIELAB conversion achieved the pre-processing task using PSNR and MSE for evaluation. The proposed fuzzy enhancement and fuzzy enhancement with brightness can improve images with lower MSE and higher PSNR for handling complicated moving images. The fuzzy enhancement and fuzzy enhancement with brightness can also make the traffic light image contours clearer. From the MSE values shown in Table 5 and Figure 7, the fuzzy enhancement and fuzzy enhancement with brightness adjustment (adjusted to 7) demonstrated significant improvement for image pre-processing.  
After evaluation, the fuzzy method can be used to enhance large fleet image pre-processing tasks. Next, the focus is on the effect of resolution on the training of traffic light models under the limitation of having a limited number of images. The enhanced 33 images were adjusted using fuzzy enhancement and added to the model training. According to the results of YOLOv9 for traffic lights, there were more than 500 epochs necessary to achieve stability. The YOLOv9 with fuzzy enhancement images obtained better performance, and image enhancement is useful for model training via fuzzy enhancement that can be flexibly adjusted for large fleets to handle pre-processing tasks. YOLOv9 with fuzzy enhancement obtained better detection performance compared to YOLOv9 with histogram equalization enhancement and YOLOv9.  
Conceptualization, C.-Y.M.; Methodology, P.K.; Software, P.K.; Validation, P.K.; Formal analysis, P.K.; Investigation, C.-Y.M.; Data curation, C.-Y.M.; writing--original draft preparation, C.-Y.M. and P.K.; writing--review and editing, C.-Y.M. and P.K.; Visualization, P.K.; Supervision, C.-Y.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by SkyEyes GPS Technology Co., Ltd., and National Science and Technology Council: NSTC 112-2622-M-035-001-.  
Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
We thank [PERSON] for proofreading this paper and [PERSON] for organizing the data.  
The authors declare that this study received funding from SkyEyes GPS Technology Co., Ltd. The funder was involved in providing data sources.
# Electro-Aero-Mechanical Model of Piezoelectric Direct-Driven Flapping-Wing Actuator
## 3 Experiment  
### Design and Fabrication  
To verify the analytical model, we measured the performance of prototypes of the flapping-wing actuation system and compared the result to the analytical calculation. Figure 4 shows the structure of the system. The system comprises two parts: a piezoelectric unimorph actuator and a wing. The wing comprises four parts (shown in Figure 4a): Leading-edge bar, flexible vein, reinforcing vein, and wing membrane. The wing membrane is a thin polyester film, and it is supported by the veins. The veins consist of a combination of titanium (Ti) reinforcing layer and polyimide (PI) flexible layer. The wing can rotate around the x-axis at the hinge structure, which is also made of the PI layer. A magnified drawing of the hinge structure is shown in Figure 4c. The hinge of the prototype comprises a torsional beam structure. Thus, \(k_{w}\) can be calculated simply as the spring constant of the torsional beams [31]:  
\[k_{w}=N\beta G_{h}\frac{w_{h}t_{h}^{3}}{L_{h}},\quad\beta\simeq\frac{1}{3}-0.2 1\frac{t_{h}}{w_{h}}\Bigg{(}1-\frac{1}{12}\bigg{(}\frac{t_{h}}{w_{h}}\bigg{)} ^{4}\Bigg{)}, \tag{41}\]  
where \(N\) and \(G_{h}\) are the number of torsional beams and the shear modulus of PI, respectively. \(t_{h}\), \(w_{h}\), and \(L_{h}\) are the thickness, width, and length of the torsional beams, respectively. The unimorph actuator consists of a piezoelectric plate and a Ti shim plate bonded by epoxy adhesive (shown in Figure 4b). We used a Pb(In\({}_{1/2}\)Nb\({}_{1/2}\))O\({}_{3}\)-Pb(Mg\({}_{1/3}\)Nb\({}_{2/3}\))O\({}_{3}\)-PbTiO\({}_{3}\) (PIN-PMN-PT) single crystal as the piezoelectric material; it is suitable for large-displacement actuators because of its extremely high piezoelectric coefficient. PIN-PMN-PT plates were obtained from TRS Technologies (United States). The Ti shim plate has slits along the longitudinal direction to let excess adhesive flow out.  
Figure 4: Structure of prototype actuation system: components of (**a**) wing and (**b**) unimorph actuator; and (**c**) assembled system and a close-up image of hinge.  
In this experiment, two prototypes with different size were measured for confirming the generality of the model. One has a 17.0-mm-long wing and a 10.5-mm-long unimorph and the other has a 32.4-mm-long wing and a 21.0-mm-long unimorph; they are respectively called \"small type\" and \"large type\" below. The other design parameters and material properties are summarized in Appendix A. Figure 5 shows photographs of the small and large types.
## 6 Experiments Validation  
### Data Acquisition  
The first verification test case is fault detection for rolling bearing. The bearing test rig consists of a motor, coupling, intermediate shaft, supporting bearings, and electrical brake, as shown in Figure 9. A piezoelectric accelerometer was installed vertically on the drive end of the motor, and another piezoelectric accelerometer was positioned on the bearing housing with a sensitivity of 1.04 mV/ms\({}^{2}\). The standard load of the bearing test rig is 680 Nm, and the final output speed is 388 rpm. Tables 3 and 4 show the parameters of the experimental bearing, among them, \(f_{0}\), \(f_{i}\), \(f_{b}\), and \(f_{c}\) are the fault characteristic frequencies of the outer ring, inner ring, rolling element, and cage of the rolling element bearing, respectively.  
Figure 8: Rolling bearing fault diagnosis flowchart.
We implement the state-of-the-art Convolutional Bidirectional Long-Short-Term Memory (CNN-BiLSTM) model to process both the temporal sequence component (i.e., transitions) and the spatial content of the pixel colors (i.e., duration of each stage) [9]. This study expands beyond individual patient health to address broader aspects of population health management. It employs a noninvasive and cost-effective approach, using comprehensive patient data from electronic health records. These records provide detailed information on medical history, including prevalent comorbidities such as cardiovascular disorders, diabetes, obesity, and hypertension, as well as demographic variables such as age, race, and gender, which have been linked to OSA, insomnia, and depression. Our focus is on elucidating the connection between fragmented sleep, its cognitive and mental health implications, and the transformative potential of machine learning in medical diagnostics and treatment. The contributions of this study are listed below. 1. The development of deep learning models for the detection of OSA, insomnia, and depression using sleep stage data from wearable devices and patient-specific medical history. 2. The construction of hypondensity and hypnogram visualizations to enrich quantitative data obtained from wearable devices. 3. The application of multimodal learning to analyze OSA in the context of its comorbidities. The structure of this paper is as follows. Section 2 details the materials and methods, Section 3 reports the results, and Section 4 presents a discussion of the results.
## 1 Introduction  
Ultrashort pulses are of great significance and application value as a tool in electro-molecular dynamics [1; 2; 3], surface science [4; 5], pump-probe experiments [6] and so on, which need attosecond (as, \(10^{-18}\) s), zeptosecond (zs, \(10^{-21}\) s), and even yoctosecond (ys, \(10^{-24}\) s) pulses. Since [PERSON] [7] and [PERSON] [8] who won the newest Nobel Prize produced the first consecutive and isolated single attosecond light pulses, respectively, in the world, the door to ultrafast physics has been opened. The shortest pulse that can be generated experimentally is only 53 as by [PERSON] team [9], but this is almost the limit of current experimental methods. One possible method in theory of producing even shorter (zeptosecond) pulses is injecting an ultra-high power coherent X-ray pulse into an ultra-relativistic flying mirror [10]; however, a very high-intensity laser and novel techniques are required but they are not yet available.  
To stably produce shorter pulses with higher energies, nonlinear Thomson/Compton scattering is a method worth considering [5; 11; 12]. When a single high-energy electron collides with an intense laser pulse, nonlinear inverse Thomson scattering (NITS, Figure 1), or Compton scattering under the Thomson limit [13; 14], can be used to explain the motion and radiation of the electron. [PERSON] et al. [15; 16] conducted detailed research on attosecond X-ray pulses generated by nonlinear Thomson scattering, pioneered in this field. In our previous research [17; 18], the generation of X/\(\gamma\)-ray pulses in the order of zeptosecond through single-electron NITS has been proven theoretically and numerically. However, the potential of NITS goes far beyond that.  
In this paper, we investigated and calculated the spatiotemporal properties of NITS pulses generated by low-energy and medium-high-energy single electron collides with a tightly focused short-pulse intense laser field for the first time. The effects of the electron's initial energy and laser intensity on the spatiotemporal properties of radiation are studied in detail. The temporal distribution of the radiated pulse has a structure of the zepto-yoctosecond and is spatially shown from the perspective of a polar angle (refers to \(\theta\) distribution) and also an azimuth angle (refers to \(\phi\) distribution). The time-compression effect was proposed and used to explain the occurrence of ultra-short pulses. Furthermore, when increasing the electron's initial energy and laser intensity, we discovered a new nonlinear phenomenon of electron radiation properties. This research elucidated the mechanism of generating zeptosecond-yoctosecond pulses through nonlinear Thomson scattering, and also provided the theoretical and numerical basis for experimentally producing shorter light pulses.