esa-sceva/satcom-chunk-collection huggingface dataset:
# Notch Fatigue Life Research Based on Critical Distance Theory
###### Abstract  
Orthotropic anisotropic steel bridge panels are widely used in civil engineering due to their advantages of light deadweight, high ultimate bearing capacity, and wide range of applications. However, their fatigue problem is serious, and the fatigue-resistant design of the steel box girder diaphragm curved cutout is still difficult. In order to effectively predict the fatigue life of this typical fatigue-susceptible detail, a fatigue test of the specimen with curved notched segments of the diaphragm was carried out. ABAQUS 2016 version finite element analysis software was used to establish three kinds of finite element simplified models of the fatigue specimens with curved notches, and the laws of the influence of different notch radii on the fatigue life of the specimens were studied. Through the finite element solution of the three arc notch specimen models with different radii under the tensile load of 50 kN their respective principal stress distributions were obtained. The critical distance theory was introduced to analyze the characteristic stresses in the stress concentration area of the arc notch, and the point method and line method were used to calculate the characteristic stresses. The fatigue life prediction models of three kinds of notched components were established by combining the material fatigue limit and FE-safe life results. The results show that the point method is more conservative than the line method; no matter if the point method or the line method is used, the characteristic stress and the notch radius are inversely proportional to each other, and it is beneficial for the fatigue life of the structure to appropriately increase the notch radius. The fatigue prediction model of steel box girder diaphraggs with curved notches based on the critical distance theory is of high accuracy, which is basically consistent with the test results, verifying the feasibility of the critical distance theory in the fatigue life prediction of notched specimens, and providing a reference for the fatigue life assessment of similar steel structures.  
orthotropic anisotropic bridge deck plate; steel box girder; cross-sectional plate; critical distance theory; curved cutout; fatigue life prediction Article
## 2 Basic Theoretical Parameters of FE-SEA Method
### Coupling Loss Factor  
#### 2.3.1 Coupling Loss Factor between Structures  
The coupling loss factor between structures represents the strength of coupling between different subsystems and represents the energy loss generated when energy is transferred in each subsystem. All subsystems transfer energy through the coupling effect of the system. When one subsystem is excited by the external system, the energy of the subsystem is transferred to the other subsystem through the coupling effect, so as to achieve energy transfer and balance. The coupling effect of different subsystems mainly has the following methods: surface connection, point connection, and line connection. There is a line connection mode between different thin plates, and the coupling loss factor can be expressed as [10; 11; 12; 13; 14]:  
\[\eta_{jk}=\frac{lc_{g}}{\pi\omega A_{j}}\tau_{jk} \tag{23}\]  
where the \(l\) is the plate connection between the curve length. \(c_{g}\) represents the velocity of the bending wave of the excitation plate, \(A_{j}\) represents the area of subsystem \(j\), \(\omega\) represents the center frequency of the research frequency band, and \(\tau_{jk}\) represents the propagation coefficient of sound waves when they are transmitted from subsystem \(j\) to subsystem \(k\).
# Bumblebee Pollination Enhances Yield and Flavor of Tomato in Gobi Desert Greenhouses
## 4 Discussion  
### Bumblebee Pollination Enhanced Tomato Yield  
Bumblebees are efficient pollinators of greenhouse tomatoes, resulting in significantly improved tomato yield. In our study, tomatoes pollinated by bumblebees showed better production performance than tomatoes treated with vibrator or PGR, resulting in higher fruit set, heavier fruit weight, and higher seed numbers, but no significant increase in fruit size. These findings are in line with previous research on bee pollination on tomato, which has consistently demonstrated that buzz-pollinating bees, e.g., bumblebees significantly increased tomato fruit weight compared to no-pollination control, while auxin treatment, vibration, or non-buzz-pollinating bees did not significantly increase fruit weight [37].  
We found that tomato flowers treated by PGR produced a slightly, but not significantly, larger fruits than buzz-pollination of bumblebee and vibrator. Some previous studies also found that tomato fruits from bumblebee pollination had a slightly but not significantly smaller size than PGR or vibrator treatments [44]. Normally, the fruit development of tomato is induced by fertilization, then the tomato ovary develops into a pericarp and the placental parenchyma fills the locular cavities with locular tissue, which encloses the seeds. Fruits can also develop without fertilization: parthenocarpic fruit-set occurs in unpollinated tomato ovaries when applicated with plant growth regulator, mainly auxins and gibberellins [45; 46; 47]. Compared with bee-pollination, the size of fruit treated by PGR increased much faster [48]. However, the application of PGR on tomato needs extreme care, for deformed fruits often occur with unsuitable concentrations, which would lead to falling price. Furthermore, it has been reported that rates of petal and stigma retention on tomato fruits were increased significantly after PGR treatments, which could result higher occurrence of grey mold on the tomato fruits [6].  
In contrast to fruit, seed development is strictly dependent on fertilization. Compared with tomato fruits from buzz-pollination, parthenocarpic tomato fruits contain fewer seed and less locular tissue. Usually, the weight of pollinated tomato fruits increases with seed quantity. In our study, only positive correlations between fruit weight and seed were found in tomato fruits by bumblebee and vibrator pollination, but not in fruits by PGR treatment. Although the use of PGR or vibrator treatment can improve fruit production compared to no pollination treatments in many crops [49; 50], we found that more variations and fluctuations of fruit yield were observed in PGR and vibrator-treated tomato. Compared with manual pollination methods, bumblebee-pollinated tomato also had a more stable fruit yield between plants.
## 4 Conclusions  
This study elaborates the influence of processing parameters on the hardness of SLM-built AlSi12 alloy samples based on a full factorial DoE technique. Also, the determined experimental outcomes were achieved via regression and ANOVA analysis. In maximizing the hardness, regression analysis was employed. From the results achieved, the following important conclusions can be drawn:  
* Increasing the laser power consequently makes the hardness increase because of the increment in laser beam intensity.
* Scanning speed does not have much impact on the resultant hardness.
* Optimized processing parameters are achieved, giving a hardness value of 131.48 Hv.  
Future research should focus on enhancing the obtained optimized AlSi12 alloy hardness and looking into the microstructural evolution and other mechanical properties by using Cu and Mg additives. This proposed alloy may be applicable to the railway industry, since, recently, aluminum has become very crucial in modernizing trains and boosting energy efficiency.  
Figure 7: Contour plot of laser power and scanning speed for hardness.  
**Author Contributions:** Writing--original draft preparation, N.K.; Review and editing, M.B.S., K.M., and R.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Research Foundation (NRF Grant 123575).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** 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.  
**Acknowledgments:** We would like to thank the Technology Innovation Agency (TIA) South Africa, Gibela Rail Transport Consortium (GRTC), National Research Foundation (NRF Grant 123575), and the Tshwane University of Technology (TUT).  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Design Scheme of SIMP Method in the Abaqus-Matlab Topology Optimization Integration Framework
### Abaqus-Matlab Interface Scheme  
The SIMP method is based on the continuum topology optimization theory, and its core is an iterative computing process. When formulating the overall plan, a complete circular mechanism needs to be established. In view of the data interaction requirementsbetween Abaqus as a finite element analysis tool and Matlab as a mathematical operation platform, system compatibility becomes a key consideration factor. Based on the above analysis, the scheme design is shown in Figure 7.  
Firstly, the basic finite element model is constructed through Abaqus. After generating the inp format file, it is transferred to the Matlab environment to implement the SIMP algorithm operation. During the iterative process of topology optimization in Matlab, there is no need for manual operation of Abaqus for finite element analysis. The calculation can be completed simply by relying on the core command-line call function of Abaqus. In the Abaqus-Matlab platform, the SIMP method is integrated through the main functions of Matlab. The calculation process mainly includes the following steps. Firstly, the initial model data is submitted to Abaqus through the inp file for finite element solution. Then, the data is extracted and analyzed in Matlab. The SIMP algorithm is used for structural optimization and the updated inp file is generated. Finally, through iterative calculation until the convergence standard is reached, the final optimization scheme is output.  
The key to building the joint platform of Abaqus and Matlab lies in establishing the data interaction channel. This platform realizes interconnection by extracting the calculation results of Abaqus through Matlab. Abaqus saves the analysis data to the document, and Matlab acquires the required information by reading the document. The inp file plays a crucial bridging role between the Abaqus/CAE preprocessing module and the Standard/Explicit solver, and is processed by the solver as the core analysis object. By transferring the inp file to the Abaqus/CAE core, Matlab realizes the platform connection of finite element analysis, providing the necessary data interface for the joint simulation of Abaqus-Matlab. The data interaction between Abaqus and Matlab is accomplished by parsing the obb file to transfer the finite element calculation results.
## 5 Results and Discussion
### Practical Implications: Dropout Rate Selection and Prediction Safety  
Our findings, particularly the subject-specific optimal dropout rates in Figure 9, highlight a crucial practical issue: how should this hyperparameter be chosen in a clinical setting? In practice, the optimal dropout rate must be determined through a calibration process tailored to each individual. This typically involves a brief data collection session for the new user, followed by a cross-validated grid search across a range of dropout rates to find the value that maximizes classification performance for that specific subject.  
In practical clinical scenarios, the selection of the dropout rate should balance performance stability and predictive conservativeness. For routine predictions, where maximizing performance is the primary goal, lower dropout rates are preferable as they maintain high accuracy and reliable model confidence. However, if a clinician requires highly cautious predictions where uncertainty estimation must be emphasized (i.e., safer predictions that penalize overconfident misclassifications), slightly higher dropout rates can be used. This approach, while potentially reducing peak accuracy, amplifies the predictive variance and increases the model's sensitivity to ambiguous cases. This highlights the need for application-specific tuning that balances performance with the demand for safe and reliable predictions.  
A critical challenge for any real-world BCI system is the out-of-distribution (OOD) generalization problem, where the distribution of real-time test data inevitably shifts away from the training data due to factors like user fatigue or changing electrode conditions. Our uncertainty-aware framework is inherently equipped to address this. The predictive variance derived from MCD can serve as a powerful OOD detection mechanism; a sharp increase in uncertainty can signal that an input is dissimilar to the training data, allowing the system to flag the prediction as unreliable. To move from detection to mitigation, future work could incorporate principles from adaptive OOD control. Inspired by recent advancements such as OOD-Control [81], our framework could be extended to dynamically adjust model parameters, like the dropout rate, in response to detected distributional shifts. Furthermore, to rigorously validate and improve robustness, a comprehensive benchmark for MI-EEG, analogous to benchmarks like OOD-Bench [82] in computer vision, could be developed. Such a benchmark would enable the systematic evaluation of how well uncertainty-based methods handle realistic OOD scenarios, paving the way for BCI systems that are not only accurate but also robust and adaptive in uncontrolled, real-world environments.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 2 Materials and Methods
### Heart Rate Extraction
#### 2.4.5 Error Analysis of RGB-Based Measurements  
RGB-based heart rate extraction relies on visible light to enable successful heart rate measurements from a patient. As the measurement setup in this work does not include a dedicated visible light source, variations in room lighting, weather, and bed placement in the ICU room can strongly affect the lighting conditions in the room. While we have previously described how we attempted to prevent and combat overexposure of face pixels, there will also be a minimum brightness level at which we can still reliably extract heart rate from RGB video frames. First, we determined the brightness level as the mean across all pixels in the face ROI and the R, G, and B channels. Then, we assessed the 5 BPM agreement, coverage, and mean absolute error (MAE) as we increased a minimum brightness threshold to determine how the RGB-based extraction was affected by the face ROI brightness.  
After we removed segments that could have poor agreement due to the brightness level, we further investigated the RGB recordings of patients for significant errors and tried to, based on investigation of the ground truth and video recordings, sketch a picture of likely causes for poor quality monitoring in those recordings. Furthermore, we will also highlight which step in the heart rate extraction seems to be the problem.
## 4 Discussion  
For all the optimal time source combinations found by the traversal algorithm, the PSO algorithm can only obtain one of the optimal time source combinations each time. For LEO satellite network application scenarios, it is important to find one optimal combination immediately, when the time source satellite is broken. In the simulation, the PSO algorithm can gain a group of optimal time source combinations within a minimum of 10 iterations, which significantly reduces the computational complexity, shortens the computation time, and can complete the satellite time re-synchronization of the LEO satellite networks more quickly.  
Although only 66 satellites are used in the design, the effectiveness of the proposed method has been verified. As the number of satellites increases, the traversal algorithm grows exponentially in computation because it needs to compute all possible scenarios, whereas the PSO algorithm is closer to the optimal result with each iteration, eliminating many useless computations. Therefore, its generalization to giant constellations is also applicable.
## 3 Materials and Methods
### Methodology and Algorithms  
To perform analytics on the available data, two approaches were employed: (1) statistical analysis; and (2) machine learning method. In the statistical analysis, variation of the number of collisions versus different classes were investigated. The underlying relationships, causes, patterns, and trends from the data were described and compared with other studies. While we only used the last 5 years of the data, a comparison with the whole dataset was performed to find any differences in patterns of the selected dataset. In the machine learning approach, the relative importance and correlation between the factors in the dataset are evaluated. Different machine learning modeling approaches could be used to investigate the effects of previously mentioned factors of collisions. Regression models [30; 31] and classification models [32; 33; 34]) were two common methods to analyze accident parameters. Classification models use data-mining approaches while the regression models assume a specific functional form to model the relationships between dependent and independent variables [34]. Classification models are independent of the assumption and focus on the state of data. Many classification models such as logistic regression, k-nearest neighbors (k-NN), Gaussian naive Bayes (GNB), support vector machines (SVM), etc. have been developed. In this study, the Auto-Sklearn (ver. 0.14.0) [35], one of the robust new Automated Machine Learning (AutoML) libraries, is used to select the best-tuned classification model. Auto-Sklearn library enables the user to automatically build and deploy advanced classification machine learning models to derive insight from the data. To implement the Auto-Sklearn method, the \"medical treatment required\" (code \"P_ISEV\") factor (Table 2), which represents the severity of a collision, was selected. This factor uses three classes of no-injury, injury, or fatality of people at the accident scene. Understanding the factor of required medical treatment as a function of the other 21 factors (criteria) could serve as a good predictor for the severity of a collision. The factors that lead to injury and fatality classes are important to consider for reducing the severity of collisions while the influencing factors on the no-injury class can be important for designing the collision avoidance warning systems to reduce false alarms. Factors that showed relative dependency with other factors, as well as those data columns that were not contributing to the collisions (e.g., P_ID, V_ID, C_YEAR, V_YEAR, C_CONF, and C_SEV) were eliminated from the analysis. Thus, the problem was simplified to find the dependency of the factor P_ISEV on the other 16 remaining factors. Before using the machine learning model, some steps were performed to prepare the data. Since the original data were imbalanced, downsampling the majority class of the original dataset to match the minority class was employed to balance the dataset. The min-max normalization technique was applied to
# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 3 Case Study
### Vibration Fault Feature Analysis  
The original vibration data are normalized by their peak absolute values to obtain relative amplitudes, which are then used for plotting. The relative vibration amplitude of the four measurement points arranged on the engine are shown in Figure 5. The relative vibration amplitude of all four sensors reached its peak at 1759.8 s. The aircraft engine also experiences a sudden failure and shuts down at 1759.8 s. The relative amplitude of the two sensors placed on the bearing housing is much smaller than that of those placed on the intermediate case. The intermediate case is located near the fan and compressor, which are two major rotating components and sources of airflow disturbances. During operation, it is subjected to significant dynamic forces, resulting in large vibrations. The maximum relative vibration amplitudes measured by the sensors V\({}_{\text{ic1}}\) and V\({}_{\text{ic2}}\) on the intermediate case are 0.84 and \(-\)1.0, respectively. Meanwhile, the maximum relative vibration amplitudes measured by the sensors V\({}_{\text{11}}\) and V\({}_{\text{12}}\) on the bearing housing are 0.11 and \(-\)0.15, respectively. The relative vibration amplitude at measurement point V\({}_{\text{ic2}}\) on the intermediate case increased from 0.10 to 0.19 after 1754 s. This nearly doubles and continues to show an increasing trend until it reaches a peak of \(-\)1.0 at 1759.8 s. The vibration signals of the other three sensors are not as significant as those at V\({}_{\text{ic2}}\) before the shutdown.
## 2 System Model
### Energy Consumption and Storage Models  
In this paper, the rechargeable battery of each EH-ST is modeled as a buffer [36], and each position in the buffer can hold one energy unit. It is assumed that packets are transmitted with equal power, which will consume one energy unit for transmitting one physical packet in a frame, and the other energy expenditures due to battery leakage or data collecting, etc. are not taken into consideration. The number of energy units that are stored in a battery of an EH-ST is denoted by \(E\in\{0,1,2,\ldots,e_{max}\}\), where \(e_{max}\) represents the capacity of the battery. Denote the amount of energy units of EH-ST \(u\) at the frame \(k\) as \(E_{u,k}\), where \(k\in\mathbb{Z}^{+}\), and the evolution of \(E_{u,k}\) is formulated as follows:  
\[E_{u,k+1}=min\left\{E_{u,k}-l_{u,k}\cdot Q_{u,k}+B_{u,k},e_{max}\right\}, \tag{5}\]  
where \(Q_{u,k}\) is the packet transmission action of EH-ST \(u\). \(Q_{u,k}=1\) if the EH-ST determines to transmit the current packet with \(l_{u,k}\) replicas, which will consume \(l_{u,k}\) energy units, otherwise the EH-ST \(u\) will remain idle for \(Q_{u,k}=0\). \(B_{u,k}\) represents the amount of energy that the EH-ST \(u\) harvested within the duration of frame \(k\), and the harvested energy within the duration of frame \(k\) can only be used for the next frame. Thus, if there is no energy left in the battery, then the EH-ST \(u\) will remain idle (\(Q_{u,k}=0\)).