esa-sceva/satcom-chunk-collection huggingface dataset:
# GE-YOLO for Weed Detection in Rice Paddy Fields
## 3 Results and Discussions
### Model Validation and Comparison with Other Algorithms  
The detection results of GE-YOLO are shown in Figure 9. As illustrated, GE-YOLO effectively detects multi-scale objects, including densely packed targets. This demonstrates that our model performs well in handling different sizes of weed targets and in detecting weeds with dense distributions. To further validate the efficacy of GE-YOLO, we compared its performance with that of other mainstream object detection algorithms. The detailed experimental results are presented in Table 6. From the results in Table 6, it is evident that GE-YOLO achieves a higher level of performance compared to other YOLO series algorithms in terms of both mAP and F1 Score. The mAP reaches 93.1%, which is the highest among all compared algorithms. The F1 Score is 90.3%, surpassing all other compared algorithms except YOLOv9. Although our model slightly lags behind YOLOv9 in terms of F1 Score, it offers a lower parameter count, reduced computational complexity, and higher FPS. Therefore, overall, our model demonstrates superior performance and high reliability in the task of weed detection in rice fields.
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 3 Methodology
### Phase 2--Damage Assessment and Failure Analysis  
This phase aims to quantitatively evaluate the potential damage to the power station's components and subcomponents as a result of the primary earthquake event and secondary events. This phase is based on the threats and the system layout defined in the previous phase.  
* Subcomponent Damage Assessment--the predicted ground motion at the power station site is applied to the fragility curves in order to calculate the probability and severity of damage to each subcomponent.
* Cumulative Damage Evaluation--aggregation of damage accumulated from the primary and secondary events to assess the overall cumulative damage sustained by the subcomponents.
* Damage and Failure Analysis--a comprehensive failure analysis is conducted, considering the interdependencies between components and the system's redundancy and resilience. Fault Tree Analysis (FTA) is utilized to provide an in-depth evaluation of failure modes and their implications.
# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 3 Results
### Univariate Regressions  
The results of UR are shown in Table 4 as a heat map. Generally, the fits between VIs and _DMY_ differed from moderate to poor depending on the VI. None of the calibration and validation _NSE_ values exceeded 0.55. Among these VIs, red-edge-related VIs had stronger correlations with _DMY_ compared to others. Short-wave infrared (SWIR)-related VIs did not show a strong ability to correlate with _DMY_. Surprisingly, accuracies for evaluation were higher than for calibration and validation (e.g., average _NSE_ of  
Figure 3: Variation in forage dry matter yield (_DMY_) of the dataset (180 samples, Table 2) at four sites in 2019 and 2020. The horizontal lines in the boxplot show the first quartile (Q1), median and third quartile (Q3) of the datasets. The upper end of the black line is the upper bound for detecting outliers (Q3 + 1.5 \(\times\) (Q3–Q1)) and the bottom end of the black line is the lower bound for detecting outliers (Q3 + 1.5 \(\times\) (Q3–Q1)). The black dot shows outlier, which was removed for the regression analyses.  
0.51 among 56 VIs for evaluation against 0.33 and 0.22 for calibration and validation). However, the evaluation dataset only had nine samples; therefore, the results must be interpreted cautiously.
## 3 Results
### Settlement Variation Regularity of K67+300 Subgrade Section  
There were 5 settlement observation points, numbered 1#-5#. Observation points 1# and 5# were located 4 m outside the slope toe on the left and right sides of the subgrade, respectively. Observation points 2# and 3# were located at the left and right slope toe of the subgrade, respectively. Observation point 3# was in the center of the subgrade. The subgrade settlement observation could only be started after the construction had been completed. Therefore, the subgrade settlement data were obtained from June to December 2020. The following information can be obtained from Figure 9.  
From the center of the subgrade to the toe of the slope, and then to the outside of the subgrade, the subgrade settlement was increasing. It is shown that the closer to the center of the subgrade, the smaller the magnitude of subgrade freeze-thaw deformation.  
Figure 9: Position variation of subgrade surface settlement observation points relative to datum point in 2020.  
The subgrade settlement observation started just after the completion of subgrade construction in May 2020. Therefore, the subgrade at this time was at the initial position, and the settlement was regarded as 0 mm. The settlement values in June and the next months were increments relative to the initial position. As the temperature rose, part of the ice in the underground soil melted into water, causing the subgrade to change from previous frost heave to gradual subsidence. So, all charts intersected at the point \"0\" on August 15. In November, the settlement reached the maximum. The subgrade settlements calculated from the observation data of observation points 1#-5# were 19.3 mm, 8.3 mm, 4.8 mm, 9.5 mm, and 21.1 mm, respectively. It can be seen that the settlement at the center of the subgrade and the slope toe was relatively small, which could ensure the safe operation of the subgrade. The subgrade settlement of the observation points 1# and 2# (with vegetation cover) is less than that of points 4# and 5# (no vegetation cover).
## 4 Conclusions  
In this study, the optical properties of apples were noninvasively determined using the proposed frequency-domain DRS system. In this system, we used two SDSs, 1 and 5 mm, and two laser diode wavelengths of 660 and 980 nm, corresponding to the chlorophyll and water absorption peaks. We found that the optical properties recovered by our DRS almost coincided with the apples' physiological changes during the 14-day study. The decrease in the \(\mu_{a}\) and \(\mu_{s}\)' at the wavelength of 660 nm at 1 mm SDS corresponded to the breakdown in the chlorophyll and enzyme of the cell walls. The decrease in the \(\mu_{a}\) recovered at SDS 5 mm at the wavelength of 980 nm after 14 days suggested that the water concentration in the apple outer flesh reduced. The decrease in the \(\mu_{s}\)' at an SDS of 5 mm and wavelength of 980 nm after 14 days were associated with apple softening during ripening. These optical properties changed more significantly with the physiological phenomena that occurred during the measurement of injured apples. Our frequency-domain DRS system could be applied to different sampling depths, especially in thin-skinned fruits, and could be used to study thick-skinned fruits by increasing the SDS. The DRS techniques might be practical tools for grading fruits or monitoring fruit changes during long-term storage.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Normal Apples (N = 5)**} & \multicolumn{2}{c}{**Injured Apple (N = 1)**} \\ \cline{2-5}  & \(\mu_{a}\) (mm\({}^{-1}\)) & \(\mu_{s}\)’(mm\({}^{-1}\)) & \(\mu_{a}\) (mm\({}^{-1}\)) & \(\mu_{s}\)’(mm\({}^{-1}\)) \\ \hline Day 1 & 0.0125 & 1.613 & 0.0128 & 1.390 \\ Day 7 & 0.0125 & 1.650 & 0.0167 & 0.592 \\ Day 14 & 0.0122 & 1.661 & 0.0176 & 0.667 \\ \hline \end{tabular}
\end{table}
Table 2: Optical properties of normal apples and injured apple recovered at 980 nm in 14 days.  
Figure 8: (**a**) Absorption and (**b**) reduced scattering coefficients of injured apples (red circles) and normal apples (black squares) recovered at the wavelength of 980 nm in 14 days.  
**Author Contributions:** All authors conceived and designed the experiments. N.-Y.C. and C.-C.C. performed the analysis experiments. N.-Y.C. was responsible for manuscript writing. All Authors contributed to the interpretation and discussion of experimental results.  
**Funding:** This research is supported by the Ministry of Science and Technology of Taiwan under Grant No. NSC-101-2221-E-006-211- and MOST-107-2221-E-006-148-.  
**Acknowledgments:** We thank [PERSON] from Tainan District Agricultural Research and Extension Station for valuable physiological information and suggestion of the research.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Experiments and Discussion
### Parameter Analysis
#### 3.3.1 Effect of Patch Size on Accuracy  
Patch sizes 17, 19, 21, 23, and 25 are compared experimentally. Excessively large patches increase the amount of computation and may cause overfitting, whereas too small patches reduce accuracy. Table 4 shows the performance of the different-sized patches on the three datasets. The highest accuracy is obtained at a patch size of 21.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Layer (Type)** & **Kernel Size** & **Stride** & **Padding** & **Output Shape** \\ \hline Input layer & & & & Input: [(21,21,10,1)] \\ Conv3D\_1 & (3,3,3) & (1,1,1) & (0,0,0) & Out\_1: (19,19,8,8) \\ Conv3D\_2 & (3,3,3) & (1,1,1) & (0,0,0) & Out\_2: (17,17,6,16) \\ Separable\_Conv3D\_3\_1\_1 & (3,3,3) & (1,1,1) & (0,0,0) & Out\_3\_1: (15,15,4,32) \\ Conv3D\_3\_1\_2 & (1,1,1) & (1,1,1) & (0,0,0) & Out\_3\_1\_2: (15,15,4,16) \\ Separable\_Conv3D\_3\_2\_1 & (3,3,3) & (1,1,1) & (0,0,0) & Out\_3\_2\_1: (15,15,4,32) \\ Conv3D\_3\_2\_2 & (1,1,1) & (1,1,1) & (0,0,0) & Out\_3\_2\_2: (15,15,4,32) \\ Separable\_Conv3D\_3\_2\_3 & (3,3,3) & (1,1,1) & (0,0,0) & Out\_3\_3: (13,13,2,32) \\ Conv3D\_3\_2\_4 & (1,1,1) & (1,1,1) & (1,1,1) & Out\_3\_2\_4: (15,15,4,16) \\ Concatenate\_1 (Out\_3\_1\_2, Out\_3\_2\_4) & & & Out\_C\_1: (15,15,4,32) \\ Residual Connection\_1 & (3,3,3) & (1,1,1) & (0,0,0) & Out\_R\_1: (15,15,4,32) \\ Add (Out\_C1, Out\_R1) & & & Out\_A1: (15,15,4,32) \\ Reshape (Out\_A1) & & & Out\_R\_E: (15,15,128) \\ Separable\_Conv2D\_4\_1\_1 & (3,3) & (1,1) & (0,0) & Out\_4\_1\_1: (13,13,64) \\ Conv2D\_4\_1 & (1,1) & (1,1) & (0,0) & Out\_4\_1\_2: (13,13,64) \\ Separable\_Conv2D\_4\_2\_1 & (3,3) & (1,1) & (0,0) & Out\_4\_2\_2: (13,13,64) \\ Conv2D\_4\_2\_2 & (1,1) & (1,1) & (0,0) & Out\_4\_2\_2: (13,13,64) \\ Separable\_Conv2D\_4\_2\_3 & (3,3) & (1,1) & (0,0) & Out\_4\_2\_3: (11,11,64) \\ Conv2D\_4\_2\_4 & (1,1) & (1,1) & (1,1) & Out\_4\_2\_4: (13,13,32) \\ Concatenate\_2 (Out\_4\_1\_2, Out\_4\_2\_4) & & & Out\_C\_2: (13,13,64) \\ Attention & & & Out\_SE: (13,13,64) \\ Residual Connection\_2 & (3,3) & (1,1) & (0,0) & Out\_R\_2: (13,13,64) \\ Add (Out\_C\_2, Out\_R\_2) & & & Out\_A\_2: (13,13,64) \\ Flatten & & & Out\_F: (10,816) \\ Linear\_1 & & & & Out\_L\_1: (256) \\ Dropout\_1 & & & Out\_D\_1: (256) \\ Linear\_2 & & & Out\_L\_2: (128) \\ Dropout\_2 & & & Out\_D\_2: (128) \\ Linear\_3 & & & Out\_L\_3: (9) \\ Total Parameters: 3,059,193 & & & \\ \hline \end{tabular}
\end{table}
Table 3: Framework structure of PU dataset.
# View-Aware Contrastive Learning for Incomplete Tabular Data with Low-Label Regimes
## 3 Materials and Method
### Evaluation Metrics  
Several standard metrics were employed to comprehensively evaluate both classification and reconstruction performance, including accuracy, F1-score, area under the curve (AUC), and normalized mean squared error (Normalized MSE). Accuracy quantifies the proportion of correctly classified instances across the entire dataset, while F1-score balances precision and recall, making it particularly suitable for imbalanced classification scenarios. AUC reflects the model's discriminative capability across varying decision thresholds, with higher values indicating better robustness. Normalized MSE is used to assess the relative reconstruction error in regression tasks by comparing predicted outputs against actual values normalized by the ground truth variance.
## 8 Conclusions  
(1) During the service life of the roadway, the plastic zone near the side of the mining face is always extended along the coal seam towards the side of the mining. The plastic zone of the roof and floor expands to the deep part of the surrounding rock. There are significant differences in the expansion degree of the plastic zone at different positions in different service periods. The expansion degree of the soft coal (rock) layer is the highest.  
(2) The direction of the stress field changes during the service period of GER. However, there are differences in the areas where the stress field direction changes in different periods. During the same period, the degree of change in the direction of the stress field at different positions of the roadway is also different. In addition, the deflection direction of the stress field of the GER is always toward the side of the mining activity.  
(3) Under the action of coal seam dip angle and stress direction, the plastic zone expands nonuniformly. When the angle between the maximum principal stress and the coal (rock) layer is 45\({}^{\circ}\) (\(\pm\)5\({}^{\circ}\)), the nonuniform expansion degree of the plastic zone is the largest, and the expansion degree in the soft rock layer is higher than that in the hard rock layer.  
(4) During the roadway excavation and the first mining periods, the plastic zone has a low degree of expansion on the roof and high side. The conventional bolt and anchor cable support is used inside the roadway. During the retaining period and the secondary mining period, the plastic zone of the surrounding rock on the high side of the roadway is significantly expanded and connected with the goal. The roof span of the roadway is  
Figure 18: Roadway section surrounding rock deformation monitoring results curve.  
thus increased. The roadside filling technique reduces the roof span and improves its overall stability.  
(5) The control scheme of the surrounding rock in the GER section of 3131 headentry was optimized. The method of roadway bolt (cable) support combined with roadside flexible formwork concrete wall-filling support was adopted. The width of the roadside filling body was 0.6 m, and the strength exceeded 40 MPa. The relative displacements of the roof and floor of the roadway were reduced from 250 mm to 125 mm, and the relative displacements on both sides were stabilized at 95 mm.  
Conceptualization, X.Q. and J.L.; methodology, J.L.; software, X.Q.; validation, C.C., J.D., Y.Z. and Z.C.; formal analysis, X.Q.; investigation, C.C., J.D., Y.Z. and Z.C.; resources, J.L.; data curation, J.L.; writing--original draft preparation, X.Q.; writing--review and editing, X.Q.; visualization, J.L.; supervision, J.L.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 51804243).  
Not applicable.  
Data Availability StatementData are contained within the article. Further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
## 2 The Procedures of Forecasting Strategy
### Hybrid Firefly and Particle Swarm Optimization (HFPSO)
#### 2.5.1 Particle Swarm Optimization (PSO) Algorithm  
PSO is one type of metaheuristic optimization method which is inspired by the behavior of fish schooling and bird flocking [33]. It is preferable in the optimization area as it only requires a few parameter adjustments in optimizing a complex structure [34]. During the optimization process in the n-dimensional search space, \(X_{i}\) = (\(x_{1}\), \(x_{2}\), \(x_{3}\),...., \(x_{n}\)), the position and the velocity of a particle areinitialized. Later, the particle will decide its trajectory according to its historical personal best (\(\mathrm{P_{best}}\)) solution and historical global best (\(\mathrm{G_{best}}\)) solution. Then, the particle moves to a new position by using the Equation (7) to Equation (9).  
The particle updates its velocity (\(\mathrm{v_{i}(t)}\)) as in Equation (7) where \(\mathrm{C_{1}}\) and \(\mathrm{C_{2}}\) are recognized as a cognitive parameter and social parameter, respectively. \(\mathrm{r_{1}}\) and \(\mathrm{r_{2}}\) are uniformly distributed numbers in the range of [0, 1] while \(\mathrm{P_{best}}\) and \(\mathrm{G_{best}}\) represent the personal best solution and global best solution, respectively. Besides, \(\mathrm{\omega}\) is the representation of inertia weight that is updated at each iteration according to Equation (8) where \(\mathrm{\omega_{max}}\) and \(\mathrm{\omega_{min}}\) are final and initial inertia weight, \(\mathrm{Itr_{max}}\) characterizes the maximum number of iteration and \(\mathrm{Itr}\) denotes the current iteration number. \(\mathrm{v_{i}(t-1)}\) refers to the previous velocity of a particle and \(\mathrm{x_{i}(t-1)}\) represents the previous position of a particle.  
\[\mathrm{v_{i}(t)}=\mathrm{\omega v_{i}(t-1)}+\mathrm{C_{1}r_{1}(P_{best}-x_{ i}(t-1))}+\mathrm{C_{2}r_{2}(G_{best}-x_{i}(t-1))} \tag{7}\]  
\[\mathrm{\omega}=\frac{\mathrm{\omega_{max}}-\mathrm{\omega_{min}}}{\mathrm{ Itr_{max}}}\mathrm{Itr} \tag{8}\]  
The current position of each particle \(\mathrm{x_{i}(t)}\) is updated by using the expression in Equation (9) where \(\mathrm{v_{i}(t)}\) signifies the current velocity of a particle.  
\[\mathrm{x_{i}(t)}=\mathrm{x_{i}(t-1)}+\mathrm{v_{i}(t)} \tag{9}\]
# New Approaches of Stochastic Models to Examine the Vibration Features in Roller Bearings
## 8 Conclusions  
This study seeks to quantify the behavior of a healthy roller bearing in comparison to inner and outer race faulty bearings, especially under increasing loads, utilizing vibration techniques. Vibrant data were systematically collected under various experimental conditions, and signal processing techniques were applied to extract significant features. While these features exhibit smooth performance in a healthy bearing, inner or outer faulty roller bearings manifest a distinctive peak or notch at the fault frequency or its harmonic, indicative of the presence of a fault. Stochastic model equations have been meticulously developed by discretely regressing inner and outer race faulty bearing features against healthy bearing features across diverse experimental conditions. The optimized and suitable model is rigorously assessed using stochastic modelling techniques. The computed stochastic coefficients offer crucial insights into the association of healthy bearing features with the structures of inner and outer race faulty bearings. These estimated models prove valuable in predicting the remaining useful life of roller element bearings affected by inner and outer race faults. Additionally, they serve as a support tool for maintainers, aiding in the development of effective maintenance strategies to minimize the risk of failure and maximize the overall lifespan of the machinery.  
Conceptualization, S.B. and A.A.S.; methodology, S.B. and M.H.; software, S.B.; validation, S.B. and A.M. and M.H.; formal analysis, S.B.; investigation, A.A.S.; resources, A.M. and M.H.; writing--original draft preparation, S.B.; writing--review and editing, S.B.; supervision, A.A.S. and A.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Mehran University of Engineering and Technology, Jamshoro.  
Publicly available datasets were analyzed in this study.
# A Component-Based Approach to Early Warning Systems: A Theoretical Model
## 1 Introduction
### Factors Influencing the Occurrence and Effect of Natural Hazards  
Natural hazard EWSs monitor, analyse, and model data on various variables influencing the occurrence and impact of natural disasters. Many complex forecasting and risk assessment models integrate multiple variables monitored through modern technologies, such as remote sensing and IoT sensors [32].  
The identification of these variables and data collection represents a key element in the development of EWSs and MHEWSs. Various technical standards, indices, indicators, and scientific studies can serve as a basis for collecting information on factors affecting natural hazards.  
For example, [PERSON] et al. (2020) [33] present variables essential for hydrodynamic modelling. While their study does not primarily focus on variables themselves, it provides a detailed description of input variables and the process of acquiring them for hydrodynamic modelling [33].  
The relationship between flood causes and consequences was studied by [PERSON] and [PERSON] (2023) [34]. Their research highlighted the differences between floods in arid regions compared to those in humid and temperate areas. The authors described in detail the relationships between geographical, climatic, hydrological, and topographical characteristics and flood dynamics [34].  
[PERSON] et al. (2022) [35] examined the impact of geographical characteristics on flood occurrence and behaviour in Slovakia. Their study considered variables influencing surface runoff, including slope, curvature, maximum five-day precipitation, river density, land cover, soil texture, lithology, and flood frequency [35].  
Regarding landslides, the primary monitored variables are precipitation and slope stability [31]. [PERSON] et al. (2018) explored landslides from both natural and economic perspectives, identifying a correlation between landslides and population density and GDP growth rate [36].  
For wildfires, [PERSON] et al. (2024) investigated changes in wildfire patterns due to terrain and meteorological variations, specifically vegetation type, slope gradient, wind speed, and wind direction [37].  
These studies represent just a fraction of the possible approaches for identifying variables influencing the occurrence and behaviour of various natural hazards.  
To enhance risk knowledge within MHEWSs, it is essential to develop a comprehensive database of variables based on validated studies and to create complex forecasting and risk assessment models. Thorough identification of all relevant variables can also support the analysis and selection of suitable measurement devices for data collection in MHEWSs.