esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating the Optimal Overall Slope Angle of Open-Pit Mines with Probabilistic Analysis
## 2 Methods of Slope Stability Analysis
### Shear Strength Reduction Technique (SSRT)  
SSRT is as an approach where the factor of safety is determined by artificially weakening or reducing soil/rock mass strength properties in steps/stages, adopting elastoplastic FEM analysis until failure/collapse of the rock slope occurs (e.g., slope fails). Numerically, failure occurs when the converged solution no longer exists. Alternatively, the factor of safety (**FOS**) is the value by which soil/rock mass has to be reduced to reach failure/collapse [47; 48; 49; 50; 51; 52]. Figure 1 depicts graphically Mohr-Coulomb yield surfaces and Equation (2) presents them mathematically.  
\[\mathbf{SRF}=\frac{\mathbf{tan}\ \mathbf{\varphi}}{\mathbf{tan}\ \mathbf{\varphi_{f}}}=\frac{\mathbf{C}}{\mathbf{C_{f}}}. \tag{2}\]  
where:  
**SRF** is the strength reduction factor, which is used to define the value of soil/rock mass strength parameters at a given stage of the analysis,  
**C & \(\mathbf{\varphi}\)** are the soil/rock mass shear strength input values/parameters (e.g., cohesion and friction, respectively),  
**C\({}_{\mathbf{f}}\) & \(\mathbf{\varphi_{f}}\)** are the soil/rock mass shear strength reduced or mobilized values used in the analysis (e.g., cohesion and friction at failure, respectively).  
It is worthy to mention that SRF is set to 1.0 at the beginning of calculations (e.g., soil/rock mass strength properties are set to their input values, **C & \(\mathbf{\varphi}\)**). In case of failure, the SRF, which is defined by Equation (2), corresponds to the factor of safety (**FOS**), aforementioned in Equation (1). Also, there are no assumptions required about the shape or location of the failure surface when adopting FEM methods of analysis. The failure occurs through the zones within the soil/rock mass, in which their shear strength is unable to resist the applied shear stress. This analysis has been conducted in static drained conditions assuming effective shear strength and deformation parameters, ignoring the effect of seismicity, groundwater level and distributed load. The numerical modelling setup is presented in the following section.
## 4 Joint Extraction on Tunnel Faces Based on Image Segmentation Neural Network Models
### Joint Extraction of Tunnel Face Based on U-Net Deep Learning Architecture
#### 4.2.3 Analysis of U-Net Convolutional Neural Network for Tunnel Face Joint Extraction  
The 8580 sample images were divided into training, validation, and test sets in a 60%, 20%, and 20% ratio. The preprocessed dataset was input into the U-Net convolutional neural network written in PyTorch using Python 3.7 for training. By calculating the loss and accuracy, the network parameters were iteratively updated to minimize the loss on the validation set. Once the minimum loss value stabilizes, the model converges.  
Under this U-Net convolutional neural network structure, the changes in the loss function and accuracy for the training and validation sets are shown in Figures 24 and 25, respectively.  
As shown in Figures 24 and 25, the U-Net achieved an accuracy of 82.2% on the training set and 82.6% on the validation set, stabilizing at epoch 29.  
The trained U-Net convolutional neural network was used to test the test set. A comparison of randomly selected predicted images and their corresponding labeled images is shown in Figure 26.  
Figure 26: Cont.  
Figure 25: Changes in accuracy for training and validation sets.  
As seen in Figure 26, despite good segmentation, it struggled with 'rough edges' and smaller targets.  
Comparing the predicted groups, it is evident that when the segmented target occupies a larger proportion of the total image, the overall segmentation effect is better. However, in the fourth and fifth groups, where the segmented target occupies a smaller proportion of the total image, non-target images appear after segmentation. This issue is related to the principle of U-Net, which calculates classification loss pixel by pixel. When the target segmentation object occupies a small portion of the entire image, the iterative loss value can easily drop very low, making it difficult for the target to be fully segmented.  
To address the shortcomings of semantic segmentation methods like the U-Net neural network, the author uses an instance segmentation algorithm that combines object detection and semantic segmentation, Mask R-CNN, to extract joints. This approach allows for precise segmentation of object edges based on bounding boxes from object detection, achieving more accurate segmentation results.
# Assessment of MicroPET Image Quality Based on Reconstruction Methods and Post-Filtering
## 3 Results
### Image Uniformity  
The uniformity of the 3 DRP and OSEM3D reconstruction methods was higher than that of FBP reconstruction method. However, the uniformity of the OSEM2D reconstruction method was lower than that of the FBP reconstruction method. In iterative reconstruction methods (OSEM2D and OSEM3D), image uniformity decreased with increasing number of iterations (Figure 3A). The uniformity values of OSEM2D_14, 16, and I8 were 8.25, 9.84, and 11.18%, respectively. However, the increase in the range of uniformity in OSEM3D was lower than that in OSEM2D. The uniformity values of OSEM3D_14, 16, and I8 were  
Figure 1: (**A**) Images of the hot rods in NEMA NU-4 were acquired via Inveon microPET. PET data were reconstructed using filtered back-projection (FBP), 2- and 3-dimensional ordered-subset expectation maximization (OSEM2D and OSEM3D, respectively), and 3-dimensional reprojection (3 DRP). A Hann filter (projection filter) was used for the 3 DRP reconstruction method only. In the OSEM reconstruction methods, iteration numbers were 4, 6, and 8. Image scale was fixed to the same scale. (**B**) The rod size was calculated via full width at half maximum in the NEMA NU-4 mouse image quality phantom. In the 2, 3, and 4 mm rods, the accuracy of OSEM2D reconstruction method was higher than that of the other methods.  
6.03, 6.20, and 6.40%, respectively. We confirmed the effect of post-Gaussian filtering in iterative reconstruction methods (Figure 3B). The image uniformity was increased with the use of a Gaussian filter of 1 or 2 mm and decreased with a 4 mm Gaussian filter. Specifically, the image uniformity in the OSEM3D reconstruction method without Gaussian filtering was higher than that with the 4 mm Gaussian filter.
## 6 Significant Challenges
In particular, incoming radiation must be sent back into exactly the same direction, with minimal reflected beam divergence and suppressed interference. More importantly, atmospheric scintillation effects and speckle may prove to significantly affect the PAT system, and worse, the measurements' SNR. Finally, this discussion would not be complete without acknowledging the challenges already faced by the water vapour backscatter LiDAR proposals that our concept largely draws from.
# New Advances and Challenges of Fall Detection Systems: A Survey
## 4 Challenges and Potentials
### How to Apply the Theories and System to Reality  
Most of the fall detection algorithms perform well in the lab or on simulated datasets. However, few cues show that they could be successfully used in real life. To apply the theories and system to reality, the issues that would be introduced by sensors and algorithms must be considered. For example, Kinect has been adopted by several fall detection systems, due to its ability of easily obtaining 3D information by tracing the human. However, the physical limit of Kinect is obvious: the effective detection distance ranges from 0.4 m to 3 m. If it is used in real life, one Kinect cannot cover an entire room. The deployment of sensors in real rooms should be considered and investigated. Besides, reliable datasets determine the performance of machine learning algorithms such as DT, SVM, and ANN. Most existing datasets are set up by simulated fall data, which may make the system less convincing.
## 2 Edge-of-Field Technologies  
The development and implementation of edge-of-field technologies occurred in response to the need to reestablish ecosystems services which were lost in the land due to the expansion of agriculture. Thus, natural processes in the land that retain P and consequently reduce the P load from agricultural areas to surface waters were lost or severely attenuated. Important ecosystems which provide these benefits include wetlands and riparian vegetation [56].  
Wetlands are waterlogged ecosystems normally presenting seasonal variations in water level, where hydrophyte plants adapted to soil anaerobic conditions are found [57]. They receive surface and subsurface water flows from uplands and decrease the P load by sedimentation of PP, sorption of DRP to sediments, and biological uptake of bioavailable forms (e.g., orthophosphate), before the water flows downstream [58]. Riparian vegetation, in turn, consists of cover plants, shrubs and trees found between uplands and along the bank of rivers and streams. This ecosystem intercepts the water flow from uplands, lowering the P load in the root zone by plant uptake and entrapment of PP before it reaches the waterways [59].  
Agricultural drainage was largely responsible for the loss of these ecosystems by reducing the water storage of the land and increasing the water flow to waterways [60; 61]. This consequently lowered local water tables and resulted in the conversion of wetlands to agricultural fields. The disruption of the hydrological regime at riparian floodplains affected the riparian vegetation, which has also been lost owing to deforestation for expansion of croplands and livestock grazing [62]. Furthermore, the natural buffer capacity of soils to sorb and retain P has been affected in agricultural catchments by the increasing levels of P saturation in response to the widespread use of mineral fertilizers and manure [63; 64; 65].  
The loss of these ecosystems and lower soil retention capacity for P, in addition to the increase in P transport through tile drains, resulted in a decreasing capacity of the land to retain P and prevent eutrophication in surface waters [56; 60]. Therefore, edge-of-field technologies have been implemented in agricultural catchments to replicate some of the natural retention processes and serve as low cost mitigation options.
# A New Concept of Instantaneous Whirling Speed for Cracked Rotor's Axis Orbit
## 2 Nonlinear Dynamic Model of a Cracked Rotor  
The dynamic model adopted in this paper has been developed in our recent publication [29; 30]. In Figure 1, _oxy_ is a fixed coordinate system, \(L\) is the length of the shaft, and \(z_{\rm c}\) is the z-coordinate of the crack position with respect to the \(o\)-_xyz_ reference system, and in this paper \(z_{\rm c}=L/2\). \(o^{\prime}x^{\prime}y^{\prime}\) is a rotating coordinate system, \(\overrightarrow{o^{\prime}N}\) is the normal direction of the crack tip, and \(H\) is a horizontal reference axis. \(o_{\rm c}\) is the center of mass, \(e_{\rm c}\) is the eccentric distance, and \(\beta\) is the imbalance orientation angle. \(\theta\) is a rotor spin angle, \(\varphi\) is a whirling angle of reference frames \(o^{\prime}x^{\prime}y^{\prime}\), and \(\psi=\theta-\varphi\) is a rotation-whirling difference angle, and \(d\) is the absolute depth of cracks.  
The general equations of motion of a cracked Jeffcott rotor can be obtained:  
\[\begin{array}{l}\left[\begin{array}{cc}m&0\\ 0&m\end{array}\right]\left[\begin{array}{c}\ddot{x}\\ \ddot{y}\end{array}\right]+\left[\begin{array}{cc}c&0\\ 0&c\end{array}\right]\left[\begin{array}{c}\dot{x}\\ \dot{y}\end{array}\right]+\left[\begin{array}{cc}k_{x}(x,y,t)&k_{xy}(x,y,t) \\ k_{xy}(x,y,t)&k_{y}(x,y,t)\end{array}\right]\left[\begin{array}{c}x\\ y\end{array}\right]\\ =me_{\rm c}\left[\begin{array}{c}\ddot{\theta}^{2}\cos(\theta+\beta)+\ddot{ \theta}\sin(\theta+\beta)\\ \dot{\theta}^{2}\sin(\theta+\beta)-\ddot{\theta}\cos(\theta+\beta)\end{array} \right]-\left[\begin{array}{c}0\\ mg\end{array}\right]\end{array} \tag{1}\]  
where \(\ddot{\theta}\) is the angular acceleration, \(\dot{\theta}\) is the angular velocity. \(k_{\rm x}\), \(k_{y}\), and \(k_{\rm xy}\) are breathing stiffnesses in the fixed coordinate system _oxy_, and they can be obtained from the stiffnesses \(k_{\rm x}\), \(k_{\rm y^{*}}\), and \(k_{\rm x^{*}y^{*}}\) in the rotational coordinate system \(o^{\prime}x^{\prime}y^{\prime}\) by Equation (2), and the stiffness expressions can be referred to citation [20].  
\[\left[\begin{array}{c}k_{x}\\ k_{y}\\ k_{xy}\end{array}\right]=\left[\begin{array}{ccc}\cos^{2}(\varphi)&\sin^{2} (\varphi)&-\sin(2\varphi)\\ \sin^{2}(\varphi)&\cos^{2}(\varphi)&\sin(2\varphi)\\ \sin(\varphi)\cos(\varphi)&-\sin(\varphi)\cos(\varphi)&\cos(2\varphi)\end{array} \right]\left[\begin{array}{c}k_{x^{*}}\\ k_{y^{*}}\\ k_{x^{*}y^{*}}\end{array}\right] \tag{2}\]
## 3 Metaheuristic Optimization Algorithms
### Analysis of Optimal Sizing and Allocation  
Table 2 summarizes the simulation results of the optimal location, size, and numbers of BESS. After applying the proposed voltage management strategy, the network's voltage is enhanced by increasing the installed BESS. Bus 53 is the optimal location to install the BESS if we have only one available battery. Adding more BESS, it can be seen that the BESS tends to be placed at the end of the feeder. This is because the voltage becomes more violated as we move away from the main feeder, as is seen in Figure 6. Furthermore, the maximum and minimum voltage magnitudes are improved to their corresponding voltage limits as the availability of BESS increases. The voltage profile comes within the predefined operation limits at each bus when 21 BESS are installed at 21 different buses with a total energy of 281.6 kWh. Moreover, the optimal operation and placing of 17 BESS is enough to overcome the undervoltage problem in the network.  
In the study network, there are 55 load/PV buses across the three phases. As shown in Figure 3, there are 20 loads/PVs connected to phase \(a\), 20 loads/PVs connected to phase \(b\), and 15 loads/PVs connected to phase \(c\). The simulation results in Table 2 indicate that the size and the location of batteries are highly influenced by the voltage magnitudes. Thus, the locations of BESS are mostly at the end of the feeder. In addition, among the optimal 21 BESS locations, 10, 8, and 3 batteries are connected to phase \(a\), \(b\), and \(c\), respectively. Phases \(a\) and \(b\) encounter the worst voltage profile; thus, the optimal locations of the batteries are mostly co-located with the PV systems connected with these phases. Despite bus 47 being at the end of the feeder, no battery is installed there. This is because the load profile at bus 47 is load profile 2, which minimizes the contribution from this bus in supporting the network voltage. At this bus, the peak PV generation coincides with the local peak load. A similar situation is found with bus 37, which is connected to phase \(b\). Therefore, the optimal siting and sizing of the available BESS are highly impacted by network topology, load profile, and the amount of PV power that flows into the grid.  
Figure 6: Network daily voltage profile with and without PV system at different load buses.
## 3 Results  
After removing articles that met the inclusion and exclusion criteria, a total of 30,764 articles were removed. The next step was identifying and removing duplicates, which meant 44 duplicate records were removed. The next step was screening the remaining 232 articles for its relevance. In total, 110 articles were removed as they were not relevant for the purpose of this study, which meant 122 articles remained for the next step. Then, we retrieved the full-text articles for the identified relevant studies. The full paper of 33 articles could not be retrieved, which meant 89 articles remained for the eligibility phase to assess the eligibility of the studies to be included in the systematic review. In this phase, the full-text articles were assessed to determine whether they met the eligibility criteria. After reviewing the full texts of the remaining papers, we completed the selection process. All discrepancies were addressed through discussion, seeking agreement from both reviewers to mitigate potential selection bias. To ensure a comprehensive review, a manual search in relevant journals was conducted, and a supplementary search in Web of Science identified five additional studies that met the inclusion criteria. Therefore, a total of 60 articles were included in this literature review. An overview of all selected studies is presented in Table 1, with the first author, year of publication, name of journal/conference, and the type of digital health (more details of these articles are presented in Table A1, Appendix A). Figure 1 presents the flowchart, which illustrates the selection process for the articles included in this systematic literature review. Figure 1: PRISMA flow chart of the systematic review on the eHealth interventions for transplantation purposes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
## 3 Traffic Model  
We propose a simple traffic model to control both the temporal and spatial variations of traffic over time. Notation used in the traffic model is summarized in Table 1 in the Appendix A.  
We consider a network with a set of nodes \(V\), and a set of time periods \(T\) for evaluation of power consumption during network operation. The length of each time period \(t\in T\) is \(\Delta t\). A traffic matrix \(D(t)\) at time period \(t\in T\) describes traffic demands between all node pairs in the network. It is composed of elements \(d_{a,b}(t)\) containing (directed) traffic demands exchanged between each ordered node pair \(a,b\in V^{2}\).  
The traffic matrices \(D(t)\) for all \(t\in T\) are obtained in the following two steps. First, we focus on temporal traffic variation, i.e., all traffic demands follow the same (relative) changes over time, without changes of spatial traffic distribution. In the second step, we add parameters to our model in order to vary spatial traffic distribution over time in a controlled way.
## 3 Mathematical Models and Validations
### Mass and Heat Transfer Model  
Mass, momentum, and energy conservation equations are three fundamental equations for the mass and heat transfer simulation in ANSYS. For an incompressible ideal gas, the mass conservation is expressed by Equation (9) [35]:  
\[\frac{\partial\rho_{a}}{\partial t}=-\
abla\cdot\left(\rho_{a}\overrightarrow{v }\right) \tag{9}\]  
where \(\rho_{a}\) is the air mass density, \(\overrightarrow{v}\) is the airflow velocity vector.  
In an inertial reference frame, the momentum conservation is expressed by Equation (10) [36]:  
\[\frac{\partial\left(\rho\overrightarrow{v}\right)}{\partial t}+\
abla\cdot \left(\rho\overrightarrow{v}\overrightarrow{v}\right))=-\
abla\text{p}+ \
abla\cdot\left(\overline{\overline{\overline{\tau}}}\right) \tag{10}\]  
where p is the static pressure, \(\overline{\overline{\overline{\tau}}}\) is the stress tensor.  
The energy conservation is expressed in Equation (11) [37]:  
\[\rho_{a}Cp_{a}\
abla T_{a}+\rho_{b}Cp_{b}\frac{\partial T_{b}}{\partial t}=k_ {a}\
abla^{2}\text{T}_{a}+k_{b}\
abla^{2}\text{T}_{b}+q_{b} \tag{11}\]  
Figure 3: Electric circuit model diagram.  
where \(\rho_{a}\), \(\rho_{b}\), \(Cp_{a}\), \(Cp_{b}\), \(k_{a}\), \(k_{b}\), T\({}_{a}\), and T\({}_{b}\) are the density, specific heat capacity, thermal conductivity, and temperature of the air and battery, respectively. \(\
abla^{2}\) is the Laplace operator, \(q_{b}\) is the battery heat generation rate.
# Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
## 5 Discussion
Notably, PCA has been demonstrated to be effective in identifying dominant patterns and temporal variations in deformation data, which has also been applied in recent research [55]. Both proposed methods have advantages and disadvantages relative to each other. In practice, MIRA's requirement for prior experience in processing n-dimensional datasets stands out as a disadvantage. However, since the steps to address this situation are outlined in this study, the disadvantage can be mitigated. Although the exact boundaries of the clusters may vary depending on the user, this issue is addressed by using machine learning-based SVM classification and ensuring the use of a sample size-independent classification tool. While the application of InCROSS is easier compared to MIRA, its main disadvantage is the need for information about a known deformation area in the region. If there is no pre-existing deformation data in the region, this issue can be overcome by utilizing deformation products obtained from InSAR studies conducted in the application area. The integrated use of both innovative methods is thought to increase the interpretability of complex InSAR data sets, and may contribute to broader applicability in various environmental conditions. The broader implications of these findings extend to the development of integrated hazard mapping frameworks. When supported by GNSS measurements and high-resolution DEMs, such frameworks can substantially improve early warning capabilities and land-use planning. Future studies should explore the operational integration of MIRA and InCROSS into regional landslide monitoring systems and assess their performance across different climatic and geological contexts.
## 5 Discussion  
Spatiotemporal filtering is a necessary step to improve the precision of position time series in processing regional GNSS time series data. In previous studies, different methods have been employed, such as Stacking [17], PCA [1; 21] and ICA [3]. The observations in these studies were regarded as equally weighted, while in fact, the formal errors were different. The assumption of equal weight is not impractical. How to define the optimal weighting function in spatiotemporal filtering is an open question [1]. Distances, correlations and formal errors are the factors that are usually considered in generating weights [2; 20; 21]. In our previous work, we realized the importance of formal errors, but this research was not comprehensive [26]. The IPCA in the present research was proposed on the basis that formal errors are not related to CME.  
With the removal of CME, RMS reduced by about 6.3% for the three components of the whole CMONOC network in China [3]. Relatively speaking, our study region was small, the CME was significant, and the RMS reductions were around 18% for North and Up components and 15% for the East component derived from IPCA. Meanwhile, from the noise analysis of CME we found that the temporal behavior of CME was not random, which coincides with [PERSON] et al. [1; 18]. The proportion of noise revealed that flicker noise was dominant in CME. Therefore, comparison of RMS before and after filtering was not enough [28]. Reductions in flicker noise were compared and we found that flicker noise reduced significantly, which aligns with the large proportion of flicker noise in CME. Further, local effects were common in GNSS stations, which may be due to unmodeled signals or the stations' surroundings, such as multipath. Since stations with strong local effects may have significant spatial responses [1; 3], the subsequent analysis may be biased. Therefore, the conventional approach would be to identify these stations through KLE and remove these stations first [1]. PCA is only appropriate for stations with weak local effects [1]. In the synthetic time series, weak local effects were included to test the performance of IPCA. The results confirmed its advantage over PCA in time series with local effects.