esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Prediction of Elderly Care Facility Locations
For example: In older urban districts with high building density (e.g., Yuhua, Tianxin, and Furong Districts), efforts should focus on expanding the service radius of existing facilities. And in emerging urban areas west of the Xiang River, these zones could be targeted for new facility construction to improve walkability and service quality. Furthermore, existing public facility resources should be fully utilized to develop different types of elderly care facilities based on the unique characteristics and needs of each region. For example, Yuhua District and Tianxin District, with their abundant medical resources, can leverage this advantage to establish integrated medical and elderly care service centers. Meanwhile, Yuelu District can utilize its educational resources to establish senior universities and social welfare centers, enriching the cultural and spiritual lives of the elderly. Beyond the central urban areas of Changsha, particularly in remote suburban regions such as Changsha County, Liuyang City, and Ningxiang City, there are also numerous streets with severe aging populations. Although these areas are relatively less developed economically, they benefit from lower land prices and beautiful natural environments. During the site selection process, the unique characteristics and advantages of these regions should be fully considered. In addition to constructing ordinary elderly care facilities suitable for local residents to meet basic needs, commercial elderly care institutions could be introduced to develop high-end facilities that attract seniors for vacation-style retirement, thereby boosting local economic development. This strategy not only addresses the diverse needs of the elderly but also promotes balanced regional economic growth.
## 3 Full Device Fabrication  
### Ge Film Deposition on Si Substrate  
In the literature, methods such as molecular beam epitaxy (MBE) [30; 31; 32], reduced pressure chemical vapor deposition (RPCVD) [33; 34; 35], rapid melt growth (RMG) [36; 37; 38], and plasma enhanced chemical vapor deposition (PECVD) [39], have been used to grow high-quality Ge films on Si substrates. Due to the 4.2% lattice mismatch between Ge and Si, defects including misfit dislocation and threading dislocation appear in the Ge film deposited on the Si substrate. These defects may act as photo carrier recombination centers and dark current generation centers [40; 41; 42], and their density can be calculated by cross-sectional transmission electron microscope (TEM) and the etch pit method [43].  
In this study, we applied RPCVD to grow a 1 \(\upmu\)m thick Ge film on a Si substrate (100 plane). Figure 7a shows the cross-sectional TEM image of the 1 \(\upmu\)m thick Ge film deposited on a Si substrate. Misfit dislocations at the Ge/Si interface along with several threading dislocations can be observed. In the Ge region away from the interface, most defects merge and disappear, resulting in a relaxed Ge layer with a high degree of single crystalline structure. Figure 7b shows the top-view atomic force microscope (AFM) image, and Figure 7c shows the top-view SEM image, both derived after applying an etch pit method. The total defect density is then estimated to be on the order of 1 \(\times\) 10\({}^{7}\) cm\({}^{-2}\).
## 5 Discussion
### The Influence of Collection Environment  
We conducted a comprehensive evaluation of various classical models using point clouds of several crops collected from different platforms and environments. Our findings indicate that the environmental context of data collection and the platform used substantially affect model performance. The environmental context of data collection significantly impacts model application. Various elements of data collection in natural conditions, such as weed interference and wind, create challenges and introduce a considerable amount of noise, which is difficult to eliminate from the point cloud data. Some models, such as CurveNet, perform well in controlled environments but show poor performance in real-world agricultural settings due to these noise factors. Additionally, real-world agricultural environments are often densely planted, making it difficult for sensors to capture the morphological structure of the inner layers of plants. This results in severe structural information loss within the point cloud data, adversely affecting the performance of nearly all models.  
Moreover, data collection using UAV platforms has been widely applied to large-scale crop objects. However, it remains challenging to acquire plants with intact structures at the organ level. Compared with terrestrial laser scanning, this method seems more suited for trait extraction at individual plant levels. High-resolution RGB images also pose a significant challenge to 3D reconstruction methods, requiring substantial computational resources. There is a long way to go in the application of drones in field settings and the extraction of organ-level traits.
# Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
## 3 Results and Discussion
### Comparison of Different Adjustment Schemes  
Since there are no high-level control points in this control network, both the method of this paper and the traditional method use the classical free network adjustment method to calculate the coordinates of the first-phase control points, and the comparison with the conventional method is mainly reflected in the second-phase data processing.
## 4 Numerical Calculation of Slopes Based on Weibull Parameter Random Distribution Model
### Slope Stability Analysis Based on Cusp Mutation Theory
#### 4.3.3 Safety Factor Variation Analysis  
A comprehensive analysis of slope stability during the excavation process, using the strength reduction method, is summarized in Table 3. The results reveal a clear correlation between the factor of safety (FOS), abrupt changes in dissipated energy and displacement, and shifts in the plastic zone distribution. As excavation progresses, the overall stability of the slope significantly declines, as evidenced by a gradual decrease in FOS values. Notably, after the thirteenth excavation stage, the FOS drops to 0.96, indicating a critically stable condition, where the risk of slope failure becomes highly significant.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation** & **Excavation** & \multirow{2}{*}{**u**} & \multirow{2}{*}{**v**} & \multirow{2}{*}{**\(\Delta\)**} & **FOS** \\
**Indicators** & & & & & \\ \hline \multirow{8}{*}{Dissipated energy} & 5 & \(-3\) & \(-5\) & 459 & 1.134 \\  & 6 & \(-4\) & \(-13\) & 4051 & 1.121 \\  & 7 & \(-6\) & \(-21\) & 8835 & 1.096 \\ \cline{1-1}  & 8 & \(-23\) & \(-99\) & 167,291 & 1.075 \\ \cline{1-1}  & 9 & \(-19\) & \(-81\) & 122,275 & 1.063 \\ \cline{1-1}  & 10 & \(-22\) & \(-101\) & 190,243 & 1.042 \\ \cline{1-1}  & 11 & \(-26\) & \(-145\) & 427,067 & 1.034 \\ \cline{1-1}  & 12 & \(-43\) & \(-309\) & 1,941,931 & 1.025 \\ \cline{1-1}  & 13 & \(-23\) & \(-29\) & \(-74\),629 & 0.962 \\ \hline \multirow{8}{*}{Displacement} & 5 & \(-3\) & \(-22\) & 12,852 & 1.134 \\ \cline{1-1}  & 6 & \(-19\) & \(-65\) & 59,203 & 1.121 \\ \cline{1-1}  & 7 & \(-6\) & \(-24\) & 13,824 & 1.096 \\ \cline{1-1}  & 8 & \(-40\) & \(-193\) & 493,723 & 1.075 \\ \cline{1-1}  & 9 & \(-13\) & \(-105\) & 280,099 & 1.063 \\ \cline{1-1}  & 10 & \(-19\) & 653 & 11,458,171 & 1.042 \\ \cline{1-1}  & 11 & \(-60\) & \(-363\) & 1,829,763 & 1.034 \\ \cline{1-1}  & 12 & \(-109\) & \(-954\) & 14,212,900 & 1.025 \\ \cline{1-1}  & 13 & \(-98.8\) & \(-452.3\) & \(-2\),191,909 & 0.962 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Corresponding table of mutation eigenvalues and safety coefficients for different excavation steps.  
Figure 11: Trend of changes in dissipated energy, displacement, and plastic zone mutation characteristic values.
## 3 Collapse Performance of Seismically Isolated Buildings
### Analysis Models of Isolated Buildings
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No. of EQ**} & \multirow{2}{*}{**Ground Motions**} & \multirow{2}{*}{**Stations**} & \multirow{2}{*}{**Duration, s**} & \multirow{2}{*}{**Magnitude**} & \multicolumn{2}{c}{**PGA, g**} \\ \cline{5-6}  & & & & & **X-Comp** & **Y-Comp** \\ \hline
1, 2 & Northridge & Beverly Hills-Mulhol & 29.99 & 6.7 & 0.416 & 0.516 \\
3, 4 & Northridge & Canyon Country-WLC & 19.99 & 6.7 & 0.410 & 0.482 \\
5, 6 & Duze, Turkey & Bolu & 55.9 & 7.1 & 0.728 & 0.822 \\
7, 8 & Hector Mine & Hector & 45.31 & 7.1 & 0.266 & 0.337 \\
9, 10 & Imperial Valley & Delta & 99.92 & 6.5 & 0.238 & 0.351 \\
11, 12 & Imperial Valley & El Centro Array \#11 & 39.035 & 6.5 & 0.364 & 0.380 \\
13, 14 & Kobe, Japan & Nishi-Akashi & 40.96 & 6.9 & 0.509 & 0.503 \\
15, 16 & Kobe, Japan & Shin-Osaka & 40.96 & 6.9 & 0.243 & 0.212 \\
17, 18 & Kocaeli, Turkey & Duze & 27.185 & 7.5 & 0.312 & 0.358 \\
19, 20 & Kocaeli, Turkey & Arcelik & 30 & 7.5 & 0.218 & 0.149 \\
21, 22 & Landers & Yermo Fire Station & 44 & 7.3 & 0.245 & 0.152 \\
23, 24 & Landers & Coolwater & 27.965 & 7.3 & 0.283 & 0.417 \\
25, 26 & Loma Prieta & Capitola & 39.955 & 6.9 & 0.529 & 0.443 \\
27, 28 & Loma Prieta & Gilroy Array \#3 & 39.945 & 6.9 & 0.555 & 0.367 \\
29, 30 & Superstition Hills & El Centro Imp.Co. & 40 & 6.5 & 0.358 & 0.258 \\
31, 32 & Superstition Hills & Poe Road (temp) & 22.3 & 6.5 & 0.446 & 0.300 \\
33, 34 & Cape Mendocino & Rio Dell Overpass & 36 & 7.0 & 0.385 & 0.549 \\
35, 36 & Chi-Chi, Taiwan & CHY101 & 90 & 7.6 & 0.353 & 0.440 \\
37, 38 & San Fernando & LA-Hollywood Stor & 28 & 6.6 & 0.210 & 0.174 \\
39, 40 & Friuli, Italy & Tolmezzo & 36.345 & 6.5 & 0.351 & 0.315 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Properties of selected historical ground motion records. Figure 7: Response spectra of scaled earthquake records for prototype isolated buildings: (**a**) High seismicity regions; (**b**) Low-seismicity regions. adopted from concrete frames of FEMA356 [29] and reinforced concrete moment resisting frames of HAZUS-MH [30]. Such collapse states of reinforced concrete frames include extensive cracking, splice failure in the nonductile column, and severe damage in short columns. Since excessive shear strains can cause problems such as the bounding and buckling, the allowable peak shear strain defined as the collapse state of isolation systems including LRBs and NRBs was conservatively set to 250% following the suggestion of [PERSON] and [PERSON] [31], although modern elastomeric isolating bearing often withstands as much as 400% of the shear strain [32]. The allowable deformation of the FSBs is larger than that of LRB and NRB type isolators.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
## 3 Calculation of PA Efficiency
The coefficients in Equation (21) may change subject to different polynomial models.
## 3 Results  
Table 1 shows the results of the cyclic fatigue and torsional resistance data (mean values and Standard Deviation) for the four tested NiTi file systems. In the cyclic fatigue test, the ETP files showed significantly higher resistance than the PTG (files \(p<0.05\)). The ET files showed significantly higher cyclic fatigue resistance than the PTU files. When comparing the files produced by the same manufacturers, both the heat-treated files (the ETP and PTG) showed a significantly longer lifespan than the non-heat-treated files. The fractured lengths were found to be comparable, respectively (\(p>0.5\)).  
Figure 1: Device for cyclic fatigue resistance tests.  
Figure 2: Device for torsional resistance tests.  
As shown in Table 2, in terms of the torsional resistance, the ETP files demonstrated no significant differences, in terms of resistance, when compared to the PTG (files \(p<0.05\)). Similarly, no significant differences were found between the ET and PTU files. For both manufacturers, the non-heat-treated files showed significantly superior torsional resistance to the heat-treated ones (the ET vs. the ETP, and the PTU vs. the PTG). When the torsional stress was applied, no statistically significant differences were found regarding the time to fracture amongst the four groups; the mean values are reported in Table 2.  
The fractographic analysis of the separated surface is needed to understand the pattern of the leaks that caused the material's failure and the mechanisms involved in the fracture process [23; 24]. The surfaces of the fractured files, caused by the torsional failure, present the typical circular abrasion marks (Figure 3), while the cyclic fatigue failure is characterized by the presence of fatigue striations on the fractured surfaces (Figure 4). In the current study, the SEM evaluation of the files exhibited the typical fractographic features of flexural and torsional fatigue behavior.
# Analytical Solutions of Water Inflow for Foundation Pit in Confined Water Stratum
###### Abstract  
This paper is aimed at the problem of inaccurate calculation of the water inflow of a foundation pit with a suspended water-proof curtain in a confined water stratum. According to the groundwater hydraulics principle, the theoretical model of the seepage field of the foundation pit is established, and the analytical formula of the water inflow for the circular, strip, and rectangular foundation pits is deduced. The seepage path influence coefficient \(\alpha\) related to the water-proof curtain insertion ratio, the thickness of the confined aquifer, and the radius of the foundation pit is proposed and obtained by the positive and negative analysis of numerical simulation. The coefficient is used to correct the equivalent seepage length in the pit and directly affect the calculation result of water inflow. Finally, based on the calculation results of the analytical solution of water inflow, the relationship between the scale of the foundation pit and the water control effect is discussed. The research results show that the theoretical analysis results in this paper are in good agreement with the numerical results and the measured data in the field: when the size of the foundation pit is too large, it is not suitable to use the suspended water-proof curtain as the water control measure.  
foundation pit; seepage fields; water-proof curtain; seepage path influence coefficient Article
## 2 RFM Optimization
### Method
In practice, the RFM models provided by the operators are most often not sufficiently accurate for 3-D extractions or precise georeferencing so that they generally need to be refined [7; 20; 21; 22]. There can indeed be significant bias introduced either by the inaccuracy of the method used or by the reference data used as an input in the determination of the RFM. The RFM may be either determined anew (\"direct refinement\") or refined by determining a corrective term (\"indirect refinement\"). We adopt the latter approach and express the corrective terms in the image space (Figure 1),  
\[\Delta c_{ij}=a_{0}+a_{1}.c_{ij}+a_{2}.r_{ij}, \tag{4}\]  
\[\Delta r_{ij}=b_{0}+b_{1}.c_{ij}+b_{2}.r_{ij}, \tag{5}\]  
where \(a_{k}\), \(b_{k}\) are the adjustment parameters for image \(i\), and \((c_{ij},r_{ij})\) are the image coordinates of point \(j\) on image \(i\). We can then use a least-squares adjustment routine to minimize the re-projection errors of a selection of tie points,  
\[F_{c_{ij}}=\Delta c_{ij}-c^{\prime}_{ij}+c_{ij}, \tag{6}\]  
\[F_{r_{ij}}=\Delta r_{ij}-r^{\prime}_{ij}+r_{ij}, \tag{7}\]  
where \((c^{\prime}_{ij},r^{\prime}_{ij})\) are the image-space coordinates of tie point \(j\) on image \(i\), \((c_{ij},r_{ij})\) are the nominal image coordinates computed with the RFM function model given in Equation (2), and \(\left(\Delta c_{ij},\Delta r_{ij}\right)\) are the bias compensation functions (cf. Equations (4) and (5)). The normal equations then write,  
\[\Delta X=\left(A^{T}A\right)^{-1}\!\!A^{T}B, \tag{8}\]  
where \(A=\frac{\partial F_{c}}{\partial X}\frac{\partial F_{c}}{\partial X}^{T}\) is the Jacobian matrix, \(B\) is the discrepancy vector and \(X\) is the parameter vector \(\left[lon_{ij}\;lat_{ij}\;alt_{ij}\;alt_{ji}\;a_{0i}\;a_{1i}\;a_{1i}\;a_{2i}\; b_{0i}\;b_{1i}\;b_{2i}a_{0i}\right]^{T}\). The method described here is implemented as an open source module of the COSI-Corr software package. This module allows: (1) to correct the RFM bias of a single image using GCPs; (2) to correct the RFM bias of a large block of overlapping images, and (3) to incorporate the bias compensation into the original supplied RFMs, similar to the method proposed in [23]. This package can be employed to process any type of image delivered with RFMs.
# A Physicist's View on Partial 3D Shape Matching
## 5 Discussion
### Computing Time  
We claimed that the framework we propose in which vertices of two meshes are first characterized using signature vectors and then placed into correspondence using optimal possibly partial transport enables a robust solution to the shape comparison problem. Such a framework, however, would be nonpractical if it proved to be computationally too expensive, a common criticism for methods that rely on optimal transport. We tracked the running times for our technique for the many experiments mentioned above to make sure that this is not the case. As we solve a nonlinear system of equations iteratively at each inverse temperature, we first note that our implementation of optimal transport heavily relies on linear algebra. Each iteration requires the solution of a linear system of equations, the linearized SPA system, which can be solved either directly or iteratively. Thus, it is anticipated that parallelization will have a significant positive impact on the entire algorithm. As a result, we created two versions of our comparison process: one that utilizes GPUs and maybe multiple CPUs. Both largely rely on the processor-specific optimized BLAS and LAPACK libraries. For the direct solver of the SPA system, we used the LAPACK routine dsysv on CPU, and the CUSOLVER routines DnDgetrf and DnDgetrs on GPU, based on CUDA. For the alternate iterative solver, we implemented a conjugate gradient solver with an incomplete LU (ILU) preconditioner. The distributions of the computing times for the different implementations of our framework (direct or iterative solver, and CPU or GPU implementations) are shown in Figure 9, while the means of those distributions are provided in Table 6. As expected, we observe a significant speedup when the comparisons are run on multiple processors: factors of 7.8 and 9.2 for the direct and for the iterative solver on an 8 CPUs/16 threads system, respectively, and factors of 28 and 241 for the same solvers on GPU. The improvement in computing time is much greater for the iterative solver. This is expected, as direct dense solvers are not yet as optimized on GPUs than they are on CPUs. We do observe that the iterative solvers are faster than the direct solver, and we advocate using the former.  
Figure 9: Boxplots of the computing times (wall clock times) of the optimal transport part of our framework for shape comparison. We compare the direct solver and the indirect solver for the linearized SPA solver, on either a single CPU (CPU1), on multiple CPUs (CPU16, i.e., Intel Core i7 processor with 8 cores/16 threads running at 4.00 GHz), and 64 GB of memory, or on a GPU system (GPU, i.e., Linux server, with Xeon Platinum 8168 CPU at 2.7 GHz, and a NVIDIA RT2080 Ti GPU card with 11 GB of memory). We included all pairwise comparisons of the shapes of TOSCA8 that are represented with around 3400 vertices (i.e., all shapes except the seahhorses), with the exclusion of self-comparisons that were found to be significantly faster, for a total of 861 comparisons. The mean values for all distributions are given in Table 6.
## 2 Background
### Internet of Things (IoT)  
Internet of things (IoT) is a branch of distributed systems that tends to allow devices to communicate automatically with minimal or no human intervention, as shown in Figure 2, and the IoT is a system of interrelated computing devices, sensors, and CPUs that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interactions [8]. An IoT-based hydroponics system is designed to monitor and/or control a hydroponic system with the help of a server. The system communicates with the server through Ethernet, Wi-Fi, GPRS, or satellite radio communication.  
As shown in Figure 2, IoT systems heavily utilized embedded devices, while ubiquitous devices are at the core of this system. Embedded devices are battery-powered devices categorized by resource constraints, and they suffer from inadequate space, speed, and small battery life because of their size. When these devices are employed in a system. A careful design has to be made to supplement these deficiencies. In this paper, we design an energy-aware model using an energy harvesting technique to handle the power requirement of the system, whereas the space constraint was neutralized by compressing our model into smaller one using knowledge distillation.  
Figure 1: Convolutional neural network adopted from superdatascience.  
Figure 2: Typical IoT system.
## 7 Simulation Results
### Traction Management in Wheel-Slip Condition  
In the last scenario (Figure 15), the aim was to investigate the response of the system with some parameters changing during the simulation. In particular, the friction coefficient was sharply decreased to show how the VCU handles wheel slipping (as described in Section 3).  
Figure 15a reports the reference speed of the vehicle (blue line), the actual speed (red line), and the measured speed (yellow line). The actual speed is the real one, which is represented by the time derivative of the position of the vehicle. The measured speed, instead, was obtained by considering the rotational speed of the wheelsets multiplied by the rolling radius.  
Figure 15 shows the motor drives torques and the variation of the friction coefficient which is provided as an input to the system. The motor torques are depicted in blue (line and dashed line for drive one and drive two, respectively) and the friction coefficient is represented in red.  
Figure 14: Simulation results in terms of speed of the vehicle and internal force reference (**a**,**c**), motor drive torques and pressures of the pneumatic brake system (**b**,**d**). The results are related to the acceleration phase (**a**,**b**) and deceleration phase (**c**,**d**).
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 4 Results
### The Sustainability of E-Health Solutions
#### 4.2.2 Sustainability Dimensions
[PERSON] and [PERSON] [25] proposed a judgment-decomposition analytic hierarchy process (JD-AHP) approach to help decision-makers in assessing the sustainability of smart e-health applications, such as the smart body analyzer. The main factors that critically influence the sustainability of a smart technology application are unobtrusiveness, supporting online social networking, relaxation of the related medical laws, future e-health market size, correct identification of need and situation. [PERSON] [23; 24] discussed the sustainability of electronic health records (EHRs). The first study discussed sustainability issues of three different models of EHR from management (i.e., conflicts of interests and access restriction for some special cases), legal (i.e., patients' bill of rights), and technological (i.e., semantic interoperability standards) aspects. Furthermore, the study proposed the Independent Health Record Banks (IHRBs) model that would help in sustaining the her, while the following paper [24] discussed sustainability considerations of IHRBs, which are legal and ethical (e.g., authentication, access control, privacy, data integrity), business (i.e., revenue and reducing the cost of data archiving), and technological (i.e., information exchange standards). These considerations act as guidelines for the new legislation needed for IHRBs to be established and operate successfully.