esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results and Discussion
### Analysis of Numerical Simulation Results of Pilot Tunnel Excavation
#### 4.2.3 Deformation Analysis of Supporting Structure  
Figure 13 shows the deformation of the normal supporting anchor bolt and the advanced supporting pipe after pilot tunnel excavation. Due to the influence of pilot tunnel excavation unloading, after the surrounding rock stress was released, part of the surrounding rock pressure directly acted on the anchor bolt connected with it, resulting in varying degrees of deformation of the anchor bolt. On the whole, the deformation of the anchor bolt was small, and the magnitude was basically 10\({}^{-4}\) m. Within 30 m of the middle pilot tunnel and the left pilot tunnel, there were some bolts with large deformation locally, with a maximum deformation of 183 mm. The bolts may have reached the yield state or even been damaged. The deformation of the advance support conduit was small, and the maximum deformation was located on both sides of the left and right pilot tunnels, with the maximum deformation of 30 mm. The permissible deformation of the advance support conduit was  
Figure 11: Vault settlement of middle pilot tunnel during pilot tunnel excavation.  
50 mm. Therefore, higher design standards and parameters should be used to improve the yield strength of the bolt when the bolt was used to strengthen the surrounding rock at the portal section of the pilot tunnel.
# Research Advances in Large Deformation Analysis and Applications of the Material Point Method
## 8 Conclusions and Outlook
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Method** & **Key Innovations** & **Limitations** \\ \hline \multirow{6}{*}{[PERSON] et al. (1994) [13]} & \multirow{6}{*}{Standard MPM} & Firstly extended the FLIP method to solid mechanics, establishing a coupling framework between material points and background grids. & Significant numerical dissipation. Incomplete contact algorithms. \\ \hline \multirow{6}{*}{[PERSON] and [PERSON] (2003) [15]} & \multirow{6}{*}{multirow{6}{*}{multi-mesh MPM}} & Assigning independent background grids to different materials to address material interface issues. & Neglecting friction; inability to ensure non-penetration conditions at the particle level. \\ \hline \multirow{6}{*}{[PERSON] and [PERSON] (2004) [30]} & \multirow{6}{*}{GIMP} & Introducing generalized interpolation shape functions to reduce grid-crossing noise. & The computational cost is slightly higher than that of the standard MPM. \\ \hline \multirow{6}{*}{[PERSON] et al. (2006) [28]} & \multirow{6}{*}{explicit material point FEM} & Automatically converting FEM nodes to MPM particles in highly deformable regions. & The treatment of the coupling interface is complex. \\ \hline \multirow{6}{*}{[PERSON] et al. (2009) [46]} & \multirow{6}{*}{adaptive MPM} & Automatically splitting material points based on strain thresholds to improve simulation accuracy. & Particle splitting increases computational load, necessitating the optimization of multiple local background grids. \\ \hline \multirow{6}{*}{[PERSON] et al. (2011) [104]} & \multirow{6}{*}{CFEMP} & Coupling finite element truss elements with the material point method on a unified background grid to enhance computational efficiency and accuracy. & Consistent meshing between the FEM domain and the MPM domain is required, which may lead to over-meshing in the FEM domain. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of main variants and developmental history of MPM.
# Performance Evaluation of IMERG GPM Products during Tropical Storm Imelda
## 2 Materials and Methods
### Methodology  
Tropical Storm Imelda predominantly affected the southeastern counties of Texas. To better track the devastation of the event, this study focuses on the precipitation downpour around the Houston and Galveston areas. IMERG products at Early-, Late-, and Final-run stages of the event were compared to the NCEP Stage-IV gridded radar product at an hourly temporal resolution for eight consecutive days, starting from 14 September and ending on 21 September, during Tropical Storm Imelda. Three days before and after landfall (17 September 2019) were included in the analysis to reduce the impact of the presence of the extreme storm event on the probabilistic statistical indices.  
Euclidean distance was used to identify the nearest radar grid to each satellite grid. For each grid of the satellite product, a nearest radar grid was found using the advantage of the satellite product's spatial spacing being three times higher than that of the radar grids. Subsequently, statistical metrics were calculated to compare the precipitation estimates of the IMERG grids and their nearest radar grids at an hourly temporal resolution. There are 40,434 NWS/NCEP radar grids versus 6577 IMERG satellite grids in Texas. However, only 2570 grids of Stage-IV radar data and 418 IMERG grids were used that cover the analysis area spanning across 17 counties located in southeast Texas.
# Network Structure and Synergy Characteristics in the Guangdong-Hong Kong-Macao Greater Bay Area
## 4 Results Analysis  
To uncover the flow patterns and synergistic dynamics among cities in the Greater Bay Area, this study constructs a dual-factor linkage network integrating enterprise investment and liner transportation data. This framework facilitates a comprehensive analysis of the interactive structure between logistics and capital flows. During data processing, topological characteristics of the network--including node centrality, network density, and clustering coefficients--were quantitatively evaluated using the NetworkX 3.2.1 library in Python 3.12.6 and Gephi 0.9.2 software. For enhanced spatial visualization and interpretability, ArcGIS 10.8 was employed to spatially map the intercity linkages, while Origin 2024 was used to illustrate indicator trends and comparative analyses.
## 3 Experimental Results and Analysis  
### Study Area and Data  
Xuzhou is the central city of the Huaihai Economic Zone, and its urbanization development is one of the important indicators of the degree of development of the Huaihai Economic Zone. Xuzhou is located in the southeastern part of the North China Plain, between longitude 116\({}^{\circ}\)22\({}^{\prime}\)-118\({}^{\circ}\)40\({}^{\prime}\) E and latitude 33\({}^{\circ}\)43\({}^{\prime}\)-34\({}^{\circ}\)58\({}^{\prime}\) N, covering an area of 11,765 km\({}^{2}\). Xuzhou City is dominated by plains, which account for more than 90% of the area, with a few hills and mountains in the central and eastern parts of the city, as shown in Figure 2. (The map of China shown here is based on the standard map with review number GS (2016) 2923 downloaded from the website of the Standard Map Service of the Ministry of Natural Resources, with no modifications to the base map). There is a large amount of farmland distributed in the central part of Xuzhou City, and the confusion between impervious surfaces and bare soil is an urgent problem for impervious surface extraction.  
The extraction of built-up areas was completed using the integration of DMSP-OLS-like data. The Landsat@ OLI/TIRS C2L2 remote sensing image data covering Xuzhou City in September and October from 2013 to 2022 were downloaded from USGS, and the overall cloudiness of the study area was ensured to be less than 20% to ensure the extraction accuracy of the impervious surfaces. [PERSON] et al. constructed a feature set by spectral and texture features, and then used nighttime lighting information and spectral information to select positive and negative samples of impervious surfaces to complete the extraction of impervious surfaces. The image is spliced and cropped to obtain the image map of Xuzhou City. Landsat@ OLI/TIRS C2L2 10-band images were used to complete the inversion of surface temperature.
###### Abstract  
Semantic segmentation of remote sensing images is an important technique for spatial analysis and geocomputation. It has important applications in the fields of military reconnaissance, urban planning, resource utilization and environmental monitoring. In order to accurately perform semantic segmentation of remote sensing images, we proposed a novel multi-scale deep features fusion and cost-sensitive loss function based segmentation network, named MFCSNet. To acquire the information of different levels in remote sensing images, we design a multi-scale feature encoding and decoding structure, which can fuse the low-level and high-level semantic information. Then a max-pooling indices up-sampling structure is designed to improve the recognition rate of the object edge and location information in the remote sensing image. In addition, the cost-sensitive loss function is designed to improve the classification accuracy of objects with fewer samples. The penalty coefficient of misclassification is designed to improve the robustness of the network model, and the batch normalization layer is also added to make the network converge faster. The experimental results show that the classification performance of MFCSNet outperforms U-Net and SegNet in classification accuracy, object details and prediction consistency.  
S 2019 9 4043 10.3390/app9194043 10.3390/app9194043 1
## 3 Experimental Results and Analysis
### Fracture Toughness of Coal
#### 3.1.2 Variation of Fracture Toughness with Loading Rate  
According to Figure 5a, for the Crack-arrester coal sample, the fracture toughness prior to immersion is measured at 6.31 MPa\(\cdot\)mm\({}^{1/2}\) (0.0002 mm/s), 6.51 MPa\(\cdot\)mm\({}^{1/2}\) (0.0005 mm/s), 7.67 MPa\(\cdot\)mm\({}^{1/2}\) (0.002 mm/s), and 7.78 MPa\(\cdot\)mm\({}^{1/2}\) (0.005 mm/s). There is no significant change in fracture toughness at low loading rates. However, when the loading rate is increased tenfold to 0.002 mm/s, the fracture toughness rises by 21.66% compared to the value at 0.0002 mm/s, indicating a significant change. As the loading rate continues to increase, the rate of change gradually stabilizes. For coal immersed for 30 days, the fracture toughness values are 4.15 MPa\(\cdot\)mm\({}^{1/2}\) (0.0002 mm/s), 4.52 MPa\(\cdot\)mm\({}^{1/2}\) (0.0005 mm/s), 4.98 MPa\(\cdot\)mm\({}^{1/2}\) (0.002 mm/s), and 5.69 MPa\(\cdot\)mm\({}^{1/2}\) (0.005 mm/s). While the fracture toughness exhibits considerable changes at low loading rates, it increases more slowly as the rate rises. From Figure 5b,c, it is evident that the trend of change for the Crack-splitter and Crack-divider types is consistent with that of the Crack-arrester type, characterized by a gradual increase in fracture toughness with increasing loading rate. The average values of all data plotted as a function of loading rate are illustrated in Figure 5d, and this trend can be described by Equation (5).  
\[y=0.4568\ln(x)+8.9771 \tag{5}\]The fracture toughness at a loading rate of 0.005 mm/s increased by 27.28% compared to 0.0002 mm/s, indicating a significant improvement in the coal's resistance to fracture. The slope of the curve suggests that the change in coal fracture toughness is more gradual when immersed in mine water than when not immersed. This is primarily attributed to the weakening of the coal matrix and layered structure by mine water, which results in reduced structural differences. The effect of loading rate on coal results in increased mechanical strength. [PERSON] et al. [35] revealed that peak stress and elastic modulus decreased with increasing loading rates. [PERSON] et al. [36] argue that when the strength of coal samples exhibits a nonlinear characteristic of initially increasing and then decreasing with elevated loading rates, it is due to the inhibiting effect of high loading rates on crack development; this leads to lower strength due to localized fracture surfaces. However, the coal samples displayed overall softening and no localized fracture surfaces under immersion in mine water, indicating a positive correlation between fracture toughness and loading rate.
## 3 Magnetic Characterization Instruments
### Giant Magnetoresistive (GMR) Sensors  
Giant magnetoresistive sensors are compact, economical, high-sensitivity, large-scale, room-temperature devices for catalyst sensing applications [93; 94]. In addition, they demand low power consumption, high thermal stability, and a relatively simple structure [95]. Moreover, their transduction efficiency is high compared to other magnetic sensor topologies, especially when the magnetic field is low [59]. Referring to their structure, GMR sensors consist of thin magnetic films, separated by a non-magnetic conducting layer of CuAgAu. The GMR sensitivity is in the plane of the film. According to [95], the thin film GMR sensor can be categorized into three different categories: (1) the sandwich, (2) the multilayer, and (3) the spin valve. The latter two present higher sensitivity compared to the sensor of the first category. The GMR sensor can be used, with success, in the field of catalytic nanoparticle characterization but also in applications like hard disk drives and magnetic recording [96]. However, the level of the noise of this device is high, higher than SQUID noise (0.1-10 pT/\(\sqrt{\mathrm{Hz}}\)) and more specifically in the range of 0.1-1 n\(\mathrm{T}\)/\(\sqrt{\mathrm{Hz}}\)[37; 97; 98]. According to [97], compared to SQUIDs magnetometers,  
Figure 1: Schematic diagram that describes the structure of a Faraday rotation magnetometer [85].  
GMR sensors could be antagonistic candidates if the field gain or magnetoresistance is further increased.  
The most significant studies in the field of nanoparticle characterization using GMR sensors are summarized below. Particularly, in [37; 59], the GMR sensor is proposed for magnetorelaxometry measurements of nanoparticles. In [93], MnFe\({}_{2}\)O\({}_{4}\) nanoparticles are characterized using a GMR sensor and a direct current measurement method. In [94], the properties of supermagnetic nanoparticles are detected utilizing a GMR sensor and permanent magnets. In [95; 96], a GMR sensor in the Wheatstone bridge is utilized for the characterization of iron oxide nanoparticles. In [99], the GMR sensor is used for medical purposes to detect the influenza A virus.
## 2 Related Work
### Model Interpretation in Medical Domain  
In the medical domain, researchers acquire the attention network and gradient to interpret segmentation models given images of magnetic resonance imaging (MRI), CT and skin lesions. SAUNet exploits the attention network to extract the global context of MRI segments [15]. Factorizer [16] solves non-negative matrix factorization as a self-attention role to segment brain tumors. The attention of both SAUNet and Factorizer is utilized to interpret the model. Moreover, gradient-based saliency maps are employed to interpret a skin cancer classifier [17]. Although they claim that their interpretations assure the decision of their model is similar to a human's, their interpretation methods are limited to the specific task, specifically the images.  
Given the time series data in the medical domain such as electrocardiogram (ECG), NEVES [24] and LIMESegment [25], we find the most important subset of the input using LIME [20]. NEVES divides the signal by the same interval, while LIMESegment separates it by the change point, which is the most different point between window slides. Although they attempt to interpret the model given medical time series data, they are limited to only consider the aspect of time axis, whereas the frequency is also the important role to diagnose in practice.
## 3 Global Loss Model of PMSM
### Fundamental and Harmonic Components of PWM Output Voltage
 \tag{19}\]  
where \(A_{00}\) is the DC offset; \(A_{0n}\) and \(B_{0n}\) are the fundamental component and base-band harmonics; \(A_{m0}\) and \(B_{m0}\) are the carrier harmonics; \(A_{mn}\) and \(B_{mn}\) are side-band harmonics. The fundamental component and harmonic components of the inverter PWM output signal can be calculated by the double Fourier integral analysis as:  
\[A_{mn}+jB_{mn}=\frac{1}{2\pi^{2}}\int_{-\pi}^{\pi}\int_{-\frac{\pi}{2}(1+M\cos y )}^{\frac{\pi}{2}(1+M\cos y)}U_{dc}\mathrm{e}^{[j(mx+ny)]}dxdy \tag{20}\]  
Substituting Equation (15) into Equations (19) and (20), the fundamental and harmonic components of the inverter phase-leg PWM output voltage can be expressed as:  
\[\left\{ \begin{array}{ll}u_{az}(t)=\frac{1}{2}U_{dc}+\frac{1}{2}U_{dc}M\cos \omega_{0}t+\frac{2U_{dc}}{\pi}\sum\limits_{m=1}^{\infty}\frac{1}{n}l_{0}(m \frac{\pi}{2}M)\sin(m\frac{\pi}{2})\cdot\cos[m(\omega_{c}t+\theta_{c})]\\ +\frac{2U_{dc}}{\pi}\sum\limits_{m=1}^{\infty}\sum\limits_{\begin{subarray}{c} n=\infty\\ n=-\infty\end{subarray}}^{n=\infty}\left\{\frac{1}{m}J_{n}(m\frac{\pi}{2}M)\sin[(m+n )\frac{\pi}{2}]\cdot\cos[m(\omega_{c}t+\theta_{c})+n\omega_{0}t]\right\}\\ n=-\infty\end{array} \right.\]  
\[ \begin{array}{ll}u_{bz}(t)=\frac{1}{2}U_{dc}+\frac{1}{2}U_{dc}M\cos(\omega _{0}t-\frac{2}{3}\pi)+\frac{2U_{dc}}{\pi}\sum\limits_{m=1}^{\infty}\frac{1}{n }l_{0}(m\frac{\pi}{2}M)\cdot\sin(m\frac{\pi}{2})\cdot\cos[m(\omega_{c}t+ \theta_{c})]\\ +\frac{2U_{dc}}{\pi}\sum\limits_{m=1}^{\infty}\sum\limits_{\begin{subarray}{c} n=\infty\\ n=-\infty\end{subarray}}^{n=\infty}\left\{\frac{1}{m}J_{n}(m\frac{\pi}{2}M)\cdot \sin[(m+n)\frac{\pi}{2}]\cdot\cos[m(\omega_{c}t+\theta_{c})+n(\omega_{0}t- \frac{2}{3}\pi)]\right\}\\ n\
eq 0\end{array}  \tag{21}\]  
where the constant term \(\frac{1}{2}U_{dc}\) is caused by the output voltage definition with respect to the negative DC bus. \(J_{n}\) (\(\underline{z}\)) is the Bessel function of the \(n\)th order. The fundamental voltage creates the fundamental current in the PMSM, which will bring the fundamental motor loss. Furthermore, the harmonic current caused by the harmonic components of inverter output voltage will generate harmonic motor losses.
[16] found that PE was more effective for engaging in MVPA than other segments during the school day. Moreover, a metanalysis by [PERSON] et al. [17] concluded that school recess interventions show promise for increasing MVPA. Despite the above-mentioned health effects and the feasibility of engaging in PA within the school context, some studies have reported low levels of PA during both PE classes and school recesses. For instance, a meta-analysis published by [PERSON] et al. [18] showed that, on average, children engaged in MVPA for 33.0% of their PE classes and were sedentary for 35.9% of their PE classes. [PERSON] et al. [19] also found low levels of MVPA during school recess are in children, a low percentage of children meet the MVPA recommendation during school recess, and overweight/obese children show still lower values in MVPA. Similarly, some meta-analyses have found that the proportion of PE lesson time during which young population are engaged in MVPA is typically less than 50% of the recommendation suggested by the World Health Organization (WHO) [20; 21]. However, only a few studies have analyzed the association between excess weight and physical fitness in relation to PA at school setting. For instance, a systematic review by [PERSON] et al. [22] found several factors associated with lower rates of obesity or obesity trajectory, such as increased minutes of recess, meeting with recommended recess and PE time, among others. Regarding physical fitness, [PERSON] et al. [23] found a higher performance in four different physical fitness tests in those schoolchildren physically active during PE and in school recess. Furthermore, [PERSON] et al. [24], showed that young people who were sedentary for more than 15 min during recess time presented lower odds of cardiorespiratory fitness. Examining the association between PA intensity during PE classes or school recesses and excess weight or global physical fitness and in children could be of great interest and importance, especially for future intervention programs or public policies related to PA. This relevance lies in three main reasons which reinforces the need of this study: first, that children spend a large part of their time in-and-around the school environment; second, the crucial role of maintaining an adequate weight status and a high physical fitness in promoting and maintaining health; and third, the low prevalence found of children meeting with the PA recommendation in Spain and in the Region of Murcia. Thus, our hypothesis is that schoolchildren who apply a higher PA intensity in PE classes and during recesses would have lower odds of having excess weight and higher odds of having high global physical fitness compared to those who apply a lower intensity of PA. Thus, the aim of this study was to explore the association between intensity of PA in PE classes or school recesses and excess weight or global physical fitness in a sample of Spanish schoolchildren.
## 2 Materials and Methods
### Data Analysis Methods
#### 2.3.4 Structural Equation Modeling  
Structural Equation Modeling (SEM), as a statistical approach that integrates the strengths of confirmatory factor analysis and path analysis, demonstrates unique value in investigating multivariate interactions within complex systems [30]. In this study, SEM was employed to construct a model of the multifactorial interactions and coupling relationships between GPP and meteorological variables. By formulating path hypotheses, the analysis focused on elucidating the direct effect of VPD on GPP, while also uncovering indirect pathways among various meteorological factors. All variables were standardized (mean = 0, SD = 1) before analysis. Raster data were resampled to match GPP resolution (500 m), and pixels with missing values were excluded. Model fitting was performed annually (2000-2020) using the lavaan package in R. Standardized path coefficients (\(\beta\)) were calculated to quantify driver contributions, with significance determined at \(p\) < 0.05 (*). The semPlot package was used for visualization. Analyses were conducted in R 4.4.1. During model validation, modification indices were utilized to optimize parameter estimation, and standardized path coefficients were applied to quantify the relative contributions of different driving factors.