esa-sceva/satcom-chunk-collection huggingface dataset:
# Digital Systems in Smart City and Infrastructure: Digital as a Service
## 2 Research Background
### Big Data  
A smart city infrastructure is based on the concept of the digital city that integrates the Internet of Things and cloud computing technologies [24], to enable automatic control and intelligence services for people and logistics in physical cities; the application of Big Data to smart cities includes an extensive sensor networks with problems and challenges that can be mitigated deploying cloud computing and data mining. There is a gap in combining the current state of the art in an integrated Big Data and Machine Learning analytical applications for IoT and Smart City application framework [25] that would help reducing development costs and enable new analytical services for citizens and urban decision makers. Big data analytics have a huge potential to enhance smart city services [26]; however, the understanding of the requirements that support its implementation and applications are key to the effective analysis and utilization to deliver success in smart city service domains. The combination of the IoT and Big Data is still an immature unexplored research with new and interesting challenges for achieving the goal of future smart cities [27]; the difficulties focus primarily on the awareness of the main smart environment characteristics and the collaboration between the technology and the business services that enable smart cities to improve their vision and principles.  
Big Data Smart City applications and potentials will be able to understand its environment through analyzing its data in order to immediately make changes to solve issues and to improve the residents' quality of life [28]; data requires to be collected from all networks, devices and sensors embedded in its infrastructure; it becomes valuable by passing different processing stages, by applying advanced analyzing Big Data platforms on data and finally by exporting the data in useful platform in order to improve any city's system application. Urban big data streamed from city sensors will become a source for smart city information [29] that will enable longer term strategic planning instead to short-term thinking about how cities function and can be managed. Two closely related emerging technology frameworks, the Internet of Things and Big Data can make smart cities efficient and responsive [30]; however, these technological advances require to be integrated in terms of physical infrastructure where digital technologies translate into better public services for inhabitants and better use of resources while reducing environmental impacts.
# Locomotion of Self-Excited Vibrating and Rotating Objects in Granular Environments
## 3 Results and Analysis
### Coupling Oscillatory Translation and Oscillatory Rotation Speed
#### 3.2.1 The Effect of Rotation Amplitude, \(\Omega\)  
We fixed the vibration amplitude at \(A=0.03\) m, the frequency at \(f=3\) Hz, and varied the angular velocity amplitude \(\Omega=\pm 10\times 2^{n}\) rad/s, with \(n=0,1,2,...,7\). Plots of the SO displacement vs. time for negative and positive \(\Omega\) are shown in Figure 6a,b. The mean rate of displacement, \(U_{z}=\langle d(\Delta z)/dt\rangle\) depends sensitively on the rotation angular velocity. Figure 6c,d summarize our following observations. (i) As established in Section 3.2, the SO rose and sank for \(\Omega\) negative or positive, respectively. (ii) The rising and sinking rates increased with \(\mid\Omega\mid\). (iii) When \(\mid\Omega\mid\) exceeded 320 rad/s, the SO either broke the top surface or stabilized very close to the bottom of the container.  
The relationship between the rising and sinking rates and \(\Omega\) can be fitted well by  
\[U_{z}=\pm C_{1}(1-e^{-C_{2}\mid\Omega\mid}) \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **(t = 0 \(\rightarrow\) t = T/4)** & **(t = T/4 \(\rightarrow\) t = T/2)** & **(t = T/2 \(\rightarrow\) t = 3T/4)** & **(t = 3T/4 \(\rightarrow\) t = T)** \\ \hline Vibration direction & \(+y\) direction & \(-y\) direction & \(-y\) direction & \(+y\) direction \\ Rotation direction & clockwise & counter-clockwise & counter-clockwise & clockwise \\ Resultant friction direction & \(+z\) direction & \(+z\) direction & \(+z\) direction & \(+z\) direction & \(+z\) direction \\ \hline \hline \end{tabular}
\end{table}
Table 3: The direction of vibration and rotation.  
Figure 6: The displacement of the SO in the \(z\) direction for fixed translational amplitude \(A=0.03\) m and oscillatory frequency \(f=3\) Hz. (**a**) The rising height for \(\Omega<0\); (**b**) the sinking depth for \(\Omega>0\); (**c**) the rising rate for \(\Omega<0\); (**d**) the sinking rate for \(\Omega>0\). The lines in (**c**) and (**d**) represent exponential fits for \(U_{z}(\Omega)\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**T (s)** & **t = 0** & **t = T/4** & **t = T/2** & **t = 3T/4** & **t = T** \\ \hline Displacement y (m) & y = 0 & y = A & y = 0 & y = \(-\)A & y = 0 \\ Rotation velocity \(\omega\) (rad/s) & \(\omega=\Omega\) & \(\omega=0\) & \(\omega=-\Omega\) & \(\omega=0\) & \(\omega=\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The corresponding values of vibration displacement and rotation velocity at one period.  
where the \(C_{1}\) and \(C_{2}\) are different constants for the rising and sinking processes. These fits are shown in Figure 6c,d.
## 3 Coal Pillar Load and Stress Distribution Law of Working Face in End-Mining Stage
### Load Analysis of RCP in Working Face Under the Influence of Mining  
During the mining process, the roof collapses to form a caving arch, where the arch feet acts on the coal in front of the working face and the collapsed gangue in the goaf. As the working face advances forward, a caving arch is formed, as illustrated in Figure 5.  
Figure 5 reveals that the load acting on the coal pillar in front of the working face is divided into two components: the weight of the rock mass within the roof caving arch behind the working face, and the load transferred following the excavation of the RR.  
The load \(P_{1}\) transferred from the caving arch above the working face to the coal pillar can be determined by Equation (2) [26]:  
\[P_{1}=\gamma\frac{4h(L+D)-W^{2}\cot\varphi_{k}}{8} \tag{2}\]  
where \(L\) represents the width of the RCP in the working face, m; \(W\) is the span of the caving arch, m; \(\varphi_{k}\) is the internal friction angle of the roof, \(34^{\circ}\); \(D\) is the support control distance of the working face, 5.7 m; and \(f_{k}\) is the rock integrity coefficient, \(f_{k}=\sigma_{c}/10=3.5\).  
The span of the caving arch is:  
\[\left\{\begin{array}{l}W=D+2M\tan(45^{\circ}-\frac{\varphi_{k}}{2})\\ f_{k}=\sigma_{c}/10\end{array}\right. \tag{3}\]  
The height of the caving arch is:  
\[H=\frac{W}{2f_{k}} \tag{4}\]  
where \(\sigma_{c}\) represents the uniaxial compressive strength of the immediate roof, which is 25.7 MPa.  
Through the application Equations (2)-(4), the load \(P_{1}\) acting on the coal pillar on one side of the goaf can be obtained as:  
\[P_{1}=\gamma\frac{4 hf_{k}(L+D)-W^{2}}{8f_{k}} \tag{5}\]  
Figure 5: The schematic diagram of caving arch above the goaf of the working face. B—solid coal; \(a\)—RR width, 4.5 m; \(b\)—RR height; \(L\)—width of RCP in working face; \(D\)—support control distance, 5.7 m; H—falling arch height; M—mining height, 2.9 m.  
Similarly, the load \(P_{2}\) transferred to the coal pillar by the RR is:  
\[P_{2}=\gamma\frac{4 hf_{k}(L+a)-a^{2}}{8f_{k}} \tag{6}\]  
where \(a\) represents the width of the RR, which is 4.5 m.  
In summary, the total load \(P\) acting on the coal pillar in front of the working face is:  
\[P=P_{1}+P_{2}=\gamma\frac{4 hf_{k}(2L+D+a)-a^{2}-W^{2}}{8f_{k}} \tag{7}\]  
From Equations (2)-(7), the average stress \(\overline{\sigma}\) in the coal pillar in front of the working face can be obtained as follows:  
\[\overline{\sigma}=\gamma\frac{4 hf_{k}(2L+D+a)-a^{2}-W^{2}}{8 Lf_{k}} \tag{8}\]  
where \(\gamma\) is the weight of the overlying strata, 25 kN/m\({}^{3}\); \(h\) is the depth of the coal seam, 155 m; \(a\) is the RR width, 4.5 m; \(L\) is the width of RCP in working face; \(D\) is the support control distance, 5.7 m; M is the mining height, 2.9 m; and \(W\) is the span of the caving arch, m.
## 1 Introduction
### Research Gap and Objectives  
While such studies offer valuable qualitative insights into fault segmentation and paleo-rupture behavior, they do not capture the contemporary deformation processes that are critical for assessing ongoing strain accumulation. To address this gap, the present study utilizes dense GNSS velocity fields to quantify contemporary strain rates across the Yangsan Fault system. By analyzing spatial patterns of crustal strain, we aim to assess the degree of stress buildup in different fault segments and evaluate their seismogenic potential. This geodetic approach complements previous geomorphic and paleoseismic investigations and contributes to a more comprehensive understanding of the fault's current tectonic state.  
Together, these studies reveal that the Yangsan Fault is not only structurally segmented and long-lived but also capable of producing large-magnitude earthquakes despite its intraplate setting. Its complex deformation history, diverse lithological controls, and evidence for both seismic and aseismic behavior make it an ideal natural laboratory for strain-based seismic risk analysis.  
In light of these insights, the present study aims to quantify crustal strain evolution in southeastern Korea over a two-year period using a high-density GNSS network anchored to national geodetic control points. By computing strain tensors from GNSS baseline vectors through elastic theory and least-squares estimation, we extract spatial and temporal variations in principal strain, maximum shear, and areal dilation. Special attention is directed toward the Yangsan Fault and its surroundings, allowing us to evaluate the consistency between geodetically inferred stress accumulation and known patterns of historical and paleoseismic activity.  
This integrative approach--linking geodetic strain modeling with fault structural analysis and seismic records--contributes to the development of early warning indicators and long-term seismic forecasting models in stable continental regions. It also enhances our understanding of fault zone processes that govern earthquake recurrence and deformation localization, offering crucial insights for national-scale seismic hazard assessment in the Korean Peninsula.
## 2 Materials and Methods
### Dimensionless Parameters  
The variables in this experiment are the size of the bubble and the size of the tube, where the size of the tube includes length and diameter. In the experiment, three different experimental voltages were used to change the size of the bubble: 60 V, 100 V, and 150 V. The average maximum diameter of the bubble in the free field corresponding to the different voltages was 11.5 mm, 18.6 mm, and 29.2 mm, respectively. The lengths of the experimental circular tubes were 100 mm, 200 mm, 400 mm, and 500 mm. In this paper, the 100 mm tube is defined as a short tube, the 200 mm tube is defined as a longer tube, and the 400 mm and 500 mm tubes are defined as long tubes. The vertical axis is the diameter of the circular tube, and the horizontal axis is the initial bubble generation position. For example, a circular tube with a length of 100 mm and a diameter of 6 mm, that is, the left-end tube length is 10 mm and the right-end tube length is 90 mm, is connected with a flange connector and combined with the bubble generation bracket to form a complete experimental device, as shown in the Figure 2.  
The key parameters in the experiment are shown in Figure 3. At the initial bubble generation moment, the time (\(t\)) is set to 0, and the oscillation period of the bubble generated by the electric spark is \(T_{0}\). The axial position along the tube is represented by the \(x\) coordinate, the right end of the tube is \(x=0\), the left end is \(x=L_{t}\), and the inner diameter of the tube is \(D_{t}\).  
The horizontal coordinate of the initial bubble is \(X_{B}\), and the dimensionless parameter of the initial bubble occurrence position relative to the length of the tube is  
\[\eta_{b}=\frac{X_{B}}{L_{t}} \tag{1}\]  
Dimensionless diameter of the tube relative to the maximum radius of the bubble is  
\[\gamma_{t}=\frac{D_{t}}{R_{max}} \tag{2}\]  
where \(R_{max}\) is the maximum radius of the bubble under free-field conditions. Another parameter of the tube is the length. The dimensionless length of the tube length relative to the maximum radius of the bubble is  
\[\gamma_{L}=\frac{L_{t}}{R_{max}} \tag{3}\]  
Figure 3: Schematic diagram of experimental cavitation parameters in a tube.  
In addition, the concept of tube diameter ratio is introduced here, corresponding to the ratio of the length of the tube to the diameter:  
\[\Lambda=\frac{L_{t}}{D_{t}} \tag{4}\]
## 3 Results and Discussion
### Average Joint Aperture of the Single Crack Model with Different Angles  
The average joint aperture values of the specimen under different stress conditions and different fracture angles are shown in Figure 22. The \(x\)-axis represents the crack angle, the \(y\)-axis represents the axial stress, and the \(z\)-axis represents the average contact hydraulic value. Under the actions of axial stress and confining pressure, the variation in the average joint aperture value is similar in different fracture angle models.  
Experimental data collected for the 60- and 70-degree angles (Figure 22) were selected for analysis; Figure 23 shows the change curves of the average joint hydraulic aperture values with different confining pressures and axial pressures. When the axial pressure was 45 MPa, the average joint hydraulic aperture values of the 60-degree and 70-degree cracks under the condition of 5-30 MPa were 3.98 \(\times\) 10\({}^{-6}\), 3.52 \(\times\) 10\({}^{-6}\), 3.07 \(\times\) 10\({}^{-6}\), 2.64 \(\times\) 10\({}^{-6}\), 2.21 \(\times\) 10\({}^{-6}\), and 1.80 \(\times\) 10\({}^{-6}\), respectively. The average joint hydraulic aperture value of the single crack model with different angles was approximately equal when the stress of the environment was kept equal. The average joint hydraulic aperture values of the 60- and 70-degree crack angles were approximately equal, but the seepage flow increased sharply, as shown in Figure 14. The main reasons for this are as follows. When the crack angle is greater than 70 degrees, the seepage channel formed by the macro crack is directly connected to the seepage boundary to form the main seepage channel (Figure 16). When the crack angle is less than 60 degrees, the macroscopic crack needs to form a seepage channel through a connection with the surrounding microscopic crack. This is a reasonable explanation for this phenomenon, because 60-degree cracks have relatively high sensitivity to axial stress and confining pressure (Figure 18), while 70-degree cracks are more sensitive to confining pressure than axial pressure (Figure 19).  
Figure 22: Average joint aperture values of different angle crack models.  
Under the actions of axial stress and confining pressure, the variation in the average joint aperture value is similar in different fracture angle models. When the crack angle ranged from 40 to 60 degrees. The average joint hydraulic aperture value increased slightly with an increase in the angle. When the crack angle was greater than 60 degrees, the average joint hydraulic aperture value was almost stable at a constant value. The average joint hydraulic aperture value increased gradually with a decrease in the axial stress.
## 3 Results  
Based on the presented exclusion criteria, 94 nodules (90 patients) were considered in the final analysis, all of which had pathology reports. In patients with s multinodular goiter, only the highest-risk nodules, with a performed FNA, were included in the final analysis. The patients were mostly females (80/90), with the mean age of 47.6 \(\pm\) 12.9 years. A high level of thyroid peroxidase antibodies (TPOAbs) or thyroglobulin antibodies (TgAbs) was found in 19 cases (20.2%).  
The main characteristics of the group are displayed in Table 1.  
Out of the 94 focal lesions, 29 proved to be malignant. The distribution of cancers is detailed in Table 2. All malignancies in our study group were differentiated variants. Most of the malignancies (96.8%) were forms of papillary thyroid carcinoma (PTC). The single borderline case (NIFTP) was also included in the malignancy group.  
From the 65 benign lesions, 38 (58.5%) were nodular hyperplasia, 13 (20%) were follicular adenomas, 4 (6.2%) were Hurthle cell adenoma, 9 (13.8%) were lymphocytic thyroiditis and 1 (1.5%) was Riedel thyroiditis.  
All thyroid focal lesions were, firstly, evaluated by means of grayscale US. The median size of the nodules was 1.9 cm (range interval 0.8-6.4 cm), with only three subcentimetric lesions. Most of the nodules were completely or almost completely solid (81.9%). The  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Number of Patients** \\ \hline Total number of nodules & 94 \\ Mean age & 47.6 \(\pm\) 12.9 (24–75) \\ Female gender & 80 (88.9%) \\ Associated AITD & 19 \\ \hline \end{tabular}
\end{table}
Table 1: Main characteristics of the study group.  
grayscale ultrasound characteristics were noted for each nodule and considered in the FNA indication (Table 3).  
We observed the following suspect features: moderate hypoechogenicity in 32 cases (34%), marked hypoechogenicity in 5 cases (5.3%), a taller-than-wide shape in 8 cases (8.5%), nonhomogeneity in 48 of the nodules (51%), irregular margins in 18 cases (19.1%), infiltrative margins in 3 cases (3.1%), and microcalcifications in 15 cases (15.9%). We defined 47 low-risk cases, 31 intermediate-risk cases and 16 high-risk cases.
# Adsorption of Cadmium and Methylene Blue Using Highly Porous Carbon from Hemp Seeds
## 4 Results and Discussion
### Isotherm, Kinetics and Thermodynamic Studies
#### 4.3.3 Thermodynamic Parameters  
Thermodynamic parameters were used to determine the spontaneity and feasibility of cadmium and methylene blue adsorption by PHSs, CHS-500 and CHS-700 (Table 3). The parameters evaluated at different temperatures show positive \(\Delta\)H\({}^{\circ}\) values for all adsorbents, which implied an endothermic reaction. Generally, an endothermic reaction increases the sorption process for pollutants as temperature increases [35]. \(\Delta\)S\({}^{\circ}\) values were also positive;  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Adsorption Isotherm Models**} & \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**PHS**} & \multicolumn{2}{c}{**CHS-500**} & \multicolumn{2}{c}{**CHS-700**} \\ \cline{3-8}  & & **Cd(II)** & **MB** & **Cd(II)** & **MB** & **Cd(II)** & **MB** \\ \hline Langmuir & \(Q_{o}\) (mg/g) & 17.02 & 19.43 & 33.95 & 50.03 & 36.88 & 52.61 \\ \hline \multirow{8}{*}{Freundlich} & \(B\) (L/mol) & 0.083 & 0.091 & 0.185 & 0.203 & 0.193 & 0.211 \\  & \(R^{2}\) & 0.992 & 0.993 & 0.992 & 0.994 & 0.991 & 0.997 \\ \cline{1-1}  & \(K_{F}\) & 0.381 & 0.418 & 0.793 & 0.815 & 0.804 & 0.860 \\ \cline{1-1}  & \(N\) & 1.037 & 1.285 & 1.761 & 1.892 & 1.779 & 1.902 \\ \cline{1-1}  & \(R^{2}\) & 0.950 & 0.971 & 0.977 & 0.980 & 0.989 & 0.985 \\ \hline Experimental & \(q_{e}\) (mg/g) & 16.21 & 19.21 & 31.12 & 49.12 & 35.08 & 53.98 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Isotherms for the adsorption of pollutants onto hemp-based adsorbents.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Kinetic Models**} & \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**PHS**} & \multicolumn{2}{c}{**CHS-500**} & \multicolumn{2}{c}{**CHS-700**} \\ \cline{3-8}  & & **Cd(II)** & **MB** & **Cd(II)** & **MB** & **Cd(II)** & **MB** \\ \hline PFOM & \(q_{e}\) (mg/g) & 10.75 & 16.60 & 26.59 & 44.81 & 29.05 & 48.49 \\  & \(K_{1}\) (min\({}^{-1}\)) & 0.014 & 0.022 & 0.036 & 0.062 & 0.040 & 0.067 \\  & \(R^{2}\) & 0.881 & 0.902 & 0.896 & 0.938 & 0.916 & 0.949 \\ PSOM & \(q_{e}\) (mg/g) & 17.56 & 20.40 & 32.09 & 50.06 & 36.74 & 54.12 \\  & \(K_{2}\) (g-mg/min) & 0.024 & 0.028 & 0.044 & 0.069 & 0.051 & 0.075 \\  & \(R^{2}\) & 0.990 & 0.993 & 0.995 & 0.991 & 0.992 & 0.996 \\ IPDM & C (mg/g) & 5.490 & 9.503 & 24.79 & 30.52 & 26.89 & 44.07 \\  & \(K_{i}\) (g/g-min\({}^{1/2}\)) & 0.016 & 0.018 & 0.034 & 0.042 & 0.037 & 0.061 \\  & \(R^{2}\) & 0.922 & 0.964 & 0.956 & 0.973 & 0.969 & 0.979 \\ \hline Experimental & \(q_{e}\) (mg/g) & 16.35 & 19.35 & 31.74 & 49.89 & 35.10 & 53.80 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetics for the adsorption of pollutants onto hemp-based adsorbents.  
this indicated increased randomness and degree of freedom for cadmium and methylene blue in the solution. The negative values for \(\Delta\)G\({}^{\circ}\) implied that the uptake of cadmium and methylene blue by PHSs, CHS-500 and CHS-700 was feasible and spontaneous.
## 4 Research Execution and Analysis
### AHP Weight Calculation
#### 4.4.2 Calculation of Weights for Individual Levels  
By normalizing the judgment matrix based on calculating its eigenvector, we can incrementally ascertain the relative weights of the matrix. The aggregated weight values at each hierarchical level are gauged relative to the overall goal and are derived sequentially from the higher to the lower levels. In the context of a multi-tiered decision-making model, the weight assigned to an individual level serves as the basis for evaluating the relative significance of the elements within that tier [70].  
The steps for calculating the individual levels sorting method are as follows:  
1. Calculate the product of each row's indicators in the judgment matrix \(M_{i}\). \[M_{i}=\prod_{j=1}^{m}a_{ij}(j=1,2,\cdots,m)\] (2)
2. Calculate the nth root of \(M_{i}\). \[W_{i}=\sqrt[n]{M_{i}}\left(i=1,2,\cdots,n\right)\] (3)
3. Normalize \(W_{i}\) to obtain the eigenvector \(\omega_{i}\). \[\omega_{i}=\frac{W_{i}}{\sum_{j=1}^{m}W_{j}}(j=1,2,\cdots,m)\] (4)
4. The formula for the maximum value of the judgment matrix is as follows: \[\lambda_{max}=\sum_{i=1}^{m}\frac{[A\omega]_{i}}{n\omega_{i}}\] (5)  
We determine the maximum eigenvalue \(\lambda_{max}\) of the judgment matrix \(A\) by considering \(n\) as the matrix's dimension, where \(A\) denotes the judgment matrix itself and \(\omega_{i}\) signifies the weight attributed to the \(i\)th indicator.  
1. Consistency test: The ratio of the Consistency Index (CI) of the judgment matrix to the Random Index (RI) is known as the Random Consistency Ratio (CR). When CR is less than 0.1, the judgment matrix is considered consistent. If CR is greater than 0.1, the judgment matrix needs to be reconstructed [71].  
The formula for calculating \(CR\) is as follows:  
\[CI=\frac{\lambda_{max}-n}{n-1} \tag{6}\]  
\[CR=\frac{CI}{RI} \tag{7}\]  
The \(RI\) value in the formula is determined according to Table 14.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Sauty’s 1-9 Scale Assignment Method**} \\ \hline
**Scale** & **Meaning** \\ \hline
1 & Indicators \(i\) and \(j\) are of equal importance. \\ \hline
3 & Indicator \(i\) is slightly more important than indicator \(j\). \\ \hline
5 & Indicator \(i\) is moderately more important than indicator \(j\). \\ \hline
7 & Indicator \(i\) is strongly more important than indicator \(j\). \\ \hline
9 & Indicator \(i\) is absolutely more important than indicator \(j\). \\ \hline
2, 4, 6, 8 & The importance of the indicators falls between the above scales. \\ \hline Reciprocal & If the comparison between factors \(i\) and \(j\) results in the judgment matrix \\  & entry \(C_{ij}\), then the comparison of factor \(j\) to \(i\) is given as \(C_{ij}=1/C_{\text{ji}}\). \\ \hline \hline \end{tabular}
\end{table}
Table 13: Relative importance scale.