esa-sceva/satcom-chunk-collection huggingface dataset:
# New Landslide Disaster Monitoring System: Case Study of Pingding Village
## 1 Introduction
On 25 June 2003, the 42-45 K section (Wuwanzi) of A Li Mountain Highway was affected by the most serious landslide in its history due to continuous torrential rain. With ceaseless rain-wash, the slumped section enlarged, and an area of nearly 120 m slipped off of the valley. As a result, the A Li Mountain Public Works Section commissioned Feng Chia University to install various transmission devices and equipment in the 31 K section of the Taiwan 18 th Line Highway. As an outcome of this installation, it was suggested that reference values for rainfall levels, water levels of barrier lakes, drilled underground water levels of deposition areas, and overflow water levels be established and then used as alarms when exceeding such values. Abnormal phenomena such as up-warp and crack-openings once again affected the dipped-slope site of the collapse zone in November 2002. To understand whether the Jiufenershan Collapse Zone was continuously sliding, the Soil and Water Conservation Bureau immediately organized the \"Jiufenershan Collapse Zone Observation Project\" to gain a primitive understanding of land surfaces. Observations of underground changes via geological drilling, land surface changes observation, the interior inclination of drilling holes, and underground observations can be used as references for emergency responses during typhoons or torrential rain [27]. The village of Pingding in the county of Yunlin, Taiwan was chosen as a study site. This village is located in a valley surrounded by steep mountains. The dynamic sediment process and the arrangement of the sensors are supposed to provide alerts six hours in advance of any potential landslides so as to protect this village. The advantage of this study is its demonstration of a real-time monitoring station in Pingding that provides the residents with a landslide warning and management system. The aim of this research was: (1) To receive real-time data and to monitor the entire study area (high landslide potential area); (2) to integrate software and hardware for preparedness to adapt to landslide hazards; (3) to send warnings to residents to avoid serious landslides. The research procedure is indicated in Figure 1.
# A Framework for User Traffic Prediction and Resource Allocation in 5G Networks
## 3 Datasets and Data Preprocessing  
In this paper, we present a framework that has two main tasks, user traffic prediction for 5G networks and user allocation in 5G networks, incorporating the predictions of the previous module. Our system consists of two modules: (i) an RNN or a Transformer-based model for user traffic prediction and (ii) a Convolutional Neural Network (CNN) model for user resource allocation. There is also an adaptive approach to handle the results of user traffic predictions to assist the user allocation module in its task based on future highs and lows of traffic at base stations.  
The data used to train and evaluate the models were obtained from two distinct sources, one for each model. The user traffic prediction dataset consists of traffic collected from a 5G mobile terminal in a dense urban setting [26]. The user allocation dataset is a synthetic dataset called DeepMIMO [27] created with the express purpose of being used by large data models such as neural networks.
## 3 Evaluation Methods of the Performance of GSP
### Computationand Simulation Method
#### 3.3.3 2S2P1D Model  
[PERSON] et al., evaluated the viscoelastic behavior of GSP according to the 2S2P1D model (composed of 2 spring units, 2 parabolic units, and 1 clay pot unit). It was found that the 2S2P1D model had a good correlation with the test results. The equation was expressed as follows [139]:  
\[E^{*}(w)=E_{e}+\frac{E_{g}-E_{e}}{1+\mu\left(iw\tau_{0}\right)^{-k}+\left(iw \tau_{0}\right)^{-h}+\left(i\omega\beta\tau_{0}\right)^{-1}} \tag{6}\]  
\(E^{*}(w)\) was the complex modulus; \(E_{e}\) was the equilibrium modulus; \(E_{g}\) was the glassy modulus; \(w\) was the angular frequency; \(\mu\) was a calibration constant; \(i\) was the complex number; \(\tau_{0}\) was the characteristic relaxation time; k and h were constant values defined as \(0<k<h<1\); and \(\beta\) was a constant that depended on the viscosity of the dashpot.  
The reinforcement effect of GSP is positively linked with the OMA void ratio, and the dynamic modulus is associated with the gradation [139]. In other words, cement can play a role to enhance the strength of GSP under high-temperature and low-frequency loads.
## 2 Materials and Methods
### Methods
#### 2.3.4 Geographical Detectors  
Geographical detectors are statistical methods to detect spatial heterogeneity and reveal its underlying driving forces, which consist of four modules: factor detection, risk detection, interaction detection, and ecological detection [57]. Geographical detectors enable the assessment of the extent of impact of multiple driving factors on response variables and the magnitude of interactions between factors. Among them, the factor detection and interaction detection modules are widely used in the analysis of driving factors of geographic phenomena and processes, and the factor detector detects the spatial heterogeneity of the dependent variable and the extent to which the independent variable explains the spatial heterogeneity of the dependent variable, as measured by the q-value. The expression is:  
\[q=1-\frac{\sum_{h=1}^{L}N_{h}\sigma_{h}^{2}}{N\sigma^{2}} \tag{12}\]  
where: \(h=1\),..., \(L\) is the stratification of variable \(Y\) or factor \(X\), i.e., classification or partitioning; \(N_{h}\) and \(N\) are the number of cells in stratum \(h\) and full area, respectively; \(\sigma_{h}^{2}\) and \(\sigma^{2}\) are the variances of \(Y\) values in stratum \(h\) and full area, respectively. \(q\)-values are between 0 and 1, and larger values indicate that the explanatory factor has a stronger effect on the study variable the stronger the driving force.  
The greatest advantage of the geographical detector, in relation to other statistical methods, is its possession of interaction detectors that can be used to identify interactions between different risk factors, i.e., to assess whether two driving factors, together, increase or decrease the explanatory power of the dependent variable or whether the effects of these factors on the dependent variable are independent of each other [58].  
The risk detector is used to detect the suitable range or type of impact of different environmental factors on NPP, which are tested by t statistics:  
\[t=\frac{\overline{Y}_{h=1}-\overline{Y}_{h=2}}{\left[\frac{Var(Y_{h=1})}{n_{h-1}} +\frac{Var(Y_{h=2})}{n_{h-2}}\right]^{\frac{1}{2}}} \tag{13}\]  
where: \(\overline{Y}_{h}\) is the mean value of NPP in subregion \(h\); \(n_{h}\) is the number of samples in subregion \(h\); \(Var\) is variance.
# Traceability of the Micro Scale Pipe Viscometer for Traceable Calibration of Dynamic Viscosity
## 2 Materials and Methods  
### Experimental Setup of Pipe Viscometer  
The current flow facility at METAS [15] has been modified to include a section with a pipe viscometer as can be seen in Figures 1 and 2. The pipe viscometer consists of a microtube with temperature and pressure sensors upstream and downstream of the microtube.  
Measuring the flowrate, the pressure drop, the temperature and including the geometrical specifications of the microtube allows for calculation of the dynamic viscosity of the liquid according to the Hagen-Poiseuille equation:  
\[\eta=\frac{\Delta P\cdot\pi\cdot r^{4}}{8\cdot Q\cdot L}, \tag{1}\]where \(\eta\) is the dynamic viscosity, \(\Delta P\) is the pressure drop over the microtube, \(Q\) is the volumetric flowrate, \(L\) is the length of the micro tube, and \(r\) is the inner radius of the microtube.
## 3 Results
### The ESW Analyses  
The ESW increased first and then decreased with the same FS under the FH of 3, 4, and 5 m, respectively (Figure 4). This change trend was most significant at the FS of 4.0 m/s, the ESW increase rate was 63.82% comparing T4 (ESW = 3.51 m) with T5 (ESW = 5.75 m), and the ESW decrease rate was 27.48% comparing T5 (ESW = 5.75 m) with T6 (ESW = 4.17 m). From Figure 4, the ESWs under an FS of 3.0 m/s were larger than those of same FH at 4.0 or 5.0 m/s. The ESW showed a monotonous downward trend with the same FH (Figure 5) obviously under the FS of 1.5, 2.0, and 2.5 m/s, respectively. This trend was obvious at the FH of 1.5 and 2.5 m, and the ESW maximum decrease rate was 52.40% comparing T1 (ESW = 5.42 m) with T7 (ESW = 2.58 m). From Figure 5, the ESWs under an FH of 2.0 m were larger than those of the same FS at 1.5 or 2.5 m. Therefore, it could be considered that the FS and the FH affected the ESWs. Comparing Figure 4 with Figure 5, it could be seen that FS had a larger effect on EWS than FH.  
The two-way ANOVA was conducted to verify the significance effect of FS and FH on ESW at the \(p\)-value = 0.05 level, and the results are shown in Table 6. The FS has a highly significant impact on ESW (\(p\) = 0.0033 \(<\) 0.01), the FH has no significant impact on the ESW (\(p\) = 0.136 \(>\) 0.05), and the interaction between FS and FH also has no significant impact on ESW (\(p\) = 0.906 \(>\) 0.05).  
Figure 4: Effective spraying width (ESW) changes under different speeds. Note: ESW1.5, ESW2.0, ESW2.5 represent the ESW when the flight height (FH) was 1.5, 2.0, and 2.5 m, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Treatment Number** & **ESW** & **_CV_** \\ \hline T1 & 5.42 m & 23.30\% \\ T2 & 5.78 m & 24.80\% \\ T3 & 4.58 m & 26.20\% \\ T4 & 3.51 m & 23.30\% \\ T5 & 5.75 m & 26.20\% \\ T6 & 4.17 m & 34.40\% \\ T7 & 2.58 m & 27.20\% \\ T8 & 2.77 m & 28.15\% \\ T9 & 2.51 m & 32.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test result data of bandwidths, droplet deposition uniformities, and penetration rates.
# Study on Dominant Frequency Attenuation of Blasting Vibration for Ultra-Small-Spacing Tunnel
## 3 Field Test  
### Overview  
The newly constructed Jiuwuji tunnel is a double-track tunnel located in the Xingyi Ring Expressway, Guizhou, China. The tunnel has a total length of 393 m and a maximum buried depth of 169 m; the exit of the tunnel is located on a steep hillside. As the blast  
Figure 1: Definitions of three dominant frequencies [28]: (**a**) typical blast vibration waveform and (**b**) Fourier amplitude spectrum.  
face was excavated from the entrance to the exit, the clearance between the two tunnels gradually decreased to 0.6 m, which belongs to the category of ultra-small-spacing tunnels. In the two tunnels, the left tunnel is a preceding one and the right tunnel is the succeeding tunnel. In this study, a series of blast vibration tests were conducted at clearance ranges from 9.4 m to 4.0 m. The blast vibration waves from the succeeding tunnel inevitably affected the structure of the preceding tunnel. The site conditions are shown in Figure 2. In the blasting test area, the average burial depth of the soil above the structures of the left and right tunnels is 100 m and 20 m, respectively; and the main surrounding rock of the tunnel is moderately weathered dolomite, with broken rock mass and developed joints and fissures.  
The design of the tunnel is shown in Figure 3. To ensure the safety and stability of the construction, the middle rock pillar was reinforced. Small grouting conduits were used. An extended hollow grouting bolt was set above the middle rock pillar, and four hollow grouting bolts were installed under the middle rock pillar, to reinforce the arch foot. The deformation space reserved for the primary support and the secondary lining was 15 cm.
## 2 Theory and Methods
### EBT-XD Apparent Dose-Rate Dependence  
When analysing the film response to PEER exposures, a super-linear response to increasing charge was found. As previously stated, \(D=\Phi\frac{1}{\rho}\frac{dE}{dx}\). Hence, as the electron energy is unchanged during the experiment, the dose should scale linearly with increasing fluence. Deviation from this behaviour could only occur due to a change in the transverse distribution of the field. If the transverse distribution is unchanged, then this suggests that the super-linear response of the film must be a dose rate or DPP dependence. To confirm this, a complimentary experiment was conducted, whereby three levels of charge were delivered to films, over three different temporal profiles corresponding to a single pulse delivery, 2 pulse delivery, and 20 pulse delivery, at a 1 Hz pulse repetition frequency (PRF). This was repeated three times. The central-axis-dose was extracted from the scanned films, normalised to charge measurements, and plotted. It is expected that if the dose rate is the true cause of the super-linear response, then the results for each level of charge delivery should converge for the 20 pulse delivery where DPP is at its lowest and the average dose rates are within the realm of clinical applications that the EBT-XD is primarily designed for.  
However, as the PEER energy of 100 MeV is outside the manufacturers usable energy range, a Monte Carlo simulation was used to estimate the validity of the range of film responses. The Geant4 toolkit [87,88] (Version 11.00p03, with the G4 EmStandardPhysics_option4 physics list) was used to produce a beam with a transverse Gaussian distribution representative of that at PEER. The experimental geometry was created within Geant4, including a slice of EBT-XD film (with the central layer sensitive volume set to water) within a PMMA phantom to score the dose in a region corresponding to that of the experimental procedure. The dose was then normalised to charge to evaluate the experimental response of EBT-XD film.
## Results  
### Chemical and Nutritional Value of Thymbra spicata  
Leaves of _T. spicata_ were fully characterized, as depicted in Table 2. The data refer to macronutrients, minerals (ash), and energy content.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compounds** & **Quantity** & **Compounds** & **Quantity** \\ \hline Total fat (g/100 g) & 4.3 & Total carbohydrates (g/100 g) & 52.7 \\ \hline Saturated fatty acids (g/100 g) & 0.9 & Total dietary fiber (g/100 g) & 12.3 \\ \hline Fatty acid methyl esters (\% of fat content) & \multicolumn{3}{c}{Simple sugars (g/100 g)} \\ \hline C12:0—Lauric acid & \textless{}0.1 & Fructose & 0.03 \\ \hline C14:0—Myuristic acid & \textless{}0.1 & Galactose & n.d \\ \hline C16:0—Palmitic acid & 17.5 & Glucose & 1.69 \\ \hline C16:1—Palmitoleic acid & \textless{}0.1 & Lactose & n.d \\ \hline C17:0—Heptadecanoic acid & \textless{}0.1 & Maltose & n.d \\ \hline C17:1—Heptadecenoic acid & \textless{}0.1 & Sucrose & 0.02 \\ \hline C18:0—Stearic acid & 2.6 & Sum sugars & 1.74 \\ \hline C18:1—Oleic acid & 40.6 & Nitrogen (g/100 g) & 0.99 \\ \hline C18:2—Linoleic acid & 35.5 & Total protein (g/100 g) & 6.19 \\ \hline C20:0—Arachic acid & 2.6 & Moisture (g/100 g) & 17.0 \\ \hline C18:3—Linolenic acid & 0.5 & Salt (g/100 g) & 0.009 \\ \hline C20:1—Eicosenoco acid & \textless{}0.1 & Ash (g/100 g) & 9.11 \\ \hline C22:0—Beenic acid & \textless{}0.1 & Energy value (Kcal/100 g) & 324 \\ \hline C22:1—Erucic acid & \textless{}0.1 & Energy value (KJ/100 g) & 1367 \\ \hline C24:0—Lignoercic acid & \textless{}0.1 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analytical values of chemical composition and nutritional values of _Thymbra spicata_ leaves.  
More than 50% of plant materials were carbohydrates (52.7 g per 100 g of the _T. spicata_ leaves). Total dietary fiber was measured at 12.3 g/100 g, and the sum of the simple sugars (listed by specific sugar types, along with their values in g per 100 g) was 1.74 g. Total protein and fat content were 6.19 and 4.3 g/100 g, respectively. The contents of saturated fatty acids were low (0.9 g), while unsaturated fatty acids were more represented. Oleic acid (C18:1) and linoleic acid (C18:2) accounted for 40.6% and 30.5% of fat content, respectively. Other constituents, such as moisture and ash, were present in moderate amounts (17.0 and 9.11 g/100 g, respectively). Finally, the energy value of _T. spicata_ leaves was 324 kcal/100 g, which is equal to 1367 kJ/100 g.
# A Multi-Plant Height Detection Method Based on Ruler-Free Monocular Computer Vision
## 3 Principles and Methods
### Segmentation of Plants
#### 3.3.2 Data Peak Finding and Boundary Selection  
As mentioned above, a single plant in the image appears as a \"peak\" in the data. Multiple plants in the same image can be segmented by finding peaks in the binary pixel data, so peak finding is necessary.  
Each \"peak\" has a maximum value (peak) and two minimum values (valley), and the adjacent \"peaks\" are divided by the valley point.  
In this study, the valley value was found to determine the boundary of the peak. However, as shown in the figure above, there are many data burrs in the figure, which is not conducive to finding the poles. Therefore, it is necessary to smooth the data before finding the extremal values. The process of peak finding for binarized data is as shown in the following Figure 8.  
Firstly, a mean filter is applied to smooth the data and remove any spurious noise [36]. This step is crucial for preparing data for accurate peak detection. Secondly, we perform the Pole search [37]. After inverting the data, the Matlab Findpeaks function is used to process the binarization count value and find the peak after inversion, which is the valley value of the original data. When calling, the minpeakdistance parameter is set to limit the minimum distance between the two peaks to ensure that no redundant boundary points are found. Finally, we perform boundary segmentation [38; 39]. Based on the identified valley points, the boundaries for segmentation are determined. This process effectively locates peaks in binary data while mitigating the impact of noise and ensuring the selection of relevant features.  
The principle of the above mean filtering is as follows:  
\[\mathrm{n_{j}}=\frac{1}{2\mathrm{r}+1}\sum_{i=j-r}^{j+r}\mathrm{n_{i}} \tag{6}\]  
\(\mathrm{n_{j}}\) represents the value corresponding to column j in the binary pixel count, and r represents the range within which the mean filter takes the mean. However, after mean filtering, the binarization matrix will be reduced by 2r columns, which is not conducive for the accurate segmentation of plant boundaries in the future. Therefore, all the zero matrices of r columns will be added to the left and right sides of the mean filtered matrix to maintain the same size of the binarization matrix after mean filtering.  
The boundary point results obtained through filtering and searching using the above methods are shown in the following Figure 9:  
Figure 8: Peak searching process of binarized data.  
Figure 9: Processing and segmentation of boundary points by filtering.
# Rapid Deployment Method for Multi-Scene UAV Base Stations for Disaster Emergency Communications
###### Abstract  
The collaborative deployment of multiple UAVs is a crucial issue in UAV-supported disaster emergency communication networks, as utilizing these UAVs as air base stations can greatly assist in restoring communication networks within disaster-stricken areas. In this paper, the problem of rapid deployment of randomly distributed UAVs in disaster scenarios is studied, and a distributed rapid deployment method for UAVs' emergency communication network is proposed; this method can cover all target deployment points while maintaining connectivity and provide maximum area coverage for the emergency communication network. To reduce the deployment complexity, we decoupled the three-dimensional UAV deployment problem into two dimensions: vertical and horizontal. For this small-area deployment scenario, a small area UAVs deployment improved-Broyden-Fletcher-Goldfarb-Shanno (SAIBFGS) algorithm is proposed via improving the Iterative step size and search direction to solve the high computational complexity of the traditional Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. In a large area deployment scenario, aiming at the problem of the premature convergence of the standard genetic algorithm (SGA), the large-area UAVs deployment elitist strategy genetic algorithm (LAESGA) is proposed through the improvement of selection, crossover, and mutation operations. The adaptation function of connectivity and coverage is solved by using SAIBFGS and LAESGA, respectively, in the horizontal dimension to obtain the optimal UAV two-dimensional deployment coordinates. Then, the transmitting power and height of the UAV base station are dynamically adjusted according to the channel characteristics and the discrete coefficients of the ground users to be rescued in different environments, which effectively improves the power consumption efficiency of the UAV base station and increases the usage time of the UAV base station, realizing the energy-saving deployment of the UAV base station. Finally, the effectiveness of the proposed method is verified via data transmission rate simulation results in different environments.  
disaster emergency communication; UAV connectivity; UAV deployment optimization; UAV coverage +
Footnote †: journal: Applied
## 1 Introductory
[PERSON] [19] derived a semi-spherical diffusion model for Newtonian fluids considering the tortuosity of porous media and the time-dependent viscosity of the slurry. [PERSON] [20] proposed a behavioral model to explain the heterogeneity observed in the radius of influence during injection. [PERSON] [21] conducted a study using Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) to investigate the permeation of cement-based grout in porous media and the filtration behavior of cement particles. [PERSON] [22], based on Cone Penetration Testing (CPT) and Standard Penetration Testing (SPT), provided confirmation of the effectiveness of micropile jet grouting in enhancing soil properties and mitigating liquefaction risk. Simultaneously, scholars have conducted research into the intricate flow of fluids in porous media. [PERSON] [23; 24] proposed an adaptive deep learning algorithm that can quantitatively predict the total phase count of reservoir fluids in equilibrium under various environmental conditions. In the reinforcement of gravelly formations, penetration grouting is commonly employed. Typically, readily available and cost-effective cement grout solutions are chosen for this purpose in engineering practice. Consequently, non-Newtonian fluids are frequently utilized for the consolidation of porous media formations. Simultaneously, numerous scholars have conducted research on non-Newtonian fluids in this context. Given the complexity of grouting geology, [PERSON] [25] investigated the influence of the diffusion path of Bingham fluids within porous media on the diffusion behavior of the grout. [PERSON] [26] studied the propagation of non-Newtonian fluids in various homogeneous or heterogeneous fracture networks. [PERSON] [27] provided a comprehensive overview of non-Newtonian flow in fractures. [PERSON] [28] presented an analysis of the radial velocity profile between parallel plates in the flow of non-Newtonian fluids within fractures, along with numerical solutions. Consequently, there has been extensive research by numerous scholars on non-Newtonian fluids in this field. In summary, numerous scholars have conducted extensive research on permeation grouting, making it a subject worthy of investigation. However, in practical engineering applications, the particle size distribution of gravel is not uniform. How the varying particle size distribution of gravel affects the permeation and diffusion of non-Newtonian fluids remains unclear. Therefore, the objective of this study is to explore how different particle size distributions of gravel impact the rules governing permeation grouting. Concurrently, this paper focuses on the widely used Bingham fluid as the research subject, investigating the permeation grouting mechanism of Bingham fluids within gravel of varying particle size distributions. The aim is to provide theoretical guidance and support for engineering practices.