esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
For the monitoring data of the Lishanyuan landslide, the sliding window method was used to extract the features, and gray relation analysis was used to screen the features. Then the PSO-optimized k-means algorithm was used to cluster. Finally, the Apriori algorithm was used to mine the strong association rules between deformation speed and rainfall characteristics to analyze the disaster factors of the Lishanyuan landslide and propose the precursors that can be used for early warning. The following conclusions were obtained from this study:  
The sliding window method was adopted to achieve feature extraction of high-frequency monitoring data, which can make full use of the data and be more representative.  
Using PSO-optimized k-means algorithm to cluster feature engineering can effectively avoid the clustering results falling into local optimal. By clustering, the numerical dataset is transformed into transaction database, and the strong association rules can be mined using the Apriori algorithm. This research developed mining of association rules of monitoring data at hourly or even minute scale. Compared with ultra-long-term monitoring data at monthly scale, we should pay more attention to short-term deformation patterns, which are more conducive to short-term real-time early warning.  
The results of association rules mining show that the high-speed deformation process of the Lishanyuan landslide is mainly affected by the combination of short-term rainfall of about 1 day and long-term heavy rainfall of about 3-7 days. A rainfall within 24 h and heavy rainfall with a cumulative rainfall greater than 130.60 mm within 7 days can be used as a precursor to identify the high-speed deformation of the landslide.  
The association rule mining algorithm used in this paper is highly complex, computationally intensive, and very time-consuming, and simpler and faster algorithms need to be explored in the future to cope with monitoring and early warning of more and more landslides. In addition, this mining process does not consider the time-series characteristics of item sets, and future research should explore sequence pattern mining, which has uncovered more and more valuable information.  
Conceptualization, J.X., D.B.; methodology, J.X., D.B.; software, J.X., D.B.; validation, J.X. and J.L.; formal analysis, J.X.; investigation, J.X.; resources, J.X. and H.H.; data curation, J.X.; writing--original draft preparation, J.X.; writing--review and editing, J.X., H.H., J.L., D.B., G.L.; visualization, D.B. and J.X.; supervision, J.X.; project administration, J.X.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key research and development program of Hunan Province of China, grant number: 2020 SK2135. Natural Resources Research Project in Hunan Province of China, grant number: 2021-15.  
Not applicable.  
We would like to thank the editor and the reviewers for helping us improve the quality of the manuscript.  
The authors declare no conflict of interest.
# Characterization of the Body-to-Body Propagation Channel for Subjects during Sports Activities
## 5 Performance Analysis
### Channel Capacity  
Channel capacity is the maximum rate at which information can be transmitted reliably over a communication channel. Normalized to the bandwidth, it can be expressed in \(\text{bps}/\text{Hz}\) as [22; 23]  
\[C_{N}=\log_{2}(1+SNR), \tag{4}\]  
where \(SNR\) is the received signal to noise ratio that can be expressed as  
\[SNR=\frac{P_{r}}{kTBN_{f}}. \tag{5}\]  
Here, \(P_{r}\) is the received power, \(k\) is the Boltzmann constant, \(T\) is the temperature in Kelvin, \(B\) is the signal bandwidth in \(\text{Hz}\) and \(N_{f}\) is the receiver linear noise figure. Due to the time-varying fading effects of the received signal power \(P_{r}\) in BBWNs, the \(SNR\) in (4) varies accordingly, making the channel capacity a random variable. Using the CDFs obtained in Section 4 from the simulation model as well as the measured results, the capacity of the channel in different scenarios and activities were simulated and analyzed. Parameters used to calculate the noise power are given in Table 4. Figure 12 shows the CDFs of the instantaneous channel capacity for different average received power \(\overline{P}_{r}\). Considering the running activity in Figure 12a, it can be observed that Scenario 1 (subject behind the other) gives the best instantaneous channel capacity most of the time. However, it is interesting to notice that, with equal average received power, the instantaneous channel capacity of the given two possible scenarios is almost equal for more than 25% of the time. This is indicated by the convergence of the two curves in their upper part. With relatively stable transceivers achievable during cycling activity, the instantaneous channel capacities obtained has steeper slopes as seen in Figure 12b. Here, Scenario 1 gives the best instantaneous channel capacity most of the time due to the presence of the LOS component, while Scenario 2 (subject beside the other) gives the worst due to its absence.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Average received power (\(\overline{P}_{r}\)) & [4 16 64] pW \\ Boltzmann constant (\(k\)) & \(1.38\times 10^{-23}\) JK\({}^{-1}\) \\ Temperature in Kelvin (\(T\)) & 290 K \\ Noise bandwidth (\(B\)) & 300 kHz \\ Receiver noise figure (\(N_{f}\)) & 6.3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters used in performance analysis simulations.  
The effect of the stability of the arms and hence the transceivers can be observed further in Figure 13, where CDFs of instantaneous channel capacity for all scenarios are plotted together. It can be observed that, with similar average received power, cycling where the arm is more stable, outperform running by having larger values in similar scenarios. This is regardless of the fact that the velocity of the subjects during cycling is more than 50% larger than that of running.
## 2 Materials and Methods
### Data and Preprocessing  
#### 2.2.1 Meteorological Data and Dendrochronological Measurements  
Model input meteorological files of Biome-BGCMuSo include daily minimum and maximum temperature, daily average temperature, daily precipitation, daily shortwave download radiation (Sard), daily average water vapor pressure difference (VPD) and sunshine hours (Daylen). Therefore, we collected the ground meteorological element-driven data (CMFD) with high spatial and temporal resolution from 1992 to 2018 in China [29]. This dataset is produced by combining the existing international Princeton reanalysis data, GLDAS data, GEWEX-SRB radiation data, and TRMM precipitation data as the background field, and combining the conventional meteorological observation data of the China Meteorological Administration, with a spatial resolution of 0.1\({}^{\circ}\)[30; 31]. To keep the same size as the Biome-BGCMuSo model simulation unit, we first used python (version 3.10) to convert 75,920  
Figure 1: The location and forest observation sample sites in the Greater Khingan Mountains in Inner Mongolia.  
(8 \(\times\) 365 \(\times\) 27) scene CFMD data from network Common Data Form (NetCDF) to Tag Image File Format (TIFF) and then use the nearest neighbor interpolation method to resample them into a resolution of 1 km (Albers projection, WGS-84 coordinate system). Then, we extracted daily minimum temperature, daily maximum temperature and daily precipitation data from the CMFD data. Finally, VPD, Sard and Daylen were calculated using the MT-CLIM 4.3 algorithm for the Biome-BGCMuSo model-driven.  
In order to verify the feasibility of parameters of the Biome-BGCMuSo model and the accuracy of the simulation results, 46 sample plots were selected from the Greater Khingan Mountains in Inner Mongolia during August 2013 and August 2016. The main forest types included _Larix smelini_ and _Betula platyphylla_ pure forest, _Larix_ and _Betula_ mixed forests, etc., and forest structural parameters and dendrochronological measurements were carried out. Collected samples were packaged back to the laboratory after the dendrochronological measurements, and the annual ring width was measured by a WinDENDRO annual ring analyzer with a measurement accuracy within 0.001 mm. The COFECHA algorithm was used for manual cross-dating, and the DBH value of each standard sample tree was obtained year by year. According to the biomass growth equations of each forest type [32; 33], the annual forest biomass increment was calculated, and also the average ratio of the biomass to the carbon of Larix _gmelini_ (0.5211) and _Betula_ platyphylla (0.491) [34]; the forest NPP for each plot was obtained, then they were used to verify the model simulation results.
# A Segmentation-Based Optimal Seamline Generation Method for SAR Image Mosaic
## 3 Results and Analysis  
### Experimental Dataset  
To assess the effectiveness of the proposed method, two sets of experiments are conducted using multiple SAR images acquired from the Chinese Gaofen-3 01 satellite. This satellite is the inaugural civil microwave remote sensing imaging satellite included in the \"National High-resolution Earth Observation System Major Project\" and is distinguished as the initial C-band multi-polarization SAR satellite, featuring a nominal resolution ranging from 1 m to 500 m. The two datasets are outlined as follows:  
1. Dataset 1: The ROI spans from 44.56\({}^{\circ}\) to 45.67\({}^{\circ}\) N latitude and 123.71\({}^{\circ}\) to 125.05\({}^{\circ}\) E longitude. This dataset comprises 10 ascending orbit images collected between November 2019 and April 2021.
2. Dataset 2: The ROI extends from 33.55\({}^{\circ}\) to 35.07\({}^{\circ}\) N latitude and 116.16\({}^{\circ}\) to 117.57\({}^{\circ}\) E longitude. This dataset consists of 21 ascending orbit images collected from October 2022 to December 2022.  
All SAR images are SLC products from the Gaofen-3 01 satellite, and the imaging mode is ultra-fine stripe (UFS). Detailed information is presented in Table 1[38]. The geometric distribution can be visualized in Figure 4. Notably, the dataset encompasses diverse viewing conditions, including various incidence angles and different-side views, contributing to a comprehensive evaluation of the proposed method.
###### Abstract
intestinalis, C. limum_) are widespread in the Bulgarian Black Sea region, relatively easy to harvest, and have a rich chemical composition. On the other hand, the brown alga (_E. crinita_) is more difficult to obtain (their habitat is the rocky seabed close to the seashore at a depth of 2 to 3 m). Freshwater algae (_Arthrospira platensis, Chlorella_ spp.) grown in a bioreactor are characterized by high yield, controlled chemical composition, ecological purity, and safety. The current research aims to compare the chemical composition of bioreactor freshwater algae with the green and brown algae described above. Knowledge of this composition is a prerequisite for their differentiated application. Parameters such as antioxidant activity, total phenolic content, protein content, rutin, chlorophyll, flavonoids, and phenolic acids are essential for evaluating the quality of raw materials in the production of dietary supplements, using them in foods, etc. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Algal Source** & **References** \\ \hline Flavonoids & _Palmaria palmata_ & [20] \\ Chlorotannins & _Sargassum pallidum_ & [36] \\ Fucus vesiculos_ & [37] \\ \hline \multirow{3}{*}{Ascorbate} & _Chondrus crispus_ & [25] \\  & _Mascourpus stellatus_ & [37] \\  & _Sargassum_ spp. & \\ \hline Vitamin A & _Kappaphycus alvarezii_ & [6] \\ \hline Fucoidan & \multirow{2}{*}{_Turbinaria conoides_} & [38] \\ Alginic acid & & \\ Laminaran & & \\ \hline Fucoidan & _Saccharina japonica_ & [16] \\ Sulfated galactans & (formerly Laminaria japonica) & [39,40] \\ (Lambda-carrageenan) & Some red seaweeds & [39,40] \\ \hline Galactans & Most of the red algae & [41] \\ Sulfated glycosaminoglycan & _Sargassum wightii_ & [42] \\ \hline Porphyan & _Porphyna_ spp. & [43] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._There is no in-depth research on the Bulgarian Black Sea region's rutin, flavonoids, and phenolic acid content of _U. intestinalis_, _C. limum_, and _E. crinita_. There is scarce data available on the antioxidant activity and total phenolic content of such algae of the Turkish Black Sea region [58], as well as on the protein, chlorophyll \(a\) and _b_[59], rutin, and flavonoids [60] of algae from the Romanian Black Sea region. Research has been carried out on Bulgarian Black Sea region algae regarding the content of \(\alpha\)--tocpherols and fatty acid composition [61; 62], as well as heavy metal content [63] and mineral content [64]. The present study aimed to fill these gaps by providing the necessary data on some macroalgae from the Bulgarian Black Sea and the microalgae _A. platensis_ (Cyanobacteria) and _Chlorella_ spp. (_Chlorophyta_) from bioreactors.
# The Behavior of Tree-Width and Path-Width Under Graph Operations and Graph Transformations
## 3 Unary Graph Operations and Graph Transformations
### Edge Addition and Edge Deletion
#### 3.2.2 Edge Addition  
Let \(G\) be a graph and \(v,w\in V(G)\) be two vertices. For \(\{v,w\}\
ot\in E(G)\), we define by \(G+\{v,w\}\) the graph we obtain from \(G\) when adding the edge \(\{v,w\}\), i.e.,  
\[G+\{v,w\}=(V(G),E(G)\cup\{\{v,w\}\}).\]  
Having defined _edge addition_, our next theorem shows that inserting an edge into a graph increases the graph's width by at most one.  
Theorem 3.2: _For a graph \(G\) and two different vertices \(v,w\in V(G)\), it holds that_  
\[tw(G) \leq tw(G+\{v,w\})\leq tw(G)+1\quad\text{and}\] \[pw(G) \leq pw(G+\{v,w\})\leq pw(G)+1.\]  
Proof.: Given a tree-decomposition (path-decomposition) for \(G\), we obtain a tree-decomposition (path-decomposition) for \(G+\{v,w\}\) by adding one of the two vertices, \(v\) or \(w\), to all its bags. Consequently, \(\text{tw}(G+\{v,w\})\leq\text{tw}(G)+1\) and \(\text{pw}(G+\{v,w\})\leq\text{pw}(G)+1\) hold.  
The lower bounds follow from the fact that a tree-decomposition (path-decomposition) for \(G+\{v,w\}\) is also a tree-decomposition (path-decomposition) for \(G\).
## 3 Numerical Simulation Analysis and Demonstration of Actual Extraction Sequence in Large Stopes
Plastic zone analysis revealed that the 60-12# working face was subjected to shear stress, which had reached the yield surface, but later returned to an elastic state. Combining this with the analysis of the void collapse, the 60-12# working face collapsed towards the 60-11# working face; however, the displacement cloud showed that the upper region close to the 60-11# working face had very little displacement, although serious collapse actually occurred, indicating that the collapse was primarily due to the poor stability of the rock mass. Field investigations indicated that the rock chamber of the 60-6# working face experienced severe collapse and the crushing failure of the intermediate pillars, mainly due to the more concentrated stress in the working face, which led to damage to the surrounding rock. This is consistent with the numerical simulation results. The misalignment of the blast holes in the 60-10# working face was analyzed through a numerical simulation, revealing a height difference of 20 m between the 60-12# and 60-8# working faces after mining, resulting in a non-vertical inclined compressive stress region that caused a significant misalignment of the blast holes. In summary, the numerical simulation aligns with the actual situation, indicating that poor rock stability is the main reason for the collapse of the void, while stress concentration leads to damage in the rock chambers and intermediate pillars. Figure 4: Simulation results after the mining of the 60-8# working face. (**a**) Maximum principal stress contour map; (**b**) minimum principal stress contour map; (**c**) displacement contour map; and (**d**) plastic zone contour map.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 3 Queueing Models
### Additional Features
#### 3.7.1 Lifetime Messages in Queueing Networks  
As already discussed, a queueing network consists of a network of (connected) queues used for the performance modelling of a system. As an example, in the context of this paper, IoT interactions can be modeled via the creation of queueing networks that consist of queueing models already described in previous subsections. According to Figure 3(a), messages that have an arrival rate \(\lambda\) are processed in the first queue of a queueing network. Such messages have a lifetime period attributed to them upon their creation which expresses the validity of the message inside the (queueing) network. Hence, as soon as the lifetime of a message elapses, the message leaves the queueing network and is said to be expired.  
To take into account the case of an expired message, we consider the time that a message spends in both server and queue. By assuming that a queueing network includes only one M/M/1 queue, a message reneges when a certain lifetime period passes without its service having started. According to the literature, such a model is the M/M/1 queue with reneging or impatient customers [42; 43; 44]. In this paper, we present the simulation of such a model via our simulator and incorporate the case of reneging in our ON/OFF models. Hence, it is possible for system designers to create networks of queues, including various queueing models enriched with the feature of the lifetime period.  
Figure 4: Queues with event expirations and finite capacity.
## 2 System Architecture
### SCADA System
#### 2.4.2 Data Exporting Script  
To automatically save the measurement values on a standard form, a custom VBS (Visual Basic Script) was developed to create a new CSV (_Comma-Separated Values_) file, whose name and location are customizable by the operator--see Figure 13. Each line begins by the context information: date and time, section number, and output voltage value. The following data refer to the measurements sequence: 2 voltage values from the electrodes, 26 propagation times, and 26 propagation velocities. The 26 propagation times and velocities refer to the ultrasonic tomography (the experimental setup has a limit of 26 measurements per section).
## 3 Numerical Simulations
### Numerical Method and Model Validation
#### 3.4.2 Model Validation  
The total efficiency (\(\eta_{tot}\)) of powder coating curing with a gas infrared heater is between 25% and 30% [37]. However, the total efficiency of powder coating curing with catalytic infrared heating equipment is unknown. Experimental data with a curing temperature of 240 \({}^{\circ}\)C were used to determine the total efficiency value. The model was then validated using curing temperatures of 230 \({}^{\circ}\)C and 220 \({}^{\circ}\)C.  
As shown in Figure 19, the simulation results closely matched the experimental results when \(\eta_{tot}\) was 0.4. The relative errors are shown in Table 8, and the maximum relative error was 7.6% at 1 min.  
Figure 19: Comparison of measured and simulated coating temperatures.  
Figure 18: Coating temperature versus time with different time steps.  
The model's accuracy was further verified using curing temperatures of 220 \({}^{\circ}\)C and 230 \({}^{\circ}\)C by changing the gas intake of the radiant panel. During the experimental process, the maximum gas flow rate of 0.20 m\({}^{3}\)/h was used for the coating in the initial heating stage to ensure that it was rapidly heated. The gas flow was reduced when the coating reached the curing temperature. When the coating was heated for 125 s, the gas flow was decreased to 0.16 m\({}^{3}\)/h. This maintained the curing temperature at 220 \({}^{\circ}\)C. When the coating was heated for 157 s, the gas flow was decreased to 0.18 m\({}^{3}\)/h. This maintained the curing temperature at 230 \({}^{\circ}\)C. After adjusting the gas flow meter, the gas flow required approximately 30 s to stabilize. Therefore, when the simulated curing temperature was 220 \({}^{\circ}\)C, the initial internal heat source value was 8.5 W/m\({}^{3}\), and at 156 s, the internal heat source value was adjusted to 6.8 W/m\({}^{3}\). When the simulated curing temperature was 230 \({}^{\circ}\)C, the initial internal heat source value was 8.5 W/m\({}^{3}\), and at 188 s, the internal heat source value was adjusted to 7.65 W/m\({}^{3}\).  
The simulation results are shown in Figure 20. The simulated temperature values were lower than the measured temperature values during the entire heating process. This was because the radiation intensity of the radiation panel was delayed after the gas flow to the radiation panel was reduced during the heating process. The maximum differences between the simulation and experimental data were 8.7% at 230 \({}^{\circ}\)C and 9.6% at 220 \({}^{\circ}\)C. Therefore, this model can accurately predict changes in coating temperatures in engineering applications.
## 3 Methodology
### Kernel Density Estimation (KDE)  
Kernel density estimation (KDE) [36] is a method to estimate the probability density function of unknown variables from the data itself [37]. Taking the above conditional quantile as the input value of KDE and selecting the appropriate window width for probability density prediction, the following quantile function can be obtained. \(Z_{i}=\hat{Q}_{Y}(\tau\mid X)\), \(i=(1,\dots,n)\), \(n\) is the number of quantiles.  
Assuming that \(Z_{1},Z_{2},\dots,Z_{n}\) is an independent quantile function derived from the estimated probability distribution, then its kernel density is estimated as: \(f_{h}(Z)=\frac{1}{mh}\sum_{i=1}^{n}K(\frac{Z-Z_{i}}{h})\), \(h\) is the window width, and \(K(\cdot)\) is the kernel function.  
Figure 4: Schematic diagram of residual blocks.  
For the selection of kernel function, there are Gaussian kernel function, matrix kernel function and trigonometric kernel function. In this paper, Gaussian kernel function is selected, and the expression is: \(K(u)=\frac{1}{\sqrt{2n}}exp[-\frac{u^{2}}{2}]\), the value range of \(h\) is \(1.8-2.0\).