esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Cluster Analysis of Small- and Medium-Sized Enterprises
#### 4.1.6 Validation of Cluster Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dependent** & **Clusters** & **Mean** & **Lower Bound 2** & **Upper Bound 2** & **\(p\)-Value 1** \\
**Variable** & **Comparison** & **Difference** & **Lower Bound 2** & **Upper Bound 2** & **\(p\)-Value 1** \\ \hline  & 2-1 & \(-\)0.9677 & \(-\)1.3565 & \(-\)0.5788 & \(2\times 10^{-5}\)*** \\  & 3-1 & 0.1157 & \(-\)0.2538 & 0.4851 & 0.848 \\ VPi4 Level 1 & 4-1 & \(-\)2.1330 & \(-\)2.5388 & \(-\)1.7272 & \(2\times 10^{-5}\)*** \\  & 3-2 & \(-\)1.0833 & \(-\)1.4070 & \(-\)0.7597 & \(2\times 10^{-5}\)*** \\  & 4-2 & 1.1654 & 0.8008 & 1.5299 & \(2\times 10^{-5}\)*** \\  & 4-3 & \(-\)2.2487 & \(-\)2.5925 & \(-\)1.9049 & \(2\times 10^{-5}\)*** \\ \hline  & 2-1 & \(-\)1.4048 & \(-\)1.7611 & \(-\)1.0486 & \(2\times 10^{-4}\)*** \\  & 3-1 & \(-\)2.0085 & \(-\)2.3470 & \(-\)1.6700 & \(2\times 10^{-4}\)*** \\  & 4-1 & \(-\)1.2576 & \(-\)1.6294 & \(-\)0.8858 & \(2\times 10^{-4}\)*** \\  & 3-2 & 0.6036 & 0.3071 & 0.9002 & \(2\times 10^{-4}\)*** \\  & 4-2 & \(-\)0.1473 & \(-\)0.4813 & 0.1868 & 0.662 \\  & 4-3 & 0.7509 & 0.4359 & 1.0659 & \(2\times 10^{-4}\)*** \\ \hline  & 2-1 & 1.6700 & 1.2660 & 2.0740 & 0.001 *** \\  & 3-1 & 0.1087 & \(-\)0.2751 & 0.4926 & 0.8824 \\ VPi4 Level 3 & 4-1 & \(-\)0.3104 & \(-\)0.7320 & 0.1111 & 0.2261 \\  & 3-2 & 1.5613 & 1.2250 & 1.8976 & 0.001 *** \\  & 4-2 & 1.9805 & 1.6017 & 2.3592 & 0.001 *** \\  & 4-3 & \(-\)0.4191 & \(-\)0.7763 & \(-\)0.0619 & 0.0142 * \\ \hline \hline \end{tabular}  
* Significant codes: *** 0.001, *** 0.01, and *** 0.05. 2 The lower and the upper end point of the confidence interval at 95% (default).  
\end{table} 
Table 5: Results of ANOVA test. this is the overlap of the factor VPi4 Level 3. Another way to verify the validity of a cluster analysis is, for example, the Dunn index, calculated as the ratio of minimum separation/maximum diameter [73; 74], 0.06623, in the paper. The higher the Dunn index value, the better the clustering is.
## 2 Materials and Methods
### Satellite Images  
PlanetScope is a satellite constellation operated by Planet Labs, designed for high-frequency Earth observation. Each PlanetScope satellite, known as Dove, is equipped with a multispectral sensor capturing imagery in the coastal blue, blue, green I, green, yellow, red, red edge, and near-infrared bands (Table 3). With a spatial resolution of approximately 3 m per pixel, PlanetScope provides daily global coverage, enabling applications in precision agriculture.
# Sixth-Generation (6G) Networks for Improved Machine-to-Machine (M2M) Communication in Industry 4.0
## 4 Discussion
### Limitations of Current Studies
Particular attention should be paid here to the health and environmental aspects of Industry 5.0, which will soon be the benchmark for meeting the requirements of the Green Deal. Despite these limitations, continuous research and development efforts are aimed at addressing the challenges and realizing the potential benefits of 6G networks for improved M2M communication in Industry 4.0.
# Estimation of Distribution Algorithms with Fuzzy Sampling for Stochastic Programming Problems
## 4 Numerical Experiments
### Global Optimization Results  
The proposed DEDA algorithm was tested to solve nonlinear programming problems using the test functions in Set A and Set B. These test functions have diverse characteristics to assess various difficulties that occur in global optimization problems. For all test results for global optimizations, the records were obtained over 100 independent runs with a maximum function evaluation of 20,000. First, Table 3 shows average errors (Av.) and standard deviation (Std.) obtained by the DEDA method using Test Set B. It reached the global optima within error gaps less than \(10^{-3}\) for 25 out of 40 test functions. \begin{table}
\begin{tabular}{l l l l} \hline \hline \(f\) & \(R=100\) & \(R=200\) & \(R=500\) \\ \hline \(RC\) & \(3.58\times 10^{-7}\) & \(3.58\times 10^{-7}\) & \(1.42\times 10^{-4}\) \\ \(GP\) & \(7.82\times 10^{-14}\) & \(7.80\times 10^{-14}\) & \(7.80\times 10^{-14}\) \\ \(R_{2}\) & \(2.90\times 10^{-3}\) & \(3.14\times 10^{-5}\) & \(1.92\times 10^{-2}\) \\ \(H_{3,4}\) & \(2.15\times 10^{-6}\) & \(2.15\times 10^{-6}\) & \(2.15\times 10^{-6}\) \\ \(S_{4,7}\) & \(5.66\times 10^{-7}\) & \(5.66\times 10^{-7}\) & \(5.66\times 10^{-7}\) \\ \(P_{40,5}\) & \(1.07\times 10^{-1}\) & \(4.54\times 10^{-1}\) & \(7.06\times 10^{-1}\) \\ \(T_{6}\) & \(2.50\times 10^{-3}\) & \(4.04\times 10^{-1}\) & \(8.50\times 10^{-5}\) \\ \(RT_{10}\) & \(3.27\times 10^{-13}\) & \(0.00\) & \(1.80\times 10^{-3}\) \\ \(R_{10}\) & \(5.40\times 10^{-1}\) & \(7.99\times 10^{-1}\) & \(1.85\) \\ \(RT_{20}\) & \(8.57\times 10\) & \(7.70\times 10\) & \(9.77\times 10\) \\ \(R_{20}\) & \(7.38\) & \(8.36\) & \(8.94\) \\ \(PW_{24}\) & \(2.05\times 10^{-10}\) & \(3.22\times 10^{-9}\) & \(2.22\times 10^{-3}\) \\ \(DP_{25}\) & \(6.80\times 10^{-1}\) & \(7.99\times 10^{-1}\) & \(9.06\times 10^{-1}\) \\ \(AK_{30}\) & \(4.44\times 10^{-15}\) & \(3.03\times 10^{-12}\) & \(3.44\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average errors using different settings of parameter \(R\) for global optimization problems using Test Set A.  
Figure 1: Average errors using different settings of parameter \(R\) for simulation-based optimization problems using Test Set C.  
The DEDA results were compared with those of the scatter search methods introduced in [10]:  
* Scatter Search (SS): The standard scatter search method. * Directed Scatter Search (DSS): An SS-based method directed by a memory-based element called gene matrix in order to increase the search diversity. Table 4 shows the average errors (Av. ), standard deviation (Std.) and success rates (Suc.) obtained by each method using Test Set A. In general, the DEDA obtained lower average errors and higher success rates than the other two methods, as can be seen in the ranks \(R^{+}\) and \(R^{-}\) in Table 5. However, there was no significant difference between these methods according to the \(p\)-values obtained by the Wilcoxon rank-sum test, as shown in Table 5.
## 3 Finite Element Modeling
### Calculation of Thickness and the Average Temperature of Freezing Wall  
On 18 May 2015, the temperature measurements were started at the temperature measurement points on the measured path of the connecting passage. By 9 June, the temperature at all the measured points dropped below 0 \({}^{\circ}\)C. A temperature-measured point on the C8 path with the highest temperature, No. 3, was selected as an example to analyze the development rate of the frozen wall. On 9 June, the temperature at this measured point fell below 0 \({}^{\circ}\)C, which indicated that this place had been frozen since then. The measured point was 0.7 m away from the freezing hole. Thus, the freezing wall development rate was 30.43 mm/d. Table 3 shows the lowest freezing wall development rate at the freezing paths.  
It can be seen from Table 3 that the slowest freezing wall development rate was 15.38 mm/d and the fastest freezing wall development rate was 44.74 mm/d. The average speed of all measurement points was 36.59 mm/d. The radius of frozen wall development in 40 days was 1463.75 mm. The thinnest thickness of the frozen curtain was 2825 mm, according to the calculation of a cylindrical intersection circle in the frozen wall. The effective frost wall thickness after the connecting passage freezing process was 2.0 m; hence, the freezing curtain thickness could reach the design requirements.  
\[t_{\rm{oc}}=t_{b}\bullet\left(1.135\ -\ 0.352\sqrt{L}\right)-0.875\frac{1}{ \sqrt[3]{E}}+0.266\sqrt{\frac{L}{E}})-0.466\]  
where \(t_{\rm{oc}}\) is the average temperature of the frozen wall; \(t_{b}\) is the temperature of the brine, \(L\) is the distance between the freezing tube, and \(E\) is the thickness of the frozen wall.  
The average temperature of the frozen wall was obtained as \(-9.859\,^{\circ}\)C, which was slightly lower than the average temperature of the frozen wall, which was \(-10\,^{\circ}\)C. The  
Figure 4: Schematic representation of the observation section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sr. No.**} & **Distance From the Freezing Tube (m)** & **Days Needed to Freeze to 0 \({}^{\circ}\)C (Days)** & **Freezing Wall Development Rate (mm/Day)** \\ \hline C1 & 0.7 & 16 & 43.75 \\ C2 & 0.85 & 17 & 50 \\ C3 & 0.2 & 13 & 15.38 \\ C4 & 0.38 & 23 & 16.52 \\ C5 & 0.7 & 17 & 41.18 \\ C6 & 0.85 & 16 & 53.13 \\ C7 & 0.85 & 19 & 44.74 \\ C8 & 0.7 & 23 & 30.43 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Slowest freeze wall development rate summary at the measured point.  
reason for the slightly higher temperature at each measured point on the C3 path was that the measured path was far away from the freezing tube or/and the installation of the measured path was affected by a serious deviation caused by the poor quality of the borehole during construction [20].
## 3 Strength Characterization
### Influence pf the Water-Cement Ratio on the Strength of the Cement  
The fillings with different water-cement ratios were prepared, and the strength values of the cemented fillings were measured at 3, 7, and 28 days to investigate the effect of the water-cement ratio on the compressive strength. The variation patterns of the cemented fillings strength with the water-cement ratio are exhibited in Figure 4, and the corresponding fitting relationships are presented in Table 6.  
According to Table 6, when the regression between the water-cement ratio and strength is fitted with a power function relationship, the accuracy of the fit is high, suggesting that the rate of strength change of the backfill can be expressed with a power function relationship when the water-cement ratio is the variable. Therefore, the UCS of the cemented backfill is a negative power function of the water-cement ratio. It can be observed from Figure 4 that the water-cement ratio for this test has a wide range of values (1.8-2.7), and the overall law of strength variation is as follows: under the given conditions, the strength of the test block decreases substantially with increasing water-cement ratio. An increase in the water-cement ratio also means that the concentration of the slurry decreases. When the water-cement ratio is at a certain level, the water content will play a vital role in the hydration process of the cement, and as the amount of added cement increases, the hydration reaction of the cement will be more intense thus leading to an increase in strength.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Equation** & & **y = a \(\times\) xb** & \\ \hline Drawing & 28-d compressive strength & 7-d compressive strength & 3-d compressive strength \\ a & \(15.93273\pm 3.91276\) & \(5.1776\pm 0.42853\) & \(3.2895\pm 0.44903\) \\ b & \(-2.03257\pm 0.32082\) & \(-1.31547\pm 0.10624\) & \(-1.04532\pm 0.17405\) \\ Reduced Chi-Sqr & 0.15017 & 0.00548 & 0.00916 \\ R\({}^{2}\) & 0.76328 & 0.92307 & 0.73858 \\ Adjusted R\({}^{2}\) & 0.74507 & 0.91715 & 0.71847 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Fitting relationships between the water–cement ratio and UCS.  
Figure 4: Effect of the water–cement ratio on the UCS.
# PSNet: Parallel-Convolution-Based U-Net for Crack Detection with Self-Gated Attention Block
## 3 Results and Discussion
### Effects of Using a Different Number of Blocks in the Parallel Convolution Module  
For the purpose of evaluating the effect of using a different number of blocks in the Parallel Convolution Module, we conducted an experiment.  
As shown in Table 4, it can be seen that the number of blocks used in the PCM could affect the final accuracy of the model. From the experimental results, we have the following observations: Firstly, the number of blocks in the PCM has different impacts on the four datasets. The range in variation is about 3%. Secondly, with the increase in the number of blocks in the PCM, the final segmentation accuracy firstly goes up, reaches the maximum value, and then drops down. The main reason is that different levels of features can be fused along with the increasing of blocks. However, it might bring some useless features for the final classification if the number of blocks continues rising. Thus, this useless information would interfere with the final segmentation.
## 5 Numerical Results
### Convergence Performance  
Considering the relationship between the DDPG-based algorithm's performance and the parameters in the system, we first conducted several experiments to find the appropriate learning rate and discount factor. Meanwhile, as shown in Figures 4 and 5, the proposed DDPG-based algorithm's convergence performance is displayed. Figure 4 depicts the average rewards under different learning rates.  
The average reward is obtained by \(\sum_{t=1}^{T_{\text{z}}}\frac{t_{\text{z}}}{N_{i}}(N_{i}=1,2,\cdots,T_{\text{ max}})\), where \(T_{\text{max}}\) denotes the maximum time steps. It can be obtained from the figure that the maximum average reward can be achieved when the learning rate is 0.01. Figure 5 is the convergence performance under different discount factors. The figure shows that the algorithm performs better than others when the discount value is 0.7. Therefore, we set the learning rate and discount value as 0.01 and 0.7 in the following experiments for the DDPG-based framework. Moreover, it can be obtained that average rewards increase with the number of training time steps and finally converge at about \(10^{4}\) rounds.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Value** \\ \hline \(M\) & Number of antennas at BS & 8 \\ \(N\times N\) & Number of IRS elements & 64 \\ \(K\) & Number of UEs & 8 \\ \(\text{pmax}\) & Power budget of BS & 10 dB \\ \(p_{k}\) & Transmit power of the UE & 30 dBm \\ \(\sigma^{2}\) & Noise variance & \(-85\) dBm \\ \(B_{k}\) & Bandwidth allocated to UE \(k\) & 2 MHz \\ \(d_{k}\) & Input data size of task & \(U[1,2]\) Mbits \\ \(c_{k}\) & Required computation cost & \(U[1,2]\) Kcycles/bit \\ \(F_{o}^{\text{tol}}\) & CPU frequency of BS server & 10 Gcycles/s \\ \(f_{l,k}\) & CPU frequency of UE & \(U[1,2]\) Gcycles/s \\ \(\xi_{k}\) & Maximum tolerable latency & 100 ms \\ \(\kappa_{o},\kappa_{l}\) & Effective capacitance coefficient & \(10^{-26}\), \(3\times 10^{-26}\) \\ \(\alpha_{\mu},\alpha_{Q}\) & Learning rate for actor and critic networks & 0.001, 0.001 \\ \(\gamma\) & Discount factor & 0.7 \\ \(\epsilon\) & Soft update factor & 0.01 \\ \(\mathcal{M}\) & Capacity of experience buffer & 10,000 \\ \(J\) & Capacity of minibatch & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameter values.  
Figure 4: Convergence performance under different learning rates.
# Effects of Transport Network Slicing on 5G Applications
## 6 Impact of Network Slicing on 5G Applications
### The Slicing Applications  
To investigate how network slicing improves applications' performance, we configured a slice with a CIR ratio of 100% for each application on the P4 switches. The solid curves in graphs (a), (c), and (e) represent the output measures of the slicing applications, which gain a significant improvement over those of the baseline applications. The solid curves in graphs (c) and (e) show that, when the link is saturated, the loss rate and latency of the slicing video with UDP increase proportionally to the background traffic volume. This phenomenon does not occur with other slicing applications with TCP because TCP congestion control reduces the sending rate when congestion occurs, limiting the number of packets dispatched to the low-priority queue in the P4 switches. However, the slicing video with UDP has more packets marked yellow dispatched to the low-priority queue. These packets contend for available bandwidth with the background traffic. Therefore, the average latencies of the video with UDP increase in proportion to background traffic.  
The solid curves in graphs (a) and (c) show that a 100% CIR ratio still leads to about 1.21% to 1.73% throughput degradation with almost 0% loss rates for the slicing smart switch, about 9% throughput degradation with about 0.4% to 0.5% loss rates for the slicing sensor fusion, and about 3% to 5% throughput degradation with about 3% to 5% loss rates for the slicing video. By contrast, the same CIR ratio reaches a throughput percentage of 100% without any packet loss for the slicing MapTalk, indicating that applications with different traffic patterns may require different CIR ratios. Therefore, we further studied the network slicing effect on applications' output measures by varying the CIR ratios to verify this idea.  
Graphs (b), (d), and (f) in Figure 7 demonstrate that increasing the CIR ratio can improve the applications' output measures when the total demand is larger than the link capacity. These figures also confirm that the smart switch, the sensor fusion, and the video require a CIR ratio above 100%. In graph (d), the CIR ratios above 100% considerably reduce the loss rates of the smart switch, the sensor fusion, and the video. For the smart switch, the loss rate drops to 0% at a 120% CIR ratio. For the sensor fusion, the loss rate drops to 0.14% at a 120% CIR ratio and 0.0035% at a 140% CIR ratio. As for the video, the loss rate drops to 0.9% at a 120% CIR ratio and 0.0017% at a 140% CIR ratio. Figure 7b indicates that a reduction in the CIR ratio causes a more significant impact on the sensor fusion than other applications. Figure 7d shows that, with the same CIR ratio, the video has a higher loss rate than other TCP applications because of TCP congestion control.
## 4 Discussion
### Limitations and Prospects of Land Cover Classification in QLM  
There are many factors that can lead to an error in land cover classification. The phenomenon of \"the same subject with different spectra\" and \"different subjects with the same spectra\" are the most vital reasons for the misclassification of land cover types. For example, misclassification of forest and grassland occurred in some areas in this study. Secondly, the limitations of the classifiers also lead to classification biases. Specifically, although the three methods achieved high classification accuracy, there were still some problems worth noting. For example, the CART classifier had the problem of confusion between bare land and water bodies. The SVM had a good recognition effect on urban areas, but it recognized a very small area of bare land as construction land. In general, the land cover types with the fewest misclassification phenomenon were water bodies and permanent snow and ice. Third, the problem of mixed pixels was also one of the sources of classification errors. In addition, the error source of classification was also caused by cloud removal and hill shadows. The cloud removal process was performed during the preprocessing of remote sensing images, but there were still very few cloud shadows and hill shadows in the composited image, which created the classification biases.  
With the gradual enrichment of remote sensing data sources, the opportunities and challenges involved in obtaining land cover products using remote sensing image identification and classification are gradually increasing. The gradual improvement of the spatial resolution of satellite data will produce more land cover products with finer spatial resolutions. The application of multisource data fusion and the continuous update and improvement of the classification algorithms will greatly improve of the accuracies of the products. Therefore, remote sensing images with higher temporal and spatial resolutions can be used as data sources for land cover studies in the future, and the classification system can be further refined. In addition, shortening the time period of the products in the QLM, such as the production of land cover products on a quarterly or monthly basis, will be more conducive to monitoring the vegetation changes in the area, and will play a better auxiliary role in the studies of seasonal changes and mutation detection of vegetation in this area.
# Dominant Channel Occupancy for Wi-Fi Backscatter Uplink in Industrial Internet of Things
## 3 Design of the DCO  
The DCO is designed to guarantee the timeliness and reliability of Wi-Fi backscatter uplink communication. To this end, the DCO provides four types of MAC configurations for the purpose of the high-priority channel access and reliable burst transmission of Wi-Fi backscatter devices. Three of them are used to support high-priority channel access, and one of them is used for reliable burst transmission. In the following subsections, we describe the design of the DCO in detail.
## 2 Beidou-Binocular Cooperative Navigation Model
### Model Framework and Implementation
#### 2.4.3 System Implementation Process  
The specific implementation process of the BBCN model is shown in Figure 6.  
Through the fusion processing of multi-sensor data, the collaborative optimization of global and local path planning, and real-time dynamic obstacle avoidance control, the BBCN model can provide efficient navigation capabilities in complex environments. The next chapter will cover the simulations and physical experiments conducted to validate the model and evaluate its actual performance.
# Privacy-Preserving Data Analytics in Internet of Medical Things
## 5 Proposed Method for Privacy Preservation in Healthcare Data
### Dataset Description
#### 5.3.1 Breast Cancer Prediction Dataset  
The Breast Cancer Prediction Dataset used in this research is publicly available on Kaggle [47]. The dataset contains information from 20,000 digital and 20,000 film-screen mammograms collected from women in the age range of 60-89 years for breast cancer prediction. It has almost 30,000 instances (patient records) with 13 attributes.
## 5 Experimental Results and Discussion
### Comparison between Single-Frequency Hammering and Variable-Frequency Hammering
#### 5.3.2 Classification Using Variable-Frequency Hammering MFCCs  
Table 5 shows the SVM classification results for two classification forms of the variable-frequency hammering MFCCs under three kernel functions, with the highest accuracy of over 97% for both classification forms, which is higher than that of the three single-frequency hammering cases. This demonstrates that the frequency conversion of MFCCs is effective at classifying concrete damage and better than the single-frequency MFCC, that is, compared with single-frequency hammering, the acoustic characteristics obtained from the acoustic signals of variable-frequency hammering are more effective at classifying the damage above the second category in Section 5.3.1.  
The experimental results showed that the classification prediction accuracy of the method based on variable-frequency hammering for concrete is high, reaching 97.5% for the two categories and 97.8% for the four categories. Compared with single-frequency hammering, it is able to distinguish multiple types of damage, surface, internal, and combined surface-internal, more effectively.