esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
The ground subsidence values above the backfilled area from the physical simulation and numerical simulation, and those measured on site, were analyzed to determine the ultimate ground subsidence and evaluate the effect of solid backfill mining on ground-surface infrastructure. It was found that the 80% filling ratio was able to control the strata and ground subsidence in the two simulated experiments and those measured on site. The maximum ground subsidence value of the field measurement is 47 mm, the maximum ground subsidence value of the physical simulation is 0.46 mm, and the maximum ground subsidence value of the numerical simulation is 50.4. These three values are much smaller than the extreme surface subsidence value expected from the prediction calculation, which was 281 mm. Moreover, due to their stress similarity, the vertical stress results from the physical analog modeling show consistency with the field measurements as well as the numerical simulation. The vertical stress of the physical simulation above the goat ranges from 0.05 to 0.1 MPa, and the peak values of the vertical stress above the left and right boundary coal pillars are, respectively, 0.081 and 0.056 MPa. These results indicate that the backfill body effectively distributed the vertical stress above the goat area, reducing the stress concentration, the vertical movement of the overlaying strata above the goat area, and ground subsidence. This was one and the same for the numerical simulation's vertical stress; above the goat it ranges from 13.7 to 16.62 MPa, and the peak value of the vertical stress around the working face edges and backfill body is 22.33 MPa. The physical and numerical simulations are consistent with the field measurement results. Furthermore, the damage level of the buildings on the ground is level I and consistent with the predicted results, without the need for repair or reinforcement. This means that the backfill body greatly reduces and controls ground subsidence and ensures the safety of ground-surface infrastructure. Contrary to the caving method, no obvious damage or cracks were detected in the ground-surface infrastructure above the working face. Therefore, a solid backfill method with an 80% filling ratio is effective and should be used for ground control in similar mining operations. This method has been shown to efficiently prevent ground subsidence and distribute vertical stress, reducing the risk of damage to surface infrastructure. To maintain a steady workflow and meet the mine's objectives, backfilling efficiency is very important. When the backfilling process is inefficient, it can affect subsequent activities and overall project schedules. The method's performance in the Tangshan coal mine implies  
Figure 18: Cumulative subsidence values of S-line measuring points.  
that it can be used reliably in other coal mines to improve ground stability and assure the safety of surface buildings. Further study and continual monitoring, especially in different mining layouts and at different sizes, should be carried out to optimize the filling ratio and improve the backfill materials for even better results.
## 4 Results and Discussion
### Results  
#### 4.2.1 Production-Inducing Effects  
South Korea and Japan are presented in Table 2. It was demonstrated that a USD 1 investment in the NG supply sector in South Korea induces production of USD 0.07919 for other industries, USD 1 for its own industry, and a total of USD 1.07919. Meanwhile, in Japan, a USD 1 investment in the NG supply sector induces the production of USD 0.86142 in other industries and USD 1 in its own industry for a total of USD 1.86142. In other words, it was found that the production-inducing effects of the NG supply sector in Japan were about 1.7 times greater than those in South Korea.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Sectors** & \\ \hline
1. & Agricultural, forest, and fishery goods \\
2. & Mined and quarried goods \\
3. & Food, beverages, and tobacco products \\
4. & Textile and leather products \\
5. & Wood, paper products, printing, and reproduction of recorded media \\
6. & Petroleum and coal products \\
7. & Chemical products \\
8. & Non-metallic mineral products \\
9. & Basic metal products \\
10. & Fabricated metal products, except machinery and furniture \\
11. & Computing machinery, electronic equipment, and optical instruments \\
12. & Electrical equipment \\
13. & Machinery and equipment \\
14. & Transport equipment, manufacturing services, and repair services of industrial equipment \\
15. & Other manufactured products \\
16. & Electricity and steam supply \\
17. & Water supply, sewage, waste treatment, and disposal services \\
18. & Construction \\
19. & Wholesale, retail trade, and commodity brokerage services \\
20. & Transportation \\
21. & Food services and accommodation \\
22. & Communications and broadcasting \\
23. & Finance and insurance \\
24. & Real estate services \\
25. & Professional, scientific, and technical services \\
26. & Business support services \\
27. & Public administration, defense, and social security services \\
28. & Education services \\
29. & Health and social care services \\
30. & Art, sports, and leisure services \\
31. & Other services \\
32. & Others \\
33. & Natural gas supply \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sector classification adopted in this study.  
In South Korea, the production-inducing effect of other industries due to investment in NG was high in the order of \"2. Mined and quarried goods\", \"23. Finance and insurance\", \"26. Business support services\", and \"20. Transportation\". In Japan, the production-inducing effect of other industries due to investment in NG was high in the order of \"2. Mined and quarried goods\", \"20. Transportation\", and \"26. Business support services\". The production-inducing effect from investment in the NG supply sector was the largest in the \"2. Mined and quarried goods\" sector in both countries. It is believed that this is because NG is one of the products of the \"2. Mined and quarried goods\" sector. In addition, because LNG is imported through sea transportation in both countries, it is judged that the production-inducing effect was significant in the \"20. Transportation\" sector.
# Vegetation Loss Measurements for Single Alley Trees in Millimeter-Wave Bands
## 4 Results of Measurements  
Measurements were conducted for Scenarios 1 and 2; for each Scenario, the other set of trees was taken into account. Then, in each scenario, the same set of trees was considered, but two directions of measurement were considered. Precisely, the transmit antenna was set in two variants at two ends of the tree row highlighted in the legend as'measurement 1' and'measurement 2'. In Figures 11 and 12, the results of the measurements are presented for the two scenarios for four carrier frequencies. Foliage attenuation is there presented in the function of foliage depth. Therefore, the point 'zero' at the depth of the foliage is the beginning of the first tree in the tree alley. In each measurement scenario, Tx was located 4 m away from the first tree corona, as presented in Figure 8.  
The general observation of the measured foliage attenuation is that the greatest increase in attenuation is between the second and third trees in the alley. This is in line with the exponential character of the loss described precisely in Section 2.  
In Figures 13-20 the measurements are presented and divided by frequency and scenario. They are compared with three reference models (COST-235, Weissberger, and FITU-R) and two statistical models with tuned parameters, according to frequency. Maximum attenuation is presented as 'Model 1' and has \(A_{m}\) and \(\gamma\) tuned, while MED, named in the figures as 'Model 2', has three parameters tuned. In addition, in the legend of each plot, the value of the root mean square error is given.  
Figure 12: Foliage attenuation for Scenario 2.  
Figure 13: Comparison of measurements and statistical models in Scenario 1 at frequency 26 GHz.  
Figure 16: Comparison of measurements and statistical models in Scenario 1 at frequency 32 GHz.  
Figure 14: Comparison of measurements and statistical models in Scenario 1 at frequency 28 GHz.  
Figure 15: Comparison of measurements and statistical models in Scenario 1 at frequency 30 GHz.  
Figure 19: Comparison of measurements and statistical models in Scenario 2 at frequency 30 GHz.  
Figure 17: Comparison of measurements and statistical models in Scenario 2 at frequency 26 GHz.  
Figure 18: Comparison of measurements and statistical models in Scenario 2 at frequency 28 GHz.  
In Figures 21 and 22, all measurements from Scenario 1 with parameter fitting can be found. The MA and MED models were found according to the minimum root mean square error (RMSE) and the minimum median of squared errors (LMedS). The minimum median error in Scenario 2 was found for the MA model (0.73 dB) and in Scenario 2 for the MED model (1.75 dB).  
Figure 21: All measurements and tuned models for Scenario 1.  
Figure 20: Comparison of measurements and statistical models in Scenario 2 at frequency 32 GHz.
## 4 The Proposed h-POQL MPPT Control
### Simulation Results
#### 4.3.2 Simulation and Validation of h-POQL MPPT Controller  
In this section, eight Q-learning controllers in the relative control zones were trained to find the optimal values of the duty cycle. The simulated results are shown in Table 7. In the next stage, different operating conditions are used to evaluate the performance of the h-POQL controller. First, the temperature of the power source is set to 25 \({}^{\circ}\)C, and the irradiation is switched between 450, 650, 750, and 950 W/m\({}^{2}\). Later, the irradiation is fixed to 1000 W/m\({}^{2}\) and the temperature changes between 15 \({}^{\circ}\)C and 35 \({}^{\circ}\)C. Results in Figures 19 and 20 show that for all cases the controller can perform with the fast convergences to the steady state and operates at the maximum power point condition, compared to the theoretical data of the PV module.  
Figure 19: Output powers under the change of irradiation.  
Figure 18: Q-learning MPPT training based on the standard test conditions (G = 1000 W/m\({}^{2}\) and T = 25\({}^{\circ}\)C).  
Finally, the proposed hybrid controller is compared with the P&O method based on the change of both temperature and irradiation, as shown in Figure 21. The results in Figure 22 illustrates that the step size of the P&O can be reduced from 0.0005 to 0.00005 in the h-POQL controller. Thus, it can overcome the oscillation drawback of the P&O method. Moreover, more power was generated by h-POQL controller with the change of weather conditions as indicated by the blue line in the graph. In conclusion, a better performance of the h-POQL over the P&O can be validated.
# Nanomaterials in Craniofacial Tissue Regeneration: A Review
## 2 Overview of Nanomaterials in Crani Tissue Engineering
### Composite Nanomaterials  
Composite-based nanomaterials are created by combining a minimum of two nanomaterials together, usually containing a matrix material and nanoscale particles. The use of such a technique allows researchers to synthesize novel nanomaterials with unique characteristics, or to harness desired physical and mechanical properties from multiple types of nanomaterials [22]. For example, in some studies, nanomaterials have been combined to create novel composite nanomaterials with similar properties to that of human bone. When creating a composite-based nanomaterial, various properties, including biocompatibility and biofunctionality, modulus of elasticity, surface hardness, degree of polymerization shrinkage, compressive strength, tensile strength, and material flow, should be considered [22; 23; 24]. In dentistry, composite-based nanomaterials are often the result of combining biocompatible and biofunctional ceramics and polymers [22].
## 3 Results and Discussion
### Emission Characteristics
#### 3.5.1 CO Emissions  
First, the overall carbon monoxide emissions (CO) for all the tested fuels are displayed in Figure 7 at different load conditions.  
The load condition features an inverse relation with the CO emission levels. The engine load plays a central role in the emissions behavior since, at high engine loads, it can be reached a non-oxygenated condition that promotes CO formation. However, the high CO formation remains high at low engine loads due to a highly lean mixture that hinders fuel burning. Thus, the flame cannot be maintained due to the limited propagation speed.  
Figure 7: CO emissions for fuels tested.  
Specifically, the ABE implementation reduces CO emissions between 11% to 33% compared to gasoline, depending on the engine load. The HHO implementation enables a further reduction of up to 22%. The enhanced behavior from the fuel blends results from promoting complete combustion, meaning that more CO is converted into CO\({}_{2}\). Additionally, the ABE incorporation facilitates oxidation due to the increased laminar flame speed and the leaning effect of its oxygenated nature. The higher volatility derived from the acetone in the ABE blends further promotes CO minimization. [PERSON] et al. (2016) encountered that the ABE features a post-flame oxidation trend that reduces CO levels.  
The implementation of HHO doping in the intake air minimizes CO levels, which can also be explained considering the direct oxygen enrichment in the air/fuel mixture. CO emission is directly linked to the air/fuel ratio within the engine and the fuel consumption. Hence, since hydroxy enrichment reduces the BSFC (Figure 6), fewer CO levels are evidenced in the exhaust gases.  
It is worth underlying the trade-off of the proposed fuel methodology since the CO minimization resulted in intensified CO\({}_{2}\) formation. Considering that international regulations claimed for integral solutions to meet CO\({}_{2}\) standards, it triggers a collateral cost impact in the proposed dual-fuel technology due to the imminent necessity to engage additional methods to promote a sustainable operation in future scenarios. [PERSON] et al. (2013) have pointed out the predominant role of advanced fuel injection systems in this goal. Moreover, the latter technology is accompanied by additional improvements in fuel economy and combustion noise that reinforce its implementation. Undoubtedly, implementing such sophisticated fuel injection systems increases the investment cost that can be only supported by substantial fuel savings while maintaining high-efficiency operation. Moreover, considering the effect of combustion phasing, the necessity to control spark timing to promote higher combustion efficiencies requires in-depth exploration of engine design characteristics and fuel injection systems that foster a techno-economic operation.
## 3 Simulations
### Influence of Image Noise  
In this case, the diameter of the circle is 50 mm and the target is placed twice. Gaussian noise is added to the circular target profile and the light stripe used for calibration. The noise level varies from \(\sigma\) = 0 mm to \(\sigma\) = 1 mm at intervals of 0.1 mm. The relative errors of  
Figure 6: Geometrical layout and dimensions of the simulation case (unit: mm).  
Figure 7: Influence of the number of circular target placements on the calibration accuracy.  
the calibration data obtained at different noise levels are depicted in Figure 8. This shows that the calibration accuracy decreases as the noise increases. In the actual experiment, the pixel size of the camera sensor is 3.5 um. According to the image processing methods described, the extraction process can reach a precision of 0.2 pixels or 0.7 um, which corresponds to a relatively small error.
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 2 Related Work
's intelligent, edge-driven, multi-source data fusion framework, combining the lightweight YOLOX-s network and transfer learning, achieves real-time vessel detection and the efficient fusion of AIS data, enhancing the navigation capabilities of autonomous surface vessels [38]. Despite significant progress in waterway perception, some limitations persist. For example, in vessel detection within inland waterways, vessels vary considerably in size, and some targets exhibit small features and low resolution. The loss of target feature information caused by convolution and pooling operations often results in misdetectionand the missed detection of small targets. To address these challenges, this paper proposes a target detection algorithm designed to enhance the features of small targets and low-resolution objects. Additionally, due to the inherent latency of AIS data, real-time matching with video data remains challenging in practical applications. To overcome this issue, this paper predicts historical AIS data to synchronize with video data, followed by data integration using a region-based partitioning matching method.
## 3 Results and Discussion
### Kinetic Parameters of Oil Oxidation  
Based on the oxidation onset temperatures obtained in the PDSC assay in different heating rates, regression analysis was carried out. The results are shown in Table 5. Activation energies ranged from 82.98 to 92.01 kJ/mol. In the case of all the studied seed oils, UAE resulted in slightly higher \(E_{a}\) values. The findings were similar to results obtained for UAE cranberry seed oil [10] and redcurrant seed oil [11]. Also, activation energy values for berry seed oils were higher than activation energies of rapeseed and sunflower oils obtained in the accelerated storage study [35]. However, similar \(E_{a}\) (97.43-99.94 kJ/mol) were obtained for olive oils by [PERSON] and [PERSON] [36]. According to [PERSON] et al. [37], \(E_{a}\) is correlated with the fatty acid profile of oil, and the more PUFAs are found in oil, the higher \(E_{a}\) values are observed. Also, bioactive compounds presence influences the \(E_{a}\) of oil. Oxidation rate constants in 100-140 \({}^{\circ}\)C ranged from 0.0018 to 0.053, with only slight differences between oils obtained in different extraction procedures. Briefly, it can be observed that extraction method does not influence the kinetic parameters of oil oxidation and that these features are mostly dependent on the source of oil.
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 3 Fabrication of Hydrogels
### Chemical Crosslinking
#### 3.3.5 Dies-Alder Reaction  
The most widely utilized click chemistry in hydrogel synthesis is the Diels-Alder reaction. The concept of click chemistry is largely based on its practical advantages including versatility, simplicity, and efficiency [180]. The Diels-Alder reaction is a highly selective and efficient cycloaddition reaction between a diene compound and a dienophile like maleimide, and it exhibits rapid reaction kinetics in aqueous solution [141].  
The Diels-Alder reaction does not mandate the use of any initiators or coupling agent nor produce byproducts yet could form a covalent bond at generally high yield and maintain its reactivity and specificity at varying conditions [141, 152]. One unique feature is that the stereochemistry of functional groups on participating molecules determines the stereochemistry of the resulting structure, enabling three-dimensional control over molecular structures [181]. This method of crosslinking is highly versatile as far as the portion of participating molecules suffice the criteria of the diene-dienophile pair; thus, it is widely compatible for both natural and synthetic hydrogels.
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 6 Experimental Results and Analysis
### Analysis of Model Performance Differences  
An intriguing aspect of our findings is that traditional machine learning models, particularly Random Forest, outperformed more complex deep learning architectures despite the latter's theoretical advantages in representation capacity. Several factors contribute to this phenomenon:  
1. Data Volume Limitations: Deep learning models typically require larger datasets to generalize effectively. Our experiment utilized a moderate-sized dataset (92 test samples), potentially limiting the full expression of deep learning capabilities. This is evidenced by the cross-validation results, where CNN showed higher mean accuracy (87.12%) than its test accuracy (79.35%), suggesting some overfitting.
2. Feature Engineering Effectiveness: Random Forest's superior performance underscores the value of our statistical feature extraction approach. The handcrafted features  
Figure 12: Comparative analysis of model complexity and inference latency, emphasizing tradeoffs between accuracy and efficiency across all classifiers.  
effectively capture the discriminative characteristics of different activities, providing Random Forest with highly informative inputs that may obviate the need for deep learning's automatic feature extraction.
3. Model-Data Compatibility: The ensemble nature of Random Forest, with its ability to handle non-linear relationships and feature interactions without making strong assumptions about data distribution, appears particularly well suited to human activity recognition data, which exhibit both statistical regularities and stochastic variations.
4. Architectural Limitations: Standard Transformer models struggled the most (65.22% accuracy), which was likely due to their reliance on global attention mechanisms that may not be optimal for capturing the localized patterns in activity data. In contrast, TransUNet's hybrid architecture (91.30% accuracy) successfully combines convolutional feature extraction with Transformer modules, approaching Random Forest's performance.  
These findings highlight an important consideration for IoT applications: the most sophisticated model is not always the most effective, particularly when computational efficiency is considered alongside accuracy. Random Forest offers an optimal balance of accuracy, interpretability, and efficiency for this specific activity recognition task.  
In summary, our experimental results validate the efficacy of our integrated system in recognizing human activity states with high accuracy and computational efficiency. The comprehensive evaluation across traditional and deep learning models, combined with statistical validation and efficiency analysis, provides valuable insights for deploying activity recognition systems in real-world IoT environments with varying resource constraints.