esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
### Ablation Experiments  
The above comparative experiments demonstrate the advantages of the LDE-Net in the semantic segmentation of outdoor environments. In order to better understand the network and evaluate the role of each module, we conducted several ablation experiments as follows. We focused on two modules in LDE-Net: local-global feature extraction modules (LGFE) and the local feature aggregation classifier (LFAC). The purpose of ablation experiments is to investigate the impact of these two modules on the overall semantic segmentation accuracy of the network.  
In Table 5, BaseNet represents the basic network without these two modules, LGFE represents the local-global feature extraction module, and LFAC represents the local feature aggregation classifier.  
As shown in Table 5, the mIoU of a network without LGFE is 2.8% lower than the LDE-Net with LGFE. The mIoU without LFAC module is 1.6% lower than that with LFAC module. Although the two modules will slightly increase the number of arguments and inference time, the increase in mIoU is significant. This shows that local feature aggregation classifiers and local-global feature extraction module can improve the context-aware ability of the classifier by referring to neighborhood features, thus improving the overall accuracy.  
From the above experiments, it can be seen that each module in LDE-Net has its own meaning, and the combination can achieve the best effect. Both LGFE and LFAC can enhance the model's local perception ability. Therefore, it is meaningful to use all modules of LDE-Net for semantic segmentation in colored outdoor environment, and good results can be obtained.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Total Time (Seconds)** & **Parameters (Millions)** & **mIoU (\%)** \\ \hline PointNet & 192 & 0.8 & 14.6 \\ PointNet++ & 9831 & 0.97 & 20.1 \\ SPG & 43,584 & 0.25 & 17.4 \\ RandLA-Net & 185 & 1.24 & 53.9 \\ LDE-Net (Our) & 191 & 1.32 & 54.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental of model complexity and inference time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **mIoU** & **Total Time** & **Parameters** \\  & **(\%)** & **(Seconds)** & **(Millions)** \\ \hline Base-Net & 50.1 & 173 & 1.10 \\ Base-Net + LGFE & 53.3 & 189 & 1.30 \\ Base-Net + LFAC & 52.1 & 190 & 1.27 \\ Base-Net + LGFE + LFAC (LDE-Net) & 54.9 & 191 & 1.32 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment of LDE-Net on SemanticKITTI dataset.
## 3 Algorithm Description
### Q-Table Initialization
The centralized approach of periodically acquiring global satellite network information and reinitializing the Q-tables can effectively reduce the computational burden of online training on satellite nodes, while simultaneously improving routing accuracy.
## 4 Conclusions
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Location**} & \multirow{3}{*}{**Model**} & \multicolumn{6}{c}{**\(R^{2}\) (\%)**} & \multicolumn{6}{c}{**RMSE (\({}^{\circ}\)C)**} \\ \cline{3-10}  & & & \multicolumn{6}{c}{**Time Step (Minutes)**} \\  & & **1** & **5** & **10** & **15** & **1** & **5** & **10** & **15** \\ \hline \multirow{8}{*}{Tri An} & Sandia \({}^{\star}\) & 94.3 & 96.1 & 97.0 & 97.5 & 2.45 & 2.06 & 1.84 & 1.72 \\  & Fairman \({}^{\star}\) & 94.4 & 96.5 & 97.5 & 98.0 & 2.69 & 2.35 & 2.17 & 2.09 \\  & PVSyst1 \({}^{\star}\) & 93.6 & 95.4 & 96.3 & 96.7 & 2.69 & 2.27 & 2.03 & 1.90 \\  & Akhassi1 \({}^{\star}\) & 94.5 & 96.5 & 97.4 & 97.8 & 4.55 & 4.46 & 4.42 & 4.41 \\  & Our model with wind \({}^{\star}\) & **95.6** & **97.8** & **98.1** & **98.1** & **2.18** & **1.63** & **1.56** & **1.55** \\ \cline{2-10}  & Lasnier & 92.5 & 93.8 & 94.4 & 94.7 & 4.19 & 4.08 & 4.03 & 4.01 \\  & PVSyst2 & 92.0 & 93.7 & 94.6 & 95.0 & 2.88 & 2.53 & 2.33 & 2.22 \\  & Akhassi2 & 91.4 & 92.5 & 93.0 & 93.2 & 5.12 & 5.08 & 5.06 & 5.05 \\  & Our model without wind & 93.3 & 95.5 & 95.7 & 95.7 & 2.63 & 2.15 & 2.09 & 2.07 \\ \hline \multirow{8}{*}{Da Nang} & Sandia \({}^{\star}\) & 95.9 & 97.3 & 97.9 & 98.2 & 2.52 & 2.22 & 2.09 & 2.01 \\  & Fairman \({}^{\star}\) & 95.2 & 96.9 & 97.6 & 98.0 & **2.20** & **1.82** & **1.64** & **1.55** \\  & PVSyst1 \({}^{\star}\) & 95.4 & 96.7 & 97.3 & 97.6 & 3.63 & 3.41 & 3.31 & 3.26 \\  & Akhassi1 \({}^{\star}\) & 95.1 & 96.6 & 97.2 & 97.5 & 2.75 & 2.61 & 2.55 & 2.52 \\  & Our model with wind \({}^{\star}\) & **96.7** & **98.5** & **98.7** & **98.8** & 2.35 & 1.95 & 1.88 & 1.87 \\ \cline{2-10}  & Lasnier & 94.6 & 95.4 & 95.8 & 96.0 & 2.21 & 2.05 & 1.96 & 1.92 \\  & PVSyst2 & 94.1 & 95.3 & 95.8 & 96.1 & 3.82 & 3.63 & 3.54 & 3.50 \\  & Akhassai2 & 93.6 & 94.2 & 94.5 & 94.6 & 3.21 & 3.14 & 3.10 & 3.07 \\  & Our model without wind & 95.0 & 96.4 & 96.5 & 96.5 & 3.72 & 3.49 & 3.46 & 3.46 \\ \hline \multirow{8}{*}{Ha Noi} & Sandia \({}^{\star}\) & 96.3 & 97.2 & 97.7 & 97.9 & 3.06 & 2.89 & 2.80 & 2.75 \\  & Fairman \({}^{\star}\) & 95.8 & 96.9 & 97.5 & 97.8 & 3.39 & 3.34 & 3.16 & 3.13 \\ \cline{1-1}  & PVSyst1 \({}^{\star}\) & 96.2 & 97.1 & 97.7 & 98.0 & **2.60** & **2.36** & **2.22** & **2.14** \\ \cline{1-1}  & Akhassi1 \({}^{\star}\) & 94.9 & 95.8 & 96.3 & 96.5 & 4.91 & 4.89 & 4.87 & 4.87 \\ \cline{1-1}  & Our model with wind \({}^{\star}\) & **96.9** & **98.2** & **98.4** & **98.5** & 2.94 & 2.66 & 2.63 & 2.63 \\ \cline{1-1} \cline{2-10}  & Lasnier & 93.7 & 94.2 & 94.5 & 94.7 & 4.84 & 4.80 & 4.78 & 4.77 \\ \cline{1-1}  & PVSyst2 & 95.6 & 96.5 & 97.0 & 97.2 & 2.94 & 2.77 & 2.66 & 2.60 \\ \cline{1-1}  & Akhassai2 & 91.4 & 91.7 & 91.9 & 92.1 & 5.14 & 5.12 & 5.11 & 5.11 \\ \cline{1-1}  & Our model without wind & 96.3 & 97.5 & 97.7 & 97.8 & 2.79 & 2.49 & 2.45 & 2.45 \\ \hline \hline \end{tabular}   
\({}^{\star}\)Models with wind speed.
## 4 Results
### Total Vcd
The corresponding value of EMI is 24.5% (19.9%) higher than OMI (TROPOMI), and these discrepancies can be attributed to the different SCD fitting parameters (Table 1), as well as the de-stripe method, used in this study. In order to evaluate the uncertainty in the retrieved SCD, we divided the Pacific clean region (60 degS-60 degN; 160 degE-180 degE) into 2 deg \(\times\) 2 deg boxes. Theoretically, the AMF of pixels within a box has little variability, and the VCD of the pixels within a box should have little difference, so the variability in the retrieved total VCD results from the error of SCD. The uncertainty of SCD can be estimated by statistically analyzing the SCD deviation between each valid EMI pixel in a box and the box mean [27]. Figure 10 shows the histograms of the absolute differences of all valid pixels in August 2018. The distribution of SCD deviation basically follows a Gaussian distribution, which demonstrated that the variability within boxes is random error. The corresponding width of the Gaussian distribution is \(0.79\times 10^{15}\) molec cm\({}^{-2}\), which was interpreted as the SCD error of all valid pixels in August 2018. Figure 8: NO\({}_{2}\) total VCD results of OMI (**a**), EMI (**b**), and TROPOMI (**c**) over the North China Plain region on 22 January, 2019. The SCD uncertainty of the current EMI NO\({}_{2}\) algorithm is 17.9% larger than that of OMI (the near real-time algorithm) after its launch of more than two years [24], and also higher than the OMI algorithms presented by [PERSON] et al. (2018). The relatively large SCD uncertainty may mainly result from the residual cloud pixels caused by the rough cloud screen method.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 8 Acoustic Emission Technique
### Timber Structures  
Multi-resonant sensors linearly distributed along a reinforced timber beam were tested by [PERSON] et al. [101] and [PERSON] et al. [102]. To eliminate the effect of the attenuation, the spectral energy is corrected by empirically derived attenuation curves. The experimental findings showed that AE is a promising technique in real-life retrofitted beams. A correlation between AE signal features and failure mechanisms for different wood species on the basis of bending tests was performed by [PERSON] et al. [103]. In this study, the AE activity was related to the degree of heterogeneity of the timber species.
## 4 Conclusions  
The effects of hydrogen injection strategies for a gasoline/hydrogen engine with various excess air ratios were investigated. The main conclusions of this research are summarized as follows:  
1. The lean-burn performance could be improved with hydrogen addition. In this study, the lean burn limit with the NHMD was the excess air ratio of 1.5, while with 20% hydrogen addition, it was the excess air ratio of 2.8;
2. With the increase in the excess air ratio, the brake thermal efficiency of the engine first increased and then decreased. Moreover, hydrogen addition improved the brake thermal efficiency. With the NHMD, the maximum brake thermal efficiency (23.83%) appeared at the excess air ratio of 1.2, while with the PPHMD, the maximum brake thermal efficiency increased to 26.86% at the excess air ratio of 1.5;
3. With the increase in the excess air ratio, the best HMD changed from the PPHMD to the SHMD. Since the engine ignition became the key factor under lean-burn conditions, the hydrogen concentration around the spark plug was the highest with the SHMD;
4. The COV of the engine increased with the increase in the excess air ratio, since the engine ignition became unstable and the engine combustion was slower under lean-burn conditions. The COV with the NHMD was higher than that with hydrogen addition, which was because hydrogen addition improved the ignition and combustion stability;
5. The HC emissions first decreased and then increased with the increase in the excess air ratio because the increase in oxygen concentration led to the deterioration of  
Figure 11: Effects of the HMD on the NOx emissions.  
combustion. The HC emissions were the highest with the NHMD and the lowest with the PHMD. The hydrogen concentration affected the HC emissions significantly. The HC emissions with the PHMD were, on average, 4.01% lower than with the PPHMD, 14.07% lower than with the SHMD and 51.96% lower than with the NHMD;
6. The NOx emissions first increased and then decreased with the increase in the excess air ratio since the engine temperature was reduced and the oxygen concentration increased. When the excess air ratio was greater than 2.0, there were almost no NOx emissions. The NOx emissions were the lowest with the NHMD and the highest with the SHMD. The NOx emissions with the NHMD were, on average, 42.22% lower than with the PHMD, 45.08% lower than with the PPHMD and 47.21% lower than that with SHMD.  
Conceptualization, S.P.; methodology, S.P.; software, S.P.; validation, S.P.; formal analysis, S.P.; investigation, S.P.; resources, J.W.; data curation, S.P.; writing--original draft preparation, S.P.; writing--review and editing, B.L. and H.D.; supervision, J.W.; project administration, Z.H.; funding acquisition, Z.H. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the National Natural Science Foundation of China (No.51876163), Shaanxi Science and Technology Innovation Team (2021 TD-22) and Major Science and Technology Projects of Inner Mongolia Autonomous Region (2021 ZD0021).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Mechanical Properties
#### 3.3.3 Elastic Modulus  
The elastic modulus of the grouting reinforcement body defines its stiffness, specifically indicating the material's resistance to deformation under applied loads. As such, it is a critical parameter in the analysis of mechanical properties. The elastic modulus is calculated by determining the slope of the linear segment in the elastic stage of the stress-strain curve for the sample using Equation (5):  
\[E=\frac{\sigma_{a2}-\sigma_{a1}}{\varepsilon_{a2}-\varepsilon_{a1}} \tag{5}\]  
where \(E\) is the elastic modulus of the grouted solid (GPa), \(\sigma_{a1}\) is the stress value (MPa) at the initial point of the linear segment on the axial stress-strain curve, and \(\sigma_{a2}\) is the stress value (MPa) at the endpoint of the same linear segment. Similarly, \(\varepsilon_{a1}\) and \(\varepsilon_{a2}\) correspond to the axial strain values at these stress points, respectively.  
The elastic modulus of each sample was calculated, and the results were organized. The influence of freeze-thaw cycles on the elastic modulus of the grouting reinforcement body is shown in the curve plotted in Figure 22.  
As shown in Figure 22, the elastic modulus of grouting reinforcement bodies, across various crack dip angles and confining pressures, exhibits a consistent trend as the number of freeze-thaw cycles increases. Specifically, the elastic modulus decreases with an increasing number of freeze-thaw cycles, with the rate of decline progressively accelerating. This behavior follows a negative exponential function. The relationship between the elastic modulus and freeze-thaw cycles of grouting reinforcement bodies under varying confining pressures is fitted using a negative exponential function, as detailed in Figure 22. The correlation coefficients for the fitting range from 0.92 to 0.99, indicating a strong fit.  
Figure 22 also demonstrates that both crack dip angle and confining pressure significantly influence the elastic modulus of the grouting reinforcement body. For the same crack dip angle and the same number of freeze-thaw cycles, the elastic modulus increases with rising confining pressure, with the most pronounced effects observed at 45\({}^{\circ}\) and 60\({}^{\circ}\) crack dip angles. The elastic modulus of samples subjected to confining pressure is notably different from those without confining pressure. This difference arises because, in the absence of confining pressure, shear slip failure occurs directly along the fracture surface once the stress on the grouting reinforcement body reaches the shear strength of the slurry-rock interface layer. However, when confining pressure is applied, the slip of the rock block is restricted, which enhances both the compressive and deformation capacities of the sample, ultimately increasing its elastic modulus.
## 4 Experimental Analysis
### Energy Dissipation and Equivalent Viscous Damping  
Energy dissipation through hysteretic damping (\(E_{d}\)) is an important parameter in the analysis of the seismic performance of the shear wall, since it reduces the earthquake damage, and, thereby, reduces the strength and ductility demands in the seismic design of the structure. The dissipated energy (\(E_{d}\)) can be calculated by the area enclosed by the hysteresis loop at each loading increment [32], as shown in Figure 13.  
Dissipated energy of the specimens in each first loading cycle is shown in Figure 14a. It was observed that the dissipated energy of all specimens exhibited a similar, considerable increase with the consecutive loading displacement, which proved the favorable energy dissipation capacity of the PRMSW. It should be noted that PMW-2 exhibited the most advantageous energy dissipation capacity because the lateral capacity of PMW-2 was larger than the other specimens, due to the higher axial compression load and the concentrated vertical rebar at the sides.  
The equivalent viscous damping ratio (\(\zeta_{eq}\)) can be used to evaluate the energy dissipation capacity of the shear wall. According to [32], the equivalent viscous damping ratio can be defined as Equation (3):  
\[\zeta_{eq}=\frac{E_{d}}{4\cdot\pi\cdot E_{s}} \tag{3}\]  
where \(E_{d}\) represents the aforementioned dissipated energy; \(E_{s}\) is the elastic strain energy of the test wall, stored in an equivalent linear elastic system in one loading cycle, and can be calculated by the trilateral area (the vertically hatched area) in Figure 13.  
Figure 12: Evolution of stiffness degradation of test walls.  
Figure 13: Calculation of energy dissipation.  
Figure 14b presents the relationship between the equivalent viscous damping ratio and the ratio of the lateral displacement of the specimen (\(\Delta\)) and the displacement corresponding to the ultimate load (\(\Delta_{u}\)). As observed, the increase of the hysteresis damping started at a displacement of about 0.5 \(\Delta_{u}\). The equivalent viscous damping ratio ranged from 9% to 13% at the ultimate load, and ranged from about 13% to 19% at the displacement of 2 times \(\Delta_{u}\). Figure 14b showed that the distribution of vertical rebar did not have significant influence on the hysteresis damping of the shear wall. By contrast, specimens PMW-1 and PMW-3A/B, both with a compression ratio of 0.10, exhibited approximately a 38% increase of \(\zeta_{eq}\) at ultimate load than that of PMW-2 and PMW-4, both with a compression ratio of 0.15, indicating that axial compression load had an obviously detrimental effect on the equivalent viscous damping ratio of the specimens. It was inferred that the shear wall with a lower axial load exhibited more efficient energy dissipation.
## 5 Specific Application of Topic Identification Models and Algorithms
### Application of Machine Learning and Deep Learning Models and Algorithms
#### 5.2.4 BERT  
The full name of BERT is Bidirectional Encoder Representation from Transformers. It is a pre-trained language representation model. It adopts the masked language model (MLM) and conducts pre-training based on the attention mechanism of two-way transformers, and achieves a novel performance in natural language processing tasks. [PERSON] classified the technical fields addressed in the papers and patent data to determine the development status of each technical field, based on a BERT-LSTM model of subject identifications of scientific and technological documents [59]. [PERSON] and [PERSON] used the optimal Bert LSTM text classification model to identify the topics in all SCI papers and patent data in the field of new energy vehicles [5]. [PERSON] took the technical relationships represented by semantic relationship between subject words in patent documents as the training data, and used the BERT pre-training model to realize the task of identifying technical relationships [60].
# Position-Monitoring-Based Hybrid Routing Protocol for 3D UAV-Based Networks
## 4 Experimental Evaluation
### Results and Discussion
#### 4.3.3 Throughput (kbps)  
The throughput is an important parameter to measure the performance of the network; it measures how many messages reach from source to destination in a given time. As UAV ad-hoc networks are sensitive to forwarding data in the required time, it is therefore necessary to have high throughput. The results in Figure 8 show that as the number of moving UAVs increases, the throughput decreases gradually. We examine our results in three scenarios: at 20, 40, and 60 m/s at different numbers of nodes. PHCC (a centroid-based scheme) maintains its throughput because it utilizes position and best links across the network. We can observe that position-based routing schemes have unstable throughput in a zigzag manner; the reason for this is that they forward messages to the UAVs present in the communication range if the destination UAV is in range or if it finds an appropriate route; otherwise, the message cannot reach. Moreover, from Figure 8, it can be observed that the proposed schemes PHCC and PHCR both maintain their throughput in different mobilities, which is more than double than benchmark schemes. PHCS decreases as the mobility speed of UAVs increases, though it offers better performance than position-based and topology (AODV)-based schemes. The PHCC's outer performance is due to the PHC selection process. such as the geographical position and number of connections. As we know, the position of the PHC is essential in UAV networks. The performance of the PHCC remains almost stable, even when the mobility of the network changes.  
Figure 8: Throughput versus number of UAV nodes at different speeds (20, 40, and 60 m/s).  
Figure 7: Delay versus Number of UAV nodes at different speeds (20, 40, and 60 m/s).
# Multi-Task Agent Hybrid Control in Sparse Maps and Complex Environmental Conditions
## 3 Dynamic Environment Target Identification and Localization (DETIL)
### Simulation Validation and Performance Analysis
clustering scheme and by 91% compared to the DBSCAN clustering scheme divided into 5 segments along the time axis. There was also a significant improvement in the accuracy of interest target localization. Figure 8 shows that the clustering scheme proposed in this study can effectively overcome the impact of cumulative errors during the agent's tasks on the accuracy of interest target localization. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Algorithm Name**} & \multicolumn{2}{c}{**Average Number of**} & \multirow{2}{*}{**Average Interest**} & \multirow{2}{*}{**Average Algorithm**} \\  & **Interest Targets** & & **Target Localization** \\  & **Found in TOP 5** & & **Accuracy (m)** & **Runtime (s)** \\ \hline DBSCAN & 1.86 & 1.42 & 0.18 \\ \hline DBSCAN (Divided & & & \\ into 5 Segments & 2.45 & 1.37 & 0.81 \\ Along Time Axis) & & & \\ \hline Spatiotemporal & & & \\ Dimension Clustering & & & \\ Based on DBSCAN & & & \\ (Ours) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation results of identification and localization using the DETIL algorithm. Figure 7: Comparison of results from different path planning algorithms. (**a**) Paths planned in a binary map using AStar, Dijkstra, and BFS algorithms. (**b**) Paths planned in a binary map using AStar, Dijkstra, and BFS algorithms displayed on an elevation map. (**c**) Paths planned in an elevation map using AStarWith and AStarWithPun algorithms displayed on a three-dimensional map (gray areas in the map represent penalty zones). (**d**) Paths planned in an elevation map using AStarWithH and AStarWithPun algorithms.
## 2 The Proposed Method
### The Proposed Algorithm
#### 2.1.4 The Calculation of Noise Residuals in Images Awaiting Source Tracing  
By inputting the image \(I^{z}\) to be traced into the InvDN, we obtain the denoised clean image \(\hat{\mathcal{C}}^{z}\). Subsequently, utilizing (1), we can calculate the noise residual \(W^{z}\) corresponding to the target image for tracing.