esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optimization Process Based on the RSM  
First, it is necessary to determine the influence of the factors to identify the optimal point in the RSM. After this is accomplished, a model can be established, and experiments can be conducted to obtain the response variables [30]. In the study conducted by [PERSON] and [PERSON] [20], three factors that influence the heat transfer and pressure drop mechanisms were included in the model. Using a central composite design based on the center points and design range, 15 experimental points were determined, as shown in Table 2. A numerical analysis was then conducted for each case to obtain the response variables \(\Delta T\) and \(\Delta P\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **L (mm)** & **W (mm)** & **H (mm)** & **\(\Delta T\) (K)** & **\(\Delta P\) (Pa)** \\ \hline
1 & 7.9 & 0.8 & 0.3125 & 17.3406 & 27.29 \\
2 & 13.6 & 0.8 & 0.3125 & 15.5593 & 35.63 \\
3 & 7.9 & 1.8 & 0.3125 & 16.894 & 22.71 \\
4 & 13.6 & 1.8 & 0.3125 & 15.1377 & 25.94 \\
5 & 7.9 & 0.8 & 0.8 & 18.38 & 31.53 \\
6 & 13.6 & 0.8 & 0.8 & 17.2649 & 40.6 \\
7 & 7.9 & 1.8 & 0.8 & 18.7918 & 32.23 \\
8 & 13.6 & 1.8 & 0.8 & 18.4825 & 43.44 \\
9 & 7.9 & 1.3 & 0.55625 & 18.6005 & 28.72 \\
10 & 13.6 & 1.3 & 0.55625 & 18.048 & 37.51 \\
11 & 10.75 & 0.8 & 0.55625 & 19.3582 & 38.69 \\
12 & 10.75 & 1.8 & 0.55625 & 19.3201 & 35.66 \\
13 & 10.75 & 1.3 & 0.3125 & 18.3369 & 32.74 \\
14 & 10.75 & 1.3 & 0.8 & 19.4143 & 38.99 \\
15 & 10.75 & 1.3 & 0.55625 & 19.369 & 37.05 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometric parameters of the RSM cases and experimental data.  
Figure 5: Validation of CFD results: comparison of the j- and f-factor with empirical correlations across Reynolds numbers.  
A regression equation associated with the effect of the objective functions \(P\) and Gv on the three shape factors was formulated as a second-order regression model to evaluate the relationship between the response variables and factors based on the numerical analysis, as shown in Equations (12) and (13) [22]. The regression was performed, and the residuals were verified to confirm that the regression was conducted correctly. The objective function \(P\) was modified to minimize the required fan power while preventing a reduction in thermal performance to achieve optimization, and the \(Q\) value of the reference shape was used as a constraint. This resulted in creating a transformed objective function for reduced pumping power. The optimization of Gv was carried out in a direction that maximizes Gv.  
\[\begin{split} P=-0.00706+0.003404L-0.00412W-0.00128H-0.000145L^{ 2}+0.00036W^{2}\\ -0.00538H^{2}-0.000081 LW+0.000492 LH+0.00572 WH\end{split} \tag{12}\]  
\[\begin{split}\text{Gv}=-0.1145+0.03217L-0.0090W+0.0907H-0.001712L^{ 2}-0.00165W^{2}\\ -0.0872H^{2}+0.000911~{}LW+0.001510~{}LH+0.01122~{}WH\end{split} \tag{13}\]
## 2 Background  
### Open-Cut Construction Method  
Open Excavation (also known as open cut, cut-and-cover, or trenching) is an excavation made in the open rather than a tunnel. The simplest method for installing a new pipe or replacing an old one entails digging a trench to the necessary depth, placing the pipe, filling the trench back in, and repairing the surface [1; 2; 3; 13]. This open-cut trench method entails excavating and exposing the existing pipe for repair or replacement, followed by backfilling. As a widely used traditional method for underground infrastructure installation or replacement, the OCCM includes several steps: excavating the trench, securing the trench walls, preparing the pipe bedding, laying the pipe, embedding it, refilling the trench, compacting the soil, and restoring the surface [14] (Figure 1).  
Advantages and Challenges of OCCM  
The five advantages of OCCM are as follows [15; 16; 17; 18]:  
* May be more cost-effective than trenchless methods in areas without pavement;
* Serves as the only viable option for repairing pipes that have collapsed or lost their slope, as these require complete removal;
* Ideal for pipes heavily obstructed by roots or debris, eliminating the need for extraction;
* Allows repairs without necessitating the removal of roots or debris from within the pipe;
* A reliable solution for addressing severe structural damage to pipelines.  
The challenges of OCCM include the following:  
Figure 1: Cross-sectional diagram of pipe installation in a trench [14].  
* High Risk of Cave-Ins: Cave-ins are the most significant danger associated with excavation, posing a higher likelihood of worker fatalities compared to other excavation-related accidents [19].
* Increased Excavation Requirements: Open-cut methods require more extensive excavation compared to trenchless techniques, resulting in higher resource and labor demands.
* Pavement Removal Costs: Removing street and sidewalk pavement is often necessary, adding to the overall expense of repairs.
* Multiple Safety Hazards: Workers face additional risks, such as falls, falling loads, exposure to hazardous atmospheres, and accidents involving mobile equipment.
* Traffic Disruptions: Open trenches at intersections in busy urban areas can significantly disrupt traffic flow and limit access to local businesses, causing inconvenience and financial losses [20].
* Environmental Impact in Sensitive Areas: Installing underground utility lines in wetlands incurs high environmental mitigation costs, increasing project expenses.
* Depth-Related Costs: Deeper installations require specialized equipment and involve higher costs for safety systems, which are critical for protecting workers in such conditions.
* Economic Impact on Businesses: Open-road construction on heavily trafficked streets in business districts can severely affect local businesses, limiting customer access and reducing revenues [21]. In the traditional OCCM for river crossings, pipelines are typically routed beneath a bridge, which significantly accelerates wear and reduces their lifespan by 50% to 75%.
## 4 Proposed Solution
### Trajectory Optimization Algorithm Based on DDPG
#### 4.3.5 Overall Algorithm  
The specific description of our algorithm is shown in Algorithm 1. Initially, in line 1, the experience replay buffer B is initialized. In lines 2-4, the actor and critic networks are randomly initialized.  
The training loop of the algorithm is located in lines 5-27, and each iteration of the loop trains the DRL network model once. This algorithm sets \(N\) scenarios for training simulations. In line 6, the parameters of each scenario are initialized. In line 7, a parameter called 'done' is set to determine the termination condition for the current scenario's training. Once a UAV runs out of energy, the training for that scenario is terminated. In lines 8-10, each UAV selects an action based on the exploration rate from either free movement or the deep learning network. In lines 11-13, we check whether there is any collision between UAVs and whether the communication between the UAV group is stable during this time slot. In line 14, we update the observation space \(\mathcal{O}\) for the user coverage, which indicates how many time slots each user has been covered from the beginning of the mission to the current time slot. In line 15, we use the newly obtained state to replace the current state of the UAV. In lines 16-17, we calculate the coverage score for each user, then obtain the overall coverage score and fairness index of the system, and finally, calculate the total reward for the current UAV group. In lines 18-20, we check whether there are any UAV power outages that would result in the end of the system's flight cycle.  
Finally, in lines 21-25, the parameters of the actor network, critic network, and target network are updated.  
```
1:Initialize the experience replay buffer B
2:FOR UAV = 1,..., M DO
3: Randomly initialize actor network and critic network
4:END FOR
5:FOR episode = 1,..., N DO
6: Initialize the environment
7:WHILE done==FALSE
8:FOR UAV = 1,..., M DO
9: select an action
10:END FOR
11:IFD_m1_m2 \(<d_{min}\) OR all the distances between any UAV and another > \(d_{comm}\) THEN
12:Return all UAVs to their previous state positions, and this action is invalidated
13:END IF
14:Update user coverage in the observation space \(\mathcal{O}\)
15:Update the current status of each UAV \(S_{t}\gets S_{t+1}\)
16:Calculate coverage scores \(c_{T}\) for all users, fairness index \(f_{t}\)
17:Calculate overall rewards \(R_{t}\)
18:IF any UAV runs out of energy THEN
19:done = TRUE
20:END IF
21:FOR UAV = 1,..., M DO
22:Update actor network
23:Update critic network
24:Update target actor network and target critic network
25:END FOR
26:END WHILE
27:END FOR
```  
**Algorithm 1** 3D trajectory optimization algorithm based on DDPG
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 4 Algorithm
### Dynamic Task Processing Algorithm Based on the Speed Variation of UAVs and Vehicles
#### 4.2.1 Local Dynamic Optimization Based on Tabu Search  
Tabu Search (TS) is an optimization search algorithm based on neighborhood selection, which uses a tabu list to record moves and avoid getting trapped in local optima after multiple iterations. In response to variations in UAV speed, this paper employs the Tabu Search algorithm to adjust the UAV routes within a single anchor point and outputs locally optimized solutions. This algorithm is named the TS-based Dynamic Processing Optimization Algorithm (TSDPOA).  
The pseudocode for the anchor point optimization algorithm based on Tabu Search is shown in Algorithm 6. It defines two basic neighborhood search structures: (1) node swapping and (2) node insertion, as seen in lines 7-11. In each iteration, the algorithm generates \(k_{1}\) swap neighborhood solutions and \(k_{2}\) insertion neighborhood solutions, selects the optimal solution from the new neighborhood, and updates the tabu list _List_. This forms the basis for the Tabu Search operations. After \(max\_iter\) iterations, an optimized solution \(\tilde{S}\) is produced.  
It is important to note that this local dynamic optimization algorithm performs optimization adjustments based on the original UAV flight route solution rather than reconstructing the UAV route solution from the remaining unserviced task points. This significantly reduces the algorithm's running time. Consequently, the algorithm can quickly make decisions and generate adjusted optimal solutions in response to changes in vehicle and UAV speeds, allowing the UAVs to execute tasks according to the new flight route plan.  
```
1:Solution \(S\) within a single anchor point;
2:Optimized solution \(\tilde{S}\);
3:Initialize the optimal solution \(\tilde{S}\gets S\) and update the current latest solution \(\hat{S}\gets S\)
4:Tabo list \(List=\emptyset\)
5:while\(max\_iter>0\)do
6: Neighborhood \(neigh=\emptyset\)
7: Count \(count=0\)
8:while\(count<k_{1}+k_{2}\)do
9:if\(count<k_{1}\)then
10: Randomly pick two routes, and randomly select a customer point from each route to exchange
11:else
12: Randomly pick two routes, and randomly select a customer point from one route to add to the other route
13:endif
14:Reorder the newly formed two routes
15:Delete any empty routes formed due to insertion operations
16:Reassign routes for UAVs to generate solutions within a single anchor point \(\leftarrow\) Call Algorithm 3
17:Insert the newly generated solution into the neighborhood \(neigh\)
18:endwhile
19:\(\hat{S}\leftarrow\) the best solution in \(neigh\)
20: Update the tabo list \(List\) based on \(\hat{S}\)
21:Calculate the cost of the optimized solution \(\hat{S}\) and the solution \(\hat{S}\), denoted as \(\tilde{T}\) and \(\tilde{T}\) respectively
22:if\(\hat{T}<\tilde{T}\)then
23:\(\tilde{S}\leftarrow\hat{S}\)
24:endif
25:endwhile
26:Return \(\tilde{S}\);
```  
**Algorithm 6** TSDPOA Algorithm
## 4 Experimental Validation
### Experimental Setup  
#### 4.1.1 Datasets and Models  
We evaluate SHAT with two widely used CNN models, ResNet-50 [1] and VGG-16 [2]. ResNet-50 with 23 M parameters is a computation-intensive model, while VGG-16 with 138 M parameters is a communication-intensive model. As the training datasets for both models, we use the CIFAR dataset that consists of 50 K training images and 10 K test images with 10 labels ([[https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)]([https://www.cs.toronto.edu/~kriz/cifar.html](https://www.cs.toronto.edu/~kriz/cifar.html)), accessed on 22 December 2021).
# Advanced Text Detection of Container Numbers via Dual-Branch Adaptive Multi-Scale Network
## 3 Methods
### Dual-Loss Strategy  
#### 3.3.1 Label Generation Method  
In the detection network for pixel-level segmentation, we employ a labeling strategy based on the Vatti clipping algorithm [33] to generate labels. For a given image of container number text detection, we define each text region as a set of segmentation lines \(G=\{S_{k}\}_{k=1}^{n}\), where \(n\) represents the number of vertices in the polygon. To obtain the ground truth segmentation mask, we apply the Vatti clipping algorithm to shrink the original polygon by \(D_{i}\) pixels, resulting in a shrunken polygon. Then, each shrunken polygon is converted into a 0/1 binary mask, serving as the ground truth for the segmentation network. The offset \(D\) is calculated using the formula \(D=A(1-r^{2})\), where \(r\) is an empirically determined shrinking ratio set to 0.4, and \(A\) and \(L\) represent the area and perimeter of the original polygon. Similarly, we generate labels for the threshold map through a similar process. Firstly, by using the same offset \(D_{i}\), we dilate the text polygon \(G\) to \(G_{d}\). The spatial region between the original polygon \(G\) and the dilated polygon \(G_{d}\) is considered the boundary of the text region. The labels for the threshold map are generated by calculating the distance to the nearest line segment in \(G\).
###### Abstract  
At present, deep ultraviolet (DUV) lasers at the wavelength of fourth harmonics of 1 \(\upmu\)m (266 nm/258 nm) and at the wavelength of 193 nm are widely utilized in science and industry. We review the generation of these DUV lasers by nonlinear frequency conversion processes using solid-state/fiber lasers as the fundamental frequency. A DUV laser at 258 nm by fourth harmonics generation (FHG) could achieve an average power of 10 W with a beam quality of M\({}^{2}\) < 1.5. Moreover, 1 W of average power at 193 nm was obtained by sum-frequency generation (SFG). A new concept of 193-nm DUV laser generation by use of the diamond Raman laser is also introduced. A proof-of-principle experiment of the diamond Raman laser is reported with the conversion efficiency of 23% from the pump to the second Stokes wavelength, which implies the potential to generate a higher power 193 nm DUV laser in the future.  
deep ultraviolet laser; frequency conversion; Raman laser 2018 8 233; doi:10.3390/app8020233
## 5 Post adsorption Characterization
### FTIR Analysis  
The FTIR spectra of Raw-RGL and AC-RGL adsorbents after adsorption are indicated in Figure 13. The graph of Raw-RGL shows the peak at 3310 cm\({}^{-1}\) due to -OH. While the -CH due to CH\({}_{2}\) and CH\({}_{3}\) shifted to 2916 and 2841 cm\({}^{-1}\), respectively. The carbonyl (-C=O) peak was clearly visible at 1724 cm\({}^{-1}\) on Raw-RGL after adsorption. There were new peaks observed at 1595, 1444 and 1304 cm\({}^{-1}\) after adsorption of ibuprofen on Raw-RGL. The spectrum for AC-RGL showed a peak of -C=O, which shifted to 1547 cm\({}^{-1}\) after adsorption and the -O-C-O peak disappeared. The changes in wavenumbers, disappearing and formation of new peaks suggest that the functional groups were involved in the adsorption process [50].  
Figure 12: After adsorption, SEM images of Raw-RGL (**A,B**) and AC-RGL (**C,D**).
## 3 Novel Broad Echo State Network
### Experimental Environment and Settings
#### 4.3.2 Results of Air Quality Dataset  
In this experiment of the air quality dataset, the predicted outputs are shown in Figure 12. It can be seen that the result of the CMBESN is closer to the actual values than other models in the general view.  
For further comparison of the results, the outputs are plotted with the boxplots in Figure 13. The middle horizontal line and the start point in each box represent the median and mean of the data. The size of the box indicates the distribution of the data. As seen in Figure 13, the mean and median of CMBESN are closest to the actual data.  
For a clear comparison, the models' errors are shown in Figure 14. It can be seen that the error variation range of CMBESN is much smaller than that of CMBLS, ESN, and GRU, which means that CMBESN has a higher prediction accuracy and fits the real curve most closely.  
Figure 11: Percentage change in evaluation metrics for CMBESN relative to each model on the MSO dataset.  
Figure 12: Prediction results of each model in the air quality data set of Fangshan District, Beijing.  
The evaluation metrics are shown in Table 5, which contains the training time, RMSE, SMAPE, MAE, and \(R^{2}\) for each model.  
Figure 15 shows the decreased percentage of SMAPE, MAE, RMSE, and the increased ratio of \(R^{2}\) when the CMBESN is compared with other models on the air quality dataset.  
Figure 14: Errors of each model on air quality dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Training Time(s)** & **SMAPE** & **MAE** & **RMSE** & \(R^{2}\) \\ \hline ESN & 2.9911 & 0.2937 & 28.8061 & 40.8411 & 0.0881 \\ BLS & 0.1120 & 0.3507 & 57.5135 & 62.1322 & 0.4730 \\ GRU & 65.9890 & 0.0889 & 12.5609 & 26.2116 & 0.6062 \\ CMBLS & 0.0717 & 0.1249 & 13.9013 & 24.8272 & 0.4889 \\ CMBESN & 58.9831 & 0.1646 & 11.1110 & 13.5795 & 0.8959 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation metrics for each model on the air quality dataset.  
Figure 13: Boxplot of the prediction results of each model for the air quality dataset.  
As seen in Table 5, the BLS model performs the worst performance. CMBLS improves performance over BLS. CMBESN improves accuracy over other networks. Meanwhile, Figure 15 shows the decreased percentage in RMSE, SMAPE, and MAE and the increased percentage in \(R^{2}\) for CMBESN, which means the CMBESN has a better performance. It can also be seen that the CMBESN model has the slightest prediction error. The training time is also shorter than GRU.  
The proposed CMBESN is tested in the experiment by comparing some related models. It can be found from the results that the prediction accuracy of the CMBESN has been improved. However, the training time has risen at the same time. Then the model should be optimized, and the experiments are conducted in Section 4.4.
## 5 Conclusions  
In this paper, a novel Microwave TVDI (MTVDI) that uses passive microwave remote sensing technology was used here to capture the canopy seasonality of tropical evergreen forests across Amazon. Our results indicated that the MTVDI showed consistent or opposite variability with photosynthesis proxies (i.e., EVI and SIF) in different subregions. The MTVDI not only performed better than other climatic dryness indicators such as PDSI. CWD, TWS, and VPD, but also showed good capability in detecting the forest areas which green up during the dry season in wet regions across Amazon tropical forests. MTVDI provides a new choice for the scientific community to investigate canopy growth dynamics in the Amazonian tropical forests.  
The following are available online at [[https://www.mdpi.com/2072-429/13/3/339/s1](https://www.mdpi.com/2072-429/13/3/339/s1)]([https://www.mdpi.com/2072-429/13/3/339/s1](https://www.mdpi.com/2072-429/13/3/339/s1)), Figure S1: The monthly MNDVI-Ts space in 2003, Figure S2: The monthly MNDVI-Ts space in 2005, Figure S3: The monthly MNDVI-Ts space in 2006, Figure S4: The monthly MNDVI-Ts space in 2007, Figure S5: The monthly MNDVI-Ts space in 2008, Figure S6: The monthly MNDVI-Ts space in 2009, Figure S7: The monthly MNDVI-Ts space in 2010, Figure S8: The seasonality of MTVDI, PDSI, CWD, VPD, and TWS in 6 sub regions of Amazonian tropical forests, Figure S9: The seasonality curve of (a) EVI, (b) SIF in 6 sub regions of Amazonian tropical forests, Table S1: The slope, intercept and R of dry edges and wet edges of MNDVI-Ts triangle in 2004, Table S2: The NSE results of MTVDI against climatic dryness indicators and canopy photosynthesis proxies, Table S3: The statistics of the absolute value of regional mean correlation coefficient (R) of the regression between MTVDI and VPD, CWD, TWS and PDSI in each sub region.  
Conceptualization, X.C. and L.L.; methodology, L.L. and X.Y.; software, L.L. and X.Y.; validation, L.L., X.Y. and F.G.; formal analysis, X.Y. and F.G.; investigation, X.Y. and F.G.; resources, X.Y.; data curation, L.L. and X.Y.; writing--original draft preparation, L.L., X.Y. and F.G.; writing--review and editing, L.L., X.C., Y.S. and G.H.; visualization, L.L.; supervision, X.C., Y.S. and G.H.; project administration, X.C. and Y.S.; funding acquisition, X.C. and Y.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 31971458, 41971275 and 41907289; the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), grant number GML2019 ZD0301; the Guangdong Basic and Applied Basic Research Foundation, grant number 2020A151501091; the GDAS Special Project of Science and Technology Development, grant number 2020 GDASYL-20200102002.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# Numerical Parametric Studies on the Stress Distribution in Rocks around Underground Silo
## 5 Numerical Results
### Safety Factors  
In this section, the safety factor is presented using the stress value obtained through 2D axial symmetric analysis. The contours of safety factor distributions provide valuable information for evaluating the structural safety of the surrounding rock mass, which is the main supporting body of the underground silo.  
The safety factor distributions according to change in the ratio of in situ horizontal stress to vertical stress (Ko) after single-step excavation of an underground silo are presented in Figures 18-20 for Ko values of 0.5, 1.0, and 2.0, respectively.  
For Ko = 0.5, the zones with lower safety factors were more extended in the horizontal direction along the storage wall, with a minimum safety factor of 1.42. The stress state in these zones, as shown in Figure 14d, were close to the triaxial extension mode where vertical and tangential stresses were contributing as major stresses and the horizontal stress as a minor stress. Overall, the surrounding rock mass was in a safe condition.  
For Ko = 1.0, the zones with lower safety factors were more-or-less uniformly concentrated around the silo, with a minimum safety factor of 1.29. The stress states in these zones, as examined in Figure 15d, were close to the deviatoric pure shear mode where the major stress was tangential stress, the intermediate stress was vertical stress, and the minor stress was horizontal stress. Overall, the surrounding rock mass was in a safe condition.  
For Ko = 2.0, the zones with lower safety factors were more widely spread around the silo compared to the cases for Ko values of 0.5 and 1.0. The stress states along the storage wall, as shown in Figure 16d, were closer to the triaxial compression mode. Overall, surrounding rock mass was in a safe condition except along the storage wall, where it almost reached ultimate strength with a minimum safety factor of 1.02.  
Figure 17: Stress state at the first element along lines C5 and D6 (Ko = 2.0): (**a**) p–q plot at Location C; (**b**) t–q plot at Location C; (**c**) p–q plot at Location D; (**d**) t–q plot at Location D.  
Figure 19: Safety factor distribution (Ko = 1.0).  
Figure 18: Safety factor distribution (Ko = 0.5).