esa-sceva/satcom-chunk-collection huggingface dataset:
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 5 PAGWO-IDWA Algorithm
### Algorithm Flowchart and Pseudo-Code  
In this paper, the improved DWA is integrated with PAGWO (termed PAGWO-IDWA) to plan paths for mobile robots and to enable them to avoid obstacles in dynamic environments, thereby enhancing the algorithm's dynamic performance and robustness. The algorithm's flowchart is presented in Figure 4:  
Figure 3: Overall Framework Flowchart.  
In this framework, PAGWO is first utilized to generate a global collision-free path, incorporating four key enhancements: (1) piecewise chaotic mapping to diversify the search space; (2) an EDP strategy to prevent premature convergence; (3) an improved nonlinear convergence factor to balance exploration and exploitation; and (4) a dynamic weighting scheme in the position update formula. Once the global path points are obtained, the improved IDWA uses these points to guide the heading angle and optimize local obstacle avoidance. Specifically, the conflict avoidance strategy is based on the relative angle and position between the mobile robot and dynamic obstacles. When a direct conflict is detected--where the robot and obstacle are aligned in a manner that poses a high risk of collision--the robot accelerates to quickly bypass the obstacle. Conversely, if a non-direct conflict is identified, the robot decelerates and stops, waiting for a safer opportunity to proceed. Moreover, similar to the conventional Dynamic Window Approach (DWA), IDWA employs an evaluation function that considers factors such as heading alignment, velocity, and obstacle clearance to determine the optimal local trajectory. By combining PAGWO's global guidance with IDWA's adaptive local maneuvering, the proposed method achieves efficient and safe navigation in dynamic environments. The pseudo-code of the PAGWO-IDWA is presented in Algorithm 1.  
Figure 4: The PAGWO-IDWA algorithm.  
```
Input: Number of grey wolves: \(n\). Maximum number of iterations: Max_iter. Dimension of the problem: dim. Grid map: G. DWA parameters: \(\lambda_{1},\lambda_{1},\lambda_{1}\). Output: The smooth optimal trajectory for the robot.
1. Initialize the Grey Wolf population using Piecewise Chaotic Mapping.
2. Initialize Alpha, Beta, and Delta wolves.
3. **While** iter < Max_iter
4. **for** i = 1: \(n\)
5. Evaluate fitness and update leaders;
6. **end for**
7. **for** i = 1: round(n/4)
8. Apply Evolutionary Population Dynamics (EPD);
9. **end for**
10. Update nonlinear convergence factor;
11. **For** i = 1: \(n\)
12. Update positions with dynamic weighting;
13. **end for**
14. Obtain the current new location;
15. Update the optimal location of the current individual
16. iter = iter + 1;
17. **end while**
18. Acquire global optimal path point information of the PAGWO;
19. Fuse the point information and put it into local planner DWA;
20. Control the robot to move according to the global optimal with Conflict avoidance strategies;
21. **Return** the smooth optimal trajectory.
```  
**Algorithm 1**PAGWO-IDWA pseudo-code
# Robustness of Double-Layer Group-Dependent Combat Network with Cascading Failure
## 4 Simulation Experiments on Robustness
### Robustness Evaluation Index
Taking the \"O\(\rightarrow\)P\(\rightarrow\)D\(\rightarrow\)A\" link as an example, the number of this link can be calculated by  
\[ \begin{split} S_{\mathrm{OPDA}}^{\mathrm{WG}}(G)& =\text{tr}\{[S_{\mathrm{OP}}\wedge(S_{\mathrm{OC}}\times S_{ \mathrm{CC}}\times S_{\mathrm{CP}})]\times\\ &[S_{\mathrm{PD}}\wedge(S_{\mathrm{PC}}\times S_{\mathrm{CC}} \times S_{\mathrm{CD}})]\times\\ &[S_{\mathrm{DA}}\wedge(S_{\mathrm{DC}}\times S_{\mathrm{CC}} \times S_{\mathrm{CA}})]\times S_{\mathrm{AO}}\},\end{split}  \tag{18}\]  
where \(\wedge\) is the Boolean operation of two matrices, that is, if the corresponding element \((i,j)\) in matrix \(\mathbf{X}\) and \(\mathbf{Y}\) are both greater than 0, then \(\mathbf{X}(i,j)\wedge\mathbf{Y}(i,j)=1\). Then, the total number of links is calculated by  
\[S_{\mathrm{links}}(G)=\sum_{i=1}^{7}S_{\mathrm{link}_{i}}(G). \tag{19}\]  
The robustness of the combat network in this paper is calculated by relative metrics. For the original combat network \(G\) which has not been attacked, the initial largest component scale is \(S_{\mathrm{huge}}(G)\). The amount of CELKs is \(S_{\mathrm{link}}(G)\). For the attacked combat network \(G^{\prime}\), the corresponding largest component scale and the number of CELKs are \(S_{\mathrm{huge}}(G^{\prime})\) and \(S_{\mathrm{links}}(G^{\prime})\), respectively. We can measure the robustness of the combat network in the following expression:  
\[R=\left(\frac{S_{\mathrm{links}}(G^{\prime})}{S_{\mathrm{links}}(G)}\right)^{ \alpha}\left(\frac{S_{\mathrm{huge}}(G^{\prime})}{S_{\mathrm{huge}}(G)} \right)^{1-\alpha}, \tag{20}\]  
where \(\alpha\) is a proportion parameter indicating the preference for two different metrics. The default value of \(\alpha\) is 0.5.
# Evaluating the Efficacy of Segment Anything Model for
###### Abstract  
Segmentation of Agricultural Remote Sensing Images (ARSIs) stands as a pivotal component within the intelligent development path of agricultural information technology. Similarly, quick and effective delineation of urban green spaces (UGSs) in high-resolution images is also increasingly needed as input in various urban simulation models. Numerous segmentation algorithms exist for ARSIs and UGSs; however, a model with exceptional generalization capabilities and accuracy remains elusive. Notably, the newly released Segment Anything Model (SAM) by META AI is gaining significant recognition in various domains for segmenting conventional images, yielding commendable results. Nevertheless, SAM's application in ARSI and UGS segmentation has been relatively limited. ARSIs and UGSs exhibit distinct image characteristics, such as prominent boundaries, larger frame sizes, and extensive data types and volumes. Presently, there is a dearth of research on how SAM can effectively handle various ARSI and UGS image types and deliver superior segmentation outcomes. Thus, as a novel attempt in this paper, we aim to evaluate SAM's compatibility with a wide array of ARSI and UGS image types. The data acquisition platform comprises both aerial and spaceborne sensors, and the study sites encompass most regions of the United States, with images of varying resolutions and frame sizes. It is noteworthy that the segmentation effect of SAM is significantly influenced by the content of the image, as well as the stability and accuracy across images of different resolutions and sizes. However, in general, our findings indicate that resolution has a minimal impact on the effectiveness of conditional SAM-based segmentation, maintaining an overall segmentation accuracy above 90%. In contrast, the unsupervised segmentation approach, SAM, exhibits performance issues, with around 55% of images (3 m and coarser resolutions) experiencing lower accuracy on low-resolution images. Whereas frame size exerts a more substantial influence, as the image size increases, the accuracy of unsupervised segmentation methods decreases extremely fast, and conditional segmentation methods also show some degree of degradation. Additionally, SAM's segmentation efficacy diminishes considerably in the case of images featuring unclear edges and minimal color distinctions. Consequently, we propose enhancing SAM's capabilities by augmenting the training dataset and fine-tuning hyperparameters to align with the demands of ARSI and UGS image segmentation. Leveraging the multispectral nature and extensive data volumes of remote sensing images, the secondary development of SAM can harness its formidable segmentation potential to elevate the overall standard of ARSI and UGS image segmentation.  
Segment Anything Model (SAM); agriculture; vegetation; urban green spaces; aerial imaging; unmanned aerial vehicles (UAVs); remote sensing; Landsat; Sentinel-2 +
Footnote †: journal:  
0
Footnote 0: _Econte sensis_
## 3 Materials and Proposed Method
### AlexNet  
The AlexNet model is characterized by containing input layers, convolutional layers, pooling layers, and fully connected (FC) layers. In general, the AlexNet transfer learning algorithm is eight layers deep that comprises 5 convolutional layers and 3 FC layers as seen in Figure 3. The network was modified by adding the Batch Normalization and Dropout layers. The sequential model was built in blocks as follows:  
**Block 1**  
* Convolutional layer 1: 96 filters, 11 kernels, 4 strides, padding is valid, and activation = relu.
* Pooling layer 1: MaxPooling2D of size 3, 2 strides, padding is valid.
* BatchNormalization layer 1 was passed before moving to the next layer.  
**Block 2:**  
* Convolutional layer 2: 256 filters, 5 kernels, 1 stride, padding is valid, and activation = relu.
* Pooling layer 2: MaxPooling2D of size 3, 2 strides, padding is valid.
* BatchNormalization layer 2 was passed before moving to the next layer.  
**Block 3:**  
* Convolutional layer 3: 384 filters, 3 kernels, 1 stride, padding is valid, and activation = relu.
* BatchNormalization layer 3 was passed before moving to the next layer.  
**Block 4:**  
* Convolutional layer 4: 384 filters, 3 kernels, 1 stride, padding is valid, and activation = relu.
* BatchNormalization layer 4 was passed before moving to the next layer.  
**Block 5:**  
* Convolutional layer 5: 256 filters, 5 kernels, 1 stride, padding is valid, and activation = relu.
* Pooling layer 5: MaxPooling2D of size 3, 2 strides, padding is valid.
* BatchNormalization layer 5 was passed before moving to the next layer.  
**BatchNormalization layer 5 was passed before moving to the next layer.  
**Block 6:**  
* Fully connected layer 1: Dense node 4096, input (227, 227,3), activation = relu.
* Dropout layer was added to prevent the model from overfitting (0.4).
* BatchNormalization layer 5 was passed before moving to the next layer.  
**Block 7:**  
* Fully connected layer 1: Dense node 4096, activation = relu.
* Dropout layer was added to prevent the model from overfitting (0.4).  
**Block 8:**  
* Output layer: Dense (7), activation = softmax.  
Figure 3: Modified AlexNet Architecture for Human Posture Image Classification.
## 4 Experiment and Results Analysis
### Baseline Methods  
We compare our model with the following models:  
* LM-LSTM-CRF [25]: It is a language model-enhanced LSTM-CRF model, which achieved competitive results on several sequence-labeling tasks;
* E2E-TBSA [6]: Two stacked LSTMs were used to perform two tasks, target boundary detection and complete ABSA, respectively, and two auxiliary components were designed;
* DOER [13]: A double-cross shared RNN framework that jointly trains ATE and ASC for two tasks, considering the relationship between aspect and polarity;
* IMN [14]: An interactive multi-task learning model for the joint extraction of joint aspect items and opinion items, as well as ASC, and introduces a novel messaging mechanism that allows information interaction between tasks;
* BERT-E2E-ABSA [16]: Applying BERT to ABSA, they constructed a series of simple but effective neural baselines for this problem, using the best-performing BERT + GRU as a reference;
* SPAN [26]: A pipelined approach in which one model is used for ATE tasks, and then another model is used for ASC tasks;
* DREGCN [27]: An end-to-end interaction architecture based on multi-task learning relying on syntactic knowledge enhancement, the model uses well-designed dependency-embedding graph convolutional networks to make full use of syntactic knowledge and also designs a simple and effective messaging mechanism to realize multi-task learning;
* DCRAN [18]: A deeply contextualized relationship-aware network that allows implicit interaction between subtasks in a more efficient way and allows two explicit self-supervised strategies for deep context and relationship-aware learning.
# Cross-Entropy Method in Application to the SIRC Model
## Appendix A Optimization by the Method of Cross-Entropy  
### Multiple Selection to Minimize the Sum of Ranks  
Let \((a_{1},\ldots,a_{N})\) be a permutation of integers \((1,2,\ldots,N)\), where all permutations are equally probable. For every \(i=1,2,\ldots,N\)\(Y_{i}\) will be the number of values \(a_{1},\ldots,a_{i}\), which are \(\leq a_{i}\). \(Y_{i}=\text{card}\{1\leq j\leq i:a_{j}\leq i\}\) is the relative rank of the \(i\)-th object. The decision-maker observe the relative ranks and the real ranks define filtration: \(\mathcal{F}_{i}=\sigma\{Y_{1},Y_{2},\ldots,Y_{i}\}\). Let \(\mathfrak{S}\) be the set of Markov time with respect to \(\{\mathcal{F}_{i}\}_{i=1}^{N}\). Define  
\[v=\inf_{\tau}\mathbf{E}(a_{\tau_{1}}+\cdots+a_{\tau_{k}}),\qquad\text{where }\tau=(\tau_{1},\ldots,\tau_{k}),\,\tau_{i}\in\mathfrak{S}.\]  
We want to find the optimal procedure \(\tau^{*}=(\tau_{1}^{*},\ldots,\tau_{k}^{*})\) and the value of the optimization problem \(v\). Let \(\mathcal{F}_{(m)_{i}}\) be \(\sigma\)-algebra generated by \((Y_{1},\ldots,Y_{m_{i}})\). If we accept  
\[Z_{(m)_{k}}=\mathbf{E}(a_{\tau_{1}}+\cdots+a_{\tau_{k}}|F_{(m)_{k}}),\]  
then  
\[v=\inf_{\tau}\mathbf{E}Z_{\tau},\qquad\tau=(\tau_{1},\ldots,\tau_{k}).\]The problem is reduced to the problem of stopping sequences multiple times \(Z_{(m)_{k}}\). As shown in [47; 48] the solution to the problem is the following strategy:  
If there are integer vectors  
\[\begin{split}&\delta^{(k)}=(\delta^{(k)}_{1},\ldots,\delta^{(k)}_{N -k+1}),\qquad 0\leqslant\delta^{(k)}_{1}\leqslant\cdots\leqslant\delta^{(k)}_{N-k} <\delta^{(k)}_{N-k+1}=N,\\ &\delta^{(2)}=(\delta^{(2)}_{k-1},\ldots,\delta^{(2)}_{N-1}), \qquad 0\leqslant\delta^{(2)}_{k-1}\leqslant\cdots\leqslant\delta^{(2)}_{N-2} <\delta^{(2)}_{N-1}=N,\\ &\delta^{(1)}=(\delta^{(1)}_{k},\ldots,\delta^{(1)}_{N}),\qquad \qquad 0\leqslant\delta^{(1)}_{k}\leqslant\cdots\leqslant\delta^{(1)}_{N-1} <\delta^{(1)}_{N}=N,\\ &\delta^{i_{1}}_{j}\leqslant\delta^{i_{2}}_{j},\qquad 1\leqslant i_{1} \leqslant i_{2}\leqslant k,\qquad\qquad\quad k-i_{1}+1\leqslant j\leqslant N -i_{2}+1\end{split} \tag{11}\]  
then  
\[\tau^{*}_{1} =min\{m_{1}:y_{m_{1}}\leqslant\delta^{(k)}_{m_{1}}\},\] \[\tau^{*}_{i} =min\{m_{i}>m_{i-1}:y_{m_{i}}\leqslant\delta^{(k-i+1)}_{m_{i}}\},\]  
on the set \(F_{i-1}=\{\omega:\tau^{*}_{1}=m_{1},\ldots,\tau^{*}_{i-1}=m_{i-1}\}\). \(i=2,\ldots,k\), \(F_{0}=\Omega\). For small \(N\) there is a possibility to get accurate values by the analytical method. The CE method solves this problem by changing the estimation problem to the optimization problem. Then by randomizing this problem using a defined family of probability density functions. With this the CE method solves this efficiently by making adaptive changes to this pdf and go in the direction of the theoretically optimal density.
# Testing the Effectiveness of Voxels for Structural Analysis
## 2 Material and Methods  
The idea of this work is to analyze the usefulness of the denoising algorithm on 3D point clouds of cultural heritage used as a basis for the creation of models suitable for structural analysis. The process has been tested on point clouds of different objects differing in shape, size, geometric complexity, materials and position in the environment. In this way, the results are based on heterogeneous samples that can provide a more accurate analysis spectrum. The objects chosen for the analysis are visible in Figure 1a-d and the parameters of the Camera and lens for the survey are summarized in Table 1:  
Figure 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline
**Object** & **Camera and Lens** & **Parameters** & **Point Cloud** \\ \hline Masonry pillar & Canon 60D + 20/16 mm lens & ISO 250, f/10 & 2,872,140 \\ [PERSON] & Canon 60D + 20 mm lens & ISO 250, f/8 & 1,060,629 \\ Scorpionide & Canon 60D + 18/60 mm lens & ISO 500, f/9 & 3,693,711 \\ Sammitic tomb & Canon 60D + 18 mm lens & ISO 250, f/8 & 11,238,209 \\ Chair & Canon 5D + 20 mm lens & ISO 800, f/5.6 & 2,646,151 \\ \hline \end{tabular}  
* The statue of [PERSON] from the tomb of Pope [PERSON] in Rome. The problems were related to the position of the statue, so it was impossible to detect the back (Figure 1a).  
\end{table}
Table 1: Camera and point cloud specification for each object surveyed.  
Figure 1: Objects surveyed and used as test objects in this study: (**a**) statue of [PERSON], (**b**) medieval pillar, (**c**) copy of scorpionide, (**d**) Samnité tomb, (**e**) chair from the Royal Palace of Caserta.  
The problem was overcome by adding more photos of the back using illuminators to better highlight the part and using a wide lens (16 mm) that increased the field of view of the camera.
2. A masonry pillar from a medieval cloister, Oppido (Italy). The problems were the presence of trees and weeds and the impossibility to detect the top of the structure (Figure 1b). This was addressed by adding images acquired with a drone. Since the cameras used were different, the point clouds were manually aligned with CloudCompare software (2.13.1).
3. A wooden reproduction of a Roman throwing weapon (scorpionide), with complex and detailed geometry and small and thin parts (Figure 1c). In this case, the use of a macro lens (60 mm) helped in capturing the small details. The point clouds were aligned manually with CloudCompare.
4. A Samnite tomb in the archaeological park of Santa Maria Capuavetere. The structure is located on one side of the park, completely covered with ivy (Figure 1d). The point cloud was intentionally left dirty to specifically analyze the impact of the denoising algorithm on very noisy data.
# Experimental Study on Permeability Characteristics of
## 3 Results and Discussion
### Determination Method for Permeability Level of the CBB  
At present, there is no unified regulation for the waterproof level of backfill materials. The evaluation of the waterproof performance of backfill materials can be achieved by referring to the classification of permeability characteristics in coal mine strata. The permeability classification of rock and soil is given based on the permeability coefficient \(K\) of rock and soil in the _Code of Water Pressure Test in Borehole for Water Resources and Hydropower Engineering (SL31-2003)_[32; 33]. The CBB of gangue slurry belongs to porous media [34; 35], and there is a conversion relationship between its permeability coefficient \(K\) and permeability \(k\), as shown in Equation (3).  
\[K=k\frac{\rho g}{\mu}=k\frac{\gamma}{\mu} \tag{3}\]  
where \(K\) is the permeability coefficient, cm/s; \(\rho\) is the density of water, g/cm\({}^{3}\); \(g\) is the gravity coefficient; g = 9.8 N/kg; \(\gamma\) is the weight of water, 9.8 kN/m\({}^{3}\); \(\mu\) is the viscosity coefficient of water, 2.98 MPa-s.  
Equation (3) can be used to convert the permeability coefficient \(K\) in the classification of rock and soil permeability into permeability \(k\), and the permeability level of the CBB is obtained, as shown in Table 2.  
The permeability classification of the CBB in Table 2 is used, and the permeability results in Figure 8 are reclassified, as shown in Figure 11. It can be seen that the permeability levels of all CBBs are within the range of poor permeability and extremely poor permeability, indicating that the gangue slurry has a good water-blocking effect after backfilling into the mining space. As shown in Figure 11, when the axial pressure exceeds 6 MPa, the permeability levels of CBBs all reach extremely poor permeability. When the cement dosage reaches 6%, the permeability level of the CBB under all axial pressure conditions has extremely poor permeability. Increasing the axial pressure of the gangue slurry, reducing the particle size range of the gangue slurry, and adding cement can all improve the permeability  
Figure 10: Permeability changes of the CBB with different cement dosages.  
level of the CBB and improve its permeability. In the actual construction process, the permeability level of the CBB should be comprehensively determined based on the injection layer of gangue backfilling and the requirements for mine water prevention and control, so as to determine the composition and ratio of gangue slurry.
## 1 Introduction
used FDS to conduct a numerical simulation of a tunnel fire with a small curvature radius and found that the curvature radius would lead to a large temperature gradient near the fire source [18]. [PERSON] simulated the fire smoke flow in a single curvature tunnel by Fluent to obtain the change in CO concentration under different curvature and ventilation environments [19]. [PERSON] et al. evaluated factors such as tunnel curvature, longitudinal ventilation operation time, and ventilation velocity in order to investigate the influence of longitudinal ventilation parameters on the fire-extinguishing effect of water mist in a curved tunnel [20]. Moreover, the characteristics of smoke diffusion at the characteristic height of the human eye and the variation in factors such as temperature, visibility, and CO concentration in curved tunnel fires that affect personal evacuation have not been realized. Even the existing codes lack specific guidelines for fire control in curved tunnels [21; 22]. This paper thus ascertains the characteristics of smoke diffusion exhibited by curved highway tunnels with varying curvatures and assesses the impact on personal evacuation. The recommended evacuation strategies and design of an adit for people are also provided. The focus lies on the distinct smoke diffusion pattern and strategies to deal with its impact on evacuations.
## Extraction methods using organic solvents
### Liquid samples preparation
On the other hand, for solvent mixtures containing low proportions of acetone, convective heating is negligible and microwave energy has to be absorbed by the solid material because the extraction solvent appears to be mostly transparent to microwaves. When low volumes of solvent are used, most of the liquid is in direct contact with the solid material. A combination of two different techniques, microwave-assisted extraction (MAE) coupled to headspace solid-phase microextraction (SPME-HS), was applied by [PERSON] et al. to determine chlorophenols in soil samples [71]. After analytes were desorbed from SPME fiber in the GC injection port, they were analysed by GC-EC detection system. Under medium power irradiation for 9 minutes, with a polyacrylate fiber, phenolic compounds were extracted with an efficiency about 90% and less than 10% RSD. A different approach, based on microwave-assisted steam distillation (MASD), was used by [PERSON] _et al_. for extracting chlorophenols from solid samples [72]. Gas chromatography (GC) with electron-capture detection was used for the analysis. MASD achieved recoveries for spiked soil samples in the range of 94-101%. Some real samples were analysed which included soil, wood, leather, textiles, dyes and certified reference materials of soil and wood samples. Limit of detection values of 12 ng\(\cdot\)g\({}^{\text{-1}}\) for pentachlorophenol and 194 ng\(\cdot\)g\({}^{\text{-1}}\) for monochlorophenol were found. Table 2 shows a summary of procedures used for the determination of phenolic compounds in solid samples using conventional extraction methods.
## 3 Results and Discussion
### Phase Shifter  
The phase shifter's initial calculated length was 4.6 mm, but, through optimization, it was adjusted to 10.60 mm, achieving a \(-45^{\circ}\) phase shift in the line. As shown in Figure 13, the optimized phase shift in the line reached \(-45.8^{\circ}\), which is consistent with similar optimizations in phase shifters that demonstrate high accuracy and effective signal manipulation, as reported by [PERSON] et al. [80] and [PERSON] et al. [81].