esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Path and Shape Planning Method
### Boundary Waypoints Detection  
The following Algorithm 1 will be used to find waypoints autonomously at the boundary of a pre-built map with random sizes and dimensions. Assuming the next step of the algorithm is to determine the waypoint \(W_{n}(br)\) staying on the right side of the known waypoint \(W_{n}(bl)\). Since they are on the same horizontal line, these two waypoints locate on \(y=y_{n}\) and \(y_{n}=y_{n}^{bl}=y_{n}^{br}\). A filter mask \(\Phi\) of \(1\times m\) with a width of 1 pixel, the length of \(m\) pixels and the value of pixels equal to 1 is created as Figure 11. The free space pixel at \((x_{n}^{bl}+r,y_{n}^{bl})\) on the map, where \(M(x_{n}^{bl}+r,y_{n}^{bl})=0\) and \(r\) is random integer value such as \(0\leq x_{n}^{bl}+r\leq M_{w}\) is selected. The first pixel \(\Phi(1,1)\) of the mask is set at this pixel location. The mask slides with the step of one pixel from this location to the right and  
Figure 10: Global path and shape planner flowchart. filters each visited pixel \((x_{n}^{bl}+r+i,y_{n}^{bl})\) on the line \(y=y_{n}^{bl}\) of the map as Figure 11. At each location, the Equation (3) is used to calculate the filtered value \(f_{w}(x_{n}^{bl}+r+i,y_{n}^{bl})\) of the mask with a same mask size partition on the map. It is worth to note that in a map created by ROS, the pixels on the right side of the map right boundary pixel have the same value of \(-1\). Similarly, all the pixels on the left side of the map left boundary pixel have the same value of \(-1\). As the results, the \(f_{w}(x_{n}^{bl}+r+i,y_{n}^{bl})\) will equal -_m_ if the partition at to-be-filtered pixel stays entirely on the undefined areas of the map. If the value \(f_{w}(x_{n}^{bl}+r+\hat{i},y_{n}^{bl})=100 th-m-1\), where value \(\hat{i}\) can be found by Equation (4) and _th_ is the parameter representing the pixel thickness of the border of the map (_th_ equals 3 pixels in this paper), then the location \((x_{n}^{bl}+r+\hat{i},y_{n}^{bl})\) in the range \([(x_{n}^{bl}+r),M_{w}]\) will correspond to the right boundary pixel on the line \(y=y_{n}^{bl}\) of the map. The value of \(m\) is chosen so that the length of the mask is sufficiently larger than the biggest value of the lengths of the obstacles because the empty spaces inside the objects boundaries where the Lidar scan is blocked are also undefined spaces and have a value of \(-1\). In this paper, \(m\) is set to 100 pixels. The right boundary waypoint on \(W_{n}(br)\) will have the coordinate \((x_{n}^{br},y_{n}^{br})\), where \(x_{n}^{br}=x_{n}^{bl}+r+\hat{i}-w_{m}/2\), \(y_{n}^{bl}=y_{n}^{br}\). Since the rpLidar is fixed at the block 2 of hTetro, the value \(w_{m}/2\) where \(w_{m}\) is the robot width in pixel is added to make the safety distance with border. The processes are the same for the case where the mask slides from right to left for determining the pixel boundary on the right side.
## 4 Results
### Temporal Patterns of Selected Sample Points  
In our focused analysis within the Hanyang district, as presented in Figure 11b, we identified clusters of buildings with significant vertical development from 2012 to 2020. The detailed scatterplot identifies random sample points, where each represents a 50 m grid, labeled SP1 to SP5, chosen to understand growth patterns over time.  
Analysis of Figure 11c reveals varied growth trajectories of selected sample points over several years, highlighting various vertical development patterns. SP2 exhibits the most dramatic vertical development, ascending to nearly 200 m, which identifies it as an outlier because, in the study area, there is no building with a height of more than 150 m. Therefore, the noise in the data appears to be the main problem in the time series analysis. This contrasts sharply with SP4, where only a minimal height increase was noted, suggesting limited development activity. Both SP3 and SP5 reached growth plateaus. SP3 capped at around 75 m, and SP5 at 150 m, indicating that their constructions were likely completed within the study period. Meanwhile, SP1 showed a rapid vertical rise from zero to 100 m starting in May 2019, stabilizing by February 2020, which points to a period of intense development followed by completion. Together, these patterns provide not just the disparate rates of growth across different parts of the selected area, but also the varying timelines and stages of the vertical development.
# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 2 Related Work
### RGB Superpixel Algorithms  
There are two major classes of RGB algorithms for computing superpixels: graph-based methods and clustering-based methods. By representing an image with a graph whose nodes are pixels, the graph-based algorithms minimize a cost function defined on the graph. Representative works include normalized cuts (NC) [18], the Felzenszwalb-Huttenlocher (FH) method [19], superpixel lattices (SL) [20], and the graph-cut-based energy optimization method (GraphCut) [21].  
Normalized cuts generates regular and compact superpixels but does not adhere to the image boundary and has a high computational cost--\(O(N^{1.5})\) time complexity for an \(N\)-pixel image. Felzenszwalb-Huttenlocher method preserves the boundary well and runs in \(O(N\text{log}N)\) time, but it produces superpixels with irregular sizes and shapes. Although SL runs in \(O(N^{1.5}\text{log}N)\) theoretically, it has an empirical time complexity of \(O(N)\), making it one of the fastest superpixel algorithms. However, its produced superpixels conform to grids rather than adhering to image boundaries. GraphCut is elegant and theoretically sound, but it is difficult to use due to the many parameters involved in the algorithm.  
The clustering-based algorithms group pixels into clusters (that is, superpixels) [22] and iteratively refine them until some convergence criteria are satisfied. Popular clustering methods are Turbopixels [23], simple linear iterative clustering (SLIC) [24; 25] and structure-sensitive superpixels (SSS) [26]. Among them, SLIC is conceptually simple, easy to implement, and highly efficient in practice [24].  
All three methods are initialized with a set of evenly distributed seeds in an image domain. They differ in their method of clustering. The Turbopixels method generates a geometric flow for each  
Figure 1: Superpixels generated by the weighted coplanar feature clustering (WCFC) algorithm on an RGB-depth (RGBD) image from the New York University (NYU ) Depth V2 dataset.  
seed and propagates them using the level set method. The superpixel boundary is the point where two flows meet. Although Turbopixels has a theoretical linear time complexity of \(O(N)\), computational results have shown that it runs slowly on real-world datasets [24]. Simple linear iterative clustering is an adaption of k-means that clusters pixels in a 5D Euclidean space, combining colors and images. Simple linear iterative clustering is conceptually simple, easy to implement, and highly efficient in practice. Structure-sensitive superpixels takes geodesic distances into account and can effectively capture the non-homogenous features in images. However, SSS is computationally expensive [24].
## 5 Reconstruction Methods  
Reconstruction-based methods use a normal dataset to train a model that attempts to encode the data into a latent space and then reconstruct the original data from this representation. Reconstruction loss is calculated by comparing the differences between the reconstructed data and the original data, as illustrated in Figure 5b. This section delves into reconstruction anomaly detection methods, focusing on four types: AE-based, VAE-based, GAN-based, Transformer-based, and Diffusion-based models.
## 2 Materials and Methods
### Comparative Analysis of Preprocessing Techniques in Image-Feature Delineation  
In the realm of image processing, preprocessing plays a pivotal role in enhancing the quality of an image or preparing an image for further operations. In this study, three pre-eminent image-processing methods were meticulously chosen and their efficacy in delineating features was determined (Figure 3).  
1. Thresholding at an intensity of 110: This technique involves setting a threshold at an intensity value of 110. Pixels with an intensity that exceeds this value are labeled as the object (in this case, a tomato), whereas those with an intensity below this value are designated as the background. This approach is particularly advantageous for discerning the primary contours of the object in scenarios in which specific lighting conditions prevail.
2. Thresholding at an intensity of 120: The functionally of this technique is similar to that mentioned above. The technique uses a slightly elevated threshold value. It has been proven to be particularly effective for images that have pronounced contrast or were captured under intense illumination.
3. Dithering: Dithering is a sophisticated method that capitalizes on quantization errors to metamorphose the image into a monochromatic version, albeit with the enhanced retention of intricate details and textures. This method is instrumental in enabling the analytical model to pinpoint and identify complex regions or areas of the tomato that exhibit low contrast, which might otherwise be overlooked when using rudimentary binarization techniques.  
It is crucial to understand that the efficacy of each method is contingent on the specific attributes of the image in question and the overarching objective of the image-processing task.
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
## 5 Performance Test  
To represent the performance of GWEC, the linearization model (Equation (6)) described above has a fast application, which is based on the assumption that \(\varepsilon\) is a small angle. However, the precession angle of the rotating gyroscope cannot be 0 when subjected to external forces from the mechanical and dynamic point of view. Figure 7 shows the power output of the linearized model and the nonlinearized equation under the same conditions, where the gyroscope rotating speed of Figure 7a-d is 8000 rpm and the speed in e-h is 12,000 rpm. The pitch amplitude of the left images is 6\({}^{\circ}\), while that of the remaining images is 14\({}^{\circ}\).  
Figure 7 shows the main differences between the linear model and the nonlinear model. In general, the power output of the linear model is greater than the nonlinear equation.  
As mentioned before, Equation (5) can be simplified to Equation (6) only when \(\varepsilon_{0}\) is at a small angle. When combined with Equation (12), it can be concluded that when \(c_{y}\) and \(J_{z}\) remain unchanged, the greater \(\dot{\varphi}\) and A are, the greater \(\varepsilon_{0}\) is. It can be clearly seen from Figure 7 that when only the pitch amplitude is different, the output with 14\({}^{\circ}\) pitch amplitude A in Figure 7b,d,f,h is much greater than the others. The speed of flywheel \(\phi\) in Figure 7e-h is also higher than in (a)-(d), which causes the increase in \(\epsilon_{0}\). Therefore, the values of \(\dot{\varphi}\) and A are both increased in Figure 7f,h, and the power difference between the linear model and the nonlinear model is more obvious than in other figures. Based on the above analysis, the following conclusions can be drawn. Under the wavelet pitch amplitude or low flywheel speeds, the linearized model can obtain an acceptable approximation of the complete nonlinear equation. However, under large pitch amplitude, or the high flywheel speed, the two models are quite different. This also verifies that the linear model can be used instead of the nonlinear model to test the characteristics of GWEC only under low-speed, small pitch amplitude conditions.  
In this section, the effects of the main parameters of the gyroscope system on the output power and energy conversion efficiency are analyzed. To test and verify the performance of the GWEC, the sea state with lower wave energy density is selected in the experimental stage. The South China Sea has a wave period of 6-8 s, while the North Bay and the Gulf of Thailand have a wave height of 0.5 to 1.5 m. In this experiment, the rated sea state is set to a wave period of 7 s, and the pitch amplitude is 10\({}^{\circ}\).
# Efficient Pruning of Detection Transformer in Remote Sensing Using Ant Colony Evolutionary Pruning
## 4 Experimental Results and Analysis
### Comparative Experiment  
To further verify the effectiveness of the ACEP method proposed in this paper, other pruning methods that can be used for DETR-like neural networks were compared, namely L1-norm prune and NS. When using these two methods to prune the attention layers, all attention heads were pruned by the same dimension instead of using individual attention heads as pruning units. After pruning, the models were fine-tuned for 30 epochs to recover performance. The detailed experimental results are shown in Table 2. From the table, it can be seen that after pruning the DETR, Deformable DETR, and DINO neural networks, our method maintained better detection performance at higher compression ratios. These results suggest that our method performs better when pruning DETR-like neural networks for remote sensing tasks.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 3 Disruption Tolerant Distributed Routing Algorithm
### Main Path Direction Calculation
#### 3.3.2 Vertical Hop Direction Calculation  
Let \(c_{j}\) represent the current satellite node phase number, \(d_{j}\) represent the destination satellite node phase number, and \(d_{v}\) represent the vertical hop direction \(d_{v}\), so \(d_{v}=2\) means forwarding in the direction of increasing phase number, \(d_{v}=0\) means forwarding in the direction of decreasing phase number, \(d_{v}=-1\) means there are no vertical hops, that is, the current satellite and the destination satellite are at the same phase. Because the orbit of the LEO satellite network is circular, the vertical hop direction not only considers the size relationship between the phase number of the current satellite and the destination satellite, but also considers the phase difference, and finally determines the choice of the vertical hop direction \(d_{v}\). The calculation method of the vertical hop direction is as follows.  
1. If \(c_{j}<d_{j}\), \(\left|d_{j}-c_{j}\right|\leq M-\left|d_{j}-c_{j}\right|\), then \(d_{v}=2\);
2. If \(c_{j}<d_{j}\), \(\left|d_{j}-c_{j}\right|>M-\left|d_{j}-c_{j}\right|\), then \(d_{v}=0\);
3. If \(c_{j}>d_{j}\), \(\left|d_{j}-c_{j}\right|\leq M-\left|d_{j}-c_{j}\right|\), then \(d_{v}=0\);
4. If \(c_{j}>d_{j}\), \(\left|d_{j}-c_{j}\right|>M-\left|d_{j}-c_{j}\right|\), then \(d_{v}=2\);
5. If \(c_{j}=d_{j}\), then \(d_{v}=-1\).
# Predicting Activities of Daily Living with Spatio-Temporal Information
## 5 Experiments
### Experiment 4: Computational Performance  
The aim of this experiment is to evaluate the computational performance of our method in terms of training time and prediction time. The training time is computed based on the time it requires to build the PPM, while the prediction time is computed based on the time it takes to predict from the trained PPM. The performance is evaluated on a desktop computer with an Intel(R) Core(TM) CPU i7-7700K @ 4.2 GHz and 64 GB memory.
## 3 Proposed Methods Based on Convolutional Neural Networks
### Spatio-Temporal Convolutional Neural Network Version 2 (ST_CNN_v2)  
The second proposed model, named spatio-temporal convolutional neural network version 2 (ST_CNN_v2), uses learning structures specific to CNNs and can capture long-range dependencies within the input time series through configurations of filters and layer strides. The model architecture is defined in Figure 4.  
Figure 4: Description of the architecture of the ST_CNN_v2 model.  
Figure 3: Description of the architecture of the ST_CNN_v1 model.  
ST_CNN_v2 features multiple one-dimensional convolutional layers with a distinct stride size. All convolutional layers use a kernel size of two and the ReLU activation function, starting with the first one. Subsequent layers introduce causal padding and increasing stride to capture the different time scales of the input time series. The output of the flattened layer is then passed through two densely connected layers with ReLU activation, with the last one featuring a single unit to predict solar radiation. MAE is chosen again as the loss function and the Adam optimizer is used for training.
## 4 Case Studies
### Case Study 3: Reducing Emissions through Optimal Route Planning  
* Problem: The maritime industry is a significant contributor to global emissions. Reducing these emissions is a priority, but traditional methods often rely on predetermined routes and schedules, which may not be optimal.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Case Study** & **Problem** & **ML Technique Used** & **Implementation** & **Results** \\ \hline Predicting Vessel & Inaccurate ETA predictions leading to port operation disruptions. & Gradient boosting ML algorithm & Model continuously updated with live AIS data. & Improved accuracy of ETA predictions; more efficient scheduling of port resources. \\ \hline Anomaly Detection for Maritime Safety [38] & Difficulty identifying anomalous behavior in vessel movements. & One-class support vector machine (SVM) & Algorithm trained on historical AIS data. & Early detection of anomalies for timely interventions; improved safety and security. \\ \hline Reducing Emissions through Optimal Route Planning [39] & High emissions from the maritime industry. & ML model for route optimization & Model continuously updated with real-time data. & Significant reduction in fuel consumption and emissions; cost savings. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of case studies.  
Solution: A research project developed an ML model that could suggest the most fuel-efficient route for vessels, based on historical data and current environmental conditions [39].
* Implementation: The model took into account various factors, including weather conditions, sea currents, and vessel characteristics. The suggested routes were continuously updated based on real-time data.
* Results: The ML model helped vessels to reduce their fuel consumption significantly, contributing to a decrease in emissions. The model also led to cost savings, highlighting the commercial benefits of sustainable practices [39].
# Design of a Combined Redrawing-Ironing Process to Manufacture a CNG Pressure Vessel Liner
## 2 Design of a Combined and Redrawing-Ironing Process  
### Existing D.D.I. Process  
The existing D.D.I. process to form the cylinder part of a liner includes drawing to reduce the diameter of the billet and ironing to reduce the thickness of the billet. In the first stage, an initial blank is manufactured into a cup shape using a tractrix die to avoid forming defects without a blank holder. In the second stage, redrawing and two ironing processes (D.I.L) are continuously conducted, and in the third stage, redrawing and ironing processes (D.I.) are performed to obtain the final thickness and diameter of the liner [1]. Theoretical design rules of the D.D.I. process are shown in Equations (1)-(12) [4].  
\(dp_{\rm i}\), \(dd_{\rm ij}\), and \(d_{\rm ij}\) are diameters of the punch, drawing die, and ironing dies at the ith stage and jth process. \(b_{\rm i}\), \(t_{\rm ij}\), and \(A_{\rm ij}\) are the draw ratio, thickness of material, and cross sectional area, respectively. Based on the design rules provided by the NK company [7], the blank diameter (\(D_{0}\)) is 1010 mm, and the initial thickness (\(t_{0}\)) is 12.5 mm. The LDR (\(b_{1}\) = 1.84) in the first stage and tip clearance (\(C_{1}\) = 5%, \(C_{2}\) = 0, and \(C_{3}\) = \(-\)5%) between the drawing die and the workpiece in each stage were found from the actual field.  
\[dp_{1}=dm_{1}-(1+C_{1})\times t_{0} \tag{1}\]  
\[dd_{1}=dm_{1}+(1+C_{1})\times t_{0} \tag{2}\]  
Figure 3: Comparison of the new combined redrawing-ironing process in 2 nd stage with conventional one.  
\[b_{1}=\frac{D_{0}}{dm_{1}} \tag{3}\]  
\[dp_{2}=dm_{21}-(1+C_{1})\times t_{0} \tag{4}\]  
\[dd_{21}=dm_{21}+(1+C_{1})\times t_{0} \tag{5}\]  
\[b_{2}=\frac{dm_{1}}{dm_{21}} \tag{6}\]  
\[di_{23}=\sqrt{\frac{4A_{23}}{\pi}+dp_{2}{}^{2}},\ 0.5A_{1}=A_{23} \tag{7}\]  
\[dp_{3}=dm_{31}-(1+C_{3})\times t_{23} \tag{8}\]  
\[dd_{3}=dm_{31}+(1+C_{3})\times t_{23} \tag{9}\]  
\[b_{3}=\frac{dm_{23}}{dm_{31}},\ dm_{23}=\frac{dp_{2}+di_{23}}{2} \tag{10}\]  
\[t_{23}=\frac{di_{23}-dp_{2}}{2}di_{32}=dp_{3}+2t_{32} \tag{11}\]  
The reduction ratio of the cross sectional area (RA) in one stage is defined as Equation (12). \(A_{0}\) is the cross sectional area before forming, and \(A_{f}\) is the cross sectional area after forming. The limit reduction ratio of the cross sectional area (LRA) in each stage is 50%, which is provided by the actual field. When ironing processes are carried out two times in one stage, each reduction ratio of the cross sectional area (RA\({}_{\rm ironing}\)) in Equation (13) should be the same, and the limit reduction ratio of the cross sectional area in the ironing process (LRA\({}_{\rm ironing}\)) is not allowed to exceed 35%. \(A_{1}\) is the cross sectional area before ironing, and \(A_{2}\) is the cross sectional area after ironing.  
\[\text{RA }=1-\frac{A_{f}}{A_{0}}\times 100(\%) \tag{12}\]  
\[\text{RA}_{\rm ironing}=1-\frac{A_{2}}{A_{1}}\times 100(\%) \tag{13}\]