esa-sceva/satcom-chunk-collection huggingface dataset:
# An Iterative Optimization Algorithm for Planning Spacecraft Pathways Through Asteroids
## 5 Other Algorithms for the ARP
### Greedy Nearest Neighbor (GNN)  
The Greedy Nearest Neighbor heuristic, henceforth referred to as GNN, is inspired by the well-known nearest neighbor heuristic used in the Traveling Salesman Problem (TSP) [43].  
The core concept of GNN is to construct a reasonably effective permutation of the asteroids by iteratively visiting the asteroid that is nearest--under Euclidean distance--to the last visited one. In order to calculate the distances, at each time step the locations of all unvisited asteroids need to be computed.  
The pseudocode for GNN is outlined in Algorithm 2. For more detailed information, we direct interested readers to [5]. Moreover, an implementation of the GNN for ARP is available at [[https://doi.org/10.5281/zenodo.5725837](https://doi.org/10.5281/zenodo.5725837)]([https://doi.org/10.5281/zenodo.5725837](https://doi.org/10.5281/zenodo.5725837)) (accessed on 10 November 2024).  
```
1:procedureGNN
2:\(s\gets a_{0}\)\(\triangleright\) Orbit of the Earth
3:\(\tau\leftarrow\tau_{0}\)\(\triangleright\) Initial epoch for the spacecraft
4:\(U\leftarrow[\mu]\)\(\triangleright\) Set of the unvisited asteroids
5:for\(i\gets 1\)to\(n-1\)do
6:\(\sigma(i)\leftarrow\arg\min_{j\in U}d_{Eucl}(s,a_{ij},\tau)\)
7:\(\left(t_{2i-1},t_{2i}\right)\leftarrow\) SLSQP\((s,a_{\sigma(i)})\)\(\triangleright\) Inner task
8:\(\tau\leftarrow\tau+t_{2i-1}+t_{2i}\)
9:\(U\gets U\setminus\{\sigma(i)\}\)
10:\(s\gets a_{\sigma(i)}\)
11:endfor
12:return\(\sigma,f(\sigma)\)
13:endprocedure
```  
**Algorithm 2** The GNN constructive heuristic
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
###### Abstract
They assume that this will result in AEs failing to reconstruct well during prediction due to anomalous images deviating from the normal manifold. These works use reconstruction errors as scores for identifying anomalies with larger meaning more likely to be anomalies. However, AEs are originally designed for reconstruction, whose performance to be tightly coupled with the size of the latent space [12]. With no knowledge of the essential dimension of the data, setting the dimension of the latent space too small or too large would cause potential problems for AE-based anomaly detection methods. A large setting may result in undetectable anomalies caused by good reconstruction of anomalous images, and conversely may result in false detection of normal as abnormal. We propose a method to circumvent the above problems by adopting an AE-like model with strong reconstruction performance for the purpose of reducing false detections. To solve the problem that anomalies can also be reconstructed caused by excessive reconstruction capability, we introduce a deep autoregressive (DAR) model. By using DAR to constrain the latent space of the anomalous image in the distribution of normal images, AE can reconstruct the normal sample closest to the corresponding anomalous image. Based on this framework, we also propose a novel anomaly score to further improve anomaly detection, which exploits the residuals of both reconstructions with and without latent space constraints. We use VQ-VAE [13] applied to the compression task as the reconstruction model for the proposed method, with VQ-VAE providing a discrete latent space that can improve the modeling efficiency of DAR. For the DAR model, as PixelSNAIL [14] can generate an image by modeling a joint distribution of pixels as well as giving the likelihood of each pixel, we use it as the tool to constrain the distribution of latent space. The proposed method is evaluated on the large-scale real-world industrial image dataset MVTec AD [15] and compared it with several major anomaly detection methods. The experiments show that our method yields better performance compared with the state of the art. The main contributions of this paper are as follows:1. We propose a novel method only using normal data for image anomaly detection. It effectively excludes the anomalous components in the latent space and avoids the unwanted reconstruction of the anomalous part, which achieves better detection results. 2. We propose a new method for anomaly score. The high anomaly scores are concentrated in the regions where anomalies are present, which will reduce the noise introduced by the reconstruction and improve precision. The remainder of this paper is organized as follows: Section 2 reviews the related work of image anomaly detection. A detailed description of our proposed method is given in Section 3. Section 4 presents experimental setups and comparisons. The conclusion is finally summarized in Section 5.
## 5 Discussion  
Most of the existing studies deal with address verification processes, such as parsing, matching, and standardizing for a specific context or country, but the proposed method in our research is designed to apply to various countries based on deep learning classification models using each country's administrative district data. The deep learning model is able to determine the level of administrative districts from a given address string and through this, the accuracy of the given address is calculated and the quality is improved by converting it into a standardized address. In addition, it is able to overcome the limitations of existing geocoding services that cannot accurately identify the administrative district level corresponding to the entered address of the manufacturers.  
Regarding the experimental results, the proposed method is able to verify and evaluate the manufacturers' addresses significantly and such tasks have not been completed yet in prior research. Prior studies did not primarily concentrate on categorizing the address components to their administrative area level and assessing the quality of the address; however, our study is able to complete such tasks. In this proposed paper, the examination and verification of the manufacturers' addresses of nations that export food to Korea are the exclusive focus of our research. Based on the results from the evaluation of three different countries, such as China, Japan, and Cambodia, the results were expressed differently based on the quality of addresses for each country. Accordingly, it is supposed that the quality of manufacturers' addresses from Cambodia is still insufficient as it could only achieve an average address accuracy of 77.30%, while China and Japan achieved a good score in average accuracy of nearly 90%. That means the addresses of manufacturers from Cambodia can be incorrectly formatted, misspelled, informal, unconventional, abbreviated, or slang, while manufacturers from China and Japan deliver the addresses that meet the standard. Anyway, it should be noted that, unlike China and Japan, in Cambodia, the number of food manufacturers registered with the Ministry of Food and Drug Safety in Korea is only 89 companies and these are used for the evaluation.  
Due to the limitation and scope, we can only focus on three countries in this study and hope to extend more in future research.
# DAG Hierarchical Schedulability Analysis for Avionics Hypervisor in Multicore Processors
## 6 Results
### Evaluations
#### 6.1.1 Sensitivity of DAG Priorities  
From some results, it is not easy to understand how the DAG attribute affects the WC maximum CT. To adapt to this situation, this experiment shows how the evaluated analysis is sensitive to some DAG features. That is, by controlling the parameters of Dag and evaluating the maximum CT in the normalized value, we can see how much the performance of the analysis has changed. Otherwise, this will not be distinguished by the WC maximum CT or schedulability analysis. Specifically, we consider the following parameters in this experiment: (i) DAG parallelism (the maximum possible width when generating a randomized DAG), \(p\), and (ii) the ratio of DAG CP to total workload %L, where %_L_ = _L/C_ \(\times\) 100%.  
Figure 7 evaluates the influence of CP length on the usefulness of the proposed method, \(m=2\). The CP varies from 60% to 90% of the total workload of the generated DAG. In this experiment, compared with the existing methods, the proposed analysis shows the most remarkable performance. For the proposed method, due to the change in the internal structure of the generated DAG (for example, \(L\) = 0.6), the WC maximum CT of rta-cpf varies with a small amount of %_L_. However, with the further increase of %_L_, rta-cpf provides a constant CT because whole non-critical workloads can be executed in parallel with the CP. In this case, the maximum CT is directly equal to the length of the CP. Similar results were obtained for rta-cpf-eo, which provided a constant CT (the length of the CP) under whole experimental settings. Note that, with the further increase of %_L_, it is completely dominated by rta-cpf. Sensitivity of CP ratio (\(m=4\), \(p=8\)) in Figure 8.  
Figure 6: WC CT of DAG using different numeral of processors.  
The method is superior to the classical method and the latest technology in general. In short, we observe that whole the tested parameters have an influence on the function of the proposed method. For rta-cpf, it is sensitive to the relationship between \(m\) and \(p\), in which low \(m\) or high \(p\) shows the usefulness of this method. These two factors have a direct impact on the CT of whole NC-joints. \(\%L\) will also significantly affect the performance of rta-cpf. In rta-cpf, a longer CP usually leads to a more accurate approximation of the maximum CT. Similar to rta-cpf-eo, rta-cpf shows better performance with the increase of% \(L\). Hence, due to its explicit execution sequence, rta-cpf-eo shows stronger performance than rta-cpf, and is not affected by parameter \(q\).
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.14 Path Planning III  
In [82], the authors empowered mobile robots to navigate successfully in unknown environments. This work used the improved Dynamic Window Approach (DWA) (for more information about DWA, please refer to [93]) and the Q-learning RL algorithm to improve autonomous navigation in varying conditions effectively. The authors adjusted the existing DWA evaluation functions and developed new ones to address specific navigation obstacles. This work overcomes the limitations of traditional route planning algorithms that rely heavily on predefined global maps.  
The work in [82] used Q-learning to dynamically modify the weights assigned to various evaluation functions inside the enhanced DWA, as shown in Figure 14. This enables the robot to modify its navigation method according to its current condition and the surrounding environment. The state space is characterized by parameters such as proximity to the goal, robot orientation in relation to the target, and the positioning of barriers. The action space comprises various combinations of weights for the evaluation functions and the distance traveled used in trajectory prediction. The changes made to the DWA evaluation functions and the use of Q-learning for adaptive parameter optimization show the effectiveness of the approach.  
The above-mentioned work demonstrates the enhanced DWA's effectiveness, revealing smoother trajectories, faster navigation times, and improved obstacle avoidance over the previous DWA. The results of the experiments further validate the actual implementation of the proposed technique, demonstrating its efficacy in navigating a real robot around both static and dynamic obstacles. The comprehensive evaluation through simulations and hardware tests confirms the practicality and efficacy of applying this approach to various applications.
## 2 Shortages of Existing Parallel Architectures  
As it has been discussed in Section 1, many offline communication systems with high transmission rate have been developed. Nonetheless, improving the communication rate of real-time systems is still a big problem that needs to be worked out, especially the baseband digital signal processing technology.  
Most parallel structures are derived from serial algorithms. Gardner [14; 15; 16] and Oerder and Meyr (O&M) [17] algorithms are the two most commonly used serial timing synchronization algorithms. The authors have developed a parallel Gardner architecture [8; 18; 19] in a Gardner algorithm based two times oversampling coherent optical system. Nevertheless, this architecture could not achieve real-time performance, mainly because their Numerically Controlled Oscillator (NCO) structure could not meet FPGA timing requirements. Besides, the loop filter dismisses information provided by some of the parallel error detectors, which leads to a rather great deviation in recovered signals. To achieve real-time performance, [PERSON] and his collaborators have successfully implemented parallel O&M on FPGA in [20]. However, its requirement of four times the oversampling costs an extremely large amount of hardware resources and needs higher speed Analog to Digital Converters (ADCs) in parallel systems.
# MA-Xnet: Mobile-Attention X-Network for Crack Detection
## 2 Proposed Methodology
### Depth-Wise Separable Convolutions  
Standard convolutional neural networks use large-size convolutional kernels to obtain larger perceptual fields. However, this approach greatly increases the number of model parameters. Compared with standard convolution, depth-wise separable convolution requires fewer parameters to be tuned, reduces possible overfitting, and allows the good real-time performance of the network due to less computation. Figure 2a represents the standard convolution; Figure 2b represents the depth-wise separable convolution. The core idea is to disassemble a complete version of the standard convolution into two independent convolutional layers. The first layer is called a depth-wise convolution, and it performs lightweight filtering by applying a single convolution filter per input channel. The second layer is a \(1\times 1\) convolution, called a pointwise convolution, which is responsible for building new features by calculating a linear combination of the input channels. The input tensor of the standard convolutional layer is calculated, as follows:  
\[L_{i}=h_{i}\times w_{i}\times d_{i} \tag{1}\]  
where \(h_{i}\) denotes the height of the image, \(w_{i}\) denotes the width of the image, and \(d_{i}\) denotes the channel of the image. After filtering with a filter of size \(k\times k\times d_{j}\), an output tensor is generated, as follows:  
\[\text{Cost}=h_{i}\cdot w_{i}\cdot d_{i}\cdot d_{j}\cdot k\cdot k \tag{2}\]  
The cost with depth-wise separable convolution is only:  
\[\text{Cost}=h_{i}\cdot w_{i}\cdot d_{i}(k^{2}+d_{j}) \tag{3}\]  
Equation (3) shows the total capacity of the depth-wise separable convolutional operation. The differences between the algorithms of Equations (2) and (3) are shown in Equation (4):  
\[\text{Diff\_Ratio}=(1/N)+(1/k^{2}) \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Input Size** & **Function** & **t** & **_n_** & **_s_** & **_c_** \\ \hline \((3,224,224)\) & Conv2D & - & 1 & 2 & 32 \\ \((32,112,112)\) & Inverted residual & 1 & 1 & 1 & 16 \\ \((16,112,112)\) & Inverted residual & 6 & 2 & 2 & 24 \\ \((24,56,56)\) & Inverted residual & 6 & 3 & 2 & 32 \\ \((32,28,28)\) & Inverted residual & 6 & 4 & 2 & 64 \\ \((64,14,14)\) & Inverted residual & 6 & 3 & 1 & 96 \\ \((96,14,14)\) & Inverted residual & 6 & 3 & 2 & 160 \\ \((160,7,7)\) & Inverted residual & 6 & 1 & 1 & 320 \\ \((320,7,7)\) & Conv2D & 6 & 1 & 1 & 1280 \\ \((1280,7,7)\) & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: The architecture of MobileNetV2.  
Diff_Ratio indicates the difference between the ratios of Equations (1) and (2). This value is approximately \(k^{2}\) times the raw difference, and it can be seen that using the depth-wise separable convolution instead of the standard convolution can significantly reduce the computational effort of the model.
## 3 Results and Discussion
### ML Evaluation  
The ROC curves of each algorithm show a binary classification. Each scenario is compared with the other three scenarios so that it is considered as a binary value of 1 while the other scenarios are represented as 0. Figure 7 shows the ROC curves output for each scenario over the others for all algorithms.  
The AUC is the indicator for the binary classification performance. In the case of RMa LoS, all algorithms achieved an AUC score above 0.997. The KNN achieved the highest AUC score in RMa NLoS classification with 0.994. In the case of UMa LoS, the KNN achieved a score of 0.994, outperforming the SVM by 0.02, while the GMM outperformed the k-Means by 0.02. The least AUC score in the case of UMa NLoS was 0.96. which corresponds with the k-Means, while the GMM surpassed the AUC score of k-Means by 0.266. The supervised learning seems to have a better AUC score than unsupervised learning because the TPR and FPR were calculated on the testing set only. The unsupervised learning models were used to cluster the combined dataset (training and validation set). Then, the evaluation was performed by calculating the minimum mean square error of each cluster and comparing it with the actual data output.  
The time evaluation for each algorithm is displayed in Table 3. The time of the training and testing were combined for the supervised learning algorithms KNN and SVM. The clustering time is shown for the unsupervised learning k-Means and GMM.  
Figure 6: PDF of k-PCA output components.  
Figure 7: Cont.  
Both supervised algorithms KNN and SVM require training using labels. The SVM performed the training and testing in a total time of 0.155 s, which is faster than the KNN. This indicates that the linear SVM could achieve 99% accuracy in less time, as the dataset distribution was suitable for SVM. The GMM performed the clustering task in 0.087 s, which was the lowest among all algorithms. The k-Means clustering time was the highest at 0.26 s. This information indicates that the GMM is more flexible and reliable in the scenario clustering task. It can be generalized for other different scenarios besides RMa LoS, RMa NLoS, UMa LoS, and UMa NLoS, as it does not require a label during training, which is a good advantage over the other algorithms.
## 4 Experiments and Discussion
### Experimental Settings  
We used PyTorch 1.10 to conduct experiments on GeForce RTX 2080 Ti. The batch size was 32, the initial learning rate was 0.001, the decay was performed every 5 rounds, and the early stop mechanism was set during training. The number of FDGCN-layers was 8, and residuals were added to each layer. In the temporal convolutional network, the kernel size \(k=2\), and the dilation factor was \(d=1\), 2, 1, 4, 1, 4, 1, 2. In the graph convolutional network, the convolutional neighborhood order = 2. The number of attention heads was \(h=8\), and the time step \(T\) was 12. We used the Adam optimizer [36] to train the model. The traffic prediction in our work is a regression problem, so in the performance evaluation stage, we adopted the regression commonly used evaluation metrics MAE, RMSE, and MAPE.
# Influence of Vegetation Filter Strip on Slope Runoff, Sediment Yield and Nutrient Loss
## 3 Results
### The Effects of the VFS on Nutrient Loss
#### 3.2.2 The Effects of the VFS on TN Loss  
A previous study have shown that nitrogen loss in runoff is positively related to slope and rainfall intensity [43]. However, in this study, the difference of TN content in runoff under different combinations was not obvious. When the slope was 5\({}^{\circ}\), 20\({}^{\circ}\), and 35\({}^{\circ}\), the reduction effects of VFS1 and VFS2 on TN in runoff were 13%, 13%, 18% and 24%, 18%, 19%, respectively; When the rainfall intensity was 30 mm/h, 60 and 90 mm/h, VFS No. 1 and VFS No. 2 can reduce TN in runoff by 8%, 17%, 20% and 14%, 26% and 21%, respectively. It can be seen that the effect of VFS in reducing runoff TN loss had no clear relationship with slope gradient and rainfall intensity. Furthermore, it can be seen from the Figure 7 that the TN loss of slope was mainly carried by runoff, which was consistent with the conclusions of [PERSON] et al. [44]. Compared with the bare land control group, VFS2 was better than VFS1 in reducing the total nitrogen loss in runoff obviously. When the slope increased to 20\({}^{\circ}\) and 35\({}^{\circ}\), there was almost no difference in the effect of the two types of VFS in reducing TN loss. When the rainfall intensity was 60 mm/h, VFS2 was more helpful to reduce the TN loss in the runoff. With rainfall intensity of 90 mm/h and slopes of 5\({}^{\circ}\) and 20\({}^{\circ}\), it was also more suitable for laying VFS2. Since in terms of reducing the TN content in runoff, VFS2 was 5% and 4% more than VFS1. However, when the rainfall intensity did not change, the conclusion obtained when the slope gradient was 35\({}^{\circ}\) was contrary. The TN content in runoff (12.40 mg/L) of VFS1 was less than that of VFS2 (13.20 mg/L). In general, VFS2 can more effectively reduce TN loss in slope runoff under various combinations of rainfall intensity and slope gradient set in the experiment. It was predicted that the VFS2 was far away from the source area at the bottom, and the path of runoff was long. Nitrogen could be retained or absorbed by plants on the surface of vegetation and soil, leading to less TN content in runoff. Nitrogen loss from soil is mainly in dissolved form. Dissolved organic nitrogen concentrated in the topsoil accounted for the highest total nitrogen content. Although the content of inorganic nitrogen in total nitrogen is relatively low, unstable nature makes it prone to denitrification and lead to total nitrogen loss. As for VFS to reduce the TN content in the sediment carried by runoff, the conclusions obtained under various conditions were different. When the slope gradient was 5\({}^{\circ}\), 20\({}^{\circ}\) and 35\({}^{\circ}\), the reduction effects of VFS1 and VFS2 on TN in sediment were 22%, 40%, 44% and 17%, 40%, 44%, respectively; the rainfall intensity was 30 mm/h, 60 mm/h, 90 mm/h, the reduction effects of VFS1 and VFS2 on TN in sediment were 24%, 30%, 46% and 21%, 30%, 49%, respectively. It can be seen that