esa-sceva/satcom-chunk-collection huggingface dataset:
# Topology Control of Low-Connection UAV Laser Network with Virtual Nodes
## 6 Experiment  
This section conducts a brief experiment on the proposed algorithm and compares it with the XTC algorithm. First, it is assumed that there are 5 nodes uniformly distributed around a given node, with each distance being \(\frac{\sqrt{2}}{3}\). The results for both the XTC and VNTC algorithms are shown in Figure 6. It can be observed that the node degree in the proximity graph constructed by the XTC algorithm is 5, while the VNTC algorithm maintains a node degree of 4.  
For a node graph distributed uniformly according to a grid, with the distance between nodes remaining \(\frac{\sqrt{2}}{3}\), the resulting graph in Figure 7 shows that the  
Figure 5: Two pairs of paired nodes.  
Figure 6: Algorithm comparison with 5 neighboring nodes.  
cycles of length 4, while the VNTC algorithm predominantly generated cycles of length 6. This is because forming a cycle of length 4 requires either 2, 3, or 4 virtual nodes. After node pairing, only one edge between virtual nodes is retained. Therefore, it is impossible for 2 virtual nodes to form a cycle of length 4. Additionally, since the proximity graph generated by the XTC algorithm does not contain cycles of length 3, 3 virtual nodes also cannot form a cycle of length 4. Consequently, only when 4 virtual nodes form a cycle of length 4 can there be a possibility of forming a cycle of length 4 in the actual graph. Due to the sequential arrangement of nodes in the graph and the fact that the node pairing process is also carried out in the order of edge lengths, the graph exhibits regular cycles of length 6. When using a pairing algorithm with random initialization, as shown in Figure 8, it can be observed that the majority of cycles in the graph have a length greater than 4.  
Since the virtual graph employs the XTC algorithm for topology control, its performance in the virtual graph is identical to that of the XTC algorithm. However, after the graph mapping, one virtual node corresponds to two actual nodes. Therefore, when a virtual node acts as an intermediary, it may trigger communication between the two actual nodes, resulting in a slight degradation in actual performance compared to the XTC performance. By randomly distributing nodes within a \(5\times 5\) area and progressively increasing the node density, the average minimum jump count between nodes is calculated at various densities. The results are depicted in Figure 9, which shows that the average minimum jump count of the VNTC algorithm is slightly greater than that of the XTC algorithm.  
Figure 8: Results of VNTC algorithm with random initialization.  
Figure 7: Algorithm comparison with grid nodes.  
Based on the fourth and fifth items in the table, the performance gap between the VNTC algorithm and the XTC algorithm in the worst-case scenario is less than 20%, while their performance in average cases is similar. Therefore, the VNTC algorithm is able to maintain a topology graph with performance close to that of the XTC algorithm while reducing the upper limit of the degree of individual nodes.
# The MADAG Strategy for Fault Location Techniques
###### Abstract  
Spectrum-based fault localization (SBFL), which utilizes spectrum information of test cases to calculate the suspiciousness of each statement in a program, can reduce developers' effort. However, applying redundant test cases from a test suite to fault localization incurs a heavy burden, especially in a restricted resource environment, and it is expensive and infeasible to inspect the results of each test input. Prioritizing/selecting appropriate test cases is important to enable the practical application of the SBFL technique. In addition, we must ensure that applying the selected tests to SBFL can achieve approximately the effectiveness of fault localization with whole tests. This paper presents a test case prioritization/selection strategy, namely the Minimal Aggregate of the Diversity of All Groups (MADAG). The MADAG strategy prioritizes/selectes test cases using information on the diversity of the execution trace of each test case. We implemented and applied the MADAG strategy to 233 faulty versions of the Siemens and UNIX programs from the Software-artifact Infrastructure Repository. The experiments show that (1) the MADAG strategy uses only 8.99 and 14.27 test cases, with an average of 18, from the Siemens and UNIX test suites, respectively, and the SBFL technique has approximate effectiveness for fault localization on all test cases and outperforms the previous best test case prioritization method; (2) we verify that applying whole tests from the test suite may not achieve the better effectiveness in fault localization compared with the tests selected by MADAG strategy.  
debugging; spectrum-based fault localization; test case prioritization; test suite reduction
## 3 Irr
### Limitations of IRR  
Even though IRR algorithms are the best known practical algorithms for obtaining small grammars, they present some weaknesses. In the first place, their greedy strategy does not guarantee that the compression gain introduced by a selected word \(\omega\) will still be interesting in the final grammar. Each time an iteration selects a substring of \(\omega\), the length of the production rule is reduced; and each time a superstring is selected, its number of occurrences is reduced. Moreover, the first choices mark some breaking points and future words may appear inside them or in another parts of the grammar, but never span over these breaking points.  
It could be argued that there may exist a score function that for every sequence scores the repeats in such a way that the order they are presented to IRR results in a smallest grammar. The following theorem proves that this is not the case.  
**Theorem 1**.: _There are infinitely many sequences \(s_{k}\), over a constant alphabet, such that for any score function \(f\), \(|\text{IRR}(s_{k},f)|\) is greater than the size of a smallest grammar for \(s_{k}\)._  
Proof.: Consider the infinite set of sequences  
\[x\alpha x\beta x\gamma x|_{1}x\beta x\gamma x\alpha x|_{2}x\gamma x\alpha x \beta x|_{3}x\alpha x\gamma x\beta x|_{4}x\beta x\alpha x\gamma x|_{5}x\gamma x \beta x\alpha x|_{6}x\alpha x|_{7}x\beta x|_{8}x\gamma x\]  
where \(\alpha,\beta\) and \(\gamma\) are any sequences of size greater than two, with pairwise disjoint alphabets not containing the symbol \(x\). These sequences exploit the fact that IRR algorithms replace all possible occurrences of the selected word. Let us define \(G^{*}(s_{k})\) as the following grammar:  
\[S \to AS_{\beta}C|_{1}BS_{\gamma}A|_{2}CS_{\alpha}B|_{3}AS_{ \gamma}B|_{4}BS_{\alpha}C|_{5}CS_{\beta}A|_{6}A|_{7}B|_{8}C\] \[A \to xS_{\alpha}x\quad B\to xS_{\beta}x\quad C\to xS_{ \gamma}x\]  
to which we add the rules of smallest grammars for \(\alpha,\beta,\gamma\) with as start symbols \(S_{\alpha},S_{\beta},S_{\gamma}\) respectively. \(|G^{*}|=42+|G_{\text{min}}(\alpha)|+|G_{\text{min}}(\beta)|+|G_{\text{min}}( \gamma)|\), where \(G_{\text{min}}(s)\) denotes a smallest grammar for sequence \(s\). Note that no IRR algorithm could generate \(G^{*}\) and, moreover, by enumeration we find that the smallest possible grammar that can be obtained with an IRR algorithm has size \(46+|G_{\text{min}}(\alpha)|+|G_{\text{min}}(\beta)|+|G_{\text{min}}(\gamma)|\). This grammar has the same production rules as \(G^{*}\). In the case where \(\alpha\),\(\beta\) and \(\gamma\) are of size one (we then replace \(S_{\alpha}\) by \(\alpha\) and so on), this results in an approximation ratio of \(\frac{46}{42}\approx 1.095\). This is a general lower bound for _any_ IRR algorithm.  
Note that Theorem 1 does not make any assumptions over the possible score function \(f\) (like, for example, polynomial running time). In order to find \(G^{*}\), the choice of occurrences that will be rewritten should be flexible when considering repeats introduced in future iterations.
# Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi
## 6 Performance Evaluation
### Analysis of the Neural Network Structure  
The hyperparameters of the proposed deep regression model are adjustable variables. There is a trade-off between the performance and computational complexity of the neural network. We evaluated the average value of the ranging error by changing the number of nodes and the number of hidden blocks, and Figure 11 shows the results. The ranging error is saturated to about 2.5 m, and having too many hidden blocks for the small number of nodes decreases the ranging accuracy. The computational complexity of the proposed model structure can be expressed as \(O(M^{2}*L)\). Therefore, as the structure becomes more complex, the computation increases excessively compared to the performance gain. In this paper, we propose the use of 64 nodes in each layer and eight hidden blocks, where the ranging accuracy is greater than the 95 th percentile value among the evaluated models, and the computational complexity is only 3.3% compared to the model with the highest ranging accuracy.
## 3 Calculation Mesh and Boundary Condition Setting
### Transient State Mesh and Boundary Condition Setting  
In the transient calculation, the dynamic nested mesh technology is used to establish the calculation domain of two concentric spheres, which are the external flow field of the flight airspace and the internal flow field, including the aircraft. The external flow field domain is stationary during the simulation of large maneuvering flight, and the internal flow field mesh, including the aircraft body rotates around the center of gravity along with the aircraft. The mesh size of the body and inlet surface is consistent with that of the steady state. An encryption area shall be set near the inlet, and the maximum size of the encryption area shall not exceed 3 mm, as shown in Figure 6. The mesh boundary layer settings are consistent with the steady state. The number of volume meshes of the conventional submerged inlet configuration is about 1.4 million, of which the boundary layer prismatic meshes account for 0.8 million and the polyhedral mesh away from the wall accounts for 0.6 million; The number of the distributed inlet configuration volume meshes is about 1.6 million, of which the boundary layer prismatic meshes account for 0.9 million and the polyhedral meshes away from the wall account for 0.7 million.  
Figure 6 shows the setting of transient boundary conditions. The pressure far-field boundary is used for the far-field, and the free-flow parameters are given according to the atmospheric conditions at 5 km altitude, \(M_{0}\) = 0.7; the outlet of the inlet adopts the pressure outlet boundary condition, and the pressure is set to the corresponding pressure when \(M_{e}\) = 0.4; the interface between dynamic and static domains is set as the overset boundary condition; the body and inlet adopt non-slip adiabatic wall boundary.  
Figure 5: Setting of Steady State Boundary Conditions.  
Figure 6: Setting of Transient Boundary Conditions.
## 4 Partitioning Detection and Connectivity Restoration
### Post-Failure Recovery  
The post-failure recovery process is initiated by the pre-designated backup upon failure detection. The scope of recovery depends on the NAS. If the backup is a non-critical actor then it simply replaces the primary and the recovery would be complete. However, if the backup is also a critical node then a cascaded relocation is performed. Basically, repositioning of actor \(A_{i}\) in response to the failure ofwill be interpreted by its backup \(A_{j}\) as if \(A_{i}\) is lost and \(A_{j}\) will thus move to replace \(A_{i}\). The detail of the recovery process is as follows:  
The pre-assigned backup immediately triggers a recovery process once it detects failure of its primary. The status of backup determines the scope of recovery which can be among the following three scenarios. First, if a backup is a non-critical node the scope of recovery will be limited because it does not require further relocations. The backup actor moves to the location of the failed primary and exchanges heartbeat messages with its new neighbors. It selects and designates a new backup since it has become a critical node at the new position. This movement alerts the other primary nodes (if any) at the previous location to choose a new backup for themselves. An illustrative example is provided in Figure 5(a), where non-critical backup \(G\) simply replaces its primary (_i.e._, \(F\)) and selects a backup for itself. The second scenario is when the failed (primary) and backup node are both critical nodes and simultaneously serving as backup for each other. This scenario is articulated in Figure 5(b). Actor \(G\) is serving as backup of another actor \(A\) and _vice versa_ as shown in Figure 5(b). The actor \(G\) detects failure of \(A\) and selects another actor \"\(C\)\" as backup, as shown in Figure 5(d). Then \(G\) moves to the position of \(A\). The newly assigned backup actor performs a cascaded relocation as discussed below and is shown in Figure 5(e), with \(G\) replacing \(A\), \(C\) replacing \(G\) and \(K\) replacing \(C\). The third scenario is when the backup is a critical node. In this case, the backup actor will notify its own backup so that the network stays connected. This scenario may trigger a series of cascaded repositioning of nodes as explained above.  
Figure 5: Applying the recovery process of PCR.
# Text Classification Model Enhanced by Unlabeled Data for LaTeX Formula
## 4 Experiments and Discussion
### Experiments and Discussion
#### 4.3.2 Ablation Experiments
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model (Pretrain Model)** & **Precision/\%** & **Recall/\%** & **F1/\%** \\ \hline BERT & 92.48 & 92.14 & 92.22 \\ BERT (Task pretrained) & 93.60 & 93.45 & 93.47 \\ BERT (Domain pretrained) & 94.00 & 93.88 & 93.91 \\ TP-BERT & 94.26 & 94.10 & 94.14 \\ \hline BERT-BERT & 94.08 & 93.67 & 93.72 \\ BERT-BERT (Task pretrained) & 94.29 & 94.11 & 94.13 \\ BERT-BERT (Domain pretrained) & 95.02 & 94.76 & 94.81 \\ UL-BERT & 95.11 & 94.98 & 94.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of different pretrained models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision/\%** & **Recall/\%** & **F1/\%** \\ \hline BiLSTM (GloVe) & 85.70 & 85.59 & 85.40 \\ BiGRU (GloVe) & 87.32 & 87.12 & 86.78 \\ CNN(GloVe) & 88.50 & 88.21 & 87.86 \\ \hline BERT & 92.48 & 92.14 & 92.22 \\ MathBERT & 93.29 & 92.87 & 92.97 \\ TP-BERT & 94.26 & 94.10 & 94.14 \\ \hline BERT-BiGRU & 92.99 & 92.58 & 92.65 \\ BERT-BiLSTM & 93.30 & 92.79 & 92.91 \\ BERT- generative model & 93.60 & 93.23 & 93.28 \\ BERT-CNN & 93.73 & 93.45 & 93.50 \\ BERT-BERT & 94.08 & 93.67 & 93.72 \\ UL-BERT & 95.11 & 94.98 & 94.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of different models. of the BERT family pretrained models is 5% higher than that of traditional models [23]. The word representation of BERT is produced by their context [24], which significantly improves the model accuracy especially on the ambiguity of LaTeX command words. * The role of Two-Stage pretraining The F1 score of UL-BERT (94.98) and the fine-tuned model with TP-BERT (94.14) is 1-2% higher than that of BERT (92.22), BERT with Task pretrained (93.47), BERT with Domain pretrained (93.91) and MathBERT (92.97). It is shown that the LaTeX formulas require further pretraining to transfer the word representation distribution from the general domain to the LaTeX formula domain. * The role of pseudo-labeling Compared with the original BERT in Table 5, the score of BERT with semi-supervised learning is improved, which proves the effectiveness of semi-supervised learning. Comparing different models that are used to generate pseudo-labels, the Bert based self-training model has the highest F1 score, which proves the effectiveness of self-training. * Pretraining with pseudo-labels UL-BERT (94.98) is almost 1% higher than fine-tuning with TP-BERT (94.14) on the F1 score. UL-BERT combined with TP-BERT and the pseudo-label is better than the other BERTs with the pseudo-label. Pseudo-labels improve the robustness and generalization of classification models that achieve higher F1 scores than pretrained models without pseudo-labels.
# Experimental Study on Cavity Formation and Ground Subsidence Behavior Based on Ground Conditions
## 5 Analysis of Ground Subsidence Mechanism
Cavity formation is observed near the groundwater level. Therefore, it is judged that ground subsidence occurs more rapidly with increasing groundwater level due to cavity formation at locations closer to the ground surface. For this reason, it appears that a groundwater level of 10 cm resulted in a large amount of soil discharge due to the gradual expansion of the cavity volume. Figure 8: Cavity formation location with respect to various groundwater levels: (**a**) 10 cm, (**b**) 20 cm, and (**c**) 30 cm. Figure 7: Time until cavity formation and ground subsidence with respect to the groundwater level under relative densities of (**a**) 60% and (**b**) 75%. Hence, experiments in which the model ground is subjected to load conditions similar to actual ground conditions are required in future research. This is expected to help identify the actual ground subsidence mechanism by preventing behavior that delays cavity formation and ground subsidence due to adhesion in the ground. The experimental results with respect to the fines content revealed that ground subsidence did not occur under most conditions with a fines content of 10% or higher. It is considered that the mixing of clay increased both the internal friction angle and cohesion of the soil, thereby enhancing its resistance to particle loss and subsidence during the experiments [35]. Therefore, the presence of clay is believed to have played a key role in preventing ground subsidence by improving the overall strength characteristics of the soil.
# A Novel Dual Mixing Attention Network for UAV-Based Vehicle Re-Identification
## 4 Analysis and Experiments
### Ablation Experiment and Analysis
#### 4.4.6 Visualization of Model Retrieval Results  
To illustrate the superiority of our model more vividly, Figure 6 shows the visualization of the top 10 ranked retrieval results for the baseline and model on the VeRi-UAV dataset. Four query images corresponding to the retrieval results are randomly shown, the first row for the baseline method and the second for our method. The images with green borders represent the correct samples retrieved, while those with red edges are the incorrect ones retrieved.  
As can be observed from Figure 6, the baseline usually focuses on the vehicle's appearance, such as color, shape, etc. Therefore, some negative matches appear due to pose and illumination similarity. Our proposed DMANet grouping features increases the variation and diversity of features, making distinguishing between vehicles with a similar appearance easier. In addition, DMANet fuses channels and spatial attention to enhance focus on critical areas and colors, shapes, and textures, making models distinguish similar-looking vehicles better. In summary, DMANet improves the model's ability to capture vehicle details, enhances vehicle differentiation, and thus improves the accuracy and robustness of vehicle reidentification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Rank-1** & **Rank-5** & **Rank-10** & **mAP** \\ \hline CA [1] & 94.44 & 95.02 & 95.83 & 77.87 \\ SA [30] & 94.72 & 95.57 & 97.43 & 78.42 \\ SA\&CA [6] & 94.78 & 95.67 & 97.64 & 78.53 \\ ACmix [49] & 95.07 & 97.31 & 97.76 & 78.52 \\ Cot [50] & 95.87 & 97.31 & 97.76 & 80.30 \\ Psa [51] & 96.59 & 97.85 & 98.12 & 80.70 \\ \hline
**Ours** & **97.04** & **98.65** & **98.83** & **86.99** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of different attention modules.  
Figure 6: **Visualization of the ranking lists of model and baseline on VeRi-UAV.** The top and bottom rows for each query show the ranking results for the baseline and joining the dual mixing attention module (DMAM), respectively. The green and red boxes denote the correct and wrong results, respectively.
# Quality Assessment of Dual-Parallel Edge Deblocking Filter Architecture for HEVC/H.265
## 3 Quality Assessment Metrics  
The HEVC standard is known to be advantageous for higher video resolutions such as HD and UHD videos with lower bit rates. The features of HEVC improve the compression ratio by 50% with an increase in the complexity by 150% compared to its former video coding standard AVC [31]. Research is ongoing to reduce the overall complexity of the coding standard without affecting the compression ratio. Overall complexity can be reduced if there is a reduction in the complexity of the various modules used in these coding standards, but reduction in computational complexity may deteriorate the quality of the encoded video stream.  
Mean square error (\(MSE\)) and \(PSNR\) are the metrics used to evaluate the objective perceptional video quality [32; 33]. Structural similarity (SSIM) is also employed to evaluate the video quality by [34; 35], but none of the metrics correlate precisely with the perceptional quality of the HVS [36]. However, SSIM provides better results with respect to the perceptional quality of the HVS using the assessment of three components, viz., perceptional impact of changes in the luminance, contrast, and structure [37]. Despite the complexity of measuring SSIM, due to the assessment of three different components, it is more reliable compared to other measures [38]. Several block-edge impairment metrics were proposed and a generalized block-edge impairment metric (DBIM) was proposed by [39], which shows the difference in the perceptual quality. Equations (1)-(3) show the quality assessment of the reconstructed video using \(PSNR\), \(MSE\), and \(SSIM\), respectively.  
\[MSE(i,j)=\frac{(\Sigma_{i=1}^{M}\sum_{j=1}^{N}[f(i,j)-F(i,j)]^{2})}{M.N} \tag{1}\]  
\[PSNR=20 log_{10}\frac{255}{\sqrt{MSE}} \tag{2}\]  
where \(f(i,j)\) is the pixel value at \(i\)th row and \(j\)th column of the original video frame, \(F(i,j)\) is the pixel value at \(i\)th row and \(j\)th column of the reconstructed/modified video frame, and \(M\) and \(N\) represent the width and the height of the video frame. The value of \(PSNR\) ranges from 30 dB to 40 dB as the quality of the modified video ranges from medium to high, respectively.  
\[SSIM=\frac{(2x\bar{y}+C_{1})(2\sigma_{xy}+C_{2})}{[\bar{x}^{2}+\bar{y}^{2}+C_ {1}](\sigma_{x}^{2}+\sigma_{y}^{2}+C_{2})} \tag{3}\]  
where \(\bar{x}\), \(\bar{y}\) are the mean of \(x\) and \(y\), respectively; \(\sigma_{x}\), \(\sigma_{y}\), and \(\sigma_{xy}\) are the variance of \(x\), the variance of \(y\), and the covariance of \(x\) and \(y\), respectively; and \(C_{1}\) and \(C_{2}\) are constants. \(SSIM\) values range between \(-1\) to \(1\), and the quality is said to be the best when the value is \(1\)[38].  
Among the various metrics for video quality assessment, \(PSNR\) is the most desired quality assessment metric owing to its lucidity. Though \(PSNR\) is used to check the quality of the video to a great extent, it does not provide the actual perceptual quality as perceived by the HVS.