esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Compilation
### Results from Data Streaming Retrieving Technique
#### 4.1.1 Data Acquisition Using a Single Reference Point  
By setting the Twitter data collection point at the midpoint of Serang, based on a four-square approach: P (\(-6.116897^{\circ}\), \(106.15948^{\circ}\)). Note: hereafter, the notations will be in decimal degrees. An experiment was conducted to adjust the range to obtain as many data as possible for each acquisition cycle. The results for each Twitter data acquisition, using a single reference point with different capturing distances, are shown in Table 1.  
A setting of 100 or 50 data was used in the single reference point method each time; the maximum amount of data obtained was 15 unique data. A setting of 50 was used for data collection for each complete iteration in the program. As a result, there was a large amount of data duplication that needed to be discarded. When the distance was set with an integer of 1, or another integer, to a limit of 5 km, the same number of unique data was obtained, which was 15. However, setting distances of 1 and 2 km produced only 1 location, representing all user locations; this was termed a cluster. Setting distances of 3, 4, and 5 km produced as much as three clusters. The term \"cluster\" in this paper indicates a point representing the location of many users, or a single user at different times but at the same location. Thus, to obtain the maximum number of clusters, the capturing distance would need to be set at 3, 4, or 5 km; however, the amount of target data does not need to be as high as 50. It was found that the data obtained was at a distance that exceeded the capturing distance setting number.
## 2 Related Works
### Global Standardization in ITU-R and 3 GPP
#### 2.2.1 Standardization Trends for IMT-2030 System in ITU-R  
In June 2023, ITU-R WP 5D reached a consensus on the draft new recommendation titled \"Framework and overall objectives of the future development of IMT for 2030 and beyond\" that addresses the trends, usage scenarios, and capabilities of IMT-2030, serving as the basis for the forthcoming standardization efforts in the development of the next generation of IMT standards [9]. 'Ubiquitous connectivity' and 'connecting the unconnected' have been incorporated into the IMT-2030 framework. Their inclusion aims to facilitate the extension of communication coverage beyond terrestrial networks, spanning into integrated networks that encompass space, air, ground, and sea, transitioning from the conventional terrestrial-based paradigm. Upon the approval of this draft new recommendation during the fourth quarter of the year 2023 by ITU-R Study Group 5 (SG5), the subsequent standardization efforts will encompass the specification of precise requirements according to the IMT-2030 standardization timeline in ITU-R [32]. This undertaking involves elaborating on exact technical specifications, functionalities, and performance requirements. These particulars will serve as guiding principles for the advancement of IMT-2030 standards and their practical implementations in 3 GPP.  
Figure 1: Relations between IMT systems and 3 GPP systems.
# A Survey on Swarm Robotics for Area Coverage Problem
## 8 Conclusions  
There is increasing usage of swarm robots in various fields and occupations. The evolution of technology in the twenty-first century has played a significant role in enabling the large-scale application of swarm robots, especially for solving area coverage problems. The algorithms used in this survey, either metaheuristic or classical, refer to using a swarm of robots in specific areas to achieve certain goals. The technology used in swarm robots relies on the development of numerous naturally inspired algorithms, like Particle Swarm Optimisation (PSO), Honey Bee Optimization (MBO), and Ant Colony Optimisation (ACO), which are the most used and suitable for robotics in this domain, and many metaheuristic swarm intelligence algorithms. These algorithms and methods are utilised according to the type of problem and the requirements of the environment, which are either known or unknown and may contain obstacles, which constitute the most complex problem, or be free of obstructions. Individual coverage is an emerging trend in recent surveys, as each bot interacts with the rest of the swarm team to achieve the goal by covering the area. Relatively better algorithms than the previous ones were used. However, there still needs to be more coverage of the concerned area. From this survey, we conclude that modern algorithms are less dependent on calculations than the previous algorithms containing elaborate mathematical models. Such algorithms are combined with a wide variety of hardware, like sensors, cameras, communication, and controlling devices, to enhance the functionality of swarm robots. Combining algorithms and hardware expands the range of applications and usage of swarm robots in numerous areas, like defence, medicine, geology, and business.  
Conceptualisation, methodology, and data curation, D.K.M., A.T.S. and F.A.R.; analysis, D.K.M.; investigation and resources, D.K.M., A.T.S. and F.A.R.; writing, D.K.M.; review and editing, A.T.S. and F.A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization
## 3 Proposed Method
### Updating \(L\)  
To solve for variable \(\mathbf{L}\), we minimize over \(h(\mathbf{L},\mathbf{S},\mathbf{Z},\mathbf{\Lambda}_{1},\mathbf{\Lambda}_{2})\) with the remaining optimization variables \((\mathbf{S},\mathbf{Z},\mathbf{\Lambda}_{1},\mathbf{\Lambda}_{2})\) fixed:  
\[\mathbf{L}_{i+1}= \underset{\mathbf{L}}{\text{arg}\,\text{min}}\,h\Big{(}\mathbf{ L},\mathbf{S}^{i},\mathbf{Z}^{i},\mathbf{\Lambda}_{1}^{i},\mathbf{\Lambda}_{2}^{i} \Big{)} \tag{9}\] \[= \underset{\mathbf{L}}{\text{arg}\,\text{min}}\|\mathbf{L}\|_{ \gamma}+\Big{\langle}\mathbf{\Lambda}_{1}^{i},\mathbf{D}-\mathbf{L}-\mathbf{S}^{i }\Big{\rangle}+\Big{\langle}\mathbf{\Lambda}_{2}^{i},\mathbf{L}-\mathbf{Z}^{i} \Big{\rangle}\] \[+\frac{\mu^{i}}{2}\bigg{(}\Big{\|}\mathbf{D}-\mathbf{L}-S^{i} \Big{\|}_{\text{F}}^{2}+\Big{\|}\mathbf{L}-\mathbf{Z}^{i}\Big{\|}_{\text{F}}^ {2}\bigg{)}\] \[= \underset{\mathbf{L}}{\text{arg}\,\text{min}}\|\mathbf{L}\|_{ \gamma}+\mu^{i}\bigg{\|}\mathbf{L}-\frac{1}{2}\bigg{(}\mathbf{D}+\mathbf{Z}^{ i}-\mathbf{S}^{i}+\frac{\mathbf{\Lambda}_{1}^{i}}{\mu^{i}}+\frac{\mathbf{\Lambda}_{2}^{i}}{ \mu^{i}}\bigg{)}\bigg{\|}_{\text{F}}^{2}\]  
To solve (9), the following theorem is introduced.  
**Theorem 1**.: _Let [42]\(A=U\Sigma_{A}V\) be the SVD of \(A\in R^{  
\[prox_{f,\mu}(\sigma_{A}):=\underset{\sigma\geq 0}{\arg\min}f(\sigma)+\frac{\mu}{2} \|\sigma-\sigma_{A}\|_{2}^{2} \tag{11}\]  
_Based on the theorem above, the optimal solution to (9) is_  
\[L_{i+1}=Udiag(\sigma^{*})V \tag{12}\]  
_where \(\sigma^{*}\), the solution to (11), can be approximated by linearizing the concave term \(f(\sigma)\) iteratively. Specifically, in the (\(k+1\)) inner iteration, \(\sigma\) can be updated as follows:_  
\[\sigma^{k+1}=\underset{\sigma\geq 0}{\arg\min}\langle\ \delta f(\sigma^{k}) \rangle+\frac{\mu^{i}}{2}\|\sigma-\sigma_{A}\|_{2}^{2} \tag{13}\]  
_where_  
\[A=\frac{1}{2}\Bigg{(}D+Z^{i}-S^{i}+\frac{\Lambda_{1}^{i}}{\mu^{i}}+\frac{ \Lambda_{2}^{i}}{\mu^{i}}\Bigg{)} \tag{14}\]  
_and \(\delta f(\sigma^{k})\) is the gradient of \(f\) at \(\sigma^{k}\)._  
Figure 2: The flowchart of the proposed method.
## 4 Experimental Result
### Result and Comparison
#### 4.3.3 Trained with General datasets and Tested with Random Spatial datasets  
This experiment learns with data that do not apply random space, and tests the random space data to find out how vulnerable the trained 3D-CNN is in a situation with little spatial information. The results of Indian Pines are presented in Table 13. The performance of the 3D-CNN models decreased significantly. Only 1D-CNN models maintain performance. As in the Indian Pines dataset, in the case of the Salinas dataset, the 3D-CNN models have poor performance, and the 1D-CNN model maintains the performance. The results of Indian Pines are presented in Table 14.  
The case of the Pavia University dataset is the same as that of other datasets. The 3D-CNN models have a poor performance, and the 1D-CNN models maintain their performance. The results of Indian Pines are presented in Table 15. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Luo Model**} & \multicolumn{2}{c}{**Li Model**} & \multicolumn{2}{c}{**[PERSON] Model**} \\ \hline
**\#** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** \\ \hline
1 & 0.647 & 97.17\% & 0.796 & 79.74\% & 0.872 & 87.08\% \\ \hline
2 & 0.834 & 95.17\% & 0.881 & 89.54\% & 0.912 & 90.97\% \\ \hline
3 & 0.000 & 0.00\% & 0.477 & 45.61\% & 0.698 & 68.71\% \\ \hline
4 & 0.487 & 38.76\% & 0.83 & 79.94\% & 0.877 & 84.34\% \\ \hline
5 & 0.839 & 72.50\% & 0.968 & 95.46\% & 0.982 & 97.03\% \\ \hline
6 & 0.026 & 1.33\% & 0.631 & 58.45\% & 0.780 & 75.32\% \\ \hline
7 & 0.000 & 0.00\% & 0.478 & 43.36\% & 0.671 & 62.99\% \\ \hline
8 & 0.198 & 13.88\% & 0.661 & 68.23\% & 0.773 & 76.10\% \\ \hline
9 & 0.889 & 80.05\% & 0.953 & 92.97\% & 0.986 & 97.42\% \\ \hline OA(\%) & 64.734\% & 78.511\% & & 85.142\% \\ \hline AA(\%) & 44.32\% & 72.59\% & & 82.22\% \\ \hline Kappa & 0.497 & & 0.714 & & 0.804 \\ \hline \multicolumn{5}{c}{**Chen Model**} & \multicolumn{2}{c}{**Hu Model**} & \multicolumn{2}{c}{**Proposed Model**} \\ \hline
**\#** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** \\ \hline
1 & 0.241 & 25.49\% & 0.871 & 88.79\% & 0.959 & 95.53\% \\ \hline
2 & 0.659 & 64.23\% & 0.913 & 98.47\% & 0.978 & 97.96\% \\ \hline
3 & 0.143 & 11.22\% & 0.607 & 58.71\% & 0.843 & 82.42\% \\ \hline
4 & 0.143 & 15.08\% & 0.875 & 80.78\% & 0.958 & 96.01\% \\ \hline
5 & 0.378 & 28.32\% & 0.993 & 98.76\% & 0.995 & 99.75\% \\ \hline
6 & 0.311 & 26.14\% & 0.629 & 48.06\% & 0.938 & 93.28\% \\ \hline
7 & 0.116 & 9.44\% & 0.693 & 61.65\% & 0.914 & 91.40\% \\ \hline
8 & 0.161 & 15.12\% & 0.827 & 83.61\% & 0.886 & 90.37\% \\ \hline
9 & 0.073 & 6.46\% & 0.999 & 99.88\% & 0.998 & 99.65\% \\ \hline OA(\%) & 39.284\% & & 85.444\% & & 95.369\% \\ \hline AA(\%) & 22.39\% & & 79.86\% & & 94.04\% \\ \hline Kappa & 0.210 & & 0.801 & & 0.939 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Classification results of the Pavia University dataset to which random spatial information is applied.
# Knowledge Graph and Personalized Answer Sequences for Programming Knowledge Tracing
## 4 Experiments
### Experiment Results
#### 4.5.2 Ablation Experiment Analysis  
In order to verify the contribution of each part of the model proposed in this paper to the prediction of students' answers under the same other conditions, we conduct ablation experiment analysis to verify the rationality of GPPKT. Table 4 presents the results of the ablation experiments, which examine the impact of each component of the GPPKT model on its performance. The methods listed represent different combinations of model components: VAE (Variational Autoencoder), KG (Knowledge Graph), and ABI (Ability Index). The AUC and ACC columns show how each combination affects the model's predictive accuracy and overall performance. The analysis of these values is essential to understand the significance of each component in enhancing the model's capability. We choose the Luogu dataset for the ablation experiments. The results of the ablation experiments are shown in Table 4 which demonstrates the permutations of each method proposed in this study, where VAE indicates that the student answer sequences processed by VAE are used, KG indicates that the knowledge graph is used, and ABI indicates that students' learning abilities are used.  
Through the ablation experiments in Table 4, we draw the following conclusions: Comparing methods (2) and (5), it is observed that the consideration of students' personalized learning abilities has a positive impact on the model's prediction because different students have varying levels of mastery over different KCs. When methods (3) and (6) are compared, it is found that the incorporation of a VAE to process students' answer sequences has a positive impact on the model's predictive capabilities. This is because each student's understanding of the exercises gradually improves during the learning process. Finally, when methods (4) and (7) are compared, it is seen that the introduction of a knowledge graph also benefits the model's predictions because KCs are interconnected, and the use of a knowledge graph allows exercise information to be better considered.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **AUC** & **ACC** \\ \hline (1) LSTM & 0.8097 & 0.7954 \\ (2) LSTM + VAE & 0.8576 & 0.8343 \\ (3) LSTM + KG & 0.8654 & 0.8361 \\ (4) LSTM + ABI & 0.8679 & 0.8380 \\ (5) LSTM + VAE + ABI & 0.8762 & 0.8425 \\ (6) LSTM + VAE + KG & 0.8749 & 0.8392 \\ (7) LSTM + KG + ABI & 0.8703 & 0.8411 \\ (**8) LSTM + VAE + KG + ABI** & **0.8840** & **0.8472** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation experiments.  
In summary, the inclusion of a knowledge graph, the use of a VAE to represent answer sequences, and the consideration of students' personalized learning abilities all have a positive impact on the model's performance.
# The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes
## 4 Results and Analysis
### Analysis of Quantitative Detection  
To better analyze the quantitative relationship between the defect depth and signal amplitude, all the defect signals were smoothed first to eliminate the influence of single-peak extrema. Second, the peak values of the echo signals at different depths were extracted and plotted. Finally, the first and last peaks of the echo signals with different depths were eliminated, and the mean of residual peaks in the signals was calculated to make the mean curve approach the peak value of the defect signals as stably as possible.  
As shown in Figure 8, for longitudinal internal defects, when the defect depth increased from 0.045 to 0.073 mm, the echo signal peak value increased from 1.36 to 2.05 V. When the defect depth increased from 0.073 to 0.10 mm, the echo signal peak value increased from 2.05 to 2.76 V. For the longitudinal defect, when the defect depth increased from 0.045 to 0.073 mm, the peak value of the  
Figure 7: Signal with and without flaw (**(a)** Depth of inner transverse flaw was 0.045 mm; **(b)** Depth of inner longitudinal flaw was 0.045 mm; **(c)** Depth of outer transverse flaw was 0.045 mm; **(d)** Depth of outer longitudinal flaw was 0.045 mm; **(e)** Depth of inner longitudinal flaw was 0.073 mm; **(f)** Depth of outer longitudinal flaw was 0.073 mm; **(g)** Depth of inner longitudinal flaw was 0.10 mm; **(h)** Depth of outer longitudinal flaw was 0.10 mm; **(i)** Signal without flaw and with strong noise; **(j)** Signal without flaw and with weak noise).  
echo signal increased from 1.36 to 2.39 V. When the defect depth increased from 0.073 to 0.10 mm, the peak value of the echo signal increased from 2.39 to 2.86 V.  
The results showed that the quantitative relationship of the internal longitudinal defects was slightly better than that of external longitudinal defects. There was a linear quantitative relationship between the depth of the longitudinal defect and the peak value of the detection signal. The depth of the detected defect could be represented by the peak value of the defect detection signal.
## 4 The Proposed Distributed Image Codec
### Block Correlation Analysis  
Consider an image divided into non-overlapping rectangular blocks of pixels. In general, there are correlations between neighbouring image blocks. If the block size is small, this correlation is high. If the image block data are correlated, we postulate that their CS measurements are also correlated. In this section, this correlation will be demonstrated empirically using four standard \(512\times 512\) pixel images: \"Lena\", \"Cameraman\", \"Goldhill\" and \"Boat\". The CS measurement rate for each block is fixed at 50%. Image block similarity is measured through the correlation coefficient and the mean squared error (MSE).  
Using a block size of \(8\times 8\) pixels, we found that the largest correlation coefficients of the CS measurements among image blocks are generally very high (\(>\)0.9). This means that we can make use of this correlation for distributed coding of CS measurements. Furthermore, the MSE values of the image data are reflected in those of the CS measurement. Hence, we can use the MSE of the CS measurements as an indication of the MSE of the original image data. Thus, similar image blocks have low MSE values of CS measurements.  
To evaluate the effect of block size on image block similarity, the percentage of correlated blocks exceeding an MSE threshold is computed for block sizes of \(64\times 64\), \(32\times 32\), \(16\times 16\) and \(8\times 8\). Figure 1 shows the results for the four images. As expected, there are more correlated blocks for smaller block sizes. Blocks with a size of \(64\times 64\) can virtually be considered independent. The percentage of blocks that can be considered correlated depends on the MSE threshold. Thus, a smaller block size with appropriate MSE threshold would be desirable for our codec.  
Figure 1: Percentage of similar blocks for the block size (64, 32, 16 and eight) for (**a**) Lena; (**b**) Boat; (**c**) Cameraman; and (**d**) Goldhill.
## 3 System Model and Assumptions
### Proposed Slice Allocation System  
This section introduces and explains the proposed adaptive slice allocation system intended for use inside the 5G C-RAN. The proposed system aims to increase resource utilization in the 5G CRAN's BBU pool of resources. In addition, it seeks to guarantee the minimum QoS requirements for each 5G network slice while maintaining isolation. The proposed system dynamically allocates the resources of the BBU pool among the three 5G slices, each with different traffic sources. Moreover, it assigns a resource portion for each 5G slice based on its QoS requirement in terms of bandwidth and delay.  
The system will initially implement a CAC to admit or reject each arriving slice request based on resource availability and slice priority. The slice priority varies dynamically to prevent requests from one slice from affecting other slices, thus ensuring slice isolation.  
In Section 3.2.1, we introduce an SSA, which is an existing slice allocation system [14,15]. We adapt the SSA system to use as a reference to compare its performance with that of the proposed system. In Sections 3.2.2 and 3.2.3, we present our proposed ASA and DA-ASA.
## 3 LEO-SMC-ICN-IoT Caching Framework
### ICN Forward Process  
For the ICN Forward Process of the LEO satellite mega-constellation network, our team has completed Section 3.2 of [41]. For ease of understanding, we will briefly reiterate it.  
The packet structure is shown in Figure 6[49; 50; 51].  
For performing the interest and data forwarding, each satellite node needs to maintain three functional components: Forwarding Information Base (FIB), Pending Interest Table (PIT), and Content Store (CS) [52]. Moreover, the interest and data forwarding steps in a satellite node are described in Figure 7 [41].  
We set the GS\({}_{1}\) as the interest requester and the GS\({}_{2}\) as the data responder. In Figure 7 [41], the GS\({}_{1}\) sends an interest with the name to a satellite node, and the satellite node checks whether a match data already exists in its local CS; if the responding data are found, then data is sent back to GS\({}_{1}\)[41]. Otherwise, the satellite node investigates if an interest with the same name in the PIT already exists; if a similar interest is not found in the  
Figure 6: Packet architecture.  
Figure 7: Forwarding process at a satellite node.  
PIT, the new interest is forwarded according to the FIB and cached in the PIT [52]. When the GS\({}_{2}\) receives the interest, data containing the requested content are sent back, and as the data arrives at a satellite node, the data are forwarded via ISLs, and the data are cached in the satellite node [41].
# Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
## 5 Evaluation and Discussion
### ANN Adversarial Example Generation
#### 5.3.2 Multi-Class ANN Model  
Using the targeted FGSM against the multi-class ANN showed a gradual negative impact during the time the epsilon value grew to 1, as seen in Figure 6(a). The accuracy score fell from 95.6% to 42.1%, and the recall score fell from 95.7% to 49.3%. The targeted FGSM aims to move the dataset in the direction of the feature class. In the non-targeted FGSM activities, the metrics had a greater impact. The metrics in the non-targeted FGSM fall sharply with an epsilon of 0.1, but insignificantly, thereafter, as seen in Figure 6(b). The recall score falls significantly in the non-targeted FGSM; however, it must be considered that an adversary might not bypass the machine learning ANN IDS. Hence, as the false-negative rate increases, there is still a significant chance that an attack is detected but incorrectly labelled as another attack, thereby creating an alert. Nevertheless, the increased false-negative rate will create more overhead for those within an organisation that triage the alerts.  
The confusion matrices of the ANN after applying the FGSM to the testing dataset in Figure 5(b), to the confusion matrix without data manipulation in Figure 4(b), it is seen that  
Figure 6: (**a**) Comparing accuracy versus recall scores in binary targeted Fast Gradient Sign Method. (**b**) Comparing accuracy versus recall scores in binary non-targeted Fast Gradient Sign Method.  
Figure 7: (**a**) Comparing accuracy versus recall scores in multi-class targeted Fast Gradient Sign Method. (**b**) Comparing accuracy vs recall scores in multi-class non-targeted Fast Gradient Sign Method.  
the model is predicting a large portion of the DDoS and DoS traffic as benign. A small part of reconnaissance attacks were classified incorrectly as benign, and interestingly theft traffic was classified with a similar accuracy degree. The takeaway is that an adversary could launch DDoS and DoS more successfully, whilst data theft attacks would still be detected.