esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Architecture and Assumptions
### Multiple-Attacker Model  
It is assumed that the attacker lurks near the sink node. Once a new message is captured by the attacker, it will execute eavesdropping and traceback attacks [30]. Furthermore, this paper considers that the attacker carries out targeted active attacks in the case of passive attacks. The attacker has the capability of traffic analysis and packet de-construction, including the ability to infer sensor node locations, and it can analyze and process eavesdropped data, deducing source locations by analyzing location information and communication data from multiple sensor nodes.  
In previous research on the source location privacy protection of UASNs, scholars often consider that there is only a single attacker within the network. However, in actual situations, there may be multiple attackers who may collaborate with other attackers to jointly implement attack actions to achieve a common attack purpose. Forms of attacker cooperation include the division of labor cooperation, where each attacker is responsible for specific aspects or links of an attack; information sharing, where attackers enhance their attack abilities by sharing techniques, strategies, and information; and coordinated attacks, where multiple attackers act together with coordinated strategies and actions to carry out an attack. The differences between single-attacker and multiple-attacker scenarios are shown in Table 1.  
It is worth noting that the attacker's actions do not cause any functional interference to the network, such as adding routing paths, changing packets, damaging sensor nodes, and so on. Additionally, the attacker can only monitor the area within the receiving range of its device. This paper assumes that the attacker's monitoring range is equal to the communication radius of the sensor node. Attackers use this eavesdropping radius to listen to the traffic in the network and try to detect the activity of the source node from it.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Single Attacker** & **Multiple Attackers** \\ \hline Number of attackers & One attacker & Multiple attackers \\ \hline Attack complexity & Usually independent operations, relatively simple attacks & More sophisticated coordinated attack strategies \\ \hline Difficulty of detection & Easier to detect because it is a single entity & Complex to detect due to distributed actions \\ \hline Network influence & Some privacy impact & Significant privacy threat, especially when acting in concert \\ \hline Countermeasures to the attack & Easier to develop coping strategies & More sophisticated coping strategies may be required \\ \hline Attack surface & Limited to the capabilities of a single attacker & Broader attack surface with diverse strategies and resources \\ \hline Crypticity & Relatively easy to remain hidden and hard to detect & Difficult to remain completely hidden \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of single-attacker and multi-attacker capabilities.
# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
###### Abstract  
Remote sensing image object detection has been studied by many researchers in recent years using deep neural networks. However, optical remote sensing images contain many scenes with small and dense objects, resulting in a high rate of misrecognition. Firstly, in this work we selected a deep layer aggregation network with updated deformable convolution layers as the backbone to extract object features. The detection and classification of objects was based on the center-point network without non-maximum suppression. Secondly, the dynamic gradient adjustment embedded into the classification loss function was put forward to harmonize the quantity imbalance between easy and hard examples, as well as between positive and negative examples. Furthermore, the complete intersection over union (CIoU) loss function was selected as the objective function of bounding box regression, which achieves better convergence speed and accuracy. Finally, in order to validate the effectiveness and precision of the dynamic gradient adjustment network (DGANet), we conducted a series of experiments in remote sensing public datasets UCAS-AOD and LEVIR. The comparison experiments demonstrate that the DGANet achieves a more accurate detection result in optical remote sensing images.  
deep learning; object detection; remote sensing images; center-point network Article  
DOI-D  
DOI-D
## 3 Experiment Results and Discussions
### Investigation of Corrosion Aspects at Weld Fractures  
In order to investigate the welds with galvanic corrosion, the fractured site of the galvan annealed steel specimen side, which had undergone a salt spray test and a tensile lap shear test, was observed by SEM and EDAX analysis. Figure 9 shows the results of SEM observation at 500, 1000, and 5000 magnifications of the area around the weld of Specimen 1. In addition, Figures 10 and 11 show the results of EDAX spectrometer analysis for the conventional DeltaSpot welding specimen (Specimen 1) and the weld bonding specimens (Specimens 3 and 4), respectively.  
In the first condition, where the structural adhesive was not applied, Zn and O were detected in the fractured area, and Fe was detected in the red rusted area where corrosion progressed greatly. The fact that Zn was detected more than Al means that the corrosion of the steel side progressed more than the Al alloy side, and the dense alumina oxide film formed on the aluminum alloy surface was more resistant to corrosion than the Zn plated on the steel.  
On the other hand, in the conditions of Specimens 3 and 4 in which the structural adhesive was applied, C and Al, which were components of the adhesive paste, were detected. Since the effect of corrosion on the specimens was relatively small, the bonding strength was maintained enough to cause fractures on the aluminum side rather than the steel side, and this is considered the reason why the higher tensile shear strength was maintained. Moreover, unlike conventional DeltaSpot welding, when the adhesive is applied, it is thought that the decrease in strength is not significant because it not  
Figure 9: Scanning electron microscopy (SEM) images of the rusted area for Specim 1 after the salt spray test (1920 h).  
only prevents galvanic corrosion due to potential difference, but also prevents crevice corrosion due to penetration of the electrolytic corrosion solution.
# Sleep Breathing Disorders' Screening Among Children
## 4 Discussion
Another study involving 602 pediatric patients found that 12.6% of the sample was at risk for sleep-related breathing disorders [38]. In this preliminary study, we observed that the SCR score was significantly higher (\(p<0.05\)) in children with oral breathing, habitual nasal obstruction, tonsillar hypertrophy, malocclusion, high Friedman score, narrow palate, adenoid phenotype, or a positive Brouillette score. It is important to emphasize that a thorough analysis of children's oral health is essential, as it enables the early detection of potential problems. A study by [PERSON] et al. [39] focusing on the oral health of children with an average age of 10 years found a high prevalence of oral diseases: 41.6% had active dental caries in permanent teeth, and 54.8% had periodontal disease. Regarding tonsillar hypertrophy, we observed a rate of 22.92%, slightly lower than in other studies, where the prevalence was over 30% [40]. While the prevalence of adenoid facies is not often reported, our findings align with those of [PERSON] et al. [41]. In contrast, we observed class II malocclusions in more than half of our sample, which is higher than the 4.4-44.7% range reported in a recent scoping review [42]. However, no individual malocclusion was statistically significant in our study. We also found that sleep respiratory disorders were not significantly affected by gender or age, and the ADHD Rating Scale did not show statistically significant results. There is ample scientific evidence supporting the correlation between malocclusions and SDB in children, indicating the importance of early intervention when either disorder is present. Studies have shown that early orthodontic treatments, such as rapid palatal expansion (RPE) or mandibular advancement with functional appliances, can be effective in managing pediatric OSAS [43; 44; 45]. Correcting structural imbalances in craniofacial morphology during growth may reduce snoring and OSA in children and adolescents. Orthodontists should play a key role in managing SDB, not only by treating dental malocclusions associated with these disorders but also by screening and identifying at-risk patients. The use of a questionnaire, such as the SCR used in our study, can be an efficient, quick, and cost-effective tool for screening the pediatric population attending orthodontic clinics regularly.
# Towards Rack Utilization in Internet Datacenters: An Approach Based on Dynamic Programming
## 2 Related Work  
Saving the power consumption of servers can increase the number of servers installed in IDC and thus the utilization of IDC. Many researchers have discussed the issue of power consumption from the level of servers [14] to that of IDC [6; 7]. Generally, the solutions are thought to be of two kinds: shutting down the idle servers [6; 7; 15] and dynamic voltage and frequency scaling (DVFS) [8; 16]. The former solution usually relies on the server consolidation technology to merge more businesses into fewer number of servers, thus shutting down those idle servers or support equipment. This method improves the power utilization of servers and lowers the power margin of servers. However, there exist some implementation issues. The work of [17] presents the disadvantages of business migration after shutdown, and the work of [6] indicates that the server consolidation may result in resource competition. The DVFS technology, derived from the Busy-Idle state switch of CPU [8], has been expanded to other components, such as memory and disk [8; 17] to reduce the power consumption of servers at low utilization. More server margin can be released in the case of low utilization of server by adopting DVFS, so as to provide more space for further improving the rack utilization.  
Both the shutdown of idle servers and DVFS can reduce power supply, which may cause the performance loss of applications. Many researchers have taken into account this issue [10; 14]. The authors of [6] obtain the business performance in various consolidation cases through the offline experiment with pairwise integration of different businesses, but it results in huge experimental cost. The work of [14] analyzes the application situation of consolidation based on the assumption that the businesses with similar utilization are also similar in performance. The authors of [10] introduce the technology of power capping, and make a tradeoff between power capping and business performance. In addition, the work in [18] creates shuffled distribution topologies, where power feed connections are permuted among servers within and across racks. The authors of [7] develop a compilation technology to lessen conflicts between different businesses in the same server.  
Different from the above work, in this paper, we aim to reduce the power fragments of each rack, thus promoting the rack utilization in IDC as well as saving leasing cost, by proposing a server placement algorithm based on dynamic programming.
# Fast Support Vector Machine for Power Quality Disturbance Classification
## 5 Simulation Results
### Harmonic Variations Detection with Cross Analysis at Obs-12  
Detection accuracy is checked for harmonic PQDs in this test, where the harmonic injections varied 0-160% in magnitude, with an increment of 10% and in a total of 17 tests. In the first column, PQD points to \(v6\) \"normal\" when there are no or very low harmonics, i.e., with harmonic magnitudes \(<\)0.3. We can see that \(v3\), score of the harmonics gets higher when the magnitude is greater than 0.4, and stays highest onward. The complex event \(v4\) and \(v5\) get low scores, involving both harmonics and voltage changes. Other PQD terms also get low scores. Table 8 shows the scores of each class for harmonic magnitude changes. We can also see that \(v7\) stays lowest through all tests.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline PQD & 0–0.4 & 0.42 & 0.6 & 0.96–1.0 & 1.04 & 1.06 & 1.1 & 1.3–1.5 \\ \hline \(v7\) & 6 & 5 & 1 & 0 & 0 & 0 & 0 & 0 \\ \hline \(v1\) & 5 & 6 & 6 & 5 & 3 & 3 & 2 & 2 \\ \hline \(v4\) & 4 & 4 & 4 & 2 & 1 & 1 & 1 & 1 \\ \hline \(v6\) & 3 & 3 & 5 & 6 & 6 & 5 & 5 & 4 \\ \hline \(v3\) & 2 & 2 & 3 & 4 & 4 & 4 & 4 & 3 \\ \hline \(v2\) & 1 & 1 & 2 & 3 & 5 & 6 & 6 & 6 \\ \hline \(v5\) & 0 & 0 & 0 & 1 & 2 & 2 & 3 & 5 \\ \hline \end{tabular}
\end{table}
Table 7: Scores of each class for voltage variation at Obs-12.  
Figure 4: Pattern of harmonics of sample S5 in p.u.  
For harmonics with a voltage magnitude lower than 0.36, the PQD was identified as \"interruption\". In the range 0.38-0.92, _v4_ was identified as \"harmonics with sag\". For voltage magnitude 0.94-1.06, _v3_ \"harmonic\" was detected. For harmonics with a voltage magnitude >1.06, _v5_ was detected as \"harmonic with swell\".
# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model
## 2 Materials and Methods
### Method Overview  
Figure 1 briefly describes the end-to-end framework structure of the proposed approach, which is called the adaptive graph learning network model (AGLNM). The model framework mainly includes a graph learning module, a graph loss mechanism, graph convolution modules, and time convolution modules. The graph learning module can mine the adaptive adjacency matrix from the data, discover the hidden association between  
nodes, and then serve as the input for the graph convolution module. The graph loss mechanism can continuously update and optimize the adaptive adjacency matrix to the real dependence between the spatial points hidden in the SST data. The graph convolution module can be used to capture the dependencies between SST spatial points. The time convolution module is used to mine the time series pattern corresponding to each space point. In addition, residual links are added before and after each pair of spatio-temporal convolution modules to avoid the problem of gradient disappearance. The output of the final model will project the hidden correlation features of the SST time series into the output dimensions of the desired prediction series. Each module of the model is explained in detail in the following sections.
# GNSS Positioning Using Mobile Devices with the Android Operating System
## 4 Results and Discussion
### Comparison between U-Blox and Smartphones Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Device** & **Method** & **East** & **North** & **Up** \\ \hline \multirow{4}{*}{Huawei} & Static & 0.437 & 0.010 & 0.402 \\  & Static & 0.189 & 0.783 & 0.797 \\  & Kinematic & 0.529 & 0.287 & 0.510 \\  & SPP & 3.584 & 7.795 & 14.788 \\  & \multirow{2}{*}{SPP} & 1.143 & 0.767 & \(-\)2.597 \\  & & 4.056 & 9.071 & 15.447 \\ \hline \multirow{4}{*}{Samsung S8+} & Static & 0.384 & 0.195 & 0.432 \\  & \multirow{2}{*}{Kinematic} & 0.221 & 0.784 & 1.041 \\  & & 0.345 & 0.201 & 1.072 \\  & & 1.456 & 0.423 & 1.674 \\  & \multirow{2}{*}{SPP} & 1.114 & 1.101 & 3.115 \\  & & 2.354 & 2.124 & 3.568 \\ \hline \multirow{4}{*}{u-blox} & Static & \(-\)0.254 & \(-\)0.947 & 0.970 \\  & & 0.385 & 0.195 & 1.404 \\ \cline{1-1}  & \multirow{2}{*}{Kinematic} & \(-\)0.262 & \(-\)0.979 & 0.678 \\ \cline{1-1}  & & 4.630 & 6.600 & 19.21 \\ \cline{1-1}  & & \(-\)0.248 & \(-\)0.922 & \(-\)3.437 \\ \cline{1-1}  & \multirow{2}{*}{SPP} & 4.910 & 6.746 & 19.134 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Accuracy (upper line for each method) and precision (lower line for each method) results considering a session length of 10 min. All results exceeding a threshold of 3 m are highlighted inside the table. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Device** & **Method** & **East** & **North** & **Up** \\ \hline \multirow{4}{*}{Huawei P10+} & Static & 0.283 & \(-\)0.222 & \(-\)0.295 \\  & & 0.242 & 0.488 & 1.124 \\  & Kinematic & 0.253 & \(-\)0.198 & \(-\)0.223 \\  & SPP & 4.205 & 7.384 & 18.997 \\  & \multirow{2}{*}{SPP} & 0.253 & \(-\)0.198 & \(-\)4.025 \\  & & 4.671 & 8.569 & 19.180 \\ \hline \multirow{4}{*}{Samsung S8+} & Static & 0.325 & \(-\)0.213 & \(-\)0.675 \\  & & 0.853 & 0.422 & 1.322 \\  & \multirow{2}{*}{Kinematic} & 0.201 & \(-\)0.185 & 0.857 \\  & & 1.106 & 0.825 & 1.984 \\  & & 1.004 & 1.032 & \(-\)0.862 \\  & & 2.012 & 1.796 & 2.965 \\ \hline \multirow{4}{*}{u-blox} & Static & \(-\)0.017 & \(-\)0.076 & \(-\)0.105 \\  & & 0.004 & 0.008 & 0.007 \\ \cline{1-1}  & \multirow{2}{*}{Kinematic} & 0.098 & 0.010 & 0.058 \\ \cline{1-1}  & & 0.194 & 0.205 & 0.357 \\ \cline{1-1}  & & 1.249 & 2.77 & \(-\)0.020 \\ \cline{1-1}  & & 1.921 & 5.119 & 4.818 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy (upper line for each method) and precision (lower line for each method) results considering a session length of 30 min. All results exceeding a threshold of 3 m are highlighted inside the table.
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
###### Abstract  
This study investigates the integration of quantum computing, classical methods, and deep learning techniques for enhanced image processing in dynamic 6G networks, while also addressing essential aspects of copyright technology and detection. Our findings indicate that quantum methods excel in rapid edge detection and feature extraction but encounter difficulties in maintaining image quality compared to classical approaches. In contrast, classical methods preserve higher image fidelity but struggle to satisfy the real-time processing requirements of 6G applications. Deep learning techniques, particularly CNNs, demonstrate potential in complex image analysis tasks but demand substantial computational resources. To promote the ethical use of AI-generated images, we introduce copyright detection mechanisms that employ advanced algorithms to identify potential infringements in generated content. This integration improves adherence to intellectual property rights and legal standards, supporting the responsible implementation of image processing technologies. We suggest that the future of image processing in 6G networks resides in hybrid systems that effectively utilize the strengths of each approach while incorporating robust copyright detection capabilities. These insights contribute to the development of efficient, high-performance image processing systems in next-generation networks, highlighting the promise of integrated quantum-classical-classical deep learning architectures within 6G environments.  
copyright detection; quantum computing; hybrid deep learning; image processing; 6G networks +
Footnote †: journal: Information  
0000
## 5 Practical Dependability Assessment
### Dependability Evaluation
Next, we evaluate the system availability with the maximum values of \(CA_{min}\) and \(AQM_{min}\) that guarantee 99% of availability, which are \(CA_{min}~{}=~{}33\%\) and \(AQM_{min}~{}=~{}18\%\), and the maximum possible values, which are the coverage values of network deployment (\(CA_{min}~{}=~{}50\%\) and \(AQM_{min}~{}=~{}28\%\)). With this, it is possible to verify, respectively, the maximum and minimum  
Figure 11: Network deployment considering QoM under visual failures. availability response which the system can present for the considered deployment. The Figure 13 shows the respective steady availability values, 88.66866% and 99.00779%. If we consider that the nodes are supplied by a power line instead of a battery, we can re-evaluate the system dependability without the effect of batteries. This can be achieved by removing the battery Markov Chain from the model or simply by setting the battery discharge rates (\(\lambda_{bt}\)) to zero, avoiding to change the fault tree model. In this case, the system availability increases to 99.97261% and 99.67175%, respectively, achieving expected results of a well formed WirelessHART network [40]. Finally, it is analyzed the impact of transforming sensors with low coverage (\(OFoV_{vs}<FoV_{min}\)) into relay sensors. Figure 14 shows an example where the sensor \(vs_{7}\) is occluded and its OFoV is less than 5%. In this case we consider that this sensor cannot provide enough visual information and then its camera is turned off to save battery, but the node keeps working re-transmitting messages. We analyze the worst case scenario, that is, the maximum possible values of \(CA_{min}\) and \(AQM_{min}\), which is the coverage values of network deployment (\(CA_{min}~{}=~{}47\%\) and \(AQM_{min}~{}=~{}26\%\)). In this case, the application availability considering \(vs_{7}\) as a visual sensor node is 89.56148%. When the sensor is considered as a relay node, the assessed availability is 89.56149%. This is a very small increase in terms of the absolute values, although, in terms of dependability, it is very significant, which shows that the proper management of power consumption turning some visual nodes into relay nodes compensates somehow the visual failure. Figure 12: Impact of (**a**) \(CA_{min}\) and (**b**) \(AQM_{min}\) on dependability evaluation. Figure 13: Availability at minimum and maximum acceptable visual failures.