esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Aerodynamic Performance Analysis of Camber Morphing and Conventional Airfoils
## 2 Problem Statement  
Research works on the computational performance of morphing technology mainly focus on the aerodynamic analysis of targeted configurations of certain morphing styles and their mechanisms [38; 39] only, or additionally, on the morphing concept's degree of deformation and power consumption [40; 41]. The main objective of this work is to clarify the actual and quantitative benefits of morphing wings regarding their \(D\) reduction by comparison of various camber morphing rates with different configuration of conventional wings generated for various flap deflection angles.  
It is important to understand and validate the benefits of morphing wings, and the methodology authors use in this paper is through a numerical approach employing a high-fidelity CFD software ANSYS Fluent. Through a rigorous analysis, the aerodynamic forces are computed over a range of \(\alpha\) for both the morphed and deflected configurations by varying the maximum camber rate for the morphing cases and varying the flap deflection angle for the deflecting cases. The computational methodology is presented in Section 2, along with the validation study, details about the ANSYS Fluent setup, grid generation, and the geometries of the airfoil configurations. The computed results are discussed in Section 3, where the authors verify any improvement of \(L/D\) in morphed configurations, for configurations matched in \(L\) between morphing and conventional wing pairs. The \(L\) profiles are matched to establish the same conditions for comparison while \(\alpha\) is varied and \(D\) analysed and compared. Lastly, the conclusions are given in Section 4. It is of interest to evaluate the actual benefits of camber morphing wings in terms of their \(L/D\) improvement, indicative of enhanced sustainability for more practical aspects of real-world applications, and mission planning along an assigned trajectory.
## 3 Results and Discussion
### Effects on Morphology  
Figure 5 displays the microscopic morphology of the as-prepared \(\beta\)-FeOOH and \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\). The \(\beta\)-FeOOH is presented as a nanorod array that vertically grows on the substrate (Figure 5a). The \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) samples also show a similar morphology except for a slight volume contraction (Figure 5b), suggesting a topological transformation from \(\beta\)-FeOOH to \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\). When Co or Ni dopant was introduced, the morphology of the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) remained almost the same as exhibited in Figure 5c.d. The three \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) samples were all comprised of bunches of nanorods. Each bunch of nanorods had a diameter of about 50 nm and each nanorod had a diameter of about 5-10 nm. The small diameter is beneficial to the transport of photoinduced holes to the semiconductor-electrolyte interface, although the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) nanorods do not have the merit of a high conductivity since they are not oriented in the (0 0 1) direction on the substrate [6; 17; 39].
# Sensors Energy Optimization for Renewable Energy-Based WBANs on Sporadic Elder Movements
## 3 Power Consumption Aspect in WBAN
Data packets are transmitted among sensor nodes with the least amount of postural movement possible. Routing protocols for data across several organizational layers [34], Network and MAC layer issues can be solved via cross-layer protocols, which improve the network as a whole. There is little doubt that energy-aware protocols, which prioritize node selection and mobility over the network's energy or power consumption, are directly linked to the average lifetime and stability of the network. Routing Protocol Based on AI Crucial data takes precedence over less-important information in the analysis. Because a relay node in a routing process has no restriction on the amount of energy it may consume, emergency or critical data is prioritized over normal traffic because it must be delivered to its destination first and because it is necessary to monitor crucial medical information. Real-world energy components can be used to model this constraint:  
PCO threshold  
The composition of the energy in the model must be changed when we look at the working period of sensors and relays. Each cycle's active and sleep time can be referred to as Power Cycle Optimization (PCO). PCO = PSO (Activation condition) + PCO (Sleep condition) + PCO (switching condition)  
PSO (Activation condition) = power utilization if the node is active condition  
PCO (Sleep condition) = power utilization if the node is sleep condition  
PCO (switching condition) = power utilization if the node is switching condition (active to sleep and sleep to active)  
To performing the precise formulation  
PCO = X PSO (Activation condition) +Y PCO (Sleep condition) + Z PCO (switching condition)  
\[\text{PCO}=\sum_{\text{i=1}}^{\text{n}}\text{PCO}_{\text{cycle}_{\text{i}}} \tag{1}\]  
We'll calculate the energy consumption of correctly transmitted packets and the lifetime of the network, just like we did in the previous study. For any connection to effectively transmit a packet, the amount of energy consumed by \(\text{PCO}_{\text{ij}}\) is:  
\[\text{PCO}_{\text{ij},\text{x}}=\frac{\text{PCO}_{\text{T}\,\text{X}}^{Z}}{ \text{PDR}_{\text{ij}}} \tag{2}\]  
In this case, \(\text{PDR}_{\text{ij}}\) specifies the probability that a packet will be transported from I to j, while PCO Z TX indicates the energy needed to transmit the packet at z dBm, as stated. This is for when you only have to jump once. Node K, which serves as a relay between the sender I and receiver (j) in the two-hop transmission scenario:  
\[\mathrm{PCO_{ij}^{K}}=\frac{2\times\mathrm{PCO_{T}^{k}\,x}}{\mathrm{PDR_{i}^{k} \mathrm{j}}} \tag{3}\]  
A homogeneous wireless body area network would use the same TX power level for all sensors, but a heterogeneous wireless body area network would use a variable TX power level for each sensor (WBAN). When dealing with more complex situations, it is necessary to make additional modifications to the framework.
# A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection
## 2 Related Work
### Transformers in Computer Vision  
Originally designed for NLP [33; 34], researchers in recent years have widely applied transformers to a variety of computer vision tasks [35; 36; 37; 38], such as image classification [39; 40; 41], object detection [42; 43; 44], and semantic segmentation [45; 46; 47; 48; 49], and all of them have achieved impressive performance. Compared with CNN-based methods [11; 50; 51], the multi-head self-attention layer in the transformer has dynamic weights and global sensory fields, which gives the transformer the advantage of modeling global contextual information.  
Transformer-based models are also turning out to be a big trend in COD. HitNet [52] realizes the refinement of high-resolution features to low-resolution feature representations with iterative feedback by establishing global loop connections between multi-scale resolutions. SARNet [16] mimics the human vision mechanism to observe the behavioral patterns of camouflaged objects and formulates a search-amplify-recognize strategy to break camouflage by zooming in on the object region and alternately focusing on the foreground and background. FSPNet [53] designs a feature shrinkage decoder with a neighbor interaction module, which aggregates neighboring features step-by-step via a shrinkage pyramid for accurate decoding. CamoFormer [20] investigates a more effective way to utilize the self-attention mechanism by assigning different features to different self-attention heads to handle foreground and background regions separately to obtain more accurate segmentation results.  
Each approach in the literature offers strengths and limitations in addressing the challenges of COD. Traditional methods excel in simpler scenarios, but they often falter in complex scenes due to their reliance on hand-crafted features. CNN-based approaches have significantly advanced the field by leveraging deep learning techniques to capture intricate details and semantic context, as evidenced by the diverse strategies displayed above. However, CNNs primarily focus on local feature extraction and struggle to capture long-range dependencies across the entire image. This limitation can hinder their ability to effectively model global contextual information, which is crucial in tasks where understanding relationships between the distant parts of the image is necessary. In contrast, transformer-based models capitalize on their ability to model global dependencies and adaptively attend to relevant features, making them robust in diverse and challenging scenes. Therefore, following the baseline of transformer-based COD approaches, we propose a new scheme to achieve more accurate camouflaged object detection. The obvious advantages of the proposed scheme are that we take into account not only the compensation between cross-level features, but also the difference, for more promising results. And we also demonstrate that our method captures more detailed and discriminative information.
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 1 Introduction
### Proposed Banking Malware Tree  
This section examines and discusses the relationships between the three banking malware variants that were discussed above and establishes a timescale of when they emerged. Figure 3 shows that Zeus was discovered in 2006 [29], Ramnit was discovered in 2010, and Zeus Panda was discovered in 2016 [30], and research indicates that they all share similar code and perform similar actions. Based on this research [30; 31; 32; 33; 34; 35; 36], this paper proposes that all banking malware variants belong to a specific family of banking malware, and this proposed family tree can be seen in Figure 4. A historical timeline of a selection of banking malware variants can be seen in Figure 3, which suggests that all banking malware variants can be traced back to one of the parent banking malware variants, Zeus, Snifula, and Gozi, as shown in Figure 4.  
The key conclusions from this research reveal that most banking malware variants belong to one of the three primary families identified in Figure 4. These variants frequently borrow code from one another, with newer malware still sharing similarities in code and behavior with those outlined in Figure 4. Despite these overlaps, banking malware continues to evolve, becoming increasingly sophisticated and more effective at targeting victims.  
The rest of the paper is organized as follows: Section 2 discusses some of the key research that has been conducted in this field. In Section 3, a problem statement is presented. Section 4 proposes a framework to detect the banking malware variants discussed in Sections 1.2-1.4, Section 5 analyzes and compares the research findings, and Section 6 concludes with a summary and conclusion.
# Identification Procedure for Design Optimization of Gravitational Electromagnetic Energy Harvesters
## 3 Results and Discussions
### Comparison with Devices in Literature  
A consistent comparison method between energy harvesters' performances is the computation of the normalized power density (NPD). This parameter is defined as the ratio between output resonance power and the harvester volume, normalized by the square of acceleration input [8]. Table 10 reports NPD values of the three configurations analyzed in this paper, the optimized harvester and similar devices found in the literature. The performances of the harvesters in this study are aligned with most of the generators developed in other researchers' works. EH2 has intermediate dimensions between the other two configurations and shows the best NPD value among the others. EH3 has the largest size but generates power not sufficiently higher than EH2, resulting in a low NPD value. The smallest harvester EH1 converts the lowest power amongst the three configurations but is better optimized than EH3 as it presents a solid NPD value. The numerical simulations on the optimized energy harvester enlighten the performance enhancement of the design process in Section 3.2. The new device manages to reach an NPD value of 2.608, which is the highest amongst the devices in the comparative study, improving the EH2 NPD value of about 52%.
## 4 Results and Discussion
### Performance under the Effects of Channel Conditions  
The performance of the proposed approach under different channel conditions when SNR = 5, 8, and 20 dB and NumSTA = 4 was evaluated as shown in Figure 4a-c for the cases of different traffic models such as Pareto, Weibull, and fBM. According to the results, the system throughput performance increased when the channel quality improved from 5 dB to 20 dB in all traffic models. In this regard, the proposed approach achieved the lowest performance of 128 Mbps in the Pareto traffic model, as shown in Figure 4b, and the maximum of 312 Mbps was achieved by the Weibull traffic model when the channel condition was the worst; i.e., SNR = 5 dB. On the contrary, under the near-ideal channel conditions; e.g., with an SNR of 20 dB as shown in the figure, the system throughput performance was almost optimal in all approaches due to the lower frame error rate that occurred under the near-ideal channel condition, and the queue delay due to retransmitted frames was less. However, the proposed approach achieved the maximum performance of 781 Mbps using the Weibull traffic model, 582 Mbps using the fBM traffic model, and  
Figure 3: Performance of average system throughput under the effects of heterogenous traffic models when SNR = 8 dB.  
385 Mbps performance using the Pareto traffic model. This indicated that the proposed approach provided a good performance in the less bursty traffic model of the Weibull and fBM traffic models than in the bursty traffic model of Pareto traffic [10]. On the contrary, the Adaptive FA Conv. approach always achieved the maximum performance because it did not consider the cost of delay. However, the performance of the proposed approach was better because it adopted a dynamic adaptive aggregation selection scheme in terms of maximum throughput with the least cost of increasing delay.  
Based on these results, we concluded that both the traffic pattern among streams in the downlink MU-MIMO channel and the channel conditions affected the system performance.
## 3 Results
### Food Vectors of Calories and Free Sugars  
The main contributors of TEI in children and adolescents were pastries (9.5% of TEI), mixed dishes (8.1% of TEI) and bread as well as r rusks (7.6% of TEI). In adults, main contributors were bread and rusks (11.9% of TEI), mixed dishes (7.5% of TEI) and pastries (7.2% of TEI) (data not shown).  
The leading contributors to FS intakes in children and adolescents are cakes and pastries (16.2% of total FS intake), sugary drinks (14.5% of total FS intake) and fruit juices (13.8% of total FS intake). In adults, leading contributors to FS intakes are sugary products (sugar, honey, jam, etc.) (17.6% of total FS intake), cakes and pastries (16.9% of total FS intake) and fruit juices (12.9% of total FS intake) (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **N** & **Mean** & \begin{tabular}{c} **Standard-** \\ **Deviation** \\ \end{tabular} & \begin{tabular}{c} **10 th** \\ **Percentile** \\ \end{tabular} & \
\begin{tabular}{c} **50 th** \\ **Percentile** \\ \end{tabular} &
\begin{tabular}{c} **90 th** \\ **Percentile** \\ \end{tabular} \\ \hline \multicolumn{6}{c}{Free sugars intake (g/day)} \\ \hline Total (\(\geq\)3 years) & 2947 & 58.2 & 0.6 & 23.1 & 52.8 & 98.0 \\ \hline Children and adolescents (3–17 years) & 1102 & 67.1 & 1.0 & 34.1 & 62.5 & 106.2 \\ \hline Adults (\(\geq\)18 years) & 1845 & 56.6 & 0.8 & 21.5 & 50.5 & 97.4 \\ \hline
3–6 years & 300 & 56.5 & 1.3 & 29.1 & 53.3 & 85.7 \\ \hline
7–12 years & 439 & 62.3 & 1.1 & 32.7 & 60.6 & 92.1 \\ \hline
13–17 years & 363 & 81.7 & 2.4 & 40.0 & 77.0 & 118.3 \\ \hline
18–34 years & 465 & 66.4 & 1.6 & 31.5 & 61.1 & 100.1 \\ \hline
35–49 years & 461 & 63.2 & 2.0 & 22.1 & 56.5 & 110.2 \\ \hline
50–64 years & 459 & 50.9 & 1.2 & 22.2 & 45.2 & 86.8 \\ \hline
65–75 years & 361 & 44.5 & 1.4 & 13.2 & 41.6 & 78.0 \\ \hline \(\geq\)76 years & 99 & 49.7 & 2.2 & 22.1 & 46.0 & 78.9 \\ \hline \multicolumn{6}{c}{Free sugars intake (\% of TEI)} \\ \hline Total (\(\geq\)3 years) & 2947 & 11.5\% & 0.1\% & 4.8\% & 11.0\% & 18.7\% \\ \hline Children and adolescents (3–17 years) & 1102 & 15.3\% & 0.1\% & 9.5\% & 15.0\% & 21.1\% \\ \hline Adults (\(\geq\)18 years) & 1845 & 10.5\% & 0.1\% & 4.3\% & 9.8\% & 17.5\% \\ \hline
3–6 years & 300 & 15.6\% & 0.3\% & 9.9\% & 15.4\% & 21.6\% \\ \hline
7–12 years & 439 & 15.1\% & 0.2\% & 9.6\% & 14.8\% & 21.0\% \\ \hline
13–17 years & 363 & 15.1\% & 0.3\% & 9.4\% & 15.0\% & 21.7\% \\ \hline
18–34 years & 465 & 12.7\% & 0.3\% & 6.1\% & 12.4\% & 19.5\% \\ \hline
35–49 years & 461 & 11.6\% & 0.3\% & 4.4\% & 11.0\% & 19.1\% \\ \hline
50–64 years & 459 & 9.3\% & 0.2\% & 3.9\% & 8.7\% & 14.7\% \\ \hline
65–75 years & 361 & 8.2\% & 0.2\% & 2.7\% & 7.7\% & 13.5\% \\ \hline \(\geq\)76 years & 99 & 9.4\% & 0.4\% & 4.4\% & 9.2\% & 15.4\% \\ \hline \end{tabular}
\end{table}
Table 2: Free sugars intake in children and adolescents as well as in adults (g/day and % of total energy intake (TEI)), stratified by age.
# First Request First Service Entanglement Routing Scheme for Quantum Networks
## 5 Evaluation Results
### Evaluation on T/W-DM Networks
\(\mathrm{R_{2},R_{3},R_{4},R_{5}}\) are four forwarding nodes and the numbers around the node represent the port of the device. A, B, C, and D are four users. The paths and the link loss of user A (B) are calculated by the FRFS routing scheme under different configurations. The detailed results are shown in the Table 3. Although the networking equipment are changed, the FRFS routing scheme can find the lowest path with different loss for users. It shows that the scheme can be applied to dynamic topological networks composed of various devices. Notablely, the network in case 5 actually has only one path to establish entanglement for user A and B due to the binding of wavelength channels and ports of DWDM.
## 3 Active Optics Correction of the 4m SiC Primary Mirror
### Surface Shape Correction of the 4m SiC Primary Mirror
#### 3.1.1 Principle of Surface Shape Correction Based on the Bending Mode
Combining \(\mathbf{F}_{Bi}\) into the bending-mode force matrix \(\mathbf{F}_{B}\),  
\[\mathbf{F}_{B}= \begin{bmatrix}\mathbf{F}_{B1}&\mathbf{F}_{B2}&\cdots&\mathbf{F}_{B54}\end{bmatrix}  \tag{8}\]The bending-mode method used \(B_{i}\) to fit the primary mirror shape \(W\) and then solved for the correction force:  
\[\mathbf{W}=\sum_{i=1}^{54}\mathbf{B}_{i}b_{i} \tag{9}\]  
The fitting result was the fitting coefficient \(b_{i}\), which was the proportion of each bending mode \(B_{i}\) to the primary mirror shape \(W\). If we multiplied and summed \(b_{i}\) with the bending-mode force \(\mathbf{F}_{Bi}\), we obtained the required force \(F\) for \(W\):  
\[\mathbf{F}=\sum_{i=1}^{54}\mathbf{F}_{Bi}b_{i}=\mathbf{F}_{B}\big{[}b_{1}\quad b_{2}\quad \cdots\quad b_{54}\big{]}^{T} \tag{10}\]  
The advantage of the bending-mode method is that the stiffness of \(B_{i}\) increased with the order. According to the SVD properties, the rms of all \(B_{i}\) was equal, and the rms of all \(\mathbf{V}_{i}\) was equal. As \(i\) increased, the value of \(\mu_{i}\) gradually decreased. Therefore, according to Equation (7), \(\mathbf{F}_{Bi}\) gradually increased. This indicated that the stiffness and frequency of \(B_{i}\) gradually increased with increasing \(i\). Therefore, if only a few low-order bending modes \(\mathbf{B}_{i}\) were used to fit \(\mathbf{W}\), the amplitude of the correction force could be effectively suppressed. In addition, owing to the dominant role of low-order aberrations in the surface error, this method could ensure a good surface correction effect and achieve an accurate surface shape. Owing to the standard orthogonality among \(B_{i}\), when fitting with the first \(q\) orders of \(\mathbf{B}_{i}\), only the first \(q\) term of \(b_{i}\) had to be retained, and the other terms had to be set to zero. The solution method for the correction force was  
\[\mathbf{F}=\mathbf{F}_{B}\big{[}b_{1}\quad\cdots\quad b_{q}\quad 0\quad\cdots\quad 0 \big{]}^{T} \tag{11}\]
## 3 Results and Discussion
### Creep Assessment  
Before any attempt to assess the creep behavior of the system, it is crucial to observe the loading stage before the actual creep interval. Figure 7a-f shows the loading stage at preset depth 1000 nm for different experimental conditions (loading speed and creep holding time), Figure 8a-f shows similar results at preset depth, and Figure 9a-f presents the results for preset depth 2000 nm.  
What is evident from these diagrams is that, especially in the case of preset depth 1000 nm (Figure 7), the curves do not appear entirely smooth, but they indicate the presence of the characteristic feature called \"serrations\". Serrations during the loading stage are a characteristic indication of micro-creep phenomena occurring during this stage [12; 33]. If serrations are expressed, part of the creep deformation occurs during the loading stage, an event that prevents the overall creep deformation from being expressed during the net creep stage [12; 33]. In the present effort, the presence of serrations is mostly observed in the case of a preset depth of 1000 nm (Figure 7), whereas the phenomenon seems to fade at a higher preset depth. Some traces of serrations can be recognized, such as in the case  
Figure 7: Loading stage curves for preset depth 1000 nm: (**a,b**) loading speed 13.4 mN/s, (**c,d**) loading speed 4.4 mN/s, and (**e,f**) loading speed 2.24 mN/s.  
of the preset depth 1500 nm (Figure 8). In the case of preset depth 2000 nm (Figure 9), the loading curves are, in general, smooth with no significant presence of serrations, i.e., no micro-creep deformation takes place prior to the actual creep deformation step. One final observation that must be mentioned is the fact that serrations, if present, are found in most of the cases associated with the lower loading speeds. This observation is in agreement with other experimental efforts [5; 6; 7; 8; 9; 10; 11; 22], according to which the most important factor influencing the formation of serrations is the strain rate. Various research efforts have shown that high strain rates during the loading stage result in the absence of serrations, i.e., lack of creep deformation during loading [5; 6; 7; 8; 9; 10; 11]. On the contrary, low strain rates usually lead to serration formation and creep deformation [5; 6; 7; 8; 9; 10; 11]. The reason for this behavior is that at high strain rates, the dislocation generation is also high, yet the applied stress is not adequate enough to activate a slip plane in order for a dislocation to move. Multiple slip planes have to be activated for dislocation movement to commence. However, the high strain rate does not provide adequate time for this activation. In simple words, at high strain rates, dislocations can multiply but not move. In this case, the loading stage curve appears quite smooth [5]. Furthermore, when no creep deformation is observed in the loading stage, the system will express its creep potential during the holding stage.  
Figure 8: Loading stage curves for preset depth 1500 nm: (**a**,**b**) loading speed 13.4 mN/s, (**c**,**d**) loading speed 4.4 mN/s, and (**e**,**f**) loading speed 2.24 mN/s.