esa-sceva/satcom-chunk-collection huggingface dataset:
# PowerBridge: Covert Air-Gap Exfiltration/Infiltration via Smart Plug
## 6 Data Encoding and Decoding
### Communication Modulation
#### 6.1.2 Modulation of Infiltration Communication  
In the infiltration scenario, it is feasible to exert control over the power state of the smart plug and transmit signals to the laptop. In order to ensure computer and power adapter safety, caution should be exercised regarding the frequency of power switching, as excessively high frequencies may lead to damage to electrical appliances. Consequently, conventional ASK, FSK, and PSK modulation techniques are unsuitable for implementation in this particular scenario. To address this concern, pulse interval modulation (PIM) can be employed for communication purposes by representing data through distinct time durations between two consecutive pulses [44]. A reference time interval denoted as T is defined within the standard document; it represents the temporal width between adjacent pulses with a duration of T. The intelligent plug generates a pulse each time it switches on or off, thereby enabling the modulation of signals based on the temporal intervals between these pulses. Figure 22 illustrates the waveform, while Table 2 presents an encoding table associated with PIM technique usage. The receiving software installed on the laptop captures and analyzes these temporal intervals between successive pulses before querying against the encoding table for decoding purposes.
# Secure Cryptographic Key Encapsulation and Recovery Scheme in Noisy Network Conditions
## 5 Key Encapsulation and Recovery Method Using CRP
### Search Space Needed for Key Recovery  
Once Algorithms 2 and 3 have been executed, the worst-case scenario required to find the correct key can be quantified mathematically. This calculation is based on the length of the sublists within the Collisions and Nomatch lists. The size of the sublist is multiplied together to obtain the total number of possible key combinations. If we denote the Collisions list as \(A\) and Nomatch list as \(B\), we will have the following:  
Let \(A=\{A_{1},A_{2},\ldots,A_{n}\}\) and \(B=\{B_{1},B_{2},\ldots,B_{m}\}\), where \(A_{i}\) and \(B_{j}\) are the sublists, and \(|A_{i}|\) and \(|B_{j}|\) are the lengths (number of elements) of the sublists in lists \(A\) and \(B\)respectively. The total number of possible combinations \(C\) can be represented as the product of the lengths of each sublist, as follows:  
\[C=\left(\prod_{i=1}^{n}|A_{i}|\right)\times\left(\prod_{j=1}^{m}|B_{j}|\right) \tag{2}\]
## 2 Materials and Methods
### DNA Extraction and SSR Analysis  
Genomic DNA was extracted using the CTAB method [14]. The extracted DNA was used for PCR analysis by first checking the DNA concentration using a spectrophotometer (Nanodrop ND-1000, Thermo Scientific, Waltham, MA, USA) and adjusting the concentration to 20 ng/uL. A total of 20 markers were used for the genetic diversity analysis (Table 2), selected from previous studies as having one primer with high polymorphism in each linkage group [9; 15]. PCR was performed in a reaction mixture of 20 uL containing 20 ng of total genomic DNA, 0.4 uM of each primer, 10 mM dNTP mixture, 10 x PCR buffer, and 1 unit Taq polymerase (Bioneer, Daejeon Korea) using a MG96G (Longgene scientific, Hangzhou, China). The amplification protocol included initial denaturation for 5 min at 94 \({}^{\circ}\)C; 35 cycles of denaturation for 30 s at 94 \({}^{\circ}\)C, annealing for 30 s at 47-57 \({}^{\circ}\)C, and extension for 60 s at 72 \({}^{\circ}\)C; and a final extension step for 10 min at 72 \({}^{\circ}\)C. Amplification products were resolved using electrophoresis though 4% polyacrylamide sequencing gel at 1600 V for 100 to 120 min in 0.5 x Tris-borate-ethylenediaminetetraacetic acid (TBE, Skaelsker, Denmark) buffer. The gel was stained with silver staining [16]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Release Period**} & \multicolumn{4}{c}{**Use Type**} \\ \cline{2-5}  & **Soy Sauce and Tofu** & **Bean Sprout** & **Cooking with Rice** & **Vegetable and Early Maturity** \\ \hline \multirow{5}{*}{Before 1980} & Jangdankechx, & & & \\  & Chungbukheek, & Iksan, & Buseok, & \\  & Hamam, & Keumamgadelip, & & \\  & Geumangsorgi, & Yukwoo 3, & & \\  & Kwangdu, & Shelby, & Kwangkyo, & 18 \\  & [PERSON], Kanglim, & & & \\  & Dongputake, & & & \\  & Jangyeob & & & \\ \hline \multirow{5}{*}{1980s} & [PERSON], [PERSON], & & & \\  & [PERSON], & [PERSON], & [PERSON], & \\  & [PERSON], & [PERSON], [PERSON], & & \\  & [PERSON], & Dankyeong, & & \\  & [PERSON], Jangkyong, & & & \\  & [PERSON] & & & \\ \hline \multirow{5}{*}{1990s} & [PERSON], & [PERSON], & [PERSON], & \\  & [PERSON], & [PERSON], & [PERSON], & [PERSON], \\  & [PERSON], & [PERSON], & & \\  & [PERSON], & [PERSON], & [PERSON], & \\  & [PERSON], & [PERSON], & & \\  & [PERSON], & [PERSON], & [PERSON], & \\  & [PERSON] & & & \\ \hline \multirow{5}{*}{After 1999} & [PERSON], [PERSON], & [PERSON], & & \\  & [PERSON], [PERSON], & [PERSON], & & \\  & [PERSON], & [PERSON], [PERSON], & [PERSON], & \\  & [PERSON], & [PERSON], & & \\  & [PERSON], & [PERSON], & & \\  & [PERSON], & & \\  & [PERSON], & [PERSON], & \\  & [PERSON], & [PERSON], & [PERSON], & \\  & [PERSON], & [PERSON], & & \\  & [PERSON], & [PERSON], & \\  & [PERSON], & [PERSON], [PERSON], & \\  & [PERSON], & & \\ \hline Total & 81 & 52 & 24 & 15 & 172 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of 172 Korean soybean varieties, classified by release years and use type.
# Integrated Motion Planning for Assembly Task with Part Manipulation Using Re-Grasping
## 3 Feasible Grasp, Part Manipulation, and Integrated Planning Algorithm
### Feasible Grasps for an Assembly Task  
In order to perform an assembly task using robotic manipulator, the grasp planning is of key importance. Finding a stable grasp for objects with arbitrary shape is an active field of research [22; 23; 24; 25], and as mentioned Grasp-RRT tries to find a grasp in a trial-and-error based way. In general, a grasp is deemed stable or feasible considering only the robot hand (or gripper) and the object, but the environment is not considered. However, for assembly, the environment must be considered and the environment also must include the partially assembled parts which change as the assembly process progresses. In this paper, we do not focus on finding stable grasps for the given object, but how to choose a feasible grasp among the given set of grasps, which make assembly possible, and how to select intermediate grasp if re-grasping is necessary. Thus, we assume that a set of grasps (\(G\)) is given and we will find a set of feasible grasps \(FG\subset G\) that can be used for assembly. While selecting the set of feasible grasps, only the hand but not the whole robot body, is checked for collision with environment as well as the partial assembly at initial and final poses. Note that we keep not just one feasible grasp but a _set_ of feasible grasps since some feasible grasp maybe actually unusable due to other constraints such as collision of the robot body environment or joint limit of the robot etc. Later, during motion planning phase, the algorithm tries to find a collision free path such that the robot can grasp the part using any one of the chosen feasible grasps. Even without considering the robot body, it may not be possible to move the part from its initial pose to its final assembly pose, using a single grasp as shown in Figure 1. In Figure 1a, the initial pose of the part to be assembled with the sub-assembly is shown, while Figure 1b shows the desired assembly formation of the parts. It can be seen that it is not possible to assemble the part in single pick, and place cycle. That is why, one or more re-grasping of a part may be needed to assemble it. Part manipulation approach using re-grasping is presented in next sub-section.  
Figure 1: (**a**) The initial pose of the part (to be assembled) and sub-assembly are shown. (**b**) The final pose of the part in the assembly is shown. It can be seen that there is no feasible grasp to pick the part from its initial pose and move to its final pose without part manipulation.
## 4 Empirical Results
### Experimental Design
* **LSTM2**: Predicts VIX using S&P 500 data with second-layer hyperparameter tuning. Similar to LSTM1 but with different hyperparameter tuning in the second layer, this model helps analyze the influence of hyperparameter optimization across layers. * **VMD LSTM 1 (1,1)**: Predicts VMD-decomposed VIX using VMD-decomposed S&P 500 data with first-layer hyperparameter tuning. This model introduces VMD to both input sources (S&P 500 and VIX) and allows evaluation of whether decomposing financial time-series data enhances prediction accuracy. * **VMD LSTM 1 (2,1)**: Predicts VMD-decomposed VIX using VMD-decomposed S&P 500 data and VMD VIX features with first-layer hyperparameter tuning. This model additionally incorporates decomposed VIX features, enabling an assessment of how including VIX-specific components improves volatility prediction. * **VMD LSTM 2 (1,1)**: Predicts VMD-decomposed VIX using VMD-decomposed S&P 500 data with second-layer hyperparameter tuning. By adjusting hyperparameters in the second layer, this model tests the sensitivity of deep learning parameter configurations in VMD-enhanced architectures. * **VMD LSTM 2 (2,1)**: Predicts VMD-decomposed VIX using VMD-decomposed S&P 500 data and VMD VIX features with second-layer hyperparameter tuning. This model represents a more refined approach by incorporating decomposed VIX features while fine-tuning deeper network layers, assessing the synergy between feature engineering and model optimization. * **VMD-Cascaded LSTM**: Implements the proposed architecture without the Attention mechanism. This model serves as a key benchmark to measure the contribution of the Attention mechanism in capturing critical market dependencies and improving VIX prediction. All models were evaluated using three metrics: MSE, RMSE, and MAE. These metrics assess the prediction accuracy by quantifying the difference between predicted and actual values. * **Mean Squared Error (MSE)**: Measures the average squared difference between predicted and actual values. It penalizes larger errors more heavily, making it sensitive to outliers. \[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}\] (16)
* **Root Mean Squared Error (RMSE)**: The square root of MSE, providing an error measure in the same unit as the target variable. It retains the sensitivity to large errors while improving interpretability. \[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}\] (17)
* **Mean Absolute Error (MAE)**: Measures the average absolute difference between predicted and actual values. Unlike MSE, it treats all errors linearly, making it more robust to outliers. \[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|\] (18)
## 4 Statistical Analysis  
As we observed in Table 15 above, the detection scores of 45 cm and 55 cm are similar. Therefore, a statistical analysis is conducted to see whether there is a significant difference between the two groups of values. We utilize the Mann-Whitney U [37] method as it is one of the most commonly used non-parametric statistical tests. Developed by [PERSON] in 1947, this non-parametric test is frequently used for small samples of data that are not normally distributed.  
In the Mann-Whitney U test, the null hypothesis states that the medians of the two respective groups are not different. An alternative hypothesis states that one median is larger than the other or that the two medians differ. If the null hypothesis is not rejected, it means that the median of each group of observations is similar. If the null hypothesis is rejected, it means the two medians differ.  
We apply the Mann-Whitney U test to our 45 cm and 55 cm as the number of samples are small, less than 30, and the detection scores are not normally distributed. Our null hypothesis (H0) and alternative hypothesis (H1) are as follows:  
**H0**: _The median of APs is equal between 45 cm and 55 cm detection scores._
**H1**: _The median of APs is not equal between 45 cm and 55 cm detection scores._  
Using the SciPy function for the Mann-Whitney U test, we obtain a \(p\)-value of 0.787. Since the \(p\)-value (0.787) is above the 0.05 significance level, we fail to reject the null hypothesis.  
We conclude that there is not enough evidence to suggest a significant difference in medians between the two datasets. As the standard deviation differs by 2.23, we recommend using 55 cm instead of 45 cm for more accurate inference.
## 4 Experimental Results
### Performance Test under Different Power Rates  
The input voltage is supplied by an AC source IT7326, while the load resistor is realized with electronic load AN23606E-600-420. Waveforms of input voltage, input current, inductor current, and output voltage are measured with an oscilloscope MDO3024. PF, THD, and power efficiency are measured using a power analyzer PA5000H. The input voltage is 220 VAC. The load changes from 266 \(\Omega\) to 100 \(\Omega\), where the output power changes from 600 W to 1600 W. According to the PA5000H data manual, the voltage, current, and power measurement error are shown in Table 2. In the proposed system, the input signal frequency is 50 Hz, and the output signal is direct current (DC). According to Table 2, the measurement error is within 0.05%. Additionally, to ensure the accuracy and credibility of the measurement result, the following steps are carried out. (1) Calibration and verification: regularly calibrate and verify the PA5000H analyzer according to manufacturer guidelines and industry standards. This helps minimize systematic errors and ensures accurate readings. (2) Measurement uncertainty analysis: perform a comprehensive measurement uncertainty analysis for our specific measurement setup. After the analysis, it was found  
\begin{table}
\begin{tabular}{c c} \hline Input voltage & 220 VAC \\ Output voltage & 400 VDC \\ Rated output power & 1600 W \\ Switching frequency & 100 kHz \\ Inductor & 350 \(\mu\)H \\ Magnetic core & NPS184060 \\ Number of windings turns & 50 \\ Enameled wire diameter & 1.63 mm \\ Output electrolytic capacitor & \(7\times 150\)\(\mu\)F/450 V \\ SiC MOSFET & C3M0030090K \\ Si MOSFET & Cool MOS IPW65R045C7 \\ Driver chip & IXDN609 SIA \\ Isolator & ACPL-4800-300E \\ Controller & TMS320 PZ80049 \\ \hline \end{tabular}
\end{table}
Table 1: Main specifications of the totem-pole bridgeless PFC converter.  
Figure 14: Ttem-pole bridgeless PFC prototype.  
that connecting the input current port to the source ground side can effectively reduce the measurement error.  
Waveforms and key performance of the PFC converter under different power rates are shown in Figure 15. The power efficiency is above 98.35% over the whole load range and achieves a peak efficiency of 98.69%. As the load power increases from 589 W to 1570 W, PF increases from 99.11% to 99.82% and THD decreases from 7.69% to 3.92%.
# Computer Vision and Image Processing Approaches for Corrosion Detection
## 4 Corrosion Detection Approaches
### Classification with SVM  
SVM is an image classification technique wherein two separate zones can be partitioned from a vector space. It is a powerful machine learning technique, especially for image segmentation and pattern recognition. SVM is a supervised learning technique used for regression, classification, and outlier detections. This technique is highly effective in dimensional spaces. SVM transforms the corrosion image using a kernel trick, and then, it locates the optimal corrosion boundaries between all possible outputs. Both colors of the corroded region, and the degree of corrosion, can be analyzed using SVM.  
The applications of SVM for corrosion detection can be observed in water pipelines [67], underwater pipelines [68], steel bars [69], bridge cables [70], equipment [71], aircraft structures [72], wind turbine blades [73], and many more. SVM can also be improved using other methods to obtain a better accuracy for corrosion detection and assessment, such as HOG [74], scale-invariant feature transform (SIFT) [75], and speeded up robust features (SURF) [76]. The corroded and non-corroded regions can be distinguished from colors [77] and textures [78] using SVM.
## 1 Introduction
As a result, more sophisticated structures need to be designed based on engineering methods using analytical, numerical, and experimental analysis to produce structures capable of withstanding the harsh conditions typically encountered in marine environments. The development of artificial reefs from an engineering, reliability, and feasibility standpoint remains relatively unexplored in the literature. Given the growing importance of these structures in the rehabilitation of marine ecosystems, it is essential to integrate clear and technically sound engineering methodologies. While aspects related to geometric design are well established--covering conceptual requirements [30; 41; 42; 43], as well as hydrodynamic and morphodynamic performance using numerical simulation [18; 44; 45; 46; 47; 48; 49; 50; 51; 52] and physical modelling [26; 53; 54]--the structural design based on primary analytical and numerical methodologies is still insufficiently addressed. Existing studies that explore structural behaviour typically focus on advanced fluid-structure interaction (FSI) simulations [49; 55; 56]. This may hinder the replication of such structures in different geographies and scenarios, due to the lack of understanding of the overall relationships between general requirements including materials and action-effect relationships, and the design approach itself. This work addresses this challenge by implementing an engineering-driven approach for the design of a geometrically complex and modular multifunctional artificial reef (MMFAR), allowing the safety verification and possibly the generalization of the methodology to other similar projects. The developed approach may also support subsequent stages of analysis based on CFD and FSI for detailed functional analysis.
# A Study of MANET Routing Protocols in Heterogeneous Networks: A Review and Performance Comparison
## 4 Results and Discussion
### Throughput Performance
#### 4.2.3 Node Throughput for DYMO Routing Protocol  
The node throughput performances of DYMO are illustrated through Figures 13-15. The effect of the laptop nodes (low mobility) on the throughput performance for the DYMO routing protocol is shown in Figure 13. The throughput shows significant variation in the early stages of the simulation but stabilizes after approximately 100 s. This stabilization indicates that DYMO effectively manages route discovery and traffic forwarding after the initial route setup, leading to consistent performance across all the laptop nodes, regardless of the network topology changes.  
The effect of the mobile nodes on the throughput performance of the DYMO routing protocol is shown in Figure 14. The graph shows a significant increase in throughput as the simulation progresses, particularly between 50 and 200 s, where the network stabilizes. The variance in throughput across the different mobile hosts is reflected in the varying mobility levels and traffic loads within the network. After 200 s, the throughput becomes more stable, indicating effective route management by DYMO in dynamic conditions, although slight fluctuations can still be observed toward the end of the simulation.  
Figure 12: BATMAN’s throughput for mobile nodes.  
Figure 13: DYMO’s throughput for laptop nodes.  
The effect of the fixed nodes on DYMO's throughput is shown in Figure 15. The blue node exhibits significantly higher throughput compared to the red node, stabilizing after approximately 150 s. This result emphasizes DYMO's capacity to maintain efficient routing for fixed nodes under heterogeneous conditions. In summary, DYMO maintains stable throughput even as the node mobility increases, unlike AODV. It outperforms the other protocols in delay and throughput, making it ideal for networks where devices move unpredictably.
# Design, Analysis, and Experiment of a Wheel-Legged Mobile Robot
## 4 Results
### Comparison and Analysis
#### 4.2.4 Material Selection  
The simulation results were compared and analyzed. The operating torque required for the nylon wheel-leg cover is relatively small and regular, and the peak torque lasts for milliseconds. However, further structural design is required to meet the climbing requirements, which increases the design difficulty and reduces its effectiveness. The peak torque also lasts for milliseconds when rubber is used for the wheel-leg cover, but it easily reaches a large friction coefficient to complete the climbing requirements. Although the operating torque is relatively large, it is within the acceptable range. For the acrylic wheel-leg cover, in addition to the large abnormal torque and long duration, the operating torque value is irregular. Therefore, this material is not favorable. The performance of different wheel-leg cover materials was evaluated in all aspects, as listed in Table 3.  
Figure 20: Simulated torque diagram for the acrylic wheel-leg cover at different speeds and friction coefficients. (**a**) Material: Acrylic. n: 10 rpm. f = 0.19. (**b**) Material: Acrylic. n: 10 rpm. f = 0.35. (**c**) Material: Acrylic. n: 10 rpm. f = 0.6. (**d**) Material: Acrylic. n: 20 rpm. f = 0.19. (**e**) Material: Acrylic. n: 20 rpm. f = 0.35. (**f**) Material: Acrylic. n: 20 rpm. f = 0.6. (**g**) Material: Acrylic. n: 30 rpm. f = 0.19. (**h**) Material: Acrylic. n: 30 rpm. f = 0.35. (**i**) Material: Acrylic. n: 30 rpm. f = 0.6.  
In summary, with priority given to climbing ability, the rubber wheel-leg cover was finally determined to increase friction based on the comprehensive torque and the motor model.