esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 7 Discussion
#### 7.1.3 Energy Consumption and Efficiency  
Energy-saving techniques in traditional WSNs are tailored to minimize energy consumption at both the node and network levels, optimizing resource utilization and extending the battery life. However, in LWSN monitoring applications, energy consumption is inherently higher due to the demands of long-distance communication and data forwarding. This is because, in a typical LWSN, a limited number of sensors are often deployed to collect data and forward it to the sink node. For such sensor nodes, energy is expended not only for long-range communications but also for multi-hop communication, where the nodes not only transmit their own data but also serve as relays for transmitting data from neighboring nodes [169]. Consequently, these sensors may experience higher energy depletion, impacting the overall network's performance.  
Techniques in traditional WSNs, which solely focus on node-level energy reduction, may overlook the energy-intensive nature of long-range communication in LWSNs. These techniques may not work well in linear deployments (which require tailored approaches to effectively balance energy consumption and network performance), resulting in suboptimal performance and a shortened network lifespan. Therefore, energy-balancing techniques that focus on overall network energy consumption and strive to balance the residual energy of sensor nodes are crucial in LWSN monitoring applications for extending the network's lifetime [170]. In addition, optimal node placement is also crucial for achieving energy efficiency in LWSNs [162; 171]. By strategically positioning sensor nodes along the linear deployment path, factors such as the communication range, node density, and energy consumption can be optimized to minimize energy expenditure and enhance the network's performance [172].
## 5 Conclusions
###### Nomenclature  
\(A\), \(B\), \(C\)constant (coefficient)  
\(C_{F}\)total skin friction coefficient  
\(C_{H}\), \(C_{K}\)directional sensitivity factor  
\(c_{f}\)local skin friction coefficient  
\(d\)diameter, mm  
\(E\)voltage, V  
\(f\)sampling frequency, Hz  
\(H\)probe height, mm  
\(H_{12}\)shape factor  
\(h\)height of riblet, \(\mu\)m  
\(k\)thermal conductivity, W/(m-K)  
\(k_{s}\)surface roughness, mm  
\(L_{0}\)integral length scale, mm  
\(m\)data length  
\(N\)data length  
\(q\)uncertainty, %  
\(R\)correlation coefficient  
\(Re\)Reynolds number  
\(s\)width of riblet, \(\mu\)m  
\(s^{+}\) nondimensional width of riblet  
\(T\)temperature, K or \({}^{\circ}\)C  
\(Tu\)turbulent kinetic energy  
\(t_{0}\)integral time scale, s  
\(U\)streamwise velocity component (\(U\)-velocity), m/s  
\(u^{\prime}\)streamwise component of velocity fluctuation, m/s  
\(u^{+}\) nondimensional \(U\)velocity  
\(u_{\tau}\)friction velocity, m/s  
\begin{tabular}{l l} \(v^{\prime}\) & spanwise component of velocity fluctuation, m/s \\ \(w^{\prime}\) & vertical component of velocity fluctuation, m/s \\ \(x\) & coordinate in streamwise direction \\ \(y\) & coordinate in spanwise direction \\ \(z\) & coordinate in vertical direction \\ \(z^{+}\) & nondimensional height \\ \(\delta\) & boundary layer thickness, mm \\ \(\delta^{\ast\ast}\) & displacement thickness, mm \\ \(\varepsilon\) & energy dissipation rage, m\({}^{2}\)/s\({}^{3}\) \\ \(v\) & kinematic viscosity, m\({}^{2}\)/s \\ \(\theta\) & momentum thickness, mm \\ \(\rho\) & density, kg/m\({}^{3}\) \\ \(\sigma\) & standard deviation \\ \(\tau_{0}\) & Reynolds stress, Pa \\ \(\Delta t\) & lag time, s \\ \end{tabular}
## 3 Van der Pol Equation and the Analysis of Gyrotron Oscillators
Despite their differences, there are notable similarities between these two types of oscillators: (i) _Nonlinearity_: Both systems exhibit nonlinear behavior leading to complex dynamics not present in linear systems; (ii) _Oscillatory nature_: Each system generates oscillations that can be analyzed using similar mathematical techniques such as phase space analysis; and (iii) _Applications in synchronization_: Both types of oscillators have applications in studying synchronization phenomena in various fields such as neuroscience (e.g., neural rhythms), engineering (e.g., circuit design, electronic oscillators), and biology (e.g., population dynamics), to name a few. The above-mentioned differences and similarities between these models make them complementary to each other. In gyrotron theory, the Adler's equation is a basis for studying phase locking and synchronization [44; 48; 50; 56; 57; 58; 59], while the Van der Pol equation  
\[\dot{F}=y, \tag{8a}\]6 Results of the reachability analysis of the Duffing equation performed for the following parameters: \(\omega=1.2\), \(\alpha=-1.0\), \(\beta=1.0\), \(\delta=0.3\) and \(\gamma=0.37\). Visualized is the set of states reachable by the Duffing oscillator starting from any initial condition with position in the interval \((0.9\div 1.1)\) for \(F\) and in the interval \((-0.1\div 0.1)\) for \(dF/dt\). The region occupied by the initial conditions is encircled by a green line. The time span is \((0\div 20T)\), where \(T=2\pi/\omega\). plays a fundamental role as a precursor (archetype) of more specific physical models used to describe and investigate different regimes of gyrotron operation [70; 79; 80; 82; 83; 84].
## 3 Results
### Processing Method for Strain Data of the Retaining Structure
The FIM could be further rewritten as the relationship between curvature and lateral displacement:  
\[\{\delta\}=-\frac{c^{2}}{144}[D]\{\omega\}+\delta^{\prime}_{0}\{z\}+\delta_{0}\{I\} \tag{8}\]  
In this equation, \([D]=[N][N]\), where \([D]\) is the matrix of lateral displacement; \([\omega]\) is the column vector of pile curvature; and \(\delta^{\prime}_{0}\) is the pile top rotation angle (\({}^{\circ}\)). \[[D]= \begin{bmatrix}0&0&0&0&0\\ 36&48&-12&0&0\\ 96&192&0&0&0&\dots\\ 144&384&84&48&-12\\ 192&576&192&192&0\\ &&\vdots\end{bmatrix}  \tag{9}\]  
The pile top rotation angle (\(\delta^{\prime}_{0}\)) could be obtained by solving the system of equations using two displacement boundary conditions. Given the known lateral displacements at the pile top (\(\delta_{0}\)) and at a specific depth h (\(\delta_{h}\)), then:  
\[\delta^{\prime}_{0}=\frac{\delta_{h}-\delta_{0}+\frac{c^{2}}{144}\{d\}\{\omega\}}{h} \tag{10}\]  
In this formula, \(\{d\}\) is the element in the h-th row of the matrix \([D]\), and \(\{\omega\}\) is the curvature of the pile. The above Formulas (3)-(10) represent the entire reasoning process of the FIM. Firstly, curvature \(\omega\) in Equation (3) was the key to calculating lateral displacement. Secondly, based on the calculation principle (4), we obtained the lateral displacement value \(\delta\) through Formula (5), the displacement first derivative function value \(\delta^{\prime}\) using Formula (7), and the matrix \(N\) (6). Solving Equations (5) and (7) simultaneously, we obtained the FIM Formula (8), which was based on the curvature \(\omega\) of numerical analysis to determine the lateral displacement \(\delta\). Finally, matrix \(D\) (9) was defined as the lateral displacement matrix. The pile top rotation angle \(\delta^{\prime}\) (Equation (10)) required two displacement conditions. To verify the effectiveness of the FIM (8), it was compared with the results of the simply supported beam test, as shown in Figure 13. Under the action of a mid-span load, the model sheet pile generated deflection, and the sheet pile presented a parabolic distribution with a total of three loadings. At the first loading, the load was 0.38 N, and the maximum deflection at the mid-span was \(-2\) mm. During the second loading, the load was 0.64 N, and the maximum deflection was \(-3.42\) mm. During the third loading, the load was 1.25 N, and the maximum deflection was \(-6.7\) mm. From the figure, it can be seen that the calculated values of the FIM were basically consistent with the measured values, with an average relative error of 1.76%. This method could accurately reflect the retaining structure's deformation distribution overall. As a result, the FIM was used for lateral displacement numerical analysis.
## 4 Finite Element Preprocessing
### Grid Sensitivity Test  
The crux of grid sensitivity testing lies in the relationship between grid density and accuracy. Theoretically, a finer grid results in higher accuracy of the solution. This is because smaller grids can better approximate complex geometries and capture local physical phenomena [25]. However, considering computational costs, as the number of grids increases, so does the computational time. Moreover, beyond a certain level of refinement, the incremental accuracy becomes less pronounced. Thus, a balance between the number of grids and accuracy is necessary. In finite element analysis, finding a suitable equilibrium between computational scale (number of grids) and accuracy is crucial. This often involves minimizing computational resources while ensuring result reliability. Figure 5 shows the trend of exhaust mass flow and radial gap layers as a function of grid quantity, indicating that changes in exhaust mass flow with an increase in grid count are minimal with a standard deviation of \(8.5\times 10^{-5}\), suggesting low variability. Therefore, the number of grids has a minor impact on simulation outcomes, with 61,207 grids being closest to the dataset's average, suggesting that selecting seven layers of radial gaps in the working chamber's fluid domain for numerical simulation is most cost-effective and reliable.  
Figure 5: Grid sensitivity test.
# AV-Teller: Browser Fingerprinting for Client-Side Security Software Identification
## 3 Strategies and Tactics for Profiling Antivirus Behavior
### Antivirus Profiling Scenarios
#### 3.2.3 Scenario 3: Phishing Page-Based Detection  
Phishing pages are malicious websites engineered to deceive users into divulging sensitive personal or financial information. These pages represent a critical cybersecurity threat, making their detection a key focus for modern antivirus solutions. Given the sophistication of phishing attacks, which often closely mimic legitimate websites, the detection performance of antivirus software plays a vital role in ensuring cybersecurity. Scenario 3 evaluates antivirus software by analyzing their phishing detection mechanisms. To maintain a safe experimental environment, test phishing pages provided by AMTSO were used instead of real phishing URLs. This approach enables the evaluation of antivirus capabilities to identify phishing threats while ensuring the safety of the testing setup.  
Figure 4 depicts the process of phishing-page detection using antivirus software. When a browser accesses a test phishing page provided by AMTSO, the antivirus system inspects the URL or analyzes the webpage content to identify the phishing characteristics. Upon detecting a phishing threat, the antivirus blocks access to the page or displays a warning, thereby enabling the evaluation of its phishing detection capabilities in a controlled environment.  
The three scenarios were selected to represent distinct and practical antivirus detection categories: (1) behavioral detection through DOM observation, (2) static signature-based detection using the EICAR test file, and (3) heuristic or URL-based detection via phishing simulation. These scenarios were selected for their reproducibility, clarity of outcome, and alignment with the detection mechanisms commonly supported by modern antiviral solutions. However, this selection does not encompass the full spectrum of real-world threats. The EICAR test file is a synthetic test artifact that does not simulate the complexity of advanced malware. Furthermore, detection techniques, such as memory-resident analysis, encrypted  
Figure 4: Phishing Page-Based detection process.  
Figure 3: Signature-Based detection process.  
traffic inspection, and cloud-assisted threat classification, were not considered in the current scope. Nevertheless, the selected scenarios provide a meaningful baseline for evaluating antivirus behavior in a controlled setting. These findings may serve as a foundation for future studies involving more sophisticated and evasive threat models. Using these three scenarios, we established a comprehensive framework for analyzing antiviral detection mechanisms and identifying vulnerabilities in browser environments. Each scenario includes experimental validation under specific detection conditions, with the goal of evaluating the performance and suggesting potential improvements. The following section provides a detailed overview of the design and implementation of the AVTeller framework that was employed to execute the scenarios and analyze the data collected from them.
# Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics
## 4 Materials and Methods
### One-Class Classification Algorithms  
For the purpose of user authentication, all users were considered as abnormal except for the authorized user. One-class classification algorithms are suitable for the task, as they are trained only using the data from the authorized user. In this study, two popular one-class classification algorithms were employed: the one-class SVM (OCSVM) and local outlier factor (LOF).
## 3 Results and Discussion
### Comprehensive Analysis of Green Tea Wine Taste by Electronic Sensory System  
E-sensory technology is an advanced analysis system based on bionic senses to evaluate samples digitally. Hence, it provides a higher accuracy and reproducibility [16]. Taste is the most intuitive indicator to evaluate the quality of tea wine. As shown in Figure 7A, saltiness and richness were more prominent in green tea wine, while the response values for bittermess, astringency and aftertaste-A were lower. Compared with the black tea wine developed by [PERSON] et al. [20], the green tea wine in this research had higher intensity of saltiness, while the sourness and bittermess of the black tea wine were more prominent. The reason for the difference may be the accumulation of organic acids and oxidation of amino acids in black tea after fermentation, resulting in an increase in sourness and decrease in saltiness compared to green tea wine [38]. Figure 7: Evaluation of electronic sensory of green tea wine quality.Radar chart of taste determination by the electronic tongue (**A**), and the aroma of tea wine by electronic nose analysis (**B**), red: W3S, yellow: W5S, blue: W6S, green: W2W, W2S and W1W. For the sensitivity of each sensor, G/G0 indicates the response value. The electronic nose requires that the maximum G/G0 of the sensor for the samples to be tested is >0.5, so the response values of W1C, W3C and W5C in green tea wine was <0.5, indicating that the aromatic compounds in the volatile gases of green tea wine are low (Table 4). It has been demonstrated that the aromatic compound content decreases with fermentation time [37]. As shown in Figure 7B, the aroma components are mainly alkanes (W1S, W3S), nitrogen oxides (W5S), alcohols aldehydes and ketones (W2S) and sulfur-containing compounds (W1W, W2W). Based on G/G0, the order of aroma contribution can be seen as W1S > W5S > W2S > W2W > W1W > W3S > W6S. PLSR analysis of the aroma components along with the e-nose results showed that all the aroma components were positively correlated with the W5S, W1S and W1W sensors (Figure 8). This includes the hydrocarbon (WIS), which has the highest contribution value, with correlated aroma components 23-30.The dimethyl sulfur of the green tea aroma belongs to sulfide (W1W), which was consistent with the results of the e-nose. Aroma and taste together determine the flavor quality of tea and are dependent on volatile and non-volatile compounds. The contribution of a particular volatile compound to the tea aroma profile depends not only on its content but also on its odor activity value (OAV) and/or flavor dilution (FD) factor [39; 40; 41; 42]. Technologies such as E-nose and E-tongue have been synthesized to discriminate sensory attributes of green tea during fermentation. The VOCs, not only serve as a homeostatic marker for plant homeopathic and environmental microorganisms, but also find utility in quality assessment and origin identification [43; 44].
## 3 Results and Discussion
### Polyphenolic Compounds
[50] in strawberry cultivars, with concentrations between 112.6 and 128.5 mg:100 g\({}^{-1}\) of DM. Although these compounds are only slightly bioavailable [51], they exhibit a variety of biological activities that contribute to the overall health benefits of fruits and vegetables. Polyphenic compounds \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **HPLC-DAD** & \multicolumn{3}{c}{**HPLC-MS**} \\ \hline
**Peak Nr.** & **RT (min)** & \(\mathbf{\lambda_{\max}}\) (nm)** & **MS\({}^{-}\) (_m/z_)** & **MS\({}^{2}\) (_m/z_)** & **Compounds** & **Content (mg:100 g\({}^{-1}\) DM)** \\ \hline
1 & 22.4 & 244, 282, 344 & ND & ND & NI & - \\
2 & 25.5 & 280, 516 & 447 & 285 & Cyandin & 45.22 (1.28) 1 \\  & & & & & 3-O-glucose (C3G) & 45.22 (1.28) 1 \\
3 & 30.1 & 262, 356 & 625 & 316 & Myricetin O-glucoside & 1.44 (0.09) 2 \\
4 & 30.6 & 262, 356 & 625 & 316 & Myricetin O-glucose & 1.71 (0.15) 2 \\  & & & & & & \\
5 & 33.8 & 258, 354 & 609 & 301 & Quercetin O-glucose & 0.75 (0.09) 2 \\  & & & & & & \\
6 & 34.2 & 258, 354 & 609 & 301 & Quercetin O-glucose & 1.64 (0.34) 2 \\
7 & 34.4 & 258, 354 & 463 & 301 & Quercetin O-glucose & 2.76 (0.21) 2 \\
8 & 35.1 & 258, 354 & 463 & 301 & Quercetin O-glucose & 2.95 (0.37) 2 \\
9 & 37.5 & 240, 356 & ND & ND & NI & - \\
10 & 48.0 & 256, 372 & 301 & ND & Quercetin & 0.14 (0.06) 2 \\ \hline \hline \end{tabular} RT: retention time; ND: undetected; NI: unidentified; MS\({}^{-}\): molecular ion mass in negative mode; MS\({}^{2}\): fragment ion mass in negative mode; 1: expressed in mg eq. cyanidin:100 g\({}^{-1}\) of DM; 2: expressed in mg eq. quercetin:100 g\({}^{-1}\) of DM.  
\end{table} 
Table 2: Identification and concentration of the main phenolic compounds found in the _A. boelmii_ fruits (example of the AbM22 sample). act as potent antioxidants and anti-inflammatory agents, contributing to the prevention of chronic diseases such as cardiovascular disease, type 2 diabetes, and certain cancers.
# Methods for Corrosion Detection in Pipes Using Thermography: A Case Study on Synthetic Datasets
## 4 Case Study
### Results and Discussion  
#### 4.3.1 Introduction to UNET+  
The UNET+ model is an advanced convolutional neural network designed for image segmentation tasks. Its architecture includes a contracting path to capture context and a symmetric expanding path that ensures precise localization. The contracting path consists of repeated applications of convolutional layers followed by max-pooling for down-sampling, capturing high-level features and context information. Conversely, the expanding path uses transposed convolutions for up-sampling, restoring the spatial resolution of the features for accurate segmentation of fine details. UNET+ incorporates additional features such as residual connections, multi-scale feature integration, and attention mechanisms, improving upon the traditional UNET architecture. These enhancements enable the model to handle variations in defect size, shape, and contrast in thermographic images more effectively.  
In the context of this work, the use of UNET+ is highly advantageous due to the complex nature of the thermal patterns associated with corrosion. Corrosion often manifests in irregular shapes and varying thermal contrasts, challenging simpler models to detect accurately. The multi-scale feature integration in UNET+ allows the model to consider both global and local features simultaneously, enhancing its ability to detect corrosion regardless of size or intensity. Additionally, the residual connections help mitigate the vanishing gradient problem during training, leading to a more stable and efficient learning process. These qualities make UNET+ not only suitable but also highly effective for detecting and characterizing corrosion in thermographic images of steel pipes.  
In this study, the UNET+ model was trained on a combination of experimental and synthetic datasets. The experimental dataset consisted of thermal images captured from steel pipes with induced defects, while the synthetic dataset was generated using Finite Element Method (FEM) simulations to augment the available data. The training process involved several preprocessing steps, including normalization and augmentation techniques to enhance the model's robustness. Figure 6 shows the UNET+ model architecture which is used in this research.  
Figure 5: Simulation results for Pipe ID 1 with a 2.54 cm defect and 70% wetness [51].
# Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis
## 5 Results and Discussion
### Resource Utilization  
In Figure 8, the resource utilization is shown for DOPs up to 256 for the inference module and 32 for the training module. It can be seen that the resource type with the highest utilization for both modules are digital signal processor (DSP)-units. The inference module can be fully parallelized with a factor of 256, corresponding to the 16 inputs (SIMD) for each of the 16 neurons (PE) of the bottleneck layer.  
In contrast, the highest DOP for the training module is 32. If the DOP is further increased, either DSP-units or LUTs exceed the available resources of the board. The higher complexity of this module can primarily be explained by the existence of forward and backward-pass for the training, higher quantization of the weights, and the need to save feature maps for the gradient calculation.  
To summarize, it can be seen that it is feasible to implement the inference and training of our ANN with high parallelism even on a low-cost FPGA. Using an FPGA with more available resources would allow for further parallelization, either by increasing the DOP of the training module or by using multiple instances of the modules to increase the throughput.
# Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review
## 3 Fault Diagnosis Methods
### Data-driven Intelligent Diagnosis Algorithm  
Traditional artificial intelligence algorithms rely on prior knowledge. For instance, the rule basis of fuzzy logic (FL) can be based on the experience of experts about PMSM faults [80]. Due to research in artificial intelligence and machine learning, many data-driven intelligent diagnosis algorithms have been proposed recently. Basic data-driven approaches include some statistical methods, such as principal component analysis (PCA) [81] and independent component analysis (ICA) [82], which are also used in feature exaction for fault diagnosis. But with the data-driven intelligent diagnosis algorithms, the fault type and severity of the motor can be automatically identified by the input data based on the given training data. These methods mainly include the neural network, support vector machine, sparse representation, deep learning and so on.