esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Several experimental burst tests have been carried out to verify burst pressure models for thermoplastic pipes [1; 13; 22; 23; 24] [PERSON] et al. performed experimental burst tests on cracked high-density polyethylene (HDPE) pipes and investigated the mechanical behavior of HDPE when subjected to internal pressure [25]. [PERSON] et al. proposed evaluation criteria of the internal pressure of rupture for pipes and experimented to validate both HDPE pipe and PVC specimens [26; 27]. In addition, a new failure analysis and prediction model using burst pressure of undamaged HDPE pipes instead of ultimate stresses were developed and verified through the damaged HDPE pipes tests with different levels of groove notches. In recent years, finite element analysis (FEA), as an effective and reliable simulation method, has been used in the limit analysis and safety assessment of water supply pipelines [28; 29]. Several common burst pressure methods, usually described as a function of pipe diameter, pipe wall thickness, pipe material, and other parameters, have been studied for thermoplastic pipes, and the corresponding design criteria have been carefully analyzed [30]. Three ductile fracture models were proposed to obtain the burst pressure of a dented aluminum pipe and were verified using FEA by [PERSON] et al. [31]. A simplified method based on multi-dimensional analysis was proposed for the analysis of the ultimate water pressure bearing capacity for large-caliber water supply pipes [32]. [PERSON] et al. revealed the influence of pipe material strength changes on burst pressure and analyzed the sources of errors in those models [33]. At present, there are still a large number of scholars carrying out the evaluation and analysis of the burst pressure of PVC pipes, using experimental testing, numerical simulation, and other methods [34]. However, precise determination of the pipe burst pressure is still unresolved when the proposed method is used for calculation, due to the diverse mechanical properties of PVC. In addition, various classic formulas also have their specific scopes of application and simplified conditions. Therefore, an analysis and predictive formulas for precise determination of burst pressure that are extensively applicable have still not been proposed. The objective of the current paper is to accurately predict the short term burst pressure of PVC pipes through a theoretical model, laboratory burst tests, and FEA. Firstly, based on the proposed average shear stress yield (ASSY) theory, a new theoretical solution for the burst pressure of pipes is obtained. Then, the PVC material tensile test and pipe burst test are employed to validate the ASSY-based solution. Finally, the predicted results are compared with a series of experiments and FEA simulations. The comparison results show that the ASSY method provides a practical solution to demands for accurate prediction of PVC pipes' burst pressure.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Equalization
#### 3.2.9 Conclusions  
As can be seen from the above presentation, the application of neural networks in channel equalization has become more than a simple application. The integration of neural networks with communication systems is starting to emerge. Figure 7 illustrates the existing neural network channel equalization in VLC. Different branches of neural networks are beginning to emerge, and many more choose to extract communication-specific features from the input data. Beyond that, fast development of computational power resources make it promising to implement ML-based modules in the field of VLC. ML-based methods with powerful nonlinear phenomenon modeling ability open a new gate to solving the inherent nonlinear problems in VLC system. However, further optimization and improvement would be needed for those ML-based equalizers in terms of computational complexity, convergence speed, and generalization. Table 3 compares the equalizers mentioned above.
## 3 Methodology
### Overview
```
Input: InSAR imagery data \(X_{s}\), temporal features \(X_{t}\), and external factors \(E\). Output: Predicted deformation risk scores \(\hat{Y}\). Step 1: Data Preprocessing Extract deformation patterns from InSAR imagery; Normalize and align temporal features \(X_{t}\); Integrate external environmental conditions \(E\). Step 2: Spatial Attention Encoding Feed \(X_{s}\) into the Spatial Attention Encoder; Generate attention maps to highlight high-risk regions; Extract spatial feature representations \(\mathbf{Z}_{s}\). Step 3: Temporal Risk Prediction Feed \(X_{t}\) into the LSTM-based Temporal Risk Predictor; Model sequential dependencies in deformation time series; Extract temporal feature representations \(\mathbf{Z}_{t}\). Step 4: Feature Fusion Combine spatial and temporal features: \(\mathbf{Z}=\text{Fusion}(\mathbf{Z}_{s},\mathbf{Z}_{t},E)\); Align multi-source information into a unified representation. Step 5: Risk Score Prediction Generate comprehensive risk scores \(\hat{Y}\) for railway segments; Assess deformation magnitudes and identify high-risk areas. Step 6: Output and Decision Support Provide risk assessment insights for railway maintenance and mitigation; return Predicted risk scores \(\hat{Y}\). ```  
**Algorithm 1**InSAR-RiskLSTM: Railway Deformation Risk Prediction
## 3 Proposed Method
### Weibull Cycle to Failure (N) Analysis
#### 4.2.3 Weibull Stress Analytical Static Family  
In order to use the analytical static stress method, the mechanical element presented in Section 4 is applied, with resulting values of \(\sigma_{1}=470\) MPa and \(\sigma_{2}=176\) MPa. Based on those principal stresses values, the Weibull stress analytical family is \(W\) (\(2.240388,\,287.610848\) MPa). Table 6 shows a summary of the comparison between the static method and the FEA simulation results.  
From \(\sigma_{1}\) and \(\sigma_{2}\) values in Table 6, we conclude that both methods, the FEA and static methods, are equivalent. Additionally, note that for this particular case the fatigue life of the welded joint can be used to estimate the fatigue life of the flat spring mechanism [29]. This can be relevant due to the AWS D1.1 provides joint \(S\)-\(N\) curves whose allowable cycle number \(N\) is less than \(10^{7}\). The joint \(S\)-\(N\) curve for the welded joint category B is shown in Figure 7.  
The relation between the stress range \(F_{SR}\) and the allowable cycle number \(N\) is given by Equation (36). Therefore, by applying Equation (36) in Equation (37) with \(C_{f}=120\times 10^{8}\), the allowable cycles \(N\) are calculated. Table 7 shows a summary of the comparison between the static method and the FEA simulation fatigue life welded joint results.  
As can be observed, the general conclusion is that the proposed method can be applied by using either the FEA or the static analytical stress approach. The efficiency of the proposed method depends on the accuracy on which the principal stresses are estimated. In this way the propose method can be applied in any mechanical analysis where \(\sigma_{1}\) and \(\sigma_{2}\) are known.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Application**} & **Maximum** & **Minimum** & & & \\  & **Principal Stress** & **Principal Stress** & **(MPa)**, & **Weibull \(\beta\)** & **Parameter** & **Cycles to** & **Reliability** \\  & **Force \(F_{1}\) = 4.63 N** & **Force \(F_{2}\) = 1.74 N** & & & \\ \hline Static & 470 & 176 & 2.240388 & 691,910,584 & 0.91 \\ \hline FEA & 491.75 & 184.8 & 2.248519 & 453,250,454 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of the comparison between the analytical static method and FEA simulation results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**\(n\) Equation (13)**} & \multirow{2}{*}{\(Y_{i}\) Equation (15)} & \multirow{2}{*}{\(t_{oi}\) Equation (17)} & \multirow{2}{*}{\(R(t_{oi})\) Equation (22)} & \multirow{2}{*}{\(\sigma_{2i}\) Equation (18)} & \multirow{2}{*}{\(\sigma_{2i}\) Equation (19)} & \multirow{2}{*}{\(N\) (Cycles)} \\  & & & & & & \\ \hline
1 & \(-\)3.4034833 & 0.220104 & 0.96729 & 66.35155 & 1308.124 & 273,683,422.9 \\ \hline  & \(-\)2.9701952 & 0.26688 & 0.95 & 80.45246 & 1078.849 & 331,846,
## 2 Non-Blind LP-DSS Watermarking Scheme
### Data Embedding and Detection  
Figure 1 shows a block diagram of the speech watermarking method based on the LP-DSS scheme, where (a,b) show the embedding process and the detection process for the security code, respectively. In Figure 1a, \(x(n)\) is divided into \(K\) frames by framing it with a rectangular window function, where \(K\) is the number of frames obtained by dividing the signal length by the window length used in the LP analysis. In each frame, the watermark \(m(n)r(n)\) are obtained by multiplying the security codes \(m(n)\) by the LP residue \(r(n)\), which is the spreading signal by \(m(n)\). The watermarked signal \(y(n)\) is obtained by embedding \(m(n)r(n)\) into the host signal \(x(n)\) in each frame, as  
\[y(n) = x(n)+am(n)r(n), \tag{4}\] \[a = 10^{l_{\text{all}}/20},\] (5) \[L_{\text{all}} = L_{\text{PHS}}-L_{\text{PWS}}+L_{\text{SSL}}, \tag{6}\]  
where \(a\) is the amplitude term to adjust the embedding strength, \(L_{\text{PHS}}\) is the power level of the host signal, \(L_{\text{PWS}}\) is the power level of the signal with the embedded security code, and \(L_{\text{SSL}}\) is the embedding-strength level in dB. The security code \(m(n)\) is defined as  
\[m(n)=\begin{cases}0,&E\{y(n)r(n)\}\leq 0\\ 1,&E\{y(n)r(n)\}>0\end{cases}, \tag{7}\]  
where \(x(n)\), \(y(n)\), and \(r(n)\) are assumed to be ergodic. The LP residue has the statistical properties \(E\{r(n)\}=0\) and \(E\{r^{2}(n)\}=1\). The security code \(m(n)\) is detected by multiplying the watermark signal \(y(n)\) by the LP residue \(r(n)\) again to obtain the expected value \(E\{y(n)r(n)\}\) using the Fourier transform. The security code \(m(n)\) is calculated as  
\[E\{y(n)r(n)\} = E\{[x(n)+am(n)r(n)]r(n)\} \tag{8}\] \[= E\{x(n)r(n)\}+E\{am(n)r^{2}(n)\}\] \[= am(n), \tag{9}\]  
where \(E\{am(n)\}\) is \(am(n)\), and \(x(n)\) and \(r(n)\) are mutually orthogonal, so the first term in Equation (8) is 0. The second term is \(E\{am(n)r^{2}(n)\}=am(n)\).  
We investigated whether the LP-DSS method can satisfy the two requirements of inaudibility and robustness by comparing it with the DSS method. The non-blind LP-DSS method was evaluated by carrying out three objective tests (perceptual validation of speech quality (PESQ), log-spectrum distortion (LSD), and bit-error-rate (BER) for various modifications to the watermarked signal) for inaudibility and robustness on 12 utterances in the Advanced Telecommunications Research Institute International (ATR) speech dataset (B set) [16]. The LP order is 12, the embedding bit rates are 4, 8, 16, 32, and 64 bps, and the embedding strength level \(L_{\text{SSL}}\) is 10 dB. The results of these three tests showed that the LP-DSS method could embed watermarks with low sound quality between 4 and 16 bps and that it had the same robustness as the DSS method.  
Figure 1: Block diagram of LP-DSS method: (**a**) embedding and (**b**) detection.
# Development of Fast Protection System with Xilinx ZYNQ SoC for RAON Heavy-Ion Accelerator
## 5 Testing of the FPS  
Tests were performed on the time of the signal from the input of the modified acquisition node to the output of the modified mitigation node. Figure 8 shows the configuration for the _FPS_ test. Figure 9 shows the diagram. Prior to this test, the control board and test equipment were initialized.  
Figure 8: Installation of the _FPS_.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Requirement** & **Implementation Detail** \\ \hline \multirow{6}{*}{Configuration} & \multirow{6}{*}{Chassis} & 19-inch 2U-height rack \\ \cline{3-3}  & & Front panel: SFP+ port 5 ea, RJ-45 1 ea, USB to JTAG 1 ea, DSUB-9 1 ea, \\ \cline{3-3}  & & SD card 1 ea, LED and character LCD for display status \\ \cline{3-3}  & & Rear panel: terminal block 48 ea for output, SMA 16 ea, 220 V AC power \\ \cline{3-3}  & & Cooling system: fan for cooling \\ \cline{3-3}  & & SoC: Xilinx ZYNQ XC7Z100-2 FFG900I \\ \cline{3-3}  & \multirow{2}{*}{Digital Board} & Memory: DDR 1 GB SPI Flash 16 MB \\ \cline{3-3}  & & Temperature sensor \\ \cline{3-3}  & & Transceiver, buffer, etc. \\ \cline{3-3}  & & SMPs and cable & Power and signal connection flame-retardant cable \\ \hline \multirow{6}{*}{Interface} & SFP+ (5) & EVG 3.25 Gbps Speed \\ \cline{3-3}  & & Single mode \\ \cline{1-1} \cline{2-3}  & Ethernet (1) & 1 Gbps \\ \cline{1-1} \cline{2-3}  & USB (1) & USB to JTAG \\ \cline{1-1} \cline{2-3}  & UART to JTAG (1) & 115,200 bps UART \\ \cline{1-1} \cline{2-3}  & Output (64) & Dry contact 32 ea, wet contact 32 ea \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hardware specification of the modified acquisition node.
# Energy-Efficient Partial LDPC Decoding for NAND Flash-Based Storage Systems
## 2 Backgrounds
### Quasi-Cyclic LDPC Codes  
The array LDPC code is suitable for adjusting the column degree of the H-matrix, as it is one of regular LDPC codes that have fixed column and row degrees. Moreover, it is one of the quasi-cyclic (QC) LDPC codes composed of shifted identity matrices of the same size [12]. Therefore, the number of check nodes can be controlled easily by eliminating some block-rows, each of which having the same size as the identity matrix. Three parameters, \(w_{c}\), \(w_{r}\), and \(p\), define an array LDPC code, where \(p\) is a prime number denoting the size of the identity matrix, and \(w_{c}\) and \(w_{r}\) represent the column and row degrees of the H-matrix, respectively. The H-matrix of the \((p,w_{r},w_{c})\) array LDPC code is  
\[\mathbf{H}=\left[\begin{array}{cccc}\mathbf{I}&\mathbf{I}&\cdots&\mathbf{I} \\ \mathbf{I}&\mathbf{A}&\cdots&\mathbf{A}^{w_{r}-1}\\ \mathbf{I}&\mathbf{A}^{2}&\cdots&\mathbf{A}^{2(w_{r}-1)}\\ \vdots&\vdots&\ddots&\vdots\\ \mathbf{I}&\mathbf{A}^{w_{c}-1}&\cdots&\mathbf{A}^{(w_{c}-1)(w_{r}-1)}\\ \end{array}\right], \tag{1}\]  
where \(\mathbf{I}\) is the \(p\times p\) identity matrix and \(\mathbf{A}\) is a matrix obtained by shifting every row of \(\mathbf{I}\) cyclically by one. When \(p=3\), for example, the corresponding matrix \(\mathbf{A}\) is  
\[\mathbf{A}=\left[\begin{array}{ccc}0&1&0\\ 0&0&1\\ 1&0&0\\ \end{array}\right]. \tag{2}\]  
Based on (2), \(\mathbf{A}^{2}\) is calculated as  
\[\mathbf{A}^{2}=\left[\begin{array}{ccc}0&1&0\\ 0&0&1\\ 1&0&0\\ \end{array}\right]\left[\begin{array}{ccc}0&1&0\\ 0&0&1\\ 1&0&0\\ \end{array}\right]=\left[\begin{array}{ccc}0&0&1\\ 1&0&0\\ 0&1&0\\ \end{array}\right]. \tag{3}\]
# Green Analytical Chemistry--Recent Innovations
## 4 Application of Green Solvents
The study found that glycerol enhanced chromatographic efficiency, controlled retention factors, and improved resolution compared to traditional organic modifiers like acetonitrile. Additionally, its biodegradation, low toxicity, and stability make it an excellent choice for green analytical chemistry. As sustainable alternatives become more essential, bio-based solvents such as glycerol offer a promising and environmentally friendly direction for chromatography. Deep eutectic solvents (DESs), a newer class of bio-based solvents, stand out for their simplicity, cost-effectiveness, and green credentials, making them perfect for a variety of analytical uses. [PERSON] et al. [84] demonstrated the potential of deep eutectic solvents (DESs) as sustainable alternatives to conventional organic solvents in gas chromatography (GC). By synthesizing a DES-based stationary phase using oleic acid and tetrabutylammonium bromide, the study successfully separated alkanes, alcohols, and aromatic compounds while reducing reliance on toxic organic solvents. The method showed moderate polarity (McReynolds constant: 1420) and enabled efficient separation of BTEX (Benzene, Toluene, Ethylbenzene, Xylene) and VOCs. Additionally, the high thermal stability, biodegradability, and recyclability of DESs reinforce their role as green solvents in analytical chemistry. This study highlights DESs as promising, eco-friendly alternatives for chromatographic applications, further expanding the scope of green analytical chemistry. The shift to these greener solvents is more than just a technological advancement. It is a cultural shift in how we approach analytical chemistry. By replacing traditional solvents with water, SC-CO\({}_{2}\), ionic liquids, and bio-based options, we are reducing chemical waste, improving safety, and making strides toward a cleaner, more sustainable future. These innovations prove that analytical chemistry can maintain its high standards of performance while actively contributing to the global effort to protect our environment.
## 3 Data-Driven Solutions Applied for the Detection of NTLs
### Supervised Methods
#### 3.1.6 Optimal Path Forest  
The supervised Optimal Path Forest (OPF) algorithm is a graph-based model used for clustering and classification applications [15; 16; 66]. Unlike SVM, which is trained to obtain an optimal hyperplane for separating classes, OPF divides the graph into optimal path trees (two or more trees), each representing a class. Each tree is connected to a prototype, and the collection of trees forms the classifier [4; 16]. This classifier can operate with overlapping classes and has a low training time [16].  
The OPF classifier has been used for the identification of NTLs in databases of industrial and commercial consumers, considering few features extracted from hourly load and demand profiles. The algorithm has shown advantages in consumer classification compared with other supervised classification methods. The limitation in its application is that it requires knowledge of the expected monthly consumption and the use of energy meters that provide hourly consumption [46]. OPF has been employed to classify customers' monthly consumption into suspicious and non-suspicious categories. To enhance the performance of the method, the use of a feature extraction algorithm called Discrete Cosine Transform has been included [47].
# FAS-Res2 net: An Improved Res2 net-Based Script Identification Method for Natural Scenes
## 4 Experiments
### Results of the Method in This Paper
#### 4.3.2 Feature Pyramid Results  
We combined the Feature Pyramid Network (FPN) module with Res2 Net to prevent information loss in shallow networks. This enabled us to simultaneously obtain semantic information from deep networks and geometric information from shallow networks, resulting in richer feature representations. This operation has also been verified in the experiment. At this time, the script identification rate reached 94.0%, which is 0.7% higher than that of the simple Res2 Net network classification identification rate.
## 4 Limitations and Future Perspectives  
Previous studies discussed in this research provide a foundation for highlighting key advantages and limitations. The strategic selection of phytopathogenic fungi from tropical fruits, followed by a controlled combination of treatments under uniform concentration and bioreaction conditions, facilitated a differential analysis of sugarcane residue degradation efficiency. Additionally, the use of PCA for dimensionality reduction allowed for the identification of underlying degradation patterns that would not be easily captured through simple post hoc tests. This approach not only ensured the reliability of experimental replicates but also identified the variables contributing the most to variance explained by the principal components. However, some limitations of this study include the absence of molecular identification techniques for strain characterization and the lack of evaluation of additional environmental factors that could influence fungal activity. Future research should focus on optimizing inoculum concentrations and assessing long-term biodegradation dynamics under variable conditions.  
The biodegradation (fermentation) process applied in this study is based on the growth of _Colletotrichum_ spp. and _Rhizopus_ spp. on an aerated solid matrix, with controlled humidity and in the absence of a free liquid phase, which characterizes the system as a solid-state fermentation (SSF). The main parameters that influence the biodegradation of lignocellulosic residues under these conditions include the following: time, temperature, pH, moisture, oxygen availability, the carbon-to-nitrogen ratio, the physical structure of the substrate, and the inoculation rate [13, 14, 20, 49]. In this study, humidity and incubation time were primarily controlled in order to preliminarily assess the biodegradation efficiency within a stable system. Other parameters were not varied in order to maintain experimental consistency and reduce sources of variability. However, future studies could include analysis of substrate particle size effects as well as supplementation with carbon- and nitrogen-enriched sources. Such investigations would help optimize process conditions and contribute to the development of sustainable and innovative strategies for agro-industrial waste biodegradation.
## 3 Results and Discussions
### Two-Probe Configuration Results
However, at an electrode spacing of 40 mm, the resistivities of the cement composites with CF lengths of 6 and 10 mm (L06-2E40 and L10-2E40) were lower than that of the cement composite with 4 mm CF length (L04 mm-2E04), for all CF content values. This implies that the CF length effect reported in previous studies [21; 42; 43] is valid only when the electrode spacing is sufficiently wide. Figure 4: Resistivity of cement composites after 28 days of curing measured through the two-probe configuration for different electrode spacings and carbon fiber (CF) lengths of (**a**) 4 mm (L04), (**b**) 6 mm (L06), and (**c**) 10 mm (L10). CF0.1–2.0 indicate the CF content (0.1–2%) incorporated into the cement composites.
# A Conceptual Design Methodology for e-VTOL Aircraft for Urban Air Mobility
## 2 Design Methodology
### Weight Estimation
An issue of the adoption of electric propulsion is the overheating of the battery pack; generally, a thermal management system must be designed to avoid it, since battery overheating can be detrimental for the battery's life [79] and for the overall safety. The design of the battery Thermal Management System (TMS) goes beyond the aim of this paper, so Equation (18) has been selected from the available literature ([80]) to estimate the mass of the battery thermal management system \(M_{TMS}\), starting from the maximum power supplied by the battery pack (\(P_{batt\ max}\)) and the battery discharge efficiency (\(\eta_{batt}\)). Propeller mass has been evaluated by using data of propeller manufacturers [81; 82; 83], and a regression model, with least square method, has been used to relate the propeller mass to the maximum power absorbed by the propeller (Figure 8). The mass estimation of the actuator system (\(M_{TS}\)) needed for the transition from fixed-wing to multi-rotor mode (and vice versa) is not trivial since it can be only designed in a more advanced design phase and no literature data are available for this type of e-VTOL. It has been assumed that its mass corresponds to the 5% of the total MTOW.