esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Future Work  
The synchrophasor communication network of a smart grid caters to the basic needs to meet the goals of the smart grid. The SCN is subjected to several challenges leading to operational hindrances. To overcome such issues, resiliency estimation of an SCN of an SG was considered in this paper. To evaluate the resiliency estimation, a simplified graph-theoretic approach was presented to model the SCN analytically. An SCN was designed, implemented, and simulated using a discrete event-based network simulator, ns-3. The resiliency estimation was conducted with the packet delivery ratio as a figure of merit. Nevertheless, the proposed graph-theoretic-based resiliency framework can be regarded as the first such attempt that can be extended to generic communication networks. The inclusion of a case study is considered future work. With respect to case study-I, the SCN was observed to lose its functionality, such that \(\xi_{\text{deg}}=-0.1\) and \(\xi_{\text{rec}}=+0.1\). Contrary to case study-I, the SCN remained in a functional state in case study-II, with \(\xi_{\text{deg}}=-0.0188\) and \(\xi_{\text{rec}}=+0.0376\). If the bandwidth available is sufficiently large compared with the background traffic and synchrophasor data rate, then an SCN with at least one backup path remains fully functional (normal state), as observed in case study-III.  
Conceptualization, A.V.J.; methodology, A.V.J.; software, A.V.J.; validation, B.A.; formal analysis, A.V.J. and B.A.; investigation, N.B. and P.T.; resources, B.A. and N.B.; data curation, A.V.J.; writing--original draft preparation, A.V.J.; supervision, N.B. and B.A.; project administration, N.B. and P.T.; funding acquisition, P.T.; writing--review and editing: A.V.J., B.A., P.T. and N.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Framework Agreement between the University of Pitesti (Romania) and King Mongkut's University of Technology North Bangkok (Thailand), in part by an International Research Partnership \"Electrical Engineering--Thai French Research Center (EE-TFRC)\" under the project framework of the Lorraine Universite d'Excellence (LUE) in cooperation between Universite de Lorraine and King Mongkut's University of Technology North Bangkok, and in part by the National Research Council of Thailand (NRCT) under Senior Research Scholar Program under Grant No. N42A640328.  
Not applicable.
## 3 Results
Figure 6 shows a sample of the RAE plotted for each signal within two categories of the dataset, as an RAE vs. frequency plot, where it can be appreciated how OBP performs in a solid way throughout all the categories, to the point where, for complex signals like those included in \"full2\", it actually brings lower errors than most of the other algorithms; it can also be observed how most of the errors in NLS are octave errors (integer RAE). The exemplified categories are \"2 harm\" and \"full2\" in order to show the behavior of the four algorithms on clean vs. unclean signals. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Comparison**} & \multicolumn{3}{c}{**Statistical Measures**} \\ \cline{2-4}  & **U** & **Pearson \(p\)** & **z-Score** \\ \hline
**RAE (OBP vs. SWIPE)** & 42 & 0.08914 & 1.70318 \\ \hline
**RAE (OBP vs. NLS)** & 30 & 0.0164 & 2.396 \\ \hline
**RAE (OBP vs. YIN)** & 52 & 0.25848 & 1.12583 \\ \hline RAE (YIN vs. SWIPE) & 60 & 0.50926 & 0.66395 \\ \hline RAE (YIN vs. NLS) & 31 & 0.01928 & 2.33827 \\ \hline RAE (SWIPE vs. NLS) & 33 & 0.02642 & 2.2228 \\ \hline
**TE (OBP vs. SWIPE)** & 0 & \textless{}0.00001 & \textless{}\textless{}\textless{}4 \\ \hline
**TE (OBP vs. NLS)** & 0 & \textless{}0.000001 & \textless{}\textless{}\textless{}4 \\ \hline
**TE (OBP vs. YIN)** & 0 & \textless{}0.000001 & \textless{}\textless{}\textless{}\textless{}4 \\ \hline TE (YIN vs. SWIPE) & 0 & \textless{}0.000001 & \textless{}\textless{}\textless{}4 \\ \hline TE (YIN vs. NLS) & 0 & \textless{}0.000001 & \textless{}\textless{}\textless{}\textless{}4 \\ \hline
**ACC-1 (OBP vs. SWIPE)** & 36 & 0.04036 & \textless{}\textless{}\textless{}\textless{}  
All the analyses, data creation, tests, simulations and algorithm implementations were performed using MATLAB(r) R2023a (by Mathworks Inc., Natick, MA, USA [43]) on a Dell Latitude E5550 computer, with an Intel Core i5 5200U processor and a 16 GB Dual-Channel DDR3 RAM. Figure 6 details an example of the percentage error (RAE) with respect to the frequency, i.e., the different signals within a category of the SYNTPITCH dataset. Figure 6: Example of RAE (percentage error) vs. frequency plots for two categories (a very clean one vs. a very complex one) of the SYNTPITCH dataset. Frequencies span from 100 Hz to 5000 Hz with a step size of 50 Hz. (**a**) “2 harm” category (blue); (**b**) “full2” category (red). The time elapsed (TE) was empirically shown to be independent of the frequency of the input; plots are thus not shown as they are erratic and only depend on the randomness within the signals, given for example by the white Gaussian noise. TE was also shown to be independent of the dataset category, i.e., from the pitch complexity of the input signal.
# Resilience of LTE-A/5G-NR Links Against Transient Electromagnetic Interference
## 3 Materials and Methods
### Key Performance Indicators  
The primary KPIs are the EVM and SINR.
## 4 Results and Analysis
### Thematic Analysis: Findings
#### 4.1.4 Complexity: Findings
The pilots also discussed that ATC presence may falsely suggest the availability of longer runways. Moreover, they argued that pilots with lower experience may struggle to question or decline ATC instructions. [PERSON] et al. [90] found that less-skilled pilots need four times more decision-making time during these phases of flight, which demonstrates the role complexity plays here. Undoubtedly, whilst these HF have been analysed separately, the evident overlap among complexity, situational awareness, and decision-making is clear. The integration of these factors is key to understanding the aviation system [91]. From the discussions, it became apparent that the impact of training can determine future pilot decision-making. To establish this further, quantifiable evidence is needed.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Algorithmic Approaches
#### 4.5.4 Biased-Random-Key Genetic Algorithm (BRKGA)  
The Biased-Random-Key Genetic Algorithm (BRKGA) is a novel approach that combines Genetic Algorithm principles with the concept of random-key representation. In essence, it follows the standard Genetic Algorithm framework, encompassing phases such as initialisation, selection, crossover, mutation, and fitness evaluation. This systematic process allows for the evolutionary refinement of solutions across generations, progressively enhancing the quality of packing arrangements, as highlighted by two studies [32; 33].  
In their work, proposed to solve the 2D and 3D packing problems, [PERSON] et al. [32] introduce an inventive fitness function to assess solution quality, demonstrating the algorithm's flexibility and adaptability. Meanwhile, [PERSON] et al. [33] employ the BRKGA to address multiple packing problems, including the Container Loading Problem (CLP), Pallet Loading Problem (PLP), and 3D Bin Packing Problem (3D-BPP). The algorithm uses a unique approach by representing solutions as vectors of random numbers, referred to as random keys. These random keys are mapped to feasible solutions in the packing problems.  
The distinctive feature of the BRKGA, setting it apart from traditional Genetic Algorithms, is the introduction of bias during the selection of parents in both crossover and mutation phases. This bias is strategically designed to favour the selection of specific random keys that exhibit the potential to yield superior solutions. By doing so, the algorithm steers itself towards the convergence of high-quality solutions, a noteworthy characteristic for optimising packing scenarios. Additionally, the incorporation of a Fuzzy Logic Controller (FLC) further enhances the algorithm's efficiency by dynamically adjusting parameters to adapt to the evolving problem landscape, as described by [PERSON] and co-authors [33].
# The Immobilization of Lipases on Porous Support by Adsorption and Hydrophobic Interaction Method
## 3 Porous Carriers Used for the Immobilization of Lipase
### Criteria
In addition, the uniform distribution of CALB on smaller particles of resin increases due to a large surface area [61], and enzymes are adsorbed rapidly for a particle size of 120 um [62]. The optimized pore size and a highly hydrophobic surface of a mesoporous silica nanoparticles (MSNs) support increased lipase adsorption and beneficial for obtaining high loading and better stability [67]. The desorption of _Thermomyces lanuginosus_ lipase from a mesoporous support is reduced due to the strong hydrophobic interaction of lipase with the internal support surface [68]. However, a study shows that ethanol pretreatment of porous supports provide the interfacial activation of lipase to be adsorbed on the hydrophobic surface [65].
# Fatigue Performance of Tunnel Invert in Newly Designed Heavy Hauilway Tunnel
## 4 Fatigue Test of Tunnel Base Structures
### Strain Evolution  
The maximum strain evolutions of the specimens are illustrated in Figure 9. We found that the maximum tensile strain increased with \(N\) and the fatigue life decreased as the dynamic load and static load increased. When \(S_{\max}\leq 0.65\) (\(\varepsilon_{\max}\leq 56\times 10^{-6}\), \(\Delta\varepsilon\leq 16\times 10^{-6}\)), the specimens were still intact after 2 million cycles and the maximum tensile strains developed in two stages. When \(S_{\max}\geq 0.7\) (\(\varepsilon_{\max}\geq 68\times 10^{-6}\), \(\Delta\varepsilon\geq 28\times 10^{-6}\)), the maximum tensile strain of the specimens developed in three stages.  
Figure 8: Layout of strain gauges: 1. Compression strain gauge (top center); 2. tensile strain gauge (bottom center).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Test Conditions**} & \multicolumn{2}{c}{**Constant Loads**} & \multicolumn{2}{c}{**Dynamic Load (kN)**} & \multicolumn{2}{c}{\(S_{\max}\)} \\ \hline \multirow{4}{*}{Dynamic load conditions} & Case 1 & & 1.6 & 0.65 \\  & Case 2 & & Lateral load: 1.5 & 2.4 & 0.70 \\  & Case 3 & & 3.6 & 0.75 \\  & Case 4 & & 4.5 & 0.80 \\ \hline \multicolumn{2}{c}{**Test conditions**} & \multicolumn{2}{c}{**Constant loads**} & \multicolumn{2}{c}{**Static load (kN)**} & \multicolumn{2}{c}{\(S_{\max}\)} \\ \hline \multirow{4}{*}{Static load conditions} & Case 5 & Dynamic load: 2.4 & 23 & 0.60 \\  & Case 6 & & 25 & 0.65 \\ \cline{1-1}  & Case 7 & Lateral load: 1.5 & 27 & 0.70 \\ \cline{1-1}  & Case 8 & & 29 & 0.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Test conditions.  
An S-shaped model was introduced to describe the characteristics of the three-stage strain evolution under the abscissa of \(N\)/\(N_{f}\) (Figure 10). The model is expressed by Equation (5) and its parameters are listed in Table 6. Figure 10 shows that the maximum tensile strain increased rapidly in stage I due to the increase in microcracks [21]; in stage II, the maximum tensile strain increased steadily and linearly as a result of stable crack growth; in stage III, the maximum tensile strain increased sharply and nonlinearly due to an unstable crack growth. The transition of stages have been specified by different authors in the range of 10 to 20% (first transition) and about 80 to 90% (second transition) of the number of cycles to failure [22].  
\[\frac{\varepsilon_{n}}{\varepsilon_{0}}=\lambda+\alpha\Bigg{(}\frac{\beta}{ \beta-N/N_{f}}-1\Bigg{)}^{\gamma} \tag{5}\]  
where \(\varepsilon_{n}\) is the maximum tensile strain of the specimen after \(N\) cycles, \(\varepsilon_{0}\) is the maximum tensile strain during the first cycle, and \(\lambda\), \(\alpha\), \(\beta\), and \(\gamma\) are the fitting parameters of the curve.  
Figure 9: \(\varepsilon_{\mathrm{n}}\)-\(N\) curves: (**a**) Dynamic and (**b**) static cases.
# Modeling Hydraulic Fracture Entering Stress Barrier: Theory and Practical Recommendations
## 3 Accuracy and Range of Applicability of Asymptotic Approach  
### Limiting Cases of Very High and Nearly Zero Stress Contrasts  
The Cauchy problem (23) and (24) has simple analytical solutions in the cases of very high (\(R_{0}\to\infty\)) and very small (\(R_{0}\to 0\)) stress barriers. In the first case, the starting Equation (16) is reduced to (20). Then, by substituting into (20), the analytical expressions for driving and stress contrast SIFs, known for a straight crack solution [55] and used to derive (23), yields that when \(t>t_{B}\), and the relative penetration is as follows:  
\[\frac{\Delta z}{x_{*}(t)}=1-\cos\left(\frac{\pi}{2}\frac{p_{net}(t)}{\Delta \sigma}\right)\approx\frac{\pi^{2}}{8}\left(\frac{p_{net}(t)}{\Delta\sigma} \right)^{2} \tag{34}\]  
Equation (34) actually reproduces the classical formula by [PERSON] [60] for the size of a plastic zone at the crack tip. Now, \(p_{net}\) replaces the tensile stress, and \(\Delta\sigma\) replaces the yield stress. The Dugdale formula is one of the most famous achievements in fracture mechanics due to its accurate prediction of the size of the plastic zone measured in experiments. Equation (34), obtained in two quite different instances, reflects the analogy between their physical pictures. The fact that the Dugdale formula is well established suggests that, in cases of high stress barriers (\(R_{0}\gg 1\)), the mathematical model developed above is reliable as well.  
In the opposite extreme case of small, in the limit zero barrier intensity (\(\Delta\sigma=0;R_{0}=0\)), ODE (23) becomes the following:  
\[\frac{dy}{dt^{\prime}}=\gamma_{x}\left[\left(\frac{1}{1+y}\right)^{\alpha_{x}} (1+t)\right]^{\omega} \tag{35}\]  
In the axisymmetric case, the exponents \(\gamma_{x}\) and \(\alpha_{x}\) are replaced to \(\gamma_{r}\) and \(\alpha_{r}\), respectively. It is easy to check, by direct substitution, that the exact solution to (35) under the Cauchy condition (24) is \(y=(1+t)^{\gamma_{x}}-1\). In fact, for the plane-strain problem, it is the classical self-similar solution [35], written in the normalized variables \(y\) and \(tt\). Similarly, for the axisymmetric problem, the exact solution \(y=(1+t)^{\gamma_{r}}-1\) is the self-similar solution given in [43]. Thus, for \(R_{0}=0\), the solution to the Cauchy problem (23) and (24) is the known self-similar solution.  
In summary, the solution to the Cauchy problem (23) and (24) provides physically sustainable and accurate results in the limiting cases of very high (\(R_{0}\gg 1\)) and very low (\(0\leq R_{0}\ll 1\)) stress barriers. By continuity, we may expect that the solution is sound in cases intermediate between the extreme.
# Extraction of Novel Bioactive Peptides from Fish Protein Hydrolysates by Enzymatic Reactions
## 4 Novel Peptides Derived from Enzymatic Hydrolysis of Fish Discards
### Antimicrobial Peptides  
Most bioactive peptides derived from fish protein hydrolysates have been shown to have a broad range of deteriorative actions against diverse microbes, including bacteria, fungi, viruses, and protozoa [79; 80]. They possess a wide range of antifungal and antimicrobial properties. Various peptides from fish with antimicrobial activities have been studied widely in the past decades for their good usability in the nutraceutical industry. The isolation of these novel peptides was also studied, as in the work of [PERSON] et al. [81], who identified novel bioactive peptides that were obtained from mudfish (_Misgurmus angulicaudatus_), which they named _misgurin_. The peptide was found to cause damage to the cell membrane in various microorganisms and has high potency. Additionally, [PERSON] et al. [82] studied anchovy hydrolysates and identified a novel peptide with a membrane-disruptive property. Furthermore, the previous authors indicated that peptide Pep39 disrupted the_E. coli_ membrane and suggested that the mechanism of Pep39 includes cytoplasmic membrane damage. [PERSON] et al. [83] characterized a novel peptide with antibacterial properties, which was isolated from hemoglobin alpha in the liver of Japanese eels. The authors discussed that peptides with concentrations of 11 \(\upmu\)M exhibited stronger activity against the pathogenic bacterium _Edwardsiella tarda_. Contrary to this, [PERSON] et al. [84] studied the skin of yellowfin tuna (_Thunnus albacares_) and was able to characterize a novel peptide with antimicrobial properties. The authors indicated that the peptide has an amino acid sequence of YFGAP, and showed its potent activities in inhibiting the growth of Gram-positive bacteria, such as _B. subtilis_, _M. luteus_, and _S. aureus_. These novel antibacterial peptides can be found in various hydrolysates derived from marine or aquatic resources, and they have great potential in nutraceutical applications.
## 3 Pre-Deformed Projection Content with Simulation-Based Fitting Method
### Pre-Deformed Projection Content of DLP  
Although there is a less obviously geometric and optical distortion of directly projected simple information such as texts or rectangles in Figure 2c,d, the keystone distortion introduced by DLP is typically perceived when general content such as the image in Figure 3b is presented. If feature points of the CCD image and the real-world objects are matched (or \(\mathbf{f}(\mathbf{\mathrm{I}})=\mathbf{\mathrm{I}}\)), the calibration of DLP projection content modeled as in Equation (3) is rewritten as:  
\[\mathbf{\mathrm{H}}^{P\to S}\cdot\left(\mathbf{\mathrm{H}}^{C\to P}\cdot\mathbf{ \mathrm{I}}^{D}\right)-\mathbf{\mathrm{I}}^{S}=0 \tag{7}\]  
Figure 3: Demonstration of calibrating DLP optical distortion by pre-deforming the desired image, which has a size of 640 \(\times\) 360 pixels and four letters of “C-Q-N-U” at corners. (**a**) Transform of the desired image through the pre-deformed, projected, and observed blocks; (**b**) the CCD-observed images of DLP projection in the O\({}^{C}\) coordinate system before calibration, and after calibration (**c**).  
When \(\mathbf{I}^{\text{S}}\) is measured at a given \(\mathbf{I}^{\text{D}}\), \(\mathbf{H}^{C\to P}\) is the inverse of \(\mathbf{H}^{P\to S}\) in Equation (7) or \(\mathbf{H}^{*}=\mathbf{H}^{C\to P}=\left(\mathbf{H}^{P\to S}\right)^{-1}\). Figure 3a shows the block diagram of perspective transform to obtain the goal of AR projection function by pre-deforming the desired image.  
Like the estimation procedure of \(\mathbf{H}^{S\to C}\) in the Section 3.2, \(\mathbf{H}^{P\to S}\) mapping the point of DLP projection image in \(\mathbf{O}^{P}\) to the corresponding point of screen image in \(\mathbf{O}^{\text{S}}\) is,  
\[\mathbf{H}^{P\to S}=\begin{bmatrix}0.338&-0.034&3.656\\ 0.011&0.318&33.666\\ -2.3e-5&-1.3e-4&1\end{bmatrix} \tag{8}\]  
Based on the pre-deformed image algorithm, the CCD-captured image of projecting content in \(\mathbf{O}^{\text{C}}\) after calibration is compared with that before calibration in Figure 3. The result shows that the DLP projection of the pre-deformed image has a smaller relative error of less than 4%, and thus the simple method for calibrating the keystone distortion by applying the pre-deformed image is verified.
# Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
## 5 Concluding Remarks and Future Work  
In this study, a novel multi-module DNN framework was proposed for intelligent traffic forecasting. The framework was designed to improve our previous system, realize a long prediction period range,  
Figure 13: Speed figure of (**a**) real data, (**b**) TW-DNN, (**c**) CNN-only, (**d**) \(k\)-nn, and (**e**) ARIMA for Case III.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Congested Link**} & \multicolumn{3}{c}{**Congestion-Free Link**} \\ \hline  & **TW-DNN** & **CNN-Only** & \(k\)**-nn** & **ARIMA** & **TW-DNN** & **CNN-Only** & \(k\)**-nn** & **ARIMA** \\ \hline Case I & **0.0** & 0.0 & 0.0 & 0.0 & **99.79** & 99.79 & 99.78 & 99.79 \\ (Regular day) & **21.41** & 0.0 & 0.0 & 0.0 & **96.29** & 95.92 & 95.91 & 95.92 \\ \hline Case III & **42.93** & 0.0 & 0.0 & 0.0 & 97.59 & 97.57 & 97.53 & **97.71** \\ (Severe weather day) & \multicolumn{3}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of predicting congestion for the test casesand obtain a weather-aware traffic prediction using short-term and long-term weather forecast data. Experimental results proved that our framework exhibits an efficient performance on forecasting traffic for all kinds of driving situations such as regular days, holidays, and days of severe weather.  
More work would be needed to improve the computation speed and accommodate the live traffic-related information encountered during driving. The system may need a new module in charge of interacting with the current ones to dynamically reflect such real-time traffic data.  
[PERSON] and [PERSON] were the main researchers who initiated and organized the research work reported in the paper; [PERSON] and [PERSON] were responsible for building the final TW-DNN to improve its functions and performance. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number: NRF-2018R1D1A1B07046195) and also by NRF-Korea (grant number: 2019R1A2C4070663).  
The authors declare no conflicts of interest.
## 5 Results and Discussions
### Parameter Studies
(a) The natural frequency increases with increases in the modulus ratio \(\left(\frac{E_{1}}{E_{2}}\right)\) for a given value of the side-thickness ratio \(\left(b/L=0.5\right)\), as shown in Figure 6 for all boundary conditions, except FF and FS boundary conditions. The natural frequency values are nearly constant for FF and FS boundary conditions. (b) The fundamental natural frequency values increase with increases in the side to thickness ratio \(\left(b/h\right)\) for a given Young's modulus ratio \(\left(\frac{E_{1}}{E_{2}}\right)\), as shown in Figure 7 for all boundary conditions except FF and FS boundaries.
# Remote Sensing of Ecosystem Water Use Efficiency in Different Ecozones of the North China Plain
## 2 Data and Methods
### Methods  
#### 2.3.1 Technical Flowchart  
Based on the remote sensing products of GPP, ET and WUE in different ecosystem functional areas in the North China Plain, this paper investigates their spatial distribution and the main influence of GPP and ET on WUE. The change trend and stability of GPP, ET and WUE in different ecosystem functional areas were analyzed, and the spatial distributions of WUE driving factors were studied. The overall technical process is shown in Figure 2.