esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The proposed scheme performs double encryption in the digital domain and analog domain, taking into account the advantages of both encryption methods. The scheme can effectively protect data transmission. Data can be recovered accurately at the receiver. When the received optical power is above \(-10\) dBm, BER can reach \(0\). The encryption method of the digital domain shows the advantage of more flexibility and can guarantee the encrypted effect. The encryption of the analog domain can reflect the more complex nonlinear dynamics of the chaotic system. The digital encryption method is derived from chaotic system mapping by computers, which requires the precision of a computer. If the equipment is not precise enough, the output sequence no longer has strict chaotic dynamics. Therefore, the enhanced dynamic characteristics of chaotic systems in optical physical layer encryption will be studied in the following research.
## 3 Results  
### Time Course of Folate Absorption  
The serum concentration of orally administered folate forms was studied in adult male rats (200-250 g) because these were readily available and the objective here was to study the time course of intestinal absorption by monitoring the concentration of folates in the serum. Following oral administration of folate forms (4 mg/kg), peak folate concentration in the serum was reached by 1 h, irrespective of the form administered (Figure 1). A substantial amount of folate was retained in the serum even after 4 h, for all forms of folate.  
The three forms of folate namely levofolinate, d,l-folinic acid, and folic acid were rapidly converted to MTHF. Less than 25% of the levofolinate and d,l-folinic acid were seen in the blood at 30 min and 60 min, and approximately 50% of the folic acid remained in the native form during the 240 min of absorption, tissue distribution, and excretion (Figure 1A). Most of the MTHF remained in its native form during and following absorption at 4 h (Figure 1B). Higher total folate concentration was reached in the serum with levofolinate and folic acid (Figure 1A,C) than with d,l-folinic acid or MTHF (Figure 1B,D) but d,l-folinic acid and levofolinate appear to be more readily converted to MTHF than folic acid (Figure 2). Overall, levofolinate appeared to be somewhat better absorbed and generated higher THF over the 4 h time frame examined.  
Initially, some formyl-THF was seen with levofolinate and d,l-folinic acid and likely represents unconverted forms but by 4 h, most of this was converted to MTHF. No folic acid was formed from any of the other forms absorbed. In the time frame studied, no formyl-THF was generated from folic acid but some THF did appear by 4 h. More THF accumulated in the serum from levofolinate than from d,l-folinic acid.  
Figure 2 shows the percentage of unconverted forms in the serum for the four forms of folate at 30 min, 1 h, 2 h, and 4 h. Unconverted folic acid accounts for more than 60% of the absorbed compound at peak and only decreases to about 50% by 4 h. MTHF on the other hand, accounts for \(\sim\)90% of the serum folate throughout the 4 h period. After reaching peak concentration at 30 min, d,l-folinic acid and levofolinate decrease substantially by 4 h.
# Cooperative Caching and Resource Allocation in Integrated Satellite-Terrestrial Networks
The main contributions in this paper can be summarized as follows. * A system model is studied by introducing a neighboring sub-network approach in integrated satellite-terrestrial networks, where the inter-satellite and satellite-terrestrial cooperative caching is considered. * The problem is formulated under multiple physical constraints, the aim is to jointly minimize the deployment costs of storage resource usage and network bandwidth consumption. * The CCRA algorithm is proposed based on a neighborhood search to find the feasible strategy in an acceptable running time. The performance is also evaluated by different experiments. The remaining parts of this paper are provided as follows. Section 2 reviews the related work concerning cooperative caching. Section 3 discusses the system model. Section 4 formulates the optimization problem. Section 5 proposes the CCRA algorithm to address the problem. Section 6 evaluates the performance of the proposed CCRA algorithm. Finally, the conclusion is provided in Section 7.
## 2 Materials and Methods
### Simulations of AGC System  
To verify the error in the fuzzy corrector's conversion factor (Figure 3b), a family of first-order polynomial signals with various slopes (converted into geometric degrees) was applied to the input terminals (\(in+\), \(in-\): terminal, Figure 3). Matlab/Simulink and Fuzzy Logic ToolBox were used to carry out this analysis. The results are presented in Figure 5. The voltage waveforms \(f_{2},f_{4},f_{6}\) shown in Figure 5 (marked in blue) are the responses of the fuzzy corrector based on triangular membership functions and the MIN t-norm operator to the family of input signals, which are the first-order polynomials with slopes \(\alpha\) = {10\({}^{\circ}\), 45\({}^{\circ}\), 89\({}^{\circ}\)}. The waveforms \(f_{1},f_{3},f_{5}\) in Figure 5 (marked in red) show the qualitative advantage of the operators PROD (the type of the implication of tuned fuzzy sets) over conventional mathematical operators used in fuzzy logic. The circles \(c_{1}\), \(c_{2}\), \(c_{3}\) in Figure 6 mark the area where a rapid increase appears in the waveforms \(f_{2},f_{4},f_{6}\) caused by the switchover (switch-case statement) between fuzzy controllers operated in parallel (\(s\), \(l\), \(h\), Figure 4). The tuned membership function and the PROD operator enables the waveform to be smoothed. Fuzzy AGC tuning relies on appropriate membership function spacing while observing the output signal.  
The main advantage of a tuned fuzzy controller is to generate a more accurate gain factor in the AGC system. This advantage can be seen between the signals \(f_{1}\)-\(f_{2},f_{3}\)-\(f_{4}\), \(f_{5}\)-\(f_{6}\) shown in Figure 5. The output signals \(f_{1},f_{3},f_{5}\) in the tuned fuzzy controller increase much faster than in a conventional controller. Thanks to this, they reach 80% of dynamic range of the ADC faster. On the basis of the results shown in Figure 5 and compared to the possibilities for digitally controlled amplifiers, fuzzy controllers have an obvious potential for use in AGC systems. It should be noted that fuzzy controllers and other technologies related to artificial intelligence are increasingly being used in the development of microprocessor systems. The main advantage of fuzzy controllers is the ease with which the input/output plane can be corrected, by changing the parameters of the rules defined by Equation (1) without the need for mathematical modeling. This type of change parameter in the fuzzy controller allows for rapid responses and high accuracy without impacting the computational complexity of the microprocessor system.  
Figure 5: Waveforms of the output voltage fuzzy AGC (see terminal _ch1_agc2_ in Figure 3b) on the first-order polynomials function with slopes (**a**) 10\({}^{\circ}\); (**b**) 45\({}^{\circ}\); (**c**) 89\({}^{\circ}\) (_n_[]—iteration).  
Figure 6: Voltage waveforms for the experimental signal of the input fuzzy AGC, where (\(V_{\alpha\
u}\)) is channel 2 and output is channel 1. (Voltage division _Ch1_, _Ch2_: 1V/Div, and time base: 200 ms/Div)).
## 3 Experimental Results
### Performance Metrics for MF Class Using Imbalanced Dataset without Feature Normalization
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Normalization** &
\begin{tabular}{c} **Data** \\ **Balance** \\ \end{tabular} & **Rec (\%)** & **Pre (\%)** & **Spe (\%)** & **Acc (\%)** & **F1 (\%)** \\ \hline DT & No & Imbalanced & 18.79 & 87.32 & 99.91 & 97.22 & 30.92 \\ SVM & No & Imbalanced & 20.91 & 88.46 & 99.91 & 97.29 & 33.82 \\ k-NN & No & Imbalanced & 50.00 & 56.70 & 98.69 & 97.08 & 53.14 \\
1D-LeNet & No & Imbalanced & 6.67 & 27.50 & 99.40 & 96.33 & 10.73 \\
1D-AlexNet & No & Imbalanced & 8.48 & 73.68 & 99.90 & 96.87 & 15.22 \\
1D-VGG16 & No & Imbalanced & 8.48 & 62.22 & 99.82 & 96.80 & 14.93 \\ \hline DT & No & Balanced & 94.24 & 96.58 & 97.26 & 95.90 & 95.40 \\ SVM & No & Balanced & 98.18 & 92.84 & 93.78 & 95.77 & 95.43 \\ k-NN & No & Balanced & 93.94 & 98.73 & 99.00 & 96.72 & 96.27 \\
1D-LeNet & No & Balanced & 22.12 & 82.02 & 96.02 & 62.70 & 34.84 \\
1D-AlexNet & No & Balanced & 75.15 & 77.26 & 81.84 & 78.83 & 76.19 \\
1D-VGG16 & No & Balanced & 15.76 & 74.29 & 95.52 & 59.56 & 26.00 \\ \hline DT & Yes & Imbalanced & 23.64 & 82.98 & 99.83 & 97.31 & 36.79 \\ SVM & Yes & Imbalanced & 19.39 & 86.49 & 99.9 & 97.23 & 31.68 \\ k-NN & Yes & Imbalanced & 45.45 & 57.47 & 98.85 & 97.08 & 50.76 \\
1D-LeNet & Yes & Imbalanced & 40.61 & 78.82 & 99.63 & 97.67 & 53.60 \\
1D-AlexNet & Yes & Imbalanced & 35.76 & 87.41 & 99.82 & 97.70 & 50.75 \\
1D-VGG16 & Yes & Imbalanced & 36.97 & 88.41 & 99.83 & 97.75 & 52.14 \\ \hline DT & Yes & Balanced & 94.55 & 95.71 & 96.52 & 95.63 & 95.12 \\ SVM & Yes & Balanced & 98.18 & 94.19 & 95.02 & 96.45 & 96.14 \\ k-NN & Yes & Balanced & 93.64 & 98.72 & 99.00 & 96.58 & 96.11 \\
**1D-LeNet** & **Yes** & **Balanced** & **97.27** & **99.38** & **99.50** & **98.50** & **98.32** \\
1D-AlexNet & Yes & Balanced & 96.67 & 99.07 & 99.25 & 98.09 & 97.85 \\
1D-VGG16 & Yes & Balanced & 97.27 & 98.17 & 98.51 & 97.95 & 97.72 \\ \hline \hline \end{tabular}  
* MF: Machine Failure, SML: Shallow Machine Learning, DL: Deep Learning, DTs: Decision Trees, SVMs: Support Vector Machines, k-NNs: k-Nearest Neighbors, Rec: recall, Pre: precision, Spe: specificity, Acc: accuracy, F1: F1-score.  
\end{table} 
Table 4: Results of SML and DL classifiers for MF fault type with imbalanced and balanced dataset (without and with normalization).
# Silane Modification of Crumb Rubber on the Rheological Properties of Rubberized Asphalt
## 3. Experimental Result and Discussion
### High Temperature Stability
The tan (\(\delta\)) of AM rapidly rose with increasing temperature and it reached near 60 at 80 \({}^{\circ}\)C. Because the non-Newtonian fluid characteristic of asphalt begins to appear with increasing temperature, at this point, the plasticity of asphalt increases and permanent deformation would occur [21]. However, AR and MAR had a similar increasing trend of tan (\(\delta\)), and tan (\(\delta\)) was significantly lower than AM. This is because of the elastomer effect of rubber, which gives asphalt a better ability to recover from deformation and significantly reduces the tan (\(\delta\)) of asphalt. In addition, when the test temperature was higher than 70 \({}^{\circ}\)C, the tan (\(\delta\)) of MAR exhibited a downward trend and differences between AR and MAR increased. MCR was more stable and continuously swelled within asphalt and chemical bonds between MCR and asphalt were accrued through the polar groups of silane coupling agent, which means MCR acts as a better elastomer with a good compatibility with asphalt. As a result of the KH-550 modification, the increasing trend of asphalt plasticity was mitigated or stopped, which prevented permanent deformation and enhancing the mechanical behavior. It can be seen that Figure 2 displays a similar decline trend of \(G^{*}\)/sin (\(\delta\)) for AM, AR, and MAR with increasing temperature, which presents a sensitivity of temperature. When the temperature had not yet reached 64 \({}^{\circ}\)C, the DSR tests of all AM samples had failed, while the \(G^{*}\)/sin (\(\delta\)) of AR and MAR samples were still 2.21 kPa and 2.99 kPa at a high temperature, respectively. In addition, the \(G^{*}\)/sin (\(\delta\)) of MAR samples were always higher than those of AR, which increased by 35.4% at 58 \({}^{\circ}\)C, 29.7% at 64 \({}^{\circ}\)C, 30.7% at 70 \({}^{\circ}\)C, and 35.3% at 76 \({}^{\circ}\)C. Because a more stable and stronger continuing integrity was formed between MCR and asphalt [9], even if the test temperature rose, the ability of MAR to transmit force, redistribute internal stress, and resist shearing deformation was much better. Through the above analysis of the DSR temperature scanning test, it can be determined that MCR helped to raise and reduce complex modulus \(S\) and damping factor tan (\(\delta\)) of asphalt under high temperature conditions. The servicing performance under a high temperature environment was improved by a stronger interface adhesion state formed between MCR and asphalt through chemical reactions, which means the dynamic mechanical properties of rubberized asphalt are enhanced by KH-550.
## 1 Introduction
Different factors, such as operational cost, customer satisfaction, load loss, and profit for charging station owners, are considered only in the objective function. In this paper, the novel charging system is proposed to eliminate the adverse effects of uncontrolled EV charging and to track the origin of the energy for the charging of EV. The methodology for the multicriteria optimization of the charging/discharging schedule of a battery and EV charging level is based on multi-attribute utility theory. The optimization criteria include the minimization of charging costs, maximization of renewable energy share, and the minimization of battery degradation. The contribution of this paper can be stated as:  
* the simultaneous optimization of charging/discharging BES and EV charging power;
* the implementation of energy tracking methodology that monitors the origin of energy from and to the battery;
* the multiobjective optimization of different conflicting criteria. This problem is solved using the genetic algorithm optimization procedure adapted to the multicriteria optimization function. After the introduction and the literature review, the tracking of the energy origin while charging and discharging the battery is explained in Section 2. The optimization model with objective function and constraints is presented in Section 3, and the illustrative example for the optimization procedure is given in Section 4. After the discussion of results in Section 5, a conclusion and further research directions are given at the end.
## 4 Image Processing
### Image Segmentation  
In this paper, HSV color space and gray scale factor are used to segment _Camellia oleifera_ fruit, and the effects of the two methods are compared. Method 1: Segment _Camellia oleifera_ fruit based on HSV color space and segment the whole _Camellia oleifera_ fruit based on color features through the inRange function to transform it into a binary image. Method 2: The RGB image is separated by a single channel to obtain a grayscale image. According to Equation (20), the grayscale factor is 1.8, where \(B_{1},G_{1}\) and \(R_{1}\) are the mean values of the _Camellia oleifera_ fruit area, \(B_{2}\), \(G_{2}\) and \(R_{2}\) are the mean values of the background area, and \(X\) is the grayscale factor. The binary graph of the _Camellia oleifera_ fruit was obtained by the threshold treatment. As can be observed from Figure 6, the HSV color space method is better than the gray factor method in segmenting _Camellia oleifera_ fruit, and the segmented binary image of _Camellia oleifera_ fruit is more complete. The image segmentation diagram is shown in Figure 6.  
\[\left\{\begin{array}{l}X\times B_{1}-G_{1}-R_{1}\geq 0\\ X\times B_{2}-G_{2}-R_{2}\leq 0\end{array}\right. \tag{20}\]  
Figure 6: Image segmentation. (**a**) Based on HSV color space, (**b**) Based on gray scale factor.
# A Survey on MLLMs in Education: Application and Future Directions
## 2 Preliminaries of MLLMs
### Examples of Prominent MLLMs
#### 2.4.1 Open-Source MLLMs
Its large context window is particularly useful for creating tools that assist in complex problem solving, such as code debugging environments or interactive literature exploration. **LLaVA (Large Language and Vision Assistant)**: LLaVA is an open-source model designed to combine visual encoders with large language models, enabling general-purpose visual and language understanding [30]. It supports tasks such as visual question answering, image captioning, and the development of interactive, multimodal educational content. One major advantage of LLaVA is its accessibility and adaptability. Being open-source, it allows educators and developers to customize the model to address specific learning needs, such as creating tailored visual aids or interactive lessons. Furthermore, it fosters innovation by providing a platform for experimentation and collaboration within the educational community. LLaVA also poses significant challenges. The implementation of the model requires considerable technical expertise and computational resources, which may not be readily available to many educational institutions. Furthermore, the absence of dedicated support services means that troubleshooting and model optimization must often be handled internally, potentially delaying adoption and reducing its usability in time-sensitive scenarios. Potential applications: LLaVA can support the creation of interactive learning environments, such as visual science tutorials or personalized art history lessons. Its adaptability makes it ideal for generating tailored visual aids for diverse subjects.
## 4 Results and Discussions
### The Effect of the Rayleigh Number  
Figure 3 shows the isothermals (on the left) and streamlines (on the right) for \(Kr=10\), \(\varepsilon=0\), \(\phi=0.5\) and \(Pr=0.71\), with \(Ra=10^{2}\), \(10^{3}\), \(10^{4}\), \(10^{5}\) and \(10^{6}\). It can be seen that, for \(Ra=10^{2}\) and \(Ra=10^{3}\), the isothermals are almost vertically distributed. From the streamlines, the air flow is very weak due to the weak buoyancy force compared to the viscous force, which indicates that the heat transfer in the cavity is dominated by thermal conduction. As \(Ra\) increases to \(10^{4}\), the isothermals in the cavity become curved, which represents the enhancement of the air flow, and the transition from conduction to natural convection takes place. For \(Ra=10^{5}\), the isothermals are almost horizontally distributed of the cavity and more intensive near the interface, for the most part. From the streamlines, two vortexes are formed due to the enhanced buoyancy force. For \(Ra=10^{6}\), the streamlines turn more chaotic, and there is a tendency for turbulent flows to form. In these cases, natural convection tends to be the main mode of the total heat transfer.  
Figure 4 shows the evolution of the average convective number of the hot interface as a function of \(Ra\). It can be seen that \(\overline{Nu}_{\rm conv}\) increases with an increasing \(Ra\). From \(Ra=10^{2}\) to \(Ra=10^{3}\), \(\overline{Nu}_{\rm conv}\) remains almost unchanged, and its growth rate becomes faster with increasing \(Ra\). At last, the growth rate of \(\overline{Nu}_{\rm conv}\) reaches a peak in the range of \(Ra=10^{5}\) to \(Ra=10^{6}\). This means that with an increasing \(Ra\), the fraction of heat transfer by natural convection increases and \(Ra\) has a more significant impact on natural convection when it is at a high value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{\(Nu\)} \\ \hline \(Ra\) & **Current Work** & **Ref. [1]** & **Diff (\%)** \\ \hline \(10^{3}\) & \(1.1178\) & \(1.114\) & \(0.34\) \\ \(10^{4}\) & \(2.2453\) & \(2.245\) & \(0.13\) \\ \(10^{5}\) & \(4.5230\) & \(4.51\) & \(0.29\) \\ \(10^{6}\) & \(8.8418\) & \(8.806\) & \(0.41\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of average Nusselt numbers for different Rayleigh numbers.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Grids Level (Number)** & \(Nu\) \\ \hline Normal (1680) & \(4.5375\) \\ Fine (2233) & \(4.5360\) \\ Finer (3024) & \(4.5230\) \\ extra fine (9625) & \(4.5230\) \\ extremely fine (29,700) & \(4.5218\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of average Nusselt numbers for different grids at \(Ra=10^{5}\).  
Figure 3: _Cont._
## 3 Results and Discussion
### The Best Values of Parameters
#### 3.2.2 Influence of Bed Height  
The breakthrough curve was examined for the adsorption of Ag\({}^{+}\) and Cu\({}^{2+}\) solutions for the effect of bed height. In order to perform this study, breakthrough curves at different bed heights (3, 6 and 9 cm) and with the same influent concentration (C\({}_{0}\) = 4000 mg/dm\({}^{3}\)) and flow rate (Q = 3.0 or Q = 4.5 cm\({}^{3}\)/min, for silver and copper respectively), were plotted. Figure 9 shows that as the bed height increases, the breakthrough curve is gentler and flattened. When the bed height increased, metal cations had more time for contact with vermiculite, which resulted in the increased removal of silver and copper ions. At larger bed heights, some of the active sites will be bypassed by metal ions, hence the total efficiency of the deposit will decrease. The slope of the breakthrough curve increased with a decreasing bed height, which resulted in a reduced mass transfer zone, and the proportion of adsorbent replete with metal ions increased.
# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 4 Research Methods
### Improved Wavelet Adaptive Denoising
#### 4.1.3 Improved Threshold Function  
Classical threshold functions primarily consist of the hard threshold [19]  
\[\hat{\omega}_{i,j}=\begin{cases}\omega_{i,j},&|\omega_{i,j}|\geq\lambda\\ 0,&|\omega_{i,j}|<\lambda\end{cases}, \tag{8}\]  
and the soft threshold [22]  
\[\hat{\omega}_{i,j}=\begin{cases}\text{sgn}(\omega_{i,j})\big{(}|\omega_{i,j}| -\lambda\big{)},&|\omega_{i,j}|\geq\lambda\\ 0,&|\omega_{i,j}|<\lambda\end{cases}, \tag{9}\]  
where \(i\) denotes the number of decomposition levels, and \(\omega_{i,j}\) is the \(j^{\text{th}}\) wavelet coefficient at level \(i\). The \(\text{sgn}(\omega_{i,j})\) symbol represents the positive or negative state of the current coefficient.  
Considering the disadvantages of both hard and soft thresholds, when the absolute value of the coefficient exceeds the threshold, the function should be as close as possible to the hard threshold. When the absolute value of the coefficient falls below the threshold, the function must maintain continuity to ensure a smooth connection between the two [42]. This paper proposes an improved threshold function:  
\[\hat{\omega}_{i,j}=\begin{cases}\text{sgn}(\omega_{i,j})\bigg{(}\omega_{i,j}-(0.1)^{\sqrt{\omega_{ij}^{2}-\lambda^{2}}}\cdot\frac{\lambda}{\alpha+1}\bigg{)},&|\omega_{i,j}|\geq\lambda\\ \text{sgn}(\omega_{i,j})\cdot\frac{\alpha\cdot\text{exp}\left(10(|\omega_{i,j} |-\lambda)\right)}{\alpha+1},&|\omega_{i,j}|<\lambda\end{cases}, \tag{10}\]  
where \(\alpha\) represents the regulating factor used to adjust the position of the function as it approaches the hard threshold. For Equation (10), at the threshold, when \(|\omega_{i,j}|\rightarrow\lambda^{+}\) and \(\lambda^{-}\), \(\hat{\omega}_{i,j}=\frac{\alpha}{\alpha+1}\lambda\), so the proposed threshold function is continuous at \(\lambda\), overcoming the defect of the hard threshold. When \(\omega_{i,j}\rightarrow\infty\), \((0.1)^{\sqrt{\omega_{ij}^{2}-\lambda^{2}}}\to 0\) at this time; \(\hat{\omega}_{i,j}\rightarrow\omega_{i,j}\) does not introduce a fixed deviation to the coefficient, solving the problem with the soft threshold. A comparison of the improved threshold function with the hard and soft thresholds is shown in Figure 3, where \(\alpha=2\). The proposed threshold function closely approximates the hard threshold in the non-threshold region, while ensuring a smooth and continuous transition within the threshold region.  
The process of the improved wavelet adaptive denoising (iWAVE) is shown in Figure 4.