esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
showed that EFs could increase the susceptibility of _Candida_ spp. against miconazole, but the effects were significant only at the second week of exposure to electromagnetic fields [44]. On the contrary, [PERSON] et al. found that long-term exposure of mice to 900 MHz GSM radiation on experimental cutaneous candidiasis could retard the wound healing and reduce the survival rate of the mice, due to induced oxidative stress, disturbance in immune cell functions, and altered gene expression [45]. The mechanism of action of EFs on C. _albicans_ could be the alteration of cellular membrane with the modification of the permeabilization [46]. However, the interpretation of previous literature is quite difficult, due to the different protocols of EFs applied, as well as the different exposure environments. Indeed, radiation parameters include wave frequency, intensity, continuous or discontinuous output, and the duration of the exposure [45]. With the introduction of CMFs, a combination of different programs has been included in the different protocols. Moreover, the outcome of EF exposure depends on the nature of both the radiation and the target molecules. Consequently, the adoption of simple, in vitro, experimental study designs could help in the development of efficacious EF therapeutic protocols and in increasing knowledge of the mechanisms of action. Considering that the device used in this study is currently set up to automatically complete one cycle of treatment until A1, for testing the adherence on polyacrylic resin, we performed the experiments only on A1 and A2. SEM images and the CFU count confirmed that _Candida albicans_ adherence on the polyacrylic discs was significantly reduced in the exposed groups. It is important to highlight that adherence to these surfaces is highly influenced by superficial roughness. Rougher acrylic surfaces were associated with a higher number of _Streptococcus_ spp., _Bacteroides gingivalis_, _Actinomyces_ spp., and _Candida albicans_[47; 48; 49]. However, in order to avoid any risk of bias, all samples, were subjected to the same finishing procedures, which are the same as those traditionally used during the production of complete dentures. The clinical implications of these results are very important, because this technology could be used to eradicate yeasts from dentures, without the use of chemical substances that could damage the resin surface and could slowly release potentially irritants into the mouths of the patients. The same device could also be used to treat the oral manifestations of candidiasis, although other in vivo and in vitro studies are necessary to confirm the efficacy of this protocol in the clinical use.
## 4 Results and Discussion
### CGCW Performance Analysis
#### 4.2.4 Influence of the Head Loss  
The influence of the head loss (\(H_{\rm G}\)) on the breakthrough curve of the CGCW is shown in Figure 10. The \(r_{\rm G}\), \(T_{\rm G}\) and are assumed to be 0.05 m and 0.0015 m, respectively. Increasing \(H_{\rm G}\) can lead to a decrease in the breakthrough time. For example, the breakthrough time for \(H_{\rm G}\) = 0.3 m is 30 years longer than that for \(H_{\rm G}\) = 10 m when \(k_{\rm G}=10^{-9}\) m/s. The main reason is that the contaminant transport in the CGCW can be accelerated with the increase of \(H_{\rm G}\). More contaminants can break through the barrier and migrate to the groundwater. Thus, the methods that can control the head loss (e.g., a pumping well) should be taken to improve the performance of the CGCW. Additionally, it can be concluded that the effect of \(H_{\rm G}\) can be weakened when the impermeability of the barrier wall is relatively good (e.g., \(k_{\rm G}\) = 10\({}^{-10}\) m/s). For example, although \(H_{\rm G}\) increases by 33-fold (from 0.3 m to 10 m), the breakthrough time of the CGCW is only 1.2 times larger than before when \(k_{\rm G}=10^{-10}\) m/s. It can also be demonstrated from the empirical equation for circular holes in Table 2 that even if the head loss is large (e.g., 10 m), the leakage is lower than 1.2 \(\times\) 10\({}^{-10}\) m\({}^{3}\)/s when \(k_{\rm G}\) = 10\({}^{-10}\) m/s.  
Figure 9: Influence of the cut-off wall hydraulic conductivity on the breakthrough curve.
# A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos
## 3 The Proposed Image Cryptosystem
### Decryption Scheme  
The decryption procedure is the inverse process of the encryption scheme, and it can be described briefly as follows:  
Step 1: Apply the same initial conditions as encryption to iterate Equations (1) and (9) to generate the key streams \(\mathrm{K_{1}}(\hat{\kappa}_{21}(\mathrm{k}))_{\mathrm{k}=1}^{\mathrm{m}}\), \(\mathrm{K_{2}}(\hat{\kappa}_{22}^{\prime}(\mathrm{k}))_{\mathrm{k}=1}^{\mathrm{ n}}\), \(\mathrm{K_{3}}[\hat{\kappa}_{1}^{\prime}(\mathrm{k})]_{\mathrm{k}=1}^{\mathrm{m}}\) and \(\mathrm{K_{4}}[\hat{\kappa}_{2}^{\prime}(\mathrm{k})]_{\mathrm{k}=1}^{\mathrm{ n}}\).  
Step 2: Execute bit shift to the left or right of the components \(\mathrm{C_{ER}}\), \(\mathrm{C_{EG}}\), and \(\mathrm{C_{EB}}\) to get the matrixes \(\mathrm{C_{3R}}\), \(\mathrm{C_{3G}}\), and \(\mathrm{C_{3B}}\).  
Step 3: Cyclic shift the rows and columns of the matrixes \(\mathrm{C_{3R}}\), \(\mathrm{C_{3G}}\), and \(\mathrm{C_{3B}}\) according to the opposite direction of step 5 in the previous subsection, then the matrixes \(\mathrm{C_{2R}}\), \(\mathrm{C_{2G}}\), and \(\mathrm{C_{2B}}\) are acquired.  
Step 4: Perform bit-wise operations utilizing Formula (19).  
\[\left\{\begin{array}{l}\mathrm{C_{1R}}=(\mathrm{C_{2R}}(\mathrm{i},\mathrm{j })\oplus\mathrm{K_{2}}(\hat{\kappa}_{22}^{\prime}(\mathrm{k}))_{\mathrm{k}=1}^ {\mathrm{n}}+256-\mathrm{K_{2}}(\hat{\kappa}_{22}^{\prime}(\mathrm{k}))_{ \mathrm{k}=1}^{\mathrm{n}})\mathrm{mod}\ 256\\ \mathrm{C_{1G}}=(\mathrm{C_{2G}}(\mathrm{i},\mathrm{j})\oplus\mathrm{K_{2}}( \hat{\kappa}_{22}^{\prime}(\mathrm{k}))_{\mathrm{k}=1}^{\mathrm{n}}+256- \mathrm{K_{2}}(\hat{\kappa}_{22}^{\prime}(\mathrm{k}))_{\mathrm{k}=1}^{\mathrm{ n}})\mathrm{mod}\ 256\\ \mathrm{C_{1B}}=(\mathrm{C_{2B}}(\mathrm{i},\mathrm{j})\oplus\mathrm{K_{2}}( \hat{\kappa}_{22}^{\prime}(\mathrm{k}))_{\mathrm{k}=1}^{\mathrm{n}}+256- \mathrm{K_{2}}(\hat{\kappa}_{22}^{\prime}(\mathrm{k}))_{\mathrm{k}=1}^{\mathrm{ n}})\mathrm{mod}\ 256\end{array}\right. \tag{19}\]  
Step 5: Implement second reverse circular shifts on the rows and columns of the three matrices, \(\mathrm{C_{1R}}\), \(\mathrm{C_{1G}}\) and \(\mathrm{C_{1B}}\), according to Equations (15) and (16).  
Step 6: Merge the three obtained matrixes to form the original image \(\mathrm{P}\).
## 2 Materials and Methods
### Binary Response Variable: Outage Presence-Absence Data  
Our modeling work is intended to predict the probability of any outage occurrence. Therefore, we used a binary response variable where a value of 1 represents a reported outage during the 2005 to 2017 period and a value of 0 indicates no reported outage. Eversource Energy's outage management system (OMS), which was used to obtain the outage locations, includes both customer outages and system outages. In this case, we only used system outages or \"trouble spots\", which are locations on the power grid where a restoration crew was needed to restore power. Hereafter, we will use the term outages to refer to these trouble spots. In this study, we only considered outages for primary overhead lines that were reported to be caused by tree or limb failures.
# Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning
## 1 Introduction  
Additive manufacturing, or 3D printing, is defined as the material deposition process from computer-aided design (CAD) models, usually layer upon layer [1; 2; 3]. Additive manufacturing (AM) is widely applied in a broad spectrum of domains, including architecture, mechanics, aeronautics, chemical industry, education, food, social culture, and medicine [4; 5]. Indeed, the importance of additive manufacturing importance has grown during the COVID-19 pandemic; several medical tools have been created to mitigate disruptions in the global medical field [6]. AM is a generic term for a group of processes which can be classified according to machine architecture into seven categories [7; 8], Table 1 summarized the different AM processes and associated technologies and materials. As is evident, the range of techniques and materials is very wide. This subsector of additive manufacturing is growing rapidly in the industrial world, with compound annual growth rates of 24.5% [9; 10], because additive manufacturing methods are more efficient and more flexible than conventional manufacturing methods, such as subtractive manufacturing and formative manufacturing. Therefore, attention is increasingly focused on the energy consumption of additive manufacturing (AM) systems. Much research has been conducted on energy consumption modeling for the AM process [10; 11; 12]. However, an efficient model for predicting energy consumption in the FDM process is still absent, in particular regarding the usage of machine learning (ML) for estimating energy use in the FDM process based on part orientation. In this work, we mainly focus on energy consumption prediction in the fused deposition modelling (FDM) process. This process is one of the most frequently applied additive manufacturing methods. In fact, FDM printers largely simplify and accelerate the production process, providing users with an efficient manufacturing environment with better material utilization and reduced time consumption. The FDM principle is based on the extrusion of molten thermoplastic filaments layer by layer to form three-dimensional parts [13]. The FDM method includes numerous printing parameters, such as percentage infill, infill style, layer thickness, printing speed, and printing temperature, which affect the quality of the final product [14, 15, 16]. The aim of the current study is to model the experimental results of energy consumption for the FDM 3D printing of isovolumetric mechanical components using several ML algorithms. The simulation results provide the expected results and demonstrate the predictive accuracy of the Gaussian process regressor (GPR) method versus other ML methods used. With our developed GPR model, we can select the appropriate orientation of the part to be printed in order to reduce energy consumption. This prediction can be used to encourage engineers as they develop intuition for novel designs with FDM technology.
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 4 Dataset Generation
### Ground Truth Orbit  
This work was performed using a Sun-synchronous orbit propagated for 120 days from 3 January 2019, and the initial orbital parameters are reported in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Orbital Parameter** & **Initial Value** \\ \hline Semi-major axis, \(a\) & 6945 km \\ Eccentricity, \(e\) & \(10^{-3}\) \\ Inclination, \(i\) & \(96.6^{\circ}\) \\ Right ascension of ascending node, \(\Omega\) & \(49.562^{\circ}\) \\ Argument of perigee, \(\omega\) & \(0^{\circ}\) \\ True anomaly, \(\
u\) & \(24.33^{\circ}\) \\ Epoch & 3 January 2019 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sun-synchronous simulated orbit’s initial orbital parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Exponent density pre-factor, \(\rho_{0}\) & \(1.3\times 10^{9}\) kg/km\({}^{3}\) \\ Atmospheric scale height, \(h_{0}\) & \(8.5\) km \\ Area-to-mass ratio, \(A/m\) & \(0.126\) m\({}^{2}\)/kg \\ Dimensionless drag coefficient, \(C_{d}\) & \(2.2\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Atmospheric drag model parameters.
# An Acoustic Feature-Based Deep Learning Model for Automatic Thai Vowel Pronunciation Recognition
## 4 Materials and Methods
### Dataset
#### 4.1.1 Dataset Design  
Currently, no public datasets for Thai vowels are available for this research objective. Most of them were collected unsystematically and unstandardized. Therefore, the dataset preparation was designed for the objective of this study. The linguist listed a set of Thai words to be recorded based on linguistic theory. Every word has the same characteristics: the same consonant, the same tone, and the same final consonant, differing only in the vowels.
# An Improved Method for Evaluating Image Sharpness Based on Edge Information
## 2 Principle and Design of the Sharpness Evaluation Method
### Edge Detection  
A Canny operator has superior overall performance compared to other edge detection operators; but, the effect of Canny edge detection depends heavily on the choice of its threshold value. If the threshold value is set too high, it will lead to missed detection and edge discontinuity. If the threshold value is set too low, there will be over-detection problems, such as the noise in measured images being wrongly detected as an edge. Therefore, in order to improve the edge detection effect, an improved Canny edge detection algorithm based on the activation mechanism is proposed in this paper.  
Figure 1: Step edge image and its corresponding grayscale change curve of pixels on a row.  
Figure 2: Impulse, roof edge images, and their corresponding grayscale change curves of pixels on a row: (**a**) impulse edge; (**b**) roof edge; (**c**) impulse edge grayscale change curve; (**d**) roof edge grayscale change curve.  
Plot (a) in Figure 3 assumes that the edge detection result is obtained under a high threshold; it can be seen that there are fewer edge points on it. Plot (b) shows the edge detection result obtained in the low-threshold case with more edge points and the appearance of a noise point marked in green. By replicating the edge information in plot (a) to plot (b), plot (c) can be procured. This process is called activation; the activated edge points are marked in red. After that, the activated edge points will activate all the other edge points adjacent to them, as shown in plot (d). Because noise tends to exist in isolation, the isolated noise point is filtered out after the activation process, which is plot (e). The edge information obtained after the detection of this improved algorithm has the characteristics of low noise and high accuracy.  
Figure 4 depicts the edge extraction process of the Lena test image using our improved algorithm. It can be clearly seen that the edge extraction result processed by the improved algorithm is less noisy than that under low threshold and more accurate than that under high threshold.
## 3 Results
### Structural Equation Modeling Analysis  
Before conducting the structural equation modeling (SEM) analysis, [PERSON]'s single-factor test was employed to assess common method bias. The analysis extracted six factors with eigenvalues greater than 1.0, and the cumulative variance explained by the first factor was 23.49%, which is well below the commonly accepted threshold of 40%. Therefore, no significant common method bias was detected in this study. Table 3 summarizes the correlation coefficients among the key study variables, indicating significant positive correlations between digital technology usage, perceived usefulness (PU), perceived ease of use (PEU), academic self-efficacy (ASE), and student classroom engagement (all _p_-values < 0.001). Subsequently, two structural equation models were constructed using Amos 28.0. The first model included digital technology usage, PU, ASE, and student engagement in PE classes, while the second model comprised digital technology usage, PEU, ASE, and student engagement. Gender, age, ethnicity, grade level, and residential location were included as control variables in both models. Table 4 presents the model fit indices, most of which were within acceptable ranges, indicating good overall model fit. Table 5 reports the path coefficients and significance levels for both structural equation models. The results demonstrated that the frequency of digital technology usage had a significant positive effect on student classroom engagement (\(\beta\) = 0.108, \(p\) < 0.001), PU (\(\beta\) = 0.506, \(p\) < 0.001), PEU (\(\beta\) = 0.487, \(p\) < 0.001), and ASE (\(\beta\) = 0.258, \(p\) < 0.001), supporting Hypotheses 1-4. Additionally, PU, PEU, and ASE also had significant positive effects on student classroom engagement (\(\beta\) = 0.572, \(p\) < 0.001; \(\beta\) = 0.535, \(p\) < 0.001; \(\beta\) = 0.256, \(p\) < 0.001), confirming Hypotheses 5, 6, and 9. Furthermore, PU and PEU were found to significantly influence ASE (\(\beta\) = 0.436, \(p\) < 0.001; \(\beta\) = 0.445, \(p\) < 0.001), supporting Hypotheses 7 and 8. These findings suggest that PU, PEU, and ASE not only serve as mediators in the relationship between digital technology usage frequency and student classroom engagement but also function as chain mediators between digital technology usage, PU, PEU, and ASE.  
Figure 6: SHAP analysis of feature contributions to model output: (**a**) SHAP waterfall plot displaying individual feature contributions; (**b**) SHAP decision plot illustrating the impact of digital technology tools on model predictions.
# Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission
## 5 Conclusions  
We studied the energy efficiency optimization problem in downlink secure multicast transmission for massive MIMO with known statistical CSI in this paper. The energy efficiency maximization problem was formulated as maximizing the ratio of the secure multicast rate to the power consumption. We then obtained the secure multicast rate by introducing its lower bound to simplify the objective problem. Via making the transmit signaling directions into a closed-form, we simplified the energy efficiency optimization of matrix-valued strategy design to a beam domain power allocation problem. Next, we designed an algorithm including two-level iteration in the beam domain, and guaranteed the  
Figure 3: The energy efficiency performance of the proposed algorithm and Monte Carlo method with the numbers of BS antennas varying from \(M=16\) to \(M=128\).  
algorithm to converge to a locally optimal point based on the MM method and Dinkelbach's transform. In addition, via substituting the multicast rate for its deterministic equivalent, the complexity of computational was significantly reduced. We made extensive simulations to verify the effectiveness of our algorithm. A large number of simulation results showed that the algorithm has fast convergence and low complexity. Compared with the rate maximization method, the proposed algorithm had more efficient and stable performance under the high transmission power constraint.  
Writing, B.J., L.Y., Y.H., and L.Q.; software, B.J. and L.Q. review and proofreading, Y.Z., L.Y., and W.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61761136016, in part by the Open Research fund of the Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Ministry of Industry and Information Technology, Nanjing University of Aeronautics and Astronautics, under Grant KF20202107, and in part by the Fundamental Research Funds for the Central Universities.  
The authors declare no conflicts of interest.
## 4 Application Studies  
### Study Networks  
For demonstration purposes, the proposed four criticality indexes are applied to four benchmark networks. The networks are real WDNs that are currently under operation and all information is obtained from network managers via personal contact. Note that a flat average demand pattern without temporal variation is applied for simplicity. Table 1 summarizes the network parameters and Figure 2 depicts the network layouts. Here, the system-average degree of node (\(DoN_{sys}\)) is defined as the average number of pipes a node is connected to, as expressed in Equation (6), and the higher value indicates that a network is densely looped. Among the four networks, Network 4 shows the highest \(DoN_{sys}\), while Network 2 has the lowest \(DoN_{sys}\).  
\[DoN_{sys}=\frac{\sum_{j=1}^{N}NodeCon_{j}}{N}, \tag{6}\]  
Here, \(DoN_{sys}\) is the system-average degree of node and \(NodeCon_{j}\) is the number of pipes connected to node \(j\).
## 3 Results and Discussion  
### Sulfur Analysis  
#### 3.1.1 Relative Standard Deviation (RSD)  
Figure 3 represents the RSD results of the original and processed data by the two different methods for the major wavelength of sulfur, by using a bar chart. The major peak wavelength of sulfur (416.3 nm) line was selected for data analysis. This is because the signal to background ratio was the lowest at 416.3 nm. Fifteen out of the sixty samples showed the smallest RSD value when applying the MSC data processing method. The original data showed the smallest RSD value in only two samples. However, forty-three out of the sixty samples showed the lowest RSD value when the SG smoothing method was applied. Some samples showed a too large RSD value when the MSC method was applied. The SG smoothing-processed data show RSD values with relatively small fluctuation.  
Figure 3: RSD result for the major wavelength of sulfur emission line.
## 3 Molecular Mechanisms of Omega-3 Fatty Acids in Cancer  
In recent decades, there has been a rise in the interest for n-3 PUFA and its mechanisms involved in the inhibition of tumor cell growth. Indeed, these compounds, such as DHA and EPA, have shown to be involved in cancer prevention, installation and development as they act in many crucial cancer hallmarks including cell cycle progression, inflammation, oxidative stress, apoptosis, angiogenesis and immune system modulation, among others [48; 49; 50]. At this point, these main roles of n-3 PUFA will be reviewed with a broader perspective on cancers and will be further discussed with a focus on melanoma in Section 5.  
However, it is important to discuss which pathways may be crucial for the antineoplastic effects of n-3 PUFAs. Numerous studies have demonstrated that n-3 PUFAs increase the production of prostaglandin E3 (PGE3) while reducing the levels of prostaglandin E2 (PGE2), which is involved in inflammatory and pro-carcinogenic pathways. Additionally, there is substantial information regarding the effects of these fatty acids on the cell cycle, specifically in regulating the expression of key proteins such as cyclin-dependent kinases (CDKs) and cyclin-dependent kinase inhibitors (CDKIs). These interactions between n-3 PUFAs and various proteins occur in separate pathways. Therefore, it is likely that the crucial molecular pathway involved in the anticarcinogenic effects of n-3 PUFAs is related to the cyclooxygenase-2 (COX-2) enzyme and the production of PGE3, which leads to a reduction in PGE2 levels.