esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Performance Evaluation  
The performance of quantitative models for droplet size and deposition distribution was evaluated by the coefficient of determination (\(R^{2}\)), root means square error (RMSE). \(R^{2}\) is also called the multiple correlation coefficient and defined as the proportion of variance explained by the regression model. This definition makes it a measure of success of predicting the dependent variable from the independent variables [48]. \(Rc^{2}\) and \(Rp^{2}\) are coefficients of determination in calibration set and prediction set, respectively, indicating the accuracy of the calibration and prediction models. The error was measured by RMSE, which included root mean square error of validation (RMSEV) and root mean square error of prediction (RMSEP). Smaller RMSE represents a better performance and higher accuracy of the model. The calculation process of these parameters is written below:  
\[R^{2}=\left(\frac{\sum_{i=1}^{N}\bigl{(}\hat{y}_{i}-\overline{\hat{y}}\bigr{)} \bigl{(}y_{i}-\overline{y}\bigr{)}}{\sqrt{\sum_{i=1}^{N}\bigl{(}\hat{y}_{i}- \overline{\hat{y}}\bigr{)}^{2}\bigl{(}y_{i}-\overline{y}\bigr{)}^{2}}}\right)^ {2} \tag{2}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\bigl{(}y_{i}-\hat{y_{i}}\bigr{)}^{2}}{N-1}} \tag{3}\]  
where \(y_{i}\) and \(\hat{y_{i}}\) are the reference and prediction values of measuring point \(i\), \(\overline{y}\) and \(\overline{\hat{y}}\) are the average of reference and prediction values, \(N\) is the number of measuring points.
## 4 Numerical Simulation Results
### Strength Parameters
#### 4.3.3 Relationship Between Internal Friction Angle and RFCA  
It is observed from Figure 11 that \(\varphi\) exhibits a trend of decreasing and then increasing as \(H\) increases. Particularly, when \(H\leq\) 0.2, \(\varphi\) is linearly negatively correlated with \(H\). Conversely, when \(H\) > 0.2, \(\varphi\) is linearly positively correlated with \(H\), as per Equation (8).  
When \(H\leq\) 0.2, \(\varphi\) is predominantly regulated by the occlusal friction between coarse aggregate particles. As \(H\) progressively increases, the skeleton effect between particles diminishes. However, when \(H\) > 0.2, with a gradual increase in the fine aggregate percentage, the skeleton effect between particles continues to decrease. Consequently, \(\varphi\) transforms from occlusal friction, provided by coarse aggregates, to sliding friction imparted by fine aggregates. As \(H\) continues to augment, the number of fine aggregate particles significantly grows, causing the sliding friction to progressively increase, and, subsequently, \(\varphi\) also rises.
## 3 Results
### Periodontal Health  
Periodontal health was assessed in 14 studies [14; 15; 22; 23; 24; 25; 26; 27; 28; 29; 30]. The most frequently assessed index was PI, which was statistically significantly lower in the group of patients treated with CA in 10 studies [7; 15; 22; 23; 24; 25; 26; 27; 29]. Several studies found no differences between the groups in this respect [2; 14; 28]. Lower PD values in the CA groups were observed in five studies [2; 7; 15; 24; 29], whereas in several others, no differences between the groups were noted [16; 22; 23]. The periodontal condition was better in the group of patientstreated with CA when assessed with the GI [7; 23; 27; 30]; BOP [15; 24; 29]; PBI--Papilla Bleeding Index [23]; SBI--Sulcus Bleeding Index [23; 26]; and GBI--Gingival Bleeding Index [23; 27]. Four studies showed no significant differences in periodontal condition between the studied groups [2; 14; 22; 26] (Table 2). No significantly higher number of periodontal bacteria was observed during orthodontic treatment in both study groups using the PCR method for the detection of bacteria [15; 24; 29], despite the fact that total biofilm masses were significantly higher in the FA groups in two studies OR 0.65 CI 95% (0.48-0.89) \(p\) < 0.005 [15] and OR 0.65 95% CI (0.48-0.89) \(p\) < 0.005 [24]. On the other hand, in the study by [PERSON] et al. [7], the enzymatic activity of _Treponema denticola, Porphromonas gingivalnis_ and _Tannerella forsythia_ (strongly associated with the development of periodontitis), assessed using the BANA test, was 5.739 times higher in the FA group than in the CA group--OR 5.739, 95% CI (1.801-18.294) \(p\) = 0.003.  
In CAT groups with lower biofilm masses, lower PI ((OR: 0.09 CI 95% (0.05-0.15) \(p\) < 0.001 [15] and OR 0.09 95% CI 90.05-0.15) \(p\) < 0.001 [24] and BOP (0.3 \(\pm\) 0.47 vs. 0.85 \(\pm\) 0.36 \(p\) < 0.001 [15] and OR 0.2 95% CI (0.11-0.36) \(p\) < 0.001 [24]) scores were observed. Also, a lower PD (2.3 \(\pm\) 0.47 vs. 2.95 \(\pm\) 0.60 \(p\) = 0.002) [15] was measured.  
In turn, [PERSON] et al. [25; 26] compared the concentration of _S. mutans_ and _Lactobacillus_ spp. in the saliva of patients treated with CAs and FAs. It was shown that in 40% of individuals treated with FAs, the level of bacteria responsible for the development of caries was dangerously high (% of subjects with bacteria level CFU \(>\) 10\({}^{5}\)_S. mutans_: CAT 10% \(p\) = NS, FAT 40% \(\chi^{2}\) = 15 \(p\) = 0.000; % of subjects with bacteria level CFU \(>\) 10\({}^{5}\)_Lactobacillus_: CAT 13.3% \(p\) = NS, FAT 40%, \(\chi^{2}\) = 15, \(p\) = 0.000 [25]; % of subjects with bacteria level CFU \(>\) 10\({}^{5}\)_S. mutans_ 8% vs. 37.5%, \(\chi^{2}\) = 10.32, \(p\) = 0.001; % of subjects with bacteria level CFU \(>\) 10\({}^{5}\)_Lactobacillus_ 2.5% vs. 37.5%, \(\chi^{2}\) = 15.313, \(p\) = 0.0001 [26]). In both studies, in FA groups, PI rates were also statistically significantly higher (API: CAT: 0 \(p\) = NS vs. FAT: 1.3 \(\pm\) 0.46 \(p\) = 0.00 [25] and PI: 0 vs. 1.4 \(\pm\) 0.5 t = -17.8 \(p\) = 0.001 [26]).
## 5 Discussion
### Mixed Cases  
The first mixed case hypothesis is H2. Recall that the null hypothesis is that there is no effect; therefore, rejecting it means that its effect can be realized. This fact is interesting, because the camera has two types. For orthographic cameras, the field of view is undefined, for perspective cameras, the authors analyzed the \(45^{\circ}\), \(60^{\circ}\), \(75^{\circ}\) and \(90^{\circ}\) fields of view. Since T1 states that the perspective camera type is better than the orthographic type, and Table 2 also proves this with respect to the fields of view, the main comparison was only carried out among the fields of view of the perspective camera as seen in Table 3. Due to these results, T2 is formed: The field of view of \(90^{\circ}\) influenced the performance of the users by increasing their probability of answering correctly. This is a significant difference on the level 0.05, but not on the level 0.01 (\(p\)-value = \(0.0225\)).  
The second mixed hypothesis was H3. This is mixed because the authors hypothesized that some rotation would help the users. This hypothesis was demonstrated to be true, but when no rotation occurred, it was also true. When the rotation was smaller than \(45^{\circ}\) in a given direction, the results showed the hypothesis to be false. It can be stated that the greatest influence on the results takes place when no, or a large rotation occurs. For this, see Tables 4 and 5. Since the rotation is influential, it was also analyzed in pairs, triplets, and in fours, and its interactions were assessed in Tables 10, 12, 19, 20, 22, 23, 28, 30, 31, 32 and 35, Tables 36-38. T3 was formed: when rotating the camera \(-45^{\circ}\), \(0^{\circ}\), \(45^{\circ}\), the performance of the users will be significantly increased (\(p\)-value = \(1.12\times 10^{-10}\)), increasing their probability of answering correctly and in pairs; it will significantly (\(p\)-value = \(0.0459\)) interact with the perspective camera type; and in triplets it will significantly (\(p\)-value = \(0.000237\)) interact with the 1:5.1, 3:1 contrast ratios and the Gear VR; and in fours it will have significant (\(p\)-value = \(0.000133\)) interactions with \(-45^{\circ}\), \(0^{\circ}\), \(45^{\circ}\) camera rotations, 1.5:1, 3:1 contrast ratios and the Gear VR.  
The third and final mixed-case hypothesis was H7, which is related to the optimal preferences in virtual environments for achieving the best HCI results. This hypothesis is only a mixed case, because originally the authors put the camera's field of view into this hypothesis. However, for the optimal preferences due to similar results between the perspective camera type and the field of view, the latter was discarded and only the former was left in. Therefore, T7 is formed: based on the previous theses, the optimal preference for the virtual environments to positively influence the correct answers on spatial ability tests by affecting the human-computer interaction is a perspective camera type, a camera rotation of \(-45^{\circ}\) or \(0^{\circ}\) or \(45^{\circ}\), a contrast ratio of 1.5:1 or 3:1, and the Gear VR display device.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 3 Micro-Supercapacitor Carbon Electrodes  
Electrodes in thin film supercapacitors are made with a layer of active material that is only a few nanometres to several micrometres thick [56]. Carbon-based materials are generally used as electrodes in EDLC supercapacitors, as they satisfy the electrode requirements, although they suffer from low specific capacitance. Various carbon materials have been used, such as activated carbon [57], templated carbon [58], carbon nanotubes [59], graphene [60], carbon onions [61], and carbide-derived carbon [62]. These carbon materials have a high surface area, and thus, they can provide high specific energy and power. They have a lower cost in comparison to other materials, but their synthesis method may be more sophisticated. The morphology of the electrode material is key as it determines the interface and thus, influences the electrochemical performance. The energy storage capacity of nanoporous carbon electrodes can be increased by matching the average pore size to the bare ion diameter because desolvation of the ions in aqueous or organic electrolytes results in a more space-efficient arrangement of ions within the nanopores [63]. Other methods to improve the energy storage capacity of supercapacitors is to use ionic liquids [58], redox electrolytes [64], or the addition of redox-active materials, such as metal oxides [65] or heteroatom doping [66].
## 6 Experiments and Discussion
### Additional Experiment on Psychaocustic Evaluation  
#### 6.3.1 Purpose  
Deep learning for generative systems generally has no test oracle for the generated results, and the metamorphic properties are not clear, so it is difficult to evaluate. Therefore, as a kind of metamorphic property for this task, we calculated the Booming Index [42], which is an evaluation index designed to evaluate the muffledness of sound, based on psychoacoustic theory, for the generated sound and compared it with the baseline for a more objective evaluation.
# Enhancing Federated Intrusion Detection with Class-Specific Dynamic Sampling
###### Abstract  
Federated Learning (FL) presents a promising approach for collaborative intrusion detection while preserving data privacy. However, current FL frameworks face challenges with non-independent and identically distributed (non-IID) data and class imbalances in network security contexts. This paper introduces Dynamic Sampling-FedIDS (DS-FedIDS), a novel framework that enhances federated intrusion detection through adaptive sampling and personalization. DS-FedIDS extends the Federated Learning with Personalization Layers (FedPer) architecture by incorporating dynamic up/down sampling to address class imbalance issues in network security datasets. The framework maintains a global model for shared attack detection while enabling client-specific adaptation through personalized layers. Our approach effectively handles heterogeneous network environments, including Content Delivery Networks, Internet of Things, and industrial systems, each with distinct traffic patterns and attack profiles. Experimental results demonstrate that DS-FedIDS outperforms baseline FedPer in accuracy and efficiency, achieving superior detection rates across diverse attack classes while maintaining reasonable training overhead. Notably, DS-FedIDS excels in detecting minority attack classes and adapting to client-specific normal traffic patterns, making it ideal for real-world intrusion detection scenarios with inherently imbalanced and heterogeneous data distributions.  
federated learning; intrusion detection systems; dynamic sampling; personalization; class imbalance; non-IID data; network security; distributed learning; adaptive sampling Article
# Energy Harvesting for TDS-OFDM in NOMA-Based Underwater Communication Systems
## 3 TDS-OFDM-NOMA Scheme
### Ambient Noise  
The main source of noise in the UWA channel is ambient noise. This ambient noise is mainly dependent on turbulence, wind waves, thermal noise, and shipping. The powerspectral density (PSD) of these sources of ambient noise is empirically formulated in dB re \(\mu\)Pa/Hz as [26]:  
\[10\log N_{t}(f)=17-30\log f \tag{25}\]  
\[10\log N_{s}(f)=40+20(s-0.5)+26\log f-60\log(f+0.03) \tag{26}\]  
\[10\log N_{w}(f)=50+7.5\,w^{\frac{1}{2}}+20\log f-40\log\,(f+0.4) \tag{27}\]  
\[10\log N_{th}(f)=-15+20\log f \tag{28}\]  
\[N(f)=N_{t}(f)+N_{s}(f)+N_{w}(f)+N_{th}(f) \tag{29}\]  
where \(N_{t}\) is the PSD of the turbulence noise at (\(f<10\) Hz). \(N_{s}\) is the PSD of the shipping noise at (\(10\,\mathrm{Hz}\sim 100\,\mathrm{Hz}\)) for a shipping activity factor, \(\mathbf{s}=0\), and \(\mathbf{s}=1\) for low and high activity, respectively. \(N_{w}\) is the PSD of noise for the wind-driven wave at (\(100\,\mathrm{Hz}\sim 100\) kHz), and \(w\) is a m/s wind speed. \(N_{th}\) is the PSD of the thermal noise, and it is the dominant one for \(f>100\) kHz.
## 3 Data Collection to Cloud: Automation and Real-Time Processing
### Data Transmission: Edge Computing and Fog Computing

Table 2: Key techniques employed for data cleaning, preprocessing, and fusion. valuable for irrigation management, where the rapid processing of sensor data directly impacts the timeliness and effectiveness of water application decisions. Looking to the future, emerging technologies like federated learning present possibilities for further addressing processing bottlenecks. This approach enables distributed processing between edge devices and the cloud while minimizing data transfer, addressing both efficiency and privacy concerns in IoT networks [76]. However, the challenges remain related to the heterogeneous and volatile nature of devices in edge and fog computing tiers, underscoring the ongoing nature of research in this area. These transmission capabilities form the foundation for the real-time operation demonstrated in the case studies (Section 7), where implementations achieve practical control of irrigation systems through timely data collection and processing. The specific architectures and protocols discussed here enable the performance characteristics detailed in those implementations.
## 3 Results and Discussion
### Analysis of Factors Affecting the Spread of HBNG Leakage under Normal Ventilation Conditions
#### 3.2.4 Different HBR  
By comparing and analyzing the simulation results of case 1 to case 5 under normal ventilation settings, we can generate concentration variation graphs of gas leakage with varied HBR (Figure 11). When gas leaks with varying HBR, the patterns remain consistent. It may be deduced that hydrogen and methane were uniformly blended prior to the occurrence of the leak. Hydrogen and methane exhibit distinct mass transfer capacities, but this disparity does not impact their leakage. The spatial distribution of methane decreases with the addition of hydrogen blending. Over time, the gas concentration on the monitoring line eventually became stable. The gas leakage concentration at HBR = 20% reached 40 vol  
Figure 10: Variation of gas concentration in HBNG (HBR = 15%) leakage with different pipeline pressures, (**a**) t = 50 s, (**b**) t = 100 s, (**c**) t = 150 s, (**d**) t = 200 s.  
of the LEL of the gas mixture, which is a two-percentage point increase compared to the value before hydrogen doping. This result indicates that hydrogen blending increases the danger of explosion.  
The distribution curves of the hydrogen and methane components in gas leaks with varying HBR at different monitoring locations over time are shown in Figure 12. At an HBR of 5%, the concentration of hydrogen leakage in the natural gas compartment remains below the warning threshold even after the leakage has stabilized, possibly attributable to the relatively low quantity of hydrogen being introduced. The warning times for the methane sensor and the hydrogen sensor exhibit close similarity when the HBR is 10%, 15%, and 20%.  
Furthermore, the inclusion of hydrogen in the gas mixture results in a reduction of its calorific value. The calorific value of hydrogen is 13 MJ/m\({}^{3}\), whereas that of methane is 38 MJ/m\({}^{3}\). The heat of combustion of hydrogen is merely 1/26 th of the heat of combustion of methane at a hydrogen integration level of 10%. Therefore, for catalytic combustion-type combustible gas alarms based on the principle of thermal effect when HBNG is in the natural gas compartment, it is recommended that the methane concentration be used as the monitoring standard.  
Figure 11: Variation of gas concentration in HBNG leakage with different HBR, (**a**) t = 50 s, (**b**) t = 100 s, (**c**) t = 150 s, (**d**) t = 200 s.
# Free Vibration Analysis of Triclinic Nanobeams Based on the Differential Quadrature Method
## 4 Numerical Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**Simply Supported**} & \multicolumn{6}{c}{**Clamped-Simply**} & \multicolumn{6}{c}{**Clamped-Clamped**} & \multicolumn{6}{c}{**Clamped-Free**} \\ \hline
**Node** & \(\mathbf{\omega_{1}}\) & \(\mathbf{\omega_{2}}\) & \(\mathbf{\omega_{3}}\) & \(\mathbf{\omega_{1}}\) & \(\mathbf{\omega_{2}}\) & \(\mathbf{\omega_{3}}\) & \(\mathbf{\omega_{1}}\) & \(\mathbf{\omega_{2}}\) & \(\mathbf{\omega_{3}}\) & \(\mathbf{\omega_{1}}\) & \(\mathbf{\omega_{2}}\) & \(\mathbf{\omega_{3}}\) \\ \hline
7 & 3.142 & 6.233 & 9.320 & 3.924 & 7.078 & 10.415 & 4.729 & 8.065 & 11.230 & 1.868 & 4.660 & 8.866 \\
9 & 3.141 & 6.286 & 9.432 & 3.925 & 7.073 & 10.250 & 4.727 & 7.866 & 11.086 & 1.873 & 4.685 & 7.587 \\
11 & 3.141 & 6.280 & 9.413 & 3.925 & 7.061 & 10.180 & 4.727 & 7.839 & 10.938 & 1.873 & 4.676 & 7.850 \\
13 & 3.141 & 6.280 & 9.413 & 3.925 & 7.062 & 10.195 & 4.727 & 7.842 & 10.978 & 1.873 & 4.676 & 7.798 \\
15 & 3.141 & 6.280 & 9.413 & 3.925 & 7.062 & 10.192 & 4.727 & 7.842 & 10.968 & 1.873 & 4.676 & 7.804 \\
17 & 3.141 & 6.280 & 9.413 & 3.925 & 7.062 & 10.192 & 4.727 & 7.842 & 10.968 & 1.873 & 4.676 & 7.804 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Convergence analysis of the size-dependent triclinic beam model (\(L\)/\(h\) = 100, \(l\) = \(\mu\) = 1 nm\({}^{2}\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline _L/h_ & \(l\) & \(\mu\) & \(\omega_{1}\) & \(\omega_{2}\) & \(\omega_{3}\) & \(\omega_{4}\) \\ \hline
20 & 0 & 0 & 3.1310 & 6.200972 & 9.159842 & 11.9746 \\  & & 1 & 3.1253 & 6.156585 & 9.01551 & 11.64893 \\  & & 2 & 3.1197 & 6.113743 & 8.881877 & 11.36322 \\  & & 3 & 3.1141 & 6.072352 & 8.757596 & 11.10944 \\ \hline
1 & 0 & 3.1367 & 6.245678 & 9.306478 & 12.31112 \\  & & 1 & 3.1310 & 6.200972 & 9.159836 & 11.97634 \\  & & 2 & 3.1254 & 6.157821 & 9.024064 & 11.68264 \\  & & 3 & 3.1198 & 6.116131 & 8.897794 & 11.42175 \\ \hline
2 & 0 & 3.1424 & 6.289444 & 9.446385 & 12.64628 \\  & & 1 & 3.1367 & 6.244425 & 9.297538 & 12.30263 \\  & & 2 & 3.1310 & 6.200971 & 9.159726 & 12.00114 \\  & & 3 & 3.1254 & 6.158989 & 9.031558 & 11.73333 \\ \hline
100 & 0 & 0 & 3.1412 & 6.279769 & 9.413272 & 12.53916 \\  & & 1 & 3.1355 & 6.234819 & 9.264947 & 12.19813 \\  & & 2 & 3.1298 & 6.191432 & 9.127618 & 11.89895 \\  & & 3 & 3.1242 & 6.149515 & 8.999898 & 11.6332 \\ \hline
1 & 0 & 3.1469 & 6.325044 & 9.563966 & 12.89129 \\  & & 1 & 3.1412 & 6.279769 & 9.413267 & 12.54065 \\  & & 2 & 3.1355 & 6.23607 & 9.273739 & 12.23304 \\  & & 3 & 3.1299 & 6.19385 & 9.143974 & 11.9598 \\ \hline
2 & 0 & 3.1525 & 6.369366 & 9.707741 & 13.24306 \\  & & 1 & 3.1468 & 6.323774 & 9.55478 & 12.88039 \\  & & 2 & 3.1412 & 6.279769 & 9.413157 & 12.56281 \\  & & 3 & 3.1355 & 6.237253 & 9.281444 & 12.28104 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Effect of the _L/h_ and small-scale parameters on the natural frequencies for a simply-simply supported triclinic nanobeam.
## 2 Performance Metrics
### Luminance and Gamma Curve  
To characterize the effect of luminance loading, the luminance of a white box with a black background was measured using the LMK when the white box size varied from a small fraction to the full screen. The white area size ratio (average pixel level (APL)) ranging from 0.01% to 1% can be used to imitate the shining stars in a black sky. According to Figure 1, the mLCD showed a higher brightness than the OLED display when \(\text{APL}\geq 1\%\). As the APL increased from 0.01% to 100%, the OLED brightness decreased from 562 nits to 431 nits, while the mLCD increased from 233 nits to 997 nits. When the bright area shrunk to a very small size, the zone crosstalk became more evident. For this reason, the mLCD brightness was adjusted to a relatively low value. For most of the image contents with \(\text{APL}\geq 1\%\), the mLCD had a much better HDR performance.  
The gamma curves of the mLCD and the OLED display panels were obtained by measuring the luminance at gray levels ranging from 0 to 255. In Figure 2a, the dots represent the measured data and the solid lines are the fitting curves. The fitted gamma values of the mLCD and the OLED display were 2.6 and 2.2, respectively. Figure 2b shows the luminance values and gamma curves at low gray levels. The OLED display deviated from the expected gamma 2.2 curve noticeably due to its much higher luminance values at the third to the eighth gray levels. As for the brightness uniformity, we used the LMK to measure the 2D luminance distributions. Taking the second gray level as an example, the mLCD panel showed a better brightness uniformity than the OLED display, as shown in Figure 3a,b. To quantitatively describe the grayscale uniformity, the root mean square (RMS) and the standard deviation (SD) were calculated. The RMS and SD values for the mLCD and the OLED display were [0.0087, 0.0138] and [0.0222, 0.0176], respectively. From Figure 3b, it can be inferred that the current control at low gray levels for the OLED display was position-dependent, leading to a relatively large luminance fluctuation.  
Figure 1: Luminance dependence on the APL.