esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Adversary Model and Assumptions  
In an IIoT network, a threat or adversary \(M\) might be internal or external. An external adversary generally uses the Internet to launch cyberattacks, such as abusing digitally equipped systems, injecting malicious content into databases, stealing confidential information, and so on, or a member of an insider group who could remain inside the network, such as a compromised IIoT device or another networked device. IIoT malware can locate and exploit weak IIoT systems and devices by manipulating devices with lower security as a platform for cyberattacks. In our investigation, we made a few other assumptions. They are as follows:  
* Trustworthy FL Aggregator: As aggregation servers are an essential part of the learning process, it is necessary that there always be some level of trust in the system that coordinates learning.
* No Malicious IIoT Device by Design: In some cases, security problems may already be present in a newly released IIoT product. These devices, however, must not be tainted or infected before they are put to their intended purpose. As a result, devices will only generate allowed interactions until an adversary M identifies and exploits any vulnerabilities, providing our model with valuable data from which to learn.
* Secure Clients: Since clients are essential components of FL learning in IIoT systems, we assume that they are secure. If such clients are compromised, the IIoT infrastructure is no longer safeguarded.
# Enhanced Heat Transfer Study of Spherical Heat Storage Based on Response Surface Methodology
## 3 Results and Discussion
The melting process of PCM within an externally heated spherical unit is quite complex. The unmelted solid moves because of the density difference between the solid and liquid phases, and typically, contact melting occurs at the bottom of the spherical unit because of the sinking of the heavier solid PCM. At this time, adding pin fins inside the spherical unit speeds up the melting of the internal PCM, and the more pin fins, the stronger the internal heat transfer effect, which correspondingly shortens the melting time of the PCM. Figure 6: Response surface (**a**) and contour plot (**b**) of the number of pin fins and water bath temperature on the complete melting time. The increase in water bath temperature and the increase of CuO nanoparticles content in Figure 8 both accelerate the melting of PCM within the spherical thermal storage unit accordingly. The addition of CuO nanoparticles increases the thermal conductivity of PCM and enhances the melting heat transfer within the spherical thermal storage unit. However, the change in thermal conductivity has a relatively small effect on the melting time compared to the increase in water bath temperature. Figure 8: Response surface (**a**) and contour plot (**b**) of water bath temperature and CuO nanoparticles mass fraction on the complete melting time. Figure 7: Response surface (**a**) and contour plot (**b**) of the number of pin fins and mass fraction of CuO nanoparticles on the complete melting time. The main objective of optimization using the response surface method is to obtain the shortest melting time. Within the range of values of the three factors, the optimum combination is chosen such that the value of melting time t is minimized. Optimization analysis of the system was carried out using Design-Expert software to obtain the optimum level of influence factors of the system as well as the optimum melting time t.  
The predicted value of \(t\) was 79.7 min, and the optimal level of each influence factor was obtained from the optimization analysis: \(N=6\), \(T=75\)\({}^{\circ}\)C, and \(M=5\) wt%. In this study, response surface methodology was applied to simulate and optimize the operational impact parameters of the melting process in a spherical thermal storage unit with a minimum number of experiments, and the mathematical analysis of variance of the response surface model verified the accuracy of the model. Experiments showed that the response surface method is accurate and reliable for optimizing the operational parameters of PCM melting in spherical thermal storage units.
# Deep-Learning Model Selection and Parameter Estimation from a Wind Power Farm in Taiwan +
###### Abstract  
Deep learning networks (DLNs) use multilayer neural networks for multiclass classification that exhibit better results in wind-power forecasting applications. However, improving the training process using proper parameter hyperisations and techniques, such as regularisation and Adam-based optimisation, remains a challenge in the design of DLNs for processing time-series data. Moreover, the most appropriate parameter for the DLN model is to solve the wind-power forecasting problem by considering the excess training algorithms, such as the optimiser, activation function, batch size, and dropout. Reinforcement learning (RN) schemes constitute a smart approach to explore the proper initial parameters for the developed DLN model, considering a balance between exploration and exploitation processes. Therefore, the present study focuses on determining the proper hyperparameters for DLN models using a Q-learning scheme for four developed models. To verify the effectiveness of the developed temporal convolution network (TCN) models, experiments with five different sets of initial parameters for the TCN model were determined by the output results of Q-learning computation. The experimental results showed that the TCN accuracy for 168 h wind power prediction reached a mean absolute percentage error of 1.41%. In evaluating the effectiveness of selection of hyperparameters for the proposed model, the performance of four DLN-based prediction models for power forecasting--TCN, long short-term memory (LSTM), recurrent neural network (RNN), and gated recurrence unit (GRU) models--were compared. The overall detection accuracy of the TCN model exhibited higher prediction accuracy compared to canonical recurrent networks (i.e., the GRU, LSTM, and RNN models).  
wind power forecasting; reinforcement learning; Q-learning; hyperparameters; parameter estimation +
Footnote †: Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Application of Generalized S-Transform in the Measurement of Dynamic Elastic Modulus
## 3 Dynamic Elastic Modulus Measurement of Rigid Materials
For the low-frequency distribution of the resonance frequency of rigid materials, according to the Nyquist sampling theory, the ADC module performs analog-to-digital conversion of the voltage signal at a sampling frequency of 40 KHz. The DMA controller responds to the trigger interrupt of ADC to move the collected data directly to RAM area, to reduce the CPU operation load. The ultimate vibration data are used as the data source of the generalized S-transform (the number of samples \(N=1024\)).
# Effect of Ion Release on Color Stability of Zirconia: A Comparative Study
## 4 Discussion
While DD showed superior performance in terms of color retention, TT zirconia may still offer value in specific clinical applications, if exposure durations and environmental conditions are carefully managed. Based on the findings of this study, it is recommended that DD multilayer zirconia with a minimum thickness of 2.0 mm be selected to achieve optimal long-term color stability and resistance to chemical degradation. The superior color stability observed in DD specimens under prolonged aging conditions supports its use in clinical situations requiring high esthetic durability. Additionally, carefully considering luting cement shade remains critical, particularly in thin zirconia restorations, due to increased translucency and potential shade influence from underlying structures. These findings offer practical guidance for material and procedural selection to enhance the long-term esthetic and functional success of zirconia-based restorations. While this study offers initial insights into how specimen thickness, immersion duration, and coloring solution aging affect zirconia color stability and ion release, several methodological limitations should be noted. As a preliminary in vitro investigation, advanced surface and structural analyses--such as spectroscopy, diffraction, and topographic profiling--were excluded due to scope and resource constraints. Additionally, the absence of a non-aged control group limits the ability to isolate the specific effects of solution aging. Other parameters critical to optical assessment, including translucency, surface roughness, and standardized imaging, were also not evaluated, thereby narrowing the interpretative depth of the findings. Future studies should adopt a more comprehensive and multidisciplinary approach to address these gaps, integrating advanced characterization techniques to unravel the physicochemical processes driving zirconia discoloration and degradation. Expanding the analysis to a broader range of material formulations and implementing clinically relevant aging models--such as pH cycling, thermomechanical loading, and dynamic salivary exposure--will enhance translational value. In vivo validation, particularly with attention to patient-specific variability in salivary composition and dietary habits, will be critical in establishing the clinical reliability of zirconia restorations. Such directions are essential for developing chemically stable, esthetically durable ceramic systems for long-term use in prosthetic dentistry.
## 5 Applications of Nanocellulose
### Biomedical Applications  
Nanocellulose is regarded as one of the most distinctive and economical natural nanomaterial, with widescale biomedical applications within the health sector. It has remarkable uses, including the creation of scaffolds, applications for drug delivery, biosensors, the dressing of wounds, diagnostic tools, and the most cutting-edge methods of tissue and cell engineering [109]. Pharmaceutical industries frequently use nanocellulose due to it being renewable, non-hazardous, biocompatible and outstandingly durable. In a recent study, nanocellulose isolated from chili post-harvest biomass residue was blended with the extract from _Hemigraphis colorata_ that displayed efficient antimicrobial activity by inhibiting the growth of _Bacillus cereus_, _E. coli_, _Pseudomonas_ sp. and _Saccharomyces cerevisiae_, showing its potential to be used as a wound dressing material [110].  
Nanocellulose has also been used in the medical industry for drug delivery to specific cells and soft tissue implants. [PERSON] et al. extracted carboxylated nanocellulose from oat husk waste biomass using ammonium persulfate oxidation, and evaluated its in vitro indomethacin drug release [111]. The results exhibited a 67% drug release in 12 h, where the chemically modified nanocellulose significantly acted as controlled indomethacinrelease carrier. Cellulose nanocrystals can be used to prepare natural hydrogel film that has the potential to be used as a drug carrier and controlled release material. Cellulosic materials, such as nanocellulose, can also be combined with the cyclodext in better drug delivery systems. Cyclodext are biologically compatible with cellulosic materials such as nanocellulose, and can therefore be covalently bond to the surface of cellulose to make the cavity of cyclodextrin accessible to guest molecules [112]. Nanocellulose is also regarded as a special scaffold material divided into three types, i.e., membrane scaffolds, bio-composite matrix and pellicle scaffolds [109]. Studies have also shown that nanocellulose in biomedical applications is nontoxic to cells, confirming its potential future applications in medical science.
# Design and Modeling of a Parent Big STAR Robot Platform That Carries a Child RSTAR
## 3 Kinematic and Dynamic Analysis
### Working Range
#### 3.4.3 Reducing Exploration Time  
Using multiple child robots can reduce the exploration time of an area substantially. Assuming that the BSTAR is carrying \(N\) (one to three) RSTAR robots which are simultaneously exploring an area, the area exploration rate \(A_{ER}\) (area scanned per time) is:  
\[A_{ER}=(N\ V_{R}+V_{B})h. \tag{17}\]  
The exploration rate of all robots simultaneously exploring (without recharging) is 15.3 m\({}^{2}\)/s and the total area that can be explored is 0.108 km\({}^{2}\). If BSTAR is acting as the charging point for the RSTAR robots, the exploration rate becomes a function of the charging time \(T_{charging}\) and the working time \(T_{duty}\) of the child robots:  
\[A_{ER}=N\cdot\frac{T_{dutyR}}{T_{dutyR}+T_{chargingR}}V_{R}\cdot h. \tag{18}\]  
If the RSTAR robots are working alone (without the BSTAR) but continuously recharging their batteries from the BSTAR (\(T_{dutyR}\) = 1.77 h and \(T_{chargingR}\) = 1 h), the \(A_{ER}\) is 6.1 m\({}^{2}\)/s and the total explored area is 0.375 km\({}^{2}\).  
This methodology makes it possible to determine the size of battery that the parent robot can carry and the maximum effective distance that the robots can scout together as a function of the range of the RSTAR. For example, beyond 40 recharging cycles, it would be more advantageous to increase the battery size of the child robot to increase the efficiency of the scouting by reducing the time and distance that the child robots spend travelling back and forth from the parent robot to the scouting target zone.
## 3 Results and Discussion
### Flow Characteristics  
Figure 7 depicts the contours of velocity in the longitudinal sections of the plain tube and enhanced tube with BTs in the case of different ball diameter ratios and spacer lengths at a Reynolds number of 20,000. At the same Reynolds number, it is clear that the flow velocity of fluid near the wall in the tube with BTs is significantly improved, compared with the plain tube. It is also noted that the maximum velocity appears near the wall region instead of the central region. This change increases the velocity gradient in the boundary layer. This effect can periodically induce intense turbulence distribution in the enhanced tube with BTs. Thus, the flow boundary layer is periodically disturbed, which is beneficial to the heat transfer between the fluid and the wall. However, this inevitably leads to a greater flow resistance as well.  
Figure 7b-d shows that the boundary layer disturbance is greatly intensified by BTs, and the relevant intensity increases with the increase of the ball diameter ratio. Thus, the flow boundary layer becomes thinner and the friction factor increases at larger ball diameter ratios, as previously shown in Figure 6. At the same time, the heat transfer rate rises as well. The contours of velocity in the longitudinal sections of the enhanced tube are presented in Figure 7d-f for different spacer lengths, respectively. It can be observed that using a smaller spacer length between the BTs is more able to frequently disturb the boundary layer, which leads to a stronger disturbance upon the fluid. Therefore, both the heat transfer rate and flow resistance will decrease with the increase in spacer length.
## 4 Results
### Evaluation Index and Parameter Setting
#### 4.1.2 Parameter Setting  
The experiment utilized a 6 GHz i9-14900k CPU with 32 GB of memory and two NVIDIA GeForce RTX 3090 GPUs with a combined memory of 48 GB to implement the proposed method. We use version 1.9.1 of the PyTorch framework. To optimize GPU memory usage and ensure fair comparisons with different methods, we partitioned the image into multiple patches, each measuring 256 \(\times\) 256. To ensure consistency in the experiment, we set the manual seed to 1. Additionally, the parameters \(\lambda_{1}\) and \(\lambda_{2}\) in the loss function were also set to 1. The network is trained using the Adam optimizer with a base learning rate of \(1\times 10^{-4}\), which is decayed by 0.5 every 200 epochs. The model was trained with a batch size of 16 for 1000 iterations, resulting in a total training time of 20 h.
# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 5 Results Simulation and Analysis
### Model Complexity Analysis  
Time complexity and space complexity are two important indexes to measure an algorithm, which are used to indicate the amount of time increase and auxiliary space required by the algorithm in the worst state. In deep learning, the Flops value is usually used to explain the time complexity of the model, and the parameters of the model are used to explain the space complexity of the model. Based on the above description of model prediction steps, the time complexity of each model training is analyzed. The complexity of a neural network is usually represented by floating-point arithmetic number Flops. Based on the results in Table 2, we can clearly see that the Flops value of the scheme proposed in this paper is the lowest, so the complexity of the scheme proposed in this paper is the lowest. Meanwhile, according to the results of Figure 12, it can be seen that the lossvalue of the scheme proposed in this paper decreases the fastest and the loss value is the smallest. We can see that the complexity of the model after optimization is lower than that before optimization. Based on the analysis, it can be concluded that the complexity of the proposed scheme is obviously better than that of the comparison algorithms.  
Figure 10: _Cont._  
Figure 10: Comparison of prediction effects of different prediction models.
# Q-MeaMetaVC: An MVC Solver of a Large-Scale Graph Based on Membrane Evolutionary Algorithms
## 1 Introduction
Each membrane splits some vertices into the secondary membrane during the splitting stage, and the optimal secondary membrane is selected for fusion during the fusion stage, which accelerates the search process. Experimental results show that MEAMVC performs well. Improving search efficiency and capabilities for large sparse graphs are the two main aspects of a better MVC solver. FastVC2 has improved the search capabilities of the local search algorithm, and PEAVC has improved search efficiency. The MEA has the characteristics of solid flexibility and high search efficiency and is suitable for parallel computing. It can better prevent the search process from falling into local optimal. This article focuses on combining the PEAF framework with the membrane evolution algorithm. The main contributions include the following:  
1. In combination with PEAF and MEAF, we designed an MVC solver called Q-MeaMetaVC aimed at large and sparse graphs. 2. We designed different membrane structures based on the characteristics of connected components in bipartite and non-bipartite graphs, in order to facilitate the solution. 3. We designed MEA operators suitable for MVC, including the BMS strategy used during the division, fast add operator to speed up the recovery speed after division, as well as selection and cytolysis operators. 4. We conducted parameter experiments and algorithm comparison experiments on Q-MeaMetaVC. In six of the test set instances, Q-MeaMetaVC obtained the optimal solutions that had not been reported before. The experimental results validate the feasibility and effectiveness of the membrane evolution algorithm in solving the MVCP of large-scale sparse graphs. The rest of this article is as follows. Section 2 introduces basic knowledge, including the operators and framework of MEA and, also, briefly introduces some algorithms of MVC to the relevant definitions and solving of the MVCP. Section 3 presents in detail the membrane structure of Q-MeaMetaVC, the algorithm framework, and the algorithm design for the bipartite and non-bipartite graphs. In Section 4, a sample shows how Q-MeaMetaVC searches for higher-quality solutions. Section 5 describes comparative experiments conducted on the parameters of Q-MeaMetaVC in recent years and the test set with the best MVC solvers. The experimental results are analyzed, verifying the feasibility of Q-MeaMetaVC. Finally, the conclusion and future research are presented in Section 6.
# V2G Strategies to Flatten the Daily Load Curve in Seoul, South Korea
## 2 Daily Load Curve Evaluation Method  
In this chapter, a method for evaluating daily load curves for vehicle charging and discharging at various charge and discharge locations in large cities is proposed. The charge/discharge load estimation assumes that people start charging their EVs from the grid after they get home from work in the afternoon. To mitigate grid peaks, it is also assumed that people will discharge electricity to the grid via their vehicles after going to work in the morning.  
The share of EVs was estimated based on the scenarios for the shares of electric vehicles in Seoul, Korea, in 2030 and 2040 through the long-term plan for electric vehicles in Seoul.  
By combining the KEPCO (Korea Electric Power Company) SMP and TOU rates with the discharge and charge rates, the charge and discharge daily load curves for Seoul in 2030 and 2040 were calculated.  
The steps for this method are described below:  
1. Calculate EV discharge probability density function at work in the morning.
2. Calculate EV charging probability density function at home after work in the afternoon.
3. Calculate the initial state of charge (SOC) of EV batteries at work and at home.
4. Calculate EV charging and discharging power per hour using the charge/discharge probability density function of the EV and the SOC state of the EV battery at work and at home.
5. Calculate the daily load curve for charging and discharging EVs in Seoul in 2030 and 2040 using the charging and discharging power of EVs based on the charge time and the discharge time either at work or at home.  
Through the proposed calculation method, accurate daily load curve data were generated based on the charge and discharge load of EVs at work and at home in Seoul. In addition, a basis for establishing a billing TOU, SMP rates, and load management plans to prevent overload of the grid system in Seoul is presented.