esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Online Optimization and Experimental Validation
### Online Optimization  
First, the step responses of the simulation and experimental studies are shown in Figure 15; it can be seen that the initial and intermediate responses of the experimental result closely match the simulated ones, as the system model is relatively accurate. However, the simulated and experimental responses deviate when the valve approaches the setpoint. Although a well-calibrated Stribeck friction curve can be used to describe the nonlinearity of friction at low valve velocity, a modeling error remains, leading to the degradation of control performance. Therefore, it was necessary to conduct an online optimization study based on the offline optimized controller to further improve the closed-loop system performance. By analyzing the offline simulation results, it was found that as long as the optimal scheduled gain function is obtained, the optimal gain-scheduled PID controller can be used under different operational conditions. Therefore, to reduce computational cost and the required number of experiments, the 0 to 50 percent opening case was selected for online optimization.  
Using the binary crossover probability and distribution index at \(P_{c}\) and \(D_{c}\) with the polynomial mutation probability and distribution index at \(P_{m}\) and \(D_{m}\) from the simulation study, the parameters for the online optimization study are listed in Table 7. Benefiting from the iterative search structure, the initial search space of \(G_{p}\), \(G_{i}\), and \(G_{d}\) can be defined in a relatively small range (\([0.99,1.01]\) here). By evaluating the results of the first search, certain saturations can be observed for \(G_{p}\), \(G_{i}\), and \(G_{d}\) near the Pareto front, which leads to search space migration in the second iteration. As can be seen from Figure 16, after migrating the search space, the overall performance of both the whole population and Pareto-front population in the second iteration (the orange dots) are improved over the first iteration (the blue dots). Finally, the yellow circled point is selected as the optimal solution, as it has a minimal distance to the origin and results in improved online optimization of the control performance (see Figure 17). In summary, a comparison of the offline fixed gain  
Figure 14: Decision variables for online NSGA-II optimization bench test.
# Blind Image Deblurring Based on Local Edges Selection
## 3 Results and Discussion
### The Convergence  
In order to verify the effectiveness of the proposed method, we test the convergence. Figure 5 shows the residual [35] of the proposed method with the iteration time increases. In Figure 5a, the residual of R, G, and B channel are calculated respectively, of the color image. Figure 5b shows the  
Figure 4: Sensitivity analysis of \(\alpha\) and \(\beta\). (**a**) The sensitivity analysis of \(\alpha\); (**b**) the sensitivity analysis of \(\beta\).  
Figure 3: (**a**) Clear image, (**b**) edges selected with the window size \(=5\times 5\), (**c**) edges selected with the window size \(=7\times 7\), (**d**) edges selected with the window size \(=9\times 9\), (**e**) blurred image, (**f**) deblurring result with the window size \(=5\times 5\), (**g**) deblurring result with the window size \(=7\times 7\), (**h**) deblurring result with the window size \(=9\times 9\).  
residual of blurring kernel. With the iteration time increases, the residual [23] gradually reduces. When the iteration reaches 20, the proposed algorithm can converge to a high precision, so the inner iteration times of the image and blurring kernel are set to be 20 in the deblurring process. From the convergence test, we can conclude that the proposed algorithm can converge to the real results of clear image and blurring kernel with high probability.
## 3 Results and Discussion
### Cells Voltage and Thermal Responses Comparisons  
The comparison between the voltage response of the fresh and aged cells at 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C are illustrated in Figure 13, respectively. A decrease in capacity of the aged cell while discharged is observed at both room and cycling temperature. Considering the long-term cycling under 35 \({}^{\circ}\)C, the capacity fade observed can be attributed to the side reactions inducing further an important loss of usable \(Li^{+}\) and active material particles participating in the electrochemical lithiation/delithiation reactions inside the cell.  
Figure 12: Temperature increase of the cell 2 at 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C under various current rates.  
The comparison between the temperature increases of the fresh and aged cells at 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C are illustrated in Figures 14 and 15, respectively. These figures highlight the impact of the choice of the temperature for assessing the thermal responses of fresh and aged cells respectively, when discharged with various currents. At both room and cycling temperatures, the aged cell exhibits more pronounced temperature rises compared to the fresh cell. After long-term cycling, the voltage of the aged cell tends to be lowered and the internal resistance tends to increase, which leads to greater heat generation and hence greater temperature rises. Further, Figures 14 and 15 reveal that when assessed at the same temperature (respectively at room or cycling), the evolutions of the temperature at the surface of the battery cell prior in contrast to after long-term cycling are although not significantly differing. These differences are less in comparison to the differences observed in the thermal behaviour of the cells when assessed at different temperatures (either room/cycling or cycling/room). At both room and cycling temperatures, the maximum difference between the fresh and aged cells surface temperature evolutions being round 5%.  
Figure 14: Temperature increase at 25 \({}^{\circ}\)C for fresh and aged cells.  
Figure 13: Discharge capacity at 25 \({}^{\circ}\)C and 35 \({}^{\circ}\)C of the fresh and aged cells at various current rates.  
In the long term, results presented in Sections 3.1 and 3.2 suggest to carry out in a systematic manner, the evaluation of batteries performances at the cycling temperature. According to this, in real applications as in automotive, it can be recommended to use the track of the temperature within the battery pack (e.g., mean) recorded by the battery management system, to define the temperature at which battery cells performances should be evaluated. In addition, room temperature can be used if needed for the comparison of battery cells performances, of same type that are though not operating at the same temperature.
## 4 Discussion
In general, 2-MTHF was the best biobased solvent to extract compounds, like phenolic compounds, due to its balanced polarity and ability to form effective hydrogen bonds with phenol groups. However, EtOAc extracted a higher amount of compounds from the SPLE burdock roots than CPME due to its moderate polarity and the ability of its carbonyl group to form hydrogen bonds, although it is not as effective as 2-MTHF. By contrast, the low polarity and more hydrophobic structure of CPME resulted in it being the least efficient method for extracting compounds from the SPLE burdock roots. Considering these results, 100% 2-MTHF biobased solvent was selected as the best method for the recovery of compounds from burdock roots. After that, an optimization of the PLE extraction was carried out by testing three different temperatures (40 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, and 160 \({}^{\circ}\)C). The extraction at these three different temperatures was compared in terms of TPC, antioxidant and anticholinergic capacities, and total peak areas of the chromatogram HPLC-DAD analysis at 280 nm. The extracts obtained at 160 \({}^{\circ}\)C presented higher TPC values and antioxidant capacity compared to the use of 40 \({}^{\circ}\)C and 100 \({}^{\circ}\)C. By contrast, the PLE extracts obtained at 100 \({}^{\circ}\)C presented higher anticholinergic capacity than the rest of the extracts. The high extraction efficiency at 160 \({}^{\circ}\)C could be due to the generation of Maillard products, which could contribute to the high TPC values and the antioxidant capacity measured by interferences with the assays, overestimating the phenolic content and the antioxidant capacity of burdock roots [46]. In addition, a brown color was observed in the extract, typical of the Maillard reaction. In fact, the HPLC-DAD analysis demonstrated that phenolic compounds were reduced when the extraction temperature increased from 100 \({}^{\circ}\)C to 160 \({}^{\circ}\)C. Thus, 100 \({}^{\circ}\)C was selected as the optimal PLE extraction temperature to obtain antioxidant and anticholinergic compounds from the burdock roots, preserving their degradation. In addition, the PLE extracts were compared with a maceration performed for 24 h. The results indicated that employing high pressure and temperatures promoted the extraction of antioxidant and anticholinergic phenolic compounds compared to maceration, regardless of the temperature used. High pressure and temperature favor the solubility of the compounds in the solvent and the breaking down of the plant's cell walls, allowing for faster and more efficient extraction than the maceration process [47]. In addition, the results suggested that prolonged extraction times during maceration could promote the degradation of bioactive phenolic compounds, obtaining lower TPC, antioxidant, and anticholinergic capacities, as can be observed in Figure 2.
## 5 Conclusions
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No.51506001), Beijing Municipal Education Commission (Grant No. KM201710005029), and National Key R&D Program of China (Grant No. 2016 YFE0124900). The authors gratefully acknowledge them for financial support of this work. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature Symbol** \begin{tabular}{l l} \(m\) & mass flow rate (kg/s) \\ \(c_{p}\) & specific heat capacity at constant pressure (kJ/kg-K) \\ \(T\) & temperature (\({}^{\circ}\)C) \\ \(\Delta T\) & pinch point temperature difference (\({}^{\circ}\)C) \\ \(h\) & enthalpy (kJ/kg) \\ \(d\) & internal diameter (mm) \\ \(D\) & external diameter (mm) \\ \(Re\) & Reynolds number \\ \(Pr\) & Prandtl number \\ \(Bo\) & boiling characteristic number \\ \(x\) & vapor quality \\ \(Q\) & heat (kW) \\ \(K\) & heat transfer coefficient (W/m\({}^{2}\)-K) \\ \(\Delta T_{m}\) & logarithmic mean temperature difference (K) \\ \(A\) & heat transfer area (m\({}^{2}\)) \\ \(W\) & work (kW) \\ \(p\) & pressure (MPa) \\ \(p_{\rm w}\) & price of working fluid (\(\$\)/kg) \\ \(M_{\rm w}\) & mass of working fluid (kg) \\ \(p_{\rm e}\) & price of electricity (\(\$\)/kW-h) \\ \(\rho\) & density (kg/m\({}^{3}\)) \\ \(\mu\) & dynamic viscosity (Pa-s) \\ \(\delta\) & thickness of the tube (mm) \\ \(\alpha\) & convective heat transfer coefficient (W/m\({}^{2}\)-K) \\ \(\lambda\) & thermal conductivity (W/m\(\cdot\)K) \\
**Subscripts** \\
1-6 & state points in the cycle \\ g & flue gas \\ wf & working fluid \\ w & cooling water \\ i & inlet \\ o & outlet \\ l & liquid \\ v & vapor \\ e & evaporator \\ c & condenser \\ E & evaporation \\ C & condensation \\ min & minimum \\ max & maximum \\ s & single-phase \\ t & two-phase \\ ex & expander \\ p & pump \\
**Abbreviation** \\ ORC & Organic Rankine Cycle \\ CEPCI & Chemical Engineering Plant Cost Index \\ NE & Net Earning \\ ROI & Return on Investment \\ COM & Cost of Maintenance \\ PP & Payback Period \\ LEC & Levelized Energy Cost \\ C\({}_{\rm PV}\) & present value of total profile in system service life \\ \end{tabular}
# Energy-Efficient Resource Allocation for Downlink Non-Orthogonal Multiple Access Systems
###### Abstract  
With the rapid popularization of intelligent terminals and the explosive growth of wireless communication service demand, future mobile communication technology will face many challenges. Non-orthogonal multiple access (NOMA) technology for 5G can provide many connections and effectively improve the frequency spectrum and energy efficiency compared to traditional orthogonal multiple access technologies. Therefore, in recent years, NOMA technology has become one of the research hotspots of numerous scholars. However, the resource allocation problem in the NOMA system, as a high-dimensional nonlinear programming problem, has not been well studied. In addition, the particle swarm optimization algorithm can also effectively find the optimal solution for complex and constrained problems. Still, at the same time, it is easy to fall into local optimal defects. In this context, we decouple the high-dimensional nonlinear programming problem to maximize system energy efficiency into sub-problems: subchannel and power allocation. Firstly, a low-complexity greedy algorithm based on the principle of worst-case subchannel priority matching is proposed to solve the subchannel assignment problem. In addition, we further apply the modified particle swarm optimization algorithm to allocate power to the NOMA downlink system, aiming to improve the energy efficiency of the communication system as much as possible under the premise of ensuring the quality of service (QoS). Simulation results show that our proposed scheme has low complexity and can significantly improve the energy efficiency of the NOMA system and achieve better user fairness.  
n +
Footnote †: journal: Applied Sciences  
communication system due to their excellent performance [6; 7; 8; 9; 10]. The main idea of NOMA technology is to use power multiplexing technology at the transmitter end and actively introduce interference, multiplex superimposed signals for transmission. It eliminates the multiple-access interference through the serial interference canceling (SIC) receiver [11], thus achieving correct demodulation for multiple users. However, interference between users in NOMA can significantly affect system performance. Based on ensuring user access fairness, using reasonable user grouping and power allocation algorithm can improve the total rate of the NOMA system [12; 13]. Therefore, studying NOMA technology's critical technology and resource allocation algorithm can effectively enhance resource utilization and system capacity, which has important significance and application value.
# Neural-Network-Assisted Polar Code Decoding Schemes
## 4 Numerical Simulation
### Performance with Different \(N_{s}\)  
When simulated with a code length of 1024, a code rate of 0.5 and \(N_{S}\) of 4, 8 and 16, the BER performance are shown in Figure 13. The BER curve of KSNNAD with \(N_{S}\) 4 is marked as KSNNAD \(N_{S}=4\), while LSNNAD with \(N_{S}\) 4 marked as LSNNAD \(N_{S}=4\), and so on.  
As shown in Figure 13, combining NNN with R1, R0, SPC, and Rep can indeed improve decoding performance, but the improvement differs with different \(N_{S}\) and different schemes. Both KSNNAD and LSNNAD have bit error performance improvement as \(N_{S}\) increases and the longer the \(N_{S}\), the smaller the improvement. KSNNAD outperforms LSNNAD due to performance boost at the key bit, regardless of the value of \(N_{S}\).
# Will Zero Vulnerability Computing (ZVC) Ever Be Possible? Testing the Hypothesis
## 1 Introduction  
Cybercrime inflicts damages totaling USD 6 trillion annually and is predicted to grow to over USD 10.5 trillion industry by 2025 [1]. A hack attack occurs **every 39 s**, and approximately **300,000** new malware programs are created daily [2]. Today, over 4.5 billion connected devices remain at risk of cyber-attack [3]. With the exponential proliferation of IoT devices [4] and the ever-growing vulnerabilities of connected devices, the cybercrime industry is poised for unstoppable growth [5].  
Advances in computer science and IoT computing have been running at ultrahigh speeds. We are looking at the future in which everything will become a connected computing device [6]. Toys, clothes, cars, door locks, contact lenses, clothes, toasters, refrigerators, washing machines, light bulbs, toothbrushes, glasses, helmets, etc., are getting \"smart\". Everything has become a branch of computer science.  
\"_Everything is now a computer. The digital nightmare is about to get much worse_\", claims Bruce Schneier in his latest book _Click Here to Kill Everybody_[7].  
A Brief History of Computers & the Origin of Their Vulnerabilities  
Back in the 1940s, Thomas Watson, boss of the giant IBM Corporation, made probably the worst prediction in history, \"the world would need no more than about five computers [8]. Eight decades later, experts are predicting 50 billion Internet-connected devices by 2030 [9].  
The first computers were gigantic calculating machines, and all they ever really did was \"crunch numbers\": solve lengthy, difficult, or tedious mathematical problems. There was no software industry; no operating systems and programs were custom written for each computer individually until IBM launched its first personal computer in 1981 with an operating system that granted third-party permissions to developers of software applications. Since then, it became standard practice to produce computers with third-party permissions. In the 1990s, with the advent of the Internet, bad actors started exploiting these permissions to write malicious programs and install them on victim computers remotely. Such attack vectors created an attack surface that introduced vulnerabilities, feeding a thriving multitrillion cybercrime industry. In the state-of-the-art, it is impossible to build a computer without third-party permissions and consequently free of vulnerabilities. Essentially, legacy computers cannot prevent malware from exploiting the inherent permissions that are available for all third-party programs. However, the network efficiencies have reached a level that even the most resource-intensive applications can now run remotely without the need for local installation [10]. This introduces the possibility of doing away with third-party permissions entirely and, consequently, eliminating the attack surface to achieve zero vulnerability. Our research on zero vulnerability computing exploits these possibilities for rendering computing devices immune to cyber-attacks.
## 7 Defenses against Privacy Attacks
### Split Learning  
Split learning divides the machine learning model between the client and server, ensuring that sensitive data remain on the client side while enabling distributed model updates [160; 161]. This approach protects against attacks aiming to steal or analyze private information by minimizing data exposure during model updates [162; 163]. By keeping data decentralized, split learning not only enhances privacy but also facilitates collaborative model training across distributed networks [164]. Figure 18 presents the block schematic of split learning.  
For instance, SplitLearn [165] enhances model privacy through distributed training mechanisms. It partitions the neural network model between clients and servers, ensuring that no single entity can access the complete model. During training, only intermediate representations such as activations and gradients are exchanged, preventing the reconstruction of the entire model from any single source. This decentralized approach supports flexible deployment options based on trust levels among the participants, maintaining robust privacy protections while enabling incremental training by new clients.  
Figure 17: Visual representation of Secure Multi-party Computation.  
Additionally, Federated Split Learning (FSL) [166] addresses security concerns by focusing on securing intermediate data exchanged during training. It applies privacy-aware loss functions and integrates differential privacy techniques to add noise, preventing attackers from reconstructing the original training data. FSL achieves strong accuracy-privacy trade-offs compared to the traditional methods, underscoring its effectiveness in protecting sensitive data and ensuring reliable FL processes.  
Moreover, Splitfed Learning (SFL) [167] incorporates robust frameworks to safeguard sensitive information. SFL divides the model between client and server, ensuring that neither party has access to the complete model parameters, thereby bolstering privacy. It integrates differential privacy techniques by perturbing the model gradients with calibrated noise and incorporates PixelDP noise layers to enhance the privacy protections during data transmission. Together, these approaches fortify the privacy of shared data in FL, providing robust defense against privacy breaches.
# Distributed Generation Cluster Division Method Considering Frequency Regulation Response Speed
## 2 Supply and Demand Flexibility of DGC for Cluster Division  
### Supply and Demand Flexibility Capacity  
The demand flexibility of the cluster is mainly determined by two parts: renewable energy DG and load. Figure 1 shows the original load and the net load curve considering wind and solar DG. Compared with the original load curve, the net load curve, after considering wind power, is more unstable and fluctuates more, which causes the system's demand flexibility to increase.  
On the other hand, the DGs within the cluster that can provide resource flexibility include energy storage equipment and gas turbines. The advantage of energy storage devices over other devices is that they have both charging and discharging capabilities and can respond in a timely manner to the volatility of renewable energy output. Gas turbines (GTs) can provide bidirectional regulation under less than full load conditions and have the ability to flexibly adjust supply upward and downward.  
According to the analysis above of the climbing DGC demand flexibility and supply flexibility of each DG within the DGC, this paper describes the cluster climbing supply-demand flexibility equilibrium in terms of a demand flexibility shortage. The cluster supply-demand flexibility equilibrium degree refers to the demand flexibility of each cluster within a certain time period after fully considering the flexibility regulation capability of each cluster. The mathematical expression is as follows:  
\[\Delta F_{i}^{\tau,M}(t)=\begin{cases}\Delta P_{X_{i}}^{\tau,M}(t)-\Delta P_{G_ {i}}^{\tau,M}(t)&,&\Delta P_{X_{i}}^{\tau,M}(t)>\Delta P_{G_{i}}^{\tau,M}(t)\\ 0&,&\Delta P_{X_{i}}^{\tau,M}(t)\leq\Delta P_{G_{i}}^{\tau,M}(t)\end{cases}, \tag{1}\]  
where \(\Delta P_{X_{i}}^{\tau,M}(t)\) is the cluster demand flexibility, characterizing the time-series climbing power of the net cluster load, which has the mathematical expression  
\[\Delta P_{X_{i}}^{\tau,M}(t)=\left|\sum_{k\in i}P_{k}(t)-\sum_{k\in i}P_{k}(t- \tau)\right|, \tag{2}\]  
where \(P_{k}(t)\) and \(P_{k}(t-\tau)\) are the net load power of node \(k\) at moments \(t\) and \(t-\tau\) 2, respectively. \(\Delta P_{G_{i}}^{\tau,M}(t)\) is the cluster supply flexibility capacity, which characterizes the comprehensive capacity of flexible resource regulation of all source load storage within the cluster, and its mathematical expression is as follows:  
\[\Delta P_{G_{i}}^{\tau,M}(t)=\sum\Bigl{(}\Delta P_{g}^{\tau,M}(t)+\Delta P_{d}^ {\tau,M}(t)+\Delta P_{s}^{\tau,M}(t)\Bigr{)}, \tag{3}\]  
Figure 1: Net load and load curve considering wind and photovoltaic distributed generation.  
where \(\Delta P_{g}^{\tau,M}(t)\), \(\Delta P_{d}^{\tau,M}(t)\), and \(\Delta P_{s}^{\tau,M}(t)\) are the supply flexibility capacity of conventional units, interruptible loads, and energy storage systems, respectively.