esa-sceva/satcom-chunk-collection huggingface dataset:
# Hierarchical Synchronization Control Strategy of Active Rear Axle Independent Steering System
## 3 Hierarchical Synchronization Control Strategy Design
### Virtual Synchronization Controller  
The dotted box in Figure 4 represents the established VRSM, which adopts the concept of a traditional rack and pinion steering gear. Considering system stiffness and damping, the dynamic model of VRSM can be described as Equation (9) according to [PERSON]'s law.  
\[m_{r}\ddot{x}_{r}+c_{r}\dot{x}_{r}+k_{r}x_{r}=F_{r}-F_{r1}-F_{r2} \tag{9}\]  
where \(m_{r}\) is the equivalent mass of the rack; \(x_{r}\) is the translational displacement of the rack; \(k_{r}\) and \(c_{r}\) are the equivalent stiffness coefficient and damping coefficient respectively; \(F_{r}\) is the virtual synchronization driving force of the steering rack; and \(F_{r1}\) and \(F_{r2}\) are the forces fed back to the steering gear by the rear wheels on both sides.  
In order to realize steering angle synchronization control, Proportional-differential (PD) feedback control law is introduced by  
\[\begin{array}{ll}F_{r}&=k_{rp}e_{s}+k_{rl}\dot{e}_{s}\\ &=k_{rp}(\delta_{r}-\delta_{r}^{*})+k_{rl}(\dot{\delta}_{r}-\dot{\delta}_{r}^{* })\end{array} \tag{10}\]  
Figure 3: The hierarchical synchronization control strategy of active rear axle independent steering (ARIS) system.  
where \(e_{s}\) is defined as the synchronization error; \(k_{rp}\) and \(k_{rd}\) are the proportional coefficient and differential coefficient respectively; and \(\delta_{r}^{*}\) is the equivalent rear axle steering angle derived from the measured rear left and right wheel steering angle. When only the relationship between the steering angles of the rear wheels is considered, according to the Ackerman principle, \(\delta_{r}^{*}\) can be inversely solved by  
\[\delta_{r}^{*}=\arctan\frac{2\tan\delta_{r1}^{*}\tan\delta_{r2}^{*}}{\tan \delta_{r1}^{*}+\tan\delta_{r2}^{*}} \tag{11}\]  
where \(\delta_{r1}^{*}\) and \(\delta_{r2}^{*}\) represent the measured wheel steering angle, respectively.  
\(x_{r}\)/\(g_{r}\) can be taken as virtual rear axle steering angle after synchronization controller. \(g_{r}\) is the radius of the virtual steering pinion gear. Referring to Equation (8), the output reference angle of VRSM can be obtained as  
\[\left\{\begin{array}{l}\delta_{r1}=\arctan\left(\frac{\rho}{\rho-w/2}\tan \frac{x_{r}}{g_{r}}\right)\\ \delta_{r2}=\arctan\left(\frac{\rho}{\rho+w/2}\tan\frac{x_{r}}{g_{r}}\right) \end{array}\right. \tag{12}\]  
In the proposed strategy, \(\delta_{r}\) is not directly calculated to get the reference angle signal of each rear wheel, but after the synchronization controller. In the presence of external interference, the forces \(F_{r1}\) and \(F_{r2}\) are fed back to the steering rack, then the VRSM senses the external disturbance and immediately adjusts the reference steering angle of the left and right rear wheels to realize instantaneous synchronization control.
## 4 Conclusions  
Magnetic beads of alginate citrate-stabilized maghemite nanoparticles and functionalized multiwalled carbon nanotubes (A/\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)/f-CNT) were prepared, characterized and used as adsorbent for methylene blue dye. The kinetic and equilibrium experimental data were well-explained by the pseudo-second order kinetic model and Sips isotherm equation, respectively. The maximum adsorption capacity determined by Sips model was of 320.5 mg g\({}^{-1}\), very close to the experimental value (307.0 mg g\({}^{-1}\)), suggesting a homogenous adsorption process in the studied range of MB concentrations. The studies of pH and temperature effect on the adsorption process revealed that the beads exhibited an important adsorptive stability over a large range of pH values (4-10) and the endothermic and spontaneous nature of the process. The statistical study using the monolayer model demonstrated the multi-molecular nature of the adsorption process, with a vertical adsorption geometry of the dye. The magnetic beads could be used at least for six adsorption/desorption successive cycles, maintaining its adsorptive capacity. According to the results of this study, the synthesized composite beads can be considered as high effective adsorbents of the model compound, revealing as excellent materials for dyes removal in flow-through adsorption WWTP applications.  
Figure 10: Desorption efficiency of MB onto A/\(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)/f-CNT composites after 6 adsorption-desorption cycles.  
**Author Contributions:** Conceptualization, S.A.-T., M.B., and N.B.; data curation, M.B., and N.B.; formal analysis, S.A.-T., M.B., and N.B.; investigation, S.A.-T., M.B., N.B., and M.M.; methodology, S.A.-T., M.B., and N.B.; project administration, M.B., and N.B.; resources, S.A.-T., M.B., N.B., and M.M.; software, S.A.-T., M.B., N.B., and M.M.; supervision, S.A.-T., and M.B.; writing-original draft, M.B.; Writing-review & Editing, S.A.-T., M.B., and N.B.  
**Funding:** This research has not received any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.  
**Acknowledgments:** The authors wish to thank University Ferhat Abbas of Setif, Algeria, for the financial support. Additionally, the authors acknowledge the scientific and technical support from Autonoma University and Complutense University of Madrid, Spain.  
**Conflicts of Interest:** The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 2 Related Works and Our Approaches  
### Related Works  
As we discussed in the previous section, an optical filter shift in ROADM nodes in an optical network impacts the QoT. A machine-learning-based approach for detecting such a shift using an Optical Spectrum Analyzer (OSA) has been reported in [21]. In this method, the spectrum of a correctly filtered signal exhibits distinct characteristics, including a flat region near the center wavelength, sharp edges, and a rounded transition between the edges and the center frequency. When a filter shift occurs, these spectral features are altered. The detection process leverages residual signals, which are calculated as the difference between the measured spectrum and the ideal spectrum. These residuals serve as input features for a machine-based algorithm to identify a filter shift effectively [22]. However, a notable limitation of this approach is the requirement for additional hardware, such as OSAs, to be deployed at each reconfigurable optical add/drop multiplexer (ROADM) node, which may increase system complexity and costs.  
In contrast to the above work, we propose a convolutional neural network (CNN)-based approach [21] that utilizes constellation data obtained prior to demodulation by the transponder. By analyzing these constellation data before the demodulation process within the Digital Signal Processor (DSP)-LSI installed in the digital coherent optical receiver, our method achieves high accuracy detection of the optical filter shift [21]. This approach directly examines the optical signal itself, enabling a precise determination of whether the signal is affected by optical filters. Furthermore, it requires only front-end ADC and feedback circuits for sampling synchronization, eliminating the need for OSAs. This simplification reduces hardware complexity and enhances the practicality of the solution while maintaining reliable detection performance.  
Asynchronous sampling methods have already been proposed for detecting the Optical Signal-to-Noise Ratio (OSNR) of optical signals [23]. They have successfully estimated OSNR for various modulation formats' optical signals by using machine learning techniques. Such information can be used for QoT estimation. However, an asynchronous sampling procedure averages time-dependent features contained in the received signal. Therefore, it will erase important information resulting from time-dependent phenomena.  
We propose a novel data acquisition method to improve the quality of data for machine learning in the next section.  
As discussed above, there have been some reports on fault diagnosis or detection of ROADMs in a photonic network. However, to the best our knowledge, there have been no reports of failure prediction of optical filters in ROADMs. We propose a novel failure prediction technique for optical filters in ROADMs in this paper.
# An Evolutionary Algorithm to Optimise a Distributed UAV Swarm Formation System
## 4 Optimisation Approach
### Evolutionary Crossover Operators  
We plan to test two different crossover operators to see which one produces better results to the UAV swarm formation problem. First, we describe the well-known uniform  
Figure 3: Differences between a canonical GA and our proposed EA shown in coloured blocks.  
crossover [24] (UCX), in which the offspring is calculated by swapping individual genes (parameters) in the chromosome (system configuration). Algorithm 3 shows the pseudocode of the UCX where it can be seen that each pair of individuals from the population are subject to crossover, if a generated random number is less than the crossover probability \(P_{c}\). Consequently, each gene could be swapped between individuals, depending on another generated random number uniformly distributed throughout the chromosome (swap probability = 0.5). Otherwise, the offspring would be an identical copy of their parents. Figure 4 shows an example of UCX for the case of three UAVs where genes 2, 3, 7, 8, 10, and 12 have been swapped between individuals. The algorithm using the UCX operator would be named EA.ucx.  
```
functionUCX(\(Pop,P_{c}\)) \(Q\leftarrow\emptyset\)\(\triangleright\) New empty working population for\(ind_{1},ind_{2}\in Pop\)do\(\triangleright\) For each pair of individuals in Pop \(offspr_{1}\gets ind_{1}\)\(\triangleright\) Copy individuals into offspring \(offspr_{2}\gets ind_{2}\) if\(RND()<P_{c}\)then\(\triangleright\) Crossover probability for\(i\gets 1,len(offspr_{1})\)do\(\triangleright\) For each parameter in offspring if\(RND()<=0.5\)then\(\triangleright\) Swap probability \(aux\gets offspr_{1}[i]\)\(\triangleright\) Swap parameter values \(offspr_{1}[i]\gets offspr_{2}[i]\) \(offspr_{2}[i]\gets aux\) endif endfor endif \(Q\gets Q\cup\{offspr_{1},offspr_{2}\}\)\(\triangleright\) Add offspring to the working population endfor return\(Q\)\(\triangleright\) Return the new population endfunction
```  
**Algorithm 3** Pseudocode of the uniform crossover (UCX).  
We also propose a novel crossover operator, the drone crossover (DCX), in which each UAV configuration is treated as a block in terms of swap probability. It can be seen in Algorithm 4 that if two individuals are selected for crossover (with the same conditions as in UCX), a whole UAV configuration might be swapped between them, depending on a generated random number (swap probability = 0.5). Figure 5 illustrates an example of DCX in which the configuration of the UAVs 2 and 3 have been swapped. We would like to test these two operator to address which one is better suited to our formation problem, making our proposed EA variants (EA.ucx and EA.dcx) even more efficient and successful.  
Figure 4: Uniform crossover (UCX).
## 2 Materials and Methods
### Determinism and State Space Representation
#### 2.2.2 Formal Procedures for Assessing the Presence of Determinism and Stationarity  
To assess the presence of determinism in PPG signal segments of a significant size, we first selected stationary fragments of the PPG records for analysis. These fragments were selected using an approach based on the second-order weak stationarity, i.e., constant mean and variance with the autocorrelation depending only on the time difference. To do this, we partitioned the data into time bins consisting of a PPG cycle. Next, the mean and variance for each bin were calculated, and then, we looked for consecutive time bins in which these values did not change significantly from those calculated for the entire time series, discarding the remainder of not statistically significant time bins. For the selected intervals, it was checked if the autocorrelation function depended on the time difference alone or not. This allowed us to ensure that the fragments of the PPG signal were representative of the dynamics of the entire time series.  
In order to examine the determinism present in the PPG data, we used the central tendency measure (CTM), as it provides a quantitative estimate for the smoothness of the attractor's trajectory. The index of smoothness \(I_{s}\) of the trajectory, defined as a ratio of the CTM of the angle variations  
Figure 4: Next-amplitude and difference plots, from the dynamic variable \(x\) of the Hénon map and the Lorenz flow with a sampling time of 2 ms: (**a,b**) Hénon map with \(\tau=1\); (**c,d**) Lorenz flow with \(\tau=1\); (**e,f**) Lorenz flow with \(\tau=50\).  
of the successive tangent vectors for the original time series to that for its surrogate data, is an effective measure for the smoothness of the trajectory and, thus, for determinism in the time series [28]. The larger the index of smoothness \(I_{s}\) of the trajectory, the less smooth is the attractor's trajectory, hence a reduced determinism presence. Since the surrogate data are linear stochastic processes with a power spectrum identical to that of the original data (raw time series), the method of surrogate data makes it possible to detect nonlinear determinism on a statistical basis [29, 30].
# Comparison of Machine Learning Algorithms Used for Skin Cancer Diagnosis
## 3 Materials  
The image data used in the conducted experiments comes from the ISIC database (The International Skin Imaging Collaboration). The ISIC project [26] is an international partnership between academia and industry to improve melanoma diagnosis by advancing digital skin imaging technology and to reduce mortality, because melanoma diagnosed and treated at an early stage is easy to treat. ISIC has developed and continues to expand a publicly available image archive for use in developing and testing new standards for automated diagnostic systems. Many associations are involved in the project, including the International Dermoscopy Society (IDS) and the International Society for Digital Imaging of the Skin (ISDIS).  
In the presented paper, a set of training and test data covering two categories of skin lesions--benign and malignant, was used. The training set consists of 2637 images divided into two categories with similar proportions between the classes, while the test data set consists of 660 images with dimensions of 224 \(\times\) 244 pixels (both sets). Sample photos from both categories are shown in Figure 1.
# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 3 Analysis of Results
### Analysis of Error Variation with Epicenter Distance, SNR, Depth, and Magnitude
#### 3.4.2 Influence of Signal-to-Noise Ratio (SNR) on Magnitude Estimation Accuracy  
Both the overall error and the large seismic error converge with increasing SNR (Figure 7). Since the near shocks can mostly be categorized as high-SNR, the higher the SNR, the smaller the mean value of the errors, which is in line with the objective law. In the first second, the overall mean error tends to be 0. However, the negative error region is more expansive, indicating that the underestimated events are more discrete, while the overestimated events are relatively concentrated. The mean errors of large earthquake events in all SNR segments are located in the negative region, and the standard deviations of the errors are significantly more significant than the overall performance, indicating that most large earthquake events are significantly underestimated and the underestimation errors are significant. However, with the time window extension, the overall error and the large seismic error gradually converge and the mean value of the large seismic error tends to be close to zero.
## 4 Conclusions  
Experimental studies on the influence of nonlinear rock loading on its permeability during fluid filtration are carried out. Three types of sandstone taken from the reservoir were used as a filtering medium: low, medium, and high permeability. Waxy oil from the same field was used as the filtered fluid. For this study, an experimental set-up was created that simulates the influx of fluid from the formation into the well, in which an elastic wave actuator is installed, which sends nonlinear loads to the near-wellbore formation zone towards the filtering oil. Based on the results of this study, the following conclusions can be drawn:  
1. The impact of a nonlinear load on the rock, through which crude oil containing paraffins and asphaltenes is filtered, contributes to a change in the permeability coefficient of the rock. In the case of samples of medium and high permeability, when elastic vibrations are activated, the permeability of the rock is restored.
2. This paper developed the idea that during the filtration of paraffin and asphaltene-containing oil, particles block the pore space and are deposited in pore throats. To unblock the pore space, there is an artificial nonlinear loading, as a result of which an impulse is transferred to the rock sample by elastic vibrations, which leads to a relative displacement of the pore channel wall and paraffin and asphaltene accumulations. Having overcome the force threshold of adhesion of deposits to the wall, with the help of oscillatory acceleration, paraffin and asphaltene particles are torn off and carried away (pushed) further along the channel by the oil flow.
3. In the case of irreversible deterioration of a part of the permeable channels, the impact of a variable load contributes to postponing the moment of blocking the remaining part of the channels and maintaining the oil flow at a constant level.
4. The impact of nonlinear loading has no significant effect in the case of already existing strong blockage of permeable channels, high concentrations of paraffins and asphaltenes, or when the wax sizes are comparable to the size of the pore channels (rock of low permeability).  
**Author Contributions:** Conceptualization, M.G.; methodology, E.R.; investigation, E.R. and E.G.; data curation, E.K.; writing--original draft preparation, E.R.; writing--review and editing, E.R.; visualization, Q.Y.; funding acquisition, M.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors would like to acknowledge the generous financial support from the Russian Science Foundation (Project No. 23-19-00699, [[https://rscf.ru/project/23-19-00699/](https://rscf.ru/project/23-19-00699/)]([https://rscf.ru/project/23-19-00699/](https://rscf.ru/project/23-19-00699/))).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Research data are not available for sharing.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Real-Time Implementation of FCS-MPC Techniques  
This section examines two control approaches within the domain of predictive control, offering a comparative analysis and subsequent detailed descriptions of each. The control approaches are FCS-MPC with variable-switching-frequency operation and FCS-MPC with variable-switching-frequency operation and switching-frequency minimization.
## 3 Results and Discussion
### Kakadu Plum Protects HepG2 Cells from Ethanol-Induced Cell Death  
The HepG2 cells were incubated with independent concentrations of KKD-NT and KKD-WA, which were nearly non-toxic with a concentration up to 200 mg/mL (Figure 3). In this study, we decided to conduct all subsequent experiments below the concentration of 200 mg/mL (20-100 mg/mL). According to the HPLC analysis, our samples contained an abundant amount of vitamin C, which can protect cells from oxidative damage by ROS reduction. However, higher levels of ascorbic acid (Vit-C) show cytotoxicity by generating excessive ROS as well as blocking the energy homeostasis on cancerous cells [53]. To explore the protective effect of KKD-NT and KKD-WA against ethanol-induced cell toxicity (Figure 4a), HepG2 cells were co-treated with 4% ethanol with or without KKD-NT and KKD-WA (0-100 mg/mL) for 24 h, and then the cell survival ratio was examined by MTT assay. KKD-NT and KKD-WA significantly inhibited the cytotoxicity of EtOH in a dose-dependent manner (Figure 4b). EtOH decreased the cell survival rate by close to 50% compared to the untreated cells, whereas the cell viability was dose-dependently increased in the KKD extracts co-treatment. The viability of the 100 mg/mL Kakadu-treated group increased significantly, indicating the protective effect of KKD-NT and KKD-WA against the cytotoxicity of EtOH. Cell viability was evidently elevated in the KKD-NT-treated group compared with the KKD-WA-treated group because of the protective function.  
Figure 3: Cytotoxicity assessment using MTT in HepG2 following the incubation of various concentrations of KKD-NT and KKD-WA for 24 h. Values are mean \(\pm\) SE of three independent experiments.  
Figure 2: Reducing power activity of KKD-NT and KKD-WA.