esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Experiment of Wideband Filters Based on Double-Layered Square-Loop Arrays in the F-Band
## 5 Conclusions  
In summary, a compact wideband MM filter with a double-layered square-loop was proposed for transmission in the F-band. The transmission through the proposed filter was analyzed from the surface current and electric field distributions. In addition, transmission of electromagnetic waves through the structure at oblique incident angles and periodic misalignment of the structure were studied. The simulated results show that the proposed structure can provide high transmittance over a wide range of incident angles and values of periodic misalignment. A prototype was fabricated using ultraviolet light photoetching combined with wet etching. The experimental results show that the 3-dB bandwidth of proposed structure is 32.1 GHz in the 101.2-133.3 GHz frequency range and the maximum transmittance is 0.89 at 127.43 GHz. The measured transmission spectra show excellent agreement with the simulation results. Based on the presented results, one can conclude that the proposed filter, which is compact and ultra-broadband, has great potential applications in millimeter wave imaging, sensing and communication systems.  
Conceptualization and writing: review and editing, J.Y.; designed, measurement, writing: original draft, Y.Y.; fabricated, P.W.; data curation, G.D.; resources, F.C. and H.L; project administration, Z.Y.  
This work is funded by the National Natural Science Foundation of China (Grant No.51607050) and the Fundamental Research Funds for the Central Universities (Grant No.JD2017)GPY0006).  
The authors declare no conflicts of interest.
## 3 Network Background  
In the near future, 5G and 6G cellular systems will be the most dominant networks, providing advanced and varied services. Both 5G and 6G networks aim to enhance user experience, optimize resource utilization, and support emerging applications such as smart cities, autonomous vehicles, and next-generation industrial automation.
## 5 Simulation Verification
### Verification with the Voltage FRT Control  
The simulation framework postulates fault \(F_{2}\) propagation at the MMC\({}_{2}\) terminal. Under this condition, the converter adopts a DC voltage regulation strategy with its reference parameter \(V_{dcrf}\) = 1.0. Figure 16 shows the results of \(I_{2}\) with constant \(V_{dcrf}\) and different \(k_{pv}\), \(k_{iv}\), \(k_{pi}\), and \(k_{ii}\). The values of \(k_{pv}\), \(k_{iv}\), \(k_{pi}\), and \(k_{ii}\) are set to 4, 100, 0.2, and 20 (underdamped circuit), 2, 100, 0.5, and 10 (overdamped circuit), and 0.4, 30, 0.2, and 4 (critical damped circuit), respectively. An identical inference emerges with the \(F_{1}\)-type fault scenario.  
To examine the precision of the presented method considering the voltage FRT control, computational analysis of DC current \(I_{2}\) evolution across distinct fault phases is conducted. Following a 2 ms fault identification period, the DC voltage reference parameter (\(U_{dcrf}\)) is instantaneously reset to zero. Figure 17 presents the comparative analysis between calculations and simulated current waveforms for \(I_{2}\), while Figure 18 delineates the operational characteristics of the dual-loop PI control system. Results from Figure 17 reveal a characteristic current trajectory featuring initial amplitude decay to null followed by inverse directional growth. Subsequent to FRT control activation, the outer-loop PI controller exhibits rapid current reference collapse with immediate limiter engagement, as visualized in Figure 18. Controller saturation in the outer loop renders its characteristics negligible in subsequent analyses. Progressive current amplification occurs concurrent with \(M_{dc}\) reduction governed by the inner current regulation loop. Peak current manifestation coincides with \(M_{dc}\) nullification at 13 ms, followed by inner-loop limiter attainment at 15 ms, terminating controller intervention. Subsequent current attenuation proceeds autonomously without regulatory influence. The estimation methodology disregarding controller saturation boundaries demonstrates significant deviations in fault current prediction, as evidenced by comparative waveform discrepancies in Figure 17.  
Comparative verification of simulated versus estimated results demonstrates the proposed methodology's capability to quantitatively assess fault currents during fault FRT operations. This analytical framework enables precise prediction of fault current characteristics under varying control parameters in practical grid scenarios.  
Figure 16: Calculation and simulation values of \(I_{2}\) with constant \(U_{dcrf}\) and different \(k_{pv}\), \(k_{iv}\), \(k_{pi}\), and \(k_{ii}\) during DC fault \(F_{2}\).
# Smart Grid Protection, Automation and Control: Challenges and Opportunities
## 3 Evolution of Substation Architectures and Digital Modernizations for Smart Substations
### Migration from Conventional to Digital Substations
The autonomous local operation of IEDs and relays at the time of a complete failure of the digital system is the last line of defence in a modern substation P&C system--the loss of synchronization due to failure or redundancy of the synchronization source, or even a cyber-attack. These devices are designed for autonomous operation in the case of failure of external communications or centralized control. They are set to function in the detection and response to overcurrent, voltage, or short-circuit conditions without relying on the wider network. Modern IEDs and relays have built-in logic and local processing that can make critical decisions based on real-time input. This ensures protection for the critical assets continuously, hence enabling active autonomous fault isolation, the tripping of breakers, or the activation of any emergency operation protocol. In the event of failure in digital systems, such as time synchronization or cyber security, these relays automatically fall back to their default operational setting based on local information while maintaining the protection of the system. Besides this, almost every substation has hardwired backup systems that provide critical protection but are reliable against failures of the digital communication network. In this way, a hybrid nature will ensure that the locally autonomous P&C devices maintain substation integrity and security in the face of failures originating from the digital systems. Eventually, the protection for substations under any conditions will be achieved by combining digital and hardwired systems.
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 9 Analysis and Discussion
### Privacy Protection Analysis against GPA  
In this research, we consider the GPA that uses a low-cost transceiver to eavesdrop the communication among the vehicles on the road. The adversary catches the beacon messages, which contain pseudo-identity and location of vehicles. It tries to match the vehicle pseudo-identities at various locations. Here, we analyze the strength of the adversary (GPA) in terms of with and without additional knowledge. The additional knowledge of adversary about a vehicle may be frequently visited locations, previous pseudonyms, and vehicle locations of interest. This information increases the strength of an adversary to identify a vehicle. The adversary without additional knowledge has no past information about a vehicle; it only tries to explore the recently collected data to identify the target vehicle. We analyze the privacy protection of our proposed scheme DGVP against the strength of an adversary with and without this additional information. Figure 24 shows the average rate of confusion generated for the adversary at various tracking times. The proposed scheme produces higher confusion or uncertainty for adversaries without extra information compared to an advanced adversary. At the start of the procedure, the adversary confusion rate is low; over time, the confusion rate increases for an adversary to link various pseudonyms of a vehicle in the vicinity. So, both adversaries are facing difficulties in tracking vehicles on the road network. Similarly, Figure 25 contains the results of the average confusion per trace in terms of vehicle density. The confusion rate is lower with fewer vehicles. With an increase in vehicle traffic density, the confusion rate increases. Although an advanced adversary has a lower confusion rate than a simple adversary, the proposed scheme still creates uncertainty for an adversary to identify a target vehicle in the network.  
Figure 24: Rate of adversary confusion at various tracking time.
# A Compact Sequentially Rotated Circularly Polarized Dielectric Resonator Antenna Array
## 2 Resonating Element  
The initiated array is composed of four resonating elements, each comprising a RDR coupled to an ES. In this section, the fundamentals of the resonating elements are described.
## 3 Simulation Results and Discussion  
To analyze the antenna characteristics of the two probes, Sections 3.1 and 3.2 include an investigation of the S\({}_{21}\) spectrum in a vacuum condition (without plasma) with various geometrical parameters, such as \(\alpha\), \(\beta\), \(\gamma\), \(a\) for the RCP, and \(e\) for the BCP. Furthermore, for simple understanding, visualizations of the antenna configurations in the RCP and BCP are provided for each condition. Afterward, in plasma conditions, the S\({}_{21}\) spectrum with various antenna distances is examined.
# Distributed Global Function Model Finding for Wireless Sensor Network Data
###### Abstract  
Function model finding has become an important tool for analysis of data collected from wireless sensor networks (WSNs). With the development of WSNs, a large number of sensors have been widely deployed so that the collected data show the characteristics of distribution and mass. For distributed and massive sensor data, traditional centralized function model finding algorithms would lead to a significant decrease in performance. To solve this problem, this paper proposes a distributed global function model finding algorithm for wireless sensor network data (DGFMF-WSND). In DGFMF-WSND, on the basis of gene expression programming (GEP), an adaptive population generation strategy based on sub-population associated evolution is applied to improve the convergence speed of GEP. Secondly, to solve the generation of global function model in distributed wireless sensor networks data, this paper provides a global model generation algorithm based on unconstrained nonlinear least squares. Four representative datasets are used to evaluate the performance of the proposed algorithm. The comparative results show that the improved GEP with adaptive population generation strategy outperforms all other algorithms on the average convergence speed, time-consumption, value of R-square, and prediction accuracy. Meanwhile, experimental results also show that DGFMF-WSND has a clear advantage in terms of time-consumption and error of fitting. Moreover, with increasing of dataset size, DGFMF-WSND also demonstrates good speed-up ratio and scale-up ratio.  
Keywords:global function model; gene expression programming; unconstrained nonlinear least squares; wireless sensor network +
Footnote †: journal: Applied Soft. 2016
## 3 Results and Discussion
### Model Selection and Prediction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **MAE** & **RMSE** ** & **Tuned Hyper-Parameters** \\ \hline RF & 0.520 & 0.648 & n\_estimators = 400, min\_samples\_leaf = 2, max\_features = ‘sqrt’, \\  & & & max\_depth = 12 \\ KNN & 0.542 & 0.653 & n\_neighbors = 8, p = 1, weights = ‘distance’ \\ SVR & 0.531 & 0.682 & kernel = ‘sigmoid’, gamma = ‘auto’, coef0 = 0, C = 0.5 \\ LightGBM & 0.557 & 0.692 & colsample\_ytree = 0.4, learning\_rate = 0.01, max\_depth = 5, \\  & & n\_estimators = 200 & n\_estimators = 200 \\ GPR & 0.549 & 0.693 & kernel = RationalQuadratic(alpha = 1, length\_scale = 1), alpha = 0.5 \\ XGBoost & 0.559 & 0.696 & learning\_rate = 0.03, n\_estimators = 100, subsample = 0.25 \\ AdaBoost & 0.566 & 0.716 & learning\_rate = 0.1, n\_estimators = 300 \\ Lasso & 0.567 & 0.726 & alpha = 0.1, tol = 0.001, max\_iter = 2000, selection = ‘random’ \\ RANSAC & 0.614 & 0.800 & stop\_probability = 0.999, min\_samples = 5, max\_trials = 500 \\ MLP & 0.667 & 0.825 & max\_iter = 200, hidden\_layer\_sizes = (200, 2), activation = ‘logistic’ \\ Statistical estimator * & 0.744 & 0.919 & \\ \hline \hline \end{tabular}  
* Statistical estimator predicts target values of test dataset as the average value of training data set. ** This table is sorted in ascending order by the RMSE value.  
\end{table} 
Table 2: Prediction performance of 10 regression models and their tuned hyper-parameters. Figure 3: Comparison results of the performances of 10 regression models (RF, KNN, SVR, LightGBM, GPR, XGBoost, AdaBoost, Lasso, RANSAC, and MLP) for an expandable yield prediction. the following chapter. The XAI method illustrates a chosen model and itemizes important features with visualization.
## 3 Results and Discussion
### Microstructure of Coals of Varying Ranks  
Figure 4 shows the background subtracted Raman spectra of the tested coal samples. The coal samples clearly showed similar spectral profiles, having two distinct diffuse and overlapped D and G bands.  
Figure 3: A typical room temperature Raman spectrum of the tested graphite.  
Figure 2: A typical room-temperature Raman spectrum of one of the tested coals, C1.  
As shown in Figure 5a, as the carbon content of the coals increased, the deconvoluted D and G bands shifted little. Within a certain range, the D and G bands of the coals seemed independent of the carbon content. However, compared with the G band, the D band shifted more noticeably, suggesting that the carbon content had a greater effect on the D band than on the G band. The D band represents the defects in the lattice of the C atom, while the G band represents the in-plane stretching vibration of the sp\({}^{2}\) hybridization of the C atom. With the increase in carbon content, the C=C in the coal samples increases, but the number of disordered substances decreases [23].  
Figure 4: Background subtracted spectra of coals of different ranks.  
Figure 5: Effect of coal rank on (**a**) the band position (P\({}_{\text{G}}\) and P\({}_{\text{D}}\)), (**b**) area ratio (I\({}_{\text{D}}\)/I\({}_{\text{G}}\)), and (**c**) width of the coal samples.  
Figure 5b shows the effect of carbon content on the area ratio \(\rm I_{D}/I_{G}\) of the deconvoluted D and G bands. As shown in Figure 5b, \(\rm I_{D}/I_{G}\) increased linearly with the carbon content in the coals. The higher the value of \(\rm I_{D}/I_{G}\), the greater the number of defects that appeared in the C atom. As a result, the defects of the C atom increased with an increase of the carbon content [24].  
The effect of the carbon content on the width (FWHM) of the deconvoluted G and D bands is presented in Figure 5c. The D band became broader as the carbon content of the coals increased. By contrast, the G band showed a negligible change. \(\rm W_{G}\) can reflect the degree of ordering of carbon materials.  
The parameters affected each other. \(\rm I_{D}/I_{G}\) is used to quantitatively evaluate the degree of disorder of carbon materials. All parameters of the peaks G and D were affected by the first coalification jump.
## 4 Results and Discussions  
### Reliability Verification of Dust-Removal Efficiency Model  
The dust collection area in the ESP can be divided into N + 1 sections. When the particle size distribution of the dust particles injected into the ESP follows an R-R distribution, the dust-removal efficiency under each particle size is referred to as the grade dust-removal efficiency. If n represents the collection efficiency of the \(i\)-th particle size in the \(j\)-th section, the graded dust-removal efficiency n can be calculated as follows:  
\[\eta_{i}=\frac{\sum_{j}\eta_{i,j}M_{i,j}}{M_{i,j}} \tag{9}\]  
where \(M_{i,j}\) is the concentration of the \(i\)-th particle size dust in the spatial area corresponding to section \(j\). When \(j\) = 1, \(M_{i,j}=M_{i,1}\) represents the \(i\)-th particle concentration at the inlet of the electrostatic precipitator.  
Obtaining the grade efficiency of the three corona zones in a two-dimensional wire-plate ESP for different particle sizes can be easily achieved by exporting the calculated data and fitting them under the specified operating conditions, as depicted in Figure 7. It is evident that the numerical simulation findings in this study exhibit a consistent overall trend with those reported in the literature [31]. Moreover, the average discrepancy between them remains within 5%, thereby confirming the reliability of the dust-removal efficiency model and the subsequent numerical simulation techniques employed in this investigation.
## 4 Results and Discussion
### Load Forecasting Results
Notably, across the five-day type, MAPE reductions obtained are 64.21%, 40.86%, 13.54%, 16.00%, and 7.24%, respectively, signifying significant improvements. Similarly, reductions were observed for MAE (62.45%, 42.70%, 14.35%, 17.35%, and 8.51%), RMSE and NRMSE (60.52%, 43.91%, 14.03%, 14.98%, and 17.30%), and MSE (84.41%, 68.54%, 26.10%, 27.72%, 31.62%). These reductions highlight the improved ability of LSSVM-IBFOA to minimize forecasting errors compared to the baseline LSSVM model. Similarly, LSSVM-IBFOA outperformed LSSVM-BFOA with minimal reductions of MAPE (26.89%, 4.85%, 4.37%, 2.80%, and 0.19%), MAE (27.33%, 9.05%, 4.36%, 3.27%, and 2.00%), RMSE and NRMSE (25.10%, 3.99%, 3.42%, 0.17%, and 10.02%), and MSE (43.91%, 7.83%, 6.72%, 0.35%, 19.03%). In terms of R\({}^{2}\), the LSSVM-IBFOA achieves the highest value (0.9880) on Monday and the lowest (0.8901) on Friday. Conversely, LSSVM-BFOA and  
Figure 7: Visualization of forecasting result for the DNN, LSSVM, LSSVM-BFOA, and LSSVM-IBFOA for **(a)** Monday; **(b)** Tuesday–Thursday; **(c)** Friday; **(d)** Saturday, and **(e)** Sunday. LSSVM exhibit a wider range of R\({}^{2}\), with Mondays reaching 0.9824 and 0.9593, and Sundays dropping to 0.7405 and 0.4719, respectively. This observation suggests that LSSVM-IBFOA maintains a consistently high R\({}^{2}\) across weekdays, indicating a strong correlation between predicted and actual values. While both LSSVM-IBFOA and LSSVM-BFOA demonstrate effectiveness compared to LSSVM and DNN, LSSVM-IBFOA outperforms LSSVM-BFOA on average. These observations on the testing dataset highlight the effectiveness of LSSVM-IBFOA in achieving superior overall forecasting accuracy. The optimization process introduced by the IBFOA algorithm empowers LSSVM to deliver forecasts with consistently lower errors and a strong positive correlation with actual values across different weekdays and weekends. These reductions highlight the effectiveness of LSSVM-IBFOA's optimization process in generalizing well to unseen data compared to LSSVM-BFOA. Figure 8 summarizes the visualization results of accuracy measures from three models focusing on the important results. The error values of the models obtained demonstrate that Monday had the best performance with high accuracy compared to other day types. This result may be attributed to the relatively fewer public holidays in the historical load data occurring on Mondays compared to other day types throughout 2021. Consequently, a similar pattern of Monday was observed across all weeks and months of the year, pro
## 3 Blockchain-Enhanced Security and Operational Efficiency in VANETs  
In this section, we provide an in-depth description of the proposed architecture, thus examining its potential impact on enhancing the security landscape and augmenting the operational efficiency of vehicular ad hoc networks (VANETs).  
Blockchain technology serves as an enabling layer in our proposed system, thus acting as the cornerstone for achieving data integrity and privacy. Transactions between vehicular nodes are verified and immutably recorded on the blockchain. Capitalizing on blockchain's intrinsic decentralization, our system distributes data across multiple nodes, thereby enhancing both data availability and resilience against system failures.
Consequently, this work aims at developing a fuzzy-based control strategy for reaching an optimum grid-connected renewable-powered BESS that enhances power quality and system stability by minimizing the VD and decreasing the TCO while considering the purchased and sold energy prices, operation and maintenance costs, charges, and taxes. To do so, a multi-objective optimization has been developed using the GA for the optimal sizing of the system, coupled with an FLC based controller for energy management. In particular, the proposed FLC controller is implemented to schedule the charging and discharging for a centralized battery, while also considering the technical and economic aspects (SoC, voltage characteristics, and on/off-peak prices of electricity). Indeed, this article proposes a FL-based control strategy which controls the charging and discharging C-rate (current) of a stationary energy storage system whose size and PV size will be defined by the GA, according to the environment response, such as voltage, on/off peak, and SoC to find the minimum TCO and VD. For clarification, the FL-based control strategy is designed and then implemented in the Simulink framework to find the best PV-battery combination to minimize the above targets. In this regard, an accurate estimation of the SoC is needed to settle the energy content of the battery and to prevent it from excessive discharge. Moreover, precise information regarding load and power generation are required for the optimal B2G and G2B services. In this research, the SoC, which indicates the percentage of the remaining energy of the battery, is introduced as a direct signal for the battery energy storage system, to avert detriment due to the excessive depth of discharge. Besides that, as the high purchase price of the storage could reduce the financial gain of the PV system [24], a sensitivity cost-effectiveness analysis has been conducted while varying the battery cost. The remainder of this paper is organized, as follows. In Section 2, the proposed grid structure is provided. Section 3 presents the optimal sizing of the PV-battery system to deal with the cost and voltage minimization. Section 4 proposes the formulations of the FL-based controller to enable B2G and G2B services and control the charging/discharging rates of the BESS, by observing the SoC, the voltage fluctuation, and the on/off-peak times. Section 5 presents the simulation results augmented with the cost-effectiveness analysis that is associated with the payback time and expectation of future economic profitability of the studied system based on saving and on-peak, off-peak prices of electricity. Finally, Section 6 concludes this paper.