esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Pattern Synthesis of Linear Array Antennas Using the Nonlinear Chaotic Grey Wolf Algorithm
## 3 Nonlinear Chaotic Grey Wolf Optimization (NCGWO) Algorithm
### Nonlinear Variable Speed Convergence Factor  
All population intelligence optimization algorithms have the problem of balancing the global search ability and local search ability. A strong global search ability can ensure the diversity of the population, and a strong local search ability can ensure a locally accurate search and accelerate the convergence of the algorithm. Therefore, the importance of balancing the global search ability and local search ability in the GWO algorithm is self-evident.  
The convergence factor of the GWO algorithm linearly decreases from 2 to 0 over successive iterations. This decrease during continuous convergence renders a strategy of linearly decreasing the convergence factor that is inadequate for holistically capturing the intricate optimization search dynamics, as noted by [28]. Accordingly, this study introduces a convergence factor by exponential attenuation:  
\[a=He^{-(t^{2}/2M^{2})}-(H-2) \tag{14}\]  
where \(e\) is the natural exponent; \(t\) is the current iteration number; \(M\) is the maximum number of iterations; \(H\) is the coefficient vector, which is used to adjust the convergence factor to make it nonlinearly decrease from 2 to 0. Figure 4 shows the nonlinear relationship between the improved convergence coefficient and the number of iterations.  
Figure 3: Population distribution of chaotic mapping.  
Based on the data plotted in Figure 4, when the number of iterations increases, the improved convergence coefficient nonlinearly decreases. The decay rate is lower during the initial stages of the algorithm to enhance the search for the global optimal solution, while it accelerates in the later stages to achieve a more precise localization of the local optimal solution. This approach effectively balances the search capabilities for both global and local solutions [29].  
Based on this description, Table 1 succinctly outlines the disparities between NCGWO and GWO algorithms.
## 1 Introduction
To this end, the Consumer Digital Twin (CDT) serves as a powerful information tool that provides automated data streams on consumers' key characteristics and personalized preferences to minimize their active involvement in operations, increase the efficiency of operations and enhance the consumer-centricity of the LEM. In this work, a LEM-CDT structure is introduced in order to bridge the gap between local flexibility potential and the consumers' preferences. Towards this end, the benefits of both concepts are leveraged. On one hand, the local flexibility is procured efficiently to the wholesale energy markets while guaranteeing monetary and social benefits for LEM's participants and at the same time the preferences of LEM members are not only respected but also, and most importantly, incorporated in the optimal LEM scheduling. By combining these concepts, the aim is to harness optimally the local flexibility capacity at the distribution grid and attract more participants at LEM initiatives in order to create local sustainable energy communities. To achieve this, CDT is an essential tool since it provides the necessary information to the LEM operator in order to consider the particularities and preferences of each participant placing them at the center of the electricity market. This user centric approach is one of the main novelties of this paper, offering an efficient solution for LEM operators to model their energy scheduling accurately and attract more participants. The envisaged LEM-CDT structure is shown in Figure 1. Flexibility stems from the prosumers' side, either in the form of production from various DERs or through DR actions. As shown, prosumers are at the center of the energy market, contributing to the energy mix with any means they own, such as DERs, EVs, or even via DR schema. Supplying flexibility has both technical advantages (e.g., quick response time) and widens the pool of potential LEM members; thus, consumers who do not own DER assets can also be members and contribute to the overall supply through their load flexibility. In addition, participation via DR schema promotes democratization within the community, as all members contribute to the aggregation generation and benefit from their participation. The stochasticity of both energy demand and generation is taken into account to ensure a constant, reliable, and cost-effective energy supply, thereby mitigating the cost of remedial action. This paper is divided into five sections: In Section 2, a comprehensive literature review for both LEMs and CDTs is provided. In Section 3, the proposed solution for the integration of CDT and LEM is presented. In Section 4, the results from experiments are displayed revealing the benefits of integrating CDT into LEM. Finally, in Section 5, the conclusions of the work are provided.
# Unsupervised Anomaly Detection and Segmentation on Dirty Datasets
## 3 Proposed Method  
As shown in Figure 2, our proposed PaIRE (Patch Iterative Robust Estimation) method consists of the following parts: feature extractor, trimming patch-level gaussian distribution (Trimming Module), iterative filtering(Iteration Module), anomaly detection and segmentation (Inference) based on anomaly scores. Each part will be described in the following.
## 1 Introduction
Due to frequent cloud coverage, SAR-based images have gained increasing attention recently as they are not affected by clouds or illumination conditions. The SAR backscatter mechanism for fields changes from surface reflection to specular reflection as indicated by a rapid decrease in the backscatter coefficient when regions are flooded due to natural or agro-practice flooding. Therefore, extensive research has been conducted on mapping the extent of paddy rice [25; 26] or flooded regions [27] using this fact. In recent studies, effectively combining optical and microwave data has become a research hotspot as this can often provide higher precision than single data sources. Data availability has been greatly improved by the implementation of the Copernicus Programme, which is an Earth observation initiative that provides unprecedented levels of observational capabilities through the operation of recently launched satellites (called Sentinels). The Sentinel-1 data are part of the first global open-access SAR dataset, and the Sentinel-2 constellation provides a significant increase in the observational capacity compared with other satellites, such as Landsat, due to its large number (13) of spectral bands, high (10-60 m) spatial resolutions, and short (five-day) revisit times. The objectives of this research are to develop a paddy rice mapping algorithm that exploits the great Earth observation capability offered by Sentinel-1/2 based on the temporal characteristics of paddy rice fields. The algorithm (i) accommodates fragmentation of agro-landscapes, (ii) achieves good performance in regions frequently covered by clouds, and (iii) deals with complex cropping systems. Some improvements are made on [PERSON]'s pixel- and phenology-based rice mapping algorithm [11]: for rice phenology, MODIS temperature time series is substituted by MODIS vegetation index time series, to which PhenoRice algorithm [19] is applied; for object-based classification, an edge-based image segmentation algorithm is applied to multiple-temporal Sentinel-2 images to extract objects (fields) [20]. The improved algorithm is named the Phenology-, Object-, and Double Source-based algorithm (PODS).
## 4 Results and Discussion
### Failure Modes  
In ABAQUS, the damage variable DAMAGET represents the degradation of the elastic tensile stiffness [32]. Its value can be changed from zero to one. The material is undamaged when the value of the damage variable equals zero, while the material undergoes a total loss of strength when the value equals one. Therefore, the damage variable DAMAGET is a quantity associated with the failure of the material, and the contour plot of this damage variable can be used to characterize the failure of S-SWJ.  
Figure 9a,b, respectively, show the distribution of damage for S-SWJ and S-CWJ when the concrete-concrete interface debonds. It can be seen that only a little damage could be observed at this moment, and the damage was mainly concentrated near the concrete-concrete interface. This phenomenon is in accordance with the observation in tests. As the compression continued, more damage occurred and finally led to the failure of specimens, as shown in Figure 10. At this point, the load reached the maximum (corresponding to a shear failure in the connection area [34]), and the damage of S-SWJ (Figure 10a) was more serious than that of S-CWJ (Figure 10c). The more serious damage of S-SWJ is mainly due to the maximum load of S-SWJ being much larger than that of S-CWJ, as presented in Section 3.5. By examining the contour plots of the bridge panel end at the concrete-concrete interface, it can be seen that the bridge panel end of S-CWJ has a more concentrated damage distribution compared to the bridge panel end of S-SWJ, as shown in Figure 10b,d. This indicates that the stress concentration at the bridge panel end can be reduced by using concave square frustum-shaped wet joint.  
Figure 8: Comparison between numerical and experimental results for both S-CWJ and S-SWJ.  
Figure 9: Contour plots of damage for (**a**) S-SWJ and (**b**) S-CWJ when the concrete–concrete interface of the specimen debonds.
###### Abstract  
Chest X-rays (CCRs) represent the first tool globally employed to detect cardiopulmonary pathologies. These acquisitions are highly affected by scattered photons due to the large field of view required. Scatter in CXRs introduces background in the images, which reduces their contrast. We developed three deep-learning-based models to estimate and correct scatter contribution to CXRs. We used a Monte Carlo (MC) ray-tracing model to simulate CXRs from human models obtained from CT scans using different configurations (depending on the availability of dual-energy acquisitions). The simulated CXRs contained the separated contribution of direct and scattered X-rays in the detector. These simulated datasets were then used as the reference for the supervised training of several NNs. Three NN models (single and dual energy) were trained with the MultiResUNet architecture. The performance of the NN models was evaluated on CXRs obtained, with an MC code, from chest CT scans of patients affected by COVID-19. The results show that the NN models were able to estimate and correct the scatter contribution to CXRs with an error of \(<\)5%, being robust to variations in the simulation setup and improving contrast in soft tissue. The single-energy model was tested on real CXRs, providing robust estimations of the scatter-corrected CXRs.  
X-ray image; scatter correction; deep learning; dual energy; Monte Carlo simulations +
Footnote †: journal: _arxiv.org/abs/1212.00810
## 2 Materials and Methods
### Experimental Design
#### 2.2.3 Calculation Method of Drift Potential  
In order to better illustrate the deposition characteristics of the nozzle under different spray conditions, this article will calculate the _DP_ (drift potential) of the nozzle under different spray parameters, which represents the relative value of drift deposition compared with nozzle spray volume. It uses numerical integration calculation methods to study the _DP_ in the vertical and horizontal directions, which are _DP_\(V\) and _DP_\(H\) values respectively.  
The calculation method of _DP_\(V\) on the vertical plane at 2 m down the nozzle is shown in Equations (2) and (3). This method was proposed by [PERSON] et al. [30], and [PERSON] et al. [31] also used this method for statistical analysis.  
\[DP_{V}=\sum_{i=1}^{5}P_{Vi}\times\Delta h_{i} \tag{2}\]  
\[P_{Vi}=(\frac{A_{Vi}\times W\times 10^{-3}}{K}\times 6\times 10^{6})/Q \tag{3}\]  
_DP_\(V\) is the potential performance of spatial drift based on numerical integration on the plane at a distance of 2 m from the nozzle, \(\mu\)L/mL; \(P_{Vi}\) is the relative drift deposition  
Figure 3: The deposition collection system.  
on the \(i\)-th vertical collection line for each 1 L of solution sprayed from the nozzle, uL/L; \(\Delta h_{i}\) is the height interval corresponding to each collection line, respectively 0.05, 0.1, 0.1, 0.1 and 0.05 m; \(A_{Vi}\) is the drift deposition on the \(i\)-th vertical collection line, mg/L; \(W\) is the volume of eluted water, 30 mL; \(K\) is the tracer concentration, 300 mg/L; \(Q\) is the nozzle flow rate, L/min.  
The DP\({}_{H}\) calculation method of the nozzle on the horizontal settlement surface is shown in Equations (4) and (5). [PERSON] [32] used this method for statistical analysis.  
\[DP_{H}=\sum_{i=1}^{6}P_{Hi}\times\Delta x_{i} \tag{4}\]  
\[P_{Hi}=(\frac{A_{Hi}\times W\times 10^{-3}}{K}\times 6\times 10^{6})/Q \tag{5}\]  
\(DP_{H}\) represents the potential performance of ground drift based on numerical integration, uL/mL; \(P_{Hi}\) is the relative drift deposition on the \(i\)-th horizontal collection line for every 1 L of solution sprayed from the nozzle, uL/L; \(A_{Hi}\) is the drift deposition on the \(i\)-th horizontal collection line, mg/L; \(\Delta x_{i}\) is the distance interval corresponding to each horizontal collection line, respectively 0.5, 1, 1, 1, 1 and 0.5 m.  
On the basis of the above calculation of the DP, when the spray pressure is 0.3 MPa, the value of spraying water is set as the reference. The values under all other spray conditions are compared with it to calculate the \(DPRP\) (Drift Potential Reduction Percentages), as shown in Equation (6):  
\[DPRP=\frac{(DP^{rs}-DP^{os})}{DP^{rs}}\times 100\% \tag{6}\]  
_DPRP_ represents the drift potential reduction percentages, %; \(DP^{rs}\) represents the drift performance under the reference spray, uL/mL; \(DP^{os}\) represents the drift performance under other spray conditions, uL/mL.
## 5 Results and Discussion
This is because in this mode, the communication between the AT86 RF215 and the SmartFusion2 is based on packets and, both when transmitting and receiving, the transceiver switches between different states of a finite state machine, with each state having a different power consumption. By contrast, in I/Q mode, the communication is based on a constant I/Q samples stream, and the transceiver stays in a fixed state. Finally, to demonstrate MIGOU's ability to operate as a hybrid radio system, an application that dynamically switches between its different modes of operation was developed. The sequence in which the modes change is based on a typical CR application in which a node wakes up from sleep mode, senses the spectrum (RX I/Q mode) to adapt its communication parameters to the environment, transmits the corresponding information (TX BB mode), and returns to sleep mode. Figure 7 shows the power consumption of MIGOU while running this application. In addition, the mode of operation in which the platform is at each moment is also shown at the top. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**MIGOU Mode**} & \multirow{2}{*}{**Operation Mode**} & \multicolumn{3}{c}{**Power Consumption (mW)**} \\ \cline{3-5}  & & **Average** & **Maximum** & **Minimum** \\ \hline BB & Transmit & 163.4 & 203.0 & 135.2 \\ BB & Receive & 154.5 & 190.4 & 133.5 \\ I/Q & Transmit & 299.6 & 324.4 & 296.7 \\ I/Q & Receive & 201.4 & 212.7 & 198.5 \\ - & Sleep & 22.8 & 26.8 & 18.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average, maximum, and minimum power consumption of MIGOU in the evaluated modes. The values were calculated using 2000 samples acquired at 1 kHz (window of 2 s). Figure 6: Power consumption of MIGOU as a function of time in the evaluated modes: (**a**) transmit in BB mode, (**b**) receive in BB mode, (**c**) transmit in I/Q mode, (**d**) receive in I/Q mode, and (**e**) sleep. Figure 7: Power consumption of MIGOU in a hybrid radio application that dynamically changed the modes of operation. Although the application was idle during the sleep mode, some power consumption peaks were observed during that time. They were caused by the OS management tasks that sporadically woke up the ARM Cortex-M3 processor.
# Illuminant Adaptive Wideband Image Synthesis Using Separated Base-Detail Layer Fusion Maps
###### Abstract
Recently, as object detection research using deep learning is actively conducted, the importance of clearly identifying objects in dark areas under the low-light condition is increasing. It is necessary to improve the important information in the images by synthesizing the visible and near-infrared image pairs. The distribution characteristics of infrared light during the day and night are different, and it is necessary to synthesize visible and infrared images adaptive to various environments. Existing methods focus on synthesizing the daytime visible and IR natural light images. However, at night, the multiplexed image synthesis should reflect the light-emitting IR characteristics. Therefore, in this study, we propose a day and night adaptive visible and near-infrared image synthesis method considering near-infrared band characteristics of both daytime and nighttime. The proposed method can be applied to fixed surveillance cameras such as CCTVs and mobile surveillance cameras used in autonomous vehicles. This paper is organized as follows: Section 2 explains the significance of near-infrared and visible-light image synthesis and previous studies, Section 3 provides a proposed synthesis method. Section 3.1 introduces a visible-light and near-infrared image acquisition camera system, Section 3.2 describes the base-detail layer decomposition process. The base layer and detail layer synthesis processes are addressed in Section 3.3 and 3.4 with a comparison of the daytime and nighttime images. Section 3.5 represents the color compensation step for resolving color distortion and imbalance caused by luminance channel synthesis. Section 4 shows the results of visible-light and near-infrared image synthesis compared to the results of previous studies. Finally, a summary and conclusions are presented in Section 5.
## 6 Conclusions  
The federated-learning-based hybrid deep-learning model ResVGG-SwinNet was developed for multi-label DDoS attack detection in IoT networks. In the hybrid architecture, component-wise assessment shows the relevance of each network. ResNet's feature-extraction skills make it good at classification, although VGGNet and Swin-Transformer enhance it. Advanced feature refinement and contextual learning are needed to identify complex, multi-pattern DDoS assaults in IoT networks. ResVGG-SwinNet is a resilient scalable DDoS detection architecture because its performance improvements justify its complexity. The suggested strategy improved detection accuracy in decentralised contexts while retaining scalability, computing economy, and privacy. The system performs better than state-of-the-art approaches using ResNet's robust feature-extraction skills, VGGNet's optimisation capabilities, and Swin-Transformer's contextual attention. Adaptive preprocessing, dynamic data balancing, and feature optimisation improved the model's ability to manage complex and dynamic IoT network traffic. DDoS attack investigation showed their causes and traits. TCP and UDP protocols were the main DDoS vectors, with 40% and 30% of the attack volume across datasets. These results emphasize the necessity for focused mitigation techniques due to significant vulnerabilities in these widely used communication protocols. The findings also showed a substantial association between network congestion indicators such as packet loss and delay and detection rate drop, emphasizing the need to optimize detection techniques under different network situations. The federated-learning architecture maintained data privacy while attaining excellent accuracy, low false alarm rates, and strong generalisability across varied datasets. The proposed framework can secure IoT networks from DDoS attacks due to its adaptability to varied IoT environmentsand developing attack patterns. Real-time implementation and more attack scenarios will be studied to verify and improve the framework's applicability.  
**Author Contributions:** Conceptualization, A.A.A. ([PERSON]), M.D.L. and R.A.; data curation, A.A.A. ([PERSON]); formal analysis, A.A.A. ([PERSON]) and R.A.; investigation, A.A.A. ([PERSON]), N.A., M.D.L. and F.S.A.; methodology, A.A.A. ([PERSON]), N.A., M.D.L., E.A. and F.S.A.; project administration, E.A.; resources, N.A. and R.A.; software, E.A. and F.S.A.; supervision, M.D.L.; validation, A.A.A. ([PERSON]), M.D.L., E.A. and R.A.; visualization, N.A. and R.A.; writing--original draft, A.A.A. ([PERSON]); writing--review and editing, N.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the University of Jeddah, Jeddah, Saudi Arabia, under grant No. (UJ-23-SRP-13). The authors, therefore, thank the University of Jeddah for its technical and financial support.  
**Data Availability Statement:** The data used in this study are available at [30].  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Rotating Lorentz Force Magnetic Bearings' Dynamics Modeling and Adaptive Controller Design
## 5 Simulation Results and Analysis
### Comparison of Rejection Performance of Perturbed Signals  
A pulse disturbance with an amplitude of 1 rad is applied to the adaptive controller, PID controller, and robust sliding mode controller, respectively, at 20 s.  
The simulation results are shown in Figures 10 and 11.  
Figure 10: Comparison of anti-interference performance under pulse signals.  
Figure 9: Comparison of angular displacement signal pointing error. (**a**) Angular displacement tracking chart. (**b**) Angular displacement pointing error curve.  
The adaptive controller has the smallest response to the pulse signal, the fastest convergence speed, and no repetitive oscillation, as can be seen in Figure 10. The convergence amplitude is excessively large, which makes it possible to damage the actuator and cause high-frequency oscillation, even if the strong sliding mode controller can quickly reduce the influence of pulse disruption. In order to successfully avoid the system-impacting effects of rapid signals like pulse and step, RLFMB forces the load compartment to spin. The adaptive controller then swiftly converges the pointing error to ensure the stability of the load pointing.  
The RLFMB receives an application of a multiple-frequency sinusoidal signal with a maximum frequency of 10 Hz and an amplitude of 0.1 rad. The simulation results are shown in Figure 11.  
As can be seen in Figure 11, an adaptive controller can maintain good directional stability even in the presence of a constant disturbance signal. The errors are fewer and the curves are smoother than with PID controllers and robust sliding mode controllers, even though some mistakes cannot be completely removed. The impact of the disturbance signal is negligible and the pointing precision is higher when the RLFMB rotates the load chamber.