esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Complete Mueller Matrix Optical Scanning Microscopy Imaging
## 2 Mueller Matrix Optical Scanning Architecture
P: pinhole. PSD: Polarization States Detector. (Pockels cells or photoelastic modulators) [28], the system is considered as a \"black box\" evaluated by a figure of merit named Equally Weighted Variance (EWV) criterion where only the global experimental noise propagation is considered [29]. In a confocal mode for the reflection imaging configuration, the double pass through the sample has to be considered [30]. Indeed, it becomes even crucial for polarization tracking through the overall Point Spread Function (PSF) volume. For this reason, a variant method of the ECM has been introduced requiring the measurements of the polarimetric properties of reference samples such as linear polarizer or waveplate at specific orientation. However, in a confocal mode it is important to recalibrate the system for each pinhole size resulting in an increasing of the number of measurements. Additionally, it is important to assume that the light is reflected by a perfect mirror at normal incidence, and that the fingerprint of the polarization features is independent of the direction of propagation of the light. This final hypothesis is not easy to achieve experimentally limiting the use of such approach for versatile implementation. Second, the microscope body is composed of successive multiple optics (lenses, filters and mirrors) that transform completely the generated polarization states. Thus, the measurement pixel-by-pixel of its optical properties could be easily performed by removing any sample in transmission or by placing a simple reflective mirror in reflection. The double pass ECM method could be also used and provides simultaneously the Mueller matrices of the encoding/decoding blocks and of the microscope body [30]. Finally, the Mueller matrix of the sample is isolated by using matrices inversion based on Mueller/Stokes formalism, as follow  
\[[M_{sample}(x,y)]=[M_{mes}(x,y)]\cdot[M_{microscope}(x,y)]^{-1}, \tag{3}\]  
where \([M_{sample}]\), \([M_{microscope}]\) and \([M_{mes}]\) are the Mueller matrices of the sample, the microscope and the total (microscope+sample). Most of the MM SLM microscope deals with slow polarization states encoding/ decoding rate, by means slower than the pixel dwell time achieved by the galvanometric scanner (GS). However, this limitation has been overcome by simply acquiring successive images with different polarization states. Most recent advances for getting the full MM images at the pixel dwell time rate have proposed a snapshot approach, inspired by Optical Coherence Tomography (OCT) setups able to get the MM in the faster way based on our knowledge. Although the full-field technique is potentially faster than the scanning technique, the latter opens the way for implementation of various imaging modalities (Mueller/confocal/nonlinear) on a same scanning microscope [31].
## 2 Energy Prediction and Fault Detection in Solar-Hydrogen Systems
### Machine Learning Techniques for Fault Detection in Solar Panels
Deep learning approaches have been applied in recent research to build effective solutions for defect identification and classification in solar photovoltaic (PV) systems. In categorizing different types of faults using thermographic images and recognizing PV arrays in aerial photos, Convolutional Neural Networks (CNNs) have demonstrated encouraging outcomes. These methods have proven to be highly accurate in identifying a variety of fault types, such as offline modules, hot spots, and cracking. In several research, the use of RGB images for fault classification has been investigated, and cracks, shadows, and dust have been identified with a moderate degree of accuracy. Transfer learning and multi-scale CNN architectures have been employed to improve classification performance and overcome challenges such as imbalanced datasets [63; 64; 65]. Additionally, researchers have proposed methods combining meta-heuristic algorithms for parameter extraction with CNN-based fault classification, showing promising results in various atmospheric conditions [66]. Table 3 details the key aspects of CNNs in detecting faults in solar panels, emphasizing their advantages in image processing, pattern recognition, and scalability [67; 68; 69; 70; 71]. CNNs excel at detecting and classifying faults, making them a vital component of contemporary solar monitoring systems. Their capacity to handle large datasets and integrate with other technologies further boosts their effectiveness in ensuring the efficiency and reliability of solar energy systems. A deep convolutional neural network architecture called ResNet-50 has shown success in a number of image categorization applications. Deeper networks can be trained by utilizing residual connections, which preserve information flow and stop gradient dissipation [72]. ResNet-50 has been successfully applied to breast cancer classification using MRI scans, achieving 92.01% accuracy [73]. In emotion recognition, ResNet-50's deep architecture allows for capturing complex patterns, offering advantages in performance and computational efficiency [74]. For poultry disease recognition, combining SURF feature analysis with K-means clustering to preprocess images before feeding them into ResNet-50 improved accuracy by 20%, reaching 93.56% [75]. These studies highlight ResNet-50's versatility and potential in various domains, including medical imaging, biometrics, and computer vision applications, demonstrating its ability to extract meaningful features and achieve high classification accuracy. By solving the vanishing gradient issue in extremely deep networks, ResNet architectures have completely changed the field of deep learning. The main invention is the residual block, which uses skip connections to make information flow easier. ResNet variants, such as ResNet-50 and ResNet-152, differ in depth but share the same fundamental building blocks [76; 77]. In an effort to improve feature map exploration, recent research has looked
# Fast Path Generation Algorithm for Mobile Robot Navigation Using Hybrid Map
## 3 Proposed Methods  
In this section, we describe a hybrid map that takes advantage of both the occupancy grid map and the topology map. The proposed RC-Map (Rectangle Map) is a hybrid map that analyzes the occupancy grid map of Figure 1 generated by the robot to generate a fast path with less memory usage. When a robot equipped with the RC-Map algorithm is given a space, it acquires surrounding information using its own sensors, generates a map based on this, and updates it. The algorithm to generate RC-Map was described in a previous study [40].
## 3 Setup
### Drift Compensation  
Due to the three-electrode technology, the measurement of the impedance spectra can only be performed during a charging process and during a discharging process, respectively, so that the state of charge of the cell changes to a certain extent during the measurement. As the measurement time at low frequencies is up to 30 s, the DC component of the voltage measurement may change during the measurement. The voltage change is particularly large at the beginning of charging or discharging processes as the slowest processes have yet to decay. The voltage signal of such a case is shown in Figure 14 as an example.  
Figure 14: Measured voltage signal of an impedance measurement whose direct component increases during the charging process.  
Figure 12: Step response of the PID current controller combined with signal generation unit.  
Figure 13: Control deviation of the PID current controller combined with signal generation unit when applying a step.  
The resulting error is minimized by modeling the DC voltage component in a linear way and subtracting it from the characteristic of the voltage. Linear functions are determined by two points. Thus, the mean values of the first sine period (\(p_{1}\)) and the last sine period (\(p_{2}\)) of the voltage signal are being determined. The mean value of a sine period without offset is zero. Therefore, for the evaluated periods, the mean value can be used to determine the offset value \(p_{i,p}\). The associated time component \(p_{i,t}\) of the points corresponds to the midpoint of each period. Thus, the point (\(p_{1}\)) of the first period is given by its components  
\[p_{i,v}=\sum_{s=0}^{s_{p}}\frac{v_{s}}{s_{p}}, \tag{20}\]  
\[p_{i,t}=\sum_{s=0}^{s_{p}}\frac{t_{s}}{s_{p}}=\frac{1}{2\cdot f}. \tag{21}\]  
Here, \(s\) implements a control variable that passes through the voltage samples \(v_{s}\) and their corresponding time points \(t_{s}\). The number of values corresponding to the period is given by \(s_{p}\) and the measured frequency by \(f_{k}\). The equation of a line described by \(p_{1}\) and \(p_{2}\) is then subtracted from the raw measurement data. As can be seen in Figure 15, the error is almost completely eliminated.
###### Abstract  
This work aims to optimize a previous self-consistent model of a single stage electrohydrodynamic (EHD) thruster for space applications. The investigated parameters were the thruster performance (propulsion force T, the thrust to power ratio T/P, the electric potential distribution, the spatial distribution for the electrons and ions, and the laminar flow velocity) under several conditions, such as the design features related to the cathode's cylindrical geometry (height and radius) and some electric parameters such as the ballast resistor, and the applied potential voltage. In addition, we examined the influence of the secondary electron emission coefficient on the plasma propellant parameters. The anode to cathode potential voltage ranges between 0.9 and 40 kV, and the ballast resistance varies between 500 and 2500 M. Argon and xenon are the working gases. We assumed the gas temperature and pressure constant, 300 K and 1.3 kPa (10 Torr), respectively. The optimal matching for Xe brings off a thrust of 3.80 uN and an efficiency T/P = 434 mN/kW, while for Ar, T = 2.75 uN, and thruster to the power of 295 mN/kW. To our knowledge, the missing data in technical literature does not allow the verification and validation (V&V) of our numerical model.  
electric propulsion; electrohydrodynamic thrusters; plasma discharge; modelling plasma fluids +
Footnote †: journal: Applied Sciences
## 5 Case Studies
### Inertia Response Analysis of Wind Farms
#### 5.3.2 Impact of Different Control Parameters on Inertia  
In order to assess the influence of various control parameters on the equivalent virtual inertia of the wind farm operating under integrated inertia control, a simulation of the wind farm was conducted at a wind speed of 9 m/s. The control variables method was employed to systematically evaluate the impact of each parameter. The results of this comparative analysis are illustrated in Figure 9 below.  
Figure 9 illustrates the impact of the integrated inertia control differential coefficient on the equivalent virtual inertia of the wind farm. As \(k_{p}\) increases from 15 to 20, there is a corresponding rise in the magnitude of the equivalent virtual inertia. Additionally, this modification results in a decrease in the time necessary for the inertia to attain its peak value, as well as a more rapid decay phase; Figure 10 demonstrates that as the proportional coefficient \(k_{d2}\) increases, the equivalent virtual inertia of the wind farm exhibits a more pronounced enhancement. This trend indicates that higher values contribute to an enhanced virtual inertia effect, potentially improving the wind farm's capability to support system stability under dynamic conditions. Conversely, Figure 11 indicates that as \(T_{f}\) it increases, the inertia response capability gradually weakens.  
Figure 10: Simulated and evaluated inertia values at different Kd2 levels.  
Figure 9: Simulated and evaluated inertia values at different Kp levels.  
In conclusion, the equivalent virtual inertia of the wind farm is significantly affected by various operational conditions and control parameters. Consequently, the assessment results may provide valuable insights for addressing the grid's future demands for inertia contributions from wind farms.
## 1 Introduction
Also, the two novel power allocation schemes have been found to maximize the upper bound of sum rate and could achieve the trade-off of outage probability between two terminals [21]. For maximizing the energy efficiency, the optimal power allocation and relay selection scheme can be obtained [22]. In [23], the power allocation and relay selection algorithm is designed by energy pricing in the decode-and-forward cooperative network. Due to the introduction of energy harvesting technology, the traditional power allocation and relay selection algorithms will no longer be applicable for novel systems. Therefore, this paper proposed a joint power allocation and relay selection algorithm for the TSR relay network. In this paper, we consider an AF-based two-way relay network with energy harvesting technology, where two sources exchange information by the TSR energy harvesting relay node. We first combine the TSR protocol with the two-way relaying system and propose an optimal power allocation and relay selection scheme, which aims to maximize max-min bidirectional instantaneous information rate. The closed-form expressions of the optimal power with the fixed TS factor and the fixed relay node can be obtained when the bidirectional link information rate is equal. Then, a time allocation scheme is used to obtain the optimal TS factor, which can maximize information rate with the optimal transmit power and the fixed relay node. Lastly, the optimal relay can be obtained with the optimal transmit power and TS factor. Simulation results show that the proposed power allocation and relay selection scheme outperforms the traditional method and the PS scheme. The rest of paper is organized as follows. Section 2 introduces the system model of the TSR energy harvesting two-way relaying network. Section 3 presents a joint power allocation, relay selection and TS factor optimization scheme. In Section 4, numerical results verify the better performance of the proposed scheme compared with traditional algorithms. Finally, Section 5 concludes this paper.
## 3 Results
### Performance Metrics and CR-ICC
It is worth noting that the two teams classified as having a high opposition level, indicating the highest level of competition, occupied the third and first positions, respectively. Therefore, no significant differences were found in the influence exerted by variations in CR-ICC values among teams with higher opposition levels. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Predictor**} & \multirow{2}{*}{**Estimator**} & \multirow{2}{*}{**SE**} & \multirow{2}{*}{**Z**} & \multirow{2}{*}{**\(p\)-Value**} & \multirow{2}{*}{**OR**} & \multicolumn{2}{c}{**95\% CI**} & \multicolumn{2}{c}{**Collinearity Analysis**} \\ \cline{6-9}  & & & & & & \multicolumn{1}{c}{**_Inf_**} & \multicolumn{1}{c}{_Sup_} & \multicolumn{1}{c}{**VIF**} & \multicolumn{1}{c}{**Tolerance**} \\ \hline Constant & 0.932 & 0.2928 & 3.182 & \(<\)0.001 & 1.293 & 1.430 & 4.507 & & \\ CR-ICC & 0.136 & 0.0228 & 5.966 & \(<\)0.001 & 1.143 & 1.095 & 1.198 & 1.13 & 0.884 \\ \hline Rol & & & & & & & & 1.02 & 0.983 \\ Middle & 0.242 & 0.206 & 1.172 & 0.241 & 1.274 & 0.849 & 1.911 & & \\ blocker–libero & \(-\)0.039 & 0.227 & \(-\)0.173 & 0.862 & 0.961 & 0.615 & 1.502 & & \\ Wing spiker–libero & \(-\)0.111 & 0.197 & \(-\)0.563 & 0.574 & 0.895 & 0.607 & 1.318 & & \\ \hline Competitive load & & & & & & & & 2.48 & 0.404 \\ (CL) & & & & & & & & 2.48 & 0.404 \\ High & & & & & & & & & 2.48 & 0.404 \\ Load–attenuated & 0.957 & 0.325 & 2.994 & 0.003 & 2.652 & 1.406 & 5.022 & & \\ load & & & & & & & & & 1.08 & 0.927 \\ \hline N\({}^{\circ}\) SET & & & & & & & & & & 1.27 & 0.788 \\
2–1 & 0.142 & 0.177 & 0.803 & 0.422 & 1.153 & 0.814 & 1.633 & & & \\
3–1 & \(-\)1.131 & 0.314 & \(-\)3.594 & \(<\)0.001 & 0.323 & 0.174 & 0.598 & & \\
4–1 & \(-\)1.177 & 0.403 & \(-\)2.921 & 0.003 & 0.308 & 0.139 & 0.679 & & \\
5–1 & \(-\)0.825 & 0.419 & \(-\)1.967 & 0.049 & 0.438 & 0.192 & 0.997 & & \\ \hline \multicolumn{2}{c}{Opposition level} & & & & & & & & 1.08 & 0.927 \\ (OL) & & & & & & & & & & \\ Mid level–high level & \(-\)0.159 & 0.154 & \(-\)1.034 & 0.301 & 0.852 & 0.629 & 1.154 & & \\ Low level –high level & \(-\)0.135 & 0.242 & \(-\)0.560 & 0.575 & 0.873 & 0.542 & 1.404 & & \\ \hline Ranking & & & & & & & & 1. The collinearity analysis of all variables, with variance inflation factor (VIF) values below 2 and tolerance greater than 0.1 (see Table 3), indicated the independence of the variables, ensuring higher accuracy in predictions by avoiding the inclusion of redundant variables and issues of variance inflation.
# Perceptual Image Hashing Using Latent Low-Rank Representation and Uniform LBP
## 4 Experiments and Analysis
### Discriminability  
To evaluate the anti-collision performance of image hashing, 696 hash codes are generated via the proposed PIH scheme, based on 696 test images from the CVG-UGR image database [41]; following this, 378,400 normalized Hamming distances are calculated between the hash pairs of different images. The histogram of the normalized Hamming distances is shown in Figure 4. One finds that the distribution of the normalized Hamming distance proximately obeys a normal distribution with a mean of \(\mu\) = 0.4825 and with a standard variation of \(\delta\) = 0.0451. Consequently, given the threshold \(\tau<\mu\), the collision probability \(P_{c}\) can be computed as follows:  
\[P_{c}(\tau)=\frac{1}{\sqrt{2\pi}\delta}{\int_{-\infty}^{\tau}{e^{(-\frac{(x-\mu )}{2\delta})}}}dx=\frac{1}{2}erfc\left(-\frac{\tau-\mu}{\sqrt{2\delta}}\right), \tag{17}\]  
where \(erfc(\bullet)\) is a Gauss error function. The collision probabilities of the proposed PIH scheme for different thresholds \(\tau\) are shown in Table 3. From this table, it can be concluded that the collision probability decreases with a decreasing threshold \(\tau\). Additionally, the hashes generated by the proposed PIH scheme have a better discriminability than some of the existing image hashing schemes [13; 24; 26].  
Figure 4: The distribution of 378,400 normalized Hamming distances.  
Figure 3: The robustness comparison in terms of the average normalized Hamming distance. (**a**) JPEG compression; (**b**) Gaussian filtering; (**c**) median filtering; (**d**) Salt & pepper noise; (**e**) Scaling, and (**f**) Rotation.
### Background and Motivation
Although FSO technology offers undeniable advantages and has a broad range of potential applications, significant technical challenges impede its widespread adoption [14]. For terrestrial applications, atmospheric conditions such as rain, fog, and turbulence can significantly degrade the signal, leading to reduced performance. Furthermore, for general applications, alignment issues and the need for precise beam pointing add to the complexity of FSO system deployment. Addressing these challenges requires the development of enabling technologies and innovative strategies to ensure the reliable performance of FSO systems in diverse environments [2; 15]. Figure 1: Potential applications of FSO systems based on the operational distance between transceivers. Figure 2: Categorization of FSO communication systems based on their applications. B2B: Building-to-Building, D2D: Device-to-Device, C2C: Chip-to-Chip, V2V: Vehicle-to-Vehicle, V2G: Vehicle-to-Ground, G2V: Ground-to-Vehicle, S2S: Satellite-to-Satellite, S2G: Satellite-to-Ground, and G2S: Ground-to-Satellite.
# Robust Space Time Adaptive Processing Methods for Synthetic Aperture Radar
## 2 SAR Simulation Models
### Influence of Platform Motion  
Consider a jammer is located at point \(J\) when the radar is located at point \(A\), and the relative angle between the jammer and the beam center of the radar is \(\theta_{J}\). When the radar moves to point \(A^{\prime}\), the relative angle between the jammer and the beam center of the radar is \(\theta^{\prime}{}_{J}\). According to the geometric relationship in Figure 2b, the difference of the angles is \(\Delta\theta_{J}\), which can be approximated as:  
\[\Delta\theta_{J}\approx\frac{V_{c}t\cos\theta_{J}}{R_{J}} \tag{9}\]  
For the conventional mode in airborne radar, the CPI is only about tens of milliseconds, so \(\Delta\theta_{J}\) is very small and can be ignored. However, the synthetic aperture time of SAR is usually a few seconds. Consider the situation with the flight speed 150 m/s, synthetic aperture time of 5 s, oblique distance \(R_{0}\) = 40 Km, and the azimuth angle of jammer \(45^{\circ}\) as an example. It can be calculated that the direction of interference relative to the platform changes up to \(1.5^{\circ}\) in the synthetic aperture time, which cannot be ignored for the STAP process. In other words, the interference is time-varying and non-stationary in the synthetic aperture time.  
Some different methods for non-stationary interference cancellation have been proposed in the literature. The first method is to select interference samples in the whole synthetic aperture time to get the adaptive weight vector with widen null by STAP directly. Higher spatial freedom of the system is needed in this method, and STAP processing cannot be carried out pulse by pulse, which greatly improves the resources of storage and consumption in the system. The second way is to form a wide null in the beam pattern in the STAP process through the null broadening algorithm [22]. A common problem is that the null depth would be usually reduced or form wide nulls that would raise the sidelobe level or broaden the main beam at the same time. The third method is to suppress the non-stationary interference by dividing the coherent integration time into several batches and using different adaptive weight vectors for each batch [23; 24]. In STAP processing, although the mainlobe can be maintained by imposing a single constraint in the desired direction, this may still cause some variations in the beam pattern depending on the differences in the strength or direction of the interference from pulse to pulse, especially for strong interference near the mainlobe, thereby undermining the consistency of the echo phase between batches and leading to degradation of azimuth focusing in the SAR image. To solve these problems, two modified STAP methods for SAR with piecewise sub-apertures and data constraints for non-stationary interference cancellation are proposed in Sections 3 and 4.
# Joint Probabilistic Hypergraph Matching Labeled Multi-Bernoulli Filter for Rigid Target Tracking
## 4 Simulation
In our experiment, the survival probability \(p_{s}=0.99\), the acceleration error is set to \(\Omega=0.1\) m/s\({}^{2}\), the mean number of clutter \(\lambda_{c}=10\) and clutter is assumed to be uniformly distributed over the surveillance area, the threshold \(\alpha\) is set to 0.5, the standard deviation of measurement noise \(\sigma_{m}\) is set to 0.1 m. The simulations are implemented by MATLAB R2019a on Intel Core i9-9750H 2.6 GHz processor and 16 GB RAM.