esa-sceva/satcom-chunk-collection huggingface dataset:
# Federated Learning-Based Resource Management with Blockchain Trust Assurance in Smart IoT
## 5 Simulation Results and Analysis  
In this section, the proposed FL-based resource management mechanism is validated by simulation. We focus on the malicious node identification method and the FL algorithm performance for simulation analysis. In the simulation, we assume that the IoT nodes are randomly distributed in a circular area with a radius of 1000 m. There is one edge server in which the FL global model aggregation is performed. Out of the total 100 IoT nodes, there are 10 malicious and untrustworthy nodes. The main simulation parameters are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline IoT Area & 1000 \(\times\) 1000 m\({}^{2}\) \\ IoT Topology & random distribution \\ MEC Servers & 1 \\ Total IoT Nodes & 100 \\ Legitimate Nodes & 90 \\ Malicious Nodes & 10 \\ Network Bandwidth & 1000 Mbps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parametersFor the proposed FL algorithm in this paper, the input data can be normalized in advance. The FL training data in this simulation are randomly generated in \([0,1]\). We distinguish between malicious and legitimate nodes based on the state of the managed resources. Here, it is assumed that the resources correspond to the nodes one by one. For the cases with two and three managed resources, the SVM decision bounds for a random node distribution are given in Figures 5 and 6, respectively. As can be seen from the figures, the legitimacy of the majority of IoT nodes can be correctly identified according to our identification criteria. However, since the nodes are randomly distributed, the decision boundaries shown in Figures 5 and 6 do not perfectly distinguish the two classes of nodes completely. In fact, a very small number of nodes are very close to the decision boundary, which leads to the possibility of misidentification, and this will affect the accuracy of the proposed FL algorithm.  
Figure 7 analyzes the accuracy of the proposed FL training algorithm in two cases. One is the case where only legitimate nodes participate in FL training after malicious nodes are identified and eliminated. The other is the case where all nodes (including malicious nodes) are involved in FL training. As expected, the algorithm accuracy in the first case is much higher than that in the second case. However, since not all malicious nodes can be perfectly eliminated, the algorithm accuracy is close to 90% even in the first case. We assume that 10% of the IoT nodes are malicious. It can also be seen from Figure 7 that,  
Figure 5: Decision boundary in 2D.  
Figure 6: Decision boundary in 3D.  
when the number of IoT nodes is small, the FL algorithm accuracy is not high because fewer nodes are involved in FL training. However, as the number of IoT nodes increases, there are more legitimate nodes involved in FL training, so a more desirable FL training accuracy is obtained.
## 2 Methodology
### Inverse Magnetic Localization Based on Adaptive LM Algorithm
#### 2.2.2 Adaptive LM Algorithm  
The Levenberg-Marquardt (LM) method is a preferred choice, as it provides good calculation accuracy and robustness [34]. During the iteration process, the estimated parameters \(\textbf{Q}_{j}=(\textbf{r}_{pj}\), \(\textbf{M}_{j})\) of the \(j\)th step are replaced by a new value, \(\textbf{Q}_{j}+\textbf{q}_{j}\), with reduced objective function. The variation **q** between each iteration step is determined by finding a minimum of the linearization of objective function \(\
abla_{q}f\) involving the Jacobian of the magnetic field at \(\textbf{Q}_{j}\) and a damping parameter \(\mu\) according to Equation (3), where \(\
abla_{q}f=0\):  
\[\textbf{B}(\textbf{Q}+\textbf{q})\approx\textbf{B}(\textbf{Q})+\textbf{J}( \textbf{q}), \tag{4a}\] \[\Big{(}\textbf{J}^{\textrm{T}}\textbf{J}+\mu\textbf{I}\Big{)}\textbf{q}=-\textbf{ J}^{\textrm{T}}\textbf{B}, \tag{4b}\]The LM method takes advantage of the global convergence properties of the steepest method and the quadratic convergence of [PERSON]'s method. However, it is known that there may be large deviations with initializations far off the final minimum. Here, we design an aLMA with real-time updated measurements and try to provide more reliable initializations for this inverse problem. As presented in Figure 3, there are three steps involved:  
1. In Step 1, initializations derived from real-time measurements are figured out to reduce the searching region. From Equation (1), we can find that the magnetic fields attenuate rapidly with the distance between the sensor and the PM. Since the sensor array is distributed along the mouth contour in our study, we take \(\mathbf{r}_{p0}=(x_{p}\ (\mathbf{B}_{m\_max}))\), \(z_{p}\ (\mathbf{B}_{m\_max}))\) and \(\mathbf{M}_{0}=(0,0)\), where \(x_{p}\ (\mathbf{B}_{m\_max})\) and \(z_{p}\ (\mathbf{B}_{m\_max})\) are the \(xz\)-plane location of the sensor with the strongest measurement.
2. A pre-estimated result \(\left(\begin{matrix}\mathbf{\tilde{-}}_{p},\mathbf{\tilde{M}}\\ \mathbf{\tilde{-}}_{p},\mathbf{\tilde{M}}\\ \end{matrix}\right)\) can be given by solving the inverse magnetic problem in Equation (3) with the LM algorithm using the initializations above, which provides initializations of the magnetic dipole with improved reliability, which is termed the first-stage LM algorithm.
3. With the pre-estimated result from the first stage LM algorithm as initializations, the inverse problem in Equation (3) is solved in the second-stage LM algorithm, from which the final localization result is determined.
# From Classic to Cutting-Edge: A Near-Perfect Global Thresholding Approach with Machine Learning
###### Abstract  
Image binarization is an important process in many computer-vision applications. This transforms the color space of the original image into black and white. Global thresholding is a quick and reliable way to achieve binarization, but it is inherently limited by image noise and uneven lighting. This paper introduces a global thresholding method that uses the results of classical global thresholding algorithms and other global image features to train a regression model via machine learning. We prove through nested cross-validation that the model can predict the best possible global threshold with an average F-measure of 90.86% and a confidence of 0.79%. We apply our approach to a popular computer vision problem, document image binarization, and compare popular metrics with the best possible values achievable through global thresholding and with the values obtained through the algorithms we used to train our model. Our results show a significant improvement over these classical global thresholding algorithms, achieving near-perfect scores on all the computed metrics. We also compared our results with state-of-the-art binarization algorithms and outperformed them on certain datasets. The global threshold obtained through our method closely approximates the ideal global threshold and could be used in a mixed local-global approach for better results.  
document image binarization; global thresholding; machine learning; image preprocessing; ideal global threshold; ensemble learning; computer vision; optical character recognition (OCR) pre-processing Article
## 3 Results and Discussion
### Effect of Adsorbent Dose  
Figure 4 shows the effect of adsorbent dosage on Zn removal % and adsorption capacity. The highest removal percentage was obtained with doses of 3 g/L and 5 g/L. The adsorption capacities, \(q\) [mg/g], increase when the adsorbent dose increases. This is a logical result because the higher amount of adsorbent can uptake more Zn ions from the solution. The higher the dosage, the greater the number of available adsorption sites there are to protect against impurities [18; 44].  
\begin{table}
\begin{tabular}{c c c} \hline
**adsorbent** & **adsorption Capacity [mg/g]** & **Reference** \\ \hline Natural zeolite & 2.21 & [36] \\ Vermiculite & 3.88 & [37] \\ Defatted rice brains & 5–17 & [38] \\ Solid residue of olive mill products & 5.40 & [39] \\ Activated carbon from almond shells & 6.65 & [40] \\ Natural zeolite & 13.02 & [35] \\ Apricot stones carbon & 13.21 & [41] \\ Biomass-based carbon & 21.44 & This study \\ Lignite & 22.83 & [42] \\ Activated carbon derived from bagasse & 31.11 & [18] \\ Peat & 52.09 & [43] \\ \hline \end{tabular}
\end{table}
Table 2: Adsorption capacities of zinc (Zn) on various adsorbents.  
Figure 3: Zinc (Zn) adsorption capacity to carbon surface in different initial concentrations. Experiments were done duplicated and the range of results were at maximum 0.3 mg/g.
## 2 Published Papers  
Strong solar radiation can produce increased ionization in the terrestrial atmosphere and change its structure. In addition, increased solar radiation has the potential to cause diverse unavoidable catastrophic events by causing abrupt ionospheric disturbances that may affect satellite signals, as well as devices on the ground.  
The focus of the series of papers [8; 9; 10] within this Special Issue is on the study and prediction of the impact of intense solar radiation on the Earth, and the analysis of atmosphere plasmas and their parameters. The authors of [8] have developed an empirical model of the lower ionosphere plasmas density, and have provided a simple approximative formula for electron density. The manuscript offers an extension of the electron-density modeling in the daytime, solar flare-perturbed ionosphere. Further, in the paper of [PERSON] et al. [9], the authors introduce a novel modeling approach and give data and plasma coefficients needed for describing and modeling this medium under intense solar radiation. They also provide a simple expression for electron density plasmas. The paper by [PERSON] et al. [10] conducted a thorough examination and analysis of the lower ionospheric response to sudden solar flares during the day, and lightning-induced electron precipitation during the night. The authors stated that the data and results are useful in modern society for Earth observation, telecommunication, and other applications.  
The energy crisis and the adaptation of the global energy structure encourage the development of renewable energies, particularly solar energy. In the next series of works, attention was focused on solar devices and modeling. [PERSON] et al. [11] suggested an analytical method for calculating and enhancing the performance of a multi-mirror combination solar dish collector. In this work, a new optical model was specifically presented, and it can be used to evaluate and improve the optical performance of a combined solar dish system.  
It is known that photovoltaic systems are an excellent solution to high energy demand. One of the methods of increasing the output of photovoltaic systems is to increase the amount of solar radiation falling on a photovoltaic panel using optical devices. In the paper by [PERSON] et al. [12], a simulation of a low concentrated photovoltaic system is conducted using the COMSOL Multiphysics software package. The authors stated that this will make it easier to design and test models before they are manufactured.
# Research on Nonlinear Vibration Characteristics of Internal Beveloid Gear Transmission System
## 3 Numerical Solution of Internal Beveloid Gear with Parallel Axes
### Analysis of Nonlinear Vibration Characteristics
Figure 2: Gear vibration\(-\)displacement response curve and spectrogram (**a,b**) the radial time domain response of external gear and internal gear; (**c,d**) the axial time domain response of external gear and internal gear; and internal gear; (**e,f**) the radial spectrogram of external gear and internal gear; (**g,h**) the axial spectrogram of external gear and internal gear. Figure 2e-h show that when the rotational speed is \(\mathrm{n}=1000\) r/min, the gear rotating frequency is \(\mathrm{n}/60\) Hz, so the meshing frequency is \(\mathrm{n}\mathrm{z}/60=750\) Hz. According to the spectrogram, it can be observed that the peaks of the radial vibration and axial vibration of the gear are at the meshing frequency, and the subsequent peaks are essentially \(n\) times the meshing frequency, which is consistent with the theoretical situation. The harmonic properties of the gear's radial vibration are highly pronounced, with a multitude of small peaks. At 2 kHz, the vibration amplitude swings significantly, the vibration is intense, and the level of nonlinearity is considerable. In the axial position, it also has noticeable multiple peaks, and the corresponding frequency value is an integer multiple of the meshing frequency, but nearly no small peaks and its periodic vibration is rather steady.
## 3 Acoustic SLAM Based on Microphone Landmarks
### EKF-SLAM Algorithm  
[PERSON], [PERSON], and [PERSON] [50] first proposed the SLAM algorithm, and used an extended Kalman filter to perform simultaneous estimations of the position of a robot and the landmarks observed. Assuming that the high-dimensional state vector \(\mathbf{X}\) is the SLAM system state, which is specified as the robot's pose (position and heading) and location of stationary landmarks observed in the environment  
\[\mathbf{X}=\left[\mathbf{X_{r}},\mathbf{X_{1}},\mathbf{X_{2}},\ldots\mathbf{ X_{M}}\right]^{T} \tag{9}\]  
where \(\mathbf{X_{r}}=\left[x_{r},y_{r},\theta_{r}\right]^{T}\mathbf{X_{r}}=\left[x_ {r},y_{r},\theta_{r}\right]^{T}\) represents the robot's pose, among which \(\left(x_{r},y_{r}\right)\) are the coordinates of the robot in the global coordinate system, and \(\theta_{r}\) represents the heading of the robot body, namely, the angle between the robot body and the \(x\)-axis of the global coordinate system. \(\mathbf{X_{i}}=\left[x_{i},y_{i}\right]^{T}\),\(\left(i=1,2,\ldots M\right)\)\(\mathbf{X_{i}}=\left[x_{i},y_{i}\right]^{T}\),\(\left(i=1,2,\ldots M\right)\) are the coordinates of the landmarks in the environment. The covariance matrix of the system is shown in Equation (10).  
\[\mathbf{P}=\left[\begin{array}{cccc}\mathbf{P_{rr}}&\mathbf{P_{r1}}&\ldots& \mathbf{P_{rM}}\\ \mathbf{P_{1r}}&\mathbf{P_{11}}&\ldots&\mathbf{P_{1M}}\\ \ldots&\ldots&\ddots&\ldots\\ \mathbf{P_{Mr}}&\mathbf{P_{M1}}&\ldots&\mathbf{P_{MM}}\end{array}\right] \tag{10}\]  
where \(\mathbf{P_{rr}}\) indicates the covariance of the robot's pose, \(\mathbf{P_{ij}}(i=1,2,\ldots,M;j=1,2,\ldots,M)\) indicates the covariance of the landmark location, \(\mathbf{P_{ri}}(i=1,2,\ldots,M)\) represents the covariance between the robot's pose and the landmark location.  
The EKF-SLAM algorithm [51] process can be summarized in three steps. Firstly, the system state and system covariance at time \(t\) are predicted based on the system state at \(t-1\) and the control input (robot motion information). Secondly, after obtaining the position and orientation of the landmarks and associating them, the state of the system is updated with the observation value of the feature  
Figure 4: Microphone localization processing chain. MTP: microphone to be positioned.  
landmarks. Finally, a new state is added to the current state to obtain an estimate of the system state and system covariance. The schematic model of the SLAM problem is shown in Figure 5. In this figure, x, u, z, and m are shown as the state of the robot, control input, observation input, and the map information of the environment, respectively.
# Sensorless Posture Detection of Reluctance Spherical Motor Based on Mutual Inductance Voltage
## 3 Particle Swarm Optimization Algorithm for 3-DoF Attitude Recognition
### Identification Principle
Step 4: After obtaining the calculated value \(U=\left[u_{i}\right]^{T}\) of the mutual inductance voltage of 12 groups of stator coils, calculate the root mean square error between the measured value and the calculated value as the fitness value \(fitness(\alpha,\beta,\gamma)=\sqrt{\frac{1}{12}\sum\limits_{i=1}^{12}\left(u_{ i}^{*}-u_{i}\right)^{2}}\) of individual algorithm population.
## 3 Materials and Methods
### Datasets  
In this work, sensor data from two different datasets are utilized. The first dataset consists of temperature sensor recordings from the dataset presented in [46]. This dataset contains load information and oil temperature data from two transformers over two years, recorded at 15-minute intervals. The oil temperature of a transformer is a significant indicator of its condition, and since this reading comes from a real sensor, we considered the temperature sensor signal from one transformer. The second dataset consists of sensor signals collected using a wireless sensor network (WSN) [47]. This WSN dataset includes temperature signals collected under both single-hop and multi-hop network settings. In this work, we considered the data collected using the multi-hop scenario. In order to perform sensor fault analysis, a dataset that includes sensor measurements for both normal and faulty measurements is required. Unfortunately, no open dataset offers sensor recordings of real sensor faults. Thus, researchers have adopted an approach to synthetically injecting faults into normal sensor signals [34; 44; 48]. To create the fault dataset in our work, we injected three types of artificial sensor faults. These three types of faults are commonly found in most of the articles on this topic. Researchers in [44; 45; 48] have included these faults in their studies. Additionally, drift, bias, and stuck faults have been studied in several real-world scenarios, such as aeroengines [23], UAVs [22], chiller systems [49], and hydrogen sensors [50]. The faults and the equations used to generate them are briefly described below.
## 4 Performance Evaluation
### Detailed Results
#### 4.3.1 Scenario #1  
Table 6 shows the achieved unfairness index of all methods as well as the total number of rebuffering events occurring in scenario #1. It should be noted that, for the number of rebuffering events, the results show an accumulated value of all running times instead of an average value. In general, all network-assisted methods provided relatively identical unfairness performances that significantly outperformed the No-Proxy method. Particularly, the fairness is improved by 4.29 times on average. The results of number of rebuffering events express the similar trend. While the proxy-based methods successfully eliminated such an impairment, the No-Proxy method performed more poorly when the number of clients increases. This is because the more clients there were in the network, the more fiercely they competed for the bandwidth. In Figure 5, the time-varying performance of each method is illustrated. Due to similar behaviors, only the subscenario 1-B is shown.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **Subscenario** & **No-Proxy** & **Reactive** & **Proactive** & **FAURAS** \\ \hline \multirow{3}{*}{\(F\)} & 1-A & 0.2512 & 0.0304 & 0.0392 & 0.0391 \\  & 1-B & 0.2551 & 0.0658 & 0.0670 & 0.0661 \\  & 1-C & 0.3879 & 0.1148 & 0.1156 & 0.1133 \\ \hline \multirow{3}{*}{\(Rebuff\)} & 1-A & 0 & 0 & 0 & 0 \\  & 1-B & 3 & 0 & 0 & 0 \\  & 1-C & 5 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance of unfairness index and number of rebuffering events in scenario #1.  
Figure 5: Time-varying performances of all methods in scenario #1. (**a**) No-Proxy; (**b**) Reactive; (**c**) Proactive; (**d**) FAURAS.  
It can be observed from Figure 5a that client \(a_{2}\) usually selected a bitrate higher than the fair share (1 Mbps) and consumed the bandwidth of other clients. For this reason, not only did client \(a_{1}\) and \(a_{3}\) fail to reach the fair bitrate, but their play-out buffers also became highly unstable and eventually depleted (as for client \(a_{3}\)). Meanwhile, under assistance of the proxies in the Reactive, Proactive and proposed FAURAS method (Figure 5b-d), all clients were able to select similar bitrates and kept the buffers stable. In the next part, the performance of the network-assisted solutions is investigated further, with respect to the ability to assist the client to adapt to the changes of the fair bandwidth.
###### Abstract
This study makes a significant contribution to the field of NLP by providing an in-depth analysis and comparison of the BERTopic model with traditional topic modeling techniques such as LDA and NMF, specifically within the context of Arabic text analysis. This research examines the impact of various pre-processing strategies addressing the challenges posed by Arabic's morphological and syntactic complexity, with a particular focus on enhancing the capacity of the BERTopic model to generate coherent and diverse topics from Arabic news articles. Furthermore, it explores hyperparameter tuning, investigating how different configurations influence BERTopic's performance in terms of coherence and diversity, with the goal of establishing optimized settings for Arabic text analysis. To the best of our knowledge, this is the first study to systematically optimize BERTopic by adjusting the \(n\)-gram range parameter and integrating different embedding models for effective Arabic topic modeling. The findings underscore the importance of a nuanced approach to pre-processing and hyperparameter tuning for improving the efficacy of topic modeling in Arabic texts.
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 2 Basic Principle and Functionality
### Key System Parameters  
A spaceborne LiDAR system is characterized by several high-level performance metrics that influence its resolution, coverage, and overall data quality [26; 30; 41; 42; 67; 68]:  
* Footprint diameter: The footprint diameter is the size of the area on Earth's surface that a single LiDAR pulse illuminates and measures. The footprint size influences the spatial resolution and the ability to detect fine-scale features on the Earth's surface.
* Horizontal resolution: The horizontal resolution is the smallest resolvable distance between two footprints, and is typical;y in the range of hundreds of meters. The higher spatial resolution allows for more detailed mapping and analysis of surface features.
* Vertical resolution: The vertical resolution is the smallest resolvable distance in the vertical direction. It depends on the application: hundreds of meters for atmospheric detection (aerosol/cloud layers) and tens of centimeters for altimetry (surface elevation and structure measurements). The improved vertical resolution improves the ability to profile atmospheric layers and surface topography.
* Accuracy: The accuracy of spaceborne LiDAR measurements depends on various factors, including the calibration of the LiDAR system, atmospheric conditions, and surface reflectance properties. Accurate calibration and correction for atmospheric effects are crucial for reliable measurements.
* Scanning pattern: The scanning pattern is the pattern in which the LiDAR system scans the ground and can be a raster or a swath pattern. The choice of scan pattern affects the coverage area and the density of data points collected.
* The LiDAR coverage/swath width: The swath width determines the LiDAR coverage. A wider swath width increases the coverage area but may reduce spatial resolution.
* Signal-to-noise ratio (SNR): SNR is critical to determining the detection capability and precision of a LiDAR system. Higher SNRs indicate more reliable and precise measurements, influencing the overall quality of the data products.  
Figure 3 includes the dimensioned parameters for high-level description of spaceborne LiDAR instrument.