esa-sceva/satcom-chunk-collection huggingface dataset:
# OTSU Multi-Threshold Image Segmentation Based on Improved Particle Swarm Algorithm
## 4 Multi-Threshold Segmentation Algorithm Based on Improved PSO
### Our Method and Process  
Compared with the classical particle swarm optimization algorithm, our improved particle swarm optimization algorithm is superior to the classical algorithm in terms of both speed and accuracy. At the same time, it also avoids the problem that the particle swarm optimization algorithm is prone to fall into local optimization, greatly improving the efficiency of image segmentation. The flow chart of our improved particle swarm optimization algorithm is shown in Figure 2.  
To a certain extent, linear learning factors and elite particle search strategies are added to the classical particle swarm algorithm, which improves the problems of slow optimization speed, low precision, low efficiency, and easy optimization in the classical particle swarm. The detailed process based on the improved particle swarm algorithm is as follows:  
Step 1. Set the parameters, which include the population size (m), dimension \((D)\), velocity range \((v)\), position range \((x)\), and maximum number of iterations \((k_{m}ax)\).  
Step 2. Initialize the population. Initialize the dimensions and vectors of particle individuals according to the threshold number \((k)\). The range of position vectors is [0255].  
Step 3. Update \(c_{1}\) and \(c_{2}\) with asynchronous monotonically decreasing individual learning factor and asynchronous monotonically increasing social learning factor, \(c_{1}(k)\) and \(c_{2}(k)\), instead of Equations (10) and (11) to individual, thereby improving the accuracy of particle optimization. Calculate the fitness values for individual particles, updating the individual extremums of particles and the global extremums of particles.  
Step 4. Iteratively update the position of the particle individual and the speed of the particle individual.  
Figure 2: Improved particle swarm algorithm flowchart.  
Step 5. 20% elite particles are selected according to the threshold segmentation contribution of individual particles.  
Step 6. Use chaotic iterative optimization to find the best fitness value for elite particles by comparing the chaotic optimization results.  
Step 7. Determine whether the current number of chaotic iterations meets the set maximum number of chaos iterations or whether the chaos optimization reaches preset accuracy. If the current number of chaos iterations meets the maximum number of chaotic iterations or the chaos optimization reaches the set accuracy, go to step 8; otherwise, go to step 2.  
Step 8. Map the optimal fitness value inversely back into the particle cluster group, calculate the fitness value of all particles in the particle cluster, and output the optimal solution if the set conditions are met; otherwise, go to step 1.
## 4 Results and Discussion
### Sensitivity Analysis
#### 4.5.2 The Use Pattern for the ReX Trailer  
An important aspect of this sensitivity analysis is the marginal application factor of the ReX trailer. If one would apply the ReX trailer for one-tenth of the driven distance rather than one-twentieth, this could have a significant influence on the comparison of the different midpoint indicators. For climate change, applying the ReX trailer for 10 percent of the time would result in a relative increase by 17 percent.  
Another aspect of the use pattern is the central issue of people tending to cover longer trips during weekends, e.g., to visit relatives. This can result in distorted availability for the ReX concept and thus, at peak demand in a lower number of users for one trailer. This sensitivity has been calculated for five users and ten users concerning the impact on climate change. Results showed no significant impact on either the vehicle cycle or the powertrain cycle, as the increase was found to remain below 2.5 percent in the worst case of 5 users.
# Regular Wave Effects on the Hydrodynamic Performance of Fine-Mesh Nettings in Sampling Nets
## 2 Material and Methods
### Numerical Simulation of the Netting Models
#### 2.4.1 Wave Generation  
In this study, the regular waves are generated using the wave generation boundary by the following equation for linear wave theory [40; 45]:  
\[\eta(x,t)=-\frac{1}{g}\frac{\partial\varphi}{\partial t}=a\text{cos}(kx- \omega t) \tag{17}\]  
\[\omega^{2}=gk\text{tanh}(kh) \tag{18}\]  
where \(\omega\)\((=2\pi/T)\) is the angular frequency of the wave and the wave number \(k\) satisfies the dispersion relation in Equation (18).  
The velocity components under linear wave theory can be expressed as follows:  
\[u_{x}=-\frac{\partial\varphi}{\partial x}=\frac{gka}{\omega}\frac{\cosh k(z+h )}{\cosh kh}\text{cos}(kx-\omega t) \tag{19}\]  
\[u_{y}=-\frac{\partial\varphi}{\partial y}=\frac{gka}{\omega}\frac{\cosh k(z+h )}{\cosh kh}\text{sin}(kx-\omega t) \tag{20}\]  
\[u_{z}=-\frac{\partial\varphi}{\partial z}=\frac{gka}{\omega}\frac{\sinh k(z+h )}{\cosh kh}\text{sin}(kx-\omega t) \tag{21}\]  
Figure 3: Schematic of the numerical wave tank: (**a**) computational domain and boundary conditions, and (**b**) mesh grid configuration.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Wave Period \(T\) (s)**} & \multirow{2}{*}{**Wave Length \(L\) (m)**} & \multicolumn{2}{c}{**Net 1**} & \multicolumn{2}{c}{**Net 2**} \\ \cline{3-6}  & & \(D_{n}\) & \(C_{n}\) & \(D_{n}\) & \(C_{n}\) \\ \hline
1.3 & 2.49 & 8,814,758 & 28.16 & 5,848,000 & 19.44 \\
1.8 & 4.03 & 10,850,000 & 47.79 & 6,476,000 & 22.23 \\
2.3 & 5.49 & 14,910,000 & 51.18 & 8,328,000 & 23.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Porous media resistance coefficients under different wave cases.
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 1 Introduction
### Problem Statement  
Achieving interpretable and trustworthy AI systems remains a significant challenge. XAI techniques aim to address this by providing users insights into model decisions. However, several limitations hinder our ability to understand and evaluate XAI frameworks, thereby impeding the progress toward genuinely interpretable AI. This section outlines these limitations, highlighting the areas where further research is crucial for advancing the field of XAI.  
Despite the rapid advancements in XAI frameworks, there exists a lack of a comprehensive understanding of the diverse attributes of these frameworks and how they cater to different use cases and technical requirements. This ambiguity hinders the optimal selection and practical application of XAI frameworks within various domains, potentially leading to transparency, trust, and responsible AI development limitations. In [4], the authors identify several limitations that hinder our understanding of XAI frameworks and their performance needs. One key aspect is the lack of specific evaluation metrics. It is difficult to gauge their effectiveness without clear metrics to assess how well XAI frameworks explain model decisions. Additionally, another study emphasizes the importance of developing explanation methods, often neglecting the role of user interpretation [2]. Even if an XAI framework produces an explanation, it is crucial to understand if the users can interpret it meaningfully.  
Furthermore, research by [5] points out that the limited details on XAI frameworks themselves create a barrier to understanding. By not delving into the specifics of the individual methods and functionalities, it is difficult to know what kind of performance to expect from these frameworks. Finally, another study by [6] emphasizes the need for empirical evaluation methods to assess XAI frameworks in practice. Developing these methods would be crucial for bridging the gap between the theoretical XAI concepts and the practical understanding of their effectiveness. By addressing these limitations, future research can move us closer to a more comprehensive understanding of XAI frameworks and their role in achieving interpretable and trustworthy AI systems.  
This research aims to bridge this knowledge gap by analyzing the key attributes of XAI frameworks, including the following:  
* Explanation types (extrinsic or intrinsic);
* Model-dependence (model-agnostic or model-specific);
* Explanation scope (global or local);
* Output format (arguments, text, visualizations, etc.);
* Use cases;
* Programming language compatibility;
* Applicability to different ML and deep learning paradigms;
* Model complexity considerations;
* Open-source availability and license type;
* Associated challenges and limitations;
* Primary strengths and potential benefits.  
By providing a structured and comparative analysis of these attributes, this research will contribute to a deeper understanding of the capabilities and limitations of XAI frameworks.
## 3 Results and Discussion
### Soil Disturbance Characteristics  
#### 3.2.1 Transverse Disturbance  
The simulation showed the effects of rake angle and motion on transverse disturbance (Figure 5). The transverse disturbance for the regular tools and the belt tools at the four rake angles showed a similarity in shape, which was the combination of a soil upheaval above the original soil surface and an inverted triangular zone below the surface (Figure 5).  
The area of transverse disturbance (_Ayz_) increased as the rake angle increased (Figure 6). However, the _Ayz_ values for the belt tools were higher than those for the regular tools. This was related to the changes in disturbance width (\(W\)) and upheaval height (\(H\)) (Figure 7). Although the \(W\) values for the belt tools were slightly lower, the \(H\) values for the belt tools were higher than those for the regular tools at the four rake angles. Therefore, the higher values of _Ayz_ for the belt tools were mainly attributed to the increased upheaval height (\(H\)).  
The areas of the underground disturbance for the two types of tools were similar because of the identical depth of cutting and the similarity in disturbance width (\(W\)); thus, the overall effect of the belt motion was acceptable considering the specific resistance of subsoiling, which is the ratio of draught force to the area of underground disturbance as shown in Equation (1) [43].  
\[K_{f}=F_{x}/A_{f}, \tag{1}\]  
where \(K_{f}\) denotes the specific resistance (kN/m\({}^{2}\)) and \(A_{f}\) denotes the area of underground disturbance.  
The specific resistance (\(K_{f}\)) for the belt tools was lower than that for the regular tools (Figure 8). The \(K_{f}\) value increased from 7.82 to 12.03 for the regular tools, while remained about 7 for the belt tools as the rake angle increased.  
Figure 8: Specific resistance (\(K_{f}\)). (Random disturbance results in slightly fluctuating \(K_{f}\) values).  
Figure 6: Area of transverse disturbance (\(Ayz\)). (The upper half of each bar represents the area of soil disturbance above the initial ground surface, while the lower half represents the area of soil disturbance below the ground surface).  
Figure 7: Disturbance width (\(W\)) and Upheaval height (\(H\)).  
The specific resistance of subsoiling is an indicator to assess the performance and efficiency of subsoiling, which reflects the draught force requirement of a subsoiler disturbing a given cross-sectional area. A lower specific resistance indicates a better subsoiling efficiency [39]. The above results indicate that the belt motion increases the subsoiling efficiency by reducing the specific resistance.
## 2 SNR Analysis of the VLC System with Light Interference  
### Optical Wireless Channel  
To integrate a built-in VLC system, we need to combine fluorescent lamps and LED to have sufficiently large power for both lighting and high-speed wireless personal area networks. In general, fluorescent lamps are located within a room, and its illumination is as wide as the lighting equipment. Thus, the experiment was conducted with an LED located at the room center under fluorescent lamps.  
In this scenario, we assume that only fluorescent lamps contribute to the ambient noise. As shown in Figure 1, the optical channel can be modeled as a linear system [21]:  
\[y(t)=h(t)\otimes x(t)+n(t) \tag{1}\]  
where the symbol \(\otimes\) denotes convolution, LED power \(x(t)\) is the input, received power \(y(t)\) is the output, and impulse response function \(h(t)\) describes the system dynamics.  
The time-average transmitted LED power is given by [21]:  
\[P(t)=\langle x(t)\rangle=\underset{T\rightarrow\infty}{\lim}\frac{1}{2T}\int \limits_{-T}^{T}x(t)dt \tag{2}\]  
In this system, the LED is located at a high position, and the irradiance of the LED for communication is smaller than the size of the room. Thus, we do not consider intersymbol interference. The channel DC gain is given by [9]:  
\[H(0)=\int\limits_{\infty}^{\infty}h(t)dt \tag{3}\]  
Fundamentally, the emitted light of the LED is according to a Lambertian emission pattern. Hence, considering only the line of sight transmission path, we have:  
\[H(0)=\frac{(m+1)\cos^{m}(\varphi)}{2\pi d^{2}}A\cos(\theta) \tag{4}\]  
where \(m\) is the order of Lambertian emission, \(\varphi\) is the transmitter semi angle, \(d\) is the distance between the transmitter and the receiver, \(A\) is the perceived area of the photo-detector, and \(\theta\) is the angle of incidence.  
The average received optical power is given by:  
\[P_{r}=H(0)P(t) \tag{5}\]  
Thus, the SNR of the output signal follows from the formula:  
\[SNR=\frac{P_{r}}{P_{noise}} \tag{6}\]  
As the ambient light comes exclusively from the fluorescent lamps based on our assumption, \(P_{noise}\) is considered as the light power of the fluorescent lamps. Basically, \(P_{noise}\) can be calculated in a manner similar to that of \(P_{r}\) with different distances and other parameters. We suppose the arrangement of the lamp to be symmetrical, so \(P_{noise}\) depends only on the distance.  
\[P_{noise}(d)=\sum_{N}H_{i\_noise}(d)P_{i\_noise} \tag{7}\]  
A glance at Equations (5) and (6) reveals that the SNR depends on the semi angle of lights, angle of incidence, and distance between the transmitter, noise sources, and the receiver. From Equation (7), we see that the variation in the semi angle affects the SNR more than the change in the distance, because \(P_{noise}\) is only a function of the distance and the increment of the distance makes \(P_{noise}\) increase. Considering the SNR variation is important for the mobility VLC analysis. The receiver response to the conditions could be used to evaluate the adaptive and robust performance of the systems.  
Figure 1: Optical wireless channel model with ambient light noise.
# Roto-Translational Control of Spacecraft in Low Earth Orbit Using Environmental Forces and Torques
## 2 Spacecraft Dynamics
### Attitude Dynamics  
Using [PERSON]'s law, the attitude dynamics of a DMD-equipped spacecraft can be expressed as  
\[\mathbf{j}\mathbf{\omega}+\mathbf{j}\dot{\mathbf{\omega}}+\omega^{\times}\mathbf{J}\mathbf{\omega}= \mathbf{\tau}_{\mathbf{D}}+\mathbf{\tau}_{\mathbf{L}}+\mathbf{\tau}_{\mathbf{GG}}+\mathbf{\delta}_{\mathbf{a}}, \tag{11}\]  
where \(\mathbf{\omega}\in\mathbb{R}^{3}\) is the angular velocity of the body with respect to the ECI frame, \(J\in\mathbb{R}^{3\times 3}\) is the inertia matrix of the spacecraft, \(\mathbf{\tau}_{\mathbf{GG}},\ \mathbf{\tau}_{\mathbf{D}},\ \mathbf{\tau}_{\mathbf{L}}\in\mathbb{R}^{3}\) are the gravity gradient, aerodynamic drag and lift torques, respectively. The vector \(\mathbf{\delta}_{\mathbf{a}}\in\mathbb{R}^{3}\) denotes disturbances torques.  
To represent the orientation of the spacecraft with respect to the ECI frame, the quaternion \(\mathbf{q}\in\mathbb{R}^{4}\) is used. In the body coordinate system, \(\mathbf{q}\) is defined as [24]  
\[\mathbf{q}\triangleq[q_{0}\ \mathbf{q}_{\mathbf{v}}{}^{T}]^{T}, \tag{12}\]  
where \(q_{0}\in\mathbb{R}\) and \(\mathbf{q}_{\mathbf{v}}\triangleq[q_{1}\ q_{2}\ q_{3}]^{T}\in\mathbb{R}^{3}\), and the property \(\mathbf{q}_{\mathbf{v}}{}^{T}\mathbf{q}_{\mathbf{v}}+q_{0}^{2}=1\) is satisfied.  
A desired attitude trajectory is specified using the desired quaternion \(\mathbf{q}_{\mathbf{d}}\triangleq[q_{0d}\ \mathbf{q}_{\mathbf{0}e}{}^{T}]^{T}\in\mathbb{R}^{4}\), and the mismatch between \(\mathbf{q}\) and \(\mathbf{q}_{\mathbf{d}}\) is expressed using the error quaternion \(\mathbf{e}\triangleq[\varrho_{0}\ \mathbf{e}_{\mathbf{v}}{}^{T}]^{T}\in\mathbb{R}^{4}\), which satisfies the property \(\mathbf{e}_{\mathbf{v}}{}^{T}\mathbf{e}_{\mathbf{v}}+\varrho_{0}^{2}=1\) and obeys the error quaternion kinematics [24]  
\[\mathbf{\dot{e}}_{\mathbf{v}} \triangleq\frac{1}{2}\big{(}\varepsilon_{\mathbf{v}}{}^{\times}+\varrho _{0}\mathcal{I}_{3}\big{)}\mathbf{\tilde{\omega}}, \tag{13}\] \[\mathbf{\dot{e}}_{0} \triangleq-\frac{1}{2}\mathbf{e}_{\mathbf{v}}{}^{T}\mathbf{\tilde{\omega}}. \tag{14}\]  
In (13) and (14), \(\mathcal{I}_{3}\in\mathbb{R}^{3\times 3}\) is the identity matrix, and \(\mathbf{\tilde{\omega}}\) denotes the error in the angular velocity of the spacecraft  
\[\mathbf{\tilde{\omega}}\triangleq\mathbf{\omega}-\widetilde{R}\mathbf{\omega}_{d}, \tag{15}\]  
where \(\mathbf{\omega}_{d}\in\mathbb{R}^{3}\) is the desired angular velocity with respect to the ECI frame, and \(\widetilde{R}\in\mathbb{R}^{3\times 3}\) is the rotation matrix used to express \(\mathbf{\omega}_{d}\) in the body coordinate system.
# Arabidopsis Plastid-RNA Polymerase RPOTp Is Involved in Abiotic Stress Tolerance
## 2 Results
### Salt Stress Differentially Affects the Transcript Abundance of Plastid-Encoded Genes  
To investigate the effect of salinity on the expression of the plastome, we studied, in Col-0 salt-stressed and non-stressed seedlings, the steady-state levels of the transcripts of the functionally  
Figure 4: Analysis by qRT-PCR of _RPOTp_ gene expression in Col-0 and the _gun1-1_ mutant in response to norllurazon (NF). _RPOTp_ expression in Col-0 and the _gun1-1_ mutant in growth media supplemented (+) or not (-) with NF. Relative values were determined by the \(2^{-\Delta\Delta\mathrm{CT}}\) method for the _RPOTp_ gene after normalization with those of the _ACTIN2_ gene, and compared to those of Col-0 in the non-supplemented media, to which a value of 1 was assigned. Each value corresponds to the mean \(\pm\) SD of three biological replicates and triplicate reactions. Asterisks indicate significant differences using a Mann–Whitney U-test (\(\star\)\(p<\) 0.01) in the supplemented vs. non-supplemented media for each genotype. Plants were collected at 6 DAS.  
different plastid genes transcribed by PEP or NEP RNA polymerases (Table 1). The selected genes encode proteins involved in photosynthesis (_psaA_, _psaB_ and _psbA_ encode Photosystem I and II reaction center proteins, respectively), protein degradation (_clpP_ encodes an ATP-dependent protease), translation (_rps18_ encodes ribosomal protein S18), lipid biosynthesis (_accD_ encodes the Carboxyltransferase \(\beta\) subunit of Acetyl-CoA carboxylase) and transcription (_rpoA_, _rpoB_ and _rpoC1_ encode the \(\alpha\), \(\beta\) and \(\beta^{\prime}\) subunits of PEP, respectively) (Table 1). Compared to the non-stressed seedlings, the presence of 100 mM NaCl lowered the transcript levels of the majority of the studied genes (Table 1). This reduction was statistically significant for the _psbA_, _rpoA_ and _rpoB_ genes (1.7-, 1.6- and 1.6-fold down-regulated, respectively), whereas no significant changes in transcript abundance were found for the remaining genes, except for _clpP_, which was slightly up-regulated (Table 1). Consequently, although salt stress induced the expression of the _RPOTp_ gene, this did not correlate with the mRNA levels of the plastid genes, which are fundamentally transcribed by RPOTp (_rpoA_, _rpoB_, _rpoC1_ and _accD_; Table 1).
## 2 Theoretical Foundations  
### HeAs  
HEAs are a class of emerging metallic materials with unique compositions and properties. These alloys are composed of multiple principal elements mixed in nearly equimolar ratios, which imparts characteristics and advantages that differ from traditional alloys. Research on high-entropy alloys began in the early 2000s, gaining widespread attention due to their diverse compositions and exceptional performance.  
The composition of HEAs is one of their most distinctive features. Unlike traditional alloys which typically consist of one primary element with a few minor additions, HEAs are usually composed of five or more principal elements mixed in nearly equimolar ratios (with each element's molar fraction ranging from 5% to 35%). For example, a typical FeCoNiCrMn HEA is composed of five elements: iron (Fe), cobalt (Co), nickel (Ni), chromium (Cr), and manganese (Mn), with each element present in nearly equal proportions. This multielement composition leads to a high mixing entropy (configurational entropy), which helps stabilize the solid solution structure of HEAs.  
Structurally, high-entropy alloys often form simple solid solution structures, such as face-centered cubic (FCC), body-centered cubic (BCC), or hexagonal close-packed (HCP) structures, rather than forming complex intermetallic compounds. This is because the high mixing entropy effectively reduces the driving force for phase separation, allowing multiple elements to be uniformly distributed within the same lattice.
# Deep-Learning-Based Framework for PET Image Reconstruction from Sinogram Domain
## 2 Methodology
### Framework Based on Conditional Generative Adversarial Network  
The iterative method is based on a hypothetical initial image. Using the step-by-step approximation method, the theoretical projection value can be compared with the actual measurement projection value, and the optimal solution is determined under the guidance of some optimization criteria. Compared with the analytical method, the iterative method has better image quality and higher image resolution in PET imaging with relative undersampling and low counting. However, the disadvantage is that the calculation is complex and slow. In the proposed method, the approximation and optimization processes are replaced with the training process of the neural network.
## 4 Discussion
The study employed an immersive metaverse environment requiring a stable Internet connection. While efforts were made to accommodate multiple users, the bandwidth limitations restricted simultaneous participation to four students. Additionally, managing headset battery life necessitated recharging after each use.