esa-sceva/satcom-chunk-collection huggingface dataset:
# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 5 Results
### Model Parameters  
Regarding feature input parameters, the speaker voiceprint (\(I_{spk}\)) had a dimension of 192 and was first projected into a 100-dimensional speaker embedding (\(E_{spk}\)) with a linear layer. The content representation (\(I_{content}\)) was a one-dimensional sequence of length \(T\) with a vocabulary size of 4096. It was embedded into a 512-channel matrix with an embedding layer and then fed into a Transformer-based encoder consisting of six blocks. Each block contained eight attention heads and a feed-forward layer with a hidden dimension of 2048. The output was content embedding \(E_{content}\). The pitch feature (\(I_{pitch}\)) was first mapped to a discrete sequence with a vocabulary size of 256, which was then transformed into a 512-channel matrix by using an embedding layer. The energy (\(I_{energy}\)) and prosody (\(I_{prosody}\)) features were both projected into 100-dimensional embeddings (\(E_{energy}\) and \(E_{prosody}\), respectively) by using linear layers.  
In the multi-dimensional perception network, the time encoding was processed by a one-dimensional convolutional layer with an output size of \(512\times 2\) to generate two modulation parameters, \(\gamma_{t}\) and \(\beta_{t}\). The multi-head self-attention module used four attention heads with a hidden dimension of 400. The conditional embedding network consisted of a multi-layer perceptron with two linear layers, where the hidden layer had a dimensionality of 400, and the output layer had a size of \(400\times 6\), producing six conditional parameters: \(\alpha_{1},\gamma_{1},\beta_{1},\alpha_{2},\gamma_{2}\), and \(\beta_{2}\). Finally, the entire multi-dimensional feature perception network outputted a 100-dimensional predicted vector field (\(v(z_{t},t)\)) via a one-dimensional convolutional layer.
## 4 Numerical Analysis
### Axial Force Analysis of CWR Fractures Using FEA
#### 4.3.1 Numerical Analysis Modeling  
Displacement boundary elements were set to allow the axial expansion and contraction of the rail, whereas the welded joints between rails were modeled using nonlinear boundary spring elements with an axial tensile force. Figure 10 shows that spring elements were set with boundary conditions that considered stiffness, assuming that the rail fracture occurred when the displacement exceeded 0.05 mm. Further, as shown in Figure 11, the load conditions were set based on axial force variations because of temperature changes [32].  
For the analysis of the load condition, the longitudinal thermal force (\(F_{p}\)) was applied as in Equation (1), and the load condition was set according to the temperature change amount as shown in Figure 11 [33]. the magnitude of axial stresses (longitudinal thermal stress) of the rails may be calculated from Equation (1):  
\[\sigma=\alpha\cdot\mathrm{E}\cdot\mathrm{\Delta t}\ \mathrm{[MPa]}, \tag{1}\]  
The magnitude of the longitudinal thermal force in the track, depending on the rail type, is given by Equation (2):  
\[F_{p}=\alpha\cdot\mathrm{E}\cdot\mathrm{\Delta t}=\sigma\cdot\mathrm{A}\ \mathrm{[kN]}, \tag{2}\]  
where,  
\(\alpha\)--thermal expansion coefficient of the rail steel [1/\({}^{\circ}\)C];  
E--Young modulus of steel [MPa];  
Figure 10: Numerical analysis modeling.  
\(\Delta\)**t**--temperature difference in the rail about the neutral temperature (the temperature at which there is no thermal stress in the rail) [**\({}^{\circ}\)**C]**: A--cross-section area of the rail [m**\({}^{2}\)**].
## 5 Experimental Results
### Dynamic Switching
Therefore, lower input power into the gate SOAs results in OSNR performance degradation, which is also confirmed by the BER curves and eye diagrams in Figure 11. The gain compensation brought by the SOAs and the resulting limited power penalty, indicates the potential scale of the PIC to higher data rates and port counts.
## 3 Subcellular Influence of Light Intensity and Spectrum on the Redox System
### Light-Intensity-Dependent Subcellular Changes in ROS and Antioxidants
This much lower glutathione concentration seems to be resulting from limited availability of the glutathione precursors glycine (from reduced photorespiration) and cysteine (from reduced sulphur uptake and incorporation) during darkness [111, 112, 113]. Interestingly, 24 h of darkness depleted _Arabidopsis_ glutathione levels in mitochondria (84%) and peroxisomes (53%), whereas AsA labelling density remained comparatively stable [51]. Results from low PPFD intensity experiments corroborate these observations. Similar to darkness, low PPFD also strongly reduced the glutathione levels in peroxisomes of _Arabidopsis_ and in peroxisomes, nuclei, and the cytosol of wheat [99, 107]. In wheat, the glutathione concentration under low PPFD may have remained unchanged inchloroplasts at least partly due to a transcriptional upregulation of the chloroplast-localized \(\gamma\)-ECS [107]. Subcellular AsA concentrations in _Arabidopsis_ were largely unaffected by low PPFD, irrespective of the length of treatment [99], which can be explained by the larger size of the ascorbate pool. High PPFD for a few hours stimulated total glutathione accumulation in wild-type _Arabidopsis_, particularly in compartments of glutathione synthesis (chloroplasts and the cytosol) and of ROS and H\({}_{2}\)O\({}_{2}\) detoxification from photorespiration (peroxisomes). High PPFD in the short term, by contrast, increased the mitochondrial glutathione concentration drastically (900%) in the glutathione-deficient Arabidopsis _pad2-1_ mutant, probably to ensure efficient respiration under high PPFD stress [99]. The compartment-specific increase in total glutathione in wheat was accompanied by the transcriptional upregulation of cytosolic GR [107]. GR plays an important role in the adjustment of the optimal GSSG/GSH ratio, which determines the half-cell reduction potential of this redox pair [114]. Using redox-responsive GFP, a gradual increase in \(E_{\text{GSH}}\) was reported in potato chloroplasts with increasing light intensity. Interestingly, chloroplasts were able to return to a normal level of glutathione oxidation state after a 14 h treatment at 1250 \(\upmu\)mol m\({}^{-2}\)s\({}^{-1}\), but not if high PPFD was combined with cold stress (3 \({}^{\circ}\)C) in mature leaves, conditions favouring PSI photoinhibition [114]. The sensitivity of chloroplasts to a combination of high PPFD and cold (or other osmotic stresses) certainly depends on the natural habitat of a plant.
## 2 Materials and Methods
### Generating Synthetic Data  
Commonly referred to as Monte Carlo simulation, the synthetic data-generating algorithm consists of a random process driven by a known probability distribution over the states of the system [12; 13]. Figure 3 introduces our implementation that generates a synthetic epileptic marker according to probabilistic rules with variable predefined parameters.  
The stochastic model introduces the following probabilities for the epimarker during seizures and outside seizure periods:  
\[\begin{array}{c}P\{E>T_{s}|seizure\}=P_{s}\\ P\{E>T_{n}|normal\}=P_{n}\end{array} \tag{8}\]  
In Equation (8), \(T_{s,n}\) are the thresholds for, correspondingly, the ictal and normal periods, and \(P_{s,n}\) are the associated probabilities. We also postulate random ictal onset  
Figure 3: General scheme for Monte Carlo type of simulating epimarker. The output signal, the synthetic epimarker, is generated according to a probabilistic model that determines the distribution of values at each time point depending on whether the system is in a predefined ictal (seizure) state or in a “normal” state.  
times. The seizure duration is a random number in the interval \([L/2,L]\), where \(L\) is a chosen maximal duration. The generated events are used as a ground truth in order to assess the performance of the detection and reinforcement processes. Figure 4 illustrates a trace of the epimarker from a real seizure as observed by our seizure-detection algorithm (top frame) and from a generated event (bottom frame). The parameters for the latter are \(\{T_{s},\ T_{n},\ P_{s},\ P_{n},\ L\}=\{0.8,\ 0.1,\ 0.96,\ 0.05,\ 30\}\).  
In the results below, a variety of scenarios are considered with both stationary and nonstationary seizure-generation parameters \(\{T_{s},\ T_{n},\ P_{s},\ P_{n},\ L\}\).  
To quantify the separation between normal and seizure states, one can use the \"confusion\" metric between two distributions \(p_{n},\ p_{s}\) defined over the states of any system:  
\[C(p_{n},\ p_{s})\equiv\sum_{states}\min(p_{n},\ p_{s}) \tag{9}\]  
Assuming in our case that \(T_{s}\geq T_{n}\), we obtain from Equation (9)  
\[C=T_{n}*\min(1-P_{n},1-P_{s})+(\ T_{s}-T_{n})*\min(P_{n},1-P_{s})+(1-T_{s})* \min(P_{n},P_{s}) \tag{10}\]
# Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means
###### Abstract  
In this paper, we present a robust and reliable framework based on L0 gradient minimization (LGM) and the fuzzy c-means (FCM) method to detect various fabric defects with diverse textures. In our framework, the L0 gradient minimization is applied to process the fabric images to eliminate the influence of background texture and preserve sharpened significant edges on fabric defects. Then, the processed fabric images are clustered by using the fuzzy c-means. Through continuous iterative calculation, the clustering centers of fabric defects and non-defects are updated to realize the defect regions segmentation. We evaluate the proposed method on various samples, which include plain fabric, twill fabric, star-patterned fabric, dot-patterned fabric, box-patterned fabric, striped fabric and statistical-texture fabric with different defect types and shapes. Experimental results demonstrate that the proposed method has a good detection performance compared with other state-of-the-art methods in terms of both subjective and objective tests. In addition, the proposed method is applicable to industrial machine vision detection with limited computational resources.  
fabric defect detection; LGM; FCM; image smoothing Article
## 3 Simulated Results and Analysis
### Effects of the Exciting Probe and the Gap between Parasitic Patches  
Consequently, the effects of the exciting probe varied according to the exciting probe length '\(l_{f}\)' whereas other parameter values were kept constant. The following parameter values were constrained throughout the experiment: the position of the rectangular cavity '\(d_{y}\)', the square patch length '\(l_{p}\)', the rectangular slot length '\(l_{s}\)', the rectangular slot width '\(w_{s}\)', the cavity length '\(c_{x}\)', the cavity width '\(c_{y}\)', the cavity height '\(c_{z}\)', and the gap between parasitic patches '\(a\)' and '\(b\)'. Figure 9 shows five cases of the measured variation of the exciting probe length, ranging from \(l_{f}\) = 4.0 to 8.0 mm versus the frequency (GHz). It could be seen from Figure 9 that the minimal return loss appeared at \(l_{f}\) = 6.0 mm, as expected in Table 1. Nonetheless, when the exciting probe length '\(l_{f}\)' deviated from 6.0 mm, the return loss value was likely to increase.  
Similarly, the exciting probe length and the effects of the gap between parasitic patches varied according to the gap between parasitic patches '\(a\)' = '\(b\)' whereas other parameter values were kept constant. The following parameter values were constrained throughout the experiment: the position of the rectangular cavity '\(d_{y}\)', the square patch length '\(l_{p}\)', the rectangular slot length '\(l_{s}\)', the rectangular slot width '\(w_{s}\)', the cavity length '\(c_{x}\)', the cavity width '\(c_{y}\)', the cavity height '\(c_{z}\)', and the exciting probe length '\(l_{f}\)'. Figure 10 showed five cases of the measured variation of the gap between parasitic patches from \(a\) = \(b\) = 0.26\(\lambda\) to 0.34\(\lambda\) mm versus the frequency (GHz). It could be seen from Figure 10 that the minimal return loss appeared at \(a\) = \(b\) = 0.30\(\lambda\) mm as expected in Table 1. However, when the gap between parasitic patches '\(a\)' = '\(b\)' deviated from 0.30\(\lambda\) mm, the return loss value was likely to increase.  
Figure 8: Simulated return loss for different values of ‘\(c_{z}\)’.  
Figure 9: Simulated return loss for different values of ‘\(l_{f}\)’.
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 4 Temperature Compensation
### Temperature Compensation of Internal Calibrator  
The internal calibrator is located inside the satellite cabin, and a test plan was formulated based on the in-orbit temperature environment of the internal calibrator. The temperature range of the test chamber is \(-10\)\({}^{\circ}\)C\(\rightarrow\)\(20\)\({}^{\circ}\)C, and the performance indicators of the internal calibrator are recorded.  
During the tests, the internal calibrator operates in non-delay state, with thermal consumption below 2 W and a small internal temperature gradient. The temperature measurement accuracy can be better than 2 \({}^{\circ}\)C, As shown in Figure 10, within the range of \(-10\)\({}^{\circ}\)C\(\rightarrow\)\(+20\)\({}^{\circ}\)C, the amplitude compensation error of the internal calibrator after temperature compensation is 0.045 dB, and the phase compensation error is 0.735\({}^{\circ}\).  
Figure 10: _Cont._  
Figure 9: Temperature compensation test results of the antenna calibration networks under temperature conditions of \(-20\)\({}^{\circ}\)C\(\rightarrow\)\(+45\)\({}^{\circ}\)C. (**a**) 5 measurements of the antenna calibration networks phase. (**b**) 5 measurements of the antenna calibration networks amplitude. (**c**) Phase compensation curve of the antenna calibration networks. (**d**) Amplitude compensation curve of the antenna calibration networks. (**e**) Phase compensation error of the antenna calibration networks. (**f**) Amplitude compensation error of the antenna calibration networks.
# Application of Virtual Reality in Learning Quantum Mechanics
###### Abstract  
Quantum mechanics is a physical theory that describes the behavior of microscopic matter. According to quantum theory, a microscopic particle may be described as either a particle or a wave, called wave-particle duality. Many students in high school or college (BC level) find it difficult to imagine that microscopic particles have both particle and wave properties. This is mainly caused by the scale of the world they see since quantum mechanics deals with things that are too small, while the wave and particle phenomena at the microscopic scale are difficult to understand, measure, or verify in the real world. In this study, virtual reality technology was used to develop teaching modules on quantum mechanics, allowing learners to see the particles and wave phenomena of electrons and photons in the microscopic world through interactive operation in virtual experiments. A teaching experiment was conducted by recruiting 60 high school students as research subjects. The control group (30 students) used physics textbooks, and the experimental group (30 students) used the virtual teaching modules for learning quantum mechanics. The analysis results show that the experimental group's learning effectiveness is higher than the control group. The questionnaire results show that students were satisfied with the learning experience using virtual teaching modules with high learning motivation and low cognitive load because virtual reality can visualize the abstract concepts of wave-particle duality and help them understand quantum mechanics.  
virtual reality; quantum mechanics; wave-particle duality; learning effectiveness; learning motivation; cognitive load; system satisfaction
# Intention Recognition for Multiple AUVs in a Collaborative Search Mission
## 2 Related Works
### Model-Based Intent Recognition Methods
#### 2.1.4 Decision Trees  
Because of its simple structure, high computational efficiency, and strong interpretability, the decision tree-based intention recognition method is widely used in many fields of intention inference missions. Belief decision tree introduces a belief function to model uncertainty, which improves the adaptability of the decision tree in a complex environment.  
The researchers proposed a novel random forest method based on the belief decision tree, which combines belief structures by a weighted average of the weights of each tree [32]. Compared with the traditional random forest simple voting or average method, the accuracy of intention recognition is significantly improved. The decision tree algorithm is used to analyze the entrepreneurial intention of young people. By analyzing factors including demographic characteristics, social environment, attitude, and so on, a classification model based on QUEST (fast, unbiased, efficient, and statistical tree) algorithm was built [33]. Aiming at the problem of spatial object intention inference, a method of sequence intention inference based on the meta-fuzzy decision tree was proposed [34]. By defining three situation functions, namely \"circling\", \"collision\", and \"observation\", the internal characteristics of target trajectory and relative attitude are modeled, and the time-space modeling is carried out in combination with the Long Short-Term Memory network (LSTM), and the task transfer is realized by using meta-learning technology.  
Decision tree-based intent recognition methods offer several advantages, including high computational efficiency, making them suitable for real-time scenarios, and strong interpretability, as their branching structure provides clear decision paths [35]. They are highly adaptable across various domains, from space target prediction to smart home monitoring, and demonstrate enhanced performance when integrated with techniques like fuzzy logic, meta-learning, and deep learning. However, decision trees are sensitive to data quality, prone to overfitting in noisy or skewed datasets, and may struggle with scalability in high-dimensional or dynamic environments. Additionally, extending decision trees with fuzzy or meta-learning techniques often requires complex rule design and parameter optimization.
## 2 Materials and Methods  
### Time Synchronization Principle  
#### 2.1.1 L-Band Satellite-Ground Two-Way Time Synchronization  
BDS achieves satellite-ground time synchronization using L-band two-way time comparison. The satellite and ground station, respectively, generate and broadcast pseudocode ranging signals based on the control of local clocks. Specifically, the ground station obtains the downlink pseudorange at the time corresponding to the ground 1 pulse per second (pps), and the satellite obtains the uplink pseudorange at the time corresponding to the satellite 1 pps. Meanwhile, the satellite sends the observed value of uplink pseudorange to the ground station, and then the ground station calculates the difference between the locally measured downlink pseudorange and the received uplink pseudorange to obtain the clock offset of satellite relative to the ground station. It lays a foundation for completing the satellite-ground two-way time comparison. The principle of L-band satellite-ground two-way time comparison is shown in Figure 1.  
The satellite-ground two-way time comparison method is used to obtain the observed satellite-ground clock offset, which can be expressed by a second-order polynomial:  
\[\Delta T_{s}-\Delta T_{g}=\frac{1}{2c}(\rho_{u}-\rho_{d})-\frac{1}{2c}(R_{u}-R _{d})-\tau_{rel}+\varepsilon \tag{1}\]  
where \(\Delta T_{s},\Delta T_{g}\), respectively, denote the satellite clock offset and the ground station clock offset; \(\rho_{u},\rho_{d}\) represent the uplink and downlink pseudorange, respectively; \(R_{u},R_{d}\) indicate the theoretical values of uplink and downlink geometric distances, respectively; \(\tau_{rel}\) represents the relativistic effect delay in the signal propagation path; \(\varepsilon\) indicates the synthetic measurement noise of the satellite-ground two-way link; and \(c\) represents the velocity of light.  
The satellite-ground two-way time comparison method is used to obtain the observed value of satellite-ground clock offset. The satellite-ground clock offset of satellite \(i\) can be expressed by a second-order polynomial [21]:  
\[\Delta T_{i}(t)=a_{0}^{i}+a_{1}^{i}(t-t_{0})+a_{2}^{i}(t-t_{0})^{2} \tag{2}\]  
where \(\Delta T_{i}(t)\) represents the discrete point of satellite-ground clock offset of satellite \(i\); \(t\) denotes the observation time of satellite-ground clock offset; \(t_{0}\) refers to the reference time of clock offset parameter; \(a_{0}^{i},a_{1}^{i},a_{2}^{i}\) represent the clock offset, clock rate, and clock drift, respectively.
# Implementation of MS Circle Map in Digital Image Encryption
## 2 Materials and Methods
### MS Circle Map  
The new chaos function in this research was formulated through the composition process of two chaos functions: MS Map and Circle Map chaos functions. The composition process of MS Map and Circle Map chaos functions can be done because both have the same degree and dimension. If the Circle Map function is expressed as f(x) and the MS Map function as g(x), then the composition function of Circle Map and MS Map is expressed as MS Circle Map function. This function is a recursive function that can be seen in the equation below (4) [30], with \(x_{n}\in(0,1)\) for \(n=0,1,2,3,\ldots\) and \(x_{0}\) as the initial value, \(r\in[1,4]\), \(\lambda\geq 1,\ \Omega\in(0,1)\), and \(K\) in real numbers.  
\[x_{n+1}=\left(\left(\frac{r\lambda x_{n}}{1+\lambda(1-x_{n})^{2}}mod\ 1+\Omega+\frac{K}{2\pi}sin\left(2\pi\left(\frac{r\lambda x_{n}}{1+\lambda(1-x _{n})^{2}}mod\ 1\right)\right)\right)mod\ 1\right) \tag{4}\]  
The results of mathematical and statistical tests of this function show that it is chaotic [31], so the function can be further used as a keystream generator for encryption and decryption processes. The proposed MS Circle Map is characterized by five key parameters x0, r, \(\lambda\), K, and \(\Omega\), allowing it to generate a significantly large key space. This extensive key space enhances its resistance to brute force attacks, making it more secure compared to traditional chaotic maps with fewer parameters. The increased complexity and unpredictability of the keystream further strengthen the encryption process, ensuring high security for digital image encryption.