esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory and Calculation Model  
### Discrete Dipole Approximation  
We calculated the self-consistent total response electric field induced in a nanogap made of a tip, single molecule, and substrate by using the DDA method:  
\[\mathbf{E}(\mathbf{r}_{i},\omega)=\mathbf{E}_{0}(\mathbf{r}_{i},\omega)+\sum_{j}V\mathbf{G}(\mathbf{r} _{i},\mathbf{r}_{j},\omega)\mathbf{P}(\mathbf{r}_{j},\omega), \tag{1}\]  
\[\mathbf{P}(\mathbf{r}_{i},\omega)=\chi(\mathbf{r}_{i},\omega)\mathbf{E}(\mathbf{r}_{i},\omega), \tag{2}\]  
where \(\mathbf{E}(\mathbf{r}_{i},\omega)\) and \(\mathbf{E}_{0}(\mathbf{r}_{i},\omega)\) represent the total response field and incident field, respectively; \(i\) is the index of cells at the coordinate \(\mathbf{r}_{i}\); and \(\omega\) is the angular frequency of the electric field. \(\mathbf{G}(\mathbf{r}_{i},\mathbf{r}_{j},\omega)\) is the free-space green function propagating in both the transverse and longitudinal electromagnetic fields. \(\mathbf{P}(\mathbf{r}_{i},\omega)\) is the polarization of the \(j\)-th cell, and the integral of the second term in Equation (1) represents the field at the \(i\)-th cell propagated from the polarization at the \(j\)-th cell. \(V\) is the volume of the unit cell. Polarization is described by local susceptibility \(\chi(\mathbf{r}_{i},\omega)\). In the DDA method, because the entire space must be divided into cells of the size necessary to describe microscopic structures, it is not realistic to perform calculations that consider the atomic-scale structures of the molecules and macroscopic PiFM system. To solve the problem of this computational size, we employed an extended DDA method that incorporates partial subdivision [23]. The photoinduced force acting on the tip was obtained as follows [24]:  
\[\mathbf{F}(\omega)=\frac{1}{2}\text{Re}\left[\sum_{i}V(\
abla\mathbf{E}^{*}(\mathbf{r}_{i },\omega))\cdot\mathbf{P}(\mathbf{r}_{i},\omega)\right], \tag{3}\]  
where the summation is performed on the cells that configure the tip.
## 3 Proposed Method
### \(F_{0}\) Contour Modeling
#### 3.1.1 Linguistic Feature Extraction  
In this section, we formally describe the representation of input features for modeling the \(F_{0}\) contour. We extracted linguistic features by using a question set and context-dependent labels of the HMM-based Isarn speech synthesis [5]. The linguistic features considered are based on multi-prosodic layers, such as syllable, word, intermediate phrase, intonation phrase, and utterance as listed in Table 1. Linguistic features can be divided into four parts: Tone, phone identity, position, and features of duration.  
Tonal features are important for predicting the \(F_{0}\) contour of syllables for Isarn speech synthesis, as investigated in our previous work [5]. Tonal features can help infer the rough curve of the contours of a tone. However, other contextual features are needed to perfectly model the \(F_{0}\) contour because using tonal features alone cannot be used to deal with complex variations in the \(F_{0}\) contour.  
Features of phone identity are combined to represent a syllable. Each syllable is represented by a one-hot vector encoding the phone identity and phone categories of the initial consonant, vowel, and final consonant. In Isarn, the final consonant is optional. We add a flag here to indicate the given syllable that is not the final consonant.  
Positional features relate the number and positions of syllables in the higher prosodic layers. When examining examples of utterances in the Isarn speech corpus, we found that the \(F_{0}\) contour is also  
Figure 3: An overview of the proposed \(F_{0}\) model.  
related to stress information, however annotation is time consuming and expensive. Stress syllables typically have a long duration and appear at the end of an utterance or intonation [34]. We thus included sectional features that consider the given position of the syllable at a higher prosodic level and duration-related features. The effect of each feature set is investigated in Section 4.4.2.
## 4 Experiments
### Comparative Experiment and Result Analysis  
#### 4.5.1 Comparisons on the LEVIR-CD Dataset  
Table 2 presents the quantitative performance evaluation results of each method. On the LEVIR-CD dataset, our proposed MDNet method significantly outperforms all comparative methods in recall, F1-score, and IoU metrics, demonstrating optimal performance. Although MDNet has an accuracy of 92.25%, slightly lower than WS-Net++'s 93.32%, it achieves a good balance between accuracy and recall (both exceeding 90%), effectively balancing false positives and false negatives. Compared with the suboptimal method WS-Net++, MDNet improves the F1-score by 0.6% and IoU by 0.54%, indicating its stronger robustness against land cover scale diversity and complex background interferences. Compared with the BiT method, which relies on pure Transformer for spatio-temporal context modeling, MDNet combines Transformer and the CAM to balance local details and global contextual information. The F1-score of 89.31% for BiT is significantly lower than that of MDNet, further confirming the complementary advantages of the two mechanisms.  
The qualitative visualization comparison results are shown in Figure 7, where the prediction results of different methods in typical scenarios differ significantly. In the first-row samples, methods such as FC-EF and SNUNet show significant missed detections (FNs, green areas) and false positives (FPs, red areas) in small building change regions, while MDNet, with the help of the EMF and CASAM, completely preserves detail information  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **P** & **R** & **F1** & **IOU** \\ \hline FC-EF & 86.91 & 80.17 & 83.4 & 71.53 \\ SNUNet & 89.18 & 87.17 & 88.16 & 78.83 \\ BiT & 89.24 & 89.37 & 89.31 & 80.68 \\ AMTNet & 91.82 & **89.71** & 90.76 & 83.08 \\ WS-Net++ & **93.32** & 88.97 & **90.96** & **83.51** \\ MDNet (Ours) & **92.25** & **90.64** & **91.56** & **84.05** \\ \hline \hline \end{tabular} The red bold font indicates the best performance and the black bold font indicates the second best.  
\end{table}
Table 2: Performance evaluation of the LEVIR-CD dataset (%).  
and effectively reduces missed detections and false positives. In the second- and third-row samples, MDNet significantly reduces green missed detection area compared with comparative methods, reflecting its effectiveness in distinguishing true change regions. The DPEM in MDNet explicitly enhances the spectral-structural differences in change regions, combined with Transformer and attention mechanisms to refine context modeling, minimizing misclassification.
# Enhancing Ignition Reliability with Tail-Groove Strut Designs in Cavity-Based Combustors
## 3 Results and Discussion  
### Dynamic Ignition Process  
#### 3.1.1 Numerical Validation  
Experimental observations were conducted to validate the numerical simulations of the ignition process. The results, presented in Figure 8, illustrate a sequence of events over a 25 ms frame, showing the development of the flame kernel from its initial formation to a fully developed flame under conditions of 700 K and 100 m/s. The ignition process can be categorized into four main stages: (1) kernel generation, (2) kernel propagation, (3) flame refilling, and (4) flame growth.  
At 0 ms, ignition begins with an electrical spark, marking the kernel generation stage. The kernel then rapidly propagates towards the region behind the strut. However, due to the insufficient spark energy to ignite the unburned gases in this region and the influence of the recirculation zone within the cavity, a flame-refilling process occurs. This refilling process involves the flame reoccupying the cavity, which provides a stable environment for further development. In the subsequent flame growth stage, the flame, having fully developed within the cavity, starts to propagate towards the region behind the strut.  
Figure 7: Error analysis of the results via Mech_Ini and Mech_Red.  
Figure 9 illustrates the radial flame distance during the ignition process. To quantitatively assess the radial flame propagation performance under different strut configurations, temperature thresholds of 1600 K, 1700 K, and 1800 K were evaluated. The results indicate that the numerical simulations for all three temperature thresholds closely match the experimental data.  
In the kernel generation stage, the electrical spark induces a peak in the radial flame distance, which rapidly declines. During kernel propagation, the flame expands rapidly, leading to an increase in radial flame distance in both experimental and numerical results. In the flame-refilling stage, this distance drops sharply and remains stable for a short period. Finally, in the flame growth stage, the radial flame distance gradually increases again as the flame fully develops. In this study, 1800 K was selected as the temperature threshold for analysis.
## 3 Materials and Methods
### Characteristics Maritime Mobile Service  
Two types of MMS receivers were considered in the studies of the one with an omni-directional antenna (System 1) and the one with a directional antenna (System 2). These parameters are described in Recommendation ITU-R M.2116. Table 6 presents characteristics of the MMS that were used in the simulations.  
Figure 5: Antenna pattern of aeronautical receiver: (a) omnidirectional antenna of System 1 and (b) directional antenna of System 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **System 1** & **System 2** & **System 2** \\  & **Airborne** & **Airborne 1** & **Airborne 2** \\ \hline Tuning range & 4400–4990 & 4400–4990 & 4400\(-\)4990 \\ Power output & 45 & 30–43 & 30–43 \\ Bandwidth (3 dB) & 1 & 5/0.008 & 5/0.008 \\ Noise figure & 3.5 & 6 & 6 \\ Thermal noise level & \(-\)110.5 & \(-\)103\(/-\)131 & \(-\)103\(/-\)131 \\ Antenna type & Omnidirectional & Directional & Directional \\ Antenna gain & 3 & 14 & 14 \\
1 st sidelobe & N/A & \(-\)1 & \(-\)1 \\ Polarization & Vertical & Vertical & Vertical \\ Antenna pattern & N/A & Uniform distribution & Uniform distribution \\  & & Rec. ITU-R M.1851 & Rec. ITU-R M.1851 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characteristics of the aeronautical mobile systems used in the simulations.  
The directional antenna of MMS was pointed to the opposite direction of the coastline since it will communicate with the maritime or aeronautical station located outside of the territorial airspace or waters of the coastal state that deploys 5G NR. Figure 6 presents the modelling of the antenna patterns of MMS omnidirectional and directional antennas.
# Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network
## 1 Introduction
As an excellent deep learning method, convolutional neural networks (CNNs) have attracted much attention because adoption of the kernel sharing mechanism greatly reduces the number of network parameters and makes network training easier. After CNNs were first investigated in the field of image recognition, they have been effectively employed in various applications such as computer version [19], face recognition [20] and target detecting [21]. A large number of investigations on fault classification have benefit from CNN's ability to extract high-level abstraction features. [PERSON] et al. [22] presented a CNN-based method, in which CNN was used to extract the representative features from the designed input data. [PERSON] et al. [23] developed a fault classification method, in which the hierarchical symbolic analysis was used to form the input of CNN, and the proposed CNN was applied to extract fault features and implement fault classification. CNN-based fault diagnosis approaches have been presented and achieved success, but they usually rely on the designed features or manually generated input without considering the appropriate input selection and noise information weakening. As a useful time-frequency analysis method, wavelet packet transform (WPT) has proved capable to obtain low-frequency and high-frequency information [24; 25; 26]. Meanwhile, the low-frequency information could be selected, and the noise information could be discarded from this time-frequency information in some way. In this regard, WPT is suitable for handling the condition signals to eliminate the effect of noise interference. In this paper, a two-step fault diagnosis method is proposed. In the first step, the WPT is used to obtain the wavelet packet coefficients from vibration signal, and then, the obtained coefficients are converted into 2-D format gray scale images through a designed data-to-image conversion method and used as the inputs of the constructed CNN. In the second step, a designed CNN model is applied to further extract the sensitive and robust discernable feature from these gray images to realize the fault identification. From the experiment's study result, the proposed fault diagnosis method achieves an excellent diagnostic accuracy. The diagnostic accuracy of each bearing condition is over 92%. Moreover, it also presents more superior fault diagnosis capability than other popular machine learning-based methods. The rest of this paper is organized as follows. The related work is described in Section 2. Section 3 shows the details of the proposed fault diagnosis method. The experimental evaluation is presented in Section 4. The conclusion is drawn in Section 5.
## 2 Materials and Methods
### Problem Discretization
#### 2.6.1 Battery Energy Step  
The battery state of energy (\(SOE_{bat}\)) is the first state variable. Starting with the variation in SOE between two step times, we can calculate the battery power, and by reversing the electric motor loss map, the mechanical power required at the motor shaft. In a backward approach, the power required to drive the vehicle is perfectly known. Accordingly, we can calculate the engine power and torque. The engine model comprises three control variables: intake pressure, relative spark advance, and air to fuel equivalence ratio. By mixing equations, we calculate the necessary intake pressure that satisfies the required engine torque for the particular values of spark advance and equivalence ratio. With this procedure, we eliminate the rounding errors between intake pressure and torque and there is no need to interpolate. Another important fact is that the all-electric mode is obtained _by construction of the graph_ without rounding errors; indeed, the lower SOE bound, which represents the maximum discharge of the battery, is defined by the all-electric mode of the vehicle.  
A parametric study was conducted on the battery net energy step size. The calculations were conducted with a step time of 1 s, for a stoichiometric equivalence ratio (\(\phi=1\)) and an optimal spark advance. Only fuel was optimized, that is \(\alpha=\beta=\gamma=0\) (see Equation (5)).  
We observed the deviation in fuel and emissions compared to a reference strategy where the discretization is very fine. We also constructed a form factor that describes how the different strategies differ from the reference one. This form factor is defined as follows (see Equation (6)):\[\text{Form Factor}=1-\frac{1}{n_{sample}}\sum_{n_{sample}}\left(\frac{abs(P_{ nittake}-P_{nittake,ref})}{P_{nittake,ref}}\right) \tag{6}\]  
Deviation in fuel consumption or emissions are very small for all values of battery energy step, as shown in Table 3. The form factor also stays very stable. This allows us to use a relatively large discretization step without affecting the accuracy of the calculations.
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 5 Conclusions  
Exploiting the navigation signals from the GPS, GLONASS, BDS, Galileo, and QZSS, the present work focused on the search for a 2D-LFC and 3D-LFC composed of 6-12 satellites, which could provide optimal spatial and temporal distributions of GNSS ROEs over the globe. Two evolutionary algorithms were applied with a fitness function based on the concept of spatio-temporal point pattern. The better solution obtained by either of them, which corresponded to the highest fitness value, was selected. The space-filling performance of the final optimal LEO constellations were further evaluated using COVs of the distribution of ROEs.  
The results showed that with the use of multi-GNSS signals, the optimized constellation configurations provided a sufficient number of ROEs with proper temporal and spatial distributions. For a certain number of LEO satellites, a PSO algorithm generally performed better than a GA. The optimal 2D-LFC generally resulted in higher fitness values compared to the optimal 3D-LFC. In other words, 2D-LFC observed more uniform ROE distributions in time and space. This is further confirmed by the comparisons of the mean and standard deviations values of the 3 h-COV for one day.  
In our future work, the 2D necklace flower constellation, which accelerates the generation of constellations in the optimization process with more control in the design, will be taken into consideration and the proposed fitness function for the evolutionary algorithms will be further improved in the design of LEO constellations for GNSS RO mission.  
[PERSON], [PERSON] and [PERSON] conceptualized the initial idea and experiment design; [PERSON] analyzed the data; [PERSON], [PERSON] and [PERSON] wrote the main manuscript text; [PERSON] and [PERSON] reviewed the paper and provided valuable insights.  
This research was funded by the National Natural Science Foundation of China (Grant No. 41774033 and 41774032), the National Key Research and Development Program of China (Grant No. 2018 YFC1503502), and the National Basic Research Program of China (973 Program) (Grant No. 2013 CB733302).  
The authors declare no conflict of interests.
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
## 11 Conclusions  
In today's digital world, fake and edited images can spread quickly and easily. With the rise of AI-generated visuals and the future threat of quantum computing, it has become much harder to verify what is real and trustworthy. Traditional methods are no longer enough to keep up with these changes. In this paper, we introduced Post-Quantum VerITAS, a new framework that brings together advanced cryptography and practical tools to help verify the authenticity of digital images. Our framework provides cryptographic tools that can help detect manipulated images, especially those shared on social media. By verifying that only permissible edits (e.g., crop, grayscale) were made to a cryptographically signed original, Post-Quantum VerITAS helps journalists, platforms, and end-users establish trust in visual content. Although we do not address sociopolitical mechanisms of disinformation, our work contributes to a growing set of digital defenses by offering post-quantum-secure, privacy-preserving image verification at scale.  
Post-Quantum VerITAS builds on the original VerITAS system by making it secure against quantum attacks. It uses lattice-based hashes, post-quantum digital signatures, and efficient zero-knowledge proofs to confirm whether an image has been edited in acceptable ways, such as cropping or resizing. At the same time, it protects the privacy of the original image and works smoothly with high-resolution photos.  
One of the most important features of this system is that it does not depend on trusted editing software. Instead, it allows anyone to independently verify that an image came from a legitimate source and that it was only edited in approved ways. This gives journalists, social media platforms, and the general public a stronger way to combat misinformation.  
Post-Quantum VerITAS offers a powerful and realistic solution for checking digital content. It helps rebuild trust by giving people the tools to confirm whether an image is authentic, even when shared online or altered for privacy and clarity.  
This work was supported by the Shota Rustaveli National Science Foundation of Georgia (SRNSFG) N FR-24-15007.  
Data Availability Statement: Data is contained within the article.  
The authors would like to thank the Shota Rustaveli National Science Foundation of Georgia for its valuable support.  
The author declares no conflicts of interest.
## 2 Materials and Methods
### PCM Enhanced Battery Packs
#### 2.1.1 Phase Change Material
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & **Temperature** & **Specific Heat** & **Volumetric Heat** & \\  & **Range [\({}^{\circ}\)C]** & **Capacity [kJ/kgK]** & **Capacity [kJ/m\({}^{3}\)K]** & **Density [kg/m\({}^{3}\)]** \\ \hline Water & 0–100 & 4.19 & 1475 & 998 \\ Gravel, Sand & 0–800 & 0.71 & 1278–1420 & 1800–2000 \\ Granite & 0–800 & 0.75 & 2062 & 2750 \\ \hline Concrete & 0–500 & 0.88 & 1672–2074 & 1900–1300 \\ Brick & 0–1000 & 0.84 & 1176–1596 & 1400–1900 \\ Iron & 0–800 & 0.47 & 3655 & 7860 \\ \hline Thermal oil & 0–400 & 1.6–1.8 & 1360–1620 & 850–900 \\ Gravel water & 0–100 & 1.32 & 2904 & 2200 \\ bed & & & 2561–2243 \\ \hline Melted salt & 150–450 & 1.30 & 1970–1725 & 1203–975 \\ \hline Sodium & 100–800 & 1.30 & 925–750 & \\ \hline \hline \end{tabular} The heat capacity of a material indicates how much heat it can store in 1 m\({}^{3}\) when the temperature is increased by 1 K.  
\end{table} 
Table 1: Heat Capacity of Materials [4]. Figure 4: Stored heat temperature—diagram [5]. counteract the phenomenon of incompatible melting and use the medium in a temperature range of around 100 \({}^{\circ}\)C [6]. The paraffin-based PCMs are characterized by being particularly harmoniously soluble, cycle-resistant, ecologically harmless, harmless to health, and not corroding metal building materials [5]. Special attention should be paid to the phase change temperature, as it is the most important criterion in selecting a PCM for a particular application. The energy density and temperature range of the most popular PCMs are depicted in Figure 5. No single material has all the properties needed for an ideal PCM. Therefore, available materials must be selected, and additional additives added to replace poor physical properties with suitable system designs [6]. The selection of suitable PCMs for passive BTMS was determined by the materials available on the market and the phase transition temperature chosen.