esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 1 Introduction
In contrast, deep learning models do not need to extract features manually, have stronger characterization capability and accuracy, automatically extract relevant information to avoid empirical interference, and directly map original time series to class probability distributions. Deep learning methods such as MagNet ([PERSON] et al., 2016), attention mechanism-based modeling ([PERSON] et al., 2016), and end-to-end CNNs ([PERSON] et al., 2016) have achieved significant results in magnitude estimation, demonstrating that neural networks can learn general relationships for estimating magnitude from raw waveforms. Although most of these studies focus on the average performance of the models, there are fewer comparative analyses of the effects of training data features and the details of the model framework, which are particularly important in practical applications. In deep learning, real-time streaming is important for incremental computation, dynamic backtracking, and implementation decisions of seismic waveform time-series maps. This is particularly important in EEW systems that emphasize real-time data processing. Therefore, it is crucial to investigate the role of specific layers in the magnitude estimation modeling framework and the degree of influence of certain features on the input data. Although magnitude estimation based on multistation data can improve accuracy, it often introduces additional delays. In this paper, we prioritize both timeliness and accuracy by adopting a single-station-based magnitude estimation method, which can provide faster results without waiting for seismic waves from multiple stations. The AI models studied by previous researchers have used various data sources, and the variability of these data sources can significantly affect the performance and results of the models. This paper compares different deep learning methods using standardized benchmark datasets. We discuss and compare the differences in model frameworks by simulating real-time streaming inputs to select the model with the best estimation performance and generalization ability. Additionally, we analyze the factors affecting the estimation and its application prospects. Our research is firmly grounded in the model selection framework for magnitude prediction problems, as presented in the highly relevant study by [PERSON] [17]. We carefully recombined three models from this prior work, a choice that was not haphazard but rather a deliberate strategy. In doing so, we aim to conduct a detailed and in-depth exploration at the fundamental mechanistic level. Our objective is to precisely dissect and explain the nuanced differences in the performance of these three models when achieving accurate earthquake magnitude prediction. Through this approach, we can contribute valuable insights into the inner workings of these models and identify areas for improvement and optimization. This, in turn, can significantly impact the advancement of the field of earthquake magnitude estimation and enhance the reliability and accuracy of future predictions.
# Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects
## 4 Results
### Result of Preprocessed Image  
To extract features from threat-related images associated with Gemini, the images are first converted from the BGR format to grayscale. Then, Canny edge detection is applied to the converted grayscale images. This process is performed using the OpenCV library. Figure 9a shows the image converted to grayscale, and Figure 9b shows the Canny edge detection image of the grayscale image.
## 1 Introduction  
Recently, a significant growth of interest in the use of Additive Manufacturing (AM) techniques for rapid prototyping and fabrication of various components has been noticed [1]. AM is a process that fuses materials layer by layer, to produce items based on 3D model data. Various AM methods differ in terms of the raw materials used and in the ways for material consolidation [2; 3]. This work focuses on AM techniques where a raw material is delivered in a form of powder and is consolidated via a laser beam or an electron beam. Laser or electron beams heat and melt the powder in order to directly produce functional metallic components. A particular example of the laser- and electron-based methods are Selective Laser Melting (SLM) and Electron Beam Melting (EBM), respectively [4; 5]. Furthermore, in 2012, the Arcam's EBM process has been cleared by the FDA for implant fabrication. For medical applications, SLM and EBM are the most popular and commercially-available powder in bed AM methods [6; 7]. The ASTM Standard F2924-14 [8] can be used for the Additive Manufacturing of Ti-6 Al-4V articles with the powder bed fusion technology. It should be noted that this standard is not dedicated to only medical devices or implants.  
The aim of the work is to describe and compare the SLM and EBM methods in terms of producing a solid implant for a medical application using a Ti-6 Al-4V alloy. The comparison of SLM and EBM systems was based on an analysis of the microstructure, crystal structure, mechanical properties and cytotoxicity of Ti-6 Al-4V samples. Particular attention was paid to the comparison of the microstructures and mechanical properties of the Ti-6 Al-4V alloy after the SLM, EBM and conventional processing. To the best knowledge of the authors, this manuscript presents the first study comparing the parts with the same dimensions, which were fabricated using similar parameters by the SLM and EBM methods. Furthermore, it is the first study where the Ti-6 Al-4V alloy was strengthened by an oxygen addition during the AM process. The anisotropy of the mechanical properties of SLM and EBM fabricated parts was investigated for metals such as: nickel-based superalloys [9; 10], stainless steel [11; 12], silumins [13; 14] and titanium alloys [15; 16]. In contrast to other work, our study is one of the first reported studies showing the anisotropy of the mechanical properties of Ti-6 Al-4V investigated on microsamples cut within the SLM and EBM fabricated material.  
The article is divided as follows. The first section describes the AM methods for the fabrication of metallic implants. The second section reviews the applications of titanium in the biomedical field and the mechanical properties of the Ti-6 Al-4V alloy. Then, a description of the experiment procedures for the comparison between SLM and EBM methods is presented. Finally, the results are shown and discussed.
# Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA
## 6 Conclusions and Future Works
**Abbreviations**  
Input Data and Indices  
ACTNEP  
ANFIS  
CMA-ES  
Couriance Matrix Adaptation Evolution Strategy  
DCTNEP  
DC transmission network Expansion planning  
DCPF  
DC power flow  
DE  
Differential Evolution  
DSTNEP  
Deterministic static transmission network expansion planning  
DTNEP  
Dynamic transmission network expansion planning  
GA  
Genetic algorithm  
HM  
Heuristic method  
LP  
Linear programming  
LPSR  
Linear population size reduction  
LSHADE-SPACMA  
Linear population size reduction Success History-based Differential Evolution with  
semi-parameter adaptation hybrid with CMA-ES  
MSDTNEP  
Multi-stage dynamic transmission network expansion planning  
SPA  
Semi-parameter adaptation  
STNEP  
Static transmission network expansion planning  
TNEP  
Transmission network expansion planning  
WDN  
Egyptian West Delta Network  
R
Line resistance  
\(P_{i}\)  
Active power injection at bus i  
Bij  
Susceptance of route between bus i and j  
\(\theta_{i^{\prime}},\theta_{j}\)  
Voltage angles at bus i and j, respectively  
\(P_{ci},P_{di}\)  
Active power generation source and the load demand (MW) at bus i, respectively  
\(C_{ij}\)  
Cost of circuit between buses i and j  
Line reactance  
\(X^{0}_{ij^{\prime}},X_{ij\_max},X_{ij}\)  
Initial number of circuits, the maximum number of circuits, and the actual number of circuits  
between buses i and j, respectively  
\(P_{ij},P_{ij\_max}\)  
Active power flow and the active power flow limit in the i-j right of way (MW), respectively  
\(P_{sh,i}\)  
Amount of load shedding  
Penalty parameter that penalizes, in the objective function, any system load shedding  
\(\alpha\)  
Maximum percentage of load shedding  
\(N_{y},y_{r}\)  
Total number of stages and stage number, respectively  
Population size  
\(D^{o}_{m,k^{\prime}},D_{m,u},D_{m,l}\)  
Initial, upper bound, and lower bound of mth component of the decision vector, respectively  
\(mu^{G}_{k}\)  
Mutant vector corresponding to each population member \(D^{G}_{k}\)  
\(F\)  
Scale factor  
\(CR\)  
Crossover rate  
\(\rm D^{G}_{Pbest}\)  
Best individual vector with the best fitness value at G generation in the population
## 2 Results and Discussion
### Volatiles Isolated by Ultrasonic Solvent Extraction
The mixture of pentane and diethyl ether (1:2 v/v) isolated syringaldehyde (33.1%) and methyl syringae (2.2%), followed by higher fatty alcohols (\(Z\))-octadec-9-en-1-ol (14.9%), hexadecan-1-ol (4.3%) and octadecan-1-ol (4.1%) (Table 1, column D). A similar chemical composition was obtained by dichloromethane extraction (Table 1, column E): syringaldehyde (35.9%), (\(Z\))-octadec-9-en-1-ol (15.8%) and hexadecan-1-ol (3.0%). From the obtained results it can be noted that the mixture of pentane and diethyl ether and dichloromethane are the most suitable solvents for _Acer_ spp. USE, while pentane cannot be used since a significantly lower percentage of syringaldehyde was identified in comparison with the other solvents. Springaldehyde is the most striking compound of _Acer_ spp. honey volatiles being dominant in all the extracts except for the non-polar pentane extract. It was not found in other single samples of Croatian tree honeys (Table 2) or in other Croatian honeys [9, 10, 11, 12, 13], although it was previously identified in maple sap and syrup [8], suggesting its good potential as an _Acer_ spp. honey marker compound. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**RI**} & \multirow{2}{*}{_Acer_ spp.} & \multirow{2}{*}{_Pseudoacacia_} & \multirow{2}{*}{_Salix_ spp.} & \multirow{2}{*}{_Salix_ spp.} & \multirow{2}{*}{_Daercus_} & _Abias alba_ & \\  & & & & _robinia_ L. & & spp. & honeyw\({}^{\text{a}}\) & Ten. & \\  & & & & & & & & honeydew\({}^{\text{b}}\) & \\ \hline
1. & 1-(2-Furyl)-2-hydroxy-ethanone & 914 & - & - & - & - & 5.1 & \\
2. & & 914 & - & - & 10.2 & 0.2 & 16.4 & 3.8 \\
3. & 4-Vinyl-2-methoxyenol & 1314 & 0.2 & 3.3 & - & - & - & - \\
4. & 4-Hydroxy-2-phenyl-ethanol & 1429 & - & 3.1 & 0.6 & 1.2 & 0.6 & - \\
5. & 2-Phenylethyl acetate\({}^{\text{a}}\) & 1492 & 3.2 & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of major volatile constituents of _Acer_ spp. honey sample and other representative Croatian tree honey samples obtained by USE with the solvent pentane-diethyl ether (1:2 v/v) followed by GC and GC/MS analysis. It has been suggested that the source of vanillin and syringaldehyde in map syrup is lignin, and it is well known that syringaldehyde is formed by the degradation of lignin as an oxidation product of sinapaldehyde [14]. Later, [PERSON] and [PERSON] [15] reported the identification of phenolic lignin monomers and related flavor compounds in dichloromethane extracts of map syrup. Different kinds of phenolic compounds were found from maple sap, concentrates of the sap and the syrup [7]: vanillic acid, syringic acid, homovanillic acid, coniferyl alcohol, vanillin, \(p\)-coumaric acid, syringaldehyde, sinapic acid, ferulic acid and coniferylaldehyde. Besides syringaldehyde, several of these compounds such as syrigenic acid (0.0-0.2%), \(p\)-coumaric acid (0.0-1.4%) or ferulic acid (0.0-1.4%) were also identified as minor components in _Acer_ spp. honey.
## 4 Solving Sample Problems in Larger Sizes with MOPSO and NSGA II Algorithms
### Investigation of \(t\)-Test on the Means of Efficient Solutions  
Statistical comparisons have been conducted on the performance metrics of the metaheuristic algorithms. Table 14 presents the output results of the \(t\)-Test on the means of efficient solutions at a 95% confidence level.  
Based on the fact that the \(p\)-value is greater than the conventional threshold of 0.05, we can conclude that the null hypothesis (equality of the means of efficient solutions) is accepted. Therefore, there is no significant difference between the means of efficient solutions obtained from solving problems using metaheuristic algorithms.  
Figure 13 also compares the averages of efficient solutions for NSGA II and MOPSO algorithms. As depicted in Figure 11, the number of efficient solutions varies across different sample problems, and no definitive conclusion can be drawn regarding the performance of the algorithms in achieving better results for this metric.  
Figure 14 illustrates a box plot for confirming or rejecting the null hypothesis for the averages of the number of efficient solutions. Based on the observations, it can be concluded that the null hypothesis is rejected due to its placement outside the confidence interval.  
Table 15 presents the statistical comparisons using the \(t\)-Test on the means of the maximum spread indicator. Additionally, Figure 15 illustrates the comparisons of the means of the maximum spread indicator across all sample problems, categorized by the NSGA II and MOPSO algorithms.  
The results in Table 15 indicate no significant difference in the means of the maximum spread indicator obtained by the NSGA II and MOPSO algorithms. In this test, the value of the test statistic P is greater than the chosen level of confidence.  
These algorithms were chosen due to their proven efficiency in handling NP-hard optimization problems and their ability to balance exploration and exploitation in search spaces.  
As observed in Figure 15, the MOPSO algorithm has obtained a higher value of MSI (Maximum Spread Indicator) compared to the NSGA II algorithm in most sample problems. This indicates that the solutions obtained from the first and second objective functions using the MOPSO algorithm have a greater spread compared to the NSGA II algorithm.  
In Figure 16, it can also be observed that the null hypothesis value falls within the 95% confidence interval for the maximum spread indicator.
## 2 Materials and Methods
### Methodology
#### 2.3.2 Analysis of Drivers of Ecosystem Change  
Geodetector, a statistical tool designed to detect spatial stratified heterogeneity and its underlying drivers, was utilized in this study. Geodetector proved invaluable for uncovering the nonlinear and interactive effects of diverse drivers on both forest and grassland [55]. Detecting spatial stratified heterogeneity is crucial for understanding ecosystem changes, as it reveals variations in ecological factors across different spatial units, allowing for targeted conservation strategies. The q-value measures the extent to which the underlying factor explains the spatially stratified heterogeneity in ecosystem change. Therefore, we use the q-statistic to quantify the relative importance of potential drivers. [PERSON] et al. [56] developed the Optimal Parameters-based Geographical Detector (OPGD) model, which determines the optimal discretization parameters and spatial scale by selecting the highest q-value from different discretization methods and combinations of continuous variable categories. In this study, trends in forest and grassland changes from 2000 to 2020 were used as the dependent variable. Independent variables included trends in elevation (\(\times 1\)), slope (\(\times 2\)), average annual temperature trends (\(\times 3\)), average annual precipitation trends (\(\times 4\)), SPEI trends (\(\times 5\)), GPP trends (\(\times 6\)), soil type (\(\times 7\)), grazing intensity trends (\(\times 8\)), cropland trends (\(\times 9\)), population distribution trends (\(\times 10\)), the distribution of tourist attractions (\(\times 11\)), and the distribution of mining sites (\(\times 12\)). These variables were employed for both the factor and interaction detector.  
The analysis of geographic variables at multiple spatial scales can reveal distinct characteristics [56]. The Optimal Parameters-based Geographical Detector (OPGD) addresses the Modifiable Areal Unit Problem (MAUP) and enhances the precision and scientific validity of identifying drivers. To identify the optimal spatial scale for assessing spatial heterogeneity in forest and grassland ecosystems, the spatial scales for all variables were categorized as 500 m, 1000 m, 1500 m, 2000 m, 2500 m, and 3000 m. Various classification methods--including natural breakpoint classification, standard deviation classification, geometric interval classification, quantile classification, and equal-interval classification--were employed to determine the optimal discrete parameter combinations. The results indicated that the 90 th percentile of the explanatory variable q achieved its maximum at a spatial scale of 3000 m for forest ecosystems and 1500 m for grassland ecosystems.
# Time-Frequency Domain Seismic Signal Denoising Based on Generative Adversarial Networks
## 2 Method
### Discriminator Network Structure
Similar to CBAM [34], LCBAM replaces the fully connected layer with \(1\times 1\) convolution in channel attention and replaces the \(7\times 7\) convolutional kernel with dilated convolution with a dilation rate of 2 in spatial attention. In a traditional generative adversarial network, the discriminator maps the input features to a single scalar representing the probability of being real or fake. This scalar is essentially the discriminator's normalized evaluation of the entire input data, which is an average assessment of all local features. Taking seismic signal denoising as an example, when the generator suppresses most of the noise but still leaves some residual noise in certain frequency bands, the overall quality of the denoised signal is good. Using a single scalar to evaluate the entire time-frequency spectrum will result in an assessment close to the real label, weakening the impact of the local residual noise. Therefore, evaluating the overall quality of the generated data with a single scalar may lead to an inaccurate assessment of some local features by the discriminator, affecting the generator's ability to remove weak noise. In this paper, the discrimination module is based on the PatchGAN structure, where the input data are divided into multiple patches, and each patch is independently evaluated. The evaluation result is an \(8\times 1\times 13\) matrix, with each element in the matrix representing a local region of the input data. The evaluation results have a strong locality, enabling better capture of the data details and improving the denoising  
Figure 4: Structure of feature extraction module. performance. Additionally, PatchGAN does not require fully connected layers, reducing computational complexity. The structure of the discrimination module is shown in Figure 5.
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 3 Methodology
### Automatic Point of Interest Recognition and Tracking
#### 3.2.2 Saliency Detection  
When one or more POIs are within range of the helicopter, the next step in the pipeline, being saliency detection, is initiated. The objective of saliency detection is to identify the  
Figure 2: Overview of the automatic point of interest recognition and tracking methodology.  
most visually interesting areas within an image or video. Since POIs are supposed to attract attention, they should be highlighted by the saliency detection algorithm. This approach enables the pipeline to focus on these specific areas when searching for POIs in later stages, eliminating the need to scan the entire image.  
After empirically testing multiple saliency detectors, the UNISA model developed by [PERSON] et al. [38] has been found to provide the best and most consistent results. One of the key advantages of the UNISAL model is its optimization for both image and video data, making it well suited for our use case. The model's focus on video data ensures temporally consistent saliency results, which is important for maintaining coherence across frames. As the model is trained on eye-tracking data, its output closely resembles the visual attention patterns of a human observer, generating a heatmap that highlights the most visually engaging regions, as illustrated in Figure 3. Consequently, the UNISAL model was selected for integration into our system.
## 6 Conclusions  
In this study, a series of vibro-acoustic coupling finite element models for sound transmission loss (TL) analysis of different ABH circular plates were established by Automatic Matched Layer (AML) method and an experimental platform for sound transmission loss measurement of ABH circular plate was constructed. The accuracy of FEM modeling and analysis has been verified by the experimental measuring for transmission loss calculation results of ABH circular plates and uniform plates, in which the four-microphone method of sound transmission in the standing wave tube was adopted to compute the TLs. Both the experimental and simulation results show that the circular plate with a single ABH embedded in the center position can significantly improve the sound transmission loss in stiffness-controlled region, and at the first-order resonance frequency and some frequencies in damping-controlled region.  
To investigate the influence mechanism of acoustic black hole parameters on sound transmission loss of ABH circular plates, the effects of diameter, orientation, number, and truncation thickness of ABHs on TLs of ABH circular plates have been analyzed. Numerical simulations reveal that there exists an interval of ABH diameter for an edge-clamped circular plate embedded with single ABH in the center position to obtain the best sound insulation performance, and the orientation of the ABH region has little effect on the sound transmission loss of the structure. It is also observed that the increase in the number of ABHs could not increase the sound transmission loss at some specific frequencies but rather reduces the sound transmission loss, and the ABH circular plates with truncation thicknesses of 0.1 mm and 0.2 mm have better sound insulation performance than those with other truncation thicknesses in the mass-controlled region. The effect of the damping layer on TL of circular plates embedded with 1 and 19 ABHs was also analyzed and the damping loss factors of the circular plate with single damped ABH of different damping layer thickness were compared. It can be concluded that the influence of damping layer mainly concentrates on the first-order resonance frequency and damping-controlled region of the plate, and at some frequencies, the greater the damping layer thickness, the worse the sound insulation performance, despite that the modal damping loss factor has been increased in the whole frequency domain.  
This study was initiated and designed by [PERSON] The set-up of the experimental platform and conducting of the experiments were done by [PERSON], [PERSON], and [PERSON] The numerical simulations, experimental data analysis, and writing the paper were completed by [PERSON] [PERSON] provided suggestions for experimental design and paper writing.  
This research was funded by the Achievement Transformation Project of Nanjing (China), grant number 201701213.  
**Acknowledgments:** The financial support from the Achievement Transformation Project of Nanjing (China) is gratefully acknowledged.  
Figure 20: Modal loss factor of ABH circular plate attached damping layers of different thicknesses.