esa-sceva/satcom-chunk-collection huggingface dataset:
# Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
## 3 Methodology
### Time Series Analysis (LiCSBAS)  
LiCSBAS is software that performs a time series analysis on the unwrapped interferograms and coherence data produced by LiCSAR. This method is integrated with tools such as GACOS (Generic Atmospheric Correction Online Service) in order to minimize the effects of atmospheric noise and other regional factors. In the time series analysis process, the deformation values at each pixel are analyzed as a function of time, and ground deformation rates are calculated using linear regression techniques. LiCSBAS provides high-precision velocity maps to the end user by removing noisy data with spatial and temporal filtering steps. The importance of this method has been emphasized in the literature in terms of wide data coverage and accuracy [33].  
Figure 4 displays the cumulative line-of-sight (LOS) velocity maps derived from the ascending and descending orbit data processed through the LiCSBAS workflow. Both maps represent the spatial distribution of surface displacements in the study area, expressed in millimeters per year (mm/year). Figure 4a corresponds to the descending orbit, with deformation velocities ranging between \(-10.996\) mm/year and \(+10.449\) mm/year. Figure 4b shows the results from the ascending orbit, exhibiting a wider deformation range between  
\(-\)38.4962 mm/year and +37.9572 mm/year. In the color scale, cooler tones (purple and blue) indicate motion toward the satellite (uplift), while warmer tones (red and orange) signify motion away from the satellite (subsidence). These maps are of critical importance for identifying and classifying mass movements, as they reflect the annual average velocity for each pixel across the region.
## 4 Experimental and Theoretical Results
### Acceleration Measured on the Scale Model of the Carbody  
Figure 11 shows the accelerance in the centre of the scale model of the carbody without anti-bending bar system, obtained by the impact hammer method. The accelerometer was glued in the middle of the model, and the hammer blow was applied to approx. 25 mm of accelerometer along the longitudinal axis of the model. The error of localization of the hammer blow is approx. 1% of the length of the scale model of the carbody. In this way, only symmetric bending modes can be considered to have been excited, the contribution of the anti-symmetric modes being marginal. In addition, acceleration measurement at the middle of the scale model of the carbody ensures that only symmetrical bending modes are caught, the most important of which, for the carbody vibration problem in railway vehicles, is the first bending mode, as shown.  
Although the frequency range of interest is between 5 and 20 Hz, it has been preferred to represent accelerance over a wider range in order to have a comprehensive picture of the vibration characteristics of the experimental scale demonstrator of the carbody. In the presented frequency range (5-200 Hz), it is possible to identify the bending modes of the scale model of the carbody located at 9.01 Hz, 44.06 Hz, 78.69 Hz and 172.3 Hz. The highest accelerance value is at the level of the first bending mode, which is 14,85 m/Ns\({}^{2}\). The other peaks are much smaller, at 0.791 m/Ns\({}^{2}\), 1.71 m/Ns\({}^{2}\) and 1.961 m/Ns\({}^{2}\).  
Figure 12 shows the accelerance in the middle of the scale model of the carbody with anti-bending bar system, determined under the same conditions as above. The frequency of the first bending mode of the scale model increases to 13.44 Hz because of the anti-bending bar system. The other bending modes also have their frequency changed, but in a much smaller proportion, at 51.56 Hz, 92.63 Hz and 165.8 Hz. It is interesting to note the appearance of the local mode at 47.44 Hz, which can be attributed to the resonance of the anti-bending bar system.  
The influence of the anti-bending bar system is also manifested in the values of accelerance peaks, especially in the first bending mode. Here, the accelerance drops to less than half of the value recorded in the scale model of the carbody without anti-bending bar system, i.e., 6.562 m/Ns\({}^{2}\). The other accelerance peak values also change due to the presence of the anti-bending bar system, but to a much lesser extent.  
Figure 11: Accelerance experimentally determined in the middle of the scale model of the carbody without anti–bending bar system.
# Ultrasound and Magnetic Resonance Imaging of the Tongue in Obstructive Sleep Apnoea
## 3 Results
### Influence of Age on the Tongue Parameters in OSA  
Table 4 shows the MRI parameters of the tongue depending on the OSA participants' age.  
As presented in Table 4, none of the US parameters of the tongue were significantly influenced by the participants' age. Using MRI, patients with OSA under 40 years of age had significantly higher values of AP, axial and coronal diameters, tongue volume and midsagittal area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Age (Years)**} \\ \hline
**Tongue Parameters** & **Age \(\leq\) 40 (n = 21)** & **Age \textgreater{} 40 (n = 43)** & _p_**-Value** \\ \hline Anteroposterior diameter (cm) & 7.83 \(\pm\) 0.67 & 7.58 \(\pm\) 0.62 & 0.0001 *** \\ \hline Axial diameter (cm) & 4.83 \(\pm\) 0.49 & 4.71 \(\pm\) 0.54 & 0.0165 ** \\ \hline Coronal diameter (cm) & 4.99 \(\pm\) 0.48 & 4.78 \(\pm\) 0.43 & 0.000 *** \\ \hline Volume (cm\({}^{3}\)) & 69.79 \(\pm\) 13.85 & 65.64 \(\pm\) 11.77 & 0.000 *** \\ \hline Area midsagittal (cm\({}^{2}\)) & 28.71 \(\pm\) 3.72 & 27.73 \(\pm\) 3.06 & 0.000 *** \\ \hline \hline \end{tabular}
\end{table}
Table 4: MRI parameters of the tongue depending on age. The parameters show the mean \(\pm\) SD values. *** indicates the significant difference at \(p\) < 0.01 level, whereas ** the significant difference at \(p\) < 0.05 level. Only the significantly different parameters are shown in the table.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Independent Variables** & \multicolumn{2}{c}{**Gender**} \\ \hline  & **Men** & **Women** \\  & **(n = 74)** & **(n = 26)** \\ \hline
**MRI Tongue Parameters** & **r\({}^{2}\)** & **r\({}^{2}\)** \\ \hline Anteroposterior diameter (cm) & 0.133 * (l) & 0.073 (l) \\ \hline Sagitttal diameter (cm) (s) & 0.050 * (s) & 0.059 (l) \\ \hline Axial diameter (cm) (l) & 0.039 ** (l) & 0.063 (l) \\ \hline Coronal diameter (cm) (l) & 0.073 ** (l) & 0.015 ** (l) \\ \hline Volume (cm\({}^{3}\)) (l) & 0.149 *** (l) & 0.196 ** (s) \\ \hline Area midsagittal (cm\({}^{2}\)) (l) & 0.186 *** (l) & 0.192 ** (s) \\ \hline Fat midsagittal (cm\({}^{2}\)) (l) & 0.043 (l) & 0.428 *** (s) \\ \hline Fat\% (l) & 0.001 (l) & 0.260 *** (s) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Stochastic relationship between MRI parameters and AHI in male and female patients. Parameters show the r\({}^{2}\) (determination coefficient) values. *** indicates the significant difference at \(p\) < 0.01 level, whereas ** the significant difference at \(p\) < 0.05 level and * the significant difference at \(p\) < 0.1. (l): linear estimation, (s): sigmoid estimation.
# A Coupled Variational System for Image Decomposition along with Edges Detection
## 5 Conclusions  
In this paper, one coupled variational system for image decomposition is proposed. In this coupled system, the vector field, structure and texture images are interwove. We design the alternating direction method, primal-dual method and Gauss-Seidel iteration for the given system. Using the proposed method, we can not only separate out the cartoon and texture parts, but the pseudo edges are extracted. Numerical experiments show the effectiveness of the proposed method compared with other variational methods.  
Conceptualization, J.X., Y.G., Y.H. and L.H.; methodology, [PERSON] and Y.H.; software, [PERSON], Y.G. and Y.H.; validation, [PERSON], Y.G. and L.H.; formal analysis, [PERSON] and Y.G.; investigation, [PERSON], Y.G., Y.H. and L.H.; resources, J.X., Y.G. and L.H.; data curation, [PERSON] and Y.G.; writing--original draft preparation, J.X. and Y.H.; writing--review and editing, [PERSON], Y.G. and L.H.; visualization, J.X. and Y.H.; project administration, J.X.; funding acquisition, J.X. and L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. U1504603), Key Scientific Research Project of Colleges and Universities in Henan Province (No. 22A120006), Guangxi Natural Science Foundation under Grant (No. 2018 GXNSFBA281086).  
Not applicable.  
The data and the code are available on demand.  
We would like to thank all the reviewers for their helpful suggestions.  
All authors declare no conflict of interest.  
Figure 8: Boy decomposition experiment. From left to right: (**a**) cartoon, texture and the pseudo edge with [39]; (**b**) cartoon, texture and the pseudo edge with [40]; (**c**) cartoon, texture and the pseudo edge with [11]; (**d**) cartoon, texture and the pseudo edge \(|\omega|\) obtained by the proposed method.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 6 Security Analysis and Formal Verification
### TLA+ Correctness Verification  
This section employs TLA+ formal specification to verify the correctness of the blockchain-based multi-layer aviation big data security sharing scheme. TLA+ is a formal specification language and toolset used for verifying the correctness of system designs. By defining states, system behaviors, and invariants, it helps identify potential design flaws and security issues, and it is widely applied in concurrent and distributed algorithms and systems.  
When running the TLC model checker in the TLA+ Toolbox, it explores all user-defined finite states. If all generated states satisfy the specified invariants, it proves that the system executes correctly within that finite state space. This section decomposes the scheme's optimal block deployment, searchable data sharing, and evaluable model sharing into discrete operations, defines corresponding state transitions for each operation, and uses TLA+ to verify the scheme's correctness, ensuring all security properties are guaranteed.
## 3 Results and Discussion  
### Publications and International Collaboration  
#### 3.1.1 Publications  
Figure 2 illustrates the annual variation in publications from 2000 to 2023, along with the annual changes in publications for the top ten countries. The black line in the graph represents the annual variation in total publications. Before 2005, there were a limited number of research articles on black carbon in the climate domain, with fewer than 50 publications per year. Since 2005, there has been a steady increase in publications annually, indicating continuous growth in this research area over the past two decades. The peak of publications was observed in 2022, reaching 449 articles. It is noteworthy that the apparent decline in the number of publications in 2023 is primarily due to incomplete data from the WOS at the time of retrieval. This discrepancy may be attributed to factors such as publication delays, data update lags, and the WOS's dynamic indexing policies. However, based on current trends, it is reasonable to infer that the actual number of publications in 2023 will not show a significant decrease compared to 2022 once the data are fully updated.  
Figure 2: Annual total publications from 2000 to 2023 (the black line) and annual publications of top ten countries (stacked bars).  
A stacked bar chart is used to display the top ten countries most active in this research field (Figure 2), including the United States, China, Germany, India, England, France, Canada, Japan, Norway, and Switzerland. Among these countries, the United States had the highest cumulative publications, totaling 2089 articles. This finding aligns with other bibliometric studies in the climate-related research field, highlighting the United States as the most active country in these studies [29; 34; 35]. Specifically, the blue bars in Figure 2 representing the United States peaked in 2016 at 171 articles and have since stabilized, averaging around 137 articles annually. The red bars representing China have shown a continuous increase in publications, reaching their peak in 2022 at 208 publications. The results indicate that before 2019, the United States was the leading country in terms of publications in this field, accounting for 39% to 62% of the total annual research output, with an average of 51%. Since 2020, China has emerged as the leading country in terms of publications, contributing 38% to 46% of the total annual research output, with an average of 42%. It is worth noting that the stacked bars for the top ten countries may surpass the total publications for a given year due to the possibility of multiple countries contributing to a same publication (Figure 2). Detailed data on the annual publications for the top ten countries in this field can be found in Supplementary Table S1.
## 1 Introduction and Related Works  
In many industrial application scenarios, people and robots need to work together [1]. In the present era of Industry 4.0, manufacturing automation is moving toward mass production and mass customization through human-robot collaboration [2, 3]. One example is the installation of solar panels. The traditional manual installation method faces the safety issues of multiple-person operation, the problem of excessive material load, and a lack of human resources, while using only robots involves too much mechanical customization and intelligent transformation, and the cost is too high. With current technology, installing solar panels via human-robot collaboration is a good choice: people engage in some sophisticated and agile operations, while robots are responsible for cooperating with people to support heavy loads and other collaborative work. There is a lot of technology involved, and one key is how to recognize and predict human actions in order to provide assistance in a safe and collaborative manner [4, 5]. In other words, making the robot automatically identify the current operational tasks and workflow of the worker is very important.  
Using human action recognition (HAR) technology [6] to identify the current operation of workers, and to provide guidance for the work of robots become necessary for human-robot collaboration. HAR refers to identifying the corresponding motion category of the motion sequence by analyzing the input continuous time series data. There are different ways (or sensors) to achieve HAR, including cameras, internet of things [7],surface electromyography devices [8], RFID [9], etc. In this paper, we mainly focus on HAR using cameras as main sensor, which takes RGB or RGB-D image sequences [10] as input. It has important application prospects in the fields of human-computer interaction, intelligent security, video censorship. It has long been a research hot spot in the field of computer vision and human-computer interaction. In recent years, with the rapid development of image processing technology and computer hardware, it has received widespread research attention.
# AWMC: Abnormal-Weather Monitoring and Curation Service Based on Dynamic Graph Embedding
### Contribution  
The key contributions of this research are summarized as follows.  
* We propose an anomaly detection method that constructs dynamic graphs from spurious relationships between weather data, and uses graph entropy to measure the similarity between two graphs. Then, we propose a dynamic graph embedding model to construct an embedding space for anomaly detection. Lastly, existing anomaly detection methods can be applied in the embedding space for anomaly detection.
* We designed an abnormal-weather monitoring and curation system (AWMC) that shows abnormal changes in the weather.
* We provide a system that visualizes weather data and anomalies. This system can help people in understanding the relationship between weather changes and the extent of anomalies. It can also help people in understanding the reasons for abnormal weather.  
The rest of this paper has the following structure. In Section 2, we describe the related work. Section 3 discusses an anomaly detection method based on dynamic graph embedding. In Section 4, the architecture and implementation of AWMC are explained. Section 5 is the evaluation of the AWMC system. Section 6 presents the conclusion and future work.
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 7 Challenges and Limitations of AI Chatbots in Infectious Disease Outbreaks
### Resource Allocation Concerns  
The deployment of AI chatbots during health crises raises concerns about resource allocation, as investment in these technologies may divert funding and attention from essential public health interventions, such as vaccination campaigns and contact tracing [163]. While chatbots offer scalability and the ability to disseminate information quickly, their cost-effectiveness and impact on epidemic management remain uncertain [164]. Allocating resources to chatbots without clear evidence of their effectiveness may reduce the overall impact of traditional health measures [165].  
To address these challenges, chatbots should be designed with cost-effective strategies in mind, such as using open source platforms and modular frameworks to enable scalability across different crises [166]. Integration with existing public health systems is a very relevant factor for ensuring that chatbots complement, rather than disrupt, traditional interventions [167]. Additionally, pilot programs and robust evaluation mechanisms can help assess chatbot effectiveness before wider deployment [168]. Socio-technical strategies are also important. Decision-making frameworks that evaluate the cost-effectiveness of chatbots should be implemented to ensure resources are allocated efficiently [169]. Engaging public health professionals, policymakers, and community representatives in the development process ensures that chatbots meet public health needs and do not undermine essential services [170].  
Research is needed to compare the cost-effectiveness of chatbots with traditional public health interventions, explore integration frameworks that align chatbot deployment with existing health goals, and assess the long-term impact of chatbot investments on public health infrastructure [171].
# Commercially Biochar Applied for Tartrazine Removal from Aqueous Solutions
## 3 Results and Discussion
### Testing the Biochar Efficiency as Tartrazine Adsorbent from Synthetic Aqueous Solutions
#### 3.2.1 The Influence of the Initial pH of the Aqueous Solution on the Adsorption Process  
First, 5 mg of biochar was stirred at 400 rpm with 5 mL of tartrazine aqueous solution with concentration of 5 mg L\({}^{-1}\), varying the pH of the solution from 2 to 12, for establishing the optimal pH for the tartrazine removal. The adsorption process was performed at room temperature for 20 min for each sample. The two phases were separated at the end of the process by filtration and the solute was analyzed. The results presented in Figure 5 are showing a very low adsorption at pH values of 4 and above (only 5% or less), while for lower pH values, there is a significant increase in removal degree up to almost 50% at pH 2. Since pH < pH\({}_{\text{pzc}}\), the adsorbent surface is positively charged and absorbs the negatively charged dye.  
The aqueous tartrazine solutions used in all subsequent tests were prepared at pH 2 in accordance with the findings of this particular example.
# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 3 Methodology
### Crack Severity Analysis
#### 3.4.2 Extracting Morphological Features  
Following the extraction of individual crack objects in the prior step, we proceeded to compute the morphological characteristics of the cracks, including length, width, area, and density. To derive the length and maximum width of the cracks, we implemented Algorithm 1. From the previous section, we have the boundaries of the contours, i.e., cracks. Let a contour \(C=[\text{X},\text{Y}]\), which is an array of two columns and \(N\) rows (i.e., length of the contour) and where \(x_{0}\), \(x_{1}\),.......\(x_{n}\in X\) denotes the rows of the image and \(y_{0}\), \(y_{1}\),.......\(y_{n}\)\(\in\)Y denotes the columns of the image. Let \((x_{0},y_{0})\) and \((x_{n},y_{n})\) are the starting point and the ending point of the contour i.e., crack respectively. To find out the length of the crack, we have calculated the distance between the starting point and the ending point of the crack boundary using the distance formula. Then, for calculating the maximum width of a crack, we have traversed from the starting column \(y_{0}\) to the ending column \(y_{n}\) of the crack boundary. During this crossing, we have found out and stored the rows where any particular column \(y_{j}\) has traveled in a list named **occurs**. We estimated the number of rows traveled by any column \(y_{j}\) when this searching loop was completed, and we appended the results for each column to a list entitled **widths**. Finally, we have searched for the maximum value in the list, and thus, we have calculated the maximum width of a crack.  
```
1 contour = [X, Y]
2 Length = \(\sqrt{\left(x_{n}-x_{0}\right)^{2}+\left(y_{n}-y_{0}\right)^{2}}\)
3 Initialize an empty list named **widths for\(i\gets(y_{0},y_{n})\)do**
4 Initialize an empty list **occurs for\(j\gets N\)do**
5 if\(y_{j}==i\)then**
6 update occurs using \(x_{j}\);
7
8 end if
9 update widths using \(\max_{(\text{occurs})}-\min_{(\text{occurs})}+1\)
10 end for
11 Maximum width = \(\max_{(\text{widths})}\)
```  
**Algorithm 1**Algorithm for length and width calculation.  
We determined the area of the contours, representing the area of individual cracks. By summing the areas of these individual cracks, we obtained the total area covered by cracks within an image. This total area was then divided by the number of pixels, yielding the density of the cracks present in the image.
## 3 Application in Age-Development fMRI Data
### Extracted Smoothness Harmonics and Their Merits
The abbreviation index indicates that sFC stands for static functional connectome. Notably, in our attempt to classify phenotypes between children and adults using both sFC and extracted smoothness as discriminative features, we conducted a simple classification analysis. Since the classification accuracy is largely determined by the classifier and the volume of the discriminating features used in training [31], we employed the respective features (sFC: \(39\times 39\); smoothness harmonics: \(39\times 39\)) to train an individual SVM and evaluated its classification performance on the testing dataset (training/testing ratio: 2/8). Over 10 repetitions (the second row of Table 1), the smoothness harmonics demonstrated a clear empirical advantage over sFC in classifying adults from children. Additionally, we also applied an off-the-shelf graph embedding approach, namely the Graph2 Vec method [12], to the age-development dataset, producing an identical number of discriminative features (\(39\times 39\)) for training another independent SVM. When evaluated on the same testing set, these graph-embedding features yielded an average classification accuracy of 76.6%, demonstrating the superiority of our approach over other static graph analysis methods. These results provide compelling evidence that the smoothness harmonics capture meaningful differences in cortical dynamics between children and adults, highlighting the potential of this approach for studying developmental processes and cognitive functions.