esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In graph visualization, the adjacency matrix is a commonly used method that expresses the relationship between N nodes through an N x N matrix [1]. Multivariate node attributes are also important information in the adjacency matrix. Still, little space is left for attributes display because the adjacency matrix expresses whether the relationship exists between two nodes and makes vital information in conspicuous [2]. The Focus+Context (F+C) technique is used to solve this issue.  
The F+C technique displays node attributes mainly by using the embedded view and multi-view method [3, 4]. In the embedded view [5, 6], the attributes of the corresponding two nodes are superimposed on the edge of the focus, which will lead to the occlusion of edge information (such as color mapping). The multi-view [3, 7] changes the encoding of the focus area for attribute displacement so that the original edge information will be covered entirely. Hence, the analyst has to constantly switch between the two views in the process of data analysis [6, 7]. Therefore, showing edge and node attributes without occlusion is challenging in the traditional F+C focus view.  
An immersive environment can be led by Virtual/Augmented/Mixed Reality, wall-size displays, and tabletops [8]. A survey of Immersive Analytics [9] reports that 3D graphics, stereo vision, and head tracking are the necessary components that should be provided in this environment. The application of immersive environments in data visualization has become a new data analysis technology [10]. Compared with the 2D view, it adds depth to increase the display space and provides new possibilities for display mode and layout [11]. In addition, new interaction methods offer a new interactive experience and help users explore data more naturally and coherently.  
Therefore, we propose a focus view model named Multivariate Fence (MVF), which uses the spatial parallel coordinate in the immersive environment to display node attributes. The MVF (Figure 1), together with the focus area, constitutes a focus data analysis workspace, which displays all information of nodes and edges simultaneously without blocking edges. This method improves the efficiency and accuracy of locating and comparing attributes between nodes and enhances the understanding of the relationship between nodes and edges of the adjacency matrix. We also conducted a controlled experiment to evaluate the performance of the MVF.  
This work contributes the following: (1) a visualization model for visualizing multidimensional properties of graph data using an immersive environment; (2) a visualization method for locating and comparing adjacency matrix nodes in an immersive environment; and (3) a VR-based prototype system for visualizing high-dimensional graph data and an evaluation.
## 3 Numerical Model and Validation
### Model Validation  
Figures 9 and 10 show the calculated vertical vibration acceleration on the soil and the first floor at 8.3 m from the track, which were compared with the measured vertical vibration at the same locations. The time domain amplitudes of the vibration response obtained from the established finite element model exhibit a close resemblance to the measured values. The simulation results demonstrate a more concentrated manifestation of train-induced vibrations, with less pronounced peak acceleration, possibly attributed to random variations in track unevenness and inherent soil layer heterogeneity.  
Figure 10: Comparison of measured and simulated time histories on the 1 st floor C2. (**a**) Measurement; (**b**) simulation.  
Figure 9: Comparison of measured and simulated time histories on the soil G1. (**a**) Measurement; (**b**) simulation.  
Figure 11 shows the comparison of the measured and simulated acceleration levels on the soil and the first floor. The simulated acceleration levels across the entire frequency range of 4-80 Hz exhibit consistency with the overall trend observed in the measured data, with the peak frequencies of vibration acceleration all occurring at the same frequencies. However, it should be noted that the simulated acceleration levels across all the frequency bands are lower than those obtained from measurements. This discrepancy may potentially arise due to uncertainties associated with track irregularities and the simplification of soil layers, among other factors.
## 3 Results and Discussion
### Overall Heat Transfer Performance for Different Flow Channels
#### 3.2.3 Overall Heat Transfer Performance  
Figure 7 shows the relationship between the core volume goodness factor (\(\eta_{g}h_{std}\alpha\)) and \(E_{std}\alpha\) of the circular-arc-shaped flow channel with nine different structural parameters. As can be seen from the figure, for any circular-arc flow channel, \(\eta_{g}h_{std}\alpha\) increases with the increase in \(E_{std}\alpha\). Under the same \(E_{std}\alpha\), #5 (instead of #4) has the best overall heat transfer performance, while #3 has the worst overall heat transfer performance. At the same time, through the comparison of nine runner structures, it is found that under the same \(E_{std}\alpha\) condition, the \(\eta_{g}h_{std}\alpha\) values of the four runner structures of #1, #2 and #3 remain the same. This indicates that, at a smaller radius, the change in the inlet height has less effect on the core volume goodness factor.  
Empirical Formulas for Heat Transfer and Flow Performance of Circular-Arc-Shaped Flow Channels with Different Structures  
The main structural parameters of the circular-arc-shaped flow channel are \(S_{f}\), \(L_{f}\), \(t_{f}\), \(F_{h}\) and \(R\). Through multiple regression analysis and the \(F\) significance test of the experimental data of nine kinds of flow channel structures, the experimental correlation formula of factor \(j\) and factor \(f\) in circular-arc-shaped flow channels was obtained, shown in Equations (17) and (18).  
Figure 8 shows the comparison between experimental correlation values and experimental values of the heat transfer and flow performance in different circular-arc flow channels with different radii. The two experimental correlations can accurately predict \(j\) and \(f\) factors, and the prediction error of 90% of the experimental data can be controlled within 10%.  
\[j=0.0791 Re^{-0.185}\Big{(}S_{f}/F_{h}\Big{)}^{0.326}\Big{(}S_{f}/R\Big{)}^{0.7 55}\Big{(}L_{f}/t_{f}\Big{)}^{-0.541} \tag{18}\]  
\[f=1.013 Re^{-0.255}\Big{(}S_{f}/F_{h}\Big{)}^{0.456}\Big{(}S_{f}/R\Big{)}^{0.395 }\Big{(}L_{f}/t_{f}\Big{)}^{-0.372} \tag{19}\]  
Figure 7: Comparison of core volume goodness factor of different circular-arc-shaped flow channels.
# Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series
###### Abstract  
The main objective of this study is to analyze the spatial and temporal variability of gross and net primary production (GPP and NPP) in Peninsulay Spain across 15 years (2004-2018) and determine the relationship of those carbon fluxes with precipitation and air temperature. A time series study of daily GPP, NPP, mean air temperature, and monthly standardized precipitation index (SPI) at 1 km spatial resolution is conducted to analyze the ecosystem status and adaptation to changing environmental conditions. Spatial variability is analyzed for vegetation and specific forest types. Temporal dynamics are examined from a multiresolution analysis based on the wavelet transform (MRA-WT). The Mann-Kendall nonparametric test and the Theil-Sen slope are applied to quantify the magnitude and direction of trends (increasing or decreasing) within the time series. The use of MRA-WT to extract the annual component from daily series increased the number of statistically significant pixels. At pixel level, larger significant GPP and NPP negative changes (\(p\)-value < 0.1) are observed, especially in southeastern Spain, eastern Mediterranean coastland, and central Spain. At annual temporal scale, forests and irrigated crops are estimated to have twice the GPP of rainfed crops, shrulands, grasslands, and sparse vegetation. Within forest types, deciduous broadleaved trees exhibited the greatest annual NPP, followed by evergreen broadleaved and evergreen needle-leaved tree species. Carbon fluxes trends were correlated with precipitation. The temporal analysis based on daily TS demonstrated an increase of accuracy in the trend estimates since more significant pixels were obtained as compared to annual resolution studies (72% as to only 17%).  
GPP; NPP; SPI; air temperature; Spain; time series Article  
Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series
# Fingerprinting Acoustic Localization Indoor Based on Cluster Analysis and Iterative Interpolation
## 4 Experimental Validation
### Analysis of the Novel Method  
The fingerprinting acoustic localization approach based on iterative interpolation and cluster analysis is presented in this work. The positioning database consisting of 72 initial RPs is divided into four sub-databases by K-Means clustering algorithm, and four adjacent RPs selected by the two-stage matching method are used for 13 test point's location estimation based on iterative interpolation.  
As Figure 8 shows, all of the estimated positions of the 13 target points obtained good concordance with the true positions. Meanwhile, the interpolation process at most target points ended in five iterations. Take Test Point 3, for instance: the location error decreased during iterative interpolation process and ended at the seventh iteration.  
To analyze the influence at different test points, the position accuracy comparison of the novel method and the original method were taken on each test point. As Figure 9 shows, the novel method brought significant improvement of positioning accuracy for 11 of the 13 test points. In the novel method, the errors of Test Points 3, 7, 9, 11 and 12 decreased more than 50% from the original location method. However, Test Points 2, 4, 5, 6, 10 and 13 were not sensitive to interpolation process because they already had relatively high positioning accuracy. It must to be pointed out that the location results of Test Points 1 and 8 got worse and result in no apparent improvement in maximum error. That is because these points were located at the boundary of two sub-databases, and their adjacent RPs were assigned to different clusters by feature clustering partition method. The causes of location error are complex and varied; to further decrease the location error, improvements of other links in fingerprinting acoustic localization process are also needed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cases** & **Total RPs** & **I-RPs** & **V-RPs** & **M-Error (m)** & **A-Error (m)** \\ \hline No interpolation & 20 & 20 & 0 & 0.4505 & 0.2385 \\  & 72 & 72 & 0 & 0.2290 & 0.0945 \\ \hline Global interpolation & 255 & 20 & 235 & 0.1656 & 0.0772 \\  & 255 & 72 & 183 & 0.1330 & 0.0534 \\ \hline Iterative interpolation & 48.9 & 20 & 28.9 & 0.1773 & 0.0791 \\  & 91.2 & 72 & 19.2 & 0.1542 & 0.0652 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The location results comparison of different fingerprinting acoustic localization methods. I-RPs, initial RPs; V-RPs, virtual RPs; M-error, maximum positioning error; A-error, average positioning error.
## 2 The Vision of Future Manufacturing Industry Networks
### M2M Communications for Manufacturing Industrial Networks  
Digital manufacturing integrates technologies of a variety of subjects, which are introduced to gain high-level automation and intelligence, such as radio communications, radio frequency identification (RFID), machine vision, interactive database, cloud service, and data analytics. To clarify the role of M2M communications in industrial applications, Figure 1 depicts the whole picture of an IIoT or digital manufacturing system. The underlying techniques are divided into four layers:  
* The physical layer includes the functional machines that perform particular industrial operations, which are the basis to achieve autonomous operations. The smart sensors, actuators, robotics, RFID, machine vision, and precision control are the exemplar enabling technologies.
* The communication layer performs the machine communications for efficient data exchange and understanding. The radio communication technologies, and communication protocols and machine networking techniques are the building blocks.  
Service and application layer handles the events, processes the data, and deals with the logic of industrial tasks. Java Message Service (JSM), representational state transfer (REST), service-oriented architecture (SOA), and cloud service architecture are alternative solutions for the management of industrial operations.
* Data and knowledge layer conducts the analysis of produced data and discovery of rules to supervise the applications. 'NoSQL' solutions such as MongoDB and Rethink DB, and machine learning promise dynamic interaction and data analysis-based intelligence.  
Since digital manufacturing is characterised by the automated configuration and autonomous collaboration of machines, the efficiency, reliability, and flexibility of machine connectivity and M2M communications become crucial parameters. There are two research focuses towards highly adaptive and responsive manufacturing. One is M2M protocols and connectivity solutions with network infrastructures based on Ethernet, Wi-Fi, cellular networks, and so forth. The other is software architecture and knowledge-based manufacturing management. Although M2M is widely accepted as a pivotal enabler of digital manufacturing, the M2M communications technology-based production line-scale machine interoperability has not been sufficiently addressed.  
Evidently, the data interactions for the interactive functionalities including data collection, data request and response, events and commands notification, and execution of service layer functions are all through the intermediary communication technologies. Machine communications and understanding play an important role in the dynamic operations. There have been numerous investigations reported in the literature on how to apply existing standards and protocols that are suitable for industrial scenarios to achieve high-performance manufacturing management frameworks.  
Figure 1: Machine-to-machine (M2M) communication in industrial internet of things (IIoT) systems.
## 3 Results and Discussion
### Desorption Experiments  
For effective use of the synthesized MIPs, it is necessary to study their desorption properties. The process of desorption is closely connected with the regeneration procedure of the sorption parameters of the sorbents. Desorption was carried out with 0.05 M nitric acid.  
Figure 16 represents the concentration of silver in the solution and in the MIPs during desorption. Figure 17 shows the desorption degree of the MIPs. From the obtained experimental data, the following dependence is observed: in the first 90 min, the desorption rate of MIP(DEGDMA) is 1-5% greater in comparison with MIP(DVB), starting from 9.10% vs. 7.80% (15 min) and ending with 35.17% vs. 35.11% (90 min). After 90 min, the desorption of silver from the MIP(DEGDMA) matrix occurs more slowly, while desorption from MIP(DVB) becomes more rapid for 3-9%. Only at the end of the experiment (180 min), the final values of the desorption degree are slightly higher for MIP(DEGDMA). Control measurements were obtained after 24 h, and the results are presented in Table 6. The equilibrium value of the desorption degree at 24 h reaches almost 80%. Thus, it can be concluded that MIPs have quite high desorption efficiency, and they could be applicable as sorbents for silver recovery.  
Figure 16: Concentration of silver nitrate during desorption in solution (**a**) and in the MIPs (**b**).  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Kinetic Model**} & \multicolumn{2}{c}{**Sorption Rate, min\({}^{-1}\)**} \\ \cline{2-3}  & **MIP(DEGDMA)** & **MIP(DVB)** \\ \hline [PERSON] & 0.0179 & 0.0113 \\ [PERSON] & 0.0172 & 0.0139 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of sorption rates for MIPs using kinetic models.  
From the obtained results, it can be observed that the desorption degree of the MIP(DEGDMA) is slightly higher than that of the MIP(DVB). This can be explained by the binding properties of DVB and DEGDMA. The binding in the MIP(DVB) is stronger; thus, the molecules of the silver template are placed tightly in the cavities. The molecular chain of DEGDMA is longer than that of DVB; consequently, the structure of the MIPs will have some voids between the cavities. Because of that, the structure becomes less dense, and the molecules of the silver template will also be less dense, which leads to easier extraction of the silver template molecules from the copolymer matrix.
# MTC-GAN Bearing Fault Diagnosis for Small Samples and Variable Operating Conditions
###### Abstract  
In response to the challenges of bearing fault diagnosis under small sample sizes and variable operating conditions, this paper proposes a novel method based on the two-dimensional analysis of vibration acceleration signals and a Multi-Task Conditional Generative Adversarial Network (MTC-GAN). This method first constructs two-dimensional images of vibration signals by leveraging the physical properties of the bearing acceleration signals and employs Local Binary Patterns (LBP) to extract subtle texture features from these images, thereby generating fault feature signatures with high discriminative power across different operating conditions. Subsequently, MTC-GAN is utilized for data augmentation, and the trained discriminator is used to perform fault classification tasks, improving classification accuracy under conditions with small sample sizes. Experimental results demonstrate that the proposed method achieves excellent fault diagnosis accuracy and robustness under both small sample sizes and varying operating conditions. Compared to traditional methods, this approach exhibits higher efficiency and reliability in handling complex operating conditions and data scarcity.  
bearing; small sample; GAN; variable operating conditions; deep learning +
Footnote †: journal: Applied sciences
# Semantic Distributed Data for Vehicular Networks Using the Inter-Planetary File System
## 1 Introduction
Figure 1 depicts a vehicle connecting to the IPFS network and instantaneously having access to all the semantic information provided by RSUs, other vehicles, and any other remote IoT peer connected to the network. Based solely on IPFS, the proposal of this paper is a completely public and distributed network, unmanaged but secure and trustworthy thanks to the redundancy provided by the existence of multiple peers and the innovative methods used to store and share the information. The resultant P2P network brings the potential and advantages of information-centric networking (ICN) [5] to vehicular networks without requiring a network architecture overhaul. Figure 1: Vehicles, road-side units (RSUs), and other IoT devices (e.g., traffic cameras and traffic lights) connected to the peer-to-peer (P2P) Inter-Planetary File System (IPFS) network to share and retrieve semantic data. IPFS is multi-platform software that works over standard IP networks. Therefore, it allows RSUs and other IoT devices to interact independently of their connection to the network. Vehicles can use specific wireless systems, such as dedicated short-range communications (DSRC), the European Telecommunications Standards Institute's (ETSI) intelligent transport systems (ITS) G5 Standard, or the 3 rd Generation Partnership Project (3 GPP) Cellular Vehicle-To-Everything Standard; and RSUs can make use of multiple interfaces, wireless and wired, to connect simultaneously to vehicles, other RSUs, and IoT devices such as traffic cameras.
# Comparative Study of Lightweight Deep Semantic Segmentation Models for Concrete Damage Detection
## 3 Details for Model Training and Evaluation
### Concrete Dataset  
To evaluate the performance of the models, an image dataset containing concrete damage, was constructed. The images were collected from different sources, including online sources, as well as from real concrete structures in South Korea. Images in the dataset contained four types of damage: cracks, efflorescence, spalling, and rebar exposure. A total of 3840 images was used, of which 90% (3440 images) were used for training and 10% (400 images) for testing the models. The images had varying resolutions, starting from a high resolution of 1600 \(\times\) 1200 to a lower resolution of 796 \(\times\) 716 in RGB format. For pixel-level classification, the data was labelled manually using an Adobe Photoshop labelling tool to create the ground truths. Figure 8 shows an example of the four types of damage in the concrete dataset with the corresponding ground truths. The damages were labeled using different colors: cracks in red, efflorescence in green, spalling in magenta,  
Figure 7: Overall architecture of DeepLabV3+ (Backbone Network = ResNet-50).  
and rebar exposure in cyan. The background was not labeled and is shown in white in the ground truth.
## 3 Results and Discussion
### Quantitative Analysis Total Flavonoid Content  
Flavonoids constitute a diverse and broad category of natural phenolic compounds. These compounds possess various hydroxyl groups, which contribute to their antioxidant activity. The effectiveness of this activity relies on the ability of flavonoids to donate hydrogen or electrons to free radicals [58]. In our study, we conducted a quantitative analysis of four different extracts of _C. axillaris_ to determine the total flavonoid content. This was achieved by precipitating the extract solution with aluminum chloride (AlCl\({}_{3}\)) in an alkalinized medium. The results were expressed as \(\mu\)g quercetin equivalent (QE) per milligram of dry extract. The results for the total flavonoid content in the various extracts are displayed in Table 3.  
Significant variations were observed in the total flavonoid content among the four different extracts, with values ranging from 41.72 \(\pm\) 2.40 ug QE/mg of dry extract weight for the aqueous extract to 283.84 \(\pm\) 7.73 \(\mu\)g QE/mg of dry extract weight for the acetone extract. The analysis of the data presented in Table 3 reveals that the choice of solvent for extraction significantly impacts the extraction of flavonoid content from dried fruits of _C. axillaris_. The statistical analysis further confirms a significant difference (\(p\) < 0.05) in the total flavonoid content among the different solvents used for extraction. The results clearly demonstrate that the acetone extract exhibited the highest total flavonoid content, while the aqueous extract displayed the lowest total flavonoid content. In previous research, the total flavonoid content of ethanolic extracts from the fruit peel and flesh of _C. axillaris_ was determined to be 62.13 and 14.96 mg QE per g of dried extract, respectively [56]. Additionally, another study reported a total flavonoid content of 63.17 mg QE per gram of dry extract in a commercially available _C. axillaris_ leather sugar hydroethanolic extract [57]. Thus, it can be observed that total flavonoid content reported in our study was comparatively higher than previous findings.