esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Stand-Alone Ground-Based Technology Applications in Transport Infrastructure Monitoring
Overall, 280 research works have been identified including journal (research) papers, review papers, conference proceedings and book chapters from 2000 to 2022, as represented in Figure 1. The first step of the procedure was based on the selection of the most relevant keywords used for research in SAR applications for infrastructure monitoring. For the SAR applications, several keywords were selected and implemented for research from the Scopus database [36], including: \"InSAR\", OR \"MT-InSAR\", (multi-temporal InSAR), OR \"DInSAR\", (differential InSAR), OR \"A-DInSAR\", (advanced DInSAR), OR \"PS\" (persistent scatterers), and \"PSI\" (persistent scatterers interferometry). The second step consists in the inclusion of the keywords \"monitoring\" AND \"infrastructure\", and the selection of four infrastructure classes historically identified as conventional areas of investigation for PSI applications: \"bridges\" OR \"railways\" OR \"highways\" OR \"runways\". The review includes the research of the selected keywords in the \"article title, keywords and abstract\" for each paper, available in the Scopus database. The trend in Figure 1a is exponential, indicating that the infrastructure monitoring area has been attracting the attention of researchers in the past two decades. Two leaps can be observed that occurred in 2010 and 2020, respectively. The first was most likely related to the year of the launching of new-generation satellite missions, which included the Italian Constellation of Small Satellites for Mediterranean Basin Observation (COSMO-SkyMed) satellites, between 2007 and 2010, and the TerraSAR-X (TSX) in 2007, with high spatial and temporal resolution. The second leap is likely related to the launch of the Sentinel-1 (S1) mission in 2014, as part of the Copernicus Programme of the European Space Agency (ESA), with regular and revisit times of 12 days. These advances increase the density of PSs extracted by high-resolution SAR data up to 6 times, as opposed to those expected by medium-resolution SAR, e.g., RADARSAT-1 and Envisat. Accordingly, the combination of high spatial density, wide coverage and high sensitivity to small deformations has allowed analysis of the deformations of individual structural elements at the network level. This significantly increases the potential of MT-InSAR techniques in infrastructure monitoring. The cumulative number of citations for the selected papers were collected for 15 years (from 2006 to 2022), as reported in Figure 2. A significant interest of researchers in the implementation of satellite-based techniques for infrastructure monitoring is proven by the increase in the yearly cumulative citations, amounting to approximately 200 citations in 2017 and more than 900 in 2021. Figure 1: (a) Scientific contributions per year related to the applications of InSAR to infrastructure monitoring. (b) Documents by type. Source: Scopus (www.scopus.com), accessed on 1 September 2022 [36]. Figure 2: Citation overview of the selected papers related to MT-InSAR for infrastructure monitoring.
## 1 Introduction
In our previous research, we compared different procedures for the making of identical test plates, compared the printed part with the Computer aided design (CAD) model and found the best measurement method for some of the small details in products [17]. In this paper, we present the results of dimensional measurements of products built on the same machine under repeatability and reproducibility conditions. The goal of the research was to verify dimensional accuracy for the product built with PolyJet process using CT scanning, but also to investigate the repeatability and reproducibility of products. A similar study was carried out by the author [PERSON] et al. [18] who investigated the procedures of selective laser sintering, fused deposition modelling and stereolithography. X-ray Computed Tomography (CT) has been increasingly used in industry for dimensional quality control. CT, compared to traditional measuring methods, enables dimensional analysis in a non-contact and non-destructive way on a wide variety of components for exterior and interior (hidden) features. Recent developments of CT machines, which include (sub)micrometre focal spot X-ray sources and improved software algorithms for reconstruction of measured volume, resulted in measurement uncertainties comparable to classical CMM (coordinate measuring machine) measurements. The capabilities of the CT machine used in this work have been extensively researched by authors [PERSON] et al. [19] and [PERSON] et al. [20], with typical standard measurement uncertainties (\(k=1\)) ranging from 5 \(\upmu\)m to 10 \(\upmu\)m for polymer parts and various measurand types (cylinder diameters, axial distances, plane to plane distance). Our own research [21], performed on a multimaterial polymer part with the same CT machine used in this work, shows standard measurement uncertainties between 3 \(\upmu\)m and 10 \(\upmu\)m, depending on the selected measurands. When these results are compared with the expected accuracy of various additive manufacturing technologies, it can be reasonably expected that CT measurements will have sufficient resolution and accuracy to reliably measure AM parts. However, the surface roughness of AM parts is typically large, which can influence the surface determination of measurands in CT scans. This effect must be corrected through the use of advanced surface determination algorithms (locally adaptive thresholding) and taken into account for measurement uncertainty [22; 23]. There are few repeatability studies on metal products produced by additive manufacturing. Thus, for example, the authors [PERSON] and [PERSON] [24] tested the repeatability on vibrations in aluminium parts and found that selective laser melting (SLM) has high repeatability and repeatability is higher in the horizontally printed product compared to vertically printed products. This was also concluded by the authors [PERSON] et al. [25]--SLM can be used to produce parts with reproducible properties, provided the powder quality and the processing parameters do not change.
# Rolling Bearing Fault Diagnosis Based on CEEMDAN and CNN-SVM
## 5 Conclusions  
Aiming at the problem that the rolling bearing vibration signal collected by an acceleration sensor is mixed with noise interference, which leads to the difficulty of fault feature extraction, a rolling bearing fault diagnosis method based on CEEMDAN and CNN-SVM is proposed in this paper. The experimental results show the feasibility of the method proposed in this paper, which effectively solves the problem of low fault diagnosis accuracy of traditional methods in the noise background and achieves an average fault diagnosis accuracy of 99.25%. The following conclusions can be drawn from the experimental results: Using the CEEMDAN algorithm to decompose the vibration signal, filter the components and reconstruct the signal can effectively reduce the noise of the original signal. The SVM with the optimal parameter penalty factor \(C\) and kernel function parameter \(\sigma\), obtained  
Figure 11: CNN-SVM model confusion matrix.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \cline{2-4}  & **Maximum Value** & **Minimum Value** & **Average Value** \\ \hline SVM & 86.25 & 81.61 & 82.71 \\ CNN & 90.54 & 88.93 & 89.73 \\ EMD+CNN & 96.25 & 94.64 & 95.11 \\ EEMD+CNN & 98.04 & 96.43 & 96.87 \\ CEEMDAN+CNN- & 98.59 & 97.86 & 98.52 \\ CEEMDAN+CNN-SVM & 100 & 98.39 & 99.25 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fault diagnosis accuracy of different models.  
by GWO optimisation search, has a better classification performance than the Softmax classifier, and the average accuracy is improved by 0.73%.  
Conceptualisation, L.S. and W.L.; methodology, L.S., W.L. and D.Y.; software, L.S.; validation, L.S., W.L. and D.Y.; formal analysis, L.S.; investigation, L.S.; resources, L.S.; data curation, L.S.; writing--original draft preparation, L.S. and W.L.; writing--review and editing, L.S., W.L. and D.Y.; visualisation, D.Y.; supervision, L.S.; project administration, L.S. and S.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Guiding Project of Science and Technology Research Programme of Hubei Provincial Department of Education, grant number B2022047.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to private.  
The authors declare no conflicts of interest.
## 5 Experiments and Results
### Comparison of Test Results with the Original Model  
The effectiveness of detecting tunnel lining defects and other targets before and after optimization can be more intuitively determined by comparing the performance of the optimized model with that of the original model on the test dataset. Similarly, the heatmap of the parameter weights of the model at the detection layer can be visualized before and after the optimization. The heatmap of the model at the detection layer of the small targets before and after the improvement is also added, allowing for the effective comparison of detecting small targets before and after model optimization. Moreover, improved thermograms were added to compare the model's focus on small target defect detection before and after model optimization, and the markedly improved regions were labeled in the improved thermograms. As shown in Figure 14a,b, the optimized model can focus on the small cracks around the ring joints in the small target detection layer and outputs the prediction results. The optimized model has higher detection accuracy and confidence for small- and medium-scale cracks at the boundary of the ring joint and near the apparent appurtenances of the lining. The optimized model can pay more attention to this boundary area and assign higher parameter weights to the defected area, which has better detection results (Figure 14c). For leakage damage, which occurs around bolt holes, grouting holes, and joints (Figure 14d), the optimized model can assign more weight to the leakage damage at bolt holes, which improves the detection rate and has higher detection confidence and better prediction frame fitting results for large-scale damage detection (Figure 14e).  
Figure 13: Grad-CAM analyzing results for ablation test results.
## 3 Results and Discussion
### Fourth Experiment  
The fourth experiment intends further to test the correlation between the RADAR's signal and the SWC in an experiment that maps the soil that would be viewed in bird's eye view as 2D. For visualization, the pondView strategy is used.  
We recorded the data from the RADAR with a top view horizontal square grid of 4 cm spacing before adding 1 L of water to the soil and one hour after it. This way, we have a map with the dry soil and another for the wet soil.  
We created three pondViews based on the RADAR's readings. One pondView shows the difference between dry and wet soil near the surface, and the other shows the same difference but deeper. Figure 8 shows the three pondView diagrams, showing different numbers of layers.  
Because the presence of water in the soil lowers the intensity of the signal read by the RADAR, it is only interesting to analyze the positive values of the difference between dry and wet soil. Figure 8 shows several pondViews with different parameters. In the near-surface pondViews, we can see that the most significant difference is between 12 and 20 cm deep because the pondView with just three layers shows not so many changes and the pondView with five layers is predominantly blue. The reason is that since we add water to the ground, gravity tends to push the water into lower levels. In the deeper pondView, we can see that the significant differences are around 24 cm depth due to two factors. The three-layer pondView has a lot of greens (24 cm depth layer's color). The five-layer pondView has a significant concentration of cyan (24 cm depth layer's color) and light green and light blue. Analyzing the entire box pondView, we can see that it is predominantly light blues (24 and 28 cm depth layer's colors), corroborating what was previously said in the experience before and what the other pondViews indicate.  
Figure 7: Experiment 3 results in the sealed container. The vertical yellow dotted lines represent the moments when the water was added, and the brown color represents the container air humidity. The blue line corresponds to the RADAR’s signal, and the red line corresponds to the control signal.
# Delamination Assessment in Composite Laminates through Local Impulse Excitation Technique (IET)
## 4 Sensitivity Analysis to Delamination
Furthermore, as the delamination moves from the center of the device, the \(\frac{\beta_{lam}}{\beta_{multam}}\) ratio increases. Indeed, as the delamination moves from the center of the device, its influence on the second mode increases as well as the modal displacement of the second mode. However, the second-order nonlinear coefficient \(\beta\) accounts for the contributions of both the first and the second modes. Accordingly, at 25 mm from the center, the \(\frac{\beta_{lam}}{\beta_{multam}}\) ratio reaches its maximum value. As such, differently from the 37 mm location, where the second mode is mainly affected by delamination, both the first and second modes are influenced by delamination at 25 mm from the center. Finally, Figure 12 shows the maps of the \(\frac{\beta_{lam}}{\beta_{multam}}\) ratio for a delamination size of 50 mm with an impulse provided at 10 mm from the center and for different positions of the delamination within the device. In particular, Figure 12a shows the \(\frac{\beta_{lam}}{\beta_{multam}}\) map for the delamination located in the center of the device; in Figure 12b, the delamination is located at 10 mm from the center of the device; in Figure 12c, it is located at 25 mm; and in Figure 12d, it is located at 37 mm. Figure 11: Results of the sensitivity analysis: maximum values of \(\frac{\beta_{lam}}{\beta_{multam}}\) ratio for simulated configurations. As shown in Figure 12, while in correspondence with the nodal line, the \(\frac{\beta_{dim}}{\beta_{random}}\) ratio is equivalently equal to zero, being here null the participation of the second mode, the map of the \(\frac{\beta_{dim}}{\beta_{random}}\) ratio, and, accordingly, the location of its maximum value, significantly varies according to the retained configuration. It is worth noting that a symmetric trend is obtained only in the case of delamination located in the center of the device. These considerations suggest that the \(\frac{\beta_{dim}}{\beta_{random}}\) ratio must be mapped within the whole inspected region in order to reliably identify the presence of delamination. Nevertheless, the consistent increase in the \(\frac{\beta_{dim}}{\beta_{random}}\) ratio from the unity confirms the good sensitivity of the local-IET to the detection of delamination. In this regard, it is worth remarking that in this work, the delamination was placed in the neutral plane of the laminate. Slight variations in the \(\frac{\beta_{dim}}{\beta_{random}}\) ratio can be expected when the delamination moves from the neutral plane, as observed by [PERSON] et al. (2015) on the natural frequencies of delaminated plates.
# Inferring Urban Social Networks from Publicly Available Data
## 3 Impact of System Parameters
### Using Real Population Density and Distances
The latter is always positive and for \(\beta=2\) the global clustering coefficient is about 5 times greater than in all graphs built in the previous cases. Again, these are desirable features in a social network model [44; 76]. Geographical distances have also an effect on the degree distribution, but it is appreciable only for \(\beta=2\) (see Figures 4 and 5). 3. **Real population and \(D(u,v)=d(u,v)^{-0.5}D(u,v)=d(u,v)^{-2}\)**: Finally, the combined use of the real population and of a penalization for long edges is clearly visible in our simulation, for both values of \(\beta\), but especially when \(\beta=2\). Real density and distances mainly influence the number of connected components and the assortativity of the network. Notice that the number of connected components is also affected by the average degree of the network: the higher the degree, the lower is the number of connected components. Specifically, the use of real data seems to favor higher assortativity values and a higher number of connected components. While a uniform population density creates a giant component whose size is in line with the giant component of an ER graph, a real density creates a smaller giant component and a higher number of connected components (see Figure 6). For uniform densities, the size of the connected components is almost always 1 (the same holds when we set \(D(u,v)=1\)); instead, for real densities, we have more components in which size is more variable. Finally, as shown in Figures 4 and 5, the degree distribution is also affected by real densities and distances. Preventing a Poisson degree distribution is a further step toward the definition of a suitable model because social networks are usually characterized by skewed degree distributions [44]. Summarizing, our tests show that penalizing long edges in (2) while using the real population density allows to construct a friendship graph deprived of the typical characteristics of ER-like homogeneous graphs. Even more, using real data, we are able to simulate some of the features usually observed in social networks. These findings support the rationale that a data driven approach is important: real population density and distances must be taken into consideration for building urban social graphs. Finally, we observe that the impact on graph construction of geographical distances, when \(\beta=0.5\), is negligible if not combined with real densities, as shown by the degree distributions (Figures 4 and 5) and values in Table 5.
## 3 Quality Control of Silicon Carbide Printed Part Formation
### Detection and Assessment Techniques
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Testing Methods** & **Testing Principle** & **Testing Parameters** & **Advantages** & **Disadvantages** & **Ref.** \\ \hline Scanning & The surface of the sample & & & The equipment is & \\  & is scanned by an electron & Surface morphology & Nanometer-scale & expensive, operationally & \\  & beam, and secondary & and defect & resolution enables & complex, and & \\  & electron signals are & characteristics & analysis of & time-consuming for & [179] \\  & collected and converted & (micron/nanometer & microscopic & inspection, with & \\  & into high- & scale) & defect origins & limitations to surface & \\  & resolution images. & & & examination only. & \\ \hline Liquid & The dye penetrates & & Cost-effective, & Highly susceptible to & \\  & surface defects through & Surface-breaking & operationally simple, & surface roughness effects, & \\  & capillary action, and color & defects & and suitable for & requiring pretreatment; & \\  & distribution is observed & (cracks, pores) & simple structural & limited to surface defect & \\  & after development. & & components & detection only. & \\ \hline  & X-rays penetrate the & & High resolution & & \\  & sample, and the internal & Internal defects & (micron-scale) & & high costs, slow \\  & structure is visualized & (porosity, crack & enabling & inspection speed, and \\  & through & distribution, density) & threshold visualization of & limitations for large-sized & \\  & three-dimensional & reconstruction. & & internal defects & components. \\ \hline  & High-frequency acoustic & Internal defects & Capable of inspecting & Requires surface & \\  & waves propagate through & (cracks, & thick components & planarization treatment & \\  & the material, and internal & delamination), & with relatively low & and presents difficulties & \\  & defects are analyzed by & thickness & cost and high & in inspecting components & \\  & reflected signals. & measurement & portability & with complex geometries. & \\ \hline  & The surface temperature & & & \\  & field is captured by an & & & \\  & infrared camera after & Internal defects & Rapid large-area & Relatively low accuracy & \\  & thermal excitation, and & & & \\  & anomalies caused by & thermal properties & Rapid large-area & (millimeter-scale) with & \\  & differences are analyzed. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative analysis of nondestructive testing methods for final components. A reasonable inspection and evaluation approach should be the result of a comprehensive consideration of the component's geometric complexity, material performance requirements, economic factors, production efficiency, and application scenarios. Its selection not only should ensure product quality and optimize production processes, but is also closely related to production efficiency and manufacturing costs.
# Increased Grain Amino Acid Content in Rice with Earthworm Castings
## 3 Results and Discussion
In this regard, it is well documented that the castings and body secretions of earthworms contain a certain amount of plant hormones and hormone-like substances [18; 19; 20]. Earthworms can also enhance microbial biomass and activity due to their castings and mucus, and the byproducts of this microbial activity include plant hormones such as abscisic acids, auxins, cytokins, ethylene, and gibberellins [21]. Although there is limited information available on the influences of plant hormones or plant hormone-like substances included in earthworm castings on amino acid metabolism in the rice crop, some relevant reports have been documented in other crops. For example, [PERSON] et al. [22] found that earthworm-worked humic substances had an auxin-like effect on nitrogen metabolism in wild carrot, and [PERSON] et al. [23] observed that gibberellic acid-like activity of vermicompost leachate resulted in an increase in amino acids in the common bean. Further study is required to determine if similar effects of plant hormones and hormone-like substances in earthworm castings are observed on amino acid metabolism in rice grains. Application of earthworm castings has the potential for increasing amino acid content in rice grains, although the practicality of any approach must be taken into account. High labor costs are the key factor limiting the adoption of organic manures. In this study, the tested earthworm castings were not specially produced but were collected from no-tillage rice-oilseed rape rotation fields, where earthworms are known to be abundant (pers. obs.). Reduced or absent soil tillage in these fields can provide earthworms with an undisturbed biotope and hence may be favorable to increasing their populations, while oilseed rape plants can provide abundant food for earthworms by producing large volumes of residues. In addition, our previous study documented that adoption of no-tillage rice-oilseed rape cropping systems can maintain crop yields while saving labor [24]. Therefore, development of similar no-tillage systems may be an indirect but feasible way to increase grain amino acid content in rice by increasing earthworm activity. The authors declare no conflict of interest.
## 3 Results and Discussion
### Impacts of Filtering  
Impacts on the number of data points are detailed, before discussing the effects on recorded metrics: relative velocity, signal-to-noise ratio and distance.  
Figure 6 shows the sizes of datasets as a function of the filtering steps. The datasets of trucks 2 and 3 were affected similarly. Downsampling (filter 5) included a mean of six data points and a mode of seven. Figure 7 is a distance histogram showing the effects of all filtering steps. Note how early filters mostly remove measurements at close distances.  
Figure 5: (**a**) SNR histograms after relative velocity filtering. (**b**) Distance histograms with different SNR threshold (after relative velocity filtering).