esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applications in Earthquake Emergency Response  
### Overview of the Applications  
When an earthquake occurs, the ground motion will be collected in a timely manner from the strong ground motion network. The real-time city-scale THA will be conducted for the target region, and the analysis results will be fed back to the decision-makers and reported on the internet in a short time. A program is developed named as \"Real-time Earthquake Damage Assessment using City-scale Time-history analysis\" (\"RED-ACT\" for short) to automatically implement the above workflow. This software is programmed in C++ and runs on the Windows operating system. To date, RED-ACT system has been applied to several earthquakes in China and other countries around the world, as listed in Table 2. Typical applications will be introduced as follows.  
Figure 9: Distribution of median (**a**) building loss ratios and (**b**) repair/rebuild time for Tsinghua Campus.
## 4 Data Products and Processing
### Accessing and Processing of the Data Product  
The complex waveform products are available at ICE-CSIC/IEEC's public open-data server ([[https://www.ice.csic.es/research/gold_rtr_mining/index.php](https://www.ice.csic.es/research/gold_rtr_mining/index.php)]([https://www.ice.csic.es/research/gold_rtr_mining/index.php](https://www.ice.csic.es/research/gold_rtr_mining/index.php)), accessed on 4 February 2022) [69], which are organized by folders for different spaceborne missions. To make it easier for users to get started with these data products, a Python script is provided through ICE-CSIC/IEEC's Gitlab repository ([[https://gitlab.ice.csic.es/earth-observation/spaceborne_gnssr_cwf_processing.git](https://gitlab.ice.csic.es/earth-observation/spaceborne_gnssr_cwf_processing.git)]([https://gitlab.ice.csic.es/earth-observation/spaceborne_gnssr_cwf_processing.git](https://gitlab.ice.csic.es/earth-observation/spaceborne_gnssr_cwf_processing.git)), accessed on 4 February 2022) as an example for processing the complex waveform products. The script consists of a sets of basic functions to properly obtain, search and analyze the complex waveform products, which are presented in Figure 5 and explained in detail below:
## 3 Results
### Application in Pathological Cases  
The optimal segmentation thresholds shown in Table 3 were used to obtain the values of \(V_{V\text{-}P}\) for each patient of groups two and three, that is, patients that present discordant defects in the images. With these values, the diagnosis was determined by the algorithm for each lung separately based on the conditions defined in Table 4. Examples of patients with either CTEPH or a respiratory pathology are shown in Figure 3. The case of CTEPH corresponds to a 64-year-old man. A region of hypoperfusion with the typical triangular morphology can be identified in the perfusion image. Due to the lower uptake, these regions were not segmented by the algorithm as the voxel values are beneath the segmentation threshold. The second case corresponds to a 63-year-old woman diagnosed with chronic obstructive pulmonary disease. A hypoventilated region can be identified in the ventilation image and no defects are present in the perfusion image. These regions were not segmented, as can be seen by the drawn contour.  
After processing all images, the cases of each group were classified into CTEPH, respiratory pathology, or none. As is mentioned in the methodology, a case was classified correctly if the algorithm returned a \"positive\" or \"cannot be ruled out\" verdict for one of the two lungs. The overall accuracy of the algorithm, determined from the 3-class confusion matrix, was 0.64. In the CTEPH group, 8 of 12 patients were correctly classified. On the other hand, 6 of 10 patients with a respiratory pathology were correctly classified. The sensitivity and specificity of each class is shown in Table 5.  
Regarding the treatment response to CTEPH, \(V_{pre}\) and \(V_{post}\) were obtained and the difference was calculated. The optimal segmentation thresholds for the perfusion image were used to calculate the perfusion volumes. The perfusion SPECT images before and after treatment for patients 1 and 2 are  
\begin{table}
\begin{tabular}{c c c} \hline
**Diagnosis** & **Left Lung** & **Right Lung** \\ \hline Positive & \(|V_{V\text{-}P}|>816.04\) cm\({}^{3}\) & \(|V_{V\text{-}P}|>328.82\) cm\({}^{3}\) \\ Cannot be ruled out & 816.04 cm\({}^{3}\geq|V_{V\text{-}P}|\geq 466.84\) cm\({}^{3}\) & 328.82 cm\({}^{3}\geq|V_{V\text{-}P}|\geq 119.01\) cm\({}^{3}\) \\ Negative & \(|V_{V\text{-}P}|<466.84\) cm\({}^{3}\) & \(|V_{V\text{-}P}|<119.01\) cm\({}^{3}\) \\ \hline \end{tabular}
\end{table}
Table 4: Quantitative diagnostic scale.  
Figure 3: Segmented SPECT ventilation (left) and perfusion (right) images of a left lung. The contour of the segmentation is drawn in yellow (ventilation) and green (perfusion) in the images and the color bar. (**a**) Patient with CTEPH and typical perfusion defects (arrow). (**b**) Patient with a respiratory pathology and typical ventilation defect (arrow).  
shown in Figure 4. An improvement can be observed in the images after the treatment for patient 1. Patient 2 shows no improvement of the segmented perfusion volume after the treatment for CTEPH. This improvement, or lack of improvement, can also be observed in the quantitative values shown in Table 6.
## 3 Results  
### Drought Shifts through MUSL: Temporal and Spatial Features  
Computing the temporal and geographical features for TVDI from MUSL during the previous 20 years using the mean value approach (Figure 4). With a constant rate of increase of 0.009/a, the TVDI for the entire year exhibits a notable rising trend. The mild and moderate drought conditions are represented by the TVDI, centered in the range of 0.53 to 0.70. Moreover, the ways that distinct seasons alter things are distinct: the highest amount of drought occurs in the spring, with the average value basically concentrated between 0.54 and 0.80. In 2010, the TVDI value was significantly lower than other years, with a TVDI value of 0.54. In 2016, the TVDI was significantly higher than in other years and reached the maximum value of 0.80, which is a severe drought year. Winter is the season with the smallest degree of drought and the largest fluctuation, with TVDI values ranging from 0.38 to 0.66, and its abrupt change point occurred in 2012 and 2016 when it shifted from drought-free to mild drought to moderate drought. The rate of increase of TVDI in summer was the smallest in all seasons (growth rate = 0.006/a, R\({}^{2}\) = 0.539), mainly distributed in Ejin Horo Banner, Shenmu County, Jingbian County, and southern Dingbian County, showing discontinuities in 2005 and 2013. Conversely, the greatest rate of increase in TVDI (growth rate = 0.013/a, R\({}^{2}\) = 0.697) was observed in winter, primarily across the western portion of the Otog Banner and Otog Front Banner. Overall, 2015 was a year of abrupt change in TVDI.  
Figure 5 depicts the geographic distribution of drought classes throughout MUSL between 2001 and 2020. The west-to-east saw a slow improvement in the drought. In the northwest of Otog Banner and Otog Front Banner in Inner Mongolia, severe and intermittent droughs first appeared in 2015 and 2016, respectively. These droughs thereafter displayed an increasing tendency, with Otog Front Banner indicating the most noticeable rise. In 2019, the severe drought and extreme drought areas reached the largest areas, accounting for 18.3% and 0.1%, respectively. The locations with the least amount of drought between 2001 and 2014 were the eastern half of the county, the southeast portion of Yulin County, the north-central region of Wushen Banner, and Ejin Horo Banner. From Figure 6, the mean annual TVDI in MUSL is 0.6, indicating moderate drought (71.9%), with most of it in Yanchi, Dingbian, and the Ordos Plateau throughout the middle and west sections. Drought-free and mild drought are also the main types of droughs in the MUSL, which are  
Figure 4: Geographical and temporal TVDI trends in MUSL seasons. (**a**) Spring, (**b**) summer, (**c**) autumn, (**d**) winter, (**e**) annual mean.  
primarily found in Shaanxi and certain areas of Inner Mongolia, with the Ordos Plateau in the middle and western region of the MUSL, Yanchi County in Ningxia, and Dingbian County in Shaanxi accounting for 71.9% of the area experiencing moderate drought.  
Figure 5: Location of MUSL drought grade from 2001 to 2020.  
Figure 6: Mean annual TVDI in MUSL (2001–2020).
## 5 Results and Discussion
### Analysis of the Manually Added Research Label of Learning Analytics
#### 5.2.2 Occasional Researchers  
Occasional researchers who deployed manual self-descriptive research labels for their research used a variety of terms, as shown in Figure 7.  
The most frequently used labels were:  
* Educational data mining;
* Machine learning;
* Educational technology;  
Figure 6: Terms’ co-occurrence map for the extracted keywords in the specialist category.  
Figure 7: Occasional researchers’ most frequent research labels.  
* E-learning;
* Data mining.  
There was a consistent approach to the self-descriptive research tags, since there was only one difference in the top five labels compared to specialists.  
In the 1990s, occasional researchers showed a similar pattern of initiating contributions, but from early 2000, there has been a continuous presence and contribution to the body of LA knowledge, as depicted in Figure 8.  
A thematic analysis of the research keywords related to occasional researchers easily differentiated them from other groups, as depicted in Figure 9, where a term co-occurrence map was constructed for occasional researchers from the extracted keywords.  
Figure 9 shows 285 terms out of 34,196, using a minimum of 40 occurrences of each term as a threshold to highlight the major keywords in the title and abstract of publications by occasional researchers in LA. An amalgamated pattern can be observed, encompassing  
Figure 8: Occasional authors’ start dates.  
Figure 9: Terms’ co-occurrence map for extracted keywords in the occasional category.  
a variety of keywords, such as LMS, flipped classroom, higher education, online course, collaborative learning, and online learning, that are associated with LA, as well as keywords not associated with this discipline, such as systematic review, mathematics, engineering, and so on.
# New Quality Control Algorithm Based on GNSS Sensing Data for a Bridge Health Monitoring System
## 2 Quality Control Algorithm and Quality Measures
### Quality Measures  
The quality control outcome is decided on the basis of the number of redundant measurements and the geometry of the satellites. At this stage, the impact of the two factors on the quality control procedure should be checked, as well as the quality of the measurements. The indications of confidence for quality control are redundancy, correlation, and quality dilution of precision (QDOP).
# Removal of Antibiotics by Biochars: A Critical Review
## 4 A Critical Review of Biochar-Based Adsorption Processes
### An Overview of Biochar-Based Adsorption Studies  
In the bibliometric survey section, it was shown that antibiotic adsorption studies are increasing in number, while in the antibiotics and bacterial cytology section, antibiotics were grouped into different classes based on their chemical structure. It is important to observe the adsorption studies of each major antibiotic group. Figure 3 provides an overview of adsorption studies with different antibiotics. Adsorption studies were predominantly performed on tetracycline, fluoroquinoline, and sulfonamide antibiotics. This is consistent with the co-occurrence map (Figure 2) and suggests that these antibiotics are selective to carbonaceous adsorbents.  
Previous studies on biochar-based removal of antibiotics were mainly performed on modified biochars. The usage of pristine biochars for the removal of antibiotics is currently limited to biochars prepared by pyrolysis, which are termed pristine biochars (PBs) [68]. However, in order to modify the adsorption performance of biochars, the first step is to understand the adsorption performance of pristine biochars by studying the biochars' properties and adsorption mechanisms.  
The adsorption mechanisms of different carbonaceous materials are not identical, although certain mechanisms, such as \(\pi\)-\(\pi\) electron donor-acceptor (EDA) interactions and hydrogen bonding, are considered both for high-surface-area carbon nanotubes and for biochars, indicating the role of intermolecular interactions in the adsorption [169]. According to [PERSON] et al. (2023), at least seven different mechanisms, including hydrogen bonding, \(\pi\)-\(\pi\) interactions, surface complexation, electrostatic interactions, pore filling, ion exchange, and hydrophobic interactions, can contribute to the adsorption of antibiotics onto biochars [49]. This excellent review also showed that antibiotic adsorption studies with biochars were mostly performed with modified biochars (approximately 65% of the studies) [49].  
Table 4 provides a comparison of proposed antibiotic adsorption mechanisms and maximum adsorption capacities for pristine biochars and modified biochars.  
The results of Table 4 suggest that the modification of biochars significantly affects their antibiotic adsorption capacity. The antibiotic adsorption mechanisms were principally studied on modified biochars, and \(\pi\)-\(\pi\) interactions were the most commonly proposed mechanisms for both types of biochars.  
Figure 3: Number of publications with “adsorption” and “different antibiotics” titles on the Web of Science (WOS).
# Biology and Regulation of Staphylococcusococcal Biofilm
## 7 Roles of Biofilm in the Tolerance to Multiple Drugs
[PERSON] called the small fraction of essentially invulnerable cells in a biofilm \"persisters\" that exhibit multidrug tolerance (MDT) [114]. In _Escherichia coli_, the toxin-antitoxin (TA) modules RelE-RelB and HipB-HipA (high-persistence) seam to play a role in the persister phenotype. The overproduction of RelE or HipA causes an increase in the persister population. HipA inhibits translation by the phosphorylation of EF-Tu [115], stimulates the RelA-dependent synthesis of (p)ppGpp [116], and phosphorylates glutamyl-tRNA synthetase (GltX), which becomes inactivated by phosphorylation by HipA [117]. RelE cleaves mRNA at the ribosomal A site with high codon specificity [118]. The overexpression of RelE or HipA leads to a slowdown translation and thus the growth of _E. coli_, which presumably protects the cells from lethal factors such as antibiotics. It is known from \(\beta\)-lactam antibiotics that they act mainly on dividing cells and are less effective on non-growing cells. In staphylococci, the generation of persister cells is less clear than in _E. coli_. There are four different families of TA systems described, but their physiological roles are elusive [119]. The chromosomal _mazEF_ system encodes the RNase toxin MazF and the antitoxin MazE [120]. MazF specifically targets UACAU sequences of _spa_ (staphylococcal protein A) and _rsbW_ (anti-sigmaB factor) in _S. aureus_ mRNA in vivo, whereas translational reporter fusions indicated that the protein levels of the encoded products were unaffected. Despite a comparable growth rate to the wild-type, an _S. aureus__maEF_ deletion mutant was more susceptible to \(\beta\)-lactam antibiotics, suggesting that the genes involved in antibiotic stress response or cell wall metabolism are controlled by this TA system [120]. Long before _E. coli_, a connection between reduced growth and increased antibiotic tolerance was described in staphylococci in the form of \"small colony variants\" (SCVs) [121]. From patients with persistent and relapsing infections, _S. aureus_ SCVs were isolated which were auxotrophs for menadione, hemin, and/or a CO\({}_{2}\) supplementation. All these SCVs were resistant to aminoglycos. The phenotype of such respiratory deficient mutants was further analyzed in a stable _hemB_ mutant of _S. aureus_[122]. Such a _hemB_ mutant showed the typical SCV phenotype, such as slow growth and a resistance to aminoglycos; it also showed decreased pigmentation, low coagulase activity, reduced hemolytic activity, and a high persistence in endothelial cells. Respiratory mutants, both those that are naturally occurring or genetically constructed, demonstrate the importance of the metabolism in virulence and drug tolerance [123]. In _S. aureus_, there are many global regulators that impact virulence factor expression in SCVs [124].
## 2 Principle of FTIR Technology
### Transmission Mode [13]  
Transmission mode was the earliest used technology since the introduction of FTIR spectroscopy to the detection of asphalt materials. Due to the demand of the IR beam to penetrate the sample, a solid or powdery test sample should be made into a thin film or plate, which is suitable for mineral powder and aggregates, but not for asphalt mixture.
## 6 Discussion
### Limitations in the NPP Estimation of GLO-PEM and Verification  
In summary, this study revealed the phenological regularity by exploring the NPP trend every eight days in the early, middle and ending growth in Northeast China, from 2001 to 2019, based on the GLO-PEM. It could provide new useful knowledge for promoting symbiotic socioeconomic-ecological interactions and regional sustainable development. However, the used model showed few limitations that needed to be further addressed. Firstly, a certain error in the accuracy verification may occur, due to the limited amount of measured data in the northeast forest. However, the simulated results have been verified by using the other remoting-sensing data (MOD17 NPP) in 2003 and 2008, proving that the accuracy of our model is very high (please see Section 4.2 for specific validation). Furthermore, the scale difference between the remote-sensing data and the ground-measured data may also lead to some errors. There were also some uncertainties when running GLO-PEM, mainly considering the temperature, precipitation, land surface water index, different forest types and so on. In the future, the NPP precision could be well enhanced if more useful data were taken into the improved model, such as human distribution factor, CO\({}_{2}\) concentration and other factors.
# Road Crash Analysis and Modeling: A Systematic Review of Methods, Data, and Emerging Technologies
## 3 Data Sources and Quality
### Strategies for Addressing Data Quality Issues
#### 3.5.2 Statistical Methods for Addressing Existing Data Issues  
When working with historical data or in contexts where improved data collection is not yet feasible, statistical methods can help identify and correct for data quality issues during analysis.  
Several methodological advances help address inherent data challenges. [PERSON] et al. [27] demonstrated that binary logit models can effectively handle heterogeneous effects of road design features and traffic conditions, while their grouped random-parameter logit models specifically address unobserved heterogeneity among crash units. This work builds on [PERSON] et al.'s [28] comprehensive framework for dealing with unobserved heterogeneity in crash analysis.  
For temporal stability issues, [PERSON] et al. [29] developed a \"mixed spline indicator pooled model\" that captures parameter changes over time while incorporating unobserved heterogeneity across severity levels and time periods. Their approach achieved 55-78% prediction accuracy for 2021 using Florida crash data from 2011-2019.  
Furthermore, targeted statistical methods can address specific types of data bias. [PERSON] and [PERSON] [30] developed a nested logit model to correct occupancy overestimation bias, while [PERSON] et al. [31] advanced methods for handling endogeneity in transportation safety studies. These approaches demonstrate how statistical techniques can compensate for known data quality issues when analyzing existing datasets.  
While both collection-stage improvements and analytical methods are valuable, [PERSON] and [PERSON] [22] note that the full impact of data quality problems on road safety analyses remains incompletely understood. This suggests that the continued development of both approaches--improving data collection and advancing statistical methods--will be necessary for comprehensive improvement in crash data quality.
## 3 The Role of Heterogeneous Private Edge Cloud System  
The role of heterogeneous private edge cloud [1] in a cloud and edge computing ecosystem is depicted in Figure 1. Several stationary and mobile devices such as personal computers, mobile robots, smartphones, and sensors available within a local area are  
\begin{table}
\begin{tabular}{l l l} \hline
**Use Case** & **Latency** & **Data Rate** \\ \hline Factory Automation & 0.25–10 ms & 1 Mbps \\ \hline Process Automation & 50–100 ms & 0.1–100 Mbps \\ \hline Intelligent Transport Systems & 10–100 ms & 10–700 Mbps \\ \hline Robotics and Telepresence & 1 ms & 100 Mbps \\ \hline Virtual and Augmented Reality & 1 ms & 1 Gbps \\ \hline Health Care & 1–10 ms & 100 Mbps \\ \hline Serious Gaming & 1 ms & 1 Gbps \\ \hline Smart Grid & 1–20 ms & 10–1500 Kbps \\ \hline Education and Culture & 5–10 ms & 1 Gbps \\ \hline \end{tabular}
\end{table}
Table 1: Latency and data rate requirements of smart city and 5G use cases [5, 6, 7, 8].  
combined to create a small-scale cloud data center. The heterogeneous private edge cloud may provide several services such as computing, data storage, data processing, and data caching. The private edge cloud can be connected to classical edge computing systems such as a mobile edge computing system or fog computing system, or it may be connected to a central virtualized cloud data center on the Internet via long-term evolution (LTE) or 5G networks.
## 2 Results
### Specific Type-A Additive Genetic Correlations (\(\rho_{\text{type-A}}\)) Between Traits  
Type-A additive genetic correlations were calculated using bivariate LMMs for each pair of traits in 2021 and 2022. Figure 2 shows a network of these correlations, including only those with absolute values greater than 0.2. Full correlation matrices and approximated standard errors are provided in Supplementary Tables S2 and S3.  
Overall, tannin molecules showed high positive genetic correlations in both years, ranging from 0.65 to 0.98. Tannins were negatively correlated with protein, with stronger correlations observed in 2022 (\(-0.46<\rho_{\text{type-A}}<-0.56\)).  
Oil content was strongly positively correlated with C18:1 (\(\rho_{\text{type-A}}>0.7\)) and negatively with C18:3 (\(\rho_{\text{type-A}}<-0.66\)). As expected, C18:1 and C18:3 were strongly negatively correlated (\(\rho_{\text{type-A}}<-0.8\)). The trade-off between oil and protein was minimal (\(\rho_{\text{type-A}}=-0.2\)).  
In 2021, positive correlations between C18:1 and catechin (\(\rho_{\text{type-A}}=0.54\)) and procyanidin B1 (\(\rho_{\text{type-A}}=0.45\)) were less favourable for breeding, as high C18:1 and low tannins are preferred to reduce off-flavour. However, correlations were weaker in 2022.  
Hexanal showed a moderate correlation (\(\rho_{\text{type-A}}=0.38\)) with its precursor C18:2, while the other lipid oxidation products showed weak or no correlations with oil and fatty acids. 1-linoleoyl glycerol and 2-hydroxyoleic acid (OHOA) were strongly correlated (\(\rho_{\text{type-A}}>0.7\)) and moderately correlated with convicine (\(\rho_{\text{type-A}}=0.45\)).  
In 2022, quercetin emerged as a key molecule, showing moderate to high genetic correlation with C18:2, protein, tannins, and p-cumaric acid.  
Figure 2: Correlation network based on type-A additive genetic correlations (\(\rho_{\text{type-A}}\)) in 2021 (**left**) and 2022 (**right**). Only absolute \(\rho_{\text{type-A}}>0.2\) are displayed. Each node (black circle) represents a different trait, and each edge (line) represents the absolute genetic correlation among traits. As the legend indicates, thick red lines represent strong correlations, while thin blue lines represent weaker correlations. The labels’ background colour groups molecules according to their chemical classes.