esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Integrating 5G and Tactile Internet to Industrial Case Studies
### Integrated and Adaptive AR Maintenance and Shop-Floor Rescheduling Framework  
Following the generalized architecture of a wireless TI system that has been presented in previous sections, the authors have used, as a reference framework, the published research work in [66] in order to integrate the necessary layers and ICT infrastructure towards 5G enabled tactile internet. The added layers and ICT components have been highlighted with the color green, as presented in the conceptual framework of Figure 16. Based on this architecture the following points can be stressed:  
Figure 14: A generalized architecture of a wireless tactile internet system (adapted from [60]).  
Figure 15: A generalized framework for TI in the beyond 5G era (adapted from [65]).  
* There are different network choices at each layer of the smart factory
* The key new concept of network slicing in 5G will enable tenants to gain different levels of connectivity from their service provider to accommodate various use cases
* To achieve this network slicing, 5G will be an all-cloud architecture and this will ultimately require the use of software defined networking (SDN) and network function virtualization (NFV)
# Recent Progress on Vegetation Remote Sensing Using Spaceborne GNSS-Reflectometry
## 3 Existing Problems
### Polarization Characteristics
GNSS-R's unique combination of circular polarization and linear polarization will provide unique convenience for extracting vegetation parameter information using polarization decomposition technology. How to obtain the most polarized combination is an urgent problem to be solved in scattering mechanism research and data analysis in the future.
## 3 Results and Discussions
### Optimum Mixing Ratio
#### 3.2.3 Types of Alkaline Reagents  
GGBFS is potentially hydraulic, i.e., it is not water-soluble, and introducing alkaline reagents is needed to stimulate its surface and promote the hydration reaction [25]. Alkaline reagents can eliminate the film on the GGBFS surface and dissolve its chemical constituents.  
Figure 4: The effect of GGBFS contents on the compressive strength of SRC. Refer to the detailed experimental conditions in Table 1.  
Figure 3: The effect of W/B ratios on the compressive strength of the SRC.  
Accordingly, dissolved ions, such as Ca, generate hydration products, increasing SRC's compressive strength [25]. NaOH, KOH, and Ca(OH)\({}_{2}\) are the most frequently used alkaline reagents; however, few studies have shown their performance. Therefore, the effects of different alkaline reagents on the compressive strength of SRC were investigated (Figure 5). After 28 days of curing, the compressive strength increased in the order Ca(OH)\({}_{2}\) > KOH > NaOH. In particular, the compressive strengths of KOH and Ca(OH)\({}_{2}\) were measured to be 4 MPa immediately after only 7 days of curing and met the disposal criteria. According to [PERSON] et al. (2013), NaOH and KOH were likely to effectively catalyze the stimulation of the surface of GGBFS, but Ca(OH)\({}_{2}\) prevented hydration products, such as CSH, from forming due to the consumption of Si by Ca [42]. On the contrary, [PERSON] et al. (2022) reported that Ca(OH)\({}_{2}\) could enhance the compressive strength of RC because the efficient interaction of Ca with SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) improved the formation of hydration products, such as CSH and calcium aluminate hydrates (CAH) [43]. These results confirmed that the interaction of Ca with SiO\({}_{2}\) formed CSH and finally improved the compressive strength.
## 3 Model Construction
### Objective Functions
#### 3.1.3 Objective Determination  
According to the correlation analysis results of the above operation objectives, the objective functions of multi-objective optimal operation model for Huangjinxia Reservoir can be constructed as follows:  
4. Water diversion objective: maximum water diversion (Equation (4))  
\[MaxW=\sum_{t=1}^{T}Q_{g}(t)\Delta t,t=1,\ 2,\ldots,T \tag{4}\]  
where \(W\) is the total water diversion; \(t\) is the index of operational periods; \(\Delta t\) is the duration of each period; \(Q_{g}(t)\) is the water diversion flow during each period \(\Delta t\); \(T\) is the total number of operational periods.  
5. Power generation objective: maximum power generation (Equation (5))  
\[MaxE=\sum_{t=1}^{T}KQ_{f}(t)H(t)\Delta t,t=1,\ 2,\ldots,T \tag{5}\]  
where \(E\) is the total power generation; \(K\) is the power output coefficient; \(Q_{f}(t)\) is the flow passing through the hydraulic turbine during each period; \(H(t)\) is the power generation head during each period.
## 4 Results and Discussions
### Impacts of ESS' SoH on Stability of the System  
The SoH of the second life batteries would be decreased over time and reduce the total capacity of the ESS system. This could directly affect the off-grid system stability in terms of energy exchange between ESS and the other components. Additionally, the difference between the initial charge of the SLBs and their state of charge at the end of the day can be used as the indicator of energy storage system stability. If the SoC at the end of the day would be much lower than SoC at the beginning of the day, it suggests that the discharging rate of the SLBs is higher than their charging rate. Therefore, the ESS charging and discharging stability depends on two parameters: storage size and generation rate. The storage size is increased by the increment of the number of SLB packs; however, it will be also affected by cycle ageing of the batteries leading to decrement in size withtthe passing of time (also charging and discharging cycles). In this section, the impacts of SLBs sizing and cycle ageing on ESS stability are investigated. The difference between the initial and the final SoC is defined as the primary indicator of system stability in this study, which could be expressed as:  
\[SoC_{D}=SoC_{i}-SoC_{f} \tag{9}\]  
where \(SoC_{D}\), \(SoC_{i}\), and \(SoC_{f}\) are second life battery SoC difference, initial SoC and final SoC, respectively.  
The effects of second life batteries' ageing on the system stability is illustrated in Figures 16 and 17 for the scenarios with extra EVCS loads. When EVCS load is applied, the stability of the system could be maintained by increasing the number of battery packs, due to the reduction of SoC difference. This translates to 4 battery packs in this study to hold the system stability at an acceptable level before reaching 2000 discharge cycles and approximately 60% SoH, since the SoC difference remains almost constant until reaching these points, as shown in Figures 16 and 17.  
The impacts of battery sizing on the energy flow in a stand-alone PV system equipped with second life ESS is investigated while the system is designed based on the worst-case scenario. The results of this study revealed the relation between SLB SoC and SoH which can be used for programming battery management systems. Since most of the PVs in residential buildings are connected to the grid, the effects of employment of different SLB ESS sizes on the energy performance of such a system is not considered which can be counted as the disadvantage of this study.  
The experimental data for Nissan Leaf, collected from the literature, are obtained by degradation analysis of the SLBs using accelerated ageing profile (charging and discharging c-rates of 1C) and consequently the ESS model is not validated against the battery empirical tests at various C-rates. Its impacts, therefore, on the degradation of the batteries is neglected in this study.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Coefficient**} & \multicolumn{8}{c}{**Parameters for MRR**} \\ \cline{2-10}  & **Current** & **Current2** & **T\({}_{\text{off}}\)** & **T\({}_{\text{on}}\)** & **A \(*\text{T}_{\text{on}}\)** & **A \(*\text{T}_{\text{off}}\)** & **T\({}_{\text{off}}\)2** & **T\({}_{\text{on}}\)2** & **Constant** \\ \hline HRB2 & \(-6.35\times 10^{-5}\) & \(7.546\times 10^{-6}\) & 0 & 0 & \(-5.888\times 10^{-6}\) & 0 & \(-2.984\times 10^{-7}\) & 0 & 0.0009 \\ \hline HRB & 0.0075 & \(-0.0009\) & \(-0.0001\) & \(3.976\times 10^{-5}\) & \(7.0927\times 10^{-5}\) & \(2.17\times 10^{-6}\) & \(3.361\times 10^{-5}\) & \(-1.489\times 10^{-7}\) & \(-0.1028\) \\ \hline Constants & \(-0.1956\) & 0.0255 & 0.0087 & \(-0.0009\) & \(-0.002\) & \(-0.0002\) & \(-0.0009\) & \(6.057\times 10^{-6}\) & 2.7796 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The coefficients of equations for MRR. The graph of the interactions for surface roughness also shows a very small slope (in the range of \(10^{-3}\)) and can be considered to be constant for the evaluation (Figures 15 and 16). The higher order coefficients also show a very small slope, except for the graph of pulse-off time2 (Figure 22), which shows that the higher-order terms can be ignored if a very precise prediction based on the hardness is not required. The graph of pulse-off time (Figure 20) shows a negative slope, indicating that the increase in hardness improves the surface finish. The trend could be due to white-layer formation and reduced craters while machining higher-hardness composite. The constant shows the maximum slope (Figure 17) which indicates that the variation in surface roughness has a larger contribution from this term if the values of the input parameters are low. For higher values, the significance of this term reduces. The coefficient of pulse-on time increases with hardness (Figure 21) which shows that the higher pulse-on time increases surface roughness whereas higher pulse-off time provides a better surface finish.
# Optimizing Faulting Prediction for Rigid Pavements Using a Hybrid SHAP-TPE-CatBoost Model
## 5 Conclusions  
The TPE-SHAP-CatBoost method was proposed in this study, which incorporates TPE for Bayesian hyperparameter tuning and introduces SHAP for both global and local interpretation of the model's output. The aim is to not only train the model efficiently and accurately, but also enhance the interpretability of the model for application in practical pavement engineering practices.  
1. The TPE-CatBoost model constructed with six variables demonstrated improved predictive results on the faulting test dataset. Compared to the TPE-CatBoost model constructed with 17 variables, there was an increase of 0.007 in R\({}^{2}\), a decrease of 0.31 in MAE, and a decrease of 0.006 in RMSE. This improvement can be attributed to the capability of Boruta to identify relevant variables and eliminate unnecessary variables, thereby generating a more accurate and efficient model.
2. Compared to TPE-RF, TPE-AdaBoost, TPE-GDBT, and TPE-LightGBM, TPE-CatBoost achieved higher R\({}^{2}\) and lower MAE and RMSE. TPE-CatBoost demonstrates greater potential for predicting Faulting.  
Figure 9: SHAP for a single sample’s prediction interpretation graph.  
3. By integrating with SHAP, TPE-SHAP-CatBoost can uncover the contributions of specific features to fault prediction, thereby enhancing the interpretability of the prediction results. According to the SHAP results, AGE, LTE, DWL, and EM are the most influential features affecting the output of IRI.  
The proposed method provides reliable reference for pavement managers to develop specific models and improve the rigid pavement management system. Additionally, by utilizing SHAP values, pavement managers can identify which variables are more significant in predicting faulting, in order to ensure optimal pavement conditions. However, there are some limitations to be noted. Despite considering multiple variables, due to limited relevant data, some hidden variables that impact Faulting may not have been explored. Nevertheless, the method proposed in this paper has been preliminarily validated, and future research should utilize more advanced high-performance testing equipment to expand the dataset and collect more accurate and comprehensive pavement performance data, to fully unleash the potential of the model.  
Conceptualization, C.W. and W.X.; methodology, W.X.; software, W.X.; validation, J.L., M.G. and J.W.; investigation, C.W.; writing--original draft preparation, W.X.; writing--review and editing, W.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors would like to thank the anonymous reviewers, and the editors for their very competent comments and helpful suggestions.  
The authors declare no conflict of interest.
# Model Experimental Study on De-Icing Method of Bridge Pylon Beam Based on Electric Heating
## 2 Model Test Design
### Test Materials
#### 2.4.3 Thermocouples  
The thermocouples (Figure 7e) are K-type Teflon temperature measurement compensation wires. A total of three thermocouples were set in the heating system to record the surface temperature changes, and the temperature resistance range was \(-60\)\({}^{\circ}\)C\(\rightarrow\)\(+250\)\({}^{\circ}\)C. The real-time temperature can be monitored and recorded by connecting with the Keysight data acquisition instrument. After the thermocouples were arranged, the temperature displayed by the data acquisition instrument was compared with that displayed by the temperature and humidity sensor and the touchscreen of the cold chamber, and it was found that the temperatures of the three were very close to each other, with an error of only 0.2 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Diameter** & **Resistance** & **Length** & **Power** & **Thermal Efficiency** & **Tensile Strength** & **Compressive Strength** \\ \hline
6 mm & \(\approx\)8.5 \(\Omega\cdot\)m\({}^{-1}\) & 14 m & 28 W\(\cdot\)m\({}^{-1}\) & 98.2\% & 40.41 MPa & 60.75 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance parameters of the CFHC.  
Figure 7: (a) Overall view of a single CFHC; (b) the sectional diagram of the CFHC; (c) stainless steel sheet; (d) heat-insulating film; (e) thermocouple wires.
## 4 Results
### Case Study: Co-Located Measurements over Mequinensa Reservoir  
The case of Mequinensa Reservoir is noteworthy because it is sampled twice, namely the North and South cuts, by the same ascending S6 track (111), with a separation of roughly 8 km. Therefore, residual differences should not be significantly affected by geophysical corrections, but only by the topography, as shown in Figure 1. The same reservoir is sampled by the descending track 248 in Caspe, but as the measurements are separated by several days with respect to those from track 111, they cannot be considered co-located.  
Figure 13 shows the correlation between the errors obtained in Mequinensa-North and Mequinensa-South for FFSAR-derived WL\({}_{4}\)s. After excluding 3 outlier measurements out of 73, which had errors larger than 30 cm, the Pearson correlation coefficient between the errors was 0.58. This indicates that the error of the measurements might be more influenced by the complexity of the terrain than the effect of the uncertainty brought by geophysical corrections.
# Evaluation of Bridge Maintenance Priorities in Megacities
## 4 Application of the Proposed Model to Eight Megacity Bridges
### Selection of Maintenance Priorities for Megacity Bridges  
The collected bridge information, as shown in Table 6, was applied in the proposed maintenance priority model using open data and calculated through Excel. Figure 4 shows an example of data entry into Excel to help prioritize bridge maintenance. Figure 5 and Table 7 show the results of the maintenance priority selection for the eight bridges to which the proposed model could be applied.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Bridge Name** & \begin{tabular}{c} **Year of** \\ **Completion** \\ \end{tabular} & **Design Load** & \begin{tabular}{c} **Seismic** \\ **Design Status** \\ \end{tabular} & **Safety Grade** & **ADT \({}^{*}\)** & \
\begin{tabular}{c} **Facility** \\ **Classification** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Lanes** \\ \end{tabular} \\ \hline A & 2000 & DB-24 & Y & B & 94,687 & Type 1 & 10 \\ B & 1986 & DB-24 & Y & B & 90,697 & Type 2 & 8 \\ C & 1993 & DB-24 & N & B & 81,575 & Type 1 & 6 \\ D & 1986 & DB-24 & Y & B & 76,673 & Type 2 & 6 \\ E & 1999 & DB-24 & Y & C & 71,574 & Type 1 & 8 \\ F & 2003 & DB-24 & Y & B & 64,522 & Type 1 & 6 \\ G & 1997 & DB-24 & N & B & 57,914 & Type 2 & 7 \\ H & 1990 & DB-24 & N & B & 53,644 & Type 2 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Data on the eight bridges to which the maintenance priority model could be applied.  
Figure 4: Examples of data input into Excel to help prioritize bridge maintenance.  
To identify the maintenance priority of megacity bridges, the BMP (Bridge Maintenance Priority) score for the A bridge, which, at 4.32 points, was calculated to be the highest scoring, meaning the A bridge should be given the highest maintenance priority among the eight comparative bridges. In contrast, the G bridge obtained a BMP score of 1.67, indicating that maintenance could be carried out last among the eight bridges. In addition, the bridge with the highest risk score was 4.40 for the H bridge, the highest importance score was 0.80 for the A, B, and C bridges, and the highest comparison score was 1.80 for the A bridge. This means that even if one of the three scores constituting the BMP score (risk score, criticality score, and comparison score) is located in a high area, the maintenance ranking may be lowered if the scoring area of another area is located in a low area. Figure 6 shows the results by maintenance priority.
# Statistics on Typhoon Intensity and Rice Damage in Vietnam and the Philippines
## 2 Data and Method
Some reports had a set of minimum and maximum values, while others reported a single value of loss. As such, we had to simplify the data for analysis. For cyclones with multiple reports of damage, we noted the minimum and maximum values reported and derived a midpoint (i.e., Minimum + Maximum divided by two). If only a single number was available, that number was used for analysis. To assess how typhoon intensity influences agricultural loss, the landfall wind speed of a typhoon was used as a proxy for typhoon intensity, and linear regression analysis was performed to determine the relationship between landfall wind speed and (i) the size of rice area damaged, (ii) the volume of rice production lost, and (iii) the value of rice crop lost. To complement our findings, we also correlate the three indicators of loss to Accumulated Cyclone Energy (ACE). ACE is a wind-based index to measure the strength and duration of a storm. The ACE index for each typhoon is calculated by summing the squares of the estimated six hourly maximum sustained wind speeds for all periods in which the system is at least tropical storm strength (winds \(\geq\) 39 mph) [24; 25]. We obtained the ACE index for each typhoon from the Digital Typhoon database, and the information, together with our analysis data, was presented in Supplementary Material S2.
###### Abstract  
The purpose of this study was to clarify the structural and ultrastructural alterations of the enamel and dentin collagen network in the deciduous teeth of children affected by osteogenesis imperfecta (OI) using field-emission in-lens scanning electron microscopy (FEI-SEM) and transmission electron microscopy (TEM) analyses. Exfoliated primary teeth were collected from children with a diagnosis of OI and from healthy individuals (\(N=24\)). Tooth slices containing both dentin and enamel were fixed, dehydrated and dried, gold sputtered, and observed using FEI-SEM. Additional dentin fragments were decalcified, dehydrated, embedded in resin, cut, and processed for TEM analysis. Under FEI-SEM, the enamel in OI-affected children showed an irregular prism distribution with the enamel hydroxyapatite crystals unpacked. Ultrastructural correlative analysis of the dentin in patients affected by OI showed an altered collagen pattern with a low density. In some areas, teeth in OI patients showed a reduction in the number of dental tubules, with odontoblastic process missing in most of the tubules. The presence of altered dentine and enamel organization in OI children was firmly established at an ultrastructural level, but additional biochemical studies are necessary in order to clarify quantitatively and qualitatively the collagenic and non-collagen proteins in this disorder.  
deciduous teeth; dentinogenesis imperfeta; osteogenesis imperfecta; enamel; dentin; ultrastructural diagnosis 2020 10, 7835; doi:10.3390/app10217835
# Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions
## 1 Introduction
In an article by [PERSON] and [PERSON], a hybrid Salp Swarm Algorithm (SSA) and Perturb & Observe MPPT method was developed, which has faster MPP tracking, a low oscillation rate, adaptability to changing circumstances, application compatibility, and the need for only one sensor [54]. In a study by [PERSON], a new Musical Chair MPPT algorithm was developed; it provides high performance, quick computing time, minimal steady-state volatility, and accurate MPP monitoring [55]. The Modified Krill Swarm Algorithm and Fuzzy-Logic-Controller-based MPPT design, presented in the work of [PERSON] et al., found a quick and precise reaction to changing irradiance circumstances and dependable power source [56]. Many studies in the literature have been examined, and, in summary, various features have been investigated. The merits and demerits of MPPT methods in the current studies in the literature are given in Table 1. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**MPPT Algorithm** & **Merits** & **Demerits** \\ \hline Lock-On Mechanism MPPT [23] & Low MPP tracking oscillation, high performance, steady performance, adaptive duty cycle ratio measurement. & Computational complexity, need for two SEPIC converters, need to extra equipments. \\ \hline P\&O-LuT Hybrid MPPT [24] & Fast MPP tracking, fast response to changes in weather conditions, high MPPT accuracy, high efficiency. & Need for extra memory. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Merits and demerits of MPPT methods.