esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Test Methods
#### 2.3.3. Improvement of the Loss Percentage Method  
[PERSON] et al. proposed a method to quantify the rate of _RAP_ particle agglomeration at different sizes (ranging from 2.36 to about 19.0 mm) using sieve fraction tests [35]. We adapted it for quantitative evaluation in this article.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Group** & **Temperatures (\({}^{\circ}\)C)** & **Rotation Times (min)** & **Rotation Speeds (RPM)** \\ \hline
1 & 60 & 30 & 280 \\
2 & 60 & 60 & 1800 \\
3 & 60 & 90 & 700 \\
4 & 80 & 30 & 1800 \\
5 & 80 & 60 & 700 \\
6 & 80 & 90 & 280 \\
7 & 90 & 30 & 700 \\
8 & 90 & 60 & 280 \\
9 & 90 & 90 & 1800 \\ \hline \end{tabular}
\end{table}
Table 2: Orthogonal test with three factors and three levels.  
\[\mathrm{PL}_{i1}=(1-\mathrm{p}_{i})\times 100\% \tag{3}\]  
\[\mathrm{PL}_{i2}=(1-\mathrm{C}_{i})\times 100\% \tag{4}\]  
where \(\mathrm{PL}_{i1}\) is the loss rate of the ith particle size following extraction (%), and \(\mathrm{p}_{i}\) is the fractionated sieve balance of the ith sieve after extraction. \(\mathrm{PL}_{i2}\) is the loss rate of the ith particle size following emulsification-separation (%), and \(\mathrm{C}_{i}\) is the fractionated sieve balance of the ith sieve after emulsification screening, with the ith sieve size not less than 2.36 mm.  
The improved method considers the mass percentage of each particle size gradation. The total degree of agglomeration is calculated by summing the difference in loss rates for each block before and after extraction and emulsification-separation.  
\[\mathrm{PL}=\sum\mathrm{PL}_{i1}-\mathrm{PL}_{i2} \tag{5}\]  
where PL is the agglomeration degree of _RAP_ particles (%).
## 3 Numerical Simulations
### Boundary and Flow Conditions  
As described above, the flow transits from pressurized to free surface in the upper flume. The starting cross-section of the pressurized flow forms the upstream boundary of the computational domain. Downstream, the domain ends up with a cross-section (exit) 0.4 m from the last step (i.e., step no. 56). The drop height from the inlet to the outlet is \(H_{T}\approx\) 3.0 m. The domain's upper boundary is parallel to the chute bottom and well above the water surface. The whole flume width is included in the domain.  
The 3D domain is meshed with structural grids. The upstream inlet is set as water velocity inlet, at which the turbulence conditions are expressed in terms of turbulence intensity level and turbulence viscosity ratio. Their embedded values in the program are 3% and 5, respectively, which are typical for flume inlet conditions [17]. The downstream and upper boundaries are defined as pressure outlet. The flume bottom and sidewalls are treated as solid wall.  
Five flow rates are examined in the study (i.e., \(Q\) = 124.77 to 239.57 L/s). The corresponding unit discharge \(q\) = \(Q\)/\(B\) = 0.31-0.60 m\({}^{2}\)/s. The inlet water velocity \(V_{\text{in}}\) = 2.6-5.0 m/s, Froude number F = 4.41-8.47, and Reynolds number R = (1.19-2.28) \(\times\) 10\({}^{5}\).
# Broadband Microstrip Antenna for 5G Wireless Systems Operating at 28 GHz
## 5 Broadband Microstrip Antenna Designed for Use in 5G Systems
The dimensions of the reference plane are determined by the following relationship [26; 27]:  
\[L_{s}=L+6h=2.58+6\times 1.57=12\text{ mm}. \tag{7}\]  
\[W_{s}=W+6h=4.17+6\times 1.57=13.5\[f_{gap}=\frac{4.65\times 10^{-18}\cdot c\cdot f_{r}}{\sqrt{2\varepsilon_{reff}}}= \frac{4.65\times 10^{-18}\times 3\times 10^{5}\times 28\times 10^{9}}{\sqrt{2 \times 1.81}}=0.21\text{ mm}. \tag{9}\]  
The last step of the numerical antenna design process is to determine the size of the feed line. By calculating the dimensions of a microstrip feed line with a characteristic impedance \(Z_{\text{C}}\) = 50 \(\Omega\), we start the determination of auxiliary variables \(a\) and \(b\) as:  
\[ \begin{split} a=\frac{Z_{\text{C}}}{60}\sqrt{\frac{\varepsilon_{r }+1}{2}}+\frac{\varepsilon_{r}-1}{\varepsilon_{r}+1}\Big{(}0.23+\frac{0.11} {\varepsilon_{r}}\Big{)}=1.158\\ b=\frac{60\pi^{2}}{Z_{\text{C}}\sqrt{\varepsilon_{r}}}=7.97\end{split}  \tag{10}\]  
Since parameter a is less than 1.52, the width and length of the feed line is determined from the following relationships [21; 23]:  
\[ \begin{split} W_{f}=\frac{2}{\pi}\Big{\{}b-1&-ln(2 b-1)+\frac{\varepsilon_{r}-1}{2\varepsilon_{r}}\Big{[}ln(b-1)+0.39-\frac{0.61} {\varepsilon_{r}}\Big{]}\Big{\}}\cdot h\\ &=3.07\text{ mm}.\end{split}  \tag{11}\]  
\[L_{f}=3\cdot h=3\times 1.57=4.71\text{ mm}. \tag{12}\]  
At this stage, the process of developing a numerical model of the designed antenna is complete. The calculations above resulted in the antenna dimensions presented in Table 1. The design of the developed antenna, as calculated, is shown in Figure 2. These data are be used to optimize the antenna in terms of bandwidth and miniaturize its dimensions.
## 2 Methodology
### Feature Engineering  
Feature engineering comprises selecting, processing, and transforming raw data into features that can be used as inputs to a machine learning algorithm [53]. It is a crucial step in the machine learning pipeline, as the quality of features can significantly impact the accuracy and performance of the model.  
Feature selection, scaling, extraction, and encoding are the standard techniques used in feature engineering [53]. Feature selection involves selecting a subset of the most relevant features from the available data. Feature scaling helps to normalize or standardize the values of features to ensure they are on a similar scale. One of the scaling operations is min-max normalization:  
\[x_{i,j}^{*}=\frac{x_{i,j}-x_{j}^{min}}{x_{j}^{max}-x_{j}^{min}},\quad\quad \forall i,j \tag{12}\]  
where \(x_{i,j}^{*}\) represents the normalized value of the \(i\)th data point for the \(j\)th feature. \(x_{i,j}\) represents the raw value of the data before normalization, and \(x_{j}^{max}\) and \(x_{j}^{min}\) are the maximum and minimum values of the \(j\)th feature, respectively.  
Feature extraction involves constructing and transforming raw data into meaningful features to be used as inputs of a machine learning model. Dimensionality reduction and decomposing are some of the feature extraction techniques. An autoencoder-based dimensionality reduction method is employed for feature extraction.  
When the relationships between the features and the target variables are not straightforward, the feature construction technique is valuable for creating new features from the existing data. In order to improve the prediction performance, this work hypothesized the engine's degradation tendency to represent a new feature. The difference between the first sensor measurement when the engine starts to operate and the last measurement is calculated, and the process is illustrated in Algorithm 1.  
In order to show the relationship between the feature \(diff_{t}\) and the hypothesized engine's degradation tendency, the correlation coefficient for the difference and the target value is calculated with the following formula:  
\[\rho_{x,\ y}
# Behavior of Fiber-Reinforced and Lime-Stabilized Clayey Soil in Triaxial Tests
## 3 Results and Discussion
### Failure Pattern  
The failure patterns of samples after the completion of the triaxial tests are compared in Figure 6 for untreated soil, fiber-reinforced soil, lime-stabilized soil, and fiber-reinforced soil stabilized with lime. The comparison shows that the failures of untreated soil and fiber-reinforced soil hold a similar pattern where no noticeable shear band is observed. This failure pattern is known as plastic failure and is consistent with the observed strain-hardening behavior shown in Figure 2a,c. In spite of this similar failure pattern, a slightly smaller lateral deformation of the sample can be noticed for fiber-reinforced soil compared to that of untreated soil. The difference in sample lateral deformation is attributable to the presence of wheat straw fibers in which the induced tensile stress will restrain the lateral swelling of Hefei clayey soil.  
A greatly different failure pattern from the plastic failure pattern discussed above characterizes the failures of lime-stabilized soil and fiber-reinforced soil stabilized with lime, as indicated in Figure 6c,d. Characteristic of this failure pattern is the appearance of a dominant shear failure band oriented at an angle from the horizontal greater than \(\pi/4\) throughout the samples. Besides this, within this failure pattern, brittleness is involved, which can be referred to as an effect of the strain-softening behavior observed in Figure 2b,d. Comparison between the shear failure bands which developed in lime-stabilized soil and in fiber-reinforced soil stabilized with lime indicates that the fibers bridging the shear failure band in the fiber-reinforced soil stabilized with lime (as shown in Figure 6d) can restrain the development of the shear failure band and thus contribute to more ductile stress-strain behavior compared to that of lime-stabilized soil.  
Figure 6: Photographs of sample failure patterns (at a 20% axial strain level) in triaxial compression tested at \(\sigma_{3}\) = 200 kPa: (**a**) untreated soil, (**b**) 0.4% fiber-reinforced soil, (**c**) 4% lime-stabilized soil, and (**d**) 0.4% fiber-reinforced soil stabilized with 4% lime.
## 7 Conclusions and Remarks  
Optimizing and designing HL systems of the buildings is one of the crucial tasks in smart cities. Effective use of HL enables buildings to be more energy efficient, reduce economic losses, as well as reduce adverse environmental impacts. This study proposed a new technique (PSO-XGBoost) to predict HL of buildings system with high reliability (RMSE = 1.124, R\({}^{2}\) = 0.990, MAE = 0.615, VAF = 98.934, MAPE = 0.024). Based on the obtained results of this study, some conclusions and remarks are drawn as:  
- The AI techniques in this study, included PSO-XGBoost, XGBoost, SVM, RF, GP, and CART, were bright candidates for estimating the HL of building systems in practice. They could predict the HL of building systems with high reliability, especially the proposed PSO-XGBoost model.  
Figure 17: The importance of the input variables used in this study.  
- The proposed PSO-XGBoost model was a robust technique, which could accurately predict the HL of building systems with a promising result (RMSE = 1.124, R\({}^{2}\) = 0.990, MAE = 0.615, VAF = 98.934, MAPE = 0.024). It should be used as an alternative tool for experimental measurements. Furthermore, building design optimization methods can also be applied based on the proposed PSO-XGBoost model to minimize heat loss for buildings.  
- Although the SVM, RF, GP, and CART models were bringing acceptable performance in this study. However, they need further research to improve the accuracy of estimating the HL of building systems, uniquely combining them with optimization algorithms.  
- OH, RA, WA, and SA are the input variables that have the most influence on the accuracy of the HL of building systems forecasting model. It should be carefully collected and used as essential variables in the development of HL forecasting models.  
Although the results of this study was perfect for evaluating and predicting the HL of building systems; however, they need to be further researched in the future works, such as improving the accuracy of the other models (i.e., XGBoost, SVM, RF, GP, and CART), or try to build several novel hybrid artificial intelligence systems based on these models and optimization algorithms. The optimization of building design is also one of the challenges of engineers in the future aim of energy-efficiency. It can be conducted based on the models of this study.  
Data collection and experimental works: L.T.L., H.N., J.Z., H.M.; Writing, discussion, analysis: L.T.L., H.N., J.D., H.M.  
This research received no external funding.  
The authors would like to thank Thanh Hoa University of Culture, Sports and Tourism, Thanh Hoa City, Vietnam, for supporting this study.  
The authors declare no conflict of interest.
## 4 Experiments
### Practical Validation
#### 4.3.2 Experimental Results  
Our model demonstrated exceptional performance, achieving over 90% precision, recall, and F1-score across all four types of test data, as presented in Table 8. The false alarm and omission cases primarily arose from the following two factors:  
* Ambiguous content representation: The data content pertaining to real-world business scenarios lacked clarity, which leads to certain omissions and false alarms in our analysis;
* Data quality challenges: In real business scenarios, we encounter issues such as typos, missing characters, and other irregularities, which contributed to certain omissions in our data processing.  
UP-SDCG demonstrated greater effectiveness in detecting customer information and service data, suggesting that it currently outperforms other models for identifying personal sensitive information. However, when it comes to financial data related to business operations and management, its performance slightly lagged due to the inherent uncertainty in the data structure. Nevertheless, UP-SDCG still achieved precision, recall, and F1-scores surpassing 90%.
# Automatic Detection of Whistler Waves in the Top-Side Ionosphere: The WhISPER Technique
## 4 Discussion and Conclusions
Indeed, the WhISPER technique is proposed as a foundational component of a new system designed to advance our understanding of whistler physics and ionospheric dynamics. It enables rapid and efficient detection of whistlers, facilitating the collection of large datasets for in-depth studies. This technique represents a significant advancement in the field of whistler detection, offering key advantages over previous methods, including high detection accuracy, computational efficiency, interpretability, and adaptability. Moreover, with appropriate modifications, the technique can be adapted for analyzing electric and magnetic field data from other Low Earth Orbit (LEO) satellites and various sources in the near-Earth space, extending its utility beyond ionospheric studies. Conceptualization, D.R. and M.P. ; methodology, D.R. ; software, D.R. ; validation, D.R., G.D. and E.P. ; formal analysis, D.R. ; investigation, D.R. and M.P. ; resources, D.R., G.D., C.N. and W.J.B. ; data curation, D.R. ; writing--original draft preparation, D.R. ; writing--review and editing, M.P., R.B., E.P. and G.D.; visualization, D.R. ; supervision, M.P. and R.B. ; project administration, M.P. and R.B. ; funding acquisition, M.P. and R.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministero dell'Universita e della Ricerca with the project PRIN2022 n\({}^{\circ}\) 2022 ZBBBRY, CUP E53D23004560006. In addition, it is partially supported by MAECI program SW-CSES CUP E63C24000230006, year 2024. Not applicable  
**Data Availability Statement:** CSES satellite data are freely available at the LEOS repository (www.leos.ac.cn, accessed on 3 September 2024); SYM-H data are freely available at the NASA Cdaweb repository ([[https://cdaweb.gsfc.nasa.gov/index.html](https://cdaweb.gsfc.nasa.gov/index.html)]([https://cdaweb.gsfc.nasa.gov/index.html](https://cdaweb.gsfc.nasa.gov/index.html)) accessed on 3 September 2024). Results can be reproduced using standard free analysis packages. Methods are fully described. The authors kindly acknowledge [PERSON] and [PERSON] at the National Space Science Data Center of the Goddard Space Flight Center for the use permission of OMNI data and the NASA CDAWeb team for making these data available. The authors thank the Italian Space Agency for the financial support under the contract ASI \"LIMADOU scienza+\" n\({}^{\circ}\) 2021-18-H1. This study was carried out within the Space It Up project funded by the Italian Space Agency, ASI, and the Ministry of University and Research, MUR, under contract n. 2024-5-E.0--CUP n. I53D24000060005.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 4 Trends and Prospects of Carbon Emission Research on Biomass Combustion
### High Precision of Data and Multi-Source Fusion  
(1) The acquisition of high-precision observational data: The advent of remote sensing technology has allowed for the utilization of high-resolution satellite data, exemplified by MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite), a standard practice for the monitoring of carbon emissions from biomass burning [39]. However, the limitations of a single data source have prompted researchers to gradually shift their attention to multi-source data fusion, which can enhance spatial and temporal resolution by integrating data from disparate sensors, including the GOSAT, OCO-2, and TROPOMI. For example, the high-resolution column-averaged carbon dioxide concentration (XCO\({}_{2}\)) observations from the OCO-2 satellite of the National Aeronautics and Space Administration (NASA) are a valuable resource for estimating the spatial and temporal distribution of fire emissions [40]. (2) Integration and validation of multi-source data: By integrating ground-based observations (e.g., TCCON), airborne sensor data, and satellite remote sensing data, researchers can construct more comprehensive and accurate biomass burning emission inventories. This multi-source data integration approach has been widely employed in global carbon budgets, facilitating more reliable estimates through comparison and validation. For instance, the integration of TCCON ground-based measurements with satellite data (e.g., GOSAT) has been shown to markedly reduce satellite observation errors [39].
# A Unified Nonlinear Elastic Model for Rock Material
## 2 Common Nonlinear Elastic Models  
### Nonlinear Elastic Models of joints  
[PERSON] [3] proposed a hyperbolic function in order to describe the deformation of joints based on their initial normal stress and maximum allowable closure.  
\[\sigma_{n}=[d_{n}/(d_{\max}-d_{n})]\sigma_{0}+\sigma_{0} \tag{1}\]  
Here, \(d_{max}\) is the maximum allowable closure, \(\sigma_{0}\) is the initial normal stress, \(\sigma_{n}\) is the normal stress and \(d_{n}\) is the normal closure.  
[PERSON] and [PERSON] [4; 5] conducted a large number of experiments on joints with different weathering degrees and types of rocks. By introducing the initial stiffness \(K_{ni}\) as an important parameter for joints, the classical BB model was established based on the [PERSON]'s hyperbolic model. The formula of the BB model is as follows:  
\[d_{n}=\sigma_{n}/[K_{ni}+(\sigma_{n}/d_{\max})] \tag{2}\]  
The above equation can be rewritten as  
\[\sigma_{n}=\frac{K_{ni}d_{\max}}{d_{\max}/d_{n}-1} \tag{3}\]  
[PERSON] and [PERSON] [6] proposed a simple exponential model with reference to the BB model. The formula of this simple exponential model is as follows:\[d_{n}=d_{\max}\bigg{[}1-\exp\bigg{(}-\frac{\sigma_{n}}{K_{ni}d_{\max}}\bigg{)} \bigg{]} \tag{4}\]  
The above equation can be rewritten as  
\[\sigma_{n}=K_{ni}d_{\max}\ln\bigg{(}\frac{1}{1-d_{n}/d_{\max}}\bigg{)} \tag{5}\]  
[PERSON] and [PERSON] [6] found that under medium stress, the normal closure calculated by the hyperbolic model was slower than that calculated using the experimental results, whereas the simple exponential model was faster. By introducing the half value \(\sigma_{1/2}\) as a new parameter, which is the stress when the joint closure reached half of the maximum allowable closure, the modified exponential model was proposed. The modified exponential model excludes initial joint stiffness \(K_{ni}\) in its formula, which is an important parameter [10], and its physical meaning is not clear.  
The formula of the modified exponential model is as follows:  
\[d_{n}=d_{\max}\bigg{\{}1-\exp\bigg{[}-\bigg{(}\frac{\sigma_{n}}{\sigma_{1/2}} \bigg{)}^{n}\ln 2\bigg{]}\bigg{\}} \tag{6}\]
# Game theory-based Routing for Wireless Sensor Networks: A Comparative Survey
## 4 Comparison of the Routing Protocols  
In this section, we qualitatively compare the routing protocols of the WSNs that were discussed earlier, in Section 3. The prime target for most of the protocols is to provide an energy-efficient routing path, because energy is the most important resource in WSNs. The major features, advantages, and limitations of different game theory-based routing protocols were compared. We also extensively compared the protocols based on their different properties and characteristics (topology, data transmission type, scalability, fault tolerance, etc.). Table 7 summarizes the qualitative comparison of the routing protocols based on a cooperative game. Routing protocols developed using a cooperative game theory can balance energy usage and have a higher packet delivery ratio. These types of protocols can be chosen for overall energy efficiency in WSN applications. CH-C-TEEM [31] is suitable for a WSN that is operating in a fading channel and it supports MIMO communication. It is highly energy efficient too. The RCFR [35] mechanism can co-exist with AODV [55], which is a standard protocol. RCFR can also be integrated with other existing standard protocols for better energy efficiency. Network engineers can choose RCFR if they intend to make WSN applications deployed with the standard protocol with energy efficiency. The assignment game approach presented in [34] can provide energy efficient routing with numerous devices being involved. It can also support heterogeneous network architecture. PRGT [44] has been developed to support opportunistic networks, and ensures cooperation among nodes for improving routing efficiency in terms of packet delivery ratio and latency, but the protocol requires a centralized scheme which makes it not deployable for emergency situations like post disaster services.