esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Accuracy** & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline RF & — & **1621/(0.043)** & **1.500**/(0.000) & **7179**/(0.000) & **1397**/(0.732) & **809**/(0.000) & **1093**/**(0.000)** \\ \hline ANN & **1621**/**(0.043)** & — & **6**/(0.000) & **1796**/(0.616) & **1474**/**(0.044)** & **1126**/(0.001) & **1246**/**(0.007)** \\ \hline LR & **1.500**/**(0.000)** & **6**/**(0.000)** & — & **2**/(0.000) & **0**/(0.000) & **0**/(0.000) & **2**/**(0.000)** \\ \hline SVM & **717**/**(0.000)** & **1796**/(0.616) & **2**/(0.000) & — & **1059**/(0.003) & **1065**/(0.001) & **1065**/**(0.005)** \\ \hline LGBM & \(1397\)/(0.732) & **1474**/**(0.044)** & **0**/(0.000) & **1059**/(0.003) & — & **818**/(0.000) & **800**/**(0.000)** \\ \hline CB & **809**/**(0.000)** & **1126**/**(0.001)** & **0**/(0.000) & **1065**/**(0.001)** & **818**/(0.000) & — & \(1267\)/ (0.402) \\ \hline XGB & **1093**/**(0.000)** & **1246**/**(0.007)** & **2**/(0.000) & **1065**/**(0.005)** & **800**/(0.000) & **1267**/(0.402) & — \\ \hline
**Precision** & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline RF & — & **347**/**(0.000)** & **679**/**(0.000)** & **1979**/(0.202) & **1078**/**(0.000)** & **188**/(0.000)** & **267**/**(0.000)** \\ \hline ANN & **347**/**(0.000)** & — & **115**/**(0.000)** & **300**/**(0.000)** & **606**/**(0.000)** & **2365**/**(0.967)** & \(2181\)/ (0.591) \\ \hline LR & **679**/**(0.000)** & **115**/**(0.000)** & — & **591**/**(0.000)** & **458**/**(0.000)** & **171**/**(0.000)** & **151**/**(0.000)** \\ \hline SVM & \(1979\)/(0.202) & **300**/**(0.000)** & **591**/**(0.000)** & — & **1620**/**(0.006)** & **44**/**(0.000)** & **91**/**(0.000)** \\ \hline LGBM & **1078**/**(0.000)** & **606**/**(0.000)** & **458**/**(0.000)** & **1620**/**(0.006)** & — & **500**/**(0.000)** & **488**/**(0.000)** \\ \hline CB & **188**/**(0.000)** & **2365**/**(0.967)** & **171**/**(0.000)** & **44**/**(0.000)** & **500**/**(0.000)** & **—** & \(1765\)/ (0.784) \\ \hline XGB & **267**/**(0.000)** & **2181**/**(0.591)** & **151**/**(0.000)** & **91**/(0.000) & **488**/**(0.000)** & \(1765\)/ (0.784) & — \\ \hline
**Recall** & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline RF & — & **468**/**(0.000)** & **0**/(0.000) & **809**/**(0.000)** & **543**/**(0.000)** & **655**/**(0.000)** & **546**/**(0.000)** \\ \hline ANN & **468**/**(0.000)** & — & **115**/**(0.000)** & **130**/**(0.000)** & **826**/**(0.000)** & **990**/**(0.000)** & **1392**/**(0.018)** \\ \hline LR & **0**/**(0.000)** & **115**/**(0.000)** & — & **0**/(0.000)** & **0**/(0.000)** & **3**/**(0.000)** & **3**,**500**/**(0.000)** \\ \hline SVM & **809**/**(0.000)** & **130**/**(0.000)** & **0**/(0.000)** & — & **507**/**(0.000)** & **4**/**(0.000)** & **65**/**(0.000)** \\ \hline LGBM & **543**/**(0.000)** & **826**/**(0.000)** & **0**/(0.000)** & **507**/**(0.000)** & — & **1281**/**(0.024)** & **1277
## 2 Mathematical Modeling and Analysis of the Proposed Detector Algorithm
### Clutter Models  
In order to show the performance of the proposed detector, three clutter models were considered for the simulation scenarios. For each scenario, a Monte Carlo simulation was carried out.
## 1 Introduction
Despite the fact Under the background of fact that that hydropower plants in southwestern China challenge a new multi-market environment, a new generation scheduling model needs to be developed. To solve the aforesaid problem and achieve a successful implementation for cascade hydropower plants in the multi-market environment, a novel long-term generation scheduling model for cascade hydropower plants is proposed. The main contributions are summarized as follows:  
(1) The correlations of the MCP between different markets is described. The prediction price is treated as a random variable that consists of its prediction value and a random prediction error. The MCP scenarios for the multi-market environment are then generated by using the Copula theory, and are further reduced by the hierarchical clustering method combined with inconsistent values. (2) A novel long-term generation scheduling model is constructed, based on the stochastic programming theory and the reduced MCP scenarios, in which the market-to-market supply energy ratio constraint and the minimum constraint of supply energy in the provincial market are creatively taken into consideration, so as to meet the actual engineering needs. (3) The effectiveness and robustness of the proposed model have been validated by applying it to perform the long-term generation scheduling of the Wu River cascade hydropower plants in China. Compared with the conventional scheduling model, the proposed model can better respond to the MCP signals and gain the optimal trading strategy for different time periods and markets. It can also adapt to different MCP scenarios, and thus provides a theoretical reference for large-scale hydropower consumption in southwestern China under the new electricity market environment. The remainder of this paper is organized as follows: In Section 2, the MCP scenarios for multiple markets are described. Section 3 is devoted to the detailed presentation of the proposed optimization model. In Section 4, the solution method for the proposed model is introduced. In Section 5, the case study and optimization results are carefully discussed. Finally, Section 6 outlines the main conclusions.
## 2 Materials and Methods
### Data Processing Methods  
XCO\({}_{2}\), XCH\({}_{4}\), and XCO were retrieved by PROFFAST software from the EM27/SUN raw spectral data following previous studies [11; 21; 22; 29]. The prior (.map) files (including temperature, pressure, density, gravity, CO\({}_{2}\), CH\({}_{4}\), H\({}_{2}\)O, CO prior values from the height of 0 to 70 km of the atmosphere with an interval of 1 km) and the intraday pressure change used for PROFFAST were acquired from the TCCON server and local measurement of Weather Station, respectively. The modulation efficiencies (ME, 0.9902), phase errors (PE, 0.0049), and calibration factors (1.00014517 for XCO\({}_{2}\) and 0.99982628 for XCH\({}_{4}\)) of EM27/SUN were obtained from the Karlsruhe Institute of Technology (KIT) and applied in the retrieval process. ME and PE values for the EM27/SUN used in this study were higher than the ensemble mean of the EM27/SUN tested in KIT (ME = 0.98417 and 0.98430 and PE = \(-\)0.00061 and \(-\)0.00068) [11; 30]. Calibration factors were close to the ideal value (1.0) and agreed well with other EM27/SUN Spectrometers [30]. Raw CO\({}_{2}\) and CH\({}_{4}\) data from the Picarro G2301 spectrometer and meteorological data from Weather Station were first averaged to hourly data and then calculated to daily average values following the method in [PERSON], et al. [31].  
Bias-corrected level 2 XCO\({}_{2}\) from OCO-2 (OCO\({}_{2}\)_L2_Lite_FP 10r) and OCO-3 satellites (OCO3_L2_Lite_FP 10r) were obtained from Goddard Earth Sciences Data and Information Services Center (disc.gsfc.nasa.gov) (accessed on 8 November 2021). Bias-corrected level 2 XCO\({}_{2}\) (V02.98) and XCH\({}_{4}\) (V02.96) from the GOSAT satellite were obtained from GOSAT Data Archive Service (GDAS, data2.gosat.nies.go.jp) (accessed on 10 September 2021). Level 2 XCO\({}_{2}\), XCH\({}_{4}\), and XCO from GOSAT-2 were obtained from GOSAT-2 Data Archive Service ([[https://prdct.gosat-2.nies.go.jp](https://prdct.gosat-2.nies.go.jp)]([https://prdct.gosat-2.nies.go.jp](https://prdct.gosat-2.nies.go.jp)), accessed on 1 March 2022). Meanwhile, offline level 2 XCH\({}_{4}\) from Sentinel-5P (S5-P) were acquired from the Sentinel-5P Pre-Operations Data Hub (s5 pub.copernicus.eu/dhus) (accessed on 10 September 2021). XCO\({}_{2}\) and XCH\({}_{4}\) from OCO-2, OCO-3, and GOSAT were selected for comparison with ground observations using a box centered at the CRCS site that spanned 5\({}^{\circ}\) latitude and 10\({}^{\circ}\) longitude on the same day, while ground-based EM27/SUN observations were selected within 1 h of the transit time of each satellite [17]. A similar method was also applied to XCH\({}_{4}\) from S5-P except the select box narrowed from 5\({}^{\circ}\)\(\times\) 10\({}^{\circ}\) to 2\({}^{\circ}\)\(\times\) 2\({}^{\circ}\) due to its large swath (2600 km) and high spatial resolution (7 \(\times\) 7 km) [32; 33].
# A Robust Hybrid Iterative Linear Detector for Massive MIMO Uplink Systems
## 3 Methods
### A Stair Matrix  
A stair matrix **S** has one of the formulas listed below.  
* \(\textbf{S}_{(ij-1)}=0,\textbf{S}_{(ij+1)}=0\) where \(i=2,4,...,2\left\lfloor\frac{K}{2}\right\rfloor\)
* \(\textbf{S}_{(ij-1)}=0,\textbf{S}_{(ij+1)}=0\) where \(i=1,3,...,2\left\lfloor\frac{K-1}{2}\right\rfloor+1\)  
In the stair matrix, off-diagonal elements on either the odd or the even row are zeros [15]. For instance, a stair matrix of \(6\times 6\) size can be written as  
\[\textbf{S}=\begin{bmatrix}\times&\times&0&0&0&0\\ 0&\times&0&0&0&0\\ 0&\times&\times&\times&0&0\\ 0&0&0&\times&0&0\\ 0&0&0&\times&\times&\times\\ 0&0&0&0&0&\times\end{bmatrix}\quad\text{or}\quad\textbf{S}=\begin{bmatrix} \times&0&0&0&0&0\\ \times&\times&\times&0&0&0\\ 0&0&\times&0&0&0\\ 0&0&\times&\times&\times&0\\ 0&0&0&0&\times&0\\ 0&0&0&0&\times&\times\end{bmatrix}\]
# Performance of Payement Temperature Prediction Models
## 2 Materials and Methods
### Literature Review
#### 2.1.5 Oman Model  
Research was undertaken in Oman (2017) to develop models to predict maximum and minimum asphalt pavement temperatures. A pavement monitoring station was set up at the Sultan Qaboos University campus to monitor air, pavement temperatures and solar radiation. Data were collected for 445 days. Daily minimum and maximum temperatures were recorded. Regression analysis was used to develop the minimum and maximum pavement temperature models, using air temperature, solar radiation and duration of solar radiation as independent variables. The maximum temperature prediction depth, however, is limited to the top 20 mm of the pavement.  
[PERSON] et al. [17] also noted that solar radiation can be estimated using the relationship described in [PERSON] et al. [20], which estimates solar radiation using the geographical latitude of any given location and day of the year.
# Characteristics of HEMP Radiation Field on Typical Transport Aircraft with Frequency Sweep Method
## 4 Conclusions  
In this work, firstly, the characteristics of the HEMP radiation field are analyzed. Then, various parameters including the electric field strength distributions are calculated with FEKO software. Afterwards, the electric field strength distributions of three layers (top layer, inner layer and bottom layer) are calculated and analyzed. The main conclusions that have been reached are as follows:  
1. Most importantly, the HEMP induced field is affected by the aircraft body. The induced electric field strength \(E(t)\) at different positions varies largely. Nevertheless, the peak value of \(E(t)\) is usually greater than the initial peak value of the electric field strength \(E_{0}\).
2. Further, the induced magnetic field strength of the aircraft is related to the HEMP radiation field strength and the change rate of the electric field strength. It is also quite large at the shape position of the aircraft, which is more likely to generate the coupling effect. The order of the magnitude of the peak magnetic field strength \(H\) is around 70 \(\mathrm{dBA/m}\).
3. In addition, the peak value of \(E(t)\) which is around 155 kV/m (three times of \(E_{0}\)) occurs at the position of \(x=0\) m, \(y=14.5\) m, \(z=6\) m. This position is near the back wing of the aircraft, which has a sharp structure. Due to the reflection, electric field strength near this position \(E(t)\) increases significantly.
4. Furthermore, the electric field strength \(E(t)\) distribution inside the aircraft oscillates with the time \(t\). This is due to the oscillatory effect caused by the field inside the aircraft fuselage, which forms a closed cavity.
5. Lastly, due to the obstruction of the fuselage and wings, the induced electric field strength \(E(t)\) at the bottom layer is smaller than that at the top and inner layers.  
The results indicate that numerical calculations with the sweep frequency method based on the FEM model can reveal the characteristics of the magnetic field strength distribution and the electric field strength distribution on/inside/outside of the aircraft. T can provide guidance and insight into the necessary protection design for the HEMP of the aircraft.  
Figure 13: Tendencies of the electric field strength \(E(t)\) of various positions of bottom layer outside the aircraft.  
**Author Contributions:** Conceptualization, Q.L.; formal analysis, G.H.; methodology, G.H.; investigation, G.H. and Q.L.; resources, Q.L.; writing-original draft preparation, G.H.; writing-review and editing, Q.L.; visualization, G.H.; supervision. Q.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Delayed-Expansion Capsule Sealing Material for Coal Mine Overburden Isolated Grouting
###### Abstract  
Grouting technology is an important method of ground reinforcement and can effectively improve the stability of engineering rock mass. During overburden isolated grouting in coal mines, the influence of unexpected fractures may lead to substantial grout leakage, resulting in ineffective grouting. The existing natural sedimentation sealing method is mainly applicable to small fractures and low grout flow, while the chemical-reagent rapid-sealing method can cause grouting channel blocking, making it less suitable for overburden isolated grouting. This paper proposes a \"capsule\" sealing method, detailing the preparation of the sealing material and evaluation of its properties through testing. The sealing material, prepared using the air suspension method, was coated with paraffin on a superabsorbent polymer (SAP) material, which has delayed expansion characteristics. Although this material does not expand within the grouting fractures of overburden rock, it expands rapidly upon entering the leakage channel, accumulating within the channel to achieve effective sealing. A simulation experimental system was designed to simulate the sealing of the slurry leakage channel, and the sealing characteristics were experimentally investigated. Under consistent particle size conditions, a higher film cover ratio led to a more pronounced delayed expansion effect and extended the time required for the sealing material to achieve its maximum expansion. When the content of sealing material with particle sizes of 20 mesh, 40 mesh, and 60 mesh, and a film ratio of 20% was 1.0%, the fractures below 4 mm were effectively sealed. When the fracture aperture is 4-6 mm, the sealing material with a covering ratio of 20% or 30% should have a minimum content of 1.5%, while the sealing material with a covering ratio of 50% should have a minimum content of 2.0%. The findings of this study outline an effective prevention and control method for the sealing of abnormal slurry leakage in overburden isolated grouting engineering.  
overburden isolated grouting; SAP film cover; \"capsule\" sealing material; flow characteristics; expansion rate +
Footnote †: journal: applied sciences
# Approaches to the Functionalization of Organosilicon Dendrones Based on Limonene
## 3 Results and Discussion
### Epoxidation of Limonene-Based Carbosilane and Carbosilane-Siloxane Dendrons  
In the first stage, we studied the possibility of epoxidizing the cyclohexene double bond of limonene organosilicon derivatives. For a more focused examination, the product of limonene hydrosilylation with 1,1,1,3,5,5,5-heptamethyltrisiloxane was chosen as the model compound (Scheme 1). The siloxane derivative was chosen to enable the selection of epoxidation conditions under which the destruction of the siloxane bond does not occur, since this type of bond is known to be unstable in acidic and basic media and in the presence of oxidizing agents. The epoxidation of limonene is fairly well described in the literature [48; 49; 50]. Various reagents are used to perform this reaction: hydrogen peroxide, oxone, m-chloroperbenzoic acid, etc. In this work, we used _m_-chloroperbenzoic acid due to its high reactivity in the epoxidation reaction.  
The resulting epoxy dendrons were mixtures of _exo_- and _endo_-isomers in a ratio of 1:1, as confirmed by NMR spectroscopy methods. Using NMR APT spectra, two signals for multiple carbon substitution (57.91 ppm and 57.61 ppm) and two signals for non-multiple carbon substitution (61.21 ppm and 59.71 ppm) were shown (Figure 1). The HSQC spectra have cross-peaks characterizing the C-H correlations at the oxirane ring, which makes it possible to attribute these signals in the carbon spectrum (Figure 2).  
The epoxidation of cyclohexene double bonds in the corresponding limonene-based dendrons was performed under similar conditions (Scheme 2). In terms of the formal dendrimer nomenclature, the LimOx-G\({}_{0.5}\)TMS\({}^{2}\) model compound can be considered as a generation-0.5 dendron. The figure shows the resulting carbosilane-siloxane dendrons of various generations: two-chain and three-chain generation-1.5 dendrons LimOx-G\({}_{1.5}\)TMS\({}^{4}\) and LimOx-G\({}_{1.5}\)TMS\({}^{6}\), and a two-chain generation-2.5 dendron LimOx-G\({}_{2.5}\)TMS\({}^{8}\). To develop the synthesis of JDs further, we decided to test the epoxidation reaction under similar conditions on a completely carbosilane dendron. CDs are known to be more chemically stable than siloxane or carbosilane-siloxane ones. However, personal observations of the authors reveal that there are some reactions that efficiently occur on dendrons with a siloxane bond but nearly do not occur on carbosilane systems. All products were isolated in high yields, 89-99%. The structure and purity of the dendrons was proved by NMR spectroscopy and GPC (Supporting Information, SI1 and SI2).  
However, it was shown that epoxidation also occurred quantitatively on a generation-1 carbosilane dendron with peripheral butyl substituents and the corresponding mixture of Lim-G\({}_{1}\)Bu\({}^{4}\)_endo_- and _exo_-epoxides was formed.
## 3 Results
### The Role of the Extract on Wet/Dry Lung Ratios
#### 3.7.2 Effects of the Extract on the Expression Pattern of the Cox-2 Protein in Lung Tissue  
The immunohistochemistry (IHC) staining result demonstrates that Cox-2 protein expression was increased in the lung tissues of LPS-induced group compared to control group tissues (Cox-2 protein not expressed). In the LPS + extract-treated group, the expression pattern of the Cox-2 protein was low compared to the LPS-treated group only (Figure 12). Moreover, the DEX + LPS group showed mild expression of the Cox-2 protein.  
Figure 11: Lung tissue histopathological analysis in different experimental groups: (**a**) control animals exhibit normal lung tissue architecture; (**b**) LPS-induced (5 mg/kg body weight) animals showed severe damage of the alveolar architecture, inflammatory cell infiltration and congestion; (**c**) the tissue alterations were significantly reduced in the extract treatment group; (**d**) in the LPS + DEX-treated group, damage of the lung tissue architecture was prevented. (Arrow indicates congestion and inflammation) (Scale bar = 100 μm).  
Figure 10: Different animal groups showing lung W/D ratios. The values denote mean \(\pm\) SEM, with eight animals in each group. The significant difference (\(p\) < 0.05) is shown by an asterisk (*) between the control group and LPS treatment groups, and by (#) between the LPS and LPS + extract groups.
## 3 Analysis of the IWOLA + FFT Algorithm Characteristics  
### Subsection  
The channel characteristics of the IWOLA filter bank can be designed based on the task requirements. By selecting different values of the decimating factor \(D\) of the IWOLA filter bank, the channel frequency responses of the IWOLA filter bank with different performances can be obtained, as shown in Figure 3. To simplify the description, \(K\) and \(D\) were set as small values. When \(D=16\), the overlap between the adjacent channels was significantly less than \(D=8\), and the pass-band gain in the sub-bands was flatter than that in the case of \(D=8\). Therefore, the performance of the IWOLA filter bank was improved  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Bandwidth = \(f_{\rm s}\)/2**} \\ \hline  & & \multicolumn{6}{c}{**Sub-Bands 0–\(K\)/2-1 (Complex Signal)**} \\ \hline
0 & 1 & \(\ldots\) & \(N_{2}\)/2–1 & \(N_{1}\)–1 & \(N_{1}\)–2 & \(\ldots\) & \(N_{1}\)–\(N_{2}\)/2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bin selection and sorting diagram. It should be noted that \(\rm f_{\rm s}\) is a sampling frequency. A signal within the bandwidth of \(\rm f_{\rm s}\)/2 is first split into K sub-bands. Each of the first \(K\)/2 sub-bands are divided into \(N_{1}\) FFT bins. Sub-bands 0–\(K\)/2 represent complex signals. Consequently, the number of selected FFT bins for Sub-bands 0–\(K\)/2–1 are all \(N_{2}\). By selecting the appropriate bins from the \(N_{1}\) FFT bins for each sub-band, the frequency-domain data for the real-valued signals within \(\rm f_{\rm s}\)/2 are obtained.  
with an increase in \(D\). However, it required more hardware resources. During the actual applications, the designs can be traded off according to specific needs.  
Under the same conditions as the IWOLA filter bank, the frequency responses of the WOLA filter bank are presented in Figure 4. The frequency response for \(D\) = 16 was superior to that for \(D\) = 8, given that the former exhibited a flatter channel gain and narrower overlaps between neighbouring sub-channels. However, more hardware resources were consumed. In comparison, the frequency response characteristic of the IWOLA filter bank was identical to that of the WOLA filter bank, with the exception of shift in the centre frequency of each sub-band by \(\pi/K\).  
Figure 3: Frequency response of the channels in the IWOLA filter bank (where D denotes a decimating factor for the IWOLA filter bank).