esa-sceva/satcom-chunk-collection huggingface dataset:
# New Method to Estimate Rock Mass Deformation Modulus Based on BQ System
## 2 The BQ Rock Mass Quality Classification Method  
### Brief Review of the BQ System  
The BQ system was first proposed to evaluate the stability of engineering rock masses and assist the design of support measures in July 1995. It has been adopted as the national standard and has been applied successfully in most rock engineering fields. It was developed based on 157 domestic civil engineering cases (i.e., 30 underground metal mines, 55 underground hydropower caverns, 21 underground defense constructions, 21 railway and highway tunnels, 21 building foundations, 8 coal mines, and 1 underground civil air defense construction). The qualities of these rock masses were evaluated on the basis of the parameters that control engineering rock mass stability, using the following equation:  
\[BQ=100+3R_{c}+250K_{v} \tag{1}\]  
Here:  
\(BQ\) is the basic quality of the rock mass.  
\(R_{c}\) is the uniaxial compressive strength (\(UCS\)), in MPa.  
\(K_{v}\) is the rock intactness index, which can be calculated from the seismic wave velocity within the rock mass, Vpm, and that within intact rock, Vpr, using the following formula:  
\[K_{V}=\frac{V_{pm}^{2}}{V_{pr}^{2}}\ \ \ 0\leq K_{V}\leq 1 \tag{2}\]  
The rock intactness index (\(K_{v}\)) can also be obtained by measuring the volumetric joint count within the rock mass (\(J_{v}\)). The relationship between these values is shown in Table 1 and Figure 1 (GB/T 50218-2014, 2014) [14].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\rm J_{v}\) joint & \multirow{2}{*}{\(<\)3} & \multirow{2}{*}{3\(\sim\)10} & \multirow{2}{*}{10–20} & \multirow{2}{*}{20–35} & \multirow{2}{*}{\(\geq\)35} \\ numbers/m\({}^{3}\) & & & & & \\ \hline K\({}_{\rm v}\) & \(>\)0.75 & 0.75–0.55 & 0.55–0.35 & 0.35–0.15 & \(\leq\)0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Correlation between K\({}_{\rm v}\) and J\({}_{\rm v}\).  
Two important constraint criteria should be applied when applying Equation (1) to obtain the BQ value: (1) when the condition that \(R_{c}>90K_{v}+30\) is met, \(90K_{v}+30\) should be substituted for \(R_{c}\); (2) when \(K_{v}>0.04R_{c}+0.4\), \(K_{v}\) should be substituted with the equation \(K_{v}=0.04R_{c}+0.4\).  
The BQ system divides rock masses into five classes according to the value obtained with Equation (1) (Table 2).  
To date, the BQ method has seen widespread use in diverse rock engineering projects in China [15; 16; 17; 18; 19; 20], and has proved a suitable classificatory method for evaluating rock mass quality.
## 3 SOA-Enabled HONoC Architecture
### Semiconductor Optical Amplifier  
The SOA is mainly used for amplifying the input optical signals, switching and frequency conversion [23]. The SOA can be integrated into a CMOS-compatible chip and thus can be implemented in the HONoC by using silicon photonics technology. The operating principle of the SOA is that electrons and holes are injected into the n-type region and the p-type region respectively, causing population inversion in the active region. Population inversion leads to stimulated emission and therefore, a photon injected into the SOA triggers another photon with the same phase, wavelength and direction. Optical gain is obtained through this process.  
In the active region, the gain per unit length of the SOA is called material gain (\(g\)) and is expressed in (5a) as a function of wavelength (\(\lambda\)) and current (\(I\)). As \(L\) increases, the SOA gain (\(G\)) increases accordingly. Here, \(g\) and \(G\) are in cm\({}^{-1}\) and dB units, respectively.  
\[g(\lambda,I)=[\Gamma a_{1}n_{0}\bigg{\{}\frac{I}{I_{0}}-1\bigg{\}}-\alpha][1- \frac{2(\lambda-1570)^{2}}{\Delta\lambda^{2}}] \tag{5a}\] \[G(\lambda,I)=10\log_{10}\Big{(}e^{L\cdot g(\lambda,\ I)}\Big{)} \tag{5b}\]  
\(\Gamma\), \(a_{1}\), \(n_{0}\), \(L\), \(I_{0}\), \(\alpha\) and \(\Delta\lambda\) depend on the material and the structural characteristics of the SOA; the definitions and values of each parameter are shown in Table 1[24]. As in Equation (5), the SOA gain is proportional to the current. The operating voltage of the SOA and the wavelength are assumed to be 1.5 V and 1550 nm, respectively. In addition, it takes 20-50 ps for the SOA to reach the target gain. The SOA model in this study is simplified for the purpose of system-level analysis; therefore, the physical effect of the amplified spontaneous emission (ASE) introduced by the SOAs is not considered. Although ASE cannot be ignored in physical implementations with contemporary silicon photonics technology, this study focuses on the emerging architecture with a matured manufacturing process that enables feasible on-chip SOAs, assuming a negligible ASE compared to the cumulated crosstalk noise. Additionally, it is also assumed that the placement of SOAs can be over-layered on waveguides in the physical layout; therefore, SOAs do not affect the spaces between optical routers.
## 2 Materials and Methods
### Data Source Layer
#### 2.1.2 Underground Utility Information  
GPR has become an integral part of any earthwork project in metropolitan cities, historical sites, utility installations, and maintenance projects. GPR is an old but very effective form of technology that is used to provide information about underground pipes, communication lines, tunnels, and archeological remains before excavation [34]. GPR is based on the simple working principle, electromagnetic energy transmitted through emitter antennas in the form of a pulse reflected from the underground target and received by receiver antenna [35]. The electromagnetic images are collected to determine the location of the underground utility. The selection of GPR depends upon the project type and information required regarding the project. The lower the frequency of the antennae, the deeper information can be obtained. The higher the frequency, the greater the resolution, i.e., scan detail. Figure 3 shows the application of GPR with a typical range of frequencies and penetration depth.  
Figure 2: Images collected during the flight of the UAV equipped with a high-resolution camera.  
Existing utility information from previous underground utility records is acquired from the concerned government and service providers before the commencement of the project. The underground utility information collected from both sources provides a piece of reliable information.
# Spatiotemporal Variation in Absorption Aerosol Optical Depth over China
## 1 Introduction
compared the mean annual spatial distribution of satellite-based OMI-AAOD (0.024 for Dhaka; 0.023 for Bhola) with the ground-based AERONET-AAOD (0.110 for Dhaka; 0.073 for Bhola) [25]. [PERSON] et al. found that the annual patterns of OMI-AAOD and AERONET-AAOD over Saudi Arabia are almost similar [26]. [PERSON] reported that the daily AAE\({}_{388-500}\) for dust events over the Tarim Basin based on the OMI is depicted to be in a range of 3.2-3.6, with an average of 3.3 [27]. To the best of our knowledge, few investigations have focused on the long-term and large-scale variation characteristics of detailed AAOD and AAE values over China, especially in typical regions, and the reasons for AAOD and AAE variations should be studied. Herein, the accuracy of the OMI AAOD with ground-based AERONET AAOD is validated, then the spatial-temporal characteristics of the AAOD and AAE across China during the period of 2005-2018 on the basis of the OMI data are studied systematically. This study aims to evaluate the present AAOD situation, AAOD and AAE characteristicswith regard to annual, seasonal, and monthly variations, and the divergence in AAE values for episode days and non-episode days across China, which will hopefully benefit governments' efforts to mitigate air pollution. The paper is arranged as follows: in Section 2, the data and method for the AAOD and AAE are introduced; in Section 3, the validation of the OMI dataset with AERONET followed by 14-year detailed characteristics of the AAOD and AAE, and the main factors leading to the variations are discussed. Finally, the main conclusions are summarized in Section 4.
# An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN
## 5 Discussion and Conclusions  
The decomposition of a propagation system into smaller subsystems has been the ultimate goal of the [PERSON], Hata and COST models. In fact, the suggestion of the breaking point-distance phenomenon in ITU recommendation P.1411 [40] follows the same idea. These attempts used crisp or Boolean logic to differentiate between a limited set of propagation conditions or scenarios. In contrast to sudden transitions, fuzzy logics make it possible to have smoother transitions, while mitigating the uncertainties within the data. ANFIS further allowed for the implementation of an expert's knowledge into the system, which addressed part of the challenges of the ANN models.  
Comparison of the models used in this investigation indicated that the ANFIS and ANN models resulted in a remarkably better performance in terms of estimation, compared to the COST W.I. model. The ANFIS model resulted in a better performance compared to the ANN model. \(E_{\sigma}\) and NSE were consistently improved by about 1 dB and 10%, respectively. ANFIS was found as a better generalization candidate. In this study, the performance of ANN in Table 1 was almost identical to the ANFIS results in Table 4. This is while the ANN was trained with 90% of the data, whereas ANFIS achieved the same results with only 20% of the data.  
Furthermore, two new parameters were added into the model without having to formulate them. \(s_{m}\) was required due to the wireless technology of choice, and \(d_{w}\) was added due to the features of the propagation environment. Inclusion of these parameters in the modeling reduced the RMSE and NSE of the ANFIS model by 0.55 dB and 7%, respectively. These two parameters, however, did not make a significant change to the ANN model results. This might be due to the limited number of measurements (380 samples) that had \(d_{w}\). This is the most likely reason, given that ANFIS, with a better generalization, could benefit from this parameter.  
In this investigation, the proposed ANFIS model was used for outdoor environments. However, it can be easily adopted for indoor propagation as well. This is as simple as providing the impacting propagation parameters for the system and roughly describing their effect using fuzzy linguistic reasoning. For instance, in an indoor environment, the effect of a higher number of walls, windows or doors on LoS can increase the loss.  
[PERSON] and [PERSON] conceived and designed and performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] contributed materials/analysis tools; [PERSON], [PERSON] and [PERSON] wrote the paper reported. This research received no external funding. The authors would like to thank Glasgow Caledonian University for funding this research. Also, Stream Technologies for facilitating the data collection and measurements, and Innovate UK (KTP). The authors declare no conflict of interest.
## 4 Simulations and Performance Estimation
### Research on Joint Optimization of Multiple Parameters  
In this section, the parameters are optimized for the dual-polarization scheme we proposed in Section 2. For the purpose of researching the dual-polarization LLO CV-QKD under relatively low PERs, as an example, the PER is set to 20 dB. Figure 5 shows the SKR versus different modulation variance (\(V_{A}\)) and transmission distance. The red curve denotes that when the transmission distance is 25 km, to maximize the SKR, the optimal \(V_{A}\) is set to 1.6. In this case, the maximum value of SKR appears at the point indicated by the small red circle, and the maximum SKR is 5.858 Mbps. Compared with previous research works [20], the SKR has been greatly improved. Furthermore, our work also laid a solid foundation for further solving the high requirements problem of PER for the dual-polarization CV-QKD system in the future. We will analyze the details in the following contents.
# High-Resolution Through-the-Wall Radar Imaging with Exploitation of Target Structure
## 4 Results
### Simulated Dataset
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **BOMP** & **BSBL** & **Overlapping GS** & **Proposed Algorithm** \\ \hline Runtime (s) & 0.45 & 175.5 & 65.5 & 180.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Executive time comparisons. Figure 3: Comparison of reconstruction results using different algorithms for the simulated scene. (**a**) Ground truth. (**b**) BP-based reconstruction result using the full data. (**c**) Reconstruction result based on the BOMP method using one-fourth of measurements with known sparsity. (**d**) Reconstruction result based on the BSBL method using one-fourth of measurements. (**e**) Reconstruction result based on the Overlapping GS method using one-fourth of measurements. (**f**) Reconstruction result based on the proposed method using one-fourth of measurements. Reconstruction Performance Analysis  
The reconstruction performance is described by calculating the NMSE between the true and estimated position of the target. To further prove the correctness of the clustered BCS method, we show the NMSE versus the undersampling rate and the NMSE versus SNR, respectively, in Figure 4a,b. In this simulation, all other parameters are kept in the above simulation in Section 4.1. The ratio varies from 10% to 30% with the step of 5%. The result comparison of NMSE versus undersampling is shown in Figure 4a. It is clear that the NMSEs generally decreases with increased undersampling rates. The NMSEs in the proposed method are generally lower than those in other methods across the undersamping ratio. It is observed that the reconstructed NMSE would reduce to 0.48, when the undersampling rate approach 0.3, compared to an NMSE of 0.76 in the Overlapping GS method, 0.94 in the BSBL method, and 1.24 in the BOMP method even with a known sparsity number. The reason for the superior performance of the proposed method over these methods may benefit from the fact that the proposed method combines group sparsity with clustered structure into the model for high-resolution imaging. We also investigate the effect of additive noise on the performance of the clustered BCS method. All other parameters are kept in the above simulation in Section 4.1. The SNR ranges from \(-\)15 dB to 15 dB with the step of 5 dB. It is demonstrated in Figure 4b that the reconstructed NMSEs of all algorithms generally increase with the increases of the noise power. It is observed that the BOMP method seems more sensitive to the noise power, and generally has the highest NMSEs as compared to the other three methods, even with a known sparsity number. The main reason could be that there is the high coherence in the measurement matrix. However, the proposed method offers more robustness to noise power and shows smaller NMSEs across SNRs, and the NMSE in the proposed method is reduced to 0.44, compared to 0.50 in the overlapping GS method, 0.56 in the BSBL method and 0.88 in the BOMP method, when the SNR approaches 15 dB.
## 3 Results
### scCGImpute Enhances the Ability to Identify Cell Types in Real Data  
In this section, four real datasets (Blakeley [34], Ting [35], Baron [36], and Zeisel [4]) are used to evaluate the ability to recognize cell types in scCGImpute. The smallest dataset consists of 30 cells, and the largest dataset contains 3005 cells. The three clustering evaluation indexes (_ARI_, _NMI_, and Purity) are selected to evaluate the results of identifying cell types. We used the cell type label of these data only for evaluating the clustering results and not for identifying cell types. The details of the pre-processing can be found in the methods. The clustering performance of the original data, the imputations of scCGImpute and other eight methods are shown in Figure 6. And the results of these three indicators are presented in a stacked way. In addition, VIPER failed to run on the Zeisel dataset for a day,  
Figure 4: The result of Pearson analysis between rawdata and scCGImpute with simulated data. In the figure, refdata represents the correlation between reference data and raw data, and scCGImpute represents the correlation between reference data and scCGImpute. Where “ns” indicates that the p value is greater than 0.05, “** indicates that the p value is greater than 0.001 and less than or equal to 0.01, and “*******” indicates that the p value is less than or equal to 0.0001. (**a**) Correlation between cells; (**b**) Correlation between genes.  
Figure 5: The result of Pearson correlation analysis between the imputation and the known concentrations of ERCCs about five sequencing methods. The \(p\) value of the \(t\)-test for each sequencing data is above the boxplot.  
so we don't show it. Among these four real datasets, compared with the original data and the data imputed by the other eight methods, scCGImpute could obtain better clustering results regardless of whether the data set was small or large. While MAGIC performs better than scCGImpute in the Zeisel dataset, scCGImpute significantly outperforms MAGIC in the other three datasets. This can be attributed to the suitability of MAGIC for large datasets. Nevertheless, the peculiarity of MAGIC's ability to adjust all gene expression levels may introduce new bias into the data. In Table 1, it can be observed that the Baron dataset has the highest dropout rate, reaching 86.9%. The performance of scCGImpute on the Baron dataset, along with the cluster analysis results of the simulate dataset, mutually confirm that scCGImpute can perform well even when dealing with high dropout rates.
# Retrospective Analysis of Functional Pain among Professional Climbers
## 3 Results
### Functional Pain Characteristics of the Climbers' and Control Groups
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Anatomical** & & & & & \\
**Location of** & **Predictors** & **Odds Ratio** & **Std. Error** & **CI** & **\(p\) (Adjusted)** & **AUC** \\ \hline \multirow{4}{*}{GHJ} & (Intercept) & 0.53 & 0.17 & 0.28–0.99 & 0.048 * & - \\ \cline{2-7}  & Gender (female = 1) & 3.34 & 1.12 & 1.74–6.44 & 0.004 * & 0.638 \\ \cline{2-7}  & Preferred style of climbing (lead = 1) & 2.04 & 0.67 & 1.07–3.88 & 0.120 & 0.582 \\ \cline{2-7}  & Performance level (exp = 1) & 1.47 & 0.52 & 0.73–2.96 & 0.438 & 0.510 \\ \hline \multirow{4}{*}{Fingers} & (Intercept) & 2.54 & 0.85 & 1.32–4.88 & 0.010 * & - \\ \cline{2-7}  & Gender (female = 1) & 0.41 & 0.14 & 0.21–0.81 & 0.020 * & 0.385 \\ \cline{2-7}  & Preferred style of climbing (lead = 1) & 1.42 & 0.49 & 0.72–2.78 & 0.409 & 0.528 \\ \cline{2-7}  & Performance level (exp = 1) & 1.96 & 0.77 & 0.90–4.25 & 0.356 & 0.575 \\ \hline \multirow{4}{*}{Thoracic spine} & (Intercept) & 0.27 & 0.09 & 0.14–0.51 & 0.004 * & - \\ \cline{2-7}  & Gender (female = 1) & 1.95 & 0.65 & 1.02–3.73 & 0.043 * & 0.580 \\ \cline{2-7}  & Preferred style of climbing (lead = 1) & 1.53 & 0.50 & 0.80–2.91 & 0.396 & 0.550 \\ \cline{2-7}  & Performance level (exp = 1) & 1.32 & 0.47 & 0.66–2.65 & 0.438 & 0.512 \\ \hline \multirow{4}{*}{Lumbosacral spine} & (Intercept) & 0.45 & 0.14 & 0.25–0.83 & 0.013 * & - \\ \cline{2-7}  & Gender (female = 1) & 1.96 & 0.62 & 1.05–3.64 & 0.043 * & 0.578 \\ \cline{1-1} \cline{2-7}  & Preferred style of climbing (lead = 1) & 0.90 & 0.28 & 0.48–1.67 & 0.739 & 0.487 \\ \cline{1-1} \cline{2-7}  & Performance level (exp = 1) & 1.32 & 0.45 & 0.68–2.59 & 0.438 & 0.520 \\ \cline{1-1} \cline{2-7}  & \multicolumn{1}{c}{GHJ—glenohumeral joint, * \(p<0.05\).} & & & & \\ \hline \hline \end{tabular}  
* \(p<0.05\); GHJ—glenohumeral joint, C—cerical spine, Th—thoracic spine, L.S—lumbosacral spine.  
\end{table} 
Table 3: Results of the logistic regression model that was used to predict pain among the climbers depending on gender, type of climbing, and level of performance. The odds of developing functional pain in the GHJ among those who preferred lead climbing was 2.04 times higher than among those who preferred bouldering. However, it is worth noting that this difference was not statistically significant. Logistic regression showed no relationship between the functional pain prevalence in other areas and the examined predictors (gender, type of climbing, and level of performance). Details are given in Table 3.
## 1 Introduction
### Summary  
The existing studies firstly clarified the hazards of oil contamination on hydraulic spool valve failure, which illustrated the importance of this study. Some scholars have shown the motion characteristics of particle transport, aggregation, rotation, collision, etc. through numerical simulation and visualization tests, which provide scientific references for the force analysis of individual particles in the clearances in this study. In addition, some scholars have analyzed the causes of spool valve hysteresis, including roughness, clearance size, spool tilt, and other factors on the spool valve hysteresis, which provides theoretical guidance for the mathematical modeling of the spool hysteresis force under the action of particles in this study. There are also some gaps in the existing studies; for example, no one has directly measured the spool valve hysteresis force under the action of particles, and no one has developed the relevant test apparatus, and only a few studies have focused on the spool clamping problem caused by particle intrusion into the spool due to the lack of a systematic theoretical framework and the difficulty of numerical calculation and simulation. Meanwhile, experimental studies and theories that take into account the multifactor coupling of particle material and size, concentration, and spool postures have not been proposed. From a microscopic point of view, it has been realized that the precise fit between the spools is extremely sensitive to oil contamination, especially solid particle contaminants. Figure 1 illustrates the process of solid particles invading valve clearance. The oil in the valve clearance flows under pressure difference, which is known as \"internal leakage\". Under the co-action of internal leakage and spool sliding, solid particles can invade the valve clearance in an extremely easy way. It can be found that the particles invading the valve clearance vary in size, and the particles whose size is closer to that of the valve clearance can cause spool clamping easier. In this study, the mathematical model of hysteresis force under the action of particles is systematically established through the clamping force measurement test, taking into account the conditions of particles and the spool postures, connecting the microscopic motion of particles in the clearances with the macroscopic phenomenon of spool valve hysteresis, which is innovative and provides the theoretical basis for the similar topics.  
Figure 1: Diagram of particles invading valve clearance.