esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Flow Field Analysis in the Fully Lubricated State
### Flow Field Analysis in the Oil Loss State  
According to Table 6, CFD simulation is performed on the flow field of the two types of bearings at each oil loss node.  
The inlet oil volume and oil volume fraction at each discrete oil volume node are extracted for the bearing with the outer ring rib structure. Matlab software is employed to perform numerical fitting, where the linear interpolation, quadratic interpolation, and power methods are applied, respectively. The obtained fitted relationships are presented in Table 7.  
Figure 15: Lubricating oil distributions at the center sections of the bearing small rollers. (**a**) Outer ring rib structure; (**b**) Inner ring rib structure.  
Figure 16: Lubricating oil distribution at the center sections of the bearing large rollers. (**a**) Outer ring rib structure; (**b**) Inner ring rib structure.  
Figure 17: Lubricating oil distributions at the small roller ribs of the bearings. (**a**) Outer ring rib structure; (**b**) Inner ring rib structure.  
Figure 18: Lubricating oil distribution at the large roller ribs of the bearing. (**a**) Outer ring rib structure; (**b**) Inner ring rib structure.  
The comparison result shows that the error is the smallest when using the quadratic interpolation method for data fitting, thus, the fitting curve equation is:  
\[y=-23.55x^{2}+1.368x+0.0021 \tag{9}\]  
Similarly, the volume fractions of lubricating oil in the bearing cavity of the bearing with the inner ring rib structure during the oil loss process are also fitted, and the obtained fitting curve equation is:  
\[y=-6.728x^{2}+0.5932x+0.0012 \tag{10}\]  
The variation patterns of the lubricating oil volume fraction in the bearing cavity with the inlet flow rate during oil loss for the two types of bearings are shown in Figure 20. It  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fitting Method** & **Fitting Formula** & **Root Mean Square (RMS)** \\ \hline Linear interpolation & \(y=0.93x+0.0032\) & \(1.4\times 10^{-3}\) \\ Quadratic interpolation & \(y=-23.55x^{2}+1.368x+0.0021\) & \(2.0\times 10^{-4}\) \\ Power & \(y=0.233x^{0.6235}\) & \(9.4\times 10^{-4}\) \\ \hline \hline \end{tabular} In the table: \(x\) is the inlet oil volume; \(y\) is the volume fraction of lubricating oil in the fluid domain.  
\end{table}
Table 7: Relationships obtained by numerical fitting.  
Figure 19: Lubricating oil distributions on various bearing components at different rotational speeds. (a) Outer ring rib structure; (b) Inner ring rib structure.  
can be observed from Figure 19 that: (1) In the oil loss state, the lubricating oil volume fraction in the cavity of the bearing with the outer ring rib structure decreases faster with the decrease of the inlet flow rate, but the lubrication effect of the bearing with the outer ring rib structure is still better than that with the inner ring rib structure; (2) In the final stage of the loss of lubrication, the volume fraction of the bearing flow field of the outer ring rib structure is twice that of the inner ring rib structure.
# A Gene Expression Programming Model for Predicting Tunnel Convergence
## 4 Prediction of Tunnel Convergence by GEP
### Data Sets
#### 4.1.2. Tunnel Geometry and Tunneling Operation  
The depth of the tunnel and the excavation area are influential geometric parameters on ground movements. Generally, an underground excavation with a large diameter tends to cause high convergence, especially when tunneling in soft soils. It is estimated that a greater volume of soil moves towards the tunnel face in large diameter tunnels, when the values of the other parameters are constant, and may lead to larger convergences. However, since the excavation area of the tunnel in this project is constant, the influence of the area on tunnel convergence was neglected.  
Figure 5: Influential parameters causing tunnel convergence.  
The tunnel depth is also an influential geometric parameter on tunnel convergence. The relationship between the tunnel depth and measured convergences for the KUR Project is shown in Figure 6. As one may notice, when the tunnel is deeper than 20 m, larger convergences generally occur.
# A Space-Borne Ka-Band Tile-Type Phased Array Transmitter Module Based on HTCC Technology
###### Abstract  
We propose a double-sided layout design method with high reliability for multi-beam phased array transmitter modules based on high-temperature co-fired ceramic technology. Efficient transmission of microwave signals and low-frequency signals is realized by using multi-layer wiring and an inter-layer transition. The proposed Ka-band transmitter module exhibits a gain of \(\geq\)26 dB, an output power of \(\geq\)21 dBm, and an overall efficiency of the component of \(\geq\)40%. The RMS accuracy of phase shift and attenuation is better than 3\({}^{\circ}\) and 0.5 dB, respectively. The proposed design method addresses the problems of high integration, lightness, and miniaturization in transmit/receive module design, and shows a good performance of heat dissipation performance and sealing capability. It can be easily expanded to large-scale phased arrays.  
space-borne; Ka-band; tile-type; phased array transmitter; high temperature co-fired ceramic +
Footnote †: journal: Applied sciences  
1
## 5 Test Results and Analysis
### Comparison of Detection Results  
To demonstrate the detection effect of the YOLOv7 algorithm on _Camelia oleifera_ fruit, the random images of _Camelia oleifera_ fruit taken at the sampling site were detected and compared with the YOLOv5s network. The detection results are shown in Figure 11. On the same data set, YOLOv7 detection takes 15.215s, and YOLOv5s detection takes 16.774s, indicating that YOLOv7 has more advantages in speed.  
Figure 11 shows that some _Camelia oleifera_ fruits with occlusion can still be detected based on the YOLOv7 algorithm. Compared with the YOLOv5s algorithm, it significantly improves the detection of the usually undetected _Camelia oleifera_ fruits.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **P(\%)** & **R(\%)** & \(mAP_[EMAIL_ADDRESS]}\)(\%)** & \(mAP_[EMAIL_ADDRESS]}\)(\%) \\ \hline YOLOv7 & 92.9 & 88.3 & 94.7 & 83.1 \\ YOLOv5s & 93.1 & 88 & 94 & 79 \\ \hline \end{tabular}
\end{table}
Table 1: Performance comparison of different detection algorithms.  
Figure 10: Precision-recall curves graphs. (**a**) YOLOv7, (**b**) YOLOv5s.
## 2 Structure, Synthesis, and Properties of Bimetallic CuFe Nanoparticles
### Synthesis and Properties
#### 2.2.1 Mechanical Milling  
CuFe bimetallic nanoparticles were synthesized by mechanical milling [37; 38; 39]. [PERSON] et al. [37] used FeCl\({}_{3}\) and CuCl\({}_{2}\) with Na to form FeCu nanoparticles by ball milling and studied their magnetic properties. The process was based on two reactions:  
\[\mathrm{FeCl_{3}+3 Na\to Fe+3 NaCl} \tag{1}\]  
\[\mathrm{CuCl_{2}+2 Na\to Cu+2 NaCl} \tag{2}\]  
The reactants were milled under Ar atmosphere up to 84 h. With the increase in ball milling duration from 3 h to 84 h, the particle size was found to increase from approximately 16 nm and 50 nm, respectively (Figure 5). They reported the bcc \(\alpha\)-Fe and fcc Cu phases formation, and the formation of additional hexagonal Fe(OH)\({}_{2}\) phase for the powders milled shorter than 24 h. They also observed that the coercivity (H\({}_{\mathrm{C}}\)) value increased with the grinding time and reached a maximum value of 33.5 kA/m for 48 h of grinding, while further grinding resulted in a decrease in the H\({}_{\mathrm{C}}\) value (Figure 5).  
CuFe nanocomposites were obtained by high-energy attrition milling of Fe nanopowder and cuprous oxide (n-Cu\({}_{2}\)O) in hexane by [PERSON] et al. [38]. The authors studied their mechanical properties and found that FeCu nanocomposite has a higher ductility compared to nanostructured Fe (the compressive yield stress of FeCu is 720 MPa, while for Fe nanopowder (30 nm) is 1100 MPa [39]).  
Figure 5: Transmission electron microscopy (TEM) images (**a–c**) of the powders for various milling times (18 h, 48 h, 84 h, respectively). Particle sizes of the powders for various milling times (**d**). Saturation magnetization (M\({}_{\mathrm{S}}\)) and coercivity (H\({}_{\mathrm{C}}\)) values of the as-milled powders for various milling times (**e**). Reproduced with permission from ref. [37]. Copyright 2002 The Japan Institute of Metals and Materials.  
Nanocomposites of CuFe/CNT (carbon nanotubes) were synthetized by two-step mechanical milling of a mixture of Cu, Fe, and CNT under argon atmosphere [39]. First, pure Fe and Cu powders in four various proportions were milled for 15 h. Next, the sample showing the maximum solubility of Fe into Cu, i.e., with 20% of Fe in the sample, was chosen, mixed with CNT, and milled for 15 h to synthesize nanocomposite samples. As a result, micron-sized agglomerates consisting of nanoparticles were created. The structural, magnetic, and electrical properties depend on the CNT addition. With the increasing of the CNT content, agglomerates' size decreased, while coercivity, magnetization, and electrical resistivity of composite samples increased.
# SAHA: A String Adaptive Hash Table for Analytical Databases
## 1 Introduction  
### Background  
We are now in the big data era. The last decade was characterized by an explosion in data-analyzing applications, managing data of various types and structures. The exponential growth of datasets imposes a big challenge on data analytic platforms, among which, one typical platform is the relational database, which is commonly used to store and extract information that can be monetized or contains other business value. An important purpose of a database system is to answer decision support queries via the SQL [1] language. For example, a typical query might be to retrieve all data values corresponding to all customers having their total account balances above required limits. It mainly utilizes two SQL operators: the join operator to associate the corresponding values and the group operator to summarize the account balances. These two operators appear in almost every decision support query. For instance, among the 99 queries of the most well-known decision support benchmark: TPC-DS [2], 80 queries contain group operators and 97 queries have join operators. Efficient implementations of these two operators will greatly reduce the decision support query's runtime and benefit interactive data exploration. There are many different implementations of group and join operators. In order to understand how analytical databases handle these operators and what optimizations can be done, we will first describe the well-known implementation of famous analytical databases, such as ClickHouse [3], Impala [4] and Hive [5], and then discuss the hot spots of these operators.
## 2 Methods
### Architecture of a Weighted Deep Ensemble for the Super-Resolution of Satellite Data: WDESen2
#### 2.2.1 Kernel Component Exploration  
For training, the B band inputs, \(y_{B}\in\mathbb{R}^{6\times W/2\times H/2}\) of \(\Psi_{\theta}\), are given by applying a composition of \(M\) and \(DS\), \(MDS:\mathbb{R}^{W\times H}\rightarrow\mathbb{R}^{W\times H}\), as in Figure 1, band-wise to the training labels \(x_{B}\in\mathbb{R}^{6\times W\times H}\). Thus, \(\Psi_{\theta}\) aims to reconstruct \(x_{B}^{i}\) from \(y_{B}^{i}=MDSx_{B}^{i}\) and the other bands, where \(x_{B}^{i}\) is one of the B bands at 20 m GSD. As \(DS:\mathbb{R}^{W/2\times H/2}\rightarrow\mathbb{R}^{W\times H}\) has a higher input dimension than output dimension, the composition \(MDS\), depicted in Figure 3, has a non-trivial kernel. Recall that the kernel of a linear map \(A:E\to F\) is defined as \(\mathcal{N}(A):=\{x\in E|u(x)=0\}\). Thus, given an input \(y_{i}\), different super-resolved solutions \(x_{i}\) may exist depending on the effect of correlation. In order to analyze the kernel component of the super-resolved bands, we use the projection operator \(P_{\mathcal{N}(MDS)}\) on the kernel \(\mathcal{N}(MDS)\):  
\[P_{\mathcal{N}(MDS)}=I-(MDS)^{\dagger}MDS:\mathbb{R}^{W\times H}\rightarrow \mathbb{R}^{W\times H},\]  
where \((MDS)^{\dagger}\) is the Moore-Penrose inverse of \(MDS\)[19], and this projection is approximated by a truncated singular value decomposition of \(MDS\). The projection is computed individually per patch and band, as shown on the left-hand side of Figure 4.
# Regolith-Rich PEEK Composite Bricks: Steps Towards Space-Ready Lunar Construction Materials
## 4 Discussion on the Material Properties
### Comparison with Standards for Bricks Classification  
This section compares the experimental results with standard and experimental benchmarks commonly used for structural applications on Earth. In this sense, the comparison adopts a conservative approach, as preliminary estimates of strength requirements on the Moon [21; 22; 23; 24] should be scaled down by the same factor as lunar gravity (1/6 of Earth's gravity). Figure 10 shows the mean bending strengths of the DoE runs (black dots), with all samples cured in air and tested at +20 \({}^{\circ}\)C. Three additional data points, associated with run 8 (compaction pressure of 7.50 MPa and binder weight of 10 wt%), represent samples cured in vacuum and tested at different temperatures. Steel fiber-reinforced concrete in three different grades serves as a reference for comparison. For samples cured in air and tested at ambient temperature, all samples at 15 wt% PEEK meet or exceed all grade requirements, regardless of compaction pressure. The 10 wt% PEEK and compaction pressures above 5 MPa already meet the 35 MPa requirements, while the 10 wt% PEEK at 7.50 MPa meets those of the 65 MPa grade. On the other side, samples cured in vacuum exhibit bending strengths that meet or significantly exceed the requirements for all grades across all tested temperatures.  
Figure 11 follows the same approach and presents the compression strength results, evaluated against the minimum requirements for various brick types as specified by ASTM standards. The analyzed combinations of compaction pressure and PEEK weight percentage accommodate the performance needs of industrial floor, building, hollow, and pedestrian/light-traffic paving bricks. Specifically, the requirements for hollow bricks and pedestrian/light-traffic paving bricks are met and exceeded with 15 wt% PEEK at compaction pressures above 2.50 MPa and 5.0 MPa, respectively. Industrial floor and  
Figure 10: Bending strength: Comparison of the requirements for three grades of steel fiber-reinforced concrete [26] against experimental data. The black dots show the bending strengths of DoE samples (cured in air, tested at +20 \({}^{\circ}\)C); the additional points associated with run 8 (same compaction pressure and binder percentage) represent samples cured in vacuum and tested at various temperatures.  
building brick requirements are satisfied with 15 wt% PEEK regardless of compaction pressure and with 10 wt% PEEK when the compaction pressures exceed 0.5 MPa and 2.50 MPa, respectively. Samples cured in vacuum meet or exceed the requirements across all cases and tested temperatures, except for the sample tested at high-temperature, where performance slightly falls below the requirements for pedestrian/light-traffic paving.  
Appendix B extends this comparison by incorporating requirements and standard values from a broader range of applications.
# Deciphering Autonomous Vehicle Regulations with Machine Learning
## 4 Results
### Categorical Associations  
Figure 5 shows the observed frequencies of the two categorical variables reflecting the bill status (Figure 2) and the stipulation for a human backup driver (Figure 3). For statistical stability, the analysis consolidated executive orders into enacted laws, vetoed into failed legislation, and no legislation into the pending category. Table 7 summarizes the results of the chi-squared statistical tests for association. The \(p\)-value for status and human backup requirement, being significantly less than 0.05, indicates there is a statistically significant association between the status of AV legislation and their requirement for a human backup driver. Figure 5 shows high values for enacted legislation that stipulates a human driver to be optional and failed legislation that requires an on-board human driver. While this test shows a significant association, it does not indicate causation.  
The \(p\)-value for status and political affiliation, being greater than 0.05, indicates that there is no statistically significant association between the status of AV legislation and the aggregate political leaning of the state. Similarly, the \(p\)-value for human backup requirement and the political leaning of a state, being greater than 0.05, indicates that there is no statistically significant association between requiring a human backup driver and the state's aggregate political leaning. This suggests that the matter is bipartisan, and that other factors may have influenced the proposed stipulation for human requirements and the outcome of the legislation. Therefore, turning to more complex ML models can offer more nuanced insights.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Statistic** & **Status vs. Human** & **Status vs. Affiliation** & **Human vs. Affiliation** \\ \hline Chi-squared & 20.79 & 5.42 & 4.15 \\ DOF & 4 & 2 & 2 \\ \(p\)-value & \(3.5\times 10^{-4}\) & 0.067 & 0.126 \\ Reject & & & \\ Independence & Yes & No & No \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the chi-squared statistical test for association.  
Figure 5: Contingency matrices to test the statistical associations of the categorical variables for (**a**) status vs. human requirement, (**b**) status vs. political affiliation, and (**c**) human requirement vs. political affiliation.
# Study of the Ionospheric Scintillation Radio Propagation Characteristics with Cosmic Observations
## 3 Experimental Results
### Goodness of Fit for Different Distributions
By comprehensively analyzing the standard deviation, kurtosis and information entropy, the fitting performance of the measured probability density distribution curve can be best fitted with the \(\alpha\)-\(\mu\) distribution. The \"mean \(\pm\) standard deviation\" is used to estimate one standard deviation for the estimation curve. The interval length and probability of the three distributions falling into the range are counted to find the best fitting interval. Table 4 lists the intervals falling into the \"mean \(\pm\) standard deviation\" of the fitting performance of the three standard distributions and the corresponding interval probability under different scintillation indices. The data are comprehensively processed from 2010 to 2019. The fitting range of the Nakagami-\(m\) distribution becomes larger with increasing the normalized S4 and approaches 100% when \(S4\geq 0.8\). The fitting range of the \(\alpha\)-\(\mu\) distribution satisfies 100% goodness of fit in the whole range, while the fitting range of the \(\kappa\)-\(\mu\) distribution has a similar trend to the Nakagami-\(m\) distribution but cannot meet 100% goodness of fit in the whole range. Figure 11 shows the profile of the skewness and kurtosis. In other studies, it has been found that the profile is parabolic centered on zero, indicating that skewness and  
Figure 10: Fitting interval of the measured curve and the three distributions (\(S4=0.7\)). Figure 9: Comparisons of RMSE, information entropy and kurtosis for two groups: the ascending solar phase (2012–2015) and the descending solar phase (2016–2019). kurtosis have an empirical quadratic relationship [44]. In relevant ionospheric studies, this is considered an indication of the local dynamics of the plasma within the scattering ionospheric layer [45]. In this work, we also investigate the skewness and kurtosis profile using the data from 2010 to 2019 of all scintillation intensities, and the profile tends to collapse to the parabolic line, which is coincident with previous studies.
# Analyzing the Performance of the S3 Object Storage API for HPC Workloads
## 4 S3 Embedded
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Benchmark/System** & **Unit** & **Lustre** & **S3 Embedded** & **S3 Remote** \\ \hline ior-easy-write & GiB/s & 3.202073 & 0.990504 & 0.827451 \\ mdtest-easy-write & kIOPS & 13.548102 & 11.819631 & 0.240857 \\ ior-hard-write & GiB/s & 0.015462 & 0.215551 & 0.010041 \\ mdtest-hard-write & kIOPS & 5.615789 & 2.226332 & 0.111696 \\ ior-easy-read & GiB/s & 7.483045 & 0.375538 & 0.311502 \\ mdtest-easy-stat & kIOPS & 21.884679 & 20.489986 & 0.123976 \\ ior-hard-read & GiB/s & 0.095884 & 0.022462 & 0.005101 \\ mdtest-hard-stat & kIOPS & 17.428926 & 6.90457 & 0.125178 \\ mdtest-easy-delete & kIOPS & 8.995095 & 7.77739 & 0.10289 \\ mdtest-hard-read & kIOPS & 1.084843 & 1.287055 & 0.249273 \\ mdtest-hard-delete & kIOPS & 8.108844 & 6.711771 & 0.249282 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: IO500 results for Lustre vs. S3 Embedded using 10N-1 PPN. The radar chart in Figure 10 shows the relative performance of S3 embedded and S3 remote in percent for three independent runs of all benchmarks. Note that the three Lustre runs are so similar that they overlap in the figure. The graph clearly shows the performance gaps of the two libraries. For the sake of comparison, all relative performance numbers for the run with s3 embr-3 are listed. Only for the ior-hard-write phase, the number is close to 100%, while it often achieves 5-10% of Lustre performance. The embedded library also lacks performance for some benchmarks, but is much better.