esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Approach to Predicting the Diffusion Radius of Fracture Grouting in Soil-Rock Mixtures
## 3 Validation Tests of the Calculation Theory of Grout Diffusion in Soil-Rock Mixtures
### Field Grouting Tests  
#### 3.2.1 Test Conditions  
A field grounding test was carried out to verify the applicability of the calculation theory of the diffusion radius of fracture grouting. Based on the tunnel project between Wangjiacheng Station and Shengjibao Station of Chongqing Rail Transit Line 4, the grouting test was carried out on the ground surface at the beginning of TBM on the right line of YK47 + 780.110-YK48 + 011.110. A total of 3 point positions were arranged in this test in a single line with an interval of 2 m to prevent cross-grouting during the test. The test hole depth was set at 3 m, convenient for grouting effect verification through excavation. The grouting of the test hole was a single grouting under the same depth (3 m). The layout of the grouting test holes is shown in Figure 8.  
The grouting test was conducted under a grouting pressure of 0.5 Mpa and a void ratio of 0.35. Slurry: ordinary Portland cement slurry; water-cement ratio: 0.6:1, 0.8:1, and 1:1. The stone content of the soil-rock mixtures measured on site was approximately 30%. The conditions of each grouting test hole are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multirow{2}{*}{**Void Ratio**} & \multirow{2}{*}{**Nonuniformity Coefficient**} & \multirow{2}{*}{**Coefficient of Curvature**} & \multirow{2}{*}{**d20 (mm)**} & \multirow{2}{*}{**Internal**} & **Shearing Strength** & \multirow{2}{*}{**Cohesive**} & \multirow{2}{*}{**Mopolous**} \\  & & & & & & & & \\  & \(e\) & Cu & \(Cc\) & & \(\boldsymbol{\varphi}\) (\({}^{\circ}\)) & \(\boldsymbol{\sigma}_{e}\) (kPa) & & \\ \hline
1 & 0.3 & 37.00 & 1.32 & 0.23 & 16.7 & 110 & 23 & 7.17 \\
2 & 0.35 & 37.00 & 1.32 & 0.23 & 16.7 & 110 & 23 & 7.17 \\
3 & 0.4 & 37.00 & 1.32 & 0.23 & 16.7 & 110 & 23 & 7.17 \\
4 & 0.3 & 56.43 & 2.93 & 0.61 & 24 & 148 & 15.3 & 7.35 \\
5 & 0.35 & 56.43 & 2.93 & 0.61 & 24 & 148 & 15.3 & 7.35 \\
6 & 0.4 & 56.43 & 2.93 & 0.61 & 24 & 148 & 15.3 & 7.35 \\
7 & 0.3 & 29.17 & 3.72 & 2.5 & 29.1 & 175 & 12.1 & 8.3 \\
8 & 0.35 & 29.17 & 3.72 & 2.5 & 29.1 & 175 & 12.1 & 8.3 \\
9 & 0.4 & 29.17 & 3.72 & 2.5 & 29.1 & 175 & 12.1 & 8.3 \\
10 & 0.3 & 22.50 & 0.43 & 0.15 & 13 & 107 & 37.4 & 7.12 \\
11–13 & 0.35 & 56.09 & 3.79 & 1 & 16.7 & 110 & 23 & 7.17 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Injected media parameters.  
Figure 8: Grouting hole layout plan.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multirow{2}{*}{**Stone Content (\%)**} & \multirow{2}{*}{**Void Ratio**} & \multirow{2}{*}{**Water-Cement Ratio**} & \multirow{2}{*}{**Grouting Pressure**} \\  & & & & & (Mpa) \\ \hline
11 & 30 & 0.35 & 0.6 & 0.5 \\
12 & 30 & 0.35 & 0.8 & 0.5 \\
13 & 30 & 0.35 & 1.0 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Grouting test condition.
## 2 Materials and Methods  
### Image Data Collection  
The image datasets were prepared using images captured by the multi-camera system in the outdoor environment. The multi-camera system contains 3 camera sensors: visible, NoIR, and thermal, as shown in Figure 1. Each camera is connected to a Raspberry Pi 3 Model B+ for image processing and data storage. The visible camera, on the right side of the figure, uses a Sony IMX219 camera sensor with an image resolution of 8 megapixels. The NoIR camera system, on the left side, consists of a camera module with a 5-megapixel Omnivision 5647 sensor (without an infrared filter), and a pair of infrared LEDs. The thermal camera, in the center, is a Seek Thermal CompactPro with a resolution of 320 \(\times\) 240 pixels, connected to the Raspberry Pi module via a USB interface, as shown in the figure; its vertical position is above both the visible and NoIR cameras. The image data were also sent to Google Drive every 5 min for easy data storage and access. The images were captured continuously for a whole day (day and night). The proposed camera systems provide a low-cost solution, where the prices of the Raspberry Pi module and visible, NoIR, and thermal cameras are about USD 45, USD 7, USD 28, and USD 384, respectively.  
The plant used for image data collection was _Veiteria zizanioides_, planted in polybags and put in an outdoor environment (yard) during image collection. Photographs of the plants and the environment taken by the visible, NoIR, and thermal cameras are shown in Figures 2-4, respectively. Due to the arrangement of the three cameras (as shown in Figure 1), the image viewpoint of each camera is different. Therefore, to make a fair comparison, we employed image warping to transform the images taken by the NoIR and thermal cameras to the match the reference image taken by the visible camera. In addition, to optimize the cameras' resolution and the computation time of the image processor, all images were resized to 600 \(\times\) 400 pixels. It is noted that even though the resized images of the thermal camera are larger than the original ones, it will not affect the performance significantly for the following reason. A thermal camera captures images based on the thermal energy emitted by the objects; thus, the parts of things appear in uniform color (as shown in Figure 4) rather than the precise color as with the visible camera. Therefore, resizing an image to a higher resolution does not change the detail of objects significantly. Figure 3a,b depict the original and transformed NoIR images, respectively. Figure 4a,b depict the original and transformed thermal images, respectively. The figures show that the viewpoints of the NoIR and thermal images are similar to that of the visible image, in the sense that the objects and their orientation in the images are almost the same. For instance, the leaves, polybags, and background objects appear in nearly the same position or area in  
Figure 1: Hardware arrangement of multi-camera system.
## 5 Material and Methods
### Laboratory Work
#### 5.2.1 Postprocessing of Data from Benzu Rock Shelter  
First, 3D laser scanning data were processed with Leica Cyclone Register 360, which revises the manual alignment undertaken with the app in Ipad Pro, and each link was checked individually to improve the percentage of overlap as much as possible. This led to a bulk point cloud error of 0.004 m, an 86% overlap, and a strength of 67%. After this, the model was exported to.pts and.e57 formats. The one used was the latter, because it not only deals with point clouds but also with the 360 HDR images to combine the laser scanning and photogrammetric data. Agisoft Metashape Professional 1.8.5 was used to process the photographs taken in the field. All photographs were imported alongside a laser scanning model and the.e57 file based on the laser scanning data. This allows the software to recognise the model and extract the 360 HDR images that will be aligned with the photographs taken manually with a Nikon reflex camera.  
The workflow to generate the final model was as follows:  
Figure 4: Recording by 3D laser scanning ofBenzü rock shelter. The location of the ranging pole marks the stratigraphic sequence excavated in 2014.  
1. Alignment. The software detects key points in the photographs (stereo couples) to calculate the position of the camera. This first step generates a disperse cloud that presents a preliminary image of the 3D model.
2. Dense point cloud. This point cloud is denser and allows for corrections, including the filtering of points according to confidence. This allows for the assessment of the quality of the 3D model generated, and the greater the confidence level, the more accurately the model represents reality.
3. Generation of the mesh, which is a 3D geometric model. This model is composed of vertexes (points), edges (unions between points), and polygons (the face formed by linking two or more points in a closed circuit). The model allows for full-colour previsualisation.
4. Texturing. This process lays an image over the mesh, adding the colours calculated based on the key points to give the final 3D model a photorealistic finish.
###### Abstract  
In the variable frequency motor drive system, because the cable impedance does not match the motor impedance, the reflection wave of the voltage wave will be generated. The superposition of reflected voltage waves can lead to overvoltage at the motor ends, which can damage the insulation structure. In this paper, the equivalent circuit models of cable and stator winding are established, respectively. The overvoltage distribution under different power supply frequencies and cable lengths is simulated and analyzed. The influence mechanism of power supply frequency and cable length on the overvoltage distribution of stator winding are studied. The simulation results show that the overvoltage of the first pulse falling edge will be superimposed on the overvoltage of the second pulse rising edge under high-frequency conditions, resulting in a further increase in the overvoltage. The voltage appears in all coils after the middle of the winding. The ground voltage is up to 1.32 times the input voltage, and the inter-turn voltage is up to 9.2 times the average voltage. The increase in cable length will lead to an increase in ground voltage, but the increase in speed will slow down after exceeding the critical length of 300 m. The maximum ground voltage can reach 1.93 times of the input voltage, which is 3.6% different from the calculation result under ideal conditions. The inter-turn voltage changes with the cable length in an N-shaped manner, up to 185 V. The results of this paper are of great significance to further study the insulation design of generator end input.  
variable frequency motor; voltage distribution; wave process +
Footnote †: journal: Applied Sciences
# A Back-to-Back Gap Waveguide-Based Packaging Structure for E-Band Radio Frequency Front-End
## 2 Unit Design of the Packaging Structure
### Back-to-Back GW Design  
The GW structure is composed of parallel metal plates and periodic nails. When the air gap between nails and plate is less than a quarter wavelength, it can be equivalent to an artificial magnetic conductor (AMC), effectively preventing electromagnetic wave leakage. As illustrated in Figure 4, with specific dimensions of \(s=0.4\) mm, \(g=0.4\) mm, \(ga=0.05\) mm, and \(h=0.8\) mm, the stopband ranging from 60.4 to 163.4 GHz is encompassed.  
As illustrated in Figure 5, the proposed back-to-back gap waveguide (GW) differs from other components of the multi-layer GW structure. It primarily consists of a central metal plate flanked by two layers of periodic nails. Covering metal plates are then added to both  
Figure 4: Dispersion diagram of the EBG.  
Figure 3: Processing model of the final packaging structure and diagram of the middle metal plate.  
sides of this central plate, resulting in a back-to-back GW structure. This structure differs from the straightforward stacking of multi-layer gap waveguides. This design minimizes the effects of air gaps and assembly misalignment on performance, while also simplifying processing and assembly.
## 3 Sampling and Analysis  
Geochemical mapping is an important tool for both mineral exploration and environmental studies [32]. China's National Geochemical Mapping Project (Regional Geochemistry National Reconnaissance) was initiated in 1979 [33], and has covered more than 6 million km\({}^{2}\) of China's territory [34]. The research area, located in the Luliangshan area, NQ, Western China, is believed to have high potential for Cu, Co, Ni, Au mineral resources. As part of the regional geochemical exploration program, 6481 stream sediment samples were collected from an area of 66,900 km\({}^{2}\) (latitude 94\({}^{\circ}\)45\({}^{\prime}\)-95\({}^{\circ}\)27\({}^{\prime}\) E, longitude 37\({}^{\circ}\)32\({}^{\prime}\)-38\({}^{\circ}\)00\({}^{\prime}\) N). For the purpose of verifying the feeble geochemical anomalies, more than 600 stream sediment samples at a density of eight samples per square kilometer were collected within an area of about 80 Km\({}^{2}\) in the research area (Figure 3).  
The sample collection and chemical analytical methods for stream sediments can be found in [PERSON] et al. [33]. We selected 11 elements that are closely associated with mineralization for this research. The detection limits for Cu, Cr, Pb, Sb, Zn, Ag, Au, As, Cd, Co, and Ni were 1, 15, 2, 0.1, 10, 0.02, 0.0003, 1, 0.05, 1, and 2 ppm, respectively. The samples were analyzed for major and trace anions using ion chromatography on a Dionex 4000i Ion Chromatograph, with an AS14 analytical column for both UV/visible absorbance detection and conductivity [37]. X-ray fluorescence (XRF) was utilized to determine the concentrations of Cu, Co, Zn, and Cr, while inductively coupled plasma-mass spectrometry (ICPMS) was used to determine the concentration of Cd. Graphite furnace-atomic absorption spectrometry (GF-AAS) was employed to measure the Au concentrations and hydride generation-atomic fluorescence spectrometry (HG-AFS) was applied to obtain Sb and As concentrations. Emission spectrometry (ES) was used to determine the Ag  
Figure 3: Map of deposits and sampling locations in the NQ research area [35; 36].  
concentrations [26; 33; 34; 38]. The statistical characteristics of stream sediment elements from L\(\rm\acute{u}\)liangshan and Shuangkoushan in the NQ area are presented in Table 1.
## 2 Materials and Methods
### Methodology
#### 2.2.1 Deep Learning for Data Integration  
The observed raw transcriptomic dataset was divided into five batches, with each batch labeled to indicate the source subject of the cells collected. To create a unified dataset from the five batches, we utilized a state-of-the-art tool for integrating single-cell data. We applied SCALEX [10], a deep-learning technique that integrates single-cell data by projecting cells into a batch-invariant, shared cell-embedding space without the need for model retraining. The core of the SCALEX approach is a variational autoencoder (VAE) that maps distinct dataset batches into a common low-dimensional space free from batch effects. To achieve this, the authors trained a batch-free encoder and a batch-specific decoder within the VAE framework. More specifically, the encoder component of SCALEX is batch-free and extracts biologically relevant latent features, while the decoder is batch-specific and uses batch information and a domain-specific batch normalization layer (DSBN) to reconstruct the original data from the latent features. The encoder maps individual cells to a common space using a generalized projection function that enables the integration of new data without requiring model retraining. Furthermore, SCALEX employs a mini-batch approach where data are sampled from all batches and subjected to batch normalization to adjust for deviation and align with the overall input distribution.  
To integrate the data, we first classified the five batches based on the health status of individuals from whom the cells were collected, categorized as a \"normal control\" \"AD\" (indicating [PERSON]'s disease). We then merged the batches into a single dataset and employed SCALEX to adjust the batch effects. We used the default parameters for this analysis. After preprocessing and integration/correction, the dataset comprised 35,685 (22,775 AD and 14,074 NC) cells and 2000 genes. Figure 2 shows four data tSNE plots depicting the data before and after integration/batch effect correction with SCALEX, with color-coded batch names and disease types. In addition, we utilized three other cutting-edge techniques for integrating single-cell data, underscoring the capacity of SCALEX (see Supplementary Materials and Figures S1-S3).  
Figure 1: Overview of the experimental computational workflow. FS: feature selection; scRNA-seq: single-cell RNA sequencing.
## 4 Key Design Requirements and System Components
### Layer 2: NW Subscribers  
Layer 2 contains the NW subscribers, whiq represent the end-users or entities within the wireless communication ecosystem that regularly seek NW coverage and demand high  
Figure 1: Proposed federated NextG NWs: High-level architecture and system components.  
quality wireless services. These subscribers can range from individual consumers to large organizations, all of whom rely on the NW for their communication needs. NW subscribers are required to have subscription identification credentials to access the NW and its services, ensuring that only authorized users can utilize the services they subscribe to.
## 2 Fuel Properties of Rice Husk and Rice Straw
RH and RS are usually located in the CHL region and in some cases also in the HLC region in Figure 2a [111]. Figure 2b shows fuel ashes dominated by components in the silicon, calcium, and potassium corners or between the calcium and potassium corners with S, C, K, and CK types, respectively [184]. Moreover, sub-types of HA, MA, LA represent high, medium, and low acid tendencies, respectively [184]. In Figure 2b, wood indicates the mean value of 249 datasets for evaluated wood pellet samples presented by [PERSON] et al. [185; 92]. According to Figure 2b, both RH and RS have very low amounts of CaO + MgO + MnO in their fuel ash compositions compared to wood. On the other hand, the silica-based group of metal oxides (SiO\({}_{2}\) + Al\({}_{2}\)O\({}_{3}\) +Fe\({}_{3}\)O\({}_{4}\) + Na\({}_{2}\)O + TiO\({}_{2}\)) is higher in both RH and RS fuel ashes compared to wood fuel ash. Among the RS and RH fuel ashes, RH fuel ash has higher K\({}_{2}\)O + P\({}_{2}\)O\({}_{5}\) + SO\({}_{3}\) + Cl\({}_{2}\)O in its inorganic composition. Figure 2b was defined based on empirical findings regarding ash melting tendencies of dedicated fuel assortments and can be divided into three distinct sections with low (<1100 \({}^{\circ}\)C), medium (1100-1300 \({}^{\circ}\)C) and high (>1300 \({}^{\circ}\)C) initial deformation ash fusion temperature (DT) [184]. RH is located close to the area associated with high DT, Figure 2b. Accordingly, it is expected that RH shows low melting tendency during the combustion process. In contrast, RS is located near the area associated with low DT; therefore, it is anticipated that RS shows a higher melting tendency during the combustion process than RH. However, some data points in Figure 2b are outside of the defined areas for which ash melt investigations are available, and it indicates some research gaps in knowledge. Therefore, scientific work must be carried out here to close the gaps. According to Table 1 and Figure 2b, the silicon content of both RH and RS is high and suitable ash melting temperatures can be expected. However, there are several strategies to improve the purity of biogenic silica, which are discussed in Section 3. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Location** & **SiO\({}_{2}\)** & **Na\({}_{2}\)O** & **K\({}_{2}\)O** & **CaO** & **MgO** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{5}\)** & **Po\({}_{2}\)O\({}_{5}\)** & **SO\({}_{3}\)** & **Cl** & **Others 1** & **Method** & **Ref.** \\ \hline
**Egypt** & 57.90 & N.R. & 14.60 & 2.30 & 3.40 & 1.00 & 1.00 & 13.80 & 3.20 & 2.30 & 0.50 & XRF 2 & [98] \\
**Cambodia** & 80.18 & N.R. & 3.89 & 1.55 & 0.81 & 0.83 & 4.29 & 3.80 & 2.05 & N.R. & 2.60 & XRF 2 & [72] \\
**Italy** & 83.20 & N.R. & 2.73 & 1.60 & 1.00 & 0.33 & 3.57 & 4.12 & 1.52 & N.R. & 1.92 & XRF 2 & [72] \\ \hline \hline \end{tabular}  
* other measured inorganic oxides. 2
* X-ray fluorescence.  
\end{table} 
Table 2: Accompanying ash constituents of RH (wt % db) from different locations.
## 3 Control Algorithm
### Objective Function  
The error during the tracking process is the difference \(\gamma\) between the renewable energy output and the aggregate air conditioning loads, as shown in Equation (7). Then we establish an objective function with the goal of minimizing overall tracking error, as described by Equations (8) and (9):  
\[Y_{o}=Y_{s}+\gamma \tag{7}\]  
\[F=\min\sum_{j=1}^{\tau}\left[Y_{o}(j)-Y_{s}(j)\right]^{2} \tag{8}\]  
Figure 4: Load fluctuation curve.  
\[Y_{s}(j) = \sum\limits_{i=1}^{m}\beta(i)Y(j)(i)\] \[m<\tau\]  
The cycling time of the tracking process is \(\tau\). \(Y_{o}\in R^{\tau\times 1}\) is the renewable energy output during \(\tau\) minutes. The fitting vector is \(Y_{s}\in R^{\tau\times 1}\), which represents the aggregate air conditioning loads within \(\tau\) minutes. Load curve combination is \(Y\in R^{\tau\times m}\). It represents the \(m\) groups of selected load curves. The coefficient \(\beta\in R^{m\times 1}\) is the multiplier of the \(m\) groups load curves, representing the number of controlled air conditionings in each group.  
\(Y_{s}\) is a \(\tau\times m\) order equation. \(\tau\) is the number of equations and \(m\) is the number of unknown variables. If there are sufficiently abundant load curves in set \(Q\), the objective function is essentially an over-determined equation, i.e., \(m<\tau\). The least square method is an effective method for solving over-determined equations. Therefore, this paper uses the least square method to solve unknown coefficients.  
Actually, the fitted value of the controlled loads should not exceed the actual renewable energy output so as to make no burden on the power grid, and \(Y_{s}\) should be less than \(Y_{o}\) (i.e., load deviation \(\gamma>0\)). Moreover, the multiplier of the load curve represents the required number of loads for each group and must be non-negative. The constraints can be described as follows:  
\[\beta(i)\geq 0,\,i\in[1,m] \tag{10}\]  
\[Y_{s}(j)\leq Y_{o}(j),\,j\in[1,\tau] \tag{11}\]
## 4 Network Layer: The Main Building Block of Fixed Networks
### Five Generations of Backbone Networks: From PDH to OTN/SDN
#### 4.2.2 Machine Learning
* The comparative evaluation of network efficiency when multi-slice QoT-aware RSA approach is used versus the traditional single-slice QoT-aware RSA approach.