esa-sceva/satcom-chunk-collection huggingface dataset:
# A LiDAR-Based Backfill Monitoring System
## 3 Methods
### Slurry Surface Fitting  
Similar to methods that combine the top surface of the load and the truck bucket to estimate load volume, the backfill monitoring system estimates the volume by combining the backfill material with the space below its boundary [25, 26]. Previous methods deployed LiDAR to scan the top surface of the trucks and directly used these data to reconstruct the load model. However, the slurry top surface remains nearly level over a large area according to the rheological properties of paste [31]. Instead of directly using the point cloud of the top surface, this method locates the entire slurry surface by using RANSAC (Random Sample Consensus, which fits models to data with outliers by randomly sampling subsets to find the best fit) algorithm to obtain the coefficients of the plane equation from data within the LiDAR range [32]. The coefficients indicate the location of the top surface in geometric space. This is equivalent to assuming that at locations far from the LiDAR, the head height remains the same as the rising slurry surface. The estimated volume would then represent the entire tunnel volume beneath the slurry top surface.  
If the plane-fitting algorithm is directly used for the point cloud model, either the slurry plane needs to be screened out among a large numbers of fitted planes, or the fitting results are possibly not correct. Since the results of each LiDAR scan are stored in chronological order, which is closely related to the process of the rising backfill slurry, as long as the subsequent scans are compared with the initial scans, the rising slurry surface with a few noise points can be screened out, as follows:  
The first ten scans are merged and named cloud_base. The subsequent point cloud models are merged by every ten scans and the merged model is named cloud_process. Each point in cloud_process is iterated and placed into the three-dimensional space of cloud_base. For each point in cloud_process, if there are fewer than three points from cloud_base within the surrounding spherical space defined by a radius threshold, this point in cloud_process should be classified as an outlier, so that the backfill slurry surface can be roughly screened out.  
On the basis of the preliminary screening, the RANSAC algorithm can be used to accurately find the backfill slurry surface, and the coefficient of the slurry plane equation can be obtained. The intersection boundary of the backfill slurry surface and the tunnel can be obtained by substituting the coefficients of the plane equation back to the tunnel.  
Figure 3: Backfill progress computation workflow.
## Materials and Methods
### Water Demand Estimation
The net irrigation requirement for the \(i\)th tertiary off-take (\(i=1\) to 120) on the \(t\)th day, [\(NIR_{t}(i)\)] during normal irrigation is expressed as [28]:  
\[NIR_{t}(i)=ET_{t}(k)+SP_{t}(k)-ER_{t}(k)+SW_{t}(k)-SW_{t-1}(k) \tag{20}\]  
The subscript k, indicates the ISA number, varies from 1 to 4; \(SW_{t}\) and \(SW_{t-1}\) is the required water depth (mm) on the \(t\)th day and the \((t-1)\)th day (depth of water at the beginning of the irrigation supply), respectively. The tertiary canals along the main canal are spaced every 400 m. Two tertiary canals supply irrigation water explicitly via a 50 mm diameter pipe to each plot. With the advancement of irrigation season, the recommended irrigation supply (allowable supply based on available water) for tertiary canals is determined, to achieve an equitable supply using [28]:  
\[q_{a}(i)=f_{i}\times\frac{a_{i}}{A_{k}}\times\left(Q_{av}\frac{A_{k}}{A}\right) \tag{21}\]  
where, \(q_{a}(i)\) is the recommended water supply for \(i\)th irrigation off-take structure (m\({}^{3}\)/s) for a particular day; and \(f_{i}\) is operation and management factor for command area under \(i\)th off-take structure. The factor depends on the management and operational decisions in the command area, under a tertiary canal, with respect to the available water for irrigation. Its value ranges from 0.5 to 1.0; 0.5 to ensure a 50 mm water depth in the paddy fields Thus, if the available irrigation water in the main canal is higher than the water demand in the field, \(f_{i}=1.0\). Moreover, the factor is calculated by considering the rice's water-sensitive growth stages in the ISAs if it is lower than the field-water demand. In Equation (21), A is the total irrigation command area of the scheme (ha), \(a_{i}\) is the irrigation command area under a tertiary canal (ha), \(A_{k}\) the total cropped area under an ISA (ha), and \(Q_{av}\) the average daily available discharge for irrigation supply (m\({}^{3}\)/s). Equation (21) estimates the daily or periodic irrigation supply for the tertiary canals. To allocate irrigation water to a particular ISA, the total recommended supply for tertiary canals under the ISA has to be lower than \(Q_{av}\) for any given irrigation cycle. To meet the recommended demand for field-water this criterion must always be met. The equal distribution of water can be accomplished through careful management and control of the off-take structures.
## 4 Results
### 3D Time Series Analysis of Selected Case Study Area  
A detailed 3D time series analysis was conducted to examine the temporal VUG dynamics within the Hanyang district. Utilizing the Stack-PSInSAR approach, this analysis provided a granular assessment of urban expansion, capturing shifts from the broader urban fabric of Wuhan down to the specific variances within distinct urban sectors.  
Figure 10 represents this analysis visually, with each data point on the 3D scatterplot corresponding to a 50 m by 50 m area, mapping out the vertical development from 2012 to 2020. Through this detailed visualization, we discern the progression of structural height increments within the district, providing a dynamic overview of the urban expansion.  
In 2012, the majority of the buildings in the district were below 25 m in height. As the years progressed, a notable upward shift in building heights was recorded, particularly after May 2015, reflecting a rapid construction of taller structures. By 2020, the architectural landscape had diversified significantly, featuring a mix of low-rise buildings to skyscrapers, some reaching as high as 200 m. The color coding of the scatterplot, with orange to dark orange points denoting buildings around 100 m and red to dark red indicating those over 140 m, highlights areas of intense vertical development.  
The detailed 3D model underscores the strength of the PSInSAR technique, beyond its conventional use for deformation monitoring. It proves instrumental in detailing the VUG patterns, especially when complemented by stack processing and post-processing. Such in-depth analysis is critical to identifying specific areas with pronounced height changes, enabling urban planners to understand and manage the complex dynamics of city development.  
Moreover, the time series approach provides valuable insights at the 50 m resolution. Analyzing each point over time within the 50-m grid reveals patterns of growth that are essential for understanding how different urban sectors evolve, especially in regions experiencing the most substantial vertical changes. The ability to track these increments over time not only illustrates the past and present landscape but also aids in forecasting future urban growth trajectories.  
Figure 10: 3D Scatterplot of VUG Development within the Hanyang district (as shown in a rectangle in Figures 1 and 8) from 2012 to 2020.  
A limitation, though, of the present analysis is that it is not possible to infer the impact of the COVID-19 pandemic emergency (if there were any) on building constructions, given that the satellite data stack used refers to the time interval until November 2020.
## 2 System and Channel Models
### Average Bit Error Rate
\begin{array}{ll}P_{b}^{RA}=\frac{q^{p}}{2\Gamma(p)}\sum\limits_{\tau=1}^{n}w _{\tau}\frac{B_{s,d}(x_{\tau})}{\Gamma\left(m_{s,d}\right)}\sum\limits_{t=0}^{ \infty}C_{s,d}G_{1,2}^{1,1}\bigg{(}\frac{m_{s,d}x_{\tau}}{\overline{\gamma}_{s,d}^{RF}}\bigg{|}\begin{array}{c}1\\ m_{s,d},0\end{array}\bigg{)}G_{2,3}^{2,1}\bigg{(}D_{s,d}(x_{\tau})\bigg{|} \begin{array}{c}E_{s,d},1\\ 0,E_{s,d},E_{s,d}\end{array}\bigg{)}\times\\ \Bigg{[}1-\left[1-\frac{B_{i,j}(x_{\tau})}{\Gamma\left(m_{i,j}\right)}\sum \limits_{t=0}^{\infty}C_{i,j}G_{1,2}^{1,1}\bigg{(}\frac{m_{i,j}x_{\tau}}{ \overline{\gamma}_{i,j}^{RF}}\bigg{|}\begin{array}{c}1\\ m_{i,j},0\end{array}\bigg{)}G_{2,3}^{2,1}\bigg{(}D_{i,j}(x_{\tau})\bigg{|} \begin{array}{c}E_{i,j},1\\ 0,E_{i,j},E_{i,j}\end{array}\bigg{)}\bigg{]}^{M}\Bigg{]}^{N}.\end{array}  \tag{19}\]  
When \(M=N=0\), Equations (18) and (19) are reduced to the BER for the hybrid FSO/RF direct link without PE and with PE, respectively.
###### Abstract  
The effect of Eu\({}_{2}\)O\({}_{3}\) concentration on the luminescence properties of double perovskite (cubic) Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors was thoroughly investigated using different synthesizing temperatures. Phosphors with the composition Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\), where Eu\({}_{2}\)O\({}_{3}\) was substituted for SrO and \(x\) was changed from 0 to 0.12, were synthesized by the solid-state method at temperatures of 900-1200 \({}^{\circ}\)C, respectively. Analysis of the X-ray diffraction (XRD) patterns showed that even when the synthesizing temperature was 1100 \({}^{\circ}\)C, secondary or unknown phases were observed in Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) ceramic powders. The effect of the concentration of Eu\({}^{3+}\) ions on the luminescence properties of the Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors was readily observable because no characteristic emission peak was observed in the Sr\({}_{2}\)ZnMoO\({}_{6}\) phosphor. Two characteristic emission peaks at 597 and 616 nm were observed, which correspond to the \({}^{5}\)D\({}_{0}\)-\({}^{7}\)F\({}_{1}\) and \({}^{5}\)D\({}_{0}\)-\({}^{7}\)F\({}_{2}\) transitions of Eu\({}^{3+}\) ions, respectively. The two characteristic emission peaks of the Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors were apparently influenced by the synthesizing temperature and the concentration of Eu\({}^{3+}\) ions. When \(x\) was larger than 0.08, a concentration quenching effect of Eu\({}^{3+}\) ions in the Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors could be observed. The lifetime of the Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors decreased as the synthesizing temperature increased. A linear relation between temperature and lifetime was obtained by using a fitting curve of \(t\) = \(-\)0.0016 \(\times\)\(T\) + 3.543, where \(t\) was lifetime and \(T\) was synthesizing temperature.  
Keywords:Sr\({}_{2-x}\)Eu\({}_{x}\)ZnMoO\({}_{6}\) phosphors; double perovskite oxides; synthesizing temperature; lifetime +
Footnote †: journal: Applied
# Hybrid Precoder Using Stiefel Manifold Optimization for Mm-Wave Massive MIMO System
## 5 Conclusions  
In this paper, a fast algorithm based on Stiefel manifold optimization on the sphere is proposed in terms of improved performance. This algorithm has high spectral efficiency, low complexity, and fast processing speed compared with existing state-of-the-art techniques such as the complex oblique manifold, the OMP algorithm, MO-AltMin, and PE-AltMin. The simulation results and the numerical observation show that the proposed algorithm is superior in terms of spectral efficiency and algorithm complexity aimed to im  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{4}{c}{**Spectral Efficiency (b/s/Hz)**} & \multirow{2}{*}{**Complexity**} \\ \cline{2-2} \cline{4-5}  & **SNR = \(-10\) dB** & **SNR = \(0\) dB** & **SNR = \(10\) dB** & \\ \hline OMP [11] & 15.4 & 24.07 & 40.8 & \(\mathcal{O}\big{(}N_{T}^{2}N_{FFT}Ns\big{)}\) \\ \hline MO-AltMin [14] & 15.9 & 25.3 & 42.4 & \(\mathcal{O}\big{(}N_{FFT}N_{T}^{3}N_{s}^{2}\big{)}\) \\ \hline PE-AltMin [14] & 15.7 & 25.1 & 41.9 & \(\mathcal{O}\big{(}N_{FFT}N_{s}^{2}\big{)}\) \\ \hline CO- Manifold [12] & 16.4 & 25.3 & 42.7 & \(\mathcal{O}\big{(}N_{FFT}N_{T}Ns\big{)}\) \\ \hline MO-MMSE [36] & 16.8 & 25.5 & 43.9 & \(\mathcal{O}\big{(}N_{FFT}N_{T}N_{s}^{2}\big{)}\) \\ \hline
**Proposed** & **16.9** & **25.5** & **44.1** & \(\mathcal{O}\big{(}N_{FFT}N_{T}N_{s}\big{)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of spectral efficiency and complexity for different algorithms for different values of the SNR.  
prove the processing speed. Many research papers show that performance does not always improve with the increase in RF chains. Therefore, future work could extend research in the field of the optimization of the number of RF chains.  
Conceptualization, D.S.; Formal analysis, D.S. and A.S.; Methodology, D.S., A.S. and M.S.; Resources, M.S. and K.L.H.; Supervision, A.S. and M.S.; Validation, M.S. and K.L.H.; Visualization, A.S.; Writing--original draft, D.S.; Writing--review and editing, M.S. and K.L.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Dong-A University research fund.  
Not Applicable.  
The authors declare no conflict of interest.
# A Nighttime and Daytime Single-Image Dehazing Method
## 2 Background Theory and Related Work
### Retinex Theory  
Retinex theory [4], in short, states that the color of an object is determined by the object's ability to reflect long-wave (red), medium-wave (green) and short-wave (blue) light rather than the absolute intensity of the reflected light. It can be expressed as follows:  
\[J(x)=R(x)A(x) \tag{4}\]  
where \(J(x)\) is the haze-free image, \(R(x)\) is the reflectance and \(A(x)\) is the ambient illumination.
# Temperature Prediction Model in the Main Ventilation System of an Underground Mine
## 3 Methods
### Model Used  
On the basis of data analysis, a generalized linear model with four parameters (see Equation (6)) and a linear model with interactions and seven parameters (see Equation (7)) were chosen for this study. A sensitivity analysis was carried out to assess the significance of the parameters from the model [39],  
\[t_{f}=a_{0}+a_{1}t_{i}+a_{2}D_{x}+a_{3}D_{c}, \tag{6}\]  
\[t_{f}=a_{0}+a_{1}t_{i}+a_{2}D_{x}+a_{3}D_{c}+a_{4}t_{i}D_{x}+a_{5}t_{i}D_{c}+a_ {6}D_{x}D_{c}, \tag{7}\]  
where t\({}_{i}\) is the temperature at the beginning of the stretch (\({}^{\circ}\)C), t\({}_{i}\) is the temperature at the end of the stretch (\({}^{\circ}\)C), D\({}_{x}\) is the length of the stretch (m), and D\({}_{c}\) is the airflow change along the stretch (m\({}^{3}\)/s).  
The model estimated the daily mean temperature at the end of the stretch as a function of the daily mean temperature at the beginning of the stretch, the length of the stretch, and airflow variation. The prediction was achieved using information from node to node, as well as with a multi-node approach. Despite the quantity of data being very large, it was necessary to obtain all parameters in all nodes to use the models proposed in Equations (6) and (7). The final amount of data could be substantially reduced due to various causes, such as sensors being broken or lost due to aggressive operation conditions, abnormal values due to heavy equipment working close to the sensor, or sensor malfunction. The use of a multi-node approach with a lag step allows obtaining more data. In this study, the following lag steps were applied:  
- Lags step of one node, using six stretches created by nodes 1, 2, 3, 4, 5, 6, and 7;  
- Lag step of two nodes, using five stretches created by nodes 1-3, 2-4, 3-5, 4-6, and 5-7;  
- Lag step of three nodes, using four stretches created by nodes 1-4, 2-5, 3-6, and 4-7.  
In addition, the multi-node system reduces the tendency toward the prediction of mean values. Figure 4 displays the variables used in the model, while Table 1 details the length, D\({}_{x}\), and the airflow variation along the intake, D\({}_{c}\).  
Figure 3: Scatter plot of the effective temperatures (\({}^{\circ}\)C) measured in situ, corresponding to the seven nodes.  
Airflow variation was caused by its allocation depending on mine planning and air requirements in different workplaces, which could vary over time. Moreover, stretches S3 and S4 had a small reduction in airflow due to leakages in the circuit.
# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 5 Results
### Scalability Analysis  
To evaluate the scalability of the data-partitioning strategy based on grid segmentation proposed in this study, experiments were conducted on datasets of different sizes and with different numbers of processes. The experimental results (as shown in Figure 11) indicate that as the number of processes increased, the parallel reading time did not continue to decrease, but instead showed a trend of first decreasing and then gradually increasing. When the number of processes exceeded a certain value, the parallel reading time increased sharply. This is because file parallel reading is also limited by disk hardware, network bandwidth, etc. Starting multiple processes also incurs overheads such as context switching and inter-process communication. By comparing the experimental results of DATASET1 and DATASET5, it was found that when the data volume had not reached a certain scale, the overheads such as context switching and inter-process communication in the parallel environment occupied a large proportion of the total parallel time, so the best parallel effect could not be achieved. When the number of processes exceeded a certain value, the time consumed by these additional overheads greatly increased in proportion to the overall parallel reading time. Therefore, when performing parallel reading, it is not a case of the more processes the better, but rather that the appropriate number of processes must be selected according to the specific data to achieve the optimal parallel effect.  
Figure 11: Illustration of the trend in the time required to read different datasets as the number of processes increases.  
Figure 10: Using the data-partitioning strategy based on grid segmentation, 4 processes were initiated to load the 5 datasets in Table 1. Also shown is the number of grids maintained by each process after grid segmentation.
# Business Impact Analysis of AMM Data: A Case Study
## 5 Results
### Risk Assessment by BIA Guidelines by Asset
#### 5.2.11 Measuring Point C4 (MPC4)  
For Category C4 measurements with possible long-distance communication, the MP can be read remotely. In most cases, it will be read by a worker in the field. In the event of a communication failure for the few sites that will be remotely read, a field worker will be sent to the site. Of the Type C meters, this is one of the least represented groups of electricity meters. _Legal and contractual obligations_: For MPC44, field reading should occur once a year, and thus there would be limited violations of Decree No. 359/2020 and Decree No. 540/2005, as well as Law No. 458/2000 and Decree No. 408/2015. Therefore, the risk is set as low. _Management and operation of the organization_: The management and operation of the organization would not be significantly affected by a large-scale communication failure, as only a small percentage of consumption points would be remotely read. The only thing that could affect the management and operation at CEZ Distribuce, a.s., is an outage of other customer sites. This could mean that the meter readings would not be completed in time. Therefore, the risk of impact is rated as medium. _Loss of credibility_: The only impact on loss of confidence could be a delay in bills due to late readings. However, this would only affect a small number of MPs; therefore, the credibility of the company would not suffer. Therefore, the risk is low. _Financial loss/disruption to operations_: In this case, only a small financial loss could occur, as this is a small group of OMs. Therefore, the risk is low. _Provision of essential services_: This is the least numerous type of metering in terms of the number of consumption points; therefore, only a low impact on society can be expected in the event of a large-scale communication failure in the form of unavailable services. A comprehensive assessment of the risks and parameters of data availability and maturity is presented in Table 23.
# Adaptive Feature Fusion for Small Object Detection
## 4 Analysis of Experimental Results  
### Ablation Experiment Results  
In order to verify the effectiveness of the proposed modules in the MMF-YOLO algorithm for small objects, the VisDrone dataset was used to perform ablation experiments on each module based on the YOLOXs model.  
Head in Table 4 represents the detection head with the modified size, and Experiment # represents the experimental results of the basic network. Experiments 1 and 2 indicate that the base network was added to MMF-Net with the adaptive fusion factor and MMF-Net without the fusion factor, respectively.  
Comparing the two with the basic network, it can be found that the proposed MMF-Net multi-branch cross-scale skip connection fusion module could effectively improve the detection accuracy of small objects, increase mAP to more than 35%, and only sacrificed a small number of parameters. Compared with the baseline, they increased by 2.63% and 2.42%, respectively. Comparing the two, it can be seen that the fusion factor \(\alpha\) could further improve the accuracy of small object detection and increase the mAP of small objects to 35.73% without bringing any parameters.  
Experiment 3 indicates that adding the improved M-CBAM attention mechanism to the base network increased mAP by 0.44%. Experiment 3 showed that when the improved Head module was added, the mAP of the model increased by 3.41% compared with the basic network, and the size of parameters was reduced by 6.44 MB. It was proven that the added large-size detection head was better than the small-size detection head for the task of small object detection, and would reduce a lot of computation. Experiment 3 means that the \(\alpha\)-MMF-Net module and the M-CBAM attention mechanism and the improved detection head were added to the network at the same time, and its mAP reached 42.23%. Compared with the basic network, the mAP increased by nearly 10%, and the number of model parameters was simplified to 29.33 MB, which was reduced by nearly 5 MB compared to the original network, which verified the effectiveness of the proposed module for small object detection tasks.
## 2 Materials and Methods
### Micro-CT Scanning Protocol and Reconstruction  
The molars were scanned with a high-resolution micro-computed tomographic scanner (SkyScan 1172; Bruker microCT, Kontich, Belgium) three times: before procedures for guaranteeing the homogeneity of samples and identifying oval-shaped canals; after instrumentation; and after applying the brushing motion. The x-ray tube was activated at 130 kV and 61 \(\upmu\)A (1 mm-thick Al filter), and the scanning was made by 360\({}^{\circ}\) rotation around the vertical axis with a rotation step of 0.5\({}^{\circ}\), a resolution of 5.66 \(\upmu\)m, and 1800 ms exposure. The images were converted into cross-sectional slices by the SkyScan software interface (NRecon v.1.6.4; Bruker micro-CT) with standardized indices for beam hardening (100%), ring artifact correction of 10, and setting of standardized min and max contrast levels (0-0.3).