esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Study of Large Uncoupling Coefficient Loading for Surface Blasting
## 2 Classical Theory of Uncoupled Charge Smooth Blasting
### Smooth Blasting Theory  
In order to achieve better smooth blasting effects, the impact pressure acting on the wall of the hole should not be greater than the compressive strength of the rock under the action of the explosive load. Radial uncoupled continuous charge and axial uncoupled charge mode are used to reduce the damage to the blast wall surface, as discussed below:  
(1) Radial uncoupled continuous charge: Under the condition of this uncoupled charging, the impact pressure \(P_{2}\) acting on the blast wall can be derived using Equation (3).  
(2) Axial uncoupled charge: In this case, the explosive properties are not changed, and blasting products are distributed homogenously. In accordance with this assumption, the density \(\rho\) of blasting products in the air column is  
\[\rho=\rho_{{}_{0}}(\frac{l_{c}}{l_{c}+l_{a}}) \tag{4}\]  
The impact pressure \(P_{3}\) acting on the blast hole wall under this condition can be calculated as  
\[P_{3}=\frac{\rho_{0}{D_{1}}^{2}}{8}(\frac{d_{c}}{d_{b}})^{6}(\frac{l_{c}}{l_{ c}+l_{a}})n \tag{5}\]  
where \(l_{a}\) is the length of the air column; \(l_{c}\) is the charge length. If the length of mud in the hole is neglected, \(l_{a}+l_{c}=l_{b}\), wherein \(l_{b}\) is the length of the borehole. Assuming that the impact pressure \(P_{3}\) is not greater than the compressive strength of the rock, the charge coefficient \(l_{L}\) can be obtained from Equation (5):  
\[l_{L}=\frac{l_{c}}{l_{b}}\leq\frac{8K_{b}S_{c}}{n\rho_{0}D^{2}}(\frac{d_{b}}{d _{c}})^{6} \tag{6}\]  
where \(S_{c}\) is uniaxial compressive strength; \(K_{b}\) is rock amplification factor of rock under dynamic loading.  
The above equation is derived based on the three assumptions stated in Section 2.1. The derived results can only be correct under the condition of \(PV^{K}\) = constant (\(n\) = 3). However, due to the large uncoupling coefficient in large diameter drilled holes, the air gap is large, and the expansion of the blasting products cannot strictly comply with the conditions of \(PV^{K}\) = constant (\(K\) = 3). Therefore, based on the blast theory, the large uncoupling coefficients should be induced according to \(PV^{K}\) = constant (\(K\) = 3) (\(n\) is arbitrary number).
## 1 Introduction
### Contributions  
This paper proposes novel efficient data-driven channel prediction algorithms that reduce pilot requirements by integrating transfer and meta-learning with a novel long-short-term decomposition (LSTD) of the linear predictors. Unlike the prior articles reviewed  
Figure 1: Illustration of the frame-based transmission system under study. At any frame \(f\), based on the previous \(N\) channels \(h_{l,f}^{N}\), we investigate the problem of optimizing the \(\delta\)-lag prediction \(\hat{h}_{l+\delta,f}\).  
above, the proposed methods apply to multi-antenna frequency-selective channels whose statistics change across frames (see Figure 1). Specific contributions are as follows.
* We develop efficient predictors for single-antenna frequency-flat channels based on transfer/meta-learned quadratic regularization. Transfer and meta-learning are used to leverage data from multiple frames in order to extract shared useful knowledge that can be used for prediction on the current frame (see Figure 2).
* Targeting multi-antenna frequency-selective channels, we introduce the LSTD-based model class of linear predictors that builds on the well-known disaggregation of standard channel models into long-term space-time signatures and fading amplitudes [5; 41; 42; 43; 44]. Accordingly, the channel is described by multipath features, such as angle of arrivals, delays, and path loss, that change slowly across the frame, as well as by fast-varying fading amplitudes. Transfer learning and meta-learning algorithms for LSTD-based prediction models are proposed that build on equilibrium propagation (EP) and alternating least squares (ALS).
* Numerical results under the 3 GPP 5G standard channel model demonstrate the impact of transfer and meta-learning on reducing the number of pilots for channel prediction, as well as the merits of the proposed LSTD parametrization. Part of this paper was presented in [45], which only covered meta-learning for the case of single-antenna frequency-flat channels. As compared to [45], this journal version includes both transfer and meta-learning, and it addresses the general scenario of multi-antenna frequency-selective channels by introducing and developing the LSTD model class of linear predictors.
# Toward Sustainable Mars Exploration: A Perspective on Collaborative Intelligent Systems
## 5 Collaborative Energy-Aware Computing
### Radiation Hardening  
When designing systems for extraterrestrial missions, important considerations include adequate device shielding, radiation effects (cosmic rays and secondary particles), and material impurities, such as radioisotopes in chip packaging [71]. These can cause malfunctions in electronic devices that can be classified into three categories: total ionization dosage, displacement damage dosage, and single-event effects [72]. While hardware optimizations, such as using bipolar integrated circuits and wide band-gap substrates [73], can contribute towards radiation hardening, software optimizations can also be used in cases of single event effects. These can lead to transient faults that can cause soft errors, where a bit in the memory can be flipped from zero to one or vice versa.  
While industry-standard methods, such as triple module redundancy (TMR) [74], can be applied to improve the tolerance against soft errors in low-resource overhead tasks, resource consumption becomes enormous when a similar technique is applied for heavy computing tasks, particularly executing AI algorithms. Hence, multiple optimization techniques have been proposed both at the hardware and software levels to improve the fault tolerance of AI algorithms [75]. One such technique for neural networks is called selective multiply-accumulate zero optimization (SMART), where the multiply-accumulate operation associated with a neuron is skipped if the input value is zero [76]. Other techniques such as fault-masking and activation clipping have also been proposed to improve the fault tolerance of AI [75]. These techniques can be used to improve the radiation hardening of various AI tasks executed by the robotic swarms on Mars while remaining energy efficient.
## 3 Results
### Results of Yolov5 Models
#### 3.1.5 Performance of Each Class on the Base and Improved Models  
A comprehensive view of the performance of each model across different classes before the introduction of the modules is presented in Table 8 and after in Table 9. The mAP@50 of each class is summarized below:  
* Occluded: The mAP@50 improved from 0.71 to 0.73 for Yolov5n, decreased from 0.821 to 0.8 for Yolov5n, and also decreased from 0.82 to 0.808 for Yolov5m;
* Displaced: The mAP@50 for Yolov5n decreased from 0.796 to 0.786, Yolov5s recorded increase from 0.736 to 0.971 while mAP@50 increase from 0.771 to 0.8;
* Perforated: The mAP@50 increased from 0.875 to 0.876 for Yolov5n, increased from 0.872 to 0.903 for Yolov5s, and increased from 0.887 to 0.919 for Yolov5m;
* Faded: The mAP@50 decreased from 0.764 to 0.719 for Yolov5n, increased from 0.765 to 0.795 for Yolov5s, and increased from 0.788 to 0.832 for Yolov5m;
* Rusted: The mAP@50 increased from 0.872 to 0.927 for Yolov5n, increased from 0.884 to 0.892 for Yolov5s, and increased slightly from 0.915 to 0.919 for Yolov5m;
* Defaced: The mAP@50 increased from 0.76 to 0.785 for Yolov5n, increased from 0.778 to 0.827 for Yolov5s, and decreased from 0.814 to 0.798 for Yolov5m;
* Good: The mAP@50 increased from 0.852 to 0.861 for Yolov5n, increased slightly from 0.86 to 0.864 for Yolov5s, and decreased from 0.869 to 0.841 for Yolov5m;
* In general, the mAP@50, precision, and recall have improved for some classes, which indicates that the modifications made to the models have been effective in improving their performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **mAP@50** \\ \hline Yolov5n & 0.735 & 0.610 & 0.630 \\ Yolov5s & 0.746 & 0.65 & 0.671 \\ Yolov5m & 0.615 & 0.701 & 0.732 \\ Mean & 0.698 & 0.654 & 0.677 \\ Variance & 0.00352 & 0.00139 & 0.00176 \\ Standard deviation & 0.059 & 0.0373 & 0.0419 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average performance of k-fold cross-validation on improved models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Metrics** & **Yolov5n** & **Yolov5s** & **Yolov5m** \\ \hline \multirow{4}{*}{Occluded} & P & 0.813 & 0.953 & 0.895 \\  & R & 0.691 & 0.648 & 0.68 \\  & mAP@50 & 0.71 & 0.821 & 0.82 \\  & P & 0.843 & 0.811 & 0.79 \\ Displaced & R & 0.657 & 0.675 & 0.7 \\  & Map@50 & 0.796 & 0.763 & 0.771 \\  & P & 0.87
# An Improved Kernelized Correlation Filter Algorithm for Underwater Target Tracking
## 1 Introduction
[16] proposed a kernelized correction filters (KCF) algorithm that generates training samples by cyclic shift; the Fourier transform can be used to perform diagonalization, which reduces the memory and computational complexity by several orders of magnitude, and the kernel trick is used to train the nonlinear filter, which greatly improves the performance of the filter on target detection. However, when the target size changes in the image sequences, it will lead to filter drift in the tracking process, resulting in tracking error. In order to solve this problem, [PERSON] et al. [17] proposed a novel scale adaptive kernelized correlation filter tracker with multiple feature integration; the proposed algorithm solved the scale change issue in object tracking through a multiple scales searching strategy. Later, [PERSON] et al. [18] proposed a scale calculation approach for visual object tracking, and the results demonstrated that this algorithm outperforms state-of-the-art methods while operating in real-time. Reference [19] adopted a deformable part-based correlation filter tracking approach to cope with challenging cases, like partial occlusion, deformation, or scale changes. In these regards, to obtain more accurate tracking results, this paper presents an improved kernelized correlation filter (IKCF) to track the underwater target in forward-looking sonar image sequences. A base sample with a dynamic continuous change scale is used to predict the best location of the target. Then, an adaptive filter update strategy is proposed to update the filter using the peak to sidelobe ratio (PSR) of the response diagram. Compared to the results of KCF, CSK, MOSSE, and mean-shift, the proposed IKCF can obtain better tracking results. Moreover, it has obvious superiority and effectiveness for a target with varying scales and for when the target disappears and appears again. Therefore, the proposed IKCF has important theoretical and practical value.
# Synergistic Effect of Nonthermal Plasma and ZnO Nanoparticles on Organic Dye Degradation
## 2 Materials and Methods
### Synthesis of ZnO Nanoparticles and Their Characterization  
The ZnO nanoparticles were synthesized via hydrothermal route. All chemicals were of analytical grade and were used without further processing. Typically, 0.05 M Zinc chloride (ZnCl\({}_{2}\)) was dissolved in 100 mL DI water under intense magnetic stirring for 1 h. During continuous stirring, 0.0028 M of C-TAB was added as a surfactant, followed by the addition 0.075 M of NaOH to maintain the pH of the reactants at 10. The solution was transferred to a hydrothermal reactor and kept in an electric furnace at a synthesis temperature of 160 \({}^{\circ}\)C for 5 h. Then, the hydrothermal reactor was cooled naturally to room temperature, the product was obtained through filtration, washed several times with DI water, and then dried at 60 \({}^{\circ}\)C in an electric furnace for 24 h. The white powder was collected for characterization and further use.  
The synthesized ZnO nanoparticles were characterized using different techniques: Scanning electron microscope FE-SEM (Thermo Fisher Quanta FEG250 SEM, OR, USA) for surface morphology, X-ray diffraction XRD (Rigaku International Corp., Tokyo, Japan) for phase and crystal structure, and Raman (SENTERRA II-BRUKER, Billerica, MA, USA) spectrometer at laser wavelength 532 nm for vibrational structure. The chemical composition of ZnO was studied with X-ray photoelectron spectroscopy (XPS Thermo Scientific, Waltham, MA, USA) using a flood gun for charge compensation, while the optical properties were evaluated with a UV-5200 spectrophotometer. The pH of all samples was measured using a calibrated pH meter (model 420A, Thermo Fisher Scientific Inc.) at 25 \({}^{\circ}\)C.
## 5 Calculation Method for Residual Bearing Capacity
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Number** & **Parameter** & \(T\)\({}^{\prime}\)C & \(d\times\)**\(\delta\)/mm** & \(\kappa\) & \(N_{\rm u}\)**(T)/kN** & \(N_{\rm u\_0}\)**/kN** & \(k_{\rm T}\) & \(N_{\rm u\_0}\)**(T)/kN** & \(N_{\rm u\_0}\)**(T)/\(N_{\rm u}\)**(T)** \\ \hline ZA-1 & \(T\) & 20 & 114 \(\times\) 3.5 & 0.570 & 2451.0 & 2490.1 & 1.000 & 2490.1 & 1.016 \\ ZA-1A & \(T\) and \(\kappa\) & 20 & 89 \(\times\) 3.8 & 0.445 & 2341.5 & 2336.1 & 1.000 & 2336.1 & 0.998 \\ ZA-1B & \(T\) and \(\kappa\) & 20 & 138 \(\times\) 3.0 & 0.690 & 2524.5 & 2435.0 & 1.000 & 2435.0 & 0.965 \\ ZA-2 & \(T\) & 200 & 114 \(\times\) 3.5 & 0.570 & 2056.5 & 2490.1 & 0.932 & 2320.8 & 1.129 \\ ZA-3 & \(T\) & 300 & 114 \(\times\) 3.5 & 0.570 & 2049.0 & 2490.1 & 0.861 & 2144.0 & 1.046 \\ ZA-4 & \(T\) & 400 & 114 \(\times\) 3.5 & 0.570 & 2075.5 & 2490.1 & 0.781 & 1944.8 & 0.959 \\ ZA-5 & \(T\) & 500 & 114 \(\times\) 3.5 & 0.570 & 1775.5 & 2490.1 & 0.701 & 1745.6 & 0.983 \\ ZA-6 & \(T\) & 600 & 114 \(\times\) 3.5 & 0.570 & 1653.0 & 2490.1 & 0.625 & 1556.3 & 0.942 \\ ZA-6A & \(T\) and \(\kappa\) & 600 & 89 \(\times\) 3.8 & 0.445 & 1628.0 & 2336.1 & 0.670 & 1565.2 & 0.961 \\ ZA-6B & \(T\) and \(\kappa\) & 600 & 138 \(\times\) 3.0 & 0.690 & 1682.0 & 2435.0 & 0.588 & 1431.8 & 0.851 \\ ZA-6-10 & \(T\) and \(a\) & 600 & 114 \(\times\) 3.5 & 0.570 & 1790.0 & 2490.1 & 0.625 & 1556.3 & 0.869 \\ ZA-6-30 & \(T\) and \(a\) & 600 & 114 \(\times\) 3.5 & 0.570 & 1578.5 & 2490.1 & 0.625 & 1556.3 & 0.986 \\ ZA-7 & \(T\) & 700 & 114 \(\times\) 3.5 & 0.570 & 1403.5 & 2490.1 & 0.555 & 1382.0 & 0.985 \\ ZA-8 & \(T\) & 800 & 114 \(\times\) 3.5 & 0.570 & 1256.0 & 2490.1 & 0.494 & 1230.1 & 0.979 \\ \hline \hline \end{tabular} Note: \(N_{\rm u\_0}\) and \(N_{\rm u\_c}\)(\(T\)) correspond to the calculated results of Equations (5) and (8), respectively. In the test, the concrete compressive strength (\(f_{\rm c}\)) was measured by the cylinder blocks, so it must convert into the cube compressive strength (\(f_{\rm cu}\)). According to Chinese code GB 50010-2010 [34] and CEB-FIP [35], the ratio value of \(f_{\rm c}\)’/\(f_{\rm cu}\) varies from 0.80 to 0.89, and it can be taken as 0.86 because of the concrete strength grade C60 used in this test.  
\end{table} 
Table 6: Test results and calculated results for specimens.
## 3 Micro-Vibration Test Principle
### The Image Point Offset Extraction and LOS Jitter Calculation of the HPSOP  
The LOS jitter and image point offset caused by micro-vibration have a significant impact on the detection clarity and communication stability of the HPSOP. Therefore, the image point offset extraction and the LOS jitter calculation are crucial in evaluating the pointing accuracy.  
Figure 4: AQZSSD with optical payload: (**a**) actual model; (**b**) simple pendulum model.
# An Improved Robust Fractal Image Compression Based on M-Estimator
## 4 Robust M-Estimator for FIC
Quadtree partitioning is a method used to represent an image as a hierarchical quadtree data structure in which the root of the tree is the initial image and each node contains four subnodes ([PERSON], 2004). A node represents a square image portion and its four subnodes correspond to the four quadrants of the square image portion. Quadtree-based FIC is the FIC algorithm used in quadtree partitioning. Since quadtree-based FIC is widely used in fractal compression algorithms, and it is already proven to be superior in performance over the fixed partitioning in the FIC algorithms, we chose quadtree-based FIC as the main frame of the proposed robust algorithm. As in the analysis above, the most time-consuming parts of the original HFIC are the eight times iterated calculations on the parameter estimation. In the proposed algorithm, we decrease it to only once with almost no sacrifice on the quality. For each range block (\(r\)) and down-sampled domain block (\(d\)), in the preprocess stage, the four quadrants of the given block are ordered based on the brightness average value (\(r^{\prime}\)). Then it is rotated to the one with the brightest quadrant in the upper left corner. After this preprocess, the matching error is compared between the range block (\(r^{\prime}\)) and domain block (\(d^{\prime}\)). Here \(r^{\prime}\) and its quadrants are all with the brightest quadrant in the upper left, as is domain block \(d^{\prime}\). Hence, in the proposed algorithm, the IRLS algorithm is applied during the matching between \(r^{\prime}\) and \(d^{\prime}\), instead of \(r\) and \(d\). Hence, the eight times of parameters estimation for each \(r\) and \(d\) is changed to be only once, which is between \(r^{\prime}\) and \(d^{\prime}\). Algorithm 3 describes the main principle of the proposed decode algorithm. ```
Input the original image, the tolerance value \(T\), Output the fractal codes
1: Pre-decimate the image into domain image and partition the original image into blocks with size of \(L\times L\)
2: Preprocess the blocks. Do flip or rotation to make the blocks with its brightest quad-rant in its upper left corner. Record the index \(k\) of the transformation for each block and its quadrants
3:while there is a block didn't encoded do
4: Fetch a range block \(r_{j}\)
5: Obtain the optimal scaling parameter \(s\) and offset parameter \(o\) by IRLS algorithm and record its corresponding fractal codes
6: Calculate the matching error \(E_{i}\) by Equation (10). 7: if\(E_{i}<T\) or the block size of the \(d_{i}\) is already small enough then
8: go to 13
9: else
10: Partitioning the current block into 4 sub-blocks (quadrants) by size of \(L_{i}/2\times L_{i}/2\)
11: go to 5
12:endif
13: Record the fractal codes with the minimum matching error. 14:endwhile
```  
**Algorithm 3** The Robust M-estimator for FIC algorithm (RMFIC)  
Two M-estimators are chosen, viz, [PERSON]'s bisquare statistics. The respective IRLS algorithms are described in Sections 3.1 and 3.2.
# Cooperative SWIPT MIMO-NOMA for Reliable THz 6G Communications
## 6 Simulation Results
### System Simulation
#### 6.2.2 OP versus Transmit Power  
Similarly, for both NOMA users, the MIMO technique provides a notable enhancement of system performance compared to the SISO scheme, as shown in Figure 7. In spite of having more dedicated power than the NU, the FU has a higher outage. This reflects the expected performance given their distinct channel variations.  
Figure 5: OP vs. Transmit Power.  
Figure 6: Rates vs. transmit power.
## 3 Results and Discussions
### Expression Change of Genes Related to Phosphotransferase System (PTS)
Gene _gnd_ encoding 6-phosphogluconate dehydrogenase was down-regulated by 9.45 times, and this enzyme catalyzed 6-phospho-D-gluconate into 5-phospho-D-ribulose and NADPH. The gene _rpiA_ encoding ribose 5-phosphate isomerase A was down-regulated by 2.32 times, and this encoded enzyme catalyzed the formation of 5-phospho-D-ribose from 5-phospho-D-xylulose. Gene _prsa_ encoding ribose-phosphate pyrophosphokinase was down-regulated by 2.85 times, and this encoded enzyme catalyzed 5-phospho-D-ribose into phosphate ribose pyrophosphate. Overall, the expressions of all the investigated genes related to the pentose phosphate pathway were down regulated, indicated this pathway was partially weakened in liquid-state fermented cells, compared with solid-state fermented cells. Three genes (two _oadA_ and one _oadB_) were down-regulated by 2.02 to 12.55 times. These three encoded the oxaloacetic decarboxylase subunit, and this enzyme subunit converted oxaloacetic acid into pyruvate in the cell. Gene _ldh_ was down-regulated by 5.75 times. This gene encoded L-lactate dehydrogenase, and this encoded enzyme catalyzed pyruvate to produce lactate. Gene _pflD_ encoding formate C-acetyltransferase was down-regulated by 2.86 times, and this encoded enzyme catalyzed the reversible conversion of pyruvate into acetyl CoA. In general, the metabolism of pyruvate into lactate and succinate was weakened in the liquid fermented cells. In addition, the gene _pta_ encoding phosphate acetyl transferase was down-regulated by 3.89 times, and this encoded enzyme catalyzed the conversion between acetyl-CoA and acetyl-phosphate. Gene _ackA_ encoding acetate kinase was down-regulated by 37.10 times, and this enzyme further catalyzed the conversion of acetyl-phosphate into acetate. Overall, the metabolic flow of pyruvate was changed.
# Task-Driven Virtual Machine Optimization Placement Model and Algorithm
## 1 Introduction
However, in [16], the focus was only on minimizing the energy consumption of a cloud data center under virtual resource constraints, and factors such as the VM rental costs and the makespan of user tasks, which significantly impact user experience, were ignored. [PERSON] et al. [15] considered both VM rental costs and the resource utilization of the cloud data center; however, the optimization model was excessively simplified by converting the multi-objective optimization problem into a single-objective one through weighted sums. This resulted in the loss of some valuable information from the original multi-objective problem. Consequently, several Pareto optimal solutions may be overlooked, and practical and meaningful optimization solutions may be missed. To balance the interests of cloud service providers and users, optimization of the makespan, rental costs, and energy consumption must be performed simultaneously. In this study, task scheduling and VM placement are integrated to investigate the task-loading-oriented VM placement problem (TLVMP). Real-world resource limitations and constraints are incorporated in cloud data centers to reduce makespan, user rental costs, and energy consumption. To realize these three objectives, the VM placement problem is modeled as a multi-objective optimization issue. Moreover, as the non-dominated sorting genetic algorithm III (NSGA-III) can solve multi-objective optimization problems with considerable flexibility and provide a reasonable solution toTLVMP problems, this improved algorithm is adapted to solve the VM placement models. Based on the improved chaos-elite NSGA-III (CE-NSGAIII), the chaos-elite VM placement (CE-VMP) method is further proposed. The main contributions are as follows:(1) To reduce the makespan, user rental costs, and energy consumption of cloud data center, the task-loading-oriented VM placement issue is abstracted as a multi-objective optimization model. (2) An improved CE-NSGAIII algorithm is presented depending on the NSGA-III to solve the aforementioned model and improve the convergence and diversity of the Pareto solution set. (3) Summarizing and concluding the above solution processes, a CE-NSGAIII-based method named the CE-VMP is further proposed for VM placement. (4) The experimental results show that the CE-NSGAIII can efficiently solve the TLVMP problem. Also, the CE-VMP method is capable of achieving better results in terms of makespan, user rental costs, and energy consumption.