esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Brief Introductions to SSA and LS-SVM
### Ssa
\begin{array}{cccc}x_{1}&x_{2}&\cdots&x_{N-(m-1)\tau}\\ x_{1+\tau}&x_{2+\tau}&\cdots&x_{N-(m-2)\tau}\\ \vdots&\vdots&\vdots&\vdots\\ x_{1+(m-1)\tau}&x_{2+(m-1)\tau}&\cdots&x_{N}\end{array} \right]\]  
Values of \(m\) and \(\tau\) can be obtained using the autocorrelation function method and false nearest neighbor method [27; 28]. The process of SSA is described as Algorithm 1. ```
Given observed signal \(x(t)\), number of time periods \(\mathrm{N}(\mathrm{N}\geq(\mathrm{D}-\mathrm{d})/2+2)\) Step 1: Calculate parameters \(m\) and \(\tau\) using the autocorrelation function method and false nearest neighbor method. Step 2: Reconstruct \(x(t)\) to multi-dimensional signal \(X(t)\) using PSR. Step 3: Divide \(X(t)\) into N continuous time periods, and calculate the mean \(\overline{x}_{i}\) and covariance \(\hat{\Sigma}_{i}\) of each time period, for an arbitrary choice of \(B^{s}\), get the estimated mean \(\overline{x}_{s,i}=B^{s}\overline{x}_{i}\) and covariance matrix \(\hat{\Sigma}_{s,i}=B^{s}\hat{\Sigma}_{i}\) in each time period. Step 4: Measure the difference between the estimated stationary signal distribution and normal distribution in each time period using Kullback-Leibler (KL) divergence. Sum all period KL divergences, and construct the objective function \(\mathrm{L}(B^{s})\) with independent variable \(B^{s}\). Minimizing the objective function \(\mathrm{L}(B^{s})\) using gradient descent based algorithm described in [25], obtain the optimal stationary projection \(\hat{B}^{s}\), and estimate the stationary source signal \(\hat{S}_{t}^{s}\mathrm{L}\big{(}\hat{B}^{s}\big{)}=\min\mathrm{L}(B^{s})=\min \sum\limits_{i=1}^{n}D_{KL}\big{[}Norm\big{(}\overline{x}_{s,i},\hat{\Sigma}_{ s,i}\big{)}||Norm(0,\mathrm{I})\big{]}\) according to Equation (2). Step 5: Construct the objective function \(\mathrm{G}(B^{n})\) with steps (1) and (2) above. Maximizing the objective function \(\mathrm{G}(B^{n})\) using the optimization algorithm in Step 4, obtain the optimal non-stationary projection \(\hat{B}^{n}\), and according to Equation (2) estimate the non-stationary source signal \(\hat{S}_{t}^{n}\mathrm{G}\big{(}\hat{B}^{n}\big{)}=\max\mathrm{G}(B^{n})=\max \sum\limits_{i=1}^{n}D_{KL}\big{[}Norm\big{(}\overline{x}_{n,i},\hat{\Sigma}_{ n,i}\big{)}||Norm(0,\mathrm{I})\big{]}\). ```  
**Algorithm 1** Finding non-stationary subspaces by SSA and PSR
# Comparative Analysis of Skeleton-Based Human Pose Estimation
## 3 Methodology
### Datasets  
The datasets of image and video sources used in this experiment were the Microsoft Common Object in Context (COCO) [35] and Penn Action [44] datasets, respectively. Table 3 shows the characteristics of the COCO and Penn Action datasets. Both datasets have 6 common upper body keypoints and 6 lower body keypoints. However, the difference between these two datasets is the number of keypoints annotated for the head. COCO has 5 keypoints, including the nose, eyes, and ears, while Penn Action only provides 1 keypoint at the head position. Figures 1 and 2 show sample images of the COCO and Penn Action datasets, respectively.  
COCO [35] is a large-scale object detection, segmentation, and captioning dataset. It is commonly used in experiments for HPE [45; 46; 47]. It consists of 330,000 images, 1.5 million object instances, 80 object categories, 91 stuff categories, and 250,000 humans with keypoints. This dataset provides annotations for the body keypoint detection, where each instance of a person is labeled with 17 keypoints. There are various versions of COCO. COCO 2017 is commonly selected for HPE experiments.  
Penn Action [44] is a video dataset, which consists of 2326 video sequences with 15 actions. It is commonly used in HPE experiments [18; 48; 49; 50]. Each video sequence contains RGB image frames and annotations. The annotations include different human actions, 2D bounding boxes to locate human positions, and skeleton keypoints in the body parts. Each instance of a human is labeled with 13 keypoints.  
Figure 1: Sample images from the COCO dataset.
## 6 Nonlinear Mathematical Model of Torque Profile  
The optimal TSF is obtained by solving the optimization problem in Equation (14). However, Equation (4) is utilized to approximate the phase torque based on a linear relationship between the  
Figure 8: Comparison of the proposed TSFs: (**a**) absolute value of the RCFL; (**b**) peak phase currents; (**c**) rms phase currents.  
phase flux linkage and the current as shown in Equation (3). An analytic invertible torque function that represents the nonlinear and saturation effects appropriately was used as follows [26]:  
\[T_{k}(\theta,i)=\frac{1}{2}\frac{L_{kp}(\theta)i^{2}}{(1+f(\theta)i^{n})^{1/n}} \tag{17}\]  
where \(L_{kp}(\theta),f(\theta)\) and \(n\) are the undetermined parameters that are dependent on the rotor position and the motor parameters.  
As shown in Figure 9, by choosing reasonable parameters for \(L_{kp}(\theta)\) and \(f(\theta)\), the characteristics of the torque-current-position obtained from Equation (17) agree well with those using FEM or those from experimental measurements. After rejecting the improbable solution, the current reference can be rewritten as Equation (18) by inverting the torque equation in Equation (17) as follows:  
\[i_{k}(\theta,T_{k})=\frac{2T_{k}}{L_{kp}(\theta)}\left[\frac{f(\theta)}{2}+ \sqrt{\frac{f^{2}(\theta)}{4}+\left(\frac{L_{0p}(\theta)}{2T_{k}}\right)^{n}} \right]^{1/n} \tag{18}\]  
The data of the two angular functions \(L_{kp}(\theta)\) and \(f(\theta)\) stored in the digital controller can be used to calculate the current reference according to Equation (18), instead of using the 3-D lookup table of \(T\)-\(i\)-\(\theta\).
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
###### Abstract
For benchmarking purposes, we compare the performance of the quantum MIMO system against an equivalent classical MIMO system operating under identical bandwidth and diversity configurations. The major contributions of this study are:  
* The introduction of a quantum superposition-enhanced video transmission system that combines quantum principles with MIMO diversity to address the challenges of transmitting compressed video over fading channels. * A comparative analysis that demonstrates the advantages of quantum-assisted transmission over classical approaches in terms of preserving video quality under adverse conditions. * A flexible simulation-based framework adaptable for evaluating end-to-end quantum communication in video or multimedia transmission, providing an effective solution to fading in real-world communication scenarios. The remainder of this paper is structured as follows. Section 2 reviews prior work on quantum communication for media transmission, classical MIMO diversity techniques, and existing quantum MIMO systems. Section 3 describes the proposed quantum MIMO transmission framework and describes the system model and simulation setup. Section 4 presents and analyzes the simulation results. Finally, Section 5 concludes the paper and discusses potential avenues for future research in quantum-enhanced video communication.
## 3 Objective Measures Overview  
In this section, we briefly describe the selected objective measures: two intrusive objective measures typically used in speech enhancement, STOI [42] and ESTOI [43]; one also intrusive intelligibility measure based on information theory, SIIB [44]; and one measure based on NISQA that estimates the mean opinion score (MOS) of the naturalness of synthetic speech [45].
# Research on 2D Laser Automatic Navigation Control for Standardized Orchard
## 2 Materials and Methods
### Navigation Control Parameter Acquisition  
In order to obtain the vehicle navigation path, this experiment needs to fit the central feature points of all trunks in a straight line. We set the coordinates of the center points of the left and right fruit trees as (\(x_{li},\,y_{li}\)) and (\(x_{ri},\,y_{ri}\)). In this experiment, the coordinates of the center points of the three fruit trees on the left and right sides of the vehicle are selected to form two groups, forming 9 groups. Use the following equation to find the midpoint coordinates (\(x_{c},\,y_{c}\)).  
\[\left\{\begin{array}{c}x_{c}=\frac{x_{li}+x_{li}}{y_{c}}\\ y_{c}=\frac{y_{li}+y_{ri}}{2}\end{array}\right. \tag{6}\]  
Set the fitted line to be \(y=ax+b\), where \(a\) represents the slope of the fitted line and \(b\) represents the intercept with the \(Y\)-axis. The best estimate of the parameters \(a\), \(b\) is obtained according to the Equation (7):  
\[\left\{\begin{array}{c}a=\frac{g(\sum\limits_{i=1}^{c}x_{c}y_{c})-\sum \limits_{i=1}^{c}x_{c}\sum\limits_{i=1}^{c}y_{c}}{g\sum\limits_{i=1}^{c}x_{c}^ {2}-(\sum\limits_{i=1}^{c}x_{c})^{2}}\\ b=\frac{(\sum\limits_{i=1}^{c}x_{gi})-\sum\limits_{i=1}^{c}x_{c}(\sum\limits_{i =1}^{c}x_{c}y_{c})}{g\sum\limits_{i=1}^{c}x_{c}^{2}-(\sum\limits_{i=1}^{c}x_{ c})^{2}}\end{array}\right. \tag{7}\]  
After completing the navigation path fitting, we need to obtain the navigation parameters for implementing the automatic navigation control of the vehicle according to the current position of the vehicle. As shown in Figure 8, The line \(S\) is a navigation path, the angle \(\theta\) between the line \(S\) and the \(Y\)-axis is the heading deviation, and the flat distance \(d\) between the center position of the laser and the line \(S\) is the lateral deviation.  
Figure 7: Extraction of the center position of the fruit tree trunk.  
The calculation equation of heading deviation \(\theta\) and lateral deviation \(d\) in this experiment are as follows:  
\[\theta=\left\{\begin{array}{c}\left(\arctan k+\frac{\pi}{2}\right)\times\frac{18 0}{\pi},k<0\\ \left(\frac{\pi}{2}-\arctan k\right)\times\frac{180}{\pi},k>0\\ d=b\times\sin\theta\end{array}\right. \tag{8}\]  
As shown in the Equation (8), \(k\) is the slope of the navigation path, and \(b\) is the intercept of the navigation path.
# Roll Eccentricity Signal Detection and Its Engineering Application Based on SFFT-IAA
## 3 Roll Eccentricity Signal Detection Based on SFFT-IAA
### Frequency Band Partition Based on SFFT
#### 3.2.1 Principles of SFFT  
Denote the finite-length discrete-time signal of length \(N\) as \(x(n)\) and its sparsity as \(K\) and \(K\ll N\). The main idea of SFFT is to assign \(N\) signal frequency points into \(B\) buckets (\(B\ll N\), and \(N\) is divisible by \(B\)). The bucketization process is shown in Figure 3.  
Given the sparsity of signals, each point of a large value will be isolated in its own bucket according to its high frequency. By superimposing the frequency points in each bucket, the long sequence of \(N\) points is transformed into a short sequence of \(B\) points, and the FFT operation is performed. Then, according to the calculation results and the binning rules, a reconstruction algorithm is designed to recover the original signal frequency spectra of \(N\) points [23; 24]. Figure 4 shows the flowchart of the SFFT algorithm.  
Figure 3: Schematic diagram of the bucketization process.
## 5 Conclusions  
This study introduces an innovative approach for EEG age clustering, named SGAAE-AC, to reveal the relationship between EEG background waves and age. This method is tailored for EEG data through an improved graph construction technique, which, by integrating graph attention autoencoders with Laplacian filters, enhances the model's capability in brain signal representation and the accuracy of clustering outcomes. Furthermore, SGAAE-AC employs a multi-objective optimization scheme and a semi-supervised learning paradigm, which further enhances the accuracy and robustness of EEG age clustering without the need for true labels. Extensive experiments have demonstrated that this model outperforms other methods across all the evaluation metrics, including ACC, NMI, ARI and F-score, showcasing its advancement and effectiveness in the field of EEG age clustering.  
Figure 8: Attention visualization of GAAE.  
The significance of this study lies in its capability to capture the dynamic changes in EEG background waves across different age stages. It not only provides new tools for the analysis of EEG signals but also offers scientists new perspectives for exploring age-related changes in brain function and unveiling the fundamental laws of human cognitive development.  
In our future projects, we plan to utilize larger and more diverse datasets to train the model. This will enable the model to learn a wider range of EEG features, potentially enhancing its accuracy and ability to generalize. Leveraging the unique capabilities of our model in EEG feature extraction and representation, we will also explore research opportunities, such as brain age prediction based on EEG signals.  
Conceptualization, J.W. and J.Z.; Methodology, J.W.; Software, J.Z. and T.C.; Validation, J.W. and J.Z.; Formal Analysis, J.Z and T.C.; Investigation, T.C.; Resources, J.W.; Data Curation, [PERSON] and [PERSON]; Writing--Original Draft Preparation, [PERSON]; Writing--Review and Editing, [PERSON]; Visualization, [PERSON]; Supervision, [PERSON]; Project Administration, [PERSON]; Funding Acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Key Research and Development Program of Yunnan Province, grant number 202102 AA100021.  
Not applicable.  
Publicly available datasets were analyzed in this study. The CHBMP dataset used in the experiments presented in this paper can be downloaded at: [[https://portal.conp.ca/dataset?id=projects/CHBMP](https://portal.conp.ca/dataset?id=projects/CHBMP)]([https://portal.conp.ca/dataset?id=projects/CHBMP](https://portal.conp.ca/dataset?id=projects/CHBMP)), accessed on 10 March 2024.  
This paper extends special thanks to the anonymous reviewers and editors whose thoughtful comments helped to improve this manuscript, and extend special thanks to the Key Research and Development Program of Yunnan Province for their financial support, specialized equipment, and data assistance in this study.  
The authors declare no conflicts of interest.
## 4 Discussion  
Fitness Costs Associated with Translational Errors and Disruption of the Aminoacyl-tRNA Pool Are Amino Acid-Specific  
The aforementioned suppressor screen identified an AlaRS allele that leads to increased accuracy against non-cognate aa-tRNAAla formation. Recombinant AlaRS R561S enzymes displayed elevated rates of non-cognate Ser-tRNAAla hydrolysis as well as a long-term increase in cognate Ala-tRNAAla deacylation. This observation provides the first direct evidence of a hyper-accurate AlaRS enzyme, and to our knowledge, the first experimental validation of an aaRS post-transfer proofreading domain with increased hydrolytic activity. Interestingly, the single AlaRS R561S mutant displays no distinguishable phenotypic perturbation compared to wild-type _E. coli_. This observation suggests that perturbation to Ala-tRNAAla fidelity may be less tolerated than an overall decrease in tRNAAla aminoacylation levels.  
It has previously been noted that in both bacteria and eukaryotes, perturbation to aaRS fidelity not only affects rates of protein mistranslation but can also alter intracellular sensing of nutritional stress and consequently leading to dysregulation of cellular stress [11; 12]. AaRSs must maintain sufficient aa-tRNA levels to keep up with the translational demand for cellular protein synthesis and also monitor amino acid availability for nutrient sensing. Both of these roles make aaRSs an important player to maintain and modulate cellular homeostasis. In _E. coli_ the accumulation of uncharged tRNA will activate the stringent response facilitated by the RelA-dependent accumulation ppGpp [32; 33]. Stringent response activation leads to down-regulation of rRNAs and ribosomal proteins, ultimately slowing down cellular growth [33; 34]. Numerous reports have indicated the detrimental cost of AlaRS-mediated translational errors, but here we suggest that those errors are more costly than perturbations to the pool of aa-tRNAAla formation. We have previously suggested that specific mistranslational errors are more tolerated in the proteome than others [13]. We would now like to expand on that observation and further suggest that not only are specific mistranslational errors more or less costly to the proteome, but defects associated with aa-tRNA formation are also amino acid-dependent as well.
## 3 Results and Discussion
### \(\text{H}_{2}\text{S}\) Emission  
The H\({}_{2}\text{S}\) emission rate of all treatments increased first and then decreased, and finally was basically close to zero (Figure 6). H\({}_{2}\text{S}\) emission in the whole composting process mainly occurred in the first 7 days, accounting for about 80% of the total H\({}_{2}\text{S}\) emission. Similar to the results of previous studies [26], H\({}_{2}\text{S}\) emissions in the process of KW composting were mainly concentrated in the early stage, which may be mainly due to the intense microbial activities in the early stage of the reactor and the rapid temperature rise that consumes oxygen in the reactor, leading to the emergence of the local anaerobic environment. Other research also indicated that the main cause of generation of H\({}_{2}\text{S}\) emission during composting was the lack of aeration [27]. Thus, the use of bulking agent provided porosity to the organic material to be compost. There is a peak of H\({}_{2}\text{S}\) emissions after turning, which was consistent with the results of existing studies [9]. Compared with T1 treatment, T2 treatment can effectively reduce the cumulative H\({}_{2}\text{S}\) emission by more than 85%. The main  
Figure 5: Evolution of NH\({}_{3}\) during composting.  
reason was that the addition of GW improves the permeability of the matrix in the process of composting, improved the hypoxia condition in the material and inhibits the generation of H\({}_{2}\)S.
## 4 Results and Discussion
### The Question of _ilr-RPCA-Back clr_
,-1,0,\cdots,0\right](i=1,2,3,\cdots,D-1), \tag{6}\]  
\[U=[u_{1},u_{2},\cdots,u_{D-2},u_{D-1}]^{T}(i=1,2,3,\cdots,D-1), \tag{7}\]  
\[ilr(X)=\sqrt{\frac{i}{i+1}}ln\left[\frac{\sqrt[i]{\prod_{j=1}^{i}x_{j}}}{x_{i+ 1}}\right](i=1,2,3,\cdots,D-1), \tag{8}\]  
\[clr(Y)=ilr(X)U \tag{9}\]  
Based on the information provided, this paper analyzes the reasons for the aforementioned problems, starting with the fundamental formula. Although the score and load can be projected into the clr space after the transformation between \(clr\) and \(ilr\), the calculation steps of \(ilr\)-\(RPCA\)-\(back\)\(clr\) reveal the following issues:  
Step 1: The original data matrix \(A\) (\(R\times D\)), after the ILR transformation, finds that the last element has not been given the value of the ILR transform, so that the matrix \(B\) (\(R\times(D-1)\)) of an element is reduced. Step 2: The RPCA transformation of data matrix \(B\) (\(R\times(D-1)\)) after \(ilr\) can only obtain \(D-1\) principal components; at this time, the elements represented are only the first \(D-1\) elements, the score is matrix \(C\) (\(R\times(D-1)\)), and the load is matrix \(D\) (\((D-1)\times(D-1)\)). Step 3: U transformation of matrix \(C\) and \(D\) transform to CLR space, \(U\) score matrix (\(R\times(D-1)\))-(\((D-1)\times D\)) to obtain a new score matrix \(E\) (\(R\cdot D\)), namely \(U^{\text{T}}\)-matrix (\(D\times(D-1)\))-(\((D-1)\times(D-1)\)) obtain the new matrix \(F\) (\(D\times(D-1)\)) and set up a corresponding contact element. It is evident that the final element does not contribute to the principal component analysis, and consequently, the transformed load obtained using the transformation formula does not contain any information pertaining to this specific element. Furthermore, the relationships among elements in the transformed load are not identical to their original relationships but rather represent comprehensive associations. Additionally, it should be noted that the relationship between \(clr\) and \(ilr\) is \(U\) instead of \(U^{\text{T}}\). Hence, it is necessary to handle the correspondence between elements in the new load matrix appropriately. Since \(ilr\) is not a matrix, \(clr=ilr\cdot U\) cannot be converted to \"\(clr=U^{\text{T}}\cdot ilr\)\". So, for the final \(clr\) space inverse transformation, the only problem is the corresponding elements of the relationship. This paper argues that the CLR, ILR, and RPCA do not have any problems, but the combination of the three should be considered carefully for the identification of geochemically combined elements and types of deposits. For this issue, this paper does not propose a definitive solution but would like to take this opportunity to raise the question and hope that the mathematical geologists will study deeply and solve this problem.
## 5 Discussion
Conversely, situations relating to mining-induced subsidence have been monitored more completely in underground coal mines because of the hysteretic nature of subsidence caused by the action of gravity on the roof of the goat. While SBAS-InSAR technology is less effective with respect to monitoring of intense subsidence in central subsidence zones [48, 49, 50], it exhibits high sensitivity to minor deformations along the edges of subsidence areas, with demonstrated reliability. Consequently, the findings of this study that employed SBAS-InSAR to analyze spatial distribution and subsidence trends in mining-affected regions are robust [51, 52, 53, 54]. These results not only enable mining operators to pinpoint subsidence zones accurately and efficiently but also clearly demonstrate the characteristics and patterns of surface subsidence induced by coal mining in the geological context of the erosional landforms of the Loess Plateau. Furthermore, such data support targeted remediation in high-risk areas while optimizing monitoring expenditures. Collectively, this evidence underscores the strong suitability of SBAS-InSAR technology for erosional landform studies in the Loess Plateau. In future research on mining-induced surface subsidence, relying exclusively on advanced InSAR technology to quantify subsidence in areas of severe subsidence presents significant challenges. However, combining InSAR technology with traditional monitoring methods (e.g., UAV remote sensing) will not only pinpoint subsidence zones with high precision and low cost but also enable targeted monitoring of critical areas [55, 56, 57, 58]. This methodology ensures accurate deformation measurements that meet the operational demands of mining sites.
## 3 Results and Discussion
### Discrimination of Bread Sample by Electronic Nose System  
Principal component analysis (PCA) was employed to detect the differences in volatile composition between the bread samples based on the calculation of a discrimination index. Thus, the higher the discrimination index, the better the discrimination. Figure 3 depicts the PCA plot that represents a map of the discrimination of the bread samples. A discrimination index of 85 was obtained, which is explained by the very different odour of the samples. The contribution rates for the two principal components (PC1 and PC2) were 99.18% and 0.67%, respectively.  
Considering only the same level of addition and comparing this with the control, a good discrimination was obtained: 93 (between the control, 5 PP, and 5 SP breads), 91 (between the control, 10 PP, and 10 SP breads), and 91 (between the control, 15 PP, and 15 SP breads), respectively. In all of the cases, the control bread was significantly separated from the protein bread samples, positioned on the left side of the PCA map.  
Figure 3: Principal component analysis (PCA) plot for the bread samples: control, pea (5 PP, 10 PP, and 15 PP), and soy (SSP, 10 SP, and 15 SP) breads.