esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Overview of the Study Area  
The Fen River Basin, China's second-largest tributary of the Yellow River, spans the central and southwestern reaches of Shanxi Province, with co-ordinates ranging from 35\({}^{\circ}\)20\({}^{\prime}\)-39\({}^{\circ}\)00\({}^{\prime}\) N to 110\({}^{\circ}\)30\({}^{\prime}\)-113\({}^{\circ}\)32\({}^{\prime}\) E [27]. Encompassing a total length of 713 km, the watershed area of the basin constitutes 25.5% of the total land area of Shanxi Province. The land use within the basin can be categorized as cropland, woodland, and grassland. Croplands predominantly occupy the central and southern regions, accounting for 39.6% of the area, whereas woodlands are concentrated in the mountainous northwestern and southeastern areas, covering 28.1%. The grassland is in the northern sector, encompassing 23.7% of the basin's total area [28] (Figure 1a). The Fen River Basin experiences a semi-arid monsoon climate with an average annual precipitation of 507 mm, characterized by variable yearly patterns and seasonal fluctuations, with the bulk of rainfall occurring between July and September. The region's average annual temperature ranges from 6 \({}^{\circ}\)C to 13 \({}^{\circ}\)C, with drought being a prevalent weather-related concern. Topographically, the basin displays an elevation gradient with higher elevations in the north and lower elevations in the south. The primary river channel follows a winding path from north to south within the Taiyuan and Linfen Basins. The basin is predominantly characterized by its agricultural production, particularly for the cultivation of wheat, corn, sorghum, and various cereals. Shanxi Province serves as a significant center for commercial grain production, with a notable concentration of corn crops in the central and southern regions (Figure 1b).
# Salt and Pepper Noise Removal with Multi-Class Dictionary Learning and L0 Norm Regularizations
## 3 Results  
The proposed method, FDLCP-L\({}_{0}\), is compared with basic AMF [2], the state-of-the-art PANO-L\({}_{1}\)[9] and total variation (TV)-L\({}_{0}\) methods [17]. Two objective evaluation criteria, the peak signal-to-noise ratio (PSNR) and the mean measure of structural similarity (MSSIM) [40], which are most widely used in image denoising and reconstruction [15; 17; 37; 41; 42], are adopted to quantitatively measure the denoising performance here. The PSNR measures average pixel distortion of the denoised image and the MSSIM specifically cares about the preserved image structures, e.g., local luminance and contrast, which are important for human visual systems [40]. Higher PSNR and MSSIM mean better denoising performance. All the computations were performed on four cores 3.6 GHz CPU desktop computer with 16 GB RAM. The typical reconstruction time of a 512 \(\times\) 512 image with AMF, PANO-L\({}_{1}\), TV-L\({}_{0}\), and FDLCP-L\({}_{0}\) are 0.25 s, 55.7 s, 6.74 s, and 473.22 s, respectively. The test images are downloaded from website [43].
## 3 Kinematic Analysis
### Inverse Velocity Solving and Jacobian
#### 3.3.2 Angular Velocity and Rate of Euler Angular  
For any type of Euler angle, the angular velocity is not simply equal to the rate of Euler angles, known as:  
\[\mathbf{\omega}\
eq\left[\begin{array}{cc}\overset{\bullet}{\psi}&\overset{ \bullet}{\theta}&\overset{\bullet}{\phi}\end{array}\right]^{T} \tag{23}\]  
Equation (14) can be written as:  
\[\overset{\bullet}{{}_{B}}\overset{\bullet}{\mathbf{R}}=\mathbf{\omega}\times\overset {A}{{}_{B}}\mathbf{R} \tag{24}\]  
Considering the orthogonality of the rotation matrix, the following can be derived:  
\[\mathbf{\omega}\times=\overset{\bullet}{{}_{B}}\overset{\bullet}{{}_{B}}\overset {A}{{}_{B}}\mathbf{R}^{T} \tag{25}\]  
where \(\mathbf{\omega}\times\) represents the skew-symmetric matrix of the angular velocity vector, and it is expressed as:  
\[\mathbf{\omega}\times\ =\ \left[\begin{array}{ccc}0&-\omega_{x}&\omega_{y}\\ \omega_{z}&0&-\omega_{x}\\ -\omega_{y}&\omega_{x}&0\end{array}\right] \tag{26}\]  
Therefore, the angular velocity vector can be solved by the following three independent equations:  
\[\begin{array}{l}\omega_{x}=r_{31}^{\bullet}r_{21}+\overset{\bullet}{r_{32} }r_{22}+\overset{\bullet}{r_{33}}r_{23}\\ \omega_{y}=r_{11}^{\bullet}r_{31}+r_{12}^{\bullet}r_{32}+r_{13}^{\bullet}r_{33 }\\ \omega_{z}=r_{21}^{\bullet}r_{11}+r_{22}^{\bullet}r_{12}+r_{23}^{\bullet}r_{13 }\end{array} \tag{27}\]  
where \(r_{ij}\) corresponds to the element in row i and column j of the rotation matrix \(\overset{A}{{}_{B}}\mathbf{R}\).  
Equation (27) can be written into matrix-form as:  
\[\left[\begin{array}{cc}\omega_{x}&\omega_{y}&\omega_{z}\end{array}\right]^{T}= \boldsymbol{T}_{0}\Big{[}\begin{array}{cc}\bullet&\bullet&\bullet\\ \
ot{\psi}&\theta&\bullet\end{array}\Big{]}^{T} \tag{28}\]  
where \(\boldsymbol{T}_{0}\) is solved as:  
\[\boldsymbol{T}_{0}=\left[\begin{array}{ccc}\cos\phi\cos\theta&-\sin\phi&0\\ \sin\phi\cos\theta&\cos\phi&0\\ -\sin\theta&0&1\end{array}\right] \tag{29}\]  
The nonlinear differential equation of Euler angles in the fixed coordinate system can be solved as:  
\[\left[\begin{array}{cc}\bullet&\bullet&\bullet\\ \
ot{\psi}&\theta&\bullet\end{array}\right]^{T}=\boldsymbol{T}_{0}{}^{-1} \big{[}\begin{array}{cc}\omega_{x}&\omega_{y}&\omega_{z}\end{array}\big{]}^{T} \tag{30}\]  
where  
\[\boldsymbol{T}_{0}^{-1}=\left[\begin{array}{ccc}\frac{\cos\phi}{\cos\theta} &\frac{\sin\phi}{\cos\theta}&0\\ -\sin\phi&\cos\phi&0\\ \frac{\cos\phi\sin\theta}{\cos\theta}&\frac{\sin\phi\sin\theta}{\cos\theta}&1 \end{array}\right] \tag{31}\]
## 1 Introduction
Since low-resolution images have smaller data sizes, less computational power and storage capacity are needed to process and store the data. This translates to cost savings and improved accessibility, especially for individuals or organizations with limited resources. Secondly, training on a small dataset allows for increased efficiency. With fewer images to process, the training time is significantly reduced, enabling faster iterations and experimentation with different model architectures and hyper-parameters. This accelerated development cycle facilitates the refinement of the classifier and speeds up the overall research or development process. Consequently, an image classifier on a small dataset of low-resolution images not only reduces hardware and resource requirements but also enhances efficiency, making it an advantageous approach in certain scenarios. The contributions of this paper include the modification and implementation of two transformer-based model configurations tailored for low-resolution leather surface defect detection, as well as an in-depth analysis of the model's performance compared to existing pre-trained DL methods. This study would be beneficial for the adoption of vision transformers (ViT) in the leather industry, enabling a faster, cheaper, and more robust leather surface defect detection system for improved product quality control. The rest of the paper is organized as follows. Section 2 provides an overview of the related work in the field. Section 3 describes the proposed method for addressing the research problem. In Section 4, we present the experiments conducted to validate the effectiveness of the proposed approach. Finally, Section 5 concludes the paper by summarizing the key findings and highlighting the contributions made in this study.
## 3 Automated Calibration of Chaboche Parameter
### PSO Formulation  
Particle Swarm Optimization (PSO) is another artificial intelligent method employed to mimic the social natural movement of some animal species such as fishes and birds. In contrast to GA, the optimization process in PSO is done by direct intelligent interaction among the population members. In each iteration, the current cost function (_MSE_) of members (particles) are evaluated and compared with its previous (local) and global values to create a new eligible population. In this method, the position and speed of each particle in each iteration are evaluated for a new particle position that minimizes the cost function. The members are selected or ranked based on best fitness value as indicated by minimized _MSE_[42]. This team working activity is mathematically explained in an equation, which presents the new position of the member as:  
\[x_{i}^{new}=x_{i}^{old}+v_{i}^{new}, \tag{32}\]  
where, \(x_{i}^{new}\) is the optimum solution for a member in the population, in our case the \(x_{i}^{new}\) is the latest Chaboche parameter that is minimized in _MSE_. \(x_{i}^{old}\) is the previous position and \(v_{i}^{new}\) is the velocity (direction) of member that can be described as:  
\[v_{i}^{new}=t_{1}r_{1}\Big{(}x_{i}^{local}-x_{i}^{old}\Big{)}+t_{2}r_{2}\Big{(} x^{global}-x_{i}^{old}\Big{)} \tag{33}\]where, \(v_{i}\) is the velocity of the particle, \(w\) is the inertial coefficient and \(r_{1}\) is a random number between 0 and 1. It is set \(t_{1}+t_{2}=4\).  
Equation (33) is schematically presented in Figure 14 in which the new position is the best position of the member satisfying the cost function (_MSE_). The best member has the optimum Chaboche parameters in Equation (33).  
The overall GA and PSO optimization process flowcharts are illustrated in Figure 15a,b, respectively, with their elements (cost function, constraints, etc.) described in detail in the following.  
Figure 14: Movement of a member.  
Figure 15: Overall optimization process flow diagram (**a**) GA (**b**) PSO.
## 3 Results
### Ablation Studies and Experimental Sensitivity Analysis  
In this section, we delve into ablation studies to meticulously assess the impact and effectiveness of various components within the HMAGNN framework. We executed experiments across diverse datasets, meticulously evaluating the individual contributions of GCN, HMAGNN, and their combined model. At the same time, we verify the impact of the trainable parameter \(\alpha\) on the combined model. This parameter dynamically adjusts the weighting between the structural of HMAGNN and the attribute features of GCN.  
As illustrated in Table 3, which summarizes the impact of combining HMAGNN with GCN on link prediction, the value of the trainable parameter \(\alpha\) varies for each dataset. On the PPA dataset, the model performed optimally when \(\alpha\) was set to 0.92. This specific configuration led to a notable enhancement in prediction accuracy, with HMAGNN (w/GCN) achieving 49.72%, outperforming both HMAGNN (w/o GCN) at 47.26% and the standalone GCN at 16.98%. This underscored the efficacy of HMAGNN in effectively utilizing structural features. On the other hand, when evaluating the DDI dataset, HMAGNN (w/o  
Figure 4: Comparison of model results on five datasets. (**a**) OGB-PPA dataset; (**b**) OGB-DDI dataset; (**c**) OGB-Collab dataset; (**d**) OGB-Citation2 dataset; (**e**) Jujube dataset.  
GCN) achieved an accuracy of 37.07%, which the standalone GCN surpassed at 44.60%. The DDI dataset, characterized by a higher number of node features than other datasets, favored the performance of GCN. However, the integration of GCN with HMAGNN significantly improved accuracy to 64.66%, reinforcing the capability of HMAGNN to effectively learn both structural and attribute features.  
Additionally, we explored the influence of local subgraph size on the experiments, constrained by the node labels' k-value. Figure 5 showcases the results obtained with different values of k (4, 8, 12, and 16) on four datasets. The overall performance was optimal when k was set to 8. On the PPA dataset,  
To assess the impact of the multi-head attention mechanism (MHA) on HMAGNN, we conducted a comparative experiment between HMAGNN (with MHA) and HMAGNN (without MHA) on the OGB dataset. As depicted in Figure 6, HMAGNN (with MHA) consistently outperformed HMAGNN (without MHA), outperforming it in all datasets. Particularly noteworthy was the 45% improvement in accuracy on the PPA dataset when the multi-head attention mechanism was integrated. This emphasizes the beneficial role of the multi-head attention mechanism in enhancing the accuracy of link prediction within the HMAGNN framework.  
Ablation studies provide valuable insights into HMAGNN's robustness and sensitivity to key components. The optimal performance achieved under specific configurations and the nuanced relationships observed highlight the intricate interplay between structural and attribute features in link prediction tasks.
# Compact Single-Frequency MOPA Using a Silica Fiber Highly Doped with Yb\({}^{3+}\)
## 1 Introduction
The high Yb doping requires a high Al and/or P concentration to mitigate photodarkening (PD), which worsens with Yb concentration and limits the output power [20]. In turn, a high Al content raises the core index, which tends to increase the number of modes, while P reduces the cross-sections and gain [21]. Finally, the use of a short fiber with a high pump absorption raises the issue of potentially detrimental thermal loading. This work is the first to investigate a fiber MOPA with an Al/Ce/F core-glass composition and a very high Yb concentration (0.25 at.% Yb). The focus was on experimentally determining the maximum obtainable power in light of the presence of residual photodarkening loss related to the high Yb concentration. Despite the practical challenges of manufacturing a glass of this composition that meets all requirements, the reported amplifier exhibits very good characteristics. It utilizes a 1-m gain fiber coiled on a passively cooled spool of small diameter (8 cm) and seeded with a diode-pumped solid-state laser. It produced a strongly linearly polarized output at 1064 nm in a clean mode with a linewidth of 150 kHz and a maximum output power of ~81.5 W for 107 W of pump power in a package slightly larger than half a liter. For comparison, the 130-W laser head of the Azurlight system [22] and the PreciLasers [23] have volumes of ~13 L and ~6.2 L, respectively. The amplifier reported here is 10 to 20 times more compact, which is made possible by the short active fiber and fewer optical components. There was no sign of SBS and no deleterious effects from the thermal load. The residual photodarkening loss was small enough that the output power decreased by only 20% over 150 h of operation.
# Data-Decoupled Scattering Imaging Method Based on Autocorrelation Enhancement
## 2 Materials and Methods
### Model and Data Design  
#### 2.2.1 Overall Algorithm Flow  
According to the autocorrelation principle, the speckle images have obvious structural features after undergoing the autocorrelation operation. However, the autocorrelation results are still inevitably influenced by noise, which affects the reconstruction results of the phase-recovery algorithm. In this section, by using the prior physical knowledge of speckle correlation, deep learning is combined with traditional algorithms. Meanwhile, the basic-element datasets generated using the autocorrelation operation graph are constructed as the initial training datasets of the network model to solve the limitation of massive datasets required by a deep neural network. The specific process is shown in Figure 3.  
Figure 2: Flow of phase-recovery algorithm: (**a**) phase-recovery algorithm flow and (**b**) the ground truth. DFT is denoted as Discrete Fourier Transform, PI as Phase Information, and IDFT as Inverse Discrete Fourier Transform.
## 4 Numerical Simulation
For the oscillation mode of the damping, one of the reasons is the oscillation of the frequency and amplitude of the single component signal when it is constructed by the integral algorithm under random excitation. Meanwhile, errors in different sampling points during the solution of the simultaneous equations can also affect the identification results. In addition to this, the interference of the random excitation force induces non-linear variation in the TV system, resulting in errors in the damping identification results. Figure 6: The stiffness identification results: (**a**) \(k_{1}\), (**b**) \(k_{2}\), and (**c**) \(k_{3}\). Figure 7: The damping identification results: (**a**) \(c_{1}\), (**b**) \(c_{2}\), and (**c**) \(c_{3}\). To further indicate the robustness of the proposed method, different SNR levels of noise (20 dB, 50 dB, 100 dB) were added to the raw vibration acceleration signal. Meanwhile, a statistical indicator called mean absolute percentage error (MAPE) was introduced here, which can evaluate the accuracy of the identified results. The same procedure shown in Section 3 was performed, and the detailed identification error is shown in Table 1.  
\[\text{MAPE}=\frac{1}{N_{\text{S}}}\sum_{i=1}^{N_{\text{S}}}\left|\frac{{{{i}_{i} }-{{\widetilde{{i}}}}_{i}}}{{{{i}_{i}}}}\right|\times 100\% \tag{51}\]  
in which \(N_{\text{S}}\) represents the total sampling point number, \({{{i}_{i}}}\) represents the theoretical value, and \({{{\widetilde{{i}}}}_{i}}\) represents the identification value. From Table 1, it can be seen that under different SNR levels, the error of stiffness can be maintained within 3%, and the damping can still ensure good identification accuracy. Through the aforementioned analysis, it can be deduced that the method proposed in this paper is effective in overcoming random noise and has good robustness and TV physical parameter traceability.
# SPACNet: A Simulation Platform of an Acoustic Cognitive Network
## 5 Field Experimental Validation
### Results and Analysis  
As the purpose of the field UACNs experiment was for general validation of the proposed SPACNet, the PDR was adopted to quantitatively evaluate the network performance of the cognitive routing and the fixed routing during the field test. The resulting PDR of the cognitive routing and fixed routing is provided in Table 3, which reveals that the cognitive routing outperformed the fixed routing with a PDR of 92.5% as compared to 75.1%. Thus, the PDR results obtained from the field network experiment were generally consistent with those of the simulation experiment as yielded by SPACNet. Note that, as the node number of the field test was much smaller than that of the simulation, the field experimental network corresponded to a bigger PDR that the simulation network due to there being much fewer collisions at a similar network load. As regards the reason behind the transmission failures, besides those caused by transmission collisions, the time-varying multipath associated with the adverse shallow water channel was also a key factor, considering that the average SNR of the receiving signal was about 8 dB, which was high enough for the DSSS modulation adopted
# HMT exerts an Anticancer Effect by Targeting PAK-1
###### Abstract  
Heamatang (HMT) is a classic medicinal formula used in traditional Chinese and Korean medicine; it contains seven distinct components, mainly of herbal origin. HMT is used as an antiaging remedy, treating urinary disorders and increasing energy and vitality. However, the therapeutic applications of this formula have not been evaluated with evidence-based science. Therefore, we assessed HMT through various in vitro methods, including cell viability assay, fluorescence-activated cell sorting assay (FACS), Western blotting, migration assay, three-dimensional (3D) cell culture, siRNA-mediated PAK-1 knockdown, and crystal violet assays. HMT decreased PAK-1 expression in PC-3 cells and inhibited cell viability, growth, and motility. The inhibition of cell motility by HMT was correlated with PAK-1-mediated inhibition of Lim domain kinase (LIMK) and cofilin. HMT induced G1 arrest and apoptosis through the transcriptional regulation of cell cycle regulatory proteins and apoptosis-related proteins (increase in c-cas3 and inhibition of PARP and BCL-2). Moreover, HMT suppressed PAK-1 expression, leading to the inhibition of AKT activities. Finally, we showed that decursin was the active ingredient involved in the inhibitory effect of HMT on PAK-1. Our findings demonstrated that HMT exerts its anticancer influence through the inhibition of PAK-1. The HMT formula could be applied in various fields, including functional health food and pharmaceutical development.  
Pak-1; prostate cancer; complementary and alternative medicine; haematang; decursin 2021
# Late Fusion-Based Video Transformer for Facial Micro-Expression Recognition
## 4 Experimental Results
### Performance of the Proposed Method  
Table 2 shows the performance of the proposed method compared to other studies on SMIC and CASME II. The numerical values of each method are taken from survey [26] or their own paper. We can find that our method achieves the best accuracy and UF1 on average in SMIC and shows comparable performance in CASME II. In the case of SMIC, the proposed method has an improvement of about 0.031 and 1.8% over previous SOTA. In the case of CASME II, the proposed method did not outperform the previous SOTA, but while the previous SOTAs, LFM and GEME, demanded additional complex methods for model learning, our model exhibits comparable performance without such a method.  
Figure 2 shows the confusion matrices of the proposed method in the validation phase. In SMIC, the proposed model has difficulty classifying 'negative', especially misclassifying 'negative' as 'positive'. This is interesting because the number of samples labeled 'negative' is the largest, and other studies classify 'negative' relatively well. We believed that these results are due to using the imbalanced sampler. In CASME II, the proposed method is relatively poor at classifying 'others' and 'disgust', which have the most data samples.
## 4 Experiments
### Object Image Clustering on COIL20  
In this experiment, we test the clustering performance of our methods on the object image dataset of COIL20. The dataset contains 32 \(\times\) 32 gray scale images of 20 objects. The object images are generally captured at different views. Some sample images of this dataset are provided in Figure 6. Here we randomly select \(c=[2,3,4,5,6,8,7,8,9,10,11]\) objects for clustering. For each selected object, 36 images are randomly picked up. For each value of \(c\), we run 30 times of clustering by randomly choosing different images for each class and the mean error is reported as the final result. The parameters \(\lambda_{1}=100,\lambda_{2}=1000\) for LLE-SSC and \(\lambda_{1}=5,\lambda_{2}=2000\) for LLE-LRR.  
The clustering results are shown in Table 6. It is shown that our proposed LLE-SSC and LLE-LRR methods still perform excellently in this experiment. Compared with the other methods, LLE-SSC has better clustering results when the classes number is relatively small, while LLE-LRR has better results when the classes number is large. Particularly, LLE-LRR has the best result of 12.97% in terms of mean error.