esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Variational Bayesian Approximation-Based Adaptive Dual Extended Kalman Filter  
In order to handle the joint estimation of the SOC and the battery model parameters as well as the unknown measurement noise covariances, we propose a variational Bayesian approximation-based dual extended Kalman filter (VB-ADEKF) in this paper. First, let us rewrite the state-space equations for SOC estimation as  
\[\left\{ \begin{array}{l}X_{k+1}=F_{k}X_{k}+G_{k}I_{L,k}+w_{k}\\ y_{k}=h(X_{k},I_{L,k},\theta_{k})+v_{k}\\ w_{k}\sim\mathcal{N}(0,Q_{k}^{x})\\ v_{k}\sim\mathcal{N}(0,\Sigma_{k}^{x})\end{array} \right. \tag{28}\]  
and the state-space equations for battery model parameters as  
\[\left\{ \begin{array}{l}\theta_{k+1}=\theta_{k}+r_{k}\\ d_{k}\quad=h(X_{k},I_{L,k},\theta_{k})+e_{k}\\ r_{k}\sim N(0,Q_{k}^{\theta})\\ e_{k}\sim N(0,\Sigma_{k}^{\theta})\end{array} \right. \tag{29}\]  
where the process noise covariances \(Q_{k}^{x}\), \(Q_{k}^{\theta}\) are assumed known, and the measurement noise variances \(\Sigma_{k}^{x}\), \(\Sigma_{k}^{\theta}\) are unknown, being assumed as stochastic variables. Then, VB-ADEKF is to alternatively solve the joint posterior distribution \(p(X_{k},\Sigma_{k}^{x}|y_{1:k})\) of the SOC and its measurement noise variance and the joint posterior distribution \(p(\theta_{k},\Sigma_{k}^{\theta}|y_{1:k})\) of the battery parameter and its measurement noise variance by VB approximation as follows:\[p(X_{k},\Sigma_{k}^{x}|y_{1:k}) \approx Q_{x}(X_{k})Q_{\Sigma}(\Sigma_{k}^{x}) \tag{30}\] \[p(\theta_{k},\Sigma_{k}^{\theta}|y_{1:k}) \approx Q_{\theta}(\theta_{k})Q_{\Sigma}(\Sigma_{k}^{\theta}) \tag{31}\]  
where \(Q_{x}(X_{k})\), \(Q_{\Sigma}(\Sigma_{k}^{x})\), \(Q_{\theta}(\theta_{k})\), and \(Q_{\Sigma}(\Sigma_{k}^{\theta})\) can be solved by Equations (23) and (24). It should be noted that the parameters in these approximating densities are actually obtained using the extended Kalman filters since the measurement function \(h(\cdot)\) is nonlinear. The filtering procedure of the VB-ADEKF algorithm is summarized in Algorithm 1. ```
(1) Initialization: \(\hat{X}_{0}\), \(\hat{\theta}_{0}\), \(P_{x,0}\), \(P_{\theta,0}\), \(Q_{0}^{x}\), \(Q_{0}^{y}\), \(\hat{\alpha}_{x,0}\), \(\hat{\beta}_{x,0}\), \(\hat{\alpha}_{\theta,0}\), \(\hat{\beta}_{\theta,0}\) (2) Prediction: \(\hat{X}_{k}^{-}=F_{k-1}\hat{X}_{k-1}+G_{k-1}I_{k,k-1}\), \(\hat{\theta}_{k}^{-}=\hat{\theta}_{k-1}\) \(P_{x,k}^{-}=F_{k-1}P_{x,k-1}F_{k-1}^{T}+Q_{k}^{x}\), \(\hat{\rho}_{\theta,k}^{-}=P_{\theta,k-1}+Q_{k}^{\theta}\) \(\hat{\alpha}_{x,k}^{-}=\rho_{x}\hat{\alpha}_{x,k-1}\), \(\hat{\beta}_{x,k}^{-}=\rho_{x}\hat{\beta}_{x,k-1}\) \(\hat{\alpha}_{\theta,k}^{-}=\rho_{\theta}\hat{\alpha}_{\theta,k-1}\), \(\hat{\beta}_{\theta,k}^{-}=\rho_{\theta}\hat{\beta}_{\theta,k-1}\)  where \(\alpha_{x}\), \(\beta_{x}\) and \(\alpha_{\theta}\), \(\hat{\beta}_{\theta}\) are the inverse gamma distribution parameters of the measurement noise covariance, \(\rho_{x}\) and \(\rho_{\theta}\) are the scale factors. (3) Update: the update of VB-ADEKF utilizes iterate filtering framework.
## 3 Results and Discussion
### Relative Density
#### 3.1.1 Archimedes Method  
Figure 2a shows the comparison between results using method 2-1 and method 2-2, respectively, for the samples with SLMed surface and with ground surface. From Figure 2a, it can be seen that the ground samples have a higher density than the ones with SLMed surfaces. This is because more liquid remains on the rough surface of the SLMed AlSi10 Mg samples when measuring the mass of liquid-impregnated samples. According to Equation (3), if the mass \(m_{\textit{net}}\) is higher than the actual value, the density is lower than the actual one. Therefore, when considering the effect of the open cavities on the density measurement, it is necessary to grind the surfaces of samples to avoid excessive residual liquid left on the surface of samples.  
Figure 2b gives the comparison of method 3-1 and method 3-2, which kept the test samples immersed in liquid (tetrachloroethylene) for 5 min and 30 min, respectively, for liquid-impregnation in a vacuum environment. Figure 2b shows that the density results of the liquid-impregnation of 5 min are higher than those of 30 min for almost all samples. This indicates that a sufficient amount of time is important for liquid-impregnation. Otherwise, if the time is insufficient, some open cavities would not be closed by liquid and would not be taken into account for the volume of the sample.  
In Figure 2c, each method selects ethanol, tetrachloroethylene, and pure silicone fluid, respectively, for liquid-impregnation. It can be found that the results using the three different methods are very close, and there is no method significantly better than the others. Therefore, the function of the vacuum pump and the liquid with a high viscosity are not exclusive requirements for measuring the density of SLMed AlSi10 Mg products if the time for liquid-impregnation is long enough.  
The relative density results using method 1 and method 4 are both shown in Figure 2d. The differences between results using method 1 and method 4 reflect that some open cavities exist in some samples, especially in the sample with a lower density. This indicated that there are more open cavities in these SLMed AlSi10 Mg samples using the suboptimal SLM processing parameters. So method 4, method 2-2, or method 3-2 (Archimedes method considering the open cavities effect) should be used for the AlSi10 Mg SLMed samples, especially for the sample with a density lower than 99%, which is measured using the Archimedes method without considering the open cavities effect according to Table 2.
# On Alpha-Expansion-Based Graph-Cut Optimization for Decoder-Side Depth Estimation
## 5 Conclusions  
This paper presents a novel hierarchical alpha-expansion-based graph-cut optimization technique for decoder-side depth estimation in immersive video applications. Our approach significantly enhances the computational efficiency and accuracy of depth map estimation, addressing the critical challenges of rendering high-quality immersive videos. Through comprehensive experimentation, we have demonstrated that our method outperforms the state-of-the-art technique, in terms of both depth accuracy and computational load, making it a viable solution for more practical immersive video applications. The proposal is particularly relevant for streaming immersive content to resource-limited receivers, where optimizing decoder-side computations is crucial for a user experience.  
The experimental results have shown that examining selected, more plausible depth levels provides more accurate estimation than examining all available labels. Therefore, future directions for research can include exploring adaptive hierarchical structures that can dynamically adjust based on content complexity and computational complexity constraints (such as the number of CPU cores or the size of RAM available in the decoder). In conclusion, the hierarchical alpha-expansion-based graph-cut optimization presents a significant step forward in the research on using decoder-side depth estimation for immersive video compression.  
Conceptualization, D.M. and D.K.; methodology, D.M.; software, D.M. and D.K.; validation, D.M.; formal analysis, D.M. and O.S.; investigation, D.M.; writing--original draft preparation, D.M.; writing--review and editing, D.K. and O.S.; visualization, D.M.; supervision, O.S.; project administration, D.M. and O.S. All authors have read and agreed to the published version of the manuscript.  
The work was supported by Ministry of Science and Higher Education.  
Not applicable.  
The research was conducted under ISO/IEC JTC1/SC29 WG04 MPEG Video Coding Common Test Conditions for MPEG Immersive Video: [[https://mpeg-miv.org/](https://mpeg-miv.org/)]([https://mpeg-miv.org/](https://mpeg-miv.org/)), accessed on 13 June 2024.  
The authors declare no conflicts of interest.  
Figure 4: The visualization of differences in depth maps estimated using alpha-expansion and the proposal. Areas estimated differently (shown as white areas) are overlaid on input view.
## 6 Conclusions  
Aiming at the low accuracy of unsupervised learning classification in security applications of the IoT, this paper proposed an unsupervised NTC method using adversarial training and deep clustering under complicate IoT environments. The introduction of the CAAE for reducing the dimension of features can avoid the huge computational cost caused by the high complexity of high-dimensional original network traffic data features. The DC cell is the key building block of our model because it not only adapts the extracted features to the clustering network but also has a lower computational overhead than the CAAE model. In the multiclassification task, we gave a method to optimize the distance between clusters and within clusters through the joint analysis of cluster error and feature learning. It also indicated that the number of clusters could be determined by the elbow method and silhouette coefficient method when the sample class was unknown. The experimental results provided showed that the model not only had good network traffic classification performance but could also run efficiently in an unsupervised environment, greatly saving the cost of labeling. Therefore, our proposed unsupervised NTC method can provide a feasible solution for real application scenarios of the IoT and also provide a new idea to improve the unsupervised clustering accuracy under complex data conditions. In the future, we plan to study the detection of abnormal network traffic, that is, to identify and detect unknown malicious attacks in intrusion detection systems and to deploy them experimentally in a real industrial IoT dataset.  
Conceptualization, W.Z. and L.Z.; methodology, L.Z. and X.Z.; software, W.Z. and P.L.; validation, L.Z.; formal analysis, X.Z. and Y.W.; investigation, W.Z. and L.Z.; resources, Y.W.; data curation, P.L.; writing--original draft preparation, W.Z. and L.Z.; writing--review and editing, X.Z. and G.G.; visualization, X.Z.; supervision, G.G.; project administration, P.L.; funding acquisition, P.L.; All authors have read and agreed to the published version of the manuscript.  
This work was funded in part by the State Key Laboratory of Millimeter Waves under grant number K202219, the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under grant number 21 KJD510001.  
The USTC-TFC2016 dataset can be find at [[https://github.com/yungs-henglu/USTC-TFC2016](https://github.com/yungs-henglu/USTC-TFC2016)]([https://github.com/yungs-henglu/USTC-TFC2016](https://github.com/yungs-henglu/USTC-TFC2016)) (accessed on 18 March 2022).  
The authors declare no conflict of interest.  
Figure 11: Performance of the DC-CAAE model at \(k=\{18,19,20,21,22\}\).
## 2 Methodology
### Network Postprocessing  
The postprocessing process consists of four steps. (1) The detection module, which uses a convolution operation to quickly find the region with the highest local probability in the heat map, which will be a joint point. (2) Point linking, which generates a group of joint pair-supported heat maps and offset maps. Subsequently, the joint pairs are ranked, and the doable joint connections are calculated with the best total chance. (3) Depth adaptation which calculates the relative proportion of the depth of world space. This process converts mismatched coordinate spaces into the same coordinate space. (4) Time filtering, where, depending on the application, if necessary, time filters are used to smooth the 3D pose results (Figure 3).  
To connect the joint points, we first calculate the position of the target joint point.  
\[(TargetX_{j},TargetY_{j})=(X_{j}+OffsetX_{j}) \tag{9}\]  
Next, we searched for a circular region centered on \((TargetX_{j},TargetY_{j})\), with \(R_{search}\) as the radius. The specific calculation method was as follows:  
\[\begin{split}& l=\sqrt{OffsetX_{j}^{2},OffsetY_{j}^{2}}\\ & R_{search}=\max(l\times a+(1-a),1)\end{split} \tag{10}\]  
For practical applications, we set \(a\) = 0.3. After testing, this value was found to be highly accurate.  
We wanted to scale the depth with the values \(\text{scale}_{\text{depth}}\), so that the coordinates were expressed in the same units.  
\[depth=\sqrt{\left(P_{x}-C_{x}\right)^{2}+\left(Py-Cy\right)^{2}+\left(P_{z}- C_{z}\right)^{2}} \tag{11}\]  
Figure 3: Specific structure diagram of detection module and image-generation module. “*” is a multiplication sign.  
\[\begin{bmatrix}u_{x}\\ u_{y}\\ 1\end{bmatrix}=\begin{bmatrix}f_{x}&0&C_{x}\\ 0&f_{y}&C_{y}\\ 0&0&1\end{bmatrix}\begin{bmatrix}r_{11}&r_{12}&r_{13}&t_{1}\\ r_{21}&r_{22}&r_{23}&t_{2}\\ r_{31}&r_{32}&r_{33}&t_{3}\end{bmatrix} \tag{12}\]  
However, we found a probabilistic correlation between the size and depth of the individuals. We assume that the size of the person is normal. Linear regression and visualization demonstrated a strong correlation between human 2D size (width and height) and \(\text{scale}_{\text{depth}}\). Therefore, we use fast calculations to obtain the \(\text{scale}_{\text{depth}}\) and multiply it by the depth. This allowed us to estimate the correct position of the human joints in the camera space without obtaining camera information.
# Stabilization of Argan Oil Nanoemulsions Using Chitosan Extracted from Pink Shrimp Shells
## 3 Results and Discussion
### Chitosan
#### 3.1.1 Deacetylation Degree and Molecular Weight  
The degree of deacetylation (DDA), a key parameter representing the fraction of deacetylated units in the polymer chain, was found to be 88.34 \(\pm\) 0.48% (Table 1). This high DDA value indicates a significant concentration of reactive amino groups, enhancing the solubility and functional properties of chitosan and making it suitable for biomedical applications [15]. The DDA and chitosan concentrations are known to influence the properties of delivery systems [16].  
In addition to DDA, molecular weight is another crucial characterization parameter. The molecular weight of the extracted chitosan, determined through intrinsic viscosity measurement, was 408.809 \(\pm\) 3.110 kDa. This falls within the medium molecular weight range (150-700 kDa) [17]. Molecular weight affects chitosan's solubility, viscosity, and degradation rate. Medium molecular weight chitosan is characterized by an intermediate polymer chain length, which results in moderate entanglement and network density in solution, leading to balanced viscosity. The molecular weight significantly influences chitosan's solubility, with lower molecular weight chitosan exhibiting higher solubility across a broader pH range, whereas higher molecular weight chitosan shows limited solubility. Furthermore, molecular weight impacts degradation rates, with higher molecular weight chitosan degrading more slowly, making it suitable for controlled-release formulations [18]. Chitosan, with a high molecular weight and a high degree of deacetylation, can also enhance epithelial permeability, improving drug transport across biological barriers [19].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Material** & **Yield (\%)** & **Ash (\%)** & **Protein (\%)** \\ \hline Shrimp shells & 10.99 \(\pm\) 1.20 & 39.00 \(\pm\) 0.28 & 9.72 \(\pm\) 0.29 \\ \hline Chitin & 29.64 \(\pm\) 1.54 & 0.98 \(\pm\) 0.08 & 0.89 \(\pm\) 0.08 \\ \hline Chitosan & 24.66 \(\pm\) 1.61 from shells & 0.93 \(\pm\) 0.06 & 0.72 \(\pm\) 0.08 \\ \(83.15\) \(\pm\) 1.69 from chitin & & & \\ \hline \end{tabular}
\end{table}
Table 1: Yield, ash content, and protein content for shrimp shells, chitin, and chitosan.  
Medium molecular weight chitosan strikes a balance between the properties of both low and high molecular weight variants. It offers better solubility compared to high molecular weight chitosan while maintaining moderate viscosity and dissolves easily in slightly acidic conditions, making it well-suited for various applications that require both processability and functionality.
## 3 Results and Discussion
### Effects of TVEE on H\({}_{2}\)O\({}_{2}\)-Induced ROS Production and Cell Viability in HDFs  
To investigate the oxidative stress-protective effect of TVEE in HDFs, ROS scavenging efficacy and cell viability were examined (Figure 2). TVEE did not show cytotoxicity at 50, 100, or 200 ug/mL; therefore, the experiment was conducted at these concentrations (Figure 2a). ROS present in HDFs were measured using DCFH-DA after treatment with 0.85 mM H\({}_{2}\)O\({}_{2}\) (Figure 2b). When 0.85 mM H\({}_{2}\)O\({}_{2}\) treatment was used to induce oxidative stress in HDFs, ROS production increased approximately four times compared with that in the control. However, TVEE treatment at concentrations of 50, 100, and 200 ug/mL improved ROS scavenging efficacy in a concentration-dependent manner. These results were similar to previous results of ROS scavenging efficacy in abalone viscera hydrolysates and _T. cornutus_ viscera protames extracts [7; 28], and corroborate the ROS scavenging effect in viscera of various gastropods. To confirm the cell viability of TVEE-treated cells, HDFs were treated with 0.85 mM H\({}_{2}\)O\({}_{2}\) (Figure 2c). The cell viability of TVEE untreated HDFs decreased to 52% compared with that in the control, but the viability of TVEE-treated cells was 65.4, 69.6, and 65.4% at concentrations of 50, 100, and 200 ug/mL, respectively.  
Figure 1: DPPH radical and hydrogen peroxide scavenging activity of TVEE. (**a**) DPPH radical scavenging activity. (**b**) Hydrogen peroxide scavenging activity. Values are expressed as mean \(\pm\) SD. * \(p\) \(<\)0.05, **_p_ \(<\)0.01, ***_p_ \(<\) 0.001 indicate values compared with N-acetylcysteine (NAC, 0.05 mg/mL).  
Figure 2: Protective effect of TVEE on H\({}_{2}\)O\({}_{2}\)-induced HDFs. (**a**) Cytotoxicity (**b**) ROS production (**c**) cell viability. Values are expressed as mean \(\pm\) SD. * \(p\) \(<\) 0.05, **_p_ \(<\) 0.01 indicate values compared with only the H\({}_{2}\)O\({}_{2}\)-induced group.
## 4 Local-Global Dynamic Multi-Graph Convolutional Neural Network Model
### Local-Global Dynamic Spatiotemporal Graph Convolution
#### 4.1.3 Global Spatiotemporal Module  
The dynamic historical connectivity graph \(G_{his}^{t}\) and the POI Similarity graph \(G_{poi}^{t}\), which capture global positional semantic information, are utilized in the Global Spatiotemporal Module. Similar to the Local Spatiotemporal Module, the Global Spatiotemporal Module also comprises two Gated Temporal Convolutions (Gated TCN). However, in the graph convolution part, the Global Spatiotemporal Module employs Chebnet [43]. Given that \(G_{poi}^{t}\) is a fully connected graph, the computational approach of Chebnet is better suited for capturing global positional semantic information. Let \(x\) denote the input of the Chebnet, \(g_{\theta}\) be the convolutional kernel, and \(\ast_{g_{club}}\) represent the Chebnet graph convolution operation, and the computation is as follows:  
\[\begin{split} A_{glo}^{t}=\alpha A_{his}^{t}+(1-\alpha)A_{poi}^{ t}\\ g_{\theta}\ast_{g_{club}}x=\sum\limits_{k=0}^{K-1}\theta_{k}T_{k}( \widetilde{L}_{glo})x\end{split} \tag{7}\]  
where \(\alpha\) is the fusion factor of \(A_{his}^{t}\) and \(A_{poi}^{t}\), \(L_{glo}\) is the global Laplacian matrix, and \(\theta\) are polynomial coefficients.  
The structure of the Global Spatiotemporal (GDST) Module is as follows: Gated Temporal Convolution-Dynamic Chebnet-Gated Temporal Convolution, and the calculation formula is given by Equation (8):  
\[\text{GDST}(X)=\Gamma\ast_{\tau}\text{ReLU}(\Theta_{Cheb}\ast_{g_{club}}( \Gamma\ast_{\tau}X)) \tag{8}\]
## 4 Real-Word Examination and Evaluation
### Test on Driver's Control Ability  
In a left turn intersection scenario, we conducted an assessment test of the vehicle control abilities among two test drivers using RPAM. Both drivers passed through the intersection between 3:00 and 4:00 p.m., and the traffic signal was green when they entered the intersection. Through three days of manual calibration, we created a simple standard schedule. By consulting the table, we obtained a standard green light passing time of 9 s for the test period at the intersection. During the verification process, Test Driver 1 took 11 s to pass through the intersection, resulting in a \(Ef\) score of 83.09, while Test Driver 2, who completed it faster with 8 seconds, scored 100.  
Simultaneously, we collected vehicle trajectories of the two test drivers passing through the intersection scene using GPS data, recorded at a frequency of 20 Hz. Since it was the same intersection, there was only one standard trajectory. Figure 9 shows a comparative illustration of the standard trajectory with the trajectories of the two drivers along with the actual satellite map of the scenario.  
The red line stands for the standard route, the blue is for Tester 1, and the green is for Tester 2. It can be seen that Tester 1's trajectory has a better fit. For the RMSE score of the two testers, Tester 1 achieved 0.25787, and Tester 2 achieved 0.42855. [PERSON]'s score of Tester 1 was 0.42396 and Tester 2 was 1.18460. Both the RMSE and Haus value of Tester 1 were better than Tester 2.  
Combining the \(Ef\), RMSE, and Hausdorff value using Equation (7), the final control ability \(Opes\) for Tester 1 was 74.16 and for Tester 2 was 76.43. Tester 2 obtained a better overall ability of performing a left turn under this left-turn scenario.  
Figure 8: The result of the real-world perception ability test.
# Seismic Response of Container Crane under Near-Field and Far-Field Ground Motions
## 1 Introduction
[PERSON] [8] used the similitude law to make a 1/50 scale container crane and studied its seismic response under vertical component earthquakes using a shaking table test test. The shaking table test results on the 1/50 scale crane agreed quite well with those of the prototype crane obtained from the finite element method. In this study, a 1/20 scale container crane was built from a prototype container crane at Gwangyang port using the similitude law. A series of shaking table tests were conducted at the Seismic Research and Test Center, Pusan National University, Yangsan Campus. Past research work on structures, such as a cable-stayed bridge [18], low-rise shear walls [19], embankment dams [20], and concrete gravity dams [21], showed that near-field ground motions have a more significant effect on the seismic response of the respective structures than far-field ground motions. [PERSON] and [PERSON] [22] showed that near-field and far-field ground motion are different in intensity, nature, and typology and suggested different scale factors for near-field and far-field ground motions. [PERSON] et al. [23; 24] studied the seismic responses of offshore platforms subjected to near-field ground motions with distinct pulses, and taking into account the pile-soil-structure interaction effects. The offshore platforms, which were steel structures and located in a sea region, were similar to container crane structures. The study showed that the near-field ground motions caused more intensive structural response than the far-field ground motions. [PERSON] et al. [25] developed fragility curves based on a local displacement ratio and deflection ratio for gantry cranes subjected to near-field ground motions. They evaluated the probability of uplift for the crane according to the earthquake's peak ground acceleration. In this study, one near-field, Gazli USSR Iran, and two far-field ground motions, Nahanni Canada, and Mineral Viginia earthquakes, were matched to a Korean elastic design response spectrum. Then, the acceleration time history of the adjusted ground motions was applied in the trolley-boom direction to investigate the seismic response of the scale container crane in terms of the internal forces in the landslide and seaside legs, drift of the portal frame, and acceleration.