esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Performance Evaluation of Actuator Component
#### 4.2.2. Shuttle Shifting Actuator  
In order to evaluate the position control performance of the shuttle shifting actuator, the response performances, such as overshoot, settling time, and steady-state error, were evaluated using the experimental test-bed shown in Figure 13. The maximum overshoot and steady-state errors were 2.58% and 0.04% when shifting from neutral to forward, and 3.11% and 0.03% when shifting from forward to neutral, respectively, as shown in Table 5. The settling time from the neutral to forward position was 0.430 s, and the settling time from forward to neutral was 0.370 s. The maximum overshoot and steady-state errors were 3.36% and 0.02% when shifting from neutral to reverse, and 2.78% and 0.03% when shifting from reverse to neutral, respectively. The settling time from the neutral to reverse position was 0.400 s, and the settling time from reverse to neutral was 0.310 s. Although there were some differences depending on the control direction, it was found that the control of the shuttle shifting actuator was performed while satisfying the control targets of a maximum overshoot of less than 5%, settling time of less than 0.500 s, and steady-state error of less than 1%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Range Shift**} & \multirow{3}{*}{\begin{tabular}{c} **Driving Shift** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Max.** \\ **Overshoot** \\ **(\%)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Partially** \\ **Engaging** \\ **(a)** \\ \end{tabular} } & \multirow{3}{*}{\begin{tabular}{c} **Fully** \\ **Engaging** \\ **(b)** \\ \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{c} **Total** \\ **(a + b)** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **Greedy-State** \\ **Error (\%)** \\ \end{tabular} } \\ \cline{3-6}  & & 1 st & 0.00 \(\pm\) 0.00 & 0.49 \(\pm\) 0.02 & 0.97 \(\pm\) 0.02 & 1.46 \(\pm\) 0.04 & 0.14 \(\pm\) 0.028 \\ \cline{2-6}  & 2 nd & 0.00 \(\pm\) 0.00 & 0.51 \(\pm\) 0.03 & 0.92 \(\pm\) 0.02 & 1.43 \(\pm\) 0.04 & 0.11 \(\pm\) 0.022 \\ \cline{2-6}  & 3 rd & 0.00 \(\pm\) 0.00 & 0.53 \(\pm\) 0.03 & 0.93 \(\pm\) 0.01 & 1.46 \(\pm\) 0.03 & 0.19 \(\pm\) 0.125 \\ \cline{2-6}  & 4 th & 0.00 \(\pm\) 0.00 & 0.51 \(\pm\) 0.02 & 0.92 \(\pm\) 0.02 & 1.43 \(\pm\) 0.04 & 0.12 \(\pm\) 0.034 \\ \hline \multirow{3}{*}{3 rd} & 1 st & 0.00 \(\pm\) 0.00 & 0.49 \(\pm\) 0.01 & 0.89 \(\pm\) 0.01 & 1.37 \(\pm\) 0.02 & 0.15 \(\pm\) 0.064 \\ \cline{2-6}  & 2 nd & 0.00 \(\pm\) 0.00 & 0.50 \(\pm\) 0.02 & 0.91 \(\pm\) 0.01 & 1.41 \(\pm\) 0.03 & 0.23 \(\pm\) 0.189 \\ \cline{2-6}  & 3 rd & 0.00 \(\pm\) 0.00 & 0.50 \(\pm\) 0.02 & 0.91 \(\pm\) 0.01 & 1.41 \(\pm\) 0.02 & 0.36 \(\pm\) 0.071 \\ \cline{2-6}  & 4 th & 0.00 \(\pm\) 0.00 & 0.49 \(\pm\) 0.03 & 1.00 \(\pm\) 0.01 & 1.49 \(\pm\) 0.05 & 0.26 \(\pm\) 0.170 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation of shifting performance for clutch actuator according to each gear stage.  
Figure 12: Position control performance of the clutch actuator according to each driving shift gear under range shift: 3 rd.
## 3 Results and Discussion
In the conducted experiments, two dynamic system limitations were observed: potential well trapping and a limited effective operating range resulting from a narrow lock-in range. The first phenomenon, potential well trapping, primarily occurred with larger bluff body masses and higher flow velocities. It involved the system transitioning into a static or quasi-static state. In this state, the bluff body did not undergo full oscillations but remained near its equilibrium position, significantly reducing the beam's vibration amplitude and the generated piezoelectric voltage. This mechanism is fundamentally caused by an increase in inertia and aerodynamic damping forces, which prevent effective vibration excitation. The second phenomenon, the narrow effective operating range, stems from the system's strong dependence on the synchronization of the vortex shedding frequency with its natural frequency. This range varied for each bluff body mass and diameter configuration, as confirmed by observations of changes in vibration frequency and RMS voltage values as a function of flow velocity. Outside the lock-in range, the system either generated chaotic vibrations (with reduced energy harvesting efficiency) or became inactive. Both phenomena limit the long-term performance of the energy harvester, especially in environments with variable flow conditions, such as natural wind. Consequently, to ensure stable and continuous system operation, it is essential to implement adaptive mechanisms for tuning the system's dynamic parameters, such as variable stiffness, movable mass, or modified bluff body geometry. This presents a challenge for future research in this field.
## 2 Materials and Methods
### Antibacterial Activity--Evaluation of the Minimum Inhibitory Concentration (MIC)  
Nine bacterial strains were used in this work acquired from the American Type Culture Collection (ATCC, Manassas, VT, USA), the BCCM/LMG Bacteria Collection (Belgian Co-Ordinated Collections of Micro-organisms, Gent, Belgium), and the Spanish Type Culture Collection (Valencia, Spain): four Gram-positive strains (_Enterococcus faecalis_ ATCC 29212, _Bacillus cereus_ ATCC 11778, _Listeria monocytogenes_ LMG 16779, and _Staphylococcus aureus_ ATCC 25923) and five Gram-negative strains (_Salmonella enterica_ subsp. _ enterica_ ATCC 13311 serovar Typhimuriumium, _Klebsiella pneumoniae_ ATCC 13883, _Proteus mirabilis_ CECT 170, _Serratia marcscens_ CECT 159, and _Acinetobacter baumannii_ LMG 1025). Strains were routinely grown on Mueller Hinton Agar (MHA) and Trypic Soy Agar (TSA).  
The susceptibility of bacteria to the coloured and non-coloured fractions and total extract from blueberries and sweet cherries was determined following the broth microdilution method [45], with some modifications. Briefly, inoculums were prepared by suspension in saline solution (NaCl 0.85% (\(w/v\))) and turbidity was adjusted to 0.5 McFarland to obtain a final concentration of approximately 5 \(\times\) 10\({}^{6}\) CFU mL\({}^{-1}\). Extracts were resuspended in saline solution. Positive growth control was conducted in similar conditions replacing tested fractions for saline solution. Gentamicin was employed as the negative control with concentrations ranging between 0.016 and 0.00003 mg mL\({}^{-1}\). Experiments were performed with concentrations ranging from 4 to 0.015 mg mL\({}^{-1}\), and posteriorly incubated at 37 \({}^{\circ}\)C for 24 h. Then, 30 \(\upmu\)L of a resazurin solution (0.01%) was added to each well and incubated for 2 h at 37 \({}^{\circ}\)C. The assay was performed in triplicate, and results were reported as modal values.
## 4 Displacement Analysis of Bowden Cable-Fabric Transmission
### Experimental Verification and Result Analysis
For each peak value, the force signal measured by the end sensor was recorded, and the force errors were calculated, as shown in Figure 16.  
Under the three different peak values of 40 N, 50 N, and 60 N, the relative root-mean square errors of the Bowden cable-fabric force transmission control based on friction/displacement compensation are 2.53%, 2.16%, and 2.07%, respectively. The results demonstrate that the control method we introduced has good reliability in precision and flexibility.
# A Novel Spatiotemporal Analysis Framework for Air Pollution Episode Association in Puli, Taiwan
## 5 Conclusions  
In this paper, we have proposed a spatiotemporal analysis framework for air pollution episode association. Classic spatiotemporal analysis approaches are statistics-based. The contribution of our paper relies on machine learning approaches, which already have successful applications in the image and video processing domains. In our previous research, we built an internet of low-cost sensors for monitoring the hourly PM\({}_{2.5}\) concentrations in Puli, Taiwan. With this big volume of PM\({}_{2.5}\) data, we transform it into a video that consists of a time series of pollution image frames. To disclose the main pollution patterns in this area, several shot boundary detection algorithms are proposed to detect the cut-frames separating the pollution episodes in the time series. Each episode corresponds to particular activities, such as pollution accumulation, transportation, scattering, and diminishing, in different spatiotemporal ways. The GEI representation scheme is applied to render the spatiotemporal dynamics of the episode such that they are able to be dealt with by the clus  
Figure 8: Some members of the first two GEI clusters generated by using the CNN method. (**a**) the first cluster. (**b**) the second cluster.  
tering method. Three clustering approaches are proposed for episode clustering, ranging from histogram-based, edge-based, and deep-learning-based. The experimental results manifest that the episodes contained in the same cluster have homogeneous patterns that appear at different times in a year. This means that some particular patterns of pollution activities emerge many times in this region that may have relations with local weather, terrain, and anthropogenic activities. The research results provide a useful clue for the causal analysis of regional pollution in our future study.  
This research was funded by the National Science and Technology Council of the ROC under Grant NSTC 110-2410-H-130-055-MY2.  
The raw data used in this study can be access at [[https://www.airq.org.tw/](https://www.airq.org.tw/)]([https://www.airq.org.tw/](https://www.airq.org.tw/)), accessed on 5 April 2023.  
The authors are grateful to [PERSON] for providing the dataset and [PERSON] and [PERSON] for their code development of the proposed algorithms.  
The author declares no conflict of interest.
## 1 Introduction
### Received Signal Model
\(p(R_{\text{A}},R_{\text{S}},\Delta\tau)\) is the polarization mismatch loss in Equation (2), \(R_{\text{R}}(\psi,\varphi)\) is the AR of the RHCP antenna at a given direction \((\psi,\varphi)\) and \(R_{\text{L}}(\psi,\varphi)\) is that of the LHCP antenna; \(R_{\text{LOS}}\) is the AR of the LOS signal and \(R_{\text{MP}}\) is that of the MP signal which is a function of the grazing angle and the electrical property of the reflective surface. \(w_{\text{R}}(t)\) and \(w_{\text{L}}(t)\) are the independent additive white Gaussian noise. Equation (4) can be rewritten in a compact form in Equation (5):  
\[x_{\text{RHCP}}(t) =A_{0}e^{\text{i}\beta_{0}}s(t-t_{0})+A_{1}e^{\text{i}\beta_{1}} s(t-t_{1})+w_{\text{R}}(t) \tag{5}\] \[x_{\text{LHCP}}(t) =B_{0}e^{\text{i}\beta_{0}^{\prime}}s(t-t_{0})+B_{1}e^{\text{i} \beta_{1}^{\prime}}s(t-t_{1})+w_{\text{L}}(t)\]  
where the amplitudes and phases considering reflection and reception are rewritten in Equation (6). \[A_{0} =a_{0}p(R_{\text{R}}(\psi_{0},\varphi_{0}),R_{\text{LOS}},\Delta \tau_{0}) \theta_{0} =\beta_{0} \tag{6}\] \[A_{1} =a_{1}|\Gamma_{\text{o}}|p(R_{\text{R}}(\psi_{1},\varphi_{1}),R_ {\text{MP}},\Delta\tau_{1}) \theta_{1} =\beta_{1}-\angle\Gamma_{\text{o}}\] \[B_{0} =a_{0}p(R_{\text{L}}(\psi_{0},\varphi_{0}),R_{\text{LOS}},\Delta \tau_{0}) \theta_{0}^{\prime} =\beta_{0}\] \[x_{\text{RHCP}}(t) =a_{0}e^{\text{i}\beta_{0}}p(R_{\text{R}}(\psi_{0},\varphi_{0}),R_{ \text{LOS}},\Delta\tau_{0})s(t-t_{0}) \tag{4}\]0  
Now we have modeled the signal received by a dual-polarization antenna considering both reflection and reception. In the next subsection, we will calculate the relative power of each component with respect to \(A_{0}^{2}\), to analyze the characteristics of the received signal.
# Recent Advances in Volatiles of Teas
## 2 Aromatic Volatile Compounds in Various Kinds of Teas
### Variation of Aromatic Volatiles between Various Kinds of Tea
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **White Tea** & **Green Tea** & **Oolong Tea** & **Black Tea** & **Pu-erh Tea** \\ \hline
1 & Hexanal & (\(E\))-Geraniol & (\(E\))-Nerolidol & (\(E\))-2-Hexenal & 1,2,3-trimethoxybenzene \\ \hline
2 & (\(E\))-2-Hexenal & Linalool & Indole & Hexanal & Linalool oxide II (cis, Furanoid) \\ \hline
3 & Linalool oxide II & (cis, Furanoid) & (E)-Nerolidol & Linalool & (\(E\))-Geraniol & Hexanal \\ \hline
4 & Linalool & (\(Z\))-Hexanoic acid & Benzeneacetaldehyde & Linalool & Linalool oxide I (trans, Furanoid) \\ \hline
5 & (\(E\))-Geraniol & Nonanal & Phytol & Linalool oxide II (cis, Furanoid) & Benzaldehyde \\ \hline
6 & Phenylethyl & alcohol & 3,7-Dimethyl-1,5,7- & \(\alpha\)-Farnese & Benzeneacetaldehyde & Benzeneacetaldehyde \\ \hline
7 & Benzaldehyde & (\(E\))-2-Hexenal & Linalool oxide I (trans, Furanoid) & Linalool oxide I (trans, Furanoid) & a-Terpineol \\ \hline
8 & Linalool oxide I & Phytol & 3,7-Dimethyl-1,5,7- & 3,7-Dimethyl-1,5,7- & 3,7-Dimethyl-1,5,7- & \(\beta\)-Ionone \\ \hline
9 & Benzeneacetaldehyde & Heptanal & Benzyl nitrile & Benzaldehyde & Cedrol \\ \hline
10 & (\(Z\))-3-Hexen-1-ol & (\(Z\))-Jasmone & Hexanal & Methyl salicylate & Linalool \\ \hline \end{tabular}  
* Extraction method: simultaneous distillation and extraction (SDE); identification method: GC/ MS tentative identification; quantitative analysis: ratio of tested compound peak area to that of the internal standard (1 mL of 5% (\(r/r\)) ethyl cytotoxic). Sample number: white tea = 8, green tea = 21, oolong tea = 27, black tea = 15, Pu-erh tea = 10. The top ten volatiles were screened based on concentration.  
\end{table} 
Table 1: Top ten volatiles in various types of tea [27]1.
## 3 MEC Implementation in Vehicular Networks  
Nowadays, the world is experiencing substantial growth in Internet usage and technology, especially in the IoT and the Internet of Vehicles (IoV). Consequently, the need for safety and mobility control of smart cars has introduced new challenges in this field. Researchers and service providers have presented new methods to address these issues and provide the best QoE. The rest of this section illustrates a general model for deploying MEC in VNs.  
The design model of MEC in VNs is depicted in Figure 5. It consists of three different layers [11], namely the user-side layer including smart cars, the MEC layer including distributed Roadside Units (RSUs) and MEC servers to provide storage and computation resources, and the core network layer which has cloud platforms and core network data servers.  
Figure 4: ETSI MEC reference architecture [60].  
Figure 5: A framework for MEC-assisted VN.
# Direction-Aware Lightweight Framework for Traditional Mongolian Document Layout Analysis
## 3 Methodology
### Modified MobileNetV3 Backbone
#### 3.1.1 Asymmetric Convolutions  
Standard square convolutions are suboptimal for vertical traditional Mongolian text as they treat both directions equally. We decompose the convolution operation into sequential vertical and horizontal components:  
\[F_{out}=F_{1\times 3}(F_{3\times 1}(F_{in})) \tag{1}\]  
This design enhances the capture of vertical stroke patterns while reducing computational cost by decomposing \(3\times 3\) convolutions into sequential \(3\times 1\) and \(1\times 3\) operations, maintaining the same effective receptive field size while achieving a 33% parameter reduction.
## 2 Related Work
The result of this simulation revealed that the estimator that the reverse-infection algorithm produced for the tree network was nearer to the real source. A further performance evaluation was conducted on many real-world networks with good outcomes. However, the assumption of a single source node only was the drawback of this model, since, in most real-world scenarios, this is close to impossible. To overcome this problem, [PERSON] et al. [36], utilized a divide-and-conquer approach to find many sources in social networks using the SIR model. The technique showed promising results with high accuracy of its estimations. However, these methods have not been directly employed in the feature selection optimization problem. Since the outbreak of the COVID-19 virus in 2020, more SIR model-based methods have been designed to detect or diagnose corona virus infection in humans. In [PERSON] et al. [37], a new coronavirus herd immunity optimizer (CHIO) was proposed which drew its inspiration from the concept of herd immunity and social distance strategy so as to protect society from contracting the virus. The herd immunity employed three main kinds of individuals: susceptible, infected and immunized; it was applied to solve engineering optimization problem. This algorithm has since been utilized to solve feature selection and classification problems, including the introduction of a novel COVID-19 diagnostic strategy, known as patient detection strategy (CFDS) [38], that combined the wrapper and filter methods for feature selection. The improved k-near neighbor (EKNN) was used for the wrapper method using the chest CT images of COVID-19 infected and non-infected patients. The results revealed the superiority of the proposed method over other, recently developed ones in terms of accuracy, sensitivity, precision, and time of execution. Similarly, the greedy search operator was incorporated with and without the CHIO to make two wrapper-based methods, which were evaluated on 23 benchmark datasets and a real-world COVID-19 dataset. Some high-dimensional datasets have been employed to assess the efficacy of the proposed methods. [PERSON] [39], boosted the efficiency of the probabilistic neural network (PNN) using CHIO to solve the classification problem. Eleven benchmark datasets were used to assess the accuracy of classification of the proposed CHIO-PNN which, on all the datasets used, produced a summative classification rate of 90.3% with a quicker rate of convergence than other methods. However, the drawback of this method was its use on low and medium rank datasets. As such, there is a concern that higher dimensional datasets may negatively impact its performance.
###### Abstract  
Surface defect detection of an automobile wheel hub is important to the automobile industry because these defects directly affect the safety and appearance of automobiles. At present, surface defect detection networks based on convolutional neural network use many pooling layers when extracting features, reducing the spatial resolution of features and preventing the accurate detection of the boundary of defects. On the basis of DeepLab v3+, we propose a semantic segmentation network for the surface defect detection of an automobile wheel hub. To solve the gridding effect of atrous convolution, the high-resolution network (HRNet) is used as the backbone network to extract high-resolution features, and the multi-scale features extracted by the Atrous Spatial Pyramid Pooling (ASPP) of DeepLab v3+ are superimposed. On the basis of the optical flow, we decouple the body and edge features of the defects to accurately detect the boundary of defects. Furthermore, in the upsampling process, a decoder can accurately obtain detection results by fusing the body, edge, and multi-scale features. We use supervised training to optimize these features. Experimental results on four defect datasets (i.e., wheels, magnetic tiles, fabrics, and welds) show that the proposed network has better F1 score, average precision, and intersection over union than SegNet, Unet, and DeepLab v3+, proving that the proposed network is effective for different defect detection scenarios.  
high-resolution network; optical flow; semantic segmentation; automobile wheel hub; surface defect detection +
Footnote †: journal: Applied Sciences  
1
# Dynamic Analysis of Modified Duffing System via Intermittent External Force and Its Application
###### Abstract  
Over the past century, a tremendous amount of work on the Duffing system has been done with continuous external force, including analytical and numerical solution methods, and the dynamic behavior of physical systems. However, hows does the Duffing oscillator behave if the external force is intermittent? This paper investigates the Duffing oscillator with intermittent external force, and a modified Duffing chaotic system is proposed. Different from the continuous-control method, an intermittent external force of cosine function was designed to control the Duffing oscillator, such that the modified Duffing (MD) system could behave chaotically. The dynamic characteristics of MD system, such as the strange attractors, Lyapunov exponent spectra, and bifurcation diagram spectra were outlined with numerical simulations. Numerical results showed that there existed a positive Lyapunov exponent in some parameter intervals. Furthermore, by combining it with chaos scrambling and chaos XOR encryption, a chaos-based encryption algorithm was designed via the pseudorandom sequence generated from the MD. Finally, feasibility and validity were verified by simulation experiments of image encryption.  
Keywords:Duffing oscillator; intermittent external force; Lyapunov exponent; chaos-based encryption +
Footnote †: journal: Applied Mechanics and Physics Letters
# A Generative Adversarial Network-Based Fault Detection Approach for Photovoltaic Panel
## 3 ppFDetector Solution
### GAN Model
#### 3.1.1 Generator Network  
As shown in Figure 3, the generator network is mainly composed of encoder network \(G_{E}\) and decoder network \(G_{D}\). The details of the encoder parameters and decoder parameters are shown in Tables 1 and 2, respectively. Firstly, the generator network \(G\) reads the input PV panel image \(x\) and passes it into the encoder network \(G_{E}\). After \(x\) is passed through the convolutional layer, batch normalization layer, the leaky linear rectification activation function layer and CBAM layer, and scaled down several times, \(x\) is compressed into a latent space vector \(z=G_{E}(x)\). The latent space vector \(z\) can theoretically be assumed  
Figure 2: The integrated photovoltaic panel fault detection workflow of ppFDetector.  
as the best representation of \(x\) with the smallest dimension. Next, the decoder network \(G_{D}\) enlarges \(z\) through the transposed convolution layer, batch normalization layer and linear rectification activation function layer. After upscaling, the latent space vector \(z\) is reconstructed into the reconstructed image \(\widetilde{x}\) corresponding to the original PV panel image \(x\), i.e., \(\widetilde{x}=G(x)\).