esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Common-Mode Voltage Analysis
Also, there is an additional common-mode voltage (\(V_{d-to-c}\)) that is defined as:  
\[V_{d-to-c}=V_{DM}\frac{L_{2}-L_{1}}{2(L_{2}+L_{1})} \tag{11}\]  
Taking into account Equation (11), the differential voltage with the unbalanced \(L_{1}\) and \(L_{2}\) inductors contributes to the common-mode voltage, increasing the leakage current. \[V_{TCM}=V_{CM}+V_{d-to-c}=\frac{V_{1N}+V_{2N}}{2}+\frac{V_{1N}+V_{2N}}{2} \frac{L_{2}-L_{1}}{(L_{2}+L_{1})} \tag{12}\]  
where: \(V_{TCM}\) is the total high-frequency common-mode voltage. By considering only one of the inductors (\(L_{1}\) or \(L_{2}\)), Equation (12) is reduced, for example, by considering only \(L_{1}\):  
\[V_{TCM}=\frac{V_{1N}+V_{2N}}{2}-\frac{V_{1N}-V_{2N}}{2}=V_{2N} \tag{13}\]  
Therefore, it is concluded that if \(L_{1}=L_{2}\), then the common mode voltage is expressed as:  
\[V_{CM}=\frac{V_{1N}-V_{2N}}{2} \tag{7}\]0  
From the model presented in Figure 6, two rules are established to eliminate or reduce the leakage current in PV systems [54]:  
* For symmetrical power topologies with zero \(V_{CM-MD}\), designing a sinusoidal pulse width modulation (SPWM) strategy to constantv CM. * Matching circuit parameters to make the sum of \(V_{CM-MD}\) and \(V_{CM}\) be a constant.
# Optical Emission Spectroscopy as a Diagnostic Tool for Characterization of Atmospheric Plasma Jets
## 1 Introduction
For Ar plasma, OH and N\({}_{2}\) species were dominant. For He plasma, N\({}_{2}\)\({}^{+}\) and O were observed. Emission profiles were measured versus a distance from the tube exit. Production of OH and O radicals was investigated. The production of OH and O was increasing with increasing Ar content in the gas mixture and with the applied voltage, but decreasing with increasing air flow. NO, OH, N\({}_{2}\) molecular, atomic N, He and O, and O\({}_{2}\)\({}^{+}\) species observed. Gas temperature was found to be \(\sim\)310 K, and the electron excitation temperature was \(\sim\)5420 K. OES was also used to investigate the generation of various species when the plasma jet was impinging onto the liquid. Besides He lines, also OH, O, N\({}_{2}\), and N\({}_{2}\)\({}^{+}\) species were detected. Rotational and vibrational temperatures from OH radical were determined in the active and afterglow region. Gas temperature slightly increased when O\({}_{2}\) was added or when moved from active to afterglow region. In the active plasma region, it was 310 and 340 K for He or He/O\({}_{2}\), respectively. Whereas \(T_{\rm vib}\) was 2200 or 2500 K, respectively.
# A Multibody Model Calibration Method for Vehicle Misuse Testing
## 5 Conclusions  
In this paper, a novel multibody model calibration method for vehicle misuse testing is proposed, utilizing a modified quarter-vehicle model.  
A severe obstacle contact process has been calibrated, where the experimental vehicle passed over relatively large speed bumps at high driving speeds and generated nonlinear responses. The complete process is divided into three stages because of the varying tire-ground contact conditions, rather than being a continuous progression influenced by minor road obstacles or slow driving speeds. The traditional quarter-vehicle model has been modified to adapt to severe misuse scenarios. An equivalent spring is added between the sprung mass and an imagined fixed ceiling to capture the recovery effect caused by the vehicle's pitch motion. Then, the three-stage, two-degree-of-freedom ordinary differential equations are analyzed theoretically to calibrate the nonlinear vehicle parameters, even without proper initial values. The effectiveness of the presented modified quarter-vehicle model and the nonlinear parameter calibration method has been validated through numerical simulation using the calibrated parameters.  
For parameter calibration involving nearly linear responses or appropriate initial values, the least squares method is employed. A comprehensive calibration workflow, encompassing actual vehicle experiments, commercial software simulation, and a theoretical analysis, is also introduced.  
The proposed model does not account for tire deformation vibration, stop-block contact, or the nonlinearity of the suspension stiffness. Subsequent research may consider incorporating these factors into the modified quarter-vehicle model to enhance the simulation accuracy and obtain more precise calibrated parameters.  
Conceptualization: C.Y.; methodology: Y.W.; software: Y.W. and X.L.; validation: H.D.; writing--original draft preparation: Y.W. and C.Y.; investigation and resources: Y.W.; writing--review and editing: H.D. and Z.X.; investigation and data curation: L.L.; supervision: C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National College Student Entrepreneurship Training Program--\"Fatigue analysis and design improvement of automotive shock absorbers based on real road spectrum\" (202411079002X), Entrepreneurship College of Chengdu University.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to the fact that the research project described in the article involves relevant confidentiality agreements, and the overall project has not been completed yet.  
Author [PERSON] was employed by the company China Merchants Testing Vehicle Technology Research Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Application of Blockchain Technology in Emergency Management Systems: A Bibliometric Analysis
## 2 Related Work  
In recent years, there has been a surge of interest in the application of blockchain technology to emergency management systems, with numerous surveys and reviews highlighting both the opportunities and challenges in this field.  
Traditional reviews in this domain primarily offer qualitative syntheses of the literature, focusing on summarizing the challenges, opportunities, and specific applications of blockchain technology in emergency contexts. These reviews typically:  
* Discuss blockchain's potential to enhance transparency, security, and coordination in emergency response [35; 36; 37; 38].
* Explore specific use cases, including the healthcare sector [39; 40; 41; 42], optimizing humanitarian supply chains [43; 44], enhancing disaster response coordination [37; 45], ensuring data integrity in crisis communication systems [45; 46], and others.  
* Highlight technical and operational challenges, including scalability, interoperability, and regulatory issues [35; 37].
* Provide and discuss conceptual frameworks and identify practical implications for emergency management stakeholders [1; 47; 48].  
While these reviews are valuable for understanding the current state of the field, they are often limited by their subjective nature and lack of quantitative analysis, which can obscure broader research dynamics and long-term trends.  
In contrast, bibliometric reviews employ quantitative methods to examine publication patterns, collaboration networks, citation dynamics, and emerging research trends. These approaches offer objective and data-driven insights that complement traditional reviews by revealing the structural and temporal evolution of the research landscape.  
In the context of blockchain in emergency management, existing bibliometric studies tend to focus on narrow subsets of applications [49; 50]. Some of these studies are limited in scope due to factors such as geographic focus or restricted time frames [51; 34]. Moreover, many previous works do not concentrate specifically on blockchain technology [52; 53; 54].  
In summary, while the existing literature confirms the significant potential of blockchain technology to improve efficiency, transparency, and resilience in emergency management applications, there is a clear lack of comprehensive bibliometric analyses that systematize current knowledge, evaluate scientific collaboration, and identify future research directions. The present study addresses that gap by providing an integrated and objective overview intended to inform both academic research and practical implementations.
## 4 Autonomous Landing Method
### Power Line Detection and Location Estimation
#### 4.1.1 OPTL Detection  
STDC-Seg is a real-time semantic segmentation network with low computational cost and high accuracy. The network has an encoder-decoder structure, as shown in Figure 3, which inputs RGB images into the network and outputs segmented images of the same size. Semantic segmentation tasks require rich spatial information and sizable perceptual fields. Nevertheless, real-time semantic segmentation networks usually sacrifice spatial resolution to increase inference speed. BiSeNet [32] is used to decouple semantic and spatial information extraction, improving inference speed while maintaining spatial resolution. However, the use of the additional spatial paths is time consuming, and borrowing backbones from other tasks (e.g., image classification) is inefficient for semantic segmentation. To address these problems, STDC-Seg adopts the feature extraction network STDC for the semantic segmentation task, adopting a shared backbone and detailed guidance to replace the spatial path. More importantly, STDC-Seg uses edge images as detailed guides for extracting spatial information. More importantly, STDC-Seg uses edge images as detailed guides for extracting spatial information. This is highly beneficial for power line segmentation, as edge images are also an essential step in traditional power line detection algorithms.  
The 1650 images selected were manually labeled using the annotation tool, and the data were augmented (color jitter, random horizontal flip, random crop and random resize) to train the CNN segmentation algorithm. The dataset was divided into 70% training, 15% validation and 15% test sets, and trained using the NVIDIA RTX 3090. Running deep learning algorithms on onboard devices is very computationally expensive and would cause problems for demanding real-time applications. The weights are converted to a frozen ONNX-based model to optimize the model. The frozen graph ensures easy hardware access optimization before generating the optimized engine. The trained weights are optimized using the TensorRT inference library to run a real-time segmentation algorithm. Figure 4 shows the training and testing stages of the segmentation algorithm.  
Figure 3: The structure of the STDC segmentation network.
## 3 Methodology
### Transformation Adversarial Module (TAM)  
The goal of TAM is to be able to generate augmented images that are hard enough while keeping the sample's identity constant. If a transformed image looses its vehicle identity, we thought that it had incorporated too much noise and discarded it from the augmented dataset. The experimental results showed that this form of filtering was quite effective. TAM is made up of three components: PGN, the recognizer, and learning target selection. Due to the adversarial module, PGN will dynamically optimize itself. The structure of TAM is shown in the green part of Figure 2. Before explaining the individual components of TAM, we elaborate the algorithm of TAM in Algorithm 2.  
The algorithm contains PGN and the recognizer. PGN outputs the paramter of the filter matrix for further augmentation. The recognizer ensures that the identity of image is unchanged. The roles of PGN and the recognizer are competitive: one is to make the sample as deformed as possible, and the other is to ensure that the identity is lost after excessive augmentation.  
```
1:selected rectangle region \(A\), original image \(x\)
2:updated PGN\({}^{\prime}\)
3:parameter \(W_{1},W_{2},W_{3},W_{4}\) is abtained from PGN: \(W_{1},W_{2},W_{3},W_{4}=\mathbf{PGN}(A)\)
4:form a \(3\times 3\) filter matrix \(S_{1}\) by these four parameters \(W_{1},W_{2},W_{3},W_{4}\), then follow the clockwise and fill in the four weight \(W_{1},W_{2},W_{3},W_{4}\) in the corresponding positions to generate the other three filter matrix \(S_{2},S_{3},S_{4}\)
5: finally, obtain augmented images \(x_{1},x_{2},x_{3},x_{4}\) based on \(S_{1},S_{2},S_{3},S_{4}\):
6:recognize image: \(\mathit{Reg}\leftarrow\mathbf{Recognizer}(x)\)
7:for\(1\leq i\leq 4\)do
8:\(x_{i}\leftarrow\mathbf{BT}(x,S_{i})\)
9:recognize image: \(\mathit{Reg}_{i}\leftarrow\mathbf{Recognizer}(x_{i})\)
10:if\(\mathit{ID}[\mathit{Reg}_{i}]=\mathit{ID}[\mathit{Reg}]\)then
11:\(\Delta_{i}\leftarrow\) measure difficulty by distance between \(\mathit{Reg}_{i}\) and \(\mathit{Reg}\)
12:endif
13:endfor
14:\(i\leftarrow\arg\max_{i}\{\Delta_{i}\mid\Delta_{i}\text{ is defined, }1\leq i\leq 4\}\)
15:if\(i\) is definedthen
16:\(\mathbf{PGN}^{\prime}\) = update PGN with \(S_{i}\)
17:else
18:\(\mathbf{PGN}^{\prime}\) = PGN
19:endif
```  
**Algorithm 2** Adversarial process of PGN and the recognizer
## 7 Results
### Evaluation Framework  
#### 7.5.1 Security Evaluation  
Hyperledger Fabric, like other technologies that use certificates and identities, can easily be compromised by users. Disclosure or theft of a blockchain user's private certificate could enable a malicious user to perform read and write instructions on the ledger. Depending on the existing policy, system security may be at risk. In this project, such a scenario is unrealistic because ordinary users and administrators lack full control over the ledger. The chaincode that runs autonomously in all the peers may also pose a system risk due to bugs. Therefore, the system should be inspected and tested for bugs. Additionally, quantum computing seriously threatens systems that use traditional encryption techniques and hashing methods. Hence, robust countermeasures are necessary to address such threats.
## 3 One-Dimensional Degradation Models Based on Stochastic Processes
### Degradation Models Based on Univariate Gamma Process with Random Effects
 \tag{10}\]  
For a fixed time, let \(Z(t)=\frac{\kappa G(t)}{\alpha nt}\), then Equation (10) can be rewritten as:  
\[f(z|\alpha)=\frac{\Gamma\left(\frac{2 at+2\kappa}{2}\right)}{\Gamma\left(\frac {2 at}{2}\right)\Gamma\left(\frac{2\kappa}{2}\right)}\left(\frac{at}{\kappa} \right)^{\frac{2 at}{2}}\frac{z^{\frac{2 at}{2}-1}}{\left(1+\frac{\kappa}{2} \right)^{\frac{2 at+2\kappa}{2}}} \tag{11}\]From Equation (11), it can be seen that the random variable \(Z(t)\) obeys an F-distribution with degrees of freedom \(2\pi t\) and \(2\kappa\), that is:  
\[Z(t)\sim F_{2\pi t,2\kappa}(z) \tag{12}\]  
According to the PDF \(f(z|\alpha)\) and the characteristic of the F-distribution, the distribution function of the degradation value \(G(\mathrm{t})\) can be derived from Equation (13):  
\[F_{G(\mathrm{t})}(g)=P(G(\mathrm{t})\leq g)=P\bigg{(}\frac{\kappa G(t)}{\alpha rt }\leq\frac{\kappa g}{\alpha rt}\bigg{)}=P\bigg{(}Z(t)\leq\frac{\kappa g}{ \alpha rt}\bigg{)}=F_{2\pi t,2\kappa}\bigg{(}\frac{\kappa g}{\alpha rt}\bigg{)} \tag{13}\]  
Suppose \(\omega\) is the failure threshold of the product whose degradation path follows the Gamma process, the life, \(T\), of the product is denoted by the time when the performance degradation \(G(\mathrm{t})\) exceeds its failure threshold for the first time, which is defined as:  
\[T=\inf\{t|G(\mathrm{t})\geq\omega,t>0\} \tag{14}\]  
Considering that the Gamma process is a strictly monotonous stochastic process, the CDF of the product's life, \(T\), is:  
\[F_{\beta}(t)=P(T\leq t)=P(G(\mathrm{t})\geq\omega)=1-F_{2\pi t,2\kappa}\bigg{(} \frac{\kappa\omega}{\alpha rt}\bigg{)} \tag{15}\]  
Therefore, the reliability function of the product that takes into consideration random effects can be expressed as:  
\[R_{\beta}(t)=1-F_{\beta}(t)=F_{2\pi t,2\kappa}\bigg{(}\frac{\kappa\omega}{ \alpha rt}\bigg{)} \tag{16}\]
## 2 Materials and Methods
### Location Estimation Methods of Autonomous Driving Robot
#### 2.2.3 Distance Measurement Using an Encoder Sensor  
The encoder sensor calculates the travel distance by determining the number of motor rotations. It is also possible to calculate the direction angle of the robot in wheel-type mobile robots using an encoder sensor based on the difference between the left and right wheels. However, wheel-type mobile robots generate slips depending on the condition of the road surface. Specifically, errors caused by slips are more likely when turning left and right than when driving in a straight line [36]. In an underground mine environment, this error is more pronounced because of the varying shape of the road and the roughness of the floor. Consequently, this study used the encoder sensor to measure only the linear travel distance. The wheel diameter of the mobile robot used in the study is 15.8 cm, the motor's gear ratio is 61, and the encoder's gear ratio is 13. Thus, the travel distance can be calculated based on the encoder count using Equation (6).  
\[\textit{Distance}\left(m\right)=2\times\pi\times 0.158\times\frac{1}{13} \times\frac{1}{61}\times\textit{Encoder Count} \tag{6}\]
# Mechanism and Effect Factor of Toughening of High-Speed Train Wheels
## 2 Materials and Experimental Methods  
### Materials  
ER7 is a commonly used material for high-speed train wheels in Europe. Table 1 presents its chemical composition. Figure 1 illustrates the fracture toughness test setup for high-speed wheels. Six test pieces are evenly distributed around the rim with each sample collected from a specific location on the rim as depicted in Figure 1. The sample size conforms to compact 30 mm thick tensile test pieces (CT 30), featuring a chevron notch with an aperture angle of 90\({}^{\circ}\) in ASTM E399 standards. For ER7 high-speed train wheels, the fracture toughness requirement, denoted as KQ, necessitates an average value exceeding 80 MPa-m1/2, with each sample having a value above 70 MPa-m1/2. The six samples are evenly distributed around the circumferential direction of the wheel rim. In production, the KQ average value can consistently reach more than 80 MPa-m1/2. However, occasionally, there may be individual KQ values below 70 MPa-m1/2, and difference values within the maximum and minimum can reach up to 40 MPa-m1/2. It is well known that many factors affect the fracture toughness level, but the issue of uniformity must be discussed [19].  
When the fracture toughness of the ER7 material is tested, the toughness value often fluctuates. A typical wheel is selected for analysis, and the fracture toughness KQ results are shown in Table 2.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Grade** & **C** & **S** & **P** & **Mn** & **Si** & **Cu** & **Cr** & **Ni** & **Mo** & **V** \\  & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** & **[\%]** \\ \hline ER7 & \(\leq\)0.52 & \(\leq\)0.015 & \(\leq\)0.020 & \(\leq\)0.80 & \(\leq\)0.40 & \(\leq\)0.30 & \(\leq\)0.30 & \(\leq\)0.30 & \(\leq\)0.08 & \(\leq\)0.06 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of the ER7 wheel.  
From the results of fracture toughness KQ, sample 1 obviously deviates from the normal level. Since the samples are uniformly distributed, it is first necessary to determine whether the fluctuation was due to the macroscopic property differences or not, especially the uneven heating and quenching. Therefore, typical samples 1, 4, and 5 are selected for hardness testing.
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 2 Materials
### Data Exploration and Pre-Processing  
Before detecting outliers in the data, it is essential to understand how the satellite's orbit and attitude affect the panel's voltage, current, and temperature. The BIRDS-4 constellation was deployed into orbit from the ISS (altitude 400 km, inclination: 51.6\({}^{\circ}\), duration: 92.6 min). Since no attitude control is applied, the satellites are in free rotation at approximately 3 \({}^{\circ}\)/s on each axis. Therefore, the panel condition is affected by two factors: the beta angle that determines the eclipse and sunlight periods and the attitude perturbation of the satellite. In Figure 4a, at a low beta angle we can observe significant temperature changes (-48 \({}^{\circ}\)C) due to the transition of the satellite from eclipse to sunlight, and a slight temperature variance (-4 \({}^{\circ}\)C) due to the satellite's rotation on its axes. In Figure 4b, at a high beta angle, we observe temperature variation (-30 \({}^{\circ}\)C) only due to the satellite's rotation.  
In addition, at a low beta angle, the power requirement from the satellite is high right after the eclipse period, so the power generation is high. However, at a high beta angle, since the satellite does not experience an eclipse throughout the orbit, the power requirement is less and so the generation is also less. The generated power of each panel shows large fluctuations due to the satellite's attitude (i.e., rotation), which causes different sides of the satellite to face the sun at different instants.
# Roll Motion Prediction of Unmanned Surface Vehicle Based on Coupled CNN and LSTM
###### Abstract  
The prediction of roll motion in unmanned surface vehicles (USVs) is vital for marine safety and the efficiency of USV operations. However, the USV roll motion at sea is a complex time-varying nonlinear and non-stationary dynamic system, which varies with time-varying environmental disturbances as well as various sailing conditions. The conventional methods have the disadvantages of low accuracy, poor robustness, and insufficient practical application ability. The rise of deep learning provides new opportunities for USV motion modeling and prediction. In this paper, a data-driven neural network model is constructed by combining a convolution neural network (CNN) with long short-term memory (LSTM) for USV roll motion prediction. The CNN is used to extract spatially relevant and local time series features of the USV sensor data. The LSTM layer is exploited to reflect the long-term movement process of the USV and predict roll motion for the next moment. The fully connected layer is utilized to decode the LSTM output and calculate the final prediction results. The effectiveness of the proposed model was proved using USV roll motion prediction experiments based on two case studies from \"JingHai-VI\" and \"JingHai-III\" USVS of Shanghai University. Experimental results on a real data set indicated that our proposed model obviously outperformed the state-of-the-art methods.  
CNN; data-driven; LSTM; roll motion prediction; unmanned surface vehicle +
Footnote †: journal:  
_Future Internet_