esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite Element Analysis on Block Shear Mechanism of Lean Duplex Stainless Steel Welded Connections
## 5 Summary and Conclusions  
An experimental program including 17 specimens was performed to investigate their block shear capacity and behavior of lean duplex stainless steel (STS329 FLD) welded connections. It was found that the American design code (AISI) significantly underestimated the block shear capacities of the lean duplex stainless steel welded connections, whereas the equation proposed by [PERSON] [12], which accounts for the constraint (stress triaxiality) effect, was found to be accurate.  
For further interpretation on the block shear mechanism of welded connections, a finite element model coupled with damage was developed. The model can provide stress and strain distributions that could not be seen in the physical tests. It was found that, at the ultimate block shear limit state, the average tensile stress involving tension resistance component is higher than the tensile strength of steel material, while the average shear stress is closed to the full strain hardening assumption of 0.6\(F_{u}\). It was also found that the constriction effect become more predominant in the specimens with a larger weld length. To clearly understand the constriction effect according to the geometric dimensions of weld length, further investigation is needed in a future paper.  
Conceptualization, Y.C. and T.K.; methodology, T.K.; software, Y.C., D.-K.K., and J.K.; validation, T.K.; formal analysis, Y.C. and J.K.; investigation, T.K. and D.-K.K.; resources, T.K. and Y.C.; data curation, T.K. and J.K.; writing--original draft preparation, Y.C.; writing--review and editing, T.K.; visualization, D.-K.K. and J.K.; supervision, T.K.; project administration, T.K.; funding acquisition, Y.C. and T.K. All authors have read and agreed to the published version of the manuscript.  
The first author is supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2018R1D1A1B07046021). The last author is supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry, & Energy (MOTIE) of the Republic of Korea (No. 20204030200080). The authors would like to thank POSCO of Korea for providing the test materials (STS329 FLD) used in this study.  
Not applicable.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest.
# Denoised Improved Envelope Spectrum for Fault Diagnosis of Aero-Engine Inter-Shaft Bearing
## 3 Experimental Validation
### Case Study on HIT Inter-Shaft Bearing Fault Diagnosis
Overall, the results highlight the superior performance of the proposed DIES method in diagnosing an inter-shaft bearing fault under strong noise and dual-frequency excitation interference. In this diagnostic case for an inter-shaft bearing, the ITD-SVD method is also employed for a performance comparison. ITD-SVD is applied to the same original signal shown in Figure 18a,b. The corresponding frequency spectra are illustrated in Figure 21a,b. As depicted in Figure 21a, ITD-SVD extracts only the HP rotor frequency \(f_{hp}\) and the 10 th harmonic of the outer race FCF \(10\times f_{o}\). Similarly, Figure 21b shows that this method just isolates the HP rotor frequency, along with several prominent interfering frequency components, but fails to extract the inner race FCF. Overall, the ITD-SVD method is effective in noise control [13], enabling the extraction of the characteristic frequency of the high-pressure rotor in an aero-engine. While the method is effective in signal decomposition and noise reduction, it still fails to extract the bearing FCF component when addressing dual-frequency excitation problems. Finally, we have summarized the comprehensive analysis results of the comparative methods in Table 3. The summary will be mainly carried out from the following aspects: whether the fault-related frequency components are extracted, whether the interference frequency components are effectively eliminated, and whether the noise components are effectively eliminated. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Whether Fault-Related** & **Whether Interference** & **Whether Noise Components** \\  & **Frequency Components** & **Frequency Components Are** & **Are Effectively Eliminated** \\  & **Are Extracted** & **Effectively Eliminated** & \\ \hline SK-ES & IMS-Yes; HIT-No & No & No \\ IES & Yes & No & Yes \\ ITD-SVD & Yes & No & Yes \\
**DIES** & **Yes** & **Yes** & **Yes** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comprehensive analysis table of several comparative methods. Figure 21: Inter-shaft bearing case: (**a**) the FFT spectrum of inner race fault signal after using ITD-SVD, (**b**) the FFT spectrum of inner race fault signal after using ITD-SVD.
## 4 Cluster-Based Distributed Compressed Sensing for QoS Routing
### Distributed QoS Routing
#### 4.3.4 Protocol Operation  
The cluster-based distributed compressed sensing approach for QoS routing is described as follows. After the establishment of network topology, member nodes and the cluster head is selected according to the correlation of adjacent video nodes. Then, \(m\) collaborative nodes are found out from member nodes to participate in DCS. The images observed by collaborative nodes are jointly compressed at the cluster head. Lastly, the cluster head sends compressed data to the sink with the objective of minimizing energy consumption and satisfying QoS requirements in delay and reliability. Considering the problem of energy consumption and load balance in the event region, we choose nodes in the area out of the event area to transmit data.  
The next-hop is selected by performing Algorithm 3. The channels of \(v_{j}\) in \(F_{i}\) are put in a set \(J\) only if the local delay and reliability requirements can be satisfied. By computing (18), the optimal channel of \(v_{j}\) is selected from the channels selected above. After all nodes in \(F_{i}\) find their optimal channel, the node that results in the lowest energy consumption among all nodes in \(F_{i}\) is selected as a forwarder. The channel \(c_{i,j*}^{*}\) of the optimal node \(j^{*}\) becomes transmission channel correspondingly.  
```
1 foreach\(v_{j}\in F_{i}\)do
2\(J\leftarrow\emptyset\);
3 foreach\(c\in C_{i,j}\)do
4 if\(p_{i,j}^{c}<P_{i,j}\&\&\&\frac{L}{p_{i,j}^{*}T_{i,j}}<T_{i,j}\)then
5\(J\gets c\);
6 end if
7
8 end for
9\(c_{i,j}^{*}\leftarrow\arg\min_{c\in J}E\left(\frac{L}{p_{i,j}^{*}},d\left(v_{i },v_{j}\right)\right)\);
10 end for
11\(j^{*}\leftarrow\arg\min_{v_{j}\in F_{i}}E\left(\frac{L}{p_{i,j}^{*}},d\left(v_ {i},v_{j}\right)\right)\) where \(\sigma=c_{i,j}^{*}\);
12\(c^{*}\gets c_{i,j^{*}}^{*}\);
```  
**Algorithm 3**QoS-Guaranteed Next Hop Selection
## 3 Implementation in a Two-Wheel Vehicle System
### Model of Two-Wheel Vehicle System  
The dynamic equations for the TWVS were derived for analysis and for the controller design. Figure 7 shows the free body diagram of the TWVS.  
A simplified version of the TWVS is shown at the top of Figure 7a. The shaft of the two wheels goes through the center of mass (cm) of the cart, and a pendulum is installed in the cm of the cart through a hinge. A motor kit is installed on each wheel to drive the wheel. Assume that there is no slip occurred between the wheels and the ground, and no torque exists between the cart and the pendulum. Moreover, this example experiment only considers the longitudinal motion. Therefore, forces and torques in the lateral motion are assumed balanced and not considered.  
In order not to distract readers from the main stream of this paper, the detailed derivation of equations of motion is provided in the Appendix A. Notation is defined either in Figure 7 or in the Appendix A. The dynamics of the TWVS are represented as follows:  
\[mlg\sin\theta = (ml^{2}+J_{p})\bar{\theta}-ml\cos\theta\bar{x}, \tag{49}\] \[\left(M+m+2M_{w}+\frac{2J_{w}}{R^{2}}\right)\bar{x}-ml\cos\theta \bar{\theta} = 2\frac{\bar{n}^{*}}{R}-ml\sin\theta\bar{\theta}^{2}. \tag{50}\]  
Define \(M_{\text{total}}=M+m+2M_{w}+2J_{w}/R^{2}\) and \(J_{s}=J_{p}+ml^{2}\). The equations of motion of the TWVS can be written as  
\[\mathbf{A}\left[\begin{array}{c}\bar{x}\\ \bar{\theta}\end{array}\right] = \mathbf{B}, \tag{51}\]  
where  
\[\mathbf{A} = \left[\begin{array}{cc}M_{\text{total}}&-ml\cos\theta\\ -ml\cos\theta&J_{s}\end{array}\right]\] \[\mathbf{B} = \left[\begin{array}{cc}2\frac{\bar{n}^{*}}{R}-ml\sin\theta\bar{ \theta}^{2}\\ mlg\sin\theta\end{array}\right]\]  
Figure 7: (**a**) Free body diagram of the cart and the pendulum; (**b**) free body diagram of the wheels  
or equivalently,  
\[\left[\begin{array}{c}\ddot{x}\\ \ddot{\theta}\end{array}\right] = \mathbf{A}^{-1}\mathbf{B} \tag{52}\] \[= \frac{1}{\Delta}\left[\begin{array}{c}-J_{s}ml\sin\theta\dot{ \theta}^{2}+\frac{2J_{s}}{K}\ddot{u}^{*}+\frac{1}{2}m^{2}gl^{2}\sin(2\theta)\\ -\frac{1}{2}m^{2}l^{2}\sin(2\theta)\dot{\theta}^{2}+\frac{2 ml\cos\theta}{K} \ddot{u}^{*}+M_{\text{total}}gl\sin\theta\end{array}\right],\]  
where \(\Delta=\det(\mathbf{A})=M_{\text{total}}J_{s}-m^{2}l^{2}\cos^{2}\theta\).
# Evaluation of 6 LoWPAN Generic Header Compression in the Context of a RPL Network
## 2 Related Work
In [13; 14], the authors define compression techniques for Internet Protocol Security (IPsec) headers and the Datagram Transport Layer Security (DTLS) headers and payload, respectively. In [14], 6 LoWPAN-GHC is used for compressing DTLS encrypted data, whilst a new compression format is defined for the various DTLS headers. No in-depth performance analysis has been conducted for this compression format. [PERSON] et al. [15] propose a technique for compressing IPv6 addresses. This technique is also applicable for SCHC. Considering that the described compression techniques are either too complex to implement on constrained devices, require a shared context or are too protocol specific, we will focus on the benefits of using 6 LoWPAN-GHC. By reducing the size of the packets, 6 LoWPAN-GHC will reduce the transmission delay and ensure a lower energy consumption during packet transmission. Nevertheless, compressing will consume energy. Thus, the compression mechanism of 6 LoWPAN-GHC qualifies as energy-efficient if the energy saved during transmission outweighs the energy spent on compression, one of the aspects investigated in this article. To the best of our knowledge, no former studies on the performance of 6 LoWPAN-GHC have been published, which is surprising for a technique that is already 9 years old. We decided to investigate its performance in the context of a RPL network.
## 6 Open Issues for Cooperative and Precoding Schemes in VLC
### Angle Diversity Receivers for Overcoming the Shadowing and Blocking Effects  
VLC are not subject to small-scale effects that are common for RF communications. This issue may lead to correlated channel responses among users, and therefore, to obtain a poor performance when applying precoding schemes that cancel or minimize the interference among users. In [161], receivers composed of multiple PDs with distinct FoVs are proposed for minimizing the correlation among the channels provided by the PDs of the same user. Similarly, ADRs may provide enough channel diversity to generate uncorrelated channel responses [76; 158]. Motivated by [135], the use of ADRs can also be managed as multi-user diversity, which may provide a performance close to the asymptotic capacity for ZF precoding. This way, in [78], the concept of vROA photodetector is proposed for modifying the orientation angle of each user in order to provide a large channel diversity that maximizes the performance of ZF precoding. However, the use of ADRs or vROA photodetectors for cooperative/precoding schemes with the aim of overcoming the shadowing and blocking effects in VLC is still an open issue that requires one to develop novel ideas and algorithms.
## 7 Interference Mitigation Strategies  
Currently, the MVDS system in Lebanon utilizes high-power transmitters with power levels ranging from 10 to 15 dBW. The receiving amplifier gain (low-noise amplifier [LNA] gain) at the MVDS receiver is 30 dB. The regulatory landscape in Lebanon lacks specific guidelines for determining the transmission power gain of microwave video distribution systems (MVDS). However, there is a long-standing adherence to the International Telecommunication Union (ITU) recommendation Rec. ITU-R S.2029, which addresses both long-term and short-term interference. Additionally, ITU recommendation Rec. ITU-R S.1432 offers several protective measures for fixed satellite services, one of which concerns the degradation of error performance and availability due to frequency sharing.  
This excerpt clarifies how interference allowances are quantified as a percentage of the system's noise power. It explains that interference equal to 10% of the system noise results in an interference-to-noise ratio (I/N) of \(-10\) dB. Furthermore, it posits that a 1 dB increase in noise leads to a tenfold increase in the bit error rate (BER). Based on this relationship, the passage provides specific I/N values for different BER percentages: \(-2.4\) dB for 0.03%of any month and 0 dB for 0.005% of any month. By extending this trend, an I/N of \(-12\) dB is estimated for 100% of any month, which is equivalent to 6% of the satellite system's noise power.  
These system parameters, particularly the transmitter's side lobe gain and power gain, contribute significantly to potential interference with GEO satellites. Analysis has shown that these power levels could severely degrade the desired signal quality at the satellite in a worst-case scenario. To mitigate this interference, we present a two-step method. The first step of the proposed solution suggests reducing the MVDS transmitted power, and the second step involves employing a new antenna design with lower side lobes. In this analysis, the contributions from intermodulation interference (IMI) and cross-polarization interference (XPI) were incorporated as constant baseline values in the total interference budget. This is justified because the primary factors dictating XPI (antenna polarization discrimination) were unchanged, and the relevant amplifying stages (e.g., MVDS transmitter, satellite transponder) were assumed to operate under conditions where their IMI generation remained consistent across the specific parameters being varied in this study. This approach allows for a focused assessment of the impact of varying ASI and MVDS side lobe interference.
# Beam Scanning and Capture of Micro Laser Communication Terminal Based on MEMS Microm mirrors
## 6 Conclusions  
This paper presented a laser beam scanning and capture model based on MEMS micromirrors that focused on fast and wide-range scanning strategies. By introducing a scanning overlap factor, the capture probability under the influence of microvibrations was enhanced. Experimental analysis validated the effectiveness of the scanning capture strategy because platform microvibration was one of the main factors affecting laser communication scanning and capture. When the system experienced certain levels of microvibration, missed scans occurred. As the beam divergence angle gradually increased, the time required to scan the entire uncertain region decreased. Increasing the beam divergence angle while keeping microvibrations constant led to smaller missed scanning areas, indicat  
Figure 11: Reduction in missing scans by increasing the overlap factor.  
ing the effectiveness of increasing the beam divergence angle to reduce microvibrations. Changing the size of the uncertain region without altering the beam divergence angle or overlap factor did not improve missed scanning caused by microvibrations. Moreover, under the same parameter settings, larger uncertain regions required more time to complete scanning. If mitigating missed scanning occurrences with a constant beam divergence angle were desired, increasing the overlap factor became necessary, but this further increased the scanning time. In practical applications, a comprehensive consideration of scanning time and coverage rate is required to select optimal parameters. The scanning capture model established in this paper provides a theoretical foundation and technical support for practical applications.  
Conceptualization, X.W., J.H. and X.M.; methodology, X.W. and X.M.; software, X.W. and C.W.; validation, P.L., Y.C. and F.J.; formal analysis, X.W. and F.W.; investigation, X.W.; resources, X.W. and Y.S.; data curation, X.W.; writing--original draft preparation, X.W.; writing--review and editing, X.W. and X.M.; visualization, X.W.; supervision, J.H. and M.X.; project administration, J.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Key R&D Program of China, grant number 2022 YFC203800.  
The data will be made available at a reasonable request to the corresponding author.  
The authors express sincere thanks for the experiments provided by the Photoelectric Tracking and Measurement Technology Laboratory, Xi'an Institute of Optics and Precision Mechanics, CAS.  
The authors declare no conflict of interest.
# Motion-Induced Phase Error Compensation Using Three-Stream Neural Networks
## 1 Introduction
[21] extracted accurate phase information from three-step phase-shift fringe patterns of low signal-to-noise ratio (SNR) and saturation by using convolutional neural network (CNN). [PERSON] et al. [22] utilized U-Net [23] architecture to suppress phase error in non-sinusoidal patterns resulting from gamma distortion, defocus, saturation, and couples of these factors. [PERSON] et al. [24] extracted multiple triple-frequency phase-shift grayscale fringes from single color fringe pattern and reconstructed 3D shape accurately. As discussed before, the FTP-based methods will lead to unreliable results in complex variant surface, and PSP-based methods are usually limited in resolving complex objects with different types of motion. In order to solve these problems, we intend to reduce the motion-induced phase error in PSP using deep learning technique. Firstly, the phase error model of three-step PSP is derived to understand the characteristics of motion-induced error. Secondly, a method based on three-stream neural networks is proposed to process three different orders of the three-step phase-shift fringe patterns in the time series, and effectively suppress the phase error. Thirdly, a virtual FPP system is constructed to perform the measurement of dynamic scenes and provides the synthetic data for network training, which shows the potential of our method in dynamic scene analysis. Experimental results prove that the method can accurately reconstruct a moving object with different motion types and achieve significant improvement compared with traditional three-step phase-shifting algorithm.
## 2 Background
Most existing approaches rely on a single feature selection technique across different classifiers, which can limit the model's ability to fully capture the complexity of EEG data. This approach may overlook important features that could improve classification accuracy. For example, while mRMR aims to reduce redundancy and maximize relevance, it might miss non-linear relationships between features. Similarly, ROC-based ranking could focus on features with high individual performance but neglect the collective importance of feature combinations. This might result in a feature set that is not optimal for all classifiers, potentially affecting both the overall performance and generalizability of the model. Although the importance of feature selection is widely acknowledged in machine learning, the effects of different feature selection techniques on specific classification modelsfor EEG data, particularly in terms of accuracy, false positives, and false negatives, remain an area of ongoing investigation. Therefore, this paper presents a study aimed at addressing the limitations of previous research by investigating the impact of a range of feature selection techniques--filter-based, wrapper-based, and embedded methods--on the performance of several machine learning models. By systematically experimenting with techniques such as Elastic Net and SVM-RFE and applying them to classifiers like Logistic Regression and the SVM, this study identifies the most effective combinations and explores the impact of feature selection techniques on classifiers' performance. The findings highlight the importance of carefully selecting and testing feature selection methods and classifiers, which could lead to more accurate and reliable results.
## 3 Materials and Methods
### Case Study
#### 3.4.1 Testbed  
Industry 4.0 consists of the integration of multiple technologies that can be deployed into several control layers. Thus, the specific multi-layer approach considered in this case study is aligned with OpenFog and comprises the IoT, Fog and Cloud layers presented in Figure 7.  
In this testbed, the data production is at the IoT layer, composed of a manufacturing cell that performs the assembly operations of a set of 3D printed parts emulating the shaft of a stepper motor. The assembly cell comprises a KUKA KR3 R540 robot [38], managed by a Siemens ET 200 SP Open Controller [39], which embeds a virtualized PLC and Windows 10 operating system. To have greater flexibility in performing the tests, the testbed also has Digital Twins (DTs) to simulate additional manufacturing cells. These DTs are modeled and simulated using the software-in-the-loop approach with Tecnomatix Process Simulate 16.0.1 [40] and PLCSIM Advanced V3.0 [41] tools. All the equipment in this testbed was sourced from the respective local distributors in Spain.  
After each batch process, the production data are sent to the Fog layer through the Fog-IoT Message Hub. The Fog layer receives the data produced by the IoT layer and turns the data collected into insights with the aim of taking decisions that improve the efficiency and productivity of the production processes. The elements that form the testbed in the Fog layer can be classified as follows:  
Figure 7: Testbed available to deploy the case study. The elements of the IoT layer are represented in green, the elements of the Fog layer in blue and the elements of the Cloud layer in orange.  
* The Fog Computing cluster is based on 12 DELL Optiplex 780 computers running Ubuntu 20.04 and connected through an unmanaged D-Link DGS-1024D gigabit switch. One of the computers acts as a Master Node while the rest of the computers act as worker nodes. Each one of the Fog Nodes runs K3s [42], a lightweight, certified Kubernetes distribution specifically designed for IoT applications.
* A DELL Optiplex 7010 is used as a gateway between the Fog Cluster and the Cloud, implementing the Fog-Cloud Interface of our proposed architecture. This enables the Fog layer to reach the Container Image Registry when needed.