esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Analysis
### Optimized Parameters of Dual-Beam Laser Polishing
Previous works adopt an effective XRD detection method and have demonstrated the reason for the grain transformation on the polished layer caused by the formation of the martensitic phase after rapid melting and cooling during laser polishing [32; 54]. The polished layer of the MS parts was characterized using quantitative XRD analysis with HighScore plus 3.0 software (Xpert Highscore 3.0, Philips Analytical Instruments, Almelo city, Netherlands). Figure 16 shows the grain content of the XRD peak area. The original LPBF MS parts mainly consisted of martensite (\(\alpha\)) and a small amount of austenite (\(\gamma\)) phases. Figure 16 shows that the XRD patterns obtained by the diffractometer were characterized by narrow \"spikes\" that were generally independent of each other. Before polishing, the XRD test result in the horizontal direction of the sample is similar to that in the longitudinal direction. After polishing, the XRD test results of the horizontal direction of the sample showed notable changes. The peak narrowness (\(\gamma\)111 and \(\gamma\)200) relatively increased after polishing, while at the same time, the content of martensite (\(\alpha\)110) decreased. In the same substance, this means that the crystallization was stronger. After polishing, the martensite content in the horizontal plane decreased from 97.5% to 90.2%. The X-ray diffraction (XRD) analysis results obtained for the samples before and after polishing are shown in Figure 16. Compared with the initial surface, the peak value of the martensite on the laser-polished surface decreased, but the peak value of the austenite phase increased, indicating that the microstructure of the laser-polished layer changed with the occurrence of grain refinement during the rapid melting process of the workpiece surface, and that the microstructure was different from that of the matrix. Figure 15: SEM images of the original formed surface and the polished layer. (**a**) Total of the original formed surface and the polished layer, (**b**) original formed surface, (**c**) melt zone and heat-affected zone, and (**d**) polished layer crystal.
## 2 Formulation of the Pantograph-catenary Interaction System and Length of EVA
### Calculation Method of the Length of EVA  
After the formulation of the pantograph-catenary interaction model, the EVA of the pantograph-catenary interaction system is determined. Note that the vibration of the catenary consists of vertical, lateral, and torsional vibrations of the contact and messenger wire, and it is difficult to present all of these results in one paper. However, the propagation of vibration can be considered as the propagation of energy, and the energy distribution of the catenary directly presents the vibration distribution situations, which is helpful in determining the EVA with most of the vibration included. Therefore, the energy distribution of the catenary is concentrated and calculated. Based on Equations (1)-(4), the vibration energy at an arbitrary location of the catenary can be calculated as:  
\[\begin{array}{l}E_{x}=\frac{1}{2}\rho_{m}A_{m}\left(\dot{y}_{mx}^{2}+\dot{ z}_{mx}^{2}\right)+\rho_{m}\left(I_{ym}+I_{zm}\right)\dot{\phi}_{mx}^{2}+ \frac{1}{2}\rho_{c}A_{c}\left(\dot{y}_{cx}^{2}+\dot{z}_{cx}^{2}\right)+\rho_{c }\left(I_{yc}+I_{zc}\right)\dot{\phi}_{cx}^{2}\\ +\frac{1}{2}E_{m}\left(I_{ym}\left(z_{mx}^{\prime\prime}\right)^{2}+I_{zm} \left(y_{mx}^{\prime\prime}\right)^{2}\right)+G_{m}I_{pm}\left(\dot{y}_{mx}^{ \prime\prime}\right)^{2}+\frac{1}{2}T_{sm}\left(\left(y_{mx}^{\prime}\right)^ {2}+\left(z_{mx}^{\prime}\right)^{2}\right)\\ +\frac{1}{2}E_{c}\left(I_{yc}\left(z_{cx}^{\prime\prime}\right)^{2}+I_{zc} \left(y_{cx}^{\prime\prime}\right)^{2}\right)+G_{c}I_{pc}\left(y_{cx}^{\prime \prime}\right)^{2}+\frac{1}{2}T_{sc}\left(\left(y_{cx}^{\prime}\right)^{2}+ \left(z_{cx}^{\prime}\right)^{2}\right)\end{array} \tag{20}\]  
where \(y_{mx}\), \(z_{mx}\), and \(\phi_{mx}\) are the vertical, lateral, and torsional motions of the messenger wire at arbitrary location x, respectively. \(y_{cx}\), \(z_{cx}\), and \(\phi_{cx}\) are the vertical, lateral, and torsional motions of the contact wire at arbitrary location x, respectively.  
Based on the energy distribution of the catenary, the length of the EVA can be further decided. As mentioned above, the EVA is determined to include most of the vibration caused by the pantograph-catenary interaction. Thus, if the total energy of the EVA converges, the length of the EVA is long enough to include most of the vibration caused by the pantograph-catenary interaction. The total energy of the EVA can be calculated by:  
\[E=\int_{x_{p}-\dfrac{1}{2}a_{e}}^{x_{p}+\dfrac{1}{2}a_{e}}E_{x}dx \tag{21}\]  
where \(x_{p}\) is the location of the pantograph, and \(a_{e}\) is the length of the EVA. The convergence of total energy is defined as the difference between two neighboring values within a tolerable error of 0.01 J.
# Multi-Connectivity for Multicast Video Streaming in Cellular Networks
## 6 Distributed Resource Allocation
### Distributed Greedy Allocation  
In the Distributed Greedy Allocation (DGA) policy, each base station allocates resources to the multicast streams by only optimizing over their individual cells. Although allocating resources in a distributed manner will result in sub-optimal resource allocation decisions as discussed above, a distributed policy allows base stations to make allocation decisions independently. Therefore, such a policy can be used for enabling MC multicasting even in the absence of a central entity that can control all the base stations in a region. Furthermore, in case of content that is highly delay sensitive, the signaling delays due to the communication between the base stations and the central controller might not be tolerable. For such applications, the DGA policy can be used to sacrifice optimality in favor of lower delays.  
The DGA policy solves the resource allocation problem \(\mathbf{K}_{\mathbf{D}}^{\star}\) for each cell independently. The distributed resource allocation problem \(\mathbf{K}_{\mathbf{D}}^{\star}\) is defined as follows. As in Section 3, \(U_{jk}\subseteq[M]\) denotes the set of users that would successfully receive the multicast content if PRB \(j\) is allocated to the multicast service in cell \(c\). Set \(\mathcal{U}_{c}=\{U_{1c},U_{2c},\ldots,U_{Nc}\}\) is the collection of such sets for cell \(c\). The distributed resource allocation problem within each cell \(c\) can now be stated as follows.  
**Definition 3** (Distributed resource allocation problem \(\mathbf{K_{D}^{c}}\)).: _For all \(c\in[C]\), given the collection of sets \(\mathcal{U}_{c}=\{U_{1c},U_{2c},\ldots,U_{Nc}\}\), determine \(j^{\star}\in[N]\) such that \(j^{\star}\in\operatorname*{arg\,max}_{j\in[N]}|U_{jc}|\)._  
To solve the distributed resource allocation problem \(\mathbf{K_{D}^{c}}\), the DGA policy at each base station allocates a PRB to the multicast stream to maximize the number of users served by it. That is, PRB \(j^{\star}\) is assigned to the multicast stream in cell \(c\) if \(j^{\star}\in\operatorname*{arg\,max}_{j}|U_{jc}|\).  
The pseudo-code for this algorithm is given in Algorithm 4. The variable \(x_{jc}\) in Algorithm 4 is an indicator random variable that is equal to 1 only when PRB \(j\) is allocated to the multicast stream in cell \(c\).  
```
Input: Sets \(\mathcal{U}_{c}=\{U_{1c},\ldots,U_{Nc}\}\) for all \(c\in[C]\)
1 Initialize \(x_{jc}=0\) for every \(j,c\)for\(c=1:C\)do
2 Assign \(j^{\star}=\operatorname*{arg\,max}_{j}|U_{jc}|\)\(x_{j^{\star}c}\gets 1\)
3 end for
```  
**Algorithm 4**Distributed Greedy Allocation algorithm
# Recent Advances in Optical Injection Locking for Visible Light Communication Applications
## 6 Conclusions  
In this review, we elucidated recent advances in the OIL technique, particularly in the visible light color regime. The ability of the OIL approach to enhance the performance of optoelectronic devices is well recognized, and there is a vast array of existing research that has used the technology for optical communication applications. We presented research concerning the enhancement effects of OIL on the characteristics of different devices, which has demonstrated satisfactory increases in output power as well as high SMSR values. We also examined studies on the application of OIL in various VLC systems. The two-stage OIL and optoelectronic feedback technique has turned out to be a salient and practical approach in these intricate real-life situations, thanks to its state-of-art characteristics of high output power and considerable stability.  
VLC is an emerging technology that has a bright future, and the development of OIL in VLC could function as a helpful road map for developing greater and more effective VLC networks and systems.  
Writing--original draft preparation, X.L. and Q.B.; visualization, J.H.; review and editing, S.Y. and Y.M.; resources, J.S. and Z.L.; investigation, J.Z.; supervision and project administration, N.C. and C.S. All authors have read and agreed to the published version of the manuscript.  
This research is partially funded by the Natural Science Foundation of China Project, grant number 62274042, 61925104; Natural Science Foundation of Shanghai, grant number 212R1406200; Major Key Project of PCL; The joint project of China Mobile Research Institute & X-NET; The Key Research and Development Program of Jiangsu Province (BE2021008-5).  
**Infrared Consent Statement:** Not applicable.  
**Data Availability Statement:** The data of this study are available from the corresponding author upon request.  
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 1 Introduction
In order to improve the signal-to-noise ratio (SNR) and the accuracy of arc fault detection, the higher-order spectrum will be introduced in this paper. Higher-order spectrum analysis is emerging as a new useful technique in signal processing; it can describe nonlinear coupling, restrain Gaussian noise and preserve phase information among different frequency components of a random process. The study in [24] introduced the higher-order spectrum in detail. In the third-order case of a random process, the higher-order spectrum reduces to the bispectrum. The two main methods used to estimate the bispectrum are the conventional and the parametric methods [24, 25, 26, 27, 28, 29]. [PERSON] and [PERSON] presented two parametric approaches consisting of the third-order recursion method and the constrained third-order mean method to estimate the bispectrum [30, 31]. Bispectrum has been widely used in many fields, such as electrical and mechanical equipment condition monitoring and diagnostics [32, 33, 34], astronomy [35], communication [36, 37] and biomedical engineering [38, 39]. Bispectrum will be used to extract common features for identifying series arc faults. In order to further improve the identification rate of arc fault, a good classification algorithm is needed. Compared with traditional classification algorithms, such as a neural network algorithm, support vector machine (SVM) can better solve the difficult problems: dimension disaster, over learning, local minima [40, 41], _etc._ It has several merits including convergence to global optimum, good generalization ability and adaptation for some small samples and high dimension data [42, 43]. In this research, least squares support vector machine (LSSVM) will be introduced to identify series arc faults. As mentioned above, many previous studies on arc fault detection have discussed mathematical models, abnormal behaviors in time domain, frequency spectrum, power spectrum, _etc._, but none of these studies offer autoregressive (AR) model and AR bispectrum analysis for arc fault detection. For the first time, this research will apply AR bispectrum to study arc faults in low-voltage AC circuits. The remainder of this paper is organized as follows. Section 2 constructs an arc fault experimental platform and collects a large amount of experimental data. Section 3 analyzes different types of load signals in arc faults and normal states based on two main methods: conventional time-frequency domain and AR bispectrum analysis. The conventional time-frequency analysis includes time domain and power spectrum analysis. In bispectrum analysis, an AR model is first modelled, then AR bispectrum is introduced. Common series arc fault features are extracted as input vectors to recognize arc faults. Section 4 introduces LSSVM and applies it to the identification of series arc faults from the load states. Finally, Section 5 summarizes the conclusions of this study.
# Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle
## 1 Introduction
PID is a well-known conventional controller that is simple and reliable [17]. However, the shortcoming of the conventional PID is its difficulty in controlling nonlinear and higher-order plants [18]. FLC mimics human perception in decision making, which turns it into an effective method to be applied in the nonlinear system [19]. One of FLC advantages is that it does not require the mathematical definition of the control plant beforehand. It has been reported that the hybrid combination of a FLC and a PID, which known as fuzzy-PID has better control performance than the conventional controller alone [20]. In the MS minimization control strategy, the estimated driver's head roll angle is treated as the desired response while the estimated passenger's head roll angle is used as the actual response. The reasons of using estimated responses are due to the absence of the driver in the autonomous vehicle  
Figure 1: General head movement of the occupants. and the impracticality of attaching motion sensors on the passengers during travel. The implementation of the estimated head roll to represent the controlled variable can be realized by developing a prediction model to produce the estimated head roll responses. As mentioned before, it is a fact that the occupant's head roll angle has correlation with the lateral acceleration. Hence, head roll estimation can be carried out by establishing a model that represents the correlation. The correlation model will be used to predict the head roll responses based on the information of lateral acceleration. In previous research works, the correlation model had been established by system identification (SI) [21; 22], artificial neural network (ANN) [23], time delay neural network (TDNN) [24] and radial basis function neural network (RBFNN) methods [25]. The success of the application of the head roll prediction model developed by the RBFNN method in MS minimization control system is proven by [PERSON] et al. [16]. Thus, similarly, this study implemented the RBFNN method to produce the prediction model. The prediction model is taken from the previous work, with the details of the modeling process available in Reference [25].
# AuxDepthNet: Real-Time Monocular 3D Object Detection with Depth-Sensitive Features
## 3 Proposed Method
### Depth-Sensitive Feature Enhancement  
Current depth-assisted approaches face challenges in generalizing to varied datasets and environments, which hampers their adaptability. Moreover, their dependency on external depth sensors or estimators not only increases hardware requirements but also  
Figure 2: The overall framework of our proposed method. AuxDepthNet enhances monocular 3D object detection by integrating depth-sensitive, context-sensitive, and depth-guided features. The Auxiliary Depth Feature (ADF) module encodes depth-related cues without pre-computed depth maps. Context-sensitive features are refined by a feature pyramid and DepthFusion Transformer (DFT), providing semantic and spatial context. The Depth Position Mapping (DPM) module embeds depth-based positional information for precise 3D localization. This integration captures local and global spatial relationships efficiently, delivering robust 2D and 3D detection.  
risks propagating inaccuracies into the detection pipeline [5; 13; 17; 18]. To address these challenges, we propose the Auxiliary Depth Feature (ADF) module, which leverages auxiliary supervision during training to implicitly learn depth-sensitive features, eliminating the need for external depth maps or estimators.  
Unlike previous methods [3; 5; 8], which rely on pre-computed depth maps or external estimators, the ADF module captures depth-sensitive features directly from the input feature map using auxiliary learning. This approach ensures scalability, improves generalization across datasets, and enables real-time applicability.  
As illustrated in Figure 3, the Auxiliary Depth Feature (ADF) module operates in three stages to enhance depth-sensitive features. First, it generates initial depth-sensitive features through an auxiliary supervision task, which predicts a probability distribution over discretized depth bins for each pixel. Next, the module learns depth prototypes by leveraging spatial-depth attention mechanisms, aggregating features across spatial dimensions to capture meaningful depth relationships. Finally, these depth prototypes are projected back to refine the feature map, producing an enhanced representation that integrates depth-sensitive information and spatial context, thereby improving the accuracy and robustness of 3D object detection.  
The ADF module is designed as a lightweight and efficient solution that enhances depth reasoning and spatial localization while maintaining low computational cost. The term 'auxiliary' refers to the fact that the module learns depth-sensitive features through an auxiliary supervision task. This task does not rely on pre-computed depth maps or external depth estimators, but instead enables the network to learn depth-related information directly from the image data during training.  
The following sections detail each step of the ADF module and its role in improving depth representation for 3D object detection.
# Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal
## 1 Introduction  
Time division multiple access (TDMA) achieves the effect of multi-user sharing communication resources by dividing a non-overlapping slot in time. TDMA has the advantages of large communication capacity, high frequency efficiency and compatibility between prime station and slave station. It has been widely used in various civil and military communication systems, such as very small aperture terminal (VSAT) satellite systems, international maritime satellite systems, iridium satellite systems and unmanned aerial vehicle communication systems [1; 2; 3; 4]. TDMA signal usually has the characteristics of burst and transient. Detection of burst start time is important for subsequent parameter estimation and demodulation.  
Since relative movement between the transmitter and receiver results in doppler frequency shift to receiving signals [5], distributed unique word (DUW) shows significant robustness for frequency shift, and it can achieve high precision frequency estimation in the subsequent frequency offset correction work. At present, many VSAT satellite burst communications use DUW instead of the traditional centralized UW. Therefore, the DUW detection method has practical application value for the burst signal using only DUW. In particular, under the background of wide use of high gain channel coding and miniaturization of sending terminal, the TDMA signal detection algorithm needs to work reliably at low SNR. This puts forward much stricter requirements for DUW detection. Therefore, DUW detection methods with high precision and robustness show great theoretical significance and application prospects.
## 3 Experiment Setup  
### Drone System  
In this experiment, the reference drone system comprised commercially available equipment used in drones shown in Figure 2a. The details of the equipment and the flight test setup are shown in the Table 1.  
Figure 1: The hierarchical network of wavelet scattering transform.  
Figure 2: Main experiment devices in this study: (**a**) drone system; (**b**) Pixhawk 2 flight control module; (**c**) GPS receiver.
## 4 NSAS Effectiveness for Underground Structures
### Shear Force Control  
With regard to the shear force control effect of the NSAS isolation system, compared to that of the CIS, the analysis results of \(\beta_{SF,\;NSAS}/\beta_{SF,\;CIS}\) and \(\gamma_{SF,\;NSAS}/\gamma_{SF,\;CIS}\) are plotted in Figure 13, for the same parameter sets of \(\chi\) and \(\kappa\) as those in the above section. These values are given in Tables 4 and 5, respectively. This is consistent with the concept that the negative-stiffness effect of NSAS provides an improved isolation effect to the isolator placed in parallel. For NSASs with different \(\chi\) and \(\kappa\) parameters, \(\beta_{SF,NSAS}/\beta_{SF,CIS}\) and \(\gamma_{SF,NSAS}/\gamma_{SF,CIS}\) were much lower than 1.0. Specifically, when \(\kappa=0.35\) and \(\chi=-0.20\), the values of \(\beta_{SF,NSAS}/\beta_{SF,CIS}\) and \(\gamma_{SF,NSAS}/\gamma_{SF,CIS}\) were 0.41 and 0.58, respectively, which indicated about 59% and 42% decreases in maximal and RMS shear force, in comparison with the case of CIS. Apart from the enhanced effectiveness of NSAS for displacement control, the negative-stiffness mechanism of NSAS yielded a more significant isolation effect in order to reduce the shear force of the central column. As an intuitive illustration, the shear force responses were plotted for the uncontrolled underground structure, as well as for the structures controlled with the NSAS isolation system (\(\kappa=0.35\), \(\chi=-0.20\)) and CIS, shown in in Figure 14. Compared with the case of the original uncontrolled central column, the maximal shear force decreased from 767 to 308 kN (for CIS), and to 127 kN (for NSAS isolation system), simultaneously reporting a significant reduction in shear force in the entire time domain.  
Figure 12: Time series of relative displacements of central column for uncontrolled underground structure (US), controlled by conventional isolation system (US-CIS) and controlled by negative stiffness amplification system (US-NSAS)-based isolation system. (**a**) Port Island and (**b**) HIK station records.  
At the end of the earthquake, as shown in Figure 14, the column exhibited a permanent load for uncontrolled underground structures due to the damage of the underground structure and the plastic soil deformations that accumulated during the earthquake loading cycles. However, for the  
Figure 14: Time series of shear force for uncontrolled underground structure (US), controlled by conventional isolation system (US-CIS) and controlled by negative-stiffness amplification system (US-NAS)-based isolation system. (**a**) Port Island and (**b**) HIK station records.  
underground structures controlled with the CIS and NSAS isolation system, the permanent load was released due to the use of isolation systems. The results are consistent with previous studies.