esa-sceva/satcom-chunk-collection huggingface dataset:
# Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model
## 2 Related Research
### Development of Anomaly Detectors  
The earliest anomaly detectors were derived from linear models. LMDD, OCSVM, and PCA are representative of linear models. This type of model primarily focuses on the deviation or dissimilarity of the data and uses a linear model to find possible interference values from a series of similar data. These interference values may be anomalous objects to look for [15-17].  
Proximity-based detection is also a major research topic of anomaly detectors. KNN (k nearest neighbors) was developed first and judges abnormal data using the distance to the k-th nearest neighbor as an outlier. Many variants have also been developed based on KNN, such as AvgKNN and MedKNN [18,19]. In addition to KNN, other neighboring anomaly detectors can be identified according to different specified outlier sources. Methods such as LOF, COL, and CBLOF use different outlier factors to discriminate and process abnormal data [20-22]. HBOS uses histograms to obtain abnormal points in the series of data [23]. The closest proximity detector is ROD (rotation-based outlier detection) [24], which learns local spatial attributes and uses [PERSON] rotation formula to construct anomaly scores. This method does not require distribution assumptions and achieves good performance during forecasting.  
The development of neural networks has also created new research directions for anomaly detection. More mature neural network detectors include AutoEncoder and VAE [25]. A self-encoding neural network is an unsupervised learning algorithm that uses a back propagation algorithm and makes the target value equal to the input value, making this type of algorithm more suitable for high-dimensional complex data. The cutting-edge neural network detectors also include SO_GAAL and MO_GAAL [26]. By generating confrontation, the generator creates abnormal data from random noise, and the discriminator determines whether the data generated are abnormal or original and normal. In the final abnormality detection process, it is only necessary to use the discriminator to determine whether data are normal or abnormal.
## 3 Modeling and Validation
### FEM Validation  
The blade stratification model of the NREL 5 MW wind turbine developed in this paper is required to meet the design parameters of the original blade. The linear mass density in the blade extension direction was calculated and compared with the official NREL report, as shown in Figure 8. The linear density is higher near the hub and lower at the blade tip, but the overall trend stays the same, the reason is that the model in NREL's official report is based on a truncated model, while the blade built in this paper is a smooth connection out of SOLIDWORKS software, and the cross-sectional dimensions are variable, leading to the difference between them.  
After ensuring the validity of the blade model, the verification of the mesh independence should be further completed to ensure that the finite element analysis accurately expresses the stresses, strains and other parameters of the structure, and to maximize the computational efficiency. In this paper, modal analysis is used to verify the grid size, only for the single blade. The finite element mesh uses a linear quadrilateral mesh with normalized structure, with mesh element S4R, and a linear triangular mesh with mesh element S3 at the tip of the blade and part of the trailing edge where there is a sharp shape.  
The grid size changes on the blade surface and the total number of grids on the whole blade are shown in Table 4: As in the meshing pattern of the fluid domain, the base size and minimum size of the blade surface were halved from 0.8 m to 0.15 m, and the number of meshes for a single blade was increased from 4671 to 35,785. Figure 9 shows the grid of single blade. The boundary conditions are fixed at the blade root and no load is added. The first six modes of a single blade are analyzed and compared with literature [18]:  
Figure 8: Mass density in the direction of blade extension.  
Figure 7: Comparison of thrust and torque at different wind speeds: **(a)** Torque; **(b)** Thrust.  
From Table 5, it can be obtained that the modal analysis of the finite element model is not highly sensitive to the mesh, and the modal frequencies calculated by the four meshes do not differ much. In order to ensure faster data transfer of the bidirectional fluid-solid coupling, the mesh size of case3 with the same settings as the fluid domain can better ensure the mesh size matching between the fluid domain and the solid domain at the intersection. The final analysis results of the first six modes of a single blade are shown in Figure 10. The first, third and fifth modes of the single blade of the wind turbine are flapwise bending, the second and fourth modes are the edgewise bending, and the sixth mode is the torsional deformation around the blade axis. It is proved that the most important deformation of the blade is the flapwise bending, followed by the edgewise bending, and the torsion is smaller.
# A Symmetric Matrix-Aided MIMO to Improve Reliability for Maritime Visible Light Communications  
[PERSON]  
1 School of Information Science and Technology, Tibet University, Lhasa 850000, China 11 School of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510006, China 11 School of Information Science and Technology, Tibet University, Lhasa 850000, China 11 School of Electronics and Information Technology, Tibet University, Lhasa 850000, China 11 School of Electronics and Information Technology, Tibet University, Lhasa 850000, China 11 School of Electronics and Information Technology, Tibet University, Lhasa 850000, China 11 School of Electronics and Information Technology, Sun Yat-sen University,  
Different from the existing works, in this paper, we propose a symmetric matrix-assisted MIMO transmission scheme. By exploiting the repeatability in both time and space domains of the elements in the symmetric matrix, the transmission reliability can be improved thanks to the enlarged minimum Euclidean distance. In this design, after the user information is converted from serial to parallel (S/P), the generated bits stream is modulated into the pulse amplitude modulation (PAM) symbol and then transformed by the symmetric matrix module. Thereafter, the double singular value decomposition (SVD) method is used to suppress the interferences between different users. Subsequently, we derive the theoretical symbol error rate (SER) expression of the system proposed in this paper. Then, the simulation results are carried out to verify the accuracy of the derivations. In addition, we compare the performance between the proposed scheme and the benchmark schemes including repetitive coding (RC), spatial modulation (SM), and spatial multiplexing (SMP).  
Briefly, the major contributions include:  
1. We propose to use a symmetric matrix-assisted MIMO transmission to improve the reliability of maritime VLC systems. By exploiting the repeatability of the elements in the symmetric matrix, the minimum Euclidean distance can be enlarged.
2. We use the double SVD to suppress the interferences between the users. Then we apply the scheme in maritime VLC systems.
3. We derive the theoretical SER expression for the proposed scheme. Then simulation results are provided to verify the effectiveness of the scheme proposed in this paper.  
The remainder of this paper will proceed with a description of the details of the maritime MIMO-VLC system characteristics in Section 2. A discussion of the symmetric matrix-aided MIMO-VLC system is given in Section 3. Then, Section 4 presents the simulation results and Section 5 concludes the findings.
## 2 Materials and Methods
### Fire Scenario Analysis  
As shown in Figure 1, a full-scale tunnel model with a center line length of 1000 m is established in this study. The tunnel section is 10.75 m wide and 7.3 m high. The materials of the floor, sidewalls, and ceiling are set as \"concrete\", which is a common material used for highway tunnels. The left portal of the tunnel is set as an air \"supply\" vent to provide longitudinal ventilation, and the other is set as \"open.\" The \"simple chemistry\" combustion model uses N-Heptane fuel ([PERSON] _et al._, 2017). Based on the fire scale for different types and numbers of vehicles published by PIARC ([PERSON] _et al._, 2017), the fire scale for full truck fires (30 MW) is chosen as HRR in consideration of the worst-case scenario. The fire source is 2.6 m in height, which is the height of a normal truck. The ambient pressure was set as 101.325 kPa, and the ambient temperature was set as 20 \({}^{\circ}\)C. The gravity is considered to be 9.81 m/s\({}^{2}\). The quality of the fuel was ignored based on the methods of previous studies, and the simulating time was set as 450 s. The determination of the curvature radius for the tunnel model relied on both existing tunnel data and compliance with minimum curvature radii mandated by applicable codes ([PERSON] _et al._, 2017; [PERSON] _et al._, 2018) corresponding to specific design speeds. In addition, in a straight tunnel, the radius of curvature is \(\infty\).
## 2 Thermal Characterization
### Experimental Measurement Method  
The LGCgE fluxmeter measurement bench is schematically presented in Figure 4; its various uses are detailed in [21; 25; 35; 36; 37]. Figure 4: Experimental apparatus. Figure 3: View of the ETMV8 Sample-gypsum board used for experimental tests. The underlying principle consists of placing the sample between two exchange plates in order to impose a homogeneous surface temperature on both sides [25]. To limit lateral heat loss and ensure a 1D transfer through the coating and gypsum boards, a guard ring made of insulating material was positioned on the lateral faces of the sample. Each exchanger plate was connected to a thermostatic bath to control the temperature stresses applied to both sides of the material. The heat flux and temperature on each side of the sample were measured throughout the test using tangential gradient fluxmeters [38] integrating a thermocouple at the center. For our case study, the temperature loadings varied between 10\({}^{\circ}\) and 30 \({}^{\circ}\)C in order to force the PCM to undergo melting and solidification cycles. From measurements carried out on the sample surface and through the use of the inverse method, the thermophysical properties of the coating, such as thermal conductivity, latent heat, specific heat capacity, and melting temperature, can be identified [37]. The temperatures imposed and measured on the sample faces constitute the input data (boundary conditions) for the numerical model, while the experimental heat fluxes serve for optimization by minimizing the difference between the numerically calculated and experimental heat fluxes. The objective is to identify, by means of inverse methods [39; 40], the parameters of the numerical model that minimize an objective function.
# Video Streaming in Urban Vehicular Environments: Junction-Aware Multipath Approach
## 4 Performance Evaluation
Syst._**2016**, _17_, 2822-2831. [CrossRef]
* (36) [PERSON]; [PERSON] Opportunistic routing for live video streaming in vehicular ad hoc networks. In Proceedings of the 15 th International Symposium on World of Wireless, Mobile and Multimedia Networks (WoWMoM), Sydney, Australia, 19 June 2014; pp. 1-3. * (37) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Video streaming over vehicular networks by a multiple path solution with error correction. In Proceedings of the International Conference on Communications (ICC), Sydney, Australia, 10-14 June 2014; pp. 580-585. * (38) [PERSON]; [PERSON] QoS-aware routing for video streaming in multi-rate Ad hoc Networks. In Proceedings of the 9 th IFIP Wireless and Mobile Networking Conference (WMNC), Colmar, France, 11-13 July 2016; pp. 193-198. * (39) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] QoE-driven dissemination of real-time videos over vehicular networks. _Comput. Commun._**2016**, _91_, 133-147. [CrossRef]
* (40) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] PBMP: Priority-based multi-path packet routing for vehicular ad hoc network system in city environment. _Trans. Emerg. Telecommun. Technol._**2016**, _27_, 1331-1344. [CrossRef]
* (41) [PERSON]; [PERSON] _A Formal Assessment of the Structural Similarity Index_; University of Regina, Department of Computer Science: Regina, SK, Canada, 2008. * (42) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Modeling video streaming over VANETs. In Proceedings of the 7 th ACM Workshop on Performance Monitoring and Measurement of Heterogeneous Wireless and Wired Networks, Kaliningrad, Russia, 4-6 September 2012; pp. 7-14. * (43) [PERSON]; [PERSON]; [PERSON] Shielding video streaming against packet losses over VANETs. _Wirel. Netw._**2016**, _22_, 2563-2577. [CrossRef]
* (44) [PERSON]; [PERSON]; [PERSON] Codeplay: Live multimedia streaming in vanets using symbol-level network coding. In Proceedings of the 18 th IEEE International Conference on Network Protocols, Kyoto, Japan, 5-8 October 2010; pp. 223-232. * (45) [PERSON]; [PERSON]; [PERSON] Hybrid error recovery protocol for video streaming in vehicle ad hoc networks. _Veh. Commun._**2018**, _12_, 110-126. [CrossRef]
# High-Input Impedance Voltage-Mode Multifunction Filter
## 3 Non-Ideal and Sensitivity Consideration  
Practically CFA is characterized by the terminals equations \(\mathrm{V_{X}}=\beta\mathrm{V_{Y}}\), \(\mathrm{I_{Z}}=\alpha\mathrm{I_{X}}\), and \(\mathrm{V_{W}}=\gamma\mathrm{V_{Z}}\)[14], where \(\beta\), \(\alpha\) and \(\gamma\) are port transfer ratios of the Y, X, and Z terminals, respectively. Taking into account the non-idealities, the denominator of the transfer functions of Figure 2 becomes  
\[\mathrm{D(s)}=\mathrm{s^{2}C_{1}C_{2}R_{2}R_{3}}+\alpha_{2}\alpha_{3}\beta_{3} \gamma_{3}\mathrm{sC_{1}R_{1}}+\alpha_{1}\alpha_{2}\alpha_{3}\beta_{2}\beta_{3} \tag{6}\]  
From Equation (6), the non-ideal \(\omega_{\mathrm{o}}\) and \(\mathrm{Q}\) in Equations (4) and (5) become  
\[\omega_{\mathrm{o}}=\sqrt{\frac{\alpha_{1}\alpha_{2}\alpha_{3}\beta_{2}\beta_{ 3}}{C_{1}C_{2}R_{2}R_{3}}} \tag{7}\]  
\[\mathrm{Q}=\frac{1}{\gamma_{3}R_{1}}\sqrt{\frac{\alpha_{1}\beta_{2}C_{2}R_{2} R_{3}}{\alpha_{2}\alpha_{3}\beta_{3}C_{1}}} \tag{8}\]  
Sensitivity is an important performance of a biquad filter, and its formal definition can be expressed as follows:  
\[\mathrm{S_{X}^{F}}=\frac{\mathrm{X}}{\mathrm{F}}\frac{\partial\mathrm{F}}{ \partial\mathrm{X}} \tag{9}\]  
where F represents one of \(\omega_{\mathrm{o}}\) and Q. X represents any of the active parameter or passive component. Using this definition, the active and passive sensitivities with respect to \(\omega_{\mathrm{o}}\) and Q can be obtained as  
\[\mathrm{S_{\alpha_{1}}^{\omega_{\mathrm{o}}}}=\mathrm{S_{\alpha_{2}}^{\omega _{\mathrm{o}}}}=\mathrm{S_{\alpha_{3}}^{\omega_{\mathrm{o}}}}=\mathrm{S_{ \beta_{2}}^{\omega_{\mathrm{o}}}}=\mathrm{S_{\beta_{3}}^{\omega_{\mathrm{o}}}} =\frac{1}{2} \tag{10}\]  
\[\mathrm{S_{C_{1}}^{\omega_{\mathrm{o}}}}=\mathrm{S_{C_{2}}^{\omega_{\mathrm{o} }}}=\mathrm{S_{R_{2}}^{\omega_{\mathrm{o}}}}=\mathrm{S_{R_{3}}^{\omega_{\mathrm{ o}}}}=-\frac{1}{2} \tag{11}\]  
\[\mathrm{S_{\alpha_{1}}^{Q}}=\mathrm{S_{\beta_{2}}^{Q}}=\mathrm{S_{C_{2}}^{Q}}= \mathrm{S_{R_{2}}^{Q}}=\mathrm{S_{R_{3}}^{Q}}=\frac{1}{2} \tag{12}\]  
\[\mathrm{S_{\alpha_{2}}^{Q}}=\mathrm{S_{\alpha_{3}}^{Q}}=\mathrm{S_{\beta_{3}}^ {Q}}=\mathrm{S_{C_{1}}^{Q}}=-\frac{1}{2} \tag{13}\]  
\[\mathrm{S_{\gamma_{3}}^{Q}}=\mathrm{S_{R_{1}}^{Q}}=-1 \tag{14}\]  
It is clearly observed from Equations (10) and (14) that the circuit has low passive and active sensitivity performances.  
Figure 3: CFA constructed using commercially available AD844 integrated circuits (ICs).
# Multi-Scale Ensemble Learning for Thermal Image Enhancement
## 3 Proposed Network
### Combination Module  
In this module, \(n+1\) pairs of residual images and confidence maps are learned to finally produce a high-quality thermal image. The confidence maps are learned so that the values at a certain position sum up to one by using a softmax layer. That is, the confidence map of a sub-network indicates how much weight should be assigned to the sub-network  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Module**} & \multirow{2}{*}{**Layer**} & \multirow{2}{*}{**Kernel Size**} & \multicolumn{2}{c}{**Dimension**} \\ \cline{3-5}  & & & **Input** & **Output** \\ \hline Mixed feature & Concat & - & & \(n\times 48\) \\ \hline Reconstruction & Conv & \(3\times 3\) & \(n\times 48\) & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The configurations of the mixed feature module and reconstruction module.  
Figure 4: Visualization of the reconstruction module.  
Figure 5: An example of a pair of (left) a confidence map and (right) a residual image (normalized and color mapped to deep green and gray for visualization).  
when producing an output. Subsequently, the output image of each sub-network is element-wisely multiplied by its confidence map and then element-wisely summed to generate a high-quality thermal image as in Equations (1).  
\[HQ_{img}=I_{mix}\otimes C_{mix}\oplus\sum_{i=1}^{n}I_{i}\otimes C_{i} \tag{1}\]  
where \(I\) and \(C\) are the predicted image and confidence map, respectively, and \(HQ_{img}\) is the finally obtainable high-quality thermal image. The number of sub-networks is \(n\), and \(I_{mix}\) and \(C_{mix}\) are the predicted image and confidence map from the reconstruction module using the concatenated feature map from the mixed feature module, respectively. \(\oplus\) and \(\otimes\) denote the element-wise summation and multiplication, respectively.  
In the training phase, confidence values and residuals are learned to achieve optimal values. Since the number of \(n\)-levels of the sub-network affect the performance, the value of \(n\) should be carefully determined. How to determine this value is discussed in Section 4.
## 3 Design of Algorithm
### Configuration  
FPGAs are important in digitalizing I&C systems in NPPs since they enable high-speed calculations with parallel processing and can be easily replaced when an FPGA device has been discontinued. We used the Digilent (Pullman, WA, USA) FPGA evaluation board Eclipse Z7 [15] equipped with Xilinx's (San Jose, CA, USA) Zynq-7000 combined with the Digilent (Pullman, WA, USA) analog-to-digital converter (ADC) Zmod ADC1410 [16] for the digital algorithm design. Figure 2 shows the configuration of digital pulse signal processing. Since the pulse signals output from a detector such as an FC are extremely short (e.g., 200 ns), the ADC for EISs must have an adequate sampling rate. Table 1 shows the features of Zmod ADC1410. We aimed to convert the analog signal to a digital signal after the preamplifier so that the SR can have a simple configuration with the minimum analog modules. This improves cost-effectiveness, scalability, and maintainability because it reduces modules such as a pulse amplifier, pulse shaper, and logarithmic amplifier indicated in Section 2, and enables easy modification with only code changes. Zynq-7000 consists of a system-on-chip-integrated processing system (PS) and a programmable logic (PL) unit. The PL is equipped with FPGA, and the PS is equipped with a processor. As shown in Figure 2, most processing functions are achieved in the PL, and the PS is only used for communication with the PC.  
Figure 2: Configuration in this study with ADC and FPGA evaluation board.
# Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection
## 3 Method
### Detection Head  
The detection head uses the 3D ROI output of the first stage as input and refines it to produce higher-quality detection results. Specifically, we refer to the voxel R-CNN [10] method. First, the ROI is divided into sub-voxels of the same size according to the 6 \(\times\) 6 \(\times\) 6 resolution. Second, the voxel features extracted by sparse convolution are aggregated to the sub-voxels of the ROI using the voxel ROI pooling module to obtain ROI features. Last, the ROI features are flattened into feature vectors and input into two MLPs: the first MLP is utilized for confidence value prediction and the second MLP is utilized for regression refinement of the 3D bounding box parameters.
# Second-Order Statistical Properties of Vehicle-to-Vehicle Rician Fading Channel
###### Abstract  
Vehicle-to-vehicle (V2V) channels exhibit highly dynamic and non-stationary characteristics, posing significant challenges in designing reliable communication systems. The level crossing rate (LCR) and average fade duration (AFD) are two important second-order statistical properties that help characterize these rapid channel changes. This paper presents an in-depth analysis of the LCR and AFD for a two-dimensional non-isotropic scattering model of a V2V Rician flat fading channel. The study investigates the influence and impact of several key parameters on LCR and AFD, focusing on the impact of high vehicle traffic density (VTD). The results closely align with available empirical data and offer valuable insights into designing robust V2V communication systems that can adapt to the rapidly evolving channel conditions.  
vehicle-to-vehicle communications; Rician fading channels; level crossing rate; average fade duration; non-isotropic scattering 2025