esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Phase Noise on the Optical Millimeter-Wave Signal in the DWDM-RoF System
## 4 Results and Discussion
After adjustment, the best result of controlling the phase imbalance was at the phase of \(5\pi/12\) where the phase noise decreased from 0.2 to 0.02 degrees. The minimum phase noise was 0.02 degrees at a controlled phase imbalance of 0.1 degrees. The theoretical objective of our study was to examine the effect of phase noise on the output of the mm-wave signal. This phase noise, originating from the phase imbalance, accrued at the stage of mm-wave modulation in the DWDM-RoF system. The phase noise was controlled using the PS by tuning the phases to reach a lower phase imbalance, resulting in a lower phase noise of the mm-wave signal. Further investigations to examine  
Figure 11: The EVM vs. the phase noise of the mm-wave signal. Figure 4:0 The mm-wave signal before and after controlling the phase imbalance in the DWDM-RoF system. the phase noise were conducted, e.g., SNR, OSNR, and EVM were calculated at different phases (\(\pi/12\), \(\pi/6\), \(\pi/4\), \(\pi/3\), and \(5\pi/12\)). We found that the best phase applied at the PS was \(5\pi/12\), where better SNR, OSNR, and EVM values were obtained. Additionally at the phase of \(5\pi/12\), the phase imbalance was controlled at 0.1 degrees and decreased the phase noise to 0.02 degrees. In addition to the theoretical expressions that proved the phase imbalance effects on the amplitude and the output power of the mm-wave signal in Equations (4)-(6), the theoretical derivation of SNR, OSNR, and EVM values showed the relationship of SNR, OSNR, and EVM to the phase imbalance \(\varphi\) and the phase noise \(\delta\) in Equations (7)-(9). The results of the simulation work support the theoretical expressions of the effect of phase noise on the mm-wave signal. The phase noise of the mm-wave signal was successfully decreased by controlling the phase imbalance of the mm-wave signal in the DWDM-RoF system.
# A Novel Technique for Semantic Segmentation of Hyperspectral Images Using Multi-View Features
## 5 Experimental Results and Discussion
### Comparison of Proposed Technique with Existing Techniques
#### 5.3.2 Multiple-Set Canonical Correlation Analysis
The enhanced computational efficiency of MCCA arises from its parallelism and intrinsic capacity to consider correlations across multiple sets of spectral bands, leading to a better understanding of the intricate relationships present in hyperspectral data. This extension not only refines the characterization of spectral information but also contributes to the optimization of the SVM-based classification process. By incorporating a broader spectrum of correlations, MCCA provides a richer feature space for SVM, facilitating more accurate and discriminative classification outcomes. Figure 15 highlights the improved OA and AA of MCCA with respect to PCCA. Table 16 highlights the improvement in computational time of MCCA-SVM as compared to PCCA-SVM. For simplicity, No. of Components is specified as 'n_comps' in the Table 16. The results in Table 16 and Figure 16 have been rigorously studied and compared by varying No. of Components for all the three HSI datasets where MCCA-SVM outshines PCCA-SVM. It is evident in Table 16 that MCCA-SVM has given results in lesser computational time as compared to PCCA-SVM. The lowest time is recorded as 3.02 s for IP dataset, 6.23 s for PU dataset and 8.59 s for SA dataset by Semantic Segmentation using MCCA-SVM. Figure 15: Comparison of PCCA-SVM and MCCA-SVM in terms of OA and AA on (**a**) Indian Pines, (**b**) Pavia University and (**c**) Salinas Valley.
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 3 Face Anti-Spoofing
### Generalization Methods on Unknown attacks
[154] extended anomaly detection by incorporating client-specific thresholds and utilizing a one-class classifier based on Gaussian distribution based statistical hypothesis to enhance performance. [PERSON] et al. [155] proposed an end-to-end anomaly detection model that introduced a pseudo-negative class using a Gaussian distribution. However, it faced issues with poor robustness. Similarly, [PERSON] et al. [156] used a one-class classifier with multi-channel CNNs for anomaly detection, relying on normal face data. [PERSON] et al. [157] further developed a client-specific anomaly detection method, adapting classifiers to each user's data for better accuracy and generalization. [PERSON] et al. [158] proposed OC-SCMNet, a one-class method that uses spoof cue maps (SCMs) to learn spoof features from live faces, aiming for zero responses for real faces while dynamically preserving spoof features in a memory bank.Besides, [PERSON] et al. [159] employed evidence theory to handle the confidence of model outputs, quantifying uncertainty and enabling the model to be more robust against unknown attacks. They also established a semantic consistency loss to ensure that feature representations remain consistent across different scenarios. Table 13 compares and summarizes generalization methods based on anomaly detection, detailing the main idea, backbone, datasets, attack types, advantages, and disadvantages. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Ref. ** & **Main Idea** & **Backbone** & **Datasets** & **Attacks** & **Advantages** & **Disadvantages** \\ \hline \multirow{5}{*}{[2]} & Deep new learning, zero-shot & \multirow{5}{*}{CNN} & Replace-Attacks & \(\gamma\) & \(\surd\) & \multirow{5}{*}{\(\surd\)} & \multirow{5}{*}{\(\surd\)} & \multirow{5}{*}{\(\surd\)} \\  & learning and & & CSA-MSD & \(\surd\) & & & & \\  & multiple classification & & SWM & \(\surd\) & \(\surd\) & & & \\  & & & CSMA-MSD & \(\surd\) & \(\surd\) & & & \\  & & & Hybrid-Attacks & \(\surd\) & \(\surd\) & & &
# Dynamic Community Detection Based on Evolutionary DeepWalk
## 4 Experimental Analysis
### Network Evaluation
#### 4.2.1 Parameter Analysis  
The parameter \(\alpha\) is used to balance the ratio of historical node characteristics to the current node characteristics. The larger the value of \(\alpha\), the greater influence of the historical network structure on the current network. To analyze the influence of the parameter \(\alpha\) in evolutionary graph embedding on dynamic network community detection, this section shows the research of \(\alpha\) in real-world networks changes in evolution. The parameter \(\alpha\) takes the value within the range of [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], and uses grid search to obtain a better result, according to the evaluation results of dynamic community detection. The specific evaluation results are shown in Figure 3. Figure 3a shows the average evaluation results of the different \(\alpha\) values with real-world dataset CW, Figure 3b shows the average evaluation results of different \(\alpha\) values with real-world dataset PS.  
As can be seen from Figure 3, when \(\alpha\) = 0.6, the results of evaluation criteria in CW and PS are the best. It can be analyzed that the history of the network structure has a greater impact on the current network structure in evolution. In the next experiment, we will use \(\alpha\) = 0.6 as the fixed coefficient for our method.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 2 Background
### Heterogeneous Networks  
A heterogeneous network generally consists of multiple network layers that operate within the same bandwidth. Recent studies on the physical layer security (PLS) of heterogeneous networks focus on developing transmission strategies that safeguard multi-tier communications. In the context of 5G, heterogeneous networks can effectively and intelligently integrate various nodes into a multi-tier structure [20; 21]. This structure, shown in Figure 3, includes macrocells with high-power nodes for extensive radio access coverage and small cells with low-power nodes for localized network access, among others [22]. Unlike traditional single-tier architectures, this multi-tier design introduces new challenges in PLS analysis [23]. The placement of both high-power and low-power nodes has a direct impact on the PLS framework, necessitating precise modeling and assessment. Addition  
Figure 2: Sixth-generation network ecosystem.  
ally, heterogeneous networks can generate significant cross-tier interference, which must be addressed when designing secure and reliable data transmission methods [24]. Moreover, users have the flexibility to connect to different tiers, such as open-access networks. Therefore, it is essential to establish user association policies that balance both QoS and security requirements.
## 4 Experiment Setup and Data Collection
### Actual jamming Experiment  
To apply jamming in this experiment, a NavWar Electronic Attack Trainer (NEAT) jammer was used to generate a real jamming signal inside an anechoic chamber. The NEAT has the capability to generate signal jamming on L1 and L2 frequencies with two levels of jamming power, namely low and high levels. Additionally, it can generate different jamming signals, such as continuous wave, chirp, wide-band, and narrow-band. For the sake of consistency, the jamming signal was applied only for L1 frequency, as in the simulated jamming case. Additionally, a programmable attenuator by Mini-Circuits was connected to the NEAT to control the jamming signal power level inserted inside the anechoic chamber. The NEAT, the mini-circuits attenuator connected to the NEAT antenna, and the mini-circuits attenuator interface are shown in Figure 6. The Orolia GSG-8 GNSS signal generator is still the main component of this experiment. However, the RF signal generated from the Orolia is transmitted to an actual GNSS antenna (Rx antenna) using a radio transmitter (Tx antenna) inside the anechoic chamber, as depicted in Figure 7. The antenna is then linked to the NovAtel PwrPak7 receiver, and data are captured using the NovAtel connect, which is the same as in the simulated experiment.  
Figure 5: Orolia experimental setup.  
Figure 6: NEAT (**left**), and mini-circuits attenuator (**right**).
## 4 Discussion
### Classification of the Tested Connections  
To determine the connection stiffness and strength in the moment-rotation envelope curve, the typed connection may be classified by EC3 [23]. A connection can be normally classified as nominally pinned, semi-rigid or rigid in terms of the connection stiffness. A connection is nominally pinned when the initial stiffness is less than \(0.5\mathrm{E_{b}}\mathrm{I_{b}}/\mathrm{L_{b}}\), where \(\mathrm{E_{b}}\), \(\mathrm{I_{b}}\), and \(\mathrm{L_{b}}\) are the elastic modulus, second moment of area, and span of the steel beam, respectively. A rigid connection has an initial  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Specimen** & \(\mathrm{K_{f}}\) **(\&\)**(\&\)**(\&\)**(\&\)**(\(\mathrm{mrad}\))** & \(\mathrm{M_{y}}\) **(\&\)**(\(\mathrm{mrad}\))** & \(\mathrm{M_{max}}\) **(\&\)**(\&\)**(\(\mathrm{kN}\)-\(\mathrm{m}\))** & \(\mathrm{\theta_{max}}\) **(\&\)  
stiffness greater than \(25 EI_{\text{b}}/L_{\text{b}}\) for a non-braced frame and \(8E_{\text{b}}I_{\text{b}}/L_{\text{b}}\) for a braced frame. A connection with an initial stiffness between these two thresholds is classified as semi-rigid. A connection can be classified as full strength, partial strength, and nominally pinned according to the connection strength. The maximum required connection resistance for nominally pinned connection design is \(0.25M_{\text{bp}}\), and the full-strength connection resistance is greater than \(M_{\text{bp}}\), where \(M_{\text{bp}}\) represents the design plastic flexural resistance of the steel beam. If the flexural resistance is located between the two thresholds, the connection is partial strength. The classification of tested connections is illustrated in Figure 13. Most blind bolted flush end-plate connections to CFST columns are classified as semi-rigid and partial strength; however, specimen ST-6, with a weak beam, is classified as semi-rigid and full-strength.
## 6 Application to a Real Dataset
### Fit the Six Models to the AirPassengers Dataset  
The six sinusoidal models were applied to the AirPassengers dataset to evaluate their predictive performance. Each model was optimized using curve-fitting techniques, and their effectiveness was assessed based on RMSE and \(R^{2}\) metrics, as summarized in Table 2. Among the models, the mixed frequency with trend model demonstrated the best performance, achieving the lowest RMSE (17.59) and the highest \(R^{2}\) (0.973). The fitted results for all models are visually depicted in Figure 15. The corresponding best-fit model for the mixed frequency with trend, which demonstrated the best performance on the training AirPassengers dataset, is:  
\[\hat{y}_{t}=0.148\sin(0.521t-1.189)+0.084\sin(1.051t+0.448)+0.01t+4.809 \tag{18}\]
# The Feasibility of Shadowed Image Restoration Using the Synthetic Aperture Focusing Technique
## 4 Discussion
Numbers above the slash of relative imaged echoes show the information of original observations and those below the slash are obtained from the results of the SAFT process. of data is required to achieve a satisfactory restoration job. In other words, a degraded restoration of an image can be achieved as a result of incomplete echo signals. In the immersed experiments, the created model is scanned by B-mode and then pitch/catch arrangements. For clarity, the theoretical echo paths for B-mode and pitch/catch scans are depicted and shown in Figure 7. In Figure 7a, we can see that when the scan is performed at SP\({}_{\text{OBS}}\), targets (T\({}_{1}\), T\({}_{2}\), and T\({}_{3}\)) that are located behind the obstacle (T\({}_{\text{OBS}}\)) might not be echoed and become invisible in the original scan. Once the scanning is performed away from SP\({}_{\text{OBS}}\), e.g., at SP\({}_{\text{FR}}\), the shadowed targets can be partly echoed. In the essence of image restoration, an original image is restored by calculating the relationship between the echo signals of N sensors disposed of inline (multi-channel scans) and summing up all demodulated echo signals detected (Figure 3). In the practical operation of the SAFT process, the performance of image restoration is also highly dominated by the beam pattern, i.e., the divergence angle of the active sensor. The beam pattern is determined by factors such as the velocity of the medium to be scanned, the frequency and size of the active sensor, and the acoustic phase characteristics of the vibrating surface [27, 28], possibly due to the effects of wavefront healing or whether the created model is scanned using B-mode or pitch/catch. The targets positioned behind T\({}_{4}\), i.e., T\({}_{5}\), T\({}_{6}\), and T\({}_{7}\), are all echoed in the scan images (Figures 4a and 5a) [30]. The radiation angle of an active sensor is proportional to the sound wave in the medium. In our immersed scan, the P-wave propagates with a slower velocity in water and limits the divergence angle of the active sensor. The limited divergence angle thus narrows the overlap of the emitted beam and reflected beam; therefore, the acquired trajectories originated by reflection/diffraction are incomplete (Figure 5a). Regardless of the scanning arrangements, target T\({}_{1}\) always fails to be echoed and restored. Our laboratory results thus show that image restoration cannot happen out of nothing. Once a target to be imaged falls within the anechoic zone, nothing can be done to restore the original image.
## 4 Shield Tunnel Load-Bearing Performance Analysis by Full-Scale Test
### Test Result
#### 4.3.2 Internal Force of Shield Tunnel Structure  
Figure 30 presents the variation curve of the bending moment under different load conditions. It can be seen from the figure that the bending moment of the shield tunnel is positive at the waist, while it is negative at the top and bottom. Because of the staggered assembly type of the tunnel segment, the bending moment exhibits an asymmetric distribution characteristic. When P2 reaches the design load, the bending moment of the structure significantly increases at the sections of \(0^{\circ}\), \(55^{\circ}\), \(135^{\circ}\), \(190^{\circ}\), and \(270^{\circ}\). The reason is that the tunnel ring joints are located around these sections, and these joints have lower stiffness than the segment concrete. Under the excavation unloading condition, the bending moment transfer effect between the middle and upper rings continues to increase. As a result, the bending moment of the tunnel structure increases significantly at the sections of \(0^{\circ}\), \(55^{\circ}\), \(125^{\circ}\) and \(190^{\circ}\). When P2 is decreased to 121 kN, the shield tunnel reaches the final failure state. Compared to the design load stage, the maximum bending moment increases by 36% in the final failure stage, and the maximum positive bending moment occurs around the section of \(270^{\circ}\).  
Figure 28: Comparison of the horizontal convergence deformation between the numerical model and the full-scale test.  
Figure 29: Cross-sectional deformation of the shield tunnel when unloading 64 kN between the numerical model and the full-scale test.  
Figure 31 shows the variation of the axial force under different load conditions. It can be seen from the figure that the distribution of the structural axial force has a certain correlation with the bending moment, as the tunnel sections with larger bending moments also have a larger axial force. Before P2 is increased to the design load, the increment of axial force is small, and the maximum axial force occurs at around the section of 270\({}^{\circ}\). When P2 enters the unloading stage, the axial force at different tunnel sections significantly increases. When P2 is decreased to 121 kN, the shield tunnel reaches the final failure state. Compared to the design load stage, the maximum axial force increases by 74.1% in the final failure stage.
###### Abstract
Although some scholars have coupled the vehicle load and temperature fields in their calculations, most of them are limited to two-dimensional models, while the coupled field of asphalt pavement structure is a three-dimensional problem. Therefore, it is difficult to accurately assess the structural mechanical response of asphalt pavements under the combined effects of actual vehicle dynamic loads and temperature fields. In this study, a typical semi-rigid asphalt pavement structure in Northwest China is taken as the research object, and the three-dimensional finite-element model of the pavement structure is established by using finite element software. The temperature field and different axle types of traffic loads are applied to the model to study the mechanical response law of semi-rigid asphalt pavement under this coupled field, and to provide a reference for the structural design of semi-rigid asphalt pavements and the prevention and treatment of reflection cracking disease.
# Data-Driven Pavement Performance: Machine Learning-Based Predictive Models
## 3 Machine Learning Algorithms
### K-Nearest Neighbour  
K-nearest neighbour (KNN) is used for classifications and regression purposes, and it is identified as a non-parametric parametric machine learning algorithm; predictions are made by finding the K-nearest data points based on the input and making an average of target values [18]. Objects are classified by identifying the neighbours in data points with the provided sample units. The Euclidean distance is used to identify the distance between an object and its neighbours [32]. The nearest neighbour technique can be used both for univariate and multivariate predictions and can be used for a wide variety of datasets [33]. Since the KNN regression requires the calculation of distances between new and existing data points, it can be computationally expensive [34]. Furthermore, the quality of predictions is governed by taking the right value for K. Furthermore, KNN can be used to identify the missing values and can also be used for putting performance characterization of asphalt pavements [4].
# Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths
## 5 Experiments
### Overall Performance  
#### 5.4.1 Results on Medium-Scale Datasets  
The results of the experiment on medium-scale datasets ACM, DBLP and IMDB are shown in Table 2. As we can see, metapath-free methods (i.e., HGT, HGB and HGPN) tend to exhibit better performances. This finding is aligned with [11], which suggests that metapaths may not be necessary for heterogeneous graph learning. Remarkably, our proposed HGPN consistently outperforms previous SOTA methods across all datasets on all metrics. In particular, HGPN achieves the greatest improvements on the IMDB dataset. IMDB is believed to be more difficult for the reason that all models perform worse on IMDB and simple methods like MLP and metapath2 vec can also obtain comparable results on ACM and DBLP. This indicates that our HGPN can better extract useful information from heterogeneity due to our model structure and novel design of different purification methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**DBLP**} & \multicolumn{2}{c}{**IMDB**} & \multicolumn{2}{c}{**ACM**} \\ \cline{2-7}  & **Macro-F1** & **Micro-F1** & **Macro-F1** & **Micro-F1** & **Macro-F1** & **Micro-F1** \\ \hline RGCN & \(91.52\pm 0.50\) & \(92.07\pm 0.50\) & \(58.85\pm 0.26\) & \(62.05\pm 0.15\) & \(91.55\pm 0.74\) & \(91.41\pm 0.75\) \\ HetSANN & \(78.55\pm 2.42\) & \(80.56\pm 1.50\) & \(49.47\pm 1.21\) & \(57.68\pm 0.44\) & \(90.02\pm 0.35\) & \(89.91\pm 0.37\) \\ HAN & \(91.67\pm 0.49\) & \(92.05\pm 0.62\) & \(57.74\pm 0.96\) & \(64.52\pm 0.50\) & \(90.89\pm 0.43\) & \(90.79\pm 0.43\) \\ MAGNN & \(93.28\pm 0.51\) & \(93.76\pm 0.45\) & \(56.46\pm 3.20\) & \(64.67\pm 1.67\) & \(90.88\pm 0.64\) & \(90.77\pm 0.65\) \\ HGT & \(93.01\pm 0.23\) & \(93.49\pm 0.25\) & \(63.00\pm 1.19\) & \(67.20\pm 0.57\) & \(91.12\pm 0.76\) & \(91.00\pm 0.76\) \\ HGB & \(94.01\pm 0.24\) & \(94.46\pm 0.22\) & \(63.53\pm 1.36\) & \(67.36\pm 0.57\) & \(93.42\pm 0.44\) & \(93.35\pm 0.45\) \\ HGPN (ours) & \(\mathbf{95.02\pm 0.18}\) & \(\mathbf{95.48\pm 0.29}\) & \(\mathbf{67.55\pm 0.46}\) & \(\mathbf{69.34\pm 0.56}\) & \(\mathbf{94.12\pm 0.24}\) & \(\mathbf{94.21\pm 0.18}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall performances on ACM, DBLP and IMDB. The numbers in bold mean the best results. All methods are tested 10 times and we report the average performances.
# An Optimal Fuzzy PID Controller Design Based on Conventional PID Control and Nonlinear Factors
## 2 The Proposed Optimal Fuzzy PID Controller Design
### The Efficient Genetic Algorithm  
The GA is employed to find the optimal parameter set for a fuzzy PID controller. Basically, the GA includes some major components, such as encoding schemes, fitness evaluations, parent selection, crossover operators, and a mutation operator. This study applied binary coding and adopted the integrated absolute error (\(IAE\)) as the performance index [22], shown in Equation (6):  
\[IAE=\sum_{i=1}^{n}\lvert e[n]\rvert. \tag{6}\]  
The fitness value will be the inverse value of the performance index, i.e., the smaller absolute sum of errors will obtain a larger fitness value.  
In this paper, we hope to enhance the GA efficiency, and discuss the crossover operation in GA computation. In some GA codes, the bits of different parameters will be concatenated into a large long string of bits. Afterwards, the crossover operation was done with only one crossover point. This will simplify the programming considerably, but may reduce GA efficiency due to the possible damage to good genes. In the proposed improved scheme, each parameter has its own crossover point, as illustrated in Figure 4. In this manner, good genes will not be easily destroyed by the crossover operation. Moreover, we will increase genetic diversity by enlarging the mutation rate. Simulation results show an efficient GA for optimal fuzzy PID controller design.  
Figure 3: Different nonlinear factor for tuning MFs. (**a**) \(\gamma=1\); (**b**) \(\gamma<1\); and (**c**) \(\gamma>1\).