esa-sceva/satcom-chunk-collection huggingface dataset:
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## 5 Simulation Analysis
### Simulation Results
#### 5.2.2 The Number of Hidden Layers  
To ensure the reliability of the proposed method of designing only one hidden layer, the proposed method is designed with three hidden layers like the existing ML method and compared with the one-hidden-layer method. As shown in Figure 9, there are little differences between proposed algorithms with 1 and 3 hidden layers in both scenarios. Therefore, it can be seen that the proposed method can be efficiently designed to complete the training quickly by reducing the complexity of ML by setting one hidden layer rather than slightly increasing the performance by setting a lot of hidden layers. Although the performance loss for weight quantization appears to occur similarly in both methods, the performance becomes unstable as the number of hidden layers increases, as shown in the \(L=6\) scenario.
## 2 Materials and Methods
### Characterization of AgNPs
#### 2.5.2 High-Resolution Transmission Electron Microscopy (HRTEM) analysis  
High-Resolution Transmission Electron Microscopy was employed to acquire highly magnified images of the morphology and size of the synthesized AgNPs. Before the analysis, a drop of sonicated AgNP latex samples was carefully deposited onto carbon-coated (Quorum Q150 TE, Laughton, UK) formvar grids (200 mesh) and subsequently dried. Following this, the samples were observed utilizing the HRTEM JEM 2100 (JEOL, Tokyo, Japan), which was equipped with Gatan software (version 3.5), operating at a voltage of 200 kV. The morphology and size of the AgNPs were ascertained through the utilization of ImageJ software (Java version 1.8.0) (\(n=5\)).
## 4 Validation of the Proposed Method
### Case 2: Experiment Results on the Jiang Nan University Bearing Dataset and Performance Analysis
#### 4.3.2 Bearing Fault Diagnosis Experiment  
Fault Diagnosis Results and Evaluation of the Proposed INPSO Optimization Method  
During the optimization stage of SVM parameters (penalty factor \(c\) and the kernel parameter \(g\)), the proposed INPSO approach was adopted to tune appropriate parameters to improve fault diagnosis accuracy. The parameter settings of the algorithm were the same as in case 1. A group of samples was randomly selected as the test set, and the proposed 1 DCNN was applied for feature extraction. Then the extracted feature vectors were respectively fed into the INPSO-SVM classifier, the SVM and PSO-SVM classifiers which were implemented as comparisons in this study. Each method was performed 10 times to reduce the effect of randomness and the diagnosis accuracy of the three classifiers are listed in Table 7. From Table 7, it can be seen that the fault diagnosis accuracy of SVM was only 91.98% without the parameters tuning process. In addition, the PSO-SVM performed better than SVM, where the average fault diagnosis accuracy was 93.3% with a standard deviation of 0.13. With the same test set, the proposed INPSO-SVM achieved the best performance in each trial and the average fault diagnosis could reach 94.186%. The standard deviation was 0.06, which indicated that the INPSO-SVM had good stability. Therefore, it could be concluded that the proposed algorithm had a stronger global optimization ability and played an important role in improving fault diagnosis accuracy.  
Comparisons Among Different Fault Diagnosis Methods  
In order to further prove that the proposed 1 DCNN-INPSO-SVM could extract robust features and improve fault diagnosis accuracy, the WPT-INPSO-SVM method was used for comparative analysis. In the comparative experiments, the points to emphasize were the same as in case 1. The classification accuracies were evaluated and compared in Table 8. Table 8 lists the results obtained using the random 10 sets of data, where each set of data was used to test the 1 DCNN-INPSO-SVM and the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Run Number**} & \multicolumn{3}{c}{**Accuracy**} \\ \cline{2-4}  & **SVM** & **PSO-SVM** & **INPSO-SVM** \\ \hline
1 & 92 & 93.17 & 94.17 \\
2 & 93.33 & 93.17 & 94.17 \\
3 & 92 & 93.33 & 94.17 \\
4 & 91.5 & 93.33 & 94.17 \\
5 & 93.33 & 93.17 & 94.17 \\
6 & 90.33 & 93.17 & 94.17 \\
7 & 91.16 & 93.33 & 94.33 \\
8 & 92.66 & 93.5 & 94.17 \\
9 & 92.16 & 93.5 & 94.17 \\
10 & 91.33 & 93.33 & 94.17 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The influence of parameter optimization on fault diagnosis accuracy.  
Figure 11: Bearing fault: (**a**) outer-race fault, (**b**) inner-race fault, and (**c**) roller element fault.  
WPT-INPSO-SVM ten times respectively. The last column of Table 8 indicates that the proposed method achieved better performance, with the highest accuracy of 97.186%, while the competitor utilizing the features extracted by WPT obtained a lower accuracy.
# A Survey on Mobility Support in Wireless Body Area Networks
## 3 Mobility Model and Communication Protocol
### Group Mobility
#### 3.2.1 Mobility Model  
The well known group mobility model is Reference Point Group Mobility (RPGM) [37] that defines the correlation between nodes in the group. In addition, column mobility, pursue and nomadic community model [38] were proposed to model a person's movement patterns in specific  
Figure 5: Operations in Reliable Multi-Path Routing.  
scenarios. In addition, Small World In Motion (SWIM) makes use of social properties to decide the next moving places [39]. N-Body model [40] is another model to extract the social information from real human movement traces by defining friendship and steadiness matrices. [PERSON] et al. [41] extended the RPGM model by introducing new parameters related to posture with posture selector and global movement models.  
Furthermore, as a special case, Random Room Mobility (RRM) [42] was proposed to cover Inter-WBAN and Inter-WBAN mobility models that describe movement of patients in the hospital. In this model, each patient moves to one room according to probability mass function. If a man enters that room, the location of Destination Point (DP) is computed by length, breadth, and height of the room. Upon designating DP, a patient's speed, pause time and velocity is set. The position information is continually generated until current position reaches the DP where Center Point (CP) is updated according to moving time and velocity of nodes on a patient's body. In particular, the group mobility model for soccer players was presented in [43]. A new DynaMo model solves the problem of the previous work which is collaborated with image processing and tracking techniques during a match. Finally, the impact of the mobility model is analyzed and compared with the original RPGM from the aspects of throughput and delay.
## 3 Methods and Data
### Land Cover Mapping of Japan  
We selected and analyzed 35 scenes from the Land Remote Sensing Satellite (Landsat) Operational Land Imager (OLI) data published by the United States Geological Survey (USGS) to develop a land cover map of Japan that takes forest types into account. Each scene was processed with an Integrated Radiometric Correction [33] method to reduce effects from the atmosphere and topography, resulting in fewer errors throughout the classification process. Landsat OLI 5 bands (bands 2-6) along with the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) [34], and Green Red Vegetation Index (GRVI) [35] were used to perform the supervised classification using a maximum likelihood algorithm. This yielded a final land cover product with 12 classes. However, our interests here are in the forest classes only, so the number of classes was reorganized into three forest type classes (coniferous, deciduous broadleaf, and evergreen broadleaf) plus one additional class (others), resulting in a total of four classes. These were utilized for further procedures along with the accuracy assessment, which is fundamental for evaluating the resulting categorical map [36]. The date of the acquired scenes included two from March, four from April, four from May, six from June, five from August, five from September and nine from October. Selection of the images was made by considering a general and critical issue for such optical images: that they were cloud-free. Not all scenes were completely cloud free. In such cases, selections were made from the clearest images that could be found. The specifications of the chosen data are listed in Table 1.
## 5 Comparison of Experimental and Numerical Results and Discussions
### Ground Reflection Experiment
Using the above scattering model, Figure 27 show the results after 1000 Monte Carlo simulations when the numbers of generated scattering waves are 1, 3, 5, and 10, separately. The solid line is the mean value, and the dashed lines are the lower and upper bounds defined by the standard deviation. The earth's surface roughness (RMS value) at this time is set to 100 \(\upmu\)m.  
These additional Monte Carlo simulation results show that when there are 1 to 5 scattering waves, the theoretical results' approximation range can cover the measurement results, except for the measured values at the UAV's altitude of 60 m. The throughput measured at 60 m can be considered as an outlier because the linear distance of the ground-reflected wave is more than 120 m, and it is not easy to measure at a distance longer than 100 m using our dedicated hardware. It can also be observed that throughput slightly increases with higher numbers of scattering waves, which means the effect of ground-reflected interference is more remarkable when there are more scattering waves. The surface roughness (RMS value) was set at 100 \(\upmu\)m in the above analysis, but in order to check whether it was appropriate or not, we performed 1000 Monte Carlo simulations with a fixed number of scattering waves of 3, while the surface roughness's values are changed to 10 \(\upmu\)m, 50 \(\upmu\)m, 500 \(\upmu\)m, and 1000 \(\upmu\)m, individually. The results are shown in Figure 28. The results show that the larger the surface roughness, the lower the throughput. This is due to Equation (14), in which a thicker surface roughness yields a smaller reflection coefficient. The results also imply that surface roughness plays an important role to understand scattering phenomenon of mmWaves. Positioning errors in multiple arrival waves of ground reflection are also taken into account. Figure 29 shows the maximum and minimum throughput values when there are positioning errors within a horizontal distance of \(\pm\)1.5 m and a vertical distance of \(\pm\)0.5 m. The results show that the measured throughput is within the range of the minimum and the maximum throughput due to positioning errors. Again, it  
Figure 27: Comparison of ground reflection Monte Carlo simulation with scattering waves. positioning error is one of the reasons for the discrepancy between the measured and the theoretical analysis results. However, at higher altitude, e.g., above 30 m, there is still an unexplainable gap that will be investigated in our future work, knowing that one of the possible reasons for this might be related to the MAC/PHY conversion efficiency \(\alpha\). Figure 28: Comparison of roughness of ground reflection Monte Carlo simulation with 3 scattering waves and different surface roughness values: (**a**) 10 \(\upmu\)m, (**b**) 50 \(\upmu\)m, (**c**) 500 \(\upmu\)m, and (**d**) 1000 \(\upmu\)m.  
Figure 29: Comparison of ground reflection Monte Carlo simulation with scattering waves considering positioning error.
# A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging
## 2 Materials and Methods
### In Vivo Experiment Setups
#### 2.4.2 Radio-Frequency (RF) Data Processing and Wall Filtering  
The RF echoes were first summed in order to separate the signal nonlinear component from the linear clutter. Then the second harmonics were filtered by a finite impulse response (FIR) bandpass filter with a 4 to 11 MHz passband and then beamformed with a delay-and-sum (DAS) beamformer. A wall filter based on the eigen-decomposition [30] was then used to separate the echoes of flowing microbubbles from the weak harmonic tissue clutter and stationary microbubbles.  
Figure 2: A simple example of the ASH method for the step count in one day: (**a**) a histogram, (**b**) an ASH estimate of 4 shifted histograms, and (**c**) an ASH estimate of 16 shifted histograms.  
Following References [10; 30], the cut-off eigen-value was chosen as 0.2-max_eigen value. This is the lowest cut-off possible in order to include slow flowing microbubbles while removing clutter artifacts.
# Validation of a Manual Methodology for Measuring Roundness and Cylindicity Tolerances
## 1 Introduction
The machines are shown as \"black boxes\" to the user, offering a numerical tolerance result, but without showing the procedure carried out for its acquisition, and many of the implications involved. For this reason, the simple use of these instruments lacks validity, as it does not provide the knowledge and reasoning bases necessary for understanding the theoretical concepts applied to other situations and even for having real control of the measuring process. Furthermore, current standards do not provide clear guidelines about working methodologies when using CME [17]. One question that may be asked is how roundness and cylindricity tolerances can be obtained, at least approximately, without using a specifically designed instrument. Therefore, it is highly recommended to establish training manual methods for obtaining these tolerances, which also allow the technicians to acquire the necessary metrological and conceptual fundamentals for the development of verification tasks in industrial environments [18]. Furthermore, the mathematical approach of cylindricity tolerance is not sufficiently described in the literature, neither in dimensional metrology nor in specific training resources [8]. The analytical calculation procedure needs to be described for the industrial community, especially those that are concerned with cylindricity, as it is more complex to be implemented analytically. For all these reasons, this paper aims to revisit the concepts and the analytical mathematical fundamentals necessary for the verification of roundness and cylindricity tolerances. Additionally, it proposes a practical experience of laboratory activity in order to establish a methodology that can be tested by future users based on three key points: the theoretical review of the relevant roundness and cylindricity principles and their mathematical definition, a laboratory approach that follows the established manual measurement procedure by using typical metrological instruments, and, finally, the verification of the obtained results by means of specifically designed instruments, i.e., CME. The main objective of this research is to improve technical skills in the field of revolution shape metrology and to demonstrate that MM permits us to obtain an acceptable accuracy if some conditions are defined.
# Disguised Full-Duplex Covert Communications
###### Abstract  
Covert communications have arisen as an effective communications security measure that overcomes some of the limitations of cryptography and physical layer security. The main objective is to completely conceal from external devices the very existence of the link for exchanging confidential messages. In this paper, we take a step further and consider a scenario in which a covert communications node disguises itself as another functional entity for even more covertness. To be specific, we study a system where a source node communicates with a seemingly receive-only destination node which, in fact, is full-duplex (FD) and covertly delivers critical messages to another hidden receiver while evading the surveillance. Our aim is to identify the achievable covert rate at the hidden receiver by optimizing the public data rate and the transmit power of the FD destination node subject to the worst-case detection error probability (DEP) of the warden. Closed-form solutions are provided, and we investigate the effects of various system parameters on the covert rate through numerical results, one of which reveals that applying more (less) destination transmit power achieves a higher covert rate when the source transmit power is low (high). Since our work provides a performance guideline from the information-theoretic point of view, we conclude this paper with a discussion on possible future research such as analyses with practical modulations and imperfect channel state information.  
physical layer security; covert communications; low probability of detection; full duplex; covert rate; detection error probability
## 4 The Improved Self-Collision Detection Technology
### Comparison of the Performance of Self-Collision Detection  
To verify the superior performance of the self-collision detection technology proposed in this article, we designed a variety of different test experiments based on these improvements of self-collision detection technology in this study. The main performance parameters include the detection accuracy and the required time for solving self-collision detection. For comparing the detection accuracy, the best approximating method is regarded as the approximate object which is the most suitable for the actual structure of mobile manipulator robot by observing different approximate results. Furthermore, we choose the required time for preprocessing process of self-collision detection and that for judging self-collision detection as another performance to prove the effectiveness of the improved self-collision detection. The specific experiments are described below.
# PTA-Sync: Packet-Train-Aided Time Synchronization for Underwater Acoustic Applications
## 3 Simulation and Results  
The simulation results of PTA-Sync are analyzed in this section. For analyzing the performance of PTA-Sync for UANets, the synchronization performance of PTA-Sync was compared with that of E2 DTS [31] and TSMA-IGTS [32], which are OVTT-based synchronization schemes. The synchronization performance metric of these protocols is the time error between CS and UN after performing synchronization where the synchronization performance is compared by changing the time elapsed after synchronization (\(T\)), changes in mean velocity of UN (\(V\_mean\)), number of messages exchanged for synchronization (\(N\_ex\)), synchronization message transmission time interval (\(I\_tx\)), network range (\(N\_r\)), and the degree of mobility randomness (\(\alpha\)).
# A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets
## 4 Method
### Architecture and Working  
#### 4.2.1 Face Extraction and Generation of Corresponding Vector  
Taking a set of \(N\) different sample images, to reduce the influence of background information and effectively improve the reliability of facial features, the pre-trained MTCNN [65] model was used. It carries out bounding box regression, probability prediction of a real face, and localization of facial landmarks (such as mouths, eyes, and noses) at the same time by applying several networks in a cascade. By detecting the geometric structure of the image, the boundary rectangle of the detected face was returned. According to the detected five facial points, the input face image was cropped from the rectangle. Face alignment, transformation, and normalization [66], which depend on the position of the located key landmark, were performed on cropped faces. After that, faces were further resized to \(128\times 128\times 3\) pixels, which is inspired by VQ-VAE [67], in which the chosen size speeds up training and sampling and captures the global structure. A set of N identified images \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) is produced. To extract 128-dimensional facial embedding from each face in \(X\), FaceNet [68], which utilizes deep convolutional networks [69; 70] (DNNs) to map face images to a compact Euclidean space, was applied, generating facial features \(V=\{v_{1},v_{2},\ldots,v_{n}\}\).
# Research on Narrow Linewidth External Cavity Semiconductor Lasers
## 3 Waveguide Structure of NLECSLs
### FBG Structure of NLECSLs  
FBG structures of NLECSLs in optical fiber transmission systems have become a research hotspot. The FBG structure of NLECSL has its AR facet facing the FBG, the FBG end coupled directly to the gain chip and the other end of the FBG acts as the end reflector of the external cavity. With AR coating on the gain chip, the lasing wavelength may be selected by choosing the appropriate FBG, as shown in Figure 3.  
In 2011, [PERSON] et al. [22] reported a 1550 nm InGaAlAs/InP quantum well, high power, low noise encapsulated ECSL demonstration. The laser consisted of a dual-channel curved channel plate coupled with an optical waveguide amplifier and a 2.5 GHz narrow bandwidth FBG passive cavity using a lens fiber. Under the bias current of 4A, ECSL generates 370 mW of fiber-coupled output power, and its Gaussian linewidth and Lorentz linewidth are 35 kHz and 1 kHz, respectively.  
In 2016, [PERSON] et al. [23] demonstrated a new integrated platform with FBG. The high thermal conductivity of silicon substrate contributes to the heat dissipation and thermalization of the device. The geometric shape of the device is precisely designed with a small inclined plane, which connects the end of integrated platform to eliminate unnecessary optical feedback, and its layout can minimize the angular coupling loss between waveguides. The laser works in a single mode at 1532.83 nm, with an output power of 9 mW and a linewidth of 14 kHz.  
In 2017, [PERSON] et al. [24] combined a semiconductor gain chip and FBG with enhanced thermal sensitivity, and demonstrated a mode-free external cavity laser design. The compact ECSL had a narrow linewidth of 35 kHz, SMSR greater than 50 dB, and the mode-free tuning range was 62.5 GHz.  
In 2019, [PERSON] et al. [25] demonstrated an ultra-low loss silicon based waveguide (0.16 dB/cm) with a linewidth of 1 kHz and an output power of more than 37 mW, and a long FBG fully integrated extended distributed Bragg reflector laser with a narrow bandwidth. The combination of narrow linewidth and high power enables it to be used in coherent communication, radio frequency photonics and optical sensing.  
In 2021, [PERSON] et al. [26] realized a 400 nm FBG InGaN-based laser diode. A narrow band FBG was fabricated under near ultraviolet light. The device has a SMSR of 44 dB and an inherent linewidth of 16 kHz.  
In 2022, [PERSON] et al. [27] reported a laser based on the FBG ECSL module near the wavelength of 1550 nm, using the combination of narrow linewidth technology and frequency stable transfer technology to narrow the laser intrinsic Lorentz linewidth to 15 kHz. Table 3 shows the performances of the FBG structure of NLECSLs. The FBG structure of NLECSL is easily obtain narrow linewidth, high SMSR and high wavelength thermal stability. It is easy to design and screen the gain chip and FBG separately; the performances of FBG structure of NLECSL can be optimized and it is very convenient to be used in fiber systems.  
Figure 3: FBG structure of NLECSLs.