esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Case Studies
### Results and Discussion
#### 5.2.5 Discussion  
As for the first three case studies, it is worth pointing out that the features extracted by Hu moments contain redundant information, and the ability of image description is not strong enough that the recognition rate is very low. Zernike moments with orthogonal polynomial basis can reconstruct the original image using a limited set of moments, having a great capability of describing the object. However, they are in the continuous domain of image coordinate space. The continuous integral is approximated by a discrete summation in the calculation, involving some numerical approximations. In contrast, the Chebyshev polynomials are discrete orthogonal polynomials, overcoming the deficiency of Hu moments and Zernike moments in terms of object representation and calculation. Thus, they can present the best performance of image description.  
In addition, the moments derived from the edge images contain more information about the high-frequency portions than those derived from the pre-processed images. As a result, the minute details of the images, such as the sharp angles of the flame, are better characterized by the contour moments. On the other hand, the global features of the flame can be better characterized by the region moments. Besides, these moments are less sensitive to noise. Hence, these advantages make the combination of region with contour moments superior to the region moments, improving the accuracy of feature extraction.  
With regard to the fourth case study, the reason for this result is that the GA method has the defects of earlier maturity and slow convergent in the later stage of evolution. So that it is not in a position to find the global optimal solution. Similarly, in the convergence of PSO method, the adjustment of each particle's velocity and position is over depend on the current optimal particle, leading to produce premature convergence and fall into the local optimal solution. However, due to the comparison of  
Figure 8: Comparison of the IFA with other methods in the fourth case study.  
any two fireflies, the image information is exchanged more adequately and effectively in the FA and IFA method, which contributes to skipping the local optimal and achieve the global optimal.  
Moreover, due to the improved attractiveness, the worse solutions can move towards the better ones, improving the convergence rate. Using the modified randomization parameter, the proposed location update of IFA is able to adjust its searching step dynamically according to the situation of iterations, improving the recognition rate of the method finally.  
From the above discussion, the conclusion can be reached that the proposed method based on RCMIs and IFA-WSVM model can achieve combustion state recognition effectively and accurately, which has been proven to be superior to methods based on HMIs and ZMIs. The proposed IFA-WSVM model has advantages over other models based on GA, PSO and FA, which can be considered as an effective classification method for combustion state recognition.
# Biometric Vibration Signal Detection Devices for Swallowing Activity Monitoring
## 2 Materials and Methods
### Location of Device Attachment to Acquire Biometric Vibration Signal  
The objective of this study was to effectively monitor swallowing and breathing activities amidst movement. Therefore, an experiment was conducted involving swallowing food while walking. It was observed that depending on the location to which the device was attached, there was a significant variance in the obtained biometric signal levels related to swallowing activity. To capture signals that best represented both swallowing and respiratory cycles, an experiment was designed, focusing on a location near the neck that was closely associated with both actions.  
The experimental scenario entailed the subject taking 10 deep breaths, swallowing saliva three times, holding a glass of water, and sequentially swallowing water three to four times. Dots were marked at 1 cm intervals on the subject's neck to denote positions, and data were recorded at each position. The 4 th position corresponded to the placement of both accelerometers on the sternum, with subsequent positions indicating upward movement at 1 cm intervals.  
Figure 2 shows the data collected at each location. The blue solid line represents the measurements from the upper accelerometer, while the red solid line represents those from the lower accelerometer, positioned approximately 2 cm below the upper accelerometer. The solid yellow line illustrates the differential signal computed from the signals obtained by the two accelerometers. Black arrows indicate instances of saliva or simulated swallowing, while red arrows denote actual swallowing actions, such as drinking water.  
Figure 2: Locations of the attached wireless measurement device and the signals acquired at each location.  
The experiment was conducted at a brisk walking pace, occurring one step per second. All transient vibrations between the ground and the foot were recorded from accelerometers 1 and 2. However, the transient signal due to the walking was eliminated in the differential signal, leaving only the breathing and swallowing signals in the yellow line. Although the breathing signal was most accurately captured at the #2 position out of the four, the swallowing signal was weak, and external activity (walking) was not completely eliminated. In the case of position #3, it demonstrated clear removal of external disturbance signals due to walking compared to position #2, while also providing clear swallowing signals. In position #4, where both accelerometers are located on the SM, the sternum movement during breathing resulted in no angle difference between the two accelerations, as described in Equation (1).  
In experiments involving multiple individuals, the device was attached between positions #2 and #3, depending on the subject, for conducting experiments. Consequently, the upper accelerometer is positioned near the LP, while the lower accelerometer is placed near the SM.
# Research on the Coverage of Ultraviolet Communication Network in the Arbitrary Polygon Area
###### Abstract  
Given that the current ultraviolet (UV) networking model is established in a regular circular area, this article studies the coverage of a UV non-line-of-sight (NLOS) communication network creatively in the arbitrary polygon area. In this paper, the UV communication model and the basic concepts of network coverage are introduced first. Then the influence parameters of the UV node communication radius are studied, and the changes of the communication radius under different work patterns are analyzed. Finally, the coverage of the square target area is simulated under different communication parameters (transmitted power, data rate and node density). The results illustrate that the smaller the transceiver elevation angles are, the better the network coverage performance is. Additionally, we numerically compare the UV network models of polygonal and circular regions, which can be used as a reference for actual networking.  
ultraviolet (UV) network; network coverage; arbitrary polygon area +
Footnote †: journal: _Photomics_  
0
Footnote 0: Copyright**: © 2021 by the authors. Licence MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Results and Discussion
### Effects of Initiator Concentration  
Figure 5 shows the effects of the initiator concentration on the mean particle size and zeta potential of St/AA submicrocapsules prepared at 0.33 and 1.0 \(\times\) 10\({}^{-3}\) mol for [AA]/[St] and SDS concentrations, respectively. As is well known, the initiator concentration greatly affects the reaction rate and extent of radical polymerization. The increase in initiator concentration at a fixed amount of surfactant will increase the possibility and frequency of droplet nucleation in a micelle. At this point, preference of AA for copolymerization with St is no longer maintained. Therefore, the hydrophilic swollen layer composed of longer polyacrylic blocks and the resulting zeta potential of St/AA submicrocapsules were assumed to be diminished for higher initiator concentrations. On the other hand, a limited amount of initiator could not properly activate the droplet nucleation. At an initiator concentration of 1.0 \(\times\) 10\({}^{-3}\) mol/mol of monomer, it was proposed that submicrocapsules were formed insufficiently, which was confirmed by large particle size and low zeta potential. As shown in Figure 5b, the St/AA  
Figure 4: SEM photographs of St/AA submicrocapsules prepared at 0.33 and 2.0 \(\times\) 10\({}^{-3}\) mol/mol of monomer for [AA]/[St] and AlBN concentration, respectively, with varying surfactant content.  
Figure 3: Effects of surfactant concentration on (**a**) mean particle size and (**b**) zeta potential of St/AA submicroparticles prepared at 0.33 and 2.0 \(\times\) 10\({}^{-3}\) mol/mol of monomer for [AA]/[St] and AlBN concentration, respectively.  
submicrocapsule prepared at 1.0 \(\times\) 10\({}^{-3}\) mol/mol of monomer of the initiator concentration shows no surface potential (almost zero potential) which means that immense coagulation of particles will occur.
# One-Step Enhancer: Deblurring and Denoising of OCT Images
## 3 Proposed Method
### Experimental Method and Performance Metrics  
An ablation study was performed to evaluate the performance of each component in the proposed image enhancement method. This consisted in evaluating the performances of each module separately: first, the denoise and the deblur modules were independently assessed. Then, the performance of the proposed model, which combines both operations into a single step operation, was evaluated. More specifically, we removed speckle and blur from the original images separately, and then used the proposed model to perform a one-step image enhancement.  
In addition, to benchmark the performance of the proposed image enhancer, non-learning (BM3D [25]), supervised learning (DnCNN [8]) and unsupervised learning (DRGAN [16]) methods were implemented. The BM3D software implements the traditional OCT image enhancement method, while the DnCNN and DRGAN models were trained on the same dataset used to train our new model.  
Performance evaluation used the same test set described above. In addition, the processing time of each method was evaluated both on CPU and GPU. Finally, a visual assessment of the four image enhancement methods was performed using sample images from the test set.  
Two metrics were used for quantitative performance assessment: peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM). PSNR is commonly used to measure the quality of reconstruction of lossy image compression codec. It offers an approximation to human perception of reconstruction quality based on differences between the reconstructed and the reference image. SSIM, on the other hand, measures the similarity between two images. The overall index of SSIM evaluates luminance, contrast and structural differences.  
\[\textbf{PSNR}=10 log_{10}(\frac{(max(I_{g}))^{2}}{\frac{1}{MN}\sum_{i}\sum_{j} (I_{c}(i,j)-I_{g}(i,j))^{2}}) \tag{23}\]where \(I_{g}\) and \(I_{c}\) are, respectively, the generated and the averaging clean images. \(M\) and \(N\) are the size of the image.  
\[\textit{SSIM}=\frac{(2\mu_{I_{g}}\mu_{I_{c}}+C_{1})(2\sigma_{I_{g}I_{c}}+C_{2})} {(\mu_{I_{g}}^{2}+\mu_{I_{c}}^{2}+C_{1})(\sigma_{I_{g}}^{2}+\sigma_{I_{c}}^{2}+ C_{2})} \tag{24}\]  
where \(\mu_{I_{g}}\), \(\mu_{I_{c}}\), \(\sigma_{I_{g}}\), \(\sigma_{I_{c}}\) and \(\sigma_{I_{g}L}\) are the local means, standard deviations and cross-covariance of images \(I_{g}\) and \(I_{c}\). The constants \(C_{1}\) and \(C_{2}\) are set according to the literature [26].
## 2 Selected Wireless Sensor Networks Standards: State-of-the-art
### IEEE 802.15.1-based Standards
#### Architecture  
The WISA architecture is limited to the physical and MAC layers, as sensors and actuators communicate exclusively with a central base station in a star topology within each cell. As mentioned previously, the  
Figure 2: WISA network elements.  
WISA specification relies on the physical layer of the IEEE 802.15.1 standard operating in the 2.4 GHz frequency band at a data rate of 1 Mb/s. The WISA specification [15] is based on _Time Division Multiple Access_ (TDMA) and _Frequency Division Duplex_ (FDD), meaning that communication from base station to sensors, defined as _downlink_ direction, and from sensors to base station, defined as _uplink_ direction, occur at different frequencies. The downlink direction is exclusively reserved for the base station that remains continuously active and manages the TDMA scheme occurring in the uplink direction. The four uplink channels are divided into superframes of 2048 \(\mu s\), which are composed of 30 timeslots able to support packets up to 64 bit length (Fig. 3). These time slots are allocated by the base station to each sensor willing to transmit data. In case of successful transmission of the sensor data, an acknowledgement is sent by the base station in the downlink. Otherwise the sensor retransmits the data in the next frame. To avoid interference and improve the reliability, frequency hopping is additionally applied after each superframe with a carrier spacing of 1 MHz.
## 3 Data Collection  
In order to establish the prediction model of PPV and optimize the blasting parameters, a statistical scheme of the PPV data, including the blast design parameters and negative elevation (\(H\)), was developed. The values of the maximum explosive charge capacity (\(W\)), monitoring distance (\(R\)), negative elevation (\(H\)), burden (\(B\)), spacing (\(S\)), subdrilling (\(H_{0}\)), powder factor (PF), and PPV were collected and recorded. In this statistical scheme, \(W\), \(R\), and \(B\) have been used by other scholars as controllable parameters to predict PPV [37], while \(S\) and PF were also shown by [PERSON] and [PERSON] [38] to be related to blasting vibration. In addition, negative elevation steps can have a decreasing effect on vibration velocity [36]. Therefore, \(H\) and \(H_{0}\) (meaning the maximum depth at which explosives are placed) were counted and used as input variables. The monitoring equipment for PPV caused by blasting vibration was the TC-4850 blasting [39] vibration tester, and the monitoring points were arranged as shown in Figure 3. R and H were determined by considering laser ranging and calibration on the construction drawings. A total of 30 blasting events were investigated. Due to topographic constraints, PPV in high-steep slope and blasting steps were monitored separately for different periods of blasting. Five measurement points were arranged in front of each blasting event. In addition, less-effective recovery data were obtained about the high-steep slope because of the large number of falling rocks. Therefore, a total of 50 sets of PPV data were recorded. The characteristics of the recorded input and output data are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Indexes** & **\(\begin{array}{c}\textbf{W}\\ \textbf{(kg)}\end{array}\)** & \(\begin{array}{c}\textbf{R}\\ \textbf{(m)}\end{array}\)** & \(\begin{array}{c}\textbf{H}\\ \textbf{(m)}\end{array}\)** & \(\begin{array}{c}\textbf{B}\\ \textbf{(m)}\end{array}\)** & \(\begin{array}{c}\textbf{S}\\ \textbf{(m)}\end{array}\)** & \(\begin{array}{c}\textbf{H_{0}}\\ \textbf{(m)}\end{array}\)** & \(\begin{array}{c}\textbf{PF}\\ \textbf{(kg/m^{3})}\end{array}\)** & \(\begin{array}{c}\textbf{PPV}\\ \textbf{(cm/s)}\end{array}\)** \\ \hline minimum & 156 & 1.8 & 20 & 3 & 5 & 0.5 & 0.25 & 0.14 \\ mean & 174.48 & 124.44 & 31.57 & 3.13 & 6.13 & 1.37 & 0.32 & 8.21 \\ maximum & 195 & 125 & 654 & 3.3 & 6.5 & 2.59 & 0.44 & 33.58 \\ standard deviation & 12.580 & 34.530 & 137.380 & 0.147 & 0.340 & 0.601 & 0.048 & 9.250 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input and output parameter characteristics.  
Figure 3: Monitoring point arrangement (a is the area for the verification of the optimized blasting parameters scheme).
# Research on Intelligent Fault Diagnosis of Rolling Bearing Based on Improved Deep Residual Network
## 4 Experimental Verifications
### Hyperparameter Setup  
Hyperparameters have a great influence on the fault diagnosis accuracy of residual neural networks. According to the paper [39], the more important hyperparameters are optimizer, learning rate, activation function, convolution kernel, and pooling kernel. The hyperparameters of the experiments were made on the basis of the empirical recommendation. In our case, ReLU activation functions are selected. The convolution kernel and pooling kernel are shown in Figure 2. Adam optimizer was used in the experiment. The learning rate is too large to convergence, too small training is too slow. In this study, the exponential decay learning rate is used to optimize this problem. Set the initial learning rate to 0.01, then gradually reduce the learning rate through iteration, and the attenuation coefficient is 0.99. The best classification effect can be achieved when the learning rate is 0.001.
# RAMC: A Rotation Adaptive Tracker with Motion Constraint for Satellite Video Single-Object Tracking
## 4 Experimental Details and Analysis
### Experimental Settings
#### 4.1.3 Implementation Details  
Considering the relatively slight object changes (e.g., illumination, deformation) in a short time, the learning rate of the rotation template is set as \(\eta_{\theta}=0.01\). The cell size and orientation of the HOG feature are set to 4 \(\times\) 4 and 9, respectively, for accurate tracking, as commonly used in [36; 40]. The other parameters related to KCF are referred to [10]. The trackers are executed on a workstation with a 3.20 GHz Intel(R) Xeon(R) Gold 6134 CPU (32-core) and NVIDIA GeForce RTX 2080 Ti GPU.  
To correctly select the fusion weight \(\omega\) for optical flow remapping, we randomly selected three of the eight SVs for the experiments. Table 2 presents the experimental details, and Figure 7 shows the AUC of the success and precision plots. It can be seen that both the AUC of the success and precision plots tended to increase and then decrease as the  
Figure 6: Visualization of different bounding boxes. (**a**) shows the external rectangle of the rotating bounding box. (**b**) shows the effect of angle deviation on the external rectangle. (**c**) presents the internal-shrinkage strategy for evaluation.  
weights increased. The results are ideal when the weight is approximately 0.36. Therefore, this weight \(\omega=0.36\) is used in subsequent experiments.
# Efficient Deep Learning-Based Detection Scheme for MIMO Communication Systems
## 3 Proposed DL-Based Detection Scheme
### One Hot Encoding (OH)  
The OH labeling strategy consists of encoding each index of one combination of symbols as an OH vector, where each entry provides a vector of zero entries except for the position of the index, where the bit label is encoded as \"1\". The output is the vector \(\mathbf{y}\) with dimensions \(Nc=2^{N_{t}}\), corresponding to the number of classes. Table 4 shows an example of this OH encoding per index for a MIMO system that contains \(N_{t}=2\) antennas and 4-QAM modulation. This is the highest-dimensional case for the three proposed methods. The activation function that is well suited for this strategy is softmax since the last decision will be taken evaluating the maximum value of the vector of probabilities obtained at the output of the softmax activation. This label encoding is recommended when there are no hardware and processing capacity constraints in the system.
## 5 Conclusions  
A method has been proposed for studying the properties of the Earth's surface tremor, measured by means of GPS, based on the use of oscillation statistics that describe the deviation or similarity to the properties of Gaussian white noise, considered as a standard of a stationary random signal. Based on the application of the principal component method to the estimation of the probability densities of the distribution of extreme properties of statistics in a moving time window, concepts of active and passive tremor and their singular points are introduced, and the trajectories of singular points are determined. The concept of tremor synchronization measure is introduced as the maximum eigenvalue of the correlation density matrix of distribution of extreme values of tremor statistics. The method is applied to the data of daily GPS time series on a network of stations located in California during the time interval 2009-2022. It was shown that singular points of both types of tremor form well-defined clusters. A hypothesis is proposed that the difference in the types of singular points of tremor is associated with different mechanisms of consolidation of crustal blocks. In particular, passive tremor may be due to the activation of chaotic block movements in the vicinity of three fragments of the San Andreas fault. Estimates of measures of synchronization of active and passive tremor are given. A hypothesis has been put forward that the strong synchronization of active tremor in 2010-2015 caused a jump in the global synchronization of the tremor of the Earth's surface and an explosive increase in the correlations of global seismic noise in 2010-2011.  
**Funding:** The work was carried out within the framework of the state assignment of the Institute of Physics of the Earth of the Russian Academy of Sciences (topic FMWU-2022-0018).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The open access data from the source: [[http://geodesy.unr.edu/gps_timeseries/tenv3/IGS14/](http://geodesy.unr.edu/gps_timeseries/tenv3/IGS14/)]([http://geodesy.unr.edu/gps_timeseries/tenv3/IGS14/](http://geodesy.unr.edu/gps_timeseries/tenv3/IGS14/)) (accessed on 1 May 2023) were used.  
**Conflicts of Interest:** The author declare no conflict of interest.  
Figure 7: (**a,b**) represent time sequences of values of the normalized maximum eigenvalue of the correlation matrix with a shift in the time window of 3 years for “active” (**a**) and “passive” (**b**) tremor.