esa-sceva/satcom-chunk-collection huggingface dataset:
# Covert Anti-Jamming Communication Based on Gaussian Coded Modulation
## 4 Undetectability Analysis
### Undetectability under an Unknown Noise Level
It causes a distortion between the distributions of the actual symbol and desired Gaussian symbols. In addition, [PERSON] must perform the normality test with a finite number of samples and a finite level of precision. This means that the error probability of the normality test is not zero even for the Gaussian signal. However, the distortion of the distribution can be compensated for by the limited resolvability [35] of the channel between the transmitter and [PERSON]. Since the channel is noisy, small distortions of the input distribution cannot affect the test output of [PERSON]. Moreover, despite all these limitations, it is obvious that the probability of being detected for the proposed signal is far lower than the probability of any conventional non-Gaussian signal.
## 3 Materials and Methods
### Proposed Approach
#### 3.1.5 Fuzzy Controller
The membership function for output is shown in Figure 3d. In all four membership functions given in Figure 3, the x-axis represents the crisp input or output values and they vary according to the variable being fuzzified or defuzzified. The y-axis represents the fuzzy membership value and it ranges from 0 to 1. The fuzzy rule base used in the proposed fuzzy controller is shown in Figure 4. The fuzzy rule base is defined using three fuzzy and one crisp input. It contains a total of 96 rules that were obtained through extensive experimentation on a synthetic dataset explained later. Input 1 is the task length, Input 2 is the total task length assigned to the VM, and Input 3 is the number of high-priority tasks on the VM. The rule base is divided into two parts based on the priority of the task. In the present work, the minimum value among all the fuzzy input values in an applicable rule is used to calculate the firing value  
Figure 3: (**a**) Fuzzy membership function for task length. (**b**) Fuzzy membership function for total task length assigned to a given VM. (**c**) Fuzzy membership function for number of high-priority tasks on a VM. (**d**) Fuzzy membership function for output regarding task assignment decision. of the output. Strong rules were selected using a threshold value (\(T_{r}\)), with all the rules having fuzzy output values equal to or above the chosen \(T_{r}\). From the strong rules firing values for each fuzzy output set, the minimum firing values were chosen for each set. The final step in obtaining the crisp output was defuzzification, and it was performed using the Center-of-Gravity method (CoG) using Equation (1). \[x_{CoG}=\frac{\int\mathrm{x}.\mu_{o}(x)\,\mathrm{d}x}{\int\mu_{o}(x)\,\mathrm{d }x} \tag{1}\]  
where \(\mu_{o}(x)\) is the curve enclosing the region formed by the intersection of output membership functions with the output firing values obtained from strong rules. \(x_{CoG}\) is the \(x\) coordinate of the CoG of the aforementioned enclosed area.
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
## 5 Proposed Algorithm
### Particle Representation and Initialization
#### 5.2.1 Determination Radius of Circular Area  
In multi-hop routing protocols, the CH nodes closer to the BS undertake more data forwarding tasks, which causes nodes near the BS area to die prematurely and generates undetectable hotspots. This is the so-called hotspot problem. To address the problem, an effective solution is to make the clusters closer to the BS smaller and make the clusters farther away from the BS larger. Small clusters near the BS are with fewer nodes and have short transmission distance to the BS. Therefore, this approach can compensate for the energy consumption of the nodes near the BS for forwarding data from the other CH nodes. Since in the proposed algorithm the distribution of the circular areas determines the location of CH nodes, the area of the circular areas near the BS should be smaller than the ones farther away from the BS, which can help to achieve the reasonable distribution of the above clusters to a certain extent. The radius of a circular area is calculated by Equation (9).  
\[R_{i}=\frac{dis_{i}}{dis_{\max}}\times(R_{\max}-d_{1})+d_{1},d_{1}\leq R_{i} \leq R_{\max} \tag{9}\]  
where \(R_{i}\) is the radius of the \(i\)th circular area, \(dis_{i}\) is the distance between the center of the \(i\)th circular area and the BS, \(dis_{\max}\) is the maximum distance between circular area centers and the BS, and \(R_{\max}\) is the maximum radius of the circular areas. The maximum radius \(R_{\max}\) is \(d_{0}/2\) in this paper. \(d_{1}\) is the minimum radius of circular areas, which is calculated by Equation (10).  
\[d_{1}=\sqrt{\frac{A}{\pi n}} \tag{10}\]  
The value of \(d_{1}\) is the average radius of the area covered by a node in the network, which can ensure that the node closest to the BS can form a cluster.
## 3 Description of WDM-RoF System
In the electrical section of the receiver, after the down conversion of RF from an optical signal and the amplification process by the repeater, a fork is used to duplicate the input signal and develop four output signals. A low-pass Bessel filter (LPF) is used to separate interesting individual RF signals from other RF signals, and then a quadrature demodulator is used to demodulate the signal. The LPF selection is intended to improve the system performance by controlling the nonlinear effects of crosstalk. The main descriptions of the components in the proposed network are as follows:  
1. Pseudo random number generator (PRPS): a bit sequence is generated here, based on various operating modes. The design of such bits is related to the random data characteristics. 2. NRZ coding: generates a pulse according to the PRBS sequence. 3. Laser: generates a continuous wave optical signal. 4. Optical modulator: this modulator depends on the effect of electro-optic principle that the external voltage is applied to change the refractive index and as a result the intensity modulation is occured. Figure 4: Receiver layout. Figure 3: WDM transmitter. PIN photodiode: the optical signal obtained from the fibre is filtered and converted to an electrical signal. 6. The performance of the RoF receiver can be measured by investigating the eye diagram after a simulated design has been prepared. The eye diagram analyzer uses a modulated signal with multiple traces to create an eye diagram. The produced eye diagram can be considered an oscilloscope display, where the diagram corresponds to minimal signal distortion owing to intersymbol interference in addition to the appearing noise in the system. Eye pattern measurements are completed based on the time domain, and the waveform distortion effect may appear in the regular BER screen of test equipment. The time interval and noise margin are a part of the information that can be obtained from the eye pattern display.
# Quality of Tomato Juice as Influenced by Non-Thermal Air Plasma Treatment
## 3 Results and Discussion
### Effect of CAP on Microorganisms Eradication
It can therefore be concluded that CAP treatment for 600 s is an effective method of erasing the total number of aerobic microorganisms in tomato juice, extending its shelf life to 10 days, without heat treatment. In case of erasing yeast and molds, the CAP treatment for 30, 60, and 120 s was much less effective. The number of yeasts and molds increased in these samples over the following days of storage, similar to the untreated control. In contrast, treatment with CAP for 300 and 600 s proved to be very efficient, since in these samples the number of yeasts and molds did not increase until the 10 th day of storage and remained below 0.5 log\({}_{10}\) CFU/mL Figure 3. Visually, no mold growth was observed on these samples until day 10 of storage. Figure 3: Number yeast and molds counts in control and CAP treated samples of unpasteurized tomato juice during refrigerated storage. Mean values (\(n=4\)) with SD are shown. a–d—statistically significant differences between the means (\(p<0.05\)). Figure 2: Number of mesophilic aerobic bacteria counts in control and CAP treated samples of unpasteurised tomato juice during refrigerated storage. Mean values (\(n=4\)) with SD are shown a–m—statistically significant differences between the means (\(p<0.05\)). As shown by the results of the latest studies published by other authors, the effectiveness of CAP in erasing sensitive strains of microorganisms in fruit juice is very high, even allowing a reduction by 8.2 log\({}_{10}\) of the pathogenic strain of _Enterococcus faecalis_. Such results were obtained by CAP treatments for 300 s with plasma jet and 420 s with SDBD in pineapple juice [35]. The results of studies by the other authors also indicate the high efficiency of plasma in the reduction in pathogenic strains of microorganisms in fruit juices [8; 11; 30; 36]. [PERSON] and [PERSON] [16] investigated the inactivation effect of CAP on _E. coli_, and the highest significant reductions (after 120 s) were achieved in apple juice (4.02 \(\pm\) 0.03 log CFU/ mL) followed by sour cherry (3.34 \(\pm\) 0.09 log CFU/mL), while the values in orange (1.59 \(\pm\) 0.17 log CFU/mL) and tomato juices (1.43 \(\pm\) 0.22 log CFU/mL) were lower, which could be attributed to the food matrix. Our research results are in line with the mentioned report because 120 s CAP treatment was ineffective in erasing the total number of aerobic microorganisms as well as yeast and molds, but extending this time had satisfactory results in reducing microbial content.
## 3 Results and Discussions
### Retrofitting Scenario Analysis  
The post-operam scenario was conducted with all the energy efficiency strategies mentioned in Section 2.3. Figure 5 shows the comparison between the ante-operand and post-operam scenario. In particular, Figure 5 shows the post-operam scenario with and without the PV production.  
It is possible to notice that the energy efficiency strategies allow for reductions in the thermal and electricity consumption by 36.9% and 72.3%, respectively. In terms of  
Figure 4: Comparison between the bill readings and the simulated energy consumption of (**a**) thermal and (**b**) electricity.  
Figure 5: Comparison between ante-operand and post-operam energy consumption scenario of (**a**) thermal and (**b**) electricity.  
the annual energy request per unit of heated surface, from ante-operand to post-operand, there is a reduction from 64.20 kWh/m\({}^{2}\) to 40.51 kWh/m\({}^{2}\). Regarding the electricity, the reduction is from 65.45 kWh/m\({}^{2}\) to 18.10 kWh/m\({}^{2}\).  
In particular, it is possible to analyse that the interventions on the external walls, roof insulation, sun protection, and the optimization of the plant management can have a significant annual heating reduction from 445,638 kWh to 275,082 kWh (without considering the amount needed to heat the domestic water that is the same value of 16,635 kWh for the ante- and post-operand scenarios), that corresponds to energy savings of 38.3%. In the cooling improvement scenario, there was the possibility to change the cooling machine with a higher efficiency one that brings an electricity reduction for cooling from 187,393 kWh to 104,491 kWh, that corresponds to energy savings of 44.2%.  
The use of LED lamps, instead of the fluorescent ones present in the ante-operand scenario, can reduce the electricity demand from 254,720 kWh to 101,350 kWh, that corresponds to energy savings of 60.2%.  
As shown in Figure 5b, the implemented PV plant can produce an annual electrical power of 104,685 kWh that can bring the annual electricity demand without PVs of 235,041 kWh to 130,356 kWh considering this technology.  
The GHG emissions from ante-operand to post-operand are summarized in Table 1. It is possible to notice that the GHG emissions are about 305.4 tCO\({}_{2}\) for the ante-operand and 113.9 tCO\({}_{2}\) for the post-operand. The energy efficiency strategies can reduce the GHG emissions by 62.7%.
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods
### AdamW Optimizer  
AdamW is an optimization algorithm that builds upon the Adam algorithm. Adam algorithm is a commonly used gradient descent optimization algorithm. Compared with conventional SGD and RMSProp algorithms, Adam can dynamically adjust the learning rate adaptively to improve the training efficiency and accuracy. However, the Adam algorithm has some issues, such as suboptimal weight decay effects. To address this problem, the AdamW algorithm [36] introduced a method for decoupling weight decay, separating weight decay from the learning rate, and making weight decay more accurate and effective. Specifically, the AdamW algorithm performs L2 regularization on the weights during parameter updates, effectively achieving the desired weight decay effect. The advantages of the AdamW algorithm include improving the model's robustness and generalization capability while reducing the risk of overfitting. In this paper, the decoupled weight decay AdamW algorithm was used to optimize the model, thereby enhancing recognition accuracy and stability. The core computation process of AdamW is as follows:  
Calculation of the moving average of the gradient \(m_{t}\):  
\[m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})g_{t} \tag{7}\]  
Calculation of the moving average of the squared gradient \(v_{t}\):  
\[v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2} \tag{8}\]  
Update parameter vector \(p_{t}\):  
\[p_{t}=p_{t-1}+\eta_{t}(\frac{\alpha\hat{m}_{t}}{\sqrt{\hat{v}_{t}+\epsilon}}+ \lambda p_{t-1}) \tag{9}\]  
where the learning rate \(\alpha=0.001\), exponential decay rates \(\beta_{1}=0.9\), exponential decay weight \(\beta_{2}=0.999\), infinitesimal value \(\epsilon=10^{-8}\), weight decay value \(\lambda\in\mathcal{R}\), initialize time step \(t=0\), parameter vector \(p_{t=0}\in\mathcal{R}^{n}\), first-moment vector \(m_{t=0}=0\), second-moment vector \(v_{t=0}=0\), schedule multiplier \(\eta_{t=0}\in\mathcal{R}\), bias-corrected \(\hat{m}_{t}=m_{t}/\left(1-\beta_{1}^{t}\right)\), and bias-corrected \(\hat{v}_{t}=v_{t}/\left(1-\beta_{2}^{t}\right)\).
# Symbol-Level Selective Channel Estimation in Packet-Based OFDM Systems
## 3 Related Work  
In the receiver, assuming perfect timing and frequency synchronization, the received symbol at subcarrier \(k\) in symbol \(m\) after removing the CP and performing an FFT can be expressed as follows:  
\[Y(m,k)=H_{p}(m,k)X(m,k)+Z(m,k), \tag{1}\]  
where \(H_{p}(m,k)\) is the perfect channel response (CR), and \(Z(m,k)\) is additive white Gaussian noise with zero mean and variance \(\sigma_{z}^{2}\), i.e., \(\mathcal{N}(0,\sigma_{z}^{2})\).  
To track channel variations in a time-varying channel, the DD channel estimation scheme is widely used and continuously updates the channel estimates [28]. First, the \(m\)-th frequency-domain received symbol \(Y(m,k)\) is compensated by the \((m-1)\)-th channel estimate \(H(m-1,k)\) as follows:  
\[\hat{T}(m,k)=\frac{Y(m,k)}{H(m-1,k)},m=1,\cdots,M. \tag{2}\]  
In particular, when \(m=1\), \(H(0,k)\) is defined as the average channel estimate obtained by dividing the first two received symbols \(Y_{0}(1,k)\) and \(Y_{0}(2,k)\) by the known long training symbol \(X_{0}(k)\), i.e., \(H(0,k)=\frac{Y_{0}(1,k)+Y_{0}(2,k)}{2X_{0}(k)}\).  
Thus, \(\hat{X}(m,k)\) is obtained by demapping \(\hat{T}(m,k)\) in accordance with the modulation scheme, and \(\hat{X}(m,k)\) is again employed to obtain an initial channel estimate \(\hat{H}(m,k)\).  
\[\hat{H}(m,k) =\frac{Y(m,k)}{\hat{X}(m,k)}\] \[=\frac{H_{p}(m,k)X(m,k)}{\hat{X}(m,k)}+\frac{Z(m,k)}{\hat{X}(m,k)}. \tag{3}\]  
\(\hat{H}(m,k)\) obtained from Equation (3) still includes both the demapping error and noise component. Therefore, various channel estimation schemes have been proposed to remove the error components of \(\hat{H}(m,k)\) effectively.  
In this section, we outline three channel estimation schemes of TRFI, DD-TT, and PLS, which are related to the proposed scheme in this paper.
## 3 Mathematical Model  
### Coordinate Systems  
The earth-fixed coordinate system O-XYZ as shown in Figure 2(a) and the body-fixed coordinate system o-xyz as shown in Figure 2(b)-2(d) were considered in this study. The velocity component of the body-fixed coordinate system was (\(u,v,w,p,q,r\)), and the position in body-fixed coordinate system was (X, Y, Z). The three Euler angles of the body-fixed coordinate system relative to the earth-fixed coordinate system were expressed as \(\phi\) (roll), \(\theta\) (pitch), and \(\psi\) (yaw). The parameters of the six degree-of-freedom (6-DOF) motion are presented in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Degree of Freedom** & **Velocity/Angular Velocity** & **Position/Angle** \\ \hline Surge & \(u\) & \(X\) \\ \hline Sway & \(v\) & \(Y\) \\ \hline Heave & \(w\) & Z \\ \hline Roll & \(p\) & \(\phi\) \\ \hline Pitch & \(q\) & \(\theta\) \\ \hline Yaw & \(r\) & \(\psi\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the six-degree-of-freedom (6-DOF) motion.  
In order to define the relationship between the body-fixed linear and angular velocities and the time derivative of the earth-fixed vehicle position coordinates, the transformation [28] can be related by  
\[\left[\begin{array}{c}\dot{X}\\ \dot{Y}\\ \dot{Z}\end{array}\right]=\left[\begin{array}{ccc}\cos\psi\cos\theta&\cos\psi \sin\phi\sin\theta&\sin\phi\sin\psi+\\ &-\sin\psi\cos\phi&\cos\psi\cos\phi\sin\theta\\ \sin\psi\cos\theta&\sin\psi\sin\phi\sin\theta&\sin\psi\cos\phi\sin\theta\\ &+\cos\psi\cos\phi&-\cos\psi\sin\phi\\ -\sin\theta&\sin\phi\cos\theta&\cos\phi\cos\theta\end{array}\right]\left[ \begin{array}{c}u\\ v\\ w\end{array}\right] \tag{13}\]  
\[\left[\begin{array}{c}\dot{\phi}\\ \dot{\theta}\\ \dot{\psi}\end{array}\right]=\left[\begin{array}{ccc}1&\sin\phi\tan\theta& \cos\phi\tan\theta\\ 0&\cos\phi&-\sin\phi\\ 0&\sin\phi/\cos\theta&\cos\phi/\cos\theta\end{array}\right]\left[\begin{array} []{c}p\\ q\\ r\end{array}\right]. \tag{14}\]
# Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression
## 2 Materials and Methods  
### Study Site and Data  
The study sites are located in Gwangjin-gu, in the central-western part of South Korea. The input grayscale images were acquired on 10 June 2013, and the reference color images were acquired on 2 June 2016; both are aerial images at 1 m resolution. Coordinate definition and geometric correction were performed on the images using Environment for Visualizing Images (ENVI) geospatial analytical software (version 4.7, HARFIS Geospatial Solutions, Broomfield, CO, USA). The coordinate system of each image was projected as World Geodetic System (WGS) 84 Universal Transverse Mercator Coordinate System (UTM) 52N, and 30 Ground Control Points (GCPs) were selected for image registration. The 30 GCPs returned a total root mean square error of 0.4970, satisfying values within 0.5 m. Then, based on these GCPs, image registration was performed using the \"Warp from GCPs: Image to Image\" tool in ENVI. Finally, to achieve reasonable computational time, only a portion of the image was extracted prior to conducting the experiments, which was selected to be 1200 \(\times\) 1200 pixels. A total of four sites were extracted and are shown in Figures 1-4.  
Figure 1: Experimental area of site 1: (**a**) input grayscale image acquired on 10 June 2013, (**b**) reference color image acquired on 2 June 2016.  
Figure 2: Experimental area of site 2: (**a**) input grayscale image acquired on 10 June 2013, (**b**) reference color image acquired on 2 June 2016.
## 6 Discussion  
In practical engineering, the vibration signals actually measured by sensors often contain lots of noise interference, which greatly increase the difficulty of extracting fault features by the 1 DCNN model. In this paper, the original signal is preprocessed denoised to improve the diagnosis accuracy. That is, sparse representation preprocessing and denoising of the original signal requires the manual removal of noise components in the signal, and the reconstructed signal after denoising is used as the input of 1 DCNN. However, the effect of this method depends on people's experience to a great extent. That is, there is a need for a rational choices of parameters, such as sparsity k, the size of dictionary atoms, and the length of samples. If the parameters are not selected properly, the reconstructed signal will lose some useful features after denoising, and it is difficult to get the best denoising effect. In this paper, the relevant parameters are adjusted by calculating the envelope spectrum kurtosis of the reconstructed signal. However, it is not guaranteed that denoising is accompanied by the removal of useful signal components, thus affecting the classification accuracy of faults. The final classification accuracy cannot be guaranteed to be the best classification that this method can achieve.  
Secondly, preprocessing denoising and feature extraction are two mutually independent processes. After the original signal is denoised by sparse representation, the reconstructed signal is used as the input of 1 DCNN, which needs different processing methods. Although the classification accuracy of faults is improved, the workload is also increased.  
In addition, the classification accuracy of the method used in this paper has been significantly improved compared with the classification accuracy of the original signal without preprocessing under different SNR. However, in the case of large noise interference, the fault classification accuracy is not very high. For example, when SNR\(=-4\) dB, the classification accuracy of the two sets of datasets can only reach 46.84% and 35.6%, and its classification effect is still a great challenge. The author will study these problems carefully in his future work.  
Figure 19: Comparison of different methods (test set of Worm Gear Reducer).