esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 2 UWAC Physical Layer Techniques
### Multiple Input Multiple Output  
MIMO schemes are briefly deliberated for the UWACs to overcome the limitation of bandwidth problems for the acoustic channel under water [73]. MIMO methods offer reasonable robustness and extensive spectral efficiency in contrast to frequency fading whilst having the simple structure of the equalizer [110; 111; 112; 113]. However, this limits the applicability of the channel estimation techniques of the MIMO system, which necessitates matrix inversion whose dimension has been proportional to the multipath spread and the number of transmitting elements. To mitigate that issue, the adaptive algorithm has been proposed that functions in a decision-directed way, does not require the inversion of the matrix, and that overcomes the problems of complexity and overhead [114]. Complexity lessening is required for important impulse response coefficient selection that concludes in the matrix inversion of the reduced size [114; 115; 116]. The following key points have been applied in the MIMO-OFDM design:  
1. At the receiver end, the Doppler shift compensation, and at the transmitter end, the null subcarriers are implanted.
2. To estimate the channel in the MIMO system, pilot tones are utilized.
3. An iterative structure of the receiver has been implemented that couples MIMO detection with channel decoding [73].  
Many features are desired for OFDM that include mature technologies and implementation of low computational complexity, which retain it as the main technology in the UWACs for a single user. However, the MIMO-FBMC development may be still limited and nontrivial while, for MIMO channels, OFDM is eased to adopt. Only FMT can deal with the same level of flexibility in MIMO channels as OFDM systems with a low bandwidth-efficient member of the FBMC system class. Consequently, the poor frequency spectrum of the subcarrier signals for the OFDM system is one of the prime problems. It can limit the present and future applicability of the OFDM system for the expansion of broadband UWAC. On the other hand, the FBMC [117] is a sophisticated method for underwater multicarrier communications.
# Image-Based Malware Detection Using \(\alpha\)-Cuts and Binary Visualisation
## 3 Materials and Methods
### Creation of Image Datasets
#### 3.3.3 Colour Tone Adjustment through Fuzzy Set Theory  
Fuzzy SetsA _fuzzy set_\(\tilde{A}\) is a generalised type of a classical set where given a _universal set_\(X\), the elements of \(X\) are assigned a value from the unit interval \([0,1]\) denoting their degree of membership to \(\tilde{A}\). The characteristic function of each fuzzy set that assigns values within this range is called a _membership function_\(\mu_{\tilde{A}}\):  
\[\mu_{\tilde{A}}:X\rightarrow[0,1].\]  
with larger values expressing a stronger membership to \(\tilde{A}\).  
One of the main advantages of fuzzy sets is the ability to express vague ideas in simple linguistic terms [67]. In our work, we wished to use this property to express the concept of colour tone of a pixel as a byproduct of its immediate environment. We continue from our previous work in [17], where we defined a Fuzzy Inference System (FIS) to decide on the lightness (or darkness) of each pixel, depending on its similarity with its neighbouring pixels to its left and to its right side. The FIS comprised two inputs and one output. The inputs denote the colour similarity to the left and the colour similarity to the right pixels, respectively, while the output denotes the adjustment in colour tone for the pixel in question. According to the FIS, the colour of the pixel we are dealing with turns darker if neighbouring pixels from both sides share the same colour (according to a certain degree), retains a neutral tone if only _some_ of the neighbouring pixels share the same colour (according to a certain degree), and turns whiter if neighbouring pixels are dissimilar to it altogether. In Table 2, we provide a summary of the FIS, while in Table 3 we provide the fuzzy rules. For more details on the construction of the FIS, the reader is advised to read our work in [17].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Character Type** & **ASCII Decimal Value** & **Colour Class** & **RGB Values** \\ \hline control & 1–31, 127 & green & (0, 1, 0) \\ extended & 128–254 & red & (1, 0, 0) \\ the NULL character & 0 & grey & (0.5, 0.5, 0.5) \\ the non-breaking space & 255 & yellow & (1, 1, 0) \\ numbers (part of printable) & 48–57 & orange & (1, 0.5, 0) \\ letters (part of printable) & 65–90, 97–122 & magenta & (1, 0, 1) \\ space (part of printable) & 32 & blue & (0, 0, 1) \\ remaining printable & 33–47, 58–64, 91–96, 123–126 & cyan & (0, 1, 1) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The colours and their corresponding RGB values for the 7 categories of the ASCII characters.
# Prioritization Based Task Offloading in UAV-Assisted Edge Networks +
## 7 Performance Evaluation
### Experimental Results
#### 7.3.2 Impact of Average Vehicle Speed (\(\overline{V}\))  
Vehicle mobility inherently has a significant effect on our framework since it changes the network topology. During handover, our offloading orchestrator reconfigures all user-related flows, but Wi-Fi reconnection and flow installation cause observable disruptions in data transfer. In the baseline case from the previous scenario, vehicles were moving at 20 km/h so, for comparison, we had chosen human walking speed as 5 km/h for low mobility and 40 km/h for the double speed comparison. In order to deal with a moderately loaded network, we set the inter-arrival task interval (\(1/\lambda\)) to 8 in this scenario.  
* Impact of Average Vehicle Speed on Penalty Results  
From Figure 10, we can see that mobility had a negative impact on performance. There are several main reasons according to the experiment data analysis. The first is that there has been more handover inference to data transfers, and the second is that offloading decision validities has lasted less; for example, task-assigned processors with good network connectivity have stayed in this mode for a much shorter duration. Only-Cloud baseline performance is the least affected by the vehicle speed changes due to its single-side stationarity. ADP seems to be the most efficient compared to the other heuristics and the Only-Cloud option.  
* Impact of Average Vehicle Speed on Task Processing Characteristics  
In Table 8, we can see the service-related details. The task failure ratio for the Case 2.3 is high in general. Remembering that the task inter-arrival time is constant throughout the scenario, an increase in \(\overline{t}_{pool}\) and cloud ratio indicates that the offloading orchestrator has issues serving incoming requests.  
* Impact of Average Vehicle Speed on Temporal Results for Task Completion  
The delay distributions are illustrated in Figure 11. As the vehicles become faster, handover overhead occurs due to the disconnections during network changes, increasing the transmission times. For all methods, the existing increase in failure ratio indicates that the offloading orchestrator has issues serving incoming requests as the speed increases.  
Figure 10: Impact of average vehicle speed on the objective function \(\mathbb{D}\).
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 5 Security Analysis
### Non-Repudiation  
At the beginning of each phase in the protocol, the entities send the cloud their temporary identities (\(\mathrm{TID_{H}}\), \(\mathrm{TID_{P}}\), \(\mathrm{TID_{D}}\)) and a MAC calculated with their real identities (\(\mathrm{ID_{H}}\), \(\mathrm{ID_{P}}\), \(\mathrm{ID_{D}}\)). The cloud also sends to the entities a MAC containing their real identities. Since real identities are known only by the cloud and each respective entity, a valid MAC can be generated only by them. The session keys established among the cloud and the entities also depend on their real identity, therefore, neither the cloud nor the entities can deny the message they originated.
## 2 Unified Modeling of LEO Satellite Constellation
### Model Implementation Based on Modelica  
The orbit, satellite and system control center are the three most important parts of an LEO satellite constellation system, so this section mainly introduces the principles for model implementation.
## 3 Structural Design and Analysis of QZS Vibration Isolation Systems
### Negative Stiffness Element Design
The critical buckling load for the LS with both ends fixed is given by the following:  
\[P_{\sigma}=\frac{4\pi^{2}E\frac{bb^{3}}{12}}{{l_{0}}^{2}} \tag{6}\]  
Here, \(P_{\sigma}\) represents the aforementioned compressive load \(F\). Given the critical load \(P_{\sigma}\), the elastic modulus \(E\) of the material, and two of the dimensions among width \(b\), length \(l_{0}\), and thickness \(h\), the unknown dimensional parameters can be solved using this formula. It should be noted that, to ensure it is greater than \(P_{\sigma}\), when thickness or width is unknown, the design dimensions should be slightly smaller than the solved result. Conversely, when the length is unknown, the design dimensions should be slightly larger than the solved  
Figure 8: Compression buckling: (**a**) no-load state; (**b**) stable state, axial load F\({}_{1}\) less than critical load, LS returns to initial position after removing external load F\({}_{2}\); (**c**) unstable state, axial load F\({}_{1}\) greater than critical load, LS remains in buckled state after removing external load F\({}_{2}\); (**d**) boundary conditions and load forms for LS buckled. result. It should be noted that leaf spring thicknesses usually depend on the metal material manufacturer and need to be selected at the design stage based on their factory thickness. Parameter optimization of leaf springs can be performed by using Equation (5) as a baseline and substituting the known parameters as well as the desired value of negative stiffness. Since Equation (5) contains only four design parameters, even if there are several unknown parameters, the range of values can be determined after several attempts of calculation, and, finally, the parameters can be corrected and calibrated by Equation (30), which is a high-precision formula derived in the subsequent chapters.
## 2 Rectifier Analysis
### Efficiency  
The RF-DC power conversion efficiency (PCE) is an important figure of merit to characterize and estimate a rectifier's performance (Figure 2). It is defined as the ratio between the output DC power \((P_{DC})\) over the rectifier input RF power \((P_{t})\)[1]  
\[\eta(\%)=\frac{P_{DC}}{P_{t}}\times 100=\frac{(V_{Out})^{2}}{R_{L}}\times\frac{1} {P_{t}}\times 100. \tag{2}\]  
\(V_{out}\) is the output DC voltage across the load resistance \(R_{L}\).
## 2 Methods
### Kinematics of Locomotion Gaits  
The articulation modules in the snake-like robot are connected in serial, as shown in Figure 2. The world coordinate system \(xyz\) is fixed in extern with origin \(O\). Absolute position and attitude are denoted by the head or tail point coordinate (\(x\), \(y\), \(z\)) in \(xyz\) and the inner body shape, respectively. To describe shape, a series of moving coordinates \(x_{1}y_{1}z_{1}\), \(x_{1}y_{1}z_{1}\),..., \(x_{n}y_{n}z_{n}\) is designated at every module from tail to head. Variables \(O_{n+1}\) and \(O_{0}\) represent the head and the tail endpoints, respectively. The \(i\)-th moving coordinate \(x_{i}y_{i}z_{i}\) in the \(i\)-th module is constructed similar to Figure 2. Variable \(y_{i}\) is the pitch joint axis, and \(z_{i}\) is the roll joint axis. The origin is designated as the crossing point of \(y_{i}\) and \(z_{i}\). The positive direction of \(z_{i}\) is determined on the basis of the contraction of the linear joint. Variable \(x_{i}\) is determined by the left-hand law of the Cartesian coordinate system. If the robot is shaped into a line, then \(x_{i}\) and \(y_{i}\) (\(i\) = 1, 2,..., \(n\)) are parallel to each other and all \(z_{i}\) (\(i\) = 1, 2,..., \(n\)) are collinear. Variables \(l_{i}\), \(\gamma_{i}\), and \(\theta_{i}\) denote the \(i\)-th translational displacement, roll angle, and pitch angle, respectively. The transformation of two adjacent module coordinates can be calculated as follows:  
\[T_{i-1}^{i}=Ry\cdot Rz\cdot P_{z}=\left[\begin{array}{cccc}C_{\theta_{i}}&0& S_{\theta_{i}}&0\\ 0&1&0&0\\ -S_{\theta_{i}}&0&C_{\theta_{i}}&0\\ 0&0&0&1\end{array}\right]\cdot\left[\begin{array}{cccc}C_{\gamma_{i}}&S_{ \gamma_{i}}&0&0\\ -S_{\gamma_{i}}&C_{\gamma_{i}}&0&0\\ 0&0&1&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]\cdot\left[\begin{array}{cccc}1&0&0&0\\ 0&1&0&0\\ 0&0&1&-l_{i}\\ 0&0&0&1\end{array}\right]=\left[\begin{array}{cccc}C_{\theta_{i}}\cdot C_{ \gamma_{i}}&C_{\theta_{i}}\cdot S_{\gamma_{i}}&\delta_{\theta_{i}}&-l_{i}\cdot S _{\theta_{i}}\\ -S_{\gamma_{i}}&C_{\gamma_{i}}&0&0\\ -S_{\theta_{i}}\cdot C_{\gamma_{i}}&-S_{\theta_{i}}\cdot S_{\gamma_{i}}&C_{ \theta_{i}}&-l_{i}\cdot C_{\theta_{i}}\\ 0&0&0&1\end{array}\right] \tag{1}\]  
Equation (1) can be used to compute the coordinates of every joint point. Variables \(l_{i}\), \(\gamma_{i}\), and \(\theta_{i}\) are used as inputs for the robot shape, which is related to gait. Kinematic analysis was performed to identify the best control input law and to establish the snake gait. The succeeding section shows the creation of the mathematical model of each gait on the basis of the aforementioned coordinate system and the mechanism.
# The Influence of Laser Linewidth on the Brillouin Shift Frequency Accuracy of BOTDR
## 4 Experiment Results and Discussions
### BFS Distribution Measurement  
To verify whether the built FS-BOTDR is capable of measuring the BFS change normally, the temperature measurements were firstly performed utilizing the five lasers mentioned above as the seed source, respectively. Figure 9 gives the configuration of the tested fiber for temperature measurement. The fiber 1 with the length of 9865 m and the fiber 3 with the length of 305 m were placed at room temperature of \(\sim\)25 \({}^{\circ}\)C. The fiber 2 with the length of 30m was placed in a thermostat, the temperature of which was adjusted to change the BFS of fiber 2. As a contrast, the BFS of fiber 1 and fiber 3 were kept nearly constant in the room temperature.  
During the experiments, the width of the probe pulse light was set to 42 ns and the peak power was set to 23.09 dBm. The thermostat temperature was sequentially adjusted to 30 \({}^{\circ}\)C, 40 \({}^{\circ}\)C, 50 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 70 \({}^{\circ}\)C, respectively. Then, the BFS distribution measured by five lasers at different temperatures are shown in Figure 9(a)-e.  
Figure 10: _Cont._  
Figure 9: Configuration of the tested fiber for temperature measurement.  
From Figure 10a-e, it can be clearly seen that all obtained BFS for fiber 2, which was placed in the thermostat, increased linearly as the temperature increased from 30 \({}^{\circ}\)C to 70 \({}^{\circ}\)C. As a contrast, the BFS for fiber 1 and fiber 3, which stayed in the room temperature of 25 \({}^{\circ}\)C, remained almost constant. Besides, the measured spatial resolution was 4.2 m, which conformed to the pulse width of 42 ns. Figure 10f plots the measured results of BFS versus temperature in detail, and the linear fitting was performed based on the measured data. It shows that the slope coefficient between the temperature and the BFS was 1.14 MHz/\({}^{\circ}\)C, neatly coincided with that we reported previously [32]. The adjusted R-square was 0.99978, which indicates a strong linear relationship.  
From the above experimental results, it is proven that the built FS-BOTDR setup was capable of measuring the BFS change normally, although utilizing five linewidth-different lasers as the seed light source, respectively. In the following section, further experiments were taken based on this setup to evaluate the influence of the laser linewidth on the BGS spectrum and the BFS accuracy in detail.
## 2 GMDH-Type Neural Network  
The main problem in designing NNs is the selection of the optimal network structure in such a way that it gives the best results. The research results, which are presented in [18] have shown good prognostic properties of MLP and RBF NNs for predicting the deviations for UTC(PL). However, an important problem in this case has been the proper selection of the NN structure, which has affected the final accuracy of deviation prediction enabling correction of the atomic clock gait. These activities have been very time-consuming (for MLP type NN it takes about 50 h, and for RBF type NN about 12 h) and do not guarantee the optimal selection of the NN structure.  
The importance in solving a problem using an NN is to train it, i.e., select parameters (weights) of neurons, so that the output values from the network match those of the training pattern. Therefore, it is necessary to search for new solutions in the field of modeling algorithms based on the processing of empirical data [19]. Such a solution may be the GMDH (Group Method of Data Handling) method belonging to the class of evolutionary algorithms [9]. This method is used in many fields related mainly to data acquisition, prediction, system modeling, and optimization. This method was proposed in 1968 by the Combined Control Systems group from the Institute of Cybernetics in Kiev, headed by Prof. [PERSON] [9]. GMDH algorithms automatically find correlations in data. The GMDH idea can be successfully applied in the design of NNs.  
The application of the GMDH method at the stage of designing the NN allows us to overcome the main problem mentioned in the first paragraph of this chapter. The structure of the GMDH network is created automatically, only on the basis of prepared training and testing data sets. That is why skillful preparation of these data sets is so important. During the training process, the network develops and evolves as long as it leads to the improvement of its effectiveness [9,20]. Before the next layer of neurons is added to the current network structure, the components of the new layer are subjected to selection for precision of processing. Neurons that do not meet the criterion for assessing the condition imposed, i.e., the processing error associated with these neurons is too large, are eliminated from the structure of the network.  
The advantages of the GMDH-type NN presented above have determined its application in the prediction of deviations for UTC(PL). Predicting the deviations for the Polish Timescale UTC(PL) by means of GMDH-type NN has been carried out on the basis of the method of time series analysis by means of the commercial GMDH Shell tool. Figure 1 presents the GMDH Shell application solver view, in which the values of the NN training parameters are selected. A detailed description of the selection of these parameters is presented in [21]. The duration of the entire process of data preparation, training the GMDH-type NN, and receiving the prediction result is approximately 50 min.
# Wind Turbine Gearbox Fault Diagnosis Based on Improved EEMD and Hilbert Square Demodulation
###### Abstract  
The rapid expansion of wind farms has accelerated research into improving the reliability of wind turbines to reduce operational and maintenance costs. A critical component in wind turbine drive-trains is the gearbox, which is prone to different types of failures due to long-term operation under tough environments, variable speeds and alternating loads. To detect gearbox fault early, a method is proposed for an effective fault diagnosis by using improved ensemble empirical mode decomposition (EEMD) and Hilbert square demodulation (HSD). The method was verified numerically by implementing the scheme on the vibration signals measured from bearing and gear test rigs. In the implementation process, the following steps were identified as being important: (1) in order to increase the accuracy of EEMD, a criterion of selecting the proper resampling frequency for raw vibration signals was developed; (2) to select the fault related intrinsic mode function (IMF) that had the biggest kurtosis index value, the resampled signal was decomposed into a series of IMFs; (3) the selected IMF was demodulated by means of HSD, and fault feature information could finally be obtained. The experimental results demonstrate the merit of the proposed method in gearbox fault diagnosis.  
wind turbine gearbox; fault diagnosis; EEMD; Hilbert square demodulation +
Footnote †: journal: Applied Sciences
## 2 Sites, Instruments, and Data  
As shown in Figure 1a, the ground-based experiment sites are Xilingole grassland, Inner Mongolia (43\({}^{\circ}\)54\({}^{\prime}\)N, 115\({}^{\circ}\)58\({}^{\prime}\)E), University of Science and Technology of China, Hefei (31\({}^{\circ}\)50\({}^{\prime}\)N, 117\({}^{\circ}\)15\({}^{\prime}\)E), and Yongxing island, Sansha (16\({}^{\circ}\)50\({}^{\prime}\)N, 112\({}^{\circ}\)20\({}^{\prime}\)E). Inner Mongolia and Hefei are inland sites, while Sansha is a marine site. Moreover, the ship-borne experiment site is in the sea (16\({}^{\circ}\)25\({}^{\prime}\)N, 110\({}^{\circ}\)16\({}^{\prime}\)E). Figure 1b shows the picture of ship-borne CDWL.
# Fluctuation-Enhanced Sensing (FES): A Promising Sensing Technique
## 6 Reducing Errors and Enhancing the Sensory Information Channel Capacity
### Photonic Excitation
Some experimental data suggest that extended exposure to UV irradiation of 280 nm LEDs damaged graphene in a channel of the FET sensor and altered the device characteristics [90]. We suppose that a too-short wavelength, i.e., too energetic, UV light can deteriorate the graphene structure. We have not observed a similar effect of deterioration induced by UV light for thick graphene flakes/TiO\({}_{2}\) gas sensing layers irradiated by UV light of longer wavelengths (362 or 394 nm). However, this issue requires further and more detailed studies. The abundance of commercial UV LEDs emitting light of various wavelengths should enable future thorough investigation. The presented exemplary experimental data of the FES method modulated by UV irradiation suggest that there is a strong potential of improving selectivity and sensitivity of gas sensing by this method, mainly when two-dimensional materials are applied. We trust, therefore, that important progresses in the FES method will be obtained by using two-dimensional materials, either in back-gated FET sensors or in low-cost gas sensing layers.
## 4 Experimental Setup and Results Analysis
### Experimental Method  
#### 4.3.1 Data Preprocessing  
For each track dataset, multiple spectrograms with a duration of 8 s were generated. The following preprocessing steps were applied to each spectrogram: grayscale conversion, edge feature enhancement, and binarization. Taking a spectrogram (denoted Figure 9a) with fewer vertical lines as an example, the results of each preprocessing step are shown in Figure 9b-d.