esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Comparison of Computation Details of Conventional Method  
Since time discontinuity can exist in electric field computation, time must be continuous during temperature computation. Therefore, the conventional FEM is not able to compute the electro-thermal coupling computation. The transformation optics method is limited by the derivation of the formula for spiral motion upward. Therefore, in this section, the comparison between the proposed method and the implicit function and level set method is focused on. In the proposed method, the rotating motion is changed by controlling the electromagnetic field in the transform optical region, which means that there's no need to consider the parameter changes of the sample due to rotating. In addition, based on the sample being a regular cube, the lifting motion region can be divided into a regular cube, and the lifting motion region can be divided using the sweeping shape elements, as shown in Figure 15a. Meanwhile, the implicit function and level set method needs to change the parameters of the entire rotating and lifting motion region; if the entire region is mesh-segmented using the sweeping shape elements, the cylindrical motion channel boundaries will be mesh-constructed incorrectly. Therefore, only the motion region can be meshed in free tetrahedral form, as shown in Figure 15b. This method brings a problem that the regular cube samples will have parameter motion due to the rotating motion, and furthermore, there is a non-smooth of the sample boundaries during the electric field computation, as shown in Figure 16, which needs to be improved by increasing the number of elements.  
Figure 15: Two different algorithms for element dissection. (**a**) Proposed method. (**b**) Implicit function and level set method only.  
In this paper, a computer model DELL XPS8950 is used, which has an Intel Core i9-12900K Processor (3.9 GHz, 16 Cores), 64 GB of DDR5 memory running at 4400 MHz, a 1 TB PCIe SSD for storage, and an 8 GB NVIDIA GeForce RTX 3060 Ti graphics card. The operating system used was Windows 11 (21H2). The Root Mean Square Error (_RMSE_) is commonly used to compare the errors between methods; we use the _RMSE_ of \(S_{11}\) to compare the accuracy between the proposed method and the _RMSE_ is defined as [23]:  
\[RMSE=\sqrt{\frac{\sum\limits_{a=1}^{m}\big{|}S_{11_{discrete}}-S_{11_{method}} \big{|}^{2}}{m}} \tag{24}\]  
where \(m\) is the total number of \(S_{11}\) at different times during the movement in a circle. The smaller _RMSE_ implies a smaller computation error. The comparison of computational details of the proposed method with the implicit function and level set method under this computer as shown in Table 2. It can be seen that when both have the same number of elements, the computational efficiency of the proposed method is slightly lower, but the _RMSE_ of \(S_{11}\) obtained with the discrete position method is lower. Moreover, when the _RMSE_ of the two methods is close, the element number of the proposed method is decreased by 1,328,507, and the computation time is decreased by 62.8% compared with the implicit function and level set method.
## 6 Numerical Examples  
In this section, four numerical examples are presented, allowing for a comparison in terms of the modeling capabilities of the investigated methods. The first two examples consider a square plate, first under tension, then under shear loading, with both setups having been studied extensively in existing literature. Although analytical solutions for these two setups do not exists, the geometry can be modelled by one SBFEM subdomain and therefore a high-fidelity reference solution can be constructed for the peak load and displacements following the first crack increment. For the last two examples, the notched plate with hole and L-shaped panel, respectively, there exist experimentally obtained crack paths to compare against. Furthermore, the test setups closely mimic crack propagation scenarios under real world conditions. For the former numerical example, modelling the complete crack path by discrete crack methods is particularly challenging, since they do not provide the capability to nucleate cracks. The later numerical example presents a similar issue, however, modelling by discrete crack methods is achieved by placing the crack tip at the re-entrant corner, effectively circumventing the nucleation issue manually. To this end, we first outline the implementation details adopted for each numerical method, then proceed to the numerical examples.
# Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model
## 4 Numerical Simulations
### Algorithm Test with Stronger Couplings  
This section presents the test results of the QV controller under stronger dynamic couplings than before. Shifts in the center of mass have been increased to \(x_{g}=-0.1\) m and \(z_{g}=0.2\) m to show changes in the signals obtained from the system. Only the tracking of the more difficult trajectory for this task was investigated.  
_Complex trajectory_. For the correct operation of the control scheme, the same set of parameters was sufficient, i.e., (27). From a comparison of Figures 3 and 7, little change can be observed in the signals investigated. The similarities are in Figures 3a-e,g and 7a-e,g, respectively. The \(\tau_{q}\) signal has the opposite sign than before, although its values are comparable in both cases (Figures 3f and 7f).  
Figure 6: Comparative CL controller results and complex trajectory: (**a**) trajectories: \(p_{d}\)—desired, \(p\)—actual; (**b**) linear errors of position; (**c**) angular errors of position; (**d**) linear velocities; (**e**) angular velocities; (**f**) force and torque applied.  
It should be noted that, despite the change in the sign of \(x_{g}\) and the increase in \(z_{g}\), the values of the control parameters may have remained the same, which may indicate their robustness to some changes in vehicle dynamics. The velocity deformations in Figures 3h and 7h clearly indicate that the coupling effects have grown (as indicated by the value of the signals at the start of the movement). For complex trajectories and strong couplings (until a change in the direction of motion), the settling time for linear position errors is about 60 s, and for angular position errors, it is about 80 s. Thus, these times are comparable for complex trajectories and weak couplings.
# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 1 Introduction
* By employing a mask decoder and complex decoder, we effectively overcome the phase recovery challenges in conventional complex-domain methods, thereby achieving enhanced speech of higher quality. * The experiment results have demonstrated that DeCGAN has better performance than other algorithms in terms of processing civil aviation control speech. The paper is organized as follows. Section 2 provides the details of our DeCGAN model. In Section 3, we demonstrate the effectiveness of our method through a series of experiments. Then, Section 4 utilizes several ablation studies to further analyze the framework of DeCGAN. We conclude our paper and discuss future research directions in Section 5.
## 8 Conclusions  
This paper introduces a CPSO optimization algorithm for the synthesis of virtual antenna arrays selected from randomly deployed sensors in the realm of CB for the purpose of optimizing the mainlobe, suppressed sidelobes, and controlled nulls in certain directions. The proposed CPSO algorithm employs a node selection technique to optimize the beampattern of the CB of sensors. Results are compared with corresponding characteristics in the case of uniform and non-uniform sensor distribution geometries taking into consideration the network connectivity. The proposed CPSO can select the active CB sensors and dynamically controlling the beampattern in order to enhance the desired signal while minimizing the sidelobes.  
More control of the beampattern is achieved using the proposed CPSO by optimizing not only the positions of sensors deployed, but also the amplitude and phase of excitation applied to each collaborative sensor in the array and exploring other array geometries. The overall conclusion is that the CB using the proposed CPSO provides a better performance compared against conventional beamforming, convex optimization, GA, and particle swarm optimization when the investigation of network connectivity is affected by the number of sensors deployed in the area of interest.  
Figure 10: Various Algorithms. (**a**) Mesh Sensors 8, (**b**) Mesh Sensors 16, (**c**) Mesh Sensors 36.  
**Author Contributions:** This work is considered as a part of the high-quality post-doctoral graduate program provided by H.A.-R. who supports M.Z.H. with the software and the knowledge to design, build, operate, maintain, and monitor complex sensor antenna system analyzer. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** M.Z.H. hereby expresses gratitude to H.A.-R. and M.Z.H. would like to thank the University of Mosul who contributed in one way to this work, with particular reference to all academic staff at the College of Computer Science and Mathematics.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Vibration in the Tunnel Arch
### Dominant Frequency Analysis of Vibration  
Fifty groups of vertical dominant frequencies were selected for statistical analysis to better understand the characteristics of tunnel blasting vibration. According to the frequency prediction relationship deduced by the dimensional theory in [18], the regression statistics of the vibration frequency of the tunnel blasting are performed, which is shown in Figure 4. The correlation coefficients of the dominant frequency regression curves at each position are all near 0.6. There are differences in the dominant frequency at different positions of the tunnel arch, and the size relationship is vault > sidewall > arch waist. In addition, in the process of tunnel blasting, millisecond delay detonator blasting vibration signal energy was primarily concentrated within 200 Hz. Based on the distribution law of the vibration velocity of the arch, the vibration velocity of each position is not much different as the distance between the measuring point and the blasting centre increases. When the vibration velocity is similar, the dominant frequency of the arch waist position is the lowest. Therefore, the risk of damage at the arch waist position is highest if the damage occurs farther away from the tunnel face.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Measuring Point Location**} & \multicolumn{3}{c}{\(K\)} & \multicolumn{3}{c}{\(\alpha\)} \\ \cline{2-7}  & \multirow{2}{*}{**Vertical**} & **Horizontal** & **Horizontal** & **Vertical** & **Horizontal** & **Horizontal** \\  & & **Tangential** & **Radial** & **Tangential** & **Radial** \\ \hline Vault & 296.2 & 126.7 & 204.3 & 1.95 & 1.91 & 1.92 \\ Arch waist & 235.1 & 123.2 & 194.2 & 1.86 & 1.83 & 1.85 \\ Sidewall & 183.7 & 96.4 & 108.3 & 1.71 & 1.69 & 1.70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of \(K\) and \(\alpha\) in three directions.  
Figure 4: Statistical law of vertical dominant frequency of the tunnel at arch structure. (**a**) At vault; (**b**) At arch waist; (**c**) At sidewall.
# Multiple Observations for Secret-Key Binding with SRAM PUFs
## 2 Notation and SRAM-PUF Statistical Model
### Notation and Definitions  
We use uppercase symbols to denote random variables and lowercase symbols to denote their realizations. We consider \(t\) enrollment observations of \(n\) SRAM cells, corresponding to \(t\) binary observation vectors of length \(n\), i.e., \(\left(x_{1}^{n},x_{2}^{n},\ldots,x_{n}^{n}\right)\). The \(i\)th observation of the \(j\)th SRAM cell is represented by \(x_{i,j}\), and assumes a value in \(\{0,1\}\). We often analyze the behavior of a single SRAM cell, in which case we omit the cell index \(j\) and we have \((x_{1},x_{2},\ldots,x_{t})\), corresponding to \(t\) observations of one SRAM cell.  
The reconstruction observation vector is represented as \(y^{n}\), with \(y_{j}\) the reconstruction observation of the \(j\)th SRAM cell. We use a distinct symbol for the reconstruction observation to emphasize its different functionality in the helper data scheme. Note, however,that the reconstruction observations have the same statistical behavior as the enrollment observations, and they may as well have been represented as \(x_{t+1}^{n}\), the \((t+1)\)th observation of the SRAM cells.  
Calligraphic letters are used for finite sets and \(|\mathcal{S}|\) denotes the cardinality of the set \(\mathcal{S}\). Finally, (conditional) entropy and mutual information are defined as in [19; 20], with the base of the log equal to 2 and the units are bits. We define the binary entropy function as  
\[h_{2}(p)\stackrel{{\Delta}}{{=}}-p\log_{2}(p)-(1-p)\log_{2}(1-p). \tag{1}\]
# Retinal OCT Images: Graph-Based Layer Segmentation and Clinical Validation +
## 2 Materials and Methods
### Efficacy of the Algorithm: Comparative Analysis  
The layer thickness values obtained from our segmentation algorithm were compared to normalized layer thickness values reported by other studies in the literature [26]. Normalization ensured a fair comparative analysis between the corresponding layers' thickness values from both studies. Statistical differences in layer thicknesses were assessed to determine which denoising technique yielded better accuracy for the graph-based segmentation algorithm. Furthermore, to assess the efficacy of each denoising technique, the layer thickness values obtained by our segmentation algorithm after Gaussian filtering and after wavelet-based denoising were each compared to the normalized layer thickness values reported in literature. This was conducted to determine which denoising approach worked best with our graph-based segmentation approach in terms of accuracy and time consumption.
# Dynamic Layered Dual-Cluster Heads Routing Algorithm Based on Krill Herd Optimization in UWSNs
###### Abstract  
Aimed at the limited energy of nodes in underwater wireless sensor networks (UWSNs) and the heavy load of cluster heads in clustering routing algorithms, this paper proposes a dynamic layered dual-cluster routing algorithm based on Krill Herd optimization in UWSNs. Cluster size is first decided by the distance between the cluster head nodes and sink node, and a dynamic layered mechanism is established to avoid the repeated selection of the same cluster head nodes. Using Krill Herd optimization algorithm selects the optimal and second optimal cluster heads, and its Lagrange model directs nodes to a high likelihood area. It ultimately realizes the functions of data collection and data transition. The simulation results show that the proposed algorithm can effectively decrease cluster energy consumption, balance the network energy consumption, and prolong the network lifetime.  
UWSNs; routing algorithm; Krill Herd optimization; dynamic layered Article
# A Review of Degradation and Life Prediction of Polyethylene
## 2 Aging Degradation of Polyethylene
### Types of Degradation
#### 2.2.2 Non-Biodegradable
The covalent bonds of polyethylene are subject to oxidative degradation, which is dependent on the chain structure of polyethylene and works in conjunction with photodegradation to form free radicals. Peroxyl radicals from oxidative  
Figure 1: Polyethylene breakdown products are depicted schematically [56]. Figure 2: Two types of Norrish reaction [57]. degradation can also act on polyethylene and cause cross-linking or chain breakage, just like the byproducts of the Norrish reaction stated above. Another process that might lead to the chemical breakdown of polymers is hydrolysis reaction [60; 61; 62]. It is significant to note that because polyethylene molecules are entirely composed of alkyl groups and lack any radical energy groups that could interact with water molecules, they cannot be hydrolyzed. As the name suggests, catalytic degradation refers to the use of catalysts to break down polyethylene. Typically, catalytic degradation is employed in scientific research or to degrade polyethylene materials. The use of an appropriate catalyst and optimal processing conditions might result in the development of the intended, more precise product, and, in some situations, prevent the formation of inferior products, giving catalytic degradation some advantages over the other degrading methods previously discussed [63]. The ability to shorten experiment durations and lower reaction temperatures during studies is a more significant benefit of catalytic degradation. Polyethylene experiences mechanical degradation most frequently as a result of the influence of various stresses on the material. These forces can occur for a variety of reasons. For example, buried polyethylene pipes may experience operational issues during installation, and wild animals may unintentionally harm them as a result of the pressure that the soil and carried material exert on them [45]. This also applies to polyethylene products used outside, such as mulch film and protective jackets used on some cables, which may experience multiple mechanical degradations under unforeseen outdoor conditions [64; 65; 66]. In general, damage to polyethylene materials caused by macroscale factors, including soil or water pressure, may not be immediately noticeable but may start to have an impact at the microscopic molecule level. Even though mechanical causes are not the primary cause of degradation, once a material has been mechanically traumatized, it may be more susceptible to the effects of biodegradation [59]. Under field circumstances, mechanical forces and other abiotic parameters (such as humidity, radiation, and contaminating substances) interact with polyethylene material.
## 5 Efficiency Analysis
### Efficiency of the Cryptographic Primitives
#### 5.1.3 Billinear Pairing  
Our scheme requires a pairing that can be efficiently computed. During the secure communication protocol we compute the image of a hash function on group elements of \(\mathbb{G}_{1}\). Thus, group elements of \(\mathbb{G}_{1}\) are ideally required to have short representations. According to [64], type 3 pairings offer short representation. We implement a type 3 pairing of 256 order in 10.376 ms using the bplib python library. Again the average time of 1000 executions on random elements is used.
## 3 Data Processing Methods
### Communication Echo Signals Achievement with an EPC  
This study employs two CBSs, with one serving as the transmitting antenna that emits signals through a single element, while the other functions as the receiving antenna, utilizing a ULA composed of eight elements. Furthermore, the configurations of both CBSs satisfy the conditions for the application of the EPC principle. Consequently, the echoes can be regarded as having been obtained from observations of the target area conducted by a ULA positioned at eight EPCs in a self-transmitting and self-receiving operational mode (details of the EPC phase compensation have been described in Section 2). In the experiment, observations of the two scenarios before and after deformation were conducted \(N\) times using a CBS, resulting in the acquisition of \(N\) echo data sets from the target area for subsequent processing.  
Figure 3: Flowchart of ground deformation detection based on a Communication Base Station.  
After EPC processing and phase compensation, the communication echo signal is still not ready for imaging processing, thus necessitating the calculation of its Power Delay Profile (PDP). The PDP refers to the distribution of received signal power across various signal delays, which can be obtained by calculating the time-domain sliding correlation between the received signal and the local signal [52]. However, the computational complexity of time-domain correlation operations is significantly high. Therefore, it is generally performed by transforming the signals into the frequency domain for multiplication (i.e., frequency-domain matched filtering), followed by an inverse transformation to the time domain to obtain the PDP [53; 54]. The calculation formula is given by the following:  
\[PDP=IFFT[X(k)S^{*}(k)]\quad k=0,1,...,K \tag{8}\]  
where \(PDP\) represents the PDP of the signal, while \(IFFT[]\) denotes the Inverse Fast Fourier Transform. The local signal sequences and the received signal sequences are denoted as \(S(k)\) and \(X(k)\), respectively. The asterisk \(*\) represents the complex conjugate, and \(K\) is the length of the signal sequences. Similarly, in the processing of radar signal data, pulse compression of Linear Frequency Modulated (LFM) signals is also achieved through frequency-domain matched filtering [55]. For the echo signal \(x(t)\) received by the radar, the result of pulse compression can be expressed in the time domain as follows:  
\[x_{pc}(t)=IFFT[FFT[x(t)]\cdot FFT[s^{*}(t)]] \tag{9}\]  
where \(x_{pc}(t)\) is the signal after pulse compression, \(FFT[]\) denotes the Fast Fourier Transform, and \(s(t)\) is local signal. It is evident that the PDP obtained from the echo signals collected by the CBS through frequency-domain matched filtering is equivalent to the pulse compression results of LFM signals. Therefore, the PDP of the echo signals can be regarded as the result of pulse compression, which can subsequently be utilized for azimuth focusing to obtain the 2D SAR image of the target.
# Long-Term EEG Component Analysis Method Based on Lasso Regression
## 2 Methods
### EEG Component Decomposition and Feature Extraction
#### 2.2.1 EEG Preprocessing  
To reduce interference, the left mastoid is selected as the reference electrode. In contrast, the average reference is usually used to amplify the fluctuation of amplitude in data analysis. That is, the EEG data is obtained by subtracting the average value of all channels.  
Then, because the effective frequency of the EEG signal is mainly concentrated below 50 Hz, a band-pass filter from 0.5 to 50 Hz is applied to the EEG signal. The finite impulse response (FIR) filters are used in signal processing. First, to remove linear trends, a high-pass filter at 0.5 Hz is applied, which is also recommended to obtain good quality ICA decompositions [27]. Then, a low-pass filter at 50 Hz is applied to remove the high-frequency.  
After that, the independent components of EEG signals are obtained by the Independent Component Analysis (ICA) method [28], which decomposes the complex signal into several independent components. Moreover, the artifacts of eye movement and muscle constriction could be removed by an independent components rejection.
## 3 Analysis of Temperature Field of Frozen Wall
### Numerical Calculation Model of Freezing Temperature Field  
#### Basic Assumptions  
1. Unfrozen soil and frozen soil are homogeneous isotropic bodies.
2. The influence of groundwater seepage on the temperature field is ignored.
3. The vertical heat transfer of shaft formation and freezing holes is ignored, and the determination of freezing temperature field is simplified to a 2D plane problem.  
4. The measured brine temperature is directly applied to the boundary of the freezing holes as a temperature load.
5. The latent heat of phase transition of the soil is released in a certain temperature range near the freezing temperature during freezing.