esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on 3D Geological Modeling Method Based on Deep Neural Networks for Drilling Data
## 3 Methods
### Optimization Algorithm
#### 3.4.3 Adaptive Moment Estimation  
In order to deal with the stochastic nature of gradient estimation in the stochastic gradient descent method, the momentum method can be utilized. While it offers stability and quicker convergence, it maintains a fixed learning rate throughout training. To overcome this limitation, [PERSON] et al. introduced the Adaptive Moment Estimation Algorithm (Adam) [45]. Adam combines the advantages of the momentum method and the RMSprop algorithm, enabling adaptive learning rate adjustments while utilizing momentum as the direction for parameter updates.  
The Adam algorithm calculates the exponentially weighted average of the gradient squared, \(\mathbf{g}^{2}\) (similar to RMSprop), and the exponentially weighted average of the gradient, \(\mathbf{g}_{\text{t}}\) (similar to the momentum), using Equations (10) and (11).  
\[\text{M}_{\text{t}}=\beta_{1}\text{M}_{\text{t}-1}+(1-\beta_{1})\mathbf{g}_{ \text{t}} \tag{10}\]  
\[\text{G}_{\text{t}}=\beta_{2}\text{G}_{\text{t}-1}+(1-\beta_{2})\mathbf{g}_{ \text{t}}\odot\mathbf{g}_{\text{t}} \tag{11}\]  
Here, \(\beta_{1}\) and \(\beta_{2}\) are the decay rates of the two moving averages, typically set empirically as \(\beta_{1}\) = 0.9 and \(\beta_{2}\) = 0.99. \(\text{M}_{\text{t}}\) and \(\text{G}_{\text{t}}\) denote the mean and the variance of the gradient without subtracting the mean. Initially assuming \(\text{M}_{0}\) = 0 and \(\text{G}_{0}\) = 0, the values of \(\text{M}_{\text{t}}\) and \(\text{G}_{\text{t}}\) will be smaller than the true mean and variance at the beginning of the iteration. This deviation becomes notable when both \(\beta_{1}\) and \(\beta_{2}\) are close to 1, and, therefore, this deviation is corrected using Equations (12) and (13). Subsequently, the parameter update differences for the Adam algorithm are calculated using Equation (14).  
\[\hat{\text{M}}_{\text{t}}=\frac{\text{M}_{\text{t}}}{1-\beta_{1}^{\text{t}}} \tag{12}\]  
\[\hat{\text{G}}_{\text{t}}=\frac{\text{G}_{\text{t}}}{1-\beta_{2}^{\text{t}}} \tag{13}\]  
\[\Delta\theta_{\text{t}}=-\frac{\alpha}{\sqrt{\hat{\text{G}}_{\text{t}}+e}}\hat {\text{M}}_{\text{t}} \tag{14}\]  
where the initial learning rate \(\alpha\) is usually set to 0.001 and can also be decayed during the iteration process.
###### Abstract
The model can be extended and applied according to different modulation and coding methods of the receiver, and the receiver EMS can be analyzed according to different electromagnetic parameter characteristics of the pulse signal; on the other hand, the research on the receiver EMC testing and evaluation method under pulsed interference environment is carried out according to BER, which can improve the reliability and accuracy of receiver interference degree prediction and EMC evaluation. The remainder of this paper is organized as follows. In Section 2, based on the traditional BER theory, where Gaussian white noise (GWN) is considered, we derive the BER formula for the receiver when pulse signals are considered as the noise. Then, numerical simulation is designed to verify the theoretical conclusions in Section 3. As a practical example, the proposed EMC evaluation method is applied to BDS in Section 4, the predictions given by the method are compared with the measured results obtained in the laboratory. Finally, in Section 5, we conclude this paper with a summary of the proposed methods and some follow-up research outlooks.
## 4 Problem Statement
\begin{array}[ ]{c}x-x_{T}(\gamma_{T})\\ y-y_{T}(\gamma_{T})\end{array} \right]=\left[ \begin{array}{cc}\cos\gamma_{T }&-\sin\gamma_{T}\\ \sin\gamma_{T}&\cos\gamma_{T}\end{array} \right]\left[ \begin{array}{c}x-x_{ T}(\gamma_{T})\\ y-y_{T}(\gamma_{T})\end{array} \right], \tag{11}\]  
\[\gamma_{T}=\arctan 2\left(y_{T}^{\prime}\left(\gamma_{T}\right),x_{T}^{\prime} \left(x_{T}\right)\right), \tag{12}\]  
Figure 2: Path following error model. where \(y_{T}^{\prime}\left(\gamma_{T}\right)=\partial y_{T}/\partial\gamma_{T}\), \(x_{T}^{\prime}\left(\gamma_{T}\right)=\partial x_{T}/\partial\gamma_{T}\). Note that, if the reference path is a straight line, \(\gamma_{T}\equiv\alpha\). Expanding Equation (11):  
\[\left\{ \begin{array}{l}x_{e}=\left(x-x_{T}(\gamma_{T})\right)\cos\gamma_{T}+ \left(y-y_{T}(\gamma_{T})\right)\sin\gamma_{T},\\ y_{e}=-\left(x-x_{T}(\gamma_{T})\right)\sin\gamma_{T}+\left(y-y_{T}(\gamma_{T} )\right)\cos\gamma_{T}.\end{array} \right. \tag{13}\]  
The projection of \(\left(x,y\right)\) on the path is employed to be \(\left(x_{T},y_{T}\right)\); hence, \(x_{e}=0\). Time differentiation of the cross-track error \(y_{e}\) is given by Equation (14):  
\[\dot{y}_{e}= -\left(\dot{x}-\dot{x}_{T}(\gamma_{T})\right)\sin\gamma_{T}+ \left(\dot{y}-\dot{y}_{T}(\gamma_{T})\right)\cos\gamma_{T} \tag{14}\] \[-\dot{\gamma_{T}}\underbrace{\left[\left(x-x_{T}(\gamma_{T}) \right)\cos\gamma_{T}+\left(y-y_{T}(\gamma_{T})\right)\sin\gamma_{T}\right]}_{ x_{e}=0}\] \[=-\dot{x}\sin\gamma_{T}+\dot{y}\cos\gamma_{T}+\dot{\gamma_{T}} \underbrace{\left(x_{T}^{\prime}\sin\gamma_{T}-y_{T}^{\prime}\cos\gamma_{T} \right)}_{\gamma_{T}=\arctan\left(y_{T}(\gamma_{T}),x_{T}^{\prime}(\gamma_{T} )\right)}\] \[=-\dot{x}\sin\gamma_{T}+\dot{y}\cos\gamma_{T}.\]  
The position and attitude vectors of AUV in \(\left[\mathrm{I}\right]\) are denoted by \(\left[x,y,\psi\right]^{\mathrm{T}}\). \(\psi\) is the current heading of AUV. The kinematics equation can be expressed as Equation (15):  
\[\left\{ \begin{array}{l}\dot{x}=u\cos\psi-v\sin\psi,\\ \dot{y}=u\sin\psi+v\cos\psi,\\ \dot{\psi}=r.\end{array} \right. \tag{15}\]  
Therefore, Equation (15) can be written as:  
\[\dot{y}_{e}=-\left(u\cos\psi-v\sin\psi\right)\sin\gamma_{T}+\left(u\sin\psi+v \cos\psi\right)\cos\gamma_{T}. \tag{16}\]  
The amplitude-phase form of Equation (16) is:  
\[\dot{y}_{e}=U\sin\left(\psi-\gamma_{T}+\beta\right), \tag{17}\]  
where \(\beta=\arctan\left(v/u\right)\) is the drift angle of AUV. \(U=\sqrt{v^{2}+u^{2}}\) is the magnitude of the velocity vector. Then, consider Equation (17) in the following form:  
\[\dot{y}_{e}=U\sin\left(\psi-\gamma_{T}\right)\cos\beta+U\cos\left(\psi-\gamma_ {T}\right)\sin\beta. \tag{18}\]  
In fact, the slide angle is small, and \(\cos\beta\approx 1\) and \(\sin\beta\approx\beta\). Equation (18) can be simplified to:  
\[\dot{y}_{e}=U\sin\left(\psi-\gamma_{T}\right)+U\cos\left(\psi-\gamma_{T} \right)\beta. \tag{19}\]
## 3 Pulse Shapes Typical of Experiments
### SASE-FEL Pulses  
In order to investigate the effects of the chaotic nature of typical FEL pulses, we have performed numerical experiments for EUV pulses randomly generated by the partial-coherence method [81]. Typical pulse shapes for the case of a photon energy of 23.0 eV, coherence time (CT) of 2 fs, and mean pulse width (MPW) of 7 fs are presented in Figure 8. The pulse shape significantly fluctuates from shot to shot, and such pulses may be considered to be composed of many pulses that have a pulse width of 2 fs on average, random phases with each other, and a whole envelope corresponding to 7 fs pulse width on average.  
Figure 8: Typical pulse shapes generated by the partial-coherence method [81] for the case of 23.0 eV photon energy, 2 fs coherence time, and 7 fs mean pulse width.  
The obtained values of \(W\) and \(\delta\) for several combinations of CT and MPW for \(\hbar\omega=21.2\), 21.3, and 23.0 eV, and some of the corresponding PAD are shown in Table 1 and Figure 9, respectively. The PADs for \(\hbar\omega=20.3\) and 24.3 eV are also shown in Figure 9. For \(\hbar\omega=20.3\) and 24.3 eV, for which the PAD hardly depends on pulse width, the PAD for a chaotic pulse is similar to that for a Gaussian pulse. For \(\hbar\omega=21.2\), 21.3, and 23.0 eV, we can see from the table that \(\delta\) takes values between those corresponding to the CT and the MPW in most cases. Also, in general, the PAD is of a shape between those for the CT and the MPW (Figure 9). It is somewhat surprising that this simple empirical rule applies to most of the combinations of CT, MPW, and photon energy studied here. This may be because different constituent pulses have basically random phases from each other, smearing out the interference between different electron bursts.  
Figure 9: Photoelectron angular distribution by a chaotic pulse for a photon energy, coherence time (CT), and mean pulse width (MPW) indicated in each panel. The target atom is helium. The red and blue curves are for Gaussian pulses whose widths are given by the CT and MPW, respectively of the chaotic pulse. For example, in panel (**b**), the photon energy, CT, and MPW, are 21.2 eV, 3.5 fs, and 7 fs, respectively.
###### Abstract  
A particle filter (PF) has been introduced for effective position estimation of moving targets for non-Gaussian and nonlinear systems. The time difference of arrival (TDOA) method using acoustic sensor array has normally been used to for estimation by concealing the location of a moving target, especially underwater. In this paper, we propose a GPU -based acceleration of target position estimation using a PF and propose an efficient system and software architecture. The proposed graphic processing unit (GPU)-based algorithm has more advantages in applying PF signal processing to a target system, which consists of large-scale Internet of Things (IoT)-driven sensors because of the parallelization which is scalable. For the TDOA measurement from the acoustic sensor array, we use the generalized cross correlation phase transform (GCC-PHAT) method to obtain the correlation coefficient of the signal using Fast Fourier Transform (FFT), and we try to accelerate the calculations of GCC-PHAT based TDOA measurements using FFT with GPU compute unified device architecture (CUDA). The proposed approach utilizes a parallelization method in the target position estimation algorithm using GPU-based PF processing. In addition, it could efficiently estimate sudden movement change of the target using GPU-based parallel computing which also can be used for multiple target tracking. It also provides scalability in extending the detection algorithm according to the increase of the number of sensors. Therefore, the proposed architecture can be applied in IoT sensing applications with a large number of sensors. The target estimation algorithm was verified using MATLAB and implemented using GPU CUDA. We implemented the proposed signal processing acceleration system using target GPU to analyze in terms of execution time. The execution time of the algorithm is reduced by 55% from to the CPU standalone operation in target embedded board, NVIDIA Jetson TX1. Also, to apply large-scaled IoT sensing applications, we use NVIDIA Tesla K40c as target GPU. The execution time of the proposed multi-state-space model-based algorithm is similar to the one-state-space model algorithm because of GPU-based parallel computing. Experimental results show that the proposed architecture is a feasible solution in terms of high-performance and area-efficient architecture.  
GPU-based acceleration; acoustic sensor; time of difference arrival (TDOA); generalized cross correlation phase transform (GCC-PHAT); garticle filter (PF); Internet of Things (IoT) +
Footnote †: journal: Applied Soft. 2017
# A Dual-Band Flexible MIMO Array Antenna for Sub-6 GHz 5G Communications
## 4 MIMO Array Antenna
### Bending Analysis of the Proposed Antenna  
One of the prominent analyses involving the design of the antenna for wearable scenarios is the bending analysis. The performance of the antenna changes when the antenna is kept in a bent position according to the human body's curvatures [6]. To study the performance of the antenna in a bending environment, the proposed antenna is analyzed with two different bending conformations (\(x\)-axis and \(y\)-axis bends) with a 50 mm radius of bend, as portrayed in Figure 15. The analysis is performed using cylindrical bends available in the CST Studio EM tool. The S-parameters of the antenna are observed under both the bending axis and compared with the performance under flat conditions, as evaluated in Figure 16. It is evident that under both scenarios, there is a slight shift in the resonance in the first band, whereas the second band does not show any variations. The bandwidth in both bands is also retained, showing the antenna's applicability in wearable applications.  
Figure 14: Schematic of the MIMO array antenna. All values in mm: Ls = 57, Ws = 50, n1 = 9, n2 = 1.5, n3 = 43.4, n4 = 10, n5 = 6.8, and n6 = 12.8.  
Figure 13: Simulated scattering properties of different MIMO design stages.
# Adaptive Regression Prefetching Algorithm by Using Big Data Application Characteristics
###### Abstract  
This paper presents an innovative prefetching algorithm for a hybrid main memory structure, which consists of DRAM and phase-change memory. To enhance the efficiency of hybrid memory hardware in serving big data technologies, the proposed design employs an application-adaptive algorithm based on big data execution characteristics. Specifically optimized for graph-processing applications, which exhibit complex and irregular memory access patterns, a dual prefetching scheme is proposed. This scheme comprises a fast-response model with low-cost algorithms for regular memory access patterns and an intelligent model based on an adaptive Gaussian-kernel-based machine-learning prefetch engine. The intelligent model can acquire knowledge from real-time data samples, capturing distinct memory access patterns via an adaptive Gaussian-kernel-based regression algorithm. These methods allow the model to self-adjust its hyperparameters at runtime, facilitating the implementation of locally weighted regression (LWR) for the Gaussian process of irregular access patterns. In addition, we introduced an efficient hybrid main memory architecture that integrates two different kinds of memory technologies, including DRAM and PCM, providing cost and energy efficiency over a DRAM-only memory structure. Based on the simulation-based experimental results, our proposed model achieved performance enhancement of 57% compared to the conventional DRAM model and of approximately 12% compared to existing prefetcher-based models.  
graph processing; regression algorithm; data prefetching; hybrid main memory; memory management
# A Comprehensive MCDM-Based Approach for Object-Oriented Metrics Selection Problems
## 4 Results of Fuzzy DEMATEL Method for Object-Oriented Metrics  
Table 9 presents the groups identified in the metrics causal diagram. We classified the set of metrics into cause metrics and effect metrics. In each group, metrics have importance levels (Low, Normal, and High). For example, in the cause group, the highest metrics are NIC and CM, and for the effect group, the highest metrics are ATFD, NOM NOC, and NCC.  
Cause metrics indicate the implication of the influencing metrics on the effect metrics. Considering the interdependence among the metrics, the detection rules should consider both the cause metrics and the related influence on the effect metrics [24]. Therefore, by selecting rules combining cause and effect metrics, we can improve the accuracy of design defect detection, giving priority to the metrics having the highest importance.  
Based on the DEMATEL method, we classified the metrics into two orthogonal dimensions. The horizontal dimension represents the metrics importance and the vertical dimension represents the cause and effect metrics. Now, we can limit the detection rule; for example, we can exclude rules with low-importance metrics from the detection process. Based on Table 9, it becomes clear that detection rules should contain as many metrics as possible from the \"important\" and \"highly important\" horizontal dimension. These  
Figure 2: Causal diagram metrics.  
\begin{table}
\begin{tabular}{c|c c c} \hline \hline  & \multicolumn{3}{c}{**Metrics**} \\ \hline \multirow{3}{*}{**Groups**} & **Low Importance** & **Important** & **High Importance** \\ \cline{2-4}  & NOP & NC & NIC \\ \cline{2-4}  & NODD & NOA & CM \\ \cline{2-4}  & NOD & & \\ \hline \multirow{3}{*}{**Effect Group**} & NOPM & NMR & ATFD \\ \cline{2-4}  & PS & NMSC & NOM \\ \cline{1-1} \cline{2-4}  & & PUC & NOC \\ \cline{1-1} \cline{2-4}  & & NCC \\ \hline \end{tabular}
\end{table}
Table 9: Metrics importance.  
rules should also combine metrics from the vertical dimension (i.e., both cause and effect metrics). Based on this finding, an excellent detection rule combines metrics such as NIC or/and CM with metrics such as ATFD or/and NOM or/and NOC or/and NCC.  
The following section explores in depth the impact of the above finding on the accuracy of design defect detection.
# Optimization of Intelligent-Reflecting-Surface-Enabled Covert Communication in Multi-Sensor Systems
## 3 System Model
### Hypothesis Test  
In the analysis of a quasi-static flat fading channel characterized by coefficients \(h_{RA}\) and \(h_{AW}\), the anticipated amplitude of the complex-value \(h_{RA}h_{AW}S_{R,j}\) is denoted as \(A\), and the expected phase angle is represented as \(\theta_{0}\). The determination of [PERSON]'s communication activity involves [PERSON] conducting a binary hypothesis test [43] based on a sequence of \(n\) observations \(\mathbf{Y}_{W}=\{Y_{W,j}\}_{i=1}^{n}\). Specifically, the null hypothesis (\(\mathcal{H}_{0}\)) and alternative hypothesis (\(\mathcal{H}_{1}\)) are formulated as:  
\[\mathcal{H}_{0}: Y_{W,i}=N_{W,i}, \tag{10}\] \[\mathcal{H}_{1}: Y_{W,i}=h_{RA}h_{AW}S_{R,i}c_{i}+N_{W,i}. \tag{11}\]  
Let \(Q_{0}^{n}\) (resp. \(Q_{1}^{(n)}\)) denote the probability distribution of [PERSON]'s \(n\) observations when \(\mathcal{H}_{0}\) (resp. \(\mathcal{H}_{1}\)) is true. [PERSON] has knowledge of the distributions \(Q_{1}^{(n)}\) and \(Q_{0}^{n}\) and can conduct an optimal statistical hypothesis test such that the sum of the probability of _missed detection_\(\mathcal{P}_{FA}\) and the probability of _false alarm_ has a lower bound, which is given by  
\[\mathcal{P}_{FA}+\mathcal{P}_{MD}\geq 1-\sqrt{\mathcal{D}(Q_{1}^{(n)}\|Q_{0}^{n })}, \tag{12}\]  
where \(\mathcal{D}(Q_{1}^{(n)}\|Q_{0}^{n})\) represents the KL divergence between \(Q_{1}^{(n)}\) and \(Q_{0}^{n}\). The objective of covert communication is to ensure the KL divergence is negligible, i.e., guaranteeing  
\[\mathcal{D}(Q_{1}^{(n)}\|Q_{0}^{n})\leq\epsilon, \tag{13}\]  
where \(\epsilon\) is an arbitrarily small value within the range (0,1).  
Given the non-uniform distribution of sensors in the target scenario, the noise generated in the complex Gaussian channel is asymmetric, i.e., \(\sigma_{W}^{2}=\sigma_{w,x}^{2}+\sigma_{w,y}^{2}\) and \(\sigma_{w,x}^{2}\
eq\sigma_{w,y}^{2}\). [PERSON] possesses knowledge regarding the construction of the codebook 2-BPSK and channel coefficient from [PERSON] to [PERSON]. The probability distributions of \(\mathbf{Y}_{W}\) under \(\mathcal{H}_{0}\) and \(\mathcal{H}_{1}\) are given by  
\[Q_{0}^{n}(x,y) =\prod_{i=1}^{n}\frac{1}{2\pi\sigma_{w,x}\sigma_{w,y}}\exp \Big{(}-\frac{x^{2}}{2\sigma_{w,x}^{2}}-\frac{y^{2}}{2\sigma_{w,y}^{2}}\Big{)}, \tag{14}\] \[Q_{1}^{(n)}(x,y) =\frac{1}{2}\sum_{t=1}^{2}\prod_{i=1}^{n}\frac{1}{2}\frac{1}{2 \pi\sigma_{w,x}\sigma_{w,y}}\bigg{[}\exp\Big{(}-\frac{(x_{i}+A\beta\cos\theta_ {t})^{2}}{2\sigma_{w,x}^{2}}-\frac{(y_{i}+A\beta\sin\theta_{t})^{2}}{2\sigma_ {w,y}^{2}}\Big{)}\] \[+\exp\bigg{(}-\frac{(x_{i}-A\beta\cos\theta_{t})^{2}}{2\sigma_{w,x}^{2}}-\frac{(y_{i}-A\beta\sin\theta_{t})^{2}}{2\sigma_{w,y}^{2}}\Big{)} \bigg{]}, \tag{15}\]  
where \(\theta_{t}=\theta_{0}+\tilde{\theta}_{t}\), \(\sigma_{w,x}^{2}\) and \(\sigma_{w,y}^{2}\) represent the variances of the Gaussian noise at Willie on the \(x\)-axis and \(y\)-axis.
# Durability and Safety Performance of Pavements with Added Photocatalysts
## 3 Results
### Mercury Intrusion Porosimetry (MIP)  
Table 5 gives the MIP findings for total porosity (volume per cent), mean pore diameter (4 V/A) and matrix bulk density for pore sizes in the 400 \(\upmu\)m to 0.007 \(\upmu\)m range. Before analysing the results, it has to be considered that a cube of approximately 1 cm\({}^{3}\) was used for MIP, intruding the Hg through the photocatalytic layer. Then, the results are taking into account the photocatalytic layer and the bulk of the matrix. Thus, the absolute values in Table 5 have only comparative purposes but cannot be considered as \"real\" values. Much more informative are the results given in Figure 3a-d that presents the differential pore size distributions for the samples. Additionally, Figure 3a shows the pore sizes ranges along with their classification into both IUPAC categories [macroopores (>50 nm), mesopores (50 nm to 2 nm) and micropores (<2 nm)] and the conventional categories for concrete [air voids (>10 \(\upmu\)m), large capillaries (50 nm to 10 \(\upmu\)m), medium capillaries (10 nm to 50 nm) and gel pores (<10 nm)].  
From results from MIP (Table 5) and the data in Table 2, it can be deduced that most of the porosity in the asphalt pavement lay outside the MIP pore size range being these a mere 0.73% of the total. Imbibition of the slurries filled the voids, generating a finer pore system with different pore size distributions for each of the slurries, as detected by MIP. Pore size in the S1-A peaked at around 12 \(\upmu\)m and no pores < 0.015 \(\upmu\)m were detected. Finer porosity is found in S2-A, with one of its two peaks positioned in the air voids zone while the other was wide and lay in the medium capillary pore area, at around 0.03 \(\upmu\)m.  
Emulsion coatings do not significantly influence the pore distribution of asphalt, almost all located in the macropores range for two of the emulsion coatings on asphalt (Figure 2b). The greater porosity in E1-A than the other emulsion asphalt samples was concentrated primarily in a (narrow peak) in the large capillary pore range. Concerning emulsions on tiles, E1-T presents a significant fraction in the air void fraction (wide peak). The rise in the fraction of mesopores (around 0.02 \(\upmu\)m) in the three emulsion-coated paving tiles relative to the Ref-T reference might also be attributed to the emulsion. T1 exhibited nearly negligible porosity, lower even than in Ref-T1, in the range studied. The curves for Ref-T2 and T2 exhibited three modes, at 70 \(\upmu\)m, 1 \(\upmu\)m and 0.03 \(\upmu\)m, with a predominant peak at the first value in sample T2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Porosity (\% Vol)**} & **Mean Pore Diameter** & **Bulk** & **UPIT** \\  & & **(4 V/A)** & **Density** & **(\(\upmu\)s)** \\  & & **(\(\upmu\)m)** & **(g/mL)** & **(\(\upmu\)s)** \\ \hline Ref-A & 0.73 & 7.337 & 2.64 & 82.8 \\ S1-A & 2.64 & 0.583 & 2.46 & 73.1 \\ S2-A & 4.92 & 0.065 & 2.28 & 47.6 \\ E1-A & 1