esa-sceva/satcom-chunk-collection huggingface dataset:
# Sequence Deep Learning for Seismic Ground Response Modeling: 1D-CNN, LSTM, and Transformer Approach
## 1 Introduction
For more advanced models, the transformer network shows a state-of-the-art performance [28] for sequential data. These models are designed to effectively capture the sequential or temporal relationships between the sequence of input features and the resulting responses. The application of sequence deep learning models has been successful throughout various civil engineering fields. [PERSON] et al. [29] used LSTM for predicting the pore pressure response in liquefiable sands under cyclic loading. [PERSON] et al. [30] and [PERSON] et al. [31] used LSTM to learn the stress-strain behavior of soils. In seismic response modeling, [PERSON] et al. [32] proposed a CNN-based model to estimate the seismic ground response based on the earthquake records. The model performance was compared with the conventional one-dimensional physics-based model. Similarly, [PERSON] et al. [33] used a CNN and LSTM for seismic response modeling, and they compared the performance with the FEM model. [PERSON] et al. [34] improved the LSTM model by using attention mechanisms [28] and applied their model to the seismic response prediction of bridges. The improved model showed a promising performance improvement compared to conventional LSTM. [PERSON] et al. [35] used the transformer, a state-of-the-art sequence model, for the structural seismic response for both linear and nonlinear systems and showed improved accuracy compared to LSTM. The above studies highlight that the data-driven approach can successfully model the seismic response for unseen conditions and potentially surpass the physics-based models under their research conditions. Although previous studies proposed CNN- and LSTM-based models for ground seismic response modeling, the transformer-based model, which is the most advanced sequence learning architecture, has not been studied for this purpose. Instead, research involving transformers has primarily focused on structural response modeling. Additionally, the comprehensive comparison between the performance of the sequence learning models for seismic response is still limited. To address these gaps, we compare the CNN, LSTM, and transformer approaches for seismic ground response modeling. Based on these sequence learning architectures, we aim to (1) develop site-specific machine learning models for seismic ground response modeling and (2) explore the validity of sequence deep learning architectures (CNN, LSTM, transformer) for learning the site response. We prepare the ground motion measurement data from the Kiban Kyoshin Network (KiK-net). The models are trained on ground motion measurement data for individual sites to consider the site-specific conditions. We then compare the performance of these data-driven models with the conventional physics-based model, SHAKE2000 [4], as a baseline to evaluate their performance and validity for seismic ground response prediction. The results provide new insights into the potential of advanced sequence deep learning models for application in seismic ground response.
## 2 Status of Smart Grids  
A phased development plan with the primary goal of spreading the advanced metering infrastructure (AMI) is a fundamental early step for putting a smart grid in place. The AMI is an entire measurement and collection system that includes meters at customers' premises, communication networks between customers and service providers, and data receiving and management systems that allow service providers to use the gathered information. It enables detailed time-based information measurements and provides frequent collection and transmission of such information.  
As depicted in Figure 1, there are broadly two kinds of systems: transmission and distribution. Electrical power is generated by power stations such as thermoelectric, nuclear, wind, hydroelectric, and geothermal power plants (namely, generators), and then transported to a substation (namely, suppliers) that splits high capacity power into smaller units and distributes them to customers' premises according to their requirements. Based on customers' power usage patterns, the central controller residing with the supplier determines transmission capacity, requests an amount of power based on expected demand from generators, and distributes it to its customers. To achieve this functionality, the central controller receives power usage information from smart meters in individual smart homes directly or indirectly (through an accumulation of usage information on the path from the furthermost to the nearest customers).  
In particular, we explore the information aspect of the distribution system. Specifically, we investigate in depth how the central controller aggregates the metering information in a privacy-preserving and reliable manner. Since metering information can be gathered from multiple independent smart meters apart from the central controller, we denote them as multi-source smart meters. Henceforth, we refer to them as smart meters or just meters for simplicity throughout the paper. (There are several interesting and innovative researches dealing with microgrid construction by formulating a probabilistic model of power generation and pollutant emission [16], and adaptive scheduling specific to vehicular network where vehicular clients frequently change their locations [17]. However, these issues are beyond the scope of our focus, which is secure aggregation and verification of metering information in smart grid systems.)
# Travel Time Prediction on Long-Distance Road Segments in Thailand
## 4. Results and Discussions
### Results of Filters  
Lastly, the results of the experiment for the selection of an appropriate filter method, as explained in Section 3.4.3, are shown in Table 5. According to our test, the MAE of the SA-LSTM with the Butterworth low-pass filter was 12.15, and this was the best result among all the compared methods. Thus, the Butterworth low-pass filter was selected as part of our approach.
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 4 Common Attacks within MANETs
### Grey Hole Attacks  
The grey hole attack, also known as a selective forwarding attack, shares similarities with the black hole attack. Both involve malicious nodes dropping packets. However, grey hole attacks are more sophisticated and unpredictable. Instead of dropping all packets like a black hole, grey hole nodes selectively drop them based on pre-determined criteria, such as packet types (e.g., dropping all UDP packets) or random intervals. This makes grey hole attacks challenging to detect because the malicious node can behave normally at times, masking its true nature. The unpredictable nature of packet dropping makes it difficult to identify the attacker and distinguish it from legitimate nodes [37].
# A Novel Measurement Standard for Surface Roughness on Involute Gears
## 2 Methods for the Analysis of Surface Roughness on Involutes
### The Involute  
The most commonly used shape of gear teeth by far is the involute, shown in Figure 1.  
All form deviations including the roughness values have to be evaluated relative to the ideal involute of the workpiece. The simplest form and position of a left flank involute \(\vec{I}\) in the \(x\)-\(y\)-plane is described by using the roll angle \(\xi\):  
\[\vec{I}=r_{\text{b}}\cdot\left[\left(\begin{array}{c}\cos(\xi)\\ \sin(\xi)\end{array}\right)-\xi\left(\begin{array}{c}\sin(\xi)\\ -\cos(\xi)\end{array}\right)\right] \tag{1}\]  
with \(r_{\text{b}}\) denoting the base circle's radius and \(\xi\) denoting the roll angle. The roll angle \(\xi\) can be expressed as function of the base circle radius \(r_{\text{b}}\) and the roll length \(l\).  
\[\xi\,=\,\frac{l}{r_{\text{b}}}. \tag{2}\]  
In contrast to common usage with respect to parameterising involute geometries with roll length \(l\), this article preferably employs roll angle \(\xi\) instead.
## 2 Syntchitic Aperture Radar
### SAR Geometry  
In Figure 1a, the SAR geometry is observed, which operates in stripmap mode and is a monostatic radar, where the magenta quadrilateral represents the SAR antenna, the green quadrilateral represents the SAR antenna footprint, the circle with a green background represents the target, and the SAR system movement direction is called azimuth and the chirp signal transmission direction is called the slant range. On the other hand, the Nadir point is also observed, where the Doppler effect is minimal; in addition, the squint angle, radar beam, and \(H\) SAR platform height are apparent.  
In Figure 1b, the radar system moves from point \(P_{1}\) to point \(P_{2}\) in the azimuth direction to capture signals backscattered from the surface of interest through the SAR antenna; therefore, the blue lines represent the transmitted signal path (slant range \(R_{1}\)) and the backscattered chirp signal (slant range \(R_{2}\)), and this occurs at time \(\Delta t\). On the other hand, Equations (3) and (4) [16] are obtained from Figure 1b.  
\[R_{1}=R(n) \tag{1}\]  
\[R_{2}=R(n+\Delta t) \tag{2}\]  
\[R(n)=\sqrt{R_{0}^{2}+\left(V_{s}n\right)^{2}} \tag{3}\]  
\[R(n+\Delta t)=\sqrt{R_{0}^{2}+\left(V_{s}(n+\Delta t)\right)^{2}} \tag{4}\]  
where \(R_{1}\) and \(R_{2}\) are the slant range, \(\Delta t\) is the time delay in acquiring backscattered signals from the surface of interest, \(n\) is the time in azimuth, and \(V_{s}\) is the SAR platform speed; therefore, the time delay is as follows:  
\[\Delta t=\frac{R_{1}+R_{2}}{c} \tag{5}\]  
In Equation (5), we have the speed of light \(c\), then replacing Equations (1) and (2) into Equation (5), we have the time delay \(\Delta t\).  
\[\Delta t=\frac{R(n)+R(n+\Delta t)}{c} \tag{6}\]  
It is known that \(\alpha=\frac{c^{2}}{c^{2}-V_{s}^{2}}\) is the Doppler factor [15; 18]. Then, using Equations (3), (4) and (6), [14] the time delay \(\Delta t\) is solved, therefore, the time delay \(\Delta t\) of the chirp signal traveling through the paths of \(R_{1}\) and \(R_{2}\) is shown in Equation (7) [18].  
Figure 1: (**a**) SAR Geometry (stripmap mode) and (**b**) chirp signal path (monostatic radar).  
\[\Delta t=2\alpha\left(\frac{R(n)}{c}+\left(\frac{V_{s}}{c}\right)^{2}n\right) \tag{7}\]
# HI-Sky: Hash Index-Based Skyline Query Processing
## 4 Performance Evaluation
### Result of Experiments
#### 4.2.1 Comparison of Changes in np  
We first measured performance changes of HI-Sky such as indexing time, skyline computation time, and the number of dominance test calls according to the np. This experiment was conducted using a varying np based on the eight-dimensional independent data on 1 M (one million) and 10 M (ten million).  
Figure 5 shows the results of performance changes of HI-Sky according to varying np. In this case, the maximum number of np that can be obtained from Equation (1) is 4 for the 1 M dataset and 5 for the 10 M dataset. The experimental result shows that the maximum number of np does not guarantee optimal performance, but it can work properly as a criterion to prevent performance degradation because the small np reduces the benefit of the data space pruning step as it creates a smaller number of GLADs and stores more data in each GLAD. On the other hand, a large np reduces the benefit of the skyline computation step because it raises the number of GLADs created to be closer to the number of data and performs more operations to determine whether a comparison between GLADs is required. However, the maximum np can be used to store the number of data close to the minimum capacity, thus reducing the number of comparisons between GLADs while properly pruning the data space.
# Bloch Oscillations in the Chains of Artificial Atoms Dressed with Photons
## 3 Approximate Analytical Solution  
### Preliminaries  
The simplest and conventional model of BO is based on a quasi-classical approximation. It results from Equations (7) and (8) with \(g=0\). Electron-photon interaction disappears (subscript \(n\) becomes unnecessary) and the equations become separated. The system reads,  
\[i\hbar\frac{\partial a_{p}}{\partial t}=-e\big{(}\mathbf{E}_{dc}\cdot\mathbf{ R}_{p}\big{)}a_{p}+t_{a}a_{p+1}+t_{a}^{*}a_{p-1} \tag{15}\]  
and the same for \(b_{p}\). (the value \(\delta e\) becomes arbitrary and is taken \(\delta e=0\)). In the absence of a dc field (\(\mathbf{E}_{dc}=0\)), the eigenstate of the system corresponds to the electrons tunneling in the periodic potential and has a form of Bloch-wave \(a_{p+1}(t)=a_{p}e^{i(\varphi-vt)}\), where \(\varphi\) is a phase shift per unit cell, coupled with the eigen frequency by \(\
u=2\hbar^{-1}t_{a}\cos\varphi\). The phase shift is coupled with the continuous quasi-momentum via relation \(h=\varphi/a\), conventionally restricted to the first Brillouin zone \([-\pi/a;\pi/a]\). Under the influence of a dc field, a given Bloch-state \(|\Psi(h_{0})\rangle=N^{-1/2}{\sum_{p}}|a_{p}\rangle e^{i(h_{0}pa-\
u(h_{0})t)}\) evolves up to a phase factor into the state \(|\Psi(h(t))\rangle\) with \(h(t)\) variation according to  
\[\frac{dh}{dt}=-\frac{e\mathbf{E}_{dc}}{\hbar} \tag{16}\]  
or \(h(t)=-e\hbar^{-1}\mathbf{E}_{dc}t+h_{0}\). Thus, this evolution is periodic with a Bloch frequency corresponding to the time required for the quasi-momentum to scan a full Brillouin zone. It is described by the substitution \(\varphi(t)=h(t)a=-\omega_{B}t+\varphi_{0}\), where \(\omega_{B}=-e\hbar^{-1}\mathbf{E}_{dc}\) is the Bloch frequency, \(h_{0}=\varphi_{0}a\). The obtained solution relates also to the rather wide wave-packet with well-defined quasi-momentum. The periodic motion of its geometrical center corresponds to the quasi-classical model of BO.  
The main idea of the analytical solution considered next is based on hypothesis that the total dynamics of quasiparticles described by Equations (8) and (9) are reducible to the superposition of two interacting partial motions: (i) internal motion, which is dictated by dressing; and (ii) external motion of the quasiparticle as a whole. The internal motion is of completely quantum origin and doesn't allow any classical interpretation. It corresponds to the Rabi-wave and plays the role of the tunneling in the simplest case of BO considered before. The external motion is a motion of geometric center and will be described quasi-classically, following Equation (16).
## 3 Results
### Anova  
There was a significant main effect of segment number on RMSE (\(F=6.0\), \(p=0.016\); Table 7). Tukey HSD pairwise comparisons indicated that the RMSE for the best-performing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Segments** & **RMSE (m \(\times\) s\({}^{-2}\))** & **JH (cm)** \\ \hline \hline
1 \({}^{\text{a}}\) & Trunk, Thighs, & 2.2 (1.1) & 30.0 (8.3) \\
2 \({}^{\text{a}}\) & Thanks, Feet & 3.0 (1.4) & 26.0 (7.3) \\
3 & Thighs & 4.3 (1.2) & 31.9 (9.4) \\
4 & Shanks & 9.0 (2.3) & 42.1 (12.9) \\
5 & Feet & 15.1 (3.3) & 46.4 (19.5) \\
6 & Trunk, Thighs & 2.2 (1.2) & 28.2 (7.7) \\
7 \({}^{\text{a}}\) & Trunk, Shanks & 2.1 (1.3) & 28.4 (7.7) \\
8 & Trunk, Feet & 2.5 (1.4) & 27.0 (7.7) \\
9 & Thighs, Shanks & 4.5 (1.1) & 34.1 (9.9) \\
10 & Thighs, Feet & 4.2 (1.1) & 33.0 (10.0) \\
11 & Shanks, Feet & 9.9 (2.4) & 43.1 (14.0) \\
12 & Trunk, Thighs, & 2.1 (1.1) & 29.5 (8.l) \\
13 \({}^{\text{a}}\) & Trunk, Thighs, Feet & 2.0 (1.2) & 28.8 (8.0) \\
14 & Trunk, Shanks, Feet & 2.2 (1.3) & 29.1 (8.1) \\
15 & Thighs, Shanks, & 4.7 (1.1) & 34.8 (10.4) \\ Feet & - & - & 28.5 \\ \hline \hline \end{tabular} Root-mean-square error (RMSE) and jump height (JH) values were estimated using 15 different IMU configurations and a tri-axial force platform (FP). RMSE values were calculated by comparing IMU-derived vertical acceleration during countermovement jumps against the force platform criterion. JH was measured using the take-off velocity method for all IMU configurations and the FP criterion. \({}^{\text{a}}\) Lowest root-mean-square error (RMSE) observed for 1-, 2-, 3-, and 4-segment models. Data are presented as mean (SD).  
\end{table}
Table 6: Central tendency and dispersion.  
1-segment IMU model was significantly larger than that of the best-performing 2-segment model (_p_ = 0.041) and 3-segment model (_p_ = 0.021). No other significant differences were observed (_p_ = 0.061-0.999); however, the comparison between the best-performing 1-segment and 4-segment models approached statistical significance (_p_ = 0.061) [26].  
There was no significant main effect of segment number on CMJ JH (_F_ = 0.693, \(p\) = 0.598; Table 8). CMJ JHs derived from the best-performing IMU models were not significantly different (_p_ = 0.91-0.99) when compared against the force platform criterion, which indicated a lack of systematic bias.
## 4 Proposed Soft Parameter Estimation Paradigms
### Closed-Loop Particle Swarm Optimization  
Basically, the PSO is stimulated by the attitude of swarming through the process of finding food sources. Several applications with a number of modified versions of the PSO were presented in the literature as in [39; 40; 41; 42; 43]. The proposed CLPSO is a modified version of the standard PSO algorithm. It modulates the boundaries by considering feedback from the output parameters (control variables and fitness function) for improving the solution quality. PSO uses particles that represent the unknown parameters to be estimated. Every particle moves through the defined search domain to find a global solution. In the proposed CPSO, the control variables velocity and location can be obtained using the following Equations:  
\[V_{new}=V_{old}+\ c_{1}\cdot rand\cdot(P_{best}-x_{old})+\ c_{2}\cdot rand\cdot( \ g_{best}-x_{old}) \tag{9}\]  
\[x_{new}=x_{old}+\ V_{new} \tag{10}\]  
where \(x_{old}\) and \(V_{old}\) are, respectively, represent the current particle position and velocity; \(P_{best}\) is the best individual particle position; \(g_{best}\) refers to the global best swarm position; the \(rand\) function refers to the number in the unity domain between (0, 1); constant \(c_{1}\), \(c_{2}\) are called the conditioned learning factors by Equation (11) as:  
\[c_{1}\ +\ c_{2}\ \geq\ 4 \tag{11}\]  
The aim is to apply this algorithm to the proposed models of PV cells. Then the CLPSO is described as follows:  
1. The positions and velocities of each swarm are started randomly.
2. The RMSE is used to evaluate the fitness of each particle.
3. For particles in the same position, their finesses are compared together to obtain the best position for a certain position. This is called the personal best \(P_{best}\). The personal best is updated at each iteration.
4. Choose the best fitness particle compared to all other particles in the historical \(P_{best}\) archive to get the global best \(g_{best}\);
5. Updating the velocity and position are carried out by Equations (9) and (10), respectively.  
* Check the objective function if it has been improved and modulate the boundaries of the search space. The modified parameters' boundaries are updated as Algorithm 1, where \(\delta\) is the modulation parameter at iteration \(k\).  
```
1:Read until iteration \(k\)
2:Update the control variables \(x\) using Equations (9) and (10)
3:Evaluate the fitness function \(RMSE_{k}\)
4:Update the upper and lower limits
5:if\(RMSE_{k}<RMSE_{k-1}\)
6:take \(\left(Upper\ limit\right)_{k}\ =\ \left(Upper\ limit\right)_{k-1}-((Upper\ limit)_{k-1}-x_{k}).\delta\)
7:\(\left(Lower\ limit\right)_{new}\ =\ \left(Lower\ limit\right)_{ old}+\ \left(x_{new}-\left(Lower\ limit\right)_{old}\right).\delta\)
8:else
9:take \(\left(Upper\ limit\right)_{k}\ =\ \left(Upper\ limit\right)_{k-1}\)
10:\(\left(Lower\ limit\right)_{k}\ =\ \left(Lower\ limit\right)_{k-1}\)
11:endif
12:\(k=k+1\)
13:Go to Step 2, and the procedure described above is repeated until the stopping criteria are satisfied
```  
**Algorithm 1** Dynamic modification of control variable parameters
# Secure Cryptographic Key Encapsulation and Recovery Scheme in Noisy Network Conditions
## 2 Background
Section 4 delves into the concept of the Challenge-Response Pair (CRP) mechanism. It explains how CRPs can be generated through various methods, particularly focusing on the use of biometric protocols and digital files. Section 5 describesa novel cryptographic protocol called Response-Based Key Encapsulation Mechanism (R-KEM) that uses a CRP mechanism to encapsulate ephemeral keys with a subset of the responses. This section provides a comprehensive explanation of the protocol's operation via illustrative examples and methods for improvement. Section 6 presents the experimental work. The results showcase the relationship between error rates and noise levels, the efficiency of key recovery in terms of throughput per second, the duration required for key recovery as a function of error occurrences, and the likelihood of failure in identifying a match. Section 7 discusses the findings and the efficacy of the proposed protocol. Section 8 outlines directions for future research.
# A Research on Fault Diagnosis of a USV Thruster Based on PCA and Entropy
## 3 Experimental Validation of Selected Fault Features
### Results of the Water-Tank Experiment
Upon comparison of the results of each experiment, it was observed that the values of the data were not constant in Case 1 and Case 2, which represented different battery voltage conditions. It was speculated that the changes in the measured data occurred according to different states of the system, such as battery voltage and the starting position of the USV. Considering these phenomena, it was determined that using a fault diagnosis method whereby each condition was classified based on simple rules, such as defining thresholds, was difficult and not appropriate in this study. Therefore, we employed a new fault diagnosis algorithm based on PCA and entropy to perform fault diagnosis with a consideration of the correlation of selected fault features.
# Future Wireless Networking Experiments Escaping Simulations
## 6 Result Analysis
### W-iLab1.t  
The results for W-iLab1.t are based on 10 nodes as compared to four nodes in POWDER. The W-iLab1.t setup consists of linear, ring, and grid topology.  
1. **UDP Bandwidth:** In W-iLab1.t, maximum UDP bandwidth is observed in ring topology at around 27 Mb/s for hop 0 because of the presence of the 5 GHz upper channel connecting node 9 to node 0. The plot shows a decrease in the bandwidth with an increase in hops for all topologies, i.e., linear, ring, grid (Figure 19).
2. **UDP Loss:** There is no almost loss until hop 7 but for hop 8 it is slightly increased, i.e., around 5 percent in linear topology. For ring topology, there is negligible loss for hop 0 and hop 4 while it is around 18 percent for mid-hop 2 and hop 3. For grid topology, the loss is negligible for hop 0 and hop 1 while it is 25 percent for hop 2, hop 3, and hop 4. The loss is 35 percent for hop 5 and then decreased to 22 percent for the last hop, i.e., hop 6. The reason for such an increase in loss in grid topology is due to the use of one node to connect to multiple nodes and perform exchange of traffic (Figure 20).  
Figure 19: W-iLab1.t UDP bandwidth.  
Figure 20: W-iLab1.t UDP loss.  
3. **UDP Jitter:** The jitter is increased as hops are increased and this is the same for linear, ring, and grid topology (Figure 21).
4. **TCP and SCTP Bandwidth:** TCP and SCTP bandwidth decrease as hops increase for linear, ring, and grid topology (Figure 22). Hop 1 bandwidth is not decreased much because of the use of the 5 GHz lower channel and the proximity to node 0 and node 2. See Figure 9 for understanding of the setup and relation to the results.
5. **TCP and UDP Latency:** UDP latency is increased in linear topology because of the distance from node 0 to node 9, i.e., from 575 \(\upmu\)s to 1 s, in order to cover more hops, while TCP latency is minimal, i.e., between 500 \(\upmu\)s and 24 ms. For other topologies, i.e., ring and grid, UDP latency is minimal (Figure 23).
6. **TCP and UDP Latency:** UDP latency is increased in linear topology because of the distance from node 0 to node 9, i.e., from 575 \(\upmu\)s to 1 s, in order to cover more hops, while TCP latency is minimal, i.