esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Coverage Percentage  
The angle between a satellite's body axis and the line of sight of its sensor or communication antenna is referred to as the off-boresight angle. It plays a significant role in determining how well a satellite's sensors or communication systems operate. For instance, a satellite built to capture high-resolution photos of the ground can feature a camera with a narrow field of view that is suited for collecting pictures at close off-boresight angles. On the other hand, a satellite may include an antenna with a broad field of view that may sustain a strong signal at greater off-boresight angles if it is intended to connect with other satellites or ground stations. Figure 9 shows the same coverage as in Figure 8 but considers that the satellites can provide coverage to a certain range or angle off their Nadir ground track. The latitude is represented by the x-axis, and the coverage percentage of the orbits is shown on the y-axis. Three separate satellites are represented by the various lines on the graph. By incorporating the off-Nadir angle, one can observe that the coverage does not reduce so sharply beyond a certain max latitude value. Figures 9 and 10 show how the fields of view at 30\({}^{\circ}\) and 60\({}^{\circ}\) vary in coverage, respectively.  
Analyzing Figures 9 and 10 proves that, for the near-equatorial regions, a satellite with a lower inclination of 24\({}^{\circ}\) is favorable to an SSO or polar-type orbit. For example, in the countries/cities at 25\({}^{\circ}\) N, such as Algeria, Egypt, Saudi Arabia and the United Arab Emirates, the daily coverage percentage is at 20%, as opposed to 8.5% for SSO. The higher coverage provides a lower revisit time and more access for a closer to real-time application of IoT. However, at higher latitudes, it is less likely to receive coverage from satellites with lower inclination angles, as their ground tracks are further away from the equator.  
Figure 10: Sixty degrees off-boresight angle.  
Figure 9: Thirty degrees off-boresight angle.
# An Energy Demodulation Based Fiber Optic Sensing System for Landslide Early-Warning
## 7 The Threshold Determination  
The basic role of the threshold setting is to ensure that the system is neither too conservative, leading to numerous false alarms, nor fail to catch real events. The threshold setup should provide adequate warning time. The alarm thresholds determination of the FOS-LW was largely based on reviews of historical data and experiences with similar monitoring projects at other locations throughout the world. Based on extensive data analysis, the following three factors are concluded to determine the alarm thresholds, namely: horizontal displacement, vertical displacement, and the broadening of existing cracks [14]. For the FOW-LS system, two types of threshold are considered. Exceedance of these thresholds will result in an alarm announcement for the specific alarm level.
## 6 Conclusions  
In short, the processing variables of eddy current testing have been experimentally studied in detail. Based on the RSM model, the most effective factor on the percentage of the signal is the depth of the defect in the present study. The ANFIS model is developed to predicate the percentage signal of the eddy current testing measurements. The ANFIS models can be used effectively to estimate the characteristics of defects. The study results can be summarized as follows:  
* For the percentage signal, the maximum error for trained and tested values is 0.023%, and the average error is 0.000493 in the ANFIS model.
* The prediction of the averaged percentage signal with the ANFIS models is in good agreement with the experimental result; it also has a smaller error.  
The results prove that material conductivity and the size of cracks directly affect the eddy current signal.  
**Acknowledgments: This work was supported by Malaysia Pahang under project vote RDU1403116.**  
**Author Contributions: [PERSON] and [PERSON] designed and arranged the structure of the article; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data preview in the previous work in this field; and [PERSON] and [PERSON] wrote the paper.**  
**Conflicts of Interest: The authors declare no conflict of interest regarding the publication of this paper.**  
Figure 10: Error between experimental and ANFIS predicted values for each sample.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Exp. Run** & **Signal \%** & **ANFIS** & **ER\%** \\ \hline
5 & 0.58 & 0.57900165 & \(-\)0.001 \\
7 & 0.72 & 0.71979543 & \(-\)0.0002 \\
11 & 0.78 & 0.78048298 & 0.000483 \\
13 & 0.86 & 0.86052741 & 0.000527 \\
19 & 0.92 & 0.91973257 & \(-\)0.00027 \\
23 & 0.4 & 0.39982569 & \(-\)0.00017 \\
28 & 0.6 & 0.59936003 & \(-\)0.00064 \\
31 & 0.68 & 0.68031255 & 0.000313 \\
36 & 0.8 & 0.79928399 & \(-\)0.00072 \\
40 & 1 & 1.0005583 & 0.000558 \\
44 & 0.56 & 0.5591191 & \(-\)0.00088 \\
46 & 0.48 & 0.47976182 & \(-\)0.00024 \\
50 & 0.75 & 0.74924405 & \(-\)0.00076 \\
52 & 0.8 & 0.79996763 & \(-\)3.2 \(\times\) 10\({}^{-5}\) \\
58 & 0.88 & 0.87939948 & \(-\)0.0006 \\  & & Maximum & 0.000998 \\  & & ABS Average & 0.000493 \\ \hline \end{tabular}
\end{table}
Table 5: Comparison between the numerical result and ANFIS models for testing data.
## Abbreviations  
\begin{tabular}{c c c} \hline \hline  & **Acronyms** & **Acronyms** \\ \hline ADC & Analog-to-digital converter & APD & Avalanche photodiode \\ BER & Bit error rate & **CAP** & **Carrier-less amplitude and phase modulation** \\ CBT & Complete binary tree & CD & Chromatic dispersion \\ CR & Clock recovery & DAC & Digital-to-analog converter \\ DCI & Data center interconnects & DD & Direct detection \\ DFE & Decision feedback equalizer & DML & Directly modulated laser \\ DMT & Discrete multi-tone & DSP & Digital signal processing \\ EML & Electro-absorption modulated lasers & FEC & Forward error correction \\ FFE & Feed-forward equalizer & FTN & Faster than Nyquist \\ HD & Hard decision & ID & Intensity directed \\ IM & Intensity modulation & ISI & Inter-symbol interference \\ KK & Kramers-Kronig & LMS & Least mean squares \\ \hline \multirow{2}{*}{MLSE} & Maximum likelihood sequence & \multirow{2}{*}{ML} & \multirow{2}{*}{Machine learning} \\  & estimation & & \\ \hline MMF & Multi-mode fiber & MZM & Mach-Zehnder modulator \\ NN & Neural network & OA & Optical amplifier \\ \hline OPBF & Optical band-pass filter & OSNR & Optical signal-to-noise ratio \\ PAM4 & Four-level pulse amplitude modulation & PD & Photodiodes \\ \hline PRBS & Pseudorandom binary sequence & QAM & **quadrature amplitude modulation** \\ ReLU & Rectified Linear Units & RLS & Recursive least squares \\
**SCO** & Sampling clock offset & SMMF & Standard single-mode fiber \\ SMO & Sequential minimal optimization & SPM & Self-phase modulation \\ SVM & Support vector machine & TIA & Trans-impedance amplifiers \\ VCSEL & Vertical cavity surface-emitting laser & VNLE & Volterra nonlinear equalizer \\ VR & virtual reality & ZF & Zero-forcing \\ \hline \hline \end{tabular}
# Modeling Activity-Time to Build Realistic Plannings in Population Synthesis in a Suburban Area
## 6 Algorithm for Time-Choices Setting
### Next Activity: Travel and Start-Time
#### 6.3.2 Start-Time of the Next Activity  
The start-time \(ST_{B}\) of the next activity \(B\), is equal to the sum of the end-time \(A_{end\_time}\) and the travel time \(TT_{A\to B}\) from \(A\) to \(B\) (see Equation (35)).  
\[ST_{B}=A_{end\_time}+TT_{A\to B} \tag{35}\]  
In the case of an activity \(B\), concerning the gap-value \(Gap_{B}<\)\(>\)\(null\), the earliest next visit time \(EVT_{B}\) is checked (see Equation (19)). To avoid waiting for the next activity, the agent has to start his next activity at \(EVT_{B}\) minus \(duration_{B}\), to calculate the earlier start-time, the \(duration_{B}\) is subtracted from \(EVT_{B}\) (see Equation (36)).  
\[ST_{B}=EVT_{B}-duration_{B} \tag{36}\]  
As a consequence, two potential start-time \(ST_{B}\) values are defined: \(ST_{B}^{1}\) refers to Equation (35) and \(ST_{B}^{2}\) refers to Equation (36). A combination of Equations (35) and (36) is presented in Equation (37)  
\[ST_{B}=max(ST_{B}^{1},ST_{B}^{2}) \tag{37}\]
# A Discrete Prey-Predator Algorithm for Cloud Task Scheduling
## 3 The Proposed Discrete Prey-Predator Algorithm for Cloud Task Scheduling
### Description of the Proposed Mechanism  
This study presents an application of the Discrete Prey-Predator algorithm to cloud task scheduling to allocate cloud tasks \(\mathrm{N}=[\mathrm{t}_{1},\mathrm{t}_{2},\mathrm{t}_{3},\ldots,\mathrm{t}_{n }]\) to the cloud providers' resources \(\mathrm{M}=[\mathrm{s}_{1},\mathrm{s}_{2},\mathrm{s}_{3},\ldots,\mathrm{s}_{m }]\). The proposed Discrete Prey-Predator algorithm generates an initial set of feasible solutions representing cloud schedules. For each solution member \(\mathrm{x}_{i}\), the algorithm calculates its fitness value, which is defined as the survival value. The calculation of the survival value is based on the encoded objective function of the proposed Discrete Prey-Predator algorithm. The proposed Discrete Prey-Predator algorithm for cloud task scheduling considers the execution time as the objective function for the population solution members (\(\mathrm{x}_{i}\)).  
In the next step, the proposed algorithm considers member \(\mathrm{x}_{i}\) with the minimum survival value as the predator and member \(\mathrm{x}_{j}\) with the maximum surviving value as prey. The prey individual that finds a hiding place is the prey individual with the best surviving value, and it is not affected by predators. Other prey individuals in the population move towards prey with better survival values.
## 2 Motivation and Related Work
### Contribution  
The design of a resilient SCN is the first key to the success of SGs. However, the extensive literature survey did not find sufficient articles in this direction. Thus, this paper envisions discussing resiliency analysis for the SCNs of SGs. The resiliency estimation of a communication network seems simple at first glance. However, in reality, it is not so. Furthermore, despite there being significant discussion of resiliency, it is very hard to find concrete work where an analytical method was validated using discrete event-based simulation for the resiliency estimation of SCNs. Thus, this paper presents a graph-theoretic approach for the analysis of the resiliency of SCNs. Moreover, a simulation framework for resiliency estimation was also proposed using discrete event-based Network Simulator (NS-3) software.  
In a nutshell, the main contributions of the paper are summarized below.  
* The concept of resiliency is discussed for the SCNs of SGs;
* A graph-theoretic approach is presented for the analytical analysis of the resiliency of the SCNs;
* Simple metrics to estimate the resiliency of an SCN have been introduced;
* State-of-art discrete event-based simulation framework for resiliency analysis of SCNs is proposed using the NS-3 simulation tools.
## 3 Materials and Methods
### Adaptive Density Homogenization
#### 3.1.1 High-Density Trajectory Cells Identification  
As mentioned before, vehicle trajectories probably manifest some specific behavior characteristics, such as changing lane, changing speed and steering off. However, due to uneven sampling density of raw trajectory data, the number of trajectories with such specific moving characteristics may greatly vary at different regions, which could make it difficult to make a uniform rule or threshold for detecting road intersection points from raw trajectory data. To acquire a density-even trajectory data, we should first recognize high-density trajectory cells and do a screening processing to ensure the uniform sampling in the whole study area. So, we partition the covering area of raw trajectory data into regular cells with the fixed size of \(w\)*\(h\). The cell width \(w\) and cell height \(h\) are calculated by the following formula.  
\[\left\{\begin{array}{l}w=k*\min\{dx_{i}|dx_{i}=|X_{i}-X_{i-1}|>0\}\\ h=k*\min\{dy_{i}|dy_{i}=|Y_{i}-Y_{i-1}|>0\}\end{array}\right. \tag{1}\]  
where \(dx_{i}\) and \(dy_{i}\) denote the absolute values of \(X\) and \(Y\) coordinate differences between two successive track points. \(k\) is a density parameter, uniformly set as 1000 here.  
Then, the proportion of track points in each cell \(G_{j}\) is computed as follows.  
\[Prop_{j}=\frac{Num(j)}{\sum_{i}Num(i)} \tag{2}\]  
where \(Num(j)\) is the number of trajectory points in the cell \(G_{j}\). The cell \(G_{j}\) will be identified as a high-density trajectory cell (renumbered as \(HG_{j}\)) when \(Prop_{j}\) is larger than a predefined threshold \(N_{grid}\).
# DDPG-MPCC: An Experience Driven Multipath Performance Oriented Congestion Control
## 1 Introduction
### Literature  
The literature on TCP congestion control design with AI and ML is very rich [13]. Of particular importance to this work is PCC [2; 3], where the sender observes the connection between its actions and its experienced performance, which allows it to consistently take actions and improve performance. The case of BBR [1] is slightly different. BBR updates its rate according to the measurement of the data delivered and the Round Trip Time (RTT), while Copa [4] implements the delay-based congestion avoidance mechanism.  
The design of MPTCP has included several advances in ML [5; 7]. Some of them used deterministic policy gradients to discover the most effective congestion management solutions, while others suggested DRL-based designs to reduce the size of out-of-order delivery from diverse pathways. Multipath congestion management strategies [5; 7; 9; 14] using DRL are pertinent to our study.  
In MPCC [5], an online learning approach is developed for multipath data networking but inherits limitations along with the PCC control mechanisms. MPCC [5] presents an innovative extension to harness surrounding bandwidths attentively, which is discussed in detail later (Section 1.2). In addition, there are several other articles that conduct DRL-based MPTCP designs with different objectives [7; 15; 16]. [PERSON] et al. [15] proposed SmartCC, which observes the environment and takes certain actions to adjust the congestion window to adapt to the varying network situations. [PERSON] et al. [16] developed a DRL agent that leverages the emerging deterministic policy gradient to train its networks and adjust the congestion window in every action. [PERSON] et al. [17] implemented a deep deterministic policy gradient in satellite communications to adjust congestion rates. [PERSON] et al. [18] proposed using a machine learning model for path management in mobile devices over mptcp where signal strength and other quality metrics associated with Wi-Fi are also taken into consideration.  
In this paper, _we develop a novel hybrid DDPG inside MPCC to intelligently drive packet flows across multiple paths where the DDPG module (i) maximizes the utility of the underlying network (ii) rewards an increase in throughput and (iii) ameliorates the adverse impact of latency and loss._
## 4 Evaluation Criteria
### Copper Loss  
Total losses in an SRM include iron loss and copper loss and the latter represent the major component of the losses. Thus, the copper loss directly influences the torque-speed performance and the drive efficiency. And the copper loss is in direct proportion to the square value of the rms phase current \(I_{rms}\), which is calculated from:  
\[I_{rms}^{2}=\frac{1}{\theta_{p}}\int_{\theta_{on}+\theta_{ov}}^{\theta_{off}- \theta_{ov}}i_{k}^{2}(\theta)d\theta+\frac{1}{\theta_{p}}\int_{\theta_{on}}^ {\theta_{on}+\theta_{ov}}\Big{[}i_{k}^{2}(\theta)+i_{k-1}^{2}(\theta)\Big{]}d\theta \tag{12}\]  
Since in the region \(\theta_{on}+\theta_{ov}\leq\theta\leq\theta_{off}-\theta_{ov}\), the \(k\)th phase produces the total torque independently as shown in Figure 4 and minimizing the rms phase current \(I_{rms}\) can be realized by decreasing the second part of Equation (12).  
Hence, by modifying Equation (12), an optimization objective function can be expressed as follow:  
\[\min:J_{1} =i_{k}^{q}(\theta)+i_{k-1}^{q}(\theta) \tag{13}\]  
where \(q\) is a bound variable satisfied \(q\geq 2\). In Equation (13), the \(q\)th-power of the current is a substitute for the integration of the averaged square value of the current during the commutation.  
A torque reference profile can now be generated for a specific TSF. The current profile and the flux linkage profile are obtained using the given torque and flux-linkage characteristics. Hence, the MAV-RCFL and the square value of the rms current can be computed. As discussed above, by optimizing the TSFs, a wide rotor speed range and less copper loss can be obtained by minimizing the MAV-RCFL and the square value of the rms current. This is the basic principle and direction for selecting an optimal TSF.
# Chaos Based Cryptographic Pseudo-Random Number Generator Template with Dynamic State Change +
## 4 One-Time Pad Stream Cipher Application  
### Application Design  
A stream cipher is an encryption mechanism that uses a cryptographically secure pseudo-random number generator and for each byte in the clear text it generates a pseudo-random byte, producing the cipher text by _XOR-ing_ the two values together. It has been proven [15] that if the generator is cryptographically secure and the same sequence is not used for multiple encryptions, the generated cipher text is impossible to discern from pseudo-random data and thus unbreakable. This section describes the implementation of such a one-time pad cipher with application on image security. The implementation uses the PNG Library in Linux to read and write images. The encryption is done on each byte on each channel on each pixel using a new pseudo-random byte. The application will write the encrypted result in the same format as the input image (grayscale, color, with or without alpha channel). The decryption is symmetric therefore it is achieved by running the same application with the encrypted image as input and the same seed used when encrypting. Running decryption with a different key yields pseudo-random data as output.
## 8 Multi-Objective Optimization Integrated with Neural Networks ANN
### Optimizing Food Supply Chains with MOO-ANN  
Supply chains in the food industry are highly complex, involving perishable goods, fluctuating demand, and diverse logistical challenges. MOO-ANN models enable real-time optimization of these systems by predicting demand patterns, optimizing transportation routes, and balancing inventory levels.  
For instance, integrating MOO-ANN in a frozen food supply chain reduced spoilage rates by 18%, improved delivery time accuracy by 12%, and cut transportation costs by 15%. These quantitative gains contribute directly to SDG 9: Industry, Innovation, and Infrastructure by fostering efficient and resilient supply chain networks [36].
# Characterization of HVSR and VRSR in the Loess Plateau of China Based on Strong-Motion Data
## 4 Results and Discussion
### Characterization of the HVSR and VRSR Mean Curve Features
#### 4.3.1 Average Site Amplification Effects and Predominant Periods of Loess Plateau Sites  
The \(A_{0}\) and \(T_{0}\) of a site are influenced by various factors, such as the soil layer structure and shear wave velocity. The HVSR and VRSR spectral ratio curves of different sites exhibit distinct characteristics. To analyze the overall features of the \(A_{0}\) and \(T_{0}\) values of loess sites, the 159 spectral ratio curves calculated in this study were averaged at different period points, resulting in the mean spectral ratio curves shown in Figure 11a,b. These mean curves can be used to roughly estimate the \(A_{0}\) and \(T_{0}\) values of loess sites.  
Figure 11: The mean curves in the Loess Plateau region. (**a**) HVSR mean curves; (**b**) VRSR mean curves.  
Figure 10: Mean power spectra for various epicentral distance ranges in the Loess Plateau region.  
As shown in Figure 11a,b, the HVSR exhibits an \(A_{0}\) value of 3.12 and a \(T_{0}\) of 0.44 s, while the VRSR shows an \(A_{0}\) of 3.12 and a \(T_{0}\) of 0.37 s. It is apparent that the \(A_{0}\) value for loess plateau sites is approximately 3, with a \(T_{0}\) of around 0.4 s.
# Hypoplastic Modeling of Soil-Structure Contact Surface Considering Initial Anisotropy and Roughness
## 2. Fundamental of the Hypoplastic Constitutive Model for the Soil-Structure Contact Surface
### Contact Surface Model Considering Initial Anisotropy and Roughness  
To enhance the model's predictive accuracy, crucial factors such as structural surface roughness and initial anisotropy, which significantly impact the mechanical response of the contact surface, are integrated into the model derived from Equation (24).
## 2 Theoretical Background
Several linear approaches have been proposed so far to extract the endmembers from a given data. These approaches can be divided into geometrical techniques that consider the mixed vector enclosed within a simplex statistical framework that determines endmembers through parameters estimation, and methods that model the linear mixture as a sparse regression problem. Given the data, the abundance of each identified endmember can be quantified by solving a constrained optimization problem, which minimizes the residual between each of the observed spectral vectors and the space spanned by the inferred endmembers [21]. It is worth noting that using linear approaches in images presenting a nonlinear mixture may not only detect all the endmembers present in the scene but also may result in the identification of non-existing endmembers. Taking into account that modeling all the possible nonlinear interactions between light and materials characterizing Raman spectroscopy is an extremely complex task, the use of NLPCA [22] is here proposed to linearize the original data into a linearized feature space while reducing the original data dimensionality. This will then allow the implementation of linear unmixing models for the extraction of endmembers and abundance estimation. In particular, the N-FINDR and SUnSAL algorithms as described in [23,24] will be considered for the endmember extraction and the solution of the constrained least square problem, respectively.