esa-sceva/satcom-chunk-collection huggingface dataset:
# Node Depth Adjustment Based Target Tracking in UWSNs Using Improved Harmony Search
## 3 Problem Formulation
### Target State Model  
In this paper, the target is assumed to be a slowly maneuvering point that moves at a known depth \(h\). For the sake of simplicity, the nearly constant turn (CT) model can be utilized. It should be noted that our scheme is not limited to the CV model; for some complex motion models, the interacting multiple model (IMM) approach can be combined with our scheme. Thus the target state is given by following equation:  
\[\mathbf{x}_{k+1}=\mathbf{F}_{k}\mathbf{x}_{k}+\mathbf{w}_{k}, \tag{1}\]  
where the target state at time \(k\) is given by \(\mathbf{x}_{k}=[x,v_{x},y,v_{y}]^{\mathrm{T}}\). \((x,y)\) is the target's location; while \(v_{x}\) and \(v_{y}\) are the corresponding velocity in \(x\) and \(y\) coordinates, respectively. \(\mathbf{F}_{k}\) is the state transition matrix; the process noise \(\mathbf{w}_{k}\) is always assumed to be Gaussian with zero mean and covariance matrix \(\mathbf{Q}_{k}\). The state transition matrix \(\mathbf{F}_{k}\) and process noise covariance matrix \(\mathbf{Q}_{k}\) are given as follows:  
\[\mathbf{F}_{k}=\begin{bmatrix}1&\frac{\sin{wT}}{w}&0&-\frac{1-\cos{wT}}{w}\\ 0&\cos{wT}&0&-\sin{wT}\\ 0&\frac{1-\cos{wT}}{w}&1&\frac{\sin{wT}}{w}\\ 0&\sin{wT}&0&\cos{wT}\end{bmatrix}, \tag{2}\]  
Figure 1: Network model (the black triangle is a moving target; the red square is the fusion center; the blue oval is the sleeping node, which is static at the water surface; the green ovals are activated nodes, which move down vertically for sensing the target according to commands from the fusion center).  
\[\mathbf{Q}_{k}=q^{2}\begin{bmatrix}\frac{2(wT-\sin wT)}{w^{3}}&\frac{1-\cos wT}{w^{2 }}&0&\frac{wT-\sin wT}{w^{2}}\\ \frac{1-\cos wT}{w^{2}}&T&-\frac{wT-\sin wT}{w^{2}}&0\\ 0&-\frac{wT-\sin wT}{w^{2}}&\frac{2(wT-\sin wT)}{w^{2}}&\frac{1-\cos wT}{w^{2} }\\ \frac{wT-\sin wT}{w^{2}}&0&\frac{1-\cos wT}{w^{2}}&T\end{bmatrix}, \tag{3}\]  
where \(T\) is the sampling interval, \(w\) is turn rate, which is known and constant, and \(q\) is the process parameter.
## 4 Simulation Setup and Results
### Comparison between the Proposed Method and Baseline Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Gain**} & \multirow{2}{*}{**Methodology**} & \multicolumn{2}{c}{**Input-Current**} & \multirow{2}{*}{\(\mathbf{D}\)} & \multirow{2}{*}{\(\mathbf{k}\)} & \multirow{2}{*}{\(\mathbf{D_{\text{Z}}}\)} \\  & & & **Ripple (\(\mathbf{\Lambda}_{\text{i}\text{z}}\))** & & \\ \cline{2-5} \multirow{2}{*}{5.8} & Proposed & **0.8227** & 0.8107 & 0.4213 & 0.9999 \\ \cline{2-5}  & Baseline & 1.278 & 0.2752 & 0.7416 & 0.6 \\ \hline \multirow{2}{*}{5.9} & Proposed & **0.8362** & 0.8142 & 0.4186 & 0.9995 \\ \cline{2-5}  & Baseline & 1.8344 & 0.7793 & 0.7419 & 0.6 \\ \hline \multirow{2}{*}{6} & Proposed & **0.8493** & 0.8177 & 0.4161 & 0.9999 \\ \cline{2-5}  & Baseline & 1.8721 & 0.7831 & 0.7442 & 0.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 3: (**a**) Comparison between proposed method and baseline algorithm based on input-current ripple, (**b**) Percentage improvement of proposed method compared with baseline algorithm.
## 5 Conclusions  
Our findings demonstrate that NIRE treatment resulted in a tissue impedance change of 13.5 Ohm relative to IRE treatment. In terms of apoptosis induction, NIRE led to an increase of 1.12 times in comparison to IRE. Furthermore, the expression of the apoptosis-associated protein BCL was amplified by 1.24 times and p21 protein by 1.29 times, and SOD2 exhibited a 0.93-fold expression in NIRE-treated tissues as compared to those treated using IRE. This enhanced response can be attributed to the interactions between NIR and cells, which seems to modulate cellular metabolism prior to IRE treatment. Importantly, we observed no adverse or toxic effects from NIR exposure on the tissue. These findings hold promise for augmenting the apoptotic efficacy of IRE in therapeutic settings.  
Conceptualization, H.B.K., H.J.J. and B.K.; investigation, H.B.K. and H.J.J.; data curation, Y.S.S. and E.S.K.; formal analysis, H.B.K., H.J.J., H.S.C. and J.M.L.; funding, H.J.J., H.J.C. and H.B.K.; methodology, B.K., H.S.C., H.S.L. and J.M.L.; project administration, B.K.; supervision, B.K.; validation, S.Y.Y., Y.T.J., H.S.C. and H.J.J.; visualization, H.B.K., Y.S.S. and E.S.K.; writing, H.B.K. and H.J.J.; review and editing, B.K., S.Y.Y., Y.T.J., H.S.C. and H.J.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Research Foundation of Korea (NRF), grant number 2022R1F1A1075102 and 2021R1 IIA1A0105/303.  
The animal study protocol was approved The Institutional Animal Care and Use Committee of the Korea University College of Medicine (KOREA-2019-0146; 2019.12.16, and KOREA-2021-0017; 2 July 2021).  
Not applicable.  
This research was funded by National Research Foundation of Korea (NRF), grant number 2022R1F1A1075102 and 2021R1 IIA1A0105/303.  
The authors declare no conflict of interest.
# Probabilistic Seismic Hazard Assessment of the Southwestern Region of Saudi Arabia
###### Abstract
The assessment of seismic hazard holds significant importance in mitigating the impact of earthquakes and is crucial for purposes such as land-use planning, civil engineering endeavors, and emergency response, particularly for substantial projects within the study area such as the Najran Valley Dam, the restoration of agricultural terraces, and various urban expansion initiatives. Remarkable projects, including those in Al-Bahah, Asir, Jazan, Najran, and the Farasan Island Marine Sanctuary, fall within the research area. Al-Bahah, renowned as a premier tourist destination, is of particular interest. Utilizing expected ground motion values derived from seismic hazard assessments becomes imperative for site evaluations, especially for significant constructions, aiding not only in infrastructure design but also in the development of urban expansion projects [1; 2; 21; 22; 23; 24; 25; 26]. These findings are essential for the planning and construction of a wide range of infrastructure components, including gas pipelines, roads, bridges, water and power lines, and communication cables. Therefore, the main goal of this work is to reduce the risk that earthquakes pose in order to make it easier to develop buildings that have improved seismic resistance. This initiative aligns with the broader vision of the Kingdom 2030, ensuring that both current and future projects adhere to standards that prioritize earthquake resilience. A novel seismic hazard assessment has been developed due to the shortcomings of the existing seismic design regulations and assessments, the absence of a thorough seismic hazard study for southwest Saudi Arabia, the unique population density, and socioeconomic dynamics of the region, among other factors. A new PSHA will be constructed as part of this initiative, tailored specifically for this region and taking into consideration two different soil conditions (B/C and C classes) in line with the National Earthquake Hazards Reduction (NEHRP) initiative [27]. PGA and SA values are highlighted in the assessment, which spans two distinct return periods (475 and 975 years).
## 2 Materials and Methods
### Data Analysis
#### 2.2.2 Screening Using Support Vector Machine Learning Variations  
Input-output data may be denoted in paired sets of (x\({}_{i}\), y\({}_{i}\)), with 1 \(\leq\) i \(\leq\) n. Support vector machine learning may be used in regression applications because it relies on a small number of observations from the training data to form the optimal hyperplane [70; 71; 72].  
Therefore, support vector machine learning is used in order to linearly fit a function, in generic terms, y = f(x), in order to best capture this input-output variable relationship. The goal is to estimate an optimal hyperplane, which is a linear function that dichotomizes the maximal margin that is created by the separation of different classes of the input-output vector datapoints. The datapoints assist in forming the maximal margin that are defined as the support vectors (\(1\leq\text{i}\leq l\)). In simplified form, the function is written as  
\[\text{y}=\text{f(x)}=\text{<w,x>}+\text{b where w}\subseteq\mathbb{R}^{\text{d}} \text{ and }\text{b}\in\mathbb{R}\]  
where <w,x> is the inner product of w and x, and b is the intercept. The Vapnik formulation [70; 71; 72] relies on the slack variables \(\xi_{i}\) and \(\xi_{i}\)* to tackle situations where infeasible constraints on the optimization problem become relevant to the goal of minimizing the norm of w. Therefore, this extended formulation for the objective function becomes  
\[\text{Minimize}\ \frac{1}{2}\|w\|^{2}+C\cdot\sum_{i=1}^{l}(\xi_{i}+\xi_{i}^{*})\]  
The positive constant \(C\) balances the levelness of the fitted function in competition with those fitting errors, which appear to be larger than the prespecified error constant, \(\epsilon\). The two conditions to accomplish this are given by the following inequalities:  
\[\begin{array}{c}y_{i}-\langle w,x_{i}\rangle-b\leq\epsilon+\xi_{i}\text{with }\xi_{i}\ \geq 0\\ -(y_{i}-\langle w,x_{i}\rangle-b)\leq\epsilon+\xi_{i}^{*}\text{with }\xi_{i}^{*}\ \geq 0 \end{array}\]  
By composing a primal objective function, a Lagrangian is used, which incorporates the objective function and the two conditions with two sets of multipliers each, these are (1) \(\eta_{\text{i}}\) and \(\eta_{\text{i}}\)* to adjust the two slack variables \(\xi_{i}\) and \(\xi_{i}\)* and (2) \(\alpha_{\text{i}}\) and \(\alpha_{\text{i}}\)* to hinge the two conditions on the Lagrangian function. By obtaining the extrema of the Lagrangian function, it has been shown that the w and b are straightforward to estimate:  
\[w=\sum_{i=1}^{l}\left(\alpha_{i}-\alpha_{i}^{*}\right)x_{i}\]  
and  
\[\max\{y_{i}-\langle w,x_{i}\rangle-\varepsilon|\alpha_{i}<C\ or\ \alpha_{i}^{*}>0\}\leq b\leq\min\ \{y_{i}-\langle w,x_{i}\rangle-\varepsilon|\alpha_{i}>0\ or\ \alpha_{i}^{*}<C\}\]  
Thus, the hyperplane is defined by estimating \(\mathbf{w}\) based only on the support vectors, and its equation is simply <w,x> + b = 0. The function between input and output is given, then, by the support vector expansion:  
\[y=f(x)=\sum_{i=1}^{l}\left(\alpha_{i}-\alpha_{i}^{*}\right)\langle x_{i},x \rangle+b\]
## 3 Hybrid Topology Architecture
### Implementation
#### 3.2.6 Topology Manager
This way, packet loss is avoided as the hybrid end device needs to be operative before the end device changing into mesh mode performs its topology change. The topology manager will not conduct any other petition for topology changes until the previous requests are completed since the effects of the topology change cannot yet be assessed. The message for the topology change includes the end device identifier and the operating mode to which the end device needs to change. Once the petition is formed, it is added to the downlink queue at the LoRaWAN server. This information is appended to the data field in the LoRaWAN message, and thus, no change is performed in the operation mechanism of the LoRaWAN protocol. To send the topology change petition, four consecutive messages are transmitted in time intervals of 5 s so as to ensure the reception of the message. Upon message reception, all end devices read the identifier to assess if they are the recipient. Messages directed to other end devices will be discarded. For an end device to change back into star topology mode, the topology manager sends a topology change message when the minimum RSSI threshold is exceeded. This downlink message must be received first by the end device in hybrid topology mode. Then, it will re-transmit the message to the end device in mesh mode. The topology manager also allows manual topology administration. Therefore, if necessary, a topology mode can be selected for a specific end device, and the topology manager will send the topology change petitions accordingly. The topology manager was implemented in a local Ubuntu server at Santiago de Cali University. This implementation does not imply any changes to the software of the remaining elements of our implementations. However, it needs to be configured with the required information regarding the MQTT broker that manages the topics where the information of the end devices is published to extract the link quality data, the application ID at the Chirpstack LoRaWAN server, and the topic where the LoRaWAN server is subscribed for data. Changing topologies in LoRaWAN networks can extend the coverage of the network and improve transmission link quality for end devices that are affected by temporary or permanent interference or obstacles. However, greater benefits can be achieved with future topology manager implementation strategies, such as reducing energy consumption, minimizing the carbon footprint of the network deployment, and even creating a network that is \"alive\" or \"sensitive\" and therefore able to respond to the dynamics of the environment.
# Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
## 5 Simulation Results
### Performance Evaluation for 5G Communications
#### 5.3.1 Covertness of Communications  
The success of covertness for 5G communications is considered in Figure 7. As in the previous figures for QPSK-modulated signals and 16 QAM-modulated signals, the proposed perturbation outperforms the Gaussian noise significantly in the high-PNR region for 5G signals. Furthermore, we observe that more power is needed for the CJ to fool the classifier at the eavesdropper when the distance between the CJ and the eavesdropper increases.
## 2 PCMs: Basic Characterization
### PCMs Classification  
PCMs are classified according to composition, phase change process, and phase change temperature. More precisely, the latter turns out to be the main parameter for the choice of a PCM, which needs to be suitable for the temperature range that occurs in the considered application. In general, the classification according to the phase change process is divided into four subcategories: solid-solid, solid-liquid, liquid-gas, and solid-gas [19]. As for the classification concerning phase change temperature, three groups are universally accepted among the authors (low, medium, and high temperature), even if there is no clear definition of the temperature ranges for each group, as shown in Table 1.  
Therefore, the classification of PCMs according to composition is still the most common in the literature due to its intrinsic simplicity, since groups of materials with a si  
\begin{table}
\begin{tabular}{c c c c} \hline
**Reference** & **Low Temperature** & **Medium Temperature** & **High Temperature** \\ \hline Abhat [15] & \textless{}120 \({}^{\circ}\)C & - & - \\ [PERSON] et al. [16] & \textless{}100 \({}^{\circ}\)C & - & - \\ [PERSON] et al. [3] & \textless{}60 \({}^{\circ}\)C & Range 80–120 \({}^{\circ}\)C & \textgreater{}150 \({}^{\circ}\)C \\ [PERSON] et al. [8] & - & - & \textgreater{}120 \({}^{\circ}\)[PERSON] et al. [18] & - & - & \textgreater{}100 \({}^{\circ}\)[PERSON] et al. [19] & \textless{}30 \({}^{\circ}\)C & Range 40–200 \({}^{\circ}\)C & \textgreater{}200 \({}^{\circ}\)C \\ \hline \end{tabular}
\end{table}
Table 1: Phase change materials classification, according to phase change temperature.  
Figure 1: Phase change materials classification according to composition.  
similar characteristics. Several authors have presented such a classification, based on three main groups: organic, inorganic, and mixtures. Each main group has subgroups, as highlighted in Figure 1, according to the classifications shown in works [2; 15; 16; 18; 19].
## 4 Experimental Results and Analysis
### Ductility
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**SC1**} & \multicolumn{2}{c}{**Yielding Point**} & \multicolumn{2}{c}{**Limit Point**} \\ \cline{2-5}  & \(M_{y}\) (kNm) & \(d_{y}\) & \(M_{\text{max}}\) & \(d_{max}\) \\ \hline Pull & 679.76 & 2.085 & 779.21 & 4 \\ Push & 663.68 & 2.098 & 757.25 & 4 \\ Mean & 671.77 & 2.092 & 768.23 & 4 \\ \hline \multirow{2}{*}{**SC2**} & \multicolumn{2}{c}{**Yielding Point**} & \multicolumn{2}{c}{**Limit Point**} \\ \cline{2-5}  & \(M_{y}\) (kNm) & \(d_{y}\) & \(M_{\text{max}}\) & \(d_{max}\) \\ \hline Pull & 799.95 & 2.415 & 927.33 & 5 \\ Push & 782.57 & 2.433 & 910.55 & 5 \\ Mean & 791.01 & 2.423 & 918.95 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Joint-bending moment and drift at different characteristic points. Figure 16: Definition of the yield point according to the general yielding method. despite the lower value of the drift when the bending-resistance moment decreased to 85% of the ultimate moment. Nevertheless, one can see that the tested specimens had a ductility coefficient greater than 2.5%. According to Eurocode 8, it can be concluded that the studied RCS joint can be used as a dissipative element in ductility class medium (DCM) structures. It should be noted that to study the force transfer mechanism from the beam to the column via an embedded steel profile, the tested specimens were designed to avoid failure in the steel beam. As a result, the ductility of the steel beam was not exploited. Therefore, the ductility coefficient obtained from these tests was not representative of the joint.
## 3 Methodology
### Waveform Exclusion  
For very small water bodies, the waveform may be highly contaminated by the land. Thus, we added additional conditions to exclude the waveforms with a poor quality.  
First, if the epoch of the reference SRTM DEM is outside the waveform window, which has a measuring range of approximately 60 m, it is likely that the on-board tracker window was unable to focus on the reflecting surface. Second, within the vicinity of land, reflections from both water and land will contribute to the received waveform, resulting in more than one peak. If the number of outstanding peaks is larger than a threshold, the waveform may be contaminated substantially by land or contains no water information at all, making it not suitable for water level retrieval. In our case, the threshold was set to 5 based on the analysis of waveforms. The number of outstanding peaks is normally less five, even with land contaminations. Finally, sigma_0, which is the backscatter coefficient for water from nadir, should be much larger than the backscatter coefficient for land, where the value is normally less than 50 dB based on the analysis in our study area. With these conditions (summarized below), we could filter out highly contaminated waveforms that were unsuitable for detecting the levels of water bodies:  
* The epoch of the reference SRTM DEM must be within the waveform window
* Number of outstanding peaks \(<5\)
* Sigma_0 (backscatter coefficient) \(>50\) dB  
After waveform exclusion, only the remaining waveforms (including both slightly contaminated and clean waveforms) were considered in our study to maximize the usage of the available measurements in the water body of interest. For a small water body, the quality of the waveform largely depends on the satellite tracks which drift daily, and the surface area changes with seasons, resulting in no clean waveform from time to time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Definition** & **Formulation** \\ \hline \(BW\)1 & Receive Bandwidth & - \\ PRF & Pulse Repetition Frequency & - \\ \(N_{p}\) & Number of Pulses in a burst & - \\ \(f_{c}\) & Carrier frequency & - \\ \(c_{0}\) & Speed of light & - \\ \(R\) & Range to the surface (distance from & - \\  & satellite to the surface) & - \\ \(H_{orb}\) & Orbit Height & - \\ \(H_{s}\) & SWH & - \\ \(\sigma_{z}\) & Standard deviation of the height PDF & \(\frac{H_{s}}{\mathcal{R}}\) \\ \(\alpha_{R}\) & Orbital factor & \(\alpha_{R}=\frac{\mathcal{R}+H_{orb}}{2\cdot\
u_{T}\cdot c_{i}\cdot N_{p}}\) \\ \(L_{x}\) & Along-track resolution & \(\frac{\alpha_{0}\cdot H_{orb}}{2\cdot\
u_{T}\cdot c_{i}\cdot N_{p}}\) \\ \(L_{y}\) & Across-track resolution & \(\sqrt{\frac{c_{0}\cdot H_{orb}}{\alpha_{R}\cdot BW}}\) \\ \(L_{z}\) & Vertical resolution & \(\frac{c_{0}}{2\cdot BW}\) \\ \hline \hline \end{tabular}  
* For a chirp pulse the bandwidth can be expressed in terms of the chirp rate (\(k_{r}\)) and pulse duration (\(T_{p}\)) as follows: \(BW=K_{r}\cdot T_{p}\).  
\end{table}
Table 4: Definition of the main parameters of the SAR ocean model [65].
# I-BIM Applied in Railway Geometric Inspection Activity: Diagnostic and Alert
## 3 BIM Modeling Process
### Main Railway Track Components  
The superstructure of a railway is the component that directly impacts the loads transported, and it is composed of rails, fasteners, sleepers and layers of ballast and sub-ballast (Figure 2).  
The European Committee for Standardization (Comite European de Normalization, CEN), which supports the emission of rules for Europe, the European Standards (ENs), established the document EN 13848:2022. This standard provides guidelines for the identification of the main rail track geometry factors, establishes the fundamental requirements to make the measurements and refers to the traditional inspection procedures, in order to ensure quality and safety on railway tracks [26]:  
* Rail track geometry components: a set of parameters considered in the description of the geometrical characteristics of the rail elements, namely, track gauge, longitudinal level, alignment, cross level and twist.
* Track geometry quality: the valuation of the geometric deviation impact, in elevation and cross direction, considering the standard geometrical characteristics of each geometric parameter relevant to supporting the necessary safety concerning the drive quality.
* Gauge: the measurement made between the inside faces of the ride rail track head.
* Running table: the top surface of the rail.
* Running surface: the bent surface identified by the longitudinal displacement of a straight rail segment orthogonal to the middle line of the rail track and the peripheral running sides.
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 4 Performance Metrics  
The methods to assign link capacity resources in the network to flows discussed in the previous section are evaluated using metrics describing the amount of allocation, the fairness and combinations of both. Definitions of these metrics are given in this section.
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
### Quantum Computing Exploits  
For some time, it has been known that the mathematical principles underpinning RSA and ECC's security, whilst computationally intensive and difficult for a classical computer-based attack, are potentially readily solvable with quantum computing hardware. [PERSON]'s algorithm [8] is notable in this respect, as it is able to factorise large numbers extremely efficiently. This development necessitates the creation of alternative techniques for secure key exchange in a 'post-quantum' world, particularly as encrypted data could be stored and recovered later using these methods. In response to this threat, the National Institute of Standards and Technology (NIST) has been a leader, initiating a competition in 2017 to identify promising post-quantum cryptography (PQC) methods [9]. These techniques rely on differing mathematical problems, believed to be resistant to both known classical and quantum computing algorithms.