esa-sceva/satcom-chunk-collection huggingface dataset:
# Event Coverage Detection and Event Source Determination in Underwater Wireless Sensor Networks
## 2 Preliminaries: Routing Tree Construction and Maintenance
Note that when \(\textit{dist}\) is almost the same in value as \(r\), the acoustic transmission loss should be high, the packet delivery ratio should be low and the bit error rate should be large [31, 32]. This means that \(\textit{uv}_{prt}\) may not be appropriate to serve as the parent of \(uv\), although \(\textit{uv}_{prt}\) is still within the communication radius of \(uv\). Therefore, a robustness factor \(\xi\in(0,1]\) is applied, such that \(\textit{uv}_{prt}\) is assumed appropriate to serve as the parent of \(uv\), when \(\textit{dist}\) is not larger than (\(\xi\times r\)). When no ACK is replied to by \(\textit{uv}_{prt}\), which suggests that \(\textit{uv}_{prt}\) cannot be the parent of \(uv\) any longer, a neighboring sensor node \(\textit{uv}_{y}\) should be chosen as the parent of \(\textit{uv}\) through exchanging the EPING-PONGcontrol packets. We refer the reader to check our previous work [33] for this procedure. Specifically, \(\textit{uv}\) will broadcast a EPING control packet:  
\[\textit{EPING}=<\textit{id}_{uv},\textit{id}_{uv},\textit{g}(\textit{uv}_{y}),\textit{HC}(\textit{uv}),\textit{pid},\textit{L}_{pkt}> \tag{4}\]  
where \(\textit{L}_{pkt}=\{<\textit{pk}\textit{id}_{src},\textit{pk}\textit{id}_{id}>\}\) represents a set of packet identifiers to be forwarded. \(\textit{pk}\textit{id}_{src}\) specifies the sensor node that has generated this sensory data packet \(pkt\) and \(\textit{pk}\textit{id}\) is the unique identifier of \(pkt\). \(\textit{uv}_{y}\) replies with a PONG control packet to \(\textit{uv}\) when it received this _EPING_ control packet:  
\[\textit{PONG}=<\textit{id}_{uv},\textit{id}_{uv},\textit{pid},\textit{HC}( \textit{uv}_{y}),\textit{queue},\textit{energy},\textit{lg}(\textit{uv}_{y}, \textit{uv}),\textit{bit}\_{mask}_{L_{pkt}},\textit{bit}\_{mask}_{JR}> \tag{5}\]where _queue_ specifies the available buffer space at \(uv_{y}\) and _energy_ refers to the resident energy at \(uv_{y}\). \(bit\_mask_{L\
eq t}\) and \(bit\_mask_{JR}\) are used for handling link asymmetries and interference. According to the set of PONG control packets received, \(uv\) selects the most appropriate sensor node, whose value of \(g(uv_{y})\) is the largest, as its parent node. Generally, a routing tree is responsible for routing data packets to \(SN\) along parent-child relations between sensor nodes, rather than selecting relay nodes whenever a sensor node has a data packet to be delivered. As shown by the evaluation result in [22], this strategy should reduce the energy consumption for sensory data gathering, aggregation and routing to \(SN\) to a certain extent, especially when the network topology is relatively steady.
## 4 _Atb_ Analysis Method and Result  
### Reasons for Time Series Analysis of IoT LOG Data  
Previous studies on _ATB_ analysis mainly relied on surveys [14; 15], but owing to the development of IoT technology, driver behaviors can be tracked in real time, enabling the acquisition of more accurate results. In this study, based on IoT log data, time series analysis was implemented to investigate _ATB_ according to FCW installation status.  
With regard to IoT log data, ADAS-FCW had been installed in the vehicles of the taxi drivers who participated in the _PBC_ analysis since 2017, as shown in Figure 8, and information on the collision risk with the front of the vehicle was continuously recorded. The recorded information was transmitted to the IoT platform in real time.  
Prior to analyzing the collision risk information collected as above for one year, it was necessary to understand the characteristics of drivers' perceptions and behavioral changes. Factors that affect drivers' perceptions, such as education, do not have a persistent effect of changing the behaviors of drivers but require periodic training and stimulation [23]. This is because the effect returns to a certain value after a certain time [24].  
The IoT log data collected in this study were also collected over a limited period of one year, so prediction of the future behavioral changes was required. The estimate techniques included linear and non-linear regression, historical average algorithms [25], smoothing techniques [26; 27], and autoregressive linear processes [28; 29; 30; 31; 32]. It has been reported that a time series analysis-based technique such as autoregressive integrated moving average (ARIMA) is one of the most precise methods for the prediction of driving behaviors compared to the other available techniques mentioned above [33; 34]. Therefore, time series analysis using the ARIMA model was performed for analysis of the effect of FCW on _ATB_.
## 5 Experimental Results
### Performance Metrics
#### 5.2.1 Consistency  
The program consistency refers to the guarantee that the program follows the required rules to provide the stable outcomes. The user's basic cybersecurity awareness consistency is calculated using the following equation:  
\[C=\frac{C_{a}}{T_{a}}\times 100\% \tag{38}\]  
where \(C\) is the cybersecurity awareness consistency, \(C_{a}\) is the number of correct answers, and \(T_{a}\) is the total number of answers.  
The data in Figure 4 demonstrate the correlation between the number of answered questions using the proposed WBCA and its comparison with other programs. The result demonstrates the consistency and versus the time users take to solve the test questions. The calculated data used to define the cybersecurity awareness consistency levels of the tested users are depicted in Figure 4a, which illustrates the best cybersecurity topic (with 85% knowledge) demonstrates that our proposed WBCA produces 99.76% consistency; whereas the contending programs show 97.4-98.75% consistency. The CKS yields 97.4% consistency that is less than other contending programs.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Components** & **Version/The Name of the System** \\ \hline Personal computer & x64 \\ Operating system & Linux CentOS 7 \\ Java version & 1.8.271u \\ Recommended screen resolution & \(920\times 1080\) \\ Processor & Intel(R) Core (TM) i3-6100H \\ Maker & Intel Xeon \\ RAM & 4096 MB \\ Video memory & 16 MB \\ Hard drive & 50 GB (/dev/sda1) \\ CPU & 2.300 GHz \\ Cache size & 8192 KB \\ \hline \hline \end{tabular}
\end{table}
Table 1: Components of the web server.
# Cooperative Standoff Tracking of Moving Targets Using Modified Lyapunov Vector Field Guidance
###### Abstract  
Cooperative standoff tracking of moving targets is an important application of fixed-wing unmanned aerial vehicles (UAVs). To cope with the problem of long convergence time and unstable tracking in cooperative target tracking, traditional Lyapunov vector field guidance (LVFG) is modified. The guidance parameter \(c\) is discussed, and the gradient descent method is utilized to develop the optimal guidance parameter search algorithm. As for tracking moving targets, an interacting multiple model-based unscented Kalman filter (IMM-UKF) estimator is built for predicting the target state, and the result is used for correcting the guidance law. Meanwhile, a speed-based controller is developed for faster convergence to the desired intervehicle phase, and the stability of the controller is proved using the Lyapunov stability theory. Numerical simulation results indicate the proposed guidance converges faster to the standoff circle without intersecting the orbit. The state estimator reduces the estimate error and the intervehicle phase converges faster to the desired phase than the traditional control method. Furthermore, extensive hardware-in-the-loop simulations are carried out to verify the feasibility of the algorithm.  
s 2020; 10.390/app10113709
## 4 Performance Evaluation
The reason is that CCSM adopts the centralized computation method which can set up a global optimal multicast delivery tree for all receivers. On the contrary, PIM-SM adopts the graft method which can only get the local optimal multicast delivery tree. Therefore, the average join delay of CCSM is better than PIM-SM. Figure 9: The multicast tree re-construction cost (hop).
## 3 pLEO Time Scale Development
### Independent Physical Clocks Onboard Distributed Platforms  
To develop an ensemble time scale for pLEOT, standard state-space models used for modern atomic clocks within a system of \(N\) independent physical clocks are considered. As illustrated in [19], one needs to focus on no more than three-state polynomial processes that are driven by white noise. These processes involve the phase, \(p_{i}(t)\), the frequency, \(f_{i}(t)\), and the frequency drift, \(r_{i}(t)\), along with random walk noise processes associated with each clock. Here, \(i\) represents the \(i\)-th member clock, where \(i\) ranges from \(1\) to \(N\). Consequently, the mathematical representation of each ensemble member clock aboard the orbital platform is defined through these states.  
\[p_{i}(t_{k+1}) =p_{i}(t_{k})+\delta f_{i}(t_{k})+\frac{\delta^{2}}{2}r_{i}(t_{k} )+\epsilon_{i}(t_{k}) \tag{30}\] \[f_{i}(t_{k+1}) =f_{i}(t_{k})+\delta r_{i}(t_{k})+\eta_{i}(t_{k})\] (31) \[r_{i}(t_{k+1}) =r_{i}(t_{k})+\alpha_{i}(t_{k}) \tag{32}\]  
where \(t_{k}\) and \(t_{k+1}\) are successive sample times related by the uniform rate \(\delta\), e.g.,  
\[t_{k+1}=t_{k}+\delta\,. \tag{33}\]  
According to Equations (30) through (32), each state of the actual clock, which approximates the behavior of an ideal theoretical clock, evolves from time \(t_{k}\) to the next time \(t_{k+1}\) by absorbing a random shock. This shock is driven by aggregate process noise, which consists of three components: \(\epsilon_{i}(t_{k})\), \(\eta_{i}(t_{k})\), and \(\alpha_{i}(t_{k})\). Specifically, these correspond to white frequency noise, random walk frequency noise, and random run, respectively. The covariance of the process noise is given by  
Figure 5: A pLEOT time scale concept for AltPNT.  
\[Q_{i}(\delta)\triangleq\left[\begin{array}{ccc}q_{1}\delta+\frac{q_{2}\beta^{3}}{2 }+\frac{q_{3}\beta^{5}}{20}&\frac{q_{2}\beta^{2}}{2}+\frac{q_{3}\beta^{4}}{8}& \frac{q_{3}\beta^{3}}{6}\\ \frac{q_{2}\beta^{2}}{2}+\frac{q_{3}\beta^{4}}{8}&q_{2}\delta+\frac{q_{3}\beta^ {3}}{3}&\frac{q_{3}\beta^{2}}{2}\\ \frac{q_{3}\beta^{3}}{6}&\frac{q_{3}\beta^{2}}{2}&q_{3}\delta\end{array}\right], \tag{34}\]  
where the diffusion coefficients \(\sigma_{1}\), \(\sigma_{2}\), and \(\sigma_{3}\) are \(q_{t}=\sigma_{t}^{2}\) for \(\iota=1,2,3\), thus driving the fundamental noises. Of note, the smaller the process noise covariance of an actual clock, the better the clock's performance.
## 2 Background
### Conditional Tabular Generative Adversarial Networks  
Tabular datasets usually include a mix of continuous and discrete variables. Thus, capturing the distribution of these datasets using GAN is not a trivial process. Therefore, a Conditional Tabular Generative Adversarial Network (CTGAN) was proposed in 2019 to deal with tabular data [7]. CTGAN differs from the original GAN in many aspects. Firstly, it uses mode-specific normalization to deal with non-gaussian distribution and multimodel data. In the mode-specific normalization process, each continuous column is processed separately. The Variational Gaussian Mixture model (VGM) is used to find the number of modes in each column. Then, the Gaussian mixture model is fitted. The learned Gaussian mixture is \(\mathbb{P}_{c_{i}}\big{(}c_{i,j}\big{)}=\sum_{k=1}^{l}\mu_{k}\mathbb{N}\big{(}c _{i,j};\eta_{k},\phi_{k}\big{)}\) where \(\mu_{k}\) is the weight of a mode \((k)\) and \(\phi_{k}\) is the standard deviation of the mode. The probability density for each mode of the value in the column is calculated as follows: \(\rho_{k}=\mu_{k}\mathbb{N}\big{(}c_{i,j};\eta_{k},\phi_{k}\big{)}\). Then each value \(c_{i,j}\) in each mode is represented as a one-hot vector \((\beta_{i,j})\) of size equal to the number of modes. Additionally, a scalar value \((\alpha_{i,j})\) is utilized to represent the value \(c_{i,j}\). Thus, each row in the dataset is represented as follows:  
\[r_{j}=\alpha_{1,j}\oplus\beta_{1,j}\oplus...\oplus\alpha_{N_{c},j}\oplus\beta_{ N_{c},j}\oplus d_{1,j}\oplus d_{N_{c},j} \tag{4}\]  
where \(d\) is the representation of discrete columns [7].  
Furthermore, a conditional vector is constructed and provided as an input to the generator with the random noise vector. Suppose we have two discrete columns \(D_{1}\) and \(D_{2}\). Let \(D_{1}=\{1,2,3\}\), \(D_{2}=\{1,2\}\) and the given condition is \(D_{2}=1\). The conditional vector will be the concatenation of all discrete columns, where all values are represented as \(0\), and the condition is represented as \(1\). Thus, the conditional vector will be \(m=[0,0,0,1,0]\). To enforce the given condition, a cross-entropy loss is used between the generated sample and the conditional vector. In CTGAN architecture, fully connected layers were used to capture all possible relations between columns. Also, CTGAN uses PacGAN architecture in the discriminator to mitigate the mode-collapse problem in the original GAN. In addition, it utilizes the Wasserstein loss function with gradient penalty to train the model [7].
# Cooperative Power-Domain NOMA Systems: An Overview
## 5 Conclusions  
This article provided a complete assessment of the literature, as well as an updated evaluation of NOMA-related work on cooperative communication with major network-allowing mechanisms in 5G and B5G networks. Based on the studies presented, it appears that research in this topic is still in its early stages, leaving the field wide open for future investigation. Integrated networks must satisfy stated criteria such as reliability, efficiency, and higher system performance in emerging technologies. This article also covered an overview of NOMA, a state-of-the-art technology that enhances spectrum efficiency, energy efficiency, outage probability, cell-edge transmission rate, and latency. NOMA's performance and advantages over OMA are also highlighted. Furthermore, the literature on hybrid NOMA, a comparison of cooperative communication-based existing NOMA schemes, and a comparison of NOMA based on MIMO are discussed. Lastly, open research challenges are highlighted.  
**Funding:** The University of Sharjah funded this work under the SEED Research Grant Project No. (2102040168).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
\begin{tabular}{l l l l} Acronym & Description & Acronym & Description \\
1G & First generation & G-RQ & Generalized Rayleigh quotient \\
2G & Second generation & GSM & Global system for mobile \\
3G & Third generation & HD & Half-duplex \\
4G & Fourth generation & HetNet & Heterogeneous network \\
5G & Fifth generation & H-NOMA & Hybrid NOMA \\ AF & Amplify and forward & ICA & Interference channel alignment \\ AR & Augmented reality & IQF & Indefinite quadratic form \\ BA & Buffer aided & JD & Joint-diagonalization \\ BER & Bit error rate & LDS & Low-density spreading \\ BRS & Best relay selection & LoS & Line-of-sight \\ BS & Base station & M2M & Machine-to-machine \\ CA & Carrier aggregation & MA & Multiple access \\ CBF & Coordinated beamforming & MISO & Multiple-input single-output \\ \end{tabular}
## 4 Experiments
### Experimental Settings
#### 4.1.4 Implementation Details  
The method is optimized using the Adam [38] optimizer. For all methods, the learning rate is set to 0.00001, and the batch size is set to 32. The method is trained using L2 loss, and early stopping is applied within 20 epochs during training. All experiments are repeated five times with different random seeds, and the final results are reported as the average of the metrics. The code is implemented in PyTorch [39]. The training/validation/test data are split in a 6/2/2 ratio consistent with Informer. The convolutional kernel of MSC-Attention is selected from 2, 4, 8, 16, 32, and 64. The DESTformer consists of two encoder layers and one decoder layer. All models are trained/tested on a NIVIDA Tesla V100 32G GPU.
## Materials and Methods  
### Study Site  
The study area is located near the Hyytiala forest field station in central Finland (61\({}^{\circ}\)50\({}^{\prime}\)N, 24\({}^{\circ}\)17\({}^{\prime}\)E) managed by the University of Helsinki. The area is covered primarily with managed boreal forest, wetlands, and agricultural plots. The forest is dominated by three tree species: evergreen deciduous Norway spruce (_Picea abies_ (L) Karst) and Scots pine (_Pinus sylvestris_ L), and deciduous silver birch (_Betula pendula_). Most of the tree stands in this area are mixed. The forest understory consists of bryophyte, shrubs, and lichen species. The growing season lasts from May until late August. The terrain is hilly, and has an average ground elevation of 160 m.  
The airborne flight campaign was flown in the morning of 3 July 2015 between 10:29 and 10:57 AM (GMT+3) under clear sky conditions. The average solar zenith angle during the flights was 48\({}^{\circ}\). The flying altitude was approximately 980 m above ground level with a planned airspeed of 70 knots. According to Hyytiala SMEAR TOC measurements, the average wind speed during the measurements was 5 m\(\cdot\)s\({}^{-1}\). The flight directions were co-aligned with the solar principal plane to minimize bidirectional reflectance function (BRDF) effects. All three flight lines were flown consecutively in northeast and southwest directions.  
We obtained imagery with an Aisa Eagle II hyperspectral scanner (AHS) (Specim-Spectral Imaging Ltd., Oulu, Finland) mounted on a tilting platform at the rear end of a Short SC.7 Skyvan research aircraft (Figure 1). AISA Eagle II AHS is a pushbroom scanner that is sensitive in the 400-970 nm spectral region. The nominal spectral resolution of the sensor is 3.3 nm at full width half maximum (FWHM) and nominal channel width is 1.2 nm. Spectral binning was used during the campaign to increase the signal level at low light conditions (low sun), leading to 128 contiguous spectral bands with a decreased spectral resolution of 4.7 nm. The AHS has a field of view (FOV) of 37.7\({}^{\circ}\) divided between 1024 pixels.  
We changed the sensor angle during the flight using a spindle motor-operated platform. We used an Oxford RT3100 IMU for post-correction of progressive and angular movements. The IMU and AHS data were temporally co-registered using a synchronization signal in the hardware. We flew the first and second flight line with the sensor tilted approximately 30\({}^{\circ}\) off-nadir, whereas line three was measured close to the nadir direction. The area covered by all three flight lines was approximately 8.5 km long and 614 m wide.  
We used the view and solar directions to calculate the average scatter angle \(\beta\) for each line, which was defined as the angle between the directions from target to sun, and target to sensor (thus, for exact backscatter, hot spot, \(\beta=0\)).
# A Survey on Variable Neighborhood Search for Sustainable Logistics
## 4 Results and Findings
### Hybridization Analysis  
Figure 5 illustrates the frequency with which VNS is hybridized with various optimization techniques across the selected pool of academic papers. Appendix A contains the correspondence between acronyms and full names. Among these methods, Simulated Annealing (SA) stands out as the most commonly combined with VNS. This is likely due to the ease with which the SA cooling schedule can be incorporated into the VNS stopping criteria, allowing a smooth integration that enhances the optimization process.  
VNS is also frequently employed as a local search operator in other evolutionary algorithms, such as the GA, evolutionary algorithm (EA), and MA. In these cases, VNS is often applied after mutation and crossover operations, refining the solutions generated by these algorithms and improving their overall effectiveness.  
The next in frequency is TS. In this hybridization, a tabu list is typically incorporated during the local search phase of VNS. The list is used to guide the selection of the next movement by preventing the algorithm from revisiting previously explored solutions, which promotes diversification and avoids local optima.  
When it comes to matheuristics, which combine Exact Methods (EMs) with VNS, various strategies are possible depending on the specific problem being addressed. In some problems, an EM is used as a post-optimization technique to enhance the solutions obtained by VNS or to obtain an initial solution [51; 52; 53]. Other approaches involve using EMs to solve smaller parts of the problem inside the VNS [54; 55]. In certain applications, such as scheduling/routing or location/routing problems, all these options are used to tackle distinct aspects of the problem, thus serving different roles within the overall algorithm [56].  
A similar pattern can be observed for PSO and Ant Colony Optimization (ACO). These techniques can either be employed to act as the primary optimization methods with VNS, serving as a local search refinement [57; 58; 27; 59], or address different stages of complex problems that are solved in conjunction, as seen in multiproblem scenarios [60].  
In contrast, other algorithms appear to be combined with VNS far less frequently. Their hybridization occurs at a notably lower rate, suggesting that their integration has been less explored in the literature.
# Enhanced Gain Difference Power Allocation for NOMA-Based Visible Light Communications
###### Abstract  
With the escalating demand for high-data-rate wireless services, visible light communication (VLC) technology has emerged as a promising complement to traditional radio frequency wireless networks. To further enhance the achievable rate and error performance in non-orthogonal multiple access-based VLC downlinks, an efficient power allocation scheme named enhanced gain difference power allocation (EGDPA) is proposed for a multiple-input multiple-output VLC system. The power factors are determined by considering users' channel gains and utilizing the residual allocation principle, which focuses on the remaining power available after allocating it to the previous users. In addition, the impacts of the user distribution and transmission power are investigated, and the performance metrics in terms of achievable data rate, energy efficiency, and bit error rate are also analytically presented. Simulation results demonstrate that energy efficiency can be significantly improved and the achievable data rate gain can be enhanced by at least 6.25% with the proposed EGDPA scheme as compared with other traditional methods, confirming its superiority and validity for efficient multi-user accessing.  
visible light communication (VLC); non-orthogonal multiple access (NOMA); power allocation Article