esa-sceva/satcom-chunk-collection huggingface dataset:
# Microseismic Location Error Due to Eikonal Traveltime Calculation
## 2 Methods
### FSM for the Eikonal Equation  
We begin by reviewing the first order fast-sweeping method for the eikonal equation. Let us consider the isotropic eikonal equation:  
\[|\
abla T|^{2}=S^{2}(\mathbf{x}), \tag{2}\]  
where \(T\) is the traveltime, \(S(\mathbf{x})\) is the slowness of the medium and \(|\cdot|\) is the Euclidean norm. This equation can be effectively solved numerically using the so called upwind discretization scheme [10]. In the 3D case, for each grid point \(\mathbf{C}\), we need to consider eight tetrahedrons (Figure 1) and solve the discretized eikonal equation:  
\[|(T_{\mathrm{C}}-T_{w},T_{\mathrm{C}}-T_{S},T_{\mathrm{C}}-T_{D})|^{2}=h^{2}S^ {2}(\mathbf{C}), \tag{3}\]  
where \(h\) is the grid interval that we assume to be the same in all three directions for simplicity. The value of \(T_{\mathrm{C}}\) can be found from this quadratic equation and should be tested for causality: \(T_{\mathrm{C}}\) must be larger than time values \(T_{W}\), \(T_{S}\), \(T_{D}\) in all three neighboring grid points used in Equation (3). In cases when the causality condition is violated, Equation (3) should be reduced to the 2D case and solved similarly for each triangular face of the selected tetrahedron (\(\Delta CWD\), \(\Delta CSD\), \(\Delta CWS\)). This happens when the normal to the wavefront is parallel to one of the coordinate planes \(XY\), \(XZ\), or \(YZ\). When the equation does not have a causal solution in the 2D case as well, the solution is propagated along the edges of corresponding triangle. For example, in case of triangle \(\Delta CWD\), the candidates for traveltime values for \(T_{\rm C}\) are \(T_{W}+hS({\bf C})\) and \(T_{D}+hS({\bf C})\). Finally, in such cases, the \(T_{\rm C}\) value is determined as minimum of all candidates computed for all eight tetrahedrons, and this value is used to update traveltime value in the grid point \({\bf C}\).  
After initialization of the traveltime volume by assigning zero at the source location and a large value at all other points, the algorithm calculates traveltimes for all the points in the domain in eight alternating orders, \"sweeping\" the whole volume several times to evaluate all directions of the propagating waves. With \(I\), \(J\), and \(K\) being a number of points in the \(X\), \(Y\), and \(Z\) directions, respectively, theses sweeping orders can be written in the following form:  
\[i=1:I,j=1:J,k=1:K\] \[i=1:I,j=J:1,k=1:K\] \[i=1:1,j=1:J,k=1:K\] \[i=I:1,j=J:1,k=1:K\] \[i=1:I,j=1:J,k=K:1\] \[i=1:1,j=1:J,k=K:1\] \[i=I:1,j=J:1,k=K:1\]  
Here \(i\), \(j\), and \(k\) are grid point indices in \(X\), \(Y\), and \(Z\) directions, respectively. The method is robust and easy to implement; however, due to the singularity at the source position (and any other point where wavefront significantly curves), the solution inevitably accumulates errors. It is known that the largest error appears in the diagonal directions [4]. As we will show in the examples section, this can be critical for the purposes of microseismic imaging.
# Fractional-Order LQR and State Observer for a Fractional-Order Vibratory System
## 2 Background
### Observability of Fractional-Order Dynamical System  
If the initial state \(\mathbf{x}(0)=\mathbf{x}_{0}\) can be determined based on the time responses of the input \(u(t)\) and the output \(y(t)\) at an arbitrary time \(t_{1}>0\), then the system defined by Equation (8) is observable. In addition, the propositions given below are equivalent [25; 26].  
1. A pair of matrices \((\mathbf{C},\ \mathbf{A})\) is observable.
2. The rank of the following observability matrix \(\mathbf{N}_{o}\) is \(n\): \[\mathbf{N}_{o}=\bigg{[}\mathbf{C}^{T}\ (\mathbf{C}\mathbf{A})^{T}\ \cdots\ \Big{(}\mathbf{C}\mathbf{A}^{n-1} \Big{)}^{T}\bigg{]}^{T}.\] (10)
# Noncoherent Channel Combining for GNSS Signal Tracking with an Adaptive Antenna Array
## 3 Proposed Method
### Implementation of the Proposed Method  
The schematic diagram of the proposed method is shown in Figure 1, and the detailed steps are summarized as follows:  
1. The auto-correlation matrix \(\mathbf{R}_{xx}\) is estimated using the received snapshots. Then, the interference is suppressed to obtain \(\overline{\mathbf{x}}(t)\) through Equation (14).
2. Each channel of \(\overline{\mathbf{x}}(t)\) is correlated with the local replica signals, and the correlator outputs are obtained, as shown in Equation (19).
3. The noncoherent channel combining is implemented by exploiting the correlator outputs using Equation (20).
4. The code and carrier discriminator outputs are obtained according to Equations (22) and (23).
5. The raw discriminator outputs are fed into loop filters, and the new local replica signals are generated for continuous signal tracking.
# Comparison of Cloud-Computing Providers for Deployment of Object-Detection Deep Learning Models
## 3 Methodology
### Dataset  
To test the deployed YOLOv8 model, we used the latest iteration of the Common Objects in Context (COCO) dataset [21]. This dataset is a large-scale image-recognition dataset for object detection, segmentation, and captioning tasks. It is a widely used dataset in computer-vision research and applications, most commonly to train and evaluate many state-of-the-art object detection and segmentation models. Researchers and developers primarily use the COCO dataset to benchmark and improve computer-vision algorithms. Therefore, it has become a standard benchmark in the field and has contributed to significant advancements in the development of computer-vision systems.  
The COCO dataset is known for several key characteristics that make it valuable for computer-vision research and bench marking. It contains tens of thousands of images captured from various sources and their corresponding bounding boxes and labels. The objects in the images are are divided into 80 classes that cover a wide range of scenes and settings, depicting everyday life, including indoor and outdoor environments. There are almost 118,000 training images available in this dataset, with a median resolution of 640 \(\times\) 480 pixels. Finally, the dataset was updated frequently to ensure it stays on par with advancements in computer-vision research. Overall, the COCO dataset is a comprehensive resource for researchers and developers working on various computer-vision tasks, including object recognition, segmentation, and image understanding. The primary reason for the choice of dataset is the fact that YOLOv8 [22] is trained on the COCO dataset, which ensures that the results obtained are not influenced by the dataset and are only dependent on the cloud platforms that the models are being deployed on.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 3 Wireless Standards at 60 GHz mmWave Band
### IEEE 802.11 ay  
IEEE 802.11 ay is an amendment to IEEE 802.11 ad standard, which supports faster and longer-range mGbps communication. The hallmark of this standard is its ultrahigh performance for fixed point-to-multipoint (P2 MP) and point-to-point (P2P) transmissions, either indoor or outdoor. It is a standalone unlicensed protocol that provides a theoretical peak data rate of up to 100 Gbps. In addition, it provides a bandwidth of 8.64 GHz as well as supports MU-MIMO. It supports channel bonding and channel aggregation [51; 58] to provide higher throughput and bandwidth. Channel bonding corresponds to when many contiguous channels are merged into a single wideband channel, there is no channel spacing between them, and they can be used as a whole hand to form a single channel. Channel aggregation, on the other hand, is frequently used to combine two or more contiguous or non-contiguous channels with channel separation between them.  
Additionally, in IEEE 802.11 ad, the MIMO link can accommodate up to eight spatial streams per station, depending on the environment, the antenna's directivity, and whether or not antenna polarization is used. Multiplexing gain is added to the beamforming gain by employing antenna arrays in MIMO communication. As a result, the spatial domain's variety is completely exploited, enhancing communication durability and resulting in extremely high SNR links that are almost resistant to fading. A detailed analysis of beamforming training, MAC schemes, channel bonding, and radio propagation of IEEE 802.11 ay is described in [51].
# A Proposal of a Troposphere Model in a GNSS Simulator for VANET Applications
## 5 Tropospheric Model Solution
### Tropospheric Collins Model
Figure 2: Example of the refraction phenomenon of the signal where \(n_{inc}\) and \(n_{tran}\) are the refractive indices of the incident and transmitted medium, respectively. The terms \(T_{z,dry}\) and \(T_{z,wet}\) at zero altitude are the following:  
\[T_{z_{0},dry}=\frac{10^{-6}k_{1}R_{d}P}{g_{m}} \tag{5}\]  
\[T_{z_{0},wet}=\frac{10^{-6}k_{2}R_{d}P}{(\lambda+1)g_{m}-\beta R_{d}}\frac{e}{T}. \tag{6}\]  
While, to calculate the delay taking into account also the height of the receiver, the following equations are used:  
\[T_{z,dry}=\left[1-\frac{\beta H}{T}\right]^{\frac{\beta}{R_{d}^{2}}}\cdot T_{ z_{0},dry} \tag{7}\]  
\[T_{z,wet}=\left[1-\frac{\beta H}{T}\right]^{\frac{(\lambda+1)\beta}{R_{d}\beta }-1}\cdot T_{z_{0},wet}, \tag{8}\]  
where \(H\) is the height of the receiver above sea level (m), \(k_{1}\) = 77.604 (K/mbar), \(k_{2}\) = 382,000 K\({}^{2}\)/mbar, \(R_{d}\) = 287.054 J/Kg/K, \(g_{m}\) = 9.784 m/s\({}^{2}\), \(eg\) = 9.80665 m/s\({}^{2}\).
###### Abstract
In this paper, we transform the problem of designing a sparse FIR MIMO DFE into an \(l_{0}\) norm minimization problem, then use the proposed scheme to solve the \(l_{0}\) norm minimization problem and obtain the sparse solution. It is verified through simulations that, when compared with the non-sparse optimum MMSE DFE, the sparse FIR MIMO DFEs designed by the proposed scheme obtain a significant reduction in the number of nonzero taps with a small performance loss. In addition, the proposed scheme provides better design accuracy than the OMP algorithm with the same nonzero taps percentage.
## 2 Materials and Methods
### Modelling Framework
#### 2.3.4 The Hybrid Model Calibration  
For model calibration (Figure 2), the values for the spectral indices outlined in Table 1 were extracted from nine cloud free Sentinel S2 images in 2018. All six indices (Table 1) were used as a proxy for f\({}_{\text{PAR}}\) to model GPP using equation 1. Field-measured PAR for 2018, along with the literature-based LUE parameters (Table 2), were also used as an input in Equation (1). As a next step, multiple linear regression was performed to test which indices significantly predicted the modelled GPP when compared with the EC-derived GPP. The results of the regression indicated that individually NDWI2, EVI, REP2, and NDVI showed a Spearman correlation between 0.8 and 0.95, whereas LSWI and MCARI showed a Spearman correlation of 0.77, as shown in Figure 3. Therefore, NDWI2, EVI, REP2, and NDVI were selected as potential candidates for the hybrid models, and the fitted regression equations for all three \(\varepsilon\) values are shown in Table 3. The adjusted R-squared values ranged from 0.97 to 0.98 for all the models, with \(p\)-values below 0.05. All the analysis and modelling work were performed using R software (version 3.6.3) and relevant R packages.
## 3 Passive Radar Array Antenna Design
### Design of a Non-Uniform Array for Optimizing the SLL Value
Results confirm that the designed NULA is able to provide the desired azimuth coverage with GLL (dB) and SLL (dB) higher than ULA with \(d=270\) mm, and maintaining the directivity higher than ULA with \(d=210\) mm (Figure 11). Figure 10: Azimuth plane radiation pattern of the NULA with five Televes 4G NOVA antennas without the radome or reflector, for 770 MHz, and optimized distances, with three steering directions: \(0^{\circ}\) (broadside), \(15^{\circ}\) and \(30^{\circ}\). Figure 9: Designed NULA based on Televés 4G NOVA antennas without the radome detailing the inter-element spacing. We have repeated the procedure with an array of 11 elements (10 inter-element distances). The results are presented in Table 3 and in Figure 12. In this case, the resultant distances (\(\mathbf{d}=[349,259,223,210,210,210,214,235,262,231]\) mm) correspond to an asymmetric NULA that provides a directivity 0.6 dB lower than that associated with an ULA of 11 elements with \(d=270\) mm, but with an SLL (dB) that is approximately 4 dB higher, which demonstrates the usefulness of the proposed method. Figure 11: Azimuth plane radiation patterns of the ULAs (\(d=210\) mm and \(270\) mm) and NULA, with five Televes 4G NOVA antennas without the radome or reflector, for \(770\) MHz. Figure 12: Azimuth plane radiation patterns of the ULAs (\(d=210\) mm and \(270\) mm) and NULA, with \(11\) Televes 4G NOVA antennas without the radome or reflector, for \(770\) MHz.
# Bayesian Optimization Based Efficient Layer Sharing for Incremental Learning
## 3 Proposed Algorithm
### Global Optimal Layer Selection via BayesOpt
#### 3.4.3 Expected Improvement (EI)  
To mitigate the limitations of PI, the proposed algorithm uses an expected improvement (EI) acquisition function to decide the next observation points [28; 31].  
\[EI(n,n_{1:k})=\begin{cases}(L(n_{k}^{*}-\mu_{k}(n))\Phi(\gamma(n))+\sigma_{k}( n)\phi(\gamma(n))&\text{if }\sigma_{k}(n)>0,\\ 0&\text{if }\sigma_{k}(n)=0,\end{cases} \tag{10}\]  
where \(\phi(.)\) denotes the standard normal density function.  
The EI concerns the predictive distribution of GP regressions to balance the trade-off of exploiting and exploring [28; 29; 30; 31]. In other words, when exploring, EI determines the next point where the surrogate variance is large. When exploiting, EI decides the next point where the surrogate mean is high. Therefore, the proposed algorithm can find optimal and accurate sharing layer point by using BayesOpt. In addition, because the proposed loss function is shaped in continuous function, the BayesOpt in the proposed algorithm can converge to the global optimal sharing layer number, meeting incremental learning conditions. More details can be found in Appendix A.
# Assessing Fatality Risks in Maritime Accidents: The Influence of Key Contributing Factors
## 3 Problem Background
### Statistics on Alcohol-Influenced Maritime Accidents  
In recent years, the prevalence of alcohol-related maritime accidents has raised significant concerns within the maritime industry and regulatory bodies. The statistics indicate that a substantial portion of marine incidents involve crew members or operators under the influence of alcohol, which contributes to navigational errors, collisions, and fatalities at sea.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline
**Stage (BAC \%)** & **State of Intoxication** \\ \hline Early drunkenness (0.10–0.15) & generosity, quickness to anger, louder voice, wobbliness when standing, possible slurred speech, reduced reaction time \\ \hline Drunkenness (0.15–0.30) & major loss of coordination/staggering, rapid breathing, repetition when speaking, nausea/vomiting, severe impairment of motor skills and judgment, blurred vision, confusion and dizziness, blackouts \\ \hline Stuppor (0.30–0.40) & inability to stand properly, confusion, incoherent speech, significant risk of loss of consciousness, danger of respiratory depression (slow and shallow breathing), possible risk of coma \\ \hline Coma (0.40–0.50) & unresponsiveness even when shaken, incontinence (urination and bowels), deep and slow breathing, coma, respiratory arrest, potential failure of the central nervous system, death \\ \hline Source: Authors, based on [14; 23]. \\ \end{tabular}
\end{table}
Table 1: _Cont._Figure 1 shows the impact of alcohol consumption on accidents in the maritime sector over the period from 2019 to 2023. It illustrates both the total number of reported accidents (accidents in chosen regions, incidents not involved) and those specifically related to alcohol consumption. The data reveal a steady increase in both categories, reflecting the growing concern about alcohol consumption as a critical factor in maritime incidents. In 2019, there were 850 reported accidents in total, with 140 of them being alcohol-related. By 2023, these numbers had risen to 1027 total accidents and 185 alcohol-related cases, showing an upward trend.  
Moreover, the chart also includes the number of fatalities linked to alcohol-related accidents, with a marked increase over the years, from 12 alcohol-related fatalities in 2019 to 22 in 2023; the data underscore the growing probability of fatal outcomes in incidents where alcohol is involved. These findings reflect global patterns reported by sources such as the EASA, U.S. Coast Guard, and maritime safety organisations [29; 30; 31]. The chart visually supports the argument that alcohol consumption in the maritime sector significantly raises the risk of both accidents and fatalities, highlighting the need for stricter regulations and prevention measures in this industry.
## 3 Results and Discussion
### Damage Efficiency in SM and RM  
In general, a unit can be regarded as a damaged area when its damage variable is higher than or equal to 0.2 [47]. In this study, the percentage of damaged area to the entire coal matrix area (PDA) was used to characterize the damage efficiency. The PDA values in S\({}_{0}\)M, S\({}_{90}\)M, S\({}_{180}\)M, S\({}_{270}\)M, and RM increased with the heating time (Figure 17). In general, the PDA values in RM were larger than in other models. This is consistent with the damage distribution shown in Figure 16. Interestingly, the PDA value nonlinearly increased with the heating time. This was because the distribution of the coal matrix's mechanical parameters was non-uniform.  
At 15 s, the PDA values were 7.49%, 0.14%, 12.83%, 0.16%, and 14.54% for S\({}_{0}\)M, S\({}_{90}\)M, S\({}_{180}\)M, S\({}_{270}\)M, and RM, respectively. From this point of view, S\({}_{0}\)M, S\({}_{180}\)M, and RM could be considered models with a high damage efficiency. Therefore, damage efficiency highly relied on the distribution of the electric field in the cavity, which depended on the heating model. In conclusion, the damage efficiency in RM was higher than in SM.  
Parameter \(\Delta D\) was used to describe the rate of the PDA value, which was defined as the increment in PDA value at 0.5 s intervals. The relation between the \(\Delta D\) of models with a high damage efficiency and the heating time is shown in Figure 18. Moreover, the periods during which the damage area connected one pyrite particle to another pyrite particle,  
Figure 16: Distribution of temperature and damage after 15 s under each model.  
Figure 17: The percentage of damage in the coal matrix.  
identified as the peak periods of damage area connectivity (PDACs), are shown via the pink histogram in Figure 18.  
The PDAC durations for S\({}_{0}\)M, S\({}_{180}\)M, and RM ranged from 2.5 to 5.0 s, 1.5 to 5.0 s, and 3.0 to 7.5 s, respectively. The start time of the PDAC in RM was later than in the other two models. This was because the electric field strength was static in SM when the electric field in certain areas was large, which made it easier to heat and damage locally. In contrast, the electric field strength in RM changed with the heating time. Therefore, there were no constant hot spots in RM, which resulted in a notably rapid increase in pyrite temperature and induced the late-starting PDACs. The PDACs in RM lasted longer compared to S\({}_{0}\)M and S\({}_{180}\)M, which could be attributed to the higher number of pyrite particles involved in the damage in RM. This resulted in more damaged areas that were interconnected. In addition, the electric field strength of the coal sample was very low in SMs, such as in S\({}_{90}\) and S\({}_{270}\), which led to the slow development of damage and did not take PDAC into account.
## 4 The Satellite Selection Strategy  
Figure 5 illustrates the satellite selection strategy derived from the above analysis, taking into account the effects of satellite observable time, elevation, and PDOP on positioning accuracy. Initially, the satellite observable time at the current location can be forecasted using two-line orbital element (TLE), and satellites are ranked according to their observation times. Next, based on Equations (13)-(16) presented in Section 4, the elevation of the observable satellites is calculated. Lastly, the satellite PDOP at the initial position is computed, and the optimal satellite combination is selected to facilitate positioning.
## 2 Research Methods and Approach
Other instances will receive the information and decide to continue the search or report its location so that some other cases continue to act. So that values in search space will be found quickly and accurately, two important parameters are the location of an instance and the search velocity. These two parameters are expressed through the formulas for updating the position and updating the velocity of the instance [30]:  
\[x^{i}(t+1)=x^{i}(t)+v^{i}(t+1) \tag{7}\]  
Figure 1: Illustration of the PSO algorithm: (**a**) particles in a swarm; (**b**) particles looking for “food”; and (**c**) food locations found and reported to gather particles. \[v^{i}(t+1)=wv^{i}+C_{1}r_{1}(p^{i}(t)-x^{i}(t))+C_{2}r_{2}(G_{\text{best}}-x^{i}(t)) \tag{8}\]  
where \(x^{i}\)--the position of instance \(i\) at different times; \(v^{i}\)--the speed of individual \(i\); \(w\)--the parameter of inertial weight; \(r_{1}\) and \(r_{2}\) are random values from 0 to 1; each individual has the best position at \(p^{i}(t)\); \(C_{1}\) and \(C_{2}\) represent the population's cognitive coefficient; and \(G_{best}\)--the best location of the entire population. Each individual is characterized by its velocity vector and position in space. The goal of structural model updates in FEM is to find values for uncertain variables. These values help the model have a behavior almost equivalent to the real object, which means the difference between numeric model results and experimental measurements is drastically reduced. To evaluate the similarity between the FE model and practical structure, an objective function is built based on the natural frequency and the mode shape of the structure [31]:  
\[Fitness=\sum_{k=1}^{n}\left[1-MAC(\varphi_{k},\widetilde{\varphi}_{k})\right]+ \sum_{k=1}^{n}\frac{\omega_{k}^{2}}{\widetilde{\omega}_{k}^{2}}=\sum_{k=1}^{ n}\left[1-\frac{(\widetilde{\varphi}_{k}^{T}\varphi_{k})^{2}}{(\varphi_{k}^{T} \widetilde{\varphi}_{k})(\varphi_{k}^{T}\widetilde{\varphi}_{k})}\right]+\sum _{k=1}^{n}\frac{(\omega_{k}-\widetilde{\omega}_{k})^{2}}{\widetilde{\omega}_{ k}^{2}} \tag{9}\]  
\[MAC=\frac{\left|\sum_{k=1}^{n}\left(\widetilde{\varphi}_{k}\right)^{T}\times \varphi_{k}\right|}{\left\{\sum_{k=1}^{n}\left(\widetilde{\varphi}_{k} \right)^{T}\times\varphi_{k}\right\}\times\left\{\sum_{k=1}^{n}\left(\varphi_ {k}\right)^{T}\times\varphi_{k}\right\}} \tag{10}\]  
where \(n\)--number of modes shape considered; _MAC_[32]--modal assurance criterion; \(\varphi_{k}\), \(\widetilde{\varphi}_{k}\) are mode shapes from the FEM and experiment; \(\omega_{k}\); \(\widetilde{\omega}_{k}\) are frequencies from the FEM and experiment; and T represents the transposed matrix. The PSO algorithm determines the objective function's minimum (convergence), and the implementation diagram is shown in Figure 2 below.