esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparisons and Experiment
### Multi-Obstacles Environment  
#### 5.2.1 Comparison with Streamline Path by Weighting Velocity of Each Single Obstacle  
In Figure 16, the proposed method is compared with the streamline path obtained via the weighting method. The maximum curvature of the pure pursuit path and streamline path are 0.50 (1/m) and 1.44 (1/m), respectively. The maximum curvature of the pure pursuit path is smaller than the streamline path.  
Figure 15: The comparison of pure pursuit and lane hopping: path (upper plot) and curvature (lower plot). Horizontal axis is the forward +x direction.
## 6 Conclusions  
In a multicast transmission, resource allocation is important to minimize the required bandwidth while enabling every user to obtain the target multicast rate. However, owing to the constraints that the resource allocation should be subject to the channel quality of the worst user and as well as the lack of channel feedback information, the performances of the conventional multicast schemes were not satisfactory. In this paper, we solved an optimal resource allocation problem for a single-cell multicast transmission scheme with a supplementary multicast channel. With the aid of full channel quality feedback, it becomes a convex optimization problem that can be solved with a computationally efficient iterative algorithm. The proposed single-cell multicast transmission scheme outperforms the previous multicast schemes in terms of required bandwidth, in which every user satisfies a target multicast rate with a given outage probability of 5%. Our future research direction is to extend the current work with  
Figure 5: Average bandwidth comparison with correlated Rayleigh fading.  
Figure 6: Minimum average bandwidth versus \(|\Omega|\).  
dynamic seamless switching between unicast and multicast modes and efficient multiplexing of two transmission modes.  
Conceptualization, D.K., D.K.K.; formal analysis, D.K., D.K.K.; data curation, D.K.; validation, D.K., D.K.K.; supervision, D.K.K.; writing-original draft preparation, D.K.; writing-review and editing. D.K., D.K.K.; visualization, D.K.; project administration, D.K.K.; funding acquisition, D.K.K. This work was supported by Inha University.  
The authors declare no conflict of interest.
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 5 Experimental Results and Analysis  
Ten sets of 3 DMOP data in OB format were used as the original data to test the performance of the algorithm. Table 1 shows the configuration of the experimental computer, and Table 2 lists the basic attributes of the ten sets of data, including the number of vertex coordinates, texture coordinates, and patch numbers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hardware** & **CPU** & **System** & **RAM** & **Hard Disk** & **Graphics** **Card** \\ \hline Model & 8 cores & Windows & 16 GB & 512 GB & GeForce GTX \\ specifications & 2.9 GHz & Windows & DDR4 & 1650 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental computer configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \
\begin{tabular}{c} **Number of Vertex** \\ **Coordinates** \\ \end{tabular} &
\begin{tabular}{c} **Number of Texture** \\ **Coordinates** \\ \end{tabular} & **Polygon Count** \\ \hline M-1 & 409,363 & 517,477 & 812,385 \\ M-2 & 434,616 & 544,407 & 862,869 \\ M-3 & 306,074 & 369,923 & 607,361 \\ M-4 & 354,881 & 447,715 & 702,367 \\ M-5 & 214,421 & 270,264 & 424,207 \\ M-6 & 411,733 & 522,175 & 816,387 \\ M-7 & 364,746 & 447,715 & 723,858 \\ M-8 & 263,998 & 333,056 & 522,892 \\ M-9 & 303,368 & 383,885 & 600,056 \\ M-10 & 452,311 & 577,170 & 897,153 \\ Average & 351,551 & 441,378 & 696,953 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic information of the experimental data.  
When embedding watermarks into vertex coordinates, it is necessary to classify, partition, or group the vertex coordinate data to enhance the robustness of the watermarking algorithm [32]. Therefore, this article compared several clustering algorithms, and the results are shown in Figure 6. Finally, the DBSCAN clustering algorithm was used to cluster the vertex coordinate data to improve the robustness and stability of the vertex coordinate watermark embedding algorithm.  
The same data was processed before and after clipping using the same clustering algorithm. The DBSCAN clustering algorithm showed minor change in clustering clusters after clipping. Comparative analysis of the clustering clusters obtained by the clustering algorithm showed that the DBSCAN clustering algorithm had less influence on the clustering results after clipping, as shown in Table 3.
# Joint Delay-Doppler Estimation Performance in a Dual Source Context
## Appendix A Details of the Derivation of the Fisher Information Matrix
### Bounds Derivation
\begin{array}{ccc}\ldots&s(nT_{s})&\ldots\end{array}  \right)_{N_{1}\leq n\leq N_{2}}^{T}, \tag{100}\] \[\boldsymbol{v}(f) =\left( \begin{array}{ccc}\ldots&e^{j2\pi fn}&\ldots\end{array}  \right)_{N_{1}\leq n\leq N_{2}}^{T},\] (101) \[\mathbf{U}\left(\Delta b\right) =\text{diag}\left( \begin{array}{ccc}\ldots&e^{-j\alpha_{s}\Delta b \pi T_{s}}&\ldots\end{array} \right)_{N_{1}\leq n\leq N_{2}},\] (102) \[\mathbf{D} =\left( \begin{array}{ccc}\ldots&n&\ldots\end{array} \right)_{N_{ 1}\leq n\leq N_{2}}^{T},\] (103) \[\mathbf{V}^{\Delta,0}\left(\frac{\Delta\tau}{T_{s}}\right) =\int_{-\frac{1}{2}}^{\frac{1}{2}}\boldsymbol{v}(f)\boldsymbol{v}^{H}( f)e^{-j2\pi f\frac{\Delta\tau}{T_{s}}}\text{d}f,\] (104) \[\left[\mathbf{V}^{\Delta,0}\left(\frac{\Delta\tau}{T_{s}}\right) \right]_{k,l} =\text{sinc}\left(k-l-\frac{\Delta\tau}{T_{s}}\right),\] \[\mathbf{V}^{\Delta,1}\left(\frac{\Delta\tau}{T_{s}}\right) =j2\pi\int_{-\frac{1}{2}}^{\frac{1}{2}}f\boldsymbol{v}(f) \boldsymbol{v}^{H}(f)e^{-j2\pi f\frac{\Delta\tau}{T_{s}}}\text{d}f,\] (105) \[\left[\mathbf{V}^{\Delta,1}\left(\frac{\Delta\tau}{T_{s}}\right) \right]_{k,l} =\frac{1}{k-l-\frac{\Delta\tau}{T_{s}}}\left(\cos\left(\pi(k-l- \frac{\Delta\tau}{T_{s}})\right)\right.\] \[\qquad-\text{sinc}\left(k-l-\frac{\Delta\tau}{T_{s}}\right) \right),\] \[\mathbf{V}^{\Delta,2}\left(\frac{\Delta\tau}{T_{s}}\right) =4\pi^{2}\int_{-\frac{1}{2}}^{\frac{1}{2}}f^{2}\boldsymbol{v}(f) \boldsymbol{v}^{H}(f)e^{-j2\pi f\frac{\Delta\tau}{T_{s}}}\text{d}f,\] (106) \[\left[\mathbf{V}^{\Delta,2}\left(\frac{\Delta\tau}{T_{s}}\right) \right]_{k,l} =\pi^{2}\text{sinc}\left(k-l-\frac{\Delta\tau}{T_{s}}\right)\] \[+2\frac{cos\left(\pi(k-l-\frac{\Delta\tau}{T_{s}})\right)-\text{ sinc}\left(k-l-\frac{\Delta\tau}{T_{s}}\right)}{\left(k-l-\frac{\Delta\tau}{T_{s}} \right)^{2}}.\]
# Rapid Estimation of Earthquake Magnitude and Source Parameters Using Genetic Algorithms
## 3 Results
### GA Hyperparameter Tuning Experiments for Okada-GA Inversion Model  
The purpose of the first-stage experiments in this study was to test the ability of the GAs to carry out inversion of [PERSON]'s model to estimate four earthquake source parameters (i.e., length, width, rake, and slip) and to obtain the hyperparameter setting that resulted in the best performance of the Okada-GA inversion model.
## Main Design of ERF-IMCS
### Image-Based Monte Carlo Localization
```
0: observation information \(z_{t}\), control information \(u_{t}\);
0: the state of robot \(s_{t}\);
1:Initialization: Generate the initial particles set \(\{x_{0}^{i}\}_{i=1}^{N}\) based on prior distribution \(p(x_{0})\), \(t=1\);
2:for\(t=1,2,...\)do
3: Motion model = \(p(x_{t}|x_{t-1},\ u_{t})\);
4: Observation model = \(p(z_{t}|x_{t})\);
5: Calculate \(\omega_{t}^{i}\) according to Equation (5);
6: Crossover: set the weight \(\omega_{t}^{i}\) as the fitness value;
7: Calculate \(p_{c}\) with low fitness value according to Equation (9);
8: Perform crossover according to Equation (6) and (7);
9: Mutation: calculate \(p_{m}\) according to Equation (10);
10: Perform Mutation according to Equation (8);
11: Normalization: \(\tilde{\omega}_{t}^{i}=\omega_{t}^{i}/\sum\limits_{i=1}^{N}\omega_{t}^{i}\)
12:endfor
13:return The state of robot at time t: \(s_{t}=\{x_{t}^{i},\tilde{\omega}_{t}^{i}\}\);
```  
**Algorithm 1** Image-based Monte Carlo localization
# Source Microphone Identification Using Swin Transformer
## 3 Method
### Input Preprocessing  
As previously stated in the database description, each file comprises recordings of three minutes. These files were sliced into equal segments of five seconds, ensuring that each segment inherits the same label as the original file. After that, the audio files are transformed into a Mel spectrogram that serves as the input to the system.  
A Mel spectrogram is a type of spectrogram that uses the Mel scale to represent frequency in the vertical axis instead of the traditional linear scale. The Mel scale is a perceptual scale of pitches that is based on the way humans hear sound. In a Mel spectrogram, the horizontal axis represents time, and the color or intensity of each point in the spectrogram represents the amplitude of the sound at a particular frequency and time. The Mel spectrograms have many advantages, such as the ability to mimic human auditory perception, reduce dimensionality, preserve resilience in noisy conditions, allow simple visualization, and be widely adopted in diverse fields. Mel spectrograms are often used in voice recognition, music classification, and other audio-signal-processing applications, and they may be easily incorporated into vision-deep neural networks for classification.  
To create a Mel spectrogram, the audio signal is first divided into short overlapping frames, and the Fourier transform is applied to each frame to obtain its frequency spectrum. The resulting spectrum is then transformed into the Mel scale using a bank of overlapping triangular filters. The logarithm of the power spectrum is taken, and the resulting values are plotted as a function of time and frequency as shown by the following equations:  
\[X_{k}=\sum_{n=0}^{N-1}w_{n}x_{n}e^{-i2\pi kn/N},\;\;k=0.\ldots N-1 \tag{1}\]  
where \(w_{n}\) represents the window function (Hamming window), and \(x_{n}\) represents the original speech signal.  
The process involves applying 128 triangular filters on a Mel frequency scale to the power spectrum to extract frequency bands. The Mel frequency scale is a perceptual scale that better matches how humans perceive frequency. The following formula converts between frequencies in Hertz (_f_) and the Mel scale (_m_):  
\[m=2595\;log\bigg{(}1+\frac{f}{700}\bigg{)} \tag{2}\]
## 2 Measuring Mobile Network Quality
Furthermore, for internet service providers, it is not possible to measure the network's quality down to the end user, but only until the last hop under their control (e.g., base station). Thus, the QoS, and especially also the QoE, of the end users can only be estimated. **Crowdsourced Network Measurements** use crowdsourcing to gather information about the quality of the network. Crowdsourcing is the methodology of processing a task by a large group of people instead of a designated agent [2]. For network measurements, crowdsourcing has three major advantages: it makes it much easier to cover a wide range of situations and users, it allows entities other than the network operator to assess the performance and other characteristics of a network independently, with a coverage that is not feasible using other methods such as drive testing, and it offers the possibility to collect statistics from end-user perspective. Thus, CNMs make it possible to get insights into the real network behavior as it is experienced by the end-user, as they use realistic hardware and software settings with heterogeneous devices, access networks, and load situations. A comparison of crowdsourcing with traditional measurement techniques and best practices how to design crowdsourced network measurements issues is made in [3]. There are two ways of doing crowdsourcing studies: active or passive measurements. Either workers are paid to actively process a task or applications on the end users' smartphones are used to collect KPIs in an active way using measurement applications. In the second case, CNMs can be seen as a special case of crowdsensing, where user devices act as environmental sensors, and thus, passively monitor the network using in-service monitoring. Crowdsourced measurement data (crowd data) offers new possibilities and can be used for various applications, such as the benchmarking of network operators, providers, technologies, or countries, as well as, e.g., for monitoring, planning, and optimization of the network. In this way, crowd data provides insights beyond the network layer, that is, at the application and user level. This makes crowd data very valuable and extends the current practice of operators to evaluate networks. The ultimate goal is to use crowd data--combined with other network and user data--to improve QoE, but also for regulatory purposes, e.g., to identify issues with coverage or network settings. Challenges, drawbacks, and benefits of CNMs are listed in [1].
## 2 Materials and Methods
### Finite Element Model Definition  
Three finite element models were developed based on the [PERSON]'s theoretical model (N) and two real patient-specific corneal geometries that were measured by a Sirius tomographer (CSO, Ferrara, Italy). The first was a healthy cornea (G0), whereas the second one was affected by keratoconus in an incipient phase (G1) according to the Amsler-Krumeich criteria [32] (provided from Vissum Hospital, Miguel Hernandez University and Kerato-conus IBERIA database, Alicante, Spain). This study was approved by the research ethics committee of the Technical University of Cartagena (CEI21_001).  
The corneal model was extended to the limbus zone, where it was attached to sclera tissue. An almost 40\({}^{\circ}\) cut angle was used according to the average measured values [38]. To implement the finite element model, the ANSYS software (Swanson Analysis System Inc., Canonsburg, PA, USA) was applied by considering the 3D elements of type SOLID 186 with four elements for thickness [21]. The mixed u-P formulation and reduced integration were considered for solving purposes.  
Figure 1 shows the different defined material zones. Light blue depicts the superior-inferior, nasal-temporal, and central zones. Red denotes the limbus zone and purple the zones in diagonal octants with low fibers' density, where a reduction in the stiffness parameter of fibers was considered according to previous works [24]. Blue zones denote the transition zones between zones with maximum and minimum fibers concentration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Materials’ Constants** & **a1 (Pa)** & **a2 (Pa)** & **k1 (Pa)** & **k2 (-)** \\ \hline Central, N-T and S-I Zones & 40,000 & \(-\)10,000 & 50,000 & 200 \\ Transition Zones & 40,000 & \(-\)10,000 & 37,500 & 200 \\ Central Oblique Zones & 40,000 & \(-\)10,000 & 25,000 & 200 \\ Limbus & 40,000 & \(-\)10,000 & 50,000 & 200 \\ \hline \hline \end{tabular}  
* N-T: Nasal-Temporal, S-I: Superior-Inferior.  
\end{table}
Table 1: Materials’ constants for anisotropic hyperelastic materials definition.  
Due to the high fibers' density in the limbus zone, where a high stiffness is reached, the displacements were constrained on the exterior face, where the cornea was joined to sclera tissue. This hypothesis avoids the rotation of the limbus zone and was applied in previous works [29].  
Intraocular pressure was applied as a pressure load in the cornea's posterior zone, but the aqueous humor influence was not considered. For Navarro's geometry, an IOP of 15 mm Hg (1998.4 Pa) was applied, whereas a respective IOP of 18 mm Hg (2399.8 Pa) and 16 mm Hg (2133.2 Pa) was used for the G0 and G1 geometries according to the measured IOP for each patient.
## 3 Security and Privacy-Preserving Solutions for IoE
### Blockchain-Based Security Solutions
#### 3.1.1 Blockchain-Based Data Storage and Security Models  
IoE systems generate large amounts of data, and it is crucial to store this data securely. Traditional data storage methods rely on centralized cloud-based solutions, which are vulnerable to SPOF risks. Blockchain can be integrated with decentralized file systems such as the InterPlanetary File System (IPFS) to enhance data integrity and security [72].  
Blockchain-based data storage models [73]:  
* _On-Chain Storage_: Small-scale IoE data is stored directly on the blockchain.
* _Off-Chain Storage_: For large-scale IoE data, integration with IPFS and decentralized databases is applied.
* _Hybrid Storage_: A mixed model where frequently accessed data is stored on-chain, while large-volume data is stored off-chain.  
These storage models and their respective advantages and disadvantages are summarized in Table 4.
# Analytically Regularized Evaluation of the Coupling of Planar Concentric Conducting Rings
## 2 Statement of the Problem
### MoM Solution: Projection  
Equation (14) can be solved by means of a suitable projection: in order to fall in the framework of Galerkin's Method, we can multiply both sides of the equation by \(\begin{bmatrix}\underline{\Psi}_{m}^{(p)}(\rho)\end{bmatrix}^{T}\) and \(\begin{bmatrix}\underline{\chi}_{m}^{(p)}(\rho)\end{bmatrix}^{T}\) with \(p=1\ldots Q\), where the superscript \(T\) stands for transpose,and integrate with respect to \(\rho d\rho\) from \(r_{ip}\) to \(r_{op}\). Such a projection is analytical on both sides of the equation; on the left hand side it is just necessary to apply again the VHT Formulas (10) and (12), whereas on the right hand side, resorting to the cylindrical wave expansion of a plane wave, it is not difficult to show that  
\[\int_{r_{ip}}^{r_{op}}\Big{[}\underline{\mathbf{\Sigma}}_{m}^{(p)}(\rho)\Big{]} ^{T}\underline{\mathbf{\Sigma}}_{inc}(\rho,z=0)\rho d\rho=j^{n-1}\frac{n}{k_{ 0}sin\theta_{i}}F_{k}^{(p)}(k_{0}sin\theta_{i}) \tag{15}\]  
\[\int_{r_{ip}}^{r_{op}}\Big{[}\underline{\mathbf{\Sigma}}_{m}^{(p)}(\rho)\Big{]} ^{T}\underline{\mathbf{\Sigma}}_{inc}(\rho,z=0)\rho d\rho=j^{n-1}\frac{dG_{k}^ {(p)}(w)}{dw}\Big{|}_{k_{0}sin\theta_{i}} \tag{16}\]  
in the Transverse Magnetic (TM) case, and  
\[\int_{r_{ip}}^{r_{op}}\Big{[}\underline{\mathbf{\Sigma}}_{m}^{(p)}(\rho) \Big{]}^{T}\underline{\mathbf{\Sigma}}_{inc}(\rho,z=0)\rho d\rho=j^{n-1}\frac {dF_{k}^{(p)}(w)}{dw}\Big{|}_{k_{0}sin\theta_{i}} \tag{17}\]  
\[\int_{r_{ip}}^{r_{op}}\Big{[}\underline{\mathbf{\Sigma}}_{m}^{(p)}(\rho) \Big{]}^{T}\underline{\mathbf{\Sigma}}_{inc}(\rho,z=0)\rho d\rho=j^{n-1}\frac {n}{k_{0}sin\theta_{i}}G_{k}^{(p)}(k_{0}sin\theta_{i}) \tag{18}\]  
in the Transverse Electric (TE) case. The problem is therefore reduced to the solution of an algebraic system of linear equations. The coefficients' matrix, that is the scattering matrix, has to be computed numerically. This is not generally an easy task, as the entries of the matrix are integrals of slowly decaying oscillating functions. Different strategies can be adopted to accelerate the numerical computation of the integrals. A first possibility is to subtract the asymptotic behavior of the kernels, thus leading to integrals of the product of four Bessel functions and powers. Such an approach has been used, for example, in [44] in a simpler, scalar case. As a matter of fact, it can be shown that the integral of the product of four Bessel functions can be analytically evaluated as a Meijer function. This is very useful for numerical calculation because currently there are efficient routines evaluating such a special function.  
A second possible approach consists of resorting to the integration in the complex plane along a suitable integration path, as performed in [47]. In such a case the integrals at hand can be written as a superposition of proper integrals and fast converging improper integrals. In both cases, an effective calculation of the scattering matrix is possible, and numerical results can be obtained without excessive computational burden.
###### Abstract  
Understanding the link between land use/land cover (LULC) patterns and water quality can establish guidelines for non-point source pollution management and sustainable development. The transboundary Shat AI-Arab river basin (Iraq-Iran) suffers from nutrient pollution problems. This study aimed to estimate flow volume, nitrogen, and phosphorus pollution in this basin and how such pollution relates to LULC and flow volume using the Soil and Water Assessment Tool (SWAT) model. The data used in the SWAT model were the Digital Elevation Model (DEM), slope, parent materials of soil, LULC, and weather data (i.e., precipitation, relative humidity, temperature, solar radiation, and wind speed). The results showed that from 2004 to 2021, the annual Total Nitrogen (TN) and Total Phosphorus (TP) outputs were 618 and 140 kg km\({}^{-2}\), respectively. The TN discharge load ranged from 27 to 6500 kg km\({}^{-2}\) yr\({}^{-1}\), while the TP discharge load ranged from 1 to 1600 kg km\({}^{-2}\) yr\({}^{-1}\). Redundancy Analysis (RDA) revealed that cropland and urban cover ratios were positively correlated with the annual TN and TP discharge loads. On the contrary, shrubland and bare land ratios were negatively correlated with the annual TN and TP discharge loads. Results showed that flow volume is positively correlated with precipitation. Both annual TN and TP discharge loads exhibited a positive correlation with flow volume and a negative correlation with subbasain area. The highest annual TN and TP discharge loads were in the middle parts of the basin, where the cultivated land and construction land are concentrated and the flow volume is high. Thus, findings suggest that the basin is sensitive to shifts in flow volume associated with global climate change and to shifts in LULC change. No study for nutrient discharge load assessment for the entire Shat AI-Arab river basin has been performed before. Hence, the novel contribution of this study will guide the hydrologists and water resource planners in the basin to establish effective water policies, climate change mitigation strategies, and environmental change adaptation strategies.  
land use/land cover; water quality; SWAT model; redundancy analysis; Shat AI-Arab river basin