esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling Uncertainty
### Geometric Brownian Motion (GBM) Price Model  
An Ito process is a type of stochastic process that is used to model the evolution of variables (here asset price) over time with both deterministic and random components. The deterministic part (known as the Drift Term) represents the average rate of change,while the random component representing fluctuation is known as the Diffusion Term. Consider a stochastic process \(x\) described in the form of the following _It\(\hat{o}\)_ process:  
\[dx_{t}=\mu(t)\cdot x_{t}\cdot dt+\sigma(t)\cdot x_{t}\cdot dz_{t} \tag{4}\]  
In this equation, \(\mu(t)\) represents the drift term, \(\sigma(t)\) represents the diffusion term, and \(dz_{t}\) captures the random shocks to the system. GBM is a special case of the _It\(\hat{o}\)_ process, with \(\mu(t)\) being a constant \(\mu\) and \(\sigma(t)\) being a constant \(\sigma\)[23]. In GBM, the logarithm of the process, denoted as \(y_{t+1}=log(x_{t+1})\), follows a normal distribution with mean and variance given by the following:  
\[y_{t+1}=log(x_{t+1})\sim\mathcal{N}(log(x_{t})+(\mu-\frac{\sigma^{2}}{2}) \Delta t,\sigma^{2}\Delta t) \tag{5}\]  
This equation states that the logarithmic value of the price at the time step \((t+1)\) is normally distributed, with the mean adjusted for the drift (\(\mu\)) and volatility (\(\sigma\)), and variance proportional to the time increment \(\Delta t\).
## 5 Conclusions  
In this study, we predicted the elastic properties of a composite material using UHMWPE powder as reinforcement and PP as a matrix, using numerical analysis and the FEM to investigate the mechanical properties of the composite. As a result of applying UHMWPE powder for reinforcement, the PP-UHMWPE powder composite was modeled as an isotropic material, and the values of the elastic properties were comparatively analyzed according to the volume fraction of UHMWPE. The numerical analysis was  
Figure 4: Comparison of shear modulus.  
Figure 5: Comparison of Poisson’s ratio.  
performed using the ROM model and Hobbs equation, and the FEM results were employed for comparison based on the homogenization method using RVEs. The key findings of this study can be summarized as follows:  
1. The elastic properties predicted using the ROM model according to the volume fraction of each material showed large errors;
2. The elastic properties predicted based on the Hobbs equation were similar to those obtained using the FEM, and the change trends of the elastic properties according to the volume fraction were also similar between the two sets of results;
3. In the prediction of the elastic properties with an isotropic composite prepared using reinforcement material with a spherical shape or in the form of a fine powder, the Hobbs equation produced results with a more similar trend to the FEA results than the ROM model did, and the former method is more appropriate for applications involving isotropic composite materials.  
In the future, it will be necessary to conduct further research on the prediction of the elastic properties of isotropic composites with high volume fractions of UHMWPE, and additional investigation is required for optimization of the volume fraction. In addition, finite element analysis and numerical analysis should be quantitatively compared and analyzed based on the experiment.  
Further, experimental studies involving the preparation of real-world PP-UHMWPE composites are required, as well as research on the factors that could be adjusted to minimize the errors among the real-world experimental results, numerical analysis results, and FEA results.  
Conceptualization, J.-H.Y.; Writing--original draft, Y.-J.J.; Writing--review & editing, writing--original draft preparation, supervision, M.-S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Grant Numbers 2022R1 GIA1012340 and 2022R1 GIA1004962). These results were supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-004).  
The authors declare no conflict of interest.
# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 2 Materials and Methods  
This study proposes a Dual-View Knowledge Transfer (DVKT) framework to address the efficiency and limited labeled data issues in RSID. As depicted in Figure 1, the framework aims to effectively transfer hierarchical and cross-layer knowledge from a well-trained teacher network to a student network based on multiple Intra-KT and Inter-KT modules. The teacher network in the proposed DVKT framework adopts our earlier SCSNet [37]. Differently, the SCSNet used in this study is pre-trained on the natural-scene-dehazing dataset RESIDE [38], instead of limited labeled remote-sensing images. The student network is 1/n-SCNNet, where the number of channels in its convolutional layers (except the last layer) is 1/n of the teacher network. Compared to the complex SCSNet, the 1/n-SCSNet model has only \(1/n^{2}\) of the parameters and computational cost of SCSNet, but its performance degrades slightly.  
Figure 1: The framework of the proposed Dual-View Knowledge Transfer (DVKT) for remote-sensing image dehazing.
# Social Media Hate Speech Detection Using Explainable Artificial Intelligence (XAI)
## 2 Materials and Methods
### Tokenization, Sentence Padding, and Lemmatization  
Tokenization is the process in which sentences are divided into smaller parts that are called tokens. These tokens serve as the basis for stemming and lemmatization and can aid in finding various patterns in the text. The natural language toolkit (NLTK) library of Python provides functions to perform word tokenization. Specifically, word tokenization can be conducted to yield either characters or subwords. For example, the word \"clearer\" can be either tokenized into \"clear\" and \"er\" or \"c-l-e-a-r-e-r\". In this study, we performed character tokenization that converts words into tokens as an array of integers, improving the efficiency of the learning process. We created a tokenizer object from a pretrained model that was imported and then fitted to the HateXplain dataset. This was achieved using the keras and TensorFlow libraries.  
Padding was performed so that all the inputs were of equal length. Neural networks require all inputs to be of same length. Originally, the raw text had words and sentences of different lengths. In exploratory data analysis, we observed that the maximum sentence  
Figure 1: Data cleaning.  
length was mostly 200. Thus, we trimmed sentences with lengths greater than 200 and padded the remaining sentences.  
Using natural language processing (NLP), word normalization was performed through lemmatization. In lemmatization, all words are reduced to their base/root forms. For instance, (1) go, going, gone, and goes are reduced to go, (2) read, reading, and reads are reduced to read, and (3) hated, hating, and hates are reduced to hate.
## 2 Study Area and Data
### Ground Synchronous Measurement
#### 2.3.1 Flexible Automatic Trihedral Corner Reflector  
The seven ground-based calibration facilities used in this experiment were flexible automatic trihedral CRs developed by CASPLOS_CCSI, which can be moved easily. Each flexible automatic CR consisted of four parts: console, fixed base, triangular trihedral CRs, and the power control box (Figure 2), which transmitted signals to complete the adjustment of the CR alignment. The distribution of the flexible automatic CR on the 12 May UFS mode and 15 May FSI mode data are shown in Figures 3 and 4, respectively.  
Each CR inner leg length was 1000 mm, inner leg length deviation was less than \(\pm\)3 mm, interplate orthogonality was less than \(\pm\)0.2\({}^{\circ}\), plate curvature was less than \(\pm\)2 mm, and C-band RCS accuracy was better than 0.16 dB. The thermal deformation deviation of the inner leg length was less than \(\pm\)3 mm, the deviation of the interplate orthogonality stability was less than \(\pm\)0.1\({}^{\circ}\), the deviation of the plate curvature stability was less than \(\pm\)2 mm, the deviation of the elevation angle adjustment was less than \(\pm\)0.1\({}^{\circ}\), the deviation of the azimuth angle adjustment was less than \(\pm\)0.1\({}^{\circ}\), and the stability of the C-band RCS was better than 0.1 dB.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Date** & **Orbit** & **Look** & **Imaging** & **Incidence** & **Polarization** & **Nominal** & **Azimuth** & **Range Pixel** \\
**Direction** & **Direction** & **Mode** & **Angle/\({}^{\circ}\)** & **Resolution/m** & **Pixel Size/m** & **Size/m** \\ \hline
12 May 2022 & Descending & Right & UFS & 28.43–30.57 & HH & 3 & 1.669818 & 1.124222 \\
15 May 2022 & Ascending & Right & FSI & 26.01–29.60 & HH/HV & 5 & 2.613357 & 1.124222 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information of two C-SAR/01 satellite SLC images.  
Figure 2: Flexible automatic trihedral corner reflector.
## 2 Materials and Methods
### Modelling
#### 2.2.4 C Factor  
CZ: The \(C\) factor values were determined for the presented study using the \"Soil Erosion Control Calculator\" [43], developed and operated by the Research Institute of Soil and Water Conservation for Ministry of Agriculture and to standardize determination of crop cover effect in soil erosion conservation. The application is publicly available and allows to determine the \(C\) factor for a given crop rotation or an individual crop. According to geographic location, the application also suggests an optimum term of individual agrotechnical operations. The calculator has been used by individual farmers to determine relevant crops and crop rotations to fulfil requirements of Cross-Compliance policy defined as 'Good Agriculture and Environmental Condition' in the Czech Republic. For this study, we selected individual crops identified within the experimental catchment and calculated their \(C\) factor values for standard technology of cultivation used in the area [22]. It combines the regional seasonality of erosivity with regional statistical data of planting and harvesting and prescribed soil cover of crops in five development periods as described in [PERSON] and [PERSON] [16]. \(C\) factor values for crop rotation has then been calculated as weighted average for crops produced within the area.  
BY: The calculation of the \(C\) factor follows an approach developed by [PERSON] [44] which allows to use the proportion of different crops within a catchment to derive a mean \(C\) factor for all arable land within the catchment, without the necessity of crop rotation and seasonal erosivity data. [PERSON] [43] calculated \(C\) factors based on the seasonality of \(R\) factors and cover of more than 200 different crop rotations (including specific management, harvesting dates, etc.) following the approach of [PERSON] et al. (1987). Based on a Monte-Carlo simulation randomly using subsets of the different crop rotations, whereas \(C\) factors were calculated before, [PERSON] [44] could show that an empirical equation using the test catchment specific proportion of small grains, root crops, root crops under much tillage, and perennial crops is suitable to estimate the \(C\) factor (R\({}^{2}\) = 0.88; \(p<0.001\); [PERSON] 2002).  
AT: In Austria, the calculation of \(C\) factors is carried out with typical management data for each agricultural production zone. Management data (planting dates, harvest dates, dates and types of management operations) are taken from regional surveys and personal interviews, and the development of plant cover and plant height is derived from model simulations of the soil-water-plant model SIMWASER (45; 46). The algorithms to calculate \(C\) factors then strictly follow the procedures of the RUSLE (47).
## 3 Model Validation
### Time Evolution of System State
#### 3.3.2 Real-World (Human-Based) Mobility Model  
In this section, we replicate, for the real-world mobility scenario, the analysis performed in Section 3.3.1 for the synthetic mobility scenario. For the sake of brevity, we plot results for a single content request rate. Specifically, Figure 12 compares model predictions and simulation results for the number of content requests, the number of content items delivered through opportunistic contacts, and the number of content items delivered using the cellular network for \(\lambda_{0}=0.5\) req/min; while Figure 13 shows the number of content copies at time \(t\) for \(\lambda_{0}=0.5\) req/min. Again, the results confirm that our ODE-based modelling approach characterises with reasonable accuracy the temporal evolution of the system state and predicts a stationary regime for the offloading process.  
Figure 12: Real-world mobility model: time evolution of the number of content items with \(T_{c}=2\) min, \(T_{s}=20\) min, and \(\lambda_{0}=0.5\) req/min.
## 4 Discussion  
The fact that the improvement of the detail area is better than over the entire test area is explained by the fact that the iterative calculation in combination with sufficient environmental information by close neighbouring points can lead to good prediction about the probability of the point status. In areas with few or no neighbouring points and thus poor environmental information, no good predictions can be made. Therefore, many non-ground points are not recognized as such.  
The inclusion of the HOVE grid allows additional, valuable conclusions about the real terrain. As mentioned, this approach is very similar to the method described by [PERSON] et al. [13] where the point cloud data were also plotted with the horizontal angle (\(\theta\)) values and the vertical angle (\(\phi\)) values. In difference to the HOVE method, where filtering is done with the angles to the adjacent points, the filtering of the tree trunks bases on the principles of scan line segmentation [27]. The method of scan line segmentation is suitable to find planar objects, but it is difficult to segment uneven surfaces with few points left.  
The HOVE method alone can also be used for three-dimensional filtering. While, in the HOVE grid, the angular deviation is calculated from the measuring lengths, the angle calculation in the Wedge method is done using the height value or the Z-coordinates. Since the lengths of the TLS are usually measured from the side, the combination of these two methods provides information from two sides. This leads to more meaningful results. A more accurate analysis of adjacent grid point data to each other in the form of interpolation would be a next step toward increasing accuracy. However, interpolation is not easy in this case. Because of the irregular distribution of the points, a non-directional method (e.g., IDW) is not useful. The spline method would probably be a suitable interpolation method.  
Because terrestrial laser scans are usually taken from the side, a HOVE-grid window can contain points of two different terrain formations (e.g., small hill before large mountain). The points of the frontal formation may not be included in the interpolation of the formation behind. A preliminary separation of the points of the respective formation via the recording lengths or automated edge detection is therefore necessary before an interpolation can be made.  
A further improvement could be achieved by the combination of point clouds measured from different recording directions. A small change of the recording centre could already lead to a large information gain.  
In any case, there is still potential for further development with the HOVE-Wedge method.
###### Abstract  
Laser-induced breakdown spectroscopy (LIBS) technology has the characteristics of small sample demand, simple sample preparation, simultaneous measurement of multiple elements and safety, which has great potential application in the rapid detection of coal quality. In this paper, 59 kinds of coal commonly used in Chinese power plants were tested by a lab-designed field-portable laser-induced breakdown spectrometer. The data set division methods and the quantitative analysis algorithm of ash content, volatile matter and calorific value of coal samples were carried out. The accuracy and prediction accuracy of three kinds of dataset partitioning methods, random selection (RS), Kennard-Stone (KS) and sample partitioning based on joint X-Y distances (SPXY), coupled with three quantitative algorithms, partial least squares regression (PLS), support vector machine regression (SVR) and random forest (RF), were compared and analyzed in this paper. The results show that the model featuring SPXY combined with RF has the best prediction performance. The R2 of ash content by the RF and SPXY method is 0.9843, the RMSEP of ash content is 1.3303 and the mean relative error (MRE) is 7.47%. The R2 of volatile matter is 0.9801, RMSEP is 0.7843 and MRE is 2.19%. The R2 of calorific value is 0.9844, RMSEP is 0.7324 and MRE is 2.27%. This study demonstrates that the field-portable LIBS device combining appropriate chemometrics algorithms has a wide application prospect in the rapid analysis of coal quality.  
Keywords:laser-induced breakdown spectroscopy; portable device; coal quality analysis; data set partitioning; PLRS; SVR; RF +
Footnote †: journal: Applied sciences
## 4 Combined Optimization of UAV Location, User Pairing, and Power Allocation
### UAV Location Optimization
Subsequently, the location of the current UAV is treated as a particle within a particle swarm and input into the SA algorithm to obtain optimal pairing results for users at that location. By substituting these pairing results into Formula (40), we can ascertain the minimum user rate after power allocation optimization. This value serves as our particle swarm fitness function. Following this, according to the PSO algorithm, we calculate the current local optimal value and update both the current UAV location and velocity vector. This process is repeated until completion of all iterations within the particle swarm. Finally, by substituting cache revenue, optimal location, optimal pairing results, and optimal power distribution results of the UAV into Formula (14), we can determine its total revenue. **Input**: Initial pricing from content providers \(v_{k,f}^{initial}\), initial location of all users,  
UAV initial location \(d_{uav}^{(0)}=(x_{uav},y_{uav},h)\)  
**Output**: Total Revenue of UAV  
1. Substituting \(v_{k,f}^{initial}\) into Algorithm 1 yields the UAV caching revenue \(U_{cache}^{*}\)
2. Initialize the particle swarm, including the particle location \(\mathbf{d}_{particle}^{(i)}\) and velocity \(\mathbf{v}_{particle}^{(i)}\) and other parameters
3. Repeat
4. Substituting the positions of the current UAV particles and users into (6) yields the path loss for all users \(PL(dB)\)
5. Substitute the channel gain \(\overline{g}(dB)=-PL\) into the SA algorithm to obtain the optimal grouping \(\mathbf{g}^{*}\) for the user at the current location
6. Substituting \(\mathbf{g}^{*}\) into (40) yields the minimum achievable user rate \(R_{m}^{*}\), which serves as the value of the fitness function \(f(d_{particle})\) for the PSO algorithm
7. According to the PSO algorithm, compute the current local optimum value and update the particle velocity \(\mathbf{v}_{particle}^{(i+1)}\) and location \(\mathbf{d}_{particle}^{(i+1)}\) according to (43) and (44)
8. Until particle swarm iteration \(L_{\text{PSO}}\) ends
9. Obtaining the UAV optimal location \(d_{uav}^{*}=(x_{uav}^{*},y_{uav}^{*},h)\), the optimal pairing scenario \(\mathbf{g}^{*}\) and power allocation result in \(\mathbf{p}_{1}^{*}=\left\{\mathbf{p}_{1,m}^{*}\right\}_{m=1}^{M}\), \(\mathbf{p}_{2}^{*}=\left\{\mathbf{p}_{2,m}^{*}\right\}_{m=1}^{M}\)
10. Substituting the above results into (14) yields the total UAV revenue  
**Algorithm 2** Joint Optimization Algorithm for UAV Cache and Charging Services  
The complexity analysis of the algorithm is as follows: since the Steinberg game part is executed first and then optimized by particle swarm optimization and a simulated annealing algorithm, the complexity of the entire algorithm can be expressed as \(O(N_{\text{PSO}}L_{\text{PSO}}\cdot M\log M+t\cdot F\log F)\).
### Contributions and Organization  
The main contributions of this paper are summarized as follows.  
* Secure transmission for a MISO-NOMA-CR network applying SWIPT and assisted by AN is studied to enhance the security of the secondary network. We jointly optimize the precoder vectors and power-splitting (PS) ratios to minimize the transmit power to the SUs and EH users, subject to the constraints of minimum signal-to-interference-plus-noise ratio (SINR) at the SUs, minimum EH for EH users, maximum available power at the secondary transmitter, denoted as SU-Tx, and maximum permissible interference with the PUs. By minimizing the transmit power for SUs and EH users, we can assign the residual power to AN to generate interference against the eavesdropper, by which the security can be improved in the underlying network.
* The formulated non-convex minimization problem is challenging to solve due to coupling between the PS ratios and transmission beamforming vectors. The proposed solution consists of two steps. First, an SDR-based algorithm converts the formulated problem into a semidefinite programming (SDP) problem. Second, a Gaussian randomization technique is applied to obtain the approximate rank-one solutions for beamforming vectors, and a linear program problem is used to guarantee the feasibility of the candidate solutions of the Gaussian randomization technique.
* The space-division multiple-access (SDMA) method and the ZF technique are studied as a comparative schemes to solve the minimization problem in which the solution to the non-convex problems are based on the SDR technique. In addition, a MISO-NOMA-CR without AN is considered to be the baseline scheme to prove the advantages of AN.
* Numerical simulations prove the superiority of the proposed approach based on NOMA with AN for increasing the security of the secondary network, in comparison with NOMA without AN, SDMA and ZF schemes. In particular, the proposed AN method shows a significant improvement in SSR over the no-AN scheme under several studied scenarios.  
The rest of this paper is organized as follows. The MISO-NOMA-CR network model with SWIPT and EH users is described in Section 2. In Section 3, the beamforming optimization with AN is presented along with the proposed solution, and the comparison SDMA and ZF schemes are formulated. The numerical results are presented in Section 4. Finally, the conclusions are presented in Section 5. Table 1 lists the main abbreviations used throughout the paper.
## 5 Challenges and Future Research Directions
### Security Challenges in UDNs  
The high density of small cells makes UDNs vulnerable to security threats such as handover manipulation, cell interference, and encryption breaches. While encryption techniques provide protection, they often require significant computational power, increasing energy consumption. Future research should focus on developing lightweight security solutions, AI-powered intrusion detection systems, and adaptive attack-defense mechanisms to enhance security without compromising efficiency [25].