esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### The Electron Deflection Trajectory in a Gaussian Pulse  
Generally, the motion trajectory of the electron mainly consists of three stages: incidence, deflection and emission [24]. In the incidence stage, the electron starts to strike the linearly polarized Gaussian laser pulse moving along the positive z-axis along the positive x-axis, eventually resulting in a cross-collision at the origin of the Cartesian coordinate system. During the deflection stage, the electron undergoes a series of oscillatory motions within the polarization plane under the action of the laser. Generally, the amplitude of the oscillatory motions first increases and then decreases. Finally, in the emission stage, the electron leaves the range of the Gaussian pulse and is no longer subject to electromagnetic forces.  
As depicted in Figure 2, the electron trajectory plots under varying laser amplitudes can be observed. Given that the incident direction of the electrons is parallel to the polarization plane of the linearly polarized laser, namely the XOZ plane, the electrons are not subject to a force in the y direction, and consequently, their trajectories remain within the XOZ plane. With the continuous enhancement of the laser amplitude, the electron trajectory exhibits virtually no change in the x-axis direction but continuously elongates in the positive z-axis direction. This is because the increase in the laser amplitude augments the longitudinal ponderomotive force of the linearly polarized laser pulse on the electron, resulting in a gradual elongation of the deflection distance of the electron along the z-axis. When the laser amplitude is extremely small, the electron merely undergoes oscillatory motions within a very narrow range before exiting. Observing Figure 2, the exit direction of the electron trajectories does not undergo a notable alteration, suggesting that the exit angle of the electron trajectories is minimally influenced by the laser amplitude.  
As shown in Figure 3, when observing the changes in electron energy, it can be discerned that the period of electron energy variation is independent of the laser amplitude. However, the magnitude of electron energy change augments with the increase in laser amplitude. The period of electron energy change is equivalent to the period of velocity variation, which is correlated with the beam waist radius and the delay time of the laser pulse but shows no distinct relationship with the laser amplitude. The expansion of the  
Figure 2: The electron trajectories under various laser amplitudes.  
laser beam waist radius can broaden the laser's action scope and extend the action time and period of the electron along the x-axis. Owing to the increase in laser amplitude, the ponderomotive force exerted on the electron in the z-axis direction intensifies, and the acceleration and deceleration processes along the z-axis become more vigorous, causing the maximum value of electron energy to be greater and the minimum value to be smaller. Consequently, overall, a larger laser amplitude will result in a greater amplitude of electron energy variation.
## 2 Materials and Methods
### MiRNA Detection  
The concentration of extracted miRNAs was determined by miRNA-specific fluorometric assay using a Qubit(r) 2.0 Fluorometer (Thermo Fischer Scientific, Waltham, MA, USA). Mature miRNAs were reverse transcribed by the miRP II RT Kit (Qiagen, Hilden, Germany). The expression of miR-483-5p, miR-205-5p, and let-7-5p was determined by the miRP SYBR Green PCR Kit (Qiagen, Hilden, Germany) using miRNA specific miRP primer Assays (Qiagen, Hilden, Germany) as previously described [9; 11]. MiR-103-3p was used as a housekeeping miRNA that proved to be an applicable reference in our previous studies [9; 10; 11]. Relative expression level was determined relative to miR-103-3p expression, where the relative expression level of a specific miRNA is equivalent with \(2^{-\Delta\text{CI}}\).
# Potential Use of Paper Mill Sludge in Improving Soil Quality for Plant Growth
## 3 Results and Discussion  
### Germination Index (GI)  
Figures 1 and 2 compare the seed germination of romaine lettuce in various soil conditions. The germination index (GI) is a comprehensive parameter that considers the germination rate and relative root length. It provides a more holistic evaluation of seed germination performance. The resulting value reflects the overall vigor and quality of the seeds. A higher germination index indicates better seed performance. If soil modifiers contain toxic substances, such as heavy metals or chemical pollutants, then they can impede the germination of seeds. In Figure 2, all the soil modifiers, except the one prepared by mixing 50% paper mill sludge with a 5:5 mixture of camellia oilseed cake and peat moss (COP55:PMS = 5:5), exhibited a GI over 100. This finding suggests that the mixtures of PMS with camellia oilseed cake (CO) and peat moss (P), as well as with oilseed cake of toothache tree (TO) and peat moss (P), hold promise as potential biostimulants for enhancing seed germination. Importantly, these mixtures were found to be safe for use, as they did not negatively affect seed germination. Moreover, it is worth noting that an increase in the amount of paper mill sludge (PMS) added to the mixtures of COP and TOP resulted in a higher germination index. This observation suggests that PMS contains organic matter and essential nutrients that can enrich the soil. [PERSON] et al. reported no acute or subacute toxic effects on seed germination when PMS prepared as pellets was applied to soil [36]. In particular, there is also a concern about contamination from toxic substances in wastewater, as PMS is a solid waste generated in the wastewater treatment process. However, [PERSON] et al. reported high seed germination rates in paper mill effluents, confirming that PMS does not introduce toxic substances from wastewater [37].  
Figure 1: Images showing sprouted seeds in various soil modifiers. COP was made from a mixture of camellia oilseed cake and peat moss, and TOP was made from a mixture of toothache oilseed cake and peat moss.  
Two soil conditions ticked in red in Figure 2, COP37:PMS = 5:5 and TOP55:PMS = 5:5, exhibited high GI values over 140 and were selected to assess their potential as soil conditioners. Under these two conditions, the soil modifiers were divided into pre- and post-composting for the performance evaluation.
# Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
## 5 Experiment
### Performance Comparison and Analysis  
We have adequately compared our model on the NGSIM dataset with a wider range of state-of-the-art trajectory prediction methods. As shown in Tables 1 and 2, our approach exhibits superior performance over existing methods. Detailed analysis shows that the residual de-noising diffusion model always maintains low mean distance error and has good long-term trajectory prediction accuracy, which indicates its robustness under different traffic conditions. It is important to note that the effectiveness of our approach is mainly reflected in comparison with MID, which is our primary benchmark. Our model achieved 9%, 11%, 12%, 8% and 26% improvement in \(minADE_{1}\), \(minADE_{5}\), \(minFDE_{1}\), \(minFDE_{5}\) and \(mAP\), respectively. These improvements can be attributed to the innovative use of residual ideas in diffusion-generated models, which effectively capture intrinsic feature connections between vehicles in the same scenario, leading to more accurate joint predictions of future trajectories. Compared with the previous deep learning algorithm model, we integrate the traditional physical constraint algorithm into the model, which makes the final trajectory prediction accuracy achieve a good effect, effectively avoiding unreasonable trajectory generation in complex traffic scenarios. In addition, we provide quantitative metrics and qualitative simulation examples to better understand the performance of the model. The specific qualitative visualizations are shown in Figure 5.
## 8 Conclusions
The increasing accuracy of the objective space description and the possibility of deep network retraining presents the opportunity to create a very precise neural approximation of optimized structure parameters and, therefore, to significantly increase the precision of GA+ANN optimization. The combined GA+ANN procedure was successfully applied to problems related to the avoidance of vibration resonance, which is a major concern for every structure subjected to periodic external excitations. The presented examples show two approaches to resonance avoidance: the maximization of the fundamental natural frequency (for external excitation frequencies possibly smaller than the fundamental natural frequency) or the maximization of a frequency gap around the external excitation frequencies (in other cases). In all examples presented in this paper, the necessary changes in natural frequencies are the result of appropriate changes in lamination angles only. In other words, no changes in the boundary conditions, geometry, or mass of the structures are introduced. A further study of the proposed optimization procedure would be of interest. The proposed problems to be discussed include the following:  
* vibration resonance avoidance in the case of a few periodic excitations with different frequencies;
* multi-criteria optimization, e.g., the maximization of the fundamental natural frequency, together with the maximization of the buckling force;
* composite material design (e.g., topological constraints, varying thickness and/or stiffness);
* a full variable-stiffness approach to lamination parameters that considers continuous changes in lamination parameters over the investigated domain;
* multi-dimensional problems with the number of design variables exceeding twenty; and
* optimization of a shell with openings. **Author Contributions:** Conceptualization, B.M. and L.Z. ; Methodology, B.M. and L.Z. ; Software, B.M. and L.Z. ; Writing--Original Draft Preparation, B.M. ; Writing--Review and Editing, L.Z. **Funding:** This research was supported by the Polish Ministry of Science and Higher Education grant to maintain research potential. **Conflicts of Interest:** The authors declare no conflict of interest.
# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 4 Performance Evaluation
### Model Performance
#### 4.2.2 Damage-Level Prediction Performance
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{\(M_{DL}(FRC,ZF)\)} & \multicolumn{3}{c}{\(M_{DL}(FRC,RN)\)} & \multicolumn{3}{c}{\(M_{DL}(SSD,RN)\)} \\ \cline{2-11}  & **AUC** & \(F_{1}\)**Score** & \(AA\) & \(AUC\) & \(F_{1}\)**Score** & \(AA\) & \(AUC\) & \(F_{1}\)**Score** & \(AA\) \\ \hline Minor & 0.59 & 0.44 & 0.44 & 0.85 & 0.71 & 0.75 & 0.55 & 0.46 & 0.47.5 \\ Moderate & 0.50 & 0.50 & 0.50 & 0.67 & 0.57 & 0.49 & 0.51 & 0.27 & 0.18.58 \\ Major & 0.58 & 0.45 & 0.45 & 0.75 & 0.62 & 0.72 & 0.60 & 0.46 & 0.65 \\ \hline OA & & 0.47 & & & 0.63 & & & & 0.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance of damage-level classification. Figure 9: Damage-level prediction using \(M_{DL}(FRC,RN)\): (**a**) minor-damage scene (predicted as moderate damage with a score of 97.4%); (**b**) minor-damage scene (correct prediction with a score of 100.0%); (**c**) major-damage scene (predicted as moderate damage with a score of 95.1%); (**d**) mislabeled moderate-damage scene (predicted as major damage with a score of 95.8%). minor-damaged buildings, moderate in major-damage prediction, and less satisfactory in minor-damage prediction at all possible variable classification thresholds.
## 3 Formulation of the Mathematical Model
### Gas Flow Regimes within the Matrix
#### 3.3.2 Adsorbed Gas Flow  
For the adsorbed gas, the adsorption/desorption process occurs with changes in reservoir pressure. In addition to that, surface diffusion also occurs because of the change in gradient concentration along the pore walls. The mass flux in surface diffusion is given by [3; 6]:  
\[M_{s}=-D_{s}\frac{\zeta_{m}C}{p}\Delta p, \tag{27}\]  
where \(M_{s}\) is the mass flux in surface diffusion, \(D_{s}\) is the surface diffusion coefficient, \(\zeta_{m}\) is the modified gas coefficient, and \(C\) is the adsorbed gas concentration on the pore walls.  
Assuming that the adsorbed gas on the pore walls is monolayer adsorption, the gas concentration decreases with decreasing reservoir pressure. Furthermore, when considering the effect of adsorbed gas content, the adsorbed gas concentration can be expressed as [3]:  
\[C=\frac{4\theta M}{\pi d_{CH_{4}}^{3}N_{A}}. \tag{28}\]
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 4 Future Trend of ML in IVLC  
Over the last decade, visible light communication has achieved rapid advances both in technologies and in applications. The fundamental impetus to this achievement is not only the progress in communication technologies including coding, modulation, and signal processing, but also the rapid development of optoelectronic chips and devices. The bandwidth of the state-of-art VLC devices can exceed 1 GHz [91], nearly 100 times larger than that of 10 years ago. However, compared with the devices used in optical fiber communication, which usually boast nearly 100 GHz bandwidth, the bandwidth for VLC is too small to afford roughly equivalent data rates with optical fiber communication. Thus, the integration of wired/wireless communication in future ultrahigh-data-rate 6G networks could still be a challenge. Coherent light sources, i.e., lasers, are supposed to have larger bandwidths. Semipolar and nonpolar LEDs are reported to increase the modulation bandwidth. Microstructure, microcavity, and plasmon may be promising new approaches to enable ultrahigh data rates [92]. The intrinsic diversified characteristics of VLC devices require AI technologies to understand the device model, optimize the transmission link, and manipulate the whole network effectively.  
The future development of IVLC in the intelligent physical layer and intelligent network layer will be presented in this part, as shown in Figure 8. We strive to provide readers with some insight.
# A Low-Profile Wideband Linear-to-Circular Polarization Conversion Slot Antenna Using Metasurface
## 1 Introduction
Next, an equivalent circuit and a phase analysis are presented to explain how the proposed antenna converts LP waves to CP waves and obtains a wide ARBW. In Section 3, the parameters affecting the ARBW of the antenna are optimized. In Section 4, the proposed CP metasurface antenna is fabricated and measured. The measured results are in good agreement with the simulated results. Finally, in Section 5, the conclusions are presented.
# Improving Flood Detection and Monitoring through Remote Sensing
## 3 Conclusions  
The papers in this Special Issue cover a broad spectrum of techniques and data analyses aimed at improving the performance of flood monitoring activities from remotely sensed data. Improvements come essentially from (i) availability of innovative data sources, such as backpack-mounted 3-D cameras or spaceborne GNSS reflectometry; (ii) precise assessment and validation of algorithm performance through comparison with independent data; (iii) integration of multiple data sources such as multi-frequency, multi-sensor, and multi-temporal satellite SAR data. Results of tests over sites located throughout the world demonstrate the great potential of such methods to bring significant innovation and increase algorithm precision in the field of inundation monitoring.  
Conceptualization, A.R., D.C., M.C. and A.D.; writing--original draft preparation, A.R.; writing--review and editing, A.R, D.C., M.C. and A.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Gratitude is due to the editors of the journal, as well as to the authors who contributed with their articles to this Special Issue. Finally, special thanks goes to the anonymous reviewers, who have contributed efficiently to improve the quality of the articles.  
The authors declare no conflict of interest.
## 5 Comparison with Related Systems  
Table 3 shows a performance comparison of the proposed UWAOSDM/PN system with the other UWA OSDM systems. The comparison is based on simulation results. As shown in the comparison, the UWAOSDM/PN has a relatively high communication rate, a lower system BER and, a low receiver complexity. The effectiveness of the proposed PN sequence-based compressive sensing channel estimation and equalization with the OMP and MMSE algorithm is demonstrated.  
The main contributions of this paper are as follows: (1) Provide a solution of combining PN sequence, OMP, and MMSE for underwater acoustic OSDM communication. Compared with the other UWA OSDM systems, the proposed UWAOSDM/PN has a relatively high communication rate, a lower system BER, and a low receiver complexity. (2) PN sequences are specially selected and designed for the system. PN sequences have three roles in this system. The header PN sequence can be used for signal detection and synchronization. The header PN sequence and the middle PN sequence can be regarded as a block pilot, respectively, which is used as the pilot to do channel estimation for the subsequent OSDM symbols. In particular, PN sequence is suitable for the OMP algorithm. The Toeplitz method is used to construct an observation matrix, and the PN sequence can be used as initial vectors to create observation matrix \(\phi\) for the pseudo randomness. In this way, the matrix \(\phi\) satisfies the important RIP metric. The three PN sequences are used for the Doppler estimation by the relative position of the three correlation peaks of sliding cross-correlation operation of the received signal. (3) For the potential application prospects in the underwater acoustic hybrid single- and multi-carrier modulations and adaptive modulation, the relationship among the OSDM vector length M, the OSDM subcarrier number N, and the underwater acoustic channel length is further studied.
# A Novel Approach to Multi-Provider Network Slice Selector for 5G and Future Communication Systems
## 6 Experimental Evaluation
### Definition of Algorithm Parameters  
After performing the collection and analysis of the network parameters, the application runs for the slice selection. This flow can be seen in the architecture of the proposed framework, illustrated in Figure 3.  
In summary, the configuration of the parameters defined in Algorithm 2 for carrying out the tests is presented in Table 4. The weights were defined in the following order of parameters: [\"Latency\", \"Jitter\", \"Loss\", \"Bandwidth\", \"Transfer\", \"Distance\", \"Reliability\"]. The configuration of the weights followed the results from the K-means algorithm, which showed the most relevant features for each slice (obtained clusters), as illustrated in Figures 9-11.  
Thus, for Test 1, the Latency and Reliability criteria were considered with higher weights. Test 2 considered the Latency and Loss criteria, and Test 3 considered the Bandwidth and Transfer criteria. Finally, Test 4 used a fair distribution of weights between the criteria evaluated and Test 5 used random distribution. Each test is formed by 250 iterations in which the values of the criteria evaluated change according to the data flows from the UEs.
# Preliminary Study of Recycled Aggregate Mortar for Electric Arc Furnace Dust Encapsulation
## 1 Introduction
The verification of the adequate encapsulation of the EAFD by means of cement-based matrices must be carried out through the study of the mechanical properties of the monoliths formed by the setting of the cement and the leaching behavior of elements that are harmful to the environment [16]. There are successful studies in which cement-based matrices are used to encapsulate EAFD [29; 30]. Even so, the immobilization of heavy metals from EAFD is not always chemically possible (adsorption) [31], it being necessary to develop matrices that allow the encapsulation of heavy metals from a physical point of view--for example, using denser matrices that do not allow the diffusion of contaminating elements [1; 32]. The cement-based matrices used to date for the encapsulation of EAFD incorporate natural sand [31], and there are no studies on the feasibility of using recycled aggregates from CDW for the manufacture of encapsulation matrices. However, mortars made with recycled aggregates from CDW are more porous than those made with natural aggregates, which can impair the diffusion leaching phenomena of monolithic mortar blocks, hence the need to study the feasibility of using recycled aggregates from CDW in the manufacture of cement-based matrices for the immobilization of EAFD. The objective of this study is to analyze the possibility of using the fine fraction of recycled concrete aggregates (RCA) for the encapsulation of EAFD in cement-based matrices. If the results of this research are favorable, two wastes can be reused together, reducing the consumption of natural resources and giving a second life to RCA- and EAFD-type waste, while promoting the new paradigm of the circular economy.
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 2 Vehicle Model
### Error Model with Incremental Control  
The bilinear discretization method is used, defining the discrete time as \(Ts\). Consequently, the continuous system (3) is converted into a discrete system.  
\[x(k+1)=A_{d}x(k)+B_{d}u(k)+C_{d}\xi \tag{5}\]  
Figure 2: Path tracking error schematic diagram.  
\[\left\{\begin{aligned} A_{d}=((I-AT_{s})/2)^{-1}((I+AT_{s})/2)\\ B_{d}=BT_{s}\\ C_{d}=CT_{s}\end{aligned}\right. \tag{6}\]  
where \(I\) is a \(4\times 4\) identity matrix.  
The selection of the front wheel angle increment as a control variable can effectively mitigate abrupt changes in the angle. This is particularly beneficial when there are significant variations in steering speed, as it can enhance stability during the steering process. By integrating the front wheel angle into the state variables, we can derive the state variables for the control equation.  
\[\xi(k+1)=\begin{bmatrix}x(k+1)\\ u(k)\end{bmatrix} \tag{7}\]  
\[\xi(k+1)=A_{k}\xi(k)+B_{k}\Delta u(k)+C_{k}\xi \tag{8}\]  
In the equation \(A_{k}=\begin{bmatrix}A_{d}&B_{d}\\ O_{1\times 4}&I_{1\times 1}\end{bmatrix}\); \(B_{k}=\begin{bmatrix}B_{d}\\ I_{1\times 1}\end{bmatrix}\); \(C_{k}=\begin{bmatrix}C_{d}\\ O_{1\times 4}\end{bmatrix}\), \(\Delta u\) denotes the incremental angle of the front wheel at time k, \(O\) signifies a zero matrix, and \(I\) represents an identity matrix.  
In the instance where the road curvature equals zero, the curvature term becomes negligible. Consequently, we can derive the following linear control system:  
\[\xi(k+1)=A_{k}\xi(k)+B_{k}\Delta u(k) \tag{9}\]