esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 6 Conclusions  
This study explored Arabic meter categorization using various deep learning and transformer models, including AraBERT, AraELECTRA, Arabic-BERT, ARBERT, MARBERT, CAMeLBERT, BiGRU, and BiLSTM models. To the best of our knowledge, no prior  
Figure 14: LIME explanation with sample text 3.  
Figure 13: LIME explanation with sample text 2.  
research has evaluated this combination of methodologies, making our contribution novel in the field.  
By applying these methodologies, we introduced new perspectives on processing and categorizing Arabic poetic meters. CAMeLBERT achieved the highest accuracy (90.62%), closely followed by BiLSTM (90.53%). These results underscore the effectiveness of transformer models in capturing the complex metrical structures in Arabic poetry, marking a significant advancement and opening new avenues for Arabic NLP research.  
Conceptualization, A.M.M.; Data Curation, S.S.; Formal Analysis, S.S.; Funding Acquisition, A.M.M.; Methodology, A.M.M. and S.S.; Project Administration, A.M.M.; Resources, A.M.M.; Software, S.S.; Supervision, A.M.M.; Validation, A.M.M.; Writing--Original Draft, A.M.M. and S.S.; Writing--Review and editing, A.M.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Kuwait University, grant number EO06/24.  
Not applicable.  
The dataset is publicly available at [25].  
The authors declare no conflicts of interest.
# RF Energy Harvesting
## 6 RF Energy Harvester Simulation  
Based on the knowledge presented in Sections 4 and 5 on the energy transferred with electromagnetic waves, a circuit that simulates RF energy harvesting may be proposed. The common use of radio frequencies makes them a perfect candidate for the source of energy that must be harvested from the environment. However, conducting experiments with RFs is difficult. This is why the possibility of simulating circuits operating at such frequencies is crucial for conducting research. Simulation makes it possible to test and develop modules and verify the impact of particular elements on the whole harvester. In this paper, three environments are compared. The first is KiCad EDA. This GNU GPL v.3 or higher licensed program is a free solution that is widely used for designing electronic modules. It includes integrated simulation software: NGSPICE. The second environment is LT Spice, which is designed to test electronic modules. It is publicly available thanks to Analog Devices and contains a large database of electronic parts that can be used in prototyping. The last program is Simscape Electrical, which is part of the MATLAB Simulink environment. In this paper, the same circuit was simulated in all environments, and the results are compared.
###### Abstract  
For indoor sensor systems, it is essential to implement an extra supporting area notification part. To inform the real-time coordinates, the time difference of arrival (TDOA) algorithm can be introduced. For these indoor localization systems, their main processes are often built based on the line of sight (LOS) scenario. However, obstacles make the off-the-shelf localization system unable to play its due role in the flexible non-line of sight (NLOS) scenario. So, it is necessary to adjust the signals according to the NLOS identification results. However, the NLOS identification methods before were not effective enough. To address these challenges, on the one hand, this paper proposes an adaptive strategy for a dual-receiving signal processing method. On the other hand, the system is matched with the homologous NLOS identification method based on a novel artificial fish school algorithm (AFSA) and the decision tree model. According to our experiments, our novel AFSA optimization method can obtain a better effect and take less time. The NLOS acoustic signal identification accuracy can be improved significantly in flexible scenarios compared with other methods. Based on these processes, the system will achieve more accurate localization results in flexible NLOS situations.  
acoustic signals; indoor localization system; decision tree; artificial fish school algorithm; dual-receiving signal processing
# Analysis of Liquefaction in Tailings Deposits by Fem Modeling of Underained Cyclic Triaxial
## 5 Strategy and Characteristics of Numerical Modeling
### Calibration of the Parameters of the UBC3D-PLM Model  
Based on the observations in the previous section, the model was calibrated to best represent the moment when liquefaction occurs, according to the liquefaction criterion used in this article (\(R_{u}>0.8\)). The parameters for the ML silt were calibrated using the results of the CTX 2 test, with a CSR of 0.12. In the case of the SM sand, a CSR of 0.16 was used based on the CTX 5 test.  
For the parameters that are common to the UBC3D-PLM and HSS models, the same values were used as are indicated in the calibrated parameters shown in Table 3 for the isotropic consolidation stage. Meanwhile, the parameters that control the elastic behavior, the hardening rule, and the densification and post-liquefaction factors of the UBC3D-PLM model were calibrated following the procedure in Figure 10.  
Table 5 presents the calibrated model parameters for both soils. It can be seen that the values of the stiffness moduli \(K_{B}^{e},~{}K_{G}^{e}\), and \(K_{G}^{p}\) that were calibrated for sand are higher than those calibrated for silt, since the experimental results of the CTX tests show that the sand has a higher initial stiffness and lower plastic strains; therefore, a higher value of \(N_{160}\) was used, resulting in larger moduli. The factors \(m_{e},~{}n_{e}\)_and_\(n_{p}\) were varied to better fit the experimental curves of the CTX tests.  
In the experimental tests, the silt reached liquefaction in fewer cycles than the sand, and consequently the parameter \(f_{dens}\) is higher for the silt since it is looser than the sand. This is directly related to the calibrated stiffness modulus \(K_{G}^{p}\) for both soils since this input parameter is used during primary loading; meanwhile, during secondary loading, the modulus \(K_{GCscapdia}^{p}\) is used, which depends on the value of \(f_{dens}\). This modulus should be decreased to a lesser extent for looser soil. The opposite is true for sand, which, having a higher input \(K_{G}^{p}\), should be decreased even more, using a lower value of \(f_{dens}\).
# Rapid and Precise Zoom Lens Design Based on Voice Coil Motors with Tunnel Magnetoresistance Sensors
## 3 Rapid and Precise Zoom System Design Example
### Magnetic-Scale Closed-Loop Control System  
The position sensor operates based on the tunnel magnetoresistance effect [25; 26]. The TMR sensor mainly includes a thin-film element consisting of two ferromagnetic layers, the free layer and the fixed layer, with a thin insulating tunnel layer between them. The magnetic field direction of the fixed layer remains constant, whereas the magnetic field direction of the free layer changes in response to the external magnetic field. When the magnetic field directions of the fixed and free layers are parallel, the resistance is minimized, and the tunnel current is maximized. Conversely, when the directions are antiparallel, the resistance reaches its maximum, and the tunnel barrier current is minimized. Its working mechanism is shown in Figure 10a.  
When the TMR sensor mounted on the zoom lens group moves relative to the fixed magnetic strip, the magnetic field of the strip changes and acts on the TMR sensor [27; 28; 29], causing the magnetization direction of the free layer to change with the external magnetic field. These changes cause the resistance of the tunnel junction to vary periodically. By measuring these periodic changes in resistance, the control system can record and adjust the position of the zoom lens group in real time, achieving precise position detection and control. The working mechanism is illustrated in Figure 10b.  
The working principle of the high-precision magnetic scale closed-loop control system involves a high-precision magnetic-scale position sensor to detect the magnetic field changes of the magnetic scale strip. This technology utilizes high-precision magnetic-scale and TMR sensors to continuously monitor the position changes of the zoom lens group, providing accurate position signals. The control system then adjusts the position of the group in real time based on these signals, achieving micron-level precise positioning control through closed-loop feedback.  
As shown in Figure 11, the closed-loop control process functions as follows:  
1. The position sensor detects the position signal;
2. The control chip outputs a control signal;
3. The driver chip outputs driving pulses, causing the zoom group to move in real time.  
This cycle repeats continuously, enabling high-precision closed-loop control. The control accuracy of the system depends on the size of a single grating unit. In this case, a single grating unit measures 2.5 \(\upmu\)m, ensuring that the positioning control accuracy of the zoom group meets the 5 \(\upmu\)m requirement.  
Figure 10: Working mechanism of position sensor: (**a**) TMR sensor working mechanism; (**b**) working mechanism of TMR sensor in position detection and control of zoom lens group.
## 3 Simulation Results  
### Laser Beam Propagation in Atmospheric Turbulence  
To quantify the turbulence's effect on laser propagation, the evolution of a laser beam across the turbulent channel and the impact of different atmospheric coherent times on the ISAI raw data are presented. The phase distribution and amplitude as well as the phase fluctuation over time of the received laser beam's \(TEM_{00}\) mode in different turbulence parameters are shown in Figure 2. Here, according to [18], the \(TEM_{00}\) mode phase fluctuation speed after the atmospheric turbulence could be quantified by the atmospheric coherent time, which was equal to 0.45 ms, 2.37 ms, and 4.50 ms for each row from left to right in Figure 2.  
The phase distribution of the laser beam distorted after propagation is shown in Figure 2. With stronger turbulence, the \(TEM_{00}\) mode amplitude and phase fluctuations become more intense. The spatial decoherence of the echo induced by the turbulence could reduce the SNR and heterodyne efficiency, while the temporal decoherence could introduce an extra random phase. Such mode crosstalk and phase instability could reduce the SNR and efficiency during heterodyne detection, resulting in coherent synthesis being impossible. It should be noted that applying adaptive optics (AO) [31; 32] technology in the ISAI process can correct the wavefront phase distortion and effectively upgrade the SNR  
Figure 2: Amplitude and phase fluctuation of \(TEM_{00}\) mode and phase distributions of received beam in different atmospheric coherent length \(r_{0}\). (**a–c**) The amplitude fluctuation, (**d–f**) The phase fluctuation of \(TEM_{00}\) mode and (**g–j**) The phase distributions of received beam in different atmospheric coherent length \(r_{0}\).  
of the heterodyne signal, but it cannot improve the phase instability of the echo induced by turbulence. However, after the requirements of the SNR are satisfied, the phase stability of the echo is the key factor for the ISAL imaging quality. Hence, it is of great significance for evaluating the influence of the phase fluctuation induced by turbulence on heterodyne detection's performance and the ISAL imaging quality. According to [18], atmospheric coherent time reflects the phase fluctuation speed of the echo in the atmospheric turbulence. Therefore, we consider that the relationship between the atmospheric coherent time and pulse duration time is the main factor to determine the ISAL imaging quality in turbulent conditions.
## 5 Climbing Experiment of the Robot Prototype
### Testing Experiment  
According to the analysis in Section 3 and for the given diameter of the cable together with those parameters, such as the clamping force of the robot, spacing of the rolling wheels and pre-tightening force of the spring were adjusted. The space between the left and right half-wheels was adjusted to be 42 mm.  
As shown in Figure 15, a climbing test was conducted on the robot. Although the surface was smooth and there were obstacles (less than 10 mm in height), the robot could climb in a stable manner. By selecting heavy objects (mass, 10 kg), the climbing ability of the robot with loads was verified. The robot could still climb normally under such loads. The test proved that the climbing speed of the robot met the required performance for cable inspection. The following conclusions were drawn by combining simulation and test, data:  
1. When masses of 10 kg were added to the robot, the robot could still climb along cables with inclination angle between 20\({}^{\circ}\) to 90\({}^{\circ}\).
2. The robot could be installed and maintained by only one worker.
3. The climbing speed could be adjusted within the range from 0 m/s to 0.164 m/s.
4. The robot could pass across obstacles over a maximum length of 10 mm.  
The climbing ability of the robot under the low-frequency and high-frequency vibration was further investigated and parameters, such as vibration direction, frequency and intensity of the vibration table could be adjusted through a controller. Based on the combined vibration in horizontal and vertical directions, the test was conducted using the combination of frequencies of 10 Hz and 100 Hz and vibration intensities of 20%, 40% and 60% (namely, 1 mm, 2 mm and 3 mm) of the maximum amplitude. The test shows that the robot could climb normally and its driving wheels did not skid. As a result, it did not leave the cable under the influence of inertial force during the vibration testing. Meanwhile, when the vibration table worked at a low frequency, the whole test system vibrated strongly, which exceeded the bearing limit of the floor of the building and limited the applicable test range. This should be addressed before in the future research.
## 2 Method
### Life Cycle Cost Calculation Method
#### 2.1.2 Model Inputs  
A list of all calculation inputs included in the three variations of the baseline model is presented in Table 1. The table classifies the input parameters into five categories and depicts the baseline value and respective source. Model-specific parameters describing the variated energy systems are marked with a \"\" before the parameter name. These parameters are only applied in the appropriate models. The first three cost categories in Table 1 (operation, implementation, and maintenance) are unit cost inputs. The last two are the lifespan of the elements and the amount (quantity) for each parameter. Figure 2: Discount rate types (**left**) and price development for different cost types (**right**). Data source [31]. Costs for energy renovation measures were obtained from the MOLIO price database for renovation works [36]. Costs related to energy-producing systems stem from Technology Data Catalogues (TDC) for individual heating installations [34] and energy-producing technologies [35]. The lifespan of envelope elements is taken from guidelines [37] and TDC [34,35] for energy supply systems. The energy demand for the operation of the building includes the energy necessary for space heating, DHW, and electricity (both for building operation and private use). Building operation-related electricity is determined by the compliance tool BE18. Private electricity demand is determined through metered data
# Landslide Displacement Prediction during the Sliding Process Using XGBoost, SVR and RNNs
## 1 Introduction
Moreover, it is difficult to judge the displacement trend of each new sliding by the displacement data collected in the past. Therefore, it is very important to select a method that can predict the slope displacement accurately during the sliding process. In the paper, we used Python to develop the landslide displacement-prediction method based on the eXtreme Gradient Boosting (XGBoost) algorithm, and optimized the hyperparameters through a genetic algorithm to improve the prediction accuracy of landslide displacement. The proposed XGBoost algorithm may effectively solve the problem of insufficient prediction accuracy of small samples and meet the actual needs for short-term displacement prediction of landslides during the sliding process.
# Enhanced Performance of nZVI/MXene@CNTs for Rapid As(III) Removal from Aqueous Solutions
## 3 Results and Discussion
### Mechanism Analysis
As shown in Figure 7, the removal process and oxidation mechanism of As(III) from water by nZVI/MXene@CNTs were obtained, based on the aforementioned results  
Figure 6: (**a**) FTIR spectra of nZVI/MXene@CNTs before and after As adsorption; (**b**) XPS spectra of nZVI/MXene@CNTs before and after As adsorption. High-resolution (**c**) C 1s; (**d**) Fe 2p; (**e**) O 1s XPS spectra of nZVI/MXene@CNTs before and after As adsorption; (**f**) High-resolution As 3d XPS spectra of nZVI/MXene@CNTs after adsorption. and analyses. In general, the adsorption and catalytic oxidation processes of As(III) by nZVI/MXene@CNTs could be explained in Equations (2)-(5). Specifically, three distinct steps might be identified in the removal of As(III): As(III) was first electrostatically adsorbed to the surface of nZVI/MXene@CNTs; then, it was oxidized to As(V) by the \(\bullet\)OH radical generated by Fe\({}^{0}\)[47]; and finally they were removed by the hydrated iron oxide by interfacial co-precipitation from the aqueous solution. \[\text{Fe}^{0}\rightarrow\text{Fe}^{2+}+2\text{e}^{-} \tag{2}\]  
\[2\text{e}^{-}+\text{O}_{2}+2\text{H}^{+}\rightarrow\text{ H}_{2}\text{O}_{2} \tag{3}\]  
\[\text{Fe}^{2+}+\text{H}_{2}\text{O}_{2}\rightarrow\text{ Fe}^{3+}+\text{OH}^{-}+\text{OH} \tag{4}\]  
\[\text{H}_{3}\text{As}\text{O}_{3}+2\cdot\text{OH}=\text{H}_{2}\text{As}\text{O}_ {4}^{-}+\text{H}^{+}+\text{H}_{2}\text{O} \tag{5}\]
## 4 Conclusions  
* Our research confirmed that the spiral grains remarkably affect shape stability. A warped bow with spiral grain values increased by almost 50% compared with lumber without spiral grain. This statement was confirmed even with statistical results where the set limit value of \(p\) < 0.05 was not exceeded, either with warp crook (\(p\) = 0.0436) or with warp bow (\(p\) = 0.00001).  
Figure 14: Statistical evaluation. Warp crook (**A**) difference between the log with and without spiral grain; (**B**) difference between tangential and radial lumber.  
Figure 13: Statistical evaluation. Warp bow (**A**) difference between the log with and without spiral grain; (**B**) difference between tangential and radial lumber.  
* Regarding warp crook, statistical significance was also confirmed for different types of lumber (radial or tangential), where the significance value reached \(p=0.0120\). This result can be explained mainly by the original position of tangential lumber, which was closer to the girth of the log, than radial lumber, which was closer to the pitch, which could lead to different shape stability.
* According to the measured values, the warps during the warp crook depend on the sizes of the spiral grain and the original position of the lumber, which means whether the lumber is closer to the girth of the log or closer to the pitch.
* According to DIN standard (4074-5) and sizes of shape change for warp bow, only one piece of lumber would be obtained in the case of lumber with spiral grain and up to twelve pieces of lumber in the case of sawn lumber without spiral grain. This result confirms that the spiral grain remarkably influences the resulting quality of the lumber.  
This research could broaden the possibilities of using hornbeam lumber (_Carpinus betulus_ L.) as a construction material.  
Conceptualization, I.K. and P.V.; methodology, H.M.U. and I.K.; software, P.V. and I.K.; validation P.V. and I.K.; formal analysis, P.V.; investigation P.V.; resources, T.V.; data curation, I.K.; writing--original draft preparation, P.V.; writing--review and editing, T.V.; visualization, H.M.U.; supervision, I.K.; project administration, P.V. and T.V.; funding acquisition, I.V and P.V. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Slovak Research and Development Agency under contract no. APVV-21-0049. Not applicable. Not applicable. Not applicable. Not applicable.  
The data presented in this study are available on request from the corresponding author. This work was supported by the Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences--project VEGA no. 1/0063/22. The authors declare no conflicts of interest.
###### Abstract
Therefore, this paper studies the optimal control of the closing angle of the subgrid parallel process in the power-system-recovery process. Based on considering the uncertainty of the wind-power output, the subgrid parallel process is realized, and the power loss load is restored to speed up the system-recovery process. Firstly, the uncertainty of wind-power output is described in the form of probability density prediction, and a probability-prediction model of wind-power output based on quantile regression LSTM is established. Based on predicting future wind-power output intervals, the probability density function of the output at each time point in the future is obtained through kernel density estimation, and various output probability scenarios of wind power are integrated through weight addition to improve the accuracy of wind-power prediction. The influence of wind-power output uncertainty on the closing angle and adjustment scheme is fully considered. Then, based on a consideration of the probability of wind-power output, a control model to reduce the phase angle difference at both ends of the parallel point is established by adjusting the active power output of the conventional units of the system \begin{table}
\begin{tabular}{c c c c} \hline
**Problem** & **Model and Method** & **Advantage** & **Shortcoming** \\ \hline \multirow{8}{*}{Handling of uncertain factors} & Stochastic optimization & Consider statistics of random variables & Poor performance outside the sample and low \\  & [29; 31; 38] & Better subadditivity, which can reflect the VaR beyond the confidence interval & It is difficult to obtain the distribution information of all random variables \\ \cline{2-4}  & Robust optimization & Better performance outside the sample & The solution result is conservative \\ \hline \multirow{8}{*}{System-recovery process} & Three-stage load recovery [34] & Improve overall load recovery rate & \\ \cline{2-4}  & Distribution network system restoration considering multiple energy sources [35] & Improve the economy of the recovery process & The impact of uncertain factors in the recovery process on the system-recovery process is not considered \\ \cline{1-1} \cline{2-4}  & Route selection for re-commissioning of power equipment [37] & Applicable to recovery planning under N-M events & The impact of uncertain factors in the recovery process is not considered \\ \cline{1-1} \cline{2-4}  & Sectional recovery of load before and after subnetwork paralleling [40] & Adopt staged a recovery to ensure that the system parallel process is not affected \\ \hline \end{tabular}
\end{table} 
Table 1: Advantages and shortcomings of current research. and assigning as the control measure the load to be restored after power failure. In the regulation process, the multiobjective collaborative optimization is carried out with the minimum output adjustment sum of conventional units and the maximum input of important loads to be restored, and the NSGA-II algorithm is used to solve the established model. Finally, the effectiveness of the proposed method and model is verified by the IEEE 39-bus system simulation.
# High Efficiency Continuous-Variable Quantum Key Distribution Based on ATSC 3.0 LDPC Codes
## 2 Preliminaries  
In this section, we first briefly present the multidimensional reconciliation protocol in the CVQKD system, and then introduce ATSC LDPC codes, which will be used to construct error-correcting codes for information reconciliation part.