esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
Although the effect of applied electric field is very limited on resistivity, it can be causing damage to coal mass and increase the temperature [51]. Moreover, other factors such as gas, moisture and degree of metamorphism have significant effect on results of electrical resistivity, but all in all, it is the result of the interaction of many factors, so there are still some shortcomings in using electrical prospecting technology to predict and predict coal-rock dynamic disasters. However, acoustic emission is an intrinsic property during deformation and failure process of coal-rock mass, and electrical resistivity is more sensitive to internal fracture than AE signal, while AE signal is more sensitive to instantaneous fracture of coal mass. Consequently, in this paper, it is necessary to establish the relationship between AE damage variable and resistivity based on the analytical formula of cumulative AE ringing, so as to sufficiently consider the influence of damage and failure on resistivity variation regularities of coal mass, and determine the point-in-time of sharp change of resistivity in coal mass, which can provide more accurate signals for the coming disaster. It can be seen from Figure 8 that the relationship is in good agreement in this paper, which makes clear that adopting the joint test method of resistivity and AE technique is accurate and reliable. However, AE parameters depend on the slenderness and samples size and have strong scale effects on the process of coal sample damage, and it is found that not only in terms of specimen peak stress changes with the different size and slenderness, but also in terms of AE signals cumulative counting changes significantly. [PERSON] et al. conducted a series of numerical simulation tests on different size and slenderness of coal samples and found AE accumulative ringing count increased with the increase of experimental size [23]. [PERSON] et al. carried out a large number of studies found that AE fractal theory can effectively evaluate crack propagation monitoring of reinforced concrete structures and masonry buildings and found that it takes into account the multi-scale character of energy dissipation has scale effects [52; 53; 54; 55]. In this paper, standard samples with a size of 100 mm and a diameter of 50 mm are used during the loading, and the scale effects are obviously not taken into account in the analysis of the relationship between resistivity and AE accumulative ringing count, which lead to the resistivity calculated by this model is obviously uncertain due to scale effects. Therefore, the model needs further improvement on this basis, and it is important to establish a more mathematical relationship between AE parameters and resistivity directly and combine with other monitoring techniques simultaneously next work. And in this course, it is significant for further describing the deformation and failure of coal-rock mass and monitoring and predicting coal and rock dynamic disasters without confining itself to the influence of sampling frequency, temperature and scale effects on resistivity.
## 1 Introduction  
Beyond 5G networks should satisfy many Quality-of-Service (QoS) requirements related to high data rates, reliability, energy efficiency and security, as well as low latency and wide radio coverage [1]. In order to achieve high spectral efficiency, high-order modulations are applied. However, as the order of modulation increases, transmission of information becomes more sensitive to both channel impairments and receiver hardware imperfections [2]. In general, signal detection can be coherent or incoherent. The application of coherent detection is motivated by potential energy gains. However, in order to realize coherent detection, it is necessary to regenerate the phase of the reference carrier. The reference carrier phase can be extracted using a phase-locked loop (PLL) [2, 3, 4, 5, 6]. The regenerated phase should follow the random signal phase fluctuations generated due to multipath propagation. Even in a channel with additive white Gaussian noise (AWGN), the extraction of the reference carrier phase is not ideal [6]. In other words, there is a difference between the signal phase at the receiver input and the regenerated carrier phase. The difference between these two phases is called the phase error or phase noise. This phase error is a random process and is known to lead to the significant degradation of the system performance [2, 3, 4, 5, 6].
###### Abstract  
The correct and safe operation of electricity grids is a fundamental consideration in guaranteeing the supply of electricity with the correct parameters to consumers. One of the key aspects is how neutral point earthing works. In grids with the neutral point grounded by a Petersen coil, it is very important to properly tune its inductance in relation to the natural capacitance of the power grid. This is important both for the proper operation of earth fault protection and from the point of view of power quality, especially voltage asymmetry. Asymmetry of phase voltages in MV networks has a very negative impact on the operation of the railway network supplied by 12-pulse rectifiers. In this paper, the authors present the influence of the detuning factor of the earth fault compensation and the length and cross-section of the line on the value of voltage asymmetry factors. As the analyses carried out have shown, significant (up to 90%) values of the zero-sequence asymmetry factor can occur at low detuning, which can contribute not only to a decrease in the quality of the transmitted electricity but also to the unnecessary activation of protections. The values of the negative-sequence voltage asymmetry factor are much smaller (do not exceed 0.5% in the analysed cases) and do not depend on the network detuning factor. As the distance from the substation increases, an increase in the values of both types of asymmetry factors is also observed, with the relationship already negligibly small at considerable distances (above 20 km).  
MV power grid; voltage asymmetry; earth fault compensation; detuning factor Article
# Preparation and Chemical Protective Clothing Application of PVDF Based Sodium Sulfonate Membrane
## 3 Results
### Mechanical Properties  
Excellent mechanical properties of the membrane are crucial for CPC application. Figure 6 shows the stress-strain curves of M1-M6 membranes. It is clearly observed that the tensile strength decrease with increasing IEC. The elongation-at-break first increases from M1 to M4 and then decreases.  
Figure 4: SEM image of (**a**) surface, (**b**) cross-section of M3.  
Figure 5: The water contact angle of M1–M6 membranes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **IEC (mmol/g)** & **WU (\%)** & **LSR (\%)** & **CA of Water (\({}^{\circ}\))** \\ \hline M1 & 0 & 1.3 & 0 & 81.9 \(\pm\) 3.5 \\ M2 & 0.28 & 8.3 & 1. 7 & 70.9 \(\pm\) 2.8 \\ M3 & 0.88 & 14.3 & 5.0 & 61.2 \(\pm\) 2.5 \\ M4 & 1.73 & 23.1 & 8. 3 & 48.0 \(\pm\) 2.6 \\ M5 & 1.95 & 53.4 & 16. 7 & 39.3 \(\pm\) 2.3 \\ M6 & 2.29 & _77.4_ & 28. 3 & 23.6 \(\pm\) 1.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Water content, ion-exchange capacity, linear swelling ratio, and water contact angle.  
The tensile strength and elongation-at-break of prepared membranes are shown in Table 3. Compared with the unmodified PVDF membrane (M1), the membrane flexibility, i.e., elongation-at-break, is significantly improved by grafting modification. The decrease in tensile strength is due to the stronger plasticizing effect of water at higher WU. The as-synthesized membrane showed tensile strength sufficient for CPC application.
# Design and Optimization of Lattice Structures: A Review
## 4 Design and Optimization of Non-Uniform Lattices  
In this part of the paper, the design and optimization of non-uniform lattice structures are mainly discussed from two aspects: gradient lattice structures design, and structure optimization. Compared with uniform lattice structures, non-uniform lattice structures make the designer realize more free design. Through the design of non-uniform lattice structures, it can meet the needs of practical application. For example, in the bio-medicine field, irregular lattice structures are similar to human bone tissue, which is more suitable for the growth of bone tissue than regular lattice structures. Therefore, scholars use Voronoi-tessellation method to design non-uniform lattice structures replacing human bone tissue [184; 185; 186; 187]. Consequently, it is an urgent need for clinical application to design non-uniform lattice structures to replace diseased bones. By optimizing the distribution, shape, and size of materials, the non-uniform lattice structures can reduce the material loss and processing time, and reduce the cost. More importantly, the non-uniform lattice structures are more excellent in performance. With the combination of Voronoi and additive manufacturing technology, fabricating non-uniform lattice structures with different gradients are achieved [76; 90; 131; 188; 189; 190]. In the fourth part of this paper, the design and optimization of non-uniform lattice structures are mainly discussed from two aspects: gradient method and structure optimization.
# Numerical Performance Investigation of a Hybrid eCVT Specialized Agricultural Tractor
## 4 Numerical Investigation
### Acceleration Tests  
The proposed acceleration tests aimed to evaluate the performance of the vehicle in the two most representative speed ranges with different payloads on the trailer. Therefore, the following tests were performed:  
* 0-15 km/h with no trailer, with a trailer with 6000 kg and 10,000 kg of payloads.
* 0-40 km/h with no trailer, with a trailer with 6000 kg and 10,000 kg of payloads.  
Table 5 shows the results of the acceleration tests, and highlights two different behaviors for the HeCVT in the test performed with a gear shift (GS) between the low and high speed ranges or in the case of a test started directly with the high speed range gear ratios (thus no GS). During the test with GS, the vehicle started in the low-speed regime and  
Figure 7: Energy management strategy for HeCVT tractor.  
changed to a high-speed regime at 15 km/h. This comparison was proposed to show that, if the tractor did not have to pull a heavy trailer, it was possible to use directly the high speed regime without a consistent difference in performance. This would save clutches and other mechanical elements from unnecessary wear and would simplify the driving experience. Results for the conventional powertrain were obtained with gearshifts at 5, 10, and 20 km/h. The conventional powertrain model results were validated by the authors in a previous work using experimental data recorded during tractor field tests in different work scenarios [22; 23]. In this work, the HeCVT architecture model was compared with the validated model of the conventional architecture with the same characteristics.  
The downsized diesel engine results showed comparable acceleration performance between the HeCVT and conventional tractors. The reason of this was in the possibility of the electric machines, in particular EM\({}_{2}\), to cooperate with the ICE through the two-stage planetary gear, providing traction power for the wheels. As an example, Figure 8 shows the simulation results of the 0-40 km/h acceleration test pulling a trailer of 10,000 kg (tare mass + payload). The first diagram shows the vehicle speed, the second the kinematic link between the ICE and the EM\({}_{2}\) rotational speed, the third the torque applied by each machine, and the last one their instantaneous power. In this test, it was possible to appreciate the transition from CD mode to CS mode at t = 33 s, when the power generated by EM\({}_{1}\) (negative sign) except for conversion losses was directly used by EM\({}_{2}\) to maintain the vehicle speed.
## 4 Computer Simulation
### Comparison of the Proposed Methods  
To facilitate a clear understanding of the performance differences between the two proposed methods, this simulation considers a single-LED illumination system with no variation in nonlinear parameters. The simulation parameters are summarized in Table 1. For the nonlinear parameter characterization, white LEDs were assumed, and the channel model assumes Additive White Gaussian Noise (AWGN). The reason for selecting 16 QAM and 64 QAM modulation schemes in this study is that these schemes, compared to QPSK or BPSK, carry a greater amount of information and require the consideration of amplitude. Consequently, they are more susceptible to nonlinear effects, thereby facilitating the verification of the effectiveness of the proposed method.  
Figure 8 presents the BER performance. The number of reference signals used was 50 for LSM and 600 for MBM in 16 QAM, and both methods employed 750 reference signals for 64 QAM. At a target BER of \(10^{-4}\), LSM demonstrated an improvement of 2.0 dB compared  
Figure 6: Concept of FSS.  
Figure 7: Proposed adaptive compensation system using FSS.  
to the nonlinear LED model for 16 QAM and 0.7 dB over MBM. At a target BER of \(10^{-2}\), for 64 QAM, a 0.3 dB enhancement was achieved.  
Figure 9 illustrates the parameter estimation accuracy under 64 QAM modulation with a fixed \(E_{b}/N_{o}\) = 21 dB. The results validate the robustness of the proposed estimation framework, particularly under high-order modulation schemes. Since \(b_{2}\) is the coefficient of the squared term, we focus on this parameter. When using 15 reference signals, the mean absolute error (MAE) of \(b_{2}\) with LSM falls to 1/6 compared to the MBM. Additionally, for the LSM, it can be observed that the MAE saturates with fewer symbols. For example, in Figure 9, for \(b_{2}\), the MAE saturates at a reference signal count of six for LSM and at nine for MBM. Therefore, with a simple calculation, the computational complexity at saturation is reduced to 1/13 in LSM.  
Figure 8: BER performance with various methods.  
Figure 9: Parameter estimation accuracy of various methods.
###### Abstract  
Landslides are known as the world's most dangerous threat in mountainous regions and pose a critical obstacle for both economic and infrastructural progress. It is, therefore, quite relevant to discuss the pattern of spatial incidence of this phenomenon. The current research manifests a set of individual and ensemble of machine learning and probabilistic approaches like an artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LR), and their ensembles such as ANN-RF, ANN-SVM, SVM-RF, SVM-LR, LR-RF, LR-ANN, ANN-LR-RF, ANN-RF-SVM, ANN-SVM-LR, RF-SVM-LR, and ANN-RF-SVM-LR for mapping landslide susceptibility in Rudraprayag district of Garhwal Himalaya, India. A landslide inventory map along with sixteen landslide conditioning factors (LCFs) was used. Randomly partitioned sets of 70%:30% were used to ascertain the goodness of fit and predictive ability of the models. The contribution of LCFs was analyzed using the RF model. The altitude and drainage density were found to be the responsible factors in causing the landslide in the study area according to the RF model. The robustness of models was assessed through three threshold dependent measures, i.e., receiver operating characteristic (ROC), precision and accuracy, and two threshold independent measures, i.e., mean-absolute-error (MAE) and root-mean-square-error (RMSE). Finally, using the compound factor (CF) method, the models were prioritized based on the results of the validation methods to choose best model. Results show that ANN-RF-LR indicated a realistic finding, concentrating only on 17.74% of the study area as highly susceptible to landslide. The ANN-RF-LR ensemble demonstrated the highest goodness of fit and predictive capacity with respective values of 87.83% (area under the success rate curve) and 93.98% (area under prediction rate curve), and the highest robustness correspondingly. These attempts will play a significant role in ensemble modeling, in building reliable and comprehensive models. The proposed ANN-RF-LR ensemble model may be used in the other geographic areas having similar geo-environmental conditions. It may also be used in other types of geo-hazard modeling.  
landslide susceptibility; ANN; machine learning algorithms; ensemble models; Rudraprayag; Garhwal Himalaya
# Infrared Dim and Small Target Detection Based on the Improved Tensor Nuclear Norm
###### Abstract  
In the face of complex scenes with strong edge contours and high levels of noise, suppressing edge contours and noise levels is challenging with infrared dim and small target detection algorithms. Many advanced algorithms suffer from high false alarm rates when facing this problem. To solve this, a new anisotropic background feature weight function based on the infrared patch tensor (IPT) model was developed in this study to characterize the background airspace difference features by effectively combining the local features with the global features to suppress the strong edge contours in the structural tensor. Secondly, to enhance the target energy in the a priori model, an improved high-order cumulative model was proposed to establish the local significance region of the target as a way to achieve energy enhancement of the significant target in the structural tensor. Finally, the energy-enhanced structural tensor was introduced into the partial sum of the sensor nuclear norm (PSTNN) model as a local feature information weight matrix; the detection results were obtained by solving the model with the help of ADMM. A series of experiments show that the algorithm in this paper achieves better detection results compared with other algorithms.  
dim and small target; anisotropic filtering; energy enhancement; PSTNN model; target detection +
Footnote †: journal: Applied Sciences  
1
Footnote 1: School of Electrical Electronics and Computer Science, Guangxi University of Science and Technology, Liuzhou 545006, China; [EMAIL_ADDRESS] (X.F.); [EMAIL_ADDRESS] (A.W.)  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 4 Experimental Evaluation of the VLC System and Experimental Results  
The following section presents the experimental evaluation of the proposed VLC system. The purpose of these tests is to confirm the functionality and viability of the system under different conditions and testing scenarios. Thus, the performances of the VLC system are investigated in different noise conditions and for variable communication distances. Moreover, the evaluation process also examines the performances of the system for different modulation techniques, coding techniques, and communication frequencies. Thus, this experimental verification investigates the potential benefits that are associated with context adaptive VLC architectures (i.e., the communication parameters are selected based on the existing context). Furthermore, the aim of the experimental verification is to confirm the compatibility between VLC and automotive applications. This objective is motivated by the fact that the proposed system is developed while using a commercial standard compliant LED traffic light and a wide FOV VLC sensor. Last but not least, these tests are also intended to confirm the benefits of the logarithmic transimpedance circuit. The experimental setup is illustrated in Figure 4, whereas its summary can be found in Table 1. Additional details concerning each test are presented within the dedicated section.  
Figure 4: Schematic of the experimental setup.
## 2 Methodology and Data
### Data
For jobseekers whose registration had not ended at the time of data extraction, we considered the last day \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Variable** & **Description** & **Values** & **Proportion** \\ \hline previous non-interventions & number of previous registrations without intervention & 0 & 87.54\% without intervention & 1 & 10.25\% & 1.79\% & \\ \hline previous interventions & number of previous registrations with intervention & 0 & 87.81\% intervention & 1 & 6.03\% & 2.29\% & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Variable** & **Description** & **Mean** & **Min** & **Max** & **Range** & **Std. Deviation** & **Median** & **Mode** \\ \hline age & age of jobseeker at the beginning of registration & 34.9 & 15.0 & 78.0 & 63.0 & 12.5 & 32.0 & 24.0 \\ \hline cumulative previous registrations & cumulative number of days of previous registrations before the period under review & 1155.1 & 0.0 & 33,722.0 & 33,722.0 & 1343.8 & 699.0 & 0.0 \\ \hline work before registration & number of days from the last occupation to the current registration & 414.7 & 0.0 & 15,805.0 & 15,805.0 & 1068.4 & 1.0 & 0.0 \\ \hline average unemployment & average number of days spent in unemployment of age & 45.07 & 0.0 & 364.94 & 364.94 & 54.2 & 26.1 & 0.0 \\ \hline duration of registration & duration of the current registration in days & 300.9 & 0.0 & 1365.0 & 1365.0 & 303.8 & 182.0 & 91.0 \\ \hline \end{tabular}
\end{table} 
Table 2: Quantitative input variables used in the study. of the period under review as the end date to establish their duration of unemployment as well. The difference in dates was calculated in months and then categorised into the already-mentioned categories: up to 3 months, 3-6 months, 6-12 months, and more than 12 months. The distribution of the categorised target variable values among jobseekers in Slovakia during the period under review is presented in Table 3. To enhance the model's precision and confidence, we applied boosting to balance the subsamples based on the target variable. This involved balancing all subsamples to have approximately the same frequency as the most numerous group of the target variable while boosting the other groups by assigning appropriate weights to the cases. The weights used for balancing are also provided in Table 3.  
From this partial analysis, it specifically follows that the long-term unemployed people represent the largest group of job seekers (note: in the case of Slovakia, the term long-term unemployed defines a person who is defined as an economically active person, but has not worked for more than 1 year, i.e., was registered in the database of job seekers during this period). On the other hand, it can be concluded that more than 70% of jobseekers were registered as unemployed for less than 1 year, i.e., the period under review was characterized by the persistence of short- and medium-term unemployment.