esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Influence of Wind Velocity on Dust Distribution in the Heading Face
The highest average dust concentration over time at a speed of \(V\) = 9 m/s is 930 mg/m\({}^{3}\); from 1 to 200 s, the dust concentration is 974 mg/m\({}^{3}\); from 201 to 400 s, it is 972 mg/m\({}^{3}\); and from 401 to 600 s, it is 844 mg/m\({}^{3}\), with dust concentrations exceeding 10 mg/m\({}^{3}\), which can affect workers' health. At \(V\) = 12 m/s, the highest average dust concentration is 682 mg/m\({}^{3}\); from 1 to 200 s, it is 914 mg/m\({}^{3}\); from 201 to 400 s, it is 788 mg/m\({}^{3}\); and from 401 to 600 s, it is 346 mg/m\({}^{3}\). For \(V\) = 15 m/s, the highest average dust concentration is 508 mg/m\({}^{3}\); from 1 to 200 s, it is 788 mg/m\({}^{3}\); from 201 to 400 s, it is 568 mg/m\({}^{3}\); and from 401 to 600 s, it is 169 mg/m\({}^{3}\). For \(V\) = 18 m/s, the highest average dust concentration is 227 mg/m\({}^{3}\); from 1 to 200 s, it is 227 mg/m\({}^{3}\); from 201 to 400 s, it is 486 mg/m\({}^{3}\); from 401 to 600 s, it is 182 mg/m\({}^{3}\); and from 401 to 600 s, it drops to 12.3 mg/m\({}^{3}\), taking 522 s to reduce the dust concentration below 10 mg/m\({}^{3}\). Figure 11: Dust concentration at \(y\) = 1.5 m.  
Figure 10: Dust distribution on the cross-section at a distance of \(y\) = 1.5 m from the tunnel floor (respiratory zone height). In Figures 12-14, the highest average dust concentration results at positions 1 m, 3 m, and 6 m from the face within the 600 s study period are as follows: at 1 m from the face with wind speed \(V\) = 9 m/s, the dust concentration is 79 mg/m\({}^{3}\); when \(V\) = 12 m/s, it is 63.1 mg/m\({}^{3}\); \(V\) = 15 m/s results in 54.5 mg/m\({}^{3}\); and when \(V\) = 18 m/s, it is 44.3 mg/m\({}^{3}\). At 3 m from the mirror with wind speed \(V\) = 9 m/s, the dust concentration is 27.9 mg/m\({}^{3}\); when \(V\) = 12 m/s, it is 19.2 mg/m\({}^{3}\); \(V\) = 15 m/s results in 11.2 mg/m\({}^{3}\); and when \(V\) =18 m/s, it is 6.98 mg/m\({}^{3}\). At 6 m from the mirror with wind speed \(V\) = 9 m/s, the dust concentration is 32.8 mg/m\({}^{3}\); when \(V\) = 12 m/s, it is 20.3 mg/m\({}^{3}\); \(V\) = 15 m/s results in 12.4 mg/m\({}^{3}\); and when \(V\) = 18 m/s, it is 6.75 mg/m\({}^{3}\). Figures 15-18 depict the relationship between average dust concentration and wind speed over the 600 s study period. Figure 14: Dust concentration at a distance of 6.0 m from the face. Figure 12: Dust concentration at a distance of 1.0 m from the face. Figure 13: Dust concentration at a distance of 3.0 m from the face. Figure 16: The relationship between the average concentration and wind speed at a position 1.0 m away from the face. Figure 7:0 The relationship between the average concentration and wind speed at a position 3.0 m away from the face. Figure 7:1 The relationship between the average concentration and wind speed at position \(y=1.5\)m.
# Cross-Layer and Energy-Aware AODV Routing Protocol for Flying Ad-Hoc Networks
## 6 Conclusions  
In this research work, we anticipated a cross-layer approach for FANETs because the major drawback in this network is maintaining reliability and connectivity by providing high efficiency and quality in routing. We optimized the network topography with an efficient communication model with the help of a well-organized cluster head selection using GSO and incorporating a cooperative MAC model. Efficient CH selection was made by using the residual energy of the network and luciferin value calculation using GSO. With the help of cooperative MAC design, the network link breakages were greatly reduced. The simulation evaluation of our research achieved good performance in terms of PSR, TP, E2E delay, and PDR. The results were calculated and compared with the earlier works, namely the AODV, SOC-GSO, and EENFC-MRP protocols. The main reason that the CLEA-AODV  
Figure 12: The PDR calculation with time.  
Figure 13: The PDR calculation with the number of UAVs.  
protocol achieved better results when compared with others, so here, we concentrated on the cross-layer approach, which combined the network layer and data link layer. We provide an optimized CH selection model, which greatly improves the cluster lifetime and reduces the energy consumption where several types of information transmissions are conducted through FANETs.  
H.S.M., M.H.M., I.A.A., S.A.M., H.M., A.H.A., M.H.H., N.F.A. and M.A.J. contributed to the analysis, programming, and writing of original drafts as well as the review and editing. All authors have read and agreed to the published version of the manuscript.  
This research work was supported and funded by the Yayasan UTP grants: (015 LC0-353) with the title 'Predicting Missing Values in Big Upstream Oil and Gas Industrial Dataset using Enhanced Evolved Bat Algorithm and Support Vector Regression', under the Center for Research in Data Science (CerDaS), Universiti Teknologi PETRONAS, Malaysia.  
The used dataset of this research is available online and has a proper citation within the paper contents.  
This research was supported by the Ministry of Higher Education (MoHE) through the Fundamental Research Grant Scheme (FRGS/1/2021/ICT01/UTHM/02/1) grant vot number K389. This research was also supported by the Department of Computer and Information Sciences (CISD), UTP, Malaysia.  
The authors declare no conflict of interest.
## 4 Experiment and Result Analysis
### Experiments Setup  
We introduce the experimental setting of the proposed algorithm. For the generative model G described in Section 3.1, the 11 generating models are 11 different objects. During image generation, first, the randomly generated 5,000,001,512 dimensional random latent space \(z\) forms the latent space \(Z\), and then we input \(Z\) into the mapping network of StyleGAN to obtain the latent space \(W\), which was input into the generator to obtain 500,000 images.  
To train the predictive model, at first, the input image was first trimmed to the image size of \(256\times 256\), and then the \(224\times 224\)-sized images were extracted with the random cropping method, thus reducing the speed of model overfitting. NIMA is trained using the loss of the validation set as a constraint on the number of training iterations, setting the stop iteration parameter that stops training when the loss on the training set exceeds that number.  
This experiment was trained on a single NVIDIA 2080 Ti with the number of iterations set to 100, batch size set to 64, convolution layer learning rate set to 0.005, and fully connected layer learning rate set to 0.0005. After each iteration, tests were conducted on the validation set, and the EMD loss and prediction accuracy were calculated. The EMD loss exceeded ten iterations and did not decrease when the training stopped.  
In the learning stage of global aesthetic decision faces, we selected the 2000 highest NIMA scores and 2000 lowest scores as positive and negative samples in \(Z\) and \(W\) of the 11 generated models to obtain a total of 22 aesthetic decision faces. Finally, we also performed the aesthetic editing of \(Z\) and \(W\) separately to obtain a global aesthetic image. The experimental procedure is shown in Figure 6  
Figure 6: The procedure of experiment.
# Analytical Performance Evaluation of Massive MIMO Techniques for SC-FDE Modulations
## 4 Receivers Design
### Mrc  
As shown in Section 4.1, the IB-DFE receiver requires the inversion of the channel matrix in the estimation of the received signal. This situation requires too much complexity when moving to an mMIMO system, where there are multiple receiving and transmitting antennas.  
As explained in [14; 15; 17], the MRC receiver combines the different received branch signals in order to maximize the received SNR ratio. In fact, the phases of the received signals are fixed and the conjugate of the channel matrix is used to weight them. The small correlation between the different transmitting and receiving antennas allows us to make the following approach:  
\[\mathbf{H}_{k,p}^{H}\mathbf{H}_{k,p}\approx R\mathbf{I}. \tag{12}\]  
When considering moderate \(R/P\) values, the residual interference cannot be neglected. In order to improve the performance of this receiver, an iterative version should be implemented, such as the IB-DFE receiver. Equation (2) is also considered by this receiver by only changing the feedforward and feedback coefficient equations. Therefore, feedback coefficients are given by Equation (9) and the feedforward coefficients are given by:  
\[\mathbf{F}_{k,p}^{T}=\kappa\mathbf{H}_{k,p}^{H}, \tag{13}\]  
where \(\kappa\) is a diagonal matrix and the element \((t,t)^{th}\) is given by \(\left(\sum_{k=0}^{N-1}\sum_{r=1}^{R}\left|H_{k,p}^{(r,t)}\right|^{2}\right)^{- 1}\).
# Fusion Text Representations to Enhance Contextual Meaning in Sentiment Classification
## 4 Experiments and Results
### Evaluation Matrix  
The evaluation matrix is a method utilized to assess the performance of a sentiment classification model in accurately identifying and categorizing the sentiment expressed in provided texts. The construction of this evaluation matrix often relies on the model's predictions when compared to the test data, which contain known labels. The following are the primary elements of a confusion matrix that is frequently employed for sentiment classification:  
* True Positive (_TP_): the number of data points correctly classified as positive;
* True Negative (_TN_): the number of data points correctly classified as negative;
* False Positive (_FP_): the number of data points incorrectly classified as positive (when they should be negative). This is also known as a Type I error;
* False Negative (_FN_): the number of data points incorrectly classified as negative (when they should be positive). This is also known as a Type II error.  
From the four components above, we can calculate several useful evaluation metrics for sentiment classification [34]:  
* _accuracy_: The percentage of total correct predictions by the model, calculated as follows:  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{17}\]_precision_: The percentage of true-positive results out of all positive results predicted by the model, calculated as follows:  
\[precision=\frac{TP}{TP+FP} \tag{18}\]  
* _Recall_ (Sensitivity): The percentage of true-positive results identified by the model out of all actual positive results, calculated as follows: \[recall=\frac{TP}{TP+FN}\] (19)
* \(F1\): a metric that combines precision and recall, providing a better understanding of the model's overall performance, calculated as follows: \[F1=\frac{2*precision*recall}{precision+recall}\] (20)
## 2 Materials and Methods
### Cu Fractionation  
Selective dissolution methods provide useful information on metal binding and mobility; nevertheless, it should be remarked that there is no selective dissolution scheme free of shortcomings [38; 39]. In the present study, the distribution of Cu in the soil solid phase was determined, in triplicate, using the four-step sequential extraction scheme proposed by the Community Bureau of Reference (BCR-method) [40]. This method is one of the most widely used and has been frequently applied to reveal the speciation and mobility of potentially toxic elements in soils and sediments.  
In brief, Step 1: 1 g of soil was extracted (16 h) in 40 mL of 0.11 M acetic acid. In supernatant after centrifugation (3000 RPM for 20 min), Cu was measured and the residue was used in the next step. Step 2: extraction of soil residue with 40 mL of 0.5 M hydroxylamine hydrochloride was performed for 16 h. In supernatant after centrifugation (3000 RPM for 20 min), Cu was measured and the residue was used in the next step. Step 3: digestion of soil residue was performed using 20 mL of 30% H\({}_{2}\)O\({}_{2}\) (pH = 2-3) at 85 \({}^{\circ}\)C in a water bath for 1 h. Later, the cover of the vessel was removed and when solution was evaporated, 50 mL of 1 M ammonium acetate was added into the soil residue. After 16 h of extraction, Cu was measured in supernatant after centrifugation (3000 RPM for 20 min), residue was used in the fourth step, also named the residual step. Step 4: the soil sample was digested in a microwave oven using 10 mL of concentrated (65%) HNO\({}_{3}\). The extracted solutions were filtrated using a 0.2 \(\upmu\)m PTFE filter. Cu concentration in diluted soil extracts was analyzed by ICP-AES (Agilent 5800). Calibration was performed using standard solutions (0.5, 1, 5, 10, 30, 50 mg L\({}^{-1}\)) prepared from an ICP-standard 23-element solution in 5%HNO\({}_{3}\) (Merck solution IV), with yttrium (Y) as internal standard. Strict quality assurance and quality control (QA/QC) were taken, including blanks, analysis of reference material (CRM 601), and analysis of laboratory control samples. The recoveries of Cu, i.e., the ratio between sum of the four steps to the total soil Cu content, ranged from 89 to 114% with an average of 98.5%.
## 4 Discussion
### Response of Rubber Plantation Ecosystem GPP to Meteorological Factors  
Rainfall dominated the trend of GPP in the study area. Rainfall was more strongly correlated with GPP than temperature. Although the interannual trend showed that the overall trend of rainfall was decreasing, this was due to the significant decrease in 2019, while both rainfall and GPP showed a decreasing trend in 2010-2015 and an increasing trend in 2015-2020 (Figure 5a). The dominant effect of rainfall on production is manifested in several aspects. Firstly, precipitation--especially dry-season precipitation--significantly affects vegetation production. The tropics have abundant moisture and vigorous vegetation growth, while limited precipitation in the dry season inhibits the enhancement of production, highlighting its dominant role in production. Secondly, rainfall can influence plants' root growth by altering the soil moisture content and soil nutrient flow. Previous studies have shown that the seasonal dynamics of vegetation root growth in tropical regions are consistent with soil moisture dynamics, with the rainy season tending to be the peak of vegetation growth and accumulation, while the growth is slower in the dry season [37]. Thirdly, abundant precipitation tends to occur in the same season with sufficient light and suitable temperature, promoting the growth of vegetation together and, thus, strengthening the dominant role of precipitation.  
The influence of temperature was mainly manifested in the limiting effect of extreme temperatures on production. The GPP of the forest was lower in the years with the highest and the lowest mean annual temperatures (Figure 5b). According to the literature [35], the optimal temperature for the photosynthesis of rubber trees is 25-30 \({}^{\circ}\)C; when the ambient temperature is below 10 \({}^{\circ}\)C, rubber tree photosynthesis stops, while above 40 \({}^{\circ}\)C rubber trees' respiration exceeds photosynthesis and growth is inhibited. The duration of high-temperature weather in the study area was long. For example, there were 88 days with maximum temperatures above 35 \({}^{\circ}\)C during 2015; the lowest temperatures generally occurred in December and January, with 10 days with minimum temperatures below 10 \({}^{\circ}\)C during 2011. Therefore, the temperature limitation of rubber plantation production in the study area mainly lies in high-temperature limitations, where the inhibited photosynthesis and enhanced respiration together reduce the accumulation of organic matter, thereby limiting the level of vegetation production. Only under chilling conditions below 10 \({}^{\circ}\)C did rubber plantations experience transient production limitations due to photosynthetic cessation. These findings show that higher rainfall has a long-term enhancement effect on rubber plantation production, while providing timely protection measures during extreme weather can prevent damage to rubber plantations. This helps with the management of rubber plantations in response to different climatic phenomena.
# Supply Systems of Non-Thermal Plasma Reactors. Construction Review with Examples of Applications
## 4 Power Electronics Supply Systems for NTPR
### AC/DC/AC PSS for a GAD Plasma Reactor  
Idea of the AC/DC/AC inverter is presented in Figure 14. System can operate both as a current and voltage source. At the beginning, plasma reactor should be supplied from voltage source. Current-voltage characteristic of the plasma reactor changes considerably during its operation from the ignition to the extinction of the discharge. Discharge development from short arc, in the zone of ignition, to the glow discharge requires from the supply to follow-up discharge voltage increase proportional to the discharge length. At the steady-state operation, voltage changes depend on the processing gas parameters, while current should be kept at constant value, as to ensure plasma to be in non-thermal and non-equilibrium state. In general, the current inverter is energized from current source, which is realized with the series impedance coil attached between DC voltage source and inverter input. Output current has the rectangular waveform while voltage waveform depends on the load properties. Usually, capacitors are connected in parallel to the load of the current inverter. From the discharge point of view capacitance connected in parallel to the plasma reactor electrodes can significantly change its characteristics: higher capacitance can decrease the inter-electrode voltage and impair the thermal conditions of the GAD [55].  
In the case the inverter operation as a current source, the output voltage of the supply in no-load is of rectangular form and output current waveform is forced as sine independently on the load character by means of pulse-width voltage modulation (PWM). Since, the GAD plasma reactor's current has practically sine waveform at the whole range of operation parameters, while voltage waveform is distorted considerably--Figure 15a, therefore it is easier to control the power to the discharge through current value changes than voltage.  
Moreover, the distorted discharge current, as it is when GAD PR is energized from the sine voltage source--Figure 15a, could faultily influence the control circuits of the inverter that may possibly result in the damage of its electronic modules. Courses of discharge voltage and current in the reactor energized from the AC/DC/AC inverter operating as a current source and a voltage source are shown in Figure 15a,b respectively. Strong voltage distortion is observed, while discharge current is sine as it is programmable forced in the supplier--Figure 15a.  
Figure 12: Resonant converter topology for DBD plasma reactor [56].  
Figure 13: Block diagram of the RF-frequency power supply of atmospheric pressure plasma jets (APPJ).
## 3 Results and Discussion
Measurement of integral of dielectric losses envelope curve for same freight with addition of known quantity of non-magnetic material (quartz sand); and
3. Freight container (railcar of known mass) undergoing the same procedure. The results are presented in Figure 7. The earth, concrete, polymers, and so on, did not affect the coil parameters. Figure 6: The change in the instantaneous values of the angle of loss at different locations of the magnetic load. Figure 7: Dependence of loss angle integral on core mass with different proportion of magnetic and non-magnetic components. A somewhat different situation arose when materials with high inductive characteristics, for example, copper or aluminum, were added to a ferromagnetic material. Adding up to 30 wt.% of copper or aluminum lowered the angle of loss of the ferromagnetic material by about 20% (Figure 8). The figure successively shows the integrals of the loss angle of an empty car, then a car loaded with 35 tons of ferromagnet (black), then cars with 35 tons of ferromagnet loaded sequentially together with 5 and 10 tons of copper (red). However, this result does not preclude the practical use of the device. Copper and aluminum are much more expensive metals than iron. The addition of non-magnetic inclusions is intended to increase, not reduce, the apparent fraction of a magnetic material according to Equation (29). Therefore, these hidden additives in ferromagnetic material have no practical or economic sense and are not found in practice. Thus, this allows us to calculate the mass fraction of non-magnetic inclusions using Equation (29). Table 2 shows the data on the railcars that passed through the non-magnetic inclusions control unit. There are two types of railcars, differing in wall height (3.2 or 3.5 m). There are also two types of scrap: large and small, noted as 5A and 3A, respectively. The calibrations were experimentally obtained for every type of scrap and railcar. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Manufacturer** & **Type of Scrap** & \multirow{2}{*}{**Total Mass (t)**} & **Empty Car** & **Wall Height** & **Non-Magnetic** \\  & **Railcar No.** & **(Figure 4)** & & **Weight (t)** & **of Railcar (m)** & **Percentage (\%)** \\ \hline
1 & 54210059 & 5A & 72.12 & 22.10 & 3.2 & 5.1 \\
2 & 96632757 & 3A & 85.46 & 21.85 & 3.5 & 3.8 \\
3 & 96631205 & 5A & 82.67 & 23.20 & 3.5 & 7.2 \\
4 & 52318037 & 5A & 80.70 & 22.70 & 3.5 & 7.1 \\
5 & 54035589 & 5A & 78.15 & 22.20 & 3.2 & 5.8 \\
6 & 56171465 & 5A & 81.10 & 23.47 & 3.5 & 8.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Magnitude of non-magnetic inclusions in real scrap metal railcars. Figure 8: The dependence of the loss angle integral on the core mass with different proportions of ferromagnetic material and copper.
# First Lidar Campaign in the Industrial Sites of Volta Redonda-RJ and Lorena-SP, Brazil
###### Abstract  
We report on the first aerosol profiling campaign in the Parafba valley, a hub connecting the region between the two largest Brazilian metropolitan areas, Sao Paulo and Rio de Janeiro. Sao Paulo Sanitation Technology Company (CETESB) air quality and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data show homogeneous behavior of the atmosphere throughout the region. A more detailed description of the particulate material in the local atmosphere, including its temporal dependence, can be obtained by using ground-based lidars. Measurements were carried out with a backscatter lidar system in two industrial cities, Volta Redonda and Lorena. The aerosol backscatter profiles present several peaks at different altitudes, indicating the presence of aerosol in several atmospheric layers. In addition, The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) air-mass back-trajectories indicate a possible detection of long-range aerosol transported from biomass burning areas of South America. The present study emphasizes the importance of investigating and monitoring the emission of particulate matter at this important hub connection between two dense populated regions of Brazil.  
lidar; CALIPSO satellite; remote sensing; aerosol monitoring; industrial emissions 2022