esa-sceva/satcom-chunk-collection huggingface dataset:
# Radio Frequency Electromagnetic Fields Exposure Assessment in Indoor Environments: A Review
## 2 Methods  
### Literature Search Strategy and Inclusion Criteria  
A literature search of the research results published between 2008 and 2018 was performed using Scopus database. The search terms were derived as a combination of the various ways of describing the exposure characteristics (e.g., radio-frequency electromagnetic fields, RF electromagnetic fields, radio frequency EMF, microwave EMF, etc.), the environment (e.g., indoor, residential, school, offices, etc.) and the exposure assessment (e.g., exposure assessment, exposure measurement, dosimetry, exposure personal measurements, exposure spot measurements, etc.). No specific EMF source was used as a search criteria.  
Only peer reviewed articles published in the English language were considered. The eligible studies had to report mean RF-EMF exposure levels for indoor environments, considering the whole exposure range (10 MHz-6 GHz).  
In case of personal measurements in which the levels of exposure were measured both indoor and outdoor, only those studies in which it was possible to clearly separate the values measured indoor from the other values were considered. In case of duplicate publications, we included the article with the most comprehensive data, while in case of publications in which results included measurements described in previous studies, also these previous studies were considered, even if published before the 2008.
## 2 Data and Methodology  
### CMAM30 Model and ERA-Interim Reanalysis  
The Canadian Middle Atmosphere Model (CMAM) is a global circulation model that provides a retrospective estimate of the chemical and dynamical evolution of the atmosphere from 1979 to 2010 [38; 39; 40]. There are two types of model outputs: 1. Regular CMAM30 (which extends up to 95 km), focusing on the troposphere-stratosphere-mesosphere region, and 2. Extended-CMAM (which extends up to 200 km), focusing on the MLT region [29; 41; 42]. CMAM30 provides model output data from 1000 hPa to \(10^{-3}\) hPa and ext-CMAM provides data from 1000 hPa to \(10^{-6}\) hPa every 6 h from 1979 to 2010 on a global grid of 96 longitudes and 48 latitudes. In the current study, our focus is on the stratosphere, and hence we used the regular CMAM30 model temperature data to examine the short-term variability of tides during the period from 2006 to 2010. A two-imensional fast Fourier transform (2D-FFT) is used to analyse temperature in the longitude (\(\lambda\)) and time (\(t\)) space to extract the different wave components using a window of \(\pm 10\) days.  
\[F(f,s)=\frac{1}{N}\sum_{t}\sum_{\lambda}f(t,\lambda)e^{-i2\pi(f+s\lambda)} \tag{1}\]  
The Fourier spectrum is a function of frequency (\(f\)) and wavenumber (s), where the frequency \(f=1\) gives information on the diurnal tides and frequency \(f=0\) on SPWs.  
Positive wavenumbers represent westward propagating waves and negative wavenumbers represent eastward propagating waves.  
The reanalysis data set, ERA-interim, is the largest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) [43]. ERA-Interim is a global atmospheric reanalysis that is available from 1 January 1979 to 31 August 2019. For the current study, we investigate the temperature and wind data from 2006 to 2010 at 37 pressure levels at a spatial resolution of \(3^{\circ}\times 3^{\circ}\). Similar to CMAM30 data analysis, ERA-Interim data are also analysed using the 2D-FFT method to extract the various wave components using a \(\pm 10\)-day window [20].  
The amplitudes of DS0, DW2, and SPW1 obtained from CMAM30 are analysed in detail using continuous wavelet transform (CWT) to find the short-term variability in the time-frequency space [44] in the stratosphere and MLT region. The Morlet function of degree 6 is used as the mother wavelet, which is basically a plane wave modulated by a Gaussian. The wavelet power spectra are investigated during the period of interest from 2006 to 2010.
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
## 2 Materials and Methods
### Metrics and Algorithms
#### 2.3.3 Tree-Based Methods  
Two tree-based methods were evaluated in this work: the _sklearn_ RandomForestRegressor algorithm was evaluated as it performed well in a quantum phase transition problem [16], and the extreme gradient boosting algorithm XGBoost was selected to investigate the performance of an ensemble algorithm, which can often rival the performance of neural networks. Tree-based algorithms are known to overfit if the maximum depth hyperparameter is too high [17; 18], and the example in Figure 6 highlights this issue. The right side of the figure shows the training and test dataset performance for max_depth = 30, which overfits the training dataset by 85%. The inferior performance of the green test dataset curve shows that the model has memorized the training data and does not perform well on the test data. The left side of the figure shows the same chart for max_depth = 10, where overfitting is limited to 15%. Even though the \"entire dataset\" metric makes it seem the max_depth = 30 model is better, it is noteworthy that the mean \"test dataset\" performance is very similar to the overfit max_depth = 30 model. The max_depth = 10 model will generalize better on unseen data, and its performance is equivalent on unseen data.  
The random forest algorithm used 200 trees, a squared error criterion, and the maximum depth parameter was swept to determine the best performance that can be obtained with <15% overfitting. In this analysis, the maximum depth was varied from 1 to 11 for each of the 2/3/4/5-qubit models to determine this parameter.  
Figure 7 shows the evaluation process for the 2-qubit model, where a model was created for each value of maximum depth ranging from 1-11. In Figure 7, the \(x\)-axis is the maximum depth parameter, and the blue line shows that model MSE improves with a higher depth parameter. The green line plots overfitting (defined in Section 2.3), showing that the algorithm quickly overfits as the maximum depth parameter is increased. The dashed green line shows a 15% overfitting threshold, which, in this case, is exceeded for a value of maximum depth > 10. The maximum depth parameter where overfitting is limited to <15% is provided in Table 1 for the 2/3/4/5-qubit models, for both the random forest and XGBoost algorithms.  
Similar to the random forest algorithm, the XGBoost maximum depth parameter was swept to determine the highest depth parameter that can be used with <15% overfitting. The results of the sweep are presented in Table 1 for the 2/3/4/5-qubit models, and it can be seen that the XGBoost algorithm overfit the data at lower values of maximum depth.  
Figure 6: Overfitting example for the random forest algorithm and the 2-qubit datasets for a maximum depth parameter of 10 (**left**) and 30 (**right**). For these examples, the MSE of the training dataset (orange), test dataset (green) and entire dataset (blue) are presented for varying levels of noise.
# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 5 Conclusions  
The paper proposed a cellular network coverage solution by using UAV nodes that are controlled and managed for reallocation and able to change position as per requirements. The UAVs have high line-of-sight (LoS), which makes these solutions more realistic for ground networks. The main objectives achieved in this paper are:  
* Designed an optimal cellular coverage solution by establishing optimal numbers of BSs in the cellular network area and managing or reallocating the nodes as per requirements.
* The proposed solution offers optimal coverage area provision, especially in congested networks. Allocation of the UAVs-BS needs to be set with maximum and minimum vertical attitude.  
Figure 5: Throughput analysis in the presence of path loss exponent.  
Figure 6: Throughput analysis in the presence of users in single macrocell.  
* The proposed solutions are evaluated and tested in simulation in terms of data delay, data throughput, and network overhead.
* The proposed solution achieved better results in terms of placement in cellular networks.  
The simulation results indicated the high performance of the proposed solution. In the future, the solution will be used in other scenarios such as underwater sensor networks (UWSN) and 6G high-speed networks. The UAV-based solution will cover more areas, especially in disaster situations, and facilitate the cellular network with more coverage. These solutions will help existing networks and provide cost-effective and feasible solutions for cover the network communication range. In future, we will deploy the proposed solution and integrate it with the 3D deployment strategy of UAVs to provide maximum wireless coverage for ground users.  
Conceptualization, S.M., A.S. and K.N.Q.; methodology, S.M., A.S., S.I. and K.N.Q.; writing-original draft, S.M., A.S., S.I. and K.N.Q.; writing-review & editing, I.T.J. and N.C.; project administration, W.N., A.A. and N.C.; funding acquisition, W.N., A.A., and N.C. All authors have read and agreed to the published version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Large Groups. (Project under grant number (RGP.2/111/43).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Experiment and Results Analysis
### Simulation Experiment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{
\begin{tabular}{c} **The** \\ **Number of** \\ **Tasks** \\ \end{tabular} } & \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Average Task**} & \multicolumn{1}{c}{**Optimum**} & \multicolumn{1}{c}{**Total Task**} & \multicolumn{1}{c}{**Total Task Energy**} & \multicolumn{1}{c}{**Total Task Cost**} \\  & & \multicolumn{1}{c}{**Execution**} & \multicolumn{1}{c}{**Fitness Value**} & \multicolumn{1}{c}{**Execution**} & \multicolumn{1}{c}{**Consumption**} & \multicolumn{1}{c}{**(CNY)**} \\ \hline \multirow{9}{*}{50} & FCFS\_PSO & 258 & 2766 & 12,905 & 3,897,650 & 146 \\  & SJF\_PSO & 266 & 2768 & 13,324 & 4,012,270 & 143 \\  & MAX\_MAX\_PSO & 264 & 2657 & 13,196 & 3,977,715 & 140 \\  & PSO & 258 & 2757 & 12,905 & 3,897,020 & 126 \\  & FCFS\_PSO & 230 & 4912 & 22,972 & 6,933,960 & 278 \\
100 & SJF\_PSO & 239 & 4876 & 23,930 & 7,189,320 & 282 \\  & MAX\_MAX\_PSO & 235 & 4653 & 23,488 & 7,072,535 & 273 \\  & PSO & 229 & 5768 & 22,972 & 6,934,260 & 278 \\  & FCFS\_PSO & 238 & 9365 & 35,629 & 10,764,345 & 423 \\
150 & SJF\_PSO & 246 & 8397 & 36,943 & 11,115,970 & 407 \\  & MAX\_MAX\_PSO & 245 & 7844 & 36,830 & 11,085,500 & 421 \\  & PSO & 237 & 8946 & 35,628 & 10,764,345 & 409 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Task execution results of different algorithms. Figure 3 shows the optimal fitness value of the particle for different task scheduling results when the task number is 50, 100, and 150. As can be seen in Figure 3, with the increase in the number of tasks, the optimal fitness value of the MAX-MAX-PSO algorithm presents a decreasing trend, which fully indicates that this algorithm is more suitable for the scheduling of a large number of long tasks. Figure 2: The working state of nodes. After many experiments and statistical average calculations, Figure 4 presents the convergence curve of four task scheduling algorithms. It can be seen in Figure 4 that the MAX-MAX-PSO algorithm always has the fastest convergence speed. Figure 4: Convergence of different algorithms. Figure 3: Optimal fitness values of different algorithms.
## 3 Data Description and Data Preprocessing  
### Data Description  
In this paper, we used data from 70 wells from a carbonate field in the Middle East to train and test the model, including the drill-bit parameters, mud logging data, and geomechanical parameters, such as the standpipe pressure, bit type, density, uniaxial compressive strength, pore pressure, gamma rays, porosity, well depth, weight on bit, torque, revolutions per minute (RPM), mud density, equivalent circulating density, friction resistance, and flow rate.  
Table 1 shows part of the data display used in this paper. There are 348,702 pieces of data in total. The means, standard deviations (std), minimum values (min), maximum values (max), and 25%, 50%, and 75% quantiles are shown in the table. The minimum depth is 1504 m, and the maximum is 4102.9 m. The minimum ROP is 0.1758 m/h, the maximum is 50 m/h, and the average is 8.12864 m/h. Among them, the data of the ROP were obtained through calculation. The original data include the time spent drilling per meter, and we calculated its reciprocal as the average ROP per meter.
# Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest
## 2 Materials and Methods
### Statistical Analysis  
To determine the most appropriate statistical tools for this study, we assessed the assumptions required for the use of parametric and non-parametric approaches [95]. We tested whether our measurements and estimates of windthrow tree mortality were normally and homogeneously distributed using the Shapiro-Wilk and Levene variance tests, respectively. As our data were not normally distributed and field subplots were nested in the transects, we further conducted a non-parametric approach. We further applied the Kruskal-Wallis test on medians [93] to assess possible differences between the distribution of estimated and measured values of windthrow tree mortality. Differences between field and remote sensing estimates were assessed using the Mann-Whitney post-hoc test, which allows for comparing paired data sets. Although the arithmetic mean is not a precise measure of central tendency for non-parametric data [93], we also reported weighted mean values of field-measured and remote-estimated tree mortality, as supported by the Central Limit Theorem [96]. We further analyzed differences among satellites using ANOVA, followed by the Tukey post-hoc test. Statistical analyses were conducted in the R 4.2.2 software [97] and based on a probability level of 95%.
## 4 Physical Similarity Simulation Experiment  
### Experimental Design  
To study the caving characteristics of the overlying strata during end mining under a goal in shallow and closely-spaced coal seams, a geometric scale of 1:100 was employed to establish a similarity simulation experiment setup [28], with a length of 100 cm, a width of 20 cm, and a height of 60 cm. The similarity material ratios for various rock layers are detailed in Table 1. Stress sensors were positioned on the model base to monitor the distribution pattern of support pressure during the mining process. The model was arranged 22 m above the 2\({}^{-2}\) coal seam, with the overlying strata replaced by equivalent loads. The 2\({}^{-2}\) coal seam was mined first, followed by the pre-excavation of an RR with a width of 4.5 m and a height of 3.0 m in the 3\({}^{-1}\) coal seam. Caving-style supports were arranged to replicate practical working conditions. Then, the 3\({}^{-1}\) coal seam was mined from the left side to simulate the caving behavior of the overlying strata during the final extraction of the lower coal seam, as shown in Figure 7. Since this study mainly focused on the caving characteristics of the overlying strata during the final extraction of the lower coal seam, the caving characteristics of the overlying strata during the mining process were not described in detail.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Lithology**} & \multirow{2}{*}{\
\begin{tabular}{c} **Stratum** \\ **Thickness** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Model** \\ **Thickness** \\ \end{tabular} } & \multirow{2}{*}{**Ratio**} & \multicolumn{4}{c}{**Materials (kg/cm)**} \\ \cline{5-10}  & & & & **Sand** & & **Gypsum** & **Calcium Carbonate** & **Fly ash** \\ \hline
1 & Silstone & 7.5 & 8 & 837 & 2.84 & 0.11 & 0.249 & \\
2 & Sandy mudstone & 2.0 & 2 & 828 & 2.84 & 0.071 & 0.284 & \\
3 & Silstone & 10 & 10 & 837 & 2.84 & 0.11 & 0.249 & \\
4 & Fine sandstone & 2.0 & 2 & 737 & 2.8 & 0.12 & 0.28 & \\
5 & 2\({}^{-2}\) coal seam & 4.5 & 4 & 20:20:15 & 1.13 & 0.06 & 0.28 & 1.13 \\
6 & Mudstone & 2.0 & 2 & 828 & 2.84 & 0.071 & 0.284 & \\
7 & Sandy mudstone & 6.5 & 7 & 828 & 2.84 & 0.071 & 0.284 & \\
8 & Silstone & 5.0 & 5 & 837 & 2.84 & 0.11 & 0.249 & \\
9 & Fine sandstone & 4.0 & 4 & 737 & 2.8 & 0.12 & 0.28 & \\
10 & Silstone & 8.5 & 9 & 837 & 2.84 & 0.11 & 0.249 & \\
11 & Sandy mudstone & 2.0 & 2 & 828 & 2.84 & 0.071 & 0.284 & \\
12 & 3\({}^{-1}\) coal seam & 2.9 & 3 & 20:20:15 & 1.13 & 0.06 & 0.28 & 1.13 \\
13 & Silstone & 2.0 & 2 & 837 & 2.84 & 0.11 & 0.249 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rock similarity material ratio parameter table.  
Figure 7: Physical similarity model design diagram.
## 7 Performance of the WLAN  
Because of the geography of the zone and as a result of a Line of Sight (LOS) analysis, the WLAN was divided into three different zones, \"Huixtla\", \"Coatan\" and \"Huehuetan\". To exemplify the performance of the network, the distribution of the Huixtla zone is shown in Figure 20. The distances from point to point correspond: 17.7 Km from Agua Prieta to Las Cruces; 11.4 Km from Agua Prieta to Victoria and 16 Km from Victoria to Las Cruces. At this point is important to say that those distances are reached using fiberglass, omni-directional high-gain 15' base station antennas with 2.1 dBi gain, which reach up to 64 Km.  
The main contribution of the proposed architecture relies on the capacity to deliver reliable information even under adverse weather conditions such as rainstorms. This can be explained as follows. Based on Figure 20, let's suppose that a rainstorm approaches the path between Agua Prieta and Las Cruces interfering with communication between both units. When this is detected, the wireless network will automatically find a different route to reach the base unit. This is, if after the maximum number of retries (three) the BTU (Agua Prieta) has not received any acknowledgement from the RTU (Las Cruces), the BTU will automatically request another RTU (Victoria) to establish a link to Las Cruces to gather the information. When this occurs the system will change the following parameters: (a) Data receive timeout will be changed from 2,000 ms to 4,000 ms (it was measured that each hop takes about 1,800 ms), due to the hops number will be doubled; (b) Destination address in the isolated RTU (Las Cruces) will change to the available RTU which will be used as a bridge (Victoria in this case); and (c) The RTU that establishes a communication link with the isolated RTU will be reconfigured as a repeater, this is, will \"listen\" to all the stations and retransmit the information to the base. The same will happen with interferences in all the routes between BTUs and RTUs in the three different basins.  
Figure 20: Distances measured between RTUs and BTU in Huixtla’s basin.
## 2 Materials and Methods
### Evaluation  
The Landsat and GOCI images of the target date were used as the true value in this test. The observation time of both datasets was 10:30 on 22 September 2019. To evaluate the model fusion results, this paper calculated the root mean square error (\(RMSE\)), average absolute difference (\(AAD\)), average relative difference (\(ARD\)), and correlation coefficient (CC) between the resulting image and the true value. The definitions of the \(RMSE\), \(AAD\), \(ARD\), and CC are as follows:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}(P_{i}-L_{i})^{2}}{n}} \tag{14}\]  
\[AAD=\sum_{i=1}^{M}\sum_{j=1}^{N}|L(i,j)-P(i,j)|/MN \tag{15}\]  
\[ARD=\sum_{i=1}^{M}\sum_{j=1}^{N}[|L(i,j)-P(i,j)|/L(i,j)|/MN \tag{16}\]  
\[CC=\frac{\sum_{i=1}^{M}\sum_{j=1}^{N}\big{(}L(i,j)-\overline{L}\big{)}\big{(} P(i,j)-\overline{P}\big{)}}{\sqrt{\sum_{i=1}^{M}\sum_{j=1}^{N}\big{(}L(i,j)- \overline{L}\big{)}^{2}}\sqrt{\sum_{i=1}^{M}\sum_{j=1}^{N}\big{(}P(i,j)- \overline{P}\big{)}^{2}}} \tag{17}\]  
where \(L(i,j)\) represents the Chl-a concentration retrieved from Landsat images on the target date, \(P(i,j)\) is the model prediction result, \(M\) and \(N\), respectively, refer to the number of rows and columns of the image, and \(n\) represents the number of valid pixels. \(AAD\) reflects the average residuals, \(ARD\) represents the percentage of these residuals relative to the true Chl-a concentration data, and CC shows the linear relationship between the predicted value and the true Chl-a concentration.
## 4 Experimental Results
### Data Pre-Processing  
In Section 4.2 of the analysis of the correlation between emergencies and urban roads, this paper has divided the indicators into positive and negative indicators based on the results. It is divided into four positive indicators and six negative indicators. The four positive indicators are road length (km) \(U_{11}\), one-way road width (m) \(U_{12}\), intersections \(U_{14}\) and rainfall (mm/min) \(U_{21}\); the six negative indicators are traffic lights \(U_{13}\), temperature (\({}^{\circ}\)C) \(U_{22}\), visibility (m) \(U_{23}\), law enforcement officers \(U_{31}\), traffic flow (vehicles/min) \(U_{32}\) and road sections under construction \(U_{33}\). Standardized processing is carried out to obtain  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Inspection Index** & **Poisson Regression Model** \\ \hline \(\chi^{2}\) & \(\chi^{2}=566.58,p(\chi^{2})<0.001\) \\ \(LLC\) & \(-1091.695\) \\ \(LL\) & \(-808.405\) \\ \(AIC\) & \(1654.811\) \\ \(BIC\) & \(1725.928\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of the Poisson regression model test.  
the measured data of each road, and one set of data after processing is shown in Table 8, where the \((+)\) sign before the indicator indicates a positive indicator, and the \((-)\) sign before the indicator indicates a negative indicator.  
The original dataset \(D\) was introduced as:  
\[D=\left[\begin{array}{ccccccccc}0.981&15&2&7&6&52&0&1.1&8&400\\ 1.141&14&5&2&4&48&1&0.5&8&600\\ 0.766&15&2&2&2&72&0&0.5&11&600\\ 1.236&10&3&11&2&98&1&0.3&9&750\\ 1.374&7&2&3&0&26&3&0.8&8&400\\ 2.279&9&3&14&3&46&2&0.6&8&600\end{array}\right]_{6\times 10}.\]  
Data processing is performed in this set of original data matrix \(D\) to obtain a normalized data matrix \(D^{*}\), as shown in Equation (39).  
\[D^{*}=\left[\begin{array}{ccccccccc}0.1421&1&1&0.4167&0&0.6389&1&0&1&0\\ 0.2479&0.875&0&0&0.33&0.6944&0.67&0.75&1&0.5714\\ 0&1&1&0&0.67&0.3611&1&0.75&0&0.5714\\ 0.3106&0.375&0.667&0.75&0.67&0&0.67&1&0.67&1\\ 0.4019&0&1&0.0833&1&1&0&0.375&1&0\\ 1&0.25&0.667&1&0.5&0.7222&0.33&0.625&1&0.5714\end{array}\right]_{6\times 10} \tag{39}\]