esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Responses of Crop Yield and Farmland Carbon Sequestration to Biochar Application  
Crop yields have always been a major focus of agricultural research and they cannot be increased without improving the soil environment and crop traits. The application of biochar can increase soil enzyme activity levels [87], soil nutrient supply [88; 89], and soil water and fertilizer storage capacity [90; 91] to create good soil conditions for crop growth. In addition, the application of biochar can promote crop root growth [92; 93], regulate crop metabolic activities [94], and increase the accumulated crop biomass [95]. [PERSON] et al. [36] showed that the application of wheat straw biochar increased rice yield by 9-28%. In addition, a meta-analysis by [PERSON] et al. [96] demonstrated that the average crop yield increased by about 10% after the application of biochar. Similar to most previous studies, we found that the application of biochar improved the soil hydrothermal conditions and increased the accumulated wheat biomass, and thus the yield increased by 3.51-16.42%.  
The agricultural soil carbon budget greatly affects soil quality and crop productivity and further affects the sustainable development of agriculture [25; 97], and thus carbon sequestration in farmland has become a research hotspot. Biochar is a highly stable carbon-containing material [98] and its application to soil can help to improve soil carbon inputs [29]. In addition, the application of biochar can enhance the carbon assimilation capacity of crops to fix more gaseous carbon, and the increased amount of carbon transported to the underground parts increases soil carbon sequestration [29; 99; 100]. Furthermore, the application of biochar can reduce the decomposition rate of soil organic carbon to slow down soil carbon losses [31; 32]. In previous soil carbon sequestration studies, the NECB was widely used to assess the changes in soil carbon storage [51; 101]. In particular, [PERSON] et al. [97] showed that vegetable fields without biochar application lost carbon and had negative NECB values, whereas vegetable fields treated with biochar sequestered carbon and had positive NECB values. Similarly, [PERSON] et al. [102] applied biochar in a maize-wheat system and obtained a positive NECB value, and thus biochar played a positive role in soil carbon sequestration. Similar to previous studies, we found that the application of biochar increased the soil carbon inputs and changed the NECB value from negative to positive to facilitate carbon sequestration in wheat fields.
# Frequency Division Multiplexing of Terahertz Waves Realized by Diffractive Optical Elements
###### Abstract  
Recently, one of the most commonly discussed applications of terahertz radiation is wireless telecommunication. It is believed that the future 6G systems will utilize this frequency range. Although the exact technology of future telecommunication systems is not yet known, it is certain that methods for increasing their bandwidth should be investigated in advance. In this paper, we present the diffractive optical elements for the frequency division multiplexing of terahertz waves. The structures have been designed as a combination of a binary phase grating and a converging diffractive lens. The grating allows for differentiating the frequencies, while the lens assures separation and focusing at the finite distance. Designed structures have been manufactured from polyamide PA12 using the SLS 3D printer and verified experimentally. Simulations and experimental results are shown for different focal lengths. Moreover, parallel data transmission is shown for two channels of different carrier frequencies propagating in the same optical path. The designed structure allowed for detecting both signals independently without observable crosstalk. The proposed diffractive elements can work in a wide range of terahertz and sub-terahertz frequencies, depending on the design assumptions. Therefore, they can be considered as an appealing solution, regardless of the band finally used by the future telecommunication systems.  
Keywords:diffractive optical elements; THz; 3D printing; multiplexing; frequency division multiplexing; telecommunication +
Footnote †: journal: Applied Sciences
## 4 Conclusions  
In summary, we have described fundamental concept and principle of the LG beams carrying OAM, the turbulence model and the GS algorithm, and emulated the atmospheric turbulence by inserting random phase screens on the transmission path. We have also demonstrated the distortion effect of different turbulence strength levels by changing refractive-index structure parameter of \(C_{n}^{2}\) and the number of phase screens of \(N\) on the transmitting LG beams. Then, based on GS algorithm, we calculate a pre-compensation phase to compensate the distorted LG beams, and give the corresponding comparisons and discussions of performances with and without pre-compensation for transmitting single and multiplexing OAM beam. The results illustrate that the parameters \(C_{n}^{2}\) and \(N\) directly determine the level of the turbulence strength, and when the turbulence strength level is selected within the correction limit range of \(C_{n}^{2}=1\times 10^{-14}\)m\({}^{-2/3}\) with \(N=100\), the LG beam can be effectively retrieved and the OAM can be directly detected by adopting GS algorithm and observing the PD spectra. In addition, according to a comparison of performance results, the retrieving performance of transmitting single OAM beam is better than that of transmitting multiplexing OAM beam.  
Conceptualization, M.I.D. and Z.W.; methodology, K.G.; software, F.S. and H.Z.; formal analysis, M.I.D., Z.W. and Y.S.; writing--original draft preparation, M.I.D. and Z.W.; writing--review and editing, Z.G., J.G., R.S. and Z.D.; supervision, Z.G.; funding acquisition, Z.G. National Natural Science Foundation of China (61775050, 11804073), Fundamental Research Funds for the Central Universities (JZ2018 HGBZ0309, JZ2018 HGTB0240). The authors declare no conflict of interest.
# Extensive Benchmarking of DFT+\(U\) Calculations for Predicting Band Gaps
## 1 Introduction
Applying \(U\) to light elements and/or \(d^{10}\) group III-IV elements (e.g., Ge, In, Sn, Pb) is still in debate in the literature [35, 78, 93, 94]. This question will be addressed in this work using DFPT [79], and in particular we will explore the sensitivity of band gaps to the application of the Hubbard \(U\) correction to \(p\) states of light elements. In this paper we present a benchmark of DFT+\(U\) using _ab initio_ Hubbard \(U\)[79] for predicting band gaps in 20 compounds containing transition-metal or \(p\)-block (group III-IV) elements, including oxides, nitrides, sulfides, oxynitrides, and oxysulfides. This is done using two types of Hubbard projector functions, namely, nonorthogonalized atomic orbitals (that we will refer to as _atomic_ orbitals, for simplicity) which are provided with pseudopotentials and which are orthonormal within each atom, and orthogonalized atomic orbitals which are obtained by orthogonalizing the atomic orbitals from different sites using the Lowdin method (that we will call _ortho-atomic_ orbitals) [95]. By inspecting the dominant character of electronic states at the top of the valence bands and at the bottom of the conduction bands we select the most relevant states and apply the Hubbard correction to them and analyze the subsequent changes in the band gaps. Our study reveals that the DFT+\(U\) band gaps are very sensitive to the type of Hubbard projectors, and that the most accurate results are obtained using _ortho-atomic_ ones. Moreover, this work shows that DFT+\(U\) with Hubbard parameters determined nonempirically can be used to predict reliable band gaps at lower computational cost than that of hybrid functionals (such as HSE06 [96, 97]) that are popular for predicting band gap values. This is important for high-throughput studies that require efficient and reliable estimates of the band gaps at moderate computational cost for various technologically relevant materials such as photocatalysts [98, 99], solar cells [100, 101], and transparent conductors [102]. The remainder of the paper is organized as follows. Section 2 briefly recalls the formulation of the DFT+\(U\) approach and how Hubbard \(U\) is computed using DFPT, and describes which materials are studied in this work. Section 3 contains the technical details of our calculations. In Section 4 we present the projected density of states for all materials of this work, the Hubbard parameter values computed used _ab initio_ methods, and the comparison of the computed band gaps with those measured experimentally. Finally,Section 5 presents our concluding remarks. Appendix A reports our convergence tests when computing \(U\), and Appendix B contains a comparison of the computational costs of HSE06 and linear-response \(U\) calculations.
## 4 Advanced Bioreactors for Hydrocarbon-Containing Wastewater Treatment
### Anaerobic and Hybrid (An anaerobic-Aerobic) Bioreactors
It was found that the algal post-treatment step was efficient in the removal of pollutants (COD removal rates were 71.7-97.8%) as well as in biomass and oil accumulations. The above laboratory and pilot studies clearly demonstrate that integrated bioreactor systems provide generally better performance for treating oily wastewater because of the synergistic effects of anaerobic and aerobic microorganisms enabling sequential degradation of recalcitrant organic compounds. However, such multi-module combinations of anaerobic and aerobic processes require large space and energy and can lead to methane emission. Recently, [PERSON] et al. [15] tested on a pilot scale a hybrid vertical anaerobic biofilm (HyVAB) reactor, which integrated a bottom anaerobic sludge bed and a top aerobic biofilm stage [69]. The reactor was operated continuously at reducing HRT (from 55 to 12 h) and increasing organic loading rate (from 3 to 33 kg COD/m\({}^{3}\)-day) for 92 days, treating high strength refinery wastewater, that resulted in 86% of the total COD and 91% of the soluble COD removal [15]. An advantage of this system is that due to the close integration of anaerobic and aerobic processes, dissolved gases (methane, H\({}_{2}\)S etc.) generated in the anaerobic stage are captured and oxidized by aerobic microorganisms, thus avoiding a methane emission and providing odorless discharges to air and water. Moreover, returning the excess aerobic sludge to the bottom anaerobic part simply by gravity simplifies the sludge treatment and contributes to a methane production. Therefore, the HyVAB reactor represents a compact integrated system with low footprint, low capital expenditures and high efficiency in organic removal, energy recovery and emission prevention. Very recently, the same research group tested a pilot HyVAB reactor for the treatment of wastewater containing metformin, a widely used antidiabetic drug [67]. To achieve complete metformin removal, it was proposed to co-digest pharmaceutical-containing wastewaterwith the wastewater rich of easily degradable organics. After a period of adaptation, the reactor was able to manage organic loads of 11 g COD/day and above 10 mg/L metformin, resulting in the removal of 98% COD and 100% metformin. It should be noted that despite numerous successful laboratory and pilot-scale applications of advanced bioreactors for the treatment of oily wastewaters, their full-scale implementation is still rare. Biological processes to treat the petroleum wastewater are usually conducted in traditional activated sludge (fixed bed or suspended bed) systems or in aerated lagoons [11]. Some companies also incorporate MBRs into their treatment trains for discharge of produced waters [70].
## 3 Data Implementation  
The methodology used in the research uses a quantitative approach that will be analyzed through a statistical analysis of the database on civil aviation accidents in the last 40 years, collected by the Aviation Safety Network [21].  
A survey of the last 40 years of accidents and economic indicators that have occurred in U.S. airspace, covering the period 1980-2021 (40 years), has been organized and analyzed by the following data:  
1. Number of accidents per year;
2. Number of fatalities and injuries per year;
3. Number of accidents by flight phases (ground, takeoff, en route, approach, and landing;
4. Number of accidents by root causes (technical, environmental, human error, and sabotage);
5. Type of flight (passenger flights, cargo flights);
6. Correlation with economic factors;
7. Correlation with the number of professionals in the operation of a flight.  
The SPSS V27 software was the scientific tool chosen for the statistical analysis of this project. Besides a statistical analysis, it is also possible to check [PERSON]'s correlations, also elaborated in SPSS, with other data taken from World Bank databases to verify the correlation of accidents with GDP growth and inflation; the same flight cycles were also performed. To settle our curiosity, we went to check with the data we found in the FAA about the number of professionals involved in the operations and verified their correlation with the accidents and the causes of human errors. [PERSON]'s correlation is the correlation method used by the most used SPSS tool for making correlations. \"Correlation is a bivariate association measure (strength) of the degree of relationship between two variables\" [22]. The Pearson correlation coefficient (r) ranges from \(-1\) to \(1\). The sign indicates the positive or negative direction of the relationship, and the value suggests the strength of the relationship between the variables. A perfect correlation (\(-1\) or \(1\)) indicates that the variables are linearly related. Conversely, a correlation of zero values indicates no linear relationship between the variables [22], although there may be a nonlinear relationship.  
In addition to the correlation model, the ARIMA (autoregressive integrated moving average) model is an autoregressive moving average model. Moving average models use the average of the recorded observations as a forecast for a given period in the future. Moving averages can be simple, centered, or weighted in these models. The models that use the simple moving average are given by:  
\[Xt=xt-1+xt-2+\ldots+xt-n/n \tag{1}\]  
where \(n\) is the number of observations (observation window) and \(Xt\) is the mean [23]. This model is also based on the plots of the autocorrelation function and the partial autocorrelation function so that, by visualizing the decays of the functions, it is possible to estimate the appropriate order of the model [24]. The ARIMA method is obtained with a set of factors. Although it checks the existing values in the previous years, i.e., if there is a change of factors in the forecast years or if the factors used are too many, there is always an associated error.
## 2 The Building, Temporary Instrumentation and Methodology
### Nonparametric Method
#### 2.4.3 Phase  
It must be taken into consideration that the functions with which the nonparametric method works are sometimes complex variables. Examples include the Fourier transform and cross-power correlations. Complex variables play an important role in calculating vibration shapes since they reveal whether the relative motion between two points occurs in the same direction.  
The numerical value given by the phase angle varies only within \(\pm\pi\). Thus, a phase angle equal to 0 between 2 different points at an analyzed frequency indicates that the direction of motion is the same, called being in phase. Otherwise, if the phase angle is \(\pm\pi\), the relative movement is opposing, known as a phase shift. The formulae used to obtain the phase angle of a complex variable are as follows:  
\[G_{xy}(f)=A_{xy}(f)+iB_{xy}(f) \tag{6}\]  
\[\left|Gxy\;(f)\right|=\sqrt{A_{xy}{}^{2}(f)+iB_{xy}{}^{2}(f)} \tag{7}\]  
\[\theta_{xy}(f)=\tan^{-1}\frac{B_{xy}(f)}{A_{xy}(f)} \tag{8}\]
###### Abstract  
At present, many high-temperature pipelines need to carry out non-stop detection under high-temperature conditions, and an ultrasonic guided wave is undoubtedly one of the solutions with the highest potential to solve the problem. However, there is a lack of research on the propagation characteristics of longitudinal guided wave modes in high-temperature pipelines. Based on the Green-Naghdi (GN) generalized thermoelastic theory, a theoretical model of thermoelastic guided waves in an orthotropic hollow cylinder with a temperature field is established by using the Legendre polynomial series expansion method. Firstly, based on the GN thermoelastic theory, the coupling equations expressed by displacement and temperature are established by introducing the rectangular window function. The curves of dispersion, displacement, and temperature of the guided wave are numerically solved by using this equation. Subsequently, the influence of the diameter-to-thickness ratio on the dispersion of the longitudinal thermoelastic guided wave is analyzed at the same temperature. Finally, the effect of temperature field variation on the phase velocity dispersion is discussed, which provides a theoretical basis for the study of the dispersion characteristics of hollow cylindrical pipes containing temperature fields.  
longitudinal thermoelastic; waveguide; Legendre orthogonal polynomials; dispersion curve; temperature field +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Reliability and Validity  
Table 2 provides an overview of the Pearson correlation coefficient matrix of the constructs under study. The analysis results show that the variables were significantly correlated and had a relationship. All variable item correlation values were in medium to high levels, ranging from 0.413 to 0.921, indicating that there is a positive correlation between the variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Description** & **Items** & **Freq.** & **\%** \\ \hline \multirow{2}{*}{Gender} & Male & 188 & 70.7 \\  & Female & 78 & 29.3 \\ \hline \multirow{4}{*}{Age} & Under 20 & 49 & 18.4 \\  & 21–30 & 135 & 50.8 \\  & 31–40 & 60 & 22.6 \\  & 41–50 & 22 & 8.3 \\ \hline \multirow{4}{*}{Occupation} & Student & 107 & 40.2 \\  & Government employees & 16 & 6.0 \\  & Professional & 45 & 16.9 \\  & Service Sector & 68 & 25.6 \\  & Others & 30 & 11.3 \\ \hline \multirow{4}{*}{Average Monthly Income} & \textless{}NTS22,000 & 87 & 32.7 \\  & NTS22,001–30,000 & 42 & 15.8 \\ \cline{1-1}  & NTS30,001–40,000 & 69 & 25.9 \\ \cline{1-1}  & NTS40,001–50,000 & 30 & 11.3 \\ \cline{1-1}  & \textgreater{}NTS50,001 & 38 & 14.3 \\ \hline \multirow{4}{*}{Reason to buy A Nintendo Switch} & Interesting and fun games & 204 & 76.7 \\ \cline{1-1}  & Recommendation of friends & 22 & 8.3 \\ \cline{1-1}  & Gift from others & 26 & 9.8 \\ \cline{1-1}  & Others & 14 & 5.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Profile of the respondents.  
To assess the reliability and validity of the constructs, we conducted a confirmatory factor analysis (CFA) to test the convergent validity of each construct. A single factor model was specified for each of the constructs. The measurement model included 22 items describing six latent constructs: PU, PEOU, PP, COMP, ATT, and BI. The factor loadings for the various question items of the COM construct ranged between 0.70 and 0.92, PU construct ranged between 0.71 and.83, PEOU ranged between 0.76 and 0.86, PP ranged between 0.73 and 0.86, ATT ranged between 0.80 and 0.84 and BI ranged between 0.77 and 0.82. Convergent validity of the constructs with all factor loadings exceeding 0.70 was demonstrated to be good to excellent on their own constructs [40]. The values of composite reliability (CR) for each construct were 0.87, 0.88, 0.86, 0.89, 0.89, and 0.84, which were all clearly above 0.70 for all constructs, providing internal consistency. The values of AVE were in the range of 0.61 to 0.70, providing a reasonable convergent validity according to [PERSON] and Larcker [41].  
The measurement model revealed a good fit, with \(\chi\)2 = 539.24, \(\chi\)2/df = 2.67, GFI = 0.83, AGFI = 0.79, NFI = 0.88, IFI = 0.92, TLI/NNFI = 0.91, CFI = 0.92, SRMR = 0.07 and RMSEA = 0.08. Therefore, all indicators met the requirement for the recommended level of the model fit [42].  
Overall, the measurement model indicated that there was a high degree of reliability as well as convergent and discriminant validity. The measurement items with 22 items measuring six latent constructs were applicable in evaluating the users' acceptance of Nintendo Switch.
## 2 Materials and Methods
### Methodology
#### 2.2.4 Method of Impact Analysis  
The random forest (RF) regression was applied to identify the factor impacting temporal variation in the FPAR time series from 1980 to 2018. The RF is based on the ensemble learning method and many Decision Trees, is assumed to consider the interactions and nonlinear relationships among predictors, and can resolve the problem of multicollinearity in multivariate regression [55]. The package \"randomForest\" in MATLAB was used to implement the RF and the data were processed as follows:  
First, each observation was standardized using the Z-score method:  
\[z_{i}=\frac{x_{i}-\mu}{\sigma} \tag{10}\]  
where, \(x_{i}\) is the raw value of year \(i\), \(z_{i}\) is its normalized value, \(\sigma\) is the standard deviation, and \(\mu\) is the mean of the \(x\).  
Second, the four variables, namely TAVG, PRCP, SWRad and CO\({}_{2}\) concentration were included so we could explore their impact on the FPAR through the random forest regressions by performing 1000 regressions with the sample. The results were quantified by the coefficient of determination and the relative importance of each variable for each regression.  
All data processing is conducted with Matlab, except for the KGE index, which is calculated with R. The spatial data are then visualized using ArcGIS 10.7. Figure S2 shows the framework for data processing and analysis in this study.