esa-sceva/satcom-chunk-collection huggingface dataset:
# Incorporating Artificial Intelligence Technology in Smart Greenhouses: Current State of the Art
## 1 Introduction
Third, current climate projection models forecast suppressed rainfall, land degradation, desertification, flooding, and other weather phenomena triggered by climate change and increased release of greenhouse gases [23; 24]. The projections documented by [23; 24] reflected the current realities in Sub-Saharan Africa [25]. The expansion of open-field agriculture to offset climate-related challenges is no longer a viable option. The European Commission Joint Research Centre estimated that three-quarters of the useful land on earth was already degraded, and the proportion of degraded land would surpass 90% by 2050 [26]. The challenge is most pronounced in developing countries that experienced the highest dryland desertification rate between 1982 and 2015. During this period, at least 5.4 million km\({}^{2}\) of land was degraded [27]. In theory, losing traditional farming dryland was a sufficient motivational factor for transitioning to smart greenhouses powered by AI technologies. However, the contrary phenomenon is true for climate-smart agriculture worldwide [28]. The current pace of transition does not match the challenges. On a positive note, the transition to precision agriculture would enable farmers to optimally use water, soil, and energy resources to minimize the negative effects of climate change on farming [29]. The rational use of agricultural resources would reduce the pressure on the natural environment, resulting in higher sustainability. The article critiques recent research on smart greenhouses and artificial intelligence technologies, including the favorable factors and the challenges that have impeded the growth of AI and smart greenhouse technologies. The study emphasized smart farming technologies, mechanization of farms, smart micro-irrigation, rapid soil analysis, pest prediction, and autonomous modification of the greenhouse microclimate, and LIDAR (light Imaging, detection, and ranging), optical and electro-optical cameras for robotic system motion and object identification (see Figure 1) [30]. The specific scope was supported by the gaps in research and the breadth of information relating to the impact of AI and IoT (Internet of Things) and data-driven agriculture, intelligent crop planning, and farm gate-to-fork systems [31; 32]. More particularly, no review has so far comprehensively mapped the state of AI and IoT application in smart greenhouse farming to help researchers and farmers of plant factories to solve the imminent problems related to global food insecurities and climate change, despite the fact that technological innovations have been centrally placed in the development of effective and sustainable solutions to these challenges. Thus, this review will enable farmers of factory plants to understand the extent to which AI and IoT can be combined to improve farming efficiency and productivity and will enable researchers to advance knowledge on this subject domain by building on the research achieved so far.
# Overload Mitigation of Inertial Grid-Forming Inverters Under Frequency Excursions
## 4 Performance Evaluation of Overload Mitigation Strategies
### Grid Frequency Oscillation Test  
Another scenario in which an overload mitigation strategy is relevant is during frequency oscillations. These events require fast overload limitation and recovery process to protect the inverter while supporting the recovery of the grid.  
In this test, the inverter operates at its rated power of 1 pu, and an oscillation of 1 Hz is introduced to the grid frequency. The amplitude of this oscillation is 0.25 Hz, as shown in Figure 14a. The active power response of the previous strategies is given in Figure 14b.  
The parallel PI has a proper overload mitigation capability, but its slow recovery reduces the active power contribution during the negative half-cycle of the frequency oscillation. Hence, the contribution of a GFM with a parallel PI strategy to oscillation damping is considerably reduced.  
The oscillation damping capability issue is solved by the EFS and AL strategies. In the AL strategy, this comes at the cost of a higher overload during the positive half cycle. In the EFS strategy, the overload capability of the parallel PI is kept, at the cost of a decreased active power during the negative half cycle.
## 4 Results
### Objective Metrics Results and Analysis  
The means and standard deviations for the IoU and task time metrics are shown in Table 2. The VR headset had a higher IoU percentage and faster average task time compared to the AR headset. It should be noted that none of the objects were mislabelled by users with either headset. This is likely because a checklist of objects and their labels were provided to participants during the mapping task.  
To evaluate H1 and H2, a single-tailed paired \(t\)-test was used to compare the objective metrics between the AR and VR headsets. The \(t\)-test results are shown in Table 3.  
From these results, we observed that the VR headset performed better than the AR headset in mapping accuracy and in task time, with the difference in the objective metrics between the two headsets being statistically significant in favor of the VR headset. While this does affirm H1 and H2, it must be noted that out of the twelve participants, seven failed to complete the mapping task in the given 8-min time limit when using the AR device, with four of these participants failing to label all six objects. The large difference in variance between devices for task times seen in Table 2 can also be attributed to this fact, as all participants spent longer than 420 s to complete their map using the AR device, while no participant failed to complete the mapping task in the time limit when using the VR device, with the longest task time being 458 s and the shortest task time being 219 s.
# A Matrix Effect Correction Method for Portable X-ray Fluorescence Data
## 3 Results and Discussion
### Evaluation of the Correction Results
#### 3.3.1 Different Correction Effects
These elements have low contents and weak characteristic X-ray fluorescence intensities, which may exacerbate the influence of the matrix effect. Figure 4: The _MAPE_ and _RMSPE_ values for the correction results of the new and SLR methods. Figure 5: The _MAE_ and _RMSE_ values for the correction results of the new and SLR methods. Generally, the influence of the matrix effect can give rise to some extremely large or small testing values called outliers during the pXRF analysis, which will deviate significantly from the changing trend of normal points (Figure 2). For example, the testing values of Co were much higher than its reference values, which may have been caused by the enhancement of the characteristic X-ray fluorescence intensity by Fe (Figure 2). The testing values of V and Cr, however, were lower than their reference values, which may have been caused by the absorption of secondary X-ray fluorescence by Fe and Ti, respectively (Figure 2). If the outliers are not omitted from the analysis results before SLR regression analysis, a realistic regression curve will not be established, and therefore, the resulting regression values will also be unreliable. Note that even if the outliers are eliminated before SLR regression analysis, a realistic regression curve cannot be obtained because the testing values involved in the regression analysis are incomplete, which will affect the use of the regression equation. Obviously, this is not a fundamental solution; in other words, the influence of the matrix effect cannot be eliminated by the SLR method. Fortunately, the MEC method can use the data of the major elements to modify the data of the outliers and, to a certain extent, weaken the influence of the matrix effect. For example, the testing values of Co, V and Cr were significantly corrected by the participation of Fe and Ti. The quality of these element data was significantly improved by the MEC method. Compared with the SLR method, the new method had better statistical parameters for all of these elements (Figures 3-5). For example, all statistical parameters of Co, Mo, Sn and Ta were greatly improved. As for Cr, W, V and Cd, only a part of the parameters was enhanced; that is, the \(R^{2}\) and \(P_{RE}\) values of Cr and W increased significantly, and the _MAE_, _MAPE_, _RMSE_ and _RMSPE_ values of V and Cd decreased significantly. The reason for the incomplete correction may be that these elements are also greatly affected by other elements aside from the selected major elements.
## 3 Results and Discussion
### Structural Characterization
Figures 4 and 5 show X-ray diffraction patterns of the Ba(Co\({}_{0.7}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) (BCZN) ceramics after sintering at 1350 \({}^{\circ}\)C for 30 h (sample _S2_) and after annealing at 1300 \({}^{\circ}\)C for 30 h (sample _S2a_). The analyses were carried out at the upper surface for both specimens and at depths of 120 and 140 \(\upmu\)m for sintered and annealed ceramics, respectively. It can be seen that the surface XRD pattern (Figure 4a) can be interpreted as a mixture of three phases Ba(Co\({}_{0.7}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) (BCZN), Ba\({}_{5}\)Nb\({}_{4}\)O\({}_{15}\) and Ba\({}_{8}\)(Co,Zn)\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\). At a depth of 120 \(\upmu\)m below the surface (Figure 4b), all reflections can be attributed to the cubic structure of Ba(Co\({}_{0.7}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) (BCZN), whereas no peaks attributable to Ba(Co,Zn)\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\) or Ba\({}_{5}\)Nb\({}_{4}\)O\({}_{15}\) were observed. These results clearly show that the Ba(Co,Zn)\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\) and Ba\({}_{5}\)Nb\({}_{4}\)O\({}_{15}\) phases are only formed on the surface of the BCZN ceramics and that they represent secondary phases. The origin of these phases is probably due to the volatilization of some Zn and Co elements during the sintering steps [12]. For post-sinter annealed ceramics it can be seen from Figure 5a that the XRD pattern confirms the presence of three phases on the surface of the ceramics: Ba(Co\({}_{0.7}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) (BCZN), Ba\({}_{5}\)Nb\({}_{4}\)O\({}_{15}\) and Ba\({}_{8}\)(Co,Zn)\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\). In addition, the relative intensities of the Ba\({}_{5}\)Nb\({}_{4}\)O\({}_{15}\) and Ba(Co,Zn)\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\) peaks increase, while those of the BCZN peaks decrease, suggesting that the amounts of the secondary phases of Ba\({}_{8}\)(Co,Zn)\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\) and Ba\({}_{5}\)Nb\({}_{4}\)O\({}_{15}\) increased on the surface of the BCZN ceramic after annealing. From the XRD pattern collected at the depth of 140 \(\upmu\)m below the surface (Figure 5b), we can see that all peaks can be attributed to the Ba(Co\({}_{0.7}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\) phase. However, data analyses show some crystal diffraction peaks at 20 = 17.72\({}^{\circ}\) (marked by an open circle), which reflects the 1:2 cation ordering within the crystal already observed in previous work [7]. Figure 4: X-ray diffractograms of a BCZN ceramic (_S2_): (**a**) at the upper surface and (**b**) at a depth of 120 \(\upmu\)m below the surface. \(\bullet\): Ba(Co\({}_{0.7}\)Zn\({}_{0.3}\))\({}_{1/3}\)Nb\({}_{2/3}\)O\({}_{3}\); \(\star\): Ba\({}_{8}\)(Co,Zn)\({}_{1}\)Nb\({}_{6}\)O\({}_{24}\); \(\blacklozenge\): Ba\({}_{5}\)Nb\({}_{4}\)O\({}_{15}\).
# Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems
## 3 System Model
### Chi-Square Distribution Channel Estimation Method  
The Chi-square distribution channel estimation method can reduce the false alarms caused by AWGN in the estimated CIRs. Reducing the false alarm probability by utilizing the properties of the Chi-square distribution is an efficient way to further improve the accuracy of channel estimation.  
The power summation of the \(n\)-th tap during \(T\) consecutive frames, i.e., from the \(M\)-th to the \((M+T-1)\)-th frame of the estimated CIR, could be represented as:  
\[\sigma_{\text{FL}}^{2}(n)=\sum_{i=M}^{M+T-1}|\hat{\mathbf{w}}_{i}(n)|^{2},\ M>0,\ 0\leq n<N. \tag{13}\]  
The power distribution function (PDF) of \(\sigma_{\text{FL},l}^{2}\) satisfies the Chi-square distribution and is given by:  
\[p_{\chi^{2}}(\sigma_{\text{FL}}^{2}(n))=\frac{1}{2^{N}\Gamma(N)}(\sigma_{\text {FL},l}^{2})^{T-1}\text{e}^{-\frac{\sigma_{\text{FL}}^{2}(n)}{2}},\ 0\leq n<N. \tag{14}\]  
In the literature [15], most of the false alarms in the estimated CIRs can be effectively reduced according to the Chi-square distribution, which can be represented by:  
\[\hat{\mathbf{h}}_{\text{Chi},i}(n)=\left\{\begin{array}{ll}\hat{\mathbf{h}}_{\text{LS },i}(n),&\sum_{i=M-T+1}^{M}\left|\hat{\mathbf{h}}_{\text{LS},i}(n)\right|^{2}\geq 3T \hat{\sigma}_{v}^{2},\ M\geq T\\ 0,&\text{otherwise}\end{array},\ 0\leq n<N.\right. \tag{15}\]  
The disadvantage of Equation (15) is that the variance of noise \(\hat{\sigma}_{v}^{2}\) has not been determined in the literature [15]. In order to define the variance of noise accurately, \(\hat{\sigma}_{v}^{2}\) can be represented as:  
\[\hat{\sigma}_{v}^{2}=\sum_{n=0}^{N-1}\frac{\left(\hat{\mathbf{h}}_{\text{LS},i}(n )-\hat{\mathbf{h}}_{\text{thre},i}(n)\right)^{2}}{N},\ \ 0\leq n<N. \tag{16}\]  
In the Chi-square distribution channel estimation method, \(T\) can be chosen from the regions of \([1,20]\). The larger the value of \(T\) is, the higher the accuracy of channel estimation is. In this article, the value of \(T\) is chosen by odd values to suppress the noise.
## 2 Materials and Methods
### Determination of Total Reduction Capacity  
The total reduction capacity was measured using the ferric reducing/antioxidant power (FRAP) assay [20], with vitamin C and vitamin E as controls; 2.5 mL of different concentrations of _Ginkgo_ flavonoid solutions were prepared in test tubes, 2.5 mL of 0.2 mol L\({}^{-1}\) PBS (pH = 6.6) buffer solution was added, and 2.5 mL of 1% potassium ferricyanide solution was added. The solution was fully mixed in a water bath at 50 \({}^{\circ}\)C, and held for 20 min. After cooling with water and adding 10% trichloroacetic acid (2 mL), fully mixing, and centrifuging at a rotation speed of 4000 r/min for 10 min, the supernatant (1 mL) was taken and 0.5 mL of 0.1% ferric chloride solution and 3.5 mL distilled water were added. A spectrophotometer was used to determine the absorption value of the solution at a wavelength of 700 nm, to evaluate the reducing strength of the sample.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Symbol**} & \multirow{2}{*}{**Variable**} & \multicolumn{3}{c}{**Range and Level**} \\ \cline{3-5}  & & \(\mathbf{-1}\) & \(\mathbf{0}\) & \(\mathbf{1}\) \\ \hline \(X_{1}\) & PEG1500 concentration (\(w\)/\(w\) \%) & 12 & 21 & 30 \\ \(X_{2}\) & (NH4)\({}_{2}\)SO\({}_{4}\) concentration (\(w\)/\(w\) \%) & 15 & 23.5 & 32 \\ \(X_{3}\) & ATPS solvent volume/leaf mass ratio (v/w mL/g) & 15 & 27.5 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters and levels for Box-Behnken design. PEG: polyethylene glycol.
# Services Sector Export in Europe
## 4 Research and Discussion
### Export of Services in European Countries with Different Levels of Economic Development
To establish whether there is any correlation between growth in service exports and GDP growth, we created a scatter plot of GDP growth and service export growth (Figure 1). A correlation analysis was conducted to estimate the correlation between the rates of GDP growth and service export growth from 2005-2016. The results of the correlation analysis demonstrate that the Pearson correlation coefficient R = 0.822 is statistically significant (\(p\)-level < 0.001). This attests the presence of a strong positive correlation between growth in service exports and GDP growth. It may be concluded that the countries with higher service export growth rates demonstrate higher GDP growth. A regression analysis was conducted to establish a linear dependence between GDP growth and service export growth. Figure 1: Correlation scatter plot of GDP growth rate and service export growth rate. The outcomes of the regression analysis of growth in service exports and GDP growth from 2005-2016 resulted in the following regression Equation:  
\[\mathsf{Y_{service\_exports\_growth}}=1.916*\mathsf{T_{GDP\_growth}}+0.160\]  
The conducted regression analysis demonstrates that the growth in GDP of 1% on average corresponds to growth in service exports of 1.96%. Considering the results of the regression analysis, this model is statistically significant (F = 74.759). 67.5% of service export growth is influenced by GDP growth (R\({}^{2}\) = 0.675) and other factors are responsible for the remaining 32.5%. The results of the regression analysis are presented in Appendix C.
# LTDA-MAC v2.0: Topology-Aware Unsynchronized Scheduling in Linear Multi-Hop UWA Networks +
## 3 Simulation Results
### Discussion
The runtime figures were obtained by executing the algorithms in MATLAB R2018a as single-core jobs on the standard node of the Viking computing cluster at University of York: Intel Xeon 6138 2.0 GHz, Centos Linux 7.3. Figure 4: LTDA-MAC schedules derived by the proposed greedy algorithm for 11-node linear UASNs deployed on 2 km and 20 km long pipelines. (**a**) A 2 km long network (200 m average distance between adjacent nodes); (**b**) 20 km long network (2 km average distance between adjacent nodes).
###### Abstract  
Background: Digital breast tomosynthesis (DBT) systems employ a sophisticated set of acquisition parameters to generate an image set, and the DBT acquisition angle is considered to be one of the most important parameters. The aim of this study was to use texture analysis to assess how the DBT acquisition angle might influence DBT images of breast parenchyma. Methods: Thirty-four patients were selected from a clinical study conducted at IRST Institute. Each patient underwent a dual DBT scan performed with Fujifilm Amulet Innovality (Fujifilm Corp, Tokyo, Japan) in standard (ST, angular range = 15\({}^{\circ}\)) and high-resolution (HR, angular range = 40\({}^{\circ}\)) modalities. Texture analysis was applied on the paired dataset using histogram-based features and gray level co-occurrence matrix (GLCM) features. Wilcoxon-signed rank and Pearson-rank tests were used to assess the statistical differences and correlations between extracted features. Results: The DBT acquisition angle did not affect histogram-based features, whereas there was a significant difference in five GLCM features (\(p<0.05\)) between DBT images generated with 15\({}^{\circ}\) and 40\({}^{\circ}\) acquisition angles. Correlation analysis showed that two GLCM features were not correlated at a \(p<0.05\) significance level. Conclusions: DBT acquisition angle affects the textures extracted from DBT images and this dependence should be considered when establishing baselines for classifiers of malignant tissue. Furthermore, texture analysis could be proposed as a quantitative method for comparing and scoring the contrast of DBT images.  
medical imaging; radiomics; tomosynthesis; acquisition angle +
Footnote †: journal: Applied Soft
## 3 Results  
### Without Source Term Results  
Table 3 shows the results for the insulated boundary condition test, without a source term. The hybrid combination is seen as the best performer in terms of score at 0.9393 \(\pm\) 0.0795, closely followed by the all-Tanh and the all-ELU models having scores of 0.8869 \(\pm\) 0.1471 and 0.8689 \(\pm\) 0.1361, respectively. Likewise, the hybrid model rated best in the average relative error category with 8.54% \(\pm\) 6.07%, narrowly beating the hybrid with 11.93% \(\pm\) 9.67%, while the all-ELU had 15.87% \(\pm\) 4.00%. Plots of the prediction comparisons show that the all-ELU model, in Figure 6, predicted accurately at just 4 points, contrary to the all-Tanh predictions also shown in Figure 6, that predicted well in the first half of the domain. The hybrid model predictions in the same figure showed the most visible consistency in the predictions. These figures were all representative of the models over the 26 realizations.  
Figure 5: Temperature distributions of insulated boundary conditions with source (**left**), where dotted lines represent the points of transition between the layers of skin; its sample density (**right**) shows how points were sampled for the resolution of the simulation, where count denotes the number of points that were sampled in the interval shown. Combined, the total number of points was 250.  
Table 4 contains the results for the convective boundary conditions without a source term. In this test, the hybrid model performed better in both R2 score and average relative error. The all-Tanh model scored second-best, which scored noticeably better than the all-ELU model. The prediction plots for the all-Tanh and hybrid models can be found in Figure 7.  
Convergence rates for these non-source experiments are depicted in Figure 8, and are representative for both insulated and convection boundary condition tests. Training behavior is observed to be largely similar in the all-Tanh and hybrid combination models, while the all-ELU model showed a stunted training behavior due to triggering the stop criteria much earlier. Failure rates were the same for both experiments, in all cases, and were observed to be less than 5% of the time.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **R2 Score** & **Avg. Relative Error** & **Iterations** \\ \hline Hybrid & 0.9524 \(\pm\) 0.0604 & 6.11\% \(\pm\) 4.69\% & 2325 \(\pm\) 818 \\ All-Tanh & 0.9448 \(\pm\) 0.0622 & 6.59\% \(\pm\) 4.31\% & 2428 \(\pm\) 917 \\ All-ELU & 0.6998 \(\pm\) 0.1476 & 21.89\% \(\pm\) 2.65\% & 233 \(\pm\) 94 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results for convective boundary condition test with no source term.  
Figure 6: Representative non-source predictions for insulated boundary conditions using hybrid activation function combination (**top left**), all-Tanh activation function combination (**top right**), and all-ELU activation function combination (**bottom**).