esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of the Biological Potential and Chemical Composition of Brazilian and Mexican Propolis
## 3 Discussion
### Antifungal Activity  
The increase in mycosis and the appearance of resistant fungal strains in patients has been increasingly observed throughout the world. This has generated the search and development of new compounds that meet the requirements of an antifungal and in that sense, Mexico and Brazil have studied the antifungal properties of ethanolic extracts of propolis in different species of yeast [104] and filamentous fungi [105; 106].  
Table 2 shows these various studies and as can be seen, most studies have been restricted to the study of ethanolic extracts of propolis against _Candida albicans_. Likewise, when comparing the antifungal activity of propolis from both countries, it is observed that the extracts from Mexico require a lower concentration to achieve antifungal activity.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Microorganism** & **Color** &
\begin{tabular}{l} **Region of the** \\ **Country** \\ \end{tabular} & **Extraction** & **MIC (ng/mL)** & **Reference** \\ \hline
**Fungi Mexico** & & & & & \\ \cline{2-6}  & ND & South & Ethanolic & 32 & [106] \\ \cline{2-6} \multirow{3}{*}{_Candida albicans_} & ND & Southeast & Ethanolic & 1.6-2.30 & [107] \\ \cline{2-6}  & Brown & Southeast & Ethanolic & 750 & \\  & Center & Ethanolic & 750-2400 & [85] \\ \hline \multicolumn{6}{l}{_Cryptococcus neoformans_} & ND & South & Ethanolic & 32 & [106] \\ \hline \multicolumn{6}{l}{_Candida albicans_} & & & \\ \multicolumn{6}{l}{(ATCC 14065)} & & \\ \multicolumn{6}{l}{_Aspergillus flavus_} & & \\ \hline \multicolumn{6}{l}{_Aspergillus fumigatus_} & ND & Center & Ethanolic & 32 & [106] \\ \hline
**Fungi Brazil** & & & & & \\ \cline{2-6} \multirow{3}{*}{_Candida albicans_} & Red & Northeast & Ethanolic/Sc-CO\({}_{2}\) & \(\geq\)1000 & [26] \\ \cline{2-6}  & Red & Northeast & Ethanolic/Sc-CO\({}_{2}\) & 1.56 & [76] \\ \cline{2-6}  & Brown & Northeast & Ethanolic/Sc-CO\({}_{2}\) & \(\geq\)1000 & [105] \\ \hline \multicolumn{6}{l}{_Malassezia pachydermatis_} & Red & Northeast & Ethanolic/Sc-CO\({}_{2}\) & 4000–8000 & [105] \\ \cline{2-6}  & Green & Southeast & Ethanolic & 4000–8000 & \\ \cline{2-6}  & Browns & South & Ethanolic & \(\geq\)16,000 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Antifungal properties of propolis of different regions of Brazil and Mexico.
## 3 Random Field Generation  
### Spatial Correlation Model  
The most common spatial correlation models of one-dimensional space are the exponential model, Gauss model, spherical model and second-order autoregressive model. The exponential model (also called Markovian decaying function) is used in this paper to describe the spatial correlation structure of the randomness of the soil. The formulas in one-dimensional and two-dimensional expressions are as follows:  
\[\rho(h)=\exp\biggl{(}-2\frac{d}{l}\biggr{)} \tag{13}\]  
\[\rho(d_{x},d_{y})=\exp\left[-\sqrt{\left(\frac{2d_{x}}{l_{x}}\right)^{2}+\left( \frac{2d_{y}}{l_{y}}\right)^{2}}\right] \tag{14}\]  
where \(\rho\) is the correlation coefficient of different positions, \(d\) is the separated distance in the random field, \(l\) is the correlation length, \(d_{x}\), \(d_{y}\) are the separated distances along  
Figure 3: The process of strength reduction for Mohr–Coulomb constitutive.  
the horizontal direction and vertical direction, respectively, and \(l_{x}\), \(l_{y}\) are the horizontal correlation length and vertical correlation length in two-dimensional space.  
A plot of this exponential model in two-dimensional space is given in Figure 4, indicating the properties at two points separated by \(l_{x}\) and \(l_{y}\).  
Figure 4 shows that the soil properties will be almost the same if the distance between two points is very close. A large correlation length implies that the soil property is correlated over a large spatial extent, resulting in a smooth variation within the soil profile. On the other hand, a small correlation length indicates that the fluctuation of the soil property is serious.
## 5 Routing Algorithm
### Q-Learning  
Q-learning is a well-known reinforcement learning technique that selects the optimal action based on the current state and receives the delayed reward with the highest value without using a specific environment model [37]. Based on inputs, three primary factors are measured within the Q-learning.  
s: State: i.e., energy level and node position.  
v: Action: i.e., accessibility of the available next hope node.  
R: Reward: i.e., successful data transmission, which is calculated by function reward.  
Whenever an agent performs an action a, it is awarded with a reward, R, instantly. Hence, we can utilize a set of factors to illustrate the working procedure: \((s_{0};v_{0};R_{1};s_{1};a_{1};R_{2};s_{2};\ldotsldots)\), which shows that agent get a reward \(R_{1}\) and changing from state \(s_{0}\) to state \(s_{1}\) with the action \(v_{0}\); when an agent reached to state \(s_{2}\) with the action \(v_{2}\), it get the reward \(R_{2}\) and so on. Furthermore, \(Q(s_{t};v_{t})\) is called Q-value which is the actual value for the state-action pairs. To show the relationship between Q-values and reward, the following formula can be use.  
\[Q:s\times v\to R. \tag{17}\]  
Additionally, Q-values are updated by the core algorithm by calculating old and new information with by using Equation (18), given below.  
\[Q_{t+1}(s_{t},v_{t})=(1-\alpha)\cdot Q_{t}(s_{t},v_{t})+\alpha\cdot[\gamma \cdot maxQ_{t}(s_{t+1},v_{t})+R_{t+1}]. \tag{18}\]  
In Equation (18), \(\alpha\) is the learning rate which is \((0\leq\alpha\leq 1)\). \((s_{t+1})\) is available next hopping nodes, \(\gamma\) is a discount factor which is \((0\leq\gamma\leq 1)\), \(maxQ_{t}(s_{t+1},v_{t})\) shows the most extreme approximation reward possible. Whenever the value is set to 0, new information  
Figure 5: Data processing framework in 5G-based smart healthcare.  
is irrelevant, and previous information is only considered. While, when \(\alpha\) is closed to 1, only the new information is considered and past information is discarded. Furthermore, the \(\gamma\) factor is vital for upcoming rewards. Whenever the value is set to 0, then the agent considers short-term rewards. When the value is set to 1, then the agent is interested in long-term rewards.  
\[E_{hop}=\frac{E_{t}}{E_{(t+1)}+1/E_{pr}} \tag{19}\]  
\[D_{hop}=\frac{D_{t}}{D_{(t+1)}+d_{t}}, \tag{20}\]  
where \(D_{t}\) defines the shortest best path, \(E_{t}\) defines the highest energy best path, \(E_{pr}\) shows the energy remaining in the \(N_{t}\) node and \(d_{t}\) is the distance between nodes.
## 7 Conclusions  
The accuracy and applicability of the electromagnetic environmental effects evaluation models have always been important parts of this research area. The effects evaluation models mainly calculate the electromagnetic environmental effect index by weighting the contribution of each evaluation index to the effect, including simple weighting, fuzzy hierarchy weighting, BP neural network weighting, statistical, and normalized interference level methods. Simple weighting and fuzzy hierarchical weighting methods are feasible to evaluate the effects in specific electromagnetic environments. Neural networks are the best choice to improve the adaptability of the proposed models in various complex electromagnetic environments. The normalized interference level method applies to the effects evaluation of equipment. However, each proposed model has certain limitations, so it cannot deal with all electromagnetic interference effects.  
The combination of objective and subjective evaluation methods is necessary to realize the unification of both evaluations. The combination of the effect mechanism method and deep neural networks can not only meet the requirement of evaluation accuracy but also solve the model's poor adaptability.  
In this paper, the studies and evaluation of the effects of the electromagnetic environment are analyzed systematically, especially for the evaluation models. Both the objective and subjective evaluation models illustrated in this review demonstrate the significant role of electromagnetic environmental effects in certain fields. More research and study  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Future Challenges** & **Basic Methods** & **Possible Solutions** & **Key Issues** \\ \hline \multirow{6}{*}{Comprehensive evaluation model} & \multicolumn{3}{c}{Analysis of the cross-linking relationships among independent evaluation models \(\rightarrow\) parameters definition of the general evaluation model \(\rightarrow\) construction of a comprehensive model involving multi-effects \(\rightarrow\) definition of the effect index} \\  & \multicolumn{3}{c}{} \\efforts are needed to improve the generality, adaptability, and evaluation convenience of the evaluation models.  
Conceptualization, M.L. and G.W.; methodology, M.L. and G.W.; formal analysis, M.L.; investigation, M.L.; resources, M.L. and G.W.; data curation, M.L.; writing--original draft preparation, M.L.; writing--review and editing, M.L. and G.W.; visualization, M.L.; supervision, G.W.; project administration, G.W.; funding acquisition, G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Project of Equipment Research Fund of China, grant number 6142205200301.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors would like to thank the National Key Laboratory on Electromagnetic Environment Effects for all the support.  
The authors declare no conflict of interest.
# Retention of the Antibiotic Cofruoxime onto Agricultural and Forest Soils
## 3 Results and Discussion
### Adsorption of CFX and Modeling
#### 3.2.2 Fitting to Adsorption Models  
Figure 2 shows the adsorption curves of the 23 soils used in this work, plotting the amount of antibiotic adsorbed versus its concentration in the equilibrium solution. Figure 2: Adsorption curves for CFX on the various soils were studied. M, maize (corn) soils; VO, vineyard soils (Ourense province); VP, vineyard soils (Pontevedra province); F, forest soils; CM and CV, control soils in maize and vineyard areas. Average values (\(n=3\)), with coefficients of variation always \(<\)5%. In addition, Table 1 shows the parameters of the adsorption equations obtained from the adjustments of the experimental data to the Freundlich, Langmuir, and Linear models. The adsorption data of most of the soils adjusted better to the Freundlich equation, as indicated by the values of the coefficient of determination obtained for most of the soils (R\({}^{2}\) above 0.9). In the Linear and Langmuir models, R\({}^{2}\) higher than 0.9 was obtained in a smaller number of soils. In addition, this last model was the most affected by high errors (mean squared errors--MSE) associated with the fitting, making it the least adequate for the soils in this study. The values of the distribution coefficient (\(K_{d}\)) of the linear model show wide variation in corn soils (3.08-247.33 L kg\({}^{-1}\)) and vineyard soils on granite (2.01-201.92 L kg\({}^{-1}\)), while the range is lower in vineyard soils on schist and slate (11.48-51.39 L kg\({}^{-1}\)), and especially in forest soils (20.43-33.43 L kg\({}^{-1}\)). To be noted that, using mean squared errors, only the MSE of different fits to the same data should be compared, as it will depend on the magnitude of the data (larger values tend to have higher variability). Future more detailed treatment of these data could be made using more sophisticated, weighted nonlinear regression, as well as modeling comparison based on likelihood ratio tests and/or Akaike's Information Criteria (AIC) and Bayesian Information Criteria (BIC). The \(K_{d}\) values are linked to soil characteristics, as indicated by the statistical results that show positive and significant correlation (\(p<0.05\)) with pH in water (r = 0.50; R\({}^{2}\) = 0.25), with the concentrations of exchangeable Ca and Mg (r = 0.58 and 0.43, R\({}^{2}\) = 0.34 and 0.18, respectively), and with eCEC (r = 0.57; R\({}^{2}\) = 0.32). To note that bivariate correlations were calculated using the [PERSON]'s correlation method, in which the obtained \"r\" values indicate the degree of correlation (values closer to 1 indicate that more relation exists between the parameters) and \"p\" is the level of significance. The squared values of r (R\({}^{2}\), coefficients of determination) indicate the fraction of the variability explained by the respective soil property.
## 3 Results
### Nanobubbling Effect  
The nanobubbling effect on DCF removal was studied in two cases. In the first case, DCF was diluting using distilled water after bubbling, and it was found that there were no changes in DCF concentration after 48 h. The second case involved bubbling the DCF solution; it was observing that the DCF concentration was about 10% lower than its initial concentration (10 mg/L) after 30 min of nanobubbling (Figure 3). The dissolved oxygen concentration was reduced with increased bubbling time, probably caused by the coalescence of the numerous bulk microbubbles [27]. The reduction in DCF concentration during the nanobubbling process was caused by OH radicals (Equation (3)), which were generated by the cleavage of the entrapped water molecular due to collapse of nanobubbles [25].  
Figure 2: DCF degradation in aqueous solution under EB irradiation.
## 3 Results
### The Directional Distribution Characteristics of EEQ  
As evidenced by Table 6 and Figure 5, the directional trend of the spatial distribution of the RESI values for each grade was similar across the four periods, mainly orienting in a southwest-northeast direction. However, in 2019, a deviation was noted for the worse grade, with its distribution trending northwest-southeast. In 1995, the ratio of the worse grade was approximately equal to one, indicating a lack of pronounced clustering for the directional distribution in these areas. The central trends of the EEQ areas in the worse and poor grades varied across different study periods. Due to the scattered distribution of the EEQ areas in the worse grade, its central trend, directional trend, and discrete level were random from 1995 to 2019.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Grade** & **Year** & **Maximum Value of Kernel Density** \\ \hline \multirow{4}{*}{Worse} & 1995 & 0.166 \\  & 2000 & 88.358 \\  & 2010 & 8.414 \\  & 2019 & 1.235 \\ \hline \multirow{4}{*}{Poor} & 1995 & 6.321 \\  & 2000 & 1074.96 \\  & 2010 & 515.246 \\  & 2019 & 446.806 \\ \hline \multirow{4}{*}{Moderate} & 1995 & 1109.51 \\  & 2000 & 1035.91 \\  & 2010 & 1084.99 \\  & 2019 & 974.859 \\ \hline \multirow{4}{*}{Good} & 1995 & 1107.85 \\  & 2000 & 1075.02 \\ \cline{1-1}  & 2010 & 1110.35 \\ \cline{1-1}  & 2019 & 1111.11 \\ \hline \multirow{4}{*}{Excellent} & 1995 & 313.196 \\ \cline{1-1}  & 2000 & 38.88 \\ \cline{1-1}  & 2010 & 42.53 \\ \cline{1-1}  & 2019 & 47.911 \\ \hline \end{tabular}
\end{table}
Table 5: Maximum value of kernel density of EEQ in each grade.  
Figure 4: Change trend of kernel density of EEQ in each grade.  
The center, orientation, and ratio of the moderate- and good-grade areas showed almost no obvious difference, suggesting that their central trend, directional trend, and discrete level adhered to a predictable spatial pattern. This underscored the dominant position of the areas of the moderate and good grades in the EEQ, which covered most of the study area. Conversely, the distribution centers of the excellent-grade areas, which were centered in the study area in 1995, experienced a southeastward shift, with the highest ratio observed in 2019. This indirectly indicated that the areas of excellent grade were small and had a scattered distribution within the study area.  
Figure 5: Change trend of direction distribution of EEQ in each grade.
# Validation of an RF Image System for Real-Time Tracking Neurosurgical Tools
###### Abstract
Secondly, due to the magnetic nature of the system, the measurements made by the tracker to detect the position of the markers can be altered by magnetic field distortion caused by metallic objects and electrically powered equipment in the surgical scenario [13; 15], which poses a considerable limitation on the materials and equipment (including the tools themselves) that can be used for the intervention. As an example, an electromagnetic-based system currently available in the market is the Aurora(r) system from NDI [16]. Hybrid optical and electromagnetic tracking systems have also been proposed [13], even including augmented reality tools as well [17], albeit always keeping the above-mentioned limitations. Given the associated limitations to each method, new technological solutions to overcome them are still being pursued. Among the available options, microwave imaging rises as an interesting alternative that can provide for continuous and non-invasive intraoperative surgical tracking while overcoming the line-of-sight and magnetic interaction problems [18]. This technique is based on the changes in the dielectric constant and dielectric losses between the different biological tissues involved. These variations in the permittivity can be seen and identified by analyzing the changes in the response of microwave antennas [19]. With these alterations in the dielectric properties of the tissues, their boundaries can be identified and the biomedical image can be built [20]. These techniques find wide use in several fields, such as cancer detection and management [21; 22]. Considering the usual contrast in dielectric properties, these techniques could also be applied to detect both surgical targets (such as tumors) and surgical tools [23]. In this work, we study the feasibility and validation of a microwave antenna-based imaging system for intra-operative surgical navigation.
## 4 Results and Analysis
### Measurement Model Assessment  
The PLS algorithm's procedure was carried out to perform confirmatory factor analysis, which included testing the reliability and validity of each statement used to constitute the constructs. The items \"To pirate digital products goes against my principles\" (DIPI02) and \"I am able to communicate at all levels to ensure appropriate resources are deployed internally or externally to minimize outages\" (PS04) did not meet the required criteria and therefore were excluded from assessment. Table 1 displays the results of the reliability of the proposed model's internal consistency and of the convergent validity analyses. The Cronbach's alpha coefficient (\(\alpha\)) was adequate for all the model constructs according to the criteria established by [PERSON] and [PERSON] [75]. The composite reliability (CR) of all latent variables was considerably greater than the 0.7 threshold [73]. The appropriate values of the two aforementioned indicators lead to the conclusion that the study's internal consistency is strong. The value of the average variance extracted (AVE) indicator ranges from 0.572 to 0.831 and is greater than 0.5 [76]. Multicollinearity is not a concern in the present study. The variance inflation factor (VIF) was utilized for the analysis of collinearity, and all values of this coefficient for the items used in the study are substantially less than 5, which is the upper limit of validity. \begin{table}
\begin{tabular}{l c c c c c} \hline
**Construct and Item Description** & **Convergent Validity** & **VIF** & **Composite Reliability** & \(\alpha\) & **AVE** \\ \hline Digital Citizenship & & & & & \\ IDL: Information and Data Literacy & & & 0.918 & 0.916 & 0.6 \\ IDL01 & 0.824 & 2.839 & & & \\ IDL02 & 0.858 & 3.209 & & & \\ IDL03 & 0.790 & 2.391 & & & \\ IDL04 & 0.830 & 2.520 & & & \\ IDL05 & 0.753 & 2.016 & & & \\ IDL06 & 0.819 & 2.613 & & & \\ IDL07 & 0.838 & 2.777 & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Measure model and constructs. Discriminant validity was evaluated using two criteria, namely the Cornell-Larcker criterion and the heterotrait-monotrait (HTMT0.85) criterion. Most studies use only one of the mentioned criteria, but due to a greater confidence in the results obtained through the structural model assessment, we applied both criteria. Table 2 demonstrates that the study's constructs satisfy the Cornell-Larcker criterion [77]. In accordance with the heterotrait-monotrait (HTMT0.85) criterion [78], Table 3 confirms that all constructs record values less than 0.85, thus satisfying the HTMT0.85 criterion. Taking into account the results in both tables, it is possible to conclude that all of the constructs used in the study contain values that meet both criteria and that satisfactory discriminant validity was attained.
## 4 Experiments
### Comparisons with SOTA Methods

Table 6: Comparison experiment on the NTU 60 dataset. an orange bar chart; the right side of the vertical axis shows the accuracy (%), represented by a green line graph. It can be observed that, compared to EleAtt-GRU [44], GFNet [45], and PRGCN [22], our model achieves higher accuracy with fewer parameters. Compared to the SGN method [28], our approach has slightly lower accuracy on the NTU 60 dataset but performs better on the NTU 120 dataset with fewer parameters. Compared to the most advanced lightweight models (CoST-GCN, CoAGCN, and CoS-TR [30]), our model achieves similar performance with fewer parameters. It is worth noting that this algorithm provides a frame-by-frame prediction approach, which significantly reduces its FLOPs, whereas our method and other comparative algorithms operate on the entire skeleton sequence. The accuracy of the EfficientGCN-B0 model [29] is higher than our method, but we can achieve slightly higher accuracy on the NTU 120 dataset X-Set benchmark, and with fewer parameters and lower computational complexity. Moreover, in 2D skeleton-based action recognition using pose estimation, our paper achieves superior results in both accuracy and efficiency. In summary, our model has lower requirements for computational resources and demonstrates good performance. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Year** & **Data** & **X-Sub** & **X-Set** \\ \hline ST-GCN [9] & 2018 & 3D Skeleton & 70.7 & 73.2 \\ SR-TSL [18] & 2018 & 3D Skeleton & 74.1 & 79.9 \\ RA-GCNv1 [10] & 2019 & 3D Skeleton & 74.4 & 79.4 \\ AS-GCN [11] & 2019 & 3D Skeleton & 77.9 & 78.5 \\
2s-AGCN [12] & 2019 & 3D Skeleton & 82.5 & 84.2 \\ SGN [28] \(\star\) & 2020 & 3D Skeleton & 79.2 & 81.5 \\ RA-GCNv2 [13] & 2021 & 3D Skeleton & 81.1 & 82.7 \\
3s-CrossCLR [46] & 2021 & 3D Skeleton & 80.5 & 80.4 \\ Tripool [47] & 2021 & 3D Skeleton & 80.1 & 82.8 \\ STAR [32] & 2021 & 3D Skeleton & 78.3 & 78.5 \\ ST-TR [23] & 2021 & 3D Skeleton & 81.9 & 84.1 \\ CoS-GCN [30] \(\star\) & 2023 & 3D Skeleton & 83.7 & 85.1 \\ CoAGCN [30] \(\star\) & 2023 & 3D Skeleton & 84.0 & 85.3 \\ CoS-TR [30] \(\star\) & 2023 & 3D Skeleton & 84.8 & 86.1 \\ EfficientGCN-B0 [29] \(\star\) & 2023 & 3D Skeleton & **86.6** & 85.0 \\ \hline EHC-GCN & 2024 & 3D Skeleton & 84.6 & 85.6 \\ EHC-GCN & 2024 & Pose & 83.7 & **87.0** \\ \hline \hline \end{tabular} \(\star\) represents the lightweight action recognition model.  
\end{table} 
Table 7: Comparison experiment on the NTU 120 dataset. Figure 7: Comparison of model complexity between the EHC-GCN and competing methods. (EleAtt-GRU [44], GFNet [45], SGN [28], PRGCN [22], STF-Net [31], EfficientGCN-B0 [29]).