esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Complexity Analysis
### Complexity Analysis of ANN and SVM-Based NLEs
#### 6.2.2 Complexity of SVM  
In order to calculate the complexity of implementing the SVM-NLE, we can split the equalization process in two steps. On the one hand, the process of estimating the ML-RLS, whose complexity does not depend whether the equalization is blind. On the other hand, the complexity of performing the IRWLS that depends on the equalization type, i.e., blind or non-blind, and on the chosen cost function, in our case [PERSON]'s or Godard's cost function. We consider that the NLE algorithm operates using a \(N_{w}\) order filter operating on data blocks of \(N\)s samples. The complexity of each iteration within the ML-RLS estimation can be calculated step by step. The first step that is the computation of \(a_{i}\) is given by \(8N_{\rm W}+3\). The second step, where the \(w_{s}\) is calculated using the least-square method, requires \(64/3{N_{s}}^{3}+18{N_{s}}^{2}\). The updating of the \(w\) vector carried out in the third step depends on particular implementation. For the case of the supervised SVM, the number of operations is \(4N_{w}+6N_{s}-1\), whereas for [PERSON]'s and [PERSON]'s based blind implementations, the number operations is \(3{N_{s}}^{2}+2{N_{s}}\) and \((3p+2){N_{s}}^{2}+(p+2){N_{s}}\) (\(p\) represents the power of the norm), respectively. Step four and step five a priori do not affect the FLOP count since they do not require any extra arithmetic manipulation. It is important to note that in all the studied cases, the computational complexity is \(O(N_{S}^{3})\), with the least square calculation the limiting stage. The computational complexity of the cost function, on the other hand, is \(O(N_{s})\) for non-blind equalization and \(O(N_{S}^{2})\) for both [PERSON]'s and [PERSON]'s cost functions and, consequently, blind equalization does not suppose a significant computational cost increment compared to the unsupervised approach.
# Immobilised Humic Substances as Low-Cost Sorbents for Emerging Contaminants
There have been several attempts to immobilize HAs on silica. [PERSON] et al. [22] proposed a green and inexpensive method for the immobilization of humic substances by thermally condensing them onto silica in order to obtain a sorbent for glucocorticoids, namely the substances of the steroidal hormone class. [PERSON] and [PERSON] [23], studying the uptake of heavy metals (Pb\({}^{2+}\), Cd\({}^{2+}\), Cu\({}^{2+}\), Zn\({}^{2+}\) and Mg\({}^{2+}\)) from an aqueous solution, found that an SiO\({}_{2}\)-immobilized HA sorbent was effective in metal uptake. One of the most important advantages of immobilized HSs is that they can be separated from solutions more effectively [24]. Humic substances can be isolated, for example, from compost and sludge, as well as the result of processing of biomass waste. Therefore, biomass waste and compost humic substances can be considered as renewable material. Many of the elaborated sorbents are developed without considering economic aspects. Humic substances are low-cost materials and thus humic-based sorbents, and especially when using the suggested approaches, they can also be considered as low-cost sorbents. There are many sorbents which are designed for the removal of heavy metals, but not that many are developed for the removal of emerging pollutants such as pharmaceuticals. This study was aimed at immobilizing humic substances and investigating their properties for potential use as a low-cost sorbent of contaminants of emerging concern (in this case, cations (Cr(III)), anions (As(V)) and pharmaceuticals (chlorpromazine)).
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 3 Research Design
### Data Preprocessing
#### 3.2.3 Replacing Missing Values  
Acquiring tweets for all days was impossible due to Twitter's seven-day restriction for retrieving tweets. As a result, the mean value was used to replace the missing sentiments. Furthermore, stock data were absent on weekends or when the stock market was down. Therefore, to retrieve relevant values, we employed a function that linearly interpolates between known data to generate unknown values. Linear interpolation is a method for approximating unknown values that seem to be among the known ones [39].
# Effects of Grazer Exclusion on Carbon Cycling in Created Freshwater Wetlands
## 3 Results
### Gas Fluxes  
Carbon dioxide fluxes differed between summer and fall and were strongly influenced by grazing (Table 1, Figure 4). Across grazer treatments, there was significantly higher NEE in the summer compared to the fall in both years (2017: \(p\) = 0.0003; 2018: \(p\) = 0.003). There was also a significant interaction in 2017 between season and treatment for NEE (\(p\) = 0.01), with summertime net uptake in caged plots more than double that of uncaged plots, whereas fall values were similar across treatments. Across years, ER was significantly higher in caged plots (2017: \(p\) = 0.02; 2018: \(p\) = 0.03); and in 2017, it was significantly higher in the summer compared to the fall (\(p\) = 0.02), but showed no interaction between treatment and season in either year. GPP showed a clear interaction between season and treatment, with a significant interaction in 2017 and a strong trend towards an interaction in 2018 (2017: \(p\) = 0.02; 2018: \(p\) = 0.06), with higher primary productivity in caged plots during the summer. In summer 2017, when the grazer effect was greatest, caged plots fixed 55% more carbon than uncaged plots. Across years and treatments there was also a significant linear relationship between aboveground biomass and GPP and ER (GPP: R\({}^{2}\) = 0.3, F\({}_{1,29}\) = 9.2, \(p\) = 0.005, ER: R\({}^{2}\) = 0.4, F\({}_{1,29}\) = 17.6, \(p\) < 0.001), with higher aboveground biomass correlated with higher GPP and ER (Figure 5).  
Figure 3: Decomposition rate of the four dominant macrophytes in caged (grey) and uncaged (white) plots. There was a significant interaction between species and treatment, and letters indicate statistical differences between average k-values, \(p\) < 0.01.  
Figure 4: Net ecosystem exchange (NEE), ecosystem respiration (ER) and gross primary production (GPP) in 2017 (**A**) and 2018 (**B**) for caged (grey) and uncaged (white) treatments. Summer measurements were taken in the period June–July, fall measurements were taken in the period August–September. Stars indicate significant differences between caged and uncaged plots (_p_ < 0.05) within a season, where there was a significant interaction between season and treatment.
## 2 Height Factor Model for GNSS Tomography
### Construction of the Height Factor Model  
In the three-dimensional voxel tomography model, the criterion for judging the availability of GNSS signals is where they puncture. In other words, only GNSS signals passing from the top boundary are selected as effective rays for the tomography system, and a number of side signals penetrating from the side of the tomography area are eliminated. Evidently, this behavior not only reduces the utilization of GNSS signals, but also generates more empty voxels in the three-dimensional model, which leads to a decline in the accuracy and stability of the tomographic results. Consequently, the HFM introduced in this paper will show the process of side signal modeling. The key for modeling side rays is the estimation of the SWD of inside signals, which is discussed in the HFM.  
For the integral rays penetrating from the top boundary, their SWDs are calculated by using Equation (2). For instance, the SWD of signal \(OM\) shown in Figure 2 is obtained by the ray path mapping of the ZWD of \(M^{\prime}\), plus the gradient delay caused by the entire troposphere. Accordingly, the SWD of inside signals (e.g., signal \(OQ\)) can be estimated by the mapping of the ZWD of ray \(OQ^{\prime}\) and the gradient delay as a result of the partial tropospheric impact. Therefore, we present the estimation of the SWD of inside signals in terms of two aspects--isotropic and anisotropic components--which are carefully discussed in the following two subsections.
## 3 Determine Inventory Standards  
### Influencing Factors and Inventory Standards of Rooftop Farming  
The use of roofs for agricultural planting activities is influenced by multiple factors, such as roof slope, height, area, bearing capacity, lighting conditions, building type, roof surrounding environment, urban regulations, etc. The more comprehensive the various factors considered, the higher the feasibility of rooftop agricultural planting activities. The influencing factors, inventory basis, and inventory standards of rooftop agricultural planting were analyzed, as shown in Table 1. In addition, the regularity, accessibility, ownership, local climate, management issues, and building location of the roof space can all affect the development of agricultural planting activities and can be analyzed and considered based on specific circumstances without going into detail here.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Influencing Factors** & **Impact on Agricultural** & **Inventory Basis** & **Inventory Standards** \\  & **Planting Activities** & & Flat roofs and pitched roofs with a slope of less than 15\% (i.e., 26\%) are suitable for roof greening [15]; “The Unified Standard for Civil Building Design” mentions that the slope of a flat roof is \(\geq\)2\% and \(<\)5\%, and the slope of a planted roof is \(\geq\)2\% and \(<\)50\% [16]. & Considering the similarity between rooftop greening and agricultural planting, roof agriculture of 40 m or more susceptible to environmental factors, and it is recommended to use 40 m as the upper limit for inventory. \\  & & Beijing requires that non-sloping rooftop public buildings with fewer than 12 floors and a height of less than 40 m should implement roof greening, with a greening area of over 30\% [17]: cities such as Meishan and Chengdu also set a ceiling height of 40 m for rooftop greening; the flat roof of public buildings with a height of no more than 50 m shall be greened according to the regulations of Shanghai City [15]. & Considering the similarity between rooftop greening and agricultural planting, roof agriculture of 40 m or more susceptible to environmental factors, and it is recommended to use 40 m as the upper limit for inventory. \\  & & [PERSON] mentioned that rooftop vegetable fields of 10–15 m\({}^{2}\) can meet the per capita annual vegetable consumption, Greenhouses require a certain degree of scale efficiency, and small greenhouses should be around 400 square meters. According to an area utilization rate of 84.7\% [18], the roof area should be over 500 square meters; the scale of greenhouse construction in China, in order of small, medium, and large, is 5000 square meters, 5000 to 20,000 square meters, and over 20,000 square meters, which is relatively large for cities; the area of urban greenhouses with different functions may vary. However, due to the lack of large rooftops in urban buildings, the scale of greenhouses can be appropriately reduced. \\ \hline \end{tabular}
\end{table}
Table 1: The influencing factors and inventory standards of rooftop agricultural planting.
# The Carbon Footprint of Milk Production on a Farm
## 3 Results and Discussion
### Main Sources of GHG Emissions, Including Sources in Milk Production
[22] observed that housed dairy farms relying heavily on imported concentrates exhibited significantly higher indirect emissions than grazing-based systems, mainly due to the embodied emissions in purchased feed and energy inputs [22]. Similarly, [PERSON] et al. [48] highlighted that increased concentrate feeding to boost the milk yield may backfire by raising off-farm emissions and production costs, thereby worsening environmental and economic performance [48]. Studies in mountain and tropical regions, such as those by [PERSON] et al. [28] and [PERSON] et al. [42], underscored the variability in indirect emission contributions due to differences in feed production, land use intensity, and the carbon sequestration capacity of grasslands [28; 42]. Tools like DairyGHG and simplified calculators (e.g., LatteGHG) allow for estimating these emissions even in data-limited contexts by considering emissions from fertiliser and feed manufacturing [9; 24]. Moreover, [PERSON] et al. [5] emphasised the importance of accounting for carbon captured during photosynthesis of forage crops, which may offset a portion of indirect emissions, especially in smallholder and pasture-based systems [5]. Despite methodological challenges and regional heterogeneity, the inclusion of indirect emissions in LCA remains essential to capture the full environmental burden of milk production and to guide mitigation strategies tailored to farm structure and resource dependency [33, 41].
###### Abstract  
In response to the urgent global threat posed by human-induced extreme climate hazards, heatwaves are still systematically under-reported and under-researched in Thailand. This region is confronting a significant rise in heat-related mortality, which has resulted in hundreds of deaths, underscoring a pressing issue that needs to be addressed. This research article is one of the first to present a solution for assessing heatwave dynamics, using machine learning (ML) algorithms and geospatial technologies in this country. It analyzes heatwave metrics like heatwave number (HWN), heatwave frequency (HWF), heatwave duration (HWD), heatwave magnitude (HWM), and heatwave amplitude (HWA), combining satellite-derived land surface temperature (LST) data with ground-based air temperature (\(T_{\text{air}}\)) observations from 1981 to 2019. The result reveals significant marked increases in both the frequency and intensity of daytime heatwaves in peri-urban areas, with the most pronounced changes being a 0.45-day/year in HWN, a 2.00-day/year in HWF, and a 0.27-day/year in HWD. This trend is notably less pronounced in urban areas. Conversely, rural regions are experiencing a significant escalation in nighttime heatwaves, with increases of 0.39 days/year in HWN, 1.44 days/year in HWF, and 0.14 days/year in HWD. Correlation analysis (\(p<0.05\)) reveals spatial heterogeneity in heatwave dynamics, with robust daytime correlations between \(T_{\text{air}}\) and LST in rural (HWN, HWF, HWD, \(r>0.90\)) and peri-urban (HWM, HWA, \(r>0.65\)) regions. This study emphasizes the importance of considering microclimate variations in heatwave analysis, offering insights for targeted intervention strategies. It demonstrates how enhancing remote sensing with ML can facilitate the spatial-temporal analysis of heatwaves across diverse environments. This approach identifies critical risk areas in Thailand, guiding resilience efforts and serving as a model for managing similar microclimate, extending the applicability of this study. Overall, the study provides policymakers and stakeholders with potent tools for climate action and effective heatwave management. Furthermore, this research contributes to mitigating the impacts of extreme climate events, promoting resilience, and fostering environmental sustainability.  
heatwaves; data gap-filling; remote sensing; satellite data; air temperature; machine learning; random forest; geospatial artificial intelligence; natural hazard; Thailand +
Footnote †: journal: Medical Imaging  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
# Experimental Analysis of Ultrasonic Multiple Scattering Attenuation through the Air with Fine Dust
## 2 Scattering Attenuation Defined by Independent Scattering Approximation  
When acoustic waves are propagated through a disordered medium similar to the air with fine dust, multiple scattering occurs and the amplitude of the waves is attenuated. The attenuation triggered by scattering has been studied via statistical approximation. As the distance between the source and receiver is comparably longer than the scattering mean free path (\(l_{S}\)), considering the contribution of infinite multiple scattering, it is a challenge to accurately express the solution for the wave motion. However, statistical approaches, such as ensemble averaging, can be taken. Ensemble averaging reduces the effect of incoherent scattering triggered by the reverberation between scatterers [15]. The ensemble averaged [PERSON]'s solution <G> in the frequency \((\omega)\) -wave number \((k)\) domain is expressed as:  
\[\langle\mathrm{G}(\omega,\mathbf{k})\rangle=\frac{1}{{k_{0}}^{2}(\omega)-\Sigma( \omega,\mathbf{k})-k^{2}} \tag{1}\]  
where \(k_{0}\) and \(\Sigma\) represent the wave number in the homogeneous background medium and the self-energy that represents the contribution of ultrasonic multiple scattering, respectively.  
Independent scattering approximation (ISA) modifies the self-energy as being independent of \(\mathbf{k}\) for a certain range of frequencies, provided the sizes of the scatterers are not too large compared to the wavelength and are completely uncorrelated [16]. The ISA concept introduces the effective wave number (\(k_{e}\)) in a quasi-homogeneous medium (effective medium) [17; 18; 19; 20; 21] as:  
\[{k_{e}}^{2}(\omega)={k_{0}}^{2}(\omega)-\Sigma(\omega) \tag{2}\]  
The effective medium is based on the hypothesis that every element in the composite acts as an isolated element embedded in a homogenous medium with effective composite properties. As the theory provides a simple approach to determining effective properties, the approach has been successfully adopted in many fluid and solid cases [22; 23; 24].  
In this study, the attenuation of the effective medium is considered as the scattering attenuation of coherent waves through air with fine dust. The attenuation is defined by the imaginary part of the effective wave number given by:  
\[\alpha=2\text{Im}(k_{e}) \tag{3}\]  
In the ISA, the attenuation also exhibits the following relationship:  
\[\alpha=n\sigma_{t} \tag{4}\]  
where \(n\) and \(\sigma_{t}\) represent the number density and scattering cross-section of one scatterer, respectively. Therefore, the defined scattering attenuation correlates with the amount of fine dust in air. Further details on the scattering attenuation in the experiment are described in the following section.
## 5 Influence of Major Factors on the Performance of Building Settlement Prediction
### Soil Parameters  
The results of the calculation of the model assessment indicators for the network input layer with the effect of the inclusion of soil parameters and without them are shown in Table 3.  
As can be seen from Table 3, when the input layer is not inputted with soil parameters, the average values of RMSE and MAE are 0.37 and 0.34, respectively, and the mean value of \(R^{2}\) is 0.87--the model performs better, and the prediction of building I is the best; while when the inputted soil parameters are inputted, the average values of RMSE and MAE are 0.31 and 0.28, respectively, and the mean value of \(R^{2}\) is 0.97--the model performs better, and the prediction of building G is the best. Therefore, the input of soil layer parameters in the input layer of the LSTM-RA-ANN model leads to an improvement in the prediction accuracy: the mean values of RMSE, MAE, and \(R^{2}\) are 0.84, 0.82, and 1.11 times higher than those when the effect of the soil layer is not taken into account, respectively, but the soil layer parameters do not have a significant overall effect on the model performance. Nevertheless, when the soil parameters are input, the predicted settlements are closer to the real ones, and the established model exhibits a better performance.  
Figure 10: Evaluation indices of LSTM-RA-ANN model in prediction of building settlements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Building**} & \multicolumn{3}{c}{**No Input of Soil Parameters**} & \multicolumn{3}{c}{**Input Soil Layer Parameters**} \\ \cline{2-7}  & **RMSE** & **MAE** & \(R^{2}\) & **RMSE** & **MAE** & \(R^{2}\) \\ \hline building F & 0.18 & 0.16 & 0.94 & 0.20 & 0.18 & 0.94 \\ building G & 0.45 & 0.39 & 0.84 & 0.17 & 0.15 & 0.97 \\ building H & 0.36 & 0.31 & 0.71 & 0.32 & 0.29 & 0.98
## 3 Methods
### Quantitative Evaluation Metric for Swarm Robustness
We believe that the importance of links is not only reflected in the topological relationship of links but also closely related to the link length. Achieving a consistent link length helps achieve a uniform distribution of nodes, which in turn contributes to a balanced distribution of node and link importance. For a link, the closer the length is to the preset optimal distance, the more critical it is and the more it should be maintained and protected. Based on the above analysis, the link importance decay coefficient \(\omega_{ij}\) is defined as:  
\[\omega_{ij}=1-\frac{||\mathbf{r}_{ij}||-d_{best}|}{d_{max}}, \tag{6}\]  
where \(d_{best}\) represents the preset optimal link length according to the requirements of avoiding collisions and maintaining connectivity, and \(\mathbf{r}_{ij}=\mathbf{r}_{i}-\mathbf{r}_{j}\) denotes the relative position. The new link importance is defined as the product of the initial importance and the link importance decay coefficient. In order to satisfy Equation (2), the decayed link importance is normalized. Finally, the link importance entropy is defined as:  
\[E_{L}=-\sum_{e_{ij}\in\mathcal{E}}\frac{\omega_{ij}p_{ij}}{\sum_{e_{ij}\in \mathcal{E}}\omega_{ij}p_{ij}}\log\frac{\omega_{ij}p_{ij}}{\sum_{e_{ij}\in \mathcal{E}}\omega_{ij}p_{ij}}. \tag{7}\]  
When the importance of all links reaches the same and all link lengths converge to the preset link length, the link importance reaches the maximum value \(\log M\), where \(M\) is the total number of links. Based on the node importance entropy \(E_{N}\) and link importance entropy \(E_{G}\), the graph importance entropy \(E_{G}\) can be defined as:  
\[E_{G}=E_{N}+E_{L}. \tag{8}\]  
In fact, the graph importance entropy is a direct measure of the swarm robustness. The larger the graph importance entropy, the more balanced the distribution of node importance and link importance, and the higher the swarm robustness.
## 4 Results and Discussion
### Modelling Results
#### 4.2.2 Performance of BAS-RF for FS and ASR Expansion Modelling  
The capability of the RF model with adjusted hyperparameters was verified by assessing the predictive precision of the test group. In Figure 12, the predicted and actual FS and ASR values in the training and test groups are presented. The relationship between the predicted and actual outcomes was estimated by calculating the _RMSE_ and correlation coefficient. Apart from minor _RMSE_ values, high correlation coefficients were observed for both training (FS: 0.9841, ASR: 0.9582) and test sets (FS: 0.9545, ASR: 0.9416). Therefore, the prediction performance of the ML models was accurate and reliable. Furthermore, the _RMSE/R_ values of the training set and test set were close illustrating that no overfitting problem existed.  
Figure 11: RSME values from 10 validation sets for (**a**) FS and (**b**) ASR modelling.
## 4 Discussion
### Limitations and Future Perspectives  
We are aware that our sample size is relatively small. However, despite this limitation, we observed that edge effects led to distinct, statistically significant differences in the WA results when evaluating complex transient signals. In our view, these effects would likely become even more pronounced with a larger sample size, further highlighting the importance of addressing this issue. Overall, we believe that this study represents a significant first step in understanding the role of edge effects in the WA of biological signals and provides a solid foundation for future, larger-scale studies.  
As another potential direction for future research, filtering out the exponential trend from the PORH signals, as discussed by [16], may be applied to assure the validity of the WA results additionally.