esa-sceva/satcom-chunk-collection huggingface dataset:
### Summary of sensors in environmental modeling  
A state-of-art of satellite sensors widely used in environmental applications and natural resources management are given in Table 1. These sensors provide data in a wide range of scales (or pixel resolutions), radiometry, band numbers, and band widths and provides distinct advantage of consistency of data, synoptic coverage, global reach, cost per unit area, repeatability, precision, and accuracy. Added to this is the long-time series of archives and pathfinder datasets (e.g., [PERSON], 2005, [PERSON] and [PERSON], 1994) that have global coverage. Much of this data is also free and accessible online.  
Many applications (e.g., [PERSON] et al., 2006) in environmental monitoring require frequent coverage of the same area. This can be maximized by using data from multiple sensors (Table 1). However, since data from these sensors are acquired in multiple resolution (spatial, spectral, radiometric), multiple bandwidth, and in varying conditions, they need to be harmonized and synthesized before being used ([PERSON] et al., 2004). This will help normalize for sensor characteristics such as pixel sizes, radiometry, spectral domain, and time of acquisitions, as well as for scales. Also, inter-sensor relationships ([PERSON], 2004) will help establish seamless monitoring of phenomenon across landscape.
## 4 Research Results
### Results of Applying AHP  
Analysis Procedure  
First, the items for the AHP analysis were established. The analysis items were those related to road subsidence among the data obtained from the sewer GIS data. They consisted of six influencing factors related to road subsidence based on the sewer pipe specifications analyzed above. The six factors are as follows:  
- Factor 1: Type of pipe  
- Factor 2: Pipe diameter  
- Factor 3: Pipeline age (installation year)  
- Factor 4: Drainage method  
- Factor 5: Status of roads around the buried pipe  
- Factor 6: Maintenance history  
A pairwise comparison scale was created using the items above and distributed to 25 experts (university professors and geotechnical researchers). The scale consisted of judging the relative importance of the selected indexes in a format that requires a pairwise comparison of the evaluation items. The relative importance (weight) and consistency index (CI) of each evaluation item were reviewed to analyze the survey. The RI in this study was 1.24 (\(n=6\)).  
The AHP analysis showed two cases where the CI was inadequate, as shown in Table 9. These two cases were excluded from further analysis to minimize the margin of error in the results. The results of 23 expert surveys were analyzed after excluding two surveys with a CI of 0.1 or higher.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multicolumn{3}{c}{**Predicted Value**} \\ \cline{2-5}  & **Road Subsidence** & **Road Subsidence** & **Classification** & **Accuracy (\%)** \\ \hline \multirow{3}{*}{Actual} & Road subsidence & \multirow{3}{*}{108} & \multirow{3}{*}{90} & \multirow{3}{*}{54.5} \\  & -not occurring & & & \\ \cline{1-1} \cline{3-5}  & Road subsidence & & & \\ \cline{1-1} \cline{3-5}  & -occurring & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Classification accuracy.  
To verify the weights calculated through the AHP analysis and the analysis method, the study examined the condition of sewer pipes and the actual incidence of road subsidence in urban areas in Korea. The total length of sewer pipes in the target area was 441,171 m. The results of assessing road subsidence risk conditions using the proposed Equation (6) are shown in Table 11.  
To check the applicability of the assessment results, sewer pipes near the section where road subsidence occurred in the pilot area were extracted for risk assessment.  
Figure 6 shows the results. Compared with the road subsidence risk assessment results of the entire sewer line, over 80% were categorized as E and F grades, indicating a high risk of road collapse. These results show that the proposed risk assessment method is effective in predicting road subsidence.
## 4 Discussion
### Limitations and Further Research Directions  
This study was limited by its cross-sectional design, which does not allow for the direct establishment of causal relationships. Mediation analyses based on cross-sectional data may yield biased estimates of indirect effects owing to the lack of temporal sequencing, potentially leading to inaccurate or misleading interpretations. Therefore, inferring longitudinal mediation processes from cross-sectional analyses should be approached cautiously.  
Despite these limitations, the cross-sectional design serves as an appropriate initial approach and provides valuable preliminary insight into the relationships under investigation. However, these findings highlight the importance of future research using a longitudinal design. Longitudinal data would allow for a more accurate understanding of temporal dynamics, provide clearer insights into mediation mechanisms, and strengthen the validity of the conclusions. While this study serves as a strong starting point, further research using longitudinal methods is essential to confirm and extend the preliminary findings.  
Another limitation of this study was the demographic composition of the sample, particularly the overrepresentation of female participants. This gender imbalance may limit the generalizability of the findings, as the effects of physical activity, self-control, and emotion regulation on life satisfaction may differ between genders owing to biological, psychological, or social differences. To address this issue, future research should prioritize recruiting a more diverse and gender-balanced sample to ensure that the results are more reflective of the general population. Additionally, exploring potential gender-specific patterns in the relationships among physical activity, self-control, emotion regulation, and life satisfaction could provide deeper insights and help in designing interventions that are sensitive to the unique needs of different demographic groups.  
Although self-reporting techniques are valuable, they have inherent limitations. The data collected through these methods may be affected by memory distortions, inclination to provide socially acceptable answers, and other social factors that can compromise the accuracy of self-reported information. Given these constraints, future research should incorporate additional methodologies along with self-reporting. Notably, research systems that leverage wearable devices show particular promise as methodological approaches worthy of further exploration in upcoming studies. Future studies should also investigate whether such a relationship exists in people who exercise for less than a year or who exercise irregularly.
## 2 Method
### Neighborhood Singular Value Decomposition Filter for CPwc  
We will theoretically describe the proposed NSVD filter in this section. For the point \(\mathbf{p}=(x_{p},z_{p})\), we create a matrix \(\mathbf{B}\) that is composed of signals from the neighbor region centered at point \(\mathbf{p}\). The matrix \(\mathbf{B}\) is defined as:  
\[\mathbf{B}=\begin{bmatrix}b_{-K,-L}&b_{-K+1,-L}&b_{-K+2,-L}&\cdots&b_{K,-L}\\ b_{-K,-L+1}&b_{-K+1,-L+1}&b_{-K+2,-L+1}&\cdots&b_{K,-L+1}\\ b_{-K,-L+2}&b_{-K+1,-L+2}&b_{-K+2,-L+2}&\cdots&b_{K,-L+2}\\ \vdots&\vdots&\vdotsa three-dimensional matrix can be obtained, and it is reshaped to form a two-dimensional matrix \(\mathbf{A^{\prime}}\) with dimensions \(((2K+1)*(2L+1),N)\). The SVD of \(\mathbf{A^{\prime}}\) is:  
\[\mathbf{A^{\prime}}=\mathbf{U^{\prime}}\mathbf{\Sigma^{\prime}}\mathbf{V^{\prime}}^{H}, \tag{7}\]  
where the diagonal matrix \(\mathbf{\Sigma^{\prime}}\) with dimension \(((2K+1)*(2L+1),N)\) contains singular values \(\lambda^{\prime}_{i}(\lambda^{\prime}_{1}>\lambda^{\prime}_{2}>\lambda^{\prime }_{3}>\cdots>\lambda^{\prime}_{N})\). \(\mathbf{U^{\prime}}\) and \(\mathbf{V^{\prime}}\) are with dimensions \(((2K+1)*(2L+1),(2K+1)*(2L+1))\) and \((N,N)\), respectively. By using the threshold \(\beta\), the matrix \(\mathbf{A^{\prime}}_{NSVD}\) of the decomposed image data in the neighbor region of the point \(\mathbf{p}\) are expressed as:  
\[\mathbf{A^{\prime}}_{NSVD}=\mathbf{U^{\prime}}\mathbf{\Sigma^{\prime}}_{\beta},\mathbf{V^{ \prime}}^{H} \tag{8}\]  
where \(\mathbf{\Sigma^{\prime}}_{\beta}\) is obtained by setting singular values \(\lambda^{\prime}_{i}\) with \(i\) larger than \(\beta\) at zero in the matrix \(\mathbf{\Sigma^{\prime}}\). Then, the spatio-angular signals in the neighbor region of the point \(\mathbf{p}=(x_{p},z_{p})\) is obtained by:  
\[s^{\prime}(x_{p}+\Delta x*k,z_{p}+\Delta z*l,\theta_{n})=\mathbf{A^{\prime}}_{NSVD }[i(x_{p}+\Delta x*k,z_{p}+\Delta z*l),j(\theta_{n})]. \tag{9}\]  
After NSVD filtering, the spatio-angular signal of the point \(\mathbf{p}=(x_{p},z_{p})\) is:  
\[s_{NSVD}(x_{p},z_{p},\theta_{n})=s^{\prime}(x_{p},z_{p},\theta_{n}). \tag{10}\]  
Finally, the CPWC output after the NSVD filter can be obtained by:  
\[S_{CPWC+NSVD}(x_{p},z_{p})=\frac{1}{N}\sum_{n=1}^{N}s_{NSVD}(x_{p},z_{p},\theta _{n}). \tag{11}\]
## 4 Results
### AI Enhances the Accuracy and Robustness of TCO Predictions in CWPs  
Regarding guiding theme 3--AI enhances the accuracy and robustness of TCO predictions in CWPs--we discovered that AI improves the accuracy and robustness of prediction models, which outperforms the traditional methods (e.g., autoregressive integrated moving average (ARIMA) [125], rule-based control (RBC) [49, 111, 127], proportional-integral-derivative (PID) control [1], and MPC [114]). For instance, [PERSON] [125] introduced a deep belief network (DBN)-based ensemble method for cooling load forecasting, outperforming traditional statistical approaches such as the ARIMA model. Their study showed that ARIMA struggles to effectively model nonlinear and non-stationary characteristics commonly observed in the cooling load data. The study by [PERSON] et al. [50] demonstrates the superiority of their LSTM and DRL models over the RBC, achieving a 3% reduction in electricity costs. They assessed the performance of the LSTM-DRL and RBC strategies using several key performance indicators (KPIs), including electricity cost, maximum peak, peak-to-average ratio (PAR), average daily peak, average daily PAR, and flexibility factor.  
Similarly, studies by [PERSON] et al. [111, 127] on AI-based MPC strategy, [PERSON] et al.'s [49] pre-trained LSTM and RL models, and [PERSON] et al.'s [30] ANN models all demonstrate enhanced performance against classical RBC and conventional baseline strategies. [PERSON] et al. [111, 127] integrated AI with MPC, resulting in improved performance for their method. These findings collectively underscore the effectiveness of integrating AI in refining these systems' predictive capabilities and operational efficiency.  
For the evaluation of these methods, several evaluation metrics have been used in AI-based studies, such as mean square error (MSE) [30], coefficient of determination (R\({}^{2}\)) [30, 49, 111, 127], root mean squared error (RMSE) [46, 49, 50, 111, 125, 127], mean absolute error (MAE) [125], mean absolute percentage error (MAPE) [50], and coefficient of the variation in the RMSE (CVRMSE) [46, 125, 127]. However, while comparisons with conventional or rule-based methods were made, a few studies did not provide specific numerical comparisons of the errors. For instance, [PERSON] et al. [49] compared their pre-trained LSTM and DRL models with conventional control methods such as PID control, MPC, and RBC using evaluation metrics such as RMSE and R\({}^{2}\). While they did not provide specific numerical comparisons, they noted that the DRL-LSTM model consistently outperformed the other control strategies across varying conditions. It was pointed out that the traditional statistical and non-data-driven methods, such as ARIMA and rule-based methods, have some limitations [125]. Therefore, researchers adopted AI-based algorithms [124] to enhance the predictive modeling of parameters that impact TCO in CWPs.
## 2 Data and Method
### Method
#### 2.3.3 Mann-Kendall Trend Assessment  
Trend significance was evaluated using a statistical rank-based nonparametric Mann-Kendall test [29, 30], which is a commonly used method to assess the significance of monotonic trends in long-term time series. It has the advantage of not assuming any distributional form for the data and has similar capabilities as its parametric counterparts. In this study, the time series of the calculated \(SRI\)s are analyzed to identify meaningful long-term trends using the Mann-Kendall statistics. In the Mann-Kendall test, the data are ranked with reference to time; each data point is treated as the reference for the data points in successive time periods [31]. The equation used to calculate the Mann-Kendall correlation coefficient (S) is defined by [PERSON] as  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}sign\big{(}SRI_{i}-SRI_{j}\big{)} \tag{4}\]  
where  
\[sign\big{(}SRI_{i}-SRI_{j}\big{)}=\begin{cases}1&if\ \ SRI_{i}-SRI_{j}<0\\ 0&if\ \ SRI_{i}-SRI_{j}=0\\ -1&if\ \ SRI_{i}-SRI_{j}>0\end{cases} \tag{5}\]  
Here, \(n\) is the length of the time series dataset and \(SRI_{i}\) and \(SRI_{j}\) are the observational stand regrowth index at times \(i\) and \(j\), respectively. According to [PERSON] and Kendall, the statistic S is approximately normal when \(n\geq 8\) with the mean and the variance as follows:\[\text{E (S)}=0 \tag{6}\] \[\text{Var}(S)=\frac{\text{n}(\text{n}-1)(2\text{n}+5)-\sum_{i-1}^{n}t_{i}( i)(i-1)(2i+5)}{18} \tag{7}\]  
where \(t_{i}\) denotes the number of ties of extent \(i\).  
The equation used to calculate Mann-Kendall significance (U and p) is as follows:  
\[\text{U}=\begin{cases}\frac{S-1}{\sqrt{\text{Var}(S)}}&\text{for}\quad S>0\\ 0&\text{for}\quad S=0\\ \frac{S+1}{\sqrt{\text{Var}(S)}}&\text{for}\quad S<0\end{cases} \tag{8}\]  
The U statistic follows the standard normal distribution with zero mean and unit variance under the null hypothesis of no trend. A positive U value indicates an upward trend; a negative value indicates a downward trend. The \(p\) value of an MK statistic \(S\) can then be determined using the normal cumulative distribution function  
\[p=2[1-\phi(|U|)] \tag{9}\]  
where \(\phi(|U|)\) denotes the cumulative distribution function of a standard normal vitiate.
## 4 Numerical Simulation  
### Geometry Model and Parameter Settings  
The battery model is divided into three parts: the PVC film, the steel shell, and the battery core. For thermal conductivity, the PVC film and steel shell are isotropic materials, and the battery core is an anisotropic material. The following four types of contact surfaces have coupled heat transfer relationships: the coolant liquid and aluminum alloy liquid cold plate, the aluminum alloy liquid cooling plate and thermal silica gel pad, the thermal silica gel pad and battery surface, and the battery core and aluminum alloy liquid cold plate. The relationship between the rest of the parts and the air in the box is a natural convective heat transfer relationship, which refers to a relationship between the shell and the external environment. The physical properties of the materials of each part of the model are shown in Table 3. The mesh system was generated in the commercial CFD software StarCCM+. A three-dimension finite element model of the packs' cooling system and batteries was then set up and is shown in Figure 7a.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Density kg/m\({}^{3}\)**} & **Specific Heat Capacity** & **Thermal Conductivity** \\  & & **J/(kg-K)** & **W/(m-K)** \\ \hline
50\% ethylene glycol & 1082 & 3300 & 0.4 \\ Cell material & 3270 & 1537 & 15.1(Axial)/1(Radial) \\ Surface steel & 7870 & 448 & 80 \\ Polyvinyl chloride film & 1140 & 1670 & 0.2 \\ Thermal silica gel pad & 2600 & 2190 & 1.5 \\ Aluminum alloy cooling plate & 2700 & 903 & 237 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical properties of the materials.  
The boundary conditions in this study are as follows:  
* A steady flow with a certain temperature at the inlet;
* Free flow at the outlet with zero pressure;
* A conjugated boundary at the solid-fluid interface;
* Uniform heat generation in the cells;
* Adiabatic at all other outer surfaces of the cells and pipes.
# Optimizing Sustainable Energy Transitions in Small Isolated Grids Using Multi-Criteria Approaches
## 4 Results and Discussion
### Scenarios Pre- and Post-Full Operation of the Hydro-Wind Power Plant
However, this shift introduced new operational dynamics, especially concerning the conventional diesel power plant. Diesel-plant-operating patterns have become more variable and less predictable. As depicted in Figure 9, there are frequent rises and falls in the plant's operation, characterized by numerous start-ups, shutdowns and fluctuations in output. These changes correspond to the inherently intermittent nature of wind energy and the need to balance supply with demand. When wind conditions are favorable, wind turbines generate electricity, reducing the need for diesel power. Conversely, when wind speeds drop, the diesel generators must quickly ramp up to compensate for the shortfall. A summary of the system performance is shown in Table 5.  
The integration of the wind-hydro plant into the system has led to a reduction in the efficiency of the traditional plant, along with an increase in fuel consumption. This increase amounts to 39.4% of specific consumption, resulting in a drop in plant efficiency 9.4 points from 33.55% to 24.08% [44]. The inefficiency and increased fuel consumption translate directly into higher variable costs. Higher fuel consumption directly leads to an increase in CO2 emissions compared to the expected. Even though the wind-hydro system is designed to reduce dependence on fossil fuels, the inefficiencies introduced by the need to balance intermittent wind energy with diesel generation result in a net increase in emissions. Consequently, despite the expectation that emissions and costs would significantly decrease with the installation of the renewable plant, the data showed that these reductions were not as substantial as anticipated. This is attributed to the considerable drop in diesel performance. The performance issues are due to the continuous start-stop maneuvers required to adjust its generation to the renewable plant's intermittent production. Furthermore, the overall efficiency of the reverse pumping cycle is not within the generally discussed interval of 70-80% [68], but rather significantly lower, around 30-40% [44]. All these issues are shown in Table 5, which offers a comprehensive summary of the main features of the current generation system as simulated for 2023, considering the efficiency decrease of the conventional power plant. It comprises detailed data on installed power, electric generation, capacity factors, and costs for the diesel power plant, wind, and reverse pumping components. Figure 9: Real power system performance of El Hierro Island (**1**) compared to that forecast by the EnergyPlan code (**2**) during representative weeks of: spring–fall (**a**), summer (**b**) and winter (**c**) (year 2023).
## 3 Methodology
### Datasets and Descriptions  
The dataset used in this paper was obtained from the ENEA CRESCO6 cluster, a data center consisting of 434 computing nodes with 20,832 cores. Each node consists of two Intel Xeon Platinum 8160 CPUs (represented as CPU1 and CPU2), each with 24 cores and a total of 192 GB of RAM, corresponding to 4 GB/core and operating at a clock frequency of 2.1 GHz. The ENEA CRESCO6 cluster has been operating since 2018. The cluster has a nominal computing power of 1.4 PFLOPS and is based on the Lenovo Think System SD530 platform. The nodes are linked with an Intel Omni-Path network and have 15 switches of 48 ports each at 100 Gb/s with a latency equal to 1 \(\upmu\)s of bandwidth. For monitoring and management purposes, each computing server/node of CRESCO6 is instrumented with onboard sensors. These sensors read the values of the operational parameters of the equipment for the entire calculation server/node. These sensors monitor temperatures at
# Evaluation of _Aspergillus flavus_ Growth on Weathered HDPE Plastics Contaminated with Diesel Fuel
## 3 Results
### Fungal Growth Dynamics on Plastic Substrates
#### 3.3.5 Influence of Incubation Temperature on Fungal Colonization  
Incubation temperature had a decisive impact on the behavior of _Aspergillus flavus_, affecting both the mycelial growth area and the formation of visible extracellular metabolites (halos). In general, a trend of increasing mycelial development was observed as incubation temperature rose from 20 \({}^{\circ}\)C to 30 \({}^{\circ}\)C, particularly on substrates containing weathered diesel.  
In the gray plastic matrices contaminated with weathered diesel, increasing temperature led to progressive increases in the average colony area--from 78.39 mm\({}^{2}\) at 20 \({}^{\circ}\)C, to 74.19 mm\({}^{2}\) at 25 \({}^{\circ}\)C, and up to 355.8 mm\({}^{2}\) at 30 \({}^{\circ}\)C. Similarly, halo expansion followed this trend, reaching a maximum area of 3109.7 mm\({}^{2}\) at 30 \({}^{\circ}\)C. These results suggest a positive synergy between elevated thermal conditions and the presence of partially degraded contaminants, which were likely more bioavailable as carbon sources.  
In contrast, white plastic samples contaminated with non-weathered diesel exhibited severely limited growth at all temperatures. This matrix proved the least favorable for _A. flavus_, with consistently low colony areas (typically under 50 mm\({}^{2}\)) and erratic halo formation. The lack of diesel weathering, combined with the potential inhibitory effects of HDPE additives typical in fuel storage tanks, may have created chemically adverse conditions for both mycelial colonization and enzymatic expression.  
The control samples (uncontaminated gray plastic) displayed moderate and stable growth. Although the average colony area increased with temperature (from 42.3 mm\({}^{2}\) to 103.15 mm\({}^{2}\)), halo formation was variable but significant, indicating metabolic activity even in the absence of hydrocarbons. This highlights the capacity of PDA medium to support baseline fungal growth, as well as the influence of thermal gradients on fungal physiology.  
In summary, incubation temperature acts as a critical modulating factor, but its effects depend strongly on the substrate type and the weathering status of the contaminant. A temperature of 30 \({}^{\circ}\)C was consistently the most favorable for _A. flavus_ growth in the presence of weathered diesel, whereas temperature effects were less pronounced or even adverse in matrices containing commercial diesel.
# Research on Satellite Network Traffic Prediction Based on Improved GRU Neural Network
## 5 Results Simulation and Analysis
### Evaluation Index and Parameter Setting of Simulation  
#### 5.3.1 Evaluating Indicator  
In this paper, the average absolute error MAE, root mean square error RMSE, and goodness of fit \(R^{2}\) are selected to evaluate the proposed prediction model:  
\[\text{MAE}=\frac{1}{N}{\sum\limits_{i=1}^{N}}|y_{i}-\widetilde{y}_{i}|, \tag{24}\]  
\[\text{RMSE}=\sqrt{\frac{1}{N}{\sum\limits_{i=1}^{N}}(y_{i}-\widetilde{y}_{i} )^{2}}, \tag{25}\]  
\[R^{2}=\frac{\Sigma(\widetilde{y}_{i}-\overline{y})^{2}}{\Sigma(y_{i}- \overline{y})^{2}}, \tag{26}\]  
\[\overline{y}=\frac{1}{N}{\sum\limits_{i=1}^{N}}y_{i}, \tag{27}\]  
where \(N\) is the number of training samples, \(y_{i}\) is the actual value, and \(\widetilde{y}_{i}\) is the predicted value of the model. RMSE can be used to reflect the change amplitude of data. The smaller the value is, the higher the accuracy of the model prediction will be. MAE is not easily affected by outliers, and the smaller the value, the smaller the error. \(R^{2}\) is used to evaluate the fitting effect of the model, and the value is (0, 1). The closer the value is to 1, the better the fitting effect of the model is.