esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study used a diagnostic model, BEPS, to simulate global NEP during 1982-2016, and combined climate data and contribution index to explore the regional contribution and the role of climate variables in regulating the NEP IAV of the global terrestrial ecosystem. We found that forests have the largest IAV compared to other vegetation types on a global scale. Furthermore, the EBF contributed the most to global NEP IAV, suggesting EBF not only plays a vital role in the global carbon sink but also plays a critical role in regulating global NEP IAV. In addition, this study indicated that the contributions of temperature to the global terrestrial NEP IAV were larger than precipitation and solar radiation. However, the correlation between NEP and temperature was negative, and the correlation with precipitation was positive, indicating that global warming is not conducive to forming a global carbon sink, and abundant rainfall could facilitate the formation of a carbon sink in global terrestrial ecosystems. Therefore, we suggest that we need to pay more attention to the tropics, especially EBF, and focus on temperature and precipitation anomalies in projecting global carbon cycle feedback on future climate change.  
Conceptualization, M.W. and J.Z.; methodology, S.W.; validation, B.C.; formal analysis, M.W.; resources, S.W.; data curation, B.C.; writing--original draft preparation, M.W.; writing--review and editing, J.Z. and S.W.; supervision, J.Z.; project administration, J.Z.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Collaborative Innovation Project of High-Quality Development of Agriculture in Fujian Province, grant number XTCXGC2021015 and XTCXGC2021021, Natural Science Foundation of Fujian Province, grant number 2021J01502, and Fujian Intelligent Agricultural Science and Technology Innovation Team (CYTD2021013-1).  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** We greatly appreciate [PERSON] of Nanjing University for providing the monthly global NEP datasets.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Preventive Maintenance Decision-Making Optimization Method for Airport Runway Composite Pavements
## 3 Results and Discussion
### Maintenance Benefits of Preventive Maintenance Maintenance Technologies  
#### 3.2.1 Performance Jump  
Figure 5 plots _PI-PCI_\({}_{b}\) fitting curves, regression models (Equation (9)), and coefficients of determination \(R^{2}\) for the five maintenance methods. The relevance between _PJ_ and _PCI_\({}_{b}\) is higher than 0.87 when composite pavements are treated with the five maintenance technologies, which, in order of increasing relevance, are crack sealing, crack filling, fog seal, thin HMA overlay, and hot-mix recycled AC. According to the five regression models of _PJ_ and _PCI_\({}_{b}\), the improvements in composite pavement performance after treatment with the different maintenance technologies could be calculated.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Region of the Airport** & **Airport Code** & \(\alpha\) & \(\beta\) \\ \hline \multirow{4}{*}{Middle runway} & CZX & 167.6163 & 0.3366 \\  & DLC & 125.9302 & 0.3105 \\  & TAO & 92.5086 & 0.2906 \\  & XMN & 114.7053 & 0.3041 \\  & SHA & 104.9870 & 0.3287 \\ \hline \multirow{4}{*}{Terminal runway} & DLC & 187.4970 & 0.3074 \\  & TAO & 133.8690 & 0.2955 \\  & XMN & 160.0943 & 0.2944 \\  & SHA & 140.8225 & 0.3094 \\ \hline \multirow{4}{*}{Taxiway} & CZX & 478.9010 & 0.2529 \\  & DLC & 264.9978 & 0.2108 \\ \cline{1-1}  & TAO & 196.3031 & 0.2037 \\ \cline{1-1}  & XMN & 226.6756 & 0.2021 \\ \hline \end{tabular}
\end{table}
Table 2: \(\alpha\) and \(\beta\) in _PCI_ regression curves.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Region of Airport** & \(a_{1}\) & \(b_{1}\) & \(c_{1}\) & \(a_{2}\) & \(b_{2}\) & \(c_{2}\) \\ \hline Middle runway & 23.9802 & 0.8943 & \(-\)0.2087 & 194.3201 & \(-\)2.8224 & \(-\)0.0845 \\ Terminal runway & 24.1403 & 0.8423 & \(-\)0.2246 & 183.1502 & \(-\)2.1321 & \(-\)0.0912 \\ Taxiway & 31.9805 & 0.8217 & \(-\)0.2456 & 171.2804 & \(-\)1.8321 & \(-\)0.0972 \\ \hline \multirow{2}{*}{**Region of Airport**} & \multicolumn{4}{c}{**Regression Coefficients**} \\  & \(a_{3}\) & \(b_{3}\) & \(c_{3}\) & \(a_{4}\) & \(b_{4}\) & \(c_{4}\) & \(d\) \\ \hline Middle runway & 1.1020 & \(-\)0.2411 & \(-\)0.0803 & 0.0453 & 0.3349 & \(-\)0.0255 & \(-\)0.0981 \\ Terminal runway & 1.4210 & \(-\)0.2121 & \(-\)0.0942 & 0.0557 & 0.3144 & \(-\)0.0211 & \(-\)0.0975 \\ Taxiway & 1.5691 & \(-\)0.2033 & \(-\)0.0987 & 0.0418 & 0.3578 & \(-\)0.0311 & \(-\)0.0985 \\ \hline \end{tabular}
\end{table}
Table 3: Regression coefficients.
# Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation
## 2 Background
### Data-Loading Pipeline  
In order to leverage GPUs for model training, data have to be prepared and grouped into batches to feed to the model during training iterations. The data-loading pipeline ingests raw data, preprocesses them through transformations, and then iteratively feeds batches of data to the deep learning model for training. The pipeline also monitors the data flow and logs relevant statistics throughout the training process.  
PyTorch's data-loading pipeline exhibits a modular design, providing flexibility in how datasets are accessed and prepared for deep learning models. Due to the expressive and flexible nature of this design, we use PyTorch to implement our approach. The data-loading pipeline in PyTorch comprises three core components that interact to achieve scalable training iterations:  
* __getitem__: Implemented within a custom Dataset subclass, the __getitem__ method is responsible for retrieving a single data sample (often a feature-label pair) and performing any necessary preprocessing on the raw data.
* __len__: Also defined within the Dataset class, __len__ returns the total number of samples within the dataset. This information is crucial for the DataLoader to determine how many iterations are needed per epoch.
* collate_fn: The collate_fn, a function passed to the DataLoader, dictates how individual samples retrieved via __getitem__ are aggregated into batches. Default stacking behavior can be overridden with custom collate_fn implementations to introduce optimizations or accommodate unique data structures.  
Collectively, these components orchestrate the seamless transition from raw data within a dataset to model-ready batches, enabling efficient training of PyTorch deep learning models. This modular design allows for scalability across devices via distributed training techniques supported by the framework. In the next subsection, we will describe the necessity and functionality of different distributed training techniques used for large-scale model training.  
Figure 2: Default batch formation steps.
## 4 Results and Discussion  
### Geochemical Background Values and Baseline Values  
The results of the geochemical background values and baseline values for the desert samples in the study area are shown in Appendix A Tables A1 and A2.  
The major elements that show significant differences can be categorized into three groups: SiO\({}_{2}\), which is related to changes in the quartz background values; CaO, which reflects changes in the carbonate background values; and MgO, whose variation is associated with changes in both carbonate and ferromagnesian silicate background values. The Hunshandak, Horqin, and Hulun Buir desert sands are characterized by high SiO\({}_{2}\) and low CaO, whereas the Kumtag, Gurbantunggut, Taklimakan, and Qaidam desert sands are mainly characterized by low SiO\({}_{2}\) and high CaO. The Badain Jaran, Ulan Buhe, Mu Us, Hobq, and Tengger desert sands have SiO\({}_{2}\) and CaO background values that fall between these two extremes (Appendix A Tables A1 and A2; Figure 3a).  
From the analysis of the background values of trace elements (Figure 3c), it can be seen that the UCC-standardized values of trace elements in the Kumtag, Gurbantunggut, Taklimakan, and Qaidam desert sands are higher than those in other desert sands. Some trace elements (Cd, Cl, S, N) in these deserts are even higher than the upper continental crust abundance [50]. In contrast, the standardized values of most trace elements in the Hunshandake, Horqin, and Hulun Buir desert sands are lower than those in other deserts. The trace element standardized values in the Badain Jaran, Ulan Buhe, Mu Us, Hobq, and Tengger desert sands fall between these two extremes.  
From the analysis of the background values of rare earth elements (Figure 3a), the UCC distribution pattern plot of rare earth element background values in the deserts show a flat trend [51; 52; 53; 54]. This indicates that the rare earth element distribution patterns in the desert sands of the study area are similar to those of the upper continental crust (UCC), and the trend in the normalized values is almost consistent with that of the major and trace elements.  
According to the ratio charts of element background values to baseline values for China's deserts (Figure 4), most of the background values for various element indicators in different desert sands are close to their baseline values, indicating that the majority of the data fall within the normal natural range. Some element indicators (such as MgO, TFe\({}_{2}\)O\({}_{3}\), CaO, and TC) are relatively enriched in the Kumtag, Taklimakan, and Qaidam deserts, while they are relatively depleted in the Hunshandak, Horqin, and Hulun Buir deserts. A few elements (such as Cl and S) show significant enrichment in the Kumtag, Gurbantunggut, Taklimakan, and Qaidam deserts [4].  
Figure 4: Ratio of Geochemical Background Values to Baseline Values in Chinese Deserts.  
Figure 3: Element Standardized Plots. (**a**). Element Standardized Plots of Major Element; (**b**). Element Standardized Plots of Trace Element; (**c**). Element Standardized Plots of Rare Earth elements.
## 5 Discussion
### Farmer's Irrigation Practice Variability  
One additional reason for missed or false negative detections of irrigation events is the variation in farmers' irrigation practices, particularly in terms of timing and quantity. It is likely that the accurate detection of irrigation is hindered because farmers do not always follow the irrigation decisions based on a soil water balance and exact crop water requirements or decisions that could be advised by Optirrig, including triggers, criteria, and doses. Instead, their irrigation decisions are influenced by a combination of factors, including their experience, local knowledge, and perceptions of water availability. They may prioritize ensuring an adequate water supply to the crops, leading to irrigation amounts that exceed the exact crop water requirements. For instance, it appeared to be challenging to detect the last irrigation applied on 26-Sep (DOY = 269) over the P1 plot. This irrigation event may not be deemed necessary from the water balance perspective of Optirrig, but farmers still apply it for reasons such as grain filling or to prevent soil crusting [98]. On the other hand, Optirrig relies on a soil water balance equation to simulate and predict the soil moisture content in the root zone of the crops. This equation takes into account factors such as precipitation, evapotranspiration, soil characteristics, and irrigation events to estimate the water balance in the soil. Therefore, when testing different irrigation dates and amounts, Optirrig determines the exact SSM induced by the irrigation and other water sources (rainfall), which may not align precisely with farmers' irrigation timing and the amounts they apply. In the present study, we consistently noticed that the detected irrigation amounts in the four plots (P1, P2, P3, and P4) were consistently lower than the actual irrigation amounts applied by the farmers in the field. This indicates a case of underestimation or missed detection of irrigation events. This discrepancy between farmers' actual irrigation practices and the detected irrigation amounts can explain the low accuracy of detecting real irrigation amounts (which is somehow expected as Optirrig is used here as a tool for analysis, not for recommendations). Farmers tend to overestimate crop water requirements and apply higher levels of irrigation that do not match the exact crop water needs.
# Application and Evaluation of Energy Conservation Technologies in Wastewater Treatment Plants
## 2 Green Energy Utilized in WWTPs
### Solar Energy and Photovoltaic (PV) Energy  
Solar energy refers to the solar radiation that reaches the earth in the form of the electromagnetic waveform. As an important energy green resource, solar energy is abundant, renewable, clean, safe and easy to access. Solar energy is one of the most widely used green energy sources in WWTPs [48]. The form of solar energy used in WWTPs is shown in Table 2.  
Solar PV power generation is a method of converting light energy into electric energy through semiconductor materials. With the improvement of solar energy conversion efficiency and the reduction of the cost of solar panels, PV is becoming more and more widespread used. Many WWTPs use PV to supply electricity to the treatment plant because their aeration tanks and other places have a huge available space for PV equipment. A 100-kW photovoltaic system was installed on the roof of the Kiheung Respia WWTP in Korea, which was estimated to produce a maximum of 155 MW h/year [44]. However, short lifespan and high construction costs of PV equipment limit the development of PV in WWTPs.  
Solar energy disinfection mainly uses ultraviolet rays in sunlight to kill bacteria in wastewater [49]. It is noteworthy that PV power generation mainly uses visible light, so some WWTPs directly combine PV power generation with ultraviolet disinfection [51]. In solar artificial wetlands powered by PV or solar heat, wastewater is discharged into the wetland for physical and biological treatment. Although the technology is simple and its cost (investment and operation) is low, it cannot deal with much wastewater. Advanced oxidation processes (AOPs) are chemical reactions that oxidize pollutants are removed via oxidation by highly reactive radicals [54]. It can degrade natural toxins, emerging pollutants, pesticides, and other harmful pollutants efficiently. The application of solar energy in AOPs mainly includes solar Fenton and photocatalytic oxidation. [PERSON] et al. assessed AOPs and found that AOPs mainly depend on the consumption of electric energy. They found that the use of solar energy can reduce environmental impacts by 90% [55]. Pretreatment technology, especially preheating, has attracted much attention in water treatment. Pretreatment can be used for wastewater treatment technologies like anaerobic digestion and reverse osmosis [56; 57]. Solar heating can be used as a part of pretreatment, which can save energy that should be consumed in the original process.
## 2 Experimental
### Preparation, Quality Tests and Physical Properties
#### 2.1.1 Surface Area and Particle Size  
Nitrogen adsorption test was utilized to identify the average surface area and the distribution of size of the particles of the carbon nanoparticles, and the result has been represented in Figure 2. The higher surface area, the higher the TP. Prior to the adsorption experiments, samples were degassed at 140 \({}^{\circ}\)C for 10 h under vacuum pressure of \(10^{-5}\) torr. Nitrogen adsorption isotherms were plotted using a Tristar instrument (Micromerities, Norcross, Georgia, GA, USA) at liquid nitrogen temperature (\(\sim\)\(-196\)\({}^{\circ}\)C). An ultra-high purity nitrogen and helium (\(>\)99.999 %) were used for the adsorption and the dead volume measurements, respectively. The Brunauer-Emmett-Teller (BET) method was employed for specific surface area evaluation [34]. The quenched solid density functional theory method (QSDFT) [35], was utilized to evaluate the particle size diameter, (PSD) for the CNP sample. This figure exhibits a Type II isotherm based on International Union of Pure and Applied Chemistry, IUPAC classification representing the presence of mesoporous structure with negligible contributions from microspores. It also shows a very narrow Type H3 hysteresis loop, representing possible aggregates particles. The calculated BET equivalent surface area and total pore volume of the sample are relatively small (8.8 m\({}^{3}\)/g and 0.027 cm\({}^{3}\)/g, respectively), indicating small porosity of the sample. Total pore volume (V\({}_{\text{P}}\)) was calculated based on nitrogen amount adsorbed at near saturation pressure converted to liquid volume. The PSD for the CNP sample is shown in Figure 3. The distribution is multimodal, showing a broad range of 4-50 nm. The intensity of the distribution curve is very small. This low intensity and broad distribution of pores confirm that the available surface area is primarily due to the particle's external surface rather than its internal porosity.  
Figure 4 shows the results of particle size test for the carbon nanoparticles. Analysis of the particle size test showed that the average particle size for the carbon nanoparticles dispersed in acetone was 50 nm. This also further confirms the size claimed by the manufacturer. This also minimizes the possibility of the deposition and scale formation within the solar collector as well.  
Figure 1: Preparation and dispersion of the carbon nanoparticles in acetone in four steps.  
Figure 2: Nitrogen adsorption isotherm obtained for the carbon nanoparticle sample.
# Prediction of Swelling Index Using Advanced Machine Learning Techniques for Cohesive Soils
## 2 Materials and Methods
### Sensitivity Analysis  
To answer the question \"Which input variables have the most or less influence on \(C_{s}\) in the proposed model?\", a sensitivity analysis has been carried out using the step-by-step method [58]. In this approach, the normalized input neurons vary at a constant rate, one at a time, while the other variables are held constant. Different constant rates (0.3, 0.6 and 0.9) are selected in the current study. For every input, the percentage of change in the output, as a result of the change in the input, is recorded. The sensitivity of each input is computed based on Equation (18):  
\[\text{Sensitivity level of }X_{j}(\%)=\frac{1}{K}\sum_{i=1}^{K}\left(\frac{\% \text{ {change in output}}}{\%\text{ {change in input}}}\right)_{i} \tag{18}\]  
where \(K\) is the number of data sets used in the study (\(K=875\)). The results of the sensitivity analysis of proposed _FS-RF_ model are shown in Figure 8. It can be noticed that \(C_{s}\) is significantly influenced by _WL_, and its sensibility ratio is between 32-38%. This parameter is closely followed by the _PI_, which gives a moderate sensitivity and is ranked second. In addition, \(W\) and \(Y_{d}\) have little effect on \(C_{s}\).  
Figure 8: Results of the sensitivity analysis of the proposed model.
## 4 Experiments and Analysis of Results
### Ablation Experiment
Deleting \(MGMI-CEHCN^{M-G}\) and \(MGMI-CEHCN^{M-I}\) was found to significantly degrade all metrics, but for the Amazon dataset, deleting \(MGMI-CEHCN^{M-I}\) did not have a significant impact on the performance, and this was probably because Amazon's average length is shorter relative to the other two datasets, and we believe that long sessions may have a higher signal-to-noise ratio relative to short sessions, because they provide more data points to distinguish between the user's real interest behavior and noise. By analyzing long sessions, short-term interests or incidental behaviors can be better filtered out, leading to more accurate modeling of user interests.
## 5 Results
### Stress-Dependent Fracture Aperture  
As the core sample is subjected to externally applied stress, the fracture aperture is expected to decrease due to the vertical displacement. The fracture aperture's vertical displacement changes spatially along the core radius, i.e., the vertical displacement is less at the core's outer surface and is at a maximum at the core center. These spatial variations in the vertical displacement (\(\delta\)) along the core radius are depicted in Figure 7a, where x/\(X\) = 0 represents the outer surface of the core and x/\(X\) = 1 represents the core's center. As expected, the vertical displacement of the fracture aperture increases when moving inwards toward the core center. It is worth mentioning that the fracture aperture decreases as the external stress increases. The figure clearly shows this behavior, i.e., the rate of increase in vertical displacement is relatively higher along the outer edge of the aperture and gradually decreases towards the core center, where the vertical displacement is at a maximum. This trend has been observed for all the different stress-loading conditions considered. From the un-stressed condition where the (uniform) fracture aperture is 130 \(\upmu\)m when an external stress of 6.9 MPa is applied, the average vertical displacement of the fracture region recorded is 21.54 \(\upmu\)m, which means that the new mean fracture aperture (h_mean) is 108.46 \(\upmu\)m. When increasing the stress loading to 9 MPa, 11 MPa, 13.1 MPa, 15.2 MPa, and 17.2 MPa, the vertical displacement increases to 25.12 \(\upmu\)m, 28.05 \(\upmu\)m, 30.74 \(\upmu\)m, 33.14 \(\upmu\)m and 35.19 \(\upmu\)m, respectively. Correspondingly, the mean fracture aperture reduces to 104.88 \(\upmu\)m, 101.95 \(\upmu\)m, 99.26 \(\upmu\)m, 96.86 \(\upmu\)m, and 94.81 \(\upmu\)m, as shown in Figure 7b.  
Figure 7: Variations in (**a**) vertical displacement of the fracture aperture along the core radius and (**b**) mean fracture aperture for different stress loadings.