esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermodynamic Analysis of Partitioned Combined Cycle using Simple Gases
## 4 Result and Discussion
### Heat Capacity Rates  
Heat capacity rate data are provided in Figure 8. As per the surface plots, the heat capacities of argon, air, nitrogen, carbon dioxide, and hydrogen at the turbine inlet were 0.5229 kJ/kg \({}^{\circ}\)C\({}^{-1}\), 1.228 kJ/kg \({}^{\circ}\)C\({}^{-1}\), 1.255 kJ/ kg \({}^{\circ}\)C\({}^{-1}\), 1.365 kJ/ kg \({}^{\circ}\)C\({}^{-1}\), and 16.27 kJ/ kg \({}^{\circ}\)C\({}^{-1}\), respectively. Higher heat capacity translates into lower flow rates and thus low volumes to be handled by individual GT components. If the heat capacity value is high, higher capacity rates are available before the turbine, and thus more power can be extracted. Such numbers are interpreted by calculating the slopes of the Brayton cycle lines with respect to temperature and pressure. The conclusion can be generalized from Table 2. Lesser slopes 'dCP/dT' and 'dCP/dP' in the compressor than in the turbine are beneficial to the overall efficiency of the gas turbine. This provides an advantage in better heat/work recovery, although the difference in slopes is most suitable in the case of argon but its high viscosity results in higher-pressure drops. Furthermore, very low heat capacity rates cause very low temperatures at the Brayton cycle turbine outlet. This then requires extra combustion for the Rankine cycle to  
Figure 7: Viscosity change for different fluids shown as 3D surface plots. Black lines represent the viscosity profiles of the partitioned Brayton cycle at 92.7% adiabatic efficiency.  
work, causing an abrupt drop in combined cycle overall efficiency (Equation (2)). On the other hand, hydrogen promises less pressure drop as well as a considerable heat capacity advantage and suitability for Rankine cycle workability. Nitrogen is better than air but carbon dioxide offers the worst-case scenario. The same also translates to the heat capacity rates for different gasses, as calculated by the optimization algorithm. The Prandtl number also becomes small with lower heat capacity values, and thus it requires a large exchanger/combustor area to avoid a Carnot efficiency penalty, as in the case of argon.  
Carbon dioxide offers high heat transfer in the combustion chamber, but its heat capacity drastically drops with a decrease in pressure. Thus, it provides the poorest heat transfer in the HRSG. This offers a considerable draw back. In the literature, carbon dioxide is extensively used at super critical conditions to avoid this.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Gas** & \multicolumn{2}{c}{**Argon**} & \multicolumn{2}{c}{**Nitrogen**} & \multicolumn{2}{c}{**Hydrogen**} & \multicolumn{2}{c}{**Carbon Dioxide**} & \multicolumn{2}{c}{**Air**} \\
**Equignment** & **Compressor** & **Turbine** & **Compressor** & **Turbine** & **Compressor** & **Turbine** & **Compressor** & **Turbine** \\ \hline dcOp(dT \(\times 10^{-4}\)) & 0.0103 & 0.0221 & 1.92 & 1.35 & 13.7 & 16.4 & 7.29 & 1.75 & 2.17 & 1.24 \\ dcOp(dP \(\times 10^{-4}\)) & 0.0029
## 1 Introduction
[13], [PERSON] and [PERSON] [14], as well as [PERSON] and [PERSON] [15] put forth constitutive models for the source and sink terms that compose the IATE. Subsequently, [PERSON] and [PERSON] [16] evaluated the performance of the bubble number density transport equation with the constitutive models in the simulation of adiabatic air-water flows. [PERSON] and [PERSON] [17] and [PERSON] et al. [18] attempted to apply the S\({}_{\gamma}\) model, which is defined as a generalized moment parameter applicable to air-water and subcooled boiling flows. Additionally, [PERSON] et al. [19] evaluated two types of population balance models, namely, the inhomogeneous multi-size group and average bubble number density models, for air-water flows. However, these mechanistic models require various additional and complicated models for the source and sink terms in the transport equations, and a large amount of calculation time in the CFD application. Meanwhile, previous researchers have presented correlation-type bubble size models that assume bubble size as one group [20; 21; 22; 23]. Furthermore, a two-group model consisting of spherical and slug bubbles has been proposed [24; 25; 26; 27][PERSON] Because these models have been developed on the basis of the average two-phase parameters and properties in the cross-section of the flow channel, they are only applicable to one-dimensional analysis[PERSON]  
In a bubbly flow, we can assume a one-group bubble size. A correlation-type bubble size model provides advantages for practical CFD applications because of its simplicity. In this study, we propose a simple semi-theoretical correlation generated on the basis of a steady-state fully developed bubble number density equation to precisely predict the local bubble diameter for a CFD application. The coefficients of this correlation were determined based on three air-water bubbly flow experiments performed in a vertical pipe under low pressure conditions. For the development of the new correlation, data not provided for the experiments, such as turbulent dissipation rate and relative velocity, were obtained from the CFD calculation. Finally, the proposed correlation and the existing correlations were evaluated against four air-water experiments under low pressure conditions.
## 3 Results
### Model Usability, Performance, and Transferability
#### 3.3.3 Model Transferability  
Here, we discuss model transferability. In this section, we assess the effort required to transfer the models to new locations, new computer systems, or new users. All three are important logistical criteria for effective model deployment.  
1. _Model generalization_ Both models appear to generalize effectively to different forested regions of the world, as both have shown strong performance in this study and others. No clear winner is evident in this regard. In terms of model realism, PPA-SiBGC has a more realistic representation of forest canopies while LANDIS-II NECN has more realistic processes, as it is a Century model variant.
2. _Availability of parameterization data_ While LANDIS-II NECN requires substantially greater parameterization data compared to PPA-SiBGC, it may often be possible to rely on previously published parameters. Meanwhile, the growth, mortality, and fecundity parameters used by PPA-SiBGC are easy to calculate using common field inventory data. PPA-SiBGC is simpler to transfer in this regard given the wide availability of forest inventory data.
3. _Size of the program_ PPA-SiBGC is approximately 500 lines of R code, while LANDIS-II NECN is estimated at 0.5 million lines of C# code.
4. _Cross-platform support_ While Linux support may soon be supported with Microsoft.NET Core, LANDIS-II NECN is written in C# and is thus limited to Microsoft Windows platforms. Meanwhile, PPA-SiBGC is written in standard R code and is fully cross-platform.
5. _Ease of training new users_ While both models have a learning curve, the practical simplicity of PPA-SiBGC may make it easier to train new users. While LANDIS-II NECN contains more mechanistic processes and related parameters, these come at the cost of confusing new users. The model wrapper library we developed as part of this work vastly eases the operation of both models. Future studies should measure the time required for new users to effectively operate both models.
# Exploring the Spatiotemporal Alterations in China's GPP Based on the DTEC Model
## 3 Results
### Spatiotemporal Variation Characteristics of Chinese Terrestrial Ecosystem GPP  
The DTEC model showed a significant increasing trend from 2001 to 2020 (Figure 4j), but there were fluctuations between years. The minimum value occurred in 2001, and the maximum value occurred in 2018. The average annual increase is 9.77 g C m\({}^{-2}\) yr\({}^{-1}\) (\(p<0.001\)). The GPP growth trends of the nine regions passed the significance test, and all showed a very significant increase (Figure 4). Among them, the NC region had the largest growth rate of 20.65 g C m\({}^{-2}\) yr\({}^{-1}\), and the lowest growth trend was in the TP region, at 2.03 g C m\({}^{-2}\) yr\({}^{-1}\).  
The spatial distribution of the multiyear average GPP in China from 2001 to 2020 is shown in Figure 5a. According to the colour of the spatial distribution of the average GPP value from 2001 to 2020, the high values were mainly distributed in the eastern  
Figure 4: Annual average trends of GPP in China as a whole and in each ecological region from 2001 to 2020, with different colours representing different ecological regions. Among them, NEC (**a**) represents Northeast China, IM (**e**) represents Inner Mongolia, NWC (**g**) represents Northwest China, NC (**c**) represents North China, CC (**i**) represents Central China, TP (**f**) represents the Tibetan Plateau, SEC (**b**) represents Southeast China, SC (**d**) represents South China, SWC (**h**) represents Southwest China, and Total (**j**) represents the overall trend of China.  
region, and the low values were mainly distributed in the western region. The overall trend decreased from the southeast to the northwest and from the coast to the interior. The areas with increasing GPP trends in China from 2001 to 2020 were 67.29%, approximately 6.46 million km\({}^{2}\), and the areas with decreasing trends were 32.71%, approximately 3.14 million km\({}^{2}\). The results revealed that the trend of GPP change differed among the different regions, and in general, the area with an increasing trend of GPP in China was larger than the area with a decreasing trend. Among them, the decreasing regions were mainly in NWC, IM, TP, and SEC, and the increasing regions were mainly in SC, CC, NC, and SWC. According to the significance test results, 43.08% of China's regional GPP changes were nonsignificant, and 56.92% of the area passed the significance test; within that range, the percentages of the areas with very significant increases, significant increases, very significant decreases, and significant decreases were 47.88%, 8.23%, 0.24%, and 0.57%, respectively (Figure 5b).
# Modelling Electricity Consumption in Cambodia Based on Remote Sensing Night-Light Images
## 5 Discussion
### Limitations Analysis  
(1) The proposed denoising method required high-quality night-light images from 2019. If the quality of the images from 2019 was poor, the overall denoising effect could be  
Figure 6: Spatial distributions of electricity consumption in Cambodia in years (**a**) 2012, (**b**) 2015, and (**c**) 2018.  
poor. In addition, recent years are not as good at denoising as previous years, e.g., 2017 is better at denoising than 2018 and 2018 is better than 2019.  
(2) The data used in this work were only available until 2019, so the latest years were not included.  
(3) As Cambodia is located in a cloudy region at low latitudes, many of its daily night-light images are heavily obscured by clouds and were unusable. Meanwhile, monthly images are only affected by cloud for some months of the year. Only December was cloud-free in all years. This means that the estimation of electricity consumption and its spatial distribution were only possible on an annual scale and not on daily or monthly scales.  
(4) When using night-light images to estimate electricity consumption and its spatial pattern, it is particularly important to denoise the images, as this estimation method is ineffective if the noise cannot be effectively removed. Moreover, a set of electricity consumption data corresponding to the year of the night-light images is required for building regression models.
## 2 Single-Camera Trilateration Algorithm
### Simulation Settings
For performance analysis, we check the effect of \(\delta^{S}\mathbf{p}_{i}\), \(\delta^{m}\mathbf{q}_{i}\), \(\delta^{m}\mathbf{f}\) and \(\delta^{m}\mathbf{q}_{0}\) separately on \(\delta^{S}\mathbf{p}_{0}\) and \(\delta^{S}\mathbf{\theta}_{c}\). For each of these input error vectors, we assume that the vector components are zero-mean random variables following the corresponding Gaussian distribution and uncorrelated with one another with the same standard deviation; we discretize the data acquisition sphere S with a set of equally gapped \(\theta\) and \(\phi\); for each point (\(\theta\), \(\phi\)) on S, 10,000 samples of the erroneous input are generated according to the random distribution; the corresponding erroneous output estimates are obtained using the proposed single-camera trilateration algorithm; by comparing the erroneous estimates with the correct camera pose corresponding to (\(\theta\), \(\phi\)) on S, the statistics of the output error are generated, and then the performance indices are calculated. In order to optimally estimate \({}^{S}\mathbf{p}_{0}\) and \(r_{i}\), we carry out the steps (2) and (3) of Algorithm 1 by solving the corresponding least-squares problems of Equations (5) and (6) respectively. The Newton-Raphson method [74] is used to search for the solutions, which has a sufficient rate of convergence. To avoid being trapped in local minima, for continuous camera position estimation, the previous camera position is used as the initial guess to estimate the current camera position, which guarantees the convergence of the solution to the actual camera position.
# Ion Exchange MIEX(r) GOLD Resin as a Promising Sorbent for the Removal of PFAS Compounds
## 4 Conclusions  
In summary, we have successfully achieved satisfactory removal efficiency for six selected PFASs (PFBA, PFBS, PFHxA, PFHxS, PFOA, and PFOS) using MIEX(r) GOLD resin. In general, the removal efficiency was found to be >99% for the selected PFAS. Briefly, significant removal (>99.999%) of PFOA, PFOS, and PFHxS was obtained. Whereas, 99.21 \(\pm\) 0.12%, 99.8 \(\pm\) 0.01%, and 99.65 \(\pm\) 0.00% removal of PFBA, PFBS, and PFHxA, respectively, was estimated. The maximum adsorption capacity for PFHxS (1.01 \(\pm\) 0.04 g/g resin) and PFOA (1.05 \(\pm\) 0.01 g/g resin) is noteworthy. In addition to these outcomes, a comprehensive study on the interference of organic and inorganic matter showed a very negligible impact on PFAS removal with >99% removal of selected PFAS compounds at low concentrations of both inorganic matter and NOM. However, a high concentration of NOM and inorganic matter showed an influence on the removal of PFOA (67.42%), but significant removal (>90%) of PFOS and PFHxS was still achievable. Moreover, >98% regeneration efficiency of PFOS and PFHxS-adsorbed resin in 70% methanol with saturated salt was achieved, which will significantly reduce the remediation cost through the reuse of the resin. Overall, MIEX(r) GOLD resin has been found to be very promising as an efficient and cost-effective sorbent for the treatment of PFAS-contaminated water.  
[PERSON] supervised and led the project. [PERSON] performed all experiments, with data analysis and manuscript writing. [PERSON] conducted PFAS analysis via LC-MS. [PERSON], [PERSON] and [PERSON] contributed to data analysis and manuscript writing. [PERSON], [PERSON] and [PERSON] involved in experimental design and analysis. All authors have read and agreed to the published version of the manuscript.  
We acknowledge IXOM Operations Pty Ltd. for providing MIEX(r) GOLD resin for the project. We also acknowledge Swinburne University of Technology Research Institute Seed Grant to support this research work.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
[PERSON], [PERSON] and [PERSON] are employees of IXOM Operations Pty Ltd. of Melbourne. IXOM partially funded the project.
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 3 Methodology
### HEA Module  
The MCI module is primarily used to extract multi-scale local contextual information. To better incorporate contextual information over large distances and enable global context extraction, we introduce the HEA module. The process is as follows: first, global average pooling is applied to obtain local regional features; then, two depthwise separable strip convolutions are used. Meanwhile, the size of the depthwise separable strip convolution kernel is set according to the depth of the MCI module group, ensuring that the receptive field increases as the MCI module group to which the HEA module belongs becomes deeper. This strengthens the MCI module's ability to capture interdependencies between distant pixels while enhancing feature extraction of the core parts of target objects. In detail:  
Regional Feature Compression: Global average pooling is performed on the input feature map to compress the spatial dimension into a regional feature vector, thereby capturing the overall contextual trend. we obtain the local regional feature \(F_{l-1,n}^{\text{pool}}\) through a \(1\times 1\) convolution after average pooling:  
\[\mathbf{F}_{l-1,n}^{\text{pool}}=\text{Conv}_{1\times 1}(\mathbf{P}_{avg}(\mathbf{X}_{l -1,n}^{(2)})),n=0,\dots,N_{l}-1 \tag{4}\]  
where \(P_{avg}\) denotes the average pooling operation.  
Long-Distance Dependency Modeling: We leverage a pair of depthwise separable strip convolutions to approximate the computational effect of a standard large-kernel depthwise separable convolution:  
\[\begin{split} F_{l-1,n}^{w}&=\text{DWConv}_{1\times k _{b}}(\mathbf{F}_{l-1,n}^{\text{pool}})\\ F_{l-1,n}^{\text{h}}&=\text{DWConv}_{k_{b}\times 1}( \mathbf{F}_{l-1,n}^{w})\end{split} \tag{5}\]  
where \(k_{b}\) is the size of the depthwise separable strip convolution kernel. To expand the receptive field of the HEA module as the MCI module to which the HEA module belongs grows deeper, we set \(k_{b}=(11+2n)\), calculating the kernel size \(k_{b}\) as a function of the MCI module depth \(n\).  
Attention Weight Generation: Lastly, our HEA module generates an attention weight \(A_{l-1,n}\), where the sigmoid function ensures that \(A_{l-1,n}\) falls within the range of (0,1). This weight is further used to enhance the output of the MCI module; the formula is provided below.  
\[A_{l-1,n}=\text{Sigmoid}(\text{Conv}_{1\times 1}(\mathbf{F}_{l-1,n}^{\text{h}})), \tag{6}\]
# A Service-Caching Strategy Assisted by Double DQN in LEO Satellite Networks
###### Abstract  
Satellite fog computing (SFC) achieves computation, caching, and other functionalities through collaboration among fog nodes. Satellites can provide real-time and reliable satellite-to-ground fusion services by pre-caching content that users may request in advance. However, due to the high-speed mobility of satellites, the complexity of user-access conditions poses a new challenge in selecting optimal caching locations and improving caching efficiency. Motivated by this, in this paper, we propose a real-time caching scheme based on a Double Deep Q-Network (Double DQN). The overarching objective is to enhance the cache hit rate. The simulation results demonstrate that the algorithm proposed in this paper improves the data hit rate by approximately 13% compared to methods without reinforcement learning assistance.  
Satellite fog computing; Double DQN; caching hit 2024 24 3370.
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 2 Materials and Methods
### Structural Features  
Each vineyard was identified with a polygon using the functions of the software QGIS (Version 3.28 LTR, Quantum GIS). Then, the structural features were noted: the area, the perimeter, and the number of vertices (N\({}^{\circ}\) of Vertices). Moreover, the length-width ratio, row spacing, and headland size were assessed, as shown in Figure 3a. In addition, the dataset was enhanced for the present study by including the row's orientation. The row's orientation was measured in cardinal degrees. A cardinal direction was assigned following the pattern displayed in Figure 3b. More in detail, east-west (EW) was assigned to rows' orientation ranging from 90\({}^{\circ}\) to 114\({}^{\circ}\) and from 248\({}^{\circ}\) to 269\({}^{\circ}\); south-east (SE) was assigned to rows' orientation ranging from 115\({}^{\circ}\) to 157\({}^{\circ}\); north-south (NS) was assigned to rows' orientation ranging from 158\({}^{\circ}\) to 202\({}^{\circ}\); finally, south-west (SW) was assigned to rows' orientation ranging from 203\({}^{\circ}\) to 247\({}^{\circ}\). The elevation and the mean slope were available from a 20 m spatial resolution Italian digital elevation model (DEM) assessed on [[https://groupware.sinanet.isprambiente.it/](https://groupware.sinanet.isprambiente.it/)]([https://groupware.sinanet.isprambiente.it/](https://groupware.sinanet.isprambiente.it/)) (accessed on 1 November 2020). Hence, the altitude of 200 m was chosen for the classification into plain and hilly locations. Moreover, the average slope was computed, and a 15% slope was chosen as the threshold value to classify vineyards into flat or steep slopes [32]. Finally, the training system was assigned based on visually evaluating the vineyard's images. \"Vertical\" (VS) was assigned if each row was detectable. The \"Alberello\" (AS) training system was assigned if each single vine was detectable. \"Horizontal\" (HS) was assigned if the vines' canopies covered the whole vineyard's surfaces. Some examples of the training system classification are displayed in Figure 4. Further details on the calculation of the structural features can be found in [31].  
Figure 2: The present flowchart sketches the methodologies used, starting from the raw data acquired and involving the data analysis and conclusions. Google Earth Engine (GEE); coefficient of variation (CV); Sentinel-2 (S2); vegetation indices (VIs).  
Finally, the smallest vineyards were excluded from the satellite monitoring. Because S2 has 10 m ground spatial resolution, all vineyards, including less than 50 pixels, were excluded: at least 50 observations are required for a meaningful spatial statistical analysis [33]. Hence, the whole dataset was reduced from 3686 to 2304 vineyards.
# Automated Defect Detection Using Threshold Value Classification Based on Thermographic Inspection
## 4 Results and Discussion
### Detectability Comparative  
After the binary process, an analysis was performed to compare the detectability improvement depending on whether or not filtering was applied. In the previous study process, comparative analysis was performed through SNR calculation, but only some specific areas using ROI were calculated. Root mean square error (RMSE) was calculated to evaluate the overall detectability of amplitude and phase images. The lower the RMSE value, the smaller the difference in the real value and the estimated value, which means that the precision is high. Therefore, it can be interpreted as meaning that the detectability is high, and the equation is as follows [3]:  
\[\text{RMSE}(\theta_{1},\theta_{2})=\sqrt{\frac{\sum_{i=1}^{n}\left(\theta_{1, i}-\theta_{2,i}\right)^{2}}{n}} \tag{10}\]  
Figure 11: Automatic defect recognition based on a threshold of 0.75 for each image: (**a**) amplitude and (**b**) phase.  
where \(\theta_{1}\) is the real value (percentage value of roundness of raw image defects), \(\theta_{2}\) is the estimated value (percentage value of roundness of binary image defect), and n is the number of areas. The real value means the roundness percentage of the actual defects, and all defects were set to 100.  
Table 2 shows the roundness values for all defects in the amplitude and phase images of filtering and non-filtering, and the calculated RMSE value for all defects. Unrecognized defects in the binary image were excluded. It can be seen that both amplitude and phase had high detectability in the filtered image. According to Table 2, the same number of defects were detected regardless of the filtering. However, in order to improve the detectability of defects, it is necessary to increase the threshold of the roundness; therefore, the application of filtering can be considered important.