esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Impact of Global Climate Change on Soil Salinization  
The combined impacts of severe weather events and salinization provide a serious challenge [40]. However, it is difficult to determine the influence of climate change on soil salinization growth [7]. Sea-level rise (SLR) and precipitation changes across wide swaths of coastal areas are both possible consequences of global climate change (GCC). Soil salinization and agricultural productivity will be affected by SLR and precipitation fluctuations. Low-lying coastal locations in South and Southeast Asia, as well as regions in sub-Saharan Africa, are particularly more vulnerable [41]. The salinization of hydromorphic soils will be enhanced by climatic change conditions caused by global warming and an expansion in aridity [42]. Through the physical, chemical, and biological aspects of soil, salinization has the capacity to affect soil health [43].  
Because of increased temperature and variations in precipitation (amount and frequency), climate change is projected to have a major effect on soils and ecosystems, changing biogeochemical and hydrological processes [44]. Apparently, increasing groundwater pumping is required to fulfill the growing crop water needs as temperatures rise. Annual precipitation levels in the eastern Mediterranean are expected to decrease by 20% by 2050, increasing the danger of summer droughts [45]. Climate change is anticipated to accelerate the pace of soil salinization over the world. This will result in the use of lower quality water, elevated irrigation-induced salinization, the intensification of dryland salinization (due to the expansion of arid and semi-arid regions and desertification), and sea-level rise, directly contaminating nearby soils or indirectly affecting soils via saline intrusion in aquifers [40].  
For example, in Australia, one of the countries most affected by both salinity and climate change, there are an estimated 1.7 million hectares of salinization or at hazard of salinization above the verified 1.047 million ha of salt-affected soils [46]. Thus, inventorying and monitoring climate change impacts on salinity are critical to determine the scope of the problem, identify trends, and develop irrigation and crop management measures that will keep these areas' agricultural production maintained [47].
# Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data
## 4 Discussion
Fourth, uncertainties in input datasets (e.g., site-level GPP, CO\({}_{2}\) products, and land use data) may introduce biases into the derived GPP and ET estimates from the XGB and RF models. We examined the effects of these datasets on global-scale GPP and ET (Table S3-S5). Using site-level GPP data derived from daytime and nighttime methods, we found similar magnitudes and trendsfor global GPP, ET and WUE as the original estimates (Table S3). In addition, we used heterogeneous CO\({}_{2}\) datasets [103] to estimate global GPP, ET, and WUE, yielding values of 129 Pg C yr\({}^{-1}\), 471 mm yr\({}^{-1}\), and 1.95 g C kg\({}^{-1}\) H\({}_{2}\)O yr\({}^{-1}\), respectively, which are close to our estimates using unified CO\({}_{2}\) datasets (Table S4). Finally, we assessed the impact of land use datasets on derived GPP, ET, and WUE, finding small biases of \(-1.28\%\), \(-1.61\%\), and \(-0.3\%\), respectively, compared to our estimates derived from dominant land cover datasets (Table S5). Despite these limitations, our derived ECSIF products showed several advantages compared to previous GPP or/and ET products and provide a reliable tool to explore WUE: (1) Comprehensive considerations of plant physiology and environmental constraints. The ECSIF products fully accounted for the effects of plant physiology (e.g., SIF) and environmental changes (e.g., diffuse radiation), which were usually ignored in previous studies especially for ET products [104, 105]. [PERSON] et al. [56] found that diffuse radiation dominated 30% of site-level GPP variability at the sub-daily scales. [PERSON] et al. [106] also found that diffuse radiation significantly contributed to ET variability due to changes in transpiration. Our results confirmed that diffuse radiation can contribute significantly, both up to 8%to GPP and ET variability (Figures 4 and S3), comparable to the contributions of VPD (8% and 10%). (2) Simultaneous derivation of GPP and ET. Previous terrestrial products typically included only GPP or ET [5], with very few studies capable of deriving both GPP and ET on a global scale. In this study, ECSIF products included six combined datasets of global GPP and ET using three SIF datasets and two ML algorithms to minimize the uncertainties from single methods or datasets. These datasets are expected to be useful for exploring water and carbon fluxes and their interactions such as WUE at regional and global scales in the future. (3) High spatiotemporal resolution. ECSIF estimated global GPP, ET, and WUE at a spatial resolution of -5 km and an 8-day temporal resolution. As a result, ECSIF products could serve as benchmarks for model validations and data explorations at a very fine resolution.
# Construction Progress and Aviation Flight Test of BDSBAS
## 2 Overview of BDSBAS
### Tropospheric Correction
#### 2.3.3 Degradation Parameter Calculation  
The degradation parameter characterizes the degree to which the correction effect decreases due to the delay between the current time \(t\) when the user uses the correction and the reference time \(t_{0}\) of the correction, and it is the basis for the PL calculation. It mainly includes the user differential range estimate (UDRE) degradation factor \(\delta\)UDRE, the fast correction degradation factor \(\varepsilon_{fc}\), the long-term correction degradation factor \(\varepsilon_{ltc}\), the distance changing correction degradation factor \(\varepsilon_{mc}\), and the application mode degradation factor \(\varepsilon_{er}\). For the complete and detailed calculation process, please refer to RTCA DO-229 (RTCA 2016).  
After obtaining the various degradation factors, the residual variance \(\sigma^{2}_{flt}\) of user measurement after error correction can be further calculated as follows:  
\[\sigma^{2}_{flt}=\left\{\begin{array}{ll}\left(\sigma_{\text{UDRE}}\cdot \delta\text{UDRE}+\varepsilon_{fc}+\varepsilon_{rc}+\varepsilon_{ltc}+ \varepsilon_{er}\right)^{2},&if\ RSS_{\text{UDRE}}=0\\ \left(\sigma_{\text{UDRE}}\cdot\delta\text{UDRE}\right)^{2}+\varepsilon^{2}_ {fc}+\varepsilon^{2}_{rc}+\varepsilon^{2}_{ltc}+\varepsilon^{2}_{er},&if\ RSS_{ \text{UDRE}}=1\end{array}\right. \tag{13}\]  
where \(\sigma_{UDRE}\) is converted from the UDE, and the two have the following exact relationship:  
\[\text{UDRE}=3.29\sigma_{\text{UDRE}} \tag{14}\]  
and the UDE parameters are broadcast in the SBAS augmentation MTs 2, 3, 4, 5, and 6 in the form of the user differential range estimate indicators (UDREI).
## 2 Basic Concepts and Work Scope
### Mobile-Device-Based Positioning
This is because in order to minimize the positioning error, this case would require increasing complexity and cost on the positioning infrastructure side, which is not aligned with our objective. In addition, this case relies on network communication, centralizing the fingerprints that are collected at the different nodes or base stations. In some use-cases with limited communication, this may degrade the reliability of the positioning estimates. Hereafter, we focus only on mobile-device-based positioning using downlink radio signals as fingerprints for the case using spatial side information (Figure 6e). This case will be benchmarked against a traditional fingerprinting method operating with downlink radio signals and no side information (Figure 6c).
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 4 Automated and Transparent Encryption and Decryption
### Automated Encryption and Decryption Model
```
1 Input: User ID of DBFS user who has created a session
2 Output: Decrypted single- and multi-user files of user
3
4 ifDBFSuser session createdthen
5 Check for any existing sessions of theuser
6 Exit if any sessions exist // do not proceed
7
8 end if /* Check for files with.enc extension for single-user files */
9 forsingleUserFiles of userdo
10 if\(file\)has\(enc\)file extensionthen
11 Retrieve file\(decryptionkey\) from a DB table // protected using Oracle Wallset
12 Add\(file\)to\(decryptionList\)
13
14 end if /* Ensure no user has active sessions for multi-user files */
15 formultiUserFiles of userdo
16 ifnootheruserwithprivilege on file has active sessionsthen
17 Retrieve\(decryptionkey\) from a DB table // protected using Oracle Wallset
18 Add\(file\)to\(decryptionList\)
19
20 end if /* Decrypt all files in the decryption list */
21 forfiles\(in\)\(decryptionList\)do
22 Create a temporary\(file\) (LOB)
23 Decrypt the original\(file\) into the temporary\(file\)
24 Point original\(file\)reference to temporary\(file\)// original file never decrypted
25
26 end
```  
**Algorithm 1**Decryption process for single- and multi-user files within ECMSDK executed upon user login.
## 2 Signal Transmission and Reception in MIMO-WCDMA Networks
### Signal Model and Receiver Operations
In Equations (2)-(4), the term \(T_{k}\) indicates the selected set of transmit antennas per active user, given defined by:\[T_{k}=\underset{T^{\prime}\in TR}{\text{argmax}}\left(\sum_{l=1}^{L}\sum_{d=1}^{ dim}\lambda_{m}\Big{(}\mathbf{H}_{k,l,T^{\prime}}^{\text{H}}\mathbf{H}_{k,l,T^{ \prime}}\Big{)}\right) \tag{7}\]  
In Equation (7), \(TR\) is the set of all possible combinations of \(dim\) transmit antennas out of \(M_{t}\) and \(\lambda_{m}(\mathbf{A})\) is the maximum eigenvalue of matrix \(\mathbf{A}\). Considering for example a 4 \(\times\) 2 MIMO orientation (i.e., \(M_{t}\) = 4 and \(M_{r}\) = 2), then the set \(TR\) will be equal to {(1,2),(1,3),(1,4),(2,3),(2,4),(3,4)}. Under the assumption of independent symbol matrices among the various users of the MIMO-WCDMA orientation, the signal power of the desired user, ISI and MAI power as well as total noise power will be given by Equations (8)-(11), respectively [16]:  
\[P_{s,k}=p_{t,k}\|\sum_{l=1}^{L}\mathbf{r}_{k,l}\mathbf{H}_{k,l,T_{k}}\mathbf{t }_{k}\|_{F} \tag{8}\]  
\[ \begin{array}{l}P_{IS,k}=\left|\sum\limits_{l=1}^{L}\sum\limits_{ \begin{subarray}{c}l^{\prime}=1\\ l^{\prime}\
eq l\end{subarray}}^{L}\big{(}\mathbf{r}_{k,l}\mathbf{H}_{k,l^{ \prime},T_{k}}\mathbf{t}_{k}\big{)}\sqrt{p_{t,k}}\Big{(}\mathbf{b}_{k,-1} \rho_{k,k,|l-l^{\prime}|}+\mathbf{b}_{k,0}\overline{\rho}_{k,k,|l-l^{\prime}| }\big{)}\right|^{2}+\\ \sum\limits_{l=1}^{L}\big{(}\mathbf{r}_{k,l}\mathbf{H}_{k,l^{\prime},T_{k}} \mathbf{t}_{k}\sqrt{p_{t,k}}\big{)}^{\text{H}}\sum\limits_{l=1}^{L}\sum\limits _{\begin{subarray}{c}l^{\prime}=1\\ l^{\prime}\
eq l\end{subarray}}^{L}\big{(}\mathbf{r}_{k,l}\mathbf{H}_{k,l^{ \prime},T_{k}}\mathbf{t}_{k}\sqrt{p_{t,k}}\big{)}\Big{(}\mathbf{b}_{k,-1}\rho_ {k,k,|l-l^{\prime}|}+\mathbf{b}_{k,0}\overline{\rho}_{k,k,|l-l^{\prime}|}\big{)} +\\ \sum\limits_{l=1}^{L}\big{(}\mathbf{r}_{k,l}\mathbf{H}_{k,l,T_{k}} \mathbf{t}_{k}\sqrt{p_{t,k}}\big{)}\left(\sum\limits_{l=1}^{L}\sum\limits_{ \begin{subarray}{c}l^{\prime}=1\\ l^{\prime}\
eq l\end{subarray}}^{L}\big{(}\mathbf{r}_{k,l}\mathbf{H}_{k,l^{ \prime},T_{k}}\mathbf{t}_{k}\sqrt{p_{t,k}}\big{)}\Big{(}\mathbf{b}_{k,-1}\rho_ {k,k,|l-l^{\prime}|}+\mathbf{b}_{k,0}\overline{\rho}_{k,k,|l-l^{\prime}|}\big{)} \right)^{\text{H}}\\ \end{array}  \tag{9}\]  
\[P_{noise}=N_{o}\sum\limits_{l=1}^{L}\left\|\mathbf{H}_{k,l,T_{k}}\mathbf{t}_ {k}\right\|_{F}^{2} \tag{11}\]  
where \(N_{o}\) is the thermal noise level and \(\left\|\mathbf{A}\right\|_{F}\) the Frobenius norm of \(\mathbf{A}\). All output expressions from Equations (8)-(11) are \(dim\times dim\) matrices. Therefore, all transmission matrices should be appropriately selected so as to provide diagonal expressions for each \(P_{s,k}\) matrix.
## 2 Numerical Methods  
### Physical Model and Boundary Conditions  
The geometric model of the levitation electromagnet module is shown in Figure 1a. This structure contains a complete set of levitation electromagnet units on one side of an electromagnet maglev car. The solid model in Figure 1a consists of 12 individual electromagnet units (EMU), which include 6 primary magnet units, 4 core-elevated magnet units and 2 end magnet units. The internal structure of the EMUs is complex. The system is constructed by an electromagnet coil, a steel core, epoxy, and glass fibers. All dimensions are normalized by the height of the EMU (\(H\) = 0.12 m). Within the levitation electromagnet module, M\({}_{1}\)-M\({}_{6}\) are the primary magnet units, H\({}_{1}\)-H\({}_{4}\) are the core-elevated magnet units, S\({}_{1}\)-S\({}_{2}\) are the end magnet units. P\({}_{\rm m}\) and P\({}_{\rm d}\) are the points on the M\({}_{3}\) surface, which is located near the gap region of M\({}_{2}\) and M\({}_{3}\). Based on the objectives of this study, the following assumptions are made to reduce the complexity of the model: (1) The electromagnet coil is considered as one bulk material with homogeneous heating power distribution. (2) The electromagnet coil is assumed to be emitting heat in a constant rate, i.e., the temperature-related heating power change is neglected. (3) For the heat transfer inside of the electromagnet module, since the epoxy functions as a bonding agent between different components, the contact thermal resistance in between the interface of the different solid materials is not considered. The material properties of each component in the levitation electromagnet module are shown in Table 1.  
The levitation electromagnet module model in Figure 1a is placed in a \(166.67H\times 28.33H\times 28.33H\) cuboid space, as illustrated in Figure 1b. The bottom surface of the model is \(8.33H\) above the ground and the geometric center of the model coincides with the geometric center of the cuboid in the horizontal plane (x-y plane in Figure 1b). The initial pressure of the fluid region is assumed to be 0 Pa (gauge pressure) and the initial temperature of both the fluid and solid regions is 295 K, respectively, which is in agreement with the experimental setup. The boundary condition of the closed system is set as non-slip isothermal walls.  
Figure 1: Schematic of model geometry. (**a**) Levitation electromagnet module and locations of measuring points; (**b**) Schematic of computational domain, where the enclosed white area represents fluid domain and the shadowed area represents the levitation electromagnet module.
## 4 Numerical Calculation
### Influence of Senderness Ratio  
Figure 5 illustrates the variation in deformation and internal force of the laterally loaded long pile with the slenderness ratio of the pile. The slenderness ratio of the pile is a key parameter affecting the lateral load-bearing performance of the pile. In this study, the slenderness ratio \(\lambda\) was taken as 20, 25, and 40 to analyze its effects on pile deformation and internal forces. The results show that pile head displacement increases nonlinearly with an increasing slenderness ratio. When \(\lambda=20\), the pile head displacement is 2.5 mm; when \(\lambda=25\), the displacement increases to 3.4 mm, an increment of approximately 36%; and when \(\lambda=40\), the displacement significantly increases to 6.0 mm, representing an increment of approximately 140% compared to \(\lambda=20\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Physical Meaning** & **Value** & **Unit** \\ \hline \(E_{\text{p}}\) & Elastic modulus of pile & \(2.5\times 10^{4}\) & MPa \\ \(L\) & Pile length & 20 & m \\ \(\
u_{1,2,3}\) & [PERSON]’s ratio of elastic foundation & 0.25 & — \\ \(H_{1,2}\) & Depth of foundation & \(3,5\) & m \\ \(E_{\text{s1}}\) & Elastic modulus of the first layer foundation & 50 & MPa \\ \(E_{\text{s2}}\) & Elastic modulus of the second layer foundation & 100 & MPa \\ \(E_{\text{s3}}\) & Elastic modulus of the third layer foundation & 200 & MPa \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical parameters of the pile and layered foundation.  
At the same time, the maximum bending moment decreases as the slenderness ratio increases, and its location gradually shifts upward along the pile shaft. When \(\lambda=20\), the maximum bending moment is 1577.6 kN\(\cdot\)m, located at 0.4 L of the pile length; when \(\lambda=25\), the maximum bending moment decreases to 1221.3 kN\(\cdot\)m and shifts upward to 0.3 L; and when \(\lambda=40\), the maximum bending moment reduces to 648.6 kN\(\cdot\)m and further shifts upward to 0.2 L. The distribution of negative shear force shows an opposite trend to that of the bending moment, decreasing with the increase in slenderness ratio while also moving upward along the pile shaft. This indicates that an increasing slenderness ratio leads to a significant increase in pile head deformation, while the internal forces in the pile decrease, resulting in reduced flexural stiffness. In practical engineering, it is necessary to comprehensively consider the reasonable matching of pile length and stiffness to ensure that pile deformation and internal forces remain within acceptable limits.  
Figure 4: Comparison of the numerical results of the present study with those of Reference [47]. (**a**) Pile foundation in homogeneous soil. (**b**) Pile foundation in layered soil.
## 2 Materials and Methods
### Calculation of Surface Temperature (Ts)  
At-sensor brightness temperature (Ts) was calculated using the following formula [35]:  
\[\text{Ts}=\frac{K2}{nl\left(\frac{\text{K1}}{L_{\lambda}}+1\right)} \tag{2}\]  
where  
Ts = Effective at-sensor brightness temperature [K];  
Figure 2: Average monthly rainfall and minimum/maximum temperatures at the Tatura weather station (latitude: 36.44\({}^{\circ}\) S; longitude: 145.27\({}^{\circ}\) E; elevation: 114 m), located within the study area.  
\(K2=\) Calibration constant 2 [K];  
\(K1=\) Calibration constant 1 [W/(m\({}^{2}\) sr \(\upmu\)m)];  
\(L_{\lambda}=\) Spectral radiance at the sensor's aperture [W/(m\({}^{2}\) sr \(\upmu\)m)];  
\(nl=\) Natural logarithm.  
For this study, Band 11 TIR2 (11,500-12,510 nm) was used for \(L_{\lambda}\).
## 5 Discussion
### BCCD Dataset Results  
In contrast to the PBC datasets, the BCCD datasets presented a more challenging scenario due to their small size, noise, and poor image quality. The Google ViT continued to demonstrate its superior performance, achieving an 88.36% validation accuracy and a validation loss close to one when fitted with the BCCD DS-6 dataset. This exceptional performance, especially when handling the most challenging dataset, DS-6, is a testament to the robustness of the Google ViT.  
When compared to the ImageNet models, the Google ViT was consistently superior across all three BCCD datasets (DS-4, DS-5, and DS-6) in terms of the accuracy difference (AD) and loss difference (LD) values. The AD values for the Google ViT were consistently lower, indicating its ability to maintain a higher accuracy. In addition, the LD values for the Google ViT remained minimal, underscoring its stability even when dealing with small, noisy, and low-quality image datasets.  
The superior performance of the Google ViT on the BCCD datasets, especially the DS-6 dataset, highlights its resilience in the face of challenging data conditions and noise. This makes it a promising choice for applications where image quality is a concern or where the dataset size is limited.
# Enhancing Ignition Reliability with Tail-Groove Strut Designs in Cavity-Based Combustors
## 2 Materials and Methods
### Flame Image-Processing Techniques  
This paper proposes image-processing methods to extract and analyze flame features during the ignition process. Quantitative image-processing techniques are developed using MATLAB R2023a algorithms. The flow chart of these methods is shown in Figure 2. The algorithm consists of three core steps: image enhancement, image segmentation, and ignition process analysis.  
Figure 1: The configuration of the simulation model. (**a**) Normal Strut. (**b**) Tail-groove Strut.  
Figure 2: Feature extraction of instantaneous flame in the ignition process.  
The raw flame luminosity captured by the high-speed camera consists of a sequence of grayscale images with a range of [0, 255]. The first step of this method (image enhancement) is removing background noise. Before ignition, the background area in the image is marked as \(U(X,Y)\). The pixel value is denoted as \(v(X,Y)\). If \((X,Y)\) falls within the \(U(X,Y)\) region, the pixel value is set to zero, otherwise it remains unchanged. This can be expressed by the following equation:  
\[v(X,Y)=\begin{cases}0&\text{if }(X,Y)\in U(X,Y)\\ v(X,Y)&\text{if }(X,Y)\
otin U(X,Y)\end{cases}, \tag{1}\]  
Subsequently, image filtering is performed using a 2D bilateral, implemented via the '\(imbilatfit\)' function and a smoothing kernel with \(\sigma_{color}=0.1\) and \(\sigma_{spatial}=15\) to enhance image quality and improve the signal-to-noise ratio. Then, to further enhance the visualization and analysis of flame structures, histogram equalization based on probability density functions was applied to linearly stretch the grayscale values.  
In image segmentation, given the grayscale stretching affects the grayscale values, image segmentation is directly performed on the filtered image using a threshold value of 10.  
After segmenting the image, pixels with grayscale values above 10, denoted as \(v(X,Y)>10\), are considered part of the flame. In the ignition process analysis, the lowest point along the \(y\)-axis of the flame is marked as the \"Flame position\". The lowest y-coordinate, \(y_{min}\), is calculated as follows:  
\[y_{min}=min\{y\ |\ v(x,y)>10\} \tag{2}\]  
This \"Flame position\" is marked with a green line in Figure 2, while the \"Cavity-strut boundary\" is indicated by a red line. The distance between the flame position and the cavity-strut boundary is considered the radial flame distance, denoted as \(H\). This distance is calculated as follows: if \(H>0\), then \(H=H\); if \(H\leq 0\), then \(H=0\).