esa-sceva/satcom-chunk-collection huggingface dataset:
# Response of Mangrove Carbon Fluxes to Drought Stress Detected by Photochemical Reflectance Index
## 2 Materials and Methods
### Statistical Analyses  
The relationships among PRI, carbon, and environmental variables across time scales were explored by various statistical analyses including Pearson correlation, linear regression, and random forest (RF). Pearson correlation and linear regression were applied to examine the PRI-carbon relationships using half-hourly and daily data, respectively, while the RF approach was used to disentangle the complicated and non-linear interactions among these variables based on monthly data. The RF is a non-parameter machine learning approach without statistical presumption of explanatory variables and thus less affected by the issues due to the nonlinearity and collinearity among explanatory variables [47; 48]. Furthermore, the RF is an ensemble algorithm by aggregating predictions from a large number of decision trees, which reduces the possibility of the overfitting issue associated with single-tree predictors. The out-of-bag (OOB) error estimation was used here to assess the generalization ability of the RF prediction [49; 50; 51]. Based on the RF approach, the relative importance and affecting direction between dependent and explanatory variables were quantified to identify the dominant factors driving the variations of PRI and carbon fluxes. In this study, three sets of RF statistical analyses were conducted. The first two sets were used to analyze the influence of environmental variables on GPP and NEE. Due to the potential lag effects, advanced time series of each environmental variable (considering one and two months ahead; expressed as var(t \(-\) 1) and var(t \(-\) 2)) were also treated as an explanatory variable in addition to itself (expressed as var(t)). The third set was used to examine how PRI was correlated with environmental variables, GPP and NEE. By assuming that PRI responses to varying environmental variables faster than carbon fluxes, advanced time series of environmental variables (considering one and two months ahead) and lagged time series of GPP and NEE (considering one and two months later; expressed as var(t + 1) and var(t + 2)) were also treated as explanatory variables in addition to themselves. It is important to note that these RF applications were not to predict PRI or carbon fluxes from environmental variables but to disentangle their interactions and compare their relative importance in a quantitative manner. All data processing and statistical analyses were performed using MATLAB software (The MathWorks, Inc., Natick, MA, USA).
# Assessing the Long-Term Evolution of Abandoned Salinized Farmland via Temporal Remote Sensing Data
## 4 Results  
### Evolution of Cropping Structure in Heta  
The variation in cropping structure during 1988-2019 is shown in Figure 8. The remote sensing results are in good agreement with the statistical data. The Hetao Irrigation District is included in the administrative region of Bayan Nur city. Consequently, the detection area is generally smaller than the statistical data (statistics include data outside the study area). In general, the results show that the cropland in Hetao has continuously expanded during the last thirty years (area of total cropland has increased from 4.7 \(\times\) 10\({}^{5}\) ha to 7.1 \(\times\) 10\({}^{5}\) ha). The spring wheat planting area initially accounted for nearly 50% of the crop planting area (1988). However, since 2000, the wheat planting area has shrunk year by year, and in 2019, it was only 6%. This is because an increasing number of local farmers have tended to plant economic crops instead (e.g., sunflower) to make higher profits. As a result, in 2000, the  
Figure 7: Example of SI map of (**a**) the abandoned salinized farmland and (**b**) the residential area.  
total area of maize and sunflower surpassed the area of wheat (2.0 \(\times\) 10\({}^{5}\) ha), and in 2019, the area of maize and sunflower accounted for 70% of the total cropland.  
The detected area for each crop was significantly underestimated in 2006 and 2011 (residual error between statistics and estimates of the total cropland area up to 10.4 \(\times\) 10\({}^{5}\) ha and 10.1 \(\times\) 10\({}^{5}\) ha, respectively). This is because the weather conditions of the study area were cloudy in those years; therefore, there were fewer remote sensing data available than in other years.
# Path Planning and Collision Avoidance in Unknown Environments for USVs Based on an Improved D* Lite
## 4 Simulation Results and Analysis
### Simulation of Multi-Goal Path Planning
#### 4.5.2 Multi-Goal Path Planning in Dynamic Environment  
To simulate the path planning in a dynamic environment, we set four random dynamic obstacles (red ellipses) based on the same environment in Figure 15. The simulation result is shown in Figure 16.  
First, the improved D* Lite plans the same path shown in Figure 15, and the USV starts to move from the start node to _Goal_1_. When it reaches position \(a\), a dynamic obstacle emerges, and the algorithm replans a new path from _Goal_1_ to position \(a\) to avoid the obstacle and keep a safe distance between the USV and the obstacle. When the USV arrives at position \(b\), another obstacle emerges and the algorithm has to replan a new path from _Goal_1_ to position \(b\). When the USV reaches _Goal_1_, the algorithm starts to plan a path from _Goal_2_ and _Goal_1_. A third obstacle emerges when the USV arrives at position \(c\). The algorithm replans a new path between goal node _Goal_2_ and position \(c\). Finally, the USV reaches _Goal_2_ and stops at the destination of _Goal_3_.
## 1 Introduction
The corresponding highly sophisticated instrumentation combines two GC columns of different polarity connected in a series by a modulator, which vastly enhances the separation efficiency and identification sensitivity compared with standard GC-MS, resulting with a much higher number of properly identified volatiles [17,18]. Despite its potentials, the utilization of GC\(\times\)GC-MS technique for studying wine volatile composition is not yet common and such studies are rare in general [19, 20, 21, 22, 23, 24, 25, 26], whereas those that aimed to differentiate wines based on variety were just a few [5, 27, 28]. The aim of this study was to investigate the utility of GC\(\times\)GC with time-of-flight mass spectrometric detection (TOF-MS) in finding varietal markers among volatile compounds in wines made from non-aromatic red grapes, known to contain almost negligible amounts of varietal aromas. For this case study, wines made from the two most important and widespread Croatian red grape varieties, Plavac mali and Teran, were characterized and differentiated. Each monovarietal wine was deliberately represented by a heterogeneous sample group in terms of geographical microlocations, pedoclimatic and growing conditions, harvest dates, and grape processing and fermentation parameters. It was considered that a powerful technique such as GC\(\times\)GC-TOF-MS would provide an in-depth description of the volatile metabolome related to the variety and be able to identify subtle differences between such wines. It was expected that this study should contribute to additional valorization of the two monovarietal wines by providing information useful in managing their production, quality, and protection from forgery.
# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
## 2 Literature Review
### Socially Aware and Resource-Aware Caching Frameworks  
Recent frameworks have explored how social metrics and node-level resource availability can guide clustering and caching in CCN architectures. These approaches often incorporate node centrality, social closeness, or communication frequency to improve cache hit probability and reduce latency. However, most existing models (e.g., CCAR, PCSBCC, and DLRUK) either use static metrics or do not jointly optimize both social and resource aspects. In contrast, the proposed SARAC4N adopts a dual-metric system that integrates weighted social centrality (w-degree, w-betweenness, and w-closeness) with real-time node workload metrics to form adaptive clusters and intelligently place content. This balance of social context and resource constraints enables better workload distribution, energy efficiency, and user-centric data delivery.  
Table 2 presents a comparative summary of selected recent studies in ICN caching, highlighting their core strategies, contextual awareness, and limitations in relation to SARAC4N.  
In reality, many state-of-the-art methods combine elements of popularity, mobility, and cooperation. SARAC4N is inherently hybrid, integrating both resource and social awareness with dynamic clustering, outperforming pure strategies.
# Parametric Optimization Study of Novel Winglets for Transonic Aircraft Wings
## 3 Methodology
### Validation  
The test case of transonic flow over the ONERA M-6 wing [33] is predominantly used for validation purposes by numerous researchers. Therefore, the present computational method is compared with existing research on the ONERA M-6 wing. Analyses are performed for the flow conditions mentioned in Table 1. The C\({}_{\text{L}}\) and C\({}_{\text{D}}\) of this study are compared with [PERSON] et al. [34], [PERSON] et al. [35], [PERSON] et al. [36], [PERSON] et al. [37], and [PERSON] et al. [38], [PERSON] [39], [PERSON] and [PERSON] [40] the maximum deviations for both the cases are in the acceptable range of around 6.98% and 7.13% respectively. Furthermore, the computational results are compared with experimental results [34] conducted by the AGARD (Advisory Group for Aerospace Research and Development). The Cp vs. X/C graph is plotted at a semi-span of 44% from the root chord, as shown in Figure 6, and the results are in good agreement.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Flow Condition: M = 0.8395, Re = 11.72 \(\times\) 10\({}^{6}\), \(\alpha\) = 3.06\({}^{\circ}\)**} \\ \hline
**References** & \(\mathbf{C_{L}}\) & \(\mathbf{C_{D}}\) & \(\mathbf{C_{L}}\) Error (\%) & \(\mathbf{C_{D}}\) Error (\%) \\ \hline This present study & 0.2543 & 0.0181 & - & - \\ [PERSON] [34] & 0.2720 & 0.0181 & 6.98 & 0 \\ [PERSON] [35] & 0.2540 & 0.0191 & 0.10 & 5.58 \\ [PERSON] [36] & 0.2697 & 0.0174 & 6.08 & 4.04 \\ [PERSON] [37] & 0.253 & 0.0168 & 0.49 & 7.13 \\ [PERSON] [38] & 0.2622 & 0.0175 & 3.13 & 3.21 \\ \hline \hline \multicolumn{2}{c}{**Flow Condition: M = 0.84, Re = 11 \(\times\) 10\({}^{6}\), \(\alpha\) = 3.06\({}^{\circ}\)**} \\ \hline
**References** & \(\mathbf{C_{L}}\) & \(\mathbf{C_{D}}\) & \(\mathbf{C_{L}}\) Error (\%) & \(\mathbf{C_{D}}\) Error (\%) \\ \hline This present study & 0.2527 & 0.0175 & - & - \\ [PERSON] [39] & 0.2677 & 0.0178 & 5.93 & 1.77 \\ [PERSON] and & 0.2550 & 0.0161 & 5.6 & 1.72 \\ [PERSON] [40] & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Validation of this study with the reported literature.  
Figure 4: A schematic of NASA CRM mesh.  
Figure 5: Meshing of inflation layer for this present numerical study.
## 3 Effects of F-T Cycles on the Mechanical Properties and Microscopic Parameters
### Quantitative Analysis of Pores under F-T Cycles  
#### 3.4.1 Effect of F-T Cycles on Average Pore Diameter Percentage and Average Pore Diameter  
The internal water migration not only caused the change of \(\rm D_{P}\), but also of the average pore diameter (\(\rm D_{h}\)) during F-T; the change of \(\rm D_{h}\) caused the changing appearance of the soil. Hence, \(\rm D_{h}\) was divided into three sections: small pores, medium pores, and large pores, whose diameters were \(<\)4 \(\rm\upmu m\), 4-16 \(\rm\upmu m\), and \(>\)16 \(\rm\upmu m\), respectively. The \(\rm D_{h}\) percentage in each section is shown in Figure 13, and \(\rm D_{h}\) versus F-T cycles are listed in Table 5.  
Figure 12: Nightingale rose chart of S2 after (**a**) 0, (**b**) 1, (**c**) 2, (**d**) 3, (**e**) 4, (**f**) 5, (**g**) 6, and (**h**) 8 F-T cycles.  
Figure 13: Percentage of \(\rm D_{h}\) under F-T cycles: (**a**) S0 and (**b**) S2.  
Figure 13a shows that the largest proportion of pores was those whose \(\rm D_{h}\) were in the range of 4-16 \(\rm\upmu m\) and \(>\)16 \(\rm\upmu m\). The pores of S0 in 4-16 \(\rm\upmu m\) increased as F-T cycles continued, and the pores of \(>\)16 \(\rm\upmu m\) decreased, indicating that F-T cycles increased the medium pores. From Figure 13b, \(\rm D_{h}\) of S2 was similar to that of S0, manifesting that the soil was mainly composed of medium pores before and after being mixed with cement. With the number of F-T cycles, the variation of the \(\rm D_{h}\) percentage of S2 was completely contrary to that of S0.  
From Table 5, the \(\rm D_{h}\) of S0 increased with F-T cycles, and that of S2 showed a similar trend with that of S0, both of them having the maximum \(\rm D_{h}\) after the second F-T cycle, which meant the soil had minimum strength under the second F-T. The \(\rm D_{h}\) of both increased with the increasing F-T cycles, indicating that F-T reduced the joint force between particles and caused the increase of pores, so the cohesion and the shear strength of both decreased. However, the \(\rm D_{h}\) of S2 was smaller than that of S0, meaning that the addition of cement reduced the effect of F-T on the bonding force between particles. The \(\rm D_{h}\) was closely related to the \"fragmentation\" and \"agglomeration\" of particles under F-T cycles.
### Types of Antenna Systems Used in Underground Mining
#### 1.2.2 Electrically Small Antennas  
Electrically Small Antennas (ESAs) are devices that transmit and receive electromagnetic waves. These antennas are typically compact in size, making them suitable for a wide range of applications [30; 31]. [PERSON] et al. [32] designed a moderately high-gain and portable ESA at 58.27 MHz, which had a multilayered semi-cylindrical shaped structure with a finite ground plane. The proposed antenna provides a gain of 3.43 dBi and measured efficiency of 92.5%, making it suitable for underground communication. [PERSON] [33] presented two electrically small reactive impedance surfaces to locate miners in emergencies in underground mines. Both antennas have a high gain of 5 dBi and radiate 10 dB more power in the front hemisphere than in the rear, and are suitable as a wearable antenna for mine rescue applications.
# Self-Guided Pixel-Wise Calibration for Low-Light Image Enhancement
## 3 Method
### Loss Function  
#### 3.3.1 Pixel-Wise Exposure Control Loss  
In the absence of reference images, leveraging the intrinsic information of low-light images or commonly accepted prior knowledge becomes crucial for guiding network convergence. However, brightness information within the low-light image cannot serve directly as a reference for enhanced image brightness. Hence, a pixel-wise exposure control loss based on prior knowledge is introduced. The goal is to prompt the local average brightness of the enhanced image to align with predefined optimal exposure levels.  
While fitting the local regional average of the enhanced image to a single predefined optimal exposure level yields a globally good exposure level, it tends to introduce undesirable local overexposure problems. To address this issue, a pixel-wise well-exposedness map is introduced. The pixel-wise well-exposedness map is obtained by compressing the low-light image into an appropriate exposure level. Our rationale is that the input image and its corresponding enhanced one should share similar neighborhood luminance variations. Therefore, the well-exposedness map, with a specific dynamic range, facilitates a more natural exposure in the enhanced image. Therefore, the expression of the loss function can be formulated as follows:  
\[\mathcal{L}_{exp}=\frac{1}{N}\sum_{k=1}^{N}|Y_{k}-\mathcal{I}|, \tag{6}\]  
where \(Y_{k}\) represents the local region mean of the output image, \(\mathcal{I}\) denotes the well-exposedness map, and \(N\) indicates the number of local regions. In our experiments, we define the dynamic range of the well-exposedness map within the interval [0.5, 0.7].
## 5 Conclusions  
This study shows that fractional-order differentiation (FOD) combined with CARS improves PLSR predictions of amino acid (AA) and tea polyphenol (TP) content in fresh leaves. Using an optimal FOD of 0.7 raised the AA test R\({}^{2}\) from 0.73 to 0.80 and lowered the RMSE from 0.30% to 0.25%. For TPs, an FOD of 0.1 increased the R\({}^{2}\) from 0.40 to 0.42 and decreased the RMSE from 4.03% to 3.96%. Achieving these accuracies in fresh leaves is notable, given their high moisture and variable surfaces. CARS also beat simple correlation-based band selection, cutting computation time while boosting accuracy. Future work will validate our models across diverse cultivars to assess genetic and environmental effects and study each compound's spectral features. The FOD+CARS algorithm also makes this method practical for automated industrial tea grading. We note that key absorption bands for amino acids and polyphenols can be linked to N-H and O-H bond vibrations. However, these bands often overlap with similar vibrations from water, cellulose, lignin, and other phenolic compounds. Such overlap can introduce confounding signals and reduce the specificity of each assignment. As a result, our current band assignments carry only moderate confidence. Further validation--using pure compounds and advanced spectroscopic databases--will be needed to resolve these overlaps and improve model reliability. Although tested on fresh tea leaves, our FOD-CARS-PLSR method can be applied to other crops by tuning the derivative order and sampling scheme. This makes it a versatile tool for biochemical sensing in precision agriculture, food safety, and environmental monitoring.  
S.L.: Writing--original draft preparation, Formal analysis, Methodology, Investigation. R.S.: Methodology, Supervision, Writing--review and editing. X.L.: Methodology, Supervision, Writing--review and editing. Y.L.: Methodology, Supervision, Writing--review and editing. L.Z.: Investigation, Writing--review and editing. X.H.: Investigation, Writing--review and editing. Y.X.: Investigation, Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the National Natural Science Foundation of China (U24A20423), the National Key Research and Development Program of China (2023 YFD1601700), the National Key Research and Development Program of China (Grant No. 2019 YFD1002203), and the Key Research and Development projects of Zhejiang Province (2021C02036).  
All data generated or analyzed in this study are available from the author upon reasonable request.  
For the purpose of refining the language and ensuring clarity in our manuscript, we used ChatGPT (OpenAI) as a language polishing tool. The use of ChatGPT was strictly limited to enhancing the English language presentation.  
The authors declare no conflicts of interest.
## 2 Methodology
### Post-Processing  
Post-processing of the OCT data was done using Matlab (version 2015a, MathWorks, Natick, MA, USA). By utilizing the peak find function in Matlab, several peaks in the OCT A-Scans (line scans perpendicular to the surface) were detected. By choosing optimized parameters and adding appropriate constraints (e.g., thresholding), only the three peaks of interest were determined. The minimum peak prominence and the minimum peak height were the parameters that were optimized.  
Using a smoothing algorithm, outliers from the peak detection could be suppressed. Phantom layers caused by autocorrelation were not detected by optimizing the algorithm in terms of intensity. The three peaks of interest correspond to the beginning of the coating layer, the end of the coating layer, and the end of the scattering layer (metal or corrosion). Composing OCT A-Scans along a line parallel to the surface provides a so called B-Scan. A typical B-Scan containing a coating and a scattering layer is shown in Figure 3. Assuming a refractive index of 1.89 for Paraloid B48N, the thickness of the coating layer was calculated by computing the path difference between the second and the first peak and dividing by the refractive index of 1.89. The second layer that was calculated was considered as a scattering layer. Thus, the distance between upper and lower boundary of the material was identified as the scattering layer thickness.
###### Abstract  
This paper presents a novel framework to extract metro tunnel cross sections (profiles) from Terrestrial Laser Scanning point clouds. The entire framework consists of two steps: tunnel central axis extraction and cross section determination. In tunnel central extraction, we propose a slice-based method to obtain an initial central axis, which is further divided into linear and nonlinear circular segments by an enhanced Random Sample Consensus (RANSAC) tunnel axis segmentation algorithm. This algorithm transforms the problem of hybrid linear and nonlinear segment extraction into a sole segmentation of linear elements defined at the tangent space rather than raw data space, significantly simplifying the tunnel axis segmentation. The extracted axis segments are then provided as input to the step of the cross section determination which generates the coarse cross-sectional points by intersecting a series of straight lines that rotate orthogonally around the tunnel axis with their local fitted quadric surface, i.e., cylindrical surface. These generated profile points are further refined and densified via solving a constrained nonlinear least squares problem. Our experiments on Nanjing metro tunnel show that the cross sectional fitting error is only 1.69 mm. Compared with the designed radius of the metro tunnel, the RMSE (Root Mean Square Error) of extracted cross sections' radii only keeps 1.60 mm. We also test our algorithm on another metro tunnel in Shanghai, and the results show that the RMSE of radii only keeps 4.60 mm which is superior to a state-of-the-art method of 6.00 mm. Apart from the accurate geometry, our approach can maintain the correct topology among cross sections, thereby guaranteeing the production of geometric tunnel model without crack defects. Moreover, we prove that our algorithm is insensitive to the missing data and point density.  
terrestrial laser scanning; tunnel central axis; tunnel cross section; enhanced RANSAC; quadric fitting; constrained nonlinear least-squares problem