esa-sceva/satcom-chunk-collection huggingface dataset:
# Monitoring Changes in the Enhanced Vegetation Index to Inform the Management of Forests
## 2 Materials and Methods
### Data  
#### 2.2.1 Vegetation Index  
We used the Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) at 16-day and 250-m resolution for the period 1 January 2003 to 31 December 2022 (20 years) as the main response variable (Table A1). EVI data (product version: MOD13Q1 v061, Terra satellite) were downloaded from the Land Processes Distributed Active Archive Center's (LP DAAC) server using the R statistical programming language (version 4.1.1, [39]) and the MODIStep package (version 2.0.9, [40]). We filtered out poor-quality pixels based on the approach suggested by [PERSON] et al. [41]. Further  
Figure 1: The study area is the Algonquin Provincial Park and the surrounding area, which we refer to as the Algonquin Greater Park Ecosystem (AGPE). Elevation (in meters above sea level, masl) is shown in (**a**). The forest’s mean age in 2019 is shown in (**b**). Protected areas within the AGPE are shown in (**c**). About 16% of forest pixels belong to protected areas. The geographic distribution of three disturbance agents in the period 2002–2020 is shown in (**d**). The gray color represents forested pixels and white non-forest pixels. The dashed black line shows the study area footprint (\(\approx\)15,000 km\({}^{2}\) or 1.5 M ha). The solid black line shows Algonquin Provincial Park’s footprint. The two perpendicular dashed lines in (**a**) divide the study area into quadrants (northwest, NW; northeast, NE; southwest, SW; and southeast, SE) to ease the description of the results. Map projection is NAD83 Statistics Canada Lambert, EPSG: 3347.  
more, non-forest pixels were masked out with a forest land cover data layer for the year 2003 by [PERSON] et al. [42]. MODIS EVI values have a valid range from \(-2000\) to 10,000 while forested landscapes are mostly associated with values \(>1800\).
# Evaluation of Light Pollution in Global Protected Areas from 1992 to 2018
## 2 Datasets
### The Harmonized Global Nighttime Light Data  
We collected the harmonized global NTL data from 1992 to 2018 as our primary data source of light pollution [2; 47]. The harmonized NTL data integrate satellite observations from the Defense Meteorological Satellite Program Operational Line-scan System (DMSP/OLS) and the Visible Infrared Imaging Radiometer Suite Day Night Band (VIIRS/DNB) data. The temporal spans of DMSP and VIIRS NTL data are from 1992 to 2013 and from 2012 to 2018, respectively, with a near-global coverage (i.e., 65 degs to 75 degN) [48]. The raw records in DMSP NTL data are digital number (DN) values (i.e., ranging from 0 to 63), notably different from the radiance in the VIIRS/DNB data. Additionally, their spatial resolutions are different, i.e., 30-arc and 15-arc in DMSP/OLI and VIIRS/DNB, respectively. To extend the interval of DMSP NTL observations, the harmonized global NTL data were generated using the kernel density approach [26; 49] and the sigmoid function, which aggregates the 15-arc resolution to 30-arc seconds and converts annual VIIRS observations to the DMSP-like NTL data [2], respectively. Given that there are more uncertainties when the DNs in NTL data are lower, we screened out pixels with DN values less than 7 in our study as suggested in [2].
# Monitoring and Improving Aircraft Maintenance Processes Using IT Systems
## 1 Introduction  
IT systems are increasingly being integrated into various aspects of life, revolutionizing how processes are managed, and their role in the operation and maintenance of aircraft is no exception. These systems provide numerous advantages, such as convenient data storage, streamlined bureaucracy, reduced paper usage, and enhanced decision-making capabilities [1]. Such benefits have driven their widespread adoption across industries, with aviation being a notable example. The aviation sector has seen a growing trend in developing IT support systems tailored to meet the unique operational needs of modern aircraft. Practically every new aircraft is now equipped with a dedicated operational support system capable of collecting extensive data [2, 3]. However, a detailed analysis of IT systems used in the Polish Air Force (PLAF), including those developed domestically and those supplied by foreign manufacturers, reveals that the vast potential of these systemsoften remains untapped [4; 5; 6]. The systems generate a wealth of information that, with proper utilization, could significantly improve operational efficiency.  
This study aims to demonstrate the critical role IT systems can play beyond their traditional use in managing and overseeing aircraft operations. By leveraging these systems to identify inefficiencies and propose targeted improvements, maintenance operations can be significantly optimized. Statistical methods for reliability analysis, well-documented in the existing literature, serve as a foundation for this research [7; 8]. However, practical examples of their application in aviation are surprisingly limited. While these methods are primarily designed to evaluate the reliability of technical components, this study explores their broader applicability in enhancing maintainability. By addressing organizational inefficiencies, statistical techniques can help refine operational workflows and improve overall performance.  
This study focuses on the M-346 BIELIK aircraft, a relatively new addition to the Polish Air Force's fleet. Despite its modern design, the aircraft's maintenance program remains underdeveloped. Manufacturer-provided documentation lacks critical details, such as specific task durations and recommended execution sequences. This lack of clarity poses significant challenges for effective planning and scheduling, leading to organizational delays and extended maintenance times. By conducting a detailed analysis of these challenges, the research aims to identify characteristics of maintenance task durations that could streamline operations, enhance efficiency, and reduce unnecessary delays [9; 10; 11].
# Camera Calibration with Phase-Shifting Wedge Grating Array
## 3 Experiments  
The performance of the proposed method has been evaluated with some experiments. The experimental setup mainly includes a camera to be calibrated and an LCD used to display the calibration patterns. The camera model is IOI Flare 2M360-CL (IO Industries Inc., London, ON, Canada) with a resolution of \(2048\times 1088\) pixels. The camera lens has a zoom lens with a focal length of 12-35 mm. The LCD model is Philips 226V4L (Koninklijke Philips N.V., Amsterdam, Holland, The Netherlands), with a resolution of 1920 \(\times\) 1080 pixels and a pixel pitch of 0.248 mm. To begin with, the camera fixed on a device was placed at a suitable distance from the LCD and the optical axis of the camera was perpendicular to the screen. Then, a suitable focal length was adjusted to capture the sharp image of the pattern. The XY plane of the world reference frame was located on the LCD, and the Z axis was perpendicular to the planar monitor. Therefore, the feature points had the same \(z=0\), which could simplify the calculation procedures. All of the feature detection methods were implemented in MATLAB R2014a (MATLAB 8.3, MathWorks, Inc., Natick, MA, USA, 2014) and Visual C++ 2012 (Visual Studio 2012 Update 5, Microsoft Corporation, Redmond, Washington, United States, 2015). The camera intrinsic parameters were estimated using the standard calibration method implemented in the MATLAB environment, with the assistance of Camera calibration toolbox [(21)] and Image  
Figure 3: A PWG array’s pattern detection. (**a**) Phase-modulated area; and, (**b**) edges of its \(2\pi\)-phase.  
Processing Toolbox [22]. The calibration accuracy can be assessed on the basis of the root-mean-square re-projection error (RMSE) of feature points [5; 14], which can be computed by the equation:  
\[\text{RMSE}=\sqrt{\frac{1}{L}\sum_{l=1}^{L}\left[\left(u_{l}-\hat{u}_{l}\right) ^{2}+\left(v_{l}-\hat{v}_{l}\right)^{2}\right]}, \tag{11}\]  
where \(L\) is the total number of the feature points, \((u_{l},v_{l})\) are the extracted feature point locations, \((\hat{u}_{l},\hat{v}_{l})\) are the re-projection point locations, which were computed from the known world coordinates of the feature point.  
The PWG array used to calibrate the camera consists of 6 \(\times\) 6 uniform PWGs, which can produce 36 feature points per viewpoint. The minimum and maximum radii of the PWG are \(r_{\text{min}}=20\) pixels and \(r_{\text{max}}=65\) pixels. The distance between adjacent PWG centers is \(d=150\) pixels. The frequency is \(f=2/\pi\).
## 3 Results
### Active Countries in the LBA Research  
Figure 7 presents the most relevant and collaborative countries in the LBAs research. The size of the nodes represents the relevance of the countries to the subject. The network \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Publications** & **Citations** & **Average** & **Av. Norm.** & **Total Link** \\  & & & **Citations** & **Citations** & **Citations** & **Strength** \\ \hline [PERSON]. & 20 & 336 & 16.80 & 0.97 & 19 \\ [PERSON]. & 11 & 268 & 24.36 & 1.41 & 5 \\ [PERSON]. & 9 & 208 & 23.11 & 1.29 & 9 \\ [PERSON]. & 6 & 142 & 23.67 & 1.09 & 6 \\ [PERSON]. & 6 & 149 & 24.83 & 1.29 & 6 \\ [PERSON]. & 6 & 47 & 7.83 & 0.71 & 5 \\ [PERSON]. & 5 & 118 & 23.60 & 1.27 & 3 \\ [PERSON]. & 5 & 154 & 30.80 & 1.62 & 5 \\ [PERSON]. & 5 & 49 & 9.80 & 0.6 & 4 \\ [PERSON]. & 5 & 45 & 9.00 & 0.83 & 5 \\ [PERSON]. & 4 & 17 & 4.25 & 0.21 & 3 \\ [PERSON]. & 4 & 46 & 11.50 & 0.64 & 4 \\ [PERSON]. & 4 & 69 & 17.25 & 1.04 & 3 \\ [PERSON]. & 4 & 57 & 14.25 & 0.55 & 4 \\ [PERSON]. & 4 & 57 & 14.25 & 0.55 & 4 \\ [PERSON]. & 4 & 64 & 16.00 & 1.21 & 4 \\ [PERSON]. & 4 & 25 & 6.25 & 0.62 & 3 \\ [PERSON]. & 4 & 25 & 6.25 & 0.62 & 3 \\ [PERSON]. & 4 & 25 & 6.25 & 0.62 & 3 \\ [PERSON]. & 3 & 17 & 5.67 & 0.28 & 3 \\ [PERSON]. & 3 & 17 & 5.67 & 0.5 & 1 \\ [PERSON]. & 3 & 24 & 8.00 & 0.6 & 0 \\ [PERSON] [PERSON]. & 3 & 10 & 3.33 & 1.58 & 0 \\ [PERSON]. & 3 & 59 & 19.67 & 1.39 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical analysis of collaborative scholars in the LBAs research. is grouped into 4 main clusters. The clusters are not necessarily related to the geographic proximity of the countries. Table 4 organizes the most relevant countries numerically and complements the information presented in Figure 7. Japan, Lithuania, Norway, Spain, and India do not appear on the network map. These countries had a total link strength equal to 0, which means that their collaborating countries did not comply with the restrictions adopted. China stands out in terms of the number of publications and citations. China's interest in LBAs research is a consequence of its large production of residual lignin due to the paper industries [58]. The total emission of black liquor in China is approximately 3.2 million tons per year, representing about 16% by mass of all wastewater from the industrial sector [20]. In addition to pulping and paper mills residues, Chinese researchers are also studying lignins generated in biorefineries to develop new admixtures [31]. Along with China, Japan and India are the main agents in the manufacture of lignin-based products in Asia [3], which justifies the interest of Japanese and Indian researchers in LBAs. Based on the number of articles published, the most relevant countries in the LBAs research include China, USA, Canada, and Spain. Considering the criteria of average normalized citations, the major influential countries include Australia, Spain, France, and Canada. The relevance of the USA and Canada in the LBAs research may be related to the growing interest of North American companies in lignin-based products. The same happens
communication systems. 6G will follow the trend of a smart and connected society, contributing significantly to smart cities and sustainable transport while aligning with green transition goals.  
However, the integration of AI and ML into 6G communication systems presents both opportunities and challenges. These advanced computational models are expected to be the cornerstone of 6G infrastructure, driving innovations and efficiencies previously unattainable. AI and ML enable more intelligent, automated, and user-centric approaches to network management and optimization in 6G systems. The adaptability and learning capabilities of these models allow for dynamic network adjustments to meet varying demands, leading to improved resource management and network efficiency. AI-driven technologies in 6G will pave the way for advanced applications such as smart cities, autonomous systems, and IoT ecosystems, where seamless, real-time communication is crucial. The integration of AI and ML in 6G is set to transform data processing, from how data are gathered and analyzed to their utilization for decision-making. This transformation is vital for supporting the massive data volumes expected in 6G networks, enabling real-time data analysis and intelligent decision-making processes at the network edge [2; 3; 4]. Figure 1 illustrates the fundamental utilization scenarios of AI-enabled wireless communication systems.  
In this article, we provide an introduction to meta-learning by covering principles, algorithms, and theory and integrate meta-learning with three potential technologies for 6G systems: RF-based communication systems, optical communication systems, and molecular communication systems. We investigate their respective characteristics and explore their seamless integration with meta-learning-based algorithms, aiming to shed light on the potential benefits and challenges associated with their incorporation.
# Enhancing Flow Field Performance of a Small Circulating Water Channel Based on Porous Grid Plate
## 4 Discussions of the Fluid Field Characteristics
### Influences of the Screens
#### 4.2.2 Influence of the Screen Porosity \(q_{2}\)  
When the screen with a small porosity is used, a lower turbulence intensity can be obtained, as well as a large friction loss. For the screen used in a water channel for rectification, the porosity is generally greater than 0.5 [30]. Figure 13 shows the velocity distribution characteristics of the fluid field when the screen has different porosities. Figure 14 displays the distribution results of the turbulence intensity along the axial direction and the lateral direction when the screens with different porosities are used. It can be seen that, with the increase of the porosity of the screen, the velocity fluctuation and turbulence intensity of the fluid field in the test section first decrease and then increase.  
Figure 12: The velocity contours (on the left) and turbulence intensity contours (on the right) when applying a screen with different layers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **One-Layer** & **Two-Layer** & **Three-Layer** \\ \hline Velocity inhomogeneity \(\beta\) & \(1.1652\) & \(1.1521\) & \(1.1240\) \\ Average turbulence intensity & 0.0984 & 0.0524 & 0.0491 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of velocity inhomogeneity and average turbulence intensity when applying a screen with different layers.  
Table 6 shows the velocity inhomogeneity and average turbulence intensity of the test section when the screens with different porosities are selected, and the pressure distribution contours are illustrated in Figure 15. It is noted that, with the increase of the porosity of the screen, the velocity fluctuation, average turbulence intensity, and pressure uniformity of the flow in the test section decrease first and, then, increase. When the porosity of the screen is 0.6361, better flow field characteristics of the test section and a lower energy loss can be obtained. Therefore, the screens with too-large or too-small porosities will reduce the flow quality of the test section. In this work, the screen with a porosity as 0.6361 was chosen.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Porosity** & **0.5026** & **0.6013** & **0.6361** & **0.6599** & **0.7088** \\ \hline Velocity inhomogeneity \(\beta\) & 1.1366 & 1.1300 & 1.1240 & 1.1262 & 1.1472 \\ Average turbulence intensity & 0.0567 & 0.0558 & 0.0491 & 0.0512 & 0.0585 \\ \hline \end{tabular}
\end{table}
Table 6: The velocity inhomogeneity and average turbulence intensity of the test section when the screens with different porosities are selected.  
Figure 14: The distribution of the turbulence intensity when the screens with different porosities are used: (**a**) along the axial direction and (**b**) along the lateral direction.  
Figure 13: The velocity distribution characteristics of the fluid field when the screen has different porosities.
## 2 A New Outlook on Smart Cities: The Network-Defined Model  
The acquisition and analysis of empirical data from the smart city networking infrastructure provides the necessary feedback that enables the evaluation of all services and the optimal use of resources regarding long-term sustainability [5]. According to the authors' opinion (and the basis of the innovate results presented in this paper), an infrastructure-defined smart city will provide even greater potential for the designation and implementation of services while being inclusive of the time-domain and frequency-domain analysis offered in the traditional service-defined model [5].  
In this context, the term \"infrastructure\" refers directly to the network deployment via which smart city services are offered. Therefore, the infrastructure-defined model that is proposed in this paper, is, in a more explicit manner, a network-defined model. Thus, the concept of smart cities is identified as its deployment: the network infrastructure. The authors' reasoning for this lies in the fundamental notion that the existence of a smart city, which allows for a singular, base-level, and functional definition as a basis for every other case study and application, is empirically validated and confirmed via its network infrastructure. Without the interconnection of its integral components and without a network infrastructure, a smart city cannot exist as such.
## 2 Experimental Database and Methods
### Dimensionality Reduction  
The incorporation of a dimensionality reduction technique such as principal component analysis (PCA) [27] can harness the vast number of input parameters observed in data science applications. PCA acts on data by diminishing the number of input features into fewer, without significant loss of information compared to the all-feature instance, and is an important pre-processing step before feeding data to the appropriate algorithm. This may be a challenging task, since one would have to incorporate both domain knowledge and a good understanding of the algorithmic learning procedure [28]. It has been successfully applied to construction data [29].  
PCA is employed to approximate the variation in \(p\) prediction variables using \(k<p\) transformed components. The result is a smaller number of input variables that still explain most of the data variance. The eight-input data set, after the application of the PCA algorithm, is transformed into a six-component set. PCA component values are shown in Table 4. Every \(PCA_{i}\) component (\(i=1\)-\(6\)) is given by  
\[PCA_{i}=\sum_{j=1}^{6}w_{j}\times x_{j} \tag{4}\]  
The elbow criterion of a scree plot has been incorporated to depict the desired number of inputs. The scree plot in Figure 4 presents the number of components vs. the proportion of the variance explained. The first six out of the total eight components are selected since they achieve 93.4% variance.  
\begin{table}
\begin{tabular}{c c c c c c c|c c} \hline \hline  & **PCA1** & **PCA2** & **PCA3** & **PCA4** & **PCA5** & **PCA6** & **PCA7** & **PCA8** \\ \hline \(D\) & 0.256 & 0.613 & 0.062 & 0.152 & \(-0.178\) & \(-0.014\) & \(-0.422\) & \(-0.567\) \\ \(H\) & 0.251 & 0.619 & 0.037 & 0.119 & \(-0.182\) & \(-0.089\)
## 3 Results  
### Variance of NIRv in Different Remote Sensing Data with Distinct Spatial Resolutions  
The NIRv can be calculated based on MODIS, Landsat-8 and Sentinel-2 by using Equation (1). Figure 2 shows the spatial distribution of Sentinel-2 NIRv monthly.  
The area along the Yangtze River in the northern part of Suzhou was a high-value area of NIRV. Additionally, the changes in NIRv in this area were significantly greater than in other parts of the city. During the growing season, in April and from July to September, NIRv reached more than 0.45. The high NIRv value in this area was a result of the high photosynthetic ability of crops planted in this region, for example, wheat and rice. Furthermore, around Tai Lake, the large water body in the southwest of the city, there was another area with a high NIRv. Forests and crops were spread throughout this area (Figure 1b), and the peak NIRv was around 0.4 from June to August.  
The NIRv in Suzhou's built area ranged from 0 to 0.1, generally, without an evidently monthly dynamic. Nevertheless, there were many points scattered in the built area with high NIRvs (ranging from 0.2 to 0.35) and also seasonal changes. The urban forest in built areas contributed to the scattered high-value points. This kind of vegetation is usually found in small urban green spaces, and was represented as discrete points by remote sensing data with high resolution.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**NO.** & **Criteria 1** & **Criteria 2** & **TR** & **BA** & **Veg** \\ \hline
1 & NDVI\({}_{8}<0.2\) & & & C1 and 2 & BRE \\
2 & \(0.2\leq\text{NDVI}_{8}<0.5\) & & & C1 and 2 & GRA \\
3 & \(0.5\leq\text{NDVI}_{8}\leq 1\) & (\(\text{NDVI}_{8}\text{-NDVI}_{12}\))/\(\text{NDVI}_{8}>0.35\) & C2 & C1 and 2 & DBF \\
4 & \(0.5\leq\text{NDVI}_{8}\leq 1\) & \(0.2<(\text{NDVI}_{8}\text{-DVI}_{12}\))/\(\text{NDVI}_{8}<0.35\) & C2 & C1 and 2 & MF \\
5 & \(0.5\leq\text{NDVI}_{8}\leq 1\) & (\(\text{NDVI}_{8}\text{-NDVI}_{12}\))/\(\text{NDVI}_{8}<0.2\) & C2 & C1 and 2 & EBF \\ \hline \end{tabular} Note: The subscript is month, NDVI\({}_{8}\) and NDVI\({}_{12}\) are monthly NDVI in August and December. C1 and C2 are criteria 1 and criteria 2, respectively. Veg is the vegetation type. TR and BA are the land cover of tree and built area. BRE and GRA are bare land and grass land, respectively.  
\end{table}
Table 1: NDVI for identifying vegetation type in land cover of tree and built area.  
For comparing the differences of NIRv derived from MODIS, Landsat-8 and Sentinel-2, the monthly averages and standard deviations of NIRv were listed in Figure 3. Sentinel-2 NIRv showed two peaks in April and July, with values of 0.13 and 0.18, respectively. The values of MODIS NIRv were generally lower than Sentinel-2 and had similar trends to the other (see Figure 3).  
Figure 2: Spatial distribution of monthly Sentinel-2 NIRv in 2021, Suzhou.  
Sentinel-2. Due to the impact of the cloud, the continuous change of Landsat-8 NIRv was hard to obtain. Additionally, current valid data of Landsat-8 were similar to MODIS NRlv, and also showed a peak in September.