esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Ecosystem Service Assessment
#### 3.1.3 Nutrient Delivery Ratio Module  
The nutrient delivery ratio (NDR) module is used for assessing the service of nutrient retention by natural vegetation [45]. An important proxy for pollution, the proportion of nutrients retained on each pixel is considered an important water quality indicator. This model generates a series of quantitative values for sediment exported from each pixel using two delivery ratios (one for nutrients transported by surface flow and the other for subsurface flow), as well as the product of this load, as follows:  
\[x_{export_{i}}=load_{surf,i}\times NDR_{surf,i}+load_{subs,i}\times NDR_{subs,i} \tag{3}\]  
In this expression, \(x_{export_{i}}\) denotes the quantitative value of nutrients exported from each pixel \(i\), while \(load_{surf,i}\) and \(load_{subs,i}\) refer to the amounts of runoff nutrients transported by surface flow and groundwater, respectively. Thus, the nutrient load on each pixel is a function of upslope area and the downslope flow path (see the InVEST documentation for further information [45]). Similarly, \(NDR_{surf,i}\) and \(NDR_{subs,i}\) are the two delivery ratios; conceptually, the former represents the ability of a downstream pixel to transport nutrients through the surface flow without retention and incorporates a topographic index, while the latter denotes simple exponential decay with distance within the stream using a defined maximum value for subsurface nutrient retention. Thus, the nutrient total at the outlet of each watershed will be the sum of the contributions from all pixels within that watershed, as follows:  
\[x_{export_{tot}}=\sum_{i}x_{export_{i}} \tag{4}\]  
We adopted the output of this module (\(x_{export_{i}}\)) to assess the water purification. Input data in this case include a digital elevation model (DEM), LULC, proxies for nutrient runoff, the watershed, the biophysical table, subsurface retention efficiency and critical distance, as well as the accumulative value of threshold flow (details are given in Appendix A.3).
# Effect of Unit-Cell Size on the Barely Visible Impact Damage in Woven Composites
## 3 Experiment and Characterisation
### Nondestructive Testing of the Impact-Induced Damage  
#### 3.3.1 Ultrasonic C-Scanning  
As a nondestructive technique (NDT), ultrasonic C-scanning was used to get an idea of the extent of damage caused by the drop-weight impact tower and to provide more knowledge about the resistance of the various architectures of interest in this research. A Midas NDT system with Zeus software is the system used to scan the impacted specimens. It is fitted with one transmitter and one receiver with a frequency of 10 MHz, and the specimens have been mounted in-between. The diameter of the crystal and the nozzle were 10 and 8 mm, respectively. The used scanning velocity was 200 mm/min.
# Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
## 4 Discussion
In Figure 5c, the coordinates of the maximum error point are (41.70, 48.12), indicating that the difference between the actual value and the predicted value is 6.42. This may be due to the error in the chlorophyll measurement process or the error of measurement equipment or operator. Therefore, it is necessary to ensure the unification and standardization of sampling and experimental processes as much as possible to reduce errors. The growth stage is also an important factor that affects the ability of the spectral index to estimate the chlorophyll content [41]. Different models have different bands of optimal vegetation index combinations at different growth stages. According to the verification effect of the test set, the PR model performed the best at the flag-picking stage, with the best vegetation index of NDVI (679, 683) and GCI (683, 679), and the best performance of the RF model at the flowering stage and maturity stage, with NDVI (407, 412), RDVI (991, 989), and NDVI (587, 720), RVI (587, 934), and GCI (587, 724), respectively. The ANN model at the maturity stage performed the best, and the optimal number of vegetations were NDVI (989, 996) and GCI (989, 996), among which the model built at the flag-picking stage performed the best. At the same time, it also generates new ideas and methods for research in related fields. In the future, we can further explore how to translate this research into agronomic practice, such as it can provide a theoretical basis for the design of active sensors based on specific bands so as to reduce the burden of large-scale hyperspectral remote sensing data processing and meet the real-time needs of precision agriculture. In addition, the relationship between other vegetation indices and wheat growth indicators can be investigated to improve the accuracy and practicability of monitoring further. Data in this study are based on samples of different growth stages, but the constructed optimized spectral index has not been validated in different environments and years. In order to improve the robustness and generalization ability of the model, the applicability of different wheat varieties needs to be further tested.
# Intra-Mode Decision Based on Lagrange Optimization Regarding Chroma Coding
## 3 Chroma Intra-Mode Decision in VVC
### Chroma Mode Decision  
RDO is utilized to select the optimal mode for chroma intra prediction in order to achieve high coding efficiency, which can be represented by the following [27]:  
\[\min\{D\}\text{ subject to }R\leq Rc, \tag{4}\]  
where the parameters of \(R\) and \(D\) represent the rate and distortion, respectively, for a given chroma block to be encoded, and the \(Rc\) is the available bitrate target. The solved RDO problem is converted into an unconstrained form using the Lagrange optimization scheme as follows:  
\[\min\{J\}\quad J=D+\lambda\cdot R, \tag{5}\]  
where the \(J\) indicates the Lagrange cost and the \(\lambda\) is the Lagrange multiplier. There are eight chroma prediction options in one block, where the \(i^{\text{th}}\) candidate mode corresponds to one operating point with distortion \(D_{i}\) and bitrate \(R_{i}\). The intra-mode decision would search for the optimal point for the chroma block by minimizing the Lagrange cost \(J\).  
Figure 3: The framework of LM in intra prediction.  
In order to decrease the chroma intra prediction complexity in VVC, the execution flow of RDO relates to two segments, rough decision and fine decision, as shown in Figure 4. The rough decision evaluates five modes with lower complexity using the Lagrange cost \(f_{\mathit{fr}}\):  
\[f_{\mathit{fr}}=\text{SATD}+\lambda_{\mathit{fr}}\cdot R_{\mathit{fr}}, \tag{6}\]  
where the SATD is the sum of absolute Hadamard transformed difference, and \(R_{\mathit{fr}}\) is the number of bits required to code the prediction mode. The three modes with the lowest \(f_{\mathit{fr}}\) are added to the candidate set for selecting the optimal prediction mode as follows:  
\[J_{\mathit{sec}}=\text{SSE}+\lambda_{\mathit{sec}}\cdot R_{\mathit{sec}}. \tag{7}\]  
\(J_{\mathit{sec}}\) represents the Lagrange cost, and SSE stands for the sum of squared error between the original and reconstructed block. \(R_{\mathit{sec}}\) denotes the total bits spent to encode the predicted residual. Here, the relationship between \(\lambda_{\mathit{fr}}\) and \(\lambda_{\mathit{sec}}\) can be expressed as follows:  
\[\lambda_{\mathit{fr}}=\sqrt{\lambda_{\mathit{sec}}}. \tag{8}\]
## 3 The Recursive Generalized Least Squares Algorithm  
As we all know, the recursive generalized least squares algorithm can identify CARAR systems [36]. The core idea is to substitute their estimates for the unmeasurable noise terms in the information vector.  
The following is the recursive generalized least squares (RGLS) algorithm for estimating the parameter vector \(\mathbf{\theta}\) of the EEAR systems:  
\[\hat{\mathbf{\theta}}(t) = \hat{\mathbf{\theta}}(t-1)+\mathbf{L}_{1}(t)[y(t)-\hat{\mathbf{\varphi}}^{ \mathrm{r}}(t)\hat{\mathbf{\theta}}(t-1)] \tag{5}\] \[\mathbf{L}_{1}(t) = \mathbf{P}_{1}(t-1)\hat{\mathbf{\varphi}}(t)[1+\hat{\mathbf{\varphi}}^{ \mathrm{r}}(t)\mathbf{P}_{1}(t-1)\hat{\mathbf{\varphi}}(t)]^{-1}\] (6) \[\mathbf{P}_{1}(t) = [\mathbf{I}-\mathbf{L}_{1}(t)\hat{\mathbf{\varphi}}^{\mathrm{r}}(t)]\mathbf{P}_{ 1}(t-1),\;\mathbf{P}_{1}(0)=p_{0}\mathbf{I}\] (7) \[\hat{\mathbf{\varphi}}(t) = \left[\begin{array}{c}\mathbf{\varphi}_{\mathrm{s}}(t)\\ \hat{\mathbf{\varphi}}_{\mathrm{n}}(t)\end{array}\right],\quad\hat{\mathbf{\theta}}(t) =\left[\begin{array}{c}\hat{\mathbf{\theta}}_{\mathrm{s}}(t)\\ \hat{\mathbf{\theta}}_{\mathrm{n}}(t)\end{array}\right]\] (8) \[\mathbf{\varphi}_{\mathrm{s}}(t) = [-y(t-1),-y(t-2),\cdots,-y(t-n_{a}),u(t-1),u(t-2),\cdots,u(t-n_{b} )]^{\mathrm{r}}\] (9) \[\hat{\mathbf{\varphi}}_{\mathrm{n}}(t) = [-\hat{w}(t-1),-\hat{w}(t-2),\cdots,-\hat{w}(t-n_{c})]^{\mathrm{r}}\] (10) \[\hat{w}(t) = y(t)-\mathbf{\varphi}_{\mathrm{s}}^{\mathrm{r}}(t)\hat{\mathbf{\theta}}_ {\mathrm{s}}(t)\] (11) \[\hat{\mathbf{\theta}}_{\mathrm{s}}(t) = [\hat{a}_{1}(t),\hat{a}_{2}(t),\cdots,\hat{a}_{n_{a}}(t),\hat{b} _{1}(t),\hat{b}_{2}(t),\cdots,\hat{b}_{n_{b}}(t)]^{\mathrm{r}}\] (12) \[\hat{\mathbf{\theta}}_{\mathrm{n}}(t) = [\hat{c}_{1}(t),\hat{c}_{2}(t),\cdots,\hat{c}_{n_{c}}(t)]^{\mathrm{ r}} \tag{13}\]  
The RGLS algorithm can estimate the parameters of EEAR systems on-line.
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 2 Materials and Methods
### Features Selection  
Screening input variables is a feature selection process for building a diagnosis model. It is used to select the significant input variables that strongly relate to the output variable. ANOVA (analysis of variance) is the discrimination tool for tests of more than two samples. Next, after the difference is found, [PERSON]'s method is used to identify differences within the sample pairs. Minitab software was used to analyze signal data with ANOVA and [PERSON]'s tests. The features studied in this research were CV, RMSSD, MAD, and SAV.
# A Structural Analysis of AI Implementation Challenges in Healthcare
## 2 Literature Review
### Data Acquisition  
Data collection is a substantial difficulty in the incorporation of AI into the healthcare sector for several reasons. First, healthcare data are frequently dispersed across several systems, in different forms, and with varied degrees of standardization, making them difficult to combine and harmonize for AI applications [4]. Second, concerns with interoperability across various healthcare platforms impede the seamless sharing of information. Third, concerns about patient privacy and data security result in severe rules that limit the sharing and use of healthcare data for AI research. Furthermore, data quality, accuracy [15], and completeness may vary, affecting the performance and generalizability of artificial intelligence models. To address these difficulties, efforts must be made to improve data interoperability, implement strong privacy rules, and encourage collaboration across healthcare organizations to construct complete and representative data sets.
###### Abstract  
In urban gas network leakage monitoring, the optimized placement of sensors plays a pivotal role in ensuring public safety and minimizing system maintenance costs. This study introduces an innovative approach that integrates hierarchical clustering with ant colony optimization (ACO) to optimize sensor layouts in urban gas networks. The hierarchical clustering technique is first employed to evaluate the strategic importance of each monitoring node, which subsequently influences the pheromone importance parameter in the ACO algorithm. Furthermore, the proposed method accounts for soil types and gas diffusion characteristics, which affect the pheromone concentration gradient, as well as the physical distances between nodes, which determine the heuristic factors in the algorithm. By finely tuning these parameters, the method achieves a significant reduction in the number of sensors required while ensuring comprehensive network coverage, thereby improving economic and operational efficiency. The optimized sensor layout not only accelerates the response to gas leaks but also enhances the system's adaptability to complex urban environments. Simulation and field test results validate the effectiveness of this optimization approach, demonstrating its practical value in advancing the safety management of urban gas networks.  
Keywords:optimal sensor placement; hierarchical clustering; ant colony optimization; geographic information system mapping +
Footnote †: journal: applied sciences
## Results
### Morphology and Textural Properties of the Aerogel Fibers
#### 3.5.3 N\({}_{2}\) Adsorption-Desorption  
The specific surface area, pore volume, and pore size distribution of the CA fibers was obtained by BET absorption and BJH desorption. The fibers isotherm curves over relative pressure are shown in Figure 7a,b, and incremental pore volume versus pore size width are displayed in Figure 7c and d. The F\({}_{8}\) has a higher N\({}_{2}\) quantity adsorbed. The isotherms curves are similar to IUPAC type IV with a hysteresis loop in the range of 0.7-1.0, representing the presence of meso and macroporous structure [47; 48]. The multipoint BET specific surface area (SA\({}_{\text{BET}}\)) results showed that F\({}_{8}\) had a SA\({}_{\text{BET}}\) of 197 m\({}^{2}\) /g while low molecular weight F\({}_{\text{c}}\) with the same concentration (6 wt.%) proved to have SA\({}_{\text{BET}}\) of 85 m\({}^{2}\)/g. The average nanofibril diameter (D\({}_{\text{average}}\)) can be calculated from skeletal density (\(\rho_{\text{skeletal}}\)) and SA\({}_{\text{BET}}\) by the formula D\({}_{\text{average}}\) = 4/(\(\rho_{\text{skeletal}}\) * SA\({}_{\text{BET}}\)), where \(\rho_{\text{skeletal}}\) is 1.501 g/cm\({}^{3}\) for cellulose [29; 47].  
The BJH method discovered that majority of pores are distributed under 50 nm in both samples. The dissimilarity was observed over the highest peak of pore size distribution as it was narrower in the F\({}_{\text{s}}\) and located in the range of 20-30 nm, while the F\({}_{\text{c}}\) had a broader distribution of pores with a shorter peak height in the range of 20-40 nm (Figure 7d). BJH method clarified that the pore volume of F\({}_{\text{s}}\) and F\({}_{\text{c}}\) was 1.03 cm\({}^{3}\)/g and 0.36 cm\({}^{3}\)/g, respectively; moreover, the BJH desorption average pore width was 19.34 and 17.47 nm for F\({}_{\text{s}}\) and F\({}_{\text{c}}\), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Total Porosity (\%)** & **Closed Porosity (\%)** & **Open Porosity (\%)** \\ \hline F\({}_{\text{c}}\) & 77.323 \(\pm\) 2.331 & 0.00052 & 77.32263 \\ F\({}_{\text{s}}\) & 71.649 \(\pm\) 3.121 & 0.00575 & 71.64738 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The porosity analysis of F\({}_{\text{c}}\) and F\({}_{\text{s}}\) obtained by \(\upmu\)-CT assessment using 3D analysis in CTAn software.  
Figure 6: \(\upmu\)-CT images of cellulose aerogel F\({}_{\text{c}}\) (**a**) and F\({}_{\text{s}}\) (**b**) in 3 different planes (X-Z, X-Y, Z-Y) with their 3D computed structure. The color bar shows the intensity of the X-ray absorbed by the fiber matrix (min: black and max: white). Scare bar length is 150 \(\upmu\)m in all images.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 6 Current Challenges and Future Research Directions
### Future Research Directions
#### 6.3.3 Theoretical and Methodological Innovation  
Facing the unique characteristics of ETC data and complex application requirements, traditional traffic theories and analysis methods require innovative breakthroughs across multiple aspects.  
The construction of next-generation traffic flow theories represents fundamental requirements for adapting to ETC data characteristics. Traditional traffic flow theories are primarily based on continuous flow assumptions and macroscopic statistical characteristics,while ETC data provides discrete, identifiable individual vehicle trajectory information. These data characteristic changes require traffic flow theories to transition from macroscopic continuous models to microscopic discrete models and from statistical average descriptions to individual behavior characterizations. New theoretical frameworks must be established to describe traffic flow evolution patterns, interaction mechanisms, and collective behavioral characteristics based on individual trajectories.  
Complex network analysis methods possess important value in ETC data applications. Urban transportation systems are essentially complex network systems with characteristics including small-world and scale-free complex network features. Based on ETC data, more accurate traffic network topological structures and dynamic characteristics can be constructed. Through complex network analysis methods, structural characteristics, evolution patterns, and the cascade effects of traffic networks can be studied. These analysis methods not only facilitate a deep understanding of the inherent patterns in urban transportation systems but also provide theoretical guidance for network optimization, resilience enhancement, and risk prevention and control.  
Deep reinforcement learning methods provide new technical pathways for traffic optimization control. Traditional traffic control methods are primarily based on rules and experience, making adaptation to complex and variable traffic environments challenging. Deep reinforcement learning can automatically discover optimal control strategies through agent-environment interactive learning. ETC data-based reinforcement learning methods can achieve more intelligent and adaptive traffic control with broad application prospects in signal optimization, route guidance, and demand management.  
The recent GNN developments demonstrate the potential for integrating advanced graph-learning techniques with ETC infrastructure. Future research directions include adapting attention mechanisms for ETC gantry networks, developing conjoint learning frameworks for multi-source ETC data fusion, and exploring traffic-aware prediction models that leverage ETC systems' unique characteristics in urban transportation networks.
# Improving Low Earth Orbit (LEO) Prediction with Accelerometer Data
###### Abstract  
Low Earth Orbit (LEO) satellites have been widely used in scientific fields or commercial applications in recent decades. The demands of the real time scientific research or real time applications require real time precise LEO orbits. Usually, the predicted orbit is one of the solutions for real time users, so it is of great importance to investigate LEO orbit prediction for users who need real time LEO orbits. The centimeter level precision orbit is needed for high precision applications. Aiming at obtaining the predicted LEO orbit with centimeter precision, this article demonstrates the traditional method to conduct orbit prediction and put forward an idea of LEO orbit prediction by using onboard accelerometer data for real time applications. The procedure of LEO orbit prediction is proposed after comparing three different estimation strategies of retrieving initial conditions and dynamic parameters. Three strategies are estimating empirical coefficients every one cycle per revolution, which is the traditional method, estimating calibration parameters of one bias of accelerometer hourly for each direction by using accelerometer data, and estimating calibration parameters of one bias and one scale factor of the accelerometer for each direction with one arc by using accelerometer data. The results show that the predicted LEO orbit precision by using the traditional method can reach 10 cm when the predicted time is shorter than 20 min, while the predicted LEO orbit with better than 5 cm for each orbit direction can be achieved with accelerometer data even to predict one hour.  
Low Earth Orbit (LEO); orbit prediction; accelerometer data; calibration parameters; empirical coefficient; initial conditions; dynamic parameters +
Footnote †: journal:  
Article Improving Low Earth Orbit (LEO) Prediction with Accelerometer Data
###### Abstract  
The aim of this work is the study of the sky conditions climatology over Greece based on the diffuse-fraction (\(\mathrm{k_{d}}\)) limits, for clear, \(\mathrm{k_{d}}\in[0,0.26]\); intermediate, \(\mathrm{k_{d}}\in(0.26,0.78)\); and overcast, \(\mathrm{k_{d}}\in(0.78,1)\) is \(\mathrm{k_{d}}\) is, therefore, used here to characterise the sky conditions over a site. Its values are estimated from diffuse and global horizontal solar irradiances the typical meteorological years of 43 selected Greek sites. The \(\mathrm{k_{d}}\) values in each specific range are equivalent to sunshine durations (SSDs) under the particular sky conditions. Annual, seasonal, and intra-annual variations in SSDs are estimated with regression equations to fit their means. Clear sk comprise 33%, intermediate 40%, and overcast 27% of the time in a year. \(\mathrm{k_{d}}\), as an atmospheric scattering index (ASI), shows dependence on the sites' geographical latitude: best-fit lines mean ASIs are derived showing no trend, while overcast skies show a slight negative trend. A comparison of the clear-sky SSDs for four Greek sites from the Global Climate Data and one site from the Academy of Sciences of Moldova with those derived from \(\mathrm{k_{d}}\) shows a remarkable difference. A new methodology is developed that results in much smaller differences. Finally, maps of the annual SSDs and ASIs are derived for Greece.  
diffuse fraction; sky status; sunshine duration; atmospheric scattering; Greece +
Footnote †: journal: _Scienced Reviews of Applied Physics_  
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