esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Accuracy Validation for Different Land Cover Types  
By analyzing the accuracy validation bar charts classified by six different land cover types for the years 2017, 2018, and 2019 (as shown in Figure 8), several key trends and insights can be revealed.  
Figure 8: Bar chart of validation accuracy metrics (R\({}^{2}\), MAE, RMSE) of FM-LST for different land cover types from 2017 to 2019.  
In the 2017 data, the R\({}^{2}\) value for the desert areas was the highest, while the R\({}^{2}\) value for the alpine meadows was the lowest. This result may indicate that the model's surface temperature prediction capability is superior for land cover types such as deserts, which could be due to the relatively uniform surface characteristics and lower vegetation coverage in desert regions. In the same year, the MAE and RMSE values for the corn regions were higher, suggesting that surface temperature prediction for this land cover type was more challenging, or there were factors not fully captured by the model.  
In 2018, the model's prediction accuracy improved across all land cover types, especially in the desert and marshland types, where the R\({}^{2}\) values reached 0.88 and 0.83, respectively, demonstrating the model's robustness and good adaptability to these surface types. Additionally, the prediction accuracy for the corn regions also significantly improved, with notable reductions in the MAE and RMSE values.  
In 2019, the model achieved the highest prediction accuracy for the wetland areas (R\({}^{2}\) = 0.91), while the R\({}^{2}\) value for the desert regions showed a slight decrease but still remained at a high level (R\({}^{2}\) = 0.85). In this year, the MAE and RMSE values for all land cover types were generally lower than those in 2017, indicating that the accuracy and reliability of the model's predictions have improved year by year.  
In summary, the model demonstrates robust predictive performance across different land cover types, with particularly significant improvements in prediction accuracy for marshland and desert environments. This result proves the model's high capability in capturing surface temperature variations, providing a reliable technical approach for surface temperature monitoring. Although prediction accuracy shows some fluctuation in certain specific land cover types, the overall trend indicates continuous improvement, further validating the feasibility and rationality of the FM-LST generation method proposed in this study.
## 2 Preliminaries for Designing a Sliding Mode Controller
### SMC Schemes
#### 2.2.2 Terminal SMC  
TSMC originated in terminal attractor theory, a nonlinear term with fractional power included in the sliding function [2; 62]. Unlike the classic SMC, TSMC provides fast finite-time convergence to the origin for continuous-time controllers and robustness against uncertainties [2; 62; 92]. Ref. [93] introduced TSMC for second-order systems, which developed a controller for a rigid links robot based on second-order systems. Consider the next single-input affine system [62]:  
\[\dot{\mathbf{x}}=\mathbf{f}(\mathbf{x},t)+\mathbf{b}(\mathbf{x},t)u+\lambda( \mathbf{x},t), \tag{9}\]  
where \(\mathbf{x}\in\mathbb{R}^{n}\) is the system state, \(u\in\mathbb{R}\) is the control law, \(\mathbf{b}(\mathbf{x},t)\
eq 0\), and \(\lambda(\mathbf{x},t)\in\mathbb{R}^{n}\) lumps internal uncertainties and external disturbances. The sliding function defined by the authors and used subsequently in other studies [10; 62; 94; 95] was as follows:  
\[\sigma(\mathbf{x},t)=\frac{d\mathbf{x}(t)}{dt}+\beta|\mathbf{x}(t)|^{\lambda} \operatorname{sgn}\big{(}\mathbf{x}(t)\big{)}, \tag{10}\]  
where \(\beta>0\), and \(0<\lambda<1\). For \(x(0)\
eq 0\) and \(\sigma=0\) the sliding dynamics reaches the \(x=0\) equilibrium point in the time given by  
\[t_{\text{\tiny{eq}}}=\beta^{-1}(1-\lambda)^{-1}|x(0)|^{1-\lambda}. \tag{11}\]  
Nonsingular TSMC (NTSMC) was designed to address singularity problems that can arise when the sliding surface derivative exhibits negative powers, leading to a controller that cannot be implemented [10; 62]. In the case of systems of one relative degree, a modification was introduced in (10) by incorporating the integral of the fractional power term to address the singularity issue presented in the TSMC; this version is called integral TSMC (ITSMC) [62; 96].  
On the other hand, both NTSMC and TSMC have a slow convergence rate when the states are far from the equilibrium point [10; 97]. For this reason, the fast TSMC (FTSMC) was introduced to reduce the convergence time [10; 62]. Furthermore, TSMC was extended to high-order systems arising higher-order TSMC (HOTSMC) [98; 99].  
The characteristics of each TSMC have been combined in different studies to take advantage of their strengths and deal with complex dynamics [58; 100; 101; 102]. However, the design of terminal sliding mode observers and controllers is more challenging than classical SMC due to the nonlinearity of the terminal sliding mode function, as well as the control of multi-input-multi-output (MIMO) systems, the optimal tuning of TSMC parameters, and its implementation [62].
# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 5 Experiment and Evaluation
### Data Preprocess  
**Differencing Method:** To better capture the trend of storage resource utilization, we applied the differencing method to transform the original storage utilization data into growth rates. The differencing method eliminates trends and seasonality in time series data by calculating the difference between consecutive time points, thereby reflecting the rate of change more effectively. This reduces non-stationarity in the data and enhances the predictive performance of the model.  
Let \(U_{t}\) denote the original storage utilization time series, where \(t\) represents the time point. The growth rate \(G_{t}\) after differencing can be calculated using the following formula:  
\[G_{t}=\frac{U_{t}-U_{t-1}}{U_{t-1}}\times 100\% \tag{4}\]  
where  
* \(U_{t}\) represents the storage utilization at the current time point;
* \(U_{t-1}\) represents the storage utilization at the previous time point;
* \(G_{t}\) represents the growth rate of storage utilization, expressed as a percentage.
## 5 Local Path Planning Based on JPS  
### Jump Point Search (JPS)  
JPS can quickly scan nodes from the underlying grid map to identify jump points with two simple neighbor-pruning rules. JPS is an online symmetry-breaking algorithm, which speeds up the searching path on uniform-cost grid maps by \"jumping over\". Jump Point Search (JPS) is an optimization to the A* search algorithm for uniform-cost grids. It reduces symmetries in the search procedure by means of graph pruning and by eliminating certain nodes in the grid based on assumptions that can be made about the current node's neighbors, as long as certain conditions relating to the grid are satisfied. As a result, the algorithm can consider long \"jumps\" along straight (horizontal, vertical, and diagonal) lines in the grid, rather than the small steps from one grid position to the next that ordinary A* considers. Jump Point Search preserves A*'s optimality, while potentially reducing its running time by an order of magnitude.  
Node \(x\) is currently being expanded in Figure 9. In both cases, we can immediately prune all grey neighbors, which can reach optimally from the parent of \(x\) without ever going through node \(x\). The arrow indicates the direction of travel from its parent, either straight or diagonal. Node \(x\) is expanded currently. The arrow indicates the direction of travel from its parent, either straight or diagonal. Note that when \(x\) is adjacent to an obstacle, the highlighted neighbors cannot be pruned in Figure 9; any alternative optimal path, from the parent of \(x\) to each of these nodes, is blocked. When you check for a node on the right of the current node of black arrow in Figure 9a,b, you should only add the node immediately to the right of the current node. For diagonal movement, we basically use the same rules as for horizontal movement, only when we move diagonal, we also check the nodes horizontally and vertically, so we are able to find a corner situation where a forced neighbor is added to the open list, or if we find our destination node in the red circle in Figure 9c,d.  
Node \(x\) is currently expanded. \(p(x)\) is its parent in Figure 10a. We recursively apply the straight pruning rule, and identify \(y\) as a jump point successor of \(x\). Meanwhile, it has a neighbor \(z\) that cannot be reached optimally except by a path visiting \(x\). The intermediate nodes are never explicitly generated. We recursively apply the diagonal pruning rule, and identify \(y\) as a jump point successor of \(x\). Before each diagonal step, we cruise straight (dashed lines) first. Only if both straight recursions fail to identify a jump point do we step diagonally again. Node \(w\) is a forced neighbor of \(x\), and it is generated as normal in Figure 10b.
## 3 Methodology
### Application of K-Core Decomposition  
To understand the core structure and identify influential participants within each GitHub project, we applied k-core decomposition to the directed graphs generated from the activity logs. This method helps in identifying the most connected and active participants, providing insights into the stability and robustness of the project's collaborative framework.  
We began by extracting activity logs from the selected GitHub repositories, identifying the sequence of activities performed by each participant, such as create, commits, pull requests, accepted, merge and rejected. Using this information, we created directed graphs where nodes represent participants and directed edges indicate the sequence of activities between them. Each edge signifies that one participant's activity was followed by another's.  
Once the directed graphs were constructed, we applied k-core decomposition to identify core participants. The k-core of a graph is the maximal subgraph in which each node has at least \(k\) connections. We iteratively removed all vertices with a degree less than \(k\) until only vertices with at least \(k\) connections remained. The resulting subgraph was the k-core, and the k-core number for each participant was the highest \(k\) for which they remained in the subgraph.  
This analysis allowed us to assess the community complexity by examining the distribution of maximum k-values across different project duration groups. Higher k-values indicated more complex and interconnected collaboration networks. By identifying core participants and comparing their activities and contributions to non-core participants, we gained valuable insights into the significant role core participants play in the development and sustainability of the projects. This comprehensive approach provided a deeper understanding of the factors that influence project success and longevity in open-source software development.  
The k-core decomposition in this study was applied to directed graphs of participant interactions. The k-core value for each participant was calculated based on the largest k value for which the participant remained in the subgraph where each node had at least k connections. This approach effectively highlights participants who are integral to the project's collaborative structure.
# Spectrum Values in Suburban/Urban Environments above 1.5 GHz
###### Abstract  
Frequency bands higher than 3 GHz will be allocated for 5G telecommunication services. Therefore, investigating the spectrum values at these frequency bands is important for developing an appropriate deployment strategy. In this paper, spectrum values above 1.5 GHz are investigated in suburban and urban environments. In the suburban environment, the relative spectrum values at different positions in a cell are analyzed for frequencies ranging from 500 MHz to 1.5 GHz, 1.5 GHz to 3 GHz, and 3 GHz to 11 GHz. In the urban environment, the maximum achievable capacity of users is calculated for frequencies ranging from 500 MHz to 40 GHz. Numerical results show that spectrum values at lower frequency bands below 5 GHz present higher values at the cell-edge in the suburban environment and in non-line-of-sight (NLoS) situations in the urban environment. However, higher frequency bands have nearly the same impact as low frequency bands at the cell center in suburban environments above 5 GHz but show advantages in outdoor urban environments with line-of-sight (LoS) above 20 GHz. It should be noted that 3.5 GHz shows the highest value in NLoS situations and the indoor domain environment in UMi scenarios, while 28 GHz gives the optimum value at the cell-center and indoor areas with LoS in UMa scenarios. When considering the spectrum value for different frequency bands, the primary consideration should be whether the environment is NLoS or LoS, and the secondary consideration should be whether the environment is indoor or outdoor.  
spectrum value; suburban; urban; maximum achievable capacity; 5G; smart city +
Footnote †: journal: Accepted: 5 December 2018; Published: 7 December 2018
# Fuzzy Logic-Based Performance Enhancement of FSO Systems Under Adverse Weather Conditions
## 3 Fuzzy Modeling for Enhancing Receiver Performance in FSO System  
### Fuzzy Logic  
FL is a logical framework that extends the conventional binary logic. In contrast to binary-valued logic with binary numbers (0 or 1), it is a multivalued logic that encompasses a broader range of values. Furthermore, FL exhibits a capacity to emulate the human presentation of experiences. In the context of binary logic, temperatures are classified as \"hot\" if they exceed a predetermined threshold, whereas temperatures below this threshold are classified as \"cold\". In contrast, within the context of FL, the temperature variable can be denoted using language notations such as \"very hot\", \"hot\", \"normal\", \"cold\", and \"very cold\". Since its emergence in the 1960s, FL has become a significant methodology in the fields of system modeling and control [22]. The model's ability to predict unknown data is a measure of its accuracy. As a result, selecting the appropriate modeling tool is a rewarding task.  
FL can generate an accurate model with a small number of training iterations. The FL-based modeling approach is divided into three stages, including fuzzification, inference system, and defuzzification, which are shown in Figure 2. During the fuzzification and defuzzification phases, membership functions (MFs) were employed to change values from crisp to fuzzy and vice versa. The rule-based nature is represented by the inference system. The rules are normally developed by an expert, but they may also be retrieved from the data themselves. The rule is written in the following format: IF (A) THEN (B), where A and B indicate the antecedent (input) and consequence (output), respectively. The Center of Gravity (COG) defuzzification strategy is widespread in Mamdani-type, whereas the Weighted Average (Wtaver) defuzzification approach is popular in TSK-type. COG is used in this study [17].
## 7 Future Research Directions
### Enhancing Energy Efficiency and Sustainability in Routing  
One crucial aspect of multi-objective message routing in EnFVs is the need to enhance energy efficiency and promote sustainability [93]. As these vehicles rely on limited energy resources, optimizing routing decisions to minimize energy consumption and promote greener transportation is of paramount importance. Therefore, future research should explore innovative approaches to integrate energy efficiency and sustainability considerations into the GA-based routing optimization. One potential direction is to develop intelligent routing algorithms that consider the energy consumption patterns of EnFVs [94]. By incorporating vehicle-specific energy models, such as battery discharge rates or power consumption rates, into the fitness evaluation process, the GA can prioritize routes that minimize energy usage. Additionally, dynamically adapting the routing decisions based on real-time energy availability and charging station locations can further optimize energy consumption. Another avenue for research is to explore the integration of renewable energy sources and charging infrastructure planning into the routing optimization process [95]. This can involve incorporating data on renewable energy generation, such as solar or wind power availability, and identifying optimal routes that enable EnFVs to recharge at strategically located charging stations powered by renewable sources. By aligning the routing decisions with sustainable energy generation and distribution, the overall energy efficiency and sustainability of the transportation system can be improved. Furthermore, considering the environmental impact of routing decisions is crucial for promoting sustainability. Future research can focus on integrating environmental factors, such as emissions and air quality, into the multi-objective routing optimization. By incorporating environmental models and data, the GA can generate routes that minimize carbon emissions or avoid areas with high pollution levels, contributing to sustainable transportation practices. Table 5 summarizes the aforementioned directions for future research in the field.
## 2 Materials and Methods
### Data Analysis  
IRT images were analyzed using a specialized software (FLIR Tools Professional, Teledyne FLIR Systems Inc., OR, USA). The measurement was performed on the skin areas around the eyes, lacrimal sac, medial cannths (especially lacrimal caruncle), and lateral cannths (Figure 1).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Environmental** & **Month** & **Minimum** & **Maximum** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline \multirow{3}{*}{Ambient temperature (\({}^{\circ}\)C)} & February & 7.2 & 11.8 & 9.03 & 1.72 \\  & November & 13.8 & 20.4 & 17.53 & 2.76 \\  & December & 6.6 & 14.1 & 11.5 & 2.6 \\  & January & 10.7 & 12.9 & 11.8 & 1.1 \\ \hline \multirow{3}{*}{Relative humidity (\%)} & February & 55.8 & 62.9 & 58.08 & 2.9 \\  & November & 45.9 & 76.8 & 58.03 & 13.46 \\ \cline{1-1}  & December & 45.9 & 74.3 & 56.56 & 9.54 \\ \cline{1-1}  & January & 51.1 & 55 & 53.05 & 1.95 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ambient temperature (\({}^{\circ}\)C) and humidity (%) conditions according to season during infrared thermography (IRT).  
Since the limited size of the ROIs can strongly influence the temperature result depending on the measurement location, it was defined as follows:  
1. Find the three points representing the maximum temperature in the three areas (lacrimal sac, medial canthus, and lateral canthus).
2. One 4 \(\times\) 4 pixel area including the maximum temperature is defined as one ROI, and three ROIs are measured per image.
3. 2 \(\times\) 2 pixels indicates the center area of 4 \(\times\) 4 pixels. Those ROIs were chosen to minimize unnecessary information and to display the temperature more accurately when reading the average pixels compared to the value of a single pixel.
# Synthesis of Silicon Carbide Powders from Methyl-Modified Silica Aerogels
## 1 Introduction
The effects of the surface methyl groups (-CH\({}_{3}\)) and mesoporous structure of the silica aerogel on the carbothermal reduction reaction were investigated for different SiO\({}_{2}\)/C ratios. The synthesized SiC powders were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM).
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 3 Proposed Scheme
### Room Reserving
#### 3.2.2 Self-Reversible Embedding  
Self-reversible embedding is to embed all bits of the target pixels in A, \(n\) LSBs of the reference pixels in A and the LSB in B into C through the PEE algorithm. \(A_{r,map}\) is the set of locations of reference  
Figure 3: Overall framework of the proposed scheme.  
pixels in A, and the image owner sends \(n\) and \(A_{r\_map}\) as shared parameters to the data hider and receiver. More importantly, \(L\_map\) is scrambled by the self-embedding key \(k_{1}\), and is embedded into B by replacing the LSB for data extraction and image recovery to achieve complete reversibility.
# Neuroprotective Effect of the Mixture of _Gastrodiae elata_ and _Glycyrrhiziae__urlensis_ In Vitro
###### Abstract  
Background: This study investigated the effect of a mixture of _Gastrodiae elata_ and _Glycyrrhiziae__urlensis_ (GGW) on \(\beta\)-amyloid-induced neuronal damage in vitro. **Methods**: For finding the appropriate GGW ratio, we performed MTT assays using the ratios of 5:5, 6:4, 7:3, 8:2 and 9:1 in SK-N-SH cell and SH-SY-SY cell. Treatment with \(\beta\)-amyloid (10 uM) caused cell death and overexpression of acetylcholinesterase--the (ACHE) in SH-SY-SY cells. \(\beta\)-amyloid treatment increased the expression of mitogen-activated protein kinases (MAPKs). In addition, we detected the antioxidant activity of GGW using DCF-DA staining of SK-N-SH cells. To determine the effect of GGW on oxidative stress, we used a hydrogen peroxidase-induced in vitro model. **Results**: We selected the ratios of 5:5 and 7:3 mixtures with the least cytotoxicity. The 7:3 ratio of GGW (GGW73) decreased the mRNA expression of \(\mathrm{ACHE}\) in SH-SY-SY cell more than the 5:5 ratio of GGW (GGW55). GGW73 suppressed p-ERK protein expression in \(\beta\)-amyloid-treated SH-SY-SY cells. Additionally, GGW73 regulated reactive oxygen species (ROS) in vitro. GGW73 treatment regulated apoptotic factors in \(\beta\)-amyloid-stimulated neuronal cells. **Conclusions**: These results suggest that GGW73 exerts neuroprotective and anti-inflammatory effects in vitro. These results also showed that GGW73 had a protective effect against H\({}_{2}\)O\({}_{2}\) induced cell damage in an in vitro model. These results indicate the potential of GGW as a therapeutic agent for cognitive disorders.  
Gastrodiae elata; Glycyrrhiziae urlensis; neuroprotective effect; SH-SY-SY cell; anti-inflammatory effect +
Footnote †: journal: Applied
# Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM
## 1 Introduction
Recently, [PERSON] and [PERSON] [31] proposed an efficient model namely CNN-LSTM using the combination of CNN and LSTM for stably predicting electric energy consumption on IHEPC dataset. The CNN-LSTM method in this study achieves almost perfect prediction performance for IHEPC dataset compared with Linear Regression, LSTM approaches.
## 3 Results and Discussion
### Characterization of nZVISLP
#### 3.1.5 Determination of Porosity and Internal Structure  
nZVISLP was analyzed by determining its Brunauer-Emmett-Teller (BET) surface area at 77 K for the determination of its porosity and internal structure (see Figure S2, Supplementary Materials). The adsorption-desorption of MB (Figure S2a, Supplementary Materials) showed an increase in the adsorption of condensed nitrogen onto the pores with an increase in pressure, and vice versa for desorption at the same temperature. Figure S2b (Supplementary Materials) shows the variation of pore radius (r\({}_{\text{p}}\)) with respect to pore volume/pore radius (dV\({}_{\text{p}}\)/dr\({}_{\text{p}}\)). It can be observed that dV\({}_{\text{p}}\)/dr\({}_{\text{p}}\) decreased with an increase in pore radius. The values of pore volume, mean pore radius, and surface area of the synthesized adsorbent were 0.061 cm\({}^{3}\)/g, 12.20 A, and 40.87 m\({}^{2}\)/g, respectively. [PERSON] et al. [33] also prepared an iron graphite nano-composite with a surface area of 47 m\({}^{2}\)/g for the removal of methylene blue. Figure S2c (Supplementary Materials) shows the BET plot where p/V (p \(-\) p\({}_{0}\)) against p/p\({}_{0}\), which shows a linear relationship. The surface area of nZVISLP was also calculated using the size determined from dynamic light scattering (DLS), which was very small (0.2 m\({}^{2}\)/g), and that determined from BET, which was 47 m\({}^{2}\)/g. The high difference between both values represents the porous nature of the adsorbent, which enhanced the adsorption of N\({}_{2}\) in the BET analysis.