esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparison of Optimum Isolation Parameters for Energy Components and PRA/PGA
Conversely, the lowest results were calculated for displacement (\(D\)) and energy ratio. According to the results of PRA/PGA, it is the lowest in \(\beta\) and the highest in energy ratio, as can be seen from Figures 7c and 7f, respectively. In the other results, Inp_En, R_En, and PRA/PGA are determined similar to each other for all ground motions. Figure 6: The convergence curves for best run of cases (**(a)** Hys_En, **(b)** Inp_En, **(c)** R_En, and **(d)** PRA/PGA). In Figure 7a, the maximum \(PRA/PGA\) is calculated for the ground motion of Denali, Alaska, with RSN number 2114. The results obtained for Duzce with RSN number 1617 revealed that the lowest values were observed for \(D\), input energy, and hysteretic energy, while the highest values were observed for \(\beta\) and energy ratio. When the constraints limiting the optimization processes are examined, it can be seen from Figure 7b that the critical ground motions for \(D\) are Landers (879) and Bam, Iran (4040). Similarly, when Figure 7c is examined, the critical ground motion limiting the \(\beta\) constraint is Duzce (1617). Furthermore, the structural responses throughout the superstructure stories were investigated according to the optimum isolator parameters. Consequently, the effectiveness of the optimization outcome regarding energy components was evaluated in comparison  
Figure 7: PRA/PGA (**a**), displacement (**b**), effective damping (**c**), input energy (**d**), hysteretic energy (**e**), and energy ratio (**f**) ground motion graphs obtained optimum isolation parameters by objective functions found. to the responses of the stories. In Figure 8, the story accelerations \((a)\) and inter-story drift rates \((ISDR)\) of the stories are presented according to the cases. Hys_En calculated the maximum values by comparing the ISDRs and story accelerations. Figure 8 shows that Imp_En and R_En determined very close story accelerations and ISDR. These outcomes are also close to the results obtained with PRA/PGA. In Figure 8b, the constraint limiting the Hys_En optimization process is ISDR. The variations in energy components by time during critical ground motions in cases are presented in Figure 9.  
Figure 8: (**a**) Story accelerations and (**b**) inter-story drift ratio (ISDR) graphs for optimum isolation parameters of cases. Figure 9: The variations of energy components by time during critical ground motions in cases. The energy composition-time graphs given in Figure 9 are the most unfavorable ground motions. So, the results of different ground motions are presented. Especially for Hys_En and Inp_En, Hector Mine is the critical ground motion. Although the aim was to minimize the hysteretic energy in Hys_En, lower hysteretic energy was obtained in Inp_En. The reason for this is that, since the input energy was minimized in Inp_En, the hysteretic energy was lower than in Hys_En.
## 3 Therapeutic Potential of Exosomes in Different Diseases
### Therapeutic Potential of Exosomes in Liver Diseases  
Exosome-based therapeutic methods for the management of different kind of diseases of the liver, including liver tumor, is progressing. Exosomes obtained from adult human liver stem cells (HLSC) were shown to inhibit the growth and survival of HepG2 and primary hepatocellular carcinoma (HCC) in vitro via the use of antitumor miRNAs [127]. Furthermore, an in vivo experiment in a murine model showed accelerated morphological and functional gains of the liver despite 70% hepatectomy as well as proliferation and apoptosis resistance of rat and human hepatocytes in vitro [128]. Additionally, exosomes obtained from human umbilical cord Mesenchymal stem cells ameliorated fibrosis of the liver induced by carbon tetrachloride (CCI\({}_{4}\)) [129]. It was demonstrated in vivo in mice that immunodeficient NOD/SCID mice with their liver engrafted with human hepatoma cells produce exosomes containing exogenous siRNA which shuttles between hepatic cells and subsequently produces CD81 siRNA [129].
# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 5 Discussion and Future Directions
### Other Ensemble Learning Algorithms  
In this study, we primarily reviewed bagging, boosting and stacking ensemble algorithms. Other ensemble learning algorithms have been developed in addition to these well-known algorithms, such as dynamic ensemble learning [115] and Bayesian additive regression trees [116]. The dynamic ensemble method, unlike static ensemble algorithms which combine fixed base learners, selects the single best learner or combines a subset of learners from the pool using a just-in-time condition that depends on the particular input pattern from which a prediction is to be made when making a prediction [117; 118; 119].  
Blending is another ensemble technique that is derived from stacking. Blending differs from stacking in that it does not use k-fold cross-validation to generate training data for the meta learner but instead uses a one-holdout set. This technique results in only a small portion of the training dataset being used to generate predictions to be used as inputs to a meta model [93; 120].
# Routing Schemes in FANETs: A Survey
## 1 Introduction
### Our Contribution  
In view of the limited research work of routing schemes in FANETs and the lack of focus on the main characteristics of FANETs, such as the three-dimensional movement and mobility, this paper provides a comprehensive survey to stimulate research interest in this topic. Various routing schemes are classified, analyzed, and discussed based on a taxonomy. Open issues are outlined.  
Table 1 summarizes papers conducting surveys on FANETs, covering various foci, which are self-explanatory given examples, as follows:  
* Motivation for the need of FANETs and an explanation of their unique characteristics;
* Comparison with other ad hoc networks, such as MANETs and VANETs;
* Requirements, such as the bandwidth and energy requirements of the flying nodes;
* Mobility models of the flying nodes, such as random waypoint and Manhattan;
* Taxonomy that covers various FANET attributes;
* Objectives, such as achieving the awareness of energy consumption and cost;
* Challenges, such as high energy consumption and number of retransmissions;
* Routing metrics, such as the residual energy level and the distance between flying nodes;
* Characteristics, such as the transmission range of flying nodes and the number of nodes in a network;
* Performance measures, such as the number of clusters in a network and cost;
* Open issues, such as enhancing QoS and addressing the dynamicity of network topology.  
\begin{table}
\begin{tabular}{l  
To the best of our knowledge, this paper is the first of its kind to provide a comprehensive survey of routing schemes in FANETs, covering a diverse range of aspects. As shown in Table 1, surveys of routing schemes in FANETs have been conducted in the literature with a diverse range of foci [18; 32; 35; 37; 39; 41], such as hierarchical and data centric approaches [18]; geographical location-based approaches [35]; as well as proactive, reactive, and hybrid routing schemes based on MANETs and VANETs [32]. In addition, although existing papers (see Table 1), including the surveys of routing schemes in FANETs, present taxonomies that categorize routing schemes and mobility models in FANETs, this paper presents a taxonomy that covers various aspects, including objectives, challenges, routing metrics, characteristics, and performance measures.
## 3 Results and Discussion  
### Zeta Potential of Coal Processed with VESFFs  
Figure 2 illustrates the zeta potential values of the coal in deionized water and four VESFFs. The presence of negatively charged functional groups, including -OH, -NH\({}_{2}\), -NH, -C=O, and -COOH, on the surface of the coal particles resulted in a negative zeta potential for coal samples from two regions subjected to treatment with deionized water (Figure 2). Following the application of VESFFs B, B\({}_{2}\), and C to the coal, a positive zeta potential was observed on the coal surface. Following the application of VESFF D to the coal samples, a negative zeta potential was observed on the coal surface. However, the electronegativity exhibited an increase when compared to that of the coal subjected to treatment with deionized water. As shown in Figure 3a, the cationic VESs and zwitterionic VESs in the B, B\({}_{2}\), and C VESFFs adsorbed on to the coal surface mainly through electrostatic attraction and Van der Waals forces. Ion pairing adsorption, which occurs through the action of electrostatic forces, involves the cationic VES molecule adsorbing onto the surface of the coal through the hydrophilic group, while the hydrophobic group extends into thesolution. The hydrophobic group in the solution interacts through Van der Waals forces to form various structural forms of adsorbed micelles, resulting in a change from a negative to positive zeta potential, as shown in Figure 2. The anionic VES in VESFF D has an opposite electrical charge to the coal surface, so that the VES generates an electrostatic repulsion on the coal surface, as illustrated in Figure 3b. The anionic VES only adsorbs on the coal surface through Van der Waals forces. Anionic and zwitterionic VES also adsorb on the coal surface through HBGs, while their hydrophilic groups extend into the solution. The HBGs interact through Van der Waals forces to form adsorbed micelles, leading to an increase in the electronegativity of the coal surface. The electrostatic force between the pore wall surface and coal particles is generally outweighed by the Van der Waals forces between the two, resulting in the adsorption of numerous coal particles onto the pore wall surface. Following treatment with VESFFs B, B2, C, and D, the electrical properties of the coal surfaces of coal samples from two regions exhibited an increase, accompanied by a notable enhancement in the electrostatic force between the pore wall and coal particles, which surpassed the Van der Waals force [42]. The mutual repulsion between the pore wall and coal particles was beneficial for the removal of particles from the coal surface. After the action of VESFFs, the coal particles blocked in the pores of coal are removed, which is beneficial to the migration of CBM.
## 6 Conclusions  
In the present study, a new water index (SWI) was constructed based on S1A and OLI-MNDWI using the multiple stepwise regression analysis method. The best classification results were obtained using the threshold of 0.2 to distinguish water and non-water from the SWI images. The advantages of extracting water using the SWI are that it is quick and more efficient than the other Machine Learning methods. In addition, the SWI threshold classification method can be applied to different regions during different periods with high quantity accuracy (approximately 99%) and overall accuracy (approximately 97%).  
A total of 41 days of water surface areas for Poyang Lake were extracted from 24 May 2015 to 14 November 2016, with an interval of 12 days. The average area of Poyang Lake was 2638.36 km\({}^{2}\) during the study period. The maximum and minimum areas were 3729.19 km\({}^{2}\) (on 17 July 2016) and 1726.73 km\({}^{2}\) (on 7 March 2016), respectively. The results showed that changes in water surface areas were dramatic within periods of 12 days. The maximum and average changed areas were 875.57 km\({}^{2}\) (with a change rate of 35% from 12 April to 24 April 2016) and 197.58 km\({}^{2}\) (with a change rate of 8.2%) in 12 days, respectively. Moreover, the change in the mid-western region of Poyang Lake was more marked, with a maximum change rate of 80% between 12 April and 24 April 2016, and an average change rate of 13%.  
Poyang Lake is composed of a number of lakes, with the number drastically fluctuating with the changes in water surface area. The number of lakes with areas of 0.1-1 km\({}^{2}\), >1-10 km\({}^{2}\), and >10-100 km\({}^{2}\) ranged from 166 to 465, from 30 to 121, and from 8 to 27, respectively, in the present study.  
These results provide scientific guidance and baseline data for high-frequency monitoring of the ecological environment and wetland management in Poyang Lake.  
**Acknowledgments:** This study was supported by the National Natural Science Foundation of China (41301390 and U1404401) and the Youth Innovation Promotion Association CAS (2017089).  
**Author Contributions:** [PERSON], [PERSON], and [PERSON] conceived and designed the methodology; [PERSON] and [PERSON] performed the methodology; [PERSON], [PERSON], and [PERSON] analyzed the data; and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Spatial Distribution and Monitoring of Land Subsidence Using Sentinel-1 SAR Data in Java, Indonesia
## 3 Results
### Spatial Distribution and Land Subsidence Rate
#### 3.1.2 Greater Jakarta  
LS in Greater Jakarta (approximately at 6.2088\({}^{\circ}\) S, 106.8456\({}^{\circ}\) E), which includes Tangerang, Jakarta, and Bekasi, has become a pressing issue due to its relatively high rate compared to other regions in Indonesia, which is up to 150 mm/year, as shown in Figure 4a. The highest LS rates in each region are highlighted in Figure 4a, including Tangerang (Figure 4(a.1)), Jakarta (Figure 4(a.2)), and Bekasi (Figure 4(a.3)), with these areas being in the alluvial plain. The time series of vertical displacement are also presented in Figure 4b, while Figure 4c shows land use and examples of field conditions. Based on Figure 4(b.1-b.3), the InSAR data series from 2017-2023 show that the LS rates in Tangerang, Jakarta, and Bekasi are up to 80 mm/year, 110 mm/year, and 150 mm/year, respectively. Figure 4(c.1-c.3) indicate that subsidence in Tangerang occurs in industrial areas, buildings in Jakarta have already sunk, and the ground level in Bekasi is now lower than the surrounding water level.  
Several studies have examined the relationship between LS and urban development in Jakarta [1]. Other studies also highlight a high correlation between LS and flood occurrences in Jakarta and its surrounding areas, such as Tanggerang and Bekasi [38; 39]. Continuous monitoring and further research are essential to identify the underlying causes of this phenomenon and to develop effective mitigation strategies [1]. Addressing LS in Greater  
Figure 3: LS in Serang City and Serang Regency: (**a**) Map of LS rate in mm/year; (**b**) Time series of LS retrieved from InSAR (2017–2023) in: (**b.1**) Serang City and (**b.2**) Serang Regency; and (**c**) land use of the region highlighted in black box areas, (**a.1**) which correspond to residential housing in Serang City (**c.1**) and (**a.2**) which correspond to industrial complexes in Serang Regency (**c.2**). Optical satellite imagery in this study is sourced from Google Earth.  
Jakarta is critical to ensure sustainable urban development and protect the livelihoods of its residents.
# Estimating the Spectral Response of Eight-Band MSFA One-Shot Cameras Using Deep Learning
## 3 Materials and Methods
### Deep-Learning Algorithms
#### 3.2.2 Deeply Recursive Convolutional Network (DRCN)  
The deeply recursive convolutional network (DRCN) is an advanced convolutional neural-network architecture for image super-resolution. This model exploits deep recursion to improve the quality of images by increasing their resolution. DRCN is based on applying the same series of convolutional layers recursively, multiple times, to extract and refine image features gradually. This recursion allows the model to be deep and efficient regarding parameters [21]. Its architecture is as follows:  
* **Input Layer:** The low-resolution image is first fed into a convolutional input layer, initialing feature extraction.
* **Recursive Convolutional Blocks:** The core of DRCN consists of several convolutional blocks that are applied recursively. Each block includes one or more convolutional layers followed by ReLU activation. The same set of layers is used multiple times to input the image.
* **Recursion:** Instead of directly stacking numerous convolutional layers, DRCN reuses the same layers to obtain an adequate depth without significantly increasing the parameters.
* **Fusion Layer:** Each recursive application's outputs are combined and fused to create a comprehensive, multi-scale image representation. This is a testament to DRCN's design's power to capture the full complexity of the input.
* **Reconstruction Layer:** Finally, a reconstruction layer takes the fused output and generates the final high-resolution image.  
Figure 8: Modified VDSR architecture.  
The equation resulting from this model is the following:  
* **I** is the input image.
* **K** is the convolution filter for feature extraction.
* **K2, K3,..., Kn** are the recursive layers' convolution filters.
* **K\({}_{\textbf{final}}\)** is the convolution filter for the reconstruction layer.
* **F** is the extracted feature maps.
* **R\({}_{\text{i}}\)** is the result of the i-**th** recursive layer.
* **O** is the output image.  
\[F=ReLU(K_{1}*I) \tag{5}\]  
\[R_{i}=ReLU(K_{i}*R_{i-1})+F\ with\ R_{0}=F \tag{6}\]  
\[O=K_{final}*R_{16} \tag{7}\]  
Algorithm 2 presents the pseudocode of the DRCN algorithm.
## 3 Data Analysis
### Comparison between S1-SSM and Precipitation
Thus, a precipitation event followed by a decrease in the air temperature to less than 0 \({}^{\circ}\)C justifies the presence of frozen soil conditions as the cause of a decrease in the estimated soil moisture values in the S1-derived SSM maps. After 29 days of dry conditions in the eastern part (Herault and Gard between 27 December 2016 and 25 January 2017), a sudden increase in the soil moisture was observed on 26 January 2017 with the beginning of a rainfall event (Figure 3g). The mean S1-SSM value in this area increased from 13 vol.% on 20 January 2017 to 32 vol.% on 26 January 2017. On the other hand, the absence of precipitation in the remaining parts of the region between 20 January 2017 and 26 January 2017 caused the soil moisture to attain lower values (16 vol.%) on 26 January 2017. The slight increase in soil moisture values in Gers between 20 and 26 January is probably due to the disappearance of the frozen conditions observed in the previous acquisition (20 January 2017). The map on 1 February 2017 (Figure 3h) shows high soil moisture values for the eastern part, which was affected by continuous raining events, and a general decrease in the western part due to the absence of rainfall between 20 January 2017 and 1 February 2017. The temporal analysis of S1-derived SSM values over a period of time shows the direct effect of raining episodes or dry conditions on S1-SSM values. Figure 3: \(Cont.\)  
Figure 3: _Cont._  
Figure 3: On the left, an S1-derived SSM map. On the right, the corresponding six days of GPM cumulative precipitation data. (**a**) 21 December 2016, (**b**) 27 December 2016, (**c**) 2 January 2017, (**d**) 8 January 2017, (**e**) 14 January 2017, (**f**) 20 January 2017, (**g**) 26 January 2017, and (**h**) 1 February 2017. Departments are represented by dashed lines.
# Performance of Payement Temperature Prediction Models
## 3 Results
#### 3.1.2 Predicted Minimum Temperature  
A summary of results for the minimum predicted temperature for both the calibrated and uncalibrated models is presented in Figure 2. The detailed results are presented in Table 2.  
The model predictions for minimum temperature were variable and inconsistent as shown by the low _VAF_ values. The lower the _MRE_ and _RMSE_ of the model-predicted minimum temperature, the more accurate the model is. For the different materials examined, the Diefenderfer model performed best for minimum temperature prediction for all the materials tested. The Diefenderfer model, however, had the least goodness of fit and _VAF_ for asphalt. Thus, the accuracy of the models for minimum predicted temperature in asphalt was inconsistent. \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Analysis**} & \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Viljeon Model**} & \multicolumn{2}{c}{**SHRP Superpave Model**} & \multicolumn{2}{c}{**Diefenderfer Model**} \\ \cline{3-8}  & & **Before** & _After_ & **Before** & _After_ & **Before** & _After_ \\  & & **Calibration** & _Calibration_ & **Calibration** & _Calibration_ & **Calibration** & _Calibration_ \\ \hline \multirow{4}{*}{\(R^{2}\)} & Asphalt & 0.8313 & _0.8313_ & 0.6694 & _0.6694_ & 0.8338 & _0.8338_ \\  & Gravel & 0.9411 & _0.9411_ & 0.8536 & _0.8536_ & 0.9126 & _0.9126_ \\  & Concrete & 0.9031 & _0.9031_ & 0.8842 & _0.8842_ & 0.8858 & _0.8858_ \\  & Block paving & 0.8017 & _0.8017_ & 0.7998 & _0.7998_ & 0.7598 & _0.7598_ \\ \hline \multirow{4}{*}{\(RMSE\)} & Asphalt & 11.0132 & _3.1463_ & 11.6609 & _5.0166_ & 8.5995 & 3.3905 \\  & Gravel & 10.6601 & _1.7083_ & 10.7882 & 2.3293 & 8.8178 & _1.5449_ \\  & Concrete & 9.4748 & _1.5738_ & 9.9504 & 1.77175 & 7.2553 & _1.9641_ \\  & Block paving & 7.0864 & _2.4441_ & 6.8919 & 2.1166 & 4.9582 & _3.0033_ \\ \hline \multirow{4}{*}{\(MRE\) (\%)} & Asphalt & 98.86 & 65.99 & 113.74 & 55.13 & 148.75 & 45.57 \\  & Gravel & 56.34 & 17.62 & 73.71 & _25.20_ & 66.27 & 24.54 \\  & Concrete & 84.51 & 39.55 & 106.91 & 22.34 & 63.91 & 23.65 \\  & Block paving & 53.62 & 29.65 & 77.22 & **29.42** & 66.42 & **41.99** \\ \hline \multirow{4}{*}{_VAF_ (\%)} & Asphalt & 83.01 & _82.06_ & 63.23 & **48.17** & 79.30 & 78.34 \\  & Gravel & 78.63 & **94.08** & 84.92 & **81.74** & 91.13 & _90.47_ \\ \cline{1-1}  & Concrete & 69.56 & **89.92** & 88.06 & 86.25 & 88.25 & _87.81_ \\ \cline{1-1}  & Block paving & 41.45 & **73.76** & 79.39 & **79.89** & 73.08 & 65.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analysis of uncalibrated and calibrated predicted maximum temperatures.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data  
#### 3.2.1. National Biomass and Carbon Dataset for the Year 2000  
The Woods Hole Research Center generated a high-resolution (30 m) \"National Biomass and Carbon Dataset for the Year 2000\" (referred to as NBCD) for the conterminous United States [59]. The dataset was based on an empirical modelling approach that combined FIA data with optical remote sensing data acquired from the Landsat ETM+ sensor, high-resolution InSAR data acquired from the Shuttle Radar Topography Mission (SRTM), products from the USGS NLCD 2001 (National Land Cover Dataset 2001, land cover and canopy density) and LANDFIRE (the Landscape Fire and Resource Management Planning Tools Project, existing vegetation type) projects, and topographic informationfrom the USGS National Elevation Dataset (NED). The NBCD provided two biomass maps based on different allometric equations to calculate tree-level biomass. The version of the NBCD map based on FIADB Tree Table to calculate tree biomass, which was also the source of the 240 m mosaic for the conterminous U.S, has often been adopted in published studies. NBCD provided the accuracy for each mapping ecoregion through validation against observed data from USDA Forest Service Forest Inventory and Analysis (FIA). Because of the good verification accuracy with FIA data, NBCD2000 is often used in forest research, however, it significantly overestimated biomass in urban areas [60; 61].