esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Drivers of the WUE Trends  
To extend the analysis of the underlying factors of the WUE change trends, we used seven factors (i.e., LCC, Mp, Mt, Mr, Kp, Kt and Kr) to explore the importance of each factor in influencing WUE trends. The relative importance of the factors was explored for significant changes in the WUE trends (\(p<0.05\)) in every region through the RF (Figure 5). The drivers explained that the percentages for the four regions from west to the east were approximately 52.96%, 65.91%, 69.26%, and 89.32%. The RMSE of the RF models for each region were 0.015, 0.014, 0.011 and 0.004, respectively. The factors that contributed the most to the WUEs in regions I to IV were Mt, Mp, Mp, and LCC. In region IV, Mt was also very important to the WUE. Overall, the climate factors were the dominant drivers of the WUE dynamics across the entire TNR, contributing to more than 84% of the trend, and the LCC played a relatively small role, accounting for less than 16%.  
To further explore the specific effects of each climate factor on the WUE, the PDP was used to describe the influences of single variations on the response (Figure S4). The relationship between the meteorological factors and the WUE was nonmonotonic. In region I, when the temperature reached approximately 19 \({}^{\circ}\)C, the WUE increased at the highest rate. The most important climate factor affecting the WUE in regions II and III was the precipitation, which had the greatest impact on the WUE in regions II and III when the precipitation reached approximately 30 mm and 50 mm, respectively. In region IV, the effect of the temperature on the WUE increased gradually and remained stable after approximately 16 \({}^{\circ}\)C.  
Figure 4: (**a**) The spatial distribution of the WUE trends (WUE-K) in northern China from 1982–2015 (\(p<0.05\)); (**b–e**) The WUE-K mean column chart of the conversions of cropland, forest and grassland in the four regions between 1990 and 2015 with standard deviation error bars. C refers to croplands, F to forests, and G to grasslands. C-C refers to the croplands in 1990, which were still croplands in 2015; C-F refers to the croplands in 1990, which were converted into forests in 2015; and C-G refers to the croplands in 1990, which were converted into grasslands in 2015. The rest of the abbreviations can be extrapolated following these conventions.
# Position-Based Formation Control Scheme for Crowds Using Short Range Distance (SRD)
## 4 Experiments
### Formation Control on a Dynamic Environment  
First, we showed that our algorithms are effective in a situation where the formation needs to be controlled in a dynamic environment. To prove that our algorithms are independent of the shape of the formation and the number of agents, we set up two scenarios. In the first scenario, we used a 3D mesh model to form a crowd formation. We divided the whole crowd into two groups and let each group have the same formation. The 3D mesh that we used was a bird-shaped insignia model. The number of agents in each group was 585. Figure 13(a),b show the 3D mesh model and the initial arrangement of the crowds in the model.  
In the second scenario, we used an analytical method to set up the formation. Similar to the first case, we created two groups and let each group have 375 agents. However, in the second case, one group had a circle formation, while the other group had a rectangle formation. Figure 13(c),d show the initial set-up of the crowds based on the analytical method. To make it more interesting, we made two groups meet in the middle of the environment for both cases when they move. To do this, we drew paths on the environment so that two groups mixed in the middle. In the existing study [17], it was difficult to observe the crowd colliding from various sides because the crowd could only show linear movement. However, our method can show various situations more. Figure 15 shows a case where we used a 3D mesh for the formation and drawn paths for the groups. From this experiment, we saw that the crowds changed direction along the tangent of the path.  
Figure 16 shows the simulation screenshots where two groups with different formation constraints were met in the middle. As we can see, the formations were broken to avoid collisions, but immediately recovered when the emergency situation was resolved. A key point in this simulation was that breaking of the formation constraints was limited to particular cells. Therefore, most of the agents kept the formations while they were moving. This feature creates a natural movement of the crowds. Figure 17 shows another simulation of different formations and different numbers of agents. From these experiments, we knew that the proposed algorithm worked in a highly dynamic environment. The formation of the crowd and its breakdown were convincing (please see the accompanying video S3 in the Supplementary Materials for a better understanding).  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Number of Agents** & **Min FPS** & **Avg FPS** \\ \hline
1 & 100 & 84 & 89 \\
2 & 200 & 67 & 72 \\
3 & 400 & 46 & 55 \\
4 & 800 & 29 & 35 \\
5 & 1600 & 13 & 16 \\ \hline \end{tabular}
\end{table}
Table 1: Minimum and average FPS values based on crowd size.  
Figure 14: Crowd formation in different ways (**a**) A 3D mesh for formation constraint; (**b**) Initial setup of crowds based on a 3D mesh; (**c**) Circle formation; (**d**) Rectangle formation.
# Theoretical Study on Geometries of Electrodes in Laboratory Electrical Resistivity Measurement
## 5 Conclusions  
In this study, the theoretical electrical resistance of cylindrical electrodes was derived for finite and infinite mediums, using equipotential surface area for the characterization of the materials. Because the radius, penetration depth, and position of the electrodes affect electrical resistance, experimental tests were performed for the verification of the theoretical equations. The conclusions obtained from this study are as follows:  
1. The theoretical electrical resistance, between two cylindrical electrodes with rounded tips, is represented as a function of the radius (\(r\)) and penetration depth (\(l\)) of the electrodes and the distance (\(L\)) between the two electrodes. The electrical resistance increases when the electrical resistivity of the material and the distance between the electrodes increase and the radius and penetration depth decrease.
2. Over short distances, the measured electrical resistance between two cylindrical electrodes shows a similar tendency to values predicted by the theoretical equation. However, as the distances increase, the electrodes are situated closer to the non-conductive (acrylic) walls of the container and the measured electrical resistance is higher than the predicted values. This boundary effect is caused by the distortion of the current flow near the non-conductive materials and it starts in about a quarter of the container width from the container wall. The boundary effect is adjusted by adopting the method of image charges.
3. It is verified that the electrical resistance between two cylindrical electrodes becomes identical to the electrical resistance between two half-buried spherical electrodes as the penetration depth becomes shallow. The cylindrical electrodes can be treated as half-buried spherical electrodes with the allowable error range. If the allowable error of the measured electrical resistance is 2%, the ratio between penetration depth and the radius is less than unity.
4. The parametric study shows that the boundary effect can be reduced when the ratio between the penetration depth and the radius (\(l\)/\(r\)) and the spacing and the penetration depth (\(L\)/\(l\)) are large. Therefore, it is recommended to employ slender electrodes or a short distance between two electrodes to avoid the boundary effect in a fixed size container.  
Conceptualization, methodology, formal analysis, investigation, data curation, writing--original draft preparation, C.-H.H. and S.-H.C.; writing--review and editing, supervision, project administration, funding acquisition, G.-C.C.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1A5A1014883).  
The authors declare no conflict of interest.
# Status of Phenological Research Using Sentinel-2 Data: A Review
## 1 Introduction
Early studies using simulated Sentinel-2 bands from hyperspectral data showed promising results in monitoring vegetation chlorophyll and landcover. Those studies also found Sentinel-2 bands to be scalable with many optical sensors, such as MODIS, VIIRS, MERIS, Landsat and RapidEye [54; 55]. Similarly, using S2-like bands from other optical sensors, [PERSON] et al., 2017 [56] found the results to be highly correlated with fresh biomass and the green area index (GAI), and able to capture short-lived phenological stages, thereby enabling precise monitoring of crop development. With a high geometric and spectral consistency with Landsat, and additional information in the short wave infra-red (SWIR) and red-edge wavelengths, Sentinel-2 provides exciting opportunities to monitor and study plant phenology in much greater detail [57; 58; 59]. The interest in using Sentinel-2 data for phenological research has clearly gained momentum in the last five years, as can be seen in Figure 1.  
This review paper evaluates the progress of phenology-related research in forests, crops and grasslands utilizing Sentinel-2 data after five years of its successful operation. This paper discusses the phenology of (a) forests (integration of multisensor data and the various vegetation indices used), (b) croplands (crop mapping and yield estimation using time series data), (c) grasslands (including monitoring of management practices) and (d) other vegetation cover classes (wetlands and urban areas). Additionally, the advances made in the application of the three unique narrow bands in the red-edge portion of the spectrum, the issues of cloud cover in optical sensors and gap filling of time series for phenological studies are analysed. Recent developments in the use of Sentinel-2 in phenological research are synthesized to identify remaining challenges and the scope for future research.
## 3 Results
### In Vivo Tests on Small Animal Models
#### 3.3.3 Post-Mortem Sectional Assessment  
The experiment was completed as scheduled. Euthanasia of the animals was performed after 4 weeks of observation. Before premedication, blood was drawn from the animals for diagnostic tests from the marginal ear vein (approximately 4 mL). After premedication, Morbidal was administered to induce central cardiac and respiratory arrest. A post-mortem examination included a macroscopic assessment of the surgical scar, the degree of wound healing, and the appearance of the tissue surrounding the implant, as well as an assessment of macroscopic changes in internal organs. Tissue sections surrounding the incision site were taken. Samples taken from lymph nodes, the nearest implant sites, and organs, such as the heart, thymus, liver, spleen, left kidney, and left lung, were secured for histopathological analyses. In the majority of animals, no significant changes were found in the area of the implantation site and in the abdominal and thoracic organs. Only in animal B7/8 an effusion was found within the implantation site.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Evaluated Group** & **Parameter** & **Normal Range** & **Control Group–Implantation** & **Study Group–Implantation** & **Control Group–**Euthanasia** & **Study Group–**Euthanasia** \\
**Group** & **Unit** & **for Animals** & **Mean** & **Std. Dev.** & **Mean** & **Std. Dev.** & **Mean** & **Std. Dev.** & **Mean** & **Std. Dev.** & **Mean** & **Std. Dev.** \\ \hline WBC & (\(\times 10^{9}/\)L) & 52.4-35 & 6.08 & 1.46 & 5.93 & 1.37 & 6.46 & 1.36 & 7.86 & 2.25 \\ \hline RBC & (\(\times 10^{12}/\)L) & 5–7.6 & 5.35 & 0.34 & 5.44 & 0.32 & 5.02 & 0.31 & 5.33 & 0.42 \\ \hline HCB & (g/L) & 105–170 & 115.00 & 9.27 & 112.90 & 5.20 & 112.00 & 7.58 & 115.60 & 6.62 \\ \hline HCT & (\%) & 31–46.0 & 33.62 & 2.83 & 33.18 & 1.58 & 32.26 & 2.46 & 33.17 & 1.76 \\ \hline PLT & (\(\times 10^{9}/\)L) & 100–512 & 184.60 & 33.76 & 126.20 & 52.65 & 172.40 & 79.58 & 194.30 & 32.32 \\ \hline ALT & (I/L) & 31–53 & 110.02 & 38.73 & 81.29 & 37.11 & 107.92 & 50.44 & 95.49 & 59.78 \\ \hline AST & (I/L) & 42-98 & 34.66 & 17.15 & 42.19 & 43.36 & 40.10 & 20.05 & 41.43 & 27.50 \\ Cr & (mg/dL) & 0.5-2.7 & 1.77 & 0.13 & 1.71 & 0.17 & 1.53 & 0.09 & 1.54 & 0.18 \\ \hline LDH & (I/L) & 59–389 & 114.22 & 26.25 & 120.63 & 50.52 & 99.70 & 34.01 & 150.00 & 80.15 \\ \hline TP & (g/L) & 60–83 & 59.50 & 4.37 & 56.69 & 4.02 & 60.42 & 1.67 & 59.15 & 2.91 \\ \hline Urea & (mg/dL) & 26–84 & 51.00 & 6.58 & 44.69 & 3.94 & 47.88 & 5.65 & 43.93 & 5.61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: CBC and biochemical parameters summary.
# De Novo Transcriptome Identifies Olfactory Genes in _Diachassimomorpha Ionigicaudata_ (Ashmead)
## 2 Materials and Methods
### Transcriptome Assembly  
Raw sequencing reads were cleaned using the trim_galore v0.5.0 and the clean data was quality controlled by FASTQC v0.11.7 ([[http://www.bioinformatics.babraham.ac.uk/projects/fastqc/](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/)]([http://www.bioinformatics.babraham.ac.uk/projects/fastqc/](http://www.bioinformatics.babraham.ac.uk/projects/fastqc/))). Then, clean reads were used to assemble the transcriptome for each sample using Trinity software with default parameters, as described [21]. In detail, high quality RNA-Seq reads were used to generate overlapping k-mers (25) and Inchworm was used to assemble sorted k-mers into transcript contigs based on the (k-1)-mer overlaps. Next, Chrysalis was used to cluster related Inchworm contigs into components by using grouped raw reads and paired read links. Then, a de Bruijn graph for each cluster was built by Chrysalis and reads were partitioned among the clusters. Finally, Butterfly was used to process the individual graphs and ultimately report the full-length transcripts. To remove redundant sequences, CD-HIT was used to cluster the assembled highly similar transcripts into Unigenes [22], which can be accessed in the NCBI TAS platform under the accession number GIF00000000. BUSCO v4 was used to evaluate the completeness of the assembled Unigenes [23] using the eukaryota_odb10 dataset.
# Employing Huber and TAP Losses to Improve Inter-SubNet in Speech Enhancement
## 3 Inter-SubNet with New Loss Function
### Discussion of Some Other Losses  
As discussed in the previous two subsections, Huber loss serves as a robust loss function, while TAP loss particularly considers the perceptual quality of enhanced speech. Here, we introduce the related losses and provide a simple comparison of them with Huber loss and TAP loss.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 6 Performances Analysis
### The Nomadic Mobility Model (NCM)
#### 6.3.3 Throughput  
Figure 14 presents an overview of throughput in a mobile network environment utilizing the NCM model, comparing scenarios with and without the Hello Flood attack. The \(x\)-axis represents the number of nodes, ranging from 0 to 60, while the \(y\)-axis indicates throughput as a percentage. Eight lines depict different combinations of mobile and attacker nodes, with varying percentages ranging from 10% to 40%. Mobile nodes represent scenarios without an attack, whereas attacker nodes represent those subjected to it. As depicted in the following figure, there is a consistent trend of throughput decreasing as the number of nodes rises, suggesting a possible congestion issue. Furthermore, networks  
Figure 13: E2 ED without and with attack using NCM.  
without attack nodes typically maintain higher throughput levels compared to those with attackers. Additionally, a rise in the percentage of mobile nodes tends to coincide with a decline in throughput, indicating the impact of mobility on data transmission efficiency. Notably, the inclusion of attacker nodes intensifies the decline in throughput, underscoring the negative effect of malicious activity on network performance.  
Through various mechanisms, the NCM model and the Hello Flood attack collaborate to reduce throughput. Firstly, the structured movement patterns in the NCM model can create congestion in specific areas of the network as nodes travel together, leading to packet collisions and increased retransmissions, thus slowing down throughput. Secondly, the Hello Flood attack floods the network with excessive traffic, saturating channels and causing interference, which further delays packet transmission and reduces throughput. Additionally, the movement patterns of nodes in the NCM model may introduce routing instabilities, resulting in suboptimal routing decisions and increased packet loss, which, when combined with the disruption caused by the Hello Flood attack on routing protocols, amplifies the decrease in throughput. Moreover, both the NCM model and the Hello Flood attack can cause more activity on the network, which can make it harder to share limited resources like bandwidth and processing power. This can cause transmission delays and lower throughput even more. Additionally, the Hello Flood attack may introduce additional processing overhead and network congestion, contributing to the reduction in throughput.
###### Abstract  
This study investigated the surface properties and biomechanical behaviors of a nanostructured titanium oxide (TiO) layer with different self-assembled monolayers (SAMs) of phosphonate on the surface of microscope slides. The surface properties of SAMs were analyzed using scanning electron microscopy, X-ray photoemission spectroscopy, and contact angle goniometry. Biomechanical behaviors were evaluated using nanoindentation with a diamond Berkovich indenter. Analytical results indicated that the homogenous nanostructured TiO surface was formed on the substrate surface after the plasma oxidation treatment. As the TiO surface was immersed with 11-phosphonoundecanoic acid solution (PUA-SAM/TiO), the formation of a uniform SAM can be observed on the sample surface. Moreover, the binding energy of O 1s demonstrated the presence of the bisphosphonate monolayer on the SAMs-coated samples. It was also found that the PUA-SAM/TiO sample not only possessed a higher wettability performance, but also exhibited low surface contact stiffness. A SAM surface with a high wettability and low contact stiffness could potentially promote biocompatibility and prevent the formation of a stress shielding effect. Therefore, the self-assembled technology is a promising approach that can be applied to the surface modification of biomedical implants for facilitating bone healing and osseointegration.  
titanium oxide; self-assembled monolayer; wettability; nanoindentation 2020_10_, 590; doi:10.3390/app10020590
# Bioinspired Kirigami Structure for Efficient Anchoring of Soft Robots via Optimization Analysis
## 4 Autonomous Crawling of the Robot  
Following a comprehensive experimental investigation of kirigami geometries, actuation speeds, and locomotion metrics, the triangular kirigami pattern with a 4 \(\times\) 20 configuration (\(l=7\) mm, 100 \(\upmu\)m PET sheet) was identified as the optimal design parameters for the autonomous soft robotic crawler. This configuration demonstrated superior crawling performance in experiments, leveraging enhanced pop-up deformation and pronounced anisotropic friction properties to generate effective surface anchoring and directional propulsion. The autonomous system builds upon microcontroller-driven experimentation and integrates a fully embedded control strategy. The prototype consists of an Arduino Nano microcontroller, a TB6612 FNG motor driver (PCBSINO Technologies Limited, Shenzhen, China), and 12 V lithium battery (YG 12V, Zhejiang Yuyao Guotai Electronic Co., Ltd., Yuyao, China), with a 5 V regulated supply delivered via an LM7805 converter. The complete system is housed in a lightweight 150 g platform (Figure 10). To support untethered and repeatable operation, we developed a custom firmware loop on the Arduino Nano that manages actuator timing, direction control, and polarity switching. This autonomous loop enables cyclic crawling strokes with precise dwell and transition intervals, eliminating the need for external computation or command input. Actuation was programmed for a 30 mm stroke, using an extension speed of 30 mm/s, a 0.5-second dwell at full extension, and a 30 mm/s retraction speed. A polarity-switching control algorithm ensures smooth and repeatable actuation cycles, enabling the kirigami structure to engage the substrate during extension and retract cleanly without compromising the pop-up geometry. This compact and energy-efficient configuration leverages the low current draw of the PM actuator and the voltage regulation for stable performance under load. Moreover, the modularity of the electronics makes the system easily adaptable for future integration of sensors or control extensions [29]. Under autonomous operation, the crawler achieved a crawling speed of \(\sim\)47 cm/min, with an average forward displacement of \(\sim\)18 mm per 30 mm actuator cycle. This corresponds to a stride efficiency of approximately \(\sim\)60%, confirming that nearly two-thirds of each actuator stroke contributed to productive locomotion (see Supplementary Video S1). These results validate the system's mechanical and structural optimization and reinforce the potential of kirigami-enhanced soft robotics for untethered and adaptable movement in constrained environments.
# UAV Formation Trajectory Planning Algorithms: A Review
## 7 Future Research Focus and Direction
### Fault Tolerance Mechanism  
Since the maneuvering area of each UAV is very small, once a collision occurs, it will affect the adjacent UAVs, and a chain effect will be generated between the UAV clusters, which will cause the mission to fail. Therefore, the fault-tolerant redundancy mechanism is an important link to ensure the safe operation of the UAV system. At present, there is no fault-tolerant mechanism design for the core and weak links of the trajectory planning algorithm so as to improve the fault-tolerant ability of unmanned formation flight. In future research, we should focus on the design of the fault-tolerant mechanism when the function of the UAV fails to avoid uncontrollable events.