esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Amino-Groups Bonding of the Allylamine Plasma-Modified Ti Samples  
Figure 8 illustrates the RSM analysis results (concentration of amino-groups) of the allylamine plasma-modified Ti sample. The maximum concentration of amino-groups (31.58 nmole/cm\({}^{2}\)) can be obtained from the plasma-polymerized sample at power 80 W, deposition time 22 min, and chamber pressure 150 mTorr. According to the RSM analysis results, it was also discovered that the concentration of amino-groups deposited on the Ti surface is evidently a direct correlation to power and time during allylamine plasma modification. However, the deposition time can be a more vital effector compared with power. In addition, it also showed that the deposition with amino-groups was negatively correlated with pressure, which indicated that the flow rate of allylamine per second could not increase the bonding of amino-groups efficiently. This feature could be attributed to the fact that there is no way to induce allylamine to perform ionization efficiently due to the lack of vacuum degree in the chamber. This situation was similar to a previous study on hydrogen plasma modification [30]. When the hydrogen pressure was increased from 2.0 Torr to 14.0 Torr, the measured hydrogen dissociation rate was reduced from 0.83% to 0.14% at some discharge conditions. Accordingly, the parameter of pressure played an important role in the effect of the concentration of amino-groups on the Ti surface using the RSM.  
Figure 8: The RSM analysis results (concentration of amino-groups) of the allylamine plasma-modified Ti sample (80 W, 22 min, and 150 mTorr).  
Figure 7: A cross-sectional TEM micrograph of the allylamine plasma-modified sample (80 W, 22 min, and 150 mTorr).
# Research on the Characteristics of Seepage Failure in the Surrounding Rock (Coal) of the Goafs
## 3 Results and Discussion
### Test Results of Coal Samples
The gradual permeability recovery suggests that the blockages were temporary, with the channels exhibiting partial self-restoration, although not to their original state. Additionally, the increase in coal chips observed in the seepage flow provides direct evidence of changes occurring within the fracture structures of the coal samples. As shown in Figure 5, these coal chips support the idea that the fracture network within the coal is not static but is continually modified during the experiments. The generation of coal chips suggests the degradation of the coal matrix, where mechanical stress and hydraulic forces cause fragments to break away and enter the fluid flow, altering the permeability dynamics [17; 35]. In addition to coal chip blockages, another significant structural change observed was the expansion and increased connectivity of fissures, particularly under repeated pressurization and the subsequent restoration of confining pressure. This phenomenon was most prominent in sample M-4, where the combination of external forces and hydraulic pressure led to the substantial, progressive modification of the fracture network. In sample M-4, repeated cycles of increasing and restoring confining pressure induced a gradual extension of existing fractures and the formation of new fissures within the coal matrix. Furthermore, the effect of confining pressure on permeability was particularly evident, with higher confining pressures causing a significant reduction in permeability. For instance, sample M-4 exhibited stable permeability at a confining pressure of 4 MPa, but when the pressure was increased to 6 MPa, a marked decline in permeability was observed after 3000 s (Figure 4b). This suggests that under increased external forces, fractures within the coal gradually closed, narrowing the seepage channels and reducing fluid flow. The coal's plastic deformation under stress compresses pore spaces and fractures, ultimately lowering permeability. This behavior highlights the sensitivity of coal's permeability to external stress, with important implications for the extraction and management of resources such as coalbed methane [35; 36; 37]. Figure 4: Curves of permeability over time (**a**) and influence of perimeter pressure on permeability (**b**). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Permeability \(K\) (10\({}^{-3}\)\(\mu\)m\({}^{2}\))** & **Sample** & **Permeability \(K\) (10\({}^{-3}\)\(\mu\)m\({}^{2}\))** \\ \hline M-1 & 0.2 & M-6 & 1.2 \\ M-2 & 0.15 & M-7 & 0.13 \\ M-3 & 0.11 & M-8 & 0.09 \\ M-4 & 0.24 & M-9 & 0.10 \\ M-5 & 0.5 & M-10 & 0.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Permeability of coal sample in saturated seepage stage.
## 4 Results and Discussion
### Wear Resistance Tests
In order to identify the wear mechanism, microscopic examinations of the sample surfaces were carried out after the tribological tests. All the samples were found to have characteristic scratches and grooves resulting from micro-cutting and micro-plowing, the presence of which indicates the abrasive wear mechanism of the samples. Examples of worn composite surfaces are shown in Figure 10. The main cause of scratches and grooves was the presence of particles of the reinforcing phase in the contact area, and also irregularities on the surface of the steel counter-sample.
# Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
## 2 Materials and Methods
### Test Verification  
To verify the accuracy of the mathematical calculation model for canopy leaf area density of an orchard based on leaf fitting, this research designs both laboratory-simulated canopy validation experiments and tests in a Nashi pear orchard with horizontal trellis cultivation. The settings for the laboratory-simulated canopy were configured by modifying the parameters, as shown specifically in Table 3. The orchard selected for this research is situated in Guanzhuangli, Fangxian Town, Danyang City, Jiangsu Province, China. The orchard adopts a standard planting pattern with row spacing of 4 m, an average tree height of 3 m, and plant spacing of 2 m. The mean temperature in the orchard is 21 \({}^{\circ}\)C, and the wind speed ranges between 1.6 m/s and 2.5 m/s. As shown in Figure 4, the self-propelled cart is positioned between two orchard rows, and the canopy on the left side of the cart's path of travel is selected as the target canopy for detection. The red arrow indicates the direction of cart movement. The cart travels 10 m in the forward direction, with sampling being conducted every 0.8 m. The sampling areas are indicated by the red rectangles in Figure 4, each with a sampling width of 0.5 m, resulting in a total of 10 sampling points. To ensure the accuracy and reliability of the data, each sampling point undergoes three detections. Concurrently, manual measurements were taken for canopy height, canopy thickness, and leaf area density within the sampling areas. The canopy thickness is defined as the distance from the farthest edge of the left canopy to the trunk of the tree. The actual leaf area density is calculated using Equation (3), as shown in [10; 15].  
\[\rho=\frac{\overline{s}\times N}{T\times H\times d} \tag{3}\]  
where T represents the canopy thickness measured at the sampling point, H represents the canopy height at the sampling point, d represents the sampling width, \(\overline{s
###### Abstract  
The ecological impacts of invasive alien plant species (IAPS) are well-documented, but a dearth of studies exist concerning its economic, livelihood, biotechnological, and health risk assessment perspectives. IAPS management is currently expanding to ecological indicator and biotechnological aspects. Hence, this study aimed to investigate the phytoremediation potential, biomedical, and bio-safety applications of _X. strumarium_ growing in different abandoned habitats in Khyber Pakhtunkhwa, Pakistan. In this study, 45 plants and soil samples were gathered from 15 abandoned sites and analyzed for Pb, Cd, Cu, and Zn concentrations; bioaccumulation (BA); and translocation factor (TF). The assayed Pb and Cd concentration was higher and above threshold in both soil-plant samples. BAF was found higher in roots than intact plants despite having a significant accumulation of Cd, Pb, and Zn, which shows high metals tolerance of this IAPS. PCA-ordination explained a high cumulative variance (98.9%) and separated roads and densely populated sites with comparatively high metals concentration, indicating the pseudometallophyte nature of _X. strumarium_. Soil, sand, and plant biomass were shown to be the major determinants affecting the heavy metals concentration and its phytoremediation significantly, which may be due to the soil's metalliferous nature in the study area. This IAPS exhibited strong translocation and hyperaccumulation capacity in different functional traits with comparatively high Pb, Cd, and Zn (\(\geq\)1 TF) mobility and, hence, can effectively be used for Pb phytoextraction and phytostabilization of Cd, Cu, and Zn, respectively. Likewise, several other non-spontaneous IAPS growing on such abandoned habitats might be promising for developing a reasonable strategic framework for heavy metals mitigation and health risk implications in this region.  
invasive plants; _X. strumarium_, bioaccumulation and translocation; phytoremediation potential nations. +
Footnote †: journal: Applied Sciences
# Signal Separation Based on Knowledge Representation
## 4 Numerical Simulation  
### Up- and Downgoing Wavefield Separation via Observed Knowledge Representation  
A simple model was built for synthetic zero-offset VSP wavefields, as shown in Figure 5. The model consists of four layers. The Ricker wavelet with a frequency of 50 Hz was used as the source signal (marked in red triangle). The grid dimensions of Model-A were 400 \(\times\) 400, with a spatial sampling interval of 5 m. The geophone array was positioned at a horizontal distance of 0.85 km (marked in dotted line). The simulation included 4000 time sampling points, with a time sampling interval of 0.5 ms. Thus, it covered the time period from 0 to 2 s.  
Figure 6 shows the synthetic VSP signal obtained by this model and the separation results of the autoencoder without knowledge guidance. The original VSP signal contains a mixture of upgoing and downgoing waves. The autoencoder network can effectively reconstruct the observed signal, while the U-Net network branches predict the upgoing and downgoing wave signals separately. It can be found that without knowledge guidance, the upgoing and downgoing waves cannot be effectively separated and contain each other's energy.  
Figure 7 shows the results of separation of upgoing and downgoing waves guided by knowledge. Experimental results demonstrate significant gradient-based feature differences between the predicted signals, achieving good separation effects. Although the separated upgoing wave signal, which is near the onset of the signal wavefield where the energy is strong, contains some energy from the downgoing wave, our focus is more on the reflected  
Figure 5: Schematic diagram of the synthetic model.  
signal. With guidance from prior knowledge, this network can effectively separate the upgoing and downgoing waves.
## 3 Data Analysis  
Several theoretical studies have shown that the elevation angle has an influence on the bistatic signal penetration down to the soil and the retrieval of vegetation parameters [33; 34]. For this reason, our statistical analysis of the GNSS-R measurements makes use of two elevation intervals (50-70\({}^{\circ}\) and 70-90\({}^{\circ}\)). Although this dependence on elevation could be reduced through the selection of a narrower range of values, 20\({}^{\circ}\) intervals were used in the present study to maintain a sufficiently high number of samples in each interval.
## 2 Related Works  
### DL-Based Detectors for Optical Remote Sensing Object Detection  
DL-based detectors have been widely applied in natural sense visual tasks. However, detectors established on NSIs need to further improve their feature extraction ability for optical remote sensing object detection tasks due to the problems of a greater intra-class difference, inter-class similarity and feature loss in ORSIs. Therefore, some heavyweight detectors have been improved and applied in ORSIs by many scholars. [PERSON] et al. [18] modified YOLOv3 with a multi-receptive field to take full advantage of the feature information and to detect optical remote sensing objects effectively. [PERSON] et al. [19] designed an end-to-end cross-scale feature fusion framework for ORSIs object detection based on Faster R-CNN with a feature pyramid network (FPN) [16]. [PERSON] et al. [20] proposed a multi-scale feature extraction network based on RetinaNet, which strengthens the detection performance of irregular objects in ORSIs. [PERSON] et al. [21] established a multi-FPN that performs well in object detection with a complex background. The above research has successfully made obvious improvements in detection accuracy, but come with the non-ignorable sacrifice of the deployability or inference speed, and thus further hinder the application of detectors in edge devices. As a consequence, some lightweight DL-based detectors have been elaborately designed and improved to facilitate the application in edge devices. [PERSON] et al. [22] designed a lightweight detector by taking advantage of YOLOv3 and DenseNet [27]. [PERSON] et al. [23] employed the backbone network of ThunerNet [28] and constructed a six-layer feature fusion pyramid to enhance the detection performance. The improved YOLOv4-tiny proposed by [PERSON] et al. [24] was constructed with an efficient channel attention mechanism to enhance the information sensitivity in each channel. [PERSON] et al. [25] established a lightweight detector for vehicle and ship detection through using a semantic transfer block and the distillation loss. Although these lightweight detectors have a better accuracy after improvement, there is still an obvious gap in the detection accuracy compared with heavyweight detectors.  
Our motivation is to propose an end-to-end detector that can achieve a higher detection accuracy, better deployability and less inference time in order to meet the requirements of edge device real-time detection. YOLOv4 [8] is one of the widely used one-stage detectors, with an impressive performance in accuracy, deployability and inference time. It has been improved and applied in various fields, such as agriculture, industry and transportation [29; 30; 31; 32], which verify its excellent generalization. In this study, YOLOv4 was utilized as the basic framework, while it was optimized from feature extraction modules, structures of the neck and the attention mechanism for a better application in optical remote sensing object detection.
## 3 Results
### The Importance of Various Variables in Relation to LST and NDVI  
RF analysis identified the primary drivers of LST and NDVI variations at the time series and national scales, respectively (Figure 5). The results highlighted the NDVI as the most important factor explaining \(\Delta\) LST variability, while FDS was the principal factor explaining \(\Delta\) NDVI variability. Specifically, the relative importance of the time series annual mean NDVI in explaining overall \(\Delta\) LST (importance = 0.65) (Figure 5c) was markedly higher than its importance in explaining the national-scale change in \(\Delta_{\rm s}\) LST (importance = 0.33) (Figure 5a). Similarly, FDS showed higher importance in explaining the time series \(\Delta\) NDVI (importance = 0.56) (Figure 5d) compared to the national-scale change in \(\Delta_{\rm s}\) NDVI (importance = 0.28) (Figure 5b). Considering these findings, alongside the observed decrease in FDS (Figure 1a) and the lack of significant differences (\(p>0.05\)) in climatic factors (Figure S6) and TWS (Figure S7) between the 1986-2000 and 2001-2016 periods, the result suggests that FDS exerts a significant influence on LST, primarily mediated through its impact on the NDVI, rather than dominant influences from the background climate or soil moisture on the NDVI trends.  
Figure 4: Relationships between FDS and LST. (**a**) Relationship between \(\Delta\) FDS and \(\Delta\) LST; (**b**) Relationship between \(\Delta\) FDS and \(\Delta\) LST for every country in every year. Orange and green dashed lines show linear fitted data between (\(\Delta\) FDS \(\leq\) 3) and (\(\Delta\) FDS \(>\) 3). (**c**) Spatial patterns of relationships between \(\Delta\) FDS and \(\Delta\) LST for every country all years. \({}^{\star}\) and \({}^{\star\star}\) symbols indicate significant change at significance levels of \(p<0.05\) and \(p<0.01\).  
Figure 5: The importance of the LST and NDVI impact factors. (**a**) The importance of the \(\Delta_{\rm s}\) LST impact factor. (**b**) The importance of the \(\Delta_{\rm s}\) NDVI impact factor. (**c**) The importance of the \(\Delta\) LST impact factor. (**d**) The importance of the \(\Delta\) NDVI impact factor.  
Correlations Between \(\Delta\) LST and \(\Delta\) NDVI, \(\Delta\) ET, and \(\Delta\) Albedo  
Linear regression analysis elucidated the relationships between the inter-period change in LST and the corresponding changes in the associated variables (Figure 6). Significant positive correlations were identified between \(\Delta_{\mathrm{s}}\) LST and both the \(\Delta_{\mathrm{s}}\) NDVI (R\({}^{2}\) = 0.54, \(p\) < 0.05) and \(\Delta_{\mathrm{s}}\) ET (R\({}^{2}\) = 0.21, \(p\) < 0.05), with the strongest association observed for the \(\Delta_{\mathrm{s}}\) NDVI (Figure 6a,b). In contrast, no statistically significant linear relationship (\(p\) > 0.05) was found between \(\Delta_{\mathrm{s}}\) LST and \(\Delta_{\mathrm{s}}\) Albedo (R\({}^{2}\) = 0.07) (Figure 6c). But, there was a significant linear relationship (R\({}^{2}\) = 0.38, \(p\) < 0.01) between \(\Delta\) LST and \(\Delta\) Albedo based on the time scale (Figure S9).
# Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery
## 3 Results  
We identified and mapped a total of 578.54 km\({}^{2}\) of mangrove ecosystem area as of 2020, with 372.04 km\({}^{2}\) located along the mainland coastal zone and 206.50 km\({}^{2}\) distributed throughout the chain of islands and cages. Our classification was highly accurate, with an overall accuracy greater than 96% and a Kappa coefficient greater than 92% (Table 1).  
We have included a static depiction of our finalized mangrove map, in which we have highlighted a selection of areas featuring small mangrove caves (Figure 2). It was not possible to highlight every such location in the static map, and many of the finer-scale details are not visible on this map, especially the many small caves along the Belize Barrier Reef. Therefore, we have included a supplemental KMZ layer depicting mangrove forested area (File S1). This KMZ layer can be opened in Google Earth or any other GIS program for a more detailed exploration of Belize's mangrove extent.  
Figure 3 compares the spectral reflectance curves for mangroves on the mainland and mangroves on the cages.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Reference Data**} \\ \cline{3-5}  & & **Mangrove** & **Non-Mangrove** & **Row Total** \\ \hline \multirow{2}{*}{**Finalized**} & **Mangrove** & 960 & 40 & _1000_ \\ \cline{2-5}  & **Non-mangrove** & 34 & 966 & _1000_ \\ \cline{2-5}  & **Column total** & _994_ & _1006_ & _2000_ \\ \hline \hline \multicolumn{5}{c}{**Overall accuracy:**} & 96.30\% \\ \hline \multicolumn{5}{c}{**Kappa coefficient:**} & 92.60\% \\ \hline \multicolumn{5}{c}{**Mangrove commission error:**} & 4.00\% \\ \hline \multicolumn{5}{c}{**Mangrove omission error:**} & 3.42\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of accuracy assessment results.  
Figure 3: Spectral reflectance curves for mangroves on the mainland and mangroves on the caves. Error bars represent 1 standard deviation from the mean.  
Figure 2: Mangrove area in Belize as of 2020. Insets provide more detailed images of mangrove cover in (**A**) Baclar Chico National Park and Marine Reserve, (**B**) South Water Caye Marine Reserve, and (**C**) Port of Honduras Marine Reserve.
## 2 Materials and Methods
### Constant-Moisture Automatic Irrigation System
#### 2.3.1 Platform Implementation  
The CAIS had two components, the Core Decision Unit (CDU) and the Sensing and Irrigation Unit (SIU). The CDU analyzed and forecasted the soil moisture. The SIU regularly sampled the soil moisture and controlled the watering system to position the irrigation point. Figure 1 shows the system architecture.  
The CDU divided the experimental plot into multiple Virtual Planting Areas (VPAs) shown in Figure 1(a). The size of each VPA was similar to the experimental plot used while building the SMF model. The CDU database stored the initial moisture value of each VPA. There was a user interface to display the moisture level of each VPA in different colors. The next irrigation interval of each VPA was also in the CDU. Every time the CAIS executed an irrigation instruction, it also sampled the soil moisture.  
The SIU, which was a three-axis device, contained the Positioning Unit (PU), Irrigation Unit (IU), and a daemon to listen to the signal from the CDU. The PU controlled the \(X\)-axis and \(Y\)-axis to locate the irrigation point of each VPA. The PU used one motor also to control the \(Z\)-axis, which installed the moisture sensing probe and the irrigation tube. The IU turned the motor clockwise to examine the soil moisture and counter-clockwise to lower the tube for watering, as shown in Figure 1(b). The IU precisely controlled the motor to probe the soil moisture in different depth layers.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Window Size** & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\ \hline
**Error(levels)** & 42.4 & 46.7 & 53 & 57.1 & 59.8 & 63.3 & 67.9 & 79.4 \\ \hline
**Window Size** & 10 & 11 & 12 & 13 & 14 & 15 & 16 & 17 \\ \hline
**Error(levels)** & 78.3 & 15.1 & 13.8 & 11.7 & 9.5 & 7.8 & 7.4 & 175.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The forecasting errors with different moving average window sizes.