esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Challenges
### High-Quality Remote Sensed Vegetation Parameters for Direct Methods  
An analytical WUE model is the unique method for mechanistically estimating WUE at present, while uncertainty still exits due to the neglected factors which may become important at shorter time scales. The first terms involved are the key meteorological controlling factors (Ca, Pa and VPD) and the physiological parameter \(g_{1}\), which can reflect bio-geographical variation in ecosystem WUE, while validation and improvement at a short time scale is a challenge by adding other important factors with unchanging ecological significance [8]. At the same time, another two main affecting factors, such as soil and drought conditions, are not coupled in the model [6; 16]. At a regional or global scale, it is also a challenge to extrapolate input meteorological data with satellite data for matching it at a pixel scale. Although many re-analysis datasets of meteorological data have been produced, their spatial resolution is relatively low. The second term is T/ET, which is evaluated via Beer's Law, which reflects the projective real foliage cover in the original equation. Partitioning T/ET is more complex than that of projective foliage cover and is also limited at the ecosystem scale from both ground-based observation and remote sensed measurement [103]. Remote sensing estimation and verification of soil evaporation and vegetation transpiration is a challenging issue. Due to the heterogeneity of land surfaces, the soil evaporation or vegetation transpiration observed at sites are still difficult to scale up to the satellite pixel scale, and it is impossible to verify the soil evaporation and vegetation transpiration based on low- and medium-resolution remote sensing data [113; 114]. Besides, the third term in the analytical model, such as evaporation from canopy interception, is also a challenge due to a poor understanding on the underlying physical processes for both observation and remote sensing measurements [102; 115]. Although \(E_{\text{i}}\) is considered to be an important component in remote sensing based on the ET algorithm, it is still difficult to independently validate this [116].  
Overall, uncertainties in remote sensing products for estimating ecosystem WUE indirectly and for directing methods coupled with an underlying mechanism and remote-sensed vegetation dynamics remain issues to be solved. High temporal resolution remote sensing datasets and high spatial resolution meteorological products matching with remote sensing pixels are also challenges for remote sensing retrieval of ecosystem WUE at large scales. The development of remote sensing techniques associated with the understanding of carbon-water coupling are both opportunities and challenges for improving WUE remote sensing.
## 3 Methods
### V-RTI Validation: Ground-Truth Comparisons  
Two ground-truth comparisons were made to better assess the quality of the V-RTI. In the first case, the distances and standard deviation between the SfM model and the Structured Light Scanner (SLS) model of the Ellesiya chapel were calculated, assuming the scanner as the ground-truth for the comparison. In the second evaluation, a direct comparison of the same parts of the copper painting acquired both in RTI and in V-RTI were juxtaposed to empirically show the differences between both techniques.
## 3 Results and Discussion
### Prediction of Characteristic Parameters after F-T Cycles  
The current prediction of soil under cyclic conditions usually revolves around the study of the fitting parameters of the model (1). In order to predict the SWCC characteristic parameters after F-T cycles directly, the relationship function between these characteristic parameters and the number of F-T cycles \(n\) according to the variation law of SWCC characteristic parameters after F-T cycles was established in this section.  
Figure 6: Changes in pore parameters and SWCC characteristic parameters after different F–T cycles. (**a**) AEV and mean diameter with F–T cycles; (**b**) Residual moisture and mean diameter with F–T cycles; (**c**) Saturation moisture and porosity with F–T cycles; (**d**) \(s_{1}\) and porosity with F–T cycles.  
Table 2 shows that in the semi-logarithmic coordinates, each increase in the number of F-T cycles causes the SWCC to undergo a shape adjustment and a shift to the lower left as a whole. The change in shape and the overall offset reduce with the increasing number of F-T cycles. The changes in SWCC were small between five and seven F-T cycles; therefore, assuming that the number of F-T cycles n increases, the SWCC will eventually become a stable state, and the characteristic parameters and the slope used to describe the curve will tend to a constant value. A mathematical function was sought to describe this law. In this study, regression analysis was performed on them by exponential function and power function, respectively, and the curves with the best fitting effect are listed in Figure 7. The fitting effect is judged by the correlation coefficient R\({}^{2}\).  
Table 4 shows the correlation coefficients of various parameters in the curve fitting. The correlation coefficient R\({}^{2}\) in the above fitting curve is greater than 0.98, which has a good fitting effect. This indicates that this method has better prediction accuracy for SWCC characteristic parameters after F-T cycles, and can provide a simple and reliable basis for the estimation of the equilibrium humidity in geotechnical engineering in the seasonally frozen area and the strength of design considering the unsaturated soil characteristics.  
Figure 7: Fit curves of SWCC characteristic parameters and F–T cycles. (**a**) Saturated moisture content and F–T cycles; (**b**) Residual moisture content and F–T cycles; (**c**) AEV and F–T cycles; (**d**) \(s_{1}\) and F–T cycles.
# Adversarial Machine Learning for NextG Covert Communications Using Multiple Antennas
## 5 Simulation Results  
We analyzed the success of covertness achieved by CJ's perturbation at the eavesdropper and the corresponding effect on the BER at the receiver. We first assumed that the CJ only had one antenna to analyze the impact of the CJ on the eavesdropper. Then, we increased the number of antennas at the CJ to observe the performance when multiple antennas are used with different methods. We compared this perturbation with random Gaussian noise transmitted by the CJ. Furthermore, we changed the location of the CJ to investigate the effects of topology and channel.
# Analyzing Delay and CO Emissions: A Simulation Study of the Median U-Turn Method at Intersections
## 2 Materials and Methods
### Simulation Schemes  
Signal controlled left-turn traffic and through traffic have higher average delays and pollutant emissions, while uncontrolled right-turn traffic has a lower average delay and pollutant emissions at an intersection. Therefore, in this paper, the right-turn traffic volume to through traffic volume ratio (R\({}_{\text{rs}}\)) is set as a constant value of 0.2 [36], and the through traffic volume and the left-turn ratio (the left-turn traffic volume to through traffic volume ratio at the entrance lane, referred to as the left-turn ratio) are used as the two traffic parameters to determine the applicable conditions of using the median U-turn method.  
The simulation designs consider through traffic volumes both below and above 500 veh/h. This approach helps reveal the characteristics of delay and emissions at intersections with varying levels of congestion and traffic volume, as shown in Figure 5. For unobstructed intersections with a through traffic volume of 500 veh/h or less, we have configured eight sets of designs. These range from 100 to 500 veh/h. The left-turn ratios are set within specific ranges and intervals depending on the through traffic volume. For example, at 100 veh/h, the left-turn ratio ranges from 0.3 to 3.9 with an interval of 0.3. Similarly, other specific ranges and intervals are set for higher volumes. At congested intersections where the through traffic volume exceeds 500 veh/h, six sets of designs are created for volumes ranging from 500 to 3000 veh/h. For instance, at 500 veh/h, the left-turn ratio is between 2.6 and 3.0 with an interval of 0.1. For volumes of 1000 to 3000 veh/h, the left-turn ratio ranges from 0.1 to 1.0 with an interval of 0.1. The signal timing for both the median U-turn and direct left-turn methods lasts 142 s. In the median U-turn method, there are two signal phases with a green light time of 68 s and a yellow light time of 3 s for north-south and east-west through traffic. The direct left-turn method features four signal phases controlling both through and left-turn traffic for north-south and east-west directions. The green light time of the north-south and east-west through phase is 40 s, the green light time of the north-south and east-west left-turn phase is 25 s, and the yellow light time is always 3 s.
## 4 Discussion
### Limitations and Considerations  
Although this study was performed in artificial bone models under controlled conditions, there are limitations in extrapolating the results directly to clinical practice.  
All surgeries were performed by a single clinician with training in guided surgery, which may have minimized variability due to operator experience. However, this also limits generalizability, and future studies should include multiple operators and simulate in vivo conditions to assess the generalizability of these results.  
Moreover, the milling cutter drill evaluated in this study was specifically designed by the manufacturer to be compatible with its surgical kits. Therefore, its effectiveness may vary across different implant systems and may also depend on the clinician's surgical technique.  
Additionally, the absence of soft tissues, intraoral access constraints, and patient-related variables (e.g., saliva, movement, and limited visibility) may have contributed to lower deviation values than those typically observed clinically.  
The angular improvement observed, although modest numerically, may hold clinical relevance, especially in prosthetically demanding cases. Therefore, the findings should be interpreted considering the working hypothesis and previous studies on cortical bone flattening and guided accuracy tools.  
Further clinical studies are required to confirm whether the observed reduction in angular deviation leads to improved implant longevity and prosthetic outcomes. Moreover, evaluating the performance of milling cutter drills from other manufacturers, or across different surgical protocols, would broaden the applicability of these results.
# Design and Evaluation of Dynamic Topology for Mega Constellation Networks
## 5 Performance Evaluation
### Total Time Delay  
Figure 8 illustrates the total delay, consisting of propagation delay and processing delay, between 10,000 randomly selected pairs of ground stations in 6 topologies at the beginning of each different time slice.  
As shown in the figure, the variation of both is slight, and the overall trend is relatively flat. The delay of STLS is significantly lower than that of MHD, with a reduction of about 12.24% due to the shorter call distance of STLS and the lower number of relay hops.  
The algorithm used in this paper goes through the dynamic link selection strategy, which can directly launch the connected ISLs according to the changing pattern of time slices, significantly reducing the computation time; therefore, the overall latency of the dynamic strategy is lower than both STLS and MHD.  
When comparing the four dynamic topologies, Time has the highest latency and the smoothest variation. In contrast, Distance has the second highest latency and the jitteriest variation because both strategies need to calculate the distances of the remaining satellites when selecting the available satellites, which takes longer to determine.  
The total time delay of the 2 strategies constructed in this paper is significantly reduced, with DTLS1 being 51.15% and DTLS2 49.34% lower compared to Time, and DTLS1 being 30.67% and DTLS2 being 28.10% lower compared to Distance. Thus, DTLS1 has more downward propagation and processing delay, by approximately 3.57%, compared to DTLS2.
## 4 Experimental Results and Discussion
### Performance of Proposed Power Control Strategies
#### 4.1.3 P&O Based on FL  
Figures 13 and 14 illustrate the experimental results of P&O based on the FL method. In the same way as previous methods, the voltage perturbation step sizes were limited to 2, 4 and 6 V to be tested and to achieve LPP1 and LPP2 points, for the same wind speed (6.5 m/s). The results show that the \(p_{WT}\) for different step sizes oscillates near to 50 W for both LPP1 and LPP2, whereas the \(v_{WT}\) varies close to 15 V and 60 V for LPP1 and LPP2, respectively. The current (\(i_{WT}\)) evolves inversely with the voltage and decreases during the transition from LPP1 to LPP2.  
Whatever the step size is used, the FL method allows fewer power oscillations around \(P_{\rm lim}\) which leads to more efficiency. However, the response timeliness differs depending on the step size. Indeed, increasing the step size leads to reaching \(P_{\rm lim}\) quickly. Moreover, the results also reveal that there are more power oscillations for LPP2 compared to LPP1. This confirms what was observed in the fixed step size and [PERSON]'s methods while comparing between LPP1 and LPP2.  
Figure 12: [PERSON]’s variable step-size P&O for LPP2: (**a**) evolution of \(\Delta v\); (**b**) evolution of \(p_{WT}\); (**c**) evolution of \(v_{WT}\); (**d**) evolution of \(i_{WT}\).  
Figure 13: Cont.  
Figure 14: FL variable step-size P&O for LPP2: (**a**) evolution of \(\Delta v\); (**b**) evolution of \(p_{WT}\); (**c**) evolution of \(v_{WT}\); (**d**) evolution of \(i_{WT}\).  
Figure 13: FL variable step-size P&O for LPP1: (**a**) evolution of \(\Delta v\); (**b**) evolution of \(p_{WT}\); (**c**) evolution of \(v_{WT}\); (**d**) evolution of \(i_{WT}\).
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 5 Case Studies
### Traffic Scenario Definition
#### 5.3.2 The Malpensa Airspace Implementation for the AMAN  
For aircraft arrival planning and simulations, MXP airspace had to be implemented in DLR's AMAN and the traffic simulation environment (Figure 16). For this simulation, the airspace does not need all of the waypoints and sector boundaries of the real airspace; therefore, only waypoints relevant for arrivals have been inserted. Departure routes were not implemented, and this display thus looks less complex and simpler than usual radar images from MXP. For sequencing, all Standard Arrival Routes (STARs) with the related Flight Management System (FMS) waypoints were assigned as specified in the Aeronautical Information Publications (AIPs) of MXP [54]. The waypoints are connected with constraints regarding maximum and minimum altitudes, as well as maximum and minimum speeds. These data are needed for the 4D trajectory calculation since it is not only speed but also flight altitudes that have an influence on flight times until touchdown.  
Figure 14: Prepared FlightRadar24 datasets from 11 May 2019 with flights operated under adverse weather conditions.  
Figure 15: Prepared FlightRadar24 datasets from 6 August 2019 with normal undisturbed weather conditions.  
MXP has two runways in the south-north direction: 35L and 35R. Because of the closeness of the Alps, landings are exclusively northbound. With seven Entry Fixes or Metering Fixes, the TMA has an average number of entry points that, while not covering all points of the compass equally, are a good compromise with respect to the Alps, other nearby airports, and restricted military areas in the vicinity of the airport. The Path Stretching Area (PSA) to the south of the airport is formed by a trombone to the east and a double-trombone to the west, which allows for an even distribution of approaches across the two runways. According to the AIP, it is not until the Initial Approach Fix (IAF) with the name INLER on Final that a decision is made as to which runway aircraft will be guided to; however, analysis of radar data has shown that controllers guide aircraft to the appropriate centerline much earlier. Runway switches on centerline or final are rather the exception.  
STARs are assigned for each aircraft based on the respective approach direction and, from the north, are also based on utilization of the western and eastern trombones. Aircraft from the north are routed accordingly via the ODINA and RIXUV fixes, aircraft from the east are routed via EVRIP and PEXUG, aircraft from the southeast are also routed via PEXUG, aircraft from the south are routed via MEBUR, aircraft from the southwest are routed via DEVOX, aircraft from the west are routed via ASTIG, and aircraft from the northwest are routed via NELAB. During the selected days for the validation, all routes were active and used by approaches.
## 4 Conclusions  
The results of FTIR, EDS, and SEM indicated that spherical AgNPs synthesized with _C. arrenesis_ leaf extract containing organic and inorganic compounds and used for the adsorption of Cu\({}^{2+}\) ions from aqueous solutions. The results demonstrated that the highest efficacy of the AgNPs for adsorbing Cu\({}^{2+}\) ions was 98.99% at pH 12 and 298 K. The experimental data agree well with the pseudo-second-order model. The AgNPs adsorbed the Cu\({}^{2+}\) ions by diffusion on the AgNPs surface and within the pores, and the film diffusion was one of the processes that controlled the Cu\({}^{2+}\) adsorption. The Langmuir model was agreed more with experimental results than the Freundlich model in its explanation of Cu\({}^{2+}\) adsorption by AgNPs. The values of q\({}_{\rm m}\) were determined as 12.09 mg/g at 298 K and 9.98 mg/g at 323 K. The negative \(\Delta G^{\circ}\) and \(\Delta H^{\circ}\) values and the positive of \(\Delta S^{\circ}\) value, confirmed that the process of the Cu\({}^{2+}\) ions adsorption on the surface of the AgNPs was a physicochemical adsorption, and both exothermic and spontaneous. The results obtained conclude that AgNPs can be applied for Cu\({}^{2+}\) adsorption from aqueous solutions.  
Conceptualization, G.M.A.-S.; Formal analysis, G.M.A.-S.; Investigation, G.M.A.-S.; Methodology, G.M.A.-S.; Project administration, G.M.A.-S.; Resources, N.A.-K.; Supervision, G.M.A.-S.; Writing--review and editing, G.M.A.-S. and N.A.-K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University, through the Fast-track Research Funding Program.  
The authors declare that there are no conflicts of interest regarding the publication of this article.
# Modeling and Simulation of Sky Survey
## 4 Design of the Model Solving Algorithm  
According to the related work section, most of the existing sky survey planning algorithms are based on heuristics, and the most used are genetic algorithms [30,32]. Genetic algorithms still have a large advantage in solving small-scale and short-time survey observation tasks, but they are strained for wide and long-time sky survey observation tasks. In this paper, we use the Staged Design Algorithm based on the idea of greedy algorithm from the constraints of the task, which can handle the observation difficulties in long-time sky survey tasks. The observation times are reserved in advance for the difficult observation sky areas, so as to solve the problem of consuming a lot of resources to observe a few sky areas individually at the late stage of observation. The Staged Design Algorithm is divided into three steps: statistical phase (observation efficiency statistics and observation difficulty statistics), prearrangement stage, and overall arrangement stage.
## 2 Materials and Methods
### Data
#### 2.2.4 Global Forest Canopy Height 2019  
The Global Forest Canopy Height 2019 (GFCH2019) model [17] is provided at [[https://glad.umd.edu/dataset/gedi/](https://glad.umd.edu/dataset/gedi/)]([https://glad.umd.edu/dataset/gedi/](https://glad.umd.edu/dataset/gedi/)) (accessed on 20 September 2020) as 7 continental mosaics with a mesh size of 0.000 25\({}^{\circ}\) (0.9\({}^{\prime\prime}\) or \(\approx\)25 m). The model was computed by integrating laser ranging data from the Global Ecosystem Dynamics Investigation (GEDI) mission onboard the International Space Station and surface reflectance data derived from Landsat [34]. The model is visualised in Figure 4a, and its histogram is provided in (c). About 84% of the study area is vegetated according to GFCH2019; the maximum canopy height is 49 m; the mean canopy height is just above 15 m; the minimum height is 3 m. In GFCH2019, a definition of a tree was adopted as woody vegetation with a height of 3 m or above.  
Figure 4: Canopy height models: (**a**) 25 m mesh size Global Forest Canopy Height 2019 (GFCH2019) model. (**b**) 1 km mesh size Forest Canopy Height Map (FCHM). Both models are plotted with the same colour bar limits. (**c**) Histogram of GFCH2019 for the area of study. (**d**) Histogram of the FCHM for the area of study.  
Figure 3: Locations and elevations of the 2233 points of the levelling dataset.  
For validation of the Global Forest Canopy Height 2019 model data, an Airborne Laser Scanner (ALS) from the United States, The Democratic Republic of the Congo, and Australia were used. [PERSON] et al., 2021 [17] defined the forest canopy height as the 90 th percentile of the ALS-based canopy height model within the Landsat pixel and pointed out that the 95 th and 90 th percentile values are preferable to the maximum value as they represent the top of the tree canopies while avoiding LiDAR observation noise. The results of the validation reported by [17] using set-aside GEDI validation data are a mean absolute error of 4.45 m and an RMS of 6.6 m and using ALS data are a mean of 6.36 m and an RMS of 9.07 m.
## 3 Methods
### Classification Module  
The node classification module utilizes the GraphSAGE method to predict the classification of nodes. Assuming that the edge generator module produces an augmented graph \(\widetilde{\text{G}}=\left\{\widetilde{\text{A}},\widetilde{\text{H}}\right\}\), \(\widetilde{\text{H}}\) is the enhanced node representation set obtained by concatenating H (embeddings of original nodes) with embeddings of synthetic nodes, \(\widetilde{\text{A}}\) is the enhanced node adjacency matrix obtained by connecting A (adjacency matrix of original nodes) with the adjacency matrix of synthetic nodes, and VLL refers to the enhanced label set after merging the synthetic nodes into the original nodes in the node set VL.  
In \(\widetilde{\text{G}}\), the sizes of different data categories become balanced, and an unbiased GCN classifier can be trained on this basis. In this module, a GraphSAGE block is used in conjunction with a linear layer for node classification on \(\widetilde{\text{G}}\).  
\[\text{h}_{\text{v}}^{2}=\sigma\bigg{(}\text{W}^{2}\text{-CONCAT}(\text{h}_{ \text{v}}^{1},\widetilde{\text{H}}\widetilde{\text{A}}[;\text{v}])\bigg{)} \tag{8}\]  
\[\text{P}_{\text{v}}=\text{softmax}\bigg{(}\sigma\bigg{(}\text{W}^{\text{c}} \text{-CONCAT}(\text{h}_{\text{v}}^{2},\text{H}^{2}\text{-}\widetilde{\text{A} }[;\text{v}])\bigg{)}\bigg{)} \tag{9}\]  
In Equation (9), H\({}^{2}\) represents the node representation matrix of the second GraphSAGE, and W is the weight parameter. P\({}_{\text{v}}\) represents the probability distribution of node v over class labels. The classifier module is optimized using cross-entropy loss.  
\[\text{Lnode}=\sum_{\text{u}\in\text{V}_{\text{L}}}\sum_{\text{C}}(1(\text{Y}_ {\text{u}}=\text{c})\cdot\text{log}(\text{P}_{\text{v}}[\text{c}])) \tag{10}\]  
During the testing process, the predicted class of node v is set as the class with the highest probability Y\({}_{\text{v}}^{\prime}\), as shown in Equation (11):  
\[\text{Y}_{\text{v}}^{\prime}=\underset{\text{c}}{\text{argmax}}\text{P}_{ \text{v},\text{c}} \tag{11}\]  
By following the above steps, the final node classification results are obtained Y\({}_{\text{v}}^{\prime}\).