esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussions
### Molecular Docking  
A molecular docking analysis was carried out on the 12 most abundant components from the root essential oil of _P. pabularia_ using the Molegro Virtual Docker program [54]. The MolDock \"rerank\" docking energies as well as the scaled molecular docking energies are summarized in Table 3. There are two ligand binding regions in human PTP-1B--the catalytic site and an allosteric site (Figure 4). Nearly all of the ligands examined docked preferentially to the allosteric binding site in PDB 1T48, and the best docking ligand was osthole. In the allosteric binding site, the coumarin rings are located in a hydrophobic sandwich formed by Phe280 and Leu192 (Figure 5). Additionally, the aromatic residues Trp291 and Phe196 form face-to-edge \(\pi\) interactions with the coumarin moiety. Residues Ala189 and Glu276 surround the isopropylidene group of osthole. There are no apparent hydrogen-bonding interactions in the docked osthole in the allosteric binding site. The active site of PTP-1B is composed of highly polar residues, including Arg24, Lys41, Arg47, and Asp48, as well as the phosphate-binding loop, Cys215-Arg22 [55]. Therefore, the active site of PTP-1B is not a likely target for the hydrophobic essential oil components of _P. pabularia_. Nevertheless, the lowest-energy docked pose of osthole with the active site of PTP-1B (PDB 2 HB1) has a scaled docking energy of \(-103.0\) kJ/mol. This docking pose of osthole shows \(\pi\)-stacking of the coumarin moiety with Phe182 and Tyr46 and is held close to the catalytic site residues of Cys215 and Arg221 (Figure 6). In addition, there are hydrogen-bonding interactions between the osthole carbonyl oxygen and the side chains of Lys120 and Arg221. Figure 4: Ribbon structure of human protein tyrosine phosphatase 1B (PTP-1B, PDB 1T48). The catalytic site (**A**) and the allosteric binding site (**B**) are shown as green cross-hatched areas.
## 3 Experimental Results
### Microstructural Evaluation  
#### 3.4.1 Micro-Pores and Micro-Cracks  
Micro-pores and micro-cracks near the ITZ in concrete were assumed to reduce the compressive strength. The microstructure of cement paste and concrete was evaluated by backscatter-mode with the SEM. Entrained air voids are spherical, leading to easy recognition using backscatter SEM. The images show clear micro-pores and micro-cracks with the darkest color. Figure 10 shows the backscatter SEM images of each high-volume coarse aggregate concrete mixture. The specimens with natural sand show higher amounts of micro-pores and micro-cracks compared to their manufactured sand counterparts. Few micro-pores and no micro-cracks were found in the G-Ms-1 mixture, while D-Ms-1 shows unwanted entrapped air to some extent, but limited micro-cracks or micro-pores near the aggregate. Overall, darker areas represent higher porosity but do not necessarily mean structural deficiency. G-Ns-1 shows a significant amount of micro-pores surrounding the aggregate, which correlates to the lower compressive strength when compared to mixtures with the dolomite coarse aggregate. D-Ns-1 has a significant amount of both micro-cracks and micro-pores, which will likely cause a reduction in compressive strength when compared to its companion mixture with manufactured sand.  
Micro-cracks could occur during the sample preparation process of cutting, fracturing, polishing the specimens, even during CTE test. Some of cracks from those processes, however, likely involve crack widths greater than 10 \(\upmu\)m, and are confined to the sample edge [42]. Thus, the micro-cracks in Figure 10 are attributed to early-age thermal cracking, which occur when the restrained thermal  
Figure 10: Backscatter SEM images showing microstructure (micro-pores and micro-cracks).  
contraction strain exceeds the tensile strain capacity of concrete. The concrete specimens with higher CTEs are expected to be more susceptible to thermal stress, and consequently, micro-cracks can occur more easily near the ITZ [43].
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
###### Abstract  
This systematic review explores the use of digital twins (DT) for sustainable agricultural water management. DTs simulate real-time agricultural environments, enabling precise resource allocation, predictive maintenance, and scenario planning. AI enhances DT performance through machine learning (ML) and data-driven insights, optimizing water usage. In this study, from an initial pool of 48 papers retrieved from well-known databases such as Scopus and Web of Science, etc., a rigorous eligibility criterion was applied, narrowing the focus to 11 pertinent studies. This review highlights major disciplines where DT technology is being applied: hydroponics, aquaponics, vertical farming, and irrigation. Additionally, the literature identifies two key sub-applications within these disciplines: the simulation and prediction of water quality and soil water. This review also explores the types and maturity levels of DT technology and key concepts within these applications. Based on their current implementation, DTs in agriculture can be categorized into two functional types: monitoring DTs, which emphasize real-time response and environmental control, and predictive DTs, which enable proactive irrigation management through environmental forecasting. AI techniques used within the DT framework were also identified based on their applications. These findings underscore the transformative role that DT technology can play in enhancing efficiency and sustainability in agricultural water management. Despite technological advancements, challenges remain, including data integration, scalability, and cost barriers. Further studies should be conducted to explore these issues within practical farming environments.  
digital twin; artificial intelligence; machine learning; precision agriculture 2025154228
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 2 Materials and Methods
### PCA-Based Weight Determination Framework
#### 2.4.4 Balanced Fusion Concept  
These PCA-derived weights form the foundation of our balanced fusion approach that integrates both provincial forecasts and main grid forecasts. This dual-source integration methodology, detailed in Section 3, mitigates potential error accumulation from the evaluation system and leverages complementary forecasting strengths.  
By dynamically balancing provincial and main grid forecasts based on reliability scores, our approach achieves superior accuracy compared to direct aggregation methods. The complete mathematical formulation and implementation details of this balanced fusion methodology are presented in Section 3.4.
# Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
## 3 Secrecy Evaluation
### Performance Analysis with Perfect Beam Alignment
#### 3.2.1 On-Off Transmission Scheme
\begin{split}\Xi_{j}\left(V,x\right)&=\int_{0}^{2 \pi}\int_{0}^{Q}p_{j}\left(r\right)\Pr\left(\gamma_{E_{e,j}}>x\left|\Phi_{E}^{ j}\right)rdrd\theta\\ &=\frac{2\pi}{\Gamma\left(N_{j}\right)}\int_{0}^{Q}\Gamma\left( N_{j},\frac{N_{0}xR_{0}^{\mu}E_{E}}{P_{l}V\mathcal{C}_{j}}\right)p_{j}\left(r \right)rdr\\ &=\frac{2\pi}{\Gamma\left(N_{j}\right)}\sum\limits_{i=1}^{T} \bar{w}_{i}^{\left(r\right)}\Gamma\left(N_{j},\frac{N_{0}x\left(r_{i}^{2}+H^{ 2}\right)^{\frac{\alpha_{j}}{T}}}{P_{l}V\mathcal{C}_{j}}\right)p_{j}\left(r_{ i}\right)r_{i},\end{split}  \tag{13}\]  
where \(\Phi_{E^{\prime}}^{j}j\in\{L,N\}\) is the set of LOS or NLOS eavesdroppers seen from \(R_{0}\). Then, the PDF of \(\gamma_{RD,j}\) is derived as  
\[ \begin{split} f_{\gamma_{R_{0}Dj}}\left(x\right)&= \left[\Pr\left(\left|I_{R_{0}D}\right|^{2}<\frac{N_{0}xR_{R_{0}D}^{\alpha_{j}}}{ P_{l}\mathcal{C}_{R_{0}D}C_{j}}\right)\right]^{\prime}\\ &=\frac{1}{\Gamma\left(N_{j}\right)}\left[\gamma\left(N_{j},\frac {N_{0}xR_{R_{0}D}^{\alpha_{j}}}{P_{l}\mathcal{C}_{R_{0}D}C_{j}}\right)\right]^ {\prime}\!\!\!,\end{split}  \tag{14}\] \[\overset{d}{=}\frac{1}{\Gamma\left(N_{j}\right)}\left(\frac{N_{0 }xR_{R_{0}D}^{\alpha_{j}}}{P_{l}\mathcal{C}_{R_{0}D}C_{j}}\right)^{N_{j}}x^{N _{j}-1}e^{-\frac{N_{0}xR_{R_{0}D}^{\alpha_{j}}}{P_{l}\mathcal{C}_{R_{0}D}C_{j}}},\]  
where step (\(d\)) is due to ([40], Equation (0.410)) and ([40], Equation (8.350.1)). Finally, we can calculate the SOP in the on-off transmission scheme by substituting (12) and (14) into (11). **Remark 1**. : _We clarify that the TP in the on-off transmission scheme is affected by the threshold \(\mu\), the transmit power \(P_{t}\), the antenna gains of the legitimate links and the altitude of UAV relays \(H\), as indicated by (9). It is worth mentioning that the TP is not a monotonous function of \(H\). Specifically, when \(H\) starts to increase, the TP increases due to the larger LOS probability of the \(R_{0}\to D\) link. But if \(H\) is large enough, the path loss is severe, which will cause the decrease of TP. Moreover, when \(P_{t}\rightarrow\infty\), the TP \(p_{tx}\to 1\) according to (9), and the SOP \(p_{spp}^{on-off}\) approaches a constant due to the fact that the secrecy capacity \(\mathcal{C}_{R_{0}D}-\mathcal{C}_{E}\) is mainly affected by the channel states of legitimate and eavesdropping links if \(P_{t}\) is large enough._
## 6 Effect of Al\({}_{2}\)O\({}_{3}\) Modifier  
Al\({}_{2}\)O\({}_{3}\) enhances the long-time stability, chemical durability, melting properties, and opacity of glass and yet it is of low cost [54]. Table 3 summarizes the mass attenuation coefficients of the studied glass with different fractions of Al\({}_{2}\)O\({}_{3}\) at a wide photon energy range from 10 keV to 20 MeV. On the other hand, Table 4 summarizes the neutron effective removal cross sections of the studied glass with different fractions of Al\({}_{2}\)O\({}_{3}\). The photon-shielding capabilities of glass have decreased with the addition of the Al\({}_{2}\)O\({}_{3}\) modifier while the neutron-shielding capabilities are enhanced.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Neutron Energy (keV)**} & \multirow{2}{*}{**Pure Glass**} & \multicolumn{6}{c}{**Glass with Na\({}_{2}\)O Modifier**} \\ \cline{3-7}  & & **1\%** & **5\%** & **10\%** & **15\%** & **20\%** \\ \cline{2-7}  & & & **Mass Attenuation** & **Coefficients (cm\({}^{2}\)/g)** & & \\ \hline
10 & 87.95085 & 87.15763 & 83.98415 & 80.01720 & 76.05025 & 72.08331 \\
20 & 25.89585 & 25.64898 & 24.66178 & 23.42780 & 22.19381 & 20.95975 \\
30 & 8.96042 & 8.87559 & 8.53644 & 8.11250 & 7.68853 & 7.26458 \\
40 & 4.19727 & 4.15831 & 4.00245 & 3.80763 & 3.61281 & 3.41798 \\
50 & 2.34578 & 2.3466 & 2.24021 & 2.13465 & 2.02908 & 1.92353 \\
60 & 1.47964 & 1.46687 & 1.41581 & 1.35198 & 1.28816 & 1.22433 \\
70 & 1.01940 & 1.01105 & 0.97769 & 0.93597 & 0.89426 & 0.85255 \\
80 & 0.74652 & 0.74078 & 0.71783 & 0.68915 & 0.66047 & 0.63179 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The photon mass attenuation coefficients of the studied glass with Al\({}_{2}\)O\({}_{3}\) modifier.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Photon Energy (keV)**} & \multirow{2}{*}{**Pure Glass**} & \multicolumn{6}{c}{**Glass with Al\({}_{2}\)O\({}_{3}\) Modifier**} \\ \cline{3-7}  & & **1\%** & **5\%** & **10\%** & **15\%** & **20\%** \\ \cline{2-7}  & & & **Mass Attenuation** & **Coefficients (cm\({}^{2}\)/g)** & & \\ \hline
10 & 87.95085 & 87.15763 & 83.98415 & 80.01720 & 76.05025 & 72.08331 \\
20 & 25.89585 & 25.64898 & 24.66178 & 23.42780 & 22.19381 & 20.95975 \\
30 & 8.96042 & 8.87559 & 8.53644 & 8.11250 & 7.68853 & 7.26458 \\
40 & 4.19727 & 4.15831 & 4.00245 & 3.80763 & 3.61281 & 3.41798 \\
50 & 2.34578 & 2.3466 & 2.24021 & 2.13465 & 2.02908 & 1.92353 \\
60 & 1.47964 & 1.46687 & 1.41581 & 1.35198 & 1.28816 & 1.22433 \\
70 & 1.01940 & 1.01105 & 0.97769 & 0.93597 & 0.89426 & 0.85255 \\
80 & 0.74652 & 0.74078 & 0.71783 & 0.68915 & 0.66047 & 0.63179 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The neutron effective removal cross sections of the studied glass with Na\({}_{2}\)O modifier.
# Optimal Design of a Hybrid Solar PV/BG-Powered Heterogeneous Network
## 5 Performance Analysis
### Power Saving Issue  
In this work, the performance comparison among existing and proposed sleep mode algorithms has been evaluated through the assumptions described in Table 7. According to the 3 GPP TS 23.203 technical specifications, the permissible delay for non-real-time applications is 107 ms, though for specifically IoT applications this is more tolerable. By using the suggested IoT resource schedule algorithm, the proposed model produced greater power saving with a permitted delay, as seen in Figures 26 and 27. Figure 26 demonstrates the proportion of consumption over packet arrival rates. The proposed method consumes less amount of energy than the existing method. In the existing method, if the number of short-cycle is 1 or 2 with a low packet arrival rate, the simulation shows a better result in terms of power consumption than the proposed one. But it is a very small region and if we assume the delay limit is 107 ms, it will be not acceptable because, from Figure 28, we can see that it crosses the delay limit. Therefore, the downlink latency (delay) and the power-saving speed are mutually acceptable. From assumption 2, the results are illustrated in Figures 28 and 29. Here, the percentage of power consumption is greatly improved using the proposed method for a long-range packet arrival rate. For N = 1, 2, or 3, existing methods perform slightly better, as illustrated in Figure 28. However, concerning packet delay, as illustrated in Figure 29, this region is unacceptable. Therefore, the proposed  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fuels** & **Emissions (Kg/Kg Fuel)** \\ \hline Rice Husk & 1.49 \\ Bituminous Coal & 2.46 \\ Natural Gas & 1.93 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Emissions by different sources [50].  
Figure 25: Carbon footprints for the different system BW.
# An Intelligent Wireless System for Field Ecology Monitoring and Forest Fire Warning
## 3 Prediction of the Fire Weather Grade  
The fire weather grade is an important index for measuring the possibility of forest fire occurrence and the difficulty level of its spread. Additionally, it is an important basis for forest fire protection management. The forecasts made by the Bureau of Meteorology are based on macroscopic data and  
Figure 5: Real-time monitoring data of the forest microclimate.  
Figure 6: Air temperature curve of the forest microclimate.  
Figure 7: Monitoring data reports of the forest microclimate.  
are not specific enough to predict the grade of forest fire risk under different altitude, vegetation, or microclimate conditions. The microenvironment monitoring system for forest ecology can acquire and compute the local meteorological factors required for forecasting the grade of forest fire risk in real time, which plays an important role in improving the fire weather forecast and correct decision making.  
According to China's Fire Weather Grades released by Forestry and Grassland Administration in 1995, the assessment indexes of fire weather grades are comprised of the daily highest air temperature \(A\), the daily lowest relative humidity \(B\), the number of consecutive days without precipitation \(C\), the daily maximum wind scale \(D\), phenology correction index \(E5\), and partial fire weather indexes. Indexes \(A\), \(B\), \(C\), and \(D\) are determined using meteorological monitoring parameters. Index \(E\) is determined based on the empirical value or monitoring value of phenological changes and substituted into the following equation [24]:  
\[A+B+C+D-E. \tag{1}\]  
The standard is used to compute the fire weather grade. Scores are given according to the stepwise intervals. Fire risk grades with major differences are computed based on the meteorological data with minor differences. However, Correction Index \(E\) does not consider the soil moisture conditions of forests.  
The system computes the fire weather index based on real-time data obtained from the field ecological monitoring station. The computation of fire weather index is smoothed using the logistic regression function [25]. The litter layer soil moisture is introduced for index correction.
# Position Selection and Collision-Free Path Planning for Fruit Picking Robots
## 3 Collision-Free Path Planning
### Comparison to Other Algorithms
In contrast to point-based path planning algorithms, MPAPF allows the geometry of the manipulator to be considered through forward kinematics, enabling collision checking at all points on the manipulator and posture changes. A smooth path is generated without post-processing, as shown in Figure 10a, and since inverse kinematics is not used during path planning, the problem of multiple solutions does not need to be addressed. The example potential maps using simple APF and MPAPF are shown in Figure 12. Red and blue respectively represent the maximum and minimum values of the potential field, with yellow in the middle. Virtual obstacles [35] or forces [36] to modify the potential map near local minima were proposed to address the local minimum problem. However, these methods can only help in local areas and a repetitive problem may occur where the object could return to the same spot. The potential map with MPAPF is formed in the joint space. The structure of the potential map with MPAPF is complicatedly transformed, as shown in Figure 12c, which helps reduce the probability of local minima. The paths are computed to minimize the sum of potential values over several discrete-time steps, allowing for movement toward areas with higher potential values if needed. This characteristic further reduces the risk of being trapped in local minima. Since the potential map expands depending on the number of joints, the complexity of the map increases as the number of joints increases. Figure 12b is for visualization purposes to illustrate the potential map in the \(xy\)-space workspace, so it may differ from the actual potential map. Figure 12: Example of potential map: (**a**) APF; (**b**) MPAPF in xy-space; (**c**) MPAPF in joint space.
# Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors +
## 3 Reactive Features and Reconfiguration in iDSS
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Satellite/Sensors** & **Number of** & **Spatial** & **Temporal** & **Sewath (km)** & **Scale of** & **Data** \\  & **Bands** & **Resolution (m)** & **Resolution (Day)** & **Resolution (Day)** & **Application** & **Availability** \\ \hline
**NOAA/AVHRR** & 5 & 1100 & 0.5 & 2800 & R–G & 1978 \\ \hline
**MODIS** & 36 & 250–1000 & 0.5 & 2330 & R–G & 1999 \\ \hline
**Suomi NPP-VIIRS** & 22 & 375–750 & 0.5 & 3040 & R–G & 2012 \\ \hline
**MERIS** & 15 & 300 & 3 & 1150 & R-G & 2002–2012 \\ \hline
**Sentinel-3 OLCI** & 21 & 300 & 2 & 1270 & R–G & 2016 \\ \hline
**Landsat** & 4–9 & 15–80 & 16 & 185 & L–G & 1972 \\ \hline
**SPOT** & 4–5 & 2.5–20 & 26 & 120 & L–R & 1986 \\ \hline
**Aster** & 14 & 15–90 & 16 & 60 & L–G & 1999 \\ \hline
**Sentinel-2 MSI** & 13 & 10–60 & 5 & 290 & L–R & 2015 \\ \hline
**IKONOS** & 5 & 1–4 & 1.5–3 & 11.3 & L–R & 1999 \\ \hline
**QuickBird** & 5 & 0.61–2.24 & 2.7 & 16.5 & L & 2001 \\ \hline
**WorldView** & 4–17 & 0.31–2.40 & 1–4 & 17.6 & L & 2007 \\ \hline
**RapidEye** & 5 & 5 & 1–5.5 & 77 & L–R & 2008 \\ \hline
**ZY-3** & 4 & 2.1–5.8 & 5 & 50 & L–R & 2012 \\ \hline
**GF-1/GF-2** & 5 & 1–16 & 4–5 & 800 & L–R & 2013 \\ \hline
**PRISMA** & 238 & 5–30 & 29 * & 30 & L–G & 2019 \\ \hline
**Hyperscout-2** & 48 & 75–85 & 4-7 & \textasci{}300 & L–G & 2020 \\ \hline \end{tabular}  
* * Relook capability of 7 days with roll manoeuvre; L, landscape; R, regional; G, global; L–R, landscape to regional; L–G, landscape to global; R–G, regional to global.  
\end{table} 
Table 2: List of optical sensors. Adapted from [61; 62]. The primary objective at that point will be to acquire as much imagery as possible and to process the data on board using astrionics, i.e., hardware accelerators. After that, the data that can be acted upon is transmitted to the owners and operators. Once the wildfire is identified, the spacecraft would use the active Attitude and Orbit Control System (AOCS) for real-time/near-real-time disaster event management, as illustrated in Figure 8, with a limit concerning the off-nadir pointing ensuring sufficient image quality. It should be noted that in this case, attitude change is only considered for payload reconfiguration, which is accomplished using a gimbal. Because LEO is densely populated, an active orbital control is used by means of an electric thruster in the event of a collision between satellites [10; 29; 63]. Figure 8: An illustrative view of iDSS reconfiguration. Figure 7: Wildfire segmentation map of the hyperspectral imagery over Australia [56].
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 3 Admission Control  
Recently, resource management literature has reported diverse admission control approaches centered on NSL. Those approaches have applied techniques such as dynamic programming [129; 130], heuristics [131; 132], and stochastic models [133; 134; 135; 136] to accept slices in environments involving mainly a unique tenant. Figure 3 presents an admission control architecture using RL and DRL to make acceptance decisions in a multitenant environment. This architecture operates as follows. Tenants send diverse NSLRs (network slice requests) to the admission control module of 5G/6G use cases. The module decides the admission or preadmission (in this case, the allocation phase takes the final admission decision) of NSLRs by employing a RL/DRL agent and a prioritizer. The agent determines a normalized weight value for each 5G/6G use case. The prioritizer uses the agent's outputs to sort the NSLRs and establish the order in which resources should be allocated in the corresponding phase. The weight values should lead to achieving a goal, for instance, obtaining the maximum profit. For example, in the raised example, the agent selects an action that, if taken, maximizes the profit. The agent learns to select actions that increase profit by considering the information on states and rewards from interaction with the environment by using, for instance, Q-learning or DQN. It is relevant to highlight that an RL-based admission control solution can be specified by defining its state space, action space, exploration and exploitation method, and reward function. In addition, the specification of a DRL-based admission control approach includes further defining its DNN structure. Note that these two points apply for specifying RL/DRL-based solutions of allocation, scheduling, and orchestration of resources.