esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
There may be small diurnal variations in soil characteristics (\(\mathrm{T_{s}}\) and SWC), which cannot be reflected in the statistical analysis, and the calculation formula for \(\mathrm{R_{e}}\) includes \(\mathrm{T_{a}}\) but not \(\mathrm{T_{s}}\). Higher temperature (\(\mathrm{T_{a}}\) and \(\mathrm{T_{s}}\)) not only stimulated subsurface respiration but also extended the growing season of deciduous forests, resulting in a greater increase in GPP compared to \(\mathrm{R_{e}}\)[60]. A similar phenomenon was observed in our study conducted in 2018. Moreover, studies have demonstrated that within an optimal temperature range, a moderate increase in air temperature (\(\mathrm{T_{a}}\)) can enhance the activity of photosynthetic enzymes, thereby improving the efficiency of PAR utilization in photosynthesis [61; 62; 63]. In our kiwifruit orchard ecosystem, VPD exerted a substantial promotive effect on GPP and \(\mathrm{R_{e}}\) (Figures 4-7). This was possibly attributed to sufficient soil moisture and deeper roots maintaining high leaf water potential and stomatal opening [64]. VPD directly affects GPP and \(\mathrm{R_{e}}\) by regulating stomatal closure, transpiration, and photosynthesis [65]. To some extent, VPD served as an indicator of atmospheric drought in humid regions [66], and VPD had a greater effect on GPP compared to RH and SWC, which was consistent with previously published results [67; 68; 69]. Numerous studies have established that the LAI is a critical driver of GPP [18; 70]. In our study, the direct path coefficients of the LAI for GPP and NEE during the vigorous growth period were 0.34 and 0.43 (\(p<0.01\)), respectively (Figure 7), indicating that the LAI plays an important role in regulating carbon flux exchange. However, during the non-vigorous growth period, the impact of the LAI on carbon fluxes appeared to be concealed. This discrepancy is primarily attributable to the high frequency of cloudy days in the study area, which results in substantial fluctuations in daily radiation and consequently prevents our current statistical methods from absolutely reflecting the relationship between daily variations in photosynthesis and the LAI. The indirect effects of environmental drivers (e.g., \(\mathrm{T_{s}}\) via LAI) were acknowledged but not fully quantified using mechanistic or process-based models, limiting the understanding of system interactions. Nevertheless, this study offered valuable insights into the carbon balance and climate responsiveness of orchard ecosystems,contributing to enhancing orchard management strategies to bolster carbon sequestration while promoting sustainable agricultural practices.
# Frequency Division Multiplexing of Terahertz Waves Realized by Diffractive Optical Elements
## 2 Materials and Methods  
### Design  
All structures have been designed as a combination of a convergent lens and a diffraction grating. Phase distribution (\(\theta\)) of the lens has been defined according to the thin off-axis lens equation:  
\[\theta=-\frac{2\pi}{\lambda}\sqrt{r^{2}+f^{2}}, \tag{2}\]  
where \(\lambda\) is the design wavelength (DWL), \(r\) is the radial coordinate in the lens plane (distance from the center of the structure), and \(f\) denotes the focal length. Then, the according transmittance (T = exp(i\(\theta\))) has been multiplied by the transmittance of the binary phase grating with a constant \(d=1.872\) mm (introducing subsequent phase shifts of 0 and \(\pi\)). The transmittance function of such a grating consists of vertical stripes with the width of 0.936 mm and values alternating between 1 and \(-1\). For the convenience of numerical simulations and manufacturing, the analytical equation has been sampled with the 0.117 um pitch. Four structures with focal lengths varying from \(f=100\) mm through \(f=200\) mm and \(f=300\) mm to \(f=400\) mm have been designed for the DWL approximately equal to 1 mm (which corresponds to the frequency of 300 GHz). Phase distributions of all designed lenses are illustrated in Figure 1.
## 7 Proposed Solution
Consequently, the ANN model helps in optimizing traffic payload, which reduces network congestion and improves the overall performance of the network. 3. _Data Plane with Firewall and Encryption:_ The data plane is responsible for forwarding the network traffic between different devices in the network. The proposed solution integrates firewalls into the data plane to prevent malicious traffic from entering the network. Firewalls monitor and control the incoming and outgoing network traffic based on the security policies defined by the network administrator. In addition to firewalls, the system uses encryption to secure the data exchange between IoT devices. The use of encryption ensures that the data exchanged between IoT devices is protected from eavesdropping, tampering, and unauthorized access. In this paper, the implementation of the ANN-based detection system is described, and its results are analyzed in the following subsections. Figure 4: Proposed system architecture.
## 4 Hardware Design
Then, after the K-Best operation, the (select min) block in Figure 6 finds the solution with the minimum costs, which is then shifted and scaled as shown in Figure 6 followed by complex-to-real transformation in order to recover the transmitted symbol \(x\). The choice of the word length can greatly affect the hardware cost of the detector and its BER performance, where longer word lengths would results in a better performance as well as higher hardware costs in terms of area overhead and energy consumption. Figure 7 shows the BER attained at SNR = 30 dB for a \(4\times 4\) MIMO, while varying the word length. As shown in Figure 7, increasing the word length results in a reduced BER performance. Our experiments indicated that a word length of 9 bits would achieve the required performance, so it was adopted in our design. The design was modelled using \"System Verilog\" and implemented using 32 nm standard CMOS technology. In the following, we will provide more details on the design of the two main blocks in our proposed architecture, namely the Element-Based Lattice Reduction module and the QR decomposition module. Additionally, we will present a detailed analysis of the design overheads.
# GIS-Based Multi-Objective Routing Approach for Street-Based Sporting-Event Routing
## 3 Results
### Data Pre-Processing  
We incorporated the data in the QGIS environment on the NGII website for all information related to transportation networks, buildings, facilities, and topography within a radius of 2 km from Gwanghwamun (Figure 3). The constructed traffic-network data included 9210 road routes, 267.88 km of main roads, and 13,619 vertices intersecting between the main routes. This means that the average road network per unit km\({}^{2}\) was around 733; thus, the road network was very complex. In addition, we established and classified urban-infrastructure data corresponding to buildings and facilities based on the data-classification system described in Section 3.  
Next, we extracted a dataset of usable road routes based on the factors set to a weight of 0 or 1. Table 5 shows the results of the data pre-processing. We aggregated the extracted data with 474 road routes, which had a cumulative length of 82.53 km; there were 1057 intersection vertices. We calculated the proportion of the data extracted through pre-processing as 5.15% of the road routes, 30.81% of the length of the road routes, and 7.76% of the intersection vertices. We extracted a higher percentage of the length of the road lines compared to the share of the extraction of the road lines and intersection vertices.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data** & **Amount (Original)** & **Amount (Pre-Processing)** & **Ratio (\%)** \\ \hline Road routes & 9210 & 474 & 5.15 \\ \hline Cumulative length & 267.88 km & 82.53 km & 30.81 \\ \hline Intersection vertices & 13,619 & 1057 & 7.76 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of pre-processing.  
Subsequently, we processed the data that met the physical requirements of a road but which could not be used for a track. The removed data consisted of completely discontinuous roads, in which the starting and ending points were disconnected, and dead-end roads, where only one vertex was connected. We removed completely discontinuous roads entirely from the dataset, and we removed dead-end roads if the width of the road was less than the width required for a vehicle to turn (Figure 4).
## 4 Discussion
Previous studies have shown discordant experimental evidences about the supposed beneficial effects of biochar on root growth. Numerous papers reported positive responses on root growth parameters [37; 38; 39; 40; 41]. On the contrary, other authors [42; 39; 43] showed negligible benefits on root growth. These discrepancies could depend on different factors: plant species, growing environment, soil properties, feedstock, biochar properties, pyrolysis conditions [15; 21; 22; 23]. Furthermore, improved concentrations of dissolved organic C after biochar addition and the adsorption of phenolic compounds by biochar may positively affect root growth [38; 43]. Moreover, biochar may stimulate a faster root turnover and changes in root morphology [43]. Although there are few detailed studies on the influence of biochar on the root system, it is generally recognized that plants with a longer root length, a larger surface area and more root tips may be able to get more nutrients and grow better [44; 45; 46]. In the present study, the addition of biochar amendment positively affected hop root growth, increasing root length and mass density, as well represented in Figure 2. Moreover, a higher presence of roots could be positively correlated with nutrient uptake, and induce potential benefits in plant growth and development, including aboveground biomass. Benefits and limitations of biochar on plant growth could depend on the types of biochar and the incorporation rates [47; 48; 49]. In our study, no statistical differences were observed between number of leaves at the moment of bines elongation and climbing bines length at harvest. Regarding hop yield, biochar seemed to have positively affected cones production. This production varies greatly during the first years of the plant vegetation. In fact, the hop plant reaches productive and qualitative stability only after the third year. Nevertheless, biochar could have helped the plant when the water requirement was greater. In fact, the summer period, characterized by dry weather conditions, caused long periods of water stress, compensated only by emergency irrigation. Hops require a high water availability for successful plant growth and cones production [27], and changes in soil moisture retention may have been one of the most important factor in explaining positive biochar effects on crop yield. Due to its high porosity and specific surface area, biochar increases soil porosity, and, consequently, the soil hydraulic properties, such as the water storage capacity and the absorption capacity [42]. Furthermore, the induced macro-porosity due to the larger particle size of biochar positively affects soil water permeability [50]. Based on our findings, the application of biochar could be a promising strategy to enhance hop plant growth and cones production. However, further investigation on plant physiological mechanisms and hop cones quality is required to identify optimal levels of biochar application and to better understand the effect of biochar on hop plant growth.
# A Lunar-Orbiting Satellite Constellation for Wireless Energy Supply
## 7 Power Budget
### Scenario 2  
In this scenario, the satellites orbit in a low elliptical lunar frozen orbit with a mean altitude equal to 100 km. As the altitude is much lower than in the first scenario, the satellite is faster and the arc of the cone is smaller. For this reason the time spent by the satellite in the transmission cone is much lower than in the first scenario as shown in Figure 15, and consequently the average power transmitted is lower. We can note in Table 3 that, as in the first scenario, increasing \(\alpha_{max}\) increases the power transmitted. The sub-case of a 10-satellite constellation shown in Figure 16 shows how there is no transmission overlap between two satellites; consequently, in this scenario the power peaks higher than the base load of the single satellite (10.34 kW) are not obtained compared to the other two scenarios. Concluding in the second scenario, the best three-satellite constellation case could provide 46 kWh of energy in one day, while the best 10-satellite constellation case could provide 155 kWh of energy in one day.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Scenario 1: Lunar Circular Polar Orbit**} \\ \hline \(\alpha_{max}\) & \multicolumn{2}{c}{50\({}^{\circ}\)} & \multicolumn{2}{c}{60\({}^{\circ}\)} & \multicolumn{2}{c}{70\({}^{\circ}\)} & \multicolumn{2}{c}{80\({}^{\circ}\)} \\ \# sat & 3 & 10 & 3 & 10 & 3 & 10 & 3 & 10 \\ \(P\) (kW) & 2.64 & 8.78 & 3.44 & 11.46 & 4.44 & 14.76 & 5.68 & 18.92 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power converted in scenario 1.  
Figure 16: _Cont._  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Scenario 2: Low Elliptical Lunar Frozen Orbit**} \\ \hline \(\alpha_{max}\) & \multicolumn{2}{c}{50\({}^{o}\)} & \multicolumn{2}{c}{60\({}^{o}\)} & \multicolumn{2}{c}{70\({}^{o}\)} & \multicolumn{2}{c}{80\({}^{o}\)} \\ \# sat & 3 & 10 & 3 & 10 & 3 & 10 & 3 & 10 \\ \(P\) (kW) & 0.56 & 2.11 & 0.81 & 2.89 & 1.21 & 4.17 & 1.92 & 6.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Power converted in scenario 2.  
Figure 15: Power vs. time with a three-satellite constellation placed in low elliptical lunar frozen orbit with different maximum transmission angles. (**a**) \(\alpha_{max}=50^{o}\); (**b**) \(\alpha_{max}=60^{o}\); (**c**) \(\alpha_{max}=70^{o}\); (**d**) \(\alpha_{max}=80^{o}\).
## 4 Results and Discussion
### The Performance of Conv1D Model at Training Set
The plot shows that the temperature values predicted by the neural network match well with the values calculated using the FEM, and the model's predictions are numerically comparable to the true temperatures during the heating phase and the cooling  
Figure 6: Two-dimensional temperature field and error distribution of Conv1D and FEM results in the deposition stage at t = 4.5 s, t = 9 s, and t = 13.5 s three substeps. phase. The relative error (RE) of the model is one of our focuses to evaluate the model's prediction performance, and its expression is shown in Equation (10)  
\[RE=\frac{\left|\hat{T}(x,y)-T(x,y)\right|}{T(x,y)}\times 100\% \tag{10}\]  
As shown in Figure 7b, in the region with a fierce temperature gradient, the model's prediction had large error fluctuations compared with the true temperature value. However, the prediction error was controlled within 20 \({}^{\circ}\)C, with a relative error is less than 1%. In the rest of the region, the prediction error could decrease to 5 \({}^{\circ}\)C or less, and its relative error was controlled within 0.5%. From the point of view of the prediction value's accuracy, the prediction accuracy of the neural network model could reach more than 99%, and most of the errors were concentrated in the region of the huge temperature gradient. Moreover, considering the aspect of the limited influence that the maximum error value, which did not exceed 20 \({}^{\circ}\)C, brings to the actual manufacturing process, this is quite acceptable.
###### Abstract  
Due to the intermittent nature of wind, there exists a major disparity between the power generation from the wind and the demand of electricity. Hence, a sophisticated maximum power point tracking (MPPT) control paradigm must be formulated for maximizing the power extraction from the wind. This research article focuses on the formulation of a nonlinear fast dynamic terminal sliding mode control (FDTSMC)-based MPPT strategy for optimizing the power extraction from a 3 kW, variable speed, fixed-pitch wind energy conversion system equipped with a permanent magnet synchronous generator. The proposed MPPT strategy is compared with the benchmark fast terminal sliding mode control, conventional sliding mode control, feedback linearization control and proportional integral derivative control-based MPPT strategies under a stochastic wind speed profile. The proposed paradigm has been found superior in its tracking performance by converging the output tracking error to zero in a finite time, realizing a high precision performance, offering fast dynamic response, reducing the chattering to a minute level and guaranteeing global robustness. The superior performance and effectiveness of the proposed FDTSMC-based MPPT control paradigm is tested and validated through extensive MATLAB/Simulink simulations.  
wind energy conversion system (WECS); maximum power point tracking (MPPT); sliding mode control (SMC); permanent magnet synchronous generator (PMSG); terminal sliding mode control (TSMC) +
Footnote †: journal: Applied Sciences
## 6 Conclusions and Directions for Future Work  
This paper applies the popular skyline query concept to improve multi-condition irrigation decision support in the agricultural field. We verified the proposed method using real-world data from Taiwan's irrigation system. A complete irrigation decision-making system usually includes first-stage target value prediction modeling and a second-stage decision algorithm. This paper focuses on the second stage. In the future, we plan to develop a new prediction model to evaluate the objective values based on generative Artificial Intelligence. We plan to use this generative Artificial Intelligence model to produce a variety of reasonable water distribution conditions for specific water supply conditions and government needs. The skyline query proposed in this paper will then be applied to find the best solutions among conflicting water allocation conditions.  
Conceptualization, C.-H.L. and Y.-C.C.; Methodology, C.-H.L.; Software, C.-H.L.; Validation, C.-H.L.; Formal analysis, C.-H.L.; Investigation, C.-H.L.; Data curation, S.-H.L.; Writing--original draft, C.-H.L.; Writing--review & editing, C.-H.L. and Y.-C.C.; Visualization, C.-H.L.; Supervision, Y.-C.C. and C.-T.S.; Project administration, Y.-C.C. and C.-T.S.; Funding acquisition, Y.-C.C. and C.-T.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Science and Technology Council, grant number 111-2121-M-005-006 and 112-2121-M-005-006.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.  
Figure 13: Number of times branches are selected in top three skyline solutions using third ranking.
## 4 Security Analysis and Discussion
### External Attacks  
#### 4.1.1 The Intercept-Resend Attack  
Consider a scenario where an eavesdropper ([PERSON]) intercepts the sequences exchanged during the public key exchange phase and measures them. Based on her measurement results, [PERSON] prepares counterfeit sequences and forwards them to the Healthcare Provider (HP). However, this malicious action will be detected during the security verification process. The detection capability stems from the protocol's use of decoy photons randomly selected from the four non-orthogonal states \(\{|0\rangle,\,|1\rangle,\,|+\rangle,\,|-\rangle\}\), which the Patient's Device (PD) and HP insert into their respective transmitted sequences. Since [PERSON] has no knowledge of either the positions or measuring bases of these decoy photons within the public key exchange sequence, her interception attempts face significant detection probabilities.  
Let us consider the detection probability for a single decoy photon. Suppose a participants sends a photon in the state \(|0\rangle\). If [PERSON] uses the Z basis (the correct basis), she measures \(|0\rangle\) with 100% certainty and introduces no error. This case occurs with probability \(\frac{1}{2}\). However, if [PERSON] chooses the X basis (incorrect basis), the state \(|0\rangle\) collapses into \(|+\rangle\) or \(|-\rangle\) with equal probability. Then, when the receiver measures in the Z basis (which is correct), the probability of obtaining the original value \(|0\rangle\) from either \(|+\rangle\) or \(|-\rangle\) is \(\frac{1}{2}\) in each case. The full expression for the probability that [PERSON]'s presence is not detected for a single decoy photon is derived as follows: \(\frac{1}{2}\times 1+\frac{1}{2}\times\left(\frac{1}{2}\times\frac{1}{2}+\frac{1}{2} \times\frac{1}{2}\right)=\frac{3}{4}\).  
This means [PERSON] has a \(\frac{3}{4}\). chance of escaping detection per intercepted decoy photon. Therefore, when \(2n\) decoy photons are embedded in the protocol, the probability that none of them detects [PERSON] is \(\left(\frac{3}{4}\right)^{2n}\). Thus, the overall probability that [PERSON] is detected is \(1-\left(\frac{3}{4}\right)^{2n}\). As n increases to practical implementation values, this detection probability approaches 1, ensuring robust security against intercept-resend attacks.
# Design of PIDD(r) Controller for Robust Performance of Process Plants
## 3 Simulation Study
### First-Order System  
A generic mathematical equation representing a first-order equation is selected to simulate a PID, PIDD2, FOPID, and PIDD* controlling a plant in a simulation. An RC low-pass filter circuit is an example of a real-life first-order system. To obtain the closed-loop transfer function of the entire system, Equation (10) is used with \(C(s)\) substituted with Equations (1), (5), (8) and (9), and \(G_{p}(s)=\frac{1}{5s+1}\). The controller gains, fractional parameters, and transient response characteristics used are shown in Table 2 and Figures 6  
Based on the simulation results, using fractional parameters leads to significant improvements in transient response characteristics compared to integer parameters. Both the FOPID and proposed PIDD\({}^{\alpha}\) controllers show better performance in overshoot, with 9.6332% and 11.068%, respectively, as well as in settling time, with 14.2615 s and 14.2194 s, respectively, in contrast to the PID and PIDD\({}^{2}\) controllers. The PID controller shows the lowest \(t_{r}\) of 3.6147 s, while the PIDD\({}^{2}\) controller performs the worst in all three transient response characteristics. Despite performing worse than FOPID in overshoots, the proposed PIDD\({}^{\alpha}\) controller outperforms the \(t_{r}\) of 3.867 s and settling time of 14.2194 s. Overall, the proposed PIDD\({}^{\alpha}\) controller demonstrates the best performance on average compared to its competitors.  
The initial set-point is designated 1 when the time is 0 to monitor set-point tracking performance. After that, at 50 s, 100 s, and 150 s, the set-point changes to 2, 3, and 1.5, respectively. These changes cover both positive and negative changes in the controller. The set-point changes at intervals of 50 s to ensure that the step responses can reach their respective steady-state values, as specified in Table 2.  
Figure 8: Disturbance rejection of controller structure for first order system.  
Figure 7: Variable set-point tracking of controller structure for first order system.  
Figure 7 shows that each controller structure can manage the output to reach each set-point change. In terms of overall performance, the proposed PIDD\({}^{\alpha}\) controller outperforms its competitors. For disturbance rejection, a disturbance magnitude of \(-0.2\) is introduced to the controller structure at the 60 s to replicate external disturbances that can take place in a real-world plant environment. Figure 8 illustrates that each controller structure can reject the disturbance by managing the output to reach the set-point following the step response. Overall, the proposed PIDD\({}^{\alpha}\) controller achieves the best overall performance compared to its competitors.