esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 2 Materials and Methods
### Tomato Leaf Sampling and Leaf Water Content Measurement  
For nondestructive measurement of the tomato leaf water status using a portable hyperspectral camera, the measuring zone was divided into three parts (Figure 1): the top leaf position (LP) (near the growing point, 250 cm from the ground, LP-1), middle leaf position (150 cm from the ground, LP-2), and bottom leaf position (60 cm from the ground, LP-3). The distribution differences of the tomato plants under a year-round cultivation condition were considered. All three plants from the five tomato cultivars described above were chosen for the tomato leaf sampling.  
Tomato leaf sampling was conducted on April 6, 2017 (267 days after seeding), for a total of 45 samples. Three groups worked as a line operation for leaf sampling to ensure measurement accuracy and to make all the measurements simultaneous. Firstly, the compound leaf was sampled in accordance with the different measuring zones. Secondly, the terminal leaflet was separated from the compound leaf, and its fresh weight (FW) was immediately measured. Thirdly, the HSI data of the terminal leaflet were obtained using a hyperspectral camera. The leaf sampling was initiated at 10:00 a.m. after irrigation and completed in two hours. All leaf samples were dried in a drying machine at 105 \({}^{\circ}\)C  
Figure 1: Overview of the tomato leaf sampling at varying leaf positions (LPs). LP-1: Top; LP-2: Middle; LP-3: Bottom.  
for 72 h. Then, the dry weight (DW) of the terminal leaflet was measured. The tomato leaf WC and equivalent water thickness (MC) were calculated in terms of the fresh weight, dry weight, and leaf area (LA) by using Formulas (1) and (2), respectively, as follows [5; 6; 7]:  
\[\text{WC}\left(\%\right)=\frac{FW-DW}{FW}\times 100\%, \tag{1}\]  
\[\text{MC}=\frac{FW-DW}{LA}. \tag{2}\]
# Analytical-Numerical Model for Temperature Prediction of a Serpentine Belt Drive System
## 1 Introduction
However, the belt drive layouts and operating conditions change considerably more frequently than the changes in the pulley and its structures. The belt drive analysis thus involves a large number of operating conditions to be simulated, and in the existing techniques, the thermal analysis on each pulley must be performed for each condition, which is extremely time-consuming. To overcome this limitation, this paper proposes a thermal model to efficiently determine the temperature plots of a belt drive system under various conditions. This model combines the advantages of both analytical and numerical analyses and provides accurate and efficient temperature maps for any belt drive system. Furthermore, an experiment was performed to validate the output temperatures under various operating conditions. The remaining paper is organized as follows: Section 2 illustrates the development of the thermal model, Section 3 describes the validation experiment for the proposed model, and Section 4 presents a comparison between the calculated and experimental values of the temperature corresponding to a variety of conditions. Finally, Section 5 presents the conclusions of this research.
## 3 Results and Discussion
### Lethal Effects of Different Sterilization Treatments on the Microorganisms
#### 3.1.1 TBC and _Escherichia coli_ Counts  
The TBC and _Escherichia coli_ in CK were at 4.25 log CFU/mL and 3.04 log MPN/mL, respectively. The TBC and _Escherichia coli_ were reduced to 0, 3.89, 1.87, 3.73, and 1.70 log CFU/mL and 0, 2.18, 0, 2.18, and 0 log MPN/mL after CTS, US, TS, USN, and TSN treatments, respectively. These findings indicated that CTS treatment was still the most effective sterilization method and had the most thoroughly lethal effect on the microorganisms. The TBC and _Escherichia coli_ fatality rates of US, TS, USN, and TSN were 56.58%, 99.59%, 69.94%, 99.72%, and 86.36%, 100%, 86.36%, and 100%, respectively. This indicatesthat US treatment alone showed limited bactericidal effects on TBC and _Escherichia coli_. However, the US-combined treatment (USN and TSN) significantly enhanced the bactericidal effect, which was consistent with [PERSON] et al. (2011) and [PERSON] et al. (2013). CTS treatment relies on a thermal effect to destroy bacterial proteins, nucleic acids, and enzyme systems for sterilization purposes (Stein  
and CTS treatment on the lethal effect on mold (\(p>0.05\)). Compared with CK, yeasts was significantly reduced after treated by TS and TSN (\(p<0.05\)). However, the effect of the TS and TSN was significantly lower than CTS (\(p<0.05\)). In addition, adding nisin led to a weaker synergistic lethal effect on yeast and mold, similar to [PERSON] et al. [11].  
In all, CTS, TS, and TSN treatments showed good sterilization effects, which can effectively guarantee the microbial safety of grape juice. Hence, these three treatments were selected for further research.
## 3 Proposed Scheme
### GTS Allocation Procedure
#### 3.3.2 0/1 Knapsack Algorithm  
When the number of GTS requests is more than the available GTSs in that session, then it is not possible for the PAN coordinator to allocate GTSs to all the legitimate requested nodes, and it needs to scrutinize them.In this work, an optimal GTS allocation mechanism based on the 0/1 knapsack algorithm is proposed. The 0/1 knapsack problem optimally scrutinizes the most valuable items within the available sack capacity.  
The nodes have different amounts of data with different priority levels that are required to be sent to the fog node acting as a PAN coordinator. In addition, all the nodes do not have the same channel capacity. If we represent the number of available GTSs in a session as \(GTS_{Arail}\), and the GTS requests received by the PAN coordinator from node \(i\) as \(GTS_{i}\), along with its calculated TOPSIS value \(TOPSIS_{i}\), we can map the GTS scrutiny problem to the 0/1 knapsack problem. This can be seen in Table 2.  
Suppose \(T\) legitimate nodes require \(N\) number of GTS slots to transmit their data to the PAN coordinator. The proposed 0/1 knapsack-based algorithm scrutinizes \(k\) number of GTS-requesting nodes to send their data in the allocated TDMA-based slots by meeting the following two constraints:  
* The requested slots should be allocated within the available limit and are represented as \[\sum_{i=1}^{T}\leq GTS_{Arail}\] (13)
* The GTS-requesting nodes must be selected to maximize the TOPSIS values of the selected nodes. \[Max\sum_{a=1}^{k}TOPSIS_{a}\] (14)  
The proposed knapsack algorithm to scrutinize the requested nodes is shown in Algorithm 3.  
\begin{table}
\begin{tabular}{c c} \hline
**GTS Allocation Problem** & **Knapsack Problem** \\ \hline Total GTS capacity in a session & Carrying capacity of the knapsack \\ \hline GTS request of an item & Weight of an item \\ \hline TOPSIS value of an item & Value of an item \\ \hline \end{tabular}
\end{table}
Table 2: Knapsack mapping table.  
```
1 Input:\(T\leftarrow\) Total Number of TDMA requesting legitimate nodes \(N\leftarrow\) Total number of TDMA slots requested by nodes \(GTS_{\text{Avail}}\leftarrow\) Available slots in a session \(i\leftarrow\) GTS-requesting node ID \(X[i,j]\leftarrow\) Fill the cell valueitthnode and jthslot \(S_{i}\leftarrow\) Slots requested by ithnode If\(T\leq GTS_{\text{Avail}}\) Allocates slots to all requesting nodes by prioritizing high TOPSIS value nodes Else Apply 0/1 knapsack algorithm //1 knapsack algorithm Initialize the first row of knapsack table with all 0 Initialize the first column of knapsack table with all 0 for all GTS-requesting nodesdo for all TDMA slotsdo if\(S_{i}\leq j\)If\(S_{i}+X[i-1,j-j_{i}]>X[i-1,j]\) \(X[i,j]=j_{i}+X[i-1,j-j_{i}]\) Else \(X[i,j]=X[i-1,j]\) EndIf \(X[i,j]=X[i-1,j]\) EndIf end if \(T\gets i\) \(N\gets j\) end if
```  
**Algorithm 3**Optimal GTS allocation criteria.
## 1 Introduction
As a drawback, not all the network voltages are optimized, and, from the theoretical point of view, it is not guaranteed that a nodal voltage could exceed normal operating bounds. However, if the number of VCZs is large enough to adequately represent the network by the PNs and the number of DERs involved in the optimization is large enough to effectively control the voltages, this latter possibility is extremely reduced. * The decentralized VOP obtained by applying APP allows for: _i._ solving in each VCZ a constrained quadratic programming problem of small dimension which presents a global minimum; and _ii._ to exchange among the VCZs only scalar values. Thanks to these two features, the distributed iterative algorithm guarantees convergence with a limited number of iterations. Finally, it is worth noticing that, differently from other APP applications [27; 29], neither fictitious nodes nor overlapping variables among interconnected VCZs are introduced to obtain the fully-distributed VOP solution, due to the adoption of PNs in VCZs. The paper is organized as follows: Section 2 illustrates the centralized formulation of the proposed VOP. In Section 3, the VOP is revisited to get its decentralized formulation, highlighting the main steps of the proposed approach that are network partitioning, MMs, and APP. Finally, in Section 4, a case study is developed referring to a 123 node distribution test feeder with 15 DERs; detailed analysis of the results are presented in various operating conditions and a different number of VCZs, giving evidence of the effectiveness of the proposed decentralized VOP and comparing its performance with centralized VOP.
## 4 Precoding Schemes
### Precoding Schemes for VLC
#### 4.2.4 Non-Perfect CSI  
As also occurs in RF systems, one of the main challenges for implementing precoding schemes in VLC systems is to provide accurate and non-stale CSI. Although the small-scale effects are often negligible in VLC systems, the mobility of the users and the presence of blocking objects may generate CSI variability. In [167], the performance of DPC, ZF and BD is analyzed for VLC systems subject to non-perfect CSI. They propose a CSI error model where the estimated channel vector of each user is some degrees out of the real value. That is, the CSI is simply not perfect and other considerations about its accuracy or freshness are not taken into account. More specific uncertainty models such as noisy and outdated CSI are considered in [118] for determining the MMSE precoding. The outdated CSI uncertainty model is also considered for max-min SINR precoding in [158]. In [168], the max-min criterion is applied to the capacity bounds subject to outdated CSI for calculating the precoding vectors. All these works show that precoding schemes are considerably penalized by non-perfect CSI. However, none of them propose to exploit the deterministic behaviour of the optical channel to minimize the impact of the CSI uncertainty, which is identified as an open issue for VLC systems.
## 3 Results
It can be observed that an enhanced sensitivity is obtained by adding a thin semiconductor film on top of the metallic layer, in comparison to configurations that uses different combinations of 2D materials [50] (i.e., structures with black phosphorus (BP) and Graphene/MoS\({}_{2}\)/WS\({}_{2}\)). It is worth mentioning that by employing NSGA II together with the TMM, the BaTiO\({}_{3}\) thin film greatly improves the performance for the structure consisting in Ag-graphene [36] or ZnO-Ag-2D materials [28] with no prior information about its refractive index. Moreover, a structure with an almost 50 deg/RIU improvement in sensitivity was obtained. Figure 6: Reflectivity curves for the optimized structures with BaTiO\({}_{3}\) and Si. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**L.N.**} & \multirow{2}{*}{**Material**} & \multicolumn{2}{c}{**Configuration**} & \multirow{2}{*}{**Sensitivity (deg/RIU)**} & \multirow{2}{*}{**FWHM (deg)**} \\  & & **Metal–Semic–2D Mat.** & & \\ \hline
1 & Graphene & 43 nm–12 nm–1 L; (BaTiO\({}_{3}\)) & 302 & 7.1 \\
2 & MoS\({}_{2}\) & 39 nm–5 nm–1 L; (Si) & 232 & 9.4 \\
3 & WS\({}_{2}\) & 43 nm–10 nm–1 L; (BaTiO\({}_{3}\)) & 302 & 8 \\ \hline \end{tabular}
\end{table} 
Table 5: Configurations with the BaTiO\({}_{3}\) and Si for which the sesntivity is enhanced.
# A Wideband Circularly Polarized Filtering Dipole Antenna
## 2 Antenna Design
### Step-by-Step Design Process  
Figure 2 presents four reference antennas along with their corresponding parametric results to illustrate the operating principle of the proposed filtering CP antenna. The design originates from a classical LHCP dipole antenna structure, while incorporating tapered elements on the dipole arms. When vertically extending the ends of the four crossed-dipole arms, a CP radiation frequency at approximately 3.75 GHz is introduced, achieving a 27% AR bandwidth (AR \(<\) 3 dB). As shown in Figure 2a, a radiation null are observed in the LHCP realized gain at 5 GHz. The upper band radiation null results from loop currents formed between adjacent dipole arms. To further enhance filtering performance, two parasitic patches are introduced parallel to the dipole arm ends. This configuration creates an additional radiation null in the upper band while introducing another CP radiation frequency around 2.7 GHz. These modifications improve both the frequency selectivity at band edges and AR bandwidth expansion. Figure 2b demonstrates that reference Ant.II achieves an expanded AR bandwidth of 40% (covering 2.43-3.58 GHz with AR \(<\) 3 dB). Moreover, the enhanced current cancellation through rectangular patch coupling generates a distinct radiation null at 4.2 GHz. By incorporating four pairs of small rectangular patches coupled to the main rectangular patch, the AR bandwidth is further expanded. As shown in Figure 2c, reference Ant.III achieves an enhanced AR bandwidth of 47% (covering 2.25-3.63 GHz with AR \(<\) 3 dB). At this stage, the LHCP gain exhibits good filtering radiation performance. To achieve a tunable radiation null in the lower band, a square-ring slot is etched on the ground plane of Ant.III, as illustrated in Figure 2d. This modification introduces a radiation null at approximately 1.2 GHz while maintaining the bandpass radiation response. Notably, the square-ring slot implementation preserves the AR bandwidth without degradation, and the LHCP gain remains stable.  
Figure 2: _Cont._  
Figure 4: _Cont._  
Figure 3: The reflection coefficients of the four reference antennas.  
Figure 2: Structure and results of four antennas.
## 3 Modeling and Methods
### Governing Equations  
The approach to producing the braking torque is to calculate the amount of braking force multiplied by the ECB radius. Because of the braking force that occurs due to electromagnetic interactions, the amount of braking force can be calculated using the Lorentz force. The magnitude of the force is calculated from the magnitude of the magnetic field and the current flowing at a certain volume and can be written in Equation (1):  
\[\overset{\rightarrow}{F}_{d}=\int_{v}\overset{\rightarrow}{J}\times \overset{\rightarrow}{BdV} \tag{1}\]  
Volume can also be calculated using the surface area with double integrals. The amount of braking torque can be written by multiplying the braking force with the radius of the drum, as Equation (2):  
\[T_{b}=\iint\limits_{s}r_{p}\times(J\times B)dS \tag{2}\]  
The magnitude of the magnetic field strength can also be written with the density of the magnetic field per unit area, or \(B=\partial\!\!\!/A\). Hence, the braking force equation can be written as Equation (3):  
\[F_{R}=I_{R,i}\cdot I_{R,e,i}\cdot\partial_{R,i}/A_{R,m,i} \tag{3}\]  
The amount of eddy currents is calculated by [PERSON]'s force equation:  
\[J=\sigma(E+v+B) \tag{4}\]  
The magnitude of the eddy currents is influenced by the presence of magnetic fields and electric fields [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]:  
\[F=\frac{I_{S}p}{2\mu_{0}}\int_{0}^{2t}Re\Big{(}B_{2x}\overline{B}_{2y}\Big{)} \Big{|}_{y=0}Dx \tag{5}\]  
\[F=\frac{1}{2\mu_{0}}\int_{0}^{2t_{p}}Re\Big{(}B_{1x}B_{1y}\Big{)}Dx \tag{6}\]  
where \(\mu_{0}\) is the air permeability. The magnitude of the magnetic field is different from that proposed by [PERSON] et al. [11] because the magnitude of the magnetic field is sought using magnetic vector potential techniques:  
\[F_{ab}=\sum_{n=1,\ odd}^{\infty}\frac{\omega_{p}p}{2\mu_{0}}\int_{0}^{2x}Re \Big{(}B_{mx}B_{my}\Big{)}dx \tag{7}\]  
The calculation of the braking system using the disk brake design can be shown by the equation as stated by [PERSON] et al. [36] and [PERSON] et al. [6]:  
\[F_{b}=\frac{1}{2\mu_{0}}\int_{0}^{2\pi}\int_{R_{i}}^{R_{0}}Re\Big{[}B_{\theta} ^{III}(r,\theta,d+g).\Big{]}\Big{]}dxd\theta \tag{8}\]The formulation to obtain the value of the braking torque is calculated from the magnitude of the magnetic field, which affects the conductor or the magnetic flux density of the air gap. The use of only mathematical analytics results in an inaccurate calculation because it does not take into account the geometry of objects. The advantage of using analytical methods is the speed in calculation and te simplicity of the equation used. On the other hand, numerical analysis using FEM produces results that have a good agreement to the real conditions, because the numerical analysis takes into account the object geometry.
## 2 Methods
### Laboratory Experiments and Mie Scattering Calculations to Evaluate LISST-VSF
#### 2.1.4 Determination of Correction Functions
The rationale for this assumption is that the scattering signal produced by the examined beads for the first 25 rings (\(\psi\) = 0.09-4.96\({}^{\circ}\)) is comparable to the baseline, while there is good signal relative to the baseline for the last ring detector. The final correction simply involves the multiplication of uncorrected \(\beta_{p}^{LISST*}(\psi)\) by the correction function \(CF_{f}(\psi)\),  
\[\beta_{p}^{REF}(\psi)=\widetilde{\beta}_{p}^{ Mie}(\psi)b_{p}^{REF}, \tag{6}\]0  
where \(\beta_{p}^{LISST}(\psi)\) is the corrected LISST-VSF measurement of volume scattering function (note that the superscript * is removed from this symbol). We also determined a correction function for \(DoLP_{p}^{LISST*}(\psi)\),  
\[\beta_{p}^{REF}(\psi)=\widetilde{\beta}_{p}^{ Mie}(\psi)b_{p}^{REF}, \tag{6}\]1where \(BF(\psi)\) quantifies a correction for potential bias in \(DoLp_{p}^{USST*}(\psi)\) obtained from the standard processing code applied to LISST-VSF measurements and \(DoLp_{p}^{REF}(\psi)\) is a reference degree of linear polarization determined from Mie scattering calculations of the two scattering matrix elements, \(p_{11}^{Mie}(\psi)\) and \(p_{12}^{Mie}(\psi)\), for a given sample of standard beads. The results for \(BF_{100}(\psi)\), \(BF_{200}(\psi)\), \(BF_{400}(\psi)\), and the final correction function \(BF_{f}(\psi)\) were obtained using a procedure similar to that for \(CF_{100}(\psi)\), \(CF_{200}(\psi)\), \(CF_{400}(\psi)\), and \(CF_{f}(\psi)\). The correction of \(DoLp_{p}^{USST*}(\psi)\) simply requires a subtraction of \(BF_{f}(\psi)\),  
\[\beta_{p}^{REF}(\psi)=\widetilde{\beta}_{p}^{ Mie}(\psi)b_{p}^{REF}, \tag{6}\]2  
where \(DoLp_{p}^{LIST}(\psi)\) is the corrected degree of linear polarization within the range of scattering angles from \(16^{\circ}\) to \(150^{\circ}\). Because the \(DoLp_{p}^{LIST*}(\psi)\) data output from standard processing of LISST-VSF measurements begins at \(\psi=16^{\circ}\), no correction for the forward scattering angles of the ring detectors (\(\psi<16^{\circ}\)) was determined.
## 5 Simulation Results and Discussion
### Airspeed and Altitude Hold  
With the help of HIL system, the developed OECS is first simulated in the aircraft level flight without the presence of wind. The simulation results of level flight, as shown in Figure 13, show that the change of pitch rate, \(\Delta q\), is regulated within \(\pm\)0.01 rad/s and \(\dot{L}_{\mathrm{s}}\) is regulated at 0.01. At the same time, the airspeed is maintained at around 20.8 m/s and the altitude shows agreement with the commanded altitude, \(h_{\mathrm{c}}\), at 150 m.  
Furthermore, aircraft level flight, as shown in Figure 14, is simulated with the presence of wind, and the largest magnitude of wind is 8 m/s. The results present that the change of pitch rate, \(\Delta q\), is regulated within \(\pm\)0.10 rad/s and \(\dot{L}_{\mathrm{s}}\) is regulated within 0.05. Moreover, the airspeed is regulated at trim airspeed of 20.3 m/s with the variation of \(\pm\)0.5 m/s. The trim airspeed is dependent on the identified aircraft energy distribution model. On the other hand, the altitude deviation is less than 2 m from the commanded altitude of 120 m. As a summary for level flight, the performance of the OECS satisfies the airspeed and altitude is successfully held.  
Figure 13: Simulation results of level flight without the presence of wind.
# Operation Scheduling Optimization for Microgrids Considering Coordination of Their Components
###### Abstract  
Operation scheduling is one of the most practical optimization problems to efficiently manage the electric power supply and demand in microgrids. Although various microgrid-related techniques have been developed, there has been no established solution to the problem until now. This is because the formulated problem becomes a complicated mixed-integer programming problem having multiple optimization variables. The authors present a framework for this problem and its effective solution to obtain an operation schedule of the microgrid components considering their coordination. In the framework, trading electricity with traditional main power grids is included in the optimization target, and uncertainty originating from variable renewable energy sources is considered. In the solution, the formulated problem is reformulated to reduce the dimensions of its solution space, and, as a result, a combined algorithm of binary particle swarm optimization and quadratic programming is applicable. Through numerical simulations and discussions of their results, the validity of the authors' proposal is verified.  
microgrids; power supply-demand management; unit commitment (UC); economic load dispatch (ELD); binary particle swarm optimization (BPSO); quadratic programming (QP); uncertainty
## 6 Application of the Optimization Techniques: Routing Protocols
### Geographical Routing Protocols
#### 6.2.1 Greedy Algorithms  
Under this approach, a node decides about the transmission path based on the position of its neighbors. To proceed, the source compares the localization of the destination with the coordinates ofits neighbors. Then, it propagates the message to the neighbor which is closest to the final destination. The process is repeated until de packet reaches the intended destination. Several metrics related to the concept of closeness have been proposed for this context. Among them, the most popular metrics are the Euclidean distance and the projected line joining the relaying node and the destination.  
With this strategy, flooding processes are restricted to one-hop and the network is able to adapt proficiently to the topological changes. This simple forwarding rule is modified according to the reliability of links in [16]. In this proposal, the unreliable neighbors are not taken into account for the retransmissions. On the other hand, the geographic information is also used in SPEED (Stateless Protocol for End-to-End Delay) to estimate the delay of the transmitted packets [17].  
Similar to this algorithm, the greedy algorithm with the'most-forward-within-R' forwarding technique opts to select the most distant neighbor of the packet holder which is closer to the final destination as the next hop [7]. In contrast, the 'nearest-forward-process' chooses the nearest neighbor that is closer to the intended destination as the next relaying node.  
The main limitation of the greedy algorithms is that the transmission may fail when the current holder of the message has no neighbors closer to the destination than itself. This could occur even when there is a feasible path between the two extremes, for instance, when an obstacle is present. Aiming at overcoming this drawback, the 'right hand' rule is suggested [18].