esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Explaining Spatial Distribution of Leaf N  
One of the best performing empirical models based on SR_RE3 was used to create leaf N maps over time (Figure 5). Figure 5 shows the realistic patterns of leaf N concentrations in the southern part, KNP and the spatial distribution and variation of leaf N is influenced by a number of variables, including seasons. The variability of leaf N is clear during peak productivity (see Figure 5, May 2016) and less during dry season (Figure 5, July and August map). In addition, mean annual precipitation, altitude (digital elevation model), slope, aspect, fire frequency and geology were analysed. The correlation results showed that mean annual precipitation and altitude significantly influence (\(p<0.05\)) the spatial distribution of leaf N concentrations (Figure 6). There is a pronounced regional effect of the west--east gradient of precipitation with the western part receiving higher precipitation. The process of nutrient uptake and dissolving of organic matter is facilitated by the availability of water or by precipitation (Kal  
Figure 5: Spatial distribution of leaf nitrogen concentrations (%) derived from field spectrometer based models applied on Sentinel-2 image. Top: end of wet season (4 and 23 May 2016), and bottom: dry season (23 July and 12 August 2016).
## 1 Introduction
These conflicting requirements necessitate a compromise between accuracy and computation performance for real-time simulators [14; 15]. The limitations of existing real-time simulators are the main motivation behind the development of the FPGA-based real-time simulator presented in this paper. This paper builds on the parallel implementation methodology of [16; 17; 18] and realizes a high performance FPGA-based real-time simulator for CHIL testing. The novelty of the proposed simulator resides in the massively parallel hardware architecture that is \"tailored\" to the solution of the mathematical model of the system under consideration. Thus, the hardware is not fixed but changes, depending on the system model, to maximize the computational performance. Moreover, this parallel hardware architecture efficiently exploits fine-grained parallelism without imposing sever communication and synchronization overhead times that can limit the performance. This simulator also provides the inherent capability to relax the accuracy and bandwidth limits of the existing real-time simulators. The rest of this paper is organized as follows. Section II elaborates on the interfacing issues associated with CHIL testing. Section III presents the architecture of the proposed FPGA-based real-time simulator. Section IV introduces the proposed techniques to interface the external control/protection platform with the simulator and the different options for monitoring of the waveforms generated inside the simulator. Sections V and VI, through a case study, provide validation and evaluation of the performance of the developed simulator. Conclusions are stated in Section VII.
## 6 Home Automation System Based on Z-Wave  
With the evolution of state-of-the-art applications and paradigms, the world is progressing toward smart cities. Smart homes are an important aspect of smart cities, wherein various mobile computing and network technologies are used. However, they are also susceptible to security threats that can cause serious issues related to privacy and safety. Z-Wave is a wireless technology that is primarily used in smart homes [81]. The Z-Wave protocol was started by the Danish company Zany's as a smart lighting control system for consumers and evolved into a home network automation mesh protocol implemented on a SOC-on-a-chip system [82]. This protocol was created in 2008 by Sigma designs company to replace other protocols, and it is now one of the most widely used protocols. Z-Wave technology is a technology of low-power radio waves developed to establish communication between household devices. Z-Wave was described as a new wireless home automation technology that uses low power and communicates at a frequency of 900 MHz and a range of about 30 m. Reference [83] describes how Z-Wave can work in a smart home system. Further studies have been conducted to implement Z-Wave in specific applications such as monitor systems [84] and lock systems [85]. In addition, some authors have also focused on the security features of Z-Wave [86; 87]. Z-wave devices have higher security due to the identification of the user and are also compatible with devices of different brands and can communicate with them [88]. The Z-Wave network supports mesh network topology, and a primary controller and several sub-controllers are responsible for establishing secure communication between devices [89]. In such a network, devices can communicate with  
Figure 7: Topologies related to Zig-Bee protocol.  
each other through intermediate nodes and, in this way, communication barriers and blind spots are eliminated. Z-Wave technology consists of three layers: the radio layer, the network layer, and the software layer. These three layers are connected to lead to a robust and secure network [90]. Figure 8 shows the architecture of this protocol.
# Disentangling Long Trajectory Contributions in Two-Colour High Harmonic Generation
## 6 Conclusions  
We have shown that CTMC simulations, whether they take the ion Coulomb force into account or not, essentially reproduce the three-step-model of HHG [10]. The extracted \(\varphi_{\max}(n)\) is virtually independent of the ion Coulomb force. Consequently, the reconstructed initial and return times in Ref. [18] are expected to be robust, and the approximations used for the calculations are valid. If exclusively short trajectories are used for the analysis, then the experimental displacement gate can be reproduced for harmonic orders \(n\geq 25\). Long trajectories, however, were needed for qualitative  
Figure 10: The displacement gate contrasts \(C(n)\) extracted from fitting Equation (14) to the experimental data (red [18]), the CTMC TIPIS model (Tunnel ionisation in Parabolic coordinates with Induced dipole and Stark Shift) with only short trajectories (blue) and the CTMC TIPIS model considering all trajectories (green). The error bars show the 95% confidence interval of the Equation (14) fit.  
Figure 9: The extracted \(\varphi_{\max}(n)\) curves for the case of only short trajectories (white), and all trajectories with phase matching (green) are compared to the experimental displacement gate plot (colour bar) [18].  
agreement between the CTMC simulations and the experiment for low harmonic orders. Their contribution can be estimated by looking at the phase matching efficiency. The displacement gate contrast strongly depends on whether long trajectories are considered or not. Agreement is only achieved again if phase matching is considered for all trajectories.  
The authors gratefully acknowledge helpful discussions with [PERSON] and [PERSON]. The data from Figure 3a,c of Ref. [18] were kindly proved by [PERSON]. A.S.L. acknowledges the Max Planck Centre of Attosecond Science (MPC-AS). This research was supported by the National Centre of Competence in Research Molecular Ultrafast Science and Technology (NCCR MUST), funded by the Swiss National Science Foundation (SNSF).  
[PERSON], [PERSON] and U.K. conceived and designed the project; [PERSON] performed the numerical simulations and analytical estimates; [PERSON] analysed the data; [PERSON] and [PERSON] interpreted the data; [PERSON] wrote the manuscript draft; [PERSON], A.S.L. and U.K. edited and revised the manuscript.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} HHG & High Harmonic Generation \\ SFA & Strong Field Approximation \\ CTMC & Classical Trajectory Monte Carlo Simulation \\ ADK & Ammosov Delone Krainov \\ TIPIS & Tunnel Ionisation in Parabolic Coordinates with Induced Polarisation and Stark Shift \\ XUV & Extreme Ultra-Violet \\ IR & Infrared \\ FWHM & Full Width Half Maximum \\ \end{tabular}
## 3 System Model
### Communication Model  
Similar to [27], the channels between the UAV and the ground nodes are assumed to be dominated by the LoS links. Therefore, the channel power gain between the UAV and the \(m\)-th ground node can be given by  
\[g=\beta_{0}d^{-2}=\frac{\beta_{0}}{h^{2}}, \tag{1}\]  
Figure 1: The UAV-assisted data collection scenario.  
where \(\beta_{0}\) is the channel gain at a reference distance of 1 m, and \(d\) is the distance between the UAV and the ground sensing device. Since this paper assumes that the UAV collects data directly above the sensing device, \(d=h\). Then, the uplink throughput between the UAV and the \(m\)-th ground node can be calculated as  
\[R_{n}=Blog_{2}(1+\gamma_{n}), \tag{2}\]  
where \(B\) is the channel bandwidth, \(\gamma_{n}=\frac{p_{n}g}{e^{2}}\) is the signal-to-noise ratio (SNR) between the UAV and the \(n\)-th ground node, \(p_{n}\) is the transmitting power of the \(n\)-th ground node, and \(\sigma^{2}\) denotes the Gaussian white noise power perceived at the UAV. The flight time of the \(i\)-th decision is then given by  
\[T_{f}^{i}=\frac{\|p^{u}(i)-p^{u}(i-1)\|}{v_{u}}, \tag{3}\]  
where \(v^{u}\) is the flying speed of the UAV. This paper assumes that the UAV is flying at a constant speed. The collecting time of the \(i\)-th decision can be given by  
\[T_{c}^{i}=\frac{s_{n}}{R_{n}}, \tag{4}\]  
where \(s_{n}\) is the size of the data generated by the \(n\)-th ground node.
# Resource Allocation in Downlink VLC-NOMA Systems for Factory Automation Scenario
## 2 System Model
### Factory Automation Scenario  
In this study, we applied the VLC-NOMA system for the factory automation scenario [13; 26]. The scenario is established based on time-division multiple access (TDMA) and superframe structure. The scenario includes periodic real-time messages sent by dedicated time slots considering beacon enable mode. The message characteristics are described in Figure 3.  
\[c\leq d\leq p \tag{27}\]  
Computation time \(c\) is less or equal to deadline \(d\), \(d\) is less or equal to period \(p\). After the released time, a message will be generated with cycle \(p\) and should be transmitted by \(d\). Every message for the VLC nodes has the characteristic mentioned above. Therefore, the message transmissions should be managed to be finished within their deadlines.  
Figure 4 shows frame structures for VLC-OMA and VLC-NOMA systems. VLC-OMA and VLC-NOMA systems have the same frame duration consisting of 25 timeslots. The beacon starts at the beginning of a frame. Each timeslot is 10 ms. The difference between VLC-OMA and VLC-NOMA is whether to superpose signals or not. Figure 3(a) shows that the VLC-OMA system transfers signal for a node in a timeslot. Instead, shown in Figure 3(b), the VLC-NOMA system can transfer two signals for two nodes in a timeslot by allocating different power levels.  
Figure 3: Message characteristic for factory automation.  
Arrangements of nodes and LED are described in Figure 5. There is one LED, as a base station, and multiple nodes. Messages for the nodes are generated periodically from the LED. The messages have the characteristics described in Figure 3. Although a previous work [1] considered one message in one timeslot, the proposed system in this study considers two messages in one timeslot as it adopts the NOMA system with the superposition coding. Next, Section 3 describes resource allocation schemes for the VLC-NOMA system in the factory automation scenario.
## 2 Materials and Methods
### In Situ Measurements
#### 2.2.1 Measurements of Vegetation Parameters  
A destructive sampling method was used to measure the leaf optical and canopy structural parameters, including Cab, Cw, Cdm, and LAI. Near the spot of spectral and flux measurements, twenty tillers above the ground within a 1 m \(\times\) 1 m sample area were cut and immediately sent to the laboratory. Meanwhile, the density of tillers in the sample area was investigated. The leaves of ten tillers were weighted and scanned with a Li-Cor 3100 area meter to calculate the LAI [51]. Several leaves of the other ten tillers were cut into pieces and uniformly mixed, and approximately 0.2 g of them was randomly picked to measure Cab using spectrophotometry [52]. All of these two-part leaves were over-dried at 60 \({}^{\circ}\)C until a constant weight was reached. The Cw and Cdm were then calculated using the measured fresh weight, dry weight, and leaf area.  
The measured results for ten fieldwork days are listed in Table 2, along with the corresponding growth stages. The vegetation samples cover different growth stages, with various optical and structural parameters, which are suitable data sets for model validation. The measured LAI values aligned with their realistic patterns across the growing season, which to some degree verifies the measuring accuracy. With the growing of wheat, the LAI continually increased from the erecting to the booting stage. Meanwhile, there was an obvious decrease with the arrival of flowering.
## 2 Theoretical Background
### Probabilistic Equivalences
#### 2.2.2 Probabilistic System Equivalences
\]  
If two probabilistic systems \(S_{i}\) and \(S_{j}\) are probabilistically identical, that is, for \(S_{i}=\left\{P_{i,k}|1\leq k\leq n\right\}\) and \(S_{j}=\left\{P_{j,k}\big{|}1\leq k\leq n\right\}\), for all the corresponding processes, \(P_{i,k}\sim P_{j,k}\) for \(1\leq k\leq n\), then the two systems are _probabilistically equivalent_, that is, \(S_{i}\approx S_{j}\). For example, System \(P_{2}^{\prime}B_{2}^{\prime}C_{2}^{\prime}\) defined below is probabilistically equivalent to the previous system \(P_{1}^{\prime}B_{1}^{\prime}C_{1}^{\prime}\) since \(P_{1}^{\prime}\sim P_{2}^{\prime}\), \(B_{1}^{\prime}\sim B_{2}^{\prime}\), and \(C_{1}^{\prime}\sim C_{2}^{\prime}\): \(P_{1}^{\prime}B_{1}^{\prime}C_{1}^{\prime}\approx P_{2}^{\prime}B_{2}^{\prime}C_{2}^ {\prime}\). \[ \begin{array}{l}P_{2}^{\prime}B_{2}^{\prime}C_{2}^{\prime}=P_{2}^{\prime}[R1 \parallel R2]\parallel B_{2}^{\prime}\parallel C_{2}^{\prime}\\ P_{2}^{\prime}=\left(PB\Big{(}\overline{\mathit{Send}\ R1}\Big{)}\{0.6\}. \mathit{put}\ R1.\mathit{put}\ R2+_{D_{1}}\ PB\Big{(}\overline{\mathit{Send} \ R2}\Big{)}\{0.4\}.\mathit{put}\ R2.\mathit{put}\ R1\right).\mathit{exit};\\ B_{2}^{\prime}=\left(PB(\mathit{Send}\ R1)\{0.7\}.\mathit{get}\ R1.\mathit{get} \ R2+_{D_{2}}\ PB(\mathit{Send}\ R2)\{0.3\}.\mathit{get}\ R2.\mathit{get}\ R2 \right).\\ \left(CB(\mathit{Send}\ R1)\{0.5\}.\mathit{put}\ R1.\mathit{put}\ R2+_{D_{3}}\ CB(\mathit{Send}\ R2)\{0.5\}. \mathit{put}\ R2.\mathit{put}\ R1).\mathit{exit};\\ C_{2}^{\prime}=\left(CB\Big{(}\overline{\mathit{Send}\ R1}\Big{)}\{0.8\}. \mathit{get}\ R1.\mathit{get}\ R2+_{D_{4}}\ CB\Big{(}\overline{\mathit{Send}\ R2}\Big{)}\{0.2\}. \mathit{get}\ R2.\mathit{get}\ R1\right).\mathit{exit};\\ R1=P_{2}^{\prime}\ put.B_{2}^{\prime}\ get.B_{2}^{\prime}\ put.C_{2}^{\prime}\ get.exit;\\ R2=P_{2}^{\prime}\ put.B_{2}^{\prime}\ get.B_{2}^{\prime}\ put.C_{2}^{\prime}\ get.exit.\end{array} \]If the difference between two probabilities \(p_{i}\) and \(p_{j}\) is within \(\Delta^{+_{D}}\), then the two systems are within the _permissible probability_ range, that is, \(S_{i}\Delta^{p}S_{j}\). For example, the two systems \(P_{1}^{\prime}B_{1}^{\prime}C_{1}^{\prime}\) and \(P_{2}^{\prime\prime}B_{2}^{\prime\prime}C_{2}^{\prime\prime}\) are identical but not probabilistic, as shown as below, their probabilistic permissibility will be within \(D=\left\langle P_{1}^{\prime}\Delta^{+_{D}}P_{2}^{\prime\prime}\right\rangle\), that is, \(P_{1}^{\prime}B_{1}^{\prime}C_{1}^{\prime}\Delta^{+_{D}}p_{2}^{\prime\prime}B _{2}^{\prime\prime}C_{2}^{\prime\prime}\), where \(D_{p}=\left\langle P_{1}^{\prime}\left(\left[0.6+_{D_{1}}0.4\right]\right) \Delta^{<0.1>}P_{2}^{\prime\prime}\left(\left[0.7+_{D_{2}}0.3\right]\right) \right\rangle\), since \(B_{1}^{\prime}\), \(C_{1}^{\prime}\) are probabilistically equivalent with \(B_{2}^{\prime\prime}\), \(C_{2}^{\prime\prime}\), respectively, that is, \(B_{1}^{\prime}\sim B_{2}^{\prime\prime}\) and \(C_{1}^{\prime}\sim C_{2}^{\prime\prime}\). \[
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results
### Additives in Edible Films
#### 3.6.3 Antimicrobials  
Edible coatings can be used as carriers of antibacterial and antifungal agents to prolong the storage life of food products and are also used as nutrient carriers to improve the nutritional profile of processed food items. As an alternative for limiting the growth of bacteria, both natural and synthetic antimicrobial agents have been created and added to various edible packaging materials [97]. The most commonly used antimicrobial substances introduced in films are nisin, enzymes, chitosan, oils, plant extracts and preservatives, bacteriocins, ethylenediaminetetraacetic acid (EDTA), metal nanoparticles, and different plant extracts and their essential oils [4; 97]. Chitosan exhibits good antibacterial activity against yeast and mold and also against Gram-positive and Gram-negative bacteria [97]. [PERSON] et al. [12] discussed the few studies on bacteriophage-treated edible films and coatings. The phage-added edible films and coatings have good antibacterial properties and are suitable for the preservation of fruits, vegetables, cheese, and meats. Table 2 shows the effect of different antimicrobial agents on edible packaging materials.
# Two-Stage Optimal Task Scheduling for Smart Home Environment Using Fog Computing Infrastructures
## 5 Conclusions  
Cloud computing does not provide efficient results in terms of bandwidth, energy consumption, network usage, latency, and many other performance parameters for time-critical applications. Therefore, fog computing assists the cloud environment to overcome the performance drawbacks of cloud computing by bringing resources near the edge of the network and distributing the load among different fog nodes. The distribution of the load in the fog environment is a significant challenge, which involves several task scheduling, resource scheduling, and job scheduling algorithms. This study proposed a two-stage optimal task scheduling (2-ST) approach for the distribution of tasks executed within a smart home among several fog nodes. To solve the task scheduling effectively, this proposed approach uses a naive-Bayes-based machine learning model for training in the first stage and optimization in the second stage using a hyperheuristic approach, which is a combination of both ACO and PSO. The proposed approach was validated by performing simulations in iFogSim, and the corresponding results showed better performance in terms of energy consumption, latency time, and network usage. In this study, we tried to use machine learning and optimization algorithms together to perform the task scheduling algorithms for a small-scale smart home environment. The reason for combining ACO and PSO was that PSO provides computational efficiency and an easy implementation, while ACO is used to solve different problems like ours, in which we had to compute intensive tasks and delay-sensitive tasks. The results showed a significant improvement, and therefore, we will use this combination for large databases in our future work. Along with this, a future work in the context of of this study is also to extend it to the prediction of future network usage, bandwidth consumption, and energy consumption by predicting the user's behavior on the basis of his/her daily routine within the smart home environment. This study was tested on small and static datasets described in Tables 4 and 5. Therefore, the proposed methodology can be tested on large and dynamic datasets.  
The authors equally contributed to this work in all aspects, with more implementation-related efforts by O.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 7: Network utilization of both the cloud and fog environment using the 2-ST and hybrid approaches for task scheduling.  
Data Availability StatementAll implementation details, sources, and data will be delivered upon request by the corresponding author [PERSON].  
The authors declare no conflict of interest.
# Stereo Digital Image Correlation in MATLAB
## 2 Framework Theory
### Correlation
#### 2.3.1 Shape Function  
The main SFs are the zero (\(\mathbf{W}^{SF0}\)), first (\(\mathbf{W}^{SF1}\)), and second-order SFs (\(\mathbf{W}^{SF2}\)) given as [43]  
\[\begin{gathered}\mathbf{W}^{SF0}\big{(}\Delta\mathbf{x}_{i},\mathbf{P}^{SF0 }\big{)}=\left[\begin{array}{ccc}1&0&u\\ 0&1&v\end{array}\right]\left[\begin{array}{c}\Delta\mathbf{x}_{i}\\ \Delta\mathbf{y}_{i}\\ 1\end{array}\right],\\ \mathbf{W}^{SF1}\big{(}\Delta\mathbf{x}_{i},\mathbf{P}^{SF1}\big{)}=\left[\begin{array}{ ccc}1+u_{x}&u_{y}&u\\ v_{x}&1+v_{y}&v\end{array}\right]\left[\begin{array}{c}\Delta\mathbf{x}_{i}\\ \Delta\mathbf{y}_{i}\\ 1\end{array}\right]\\ \text{and }\mathbf{W}^{SF2}\big{(}\Delta\mathbf{x}_{i},\mathbf{P}^{SF2}\big{)}=\left[ \begin{array}{cccc}\frac{1}{2}u_{xx}&u_{xy}&\frac{1}{2}u_{yy}&1+u_{x}&u_{y}&u \\ \frac{1}{2}v_{xx}&v_{xy}&\frac{1}{2}v_{yy}&v_{x}&1+v_{y}&v\end{array}\right] \left[\begin{array}{c}\Delta\mathbf{x}_{i}^{2}\\ \Delta\mathbf{x}_{i}\mathbf{y}_{i}\\ \Delta\mathbf{y}_{i}^{2}\\ \Delta\mathbf{x}_{i}\\ \Delta\mathbf{y}_{i}\\ 1\end{array}\right].\end{gathered} \tag{9}\]  
Their sets of SFPs are given as  
\[\begin{gathered}\mathbf{P}^{SF0}=\left[\begin{array}{cc}u&v\\ \end{array}\right]^{T},\\ \mathbf{P}^{SF1}=\left[\begin{array}{cccccc}u&u_{x}&u_{y}&v&v_{x}&v_{y}\end{array} \right]^{T}\\ \text{and }\mathbf{P}^{SF2}=\left[\begin{array}{cccccc}u&u_{x}&u_{y}&u_{xx}&u_{xy}&u_{yy} &v_{x}&v_{y}&v_{xx}&v_{xy}&v_{yy}\end{array}\right]^{T}.\end{gathered} \tag{10}\]  
Here \(u\) and \(v\) represent the displacement of \(\mathbf{f}\) in the x- and y-directions respectively. Furthermore, \(u_{x}\), \(u_{xx}\), \(v_{y}\), and \(v_{yy}\) define stretching of \(\mathbf{f}\), whereas \(u_{y}\), \(v_{x}\), \(u_{yy}\), \(v_{xx}\), \(u_{xy}\), and \(v_{xy}\) define shearing. Although higher-order SFs more reliably determine displacements in complex displacement fields by accounting for more complex deformation, the DIC process is only concerned with the displacements determined by correlation.