esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Study Site and Crop Status  
For the 2014 harvest season, two commercial avocado blocks (cv. Haas) (Blocks 1 and 2) located near the township of Childers, Queensland, Australia (Figure 1), were selected. For the 2016 and 2017 harvest season, three additional orchard blocks (cv. Haas) near Childers (Blocks 3, 4 and 5) were selected (Figure 1). The study area is located between longitudes 152.12\({}^{\circ}\)E and 152.38\({}^{\circ}\)E and latitudes 25.11\({}^{\circ}\)S and 25.23\({}^{\circ}\)S. The region experiences a subtropical climate with long hot summers and mild winters. The mean annual rainfall between January and December is 1026.4 mm (1942-2016), the mean maximum temperature is 26.7 \({}^{\circ}\)C, reaching a peak in January (30.2 \({}^{\circ}\)C), and the mean minimum temperature is 16.4\({}^{\circ}\)C, with a low occurring in July (10.2 \({}^{\circ}\)C) (1959-2016) [38].  
The trees in Block 1 were planted in 2006, Block 2 in 2005, Block 3 and 4 in 1998, and Block 5 in 2007. All orchards are commercially owned and managed. Each block receives post-harvest pruning in late winter or early spring where major limbs are removed to maintain orchard access between rows and enhance light interception. Flowering occurs between early September to mid-October, followed by two fruit drop events usually between late October to January [39]. Harvesting of avocado fruits normally occurs over a split harvest where the larger fruits are removed first, allowing more time for the smaller fruit to grow [40]. In Childers, harvest commences in April depending on fruit maturity, which is determined by ripening and dry matter testing, and continues until late July. Avocado fruit yields are extremely variable across farms and regions depending on variety, season and level of management as well as from biannual bearing that can occur at varying degrees. The average yield across Childers region is over 10 tonnes per hectare.
## 4 Extended Parameter Analysis
### Finite Element Numerical Model  
#### 4.1.1 Material Constitutive Relationship and Parameter Setting  
As listed in Table 3, the material parameters of the concrete, rebar, stirrups, bolts, and welded wire meshes were set according to the measured values. A solid element was used to simulate concrete. The plastic-damage model provided by ABAQUS was adopted as the constitutive relationship between the concrete materials of the core pier column, filling layer, and segment sleeve in the elastic-plastic stage (i.e., nonlinear behaviors) and the irreversible concrete damage, such as plastic deformation accumulation and stiffness degradation. The plastic behavior of concrete was primarily defined using five parameters: dilation angle \(\psi\), flow potential deviation value \(\epsilon\), ratio of ultimate compressive strength of biaxial to uniaxial \(\sigma_{b0}/\sigma_{c0}\), ratio of the second stress invariants on the meridian plane of tension and compression \(K_{c}\), and viscosity coefficient \(\
u\); each parameter was obtained with reference to a previous study [47] (Table 5). The stress-strain curve of concrete was determined according to the previously described code [48, 56], and the damage factors of the plastic-damage model were calculated according to the energy equivalent principle proposed by [PERSON] [49, 50]. The rebars, stirrups, bolts, and welded wire meshes were simulated using a wire element, and a double broken line was adopted as the constitutive relationship of the steel. Schematics of the stress-strain curves of concrete and steel bars are shown in Figure 17. The tensile and compressive stress-strain curves of concrete were plotted in the same coordinate system, however, different proportions were obtained. Additionally, \(f_{c,r}\) and \(\varepsilon_{c,r}\) denote the representative values of the uniaxial compressive strength of the concrete and the corresponding peak compression strain, respectively;\(f_{t,r}\) and \(\varepsilon_{t,r}\) denote the representative values of the uniaxial tensile strength of the concrete and the corresponding peak tensile strain, respectively; \(\varepsilon_{c,u}\) represents the ultimate compressive strength of concrete;\(f_{y,r}\) and \(\varepsilon_{y}\) denote the representative value of the yield strength of the steel bar and corresponding yield strain, respectively;\(f_{st,r}\) and \(\varepsilon_{u}\) denote the representative value of the ultimate strength of steel bar and corresponding peak strain, respectively.
## 3 Numerical Modelling
### Mesh Model
#### 3.4.1 Finite Element Method  
The target structure consisted of three components: the concrete pad, the PDF panel, and the steel beam frame. SOLID186 is a high-order 3D 20-node element with three degrees of freedom per node, and was defined and generated for modelling the concrete pad and steel beam frame. SHELL181 is a four-node element with six degrees of freedom at each node, and was defined and generated for modelling the PDF panel.  
The mesh dependency of the FEM model was determined via linear quasi-static analysis. Five element sizes (50, 100, 150, 200, 250 mm) were tested for the steel beam frame,  
Figure 8: Applied zero period acceleration (yellow arrow) in quasi-static structural analysis. (a) CASE I-1; (b) CASE I-2; (c) CASE I-3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Load Case**} & \multicolumn{1}{c}{**Factor (-)**} & \multicolumn{1}{c}{\(\boldsymbol{\alpha}\) **(deg)**} & \multicolumn{1}{c}{**Load (g)**} \\ \hline \multirow{4}{*}{CASE I-1} & _x-axis_ & 1.5 & & 0.3540 \\  & _y-axis_ & 1.5 & 0.0 & 0.0000 \\  & _z-axis_ & 0.8 & & 0.1888 \\ \hline \multirow{4}{*}{CASE I-2} & _x-axis_ & 1.5 & & 0.2440 \\  & _y-axis_ & 1.5 & 45.0 & 0.2440 \\  & _z-axis_ & 0.8 & & 0.1888 \\ \hline \multirow{4}{*}{CASE I-3} & _x-axis_ & 1.5 & & 0.0000 \\  & _y-axis_ & 1.5 & 90.0 & 0.3540 \\ \cline{1-1}  & _z-axis_ & 0.8 & & 0.1888 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Load case in the quasi-static structural analysis.  
which was the primary structural component of the water tank. Figure 9 depicts the results of the mesh dependency test of the maximum total deformation in terms of the mesh size. As can be seen, the proper element size was determined by M3, and was 150 mm.
## 3 Undifferentiated Cells (Stem Cells)
### Adult Stem Cells
#### 3.4.1 Bone Marrow Stem Cells (BM-MSCs)
##### 3.4.1.2 Applications in BTE  
Similar to other stem cells, this type of cells can undergo osteogenic differentiation in vitro when cultured in the presence of dexamethasone, inorganic phosphate, and ascorbic acid [170; 171]. The differentiation of BM-MSCs toward osteoblastic lineage cells is regulated through various systemic hormones (e.g., estrogens) and local growth factors. The bone morphogenetic protein (BMP) family, the transforming growth factor-beta (TGF-\(\beta\)), and the fibroblast growth factor-2 (FGF-2) are the most well-defined factors regarding the osteogenic differentiation [172].  
The therapeutic effects of BM-MSCs are associated with the secretion of large amounts of different bioactive molecules (e.g., FGF-2, Ang-1, and G-CSF), which provide a regenerative microenvironment with a trophic activity for the damaged bone tissue [173]. In addition, BM-MSCs homing to the sites of bone tissue injury is considered another factor that enhances their therapeutic effect [174].  
It has been reported that only a subset of high-proliferating single colony-derived BM-MSC clones (60%) is able to form ectopic bone tissue after in vivo transplantation into immunocompromised mice [175; 176]. However, there are a large number of successful bone healing procedures using these cells (direct injection or in combination with 3-D scaffolds) in both calvarial and long bones in different animal models [177; 178; 179].  
Due to their supreme properties, BM-MSCs have been used in preclinical and clinical models for repair and regeneration of the bone [180; 181]. With the aim of enhancing the total-body bone mineral content and subsequent osteogenesis in children with imperfect osteogenesis, [PERSON] and colleagues conducted the first clinical trials with BM-MSCs [182]. Since then, a large number of studies have been performed on these cells for treating patients who suffer from osteoarthritis and bone defects [183].
## 3 Results and Discussion
### Estimation Results
One of the purposes of estimating the model with covariates is to check for internal consistency or theoretical validity. Except for the estimated coefficient for Head variable, all the coefficient estimates were statistically significant. Thus, it seems that the model employed in this paper secured internal consistency. As explained above, the sign of the coefficient means the direction of the effect of the variable on the likelihood of responding \"yes\" to a provided bid. The coefficient estimates for Education, Age, and Income variables had statistical significance. Respondents with higher levels of education had a higher possibility than respondents with lower levels of education. The age of the respondents was negatively correlated with the possibility. An interviewee with a higher income was more likely to answer \"yes\" to a proposed bid than an interviewee with a lower income. Contrary to our prior expectations, the estimates of the coefficient for the Head variable was not statistically significant at a level of 10%. Whether or not the respondent was the head of the household did not affect the respondent's determination of WTP for the reduction. This was quite an interesting finding because it is often thought in the country that this variable will affect the respondent's decision on WTP. The important purpose of estimating a model containing covariates is to verify the theoretical validity or internal consistency of the model. The model used in this study appeared to be meeting these since the estimation results were overall significant. Table 4 also presents the yearly household average WTP estimate and its confidence intervals. The mean WTP was KRW 5412 (USD 4.82), which is not much different from the results given in Table 2 (KRW 5523 or USD 4.92). In addition, the confidence intervals were much the same as those given in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Definitions** & **Mean** & **Standard Deviation** \\ \hline Education & Education level of the respondent in years & 14.10 & 2.23 \\ Income & Monthly income of the respondent’s household & 4.91 & 2.14 \\  & (unit: million Korean won = USD 891) & & \\ Head & Dummy for the respondent’s being head of & 0.53 & 0.50 \\ Age & Age of the respondent (unit: years) & 47.78 & 9.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Information about some variables considered in this study.
# Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems
## 2 System Model and Factor Graph
### Factor Graph Representation  
Based on (2) and (4)-(10), the joint PDF of all unknown variables given by the observation \(\mathbf{Y}\) can be factorized as  
\[\begin{split}& p(\mathbf{B},\mathbf{Z},\mathbf{S},\mathbf{V},\mathbf{p}^{10},\mathbf{p}^{01}| \mathbf{Y})\\ &=p(\mathbf{Y}|\mathbf{Z})p(\mathbf{Z}|\mathbf{B})p(\mathbf{B}|\mathbf{S},\mathbf{V},\mathbf{p}^{1 0},\mathbf{p}^{01})\\ &\propto\prod_{j=1}^{J}\Big{\{}\prod_{n=1}^{N}f_{\
u_{n,j}}(y_{ n,j},z_{n,j})f_{Z_{n,j}}(z_{n,j},\mathbf{b}_{j})\prod_{k=1}^{K}f_{B_{k,j}}(b_{k,j},s _{k,j},v_{k,j})f_{\eta_{k,j}}(v_{k,j})\Big{\}}\\ &\prod_{k=1}^{K}\Big{\{}f_{p_{k}^{01}}(p_{k}^{01})f_{p_{k}^{10}} (p_{k}^{10})f_{S_{k,1}}(s_{k,1},p_{k}^{10})\prod_{j=2}^{J}f_{S_{k,j}}(s_{k,j},s_{k,j-1},p_{k}^{10},p_{k}^{01})\Big{\}}.\end{split} \tag{11}\]  
The factor graph of (11) is illustrated in Figure 2. In addition, local functions and their corresponding distributions are shown in Table 2. The novel MP algorithm is designed in the next Section.  
\begin{table}
\begin{tabular}{c l} \hline \hline \multicolumn{1}{c}{**Assumption**} & \multicolumn{1}{c}{**Description**} \\ \hline Transmission scheme & GF-NOMA scheme is employed for multiple UEs \\ Single antenna & Both UAV-BS and UEs are equipped with a single antenna \\ Independent UE AS & Activity states of different UEs are assumed to be independent \\ Channel prior model & The BG-MC probability model is utilized \\ GF-NOMA technique & MC-CDMA scheme, a hybrid of CDMA and OFDM, is utilized \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main Considered Assumptions.  
Figure 2: Factor graph representation for UAV-BS-based GF-NOMA detectors.
## 4 Results
### Calculation of Synthetic LiDAR Data
#### 4.1.1 Error Case Discrimination
In the RHI scans, errors of the same magnitude are seen at \(\omega_{\phi}=4^{\circ}\)/s with \(\varepsilon_{\overline{\mu}}~{}=~{}0.31\) % and at \(\omega_{\phi}=4^{\circ}\)/s with \(\varepsilon_{\sigma_{u}}~{}=~{}2.78\) %. By averaging all points in the wake, \(-1\leq\gamma D^{-1}\leq 1\) and \(-1\leq\zeta D^{-1}\leq 1\), the representation of wind speed with the LiDAR measurements is improved. The average wind-speed error (\(\varepsilon_{\overline{\mu}}\)) is smaller than the centreline error in the PPI and RHI scans. However, since the standard-deviation error (\(\varepsilon_{\sigma_{u}}\)) is more pronounced than the mean wind-speed error (\(\varepsilon_{\overline{\mu}}\)), we conclude that volume averaging of the LiDAR measurements and the associated time shift within the scans accurately reproduces the mean wind speed, although the measured values are widely dispersed. In this case, the minimal error of the standard deviation is \(\varepsilon_{\sigma_{u}}=5.40\) % at \(\omega_{\phi}=3^{\circ}\)/s for the PPI scans and \(\varepsilon_{\sigma_{u}}=4.98\) % at \(\omega_{\phi}=3^{\circ}\)/s for the RHI scans. This minimum indicates that the local turbulence intensity averaged over the wake can be reproduced with an accuracy of \(\sim\)5 % in absolute terms. As expected, the free-flow errors for the PPI scan are the lowest of any in our analysis. Since the RHI scans mainly map the wake due to the scan geometry (Figure 9), free flow could not be defined exactly. These values are given for completeness, but should not be used for drawing generalised conclusions. The PPI free-stream error shows similar behaviour to that of the averaged wake errors, which is likely due to the process of area averaging. The errors are scaled over the scan speeds towards lower values. The minimum of \(\varepsilon_{\overline{\mu}}=0.17\)% occurs at \(\omega_{\phi}=6^{\circ}\)/s, and the minimum of \(\varepsilon_{\sigma_{u}}=3.68\)% occurs at \(\omega_{\phi}=5^{\circ}\)/s, which are comparable to the centreline errors. Since the turbulence intensity increases considerably compared to the ambient conditions in the high-shear areas of the wakes (Figure 8i), we deduce that, with lower turbulence intensities, the mapping error is minimal so the optimal scanning speed is faster. This relationship means that the more a flow fluctuates, measuring spatial structure becomes more important than measuring temporal changes. The data corresponding to Figure 9 is listed in Tables A1 and A2 in Appendix B.
## 5 Conclusions  
Contact-based communications are increasingly considered a promising Smart City enabling communication technology. In this paper, we introduced a new diffusion approach, Friendly-Sharing, where users are informed about the remaining time needed to interchange the messages stored in their buffers. In this way, the user can decide to wait longer in order to increase the number of messages exchanged, thus increasing the delivery rate.  
We used the ONE simulator with real human mobility traces and realistic message generation patterns based on social networking applications to evaluate the performance of the Friendly-Sharing diffusion. The behaviour of students produced interesting contact patterns: nodes with low mobility have more contacts than high mobility ones.  
We showed that the greater the number of messages in local buffers, the better the diffusion (in terms of delivery probability and latency). Thus, a key aspect for efficiency is the buffer management policy, especially when there is no full message interchange. The experiments show that the best results were obtained when forwarding the smallest messages first and dropping the biggest ones to make room for incoming transmissions. Regarding the waiting time for Friendly-Sharing, we also determined that when nodes wait even an extra two seconds each time they contact, it improves the delivery performance while not having a relevant impact on the buffer occupancy or on the protocol overhead.  
As a proof-of-concept, we showed that Friendly-Sharing can be easily introduced in messaging applications and can effectively improve the collaboration, increasing the delivery ratio and reducing the latency of message delivery with a limited impact on buffer utilisation.  
This work was partially supported by the _Ministerio de Economia y Competitividad, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Socidad, Proyectos I+D+1 2014_, Spain, under Grant TEC2014-52690-R, the _Generalitat Valenciana_, Spain, under Grant AICO/2015/108, the Universidad Laica Eloy Alfaro de Manabi, Ecuador, and SENESCYT, Ecuador.  
Figure 11: Delivery ratio (**a**) and latency (**b**) for different contact stop probabilities (16 s max. stop).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed to the review and validation of the simulation tools; and [PERSON], [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
# A New One-Dimensional Compound Chaotic System and Its Application in High-Speed Image Encryption
## 4 Experimental Test and Security Analysis
### Correlation Analysis of Adjacent Pixels  
The adjacent pixel values of meaningful plaintext images are usually highly correlated, so the correlation coefficient is close to 1. This strong correlation must be broken in the encryption process in order to destroy the relationship between the plaintext image and the encrypted image. The calculation equation of correlation coefficient \(r_{\mathbf{xy}}\) is expressed by Equations (30)-(33):  
\[E(\mathbf{x})=\frac{1}{N_{xy}}\sum_{i=1}^{N_{xy}}x_{i} \tag{30}\]  
\[D(\mathbf{x})=\frac{1}{N_{xy}}\sum_{i=1}^{N_{xy}}(x_{i}-E(\mathbf{x}))^{2} \tag{31}\]  
\[\mathrm{cov}(\mathbf{x},\mathbf{y})=\frac{1}{N_{xy}}\sum_{i=1}^{N_{xy}}(x_{i}- E(\mathbf{x}))(y_{i}-E(\mathbf{y})) \tag{32}\]  
\[r_{\mathbf{xy}}=\mathrm{cov}(\mathbf{x},\mathbf{y})/\sqrt{D(\mathbf{x})}\sqrt {D(\mathbf{y})} \tag{33}\]  
We calculated the correlation coefficients between all adjacent pixel pairs in the horizontal, vertical and diagonal directions of a size of 256 \(\times\) 256 gray image Lena and its ciphertext image. The results are listed in Table 3.  
From Table 3, it can be seen that the correlation coefficient of the original plaintext image is very close to 1, but the correlation coefficient of the encrypted image is very close to 0. Table 3 also lists the correlation coefficient results of the Lena image encrypted in several recently published algorithms. In contrast, our algorithm has certain advantages.  
Figure 8: _Cont_.  
Figure 8: Four test images and their ciphertext images and histograms. (**a**) Plaintext image Lena; (**b**) histogram of plaintext image Lena; (**c**) ciphertext image Lena; (**d**) histogram of ciphertext image Lena; (**e**) plaintext image cameraman; (**f**) histogram of plaintext image cameraman; (**g**) ciphertext image cameraman; (**h**) histogram of ciphertext image cameraman; (**i**) plaintext image baboon; (**j**) histogram of plaintext image baboon; (**k**) ciphertext image baboon; (**l**) histogram of ciphertext image baboon.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Horizontal** & **Vertical** & **Diagonal** \\ \hline Plain image & \(0.9401\) & \(0.9695\) & \(0.9180\) \\ Cipher image (Ours) & \(0.0062\) & \(-0.0001\) & \(0.0018\) \\ Cipher image [33] & \(-0.0016\) & \(0.0043\) & \(-0.0026\) \\ Cipher image [34] & \(-0.0017\) & \(-0.0084\) & \(-0.0019\) \\ Cipher image [35] & \(0.0010\) & \(-0.0009\) & \(0.0009\) \\ Cipher image [36] & \(-0.0086\) & \(-0.1020\) & \(0.0125\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparing results of correlation coefficients for Lena (\(256\times 256\)).
# Investigating the Performance of Red and Far-Red SIF for Monitoring GPP of Alpine Meadow Ecosystems
## 2 Materials and Methods
### Canopy SIF Retrieval  
Canopy SIF (SIF\({}_{\text{Red}}\), SIF\({}_{\text{Far-red}}\)) for red and far-red bands were retrieved with spectral fitting method (SFM). The prerequisite assumption for extracting SIF from the reflected radiance spectrum of surface vegetation is that reflectance and fluorescence emission obey [PERSON]'s cosine law, whereby the upwelling radiance of vegetation is coupled by two components: the solar flux reflected, and the fluorescence emitted [51; 52]. SFM supposes that reflectance spectra and fluorescence spectra in chosen spectral intervals are functions of wavelengths that can be described by mathematical functions rather than that they are spectrally constant [52]. Here, the reflectance and fluorescence spectra were fitted separately with quadratic functions:  
\[L(\lambda)=\frac{r(\lambda)E(\lambda)}{\pi}+F(\lambda)=\frac{\left(a\lambda^{2 }+b\lambda+c\right)E(\lambda)}{\pi}+\left(m\lambda^{2}+n\lambda+p\right) \tag{1}\]  
where \(\lambda\) is wavelength, \(L(\lambda)\) is the radiance upwelling from vegetation, \(r(\lambda)\) is the reflectance (without the emission component), \(E(\lambda)\) is the incoming total solar irradiance to the target, \(F(\lambda)\) is the solar-induced fluorescence, and \(a\), \(b\), \(c\), \(m\), \(n\), and \(p\) are the spectral fitting coefficients, respectively.
## 1 Introduction
[PERSON] et al. [35] devoted the 3-D peristaltic flow of Carreau fluid with magnetic field. Peristaltic transport of Carreau fluid through an asymmetric conduit with magnetic field was incorporated by [PERSON] et al. [36]. Moreover, [PERSON] et al. [37] depicted the peristaltic flow of MHD Carreau fluid in curved channel. Different Non-Newtonian models are used to describe the rheological property for blood. The above-mentioned models describe the impact of Non-Newtonian fluids only in peristalsis and not in EOF. Thus, we have the motivation to observe the effects of heat transfer in EOF caused by peristaltic wave through the microchannel and to examine the Carreau fluid model along viscous dissipation. Based on low Reynolds number and Debye Huckel linearization approximations, the Poisson-Boltzmann equation formally solves the potential distribution and axial velocity for Carreau fluid. Likewise, temperature equation is solved for Carreau fluid in the presence of viscous dissipation. A regular perturbation method is used to solve the current problem. A complete parametric study is performed to find out the effects of Weissenberg number \(W_{e}\), power law index/fluid index \(n_{i}\), electroosmotic parameter \(m_{e}\), Helmholtz-Smoluchowski velocity \(U_{HS}\), volume flow rate \(\theta\) and \(B_{r}\) on the thermal features of the flow. To the best of my knowledge, no such analysis has been proposed before.