esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Dynamic Study  
A numerical model was developed using the explicit dynamics module in ANSYS. The body of the MP structure (in green in Figure 12a) is deformable, while its bottom surface is fixed. The launcher is made of steel (in brown in Figure 12a), and the meshing elements are tetrahedral. [PERSON]'s modulus of the MP material was set to 692 MPa, based on our results from static experiments. However, [PERSON]'s ratio was assigned a standard value of 0.3 since no experiments were conducted to determine this parameter. Other factors, such as non-linearities and material rupture, were also not considered, and this must be taken into account when interpreting the results. Nonetheless, the primary purpose of these simulations was not to precisely replicate the experimental outcomes but only to check if the ranking of the geometries according on their shock absorption efficiency is the same than that based on the experimental data (i.e., which geometry is more effective in absorbing a given shock). Examples of simulated crush for both types of geometries are shown in Figure 12b,c.  
Figure 11: Results of the static study for various percentages of white glue: simulation vs. experiments.  
Figure 12: _Cont._The results from both simulations and experiments are plotted in Figure 13. The results indicate that the pyramidal geometry crushes more easily than the conical geometry under a given impact energy. This could be attributed to the fact that pyramids have sharper angles, where stress is more concentrated, leading to plastic deformation and subsequent mechanical failure in those regions. As a result, the pyramidal geometry exhibits a lower capacity to absorb high impact energies. For instance, at 60 J, the pyramid is already fully crushed, corresponding to the sample's height of 30 mm. Conversely, in the conical geometry, the impact forces are more evenly distributed, resulting in a higher absorption capacity before failure. The simulations exhibit a similar trend, with the pyramids crushing more than the cones under the same impact energy. However, the difference observed between the two geometries is more pronounced in the simulations, likely because the back plate on which the samples are mounted was not modelled. This omission leads to simulated crushing values that exceed reality, even surpassing 30 mm, which is impossible in the experiments.  
Figure 12: Crash tests simulations: model (**a**); cones (**b**) vs. pyramids (**c**); same launcher velocity (3.6 m/s, which corresponds to an impact energy of 36 J). The deformations displayed are on the vertical axis.  
Figure 13: Dynamic study results for various launcher speeds: experiments vs. simulations.
# Fundamental Spacetime Representations of Quantum Antenna Systems
## 6 Quantum Antenna Radiation Patterns: Basic Constructions
### Constructing the Quantum Antenna Directivity Pattern
: _The positive number \(\alpha_{k}\) is inserted into (69) in order to ensure that the total probability of radiation at all angles, evaluated at a single radial momentum value \(k\), is equal to that of an isotropic source.23 Intuitively, \(\alpha_{k}\) represents the total radiation angular density emitted by the \(q\)-antenna, so the ratio \(\mathcal{D}\) given by (69) is the relative radiation intensity along one direction with respect to a standard isotropic source_  
\[\Pr\{\mathbf{k}\in Q\}=\frac{1}{\alpha}\int_{\mathbf{k}\in Q}\frac{d^{3}k}{(2\pi)^{3} }\frac{|J(\mathbf{k})|^{2}}{2\omega_{\mathbf{k}}}, \tag{62}\]1  
_where \(P_{0}\) is the constant radiation angular probability of such an isotropic reference antenna whose radiation angular density is \(\mathcal{D}_{0}\). That is, we have_  
\[\Pr\{\mathbf{k}\in Q\}=\frac{1}{\alpha}\int_{\mathbf{k}\in Q}\frac{d^{3}k}{(2\pi)^{3} }\frac{|J(\mathbf{k})|^{2}}{2\omega_{\mathbf{k}}}, \tag{62}\]2  
_as expected from a typical directivity expression [35]. Physically, the ratio \(4\pi\alpha_{k}/\alpha\) in (71) represents the fraction of the total quantum radiation contained in a sphere with radius \(k\) in momentum space relative to the total radiation obtained by including contributions coming from all values of the momentum magnitude \(k:=|\mathbf{k}|\). The ability of a source to direct power along certain directions is measured by angle-dependent generalizations of such total ratios. Then, Definition 6 of the q-antenna directivity is a natural generalization of the corresponding definition in classical antenna theory as traditionally presented in texts such as [27,35].
# Physical Properties of _Jatropha curcas_ L. Fruits and Seeds with Respect to Their Maturity Stage
## 3 Results and Discussion
### Geometric Characteristics of _J. curcas_ fruits and Seeds
#### 3.2.1 Fruits  
The results showed that moisture content, unit mass, and dimensions of green fruits were considerably higher than for yellow and black fruits. The large width and thickness indicate a thick layer of moist flesh in the fruit hulls at this maturity stage. As the fruits were maturing, the unit mass decreased from 8.23 to 2.51 g, the surface area decreased from 2164 to 1873 mm\({}^{2}\), and the specific surface area escalated from 1.2 to 1.6 cm\({}^{2}\)/cm\({}^{3}\). The dimensions of yellow fruits, such as length, width, and thickness were slightly smaller than those of yellow _J. curcas_ fruits from Malaysia investigated by [PERSON], [PERSON], [PERSON], and [PERSON] [23], but similar to the ones from Thailand [24]. Regardless of the pericarp color, the shape of _J. curcas_ fruits was detected as spherical, since the sphericity of all maturity stages was higher than 0.7 [25]. Compared to black _J. curcas_ fruits from India [15], the surface area of the black fruits was similar, width and thickness were higher, and the length was substantially lower.
# Near-Real-Time Detection of Insect Outbreaks in Urban Trees Using a PlanetScope Time Series
## 2 Materials and Methods
### Field Data Survey: Geographical Location of Pinus pinea
This Regional Park covers an area of 23,115 ha and includes a variety of natural environments, such as coastal areas, pine forests, woodlands, wetlands, and Lake Massaciuccoli ([[https://www.parcosanrosose.org/pianificazione-e-governo-del-territorio/piani-di-gestione/](https://www.parcosanrosose.org/pianificazione-e-governo-del-territorio/piani-di-gestione/)]([https://www.parcosanrosose.org/pianificazione-e-governo-del-territorio/piani-di-gestione/](https://www.parcosanrosose.org/pianificazione-e-governo-del-territorio/piani-di-gestione/)) accessed on 10 October 2024). Due to its size and landscape diversity, the park is an important area for biodiversity conservation and for the protection of natural habitats typical of the Tyrrhenian coast. The park's area is 50% forested, dominated by pine forests (both _Pinus pinaster_ Ait. and _Pinus pinea_ L.) [37] that have not yet been infested by _T. parvicornis_. Approximately 230 plants were surveyed over an area of 10 ha.
## 5 Simulation Results
### Simulations of DIB  
Many underwater modems's operating input voltage is 12 V DC, and we take 10 mAh as one unit of battery energy. Converted to Joule, \(E_{unit}\) is 432 J.  
Figure 15 shows the energy assignment with different internode distance in shallow water. The number of super nodes is 10 and the number of basic nodes is 21. We vary the internode distance \(r\) from 50  
Figure 14: Energy dissipation vs sensor nodes.  
Figure 13: Energy gradations vs network lifetime.  
m to 150 m. The figure shows the assigned battery of nodes is nearly a straight line which verifies the rightness of Equation (18). The slope of the line increases as the internode distance increases. The reason is that as the internode distance increases, the energy consumption on one hop transmission increases. Considering the limited battery power levels of manufacturing batteries, lower internode distance may be easy to find enough assigned battery power levels to achieve balanced energy consumption.  
Figure 16 illustrates the energy assignment with different nodes and different internode distances. We observe that as the number of nodes increases, the assigned battery ratio of the first node to the last node increases. This shows that as the number of nodes increases, the first node will relay more packets to the sink, thus its energy consumption increases. However, with fixed internode distance and varying the number of nodes, the slope of the assigned battery line is nearly the same.  
Figure 16: Energy assignment with different nodes.  
Figure 15: Energy assignment with different internode distance.  
Figure 17 shows the energy assignment with different frequency and depth in shallow water. The total number of nodes is 10, and \(r\) is 100 m. It can be seen that both depth of water and the frequency affect on battery power assignment, because as the depth of water or the frequency increases, the energy consumption of nodes increase.
## 1 Introduction
### State-of-the-Art of Large Antenna Array for Communication or Sensing
#### 1.1.2 Sparse Modular Subarray  
A narrow beamwidth of the radiation pattern of a phased array requires a large array aperture size. To decrease the complexity of a large aperture, sparse modular arrays were studied in Ref. [10] where the positions of the modular uniform subarrays were optimized for the desired beamwidth and SLL. When sparse arrays are considered, clustering antennas is another solution to avoid the complexity of transmission lines from the RF front-end to antennas. For instance, in Refs. [19; 20], the BS antenna array was designed based on irregular clustering and sequential rotation, where the optimization problem considered the inter-element spacing, aperture size, and modular layout design. Due to a large transmission distance in satellite communications, a larger aperture is desirable to lower beamwidth, therefore the solutions for the apertures in non-terrestrial or aerial-terrestrial communications are a source of inspiration for designing JCAS BS in this paper. For instance, sparse subarrays are proposed in satellite-terrestrial integrated networks [21]. In non-terrestrial networks, a sunflower sparse array of uniform hexagonal subarrays was designed for satellite applications in Refs. [15; 22].  
Depending on whether separating [23] or sharing waveforms [24] between communication and sensing, the requirements for JCAS apertures are different, as in, e.g., the number and locations of antennas and the interference mitigation. In case of the presence of a dominant LoS path or a reflected path with high SNR, a sparse large array is beneficial for better angle-of-arrival (AoA) estimation [8; 10]. We intend to exploit the solutions mentioned above to design a novel large JCAS shared aperture with an affordable number of phase shifters.
## 2 Materials and Methods
### Evaluation Metrics and Statistical Analysis  
Several metrics were calculated to evaluate the performance of classifiers built using different ML techniques and resampling methods. True positives (TP) indicate the number of patients correctly classified as having the outcome whereas true negatives (TN) mean the number of patients correctly classified as not having the outcome. False positives (FP) indicate the number of patients incorrectly classified as having the outcome while false negatives (FN) mean the number of patients incorrectly classified as not having the outcome. Accuracy is (TP + TN) / (TP + TN + FP + FN). Sensitivity is TP / (TP + FN) whereas specificity is TN / (TN + FP). The receiver operating characteristic (ROC) curve displays the full picture of the trade-off between sensitivity and specificity by plotting sensitivity as a function of (1 - specificity) for all possible thresholds. The performance of classifiers was compared according to the area under the ROC curve (AUC).  
Continuous variables were reported with means and standard deviations or medians with interquartile ranges, and categorical variables were reported with counts and percentages. Clinical features between patient groups were compared by Chi-square tests for categorical variables and _t_-tests or Mann-Whitney U tests for continuous variables. The average of the estimates from the 30 dataset pairs were compared using paired _t_-tests. All statistical analyses were performed using Stata 15.1 (StataCorp, College Station, Texas). Two-tailed \(p\) values < 0.05 were considered statistically significant.
# Global Dynamics of a Predator-Prey System with Cooperative Hunting
## 5 Complete Description of the Dynamic Behaviors
### Global Dynamics of the Case \((\beta,c)\in\Lambda_{2}\)
#### 5.2.4. Scenario \((\beta,c)\in\Lambda_{2c}\)  
We consider the global dynamics for the scenario \((\beta,c)\in\Lambda_{2c}\), i.e., \(\beta>1\) and \(c\geq f_{2}(\beta)\). According to Theorem 3, \(E_{2}^{*}\) is always a source for all \(d\in(0,F(x_{0}))\). Similar to Theorem 12, when \(d\in(0,c]\), we have the following result.  
If \((\beta,c)\in\Lambda_{2c}\) and \(d\in(0,c]\), System (3) has a stable limit cycle (see Figure 13a).  
When \(d\in(c,F(x_{0}))\), the dynamics of (3) are very complex. Through the numerical simulation, there exists \(d^{\sharp}\in(c,F(x_{0}))\) such that  
1. When \(d\in(c,d^{\sharp})\), (3) has a stable limit cycle surrounding \(E_{2}^{*}\), which is the \(\omega\)-limit set of the unstable manifold \(\Gamma_{2}^{u}\) of \(E_{1}^{*}\) (see Figure 13b).
2. When \(d=d^{\sharp}\), \(\Gamma_{2}^{u}=\Gamma_{2}^{s}\), that is, System (3) has a homoclinic loop of the saddle \(E_{1}^{*}\) (see Figure 13c). Outside the homoclinic loop, trajectories converge to the boundary equilibrium \(E_{1}\), whereas inside the homoclinic loop, trajectories converge toward it (see Figure 13c);
3. When \(d\in(d^{\sharp},F(x_{0}))\), the homoclinic loop is broken and all trajectories (except the stable manifolds of \(E_{1}^{*}\)) converge to the boundary equilibrium \(E_{1}\) (see Figure 13d).  
For the scenario \((\beta,c)\in\Lambda_{2c}\), Theorem 13 and the numerical simulation (see Figure 13) imply the following biological implications:  
1. When the mortality of the predator is small (\(d\leq c\)), two species coexist (see Figure 13a) in an oscillatory mode.
2. When \(d\) is large such that \(c<d<d^{\sharp}\), System (3) has two attractors: one is the boundary equilibrium \(E_{1}(0,1)\) and the other is the stable limit cycle surrounding \(E_{2}^{*}\). The separatricesof the attracting basins of these two attractors are the stable manifolds \(\Gamma_{1}^{s}\) and \(\Gamma_{2}^{s}\) of the saddle \(E_{1}^{s}\) (see Figure 12b). As \(d\) increases from \(c\) to \(d^{\sharp}\), the attracting basin of the limit cycle dwindles and eventually shrinks into a homoclinic loop of \(E_{1}^{s}\) (see Figure 12c). So, when \(c<d\leq d^{\sharp}\), the coexistence of the prey and predator depends on the initial values._
3. _As_ \(d\) _becomes larger (_\(d>d^{\sharp}\)_), System (_3_) only has one boundary attractor_ \(E_{1}(0,1)\) _and the coexistence of the prey and predator becomes impossible (see Figures 4b,c, 6b, and 12d)._
## 3 Results
### Outcomes
#### 3.2.1 Problematic Smartphone Use
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Post-Hoc Test**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Std.**} & \multicolumn{2}{c}{**C1**} \\ \hline \multirow{3}{*}{**Group**} & \multirow{3}{*}{**(I) Time**} & \multirow{3}{*}{**(J) Time**} & \multirow{3}{*}{**(J) Time**} & \multirow{3}{*}{**Diff.**} & \multirow{3}{*}{**Error**} & \multirow{3}{*}{**Sig. *} & \multirow{3}{*}{**Lower Bound**} & **Upper Bound** \\ \cline{5-8} \cline{7-9}  & & 0 week & 8 weeks & 0.803 & 0.678 & 0.712 & \(-\)0.829 & 2.435 \\ \cline{5-8} \multirow{3}{*}{**S-scale**} & \multirow{3}{*}{SUC} & 0 week & 12 weeks & 0.561 & 0.862 & 1.000 & \(-\)1.519 & 2.641 \\ \cline{5-8} \multirow{3}{*}{**S-scale**} & 8 weeks & 12 weeks & \(-\)0.242 & 0.678 & 1.000 & \(-\)1.874 & 1.389 \\ \cline{2-8} \multirow{3}{*}{**S-scale**} & \multirow{3}{*}{PSU} & 0 week & 8 weeks & 4.919 * & 0.495 & \(<\)0.001 & 3.729 & 6.110 \\ \cline{5-8} \multirow{3}{*}{**S-scale**} & 0 week & 12 weeks & 6.290 * & 0.629 & \(<\)0.001 & 4.773 & 7.808 \\ \cline{5-8} \multirow{3}{*}{**S-scale**} & 8 weeks & 12 weeks & 1.371 * & 0.495 & 0.018 & 0.181 & 2.561 \\ \hline \multirow{3}{*}{**S-scale**} & \multirow{3}{*}{SUC} & 0 week & 8 weeks & 0.303 & 0.504 & 1.000 & \(-\)0.909 & 1.515 \\ \cline{2-8} \multirow{3}{*}{**GAD**} & 0 week & 12 weeks & 0.076 & 0.552 & 1.000 & \(-\)1.258 & 1.409 \\ \cline{2-8} \multirow{3}{*}{**GAD**} & 8 weeks & 12 weeks & \(-\)0.227 & 0.504 & 1.000 & \(-\)1.439 & 0.985 \\ \cline{2-8} \multirow{3}{*}{**PSU**} & 0 week & 8 weeks & 1.815 * & 0.367 & \(<\)0.001 & 0.930 & 2.699 \\ \cline{2-8} \multirow{3}{*}{**PSU**} & 0 week & 12 weeks & 2.258 * & 0.403 & \(<\)0.001 & 1.285 & 3.231 \\ \cline{2-8} \multirow{3}{*}{**PSU**} & 8 weeks & 12 weeks & 0.444 & 0.367 & 0.685 & \(-\)0.441 & 1.328 \\ \hline \multirow{3}{*}{**PSU**} & \multirow{3}{*}{SUC} & 0 week & 8 weeks & 1.091 & 0.620 & 0.238 & \(-\)0.401 & 2.583 \\ \cline{2-8} \multirow{3}{*}{**PSU**} & 0 week & 12 weeks & 0.424 & 0.637 & 1.000 & \(-\)1.113 & 1.962 \\ \cline{2-8} \cline{2-8}  & 8 weeks & 12 weeks & \(-\)0.667 & 0.620 & 0.849 & \(-\)2.159 & 0.825 \\ \cline{2-8} \multirow{3}{*}{**PHQ**} & 0 week & 8 weeks & 2.210 * & 0.452 & \(<\)0.001 & 1.121 & 3.298 \\ \cline{2-8} \multirow{3}{*}{**PSU**} & 0 week & 12 weeks & 2.927 * & 0.465 & \(<\)0.001 & 1.806 & 4.049 \\ \cline{2-8} \cline{2-8}  & 8 weeks & 12 weeks & 0.718 & 0.452 & 0.340 & \(-\)0.371 & 1.806 \\ \hline \hline \end{tabular} Statistically significant associations (* \(p<\) 0.05), Bonferroni correction were used, Confidence interval (CI), Smartphone Use Control (SUC), Problematic Smartphone Use (PUC), Smartphone Addiction Proness Scale (S-scale), Generalized Anxiety Disorder Scale (GAD), The Patient Health Questionnaire (PHQ).  
\end{table} 
Table 2: Results of post hoc analysis (S-scale, GAD, PHQ).
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 3 Qualitative Benchmark for Analysis  
The current MM mechanisms are suitable for the 2 nd, 3 rd, and 4 th generation, but cannot perform well for 5G and later network scenarios. Hence, there is a need to explore whether these mechanisms can be applied partially or as a whole to the stack of 5G and future networks. So, in this section, we analyze these MM mechanisms based on their usage of energy, adaptability, dependability, and network extensibility. As a portion of this analysis, initially, we present a thorough explanation of these criteria, as follows.  
**(i) Adaptability** is a way of qualitative analysis in which we measure the adaptability aspects of the MM mechanisms for the network. **(ii) Dependability** will decide whether the MM mechanisms will be capable of confirming uninterrupted services in any network. **(iii) Extensibility** feature lets one define if the MM mechanism can offer the services to the increased number of devices with a parallel increase in demanded QoS with a heterogeneity mobility profile. **(iv) Energy efficiency** concerns whether portable devices are limited in storage and energy due to their petty design. The energy efficiency will determine whether the MM mechanisms will be capable of ensuring uninterrupted services in any network with prevailing power resources [31].  
We arranged the above benchmarks (i, ii, iii, iv) for analysis into a list of parameters for each criterion and present them in Table 2. We present the acquiescence with each prescribed parameter in Table 2 for extensibility, adaptability, dependability, and energy efficiency. Now, we elaborate upon the parameter requirement relationships that have been shown in Table 1 in order to improve the completeness of the assessment criteria.  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l} \hline \hline
# Design of a NARX-ANN-Based SP Controller for Control of an Irrigation Main Canal Pool
## 3 Design of the NARX-ANN-Based SP Controller
Figure 9 illustrates the diagram of our proposal for the modified SP structure with the non-linear NARX-ANN-based models for the water distribution control in the studied canal pool, where \(K(t)\) is a gain tuning block to achieve an adequate estimate \(\hat{D}(t)\) of the load disturbance \(D(t)\). If there is no load disturbance, \(D(t)=0\), and the error signal is \(e(t)\cong 0\). However, when the load disturbance \(D(t)\) appears, we have that \(e(t)\
eq 0\). If the error signal returns to \(e(t)\cong 0\) by tuning the \(\hat{K}_{D}(t)\) of the gain tuning block, then the output signal \(\hat{D}(t)\) of the gain tuning block is a suitable estimation of the load disturbance \(D(t)\). Figure 9: Proposed modified structure of the SP. The autotuning algorithm of the gain block \(K(t)\) is the following:  
\[\hat{K}_{D}(t)=\hat{K}_{D}(t-1)\frac{y(t-1)}{y_{m1}(t-1)} \tag{15}\]  
If \(y(t-1)=y_{m1}(t-1)\) the error signal \(e(t-1)=0\) and consequently \(\hat{K}_{D}(t)=\hat{K}_{D}(t-1)\). But if \(y(t-1)\
eq y_{m1}(t-1)\) the error signal \(e(t-1)\
eq 0\) and therefore, the new \(\hat{K}_{D}(t)\) is obtained through multiplying \(\hat{K}_{D}(t-1)\) by a tuning gain obtained as \(y(t-1)/y_{m1}(t-1)\). The linear PI controller in the discrete time domain is represented as [80]:  
\[u(t)=K_{C}e_{2}(t)+K_{I}T_{s}\sum_{j=0}^{t}e_{2}(j) \tag{16}\]  
where \(K_{I}=K_{C}/T_{I}\), \(T_{s}\) is the sampling period. In the proposed modified structure of the SP, the design of the controller \(C(t)\) was carried out based on the following non-linear PI control law [46]:  
\[u(t)=f\left[e_{2}(t),T_{s}\sum_{i=0}^{t}e_{2}(i)\right] \tag{17}\]  
where \(f(\cdot)\) is a nonlinear function represented by an ANN, \(e_{2}(t)\) is the control system error, \(e_{2}(t)=r(t)-y(t)+y_{m1}(t)-y_{m2}(t)\), and \(r(t)\) is the setpoint. A multilayer feedforward with tree layers ANN was used to design the nonlinear ANN PI controller. The input layer has two nodes (the system error \(e_{2}(t)\) and the sum of the system error \(e_{2}(i)\)), the hidden layer has 5 nodes, and the output layer 1 node. The neuron activation function of the input layer is the linear function \(f_{i}(x)=x\), of the hidden layer is the sigmoid function \(f_{h}(x)=1/1+e^{-x}\), and of the output layer is \(f_{0}(x)=x\). Figure 10 illustrates the diagram with the architecture of the designed nonlinear ANN PI controller. The data for training this controller was obtained from a discrete-time linear PI controller installed in the canal pool under study. In addition, using the frequency domain method, a conventional PI controller embedded in a SP structure was designed with specifications: gain crossover frequency \(\omega_{c}=10^{-3}\) rad/s to achieve a settling time around \(T_{s}=L+\pi/\omega_{c}\approx 3400\) s and phase  
Figure 10: Architecture of the designed nonlinear ANN PI controller. margin \(\phi_{m}=80^{\circ}\) to obtain a nearly zero overshoot. The controller tuned with these specifications was:  
\[C(s)=0.087(1+\frac{1}{70.56s}) \tag{18}\]