esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Dynamic Model of Manipulator  
The dynamic robotic model is represented as [1]:  
\[M(\alpha)\ddot{\alpha}+C(\alpha,\dot{\alpha})\dot{\alpha}+G(\alpha)=\tau+d. \tag{1}\]  
Here, \(\alpha\), \(\dot{\alpha}\), and \(\ddot{\alpha}\in\mathbb{R}^{n\times 1}\) denote the joint angle position, angular velocity, and angular acceleration, respectively. The inertia matrix is given by \(M(\alpha)=\bar{M}(\alpha)+M_{\Delta}(\alpha)\in\mathbb{R}^{n\times n}\), where \(\bar{M}(\alpha)\) and \(M_{\Delta}(\alpha)\) represent the nominal and uncertain terms of the inertia matrix. Similarly, the centrifugal and Coriolis force matrix is denoted as \(C(\alpha,\dot{\alpha})=\bar{C}(\alpha,\dot{\alpha})+C_{\Delta}(\alpha,\dot{ \alpha})\in\mathbb{R}^{n\times n}\), with \(\bar{C}(\alpha,\dot{\alpha})\) and \(C_{\Delta}(\alpha,\dot{\alpha})\) representing the nominal and uncertain terms, respectively. The gravity vector is denoted as \(G(\alpha)=\bar{G}(\alpha)+G_{\Delta}(\alpha)\in\mathbb{R}^{n\times 1}\), where \(\bar{G}(\alpha)\)and \(G_{\Delta}(\alpha)\) represent the nominal and uncertain terms of the gravity vector. The external disturbance is represented by the vector \(d\in\mathbb{R}^{n\times 1}\), while the control input torque is denoted as \(\tau\in\mathbb{R}^{n\times 1}\). It is important to note that \(M_{\Delta}(\alpha)\), \(C_{\Delta}(\alpha,\dot{\alpha})\), and \(G_{\Delta}(\alpha)\) correspond to the uncertain terms in the dynamic model, with their exact values not known in advance.  
**Property 1**.: _In Equation (1), \(M\) is bounded, symmetric, positive definite, and possesses the following property:_  
\[\lambda_{\min}(M)\|s\|^{2}\leq s^{T}M\!s\leq\lambda_{\max}(M)\|s\|^{2}. \tag{2}\]  
_Here, \(s\in\mathbb{R}^{n\times 1}\) represents any nonzero vector._  
**Property 2**.: _The following passivity property can be obtained:_  
\[s^{T}\Big{(}\dot{M}-2C\Big{)}s=0\, \tag{3}\]  
_where \(s\) represents a vector in \(\mathbb{R}^{n\times 1}\)._  
Let \(\alpha_{1}=\alpha\), \(\alpha_{2}=\dot{\alpha}\), and \(\dot{\alpha}_{2}=\ddot{\alpha}\). Equation (1) can be rewritten in a form suitable for the control design, as follows:  
\[\dot{\alpha}_{2}=\bar{M}^{-1}(\alpha_{1})(-\bar{C}(\alpha_{1},\alpha_{2}) \alpha_{2}-\bar{C}(\alpha_{1})+\mathcal{F}+\tau). \tag{4}\]  
where \(\mathcal{F}=-M_{\Delta}(\alpha_{1})\dot{\alpha}_{2}-C_{\Delta}(\alpha_{1}, \alpha_{2})\alpha_{2}-G_{\Delta}(\alpha_{1})+d\in\mathbb{R}^{n\times 1}\) represents the uncertain term vector.  
This paper addresses challenges in controlling robot manipulators by improving response time, minimizing tracking errors and chattering, and increasing controller robustness. It achieves these objectives by designing a fixed-time sliding mode function based on PPCA, utilizing an RBFNN to approximate the robot's dynamics and handle uncertainty, and developing a novel fixed-time TSMC method. The approach eliminates the need for the robot's dynamical model while ensuring global fixed-time convergence and prescribed performance.
## 4 Results and Discussion
### DIC, DIC-PDDO and FE Strains Comparison  
In order to validate the DIC and DIC-PDDO results, comparison of the DIC and DIC-PDDO strains against the FE simulation strains computed on in-house tensile experiment specimen surface is presented in Figure 9, showing a good agreement in terms of magnitude and location of maximum strains, as well as distribution and magnitude of the overall strain fields. Based on the results from Figures 7 and 8, VSG of size 57 px was used for DIC and the PD absolute horizon diameter of \(2\delta=24\) px (1.04 mm) was selected for DIC-PDDO. A difference of 8% for both \(\left|\varepsilon_{y}\right|_{max}\) and \(\left|\varepsilon_{x}\right|_{max}\) strains is obtained between the DIC-PDDO and the FE results. It is also worth noticing the asymmetric profile of the strain field in Figure 9 (which is due to imperfect circular profile of the central hole and imperfect alignment between specimen and loading in the testing machine; the method, used to evaluate and reduce those effects, is defined in Section 3.3) shows bigger differences between DIC and FE strains than the differences between DIC and DIC-PDDO strains. Moreover, DIC smears the maximum strain value near the hole and thus FE simulation results higher maximum strains. Nevertheless, strain fields, captured by the experimental DIC measurement can be validated by the FEA computational results and are well reproduced by the DIC-PDDO method in terms of both distribution and magnitude.  
Figure 8: Effect of the PD horizon size on the strain values computed on in-house tensile experiment specimen surface by the DIC-PDDO method: (**a**) \(\varepsilon_{y}\) strains, (**b**) \(\varepsilon_{x}\) strains.
# A Survey of Precision Formation Relative State Measurement Technology for Distributed Spacecraft
## 3 Analysis of the Characteristics of Relative State Measurement Technology
### Composite Relative Measurement Technology of Spacecraft Formation
#### 3.6.3 GPS + Vision Coupling Measurement Technology  
The aim of the GPS + visual composite relative measurement technology is to solve the problems of low visibility and the poor real-time performance of GPS signals in high orbits. The composite GPS relative measurement technology takes full advantage of the real-time capability and robustness of vision sensors. It overcomes the shortcomings of the small measurement range of vision sensors and improves the overall measurement performance [64]. The multi-sensor fusion of the relative state results, measured independently by the GPS and the vision system, improves the overall measurement accuracy, which uses a high-precision loose-coupling method [65]. It first uses the GPS orbit filter on the satellite to determine the absolute motion state of the satellite and then combines the information from the monocular camera with the GPS carrier phase observation information shared by the communication link, to obtain the relative motion state information. Finally, the relative state information is combined with orbital dynamic prediction for filtering. Obtaining high-rate state information can significantly improve the speed of solving the ambiguity of the full circumference of the carrier and gain more accurate relative state information; the process is shown in Figure 24.  
Figure 23: Schematic diagram of the GPS + RF combined relative state measurement.
# Designing a Novel High-Performance FBG-OADM Based on Finite Element and Eigenmode Expansion Methods
## Novel FBG-OADM
Thus, the equivalent RI (i.e., \(n_{neff}^{core}\); \(n_{2}<n_{neff}^{core}<n_{1}\)) of the core mode as well as nearly infinite number of equivalent RIs (i.e., \(n_{neff}^{cladding}\); \(n_{3}<n_{neff}^{cladding}<n_{2}\)) of the cladding modes can be determined, where \(n_{1}\) is the RI of the core layer, \(n_{2}\) is the RI of the cladding layer, \(n_{3}\) is the RI of the surround layer, \(n_{neff}^{core}\) is the effective RI of the core mode, and \(n_{neff}^{cladding}\) is the effective RI of the cladding mode. FBGs involve irradiating UV light signals on phase masks with a specific cycle to cause periodic variation in the RI of the fiber core. These variations in the core RI are called fiber gratings. A uniform fiber grating can be expressed using the following mathematical equation [12; 15]:  
\[n_{1}(z)=n_{1}+\delta n\bigg{[}1+\cos\bigg{(}\frac{2\pi}{\Lambda_{FBG}}z\bigg{)} \bigg{]} \tag{1}\]  
where \(\delta n\) is the peak induced-index change, \(n_{1}\) is the RI of the core, and \(\Lambda_{FBG}\) is the FBG period. Mathematically, before light signals are affected by fiber gratings, all guided modes in the optical fiber display pairwise orthogonality. In order words, powers between the modes do not couple to, or swap with, each other during propagation. However, as light signals become affected by fiber gratings, powers between the modes undergo perturbation, causing coupling and crosstalk between the modes. \begin{table}
\begin{tabular}{c c c} \hline
**LPG** & **LPG** & **2 \(\times\) 2 Coupler** & **LPG-OADM** \\ \hline FBG & FBG & \(2\times 2\) coupler & FBG-OADM \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of spectrums between the LPG-OADM and FBG-OADM.
# Multi-Band Polarization Imaging in a Harsh Sea Fog Environment
## 4 Experimental Results and Analysis
### Analysis of the Effect of Optical Thickness on Polarization Imaging
The analysis of the experimental histogram reveals that as the value of sea spray optical thickness increases, the polarization contrast decreases and the image blurs, but the polarization contrast in the 670 nm band is greater and more favorable for observation at varying sea spray optical thicknesses. At an optical thickness of 0.75 in this band, the polarization contrast of the polarization image increases from 1.96% to 3.86%, a 1.9% increase compared to the polarization contrast of the intensity image, whereas the bands of 530 nm and 450 nm increase by 1.08% and 0.28%, respectively, indicating that when the optical thickness value is 0.75, the polarization contrast value of the polarization image at 670 nm increases more. The polarization contrast values of the polarization pictures at 670 nm and 530 nm grow almost twice as much as the polarization contrast values of the intensity images as the optical thickness increases. When the optical thickness increased from 0.52 to 0.75, the polarization contrast of the polarization images decreased by approximately 4.55 percent, 4.68 percent, and 5.29 percent at 670 nm, 530 nm, and 450 nm, respectively, due to the increase in the radius of the sea salt aerosol particles, which decreased the polarization of the images. Figure 14 shows the objective assessment indices of the photographs under varying sea fog optical thicknesses. According to the data in the figure, it is intuitively apparent that the greater the optical thickness, the lower the objective indexes, the poorer the visual effect, and the greater the loss of detail information. Moreover, as the optical thickness increases, the evaluation indexes of the polarized image at 670 nm increase, and it can be assumed that the imaging effect of this band is the best, the brightness of the image is high, and the surface features of the tar are clearly visible. Compared to the other two bands, the image information is more comprehensive the more polarization information of the target is supplied. Figure 13: Contrast of multi-band images with different sea fog visibility (**a**) Intensity image; (**b**) Polarization image.
## 3 Proactive Geospatial Information Services (PGIS)
### Concept of PGIS
#### 3.1.2 Workflow of PGIS  
There are mainly three steps to implement a PGIS application. The first step is task explanation, which is the procedure of understanding user's demands according to the related context and obtaining exact information as to what the user requires (labeled as 1 in Figure 1). The key problem in this step is to appropriately describe user's demands and map them to appropriate data descriptions. A common way to achieve this is to decompose the complex task into small simple tasks and map them to a series of data dimensions. For instance, a disaster monitoring task can be mapped to data dimensions such as temporal data (e.g., satellite images on a specific date), spatial data (e.g., satellite images covering the disaster area with a resolution of 1 m/pixel), and spectrum data (e.g., satellite images with visible bands and infrared band).  
The second step is data acquisition. In PGIS, data can be acquired from two sources. One is to collect data using sensors (labeled as 2 in Figure 1). The other is to retrieve historical data from the existing GI infrastructure (labeled as 2' in Figure 1). The key issues in this step are to schedule and cooperate with appropriate sensors according to the tasks and to share and interoperate the existing geospatial data appropriately.  
The third step is automated data processing. In PGIS, data processing allows manual data processing tasks to be automated via a geo-information processing service chain (GIPSC) [6]. This can be achieved by aggregating and combining small granularity GI services into complex processing service chains (PSC), able to provide on-demand GI services flexibly (labeled as 3 in Figure 1). Technologies such as service-oriented computing [5] and cloud computing [32] are promised methods to help build an automated data processing system in PGIS.
## 3 Sensing Per Crop
### Sensing Apple Diseases  
Apple scab is caused by the fungi _Venturia inaequalis_ and is an economically important disease for apple producers. This disease causes almost as much loss to apple growers as all the rest of the apple diseases put together [151]. The scab fungus attacks leaves, stems, and fruit. The apple scab fungus overwinters in the dead apple leaves under the trees [152]. Depending on the temperature, first visible symptoms may show as soon as 8 days after the initial penetration by the ascospore. Hundreds of new spores are formed in the infection lesion and rain disperses the spores from the infection lesion to healthy leaves and to the young developing fruit, where they start a secondary infection.  
The apple Black Rot is a fungus disease caused by _Physalospora obtuse_ that occurs throughout the warmer regions of the world. The fungus attacks fruit, leaves, and limbs. Infection of the fruit may occur from the time the fruit is initiated until harvest, causing postharvest decay. The disease first appears as a small brown spot any place on the surface of the fruit. The black rot infection develops slowly, and complete decay of the fruit usually does not occur until the fruit is mature and turns black [153]. As the rot progresses, the decayed tissue is firm and leathery. Symptoms first appear on the leaves as small, dark purplish spots.  
Fire Blight, caused by the bacterium _Erwinia amylovora,_ is one of the most destructive diseases of apple and pear in the United States. The fire blight bacterium may attack any part of the tree from the roots to the leaves. The disease usually appears in the spring as blossom, leaf, and twig blight. Infected blossoms suddenly wilt and soon turn light to dark brown. As the disease progresses beyond, it invades the fruit spur and spreads out into the leaves. The leaves wilt and the entire spur growth turns brown on apple or dark brown to black on pear and dies [154]. The invading bacteria progress more rapidly down the shoots or twigs than in the fruit spur. A severely infected apple or pear tree may have so many blight terminals that it has the appearance of being scorched or burned by fire. The diseased fruit is firm and later leathery, shriveling and turning brown on apple or black on pear and usually remains attached to the spur.
# An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
## 4 Test Case Simulations and Results
### Results: Trapezoidal-C Shape  
Finally, Table 6 reports the efficiency calculated for all the dynamical shading scenarios characterized by the Trapezoidal-C shape's objects. Figure 10 shows, as example, the diagrams of the harvested power (solid blue line) and maximum harvestable power (dotted red line) obtained in case of dynamical shading caused by the trapezoidal-A shape 4 \(\times\) 1 shading object and controlling the off grid PV system, with the best performing traditional MPPT algorithm (P&O--A) and the best performing PSO-based MPPT algorithm (PSO--5p).  
The obtained results are again quite similar to those obtained in the previous cases. The efficiency of the best PSO-based MPPT algorithm in this last case ranges from 2.86% (3 \(\times\) 1, P&O--A vs. PSO--3p) to 5.18% (2 \(\times\) 1, P&O--B vs. PSO--5p) lower than the best hill climbing technique, confirming once more the same performance behavior, which in these simulations is independent of the particular applied dynamic shading conditions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Efficiency**} \\ \cline{2-4}
**MPPT** & **2 \(\times\) 1** & **3 \(\times\) 1** & **4 \(\times\) 1** \\ \hline Perturb \& Observe—A & 97.39\% & 94.52\% & 95.41\% \\ Perturb \& Observe—B & 97.91\% & 95.81\% & 91.14\% \\ Perturb \& Observe—C & 95.62\% & 92.58\% & 93.47\% \\ Incremental Conductance & 97.06\% & 92.94\% & 92.83\% \\ Constant Voltage & 85.98\% & 69.60\% & 66.15\% \\ Open Voltage & 85.32\% & 68.92\% & 75.62\% \\ Short Current Pulse & 91.98\% & 91.77\% & 91.17\% \\ PSO—3 particles & 93.40\% & 90.47\% & 91.99\% \\ PSO—4 particles & 92.34\% & 91.82\% & 90.11\% \\ PSO—5 particles & 91.25\% & 89.27\% & 89.30\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Efficiency—Trapezoidal-B shapes (simulations).  
Figure 9: Trapezoidal-B shape—4 \(\times\) 1: (**a**) P&O—A; (**b**) PSO 3 particles.
## 3 Simulation Results and Interpretation
### Scenario 1: The Effect of Distance
#### 3.1.1 Simulation with Clutter Model 1  
Figure 16 presents the simulation of two targets with clutter model 1 (targets drowned in a cluttered Weibull distribution). The clutter according to the first model is of the Weibull type; the shape parameter a and scale parameter b are chosen arbitrarily such that a = 25 and b = 0.9.  
In Figure 16, we have the results obtained from the simulation using the OS-CFAR detection method (Figure 16a) and the proposed detection method (Figure 16b). We observe that in Figure 16b, the proposed detector finds the position of the two targets at M = 36 for the first one and at M = 43 for the second one. Compared to the conventional OS-CFAR detector, which detects a single target in the 84 km range (Figure 16a) and the second target is missed. The proposed detector has been able to detect the two targets simulated here, while the OS-CFAR method detected a single target.  
We conducted another simulation, increasing the shape parameter a to 200 while keeping the scale parameter b at 0.9.  
In Figure 17 we can observe in that (a) the target amplitude and false alarm rate are very high when it comes to the OSCFAR detector, while in (b) the proposed detector efficiently work on reducing the false alarm rate and is able to distinguish the target from the false alarm, the proposed detector is able to distinguish the target positions at M = 38 for the first target and at M = 43 for the second target (see Figure 17b). On the other hand,  
Figure 16: Targets simulated with clutter model 1 (a = 25 and b = 0.9) using (**a**) the conventional OS-CFAR and (**b**) the proposed detector.  
Figure 15: Targets simulated without clutter using (**a**) the conventional OS-CFAR detector and (**b**) the proposed detector.  
the conventional OS-CFAR detector detects multiple targets (Figure 17a), one of which is located at a range of 84 km, while the remaining targets are false alarms.  
To further evaluate the efficiency of the proposed detector, we varied the shape parameter a within the range of 0 \(<\) a \(\leq\) 200 and the scale parameter b below 0.5 (b \(<\) 0.5).  
In Figure 18, we observe that the target amplitudes are lesser than in the previous simulation, but the proposed detector (Figure 18b) easily distinguish one target at M = 38 then hardly distinguish the second one at M = 39, while the OS-CFAR detector consider the actual targets and several false alarm as targets as displayed in Figure 18a. When the scale parameter is less than 0.5 and the shape parameter exceeds 200, the conventional OS-CFAR detector (see Figure 18a) fails to locate the targets and the proposed detector (see Figure 18b) hardly distinguishes the two targets. This test allows us to identify the limit in shape and scale when using our detector in the case of targets that are drowned in a cluttered Weibull distribution.
# Multimodal Magnetic-Plasmonic Nanoparticles for Biomedical Applications
## 2 Synthetic Methods
### Thermal Decomposition Techniques
#### 2.2.3 Thermal Decomposition for Synthesis of Magnetic Core/Polymer Shell/Plasmonic Nanoparticles  
Green approaches to the syntheses of magnetic plasmonic approach based on thermal decomposition are also of interest, although these can be more difficult as organic solvents are generally a prerequisite for this type of approach. However, as demonstrated by [PERSON] et al., an aqueous based thermal decomposition reaction in benzyl alcohol can be carried out through the use of a microwave [44]. The Fe\({}_{3}\)O\({}_{4}\) nanoparticles are synthesised in the presence of PVP; and the resultant nanoparticles are then dispersed in an ethylene glycol solution containing HAuCl\({}_{4}\) and PVP--again, these are placed in the microwave in order to obtain triangular gold nanoparticles studded with Fe\({}_{3}\)O\({}_{4}\) nanoparticles.  
PEG is a popular choice as it is biocompatible and can be readily functionalised with a number of desirable functional groups. In some cases, this intermediate layer can also aid the formation of a novel morphology, such as in work carried out by [PERSON] et al. [45]. In this work, the Fe\({}_{3}\)O\({}_{4}\) first underwent a ligand exchange to obtain an outer layer of PEG. The resultant nanoparticles were then coated with poly-L-lysine, which aids the seeding and growth of a trisoctahedral gold shell. PEG is also used in the ligand mediated self-assembly of hybrid plasmonic superparamagnetic nanostructures [46]. Au nanorods, initially synthesised with CTAB as an outer stabilising layer, are replaced with bifunctional PEG groups that have both surface carboxylic acid and thiol groups. This replacement of the CTAB with the bifunctional PEG layer drastically improves the biocompatibility of the Au nanorods. Fe\({}_{3}\)O\({}_{4}\) TREG (triethylene glycol) functionalised nanoparticles were then added to the Au nanorod solution; and, as the TREG groups are easily displaced by the -COOH terminal groups on the bifunctional PEG coated Au nanorods, the product is Fe\({}_{3}\)O\({}_{4}\) studded Au nanorods.
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 4 System Model for A-MAC
### Validation Level
#### 4.3.1 Elementary Validation  
The vehicles generate the factors \(\zeta\) using specific ID and security key as discussed in Equations (2) and (3). If they are identical, the vehicles are authenticated. If they are not trustworthy, they must re-register until authentication succeeds. Vehicle \(\varrho_{0}\) is required to communicate with other entities, which is return factor \(\zeta_{\varrho_{0}}\) corresponding to Equation (3). If they are equal, vehicle \(\varrho_{0}\) is validated, and it is eligible to communicate with other entities. The vehicle's authorization processing is comparatively simple.