esa-sceva/satcom-chunk-collection huggingface dataset:
# _remote sensing_
## 2 Scientific Background
Acquisitions taken at nadir offer an imagery less distorted by the surface's topographic effects and characterized by minimal influence from atmospheric scattering. Those are usually used as reference to navigate the other images acquired from the other off-nadir angles, to ease the calibration of overall imagery and as comparison with other cameras to determine how the imagery appearance changes with the view angle (bidirectional reflectance measurement). Acquisitions taken with a view-angle of \(\pm\)26.1 degrees can be used in stereoscopic image mapping (exploiting parallax from the different view angles to determine topographic heights and clouds' heights), ensuring a base/height ratio near unity for stereo work. Furthermore, these acquisitions can provide a view sufficiently separated from nadir to have independent information, but not so large that the angular reflectance variations due to geometric effects close to the vertical are missed [39]. Images taken with a view-angle of \(\pm\)45.6 degrees ensure a sensitivity to aerosols property (obtained only when looking at the Earth's surface at an angle) that increases with the viewing angle [39]. Acquisitions taken with a view-angle of \(\pm\)60.0 degrees provide observations through the atmosphere with twice the amount of air compared to the vertical view, and present minimized directionally oriented reflectance variations among many different types, to estimate the amount of reflection at each ground point (hemispherical albedo) [39]. Imagery taken with a view-angle of \(\pm\)70.5 provide maximal sensitivity to off-nadir effects. Both forward and afterward pointing capabilities are required to get similar acquisitions with respect to sunlight as the spacecraft passes over the northern and the southern hemispheres of Earth, because the lighting effects are symmetrical about the Earth's equator [39]. The combination of four spectral bands and off-nadir angles from 0 to 70.5 degrees allows obtaining data and collecting information within the applications described in Table 1, in agreement with the NASA-JPL MISR payload [40]. The HORUS Earth Observation payload potentially covers the full range of view angles, depending on the orbital height, which actually includes all the NASA-JPL MISR view angles, as discussed in detail in Section 3. The optimal local time for acquiring this type of information over the targeted areas is about 10:30 a.m., ensuring a sufficiently high elevation of the Sun, enough light for the photographic exposure, while producing enough shadows to guarantee contrast-rich images [41]. Furthermore, this local time is particularly suitable for production agriculture because this allows for early morning fog to lift, lets plants reach their normal, unstressed metabolic state, avoids afternoon cloud buildup, and avoids thermal stress which occurs around 6 pm on hot days [42].
# Optimization of Fuzzy Logic Controller Used for a Differential Drive Wheeled Mobile Robot
## 2 Problem Description
### Design of the Initial Fuzzy Logic Controller
#### 3.1.2 Constructing the Membership Functions and Rules  
The triangular MFs are considered as a base form of the MFs. For each input and output, five MFs have been parametrized. The notations for the linguistic variables used to represent the MFs of input_1 are Very Close (VC), Close (C), Medium (M), Far (F), Very Far (VF). The notations for the linguistic variables used to represent the MFs of input_2 are Big Negative (BN), Negative (N), Zero (Z), Positive (P), Big Positive (BP). The notations for the linguistic variables used to represent the MFs of output_1 are Very Small Right (VSR), Small Right (SR), Medium Big Right (MBR), Big Right (BR), Very Big Right (VBR). The notations for the linguistic variables used to represent the MFs of output_2 are Very Small Left (VSL), Small Left (SL), Medium Big Left (MBL), Big Left (BL), Very Big Left (VBL). Based on the parameters of the robot, the given path, experiment, and experience, the parameters of all MFs are determined. These parameters are listed in Tables 1 and 2, and depicted in Figure 7.  
Based on heuristics, the rules are selected for the FLC to generate each output (\(\omega_{l}\), \(\omega_{r}\)) of the DDWMR in order to control the DDWMR to move to the waypoint. These rules are listed in Tables 3 and 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**The Distance Error \(\Delta D\) (m)/The Angle Error \(\Delta\theta\) (rad)**} \\ \hline
**Name** & **Notation** & **Parameters** \\ \hline Very Close/Big Negative & VC/BN & [0 0 2.5]/[-\(\pi\)-\(\pi\)-\(\pi\)/2] \\ Close/Negative & C/N & [0 2.5 5]/[-\(\pi\)-\(\pi\)-\(\pi\)/2 0] \\ Medium/Zero & M/Z & [2.5 5 7.5]/[-\(\pi\)/2 0 \(\pi\)/2] \\ Far/Positive & F/P & [5 7.5 10]/[0 \(\pi\)/2 \(\pi\)] \\ Very Far/Big Positive & VF/BP & [7.5 10 10]/[\(\pi\)/2 \(\pi\) \(\pi\)] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for inputs of the initial fuzzy logic controller.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**The \(\omega_{r}\) (rad/s)/The \(\omega_{l}\) (rad/s)**} \\ \hline
**Name** & **Notation** & **Parameters** \\ \hline Very Small Right/Left & VSR/VSL & [0 0 7.5]/[0 0 7.5] \\ Small Right/Left & SR/SL & [0 7.5 15]/[0 7.5 15] \\ Medium Big Right/Left & MBR/MBL & [7.5 15 22.5]/[7.5 15 22.5] \\ Big Right/Left & BR/BL & [15 22.5 30]/[15 22.5 30] \\ Very Big Right/Left & VBR/VBL & [22.5 30 30]/[22.5 30 30] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used for outputs of the initial fuzzy logic controller.  
This FLC is used to control the DDWMR to move to the goal through all the waypoints sequentially. The next subsection will present the optimization process using the GA for this FLC to minimize the energy consumption of the robot.
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 4 Attack Types
### Access Attacks  
**Counterfeiting.** Also known as impersonation, spoofing or identity fraud attacks, counterfeiting involves inserting a malicious node into a network that will pretend to be a legitimate node that is already in the network. It can be carried out using a new node with a fake ID that is inserted into the network, or by changing the ID of a compromised node already in the network to make it appear as a different node. This can be achieved when a relaying node steals the ID of a node from relayed data packets and uses this on its own packets [39]. These attacks can be used to gain access to networks and launch further attacks and, in some cases, divert the mitigation actions of the trust model back to the node that is being counterfeited, causing the legitimate node to be ejected. The authors of [39] propose a detection method for impersonation attacks based on bloom filters and the use of hashes for WSNs.  
**Man-in-the-middle (MitM)**. One of the more common cyber-attacks in all areas, the MitM attack involves an attacker injecting itself into an ongoing communication between two legitimate parties [40]. The attacker becomes an intermediary in the communication, whereby both legitimate parties still believe they are communicating directly with one another, but, in fact, they are unknowingly communicating with one another through the MitM. MitM attacks can be passive in nature, simply eavesdropping or running a traffic analysis, although often they are used to launch further attacks, such as those in the payload or DoS categories.
# A Review of Document Image Enhancement Based on Document Degradation Problem
## 2 Document Image Degradation Problems
### Deblur  
Converting documents to digital images makes it difficult to control some unpredictable degradation factors fully. Among them, how to manage image clarity is an important issue. How to control fuzziness is one of the important concerns. One of the most common examples is the blurring of document images that can be introduced during filming. For example, moving the focus of the lens and camera vibration can all result in blurred images. Figure 2f shows an example of a blurry document image.  
Image deblurring technology aims to restore the fuzzy image to the original image. As one of the important research areas in the field of image restoration, there have been many excellent achievements in this regard. The traditional method is mainly the filtering method, generally used to directly restore clear images through deconvolution [18]. With the extensive application of deep learning in recent years, scholars have proposed many effective deblurring models to achieve better results in document image deblurring.
# Sorghum Flour: A Valuable Ingredient for Bakery Industry?
## 3 Results and Discussion
### Fatty Acid Composition  
In Table 5 is given fatty acid composition of seed oil of whole wheat and sorghum seed.  
The total oil content in sorghum seeds was 3.70% (w/w), in favor of whole wheat which had a value of 2.35% (w/w). The results revealed that, unlike whole wheat, sorghum seeds had a higher oleic acid content (35.06% respectively 16.55%). It was observed that monounsaturated fatty acids (MUFA) content of sorghum was double that of whole wheat, and the polyunsaturated fatty acids (PUFA) content of sorghum was slightly lower. Besides, the (MUFA + PUFA) content of sorghum was higher than that of whole wheat.  
In Figure 3, it is presented Spectra of GC-MS of fatty acid methyl esters of sorghum, and in Figure 4 it is presented the spectra of GC-MS for fatty acid methyl esters of whole wheat.  
These results recommend that _Sorghum bicolor_ seed oils can be used as a potential dietary source of MUFA and PUFA [36].  
Figure 4: Spectra of GC–MS for fatty acid methyl esters of whole wheat.  
Figure 3: Spectra of GC–MS of fatty acid methyl esters of sorghum.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Fatty Acids** & **Rt (Min.)** & **Sorghum** & **Whole Wheat** \\ \hline C14:0 & Tetradecanoic/Miristic & 26.21 & 0.04 \(\pm\) 0.00 & 0.10 \(\pm\) 0.01 \\ C16:0 & Hexadecanoic/Palmitic & 33.58 & 17.28 \(\pm\) 0.92 & 21.23 \(\pm\) 0.15 \\ C16:In7 & _Cis_-9-hexadecenoic/Palmitic & 35.00 & 0.67 \(\pm\) 0.08 & 0.16 \(\pm\) 0.02 \\ C18:0 & Octadecanoic/Stearic & 40.47 & 1.83 \(\pm\) 0.23 & 1.22 \(\pm\) 0.15 \\ C18:In9 & _Cis_-9-octadecenoic/Oleic & 41.68 & 34.38 \(\pm\) 0.74 & 16.55 \(\pm\) 0.14 \\ C18:In11 & _Cis_-11-octadecenoic/Cis-vaccenic & 41.94 & 2.28 \(\pm\) 0.08 & 1.34 \(\pm\) 0.13 \\ C18:In6 & _Cis_-91-21-octadecenoic/Linoleic & 43.83 & 42.29 \(\pm\) 0.71 & 55.25 \(\pm\) 0.67 \\ C18:3n3 & _Cis_-91,215-octadecacentenoic/\(\alpha\)-Linoleic & 46.27 & 0.76 \(\pm\) 0.08 & 3.03 \(\pm\) 0.01 \\ C20:0 & Eicosanio/Arahidic & 46.9 & 0.14 \(\pm\) 0.02 & 0.11 \(\pm\) 0.01 \\ C20:In9 & _Cis_-11-eicosenoic/Gondoic & 48.06 & 0.17 \(\pm\) 0.02 & 0.72 \(\pm\) 0.05 \\ C22:0 & Docosanoic/Behenic & 52.86 & 0.06 \(\pm\) 0.01 & 0.12 \(\pm\) 0.01 \\ C24:0 & Tetracosanoic/Lignoceric & 58.38 & 0.10 \(\pm\) 0.01 & 0.18 \(\pm\) 0.01 \\ \hline  & SFA, \% & & 19.45 & 22.96 \\  & MUFA, \% & & 37.50 & 18.77 \\  & PUFA, \% & & 43.05 & 58.28 \\  & MUFA + PUFA, \% & & 80.55 & 77.04 \\  & omega-3, \% & & 0.76 & 3.03 \\  & omega-6, \% & & 42.29 & 55.25 \\  & omega-9, \% & & 34.55 & 17.27 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fatty acid composition of seed oil of whole wheat and sorghum seed.
# Precoded Generalized Spatial Modulation for Downlink MIMO Transmissions in Beyond 5G Networks
## 3 Transmitter and Receiver Structure
### Receiver Design
In terms of computational complexity, the BD precoding requires the computation of \(N_{u}\) SVDs, which is the step with the heaviest cost resulting in a complexity order of \(O\Bigl{(}N_{u}{}^{4}N_{rx}{}^{3}+N_{u}{}^{2}N_{tx}^{2}N_{rx}\Bigr{)}\). This cost is supported by the BS which typically can have higher computational capabilities. More critical is the required computational complexity at the users. Regarding the receiver, the s-update step (line 5 of Table 2) has the highest cost as it involves an \(N_{tx}/N_{u}\times N_{tx}/N_{u}\) matrix inversion (although it is only computed in the beginning of the algorithm). Considering a fixed number of iterations, the total complexity order is \(O\Bigl{(}(N_{tx}/N_{u})^{3}\Bigr{)}\). For comparison, the complexity order of MLD is \(O\Bigl{(}N_{comb}M^{N_{u}}\Bigr{)}\) (with \(N_{comb}=2 \begin{smallmatrix}\log_{2}{(N_{x}}&N_{s}\\ N_{a}&)\end{smallmatrix} \)), of a linear MMSE is \(O\Bigl{(}(N_{tx}/N_{u})^{3}\Bigr{)}\), of OB-MMSE [33] is \(O\Bigl{(}N_{comb}N_{a}{}^{3}\Bigr{)}\) and of multipath matching pursuit with slicing (sMMP) [34] is \(O\Bigl{(}\frac{N_{tx}}{N_{tx}}N_{tx}\Bigl{(}1-T^{k}\Bigr{)}/(1-T)+\Bigl{(}N_{ a}{}^{2}N_{rx}+N_{a}{}^{3}\Bigr{)}T^{k}\Bigr{)}\) (T is the number of child candidates expanded at each iteration). Therefore, the proposed approach has a similar complexity order to the linear MMSE. Note that the complexity of OB-MMSE does not grow exponentially with \begin{table}
\begin{tabular}{l l} \hline \hline
1: & **Input**\(\hat{\mathbf{H}}_{k},\mathbf{y}_{k},\mathbf{s}_{k},\mathbf{s}_{k}{}^{(Q)},f_{ best},P\) \\
2: & \(\overline{D}_{0}=D\), \(I\leftarrow\text{supp}(\mathbf{s})\) \\
3: & **for**\(p=1,\ldots\)\(P\)**do** \\
4: & \(\overline{D}_{p}=\overline{D}_{p-1}\backslash\{\mathbf{s}:\text{supp}( \mathbf{s})=I\}\). \\
5: & \(\mathbf{x}\leftarrow\prod_{\overline{D}_{p}}\Bigl{(}\mathbf{s}_{k}{}^{(Q)} \Bigr{)}\) \\
6: & \(I\leftarrow\text{supp}(\mathbf{x})\). \\
7: & \(\hat{\mathbf{s}}_{l}{}^{candidate}\leftarrow\hat{\mathbf{s}}_{l}{}^{candidate} \leftarrow\prod_{\mathcal{A}^{\text{TNa}}}\Bigl{(}\mathbf{s}_{l}{}^{candidate} \Bigr{)}\) \\
8: & **if**\(f(\hat{\mathbf{s}}^{candidate})<f_{best}\)**then** \\
9: & \(\hat{\mathbf{s}}_{k,\overline{I}}\gets 0,\hat{\mathbf{s}}_{k,\overline{I}} \leftarrow\hat{\mathbf{s}}_{l}{}^{candidate}\). \\
10: & \(f_{best}=f(\hat{\mathbf{s}}^{candidate})\). \\
11: & **endif** \\
12: & **end for.** \\
13: & **Output**: \(\hat{\mathbf{s}}_{k}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Solution refinement algorithm based on a closest neighbor search for user \(k\). the signal constellation size, M, like in the case of MLD, but it still depends on \(N_{comb}=2\) which can restrict its use when a large number of bits are conveyed on antenna indices.
## 4 Conclusions  
The PV modules synthesizing the PV array of a PV energy production system may operate under fast-changing partial shading conditions (e.g., in wearable PV systems, building-integrated PV systems, etc.). In order to maximize the energy production of the PV system under such operating conditions, a global maximum power point tracking (GMPPT) process must be applied to detect the position of the global maximum power point (MPP) in the minimum possible search time.  
This paper presented a novel GMPPT method which is based on the application of a machine-learning algorithm. Compared to the existing GMPPT techniques, the proposed method has the advantage that it does not require knowledge of either the operational characteristics of the PV modules comprising the PV system or the PV array structure. Also, due to its learning capability, it is capable of detecting the GMPP in significantly fewer search steps, even when unknown partial shading patterns are applied to the PV array. This feature is due to the inherent capability of the proposed GMPPT algorithm to retain the prior knowledge acquired about the behavior and attributes of its environment (i.e., the partially-shaded PV array of the PV system) during its previous executions and exploit this knowledge during its future executions in order to detect the position of the GMPP in less time.  
The numerical results presented in the paper demonstrated that by applying the proposed Q-learning-based GMPPT algorithm, the time required for detecting the global MPP when unknown partial shading patterns are applied is reduced by 80.5%-98.3% compared to the convergence time required by a GMPPT process based on the PSO algorithm, while, simultaneously achieving a similar GMPP detection accuracy (i.e., MPPT efficiency).  
Figure 19: The GMPPT process trajectory for shading pattern 9 during the execution of: (**a**) the proposed Q-learning-based method and (**b**) the PSO-based method.  
Future work includes the experimental evaluation of the proposed GMPPT method in order to assess its effect on the long-term energy production performance of PV systems, subject to rapidly changing incident solar irradiation conditions.  
Conceptualization, E.K. and M.G.L.; Methodology, C.K., E.K. and M.G.L.; Software, C.K.; Validation, C.K. and E.K.; Writing--Original Draft Preparation, C.K. and E.K.; Writing--Review & Editing, C.K., E.K. and M.G.L.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
###### Abstract  
The aim of this study was to determine the efficacy and predictability of maxillary denoalveolar expansion clear aligners in unilateral, bilateral, and single-tooth types of crossbite. This prospective analytical study enrolled adults with dentoalveolar posterior crossbite. Orthoanalyzer(tm) 1.7 software was used to analyze stl files of digital models before treatment (T1), predicted post-treatment outcome (T2), and observed outcome (T3). Changes in streamline width, first and second interpremolar widths, and intermolar width were compared using a mixed ANOVA model for repeated measures (\(\alpha\) = 0.05). The study included 46 patients aged 20-60 years, 15 with unilateral, 15 bilateral, and 16 single-tooth crossbite. In all crossbite groups, expansion was largest at the second premolar level (unilateral: 2.54 mm; bilateral:, 4.86 mm; single-tooth: 3.41 mm) (ANOVA \(p<0.001\)) and smallest at the canine level. Expansion predictability was 90% at the first premolar level in the single-tooth crossbite group, 86% at the second premolar level in the bilateral crossbite group, and 79% at the second premolar level in the unilateral crossbite group. No significant difference was found between the predicted and observed expansion in any crossbite group. Dentoalveolar expansion using differential anchorage techniques with clear aligners is highly predictable, although the treatment plan should consider overcorrection of the expansion movement to achieve the planned outcome.  
dentoalveolar expansion; clear aligners; transverse malocclusion; crossbite; orthodontic treatment
# Experimental Study on Phase Scintillation of Optical Transmission in Atmospheric Turbulence
## 3 Experimental Measurements
### Gaussian Curvature Positive and Negative Peak Values
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Weather** & **Mean Positive Peak Value (\(\upmu\)m\({}^{-1}\))** & **Standard Deviation of Positive Peak Value (\(\upmu\)m\({}^{-1}\))** & **Mean Negative Peak Value (\(\upmu\)m\({}^{-1}\))** & **Standard Deviation of Negative Peak Value (\(\upmu\)m\({}^{-1}\))** \\ \hline Sunny & 0.154 & \(8.83\times 10^{-3}\) & \(-0.3\times 10^{-3}\) & 9.7 \(\times\) 10\({}^{-5}\) \\ Cloudy & 0.107 & \(1.49\times 10^{-3}\) & \(-0.1\times 10^{-3}\) & 6.9 \(\times\) 10\({}^{-5}\) \\ Light Rain & 0.297 & 0.054 & \(-2.75\times 10^{-3}\) & 4.5 \(\times\) 10\({}^{-3}\) \\ Heavy Rain & 0.345 & 0.043 & \(-0.447\) & 0.059 \\ Dust Storm & 0.412 & 0.043 & \(-0.057\) & 0.013 \\ Snowy & 0.530 & 0.074 & \(-0.850\) & 0.125 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical distribution of positive and negative Gaussian curvature of samples for link 1. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Weather** & **Mean Positive Peak Value (\(\upmu\)m\({}^{-1}\))** & **Standard Deviation of Positive Peak Value (\(\upmu\)m\({}^{-1}\))** & **Mean Negative Peak Value (\(\upmu\)m\({}^{-1}\))** & **Standard Deviation of Negative Peak Value (\(\upmu\)m\({}^{-1}\))** \\ \hline Sunny & 0.054 & \(5.57\times 10^{-3}\) & \(-6.7\times 10^{-3}\) & 3.23 \(\times\) 10\({}^{-3}\) \\ Cloudy & 0.014 & \(2.71\times 10^{-3}\) & \(-9.4\times 10^{-3}\) & 6.68 \(\times\) 10\({}^{-3}\) \\ Light Rain & 0.378 & 0.032 & \(-0.076\) & 0.04 \\ Heavy Rain & 0.291 & 0.106 & \(-0.393\) & 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical distribution of positive and negative Gaussian curvature of samples for link 2. Figure 15: Positive and negative Gaussian curvature peak values of measured samples in link 3.  
wavefront distortion is greatest under heavy rain conditions, with the largest fluctuations in both the negative and positive peak values. The positive peak values fluctuate more under light rain, but the negative peak values fluctuate less. The wavefront distortion is minimal under sunny and cloudy conditions. The degree of local wavefront distortion decreases in the order of heavy rain, light rain, sunny, and cloudy. In link 3, the positive peak values decrease in the order of sunny and cloudy, while the negative peak values decrease in the order of cloudy and sunny. The positive peak values fluctuate more under cloudy conditions, and the negative peak values fluctuate more under sunny conditions, indicating significant changes in local distortion. The degree of local wavefront distortion decreases in the order of sunny and cloudy.
## 1 Introduction  
The relation between the rain intensity \(R\) (in mm/h) and the attenuation of a microwave wireless signal \(A\) traveling in the atmosphere is relatively simple:  
\[A=aR^{b}l \tag{1}\]  
where \(A\) is in dB, \(l\) is the path length (in km) of the link and \(a,b\) are constants, depending on the frequency and the polarization of the signal, as well as on the drop size distribution (DSD) of the rain, which is considered as typical to an area. This relation is a simplified model of complex physical relations [1; 2] which has empirically been found to be a good approximation for the rain-induced signal's attenuation, for microwave frequencies and for links of length of about 0.5-20 km. Equation (1), which become linear (\(b=1\)) for a certain choice of signal parameters, first suggested in Reference [3]. This raised the idea to use microwave links (MLs) for rainfall measurements in the early 90s [4; 5; 6], and it was experimentally tested in a multinational European project [7]. However, as the installation of dedicated MLs is costly, in combination with their limited coverage and questionable accuracy, this idea has not spread. In 2006, [PERSON] et al. [8] first demonstrated the idea of taking advantage of the existing, widely spread, cellular communication technology, and used the MLs, which are part of its backhaul network, for environmental monitoring. While the relation (1) still forms the basis of this idea, the use of commercial microwave links (CMLs) instead of dedicated MLs as in Reference [9], brings new opportunities, as well as challenges. The major opportunity is obvious: the availability of millions of potential virtual meteorological sensors almost everywhere on Earth, with no costs for installation, maintenance, or communication. Since 2006, interest in this technology has rapidly increased and many research groups around the world are contributing to it. However, the fact that it has not yet been commercialized is indicative of the challenges its implementation poses.  
In this paper, I will review the most advanced CML technology and its future directions as regards becoming an operational environmental monitoring system.