esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Most of the previous works in mobile crowd sensing utilized more infrastructure-based transmission available at the respective locality or geographical limits during the sensing time. QACM exploits the opportunistic transmission by hiring the mobile users that provide greater flexibility in providing the necessary hardware resources like advanced sensors bandwidth and software support due to advanced devices with the fastest growing technologies. However, the reliability, transmission time, and crowd sensing efficiency are most important factors in improving QOI in MCS. Many previous works attempt to bring the QOI to the required level. In QACM, very effective parameters have been devised to increase the reliability, reduce the transmission time, and increase the efficiency of crowd sensing. The parameters \(R_{bd}\), \(T_{ri}\), \(T_{rt}\), and \(M_{qs}\) strengthened the ability of QACM to increase efficiency in MCS. Here, categorization of sensing devices based on their sensing capacity brought great changes in sensing efficiency and bandwidth allocations based on high-, medium-, and low-range sensitivity requirement applications. This work can be extended to highly sensitive MCS applications like defense surveillance systems, medical applications, forest surveillance system, agricultural crops security and monitoring, etc.  
Conceptualization, B.M.T., M.G., S.A.J., M.A.K.M., K.A., P.P.B.M. and P.K.B.N.; methodology, B.M.T., M.G., S.A.J., M.A.K.M., K.A., P.P.B.M. and P.K.B.N.; software, B.M.T., M.G., S.A.J., M.A.K.M., K.A., P.P.B.M. and P.K.B.N.; validation, B.M.T., M.G., S.A.J., M.A.K.M., K.A., P.P.B.M. and P.K.B.N.; formal analysis, B.M.T., M.G., S.A.J., M.A.K.M., K.A., P.P.B.M. and P.K.B.N.; investigation, B.M.T., M.G., S.A.J., M.A.K.M., K.A., P.P.P.B.M. and P.K.B.N.; resources, B.M.T., M.G., S.A.J., M.A.K.M., K.A., P.P.B.M. and P.K.B.N.; rating curation, B.M.T., M.G., S.A.J., M.A.K.M., K.A., P.P.B.M. and P.K.B.N.; writing--original draft preparation, B.M.T., M.G., S.A.J., M.A.K.M. and K.A.; writing--view and editing, B.M.T., M.G., S.A.J., M.A.K.M., K.A., P.P.B.M. and P.K.B.N.; visualization, B.M.T., M.G., S.A.J., M.A.K.M., K.A., P.P.B.M. and P.K.B.N.; supervision, B.M.T., M.G., S.A.J., M.A.K.M., K.A., P.P.B.M. and P.K.B.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors have not created any new data.  
The authors declare no conflict of interest.  
Figure 4: Crowd sensing efficiency.
Therefore, the aim of this study was to investigate the effect of beam-forming strategies (DaS, Lu's-fk and Stolt's-fk) for plane-wave imaging on the accuracy of displacement estimates. Furthermore, the effect of the line density, apodization (in DaS) and angular weighting (in Lu's-fk and Stolt's-fk) were evaluated, and four different transducers were used with central frequencies varying between 5 and 21 MHz representing the whole range of clinically used linear array transducers. In this study, we evaluated the performance of displacement and strain estimation by using a rotating phantom. The advantage of a rotating phantom is that a displacement field is induced with axial and lateral displacements varying separately in magnitude instead of having pure axial or lateral displacements with same magnitude as obtained by linear translation of a phantom. Furthermore, the gradients of the axial and lateral displacement field were used to evaluate the accuracy of the displacement estimations because these gradients should be zero (see Materials and Methods section) without requiring exact knowledge of the reference displacement field. These gradients can also be considered as strains since strains are calculated the same way and are equal to their gradient.
## 2 Materials and Methods
### Discriminant Analysis Algorithms  
#### 2.6.1 Linear Discriminant Analysis  
Linear discriminant analysis (LDA) is a common discriminant analysis method that finds the optimal boundaries between discriminant analyses, maximizing class separability and minimizing within-class variability [33]. LDA allows the projection of high dimensional data onto low-dimensional space to better cluster samples within a class, so samples of different categories are maximally separated [34].
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 3 Performance Requirements in Digital Watermarking
### Robustness  
This measures the capability of the hidden watermark to resist any signal-processing manipulation. These modifications define all attacks, whether intentional or non-intentional. The first type of attack is aimed at damaging the invisible watermark, while the second type of attack is not explicitly aimed at modifying the watermark but rather at removing it. The level of robustness varies depending on the watermarking approach used, and certain techniques are effective against some attacks but fail against others. Based on robustness criteria, three types of digital watermarking are identified:  
**Robust watermarking:** In this case, the watermark must be resistant to different attacks on the digital document, and the detection of the watermark must be effective even under those manipulations [47]. This watermarking technique is suitable for a variety of applications, including copyright protection, fingerprinting, broadcast monitoring, and copy control [48].  
**Fragile watermarking:** In fragile watermarking, the mark is extremely sensitive to the modifications of the watermarked document [49; 50]. This technique is used to prove the authenticity and integrity of multimedia data. A fragile watermarking technique is designed to detect (with a high probability) any kind of manipulation of the watermarked document, including both incidental and intentional attacks. A comparison of the extracted watermark and the original watermark is performed to identify if the document is modified or not.  
**Semi-fragile watermarking:** This type of watermarking combines the characteristics of robust and fragile watermarking into an intermediate situation in which the watermark is robust to a defined set of attacks but fragile to others [51; 52].  
Generally, two indices are mostly adopted to measure the robustness of watermarking methods:  
**Normalized Correlation (NC):** NC computes the similarity and difference between the original watermark and the extracted watermark. The NC value of a good watermarking algorithm should be \(\geq\)0.7. Ideal algorithms provide an NC equal to 1. NC is defined as follows:  
\[NC=\frac{\sum_{i=1}^{X}\sum_{j=1}^{Y}(W_{org_{ij}}\times W_{ext_{ij}})}{\sum _{i=1}^{X}\sum_{j=1}^{Y}W_{org_{ij}^{2}}} \tag{2}\]where \(W_{org_{ij}}\) and \(W_{ext_{ij}}\) are the original watermark and extracted watermark, respectively, of size \(X\times Y\).  
**Bit Error Rate (BER):** BER is also one of the measurements that evaluate the robustness of a watermarking technique. BER computes the ratio value of incorrectly extracted watermark bits from the inserted watermark bits. The closer the BER value is to 0, the greater the similarity between the extracted watermark and the original one, indicating more robustness.  
\[BER=\frac{NI}{NT} \tag{3}\]  
NI is the number of incorrectly extracted bits, while NT is the total number of bits transmitted.
# Radix-2\({}^{2}\) Algorithm for the Odd New Mersenne Number Transform (ONMNT)
## 4 Derivation of Radix-\(2^{2}\) Algorithm for IONMNT  
The derivation of the radix-\(2^{2}\) algorithm starts by replacing \(\left(\frac{2k+1}{2}\right)\) with \(\left(\frac{2k+1}{2}+\lambda\frac{N}{4}\right)\) and \(n\) with \(4n+l\) in (2), where \(\lambda=0,1,2,3\) and \(l=0,1,2,3\), ignoring \((N^{-1})\) in (3):  
\[x(4n+l)=\bigg{\langle}\sum_{k=0}^{N-1}X_{o}(k)\sum_{\lambda=0}^{3}\beta\bigg{\{} (4n+l)\bigg{(}\frac{2k+1}{2}+\lambda\frac{N}{4}\bigg{)}\bigg{\}}\bigg{\rangle} _{M_{p}}. \tag{38}\]  
The twiddle factor from (38) can be written as follows:  
\[ \begin{split}&\beta\bigg{\{}(4n+l)\bigg{(}\frac{2k+1}{2}+\lambda \frac{N}{4}\bigg{)}\bigg{\}}\\ =&\beta\bigg{\{}\bigg{(}4n\frac{2k+1}{2}+\lambda nN \bigg{)}\bigg{(}\frac{2k+1}{2}l+\lambda l\frac{N}{4}\bigg{)}\bigg{\}}\\ =&\beta\bigg{\{}\bigg{(}\frac{2k+1}{2}l+\lambda l \frac{N}{4}\bigg{)}\bigg{(}4n\frac{2k+1}{2}+\lambda nN\bigg{)}\bigg{\}}\end{split}  \tag{39}\]  
Figure 2: A signal flow graph of radix-\(2^{2}\) ONMNT for the transform length \(N=16\) ; solid lines and dashed lines represent addition and subtraction operations, respectively. Using the relationship \(\beta(m+n)=\beta_{1}(m).\beta(n)+\beta_{2}(m).\beta(-n)\) and the periodic properties of \(\beta(. )\), (39) can be written as follows:  
\[\beta\bigg{\{}\bigg{(}\frac{2k+1}{2}l+\lambda l\frac{N}{4}\bigg{)} \bigg{(}4n\frac{2k+1}{2}+\lambda nN\bigg{)}\bigg{\}}\\ =\beta_{1}\bigg{(}\frac{2k+1}{2}l+\lambda l\frac{N}{4}\bigg{)} \beta\bigg{(}4n\frac{2k+1}{2}\bigg{)}+\beta_{2}\bigg{(}\frac{2k+1}{2}l+\lambda l \frac{N}{4}\bigg{)}\beta\bigg{(}-4n\frac{2k+1}{2}\bigg{)} \tag{40}\]  
\[\beta_{1}(m+n)=\beta_{1}(m)\beta_{1}(n)-\beta_{2}(m)\beta_{2}(n) \tag{41}\]  
\[\beta_{2}(m+n)=\beta_{1}(m)\beta_{2}(n)+\beta_{2}(m)\beta_{1}(n). \tag{42}\]  
Using the relationships (41) and (42) yields  
\[\beta_{1}\bigg{(}\frac{2k+1}{2}l+\lambda l\frac{N}{4}\bigg{)}=\beta_{1}\bigg{(} \frac{2k+1}{2}l\bigg{)}\beta_{1}\bigg{(}\lambda l\frac{N}{4}\bigg{)}-\beta_{2 }\bigg{(}\frac{2k+1}{2}l\bigg{)}\beta_{2}\bigg{(}\lambda l\frac{N}{4}\bigg{)} \tag{43}\]  
and  
\[\beta_{2}\bigg{(}\frac{2k+1}{2}l+\lambda l\frac{N}{4}\bigg{)}=\beta_{1}\bigg{(} \frac{2k+1}{2}l\bigg{)}\beta_{2}\bigg{(}\lambda l\frac{N}{4}\bigg{)}+\beta_{2 }\bigg{(}\frac{2k+1}{2}l\bigg{)}\beta_{1}\bigg{(}\lambda l\frac{N}{4}\bigg{)}. \tag{44}\]  
Substituting (43) and (44) into (40) gives the updated values of \(\beta(.
## 4 Simulation Results  
The capability of the ROA in MPPT solving is evaluated in different patterns of PV configurations. Additionally, the capability of the ROA is compared with that of the MRFO  
Figure 5: Flow chart of ROA implementation in proposed MPPT solutions.  
and PSO algorithms in achieving the GMPP. The PV module parameters are given in Table 2. The peak power of a PV module is 56.75 W [9]. A DC/DC boost converter is applied to the MPPT system and its data are presented in Table 3. The size of the population and the maximum iteration of the ROA and PSO methods are 8 and 200, respectively.  
In order to implement the proposed method, the following patterns are considered in STC (pattern 1) and PSC (patterns 2 and 3). It should be noted that the PV configuration consists of two modules in series as a 2S structure.  
Pattern (1) STC with uniform radiation of 1000 W/m\({}^{2}\);  
Pattern (2) PSC with non-uniform radiation of 1000 and 700 W/m\({}^{2}\);  
Pattern (3) PSC with non-uniform radiation of 900 and 600 W/m\({}^{2}\).  
The characteristic curves of the all patterns are depicted in Figures 6-8. As can be seen, characteristic curves of pattern 1 has one peak and patterns 2-3 have two peaks due to PSC. The obtained peak power of patterns 1-3
# Plant Disease Detection Using Deep Convolutional Neural Network
## 3 Materials and Methods  
This section provides a complete description of the architecture and training process of the proposed DCNN model with the experimental setup and dataset preparation. The proposed plant leaf disease detection model pipeline starts with dataset preparation and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Article** & **Year** & **Specie** & **Number of Classes** & **Number of Images** & **Architecture** & **Accuracy (\%)** \\ \hline [22] & 2017 & Maize & 2 & 1796 & Custom & 96.7 \\ [23] & 2017 & Wheat & 3 & 3500 & Custom & 81.04 \\ [24] & 2015 & Cucumber & 3 & 800 & Custom & 94.9 \\ [25] & 2016 & Apple & 5 & 1450 & AlexNet & 97.3 \\ [26] & 2018 & Tomato & 7 & 13,262 & VGG16 Net & 97.29 \\ [27] & 2018 & Maize & 9 & 3060 & GoogLeNet & 98.9 \\ [28] & 2017 & Tomato & 9 & 14,828 & GoogLeNet & 99.18 \\ [29] & 2017 & Rice & 10 & 500 & AlexNet & 95.48 \\ [2] & 2016 & Multiple & 15 & 4483 & CaffeNet & 96.3 \\ [30] & 2016 & Multiple & 38 & 54,306 & GoogLeNet & 99.35 \\ [7] & 2018 & Multiple & 38 & 54,323 & InceptionV3 Net & 99.76 \\ [3] & 2019 & Multiple & 39 & 61,486 & Custom & 96.46 \\ [8] & 2018 & Multiple & 58 & 87,848 & VGG16 Net & 99.53 \\ [31] & 2019 & Multiple & 79 & 46,409 & GoogLeNet & 86.5 \\ [15] & 2020 & Tomato & 10 & 18,160 & Custom & 98.7 \\ [14] & 2021 & Tomato & 10 & 3000 & Custom & 98.49 \\ [5] & 2021 & Tomato & 10 & 18,345 & AlexNet & 98.0 \\ [1] & 2022 & Multiple & 38 & 240,000 & Custom & 98.41 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of different DCNN architecture.  
ends with model prediction. Python 3.7 programming language and TensorFlow 2.9.1, numpy Version 1.19. 2, matplotlib Version 3.5.2 and OpenCV Version 4.5.5 libraries are used for dataset preparation and DCNN model implementation. Data preparation, preprocessing, model designing and prediction tasks are performed using an HP Z240 workstation with an Intel Core i7 CPU and sixteen gigabytes of random access memory. The training and testing process of the proposed DCNN and existing state-of-the-art techniques were performed using an NVidia DGX-1 deep learning server station. The deep learning server includes two Intel Xeon E5-2694 Version 4 CPUs and eight Tesla P100 GPUs for accelerating the training process of deep neural networks. In subsequent subsections, each phase of the proposed plant leaf disease detection pipeline are discussed in detail. First, the details of the dataset preparation and preprocess are discussed in the next subsection.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 5 Reinforcement Learning Based Power Allocation  
Reinforcement learning (a type of machine learning) has recently been used in the domain of resource allocation in wireless networks. Q-learning, which is basically a subtype of reinforcement learning has found its way to wireless communication problems. If we specifically talk about D2D communication, some authors have researched on that including the author in [24] has used deep reinforcement learning for allocating resources to vehicles in a network scenario composed of V2V links with stringent latency and V2I links with high capacity requirements. The author in [25] has used a distributed reinforcement learning method, where each agent in the network keeps its own table of Q-values.  
Q-Learning involves agents, actions, states, and rewards. The learning in this method is based on interaction with the environment. It is represented by a tuple \(<S\), \(A\), \(T\), \(R(s,a)>\), where \(S\) is the set of states in the environment, \(A\) is the agent, \(T\) is a state transition probability, and \(R\) is the reward function.
# Higher-Order-Mode Triple Band Circularly Polarized Rectangular Dielectric Resonator Antenna
## 2 Materials and Methods  
A DRA using a new probe double stub feeding strip and a parasitic patch has been proposed, as depicted in Figure 1. The antenna is modeled using the CST\({}^{\circledR}\) Microwave Studio. The rectangular DRA (H = 26.1 mm, B= 25.4 mm, and C = 14.3 mm) with dielectric constant \(\varepsilon_{r}\) = 10 is directly placed in the center of a ground plane (40 \(\times\) 40 mm\({}^{2}\)). The feed connected to the exciting port is optimized with the double stub, while a parasitic patch is deployed at an optimized distance from the dual stub feed on the DRA surface. Electromagnetic coupling is used to excite the parasitic patch. The resonance frequency is controlled by optimizing the length, structure, and position of the feeding probe.  
In the next section, the antenna design and optimization procedure to obtain a circularly polarized antenna is discussed in detail. The circularly polarized DRA's performance excited by double stub feeding strip with and without the parasitic patch is explained and compared. Implementing a single DRA structure, several modes have been generated which produce multiple resonant frequencies. The proposed design is selected based on enhanced impedance and axial ratio bandwidths at the desired frequency band. The works done are motivated from previous design [27], where the initial size of the DRA is from the design.
# Autonomous Operation Method of Multi-DOF Robotic Arm Based on Binocular Vision
## 2 Materials and Methods
### Multi-DOF Robotic Arm Trajectory Planning Method  
Genetic algorithm is a method derived from the theory of evolution that searches for the optimal solution by simulating the natural evolution process [19]. It has always been the most important algorithm for solving constrained optimization problems [20]. Further, the trajectory planning method based on joint space has a smaller amount of calculation because it can avoid the inverse kinematics process and directly obtain the optimal motion angle value of each joint compared with the trajectory planning method based on Cartesian space, which is convenient to implement in actual engineering. Thus, we use the genetic algorithm to plan the trajectory of the multi-DOF robotic arm in joint space to improve the working efficiency of the robotic arm. The goal is to take the robotic arm to grasp the target object with the shortest moving time and the minimum grab error. Each joint motion variable is a parameter to be optimized.
# A Review of the Water Desalination Technologies
## 2 The State of the Art
### Vapor Compression (VC)  
Vapor Compression is a common technique used in the desalination sector, based on the liquid-vapor phase transition.  
To clarify the process, Figure 8 is considered, reporting the case of a Mechanical Vapor Compression (MVC) unit.  
A vapor compressor is used to extract vapor produced inside the chamber. Due to the compression, the vapor increases its temperature and pressure. By raising the temperature and using a heat exchanger, the pressurized vapor can transfer heat to the saline water inside the chamber and produce vapor.  
To minimize the energy consumption of the process, a heat recovery exchanger is used to transfer heat from the brine discharge and the condensed feedwater to the saline feedwater [31].  
After the preheating, the saline water is mixed with a brine recirculation flow. This solution is sprayed externally on the main heat exchanger inside the desalination unit.  
MVC essentially requires electricity to run the process; therefore, a small stand-alone desalination unit can be realized to satisfy a freshwater demand ranging from 100 to 3000 m\({}^{3}\)/day.  
The same approach is adopted in the Thermal Vapor Compression (TVC) unit, reported in Figure 9.  
Figure 8: A schema of a simple Mechanical Vapor Compression desalination unit.  
Figure 7: A schema of a more efficient MSF desalination unit.  
The only significant difference is related to the method used to increase the vapor pressure. In TVC a thermal compressor is adopted, which is supplied by high pressure steam, normally spilled from a power plant.  
TVC requires thermal and electrical energy. The first one is used for the thermal compression, the latter one for the circulation pumps. TVC is sometimes assembled with MED units, realizing a hybrid system called a MED-TVC desalination plant, as shown in Figure 10.  
The comparison between the picture reported above and the MED diagram shows a few differences. The steam supply is used to produce the vacuum inside the condenser and the last effect of MED unit. The contaminated steam is condensed inside the first effect and added to the freshwater output [32]. This configuration is used to satisfy significant freshwater demand, between 10,000 and 30,000 m\({}^{3}\)/day [6].
## 2 Establishment of Pipe-NES Coupling Dynamic Model  
An NES is composed of a mass oscillator, a cubic stiffness spring and damping, which dissipate the energy transmitted by the main system and then reduce the vibration of the main system. Due to the cubic stiffness spring, an NES system has a variable natural frequency, which can achieve significant vibration suppression effects [14]. The first mode of the deep-water jacket pipe is more likely to cause wind-vortex-induced vibration. The pipe is treated as a single-degree-of-freedom mass-spring-damping system [15] (the main system). The system of the pipe with an NES (the subsystem) is shown in Figure 1.  
Considering an NES, the transverse vibration of a pipe induced by the wind-vortex force is written as follows [16]:  
\[\begin{cases}m_{1}\ddot{x}_{1}+c_{1}\dot{x}_{1}+k_{1}x_{1}+c_{nes}(\dot{x}_{1}- \dot{x}_{2})+k_{nes}(x_{1}-x_{2})^{3}=f_{L}\\ m_{nes}\ddot{x}_{2}+c_{nes}(\dot{x}_{2}-\dot{x}_{1})+k_{nes}(x_{2}-x_{1})^{3}=0 \end{cases} \tag{1}\]  
where \(m_{1}\) is the mass of the pipe and \(m_{nes}\) is the mass of the NES. \(c_{1}\) is the damping of the pipe, and \(c_{nes}\) is the damping of the NES. \(k_{1}\) is the structural stiffness of the pipe, and  
Figure 1: Vortex-induced vibration model of a pipe.  
is the cubic stiffness of the NES. \(x_{1}\), \(\dot{x}_{1}\), \(\ddot{x}_{1}\) are the transverse displacement, velocity and acceleration of the main system (pipe), respectively. \(x_{2}\), \(\dot{x}_{2}\), \(\ddot{x}_{2}\) are the displacement, velocity and acceleration of the subsystem (NES) in the direction of wind force, respectively.  
The wind-vortex-induced force on the pipe is treated as the harmonic load [17], as follows:  
\[f_{L}=\frac{1}{2}C_{L}\rho U^{2}D\cos(\Omega t)=F_{L}\cos(\Omega t) \tag{2}\]  
where \(\Omega\) is the vortex shedding frequency. To simplify the derivation process, Equation (1) is replaced by the following variables:  
\[\xi_{1}=\frac{c_{1}}{m_{1}},\ \xi_{2}=\frac{c_{nes}}{m_{1}},\ \omega^{2}=\frac{k_{1 }}{m_{1}},\ k=\frac{k_{nes}}{m_{1}},\ \rho_{2}=\frac{m_{nes}}{m_{1}},\ F=\frac{F_{L}}{m_{1}},\ z=x_{2}-x_{1}\]  
where \(z\) is the relative displacement of the pipe and NES. The mass of an NES is smaller than that of the pipe. To develop an approximated solution of the equation by using the multi-scale mixed harmonic balance method, the small value parameter \(\varepsilon\) is introduced to rescale some variables. The motion equation after replacing the variables is:  
\[\left\{\begin{aligned} \ddot{x}_{1}+\varepsilon\ddot{x}_{1} \dot{x}_{1}+\omega^{2}x_{1}-\varepsilon\ddot{x}_{2}\dot{z}-\varepsilon\kappa z ^{3}=\varepsilon F\cos(\Omega t)\\ \varepsilon\rho_{2}(\ddot{z}+\ddot{x}_{1})+\varepsilon\ddot{x}_{2} \dot{z}+\varepsilon\kappa z^{3}=0\end{aligned}\right. \tag{3}\]  
where \(\dot{z}\) is the derivative of \(z\); \(\ddot{z}\) is the derivative of \(\dot{z}\). The dot represents the derivative of the corresponding parameter.