esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Theoretical Simulations  
Theoretical simulations of different natural SERS substrates (peafowl, diatoms and opal) were performed using the finite difference time domain method [48]. The designs of these structures were constructed to mimic the actual structures of the devices. The electric field distribution over the nanostructures was calculated using Lumerical, a commercial software package. The mesh size was fixed to 1 nm in the \(X\), \(Y\) and \(Z\) directions for all calculations. Regarding paefowl, a gold bar (side length = 370 nm, material dispersion from [PERSON] and [PERSON] [49]) with many overriding functionsof dielectric materials (refractive index = 1, side length = 50 nm) was designed in such a manner that the refractive index of the metal area where dielectric bars were inserted became equal to the dielectric one. The gap between the adjacent dielectric materials wais 35 nm. Periodic boundary conditions in the \(X\) and \(Y\) directions were chosen to calculate the distribution of the electric field (EF). A plane wave with polarization along the horizontal axis at 633 nm was employed to illuminate the pefaowl device from the top. Regarding the opal's structure, many gold spheres (radius = 40 nm, material dispersion from Johnson and [PERSON] [49]) were immersed in the dielectric material to resemble the design of an opal. The gap between two gold nanosphere was fixed to 5 nm. Periodic boundary conditions in the \(X\) and \(Y\) directions were adopted to calculate the distribution of the EF. An incident wavelength of 633 nm was chosen to illuminate the device, and polarization was fixed to an angle of 45\({}^{\circ}\). Regarding the diatoms, we first constructed four circular toroid structures with inner radii of 905 nm and ring widths of 100 nm. The height of the gold ring structures was 92 nm. Within this toroid, many small, circular toroid structures of 105 nm radius (height of gold ring = 10 nm, ring width = 10 nm) were placed randomly. These structures were placed over a dielectric surface (refractive index = 1) deposited over a gold bar. The calculations were made by illuminating the device with an incident wavelength of 633 nm. The incident field considered was 1 V/m.
# Blind Watermarking of Color Medical Images Using Hadamard Transform and Fractional-Order Moments
## 3 Preliminaries
### Accurate MfrLFM Computation  
The selection of the accurate computation of MfrLFMs is one of the basic foundation rules of the proposed watermarking method. Any geometric distortion directly affects the robustness of the watermarking system over different geometric attacks, especially in medical images due to its high sensitivity to any distortion, which in turn leads to misdiagnosis. The computing of an accurate, kernel-based approach was introduced in [52]. MfrLFMs can be represented in polar coordinates (see Figure 1a) since they are symbolized in a unit circle. Each red point possesses eight point similarities, whereas each blue point possesses four similarities (see Figure 1b). The coefficient matrix of the MfrLFMs is calculated utilizing the equations:  
\[MFrM_{pq}=\frac{2p+1}{2\pi}\sum_{i}\sum_{j}k_{pq}\big{(}r_{i},\theta_{ij} \big{)}\ \hat{f}_{C}\big{(}r_{i},\theta_{ij}\big{)} \tag{15}\]  
where  
\[k_{pq}\big{(}r_{i},\theta_{ij}\big{)}=I_{p}\big{(}r_{i}\big{)}I_{q}\big{(} \theta_{ij}\big{)} \tag{16}\]  
The kernels functions [53] are defined as  
\[I_{q}\big{(}\theta_{ij}\big{)}=\int_{v_{ij}}^{v_{i,j+1}}e^{-\hat{i}q\theta}\ d\theta \tag{17}\]  
\[I_{p}(r_{i})=\int_{U_{i}}^{U_{i+1}}L_{p}\ (\alpha,r)\ rdr=\int_{U_{i}}^{U_{i+1}}R (r)dr \tag{18}\]  
where  
\[R(r)=L_{p}(\alpha,r)r \tag{19}\]  
The upper and lower limits are  
\[V_{i,j+1}=\theta_{i,j}+\Delta\theta_{i,j}/2;\ V_{i,j}=\theta_{i,j}-\Delta \theta_{i,j}/2 \tag{20}\]  
\[U_{i+1}=R_{i}+\Delta R_{i}/2;U_{i}=R_{i}-\Delta R_{i}/2 \tag{21}\]  
Compute \(J_{q}\big{(}\theta_{i,j}\big{)}\) in the exact form  
\[J_{q}\big{(}\theta_{ij}\big{)}=\left\{\begin{array}{c}\frac{1}{q}\left(e^{- \hat{i}\ q\ V_{i,j+1}}-e^{-\hat{i}\ q\ V_{i,j}}\right),q\
eq 0\\ V_{i,j+1}-V_{i,j},\ \ q=0\end{array}\right. \tag{22}\]  
Figure 1: (a) Cartesian coordinates; (b) polar coordinates.  
Then, calculate the radial kernel \(l_{p}(r_{i})\) using suitable, accurate computation in [54] using the following equation:  
\[l_{p}(r_{i})=\int_{Ui}^{Ui+1}R(r)dr\approx\ \frac{(U_{i+1}-U_{i})}{2T}\ \sum\limits_{i=1}^{2T}wR(U_{i}+\frac{(U_{i+1}-U_{i})(l-0.5)}{2T}t) \tag{23}\]  
Symbols \"\(w\)\" and \"\(t\)\" indicate the weights, where \(T=2^{l}\) and \(l=0,1,2,\ldots\ldots\cdot c-l\), the value of \(t\) can be explained in respect of the integrity of \(U_{i}\) and \(U_{i+1}\) is the order of numeral integration, and the value of \(w\) is restricted where \(\sum\limits_{l=0}^{c-1}w=2\).
## 1 Introduction
### Motivation  
There are three main objectives to evaluate computer states: computer selection, computer designing, and configuration enhancement [6]. In all these fields, the goal is to optimize the response of the actual or projected system to the actual or predicted workload. Assessment of the state of computersis attempting to define how well a particular system corresponds or can satisfy certain processing requirements for data or planned resources. There are several available assessment methods, the bulk of which are appropriate to some of the objectives, for instance, synthetic programs and benchmarks are considered the best evaluation methods. However, these evaluation techniques are not based on an analysis of real data taken from the existing real systems. All fuzzy based CPU utilization predictive models are based on tendency and previous workload states [7] or consider historical data of CPU (cores, response time), memory (reading, writing, swapping time, queue), bus (I/O throughput, latency), etc., that are too long. Moreover, each object has a variety of pointers (time, workload, frequency, and percent) that complicates the evaluation task. However, all of the above methods are complicated despite a good result. Many of them require data that are difficult to obtain for each user and require special programming skills, such as machine learning algorithms, reference datasets for evaluating performance and in-depth knowledge of computer architecture. Moreover, the results of evaluating the performance or resource utilization presented by the approaches discussed above are not fully understandable to the user. Based on the explanations above, it is necessary to simplify and convert all data types to unequal metrics, i.e., linguistic variables using fuzzy set theory. This paper aims to build an evaluation model for any type of computer systems from personal computers to large mainframes and supercomputers that can be used by non-professional users who have no analytical knowledge about hardware components. The proposed model should evaluate the state of the computer system, CPU utilization, determine the incompatibility and bottleneck of hardware components and predict the CPU utilization in given condition cases.
## 3 Results and Discussion
### Effect of Fractionation on Lignin Nanoparticle Synthesis  
#### 3.3.1 Particle Size and Shape  
The SL nanoparticles were produced at a fixed concentration (2 mg/mL), which produced more stable nanoparticle colloids than lower concentration, and different solvent-extracted fractions (F1, F2, F3, and F4). We did not perform the nanoparticle synthesis experiment with F5 due to its low solubility in THF. Table 3 shows the diameter and PDI of the nanoparticles formed by different molecular weight fractions. Generally, the higher molecular weight component, the larger the polymeric nanoparticle size can be observed. F1 and F2 show lower molecular weights than the raw material, but there was no trend of decreasing particle size in this study. The average particle size was very similar between NP-C2 and NP-F1 (468 nm and 474 nm in peak size, respectively). In the case of NP-F2 and NP-F3, the size increased with further fractionation processing. At all conditions, the PDI value exceeded 0.5, which indicates an unstable and heterogeneous distribution of lignin nanoparticle sizes was obtained. To produce a stable size distribution vigorous stirring is recommended rather than slow stirring [12]. Thus, we concluded that the adjustment of the stirring rate was needed in our future study.  
The diameter and structure of lignin fraction nanoparticles were observed by TEM imaging (Figure 2D-F). NP-F1 (Figure 2D) and NP-F2 (Figure 2E) showed a symmetric structure, whereas NP-F3 (Figure 2F) and NP-F4 (data not shown) showed an asymmetric and partially broken shape. A hypothesis is proposed in which the structural differences between raw lignin and its fractions are the cause of the particle asymmetry. Several studies reported the differences in functional groups, elemental composition, and thermal properties between fractionated lignins [14,15]. Unfortunately, SL, F3, and F4 had similar amounts of phenolic OH and methoxyl groups (Table 1). Even the molecular weights of each sample show no significant difference. Therefore, it is difficult to explain the collapsed particle shape of NP-F3 and NP-F4 with the functional group difference. It is also reported that the diffusion coefficient, which represents water miscibility and polymer solubility of solvent, is affected by the polymer functional group [26]. Further studies of functional group and molecular linkage of lignin fractions are needed to determine the structural asymmetry of large molecular weight fraction nanoparticles.
# Assessment of Active Wheelset Steering System Using Computer Simulations and Roller Rig Tests
## 4 Scaled Roller Rig Experiments
### Experimental Bogie Setup
#### 4.2.1 Wheelset Steering Mechanism  
The wheelsets are steered by an actively controlled steering mechanism (Figure 16). The actuator is a permanent magnet synchronous servomotor (item 1) with rated torque 2.5 Nm. The actuator torque is transmitted via toothed belt (item 2) to the steering rod (item 3) and then to the wheelset by pair of linkages (item 5). Each wheelset is controlled independently to a desired value of yaw angle between wheelset and bogie frame by analogue voltage signal connected to the servomotor controller.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 1 Introduction
### Motivation  
False news was mainly used to propagate rumors, satire, or fun. With time, politicians started using it to sway public sentiments. As stated by the Wall Street Journal, of all color photos published in the United States, 10% of them had been, without a doubt, altered or retouched [9]. In 2002, the photograph of then-president [PERSON] reading a children's book upturned was shared like wildfire Figure 4. With the arrival of GAN technology, forged images can be cloned/altered very closely to authentic images.  
The harmful impacts became apparent when they led to grave consequences, including mob lynching, religious disputes, and providing patients with the incorrect treatment counsel. Since the beginning, deepfakes have caused security concerns. Deepfakes have a significant adverse effect known as character assassination. It is noticed that images and videos are spread more to gather additional attention than text. Fake images over fake news must be detected in time, and their dissemination must be mitigated. This paper does a comprehensive survey of digital image tampering detection techniques. The survey considers classical image forgery detection techniques based on forensic features to modern deep learning multi-modal techniques. It also shares this field's current challenges and limitations for further research. Deep learning methods in detecting forged images can be the most efficient solution to this problem.
# A Single-Rate Multicast Congestion Control (SRMCC) Mechanism in Information-Centric Networking
## 4 Rate Adaptation
### Rate Calculation
#### 4.2.2 Light Occupancy Stage  
After the startup stage, the queue begins to be occupied. Due to the occupancy ratio being relatively low at this time, the sending rate should continue to be increased. As the occupancy ratio increases, the growth rate of the sending rate should be reduced. Therefore, we introduce a quadratic function to adjust the value of \(w\). At this stage, \(O_{th}\) is set as \(O_{th1}\).  
\[w=1-\frac{3}{4}\times\frac{O(t)^{2}}{O_{th1}^{2}} \tag{7}\]  
Then \(R(t+1)_{min}\) or \(R(t+1)_{max}\) are adjusted for the next round according to Equations (3) and (4).
## 2 Results and Discussion
### Discrimination of Different Flower Stages by Electronic Nose
As for the wilted-flower stage, 27 volatile compounds belonging to different chemical classes were identified: hydrocarbons (74.29%), aldehydes (19.30%), and esters (3.18%). The most abundant compound was \(\beta\)-caryophyllene, accounting for about 21.15% of the total GC peak area, followed by D-germacrene (19.50%), \(\beta\)-cubebene (0.69%), \(\alpha\)-farnesene (15.69%), \(\alpha\)-caryophyllene (8.37%), and benzaldehyde (4.19%). With respect to other flower stages, the wilted-flower stage was characterized by higher aldehydes. As for the dried flower stage, 32 volatile compounds belonging to different chemical classes were identified: hydrocarbons (84.88%), aldehydes (6.46%), and esters (0.69%). The most abundant compound was \(\beta\)-caryophyllene, accounting for about 27.90% of the total GC peak area, followed by [\(S\)-(\(R\)*,\(S\)*)]-5-(1,5-dimethylhexen-4-yl)-2-methyl-1,3-cyclohexa-1,3-diene (14.11%), \(\beta\)-cubebene (10.01%), \(\alpha\)-caryophyllene (9.04%), and d-germacrene (9.03%). With respect to other flower stages, the dried flower stage was characterized by higher hydrocarbons.
###### Abstract  
At the field of nanometer positioning and machining, high-precision tracking is a key technology of the micro-positioning platform which is driven by a voice coil motor. To improve the tracking accuracy and response speed, the sliding-mode active disturbance rejection control is proposed. The mathematical model of the micro-positioning platform control system  
platform is introduced directly, it will affect the control accuracy. Active disturbance rejection control can effectively suppress disturbances and improve the control accuracy [18]. Some scholars have worked on solving the control problems of the system by combining sliding-mode and active disturbance rejection control. In [19], [PERSON] et al. presented a high-precision position control scheme for a Permanent Magnet Synchronous Motor driven airborne star tracker based on active disturbance rejection control to overcome the disturbance and improve the robustness of the system. In [20], [PERSON] et al. proposed an integral sliding-mode control with improved nonlinear extended-state observer and uncertain gain adaptive law by addressing the issues of the dead zone and disturbance, as well as the specific performance requirements, such as no overshoot and chattering. In [21], [PERSON] et al. proposed a modified linear-nonlinear switching active disturbance rejection control voltage controller to ensure the stability of high-speed operation of permanent magnet synchronous generators in electrified aircraft scenarios.  
In addition, the stability of the control system is very important. Nowadays, in the field of nanometer positioning and machining, the Nyquist criterion method is extensively used [22]. For nonlinear control systems, the main methods are the variable structure control [23], differential geometry [24] and Lyapunov stability analysis methods [10; 11], among which the Lyapunov method is more convenient to apply.  
Inspired by the above research, this paper combines the advantages of sliding-mode control and active disturbance rejection control. A sliding-mode active disturbance rejection control algorithm is proposed based on a second-order model of the micro-positioning platform, which uses the unknown terms in the model as internal perturbations. The proposed model uses observers to compensate for the internal and external perturbations to improve the control accuracy. The equivalent sliding mode is designed to replace the PD term in conventional active disturbance rejection control. In the simulation, the tracking results are compared with a single sliding-mode control to verify the control effectiveness of the proposed control algorithm.
# Artificial Intelligence Enabled Routing in Software Defined Networking
## 3 Proposed AIER Mechanism
### AIER Mechanism
#### 3.2.1 Collection of Training Data  
Prior to training the ANN model, an adequately large set of training data is collected in which each record contains a congestion flag, the generation rates of all data flows, and every allocated path from a source to a destination. As illustrated in Figure 8, let \(n\) = 3, \(m\) = 1, and \(R\) = 3 as an example. Table 1 shows the training data set, including every field of each record and several data samples. Field \"C\" can be 1 or 0 depending on whether there exists one or more OpenFlow switch whose queuing length is larger than 80% along the allocated path. If yes, then \(C\) is 1; otherwise, \(C\) is 0. The \(d\) fields immediately following from Field \"C\" represent the data generation rates of \(d\) data flows. The last \(d\) fields indicate the allocated path number (belonging to {0, 1, 2}) for each data flow.  
Figure 6: Software-defined networking (SDN) architecture with the proposed artificial intelligence enabled routing (AIER).
###### Abstract  
The first step in identifying fruits on trees is to develop garden robots for different purposes such as fruit harvesting and spatial specific spraying. Due to the natural conditions of the fruit orchards and the unevenness of the various objects throughout it, usage of the controlled conditions is very difficult. As a result, these operations should be performed in natural conditions, both in light and in the background. Due to the dependency of other garden robot operations on the fruit identification stage, this step must be performed precisely. Therefore, the purpose of this paper was to design an identification algorithm in orchard conditions using a combination of video processing and majority voting based on different hybrid artificial neural networks. The different steps of designing this algorithm were: (1) Recording video of different plum orchards at different light intensities; (2) converting the videos produced into its frames; (3) extracting different color properties from pixels; (4) selecting effective properties from color extraction properties using hybrid artificial neural network-harmony search (ANN-HS); and (5) classification using majority voting based on three classifiers of artificial neural network-bees algorithm (ANN-BA), artificial neural network-biogeography-based optimization (ANN-BBO), and artificial neural network-firefly algorithm (ANN-FA). Most effective features selected by the hybrid ANN-HS consisted of the third channel in hue saturation lightness (HSL) color space, the second channel in lightness chroma hue (LCH) color space, the first channel in L*a*b* color space, and the first channel in hue saturation intensity (HSI). The results showed that the accuracy of the majority voting method in the best execution and in 500 executions was 98.01% and 97.20%, respectively. Based on different performance evaluation criteria of the classifiers, it was found that the majority voting method had a higher performance.  
artificial intelligence; precision agriculture; agricultural robot; optimization algorithm; online operation; segmentation +
Footnote †: journal: Applied Sciences
## 3 Propagation Constant \(\beta\) and Fundamental Mode HE\({}_{11}\)
The \(\cos\left(2(\phi-\phi_{0})\right)\) terms in Equation (13) break the symmetry and make much larger evanescent field in the regions, where the field component is transversal to the fiber surface. Figure 9b shows the spatial distribution of the field along the different polarization axis. The \(z\)-component of the electric field disappears while the transversed component is parallel to the fiber surface, and it becomes the maximum value while the transversed component is perpendicular to the fiber surface. For the HE\({}_{11}\) mode with quasi-linear polarization, the evanescent field on the surface of an MNF can reach the maximum intensity at \(a/\lambda=0.24\). In fiber-based sensing application, the diameter-dependent power density at the fiber-air interface is important to design possible strong interaction outside the media [48].
## 3 VLC for Underwater Communication  
Underwater VLC (UVLC) is vital for applications like oceanographic data collection, environmental monitoring, and AUVs, where high data rates and low latency are crucial [22]. This section discusses the technical details of UVLC in detail.
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 3 Dynamic Multiple Junction Based Source Routing Protocol
### DMJSR Protocol Overview  
It is a novel position based routing protocol capable of finding robust routes in city environment by considering traffic density and shortest distance to the destination. The proposed protocol in this paper uses multi-hop communication for routing of data in VANETs. The main objective of this routing protocol is to ensure the selection of paths having more connectivity within city environments to enhance the performance of the network.
# Flexible Thermo-Optic Variable Attenuator based on Long-Range Surface Plasmon-Polariton Waveguides
Besides, the mode size of the LRSPP waveguide can be adjusted by changing the thickness and width of the stripe core to obtain a low coupling loss with other kinds of waveguide devices or fibers. Based on the sensitivity to interface property of the LRSPP waveguide mentioned above, several VOAs have been proposed and investigated. For example, a LRSPP VOA that is based on electro-optic controllable liquid crystals and polymers is investigated and it exhibits a good performance of low power, high extinction ratio, and low insertion loss [34]. For both polarization use, a plasmonic nanowire-based thermo-optic VOA with a cross-section of 250 nm \(\times\) 250 nm shows a low polarization dependent loss of \(\pm\)2.5 dB with a compact footprint of 1 mm [35]. Another thermo-optic LRSPP modulator with a length of 1 cm is investigated at telecom wavelengths and it shows low driving power and high extinction ratio [36]. However, most of these VOAs use metal strip core as the heater, which will limit the physical lifetime of the device because the thin metal strip core may be wrinkled and damaged at a high temperature. In this paper, we design a flexible VOA with a structure consisting of LRSPP waveguide and a thermal heater. By using a simple spin-coating, photolithography, and wet etching process, the device has been fabricated and then characterized at a wavelength of 1.55 \(\upmu\)m. The performance of optical attenuation and high speed data transmission are measured.