esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### The Seto Inland Sea  
The Seto Inland Sea has distinct characteristics that are worthy of studying with the aim of developing a digital twin of the SIS. The first is its location. The SIS is threatened by rainfall-induced flood events [16], typhoon-induced storm surges [17; 18] and earthquake-induced tsunamis [11; 19]. Additionally, Kuroshio currents pass in front of the SIS in the Pacific. The effect of the convergence of these diverse natural phenomena should be investigated in-depth to prepare for and prevent possible risk factors.  
The second notable characteristic is its complexity. The SIS contains an archipelago with two main channels connecting it to the Pacific Ocean. Through narrow and complex straits, tidal waves flowing in from these channels converge near Hiuchi Nada and Bisan Seto in the central part of the SIS. This results in difficulties in reproducing tides in the SIS [20], and although tides were reproduced in a previous study, a quite large root-mean square error (RMSE) of 40 cm was calculated in the inner part of the SIS [21]. Understanding the spatial distributions of currents in these areas is critical for environmental impact assessments, ocean energy resource assessments, marine safety, navigation, sedimentation, aquaculture, and other factors.  
The third characteristic is its geographical isolation. The shape of the SIS is an ellipse with a major (minor) axis of approximately 450 km (75 km). However, the widths of its entry channels are 40 km (Bungo and Kii), and the width of the Kanmon strait is 1.5 km. Therefore,  
Figure 1: (**a**) Study area, the Seto Inland Sea (SIS), with tidal, mooring, and temperature observation stations and the modeling domain. Panels (**b**,**c**) depict the high-resolution unstructured meshes resolving the narrow straits near Kure and Osaka Bay, respectively.  
the governing characteristics of the volume of the SIS are determined by a few conditions: the boundary conditions of the narrow entrance, freshwater discharged from rivers, and atmospheric conditions in combination with the geographic, topographic/bathymetric and geometric features of the SIS. This means that once an SIS modeling system is established successfully, the quasi-isolated system will be more efficiently manageable, and any scenario cases will be effectively testable by controlling the limiting conditions.
## 2 Proposed Antenna Design
### Analysis and Verification  
Figure 9 shows the bore-sight gain results according to the number of reflector patches. The \(M\) and \(N\) values are varied from 3 to 9 to observe the gain properties of the proposed antenna. The resulting maximum bore-sight gain of 1.6 dBi is observed when the patches of the lattice-structure reflector are arranged with a 4 \(\times\) 6 (\(M\times N\)) planar array. We further examine the gain characteristics by changing other reflector patch parameters to obtain the optimum bore-sight gain. Figure 10a illustrates the bore-sight gain in accordance with the gap between and the width of the reflector patches. In the fixed 4 \(\times\) 6 (\(M\times N\)) arrangement, the bore-sight gain is observed to be in the ranges of 1.5 mm \(\leq w_{5}\leq\) 2.5 mm and 0.1 mm \(\leq g_{3}\leq\) 0.9 mm. The increment of the reflector patch width usually improves the bore-sight gain, while that of the gap reduces the bore-sight gain. In addition, these parameters can adjust the reflection coefficients for impedance matching characteristics, as shown in Figure 10b. When \(w_{5}\) is less than 2 mm, and \(g_{3}\) is more than 0.5 mm, impedance matching for the proposed antenna improves. From the results, the optimal values for parameters \(w_{5}\) and \(g_{3}\) of the lattice-structure reflector can be determined to be 2 mm and 0.5 mm. Furthermore, a patch width of \(w_{5}\) can improve the bore-sight gain through the minimization of the pattern distortion, and there is another key design parameter of \(w_{3}\) that can adjust the impedance matching characteristic of the proposed antenna. In addition, we simulate the proposed antenna mounted on an actual vehicle model to observe the vehicle platform effects. The partial vehicle model, where the dimensions were over 20 wavelengths at 28 GHz, is used to analyze the radiation characteristics of the proposed antenna due to the limited computational resources. Figure 11a illustrates the simulated 3D  
Figure 8: Three-dimensional patterns: (**a**) at 26 GHz; (**b**) at 28 GHz; (**c**) at 30 GHz.  
radiation patterns according to the mount location of the proposed antenna: front, rear, left side, and right side of the vehicle window. The radiation patterns of the proposed antenna with the vehicle platform are similar to those of the stand-alone antenna, but it has small fluctuations due to the ground effect, as can be seen in Figure 11b. The results confirm that the proposed antenna is feasible for 5G communication in vehicle applications with high gain performance by optimizing the parasitic elements and the lattice-structure reflector.  
Figure 10: Bore-sight gain and reflection coefficient in accordance with the variations of parameters \(w_{5}\) and \(g_{3}\): (**a**) bore-sight gain; (**b**) reflection coefficient.  
Figure 9: Bore-sight gain in accordance with variations of the number of reflectors.
# Investigation of the Scattering Noise in Underwater Optical Wireless Communications
## 1 Introduction
In [17], an experimental 622 Mb/s VLC system employing a water tank (50 \(\times\) 34 \(\times\) 34 cm\({}^{3}\)) was reported with the received power levels of \(-\)32 and \(-\)21 dBm for clear and harbor waters, respectively at the FEC BER limit of \(3\times 10^{-3}\). However, in these works, the scattering noise was not considered. Absorption and scattering, which have been extensively studied for different water types, can be modelled using Monte Carlo (MC) numerical simulations to determine the channel impulse response (IR) [18; 19; 20]. In UOWC systems, there are several noise sources, including background noise, thermal noise, dark current noise, and shot noise, which are modelled as the additive white Gaussian noise (AWGN). Note, the background noise strongly depends on the operating wavelength and geographical locations due to (i) diffused extended background light; and (ii) ambient light (i.e., Sun or other stellar (point) objects); and (iii) scattered lights, which could be the dominant noise in most cases. This effect can be diminished by using optical filters [21]. In general, deep oceans are less noisy than harbor waters due to no light penetration [22]. However, there is an additional noise, which we refer to it as the scattering noise. It is a channel-dependent noise due to random variation of the number of photons being collected at the Rx for a fixed transmit optical power level. There is some work to model shot noise and thermal noises. In [23], the general formula of shot noise was derived considering time-varying photon rates, and the signal-to-noise ratio (SNR) in nonstationary light signals is defined. In this work, it was mentioned that under which conditions the well-known formula can be applied. In [24], a method to calculate the thermal noise due to photon fluctuations for individual detectors and the correlation of these fluctuations among various detectors is introduced. In this work, an analytical model for the scattering noise is introduced, which has not been investigated before to the best of the author's knowledge. Moreover, the effect of scattering noise on the UOWC system performance is investigated by estimating the BER. We consider a UOWC link for three types of muddy waters and a range of transmission link spans using MC simulations to determine the scattering noise coefficients. The rest of this paper is organized as follows. In Section 2, the system model of UOWC is illustrated. In Section 3, the UOWC channel model is presented; in Section 4, the model of scattering noise is calculated. In Section 5, simulation and numerical results are given, and in Section 6, the paper is concluded.
## 2 Materials and Methods
### Comparative Feature-Guided Network
#### 2.4.1 Comparative Difference in Eccentric Photorefraction Images  
As discussed in Section 1.2.1, real human eyes with different diopters produce crescent-shaped bright areas of different widths under the same eccentricity of infrared light, or real human eyes with the same diopters produce crescent-shaped bright areas of different widths under different eccentricities of infrared light. The refraction and reflection of light at the interfaces of the cornea, lens, and retina generally follow [PERSON]'s law, a linear optical process. These interfaces are linear media and do not involve nonlinear materials or phenomena. The path and phase changes of light can be considered linear. The reflection paths of light in the pupil vary under different eccentricities, but as long as the propagation and reflection of light within the eye are based on linear optical principles, the entire process remains linear. Eccentric photorefraction measures the refractive state of the eye by examining the changes in reflected light. The relationship between the input light and the output signal can be described by a linear equation, making the entire optical system linear. Therefore, the light intensity distribution of the pupil under different lighting modes is the result of the superimposition of reflected light from different eccentric infrared light  
Figure 11: Comparative Feature-Guided Network.  
sources. The light intensity is calculated using Equations (2) and (3), \(\theta_{i}\) is the angle of incidence, \(\theta_{i}\) is the angle of refraction, and \(I\) is the light intensity.  
\[I_{r}=I_{0}\times\frac{1}{2}\Bigg{[}\bigg{(}\frac{\sin(\theta_{i}-\theta_{t})}{ \sin(\theta_{i}+\theta_{t})}\bigg{)}^{2}+\bigg{(}\frac{\tan(\theta_{i}-\theta_ {t})}{\tan(\theta_{i}+\theta_{t})}\bigg{)}^{2}\Bigg{]}\times\cos(\theta_{i}) \tag{2}\]  
\[I^{total}=I_{r,1}+I_{r,2}+I_{r,3}+\cdots+I_{r,n} \tag{3}\]  
We take the lighting mode 1 image as the reference image (i.e., when the eccentricity of the infrared light is 0), where the light does not cause significant changes in the pupil image's light intensity distribution due to refractive errors. Since the eccentric photorefraction image acquisition system is linear, we subtract the reference image from the other lighting mode images to enhance the image features under other lighting modes (when the eccentricity of the infrared light is not 0). As shown in Figure 12, the contrast-enhanced pupil images under eccentric light illumination show significantly enhanced variation features compared to the original images. The crescent bright spots are opposite in position for myopia and hyperopia, and the background noise is reduced, with most feature-irrelevant pixels reduced to zero.  
Applying the same comparative processing to the model eyes, as shown in Figure 13, the changes in the bright area in different diopters are more regular and standardized, serving as a standard feature. Subsequently, we process the feature maps based on this phenomenon.
# The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Standley Model
## 4 Results and Discussion  
### Numerical Simulation  
In this section, the path tracking algorithms including SM, FSM and the proposed PSO-FSM are verified in autonomous turning under different scenarios using numerical simulation. A typical U turning trajectory is stimulated to verify the superiority of PSO-FSM. The flowchart of the numerical simulation is shown in Figure 6, where the vehicle kinematic model defined by (1) is employed to update the vehicle state, and the particle flock size is 20, the inertia coefficient \(\omega\) = 0.5, the learning factors \(c_{1}\) = 1, \(c_{2}\) = 2, the maximum number of iterations \(K\) = 200 and the termination threshold \(J\) = 0.8. These parameters not only reduce the occurrence of large errors but also prevents the overshoot of the system output.  
Figure 7a shows the test result when the vehicle runs at 1 m/s with small time delay of steering control system, which is an ideal work condition for agricultural machinery. It can be seen that both FSM and PSO-FSM achieve better performance than SM, especially when the curvature is variable, whereas PSO-FSM does not outperform FSM, obviously because the gain coefficient \(\alpha\) does not have a direct modification on the expected front wheel angle. Figure 7b shows the results in the high-speed operation scenario, where the vehicle speed  
Figure 6: Flowchart of simulation experiment.  
is increased to 3 m/s with other conditions unchanged. It is notable that the PSO-FSM achieves the best accuracy among the involved algorithms, and both SM and FSM have an obvious performance degradation. Once a low driving speed is given, e.g., 1 m/s, the actuator saturation situation and state delay of the control signal would have a great effect on the steering control system, which suits the actual operation scenario. By importing the control input limitation to the steering control system, i.e., scale the expected steering angle every control period by multiplying the random coefficient between 0 and 1, the steering actuator saturation can be stimulated. The corresponding result is shown in Figure 7c, which indicates when the vehicle starts turning with obvious curvature variation, PSO-FSM not only improves the response speed of steering control system, but also achieves better stability once the autonomous turning is finished. The detailed simulation results are listed in Table 2, where the mean absolute error (MAE) and root mean square error (RMSE) are taken as the performance metrics. As we can see, PSO-FSM achieves a much better result than FSM and SM, especially when the scenarios of high-speed or random actuator saturation appear.
## 1 Introduction  
The design of highly complex spiro-heterocycles with multifunctional and potential pharmaceutical efficacy has attracted considerable attention from synthetic and medicinal chemists [1]. One of the most privileged _aza_-heterocyclic scaffolds is spiro[pyrrolidine-oxindole] [2], which is present in natural products and useful as a building block for the synthesis of significant biologically active compounds. This class of _aza_-heterocyclic compounds has gained great interest, owing to several reports of its pharmaceutical potency, including anticancer [3], antitumor [4], 5-HT3 receptor antagonist [5],acetylcholinesteraseerase-inhibitory [6], antibacterial [7], antibiotic [8], and MDM2-p53 inhibitor [9] effects; selective cyclooxygenase COX-1 with TNF-\(\alpha\) and IL-6 inhibitors [10]; and potential hypoglycemic dual inhibitory activity against \(\alpha\)-amylase and \(\alpha\)-glucosidase [11] (Figure 1). To date, prolonged efforts have been exerted to expand divergent complexity and to develop efficient synthetic routes for these valuable privileged _aza_-heterocyclic scaffolds, which would remarkably enhance their bioactivity [12; 1]. In particular, [3+2] cycloaddition is one of the most efficient synthetic approaches to produce these valuable scaffolds with stereoselective method and high yield [13]. To extend our previous research, we explored the effect of halogen substitution on the isatin ring.  
Our previous studies [9] revealed that the presence of dihalide substitution on acyl moiety substantially increased the anticancer activity of the resulting product(s). Moreover, it was reported [9] that chlorinated indole moiety retained better activity, as illustrated in Figure 1a.  
Figure 1: Natural (Spirotryprostatin A and B) and other synthetic spirooxindole scaffolds with high biological importance and structure-activity relationship.  
Subsequently, this study was designed to introduce two bromo atoms on the indole ring, presumably to enhance the activity of the examined spirooxindole compounds shown in Figure 1. The _aza_-heterocyclic compounds were prepared via a multicomponent eco-friendly strategy using oxindole as a core structure. The resulting hybrids were biologically evaluated using an _in vitro_ antiproliferative assay against three different cell lines for liver, breast, and colorectal cancer. In addition, molecular properties and lipophilicity studies were conducted to get insight about \"drug properties consideration\" and to discover the compounds' structure-property relationship (SPR).
## 2 System and Channel Model  
### System Model  
We consider a secure FSO relaying communication network, comprising of one BS, one UAV relay, one eavesdropper, and multiple sensors, as shown in Figure 1. Since the complexity of implementing a wavelength division multiplex (WDM) is extremely high, we use authentication-based non-orthogonal multiple access (NOMA) to simultaneously transmit information to multiple sensors in this case. More specifically, the BS first transfers data to the UAV. After that, the UAV decodes the received information and forwards it to all sensors. An optical phased array can be used to facilitate the alignment and tracking of an optical beam [28], to perform accurate dynamic alignment for FSO communication. Note that all the sensors need to send signal to the UAV for security authentication, and then they are authorized to access the network for information transmission and reception. An eavesdropper also requires security authentication from the UAV, as a normal sensor. We assume that the probability that the UAV identifies an eavesdropper as a normal sensor is \(P_{secr}\), which is also regarded as the secrecy outage of data transmission. SIC is used to decode the received information at the UAV and all sensors. According to [29], an UAV can employ spatial light modulation and a lens system to generate multiple light beams to point at authenticated sensors. Such a system is very simple to realize in real application scenarios. In addition, we ignore the transfer efficiency from the electrical signal to optical signal, and direct optical link is supposed to be not available due to the existence of obstacles. Within the first phase of the information transmission, the received signal of the UAV is denoted by  
\[y_{r}(t)=(s_{1}(t)+s_{2}(t)+\cdots+s_{M}(t))\sqrt{I_{SR}}+N_{R}(t), \tag{1}\]  
where \(I_{SR}\) denotes the channel power gain of the FSO link from the BS to the UAV, \(s_{i}(t)\) denotes the transmitted signal for sensor \(i\), \(i=1,\ 2,\ \cdots,\ M\), and \(N_{R}(t)\) denotes the received noise signal at the UAV. Within the second phase of information transmission, the received signal of sensor \(i\), \(i=1,\ 2,\ \cdots,\ M\), is given by  
\[y_{D_{i}}(t)=s_{i}(t)\sqrt{I_{RD_{i}}}+N_{D_{i}}(t), \tag{2}\]  
where \(I_{RD_{i}}\) denotes the channel power gain of the FSO link from the UAV to sensor \(i\), and \(N_{D_{i}}(t)\) denotes the noise signal received at sensor \(i\).
## Materials and Methods
### Lipids Contents and Fatty Acid Profiles  
Total lipids were prepared according to the method reported by [PERSON] et al. [PERSON] et al. (2018). Briefly, microalgae fresh biomass (2 g) was homogenized with 20 mL of chloroform/methanol (2:1, _v/v_) using a tissue disrupter (IKA ULTRA-TURRAX T 25 digital; IKA-WERKE, Staufen, Germany). Non-lipid impurities were removed by washing with KCl (0.88%, _w/v_). Lipid weight was determined gravimetrically after evaporating the solvent under a stream of nitrogen and overnight vacuum drying. Fatty acid methyl esters (FAME) were prepared by acid-catalyzed trans-esterification of total lipids using 2 mL of 1% sulfuric acid (_v/v_) in methanol, at 50 \({}^{\circ}\)C for 24 h. Methyl esters were extracted twice in 5 mL of hexane-diethyl ether (1:1, _v/v_) after neutralization with 2 mL of 2% KHCO\({}_{3}\), dried under a stream of nitrogen, then dissolved in 1 mL of iso-hexane. The obtained FAME were analyzed through gas-liquid chromatography in an SPTM 2560 flexible fused silica capillary column (length 100 mm, internal diameter 0.25 mm, film thickness of 0.20 mm SUPELCO) in a Hewlett-Packard 5890 gas chromatograph. The oven temperature program was set according to the following. The initial temperature was 140 \({}^{\circ}\)C, then it increased at a rate of 3 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C, followed by 2 \({}^{\circ}\)C/min to reach 240 \({}^{\circ}\)C and then it was held there for 12 min. The injector and flame ionization detector were set at 260 \({}^{\circ}\)C. Helium was used as the carrier gas at a column pressure of 300 kPa. Peaks were identified by comparing their retention times with appropriate FAME standards from the Sigma Chemical Company (St. Louis, MO, USA). Data for the individual components are expressed as a percentage of total content. Each extraction experiment was performed in triplicate, and each extract was independently analyzed three times.
# Reversible and Plausibly Deniable Covert Channels in One-Time Passwords Based on Hash Chains +
###### Abstract  
Covert channels enable stealthy communications over innocent appearing carriers. They are increasingly applied in the network context. However, little work is available that exploits cryptographic primitives in the networking context to establish such covert communications. We present a covert channel between two devices where one device authenticates itself with Lamport's one-time passwords based on a cryptographic hash function. Our channel enables plausible deniability jointly with reversibility and is applicable in different contexts, such as traditional TCP/IP networks, CPS/IoT communication, blockchain-driven systems and local inter-process communications that apply hash chains. We also present countermeasures to detect the presence of such a covert channel, which are non-trivial because hash values are random-looking binary strings, so that deviations are not likely to be detected. We report on experimental results with MD5 and SHA-3 hash functions for two covert channel variants running in a localhost setup. In particular, we evaluate the channels' time performance, conduct statistical tests using the NIST suite and run a test for matching hash values between legitimate and covert environments to determine our channels' stealthiness.  
c 2021 11 731 1
## 3 Distributed Mission Scheduling Model
### APSOA for Single Satellite Scheduling
#### 3.3.2 APSOA Procedure  
In static scheduling when all observation requests are proposed beforehand, the optimization results in each round of the game will have an impact on the overall scheduling plan of the system. In order to improve the solving efficiency and final payoff of the distributed optimization algorithm, each satellite needs to output a satisfactory sub-plan in a relatively short time. This paper designs an APSOA, whose inertia weight parameter \(\lambda\) will be adaptively adjusted as the iterations evolve, so that the solving speed will gradually increase, and convergence will occur as soon as possible.  
\[\lambda=\lambda_{\text{max}}-\frac{(\lambda_{max}-\lambda_{\text{min}})\cdot t }{T_{max}} \tag{15}\]  
where \(T_{max}\) is the maximum iteration number, \(t\) is the current iteration number, \(\lambda_{min}\) is the minimum inertia weight parameter, and \(\lambda_{max}\) is the maximum inertia weight parameter. The pseudo code of the APSOA is shown in Appendix A.  
Figure 2: Solution coding scheme.