esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Approach
### Dynamic Spectrum Averaging  
Researchers from [54], presented an algorithm for OFDR data processing. It is an averaging with a variable length (dynamic) window. The algorithm includes the following steps:  
1. A choice of scanning window with the length of \(w^{\prime}\), which allows fitting the most significant event on an OFDR trace consisting of \(N\) samples into it (Figure 4a);
2. The process of spatial domain scanning using this window. During the scanning process, for all points from \(w^{\prime}/2\) to \(N-w^{\prime}/2\), the standard deviation \(\sigma\) of the signal values \(P\) at the points included in this window is calculated;
3. Normalization of the obtained \(\sigma\) values to 1 (\(\sigma=\sigma_{norm}\));
4. A filtered trace (Figure 4b) calculation according to \[i-\frac{w_{j}}{2}\geq 0;\;i-\frac{w_{j}}{2}<N\to P_{d(j)}=\frac{1}{w_{j}} \sum_{i-\frac{w_{j}}{2}}^{i+\frac{w_{j}}{2}}P_{i},\;w_{j}=w^{\prime}(1-\sigma _{norm})\] (2)  
Figure 3: Frequency spectrum of events that took place at 3500 m along the fiber acquired using semiconductor laser (**a**) and a commercial fiber laser (**b**). The insets show the value of the SNR as a function of frequency. The setup used is shown in Figure 2.  
Describing the proposed method in simple terms, it can be said that the algorithm focuses on sharp and significant changes in the spectrum. The higher the peak found when moving along a discretely given spectrum, the fewer samples on both sides of it will be used for averaging. The more abruptly the event gains its maximum intensity, the less it will be blurred. In order to minimize dynamic distortions of the original signal, in this study, the dependence of the window size on the standard deviation is assumed to be linear. Further work is expected to study the influence of the nature of this dependence on the output signal.  
As an advantage of the algorithm, the simplicity and low computing resources requirements are also worth noting, and, in addition, the potential ability to work with any data. The main disadvantage here seems to be the possible loss of a low-intensity signal. Since an OFDR-trace is actually an FFT spectrum of backscattered data, and therefore has a similar nature to DAS spectra, it was decided to test this algorithm to improve the quality of distributed acoustic sensor data.
## 2 Problem Statement  
### Mathematical Preliminaries  
The Euclidean norm of a vector or the induced norm of a matrix is represented by \(\left\|\cdot\right\|\). The \(n\times n\) identity matrix is denoted by \(I_{n}\). For the given vector \(\mathbf{x}=\left[\begin{array}{cc}x_{1},&x_{2},&x_{2}\end{array}\right]^{T}\in \Re^{3}\) and a constant \(\zeta\in\Re\), we define \(\mathbf{x}^{\zeta}=\left[\begin{array}{cc}x_{1}^{\zeta},&x_{1}^{\zeta},&x_{1}^{ \zeta}\end{array}\right]^{T}\), \(\text{sig}^{\zeta}(\mathbf{x})=\left[\begin{array}{cc}|x_{1}|^{\zeta}\text{sgn }(x_{1}),&|x_{2}|^{\zeta}\text{sgn}(x_{2}),&|x_{3}|^{\zeta}\text{sgn}(x_{3}) \end{array}\right]^{T}\) in which \(\text{sgn}(\cdot)\) represents the sign function, and the skew symmetric matrix \(S(\mathbf{x})\in\Re^{3\times 3}\) is defined as  
\[S(\mathbf{x})=\mathbf{x}^{\times}=\left[\begin{array}{ccc}0&-x_{3}&x_{2}\\ x_{3}&0&-x_{1}\\ -x_{2}&x_{1}&0\end{array}\right].\]  
[[31]] If there exists a continuous radially unbound function \(V\): \(\Re^{n}\to\Re^{+}\cup\left[0\right]\), any solution \(\mathbf{x}(t)\) of \(V\) satisfies the inequality \(\dot{V}(\mathbf{x})\leq-(\alpha V^{p}(\mathbf{x})+\beta V^{q}(\mathbf{x}))^{k}\), where \(\alpha,\ \beta,\ p,\ q,\ k>0\) and \(pk<1,\ qk>1\), then, for the nonlinear system \(\dot{\mathbf{x}}=f(\mathbf{x},t)\), the equilibrium point is fixed-time stable and the stability time satisfies the inequality constraint \(T(x_{0})\leq 1/\alpha^{k}(1-pk)+\left(1/\beta^{k}(qk-1)\right)\), \(\forall\mathbf{x}_{0}\in\Re^{n}\).  
Let \(k=1\) in Lemma 1, assume that there is a Lyapunov function \(V(\mathbf{x}(t))\): \(\Re^{n}\to\Re^{+}\cup\left\{0\right\}\), and \(\dot{V}(\mathbf{x})\leq-\alpha V^{p}(\mathbf{x})+\beta V^{q}(\mathbf{x})\), where \(\alpha,\ \beta>0,\ p<1\), and \(q>1\). Then, for the nonlinear system \(\dot{\mathbf{x}}=f(\mathbf{x},t)\), the equilibrium point is fixed-time stable and the settling time \(T(x_{0})\leq 1/\alpha(1-p)+(1/\beta(q-1))\), \(\forall\mathbf{x}_{0}\in\Re^{n}\).  
**Lemma 2**.: _For \(\mathbf{x}=\left[\begin{array}{cc}x_{1},&x_{2},&x_{3}\end{array}\right]^{T}\in\Re^{3}\) and \(\zeta>0\), the following inequality holds:_  
\[\mathbf{x}^{T}\mathrm{sig}^{\zeta}(\mathbf{x})=\left[\begin{array}{cc}x_{1}|x_{1}|^{ \zeta}\mathrm{sgn}(x_{1}),&x_{2}|x_{2}|^{\zeta}\mathrm{sgn}(x_{2}),&x_{3}|x_{3} |^{\zeta}\mathrm{sgn}(x_{3})\end{array}\right]^{T}\leq\|\mathbf{x}\|^{\zeta+1}.\]
## 4 Simulation Results and Analyses
### Recording of EV Charging  
It is necessary that the EV charging station has the function of recording data in real-time, which will provide a data base for adjustment and future planning of the station. The recording of EV charging station is shown in Figure 19. It can be seen that N1, N2, N3, N4, and N5 are the number of EVs arrived at the station, the number of EVs being charged, the number of EVs fully charged, the number of EVs on waiting, and the number of departing EVs, respectively.  
One notes that N1 is the sum of N2, N3, N4, and N5. The equation is expressed by Equation (12):  
\[N1=N2+N3+N4+N5 \tag{12}\]  
The results show that most of the EVs arrived at the charging station choose to be charged. When the available power of the charging station is insufficient, the drivers are more inclined to wait or choose other charging modes. Thus, the current planning of this charging station is reasonable.  
Figure 19: Recording of EV charging station.  
The management strategy has a strong applicability in real life. At the physical level, EV charging stations based on a DC microgrid, consisting of PV source, storage system, public grid connection, are easy to design and implement in shopping malls, school parking lots, community parking lots, etc. The PV-based EV charging station takes full advantage of renewable energy that PV sources provide, improves peak performance of the public grid without increasing the grid capacity and, most importantly, it has succeeded in achieving social acceptance during a social acceptability study [20; 21]. In addition, the proposed power management deals with the uncertainties of EV driver's behavior considering its arbitrary and random choices through the human-computer interface, which considers most of the drivers' choices and is easy to be accepted by users.
## 2 Development of Algorithms  
**Efficient fifth-order algorithm**:  
The method proposed below is a two-step weighted iterative algorithm (known as \(5^{th}PJ\)), which is based on algorithm (2):  
\[\begin{split}& G_{2^{nd}NM}(x^{(r)})=x^{(r)}-[\Phi^{\prime}(x^{(r) })]^{-1}\Phi(x^{(r)}),\\ & x^{(r+1)}=G_{5^{th}PJ}(x^{(r)})=G_{2^{nd}NM}(x^{(r)})-H_{1}(x^{ (r)})[\[PERSON]^{\prime}(G_{2^{nd}NM}(x^{(r)}))]^{-1}\Phi(G_{2^{nd}NM}(x^{(r)})), \\ &\text{where}\;\;H_{1}(x^{(r)})=I+\frac{1}{4}(\tau(x^{(r)})-I)^{2},\quad\tau(x^{(r)})=[\[PERSON]^{\prime}(x^{(r)})]^{-1}\Phi^{\prime}(G_{2^{nd}NM}), \end{split} \tag{7}\]  
where \(I\) denotes \(n\times n\) identity matrix. In the above algorithm, a weight function \(H_{1}(x^{(r)})\) is suitably chosen to produce fifth-order convergence keeping same number of function evaluations as in method (2).  
**Efficient \((3k+5)^{th}\)-order algorithm**:  
With the \(5^{th}PJ\) algorithm as the base, we introduce new steps to obtain the following new algorithm (known as \((3k+5)^{th}PJ\)) by evaluating a new function whenever a new step is included  
\[\begin{split}& x^{(r+1)}=G_{(3k+5)^{th}PJ}(x^{(r)})=\mu_{k}(x^{(r)} ),\\ &\mu_{j}(x^{(r)})=\mu_{j-1}(x^{(r)})-H_{2}(x^{(r)})[\Phi^{\prime} (G_{2^{nd}NM}(x^{(r)}))]^{-1}\Phi(\mu_{j-1}(x^{(r)})),\\ &\text{where}\;\;H_{2}(x^{(r)})=I+\frac{1}{2}(\tau(x^{(r)})-I)^{2},\\ &\mu_{0}(x^{(r)})=G_{5^{th}PJ}(x^{(r)}),\quad j=1,2,...,k,\,k\geq 1.\end{split} \tag{8}\]  
The above algorithm has convergence order \(3k+5\). The case \(k=0\) produces the \(5^{th}PJ\) algorithm.
## 4 Results and Discussion
### Flow Uniformity
The solution of Equation (18) under the given boundary values are computed with 5 th order Runge-Kutta method [22]: see the graphical result in Figure 10. This scheme works also in non-Newtonian fluid. Therefore, the thickness of boundary layer from the slanted side wall of diffuser in Figure 9 is easily found as the following scheme:  
\[Re_{PL}=\frac{u_{\theta}^{2-n}L_{\theta}^{n}\rho}{m} \tag{19}\]  
\[u_{\theta}=\frac{\dot{m}_{i}}{\rho L_{\theta}h}\text{cos }\theta \tag{20}\]  
Figure 9:0  
Figure 9:1  
Figure 9:2 Velocity profile at the outlet with transverse obstacles: (**a**) circles, (**b**) 2:1 longitudinal ellipses. Figure 9:3 Velocity profiles for non-Newtonian fluid (\(n=0.7669\)). \begin{table}
\begin{tabular}{c c c} \hline
**Case** & **Gap [mm]** & **Cross-Sectional Shape** \\ \hline
1\(\sim\)6 & 2\(\sim\)7 (interval: 1) & Circle \\ \hline
7\(\sim\)12 & 2\(\sim\)7 (interval: 1) & 2:1 Ellipse (longitudinal) \\ \hline \end{tabular}
\end{table} 
Table 5: Computational cases. As seen in Figure 11a,b, Cases 1 and 7 reveals that there are overshoots of velocity at the boundary layer region, and the central velocity deficits due to the continuity of flow. In Figure 12, the difference of RMS value is not remarkable without cutting the edge or filter out the effect of the side diffuser walls, but with cutting the boundary data, Cases 2 and 8 are picked as the least RMS, which means the optimal flow uniformity, or the flattest central velocity profile at the wake of obstacles array.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 3 Threats and Challenges in Underwater Wireless Sensor Networks (UWSNs)
### Black Hole/Gray Hole Attack  
In this attack, an attacker acts as a black hole by broadcasting forged routing information with the lowest cost or shortest path toward destination. The victim nodes would select this path as the optimum route which actually goes through the attacker. As a consequence, the attacker can attract and pull in all the traffic of the network. Moreover, the attacker may analyze, modify, or even drop packets at will. If the attacker drops all data packets, this attack is known as a black hole attack. If it selectively drops some critical packets, it is known as a gray hole attack.  
As illustrated in Figure 6a, based on the location information, the packets from Node1 will be relayed by Node2 and Node3 to Node4. A black hole node will broadcast its false location to disguise itself as the node nearest to the sink node. As illustrated in Figure 6b, nodes in the network mistake the black hole node as the best choice to relay packets to the sink node. Hence, all the packets would be captured by the black hole node.  
Figure 5: Misdirection attack; (a) normal route; (b) misdirection route.  
Figure 6: Black hole attack; (a) normal route; (b) black hole route.  
This type of attack is harmful for sensor nodes that are deployed considerably far from the sink node. In a smarter way, the attacker may drop critical packets during a certain time period or with a certain percentage, making it more difficult to be detected. Some existing work has addressed this attack and feasible countermeasures have been proposed. In [38], an efficient and trust-based distributed intrusion detection system is proposed to defend against single and cooperative gray hole and black hole attacks. A secure routing protocol based on elliptic curve cryptography (ECC) is proposed in [39] to detect and defend false reports and gray hole attacks. A trust-based clustering protocol is proposed to detect the gray hole and prevent compromised nodes from becoming cluster head [40].
# Rapid Tracking Satellite Servo Control for Three-Axis Satcom-on-the-Move Antenna
## 5 Conclusions  
For the mechanical structure of a three-axis SOTM antenna, this paper introduces a kinematic modeling method for the three-axis SOTM antenna based on a new modified Denavit-Hartenberg method. To meet the modeling requirements of the MDH method, virtual and auxiliary coordinate systems are designed and added to the global coordinate of the three-axis SOTM antenna to obtain its kinematic model. The SOTM antenna needs to adjust its pointing direction in real time according to the changes in the location and attitude of the moving carrier. Therefore, this paper designs a servo control system based on ADRC and introduces a smooth and continuous fal function of ADRC to enhance the tracking speed of the servo control system and reduce the overshoot of the output response. In addition, aiming at the drawback of the complex calculation of the improved fal function, a piecewise linear approximation method is designed, which transforms the complex nonlinear function operation into a simple linear function operation, thereby reducing the engineering implementation difficulty of the modified ADRC. Finally, the designed scheme was validated through experiments. The experiment results show that, compared to conventional ADRC, the proposed servo control method based on modified ADRC achieved higher antenna tracking satellite accuracy and improved communication performance.  
Conceptualization, J.R. and X.J.; methodology, J.R., L.H., S.S. and Y.W.; software, [PERSON] and J.L.; validation, X.J., S.S., J.L. and Y.W.; formal analysis, J.R.; investigation, J.R. and J.L.; resources, L.H.; data curation, X.J. and L.H.; writing--original draft preparation, J.R., X.J. and L.H.; writing--review and editing, J.R., X.J., S.S., Y.W. and J.L.; supervision, X.J. and Y.W.; project administration, X.J. and L.H.; funding acquisition, X.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Qin Chuang Yuan Construction of Two Chain Integration Special Project, grant number 23 LLRH0006.  
The data used to support the findings of this study are included within the article.  
Authors [PERSON] and [PERSON] were employed by the Satpro M&C Tech Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflict of Interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Conventional ADRC** & **Modified ADRC** \\ \hline Mean Value & 78.31 & 68.17 \\ Variance & 3341.67 & 1068.16 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical characteristics of communication latency during the testing process.
## 2 Results
### Effects of JHA Injection on Transcript Levels of DaAST and DaJHAMT  
To reveal the molecular mechanism of the influence of JHA on the expression of _DaAST_ and _DaJHAMT_, the relative expression profiles of _DaAST_ and _DaJHAMT_ were analyzed by qRT-PCR at several time points after JHA treatment. We first examined the effects of JHA application in larvae. The qRT-PCR analysis showed that both doses (5 and 100 \(\upmu\)g/\(\upmu\)L of methoprene) of JHA application significantly influenced the expression of _DaAST_ and _DaJHAMT_ (\(p<0.05\)) (Figure 5). JHA treatments increased the _DaAST_ and _DaJHAMT_ expressions from 0 to 24 h at both doses (5 \(\upmu\)g/\(\upmu\)L and 100 \(\upmu\)g/\(\upmu\)L), then increased expression from 24 to 72 h post treatment (Figure 5A,C). The expression of _DaJHAMT_ was upregulated from 0 to 48 h after treatment (25 \(\upmu\)g/\(\upmu\)L), compared with the larvae controls. The expression of _DaAST_ and _DaJHAMT_ between larvae treated with different doses only differed significantly at 24 h post treatment. Next, we examined the effects of JHA application on pupae. Compared with the pupae of the control group, there was no significant difference in the expression of _DaAST_ at different times at all doses (Figure 5B). The expression of _DaJHAMT_ in the 5 \(\upmu\)g/\(\upmu\)L treatment group increased with time until 72 h post treatment, however, there was no significant difference over time for other doses (Figure 5D). The expression levels of _DaAST_ and _DaJHAMT_ between the pupae treated with different doses were only significantly different at 48 h post treatment, and pupae at a dose of 25 \(\upmu\)g/\(\upmu\)L had the highest expression levels compared with those treated with 5 \(\upmu\)g/\(\upmu\)L and 100 \(\upmu\)g/\(\upmu\)L.  
Figure 4: Relative expression of _DaAST_ and _DaJHAMT_ genes (mean \(\pm\) SE) in different tissues of larvae, pupae and emergent adults. (**A**) _DaAST_ of larvae and pupae; (**B**) _DaJHAMT_ of larvae and pupae; (**C**) _DaAST_ of emergent adults and (**D**) _DaJHAMT_ of emergent adults. The tissues included the head (H), anterior midgut (AMg), hindgut (Hg), Malpighian tubule (Mt), fat body (Fb), reproductive organ (Ro: testes of males and ovaries of females), antennae (Ant), gut (G) and epidermis (E) of \(D\). _armandi_. The results represent the means \(\pm\) SE of three independent experiments. In both graphs, transcript levels are represented as relative expression to the head (H) of males and females. All templates were normalized with _CYP4G55_ and _\(\beta\)-actin_. The 2\({}^{-\Delta\Delta\text{C}}\) and SE values were used for plotting. Uppercase letters mark significant differences in females or larvae, while lowercase letters mark males or pupae (one-way ANOVA, \(p<0.05\), with [PERSON]’s test of multiple comparisons).
## 2 Materials and Methods
### Optical Ray Tracing Simulation  
An optical analysis was performed using the Monte Carlo-based ray tracer software Tonatiuh [27,30] to obtain the total irradiation (energy flux) that the CPC absorber surface receives at different times of the day. The Tonatiuh software was developed for optical simulations of solar concentrating systems and has been successfully used to simulate CPC solar collectors. The 3D geometry of both the reflector and absorber of the CPC collector was built in the Tonatiuh software (Figure 4). For the simulations, three different geometries were modeled, i.e., CPC collector without fins (Figure 4a), with segmented fins (Figure 4b) and with continuous fins (Figure 4c). The location for this numerical study was the city of Merida (Section 2.2), and the solar parameters were an angular distribution (pillbox) [16], an irradiation of 1000 W/m\({}^{2}\) and a total of 1 million rays for the analysis. The rays intersecting a surface are called photons. The output of the ray-tracing analysis is the coordinates of the intersection point on the absorber surface of each individual photon and its energy in tabular form. Figure 4d,e illustrate the photons intersecting the surface of the CPC collector with an absorber with segmented fins and a close-up view, respectively.  
Figure 3: Schematic of the numerical methodology: (**a**) ray tracing analysis in Tonatiuh; (**b**) data processing in MATLAB; (**c**) coupled FEM-CFD thermal analysis in Abaqus.
# Effects of the Damping Parameters on the Opening and Closing Characteristics of Vent Valves
## 3 Results  
### Different Diameters of Damping Hole A  
The damping hole A is located at the front end of the middle nozzle of the support. Figure 11 shows the opening and closing characteristic curves of the valve under different shell damping hole diameters. It can be seen from Figure 11a that, with the increase in diameter of damping hole A, the middle pressure of the support began to rise when the control pressure was small. When it rose to a certain value, the middle pressure of the support rapidly rose to equal the control pressure, which was especially obvious when \(Z_{1}\) was equal to 3.0 mm. It can be seen from Figure 11b that the pressure of the piston control chamber would have a sudden increase or decrease, which is the key to controlling the opening and closing of the valve. With the increase in diameter of damping hole A, the control pressure required by this sudden change point would decrease. From Figure 11c,d, it can be seen that the complete opening and closing action of the valve led to a mutation in the control chamber pressure, characterized by the change in the piston displacement, when \(Z_{1}\) was equal to a 1.0-mm piston displacement, along with the change in the piston chamber pressure leading to a small oscillation appearing. The oscillation was most serious when \(Z_{1}\) was 3 mm and the most stable when \(Z_{1}\) was equal to 0.5 mm because damping hole A was small, which required a great deal of control pressure for the piston to move. Therefore, the recommended size for damping hole A is between 0.1 mm and 1.0 mm.  
Figure 10: Results of vent valve test and simulated open-close pressure ratio.
# Wide-Aperture Bimorph Deformable Mirror for Beam Focusing in 4.2 PW Ti:Sa Laser
## 1 Introduction  
At present, many modern laser facilities have already achieved a peak power of several PW [1; 2; 3], which creates the unique conditions for fundamental research on laser-matter interaction. The intensity of the focused radiation of this type of laser is mainly in the range of 10\({}^{19}\)-10\({}^{21}\) W/cm\({}^{2}\), which is sufficient for carrying out the relativistic optical experiments on the acceleration of the electrons and ions, generation of X-ray and gamma radiation, etc. [4]. However, to study the extreme phenomena in quantum electrodynamics, such as nonlinear Compton scattering, physical interaction of the light beams, and vacuum birefringence, the required intensity must be higher than 10\({}^{22}\) W/cm\({}^{2}\)[5]. Increasing the intensity to the specified levels in petawatt lasers is currently possible only with the use of adaptive optics [6; 7; 8].  
The multi-petawatt Ti:Sa laser consists of a femtosecond oscillator with an amplifier, a grating pulse stretcher, an OPCCPA preamplifier, power amplifier stages and a grating pulse compressor. According to this scheme, a Ti:Sa laser with a power of 4.2 PW (energy and pulse duration of 83 J and 20 fs, respectively), operating with a pulse repetition rate of 0.1 Hz, was built in CoReLS (Korea) [3]. Measurement of the wavefront (WF) distortion of PW radiation by a wavefront sensor (WFS) located after the pulse compressor showed the presence of the aberrations with an amplitude of about 3 \(\upmu\)m (P-V) [9]. The main sources of WF distortions are the thermal deformations of the active elements of the amplifying stages due to the powerful optical pumping as well as aberrations of the wide-aperture optical elements located after the amplifiers are used for the beam expansion and direction, as well as pulse compression. In addition, the focusing parabolic mirror in the interaction chamber will also introduce significant distortions. To compensate for WF aberrations, the wide aperture adaptive optical system (AOS) was installed in the optical setup of the laser facility. The unique part of this system was the bimorph-type 320 mm deformable mirror(DM) that was specially designed for this laser in order to maximally increase the beam intensity on the target. This article describes the features and the benefits of the 320 mm bimorph deformable mirror application to achieve the intensity of more than \(10^{23}\) W/cm\({}^{2}\) of Ti:Sa laser [10].