esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Rate Optimization
### Initialization
```
Input:\(\theta\) - Output of Algorithm 1 Result:\(\theta_{1}\) - Final optimized solution foreachrow in \(V\)do \(subcarriers(row)\leftarrow\sum_{j=1}^{4096}|V(i,j)|^{2}\) end foreach\([\sim,sorted\_subcarrier\_indices]\leftarrow\text{sort}(subcarriers)\)\(\theta_{1}\leftarrow\theta\) foreachrow in \(V\)do \(\text{index}\leftarrowsorted\_subcarrier\_indices(row)\) foreach\(column\) in \(V\)do if\(V(row,column)\) in \(I\) or \(II\) quadrantthen \(\theta_{2}(column)\gets 1\) else \(\theta_{2}(column)\leftarrow-1\)  end if  end foreach\(max\_iter\leftarrow\max(501-row,200)\) repeat \(\text{ran}\leftarrow\text{random number in }[1,4096]\) if\(\theta_{2}(ran)\)\(l\)= \(\theta_{1}(ran)\)then \(\theta_{3}\leftarrow\theta_{1}\) \(\theta_{3}(ran)\leftarrow\text{flip\_element}(\theta_{3}(ran))\) \(r1\leftarrow\text{find\_rate}(\theta_{1})\); \(r2\leftarrow\text{find\_rate}(\theta_{3})\) \(\theta_{1}(ran)\leftarrow(r1<r2)\)? \(\theta_{2}(ran)\): \(\theta_{1}(ran)\)  end if until\(max\_iter\);  end foreach
```
**Algorithm 3**Parametric optimization

### Further Optimization

The \(\theta\) vector obtained in the previous step is the input in this stage. The rows of \(\mathbf{V}\) are arranged in descending order, based on the sum of the square of the magnitude of the elements. For every row of \(\mathbf{V}\) which corresponds to a particular subcarrier, a temporary IRS configuration \(\mathbf{\theta_{2}}\) is calculated using the principles of Algorithm 1. A greedy solution is then used to improve the rate further. The number of steps in the greedy solution is kept high in the initial rows (which corresponds to higher magnitude) and is gradually decreased to cater to the time complexity. This approach is illustrated in Algorithm 2. ``` Input:\(\theta\) - Output of Algorithm 1 Result:\(\theta_{1}\) - Final optimized solution foreachrow in \(V\)do \(subcarriers(row)\leftarrow\sum_{j=1}^{4096}|V(i,j)|^{2}\) end foreach\([\sim,sorted\_subcarrier\_indices]\leftarrow\text{sort}(subcarriers)\)\(\theta_{1}\leftarrow\theta\) foreachrow in \(V\)do \(\text{index}\leftarrowsorted\_subcarrier\_indices(row)\) \(\theta_{2}(column)\leftarrow\text{index}\) \(\theta_{2}(column)\leftarrow\text{index
# Structural Damage Localization by the Principal Eigenvector of Modal Flexibility Change
## 2 Theory
### Deflection Estimated by Modal Flexibility Change and PE Method for Damage Localization  
In Equation (9), the flexibility change matrix \(\Delta F\) can be calculated approximately using the first few low-frequency modes, as shown in [34; 35]  
\[\Delta F=\sum_{j=1}^{m}\frac{1}{\lambda_{dj}}\phi_{dj}\phi_{dj}^{T}-\sum_{j=1}^{ m}\frac{1}{\lambda_{uj}}\phi_{uj}\phi_{uj}^{T} \tag{19}\]  
where \(m\) is the number of measured modes, \(\lambda_{uj}\) and \(\lambda_{dj}\) are the eigenvalues of the structure before and after damage, \(\varphi_{uj}\) and \(\varphi_{dj}\) are the corresponding eigenvectors, respectively. For the damaged structure, the modal data can be acquired through a modal test in practice. For the intact structure, the modal data are acquired through solving a generalized eigenvalue problem of the undamaged FEM or by the modal experiment.  
Using matrix theory [32; 33], the principal eigenvector of \(\Delta F\) can be given by the solutions of the following eigenvalue problem:  
\[\Delta F\left(\frac{\xi_{1}}{\lambda_{1}}\right)=\xi_{1} \tag{20}\]  
where \(\lambda_{1}\) is the principal eigenvalue and \(\xi_{1}\) is the principal eigenvector of \(\Delta F\), respectively. According to Equation (20), the physical meaning of the vector \(\xi_{1}\) is the structural deflection variation before and after damage by considering \(\frac{1}{\lambda_{1}}\xi_{1}\) as a load vector. Thus, according to the principle stated in Section 2.1, structural defects can be located in the following two ways: (1) the turning points in the shape of \(\xi_{1}\) are the locations of defects; (2) the sharp peaks in the curvature of \(\xi_{1}\) indicate the locations of structural defects. It is known that the curvature of \(\xi_{1}\) can be computed using the central difference approximation as follows:  
\[\left(\xi_{1}^{i}\right)^{\prime\prime}=\frac{\xi_{1}^{i+1}-2\xi_{1}^{i}+\xi_ {1}^{i-1}}{h^{2}} \tag{21}\]  
where \(\zeta_{1}^{i}\) is the \(i\)th coefficient of \(\xi_{1}\), \(h\) is the length of structural element, and \(\left(\xi_{1}^{i}\right)^{\prime\prime}\) is the \(i\)th curvature coefficient of \(\xi_{1}\).
# Design Study of a Large-Angle Optical Scanning System for MEMS LIDAR
## 4 Results and Discussion  
Setting the image distance to be 100 m, the point diagram distributions when the MEMS mirror rotates from \(0^{\circ}\) to \(5^{\circ}\) can be obtained as shown in Figure 9. The distribution of the spots are generally uniform and symmetrical, and are all within the Airy disks. When the MEMS mirror rotates from \(0^{\circ}\) to \(5^{\circ}\), the corresponding spot RMS radius is maximum.  
Set the laser wavelength to be 1520 nm, 1550 nm, and 1570 nm, Figure 10 shows the spots diagram at \(5^{\circ}\). The spots all present symmetrical distributions, and the maximum  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Surface** & **Radius/mm** & **Thickness/mm** & **Glass** & **Diameter/mm** \\ \hline Object & Infinity & 1267 & & \\ Stop & Infinity & 20 & & 5 \\
2 & 36.314 & 2.646 & F8 & 15 \\
3 & \(-\)44.353 & 3.346 & KCL & 15 \\
4 & 82.783 & 10 & & 15 \\
5 & Infinity & \(-\)12 & MIRROR & 8 \\
6 & Infinity & 15 & MIRROR & 5 \\
7 & \(-\)26.446 & 2.288 & SF67 & 16 \\
8 & \(-\)21.596 & 3.861 & & 16 \\
9 & \(-\)13.605 & 1 & LITHOSIL-Q & 20 \\
10 & \(-\)44.966 & 3.568 & & 20 \\
11 & \(-\)33.876 & 1.303 & LASF35 & 20 \\
12 & \(-\)28.725 & 1.142 & & 20 \\
13 & \(-\)1521.767 & 2.442 & LASF35 & 24 \\
14 & \(-\)49.862 & 2.838 & & 24 \\
15 & 286.426 & 3.452 & SF66 & 24 \\
16 & \(-\)79.392 & 22.076 & & 24 \\
17 & 33.650 & 0.955 & BAF8 & 22 \\
18 & 32.590 & 4.256 & SF66 & 22 \\
19 & \(-\)30.570 & 5 & & 22 \\
20 & 30.570 & 6.252 & SF66 & 22 \\
21 & \(-\)32.590 & 0.955 & BAF8 & 22 \\
22 & \(-\)33.650 & — & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Design parameters of MEMS scanning optical system.  
Figure 8: Three-dimensional structure the large-angle MEMS scanning optical system.  
Figure 9: The distributions of the point diagram with different angles, the circles are the Airy disks.  
RMS radius are all about 7 cm, which shows prefect achromatic property, suiting for FMCW application.  
According to Equation (3), the theoretical FOV of the system with various angles can be calculated. The results obtained by ZEMAX ray tracing compared with that of the theoretical calculation are shown in Figure 11. The linear fitting shows that the FOV extending factor for the designed system is about 2.78, which is 2.56 according to the theoretical calculation. The difference between the simulation results and the theoretical calculation increases with the FOV, but the maximum error does not exceed 1.41\({}^{\circ}\), showing excellent agreement. When the rotation angle of the MEMS mirror is 5\({}^{\circ}\), the FOV of the system is the largest, which is 28.5 \(\times\) 2 = 57\({}^{\circ}\), and the maximum beam divergence is less than 0.5 mrad. The total length of the optical system is less than 12 cm. Compared with the traditional optical angle expansion system, our new design has distinct advantage in the FOV, which is usually less than 30\({}^{\circ}\) in real existing LIDAR system.  
Figure 11: Comparison of the FOV between theoretical calculation and simulation.  
Figure 10: Spot diagrams at 5\({}^{\circ}\).
# A Method for Precise Tracking Control of Pneumatic Artificial-Muscle-Driven Exoskeletal Robot
###### Abstract  
Exoskeletal robots are of critical importance in the domain of mechanical boosting. The pneumatic artificial muscle (PAM) is commonly used as a flexible actuator in exoskeletal robots designed for upper limbs due to its high power-to-weight ratio, conformability, and safety. This study establishes a new model based on the existing model to improve its control precision by implementing elastic and frictional forces and empirical coefficients, battling against the time-variant hysteresis that PAM's output force exhibits. In the meantime, a BP neural network is employed in reverse modeling, followed by the adoption of the least-square-based particle swarm optimization algorithm in order to determine the optimized parameter values. PAM provides the Upper Limb Exoskeletal Robot with appropriate auxiliary power, which can be adjusted to accommodate variations in posture change during the lifting process. PAM is also capable of handling variable loads based on the principle of torque balance, constructing a control system according to the inverse dynamics of exoskeletal robots accompanied by an inverse model of PAM's output force, and finally, rendering tracking control of the elbow angle during the auxiliary process possible. Finally, the tracking error results are calculated and shown; the maximum angular error in the tracking process is 0.0175 rad, MAE value is 0.0038 rad, RMSE value is 0.0048 rad, and IEAT value is 4.6426 rad. This control method is able to improve the precision of tracking control of the elbow angle of the upper limb-exoskeleton coupled system during the process of lifting goods.  
pneumatic artificial muscle; Upper Limb Exoskeleton; variable load; tracking control Article  
A Method for Precise Tracking Artificial-Muscle-Driven Exoskeletal Robot  
[PERSON] [PERSON] [PERSON]  
College of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China; [EMAIL_ADDRESS] (G.M.); [EMAIL_ADDRESS] (H.J.); [EMAIL_ADDRESS] (J.X.) Correspondence: [EMAIL_ADDRESS]
## 3 Observation of Scintillation in OEM7 Measurements
### Amplitude fading across frequencies  
To understand scintillation periodicity, its spectral characteristics and its impact on different frequency bands, power spectral analysis was conducted. Initially, the C/N\({}_{0}\) values were converted from dB-Hz to a ratio. Subsequently, detrending was applied, followed by a Fourier transform using a Fast Fourier transform (FFT).  
Figure 6a illustrates the time series of C/N\({}_{0}\) values for GPS PRN 18 at three distinct frequencies. These plots reveal that lower frequencies, specifically L2 and L5, experience more profound fades compared to L1. This was also studied for Be  
Figure 5: Examples of signal fading and loss of lock.  
Figure 6b shows the power spectral values of GPS PRN 18 detrended C/N\({}_{0}\) values with (pink shaded area) and without (blue shaded area) scintillation impact. As anticipated, the amplitude variation in the signal during normal conditions is notably lower compared to during severe scintillation conditions. The frequency response analysis indicates that the majority of signal variations occur between 0.05 and 0.2 Hz, suggesting that scintillation events occur approximately every 5 to 20 s. Figure 6c compares the power spectral values of GPS PRN 18 across L1 CA, L2C and L5 frequencies. The lower frequencies exhibit a broader range of amplitude fluctuations, resulting in higher power spectral values.
## 3 Routing in Satellite Networks
### Routing Protocols in LEOs
#### 3.3.1 Asynchronous Transfer Mode (ATM)  
ATM is a network technique in which services such as audio, video, and data are transmitted in small, 53-byte stabled-size cells. ATM is an International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) standard. It is a switching and multiplexing technique that blends packet switching and circuit switching for data transmission. It obtains the volume and stationary transmission delay avails of circuit switching. Packet switching, on the other hand, receives the benefits of elasticity and productivity for discontinuous traffic. ATM can be called the next generation of packet switching. ATM creates virtual circuits to blend these two techniques and was originally designed for terrestrial networks. Satellite networks use time division multiplexing (TDM) technology. TDM technology addresses time division, and each time division is assigned to one user. No other user can transmit data in the time zone assigned to this user. This means that if the user using it in that time period has no data to send, the time slot is occupied unnecessarily. Because ATM technology is asynchronous, time zones are used more flexibly. In other words, asynchronous ATM technology is more productive than synchronic techniques such as TDM. For this reason, it will be beneficial for use in satellite transmission systems. Since ATM networks are connection-oriented, two points must establish a connection before they can exchange data. For this, the connection setup package is sent. Thus, a virtual circuit is created [104; 105].  
The key advantage of ATM over TDM lies in its efficient use of bandwidth and support for dynamic traffic patterns. In TDM systems, time slots are pre-assigned to users regardless of whether they have data to transmit, leading to potential inefficiencies when some users remain idle [104; 105]. ATM, in contrast, does not reserve fixed time slots for each user. Instead, it transmits data in small, fixed-size cells only when there is actual data to send. This asynchronous nature allows ATM to dynamically allocate bandwidth based on demand, minimizing idle time and maximizing channel utilization. Furthermore, ATM is more suitable for heterogeneous data types (e.g., voice, video, and data) due to its ability to handle variable bit-rate traffic with low latency and high quality of service (QoS). This flexibility and efficiency make ATM particularly advantageous for satellite networks, where bandwidth is a limited and costly resource.
# Lightweight Physical Layer Aided Key Agreement and Authentication for the Internet of Things +
## 4 Performance Analysis
### Experiment Analysis  
In this subsection, we implement an experiment to demonstrate the practical performance of the proposed PL-AKA. To this end, a USRP based test-bed is designed in an LTE communication environment. In the experiment, phase sanitization introduced in [20] is employed to compensate for the effect of the carrier frequency offset (CFO) and sampling timing offset (STO) for channel reciprocity. Figure 4 shows the reciprocity of sanitized channel phase (\(\theta\)) in our preliminary test [21]. From this result, we simply suppose that a BS transmits the challenge signal to an user equipment (UE) for PHY-CRAM. For the performance test of PL-AKA, the test statistics are defined and the probability of \"Gray\" (i.e., \(\lambda\)) is calculated to compare the signaling overhead with the conventional scheme.  
Figure 3: Signaling overheads of PL-AKA and EPS-AKA over various target false alarm probabilities (simulation results).  
Figure 2: Comparison of the probability density functions.  
Experiment Setup  
Figure 5 illustrates the experimental environment. The experiment is designed with 2 NI-USRP 2944R [22] and LTE application framework 2.0 [23] running on Labview NKG 4.0 [24] of National Instrument. The host-PC is Dell Insprion 3650 [25] with Intel Core i5, and having 16 GB of RAM. Each USRP is connected to PCI express port of the PC and one USRP is served as a BS, and the other is served as an UE. To measure CSI according to a physical location, the UE is settled onto i-ROBO PSA-125-S motion stage [26] with Autonics PMC-1 HS-USB controller [27]. UE moves 50 locations that are 2 cm apart from each other, measuring the 1000 CSI frames in each location. In the given situation, BS and UE are remotely controlled by a laptop to avoid channel distortion and maintain the channel coherence time as long as possible. The experiment is operated for 10 h. Tables 2 and 3 indicate the USRP specifications and communication parameters, respectively.  
Figure 4: Preliminary test for verifying channel reciprocity of sanitized phase [21].  
Figure 5: Experiment setup for measuring physical channels.
# An Overview of Non-Isolated Hybrid Switched-Capacitor Step-Up DC-DC Converters
## 1 Introduction
Particularly converters under study are non-isolated and unidirectional in terms of power flow; most of them can be adapted to provide a bidirectional power flow, with the cost of increasing the number of transistors. The two main types of SC circuits are pure SC circuits [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39] and resonant SC circuits [40; 41; 42; 43; 44; 45; 102; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 67; 68; 69; 70; 71; 72; 74; 75; 76; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 89; 91; 84; 86; 88; 87; 8 the combination of resonant SC circuits with traditional converters. However, a short overview of pure SC circuits is presented as an introduction. Then, the resonant charge interchange will also be introduced. Before going to topologies, the reader may look at [13; 14; 15; 16] for a more exhaustive overview of the pure or classical SC circuits. The main classification we can mention of SC circuits is shown in Figure 1, and is separated into two main types: _(i)_ First, the circuits that do not need transistors are usually made of diodes and capacitors only, and these types of converters can be divided into, _(i.1)_ ac-to-dc rectifiers with voltage multiplication characteristics, and the other type of SC converter that does not need transistors which is _(i.2)_ circuits fed by digital signals used to produce a voltage larger than the digital input level. SC circuits based on transistors can be divided into three main types, _(ii.1)_ pure switched-capacitor circuits (circuits without inductors), _(ii.2)_ SC converters with resonant charge interchange (circuits with tiny inductors), and finally, _(ii.3)_ SC converters (either pure or resonant) combined with stored energy inductors ( inductors in which linear ripple approximation can be applied to their current). Let us start with a short overview of those different circuits.
## 2 Background
### Evolution of V2X Communication Systems
As more and more communicating vehicles and intelligent roads appear in the transportation infrastructure and advanced V2X systems will proliferate with ever-increasing capacity requirements, it is foreseeable that the resources available with the current radio and channel access practices will not meet the essential requirements, such as high reliability, fast data rates, and low latency [15].
## 2 Hybrid System and Techno-Economic Analysis Method
### The Design of Hybrid System Components  
#### 2.2.1 Solar PV Models  
PV technology is a renewable power supplier based on solar irradiation, which produced DC electricity to the DC bus bar. In this study, the size of the PV model depends on system constraints such as the minimum fraction of renewable energy considered, maximum capacity of grid sold back, the density of solar irradiation, and load required. The PV models should be sized to be able to deliver power to peak load and provide excess power to the hydrogen system, for selling power back to the grid. In Homer, the formula presented the PV output power is illustrated in Equation (1) [64],  
\[P_{PV}=\gamma_{PV}f_{PV}\bigg{(}\frac{G_{T}}{G_{T,STC}}\bigg{)}[1+\ \propto_{P}\ (T_{C}-T_{C,STC})] \tag{1}\]  
where, \(P_{PV}\) is the output power from PV array in (kW), \(\gamma_{PV}\) is the capacity of PV array in (kW). \(f_{PV}\) is the derating factor in (%). \(G_{T}\) the instantaneous solar irradiation in (kW/m\({}^{2}\)). \(G_{T,STC}\) is the solar irradiation at the standard condition and it is 1 kW/m\({}^{2}\). \(\propto_{P}\) is the temperature coefficient of power (%/c). \(T_{C}\) is the nominal operation of cell temperature in current time step in (\({}^{\circ}\)C), and \(T_{C,STC}\) is the cell temperature at standard condition 25 \({}^{\circ}\)C. From Equation (1), it can be seen that various vectors can affect power produced from PV models, includes solar irradiation and ambient temperature. The technical and economic characteristics of PV models in our proposed system come from the manufacturer [65]. According to the manufacturer's datasheet, the lifetime of models is 25 years, model efficiency is 17%, the derating factor is 90%, and ground reflectance is 20%. As the temperature effect on PV model power generation, the nominal operation of cell temperature is taken 45 \({}^{\circ}\)C, and the temperature coefficient on power is \(-\)0.41%/\({}^{\circ}\)C. The economic cost of models is 1200 $/kW. 30 $/year for capital cost and operation and maintenance (O and M) cost, respectively. In addition, replacement cost is considered as zero due to the long life of PV models.
# Fabrication Tolerances' Impact on an ODAC-Based PAM-4 Transmitter
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AM & amplitude modulator \\ ASPIC & application specific photonic integrated circuits \\ CW & continuous wave \\ eDAC & electrical digital to analog converter \\ ER & extinction ratio \\ FPGA & field programmable gateway arrays \\ FPPGA & field programmable photonic gateway arrays \\ IoTs & Internet of Things \\ \(K_{in}\) & input coupling factor \\ \(K_{out}\) & output coupling factor \\ LSB & least significant bit \\ NRZ & non-return-to-zero \\ MSB & most significant bit \\ MZM & Mach Zehnder modulator \\ oDAC & optical digital to analog converter \\ OSNR & optical signal to noise ratio \\ PAM-4 & pulse amplitude modulator level four \\ PAM-8 & pulse amplitude modulator level eight \\ PAM-16 & pulse amplitude modulator level sixteen \\ PIC & Photonic integrated circuits \\ PUC & programmable unit cell \\ PM & phase modulator \\ PS & phase shifter \\ PUC & programmable unit core \\ QAM & quadrature amplitude modulation \\ SER & symbol error rate \\ SiP & Silicon Photonics \\ WDM & wavelength division multiplexing \\ \end{tabular}  
Appendix A Probability density of the four PAM levels is the combination of four Gaussian functions and can be written from a generalized Gaussian probability density function as [28; 29]  
\[f(x)=\sum_{i=0}^{3}\frac{1}{\sigma_{i}\sqrt{2\pi}}e^{\left[-\left(\frac{|x-y_{i }|}{\sigma_{i}\sqrt{2}}\right)^{2}\right]} \tag{1}\]