esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 4 Simulation Studies for a 6-DOF Manipulator  
As described in this section, a 6-DOF manipulator was taken as an example to carry out the simulation studies. First, the forward and inverse kinematics model was established, and then the stiffness and stress of the joint were analyzed to determine the influence of the joint stiffness on the end position error. Second, the stiffness variation of the manipulator in its motion space was determined. Last, the angular stiffness space position error model was established to simulate and calculate the stiffness and error changes in the motion trajectory.
## 4 Mechanism Analysis
### Model of Analysis and Prototype  
The prototype shown in Figure 6 is geometrically the same as the model in Section 3. The back frame is made of resin material whose density is \(1.2\,\times 10^{3}\) kg/m\({}^{3}\). The four wheels of the trolley are made of steel and the density is \(7.8\,\times 10^{3}\) kg/m\({}^{3}\). After adding load to the trolley, the masses of the trolley and back frame are 513.9 g and 292.3 g. The trolley is driven by the motor with wireless remote control. To easily observe the movement of the trolley and the rotation angle of the structure, the transparent side plates have a scale line every five degrees from the center along the radial direction. By measuring the size of each part for the prototype model, the actual initial values of each control variable in Table 3 are improved to \(a=0.024\), \(b=0.6\), \(c=0.637\), \(n=\)1, \(a_{1}=0.024\), and \(b_{1}=0.6\).  
The software Adams (MSC, Los Angeles, CA, USA, 2013) is employed here to study the elevation angle adjustment process of the FCRA. To match the prototype model test, the non-dimensional control parameters of the analysis model in ADMAS are kept consistent with the prototype model. To facilitate the display of internal structure, the shell is hidden in Figure 7b. The rotating pairs are arranged at the center of the back frame and the axles of the trolley. The same as the prototype model, the drive of the analysis model is applied to rotating pairs of back wheels. The contact friction is defined at the contact surfaces between the trolley and tracks. The tracks and the back frame shell are in a fixed form.
# Modified Cuckoo Search Algorithm with Variational Parameters and Logistic Map
## 1 Introduction  
Optimization problems are prevalent in society, such as profit maximization, minimum error, and so on [1; 2]. To solve this kind of problems, many Meta-heuristic algorithms have been proposed, such as genetic algorithms (GA) [3; 4; 5], tabu search [6; 7; 8], simulated annealing [9; 10; 11], particle swarm optimization [12; 13; 14], ant colony optimization [15; 16; 17], etc. Cuckoo search (CS) [18] is inspired by nature, which is a concise method and easy to implement. Cuckoo search is widely used in the real world. For example, [PERSON] et al. [19] developed a new approach which is CS algorithm in cutting stock problem. [PERSON] et al. [20] proposed a new framework for band selection problem based on binary cuckoo search. However, CS method is not perfect, the main drawbacks being it easily falls into the local optimal solution and the slow rate of convergence [21]. [PERSON] and [PERSON] [22] used self adaptive parameter method to improve CS. [PERSON] et al. [23] presented a novel cuckoo search based on chaos theory and elitism. [PERSON] et al. [24] proposed a Chaos-enhanced cuckoo search that use logistic map to ameliorate CS. [PERSON] and [PERSON] [25] proposed a cuckoo search algorithm based on frog leaping local search and chaos theory. [PERSON] and [PERSON] [26] used Gaussian distribution to initiate the CS algorithm, which only considered the initial part was not comprehensive. [PERSON] and [PERSON] [27] used a DE algorithm and a CS algorithm to propose a new hybrid optimization algorithm. These algorithms actually have some improvement; however, some of the them make the CS become difficult to implement, while others increase the complexity of the CS. Therefore, many future studies are necessary to develop new effective cuckoo search algorithms for optimization problems [22].  
[PERSON] et al. [28] found that the population reduction and usage of biased random walk are the most frequently used modifications. To improve the CS algorithm, this paper chooses a different method. This paper proposes modified cuckoo search algorithm with variational parameters and logistic sequences (VLCS). VLCS uses logistic map of each dimension to initialize host nest locationand update the location of host nest beyond the boundary, which can guarantee that the location of the nest is only calculated once. VLCS also uses coefficient function to change \(\alpha\) and \(p_{a}\) at next generation, which greatly accelerates the convergence rate.  
The rest of this paper is organized as follows. Section 2 describes the CS algorithm and analyzes the defects of CS algorithm. Section 3 proposes the corresponding solution for the drawbacks of CS algorithm and the VLCS algorithm. Simulation experiments are presented in Section 4. Finally, conclusions are presented in Section 5.
# A Novel OFDM-Based Time Domain Quadrature GSM for Visible Light Communication System
## 5 Simulations  
In this section, the SE and the BER performance of the two proposed OFDM-based QG-MIMO schemes are investigated in the case of a \(4\times 4\) VLC-MIMO system. In addition, a typical \(4\) m \(\times\) 4 m \(\times\) 3 m room is considered where the \(2\times 2\) LED array is mounted at the center of the ceiling and the receiving plane is set 0.85 m above the floor. The relevant simulation parameters are listed in Table 5. The SEs of the systems listed in Table 3 are first compared, and then Monte Carlo simulations are used to compare the BER performance of these systems under different conditions. Also, an OFDM modulation involving 255 sub-carriers and 512 IFFT/FFT points is used. In order to make the performance evaluation more fair, we assume in the simulation that the data transmission of each channel is synchronous.
## 2 Materials and Methods
### Laser Modification  
The chalcogenide film laser modification was carried out by continuous radiation of an LSR405 CP-1W semiconductor laser with a wavelength of 405 nm as well as by nanosecond pulses of a fiber laser based on the Minimark-2 complex (OOO Laser Center, Saint-Petersburg, Russia) with a wavelength of 1064 nm.  
In the mode of irradiation with continuous radiation of a semiconductor laser, the central part of the laser spot was diaphragmed, after which the radiation was focused by a lens with a magnification of 20\(\times\) and a numerical aperture of NA = 0.65. The sample with the film was fixed on a three-coordinate table, Thorlabs MTS50/M-Z8 (Figure 1), and processing was carried out in the focal plane of the lens. An ocular camera was used to visualize the irradiation zone, and the sample was moved into the image plane of the lens. The power density of the laser radiation acting on the film ranged from 0.74 to 1.09 kW/cm\({}^{2}\), with a spot diameter of about 30 \(\upmu\)m. The intensity profile in the cross-section of the laser beam hitting the film was flat on top (top-flat). The film structure was modified at a laser spot scanning speed of 1 mm/s.  
To modify the film structure with NIR laser pulses, a two-mirror galvanometric scanner was used to ensure spot motion along a given trajectory. After the scanner mirrors, the laser beam was focused by an F-theta lens with a processing field of 100 \(\times\) 100 mm (Figure 2). The films were processed in the F-theta lens focal plane. The maximum power density of the laser radiation incident on the film in a laser spot with a diameter of about 50 \(\upmu\)m reached 1.45 kW/cm\({}^{2}\) with a pulse duration of 4 ns to 20 ns and a pulse repetition rate of 5 kHz to 50 kHz. The laser spot velocity varied from 13 to 120 \(\upmu\)m/s.
## 5 Discussion
Finally, although the proposed protocol uses TDMA for handshaking and has some common features of TDMA, such as collision-free transmission and stable end-to-end delay, it is different from the TDMA-like protocols because it is basically content-based, where the data channels are dynamically allocated to different nodes in different transmission cycles. It is well known that the content-free protocols are more efficient for heavy, constant and balance traffics, while the content-based protocols are more suitable for light, burst and unbalance traffics. The proposed protocol can be viewed as a compromise of the content-free and content-based methods. Therefore, it outperforms the conventional handshaking based protocols but will not be as good as the TDMA-like protocols in the former situations, because the control packet exchanging will occupy a part of bandwidth while there is no control traffic in TDMA. However, the proposed protocol has the potential ability to outperform TDMA-like protocols for burst and unbalance traffics.
# Unsupervised Machine Learning Techniques for Detecting PLC Process Control Anomalies
## 5 Results and Discussions
### Summary of Results  
The overall performance of the detection algorithms is summarized in this section. Figure 9 shows box plots of OCSVM, OCNN, and IF results distributions with the outlier test case labeled where applicable. IF has the highest median accuracy of 88%. Furthermore, accuracy and recall box plots in Figure 9 show that all methods have outlier performance more than 1.5 times the interquartile value on test set 5. However, the precision box plot of Figure 9 indicates that IF's precision value of 98% on test set 2 is an outlier. The precision box plot shows no outliers for OCSVM and OCNN. Lastly, the F1-score box plot in Figure 9 shows that the only F1-score outlier is OCNN's result on test set 5 and the F1-scores distribution of IF is right-skewed. Therefore, the overall results indicate that all the detection models find it challenging to detect anomalies in test set 5. Nevertheless, all the detection models have similar performance distributions on test sets 1-4.  
It is insightful to compare the detection models' average performance with the reported results in [13] on all evaluation across test sets 1-3: IF performs about 5% better than OCSVM, OCNN, and [13] in accuracy, precision, and recall. However, in terms of F1-score, IF averages about 7% over OCSVM, OCNN, and [13]. In all evaluation metrics, OCNN and OCSVM perform similarly. Figure 10 shows the comparison between the detection models in this work and the reported results in [13]. Test sets 1-3 in this work are created to be the same sizes as the test sets in [13]; however, the exact nature of anomalies and their relative distributions in the three test sets is not provided in [13]. It is surmised that the difference in validation performance between the OCSVM in this work and the reported results of the OCSVM in [13] is due to these anomalies variations. Moreover, the reported result in [13] has a precision of 100% in all test cases, but a low recall perform  
Figure 9: Box plot of OCSVM, OCNN, and IF performance distribution.  
three test sets--which could potentially indicate overfitting of their model. Overall, IF achieved the best performance on all three test sets.  
Test sets 4 and 5 consist of timing bits anomalies unique to this work, and such errors were not considered in [13]. Figure 11 shows the average performance comparison between OCSVM, OCNN, and IF on test sets 4 and 5. IF's average performance is higher than that of OCNN and OCSVM on all the evaluation metrics, whereas OCNN and OCSVM perform similarly.
## 3 Experiment and Result
### 2D Wind Field Retrieval and Verification  
A comparison with the measurements from the anemometers was carried out at BDIA to determine the capability and estimate the accuracy of 2D wind retrieved from the CDWL. The accuracies of the ultrasonic anemometer (WMT703, Vaisala) for wind speed and wind direction were found to be \(\pm\)0.1 m/s and \(\pm\)2\({}^{\circ}\), respectively. An anemometer is located at the middle and at both ends of each runway. The wind data from 9 anemometers located at 17R/35L, 17L/35R, and 01L/19R, were used, which were classified into three groups (0-2 km, 2-4 km, and 4-6 km), according to the distance from the lidar. As raw data management, the carrier-to-noise ratio (CNR) criterion was set to \(-\)27 dB, where the radial velocity error of the CDWL does not exceed 0.2 m/s [37]. The angle accuracy reaches 0.01\({}^{\circ}\)  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Wavelength & 1.5 \(\upmu\)m \\ Pulse Energy & 300 \(\upmu\)J \\ Diameter of telescope & 80 mm \\ Repetition frequency & 10 kHz \\ Diameter of telescope & 80 mm \\ Temporal resolution & 0.2–1 s \\ Spatial resolution & 30 m \\ Angle accuracy & 0.01\({}^{\circ}\) \\ Maximum range & 13 km \\ Azimuth scanning range & 0–360\({}^{\circ}\) \\ Zenith scanning range & 0–90\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key Parameters of the CDWL.  
Figure 2: (**a**) The location of the CDWL at BDIA, (**b**) Photograph of the CDWL.  
Thus, the control precision of tangential displacement is guaranteed, which helps improve the accuracy of retrieval wind. The comparison results for the three consecutive months are shown in Figure 3.  
Figure 3: Comparison results of wind speed and direction measured by anemometers and retrieved from the CDWL. (**a1–a6**), (**b1–b6**) and (**c1–c6**) shows the comparison result of June, July, and August, respectively. The detail linear fitting results are also given in the figure. The color of each points represents the normalization probability density using the kernel density estimation. Positions of anemometers are marked in the head of each group of diagrams in red font.  
The wind speed is mainly concentrated at 1-3 m/s, while the wind direction varies according to the month. The lidar observations are highly correlated with anemometer observations, with a correlation coefficient of >0.95. The slope of line fitting of the wind speed >0.93, and that of the wind direction >0.99. The bias is marginal between the results of anemometers and CDWL. The magnitude of the RMS for wind speed is less than 0.6 m/s. The magnitude of the RMS for wind direction is less than \(11^{\circ}\).
# Flexible Antennas for a Sub-6 GHz 5G Band:
## 1 Introduction
The main applications of flexible antennas are given in Figure 2.  
Materials with good electrical and mechanical properties, as well as a low relative permittivity and loss tangent values are needed for the design of a flexible antenna [14]. Another requirement of these antennas is that they should operate with minimum deterioration in the proximity of the human body. The field of wearable antennas is challenging concerning fabrication. The space constraint while placed on specific body positions, the effects of the host body and the performance deterioration due to various structural deformations need to be considered while designing such wearable antennas [15]. In this paper, a detailed review of flexible antennas for the sub-6 GHz 5G band is reviewed. The main contribution of this article is as follows:  
* Materials and design parameters needed for the design of flexible antennas are reviewed. * A detailed classification of a single element, array, and MIMO flexible antennas in the recent literature is studied and a detailed analysis of different methodologies and their effect on the flexible antenna performance characteristics, such as reflection coefficient, gain, radiation pattern, the effect due to bending and the SAR are reviewed. * Based on that, single element flexible antennas for sub-6 GHz 5G are reviewed by classifying the same into slot/slit-based, DGS-based, metamaterial-based, DRA-based, and transparent flexible antennas. * Flexible array antennas for the abovesaid band are classified into slot/slit based, metamaterial based and transparent flexible antennas. * The current state of the art in MIMO flexible antennas for the sub-6 GHz 5G band is reviewed with the classification of the slot/slit based, DGS-based, metamaterial-based, DRA-based, and transparent flexible MIMO antennas. * Further, several design challenges and possible solutions for the implementation of flexible antennas are discussed. The entire paper is organized as follows: Section 1 gives the introduction and theoretical concepts of the flexible antenna and the 5G technology. The materials for the design of flexible antennas are elaborated upon in Section 2. Parameters to be considered  
Figure 2: Applications of a flexible antenna. in the flexible antenna for sub-6 GHz 5G are presented in Section 3. Flexible antennas for a sub-6 GHz 5G band are reviewed in detail in Section 4. Challenges in designing a flexible antenna in sub-6 GHz 5G are given in Section 5, followed by discussions and conclusion in Sections 6 and 7, respectively.
## 2 Methods
### Functional Models for the Determination of the Satellite Acceleration  
#### 2.1.1 Numerical Differentiation Method  
The numerical differentiation approach is often used to derive the second order derivative from the time series {z(t)}; a functional model with constant coefficients is fitted to the time series and is subsequently differentiated analytically to obtain its derivatives. There are numerous numerical differentiators that could be used in this approach, including polynomial interpolation, Newton-Gregory differentiation, and Taylor-MacLaurin differentiation [26]. With reference to the analysis of the computational efficiency and performance in [PERSON] et al. [8], we adopted the sixth-order seven-point polynomial fitting in this article. The basic formula for polynomial interpolation depending on the time difference \(\tau\) is expressed as [26]:  
\[z\left(t_{0}+\tau_{i}\right)_{N}=\sum_{j=0}^{N}c_{j}\tau_{i}^{j},\tau_{i}= \left(i-\left[\frac{M}{2}\right]\right)\Delta t \tag{1}\]  
where \(z(t_{0}+\tau_{i})\) is the arbitrary time series; \(i\) is the index of interpolated points; \(t_{0}\) is the time at which the polynomial is evaluated; \(\tau_{i}\) is the time difference between \(t_{0}\) and the actual data point; \(M\) is the total number of data points used for the interpolation; \(N\) is the degree of the polynomial; \(\Delta t\) is the sampling interval; and \(c_{j}\) is the polynomial coefficient.  
The polynomial was fitted to numbers of neighboring points along the satellite track and then the polynomial coefficients were estimated through a classical least squares [18; 26]. Due to polynomial oscillations at the interval boundaries, the polynomial was evaluated at the central point. Finally, we respectively differentiated Equation (1) once and twice, which provided the following formula:  
\[\begin{array}{rl}\dot{z}(t_{0})&=&g^{T}\cdot z\\ \ddot{z}(t_{0})&=&h^{T}\cdot z\end{array} \tag{2}\]  
where \(\dot{z}(t_{0}),\ddot{z}(t_{0})\) are the first and second derivatives of the time series; \(g\) and \(h\) are the differentiation operator with the constant coefficients corresponding to the sixth-order seven-point polynomial, which are shown in the following:  
\[\begin{array}{rl}g^{T}=\left[\begin{array}{rrrrrr}-1&\frac{3}{20\Delta t }&\frac{3}{4\Delta t}&0&\frac{3}{4\Delta t}&\frac{-3}{20\Delta t}&\frac{1}{60 \Delta t}\end{array}\right]\\ h^{T}=\left[\begin{array}{rrrrrr}1&\frac{1}{90\Delta t^{2}}&\frac{-3}{20 \Delta t^{2}}&\frac{3}{2\Delta t^{2}}&\frac{-49}{18\Delta t^{2}}&\frac{3}{2 \Delta t^{2}}&\frac{-3}{20\Delta t^{2}}&\frac{1}{90\Delta t^{2}}\end{array} \right]\end{array} \tag{3}\]
## 2 Modeling of Dynamic Rotational Error for Spindle-Rolling Bearing System  
Angular contact ball bearings are widely used in the SRB system. The nonlinear and time-varying characteristics of the bearing, under the influence of geometric error, bearing structure and dynamic load, is the root cause of the nonlinearity and has great important effects on the dynamic rotational error of the SRB system. In this section, the dynamic model of the SRB system considering the nonlinear and time-varying behaviors of the bearing are established, based on which the dynamic rotational error is evaluated.
## 2 Materials and Methods
### Experimental Design Methods
#### 2.1.1 Classical Methods  
_One factor at a time:_ According to classical experimental design approaches, the easiest and most frequently applied method is to observe the changes in product or process performance by changing the level of a single factor in each experiment. As the number of factors at hand increases, different classical designs that can be applied begin to appear.  
_Full factorial experimental design:_ The experimental design method that seems most 'optimal' in terms of evaluating and interpreting the effects of factors is the full factorial experimental design approach because all possible combinations of different levels of factors are evaluated. In such designs, an equal number of test results are taken from each level of each factor and they are compared with each other. This method can only be used when very few factors are involved because the number of experiments required increases rapidly with the number of factors and their levels.  
_Partial factorial experimental design:_ With this approach, only a consciously chosen part of the possible combinations is tried and evaluated. In this way, all resources, especially manpower, as well as time and money are saved during the experiments. With this design method, orthogonal indexes are used. By changing the level of more than one factor in each experiment, it is ensured that the levels of all factors are tested with a small number of experiments. In order to reduce the number of experiments for partial factorial experimental design, \"high value\" and \"low value\" are chosen as the two possible factor levels. Thus, instead of performing all combinations of experiments, only the effects of the factors and levels that are thought to affect the results in terms of the performance characteristics can be investigated [17].
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
### Wideband vs. Narrowband Systems in Modern Applications  
A beamformer may feed an antenna array with delays in a manner such that the transmitted waves interfere constructively in the far-field region along one or more desired directions. Destructive interference occurs along other directions, as determined by the zeros of the array factor polynomial, in order to achieve directional energy transmission and minimum harmful interference system wide. An aperture consisting of a number of uniformly spaced antennas driven by RF power amplifiers (PAs) enables a number of independent plane waves to be launched from a single array.  
Assuming all of the transmit antennas are identical to each other, the far-field beam pattern is the product of the array factor and the element pattern of the antennas. Wideband beamforming refers to the situation when the bandwidth of interest is at least 10% of the center frequency of the operational band. The number of elements used in a transmit array determines the sharpness and angular accuracy of the beam. The sharpest possible beams are achieved when all of the beamforming weights are unity within the frequency band, leading to a rectangular windowing operation with known side-lobe levels of about \(-13.6\) dB for a large number of elements. Such beams are achieved using true-time delay beamformers. However, use cases in massive-MIMO for 5G/6G systems exist where multiple independent beams are required for illumination of different angular ranges by independent information-carrying signals. For serving non-LOS receivers, a single information-carrying signal needs to be directed to multiple directions with nulls placed at predetermined directions.