esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The rapid proliferation of the Internet has been driving communication networks closer and closer to their limits, while available bandwidth is disappearing due to ever-increasing network loads. In the past decade, optical fiber communication technology has increased the per fiber data rate from 10 Tb/s to over 10 Pb/s [1]. A major explosion came after the maturity of coherent detection and advanced digital signal processing (DSP), which enabled the achievement of high spectral and energy efficiency. It is difficult to overstate the impact that optical coherent technologies have had in both generating and supporting the revolution of optical communications over the last 10 years.  
As one of the key enablers in the coherent evolution of fiber communication systems, DSP has made the innovation of high-order modulation formats possible in increasing spectral/power efficiency. DSP can also compensate almost all linear and nonlinear distortions, and improve noise tolerance in fiber systems. It provides a promising electrical solution for many problems in the optical domain. For example, troublesome channel impairments due to chromatic dispersion (CD) and polarization mode dispersion (PMD) can be almost fully compensated through DSP-based equalizers. Furthermore, DSP has the potential to ease the requirements for many optical components and can even take the place of some optical functions.
# Inertial-Navigation-Aided Single-Satellite Highly Dynamic Positioning Algorithm
## 4 Simulation and Result Analysis
### Orbital-Error Analysis  
In the first part, considering the impact of orbit-determination error on the single-satellite positioning results, we set a simulation time at 3000 s and single integration time at 150 s.The inertial component parameters that were utilized in the actual measurement of INS data included gyro zero bias \(10^{-3\circ}/h\); gyro random walk was \(10^{-4\circ}/sqrt(h)\); initial attitude error was \(2^{\prime\prime},2^{\prime\prime},2^{\prime\prime}\); INS initial error was 20 m; movement speed was 20 m/s. The main direction of the target was the \(y\) axis direction and a height error of 1 m was included in the height direction. In order to eliminate random errors, the orbit-determination error was taken from 0 to 100 m at intervals of 1 m and the remaining errors were all 0. The simulation was repeated 100 times under the same experiment conditions. Figures 2 and 3 show the results of the orbit error.  
Figure 2 shows the mean of the positioning errors of the \(x,y,z\) axis for different orbital errors. It can be seen in Figure 2 that the positioning error was positively correlated with the orbital error; at the same time, the \(y\) and \(z\) axis errors were greater than that of the \(x\) axis because the movement direction was mainly on the \(y\) axis and height error was added in the \(z\) axis. Figure 3 shows the performance comparison of the absolute error values (referred to as RMS) for the three methods under single orbital-error conditions, where the error of the least-squares positioning algorithm was much larger than that of the other two algorithms. To facilitate comparison, the display range of the shaft was reduced. It can be seen in Figure 3 that our proposed algorithm was superior to the EKF algorithm, which is much better than the algorithm of least squares-Newton iteration.
## 3 Vehicle Control Unit Description
### Motor Torque Management  
In the starting phase, the motor torque must be controlled according to an adhesion condition to avoid wheel slipping. At the beginning, the VCU provides a driving torque to only the rear axle. As reported in Figure 3, when a driving torque \(T_{d,ref}\) is demanded by the speed control loop, it is split among the two traction motors according to \(k_{d}\) as reported in Equation (1):  
\[\left\{\begin{array}{c}T_{d,F}=k_{d}T_{d,ref}\\ T_{d,R}=(1-k_{d})T_{d,ref}\end{array}\right. \tag{1}\]  
In Equation (1), \(k_{d}\) is a function of the vehicle speed and direction of motion (forward or reverse) such that the driving axle, when starting from a still condition, is always in the back (with respect to direction of motion). This allows one to reduce the risk of wheel slipping as the backward axle is loaded by load transfer which, for the vehicle considered, cannot be neglected (this typically happens with trains). Furthermore, this choice allows the VCU to estimate the real vehicle velocity by considering the peripheral speed of the front axle to be equal to the vehicle speed, i.e., the longitudinal creepage value is negligible. In this way, by comparing the two rotational speeds, it is possible to detect when the adhesion condition is no longer fulfilled for the driving wheelset. When the torque on the rear axle reaches the maximum value, i.e., the maximum motor torque according to the motor speed, an increasing torque is applied also on the front axle (the two torques will be the same once the speed reaches a set threshold). If the friction coefficient change along the path, wheel slipping could easily occur. Then, the VCU algorithm has been implemented to efficiently avoid such a condition. The real speed of the vehicle is then estimated by using a GPS sensor fused with the wheelset speeds. When the estimated speed is significantly different from one wheelset speed, the traction cut-off of the VCU is triggered. This procedure sets the motor torque to zero, allowing the wheelset to recover from a slip. Then, the VCU provides the wheelset with a smaller torque, which is then increased if the adhesion condition is fulfilled. This works like a car's emergency braking system [19]. This loop of the VCU algorithm is performed whenever a slip is detected.
## 2 Materials and Methods
### Statistical Analysis  
The Statistical Package for Social Sciences (SPSS) v.24 program was used to compare the means of variables (IBM, Armonk, NY: IBM Corp). Descriptive statistics (mean \(\pm\) SD) were used to report the characteristics of conditional factors. Shapiro-Wilk statistical test was used to determine whether the quantitative variables fulfil the criterion of normality. Pearson correlation coefficient (\(r\)) with 95% confidence intervals (CI) via bootstrapping was used to establish relationships between anthropometric characteristics, jump test, and bench pull test results with rowing performance. The magnitude of the correlation coefficient was interpreted with the following thresholds: 0.0-0.09 (trivial); 0.1-0.29 (small); 0.3-0.49 (moderate); 0.5-0.69 (strong); 0.7-0.89 (very strong); 0.9-0.99 (nearly perfect); and 1.0 (perfect) [37]. A stepwise multiple regression analysis was used to predict the 2000 m test performance. Additionally, a proportional curvilinear allometric scaling of 2000 m test performance was considered to identify key determinants of rowing performance [38; 39]. For both regression techniques, predictors were iteratively adding and removing to the predictive model to find the subset of variables resulting in models which explained 50% or more of the variance of the data (R\({}^{2}>0.5\)). The resulting best-fit equations for the best predictive models of rowing performance within the current population for male and female rowers were also shown, together with adjusted R\({}^{2}\) to account for non-significant predictors in the regression models. Statistical significance was set at \(p<0.05\).
## 3 Results and Discussion
In addition to the control result expressed as a percentage of the ratio of correct points to all (positive result above 50%), the report result is presented numerically for individual control points (CP). The first numerical value, 3, represents the number of points to check. The numerical values of 2 and 1 mean compatible points and non-consistent points, respectively (Figure 12, item 3). In addition, the e-Lean application indicates the employee who performs the control activity (Figure 12, item 4) and defines the status of the control result, its confirmation, and comments in the event of rejection (Figure 12, item 5). Another screenshot of the e-Lean application shows a list of machines in individual areas of the enterprise (Figure 13, item 1). In this part of the application there is a panel for managing production machines (Figure 13, item 2), and it is possible to correct information about machines by editing (Figure 13, item 3) or deleting machines (Figure 13, item 4), as well as view statistical data (Figure 13, item 5) and TPM control reports for individual machines (Figure 13, item 6). Figure 11: Screenshot showing selected functionality of the e-Lean application—CNC Machine Tool 63,770 overview. In addition, the application can be used as a motivator that encourages employees to perform review activities because it contains a bonus system that grants a specific pool of points for each activity, which the employer can include in the bonus process. The TPM application was tested at Technonicol Insulation. After that, the first update to the application was carried out and preparation for implementation in automotive plants was undertaken. The results of the implementation turned out to be very promising. Figure 12: Screenshot of TPM reports in e-Lean. Figure 4:0 Screenshot of the e-Lean application showing an overview of the company’s machines. Table 1 shows the averaged results from at least three measurements obtained during a TPM inspection carried out on a production line in a construction company. The average control time consisting of carrying out three control points (polymerization furnace, packing area, and defibering machines) was reduced from 16,200 to 13,923 s. Thus, thanks to the use of the application, the effectiveness of the TPM tool was increased by approx. 15% compared to previously used non-digital solutions.
_Appl. Sci._**2014**, \(4\), 148-157; doi:10.3390/app4020148  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Shift-Peristrophic Multiplexing for High Density Holographic Data Storage**  
**[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] ***  
Department of Applied Electronics, Tokyo University of Science, 6-3-1, Niijuku, Katsushika-ku, Tokyo 125-8585, Japan; E-Mails: [EMAIL_ADDRESS](Z.U.); [EMAIL_ADDRESS] (H.K.); [EMAIL_ADDRESS] (Y.T.); [EMAIL_ADDRESS] (S.Y.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +81-3-5876-1437; Fax: +81-3-5876-1639.  
_Received: 21 November 2013; in revised form: 5 March 2014 / Accepted: 17 March 2014 / Published: 31 March 2014_  
**Abstract:** Holographic data storage is a promising technology that provides very large data storage capacity, and the multiplexing method plays a significant role in increasing this capacity. Various multiplexing methods have been previously researched. In the present study, we propose a shift-peristrophic multiplexing technique that uses spherical reference waves, and experimentally verify that this method efficiently increases the data capacity. In the proposed method, a series of holograms is recorded with shift multiplexing, in which the recording material is rotated with its axis perpendicular to the material's surface. By iterating this procedure, multiplicity is shown to improve. This method achieves more than 1 Tbits/inch\({}^{2}\) data density recording. Furthermore, a capacity increase of several TB per disk is expected by maximizing the recording medium performance.  
**Keywords:** holographic data storage; shift multiplexing; spherical wave; peristrophic
## 1 Introduction
Type III or random censoring occurs when each subject, earthquake maxima in this case, has a censoring time. In the case of generalized extreme distribution, some studies [17; 18] have investigated the estimation of the extreme value index when the data are subject to random censorship. [PERSON] [19] used the GEV distribution for the analysis of censored flood samples. The other distributions belonging to the generalized extreme value distribution, such as the Weibull, Gumbel and Fretche distributions, have also been widely used in the case of censored samples for both classical and Bayesian setups [20; 21; 22]. The estimation of the parameters of the GEV distribution with censored data has been carried out using several approaches, including maximum likelihood [23; 24] and partial probability weighted moments and L-moments [25]. The L-moments estimators are less subject to estimation biases being more efficient than those obtained by the method of moments and are sometimes more accurate in small samples than those obtained by maximum likelihood [19; 26]. [PERSON] [26] showed that, in the case of outliers in the data, the method of L-moments is more robust than the conventional moments methods, since these are linear functions of the data. Subsequently, [PERSON] [27] extended the theory of L-moments to the analysis of censored data. Analysis of censored data is required when data are no consistent, for example, when a sensor with calibration problems gathers values falling above or below a threshold, or in medical studies when patients have to leave the study due to health difficulties. However, on certain occasions, it is required to artificially censor the value measured in data, in order to guarantee obtaining long periodsof return, particularly in the analysis of extreme flood values [19]. Extreme value of earthquake intensity data can also be studied by means of Type III censorship because the threshold used in the censorship mechanism is a random variable. In this sense, the main objective of this study was to analyze earthquake intensity data with the extreme value model corrected by Type III censorship or random censorship. We justify the random censorship in our analysis by the following fact. It is known that an earthquake occurred above a random threshold in certain blocks used to determine the block maxima; however, their current values are unknown and are marked as censored values.
## 4 Results
### Variation of Recovered Energy per Circuit  
The obtained results vary with the racetracks' three main characteristics: the number of turns, lap distance and the average speed.  
In Figure 11, a linear regression of the recuperated energy results over average track speed was drawn for all the circuits. The obtained linear model explains about 80% (\(R^{2}=0.804\)) of the variability in results. Hence, there is a direct correlation between the amount of energy that can be recuperated in a circuit and the average speed it takes a vehicle to run said circuit.  
Linear regressions of the obtained recuperated energy results over the tracks' number of turns and lap distances were also obtained in Figure 12 and Figure 13, respectively.  
The lap distance's linear regression with the results has a value of \(R^{2}=0.263\), which is much lower than the value obtained for the average speed. This variable does not clearly explain the variation in recuperated energy, although they are related to a certain extent, as seen in Figure 13.  
The variable which is the least connected with energy recuperation is the number of turns, with a linear regression value of \(R^{2}=0.0173\). The lack of relation is best seen in Figure 12.  
Figures 14 and 15 visually represent how the recuperated energy and average speed vary with the other two track variables.  
As per Figure 14, there is no clear dependence of recuperated energy and average speed with the track's number of turns. Recuperated energy (bubble size) increases for lower average speeds, on the \(x\) axis, from 7.88% to a maximum of 14%. The number of turns (\(y\) axis) does not have a connection with average speed since recuperated energy values for  
Figure 11: Linear regression of the recuperated energy percentage over average speed.  
Figure 10: Recuperated energy percentage histogram.  
tracks with the least number of turns are almost equal to those obtained for tracks with the highest number of turns.  
Figure 14: Percentage of recuperated energy varying with the number of turns and average speed.  
Figure 12: Linear regression of the recuperated energy percentage over the number of turns.  
Figure 13: Linear regression of the recuperated energy percentage over lap distance.  
Figure 15 shows a slight connection between recuperated energy, average speed and lap distance. The \(y\) axis is now represented by the lap distance, and the recuperated energy values (bubble size) seem to increase not only with decreasing average speeds but also with decreasing lap distances.
## 2 Robot Control System
### Cost Adaption Based on Search Expansion Direction
The function _calculateCostsToReachCell_ symbolizes the standard calculation of an expanded cell in the potential map, as shown in Equation (3):  
\[potential=\Phi\big{(}c_{prev}.x,c_{prev}.y\big{)}+M\big{(}c_{exp}.x,c_{exp}.y \big{)}+\Omega_{mv}, \tag{3}\]  
where \(c_{exp}\) is the expanded cell, \(c_{prev}\) is the previous cell, and \(\Omega_{mv}\) is the cost value to move from one cell to a neighboring cell. The function is triggered in case the expanded cell is not part of the corridor zone or the expanded cell is not determined as a right-hand driving cell. Therefore, in these cases, the default behaviors of the global planners are not changed. If the expanded cell is part of a corridor zone and neighbors a guard rail zone, the cell is of interest and further analyzed. Four cases are differentiated:  
1. The guard rail zone is a neighbor in the negative y-direction in the costmap;
2. The guard rail zone is a neighbor in the positive y-direction in the costmap;
3. The guard rail zone is a neighbor in the negative x-direction in the costmap; or
4. The guard rail zone is a neighbor in the positive x-direction in the costmap. Depending on the relative position of the expanded cell in relation to the guard rail zone and the already expanded cells stored in the potential \(P\), the cell is defined as a right-hand driving cell. In the case of a right-hand driving cell, the calculation of the cell potential is changed from Equation (3). The applied formula is shown in Equation (4):  
\[potential=\Phi(x_{i},y_{i})+M_{Corridor}\ /\ 4 \tag{4}\]  
The coordinates \(x_{i},y_{i}\), with \(i=\{1,2,3,4\}\), reflect the above mentioned four cases. \(M_{Corridor}\) is the cost value of a cell inside the corridor zone. The presented approach can be applied to any zone in any direction, as long as a particular cost gradient is present between the zone and the guard rail zone. Figure 6 shows an example scenario for a large-scale planning scenario in a simulated production environment. The red dot represents the start position and the green dot the goal position. The costmap is divided into navigation zones according to Section 2.1. Figure 6 shows the default behavior of the implemented A* planner. As desired, the resulting path is mostly located inside the corridor zone. Figure 6 shows the resulting right driving behavior by applying our approach.
## 5 Analysis and Results  
The construction method of a Korean tunnel is different from the one of a Norwegian tunnel, not to mention tunnel sizes. For example, the rock mass classification is conducted by the Q-value, and a concrete lining is installed for extremely poor rock mass quality in Norway. On the other hand, the rock mass quality is classified by RMR, and a concrete lining is always installed for the whole length of a tunnel in Korea. Therefore, to consider the characteristics in the tunnelling practice of Korea, the analysis starts from the modification of TUNSIM-Q that was established based on a Norwegian tunnel specification.  
The second step is to set up the tunnel sizes, such as the cross-sectional area, perimeter length of a tunnel, round length, etc., as well as the tunnel support types based on a Korean tunnel specification. The rock mass conditions such as SPR, DRI, and VHNR are set up according to the converted Q-value given for each tunnel support type.  
The final step is to analyse the operation times for each tunnelling operation such as drilling, charging and blasting, shotcreting, and rock bolting, etc., for the standard Q-values given by each tunnel support type, followed by the basic, standard, gross, tunnel and total construction time by summing up the operation times corresponding to each construction time category and correction factors.
###### Abstract  
The environment for acquiring microseismic signals is always filled with complex noise, leading to the presence of abundant invalid signals in the collected data and greatly disturbing effective microseismic signals. Regarding the identification of effective microseismic signals with a low signal-to-noise ratio, a method for identifying effective microseismic signals in a strong-noise environment by using the variational mode decomposition (VMD) and genetic algorithm (GA)-based optimized support vector machine (SVM) model is proposed. Microseismic signals with a low signal-to-noise ratio are adaptively decomposed into several intrinsic mode functions (IMFs) by using VMD. The characteristics of such IMFs are extracted and used as a basis for the determination of signal validity. The SVM model is optimized by utilizing GA to obtain the optimal penalty factor c and the kernel function parameter g. The availability of IMF components is judged by the optimized SVM model, based on which the effectiveness of microseismic signals is further identified. By applying the algorithm to the microseismic signals with artificially added noise, the effective microseismic signals and ineffective noise are discriminated, verifying the feasibility of the algorithm. After processing the microseismic records collected in the field, we effectively judge the effectiveness of microseismic signals, suppress the interfering noise in the data and greatly improve the signal-to-noise ratio of the seismic records. The results show that the method for identifying effective microseismic signals based on VMD and GA-SVM can well discriminate between effective and ineffective microseismic signals, which is very significant and provides technical support for microseismic monitoring in a strong-noise environment.  
microseismic monitoring; variational mode decomposition; genetic algorithm-based optimization; support vector machine +
Footnote †: journal: Applied Sciences