esa-sceva/satcom-chunk-collection huggingface dataset:
# Near-Optimal Heuristics for Just-In-Time Jobs Maximization in Flow Shop Scheduling
## 5 Computational Experiments and Results
### Experiment 1: Relative Comparison of the Proposed Heuristics
#### Enumeration Method
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline Group 1 & H6 & H5 & H2 & H1 & H4 & H3 & H9 & H8 & H10 & H7 \\  & 0.2 & 0.4 & 1.1 & 1.2 & 3.7 & 7.0 & 9.6 & 16.2 & 21.1 & 41.7 \\ \hline Group 2 & H6 & H5 & H2 & H1 & H4 & H3 & H9 & H10 & H8 & H7 \\  & 0.2 & 0.3 & 0.6 & 2.4 & 7.0 & 11.9 & 31.4 & 61.4 & 68.1 & 76.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overall performance rankings (RPD) of proposed heuristics in relation to EM for Group 1 and to the best found solution for Group 2. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(n\) & **H1** & **H2** & **H3** & **H4** & **H5** & **H6** & **H7** & **H8** & **H9** & **H10** \\ \hline
5 & 1.0 & 1.1 & 5.4 & 1.8 & 0.4 & 0.2 & 29.1 & 5.5 & 5.7 & 12.7 \\
6 & 1.2 & 0.9 & 7.4 & 3.4 & 0.4 & 0.1 & 35.8 & 9.4 & 7.3 & 17.7 \\
7 & 1.2 & 1.2 & 6.4 & 3.4 & 0.4 & 0.2 & 43.9 & 16.2 & 8.9 & 20.0 \\
8 & 1.5 & 1.0 & 7.8 & 4.8 & 0.3 & 0.3 & 49.3 & 21.8 & 11.6 & 25.3 \\
10 & 1.2 & 1.1 & 8.3 & 4.9 & 0.4 & 0.3 & 50.6 & 28.3 & 14.5 & 29.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of performances (RPD) of heuristics by number of jobs for Group 1. In terms of the numbers of machines, as can be seen in Tables 7 and 8, the results indicate, in each group, high stability with relatively small variations in their RPD amplitudes. This may suggest that the number of machines was not a relevant factor in performances for the problem addressed. Table 9 presents each group's deviations for the four defined scenarios, varying the values of \(T\) and \(R\) due date factors. These results were consistent with those obtained from the previous analyses of ranking methods, with H6 presenting the best result followed by H5. The results of different scenarios suggest that variations in the tardiness factor and due date range did not exert any relevant influence. It is interesting to note the two top heuristics, H6 and H5, obtained identical results for both groups in Scenarios 2 and 4 characterized by their wide due date ranges. That is, when the interval between due dates was large, neighborhood searches did not provide improvements.
## 2 Problem
While SoC detection is very difficult this way, the behavior on the other hand has the advantage that applications using a zinc-air cell can be optimized for very constant voltage ranges. In this article, therefore, it is analyzed whether it is possible to develop a method for detecting the SoC of zinc-air batteries with the help of alternative methods. EIS turned out to be a promising measurement technique to obtain measurement data which depend on the SoC. Due to the special electrode arrangement of the used zinc-air cells, an adapted measurement hardware is presented. The previous approaches always specified a fixed DC current that was used during measuring the impedance spectra [29; 30; 31]. This principle can also be applied in practice. However, the current working point must then be left in order to use the working point that was used for training the models. When charging, this can lead to situations where energy generated by a photovoltaic system cannot be utilized at this point in time. When discharging, the energy of the new working point may not be sufficient and additional energy from the grid is needed reach the desired working point. In both cases, there is a monetary loss. This article therefore analyzes whether it is also possible to create a model that generalizes the DC current or the working point. In detail, this means that the models are trained with data from different working points and thus an evaluation with different direct currents is also possible. The acquired measurement data are then combined methods of artificial intelligence to determine the SoC as accurately as possible. Therefore the focus is on a model that generalizes the DC current (=the working point) of the battery during an EIS measurement. Figure 5 illustrates why this aspect is particularly difficult. The figure shows impedance measurements at different SoC (Ah values) and DC currents (A values). One can see that an increase in the DC current behaves similar to a change in the SoC. Thus, the characteristics are sorted according to their DC currents and not according to the SoC. Figure 4: Successive charging and discharging of a zinc–air battery with constant current.
# Key Distribution Scheme for Optical Fiber Channel Based on SNR Feature Measurement
## 2 Key Generation Scheme for Optical Fiber Communication
### Key Evaluation Index  
In this paper, the SNR as the channel physical layer feature is introduced to evaluate the system performance. The calculation of the SNR is shown as follows: The bit resolution of signal quantization is N, the quantization noise variance is \(\sigma_{\varepsilon}^{2}\), and the signal's power is \(\sigma_{s}^{2}\). The expression of SNR is shown in Equation (6) [24].  
\[SNR=10\log_{10}\frac{\sigma_{s}^{2}}{\sigma_{\varepsilon}^{2}}=6.02N+10.79+10 \log_{10}\sigma_{s}^{2}(dB) \tag{6}\]  
\[KEY=\left\{\begin{array}{ll}0&if\ SNR<ave(SNR)*(1-\alpha_{q})\\ 1&if\ SNR>ave(SNR)*(1+\alpha_{q})\end{array}\right. \tag{7}\]  
[PERSON] and [PERSON] obtain the SNR through the loopback measurement and then quantize it to generate the key. As presented in Equation (7), \(ave\) is the average value of the SNR, and \(\alpha_{q}\) is the quantization coefficient.  
KGR is the number of keys generated in a period divided by the test time. The details are shown in Equation (8).  
\[KGR=\frac{R}{\ell_{N}}(1-\alpha_{q})(1-\xi) \tag{8}\]  
\(R\) is the signal transmission rate of 10 Gb/s, and \(\ell_{N}\) is the segment length of the received signal. Moreover, \(\alpha_{q}\) denotes the outliers during quantization corresponding to the number of key bits generated after each symbol is quantized as 0.3, and \(\xi\) is the discarding rate of privacy amplification (i.e., bit rate discarded in privacy amplification).  
\[KCR=1-\frac{\sum\limits_{j}^{\ell_{N}}\lvert K_{[PERSON]}(j)-K_{[PERSON]}(j)\rvert}{ \ell_{N}} \tag{9}\]  
The KCR is the consistency rate of the key generated by [PERSON] and [PERSON], and the partition length of the key is \(\ell_{N}\), as shown in Equation (9).  
\[KER=\frac{\sum\limits_{j}^{\ell_{N}}\lvert K_{[PERSON]}(j)-K_{[PERSON]}(j)\rvert}{\ell _{N}} \tag{10}\]  
The key error rate (KER) is the error rate of the key generated by [PERSON] and [PERSON], as shown in Equation (10).
## 1 Introduction
### Data Set  
More than seven weeks of tracking data from days 130 to 180 in 2012 were made available for this study by the GNSS Research Center at Wuhan University, with the permission of the BeiDou authorities. During this period, two satellites G2 and M1 were unavailable and three satellites G6, M5, and M6 were not yet launched. Therefore, there were eleven satellites in operation: four GEOs (C01, C03, C04, C05), five IGSOs (C06, C07, C08, C09, C10), and two MEOs (C11, C12). The ground tracks of the operational satellites are illustrated in Figure 1 together with the tracking stations for a better understanding of the observing geometry. For example, C01 and C04 are at the eastern edge and C03 and C05 on the western side of the tracking network, so the international stations on the western side improve the tracking geometry for C03 and C05 much more significantly than for C01 and C04. During the test time, maneuvers were detected on satellite C01 on days 149 and 179, C03 on 154, C04 on 144, C07 on 137, C08 on day 173, and C12 on 139.  
Currently, the daily files are transferred from each station to the GNSS research center automatically. Details of data availability of each station during the selected test period are given in Figure 2. Because most of the stations were set up shortly before the data period and running in a test mode, long gaps exist due to hardware and software failures and communication problems as well. Several stations, for example, XIAN, SIGP, and LASA just have data at the beginning of the test period. On some days, there is half number of the stations without data, which should be considered carefully in the impact study of network geometry.  
Figure 1: Ground tracks of the BeiDou satellites and distribution of the experimental tracking stations (for details see text).
# An ECG Signal De-Noising Approach Based on Wavelet Energy and Sub-Band Smoothing Filter
###### Abstract  
Electrocardiographic (ECG) signal is essential to diagnose and analyse cardiac disease. However, ECG signals are susceptible to be contaminated with various noises, which affect the application value of ECG signals. In this paper, we propose an ECG signal de-noising method using wavelet energy and a sub-band smoothing filter. Unlike the traditional wavelet threshold de-noising method, which carries out threshold processing for all wavelet coefficients, the wavelet coefficients that require threshold de-noising are selected according to the wavelet energy and other wavelet coefficients remain unchanged in the proposed method. Moreover, The sub-band smoothing filter is adopted to further de-noise the ECG signal and improve the ECG signal quality. The ECG signals of the standard MIT-BIH database are adopted to verify the proposed method using MATLAB software. The performance of the proposed approach is assessed using Signal-To-Noise ratio (SNR), Mean Square Error (MSE) and percent root mean square difference (PRD). The experimental results illustrate that the proposed method can effectively remove noise from the noisy ECG signals in comparison to the existing methods.  
ECG; discrete wavelet transform; wavelet energy; smoothing filter +
Footnote †: journal: Applied Sciences  
1
## 2 Materials and Methods
### HPLC Analysis of Phenolic Acids and Flavonoids  
Phenolic acids and flavonoids in the barley and oat samples were analyzed by High Performance Liquid Chromatography (HPLC) using Shimadzu HPLC system (USA Manufacturing Inc., Canby, OR, USA) equipped with two LC-20 AD pumps, a SIL-20 AC autosampler, a CMB-20A system controller, a CTD-20 AC oven, an ultraviolet-visible SPD-20 AV detector and a Fusion-RP 80A column (250 mm \(\times\) 4.60 mm, particle size: 4 \(\upmu\)m) [27]. Two hundred milligrams of grain powder were used to prepare extract with 80% methanol, using an ultrasonic bath (10 min, 30 \({}^{\circ}\)C, 5500 Hz). Next, the samples were centrifuged (5 min, 3180\(\times\)\(g\), 2 \({}^{\circ}\)C), and then the 1-mL aliquots of the supernatant were transferred to HPLC vials and analyzed by HPLC. Water with acetonitrile (10% in Phase A and 55% in Phase B) were used as a gradient solvent. The time of the analysis was 36 min. The gradient program was as follows: 0-21.00 min: 95% Solvent A and 5% Solvent B; 21.01-25.00 min: 50% Solvent A and 50% Solvent B; 25.01-27.00 min: 20% Solvent A and 80% Solvent B; 27.01-32.00 min: 20% Solvent A and 80% Solvent B; and 32.01-36.00 min: 95% Solvent A and 5% Solvent B. The flow rate was 1 mL min\({}^{-1}\). The wavelength range used was 270-360 nm. The column temperature was 30 \({}^{\circ}\)C. For particular compounds identification, the Fluka and Sigma-Aldrich (Poznan, Poland) external standards of phenolic compounds with purities of 95.00-99.99% were used. HPLC chromatograms presenting time of retention and peaks of the phenolic acids and flavonoids identified in oat and barley grain samples are presented in Figures S1 and S2, respectively. The concentrations of phenolic compounds were calculated based on the standard curves (Figures S4 and S5).
## 3 Benefits of Biochar Amendment in Green Roofs
### Runoff Retention  
As mentioned earlier, the impact of biochar in increasing runoff retention has been extensively researched. The comparisons between conventional and biochar-based GRs in terms of runoff retention rates are summarized in Table 2. A variety of study approaches were adopted, consisting of soil column, pilot-scale, field-scale, and software modeling. Biochar used in GRs has been manufactured from a variety of feedstocks such as birth, cedar, coconut shells, mixed hardwoods, and urban green wastes. The pyrolysis temperature has ranged from around 400 \({}^{\circ}\)C to 750 \({}^{\circ}\)C. While the retention rates of GRs after biochar amendment have been reported from both unvegetated and vegetated substrates, the results in Table 2 were only extracted from the latter, to accurately reflect an actual GR system, which must be covered by plants. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Reference**} & \multirow{2}{*}{**Approach**} & \multirow{2}{*}{**Substrate**} & \multirow{2}{*}{**Biochar Setup**} & \multirow{2}{*}{**Retention Rate without Biochar (\%)**} & \multirow{2}{*}{**Retention Rate with Biochar (\%)**} \\  & & & & & & 80 (summer), \\ \multirow{4}{*}{[64]} & \multirow{2}{*}{Field-scale} & Recycled, crushed brick, & \multirow{2}{*}{Buried biochar} & \multirow{2}{*}{70 (summer), 50 (autumn)} & \multirow{2}{*}{64 (rainy autumn)} \\  & compost, crushed bark, & & & & \\ \cline{3-3} \cline{5-6}  & and sphagnum moss & & & & 50 (autumn) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Water retention capacity of biochar-amended green roofs. Figure 7: The number of studies examining the benefits of biochar in green roofs. As expected, biochar-amended GRs retained more water when compared to those without biochar. For example, [PERSON], [PERSON], [PERSON] and [PERSON] [64] found that in summer, biochar applied either at the top (surface biochar) or at the bottom (buried biochar) of the GR substrate improved runoff retention by 5% and 10% (from 70% to 75% and 80%), respectively. The improvement was even more pronounced in autumn with more frequent rainfall, with 14% more retention in the case of buried biochar (from 50% to 64%), whereas surface biochar had no impact. In the study by [PERSON], [PERSON] and [PERSON] [65], all biochar-amended GR test beds outperformed the conventional one in terms of runoff retention during nine artificial rainfall events. More specifically, test beds with fine biochar particles, either thoroughly mixed into GR substrates or applied on the surface of GRs, exhibited the best performance. [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [73]
## 3 FMMs for Discrete Channel Modeling
### Estimation of FMM Parameters
Algorithm 1 demonstrates the step-by-step procedure for the estimation of the FMM parameters using the selected BWA. ```
Input: Initial model parameter \(\Gamma=(A,B,\pi)\); training data: the experimentally generated bit error sequence \(O=\{o_{1},\dots,o_{T}\}\) // T is the length of the bit error sequence T--length of the bit error sequence. Result: Estimates of \(A\)-matrix \(a_{i,j}\)
1 Step 0: Initialize model parameters \(A_{0}\), \(B_{0}\) and \(\pi_{0}\). 2 Step 1: (E-step) forward phase; calculate forward probability parameters. // Three-step calculation of forward probabilities: \(\alpha_{1}(i)=\pi_{i}b_{i}(o_{1}),i=1,2,\dots,N\). // Initialization process \(\alpha_{t+1}(j)=\left[\sum_{i=1}^{N}\alpha_{t}(i)a_{ij}\right]b_{j}(o_{t+1})\) // Induction process for \(1\leq t\leq T-1,1\leq j\leq N\). \(\Pr[\O|\Gamma]=\sum_{i=1}^{N}\pi_{T}(i)\beta_{T}(i)\) // Termination Step 2: Calculate the backward probability parameters in the backward phase (E-step). \(\beta_{T}(1)=1,i=1,2,\dots,N\) // Initialization Process \(\beta_{t}(i)=\sum_{j=1}^{N}\beta_{t+1}(j)b_{j}(o_{t+1})a_{ij}\), // Induction Process for \(1\leq t\leq T-1,1\leq j\leq N\). Step 3: Calculate model parameter re-estimation variables and the update process (M-step). // Computation of updates at the M-Phase \(\gamma_{t}(i)=\frac{a_{t}(i)\beta_{t}(i)}{\sum_{i=1}^{N}a_{t}(i)\beta_{t}(i)}, i=1,2,\dots,N\). \(\xi_{t}(i,j)=\frac{a_{t}(i)a_{t}b_{i}(o_{t+1})\beta_{t+1}(j)}{\sum_{i=1}^{N} \sum_{i=1}^{N}a_{t}(i)a_{i}b_{i}(o_{t+1})\beta_{t+1}(j)}\) // Determination of the new state transition probability \(\delta_{i,j}=\frac{\sum_{i=1}^{T-1}\xi_{t}(i,j)}{\sum_{i=1}^{T-1}\gamma_{t}(i)},\pi_{i}=\alpha_{1}(i)\beta_{1}(i)\), \(i=1,2,3\). Step 4: The algorithm can be stopped if convergence has been reached, and the estimated model parameters can be output. If not, the algorithm will be fed back with the most recent \(A\) estimates, and steps 1-3 will be repeated until convergence has been reached. ```  
**Algorithm 1**Baum-Welch Algorithm
## 4 Discussion
For a better comparison, the results found when HSI images are used and the DELF algorithm is applied after the SSBS band selection are shown. However, in an in-vivo scenario, a camera setting that is able to construct the data cube during the acquisition phase to facilitate the real-time detection and usage of skin features to directly provide navigational feedback is required. Furthermore, some technical aspects should be taken into account, such as the illumination conditions, which are less controlled in the operating room. For an in-vivo study, the above-described issues should be considered. There are two major stages, which are application/acquisition and data processing. This paper focused mostly on the data processing rather than data acquisition, and we have contributed to making this processing efficient for real-time operation. The next step is to redesign the data acquisition procedure and optimize the illumination intensity, which will create the desired improvements needed for real-time clinical application. We show a clear improvement when detecting local features in HS skin images while employing an expensive camera. An important trade-off should be considered, where employing an HS camera in a broad wavelength range (VIS-NIR) would increase the cost of the procedure. The conclusion is that more spectral information incurs higher system costs. The penetration depth of the HSI camera used in this study has been found to be hundreds of micrometres to a maximum of 1 mm dependent on the wavelength [23], which limits its clinical applicability to the superficial layers of the target tissue, although it was suitable for the scope of this paper. In this work, we have computed a 2D displacement of the skin features. For future work, we want to implement our system in a 3D stereo-vision scenario, where the 3D location of the skin features can be assessed and used to correct for patient motion.
# An Integrity Monitoring Method for Navigation Satellites Based on Multi-Source Observation Links
## 4 Analysis of Optimization Strategies  
### Coverage Analysis of Satellite-Based Observation Unit  
Based on the orbit inclination and altitude of BDS-3 MEO satellites, the spatial region surrounding the Earth could be divided into 8242 grid points by \(2^{\circ}\times 2^{\circ}\) intervals. With six IGS stations (BJFS, CHAN, HKSL, LHAZ, URUM, and WUH2) in China chosen as ground monitoring stations, the basic scenario is shown in Figure 7. Moreover, LEO satellites with an orbit height of 1000 km and an inclination of \(55^{\circ}\) could effectively observe the grid points. The observation elevation angle of receivers for the grids is set to be \(10^{\circ}\).  
Figure 7: Divided grid points and chosen ground monitoring stations.  
Through coverage analyses with the support of different LEO satellites to grid points, the observation performance of LEO satellites to navigation satellites could be approximately obtained. Supposing the satellite-based observation unit has 6, 12, 30, or 60 satellites, the designed scenarios and test results are shown in Table 2.  
As shown in Table 2, the following can be seen:  
1. \(C_{avg}\) is 3.47, 5.17, 10.36, and 18.75 in scenarios 2 to 5, respectively, which have nearly 1 time, 2 times, 5 times, and 10 times of improvement, respectively, compared with the results in scenario 1.
2. As shown in Figures 8-10, the multiple number and coverage rate in scenario 3 could change with the motion of LEO satellites. The uncovered grids also change greatly.
3. With the support of 30 LEO satellites, the coverage rate of four-multiple numbers can reach 100%. That means any grid can be simultaneously monitored by more than four LEO satellites, making it possible to calculate integrity parameters in real time.
4. LEO satellites can effectively make up for the shortage of ground monitoring stations and realize real-time return of navigation messages for navigation satellites. Nevertheless, an increase in LEO satellites also brings the problem of data redundancy.
## 4 Experiment
### Location Estimation by Existing Methods  
#### 4.2.1 Location Estimation by SVM  
In SVM-based location estimation methods (described in Section 2.1.3), accuracy is affected by the kernel function [37] and parameters. When different kernel functions are used, the SVM algorithm is also different.  
There are four general types of kernel functions: linear, polynomial, radial basis function (RBF), and sigmoid. The polynomial kernel function and RBF are applied to the RSSI value distribution for nonlinear and non-Gaussian states [38]. Their equations are expressed as follows. The angle brackets represent the dot product.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**BLE Products** & **BLEAD-B Ver.2**[35] \\ \hline Standard & Bluetooth Ver.4.0 \\ Size & Dia. 50 \(\times\) 16.5 mm \\ Weight & 28 g \\ Range & Approx. 5–100 m \\ \hline
**Locating Device** & **Honor 8**[36] \\ \hline OS & Android ver.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of experiment devices.  
Figure 8: (**a**) Layout of furniture and location of beacons. (**b**) Layout of fingerprint points. The grids on the floor represent floor tiles with a side length of approximately 46.5 cm.  
\[K_{\text{polynomial}}(x,y) = (<x,y>+p)^{q}\qquad p\geqslant 0,q\in N^{+} \tag{16}\] \[K_{\text{RBF}}(x,y) = \exp\left(-\frac{\parallel x-y\parallel^{2}}{2\sigma^{2}}\right) \qquad\sigma\geqslant 0 \tag{17}\]  
In this study, grid search is used to determine the parameters of the optimal solutions for two kernel functions. The following table counts the location estimation errors of the two kernel functions, and it can be seen that the radial basis kernel function is more accurate (Table 2).
## 2 Energy Dissipation Mechanism of Particle Collisions
### Analysis of Energy Consumption of Tangential Particle Collisions  
The particle tangential collision contact model is shown in Figure 3.  
Where \(k_{t}\), \(c_{t}\), and \(\mu_{f}\) are the tangential stiffness, damping coefficient, and friction coefficient between particles, respectively. When the particles collide only in the tangential direction without relative sliding, the energy consumption in the tangential direction is expressed as  
\[\Delta E_{t}=\frac{1}{2}\frac{m_{i}m_{j}}{m_{i}+m_{j}}\Big{(}1-e_{t}^{2}\Big{)} \left|v_{rt}^{-}\right|^{2} \tag{3}\]  
The tangential coefficient of restitution is expressed as  
\[e_{t}=\frac{v_{ft}^{+}-v_{it}^{+}}{v_{in}^{-}-v_{jn}^{-}} \tag{4}\]  
Herein, \(v_{it}\) and \(v_{jn}\) are the velocities of the particles; the superscripts '\(-\)' and '\(+\)' represent the conditions before and after tangential collisions, respectively.  
When the tangential force between particles is greater than the maximum static friction, relative sliding between the particles begins to occur. The friction energy consumption will replace the tangential collision energy consumption, and its friction energy consumption is expressed as  
\[\Delta E_{f}=\mu_{f}\left|F_{n}\delta_{t}\right| \tag{5}\]  
where \(\delta_{t}\) is the tangential relative displacement between particles, and \(F_{n}\) is the normal force between particles, the expression of which is obtained from [PERSON] contact theory [27] as  
\[F_{n}=\frac{4}{3}E^{*}\sqrt{R^{*}}\delta_{n}^{3} \tag{6}\]  
where \(E^{*}=E_{i}^{*}E_{j}^{*}/E_{i}^{*}+E_{j}^{*}\), \(R=R_{i}R_{j}/R_{i}+R_{j}\), \(E_{i}\) and \(E_{j}\) are the modulus of elasticity, and \(\mu_{i}\) and \(\mu_{j}\) are the Poisson's ratio of the particles, respectively. The total quantity of dissipated energy is achieved by normal and tangential collisions; the total energy consumption can be expressed as  
\[E=\Sigma\Delta E_{n}+\Sigma\Delta E_{t}+\Sigma\Delta E_{f} \tag{7}\]  
Figure 3: Particle tangential collision contact model.