esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Actor-Critic Methods
### Deep Deterministic Policy Gradient
#### 6.1.2 Dynamic Path Planning Using an Improved DDPG Algorithm  
The study by [PERSON] et al. [28] builds on the DDPG framework by incorporating several enhancements to address its limitations, such as low success rate and slow convergence in dynamic environments. These enhancements include replacing the optimizer, adding prioritized experience replay, introducing transfer learning, and using a curiosity module.  
To improve convergence speed and accuracy, the RAdam optimizer is integrated into the DDPG algorithm. The RAdam update rule is given by the following:  
\[\theta_{t+1}=\theta_{t}-\alpha_{t}r_{t}\frac{\hat{m}_{t}}{\sqrt{\hat{v}_{t}}}, \tag{60}\]  
where  
\[\hat{v}_{t}=\frac{v_{t}}{1-\beta_{2}^{t}},\quad v_{t}=\beta_{2}v_{t-1}+(1- \beta_{2})g_{t}^{2}, \tag{61}\]  
\[\hat{m}_{t}=\frac{m_{t}}{1-\beta_{1}^{t}},\quad m_{t}=\beta_{1}m_{t-1}+(1- \beta_{1})g_{t}, \tag{62}\]  
\[r_{t}=\sqrt{\frac{\rho_{t}-4}{\rho_{\infty}-4}\cdot\frac{\rho_{t}-2}{\rho_{ \infty}-2}}, \tag{63}\]  
and \(\rho_{t}\) and \(\rho_{\infty}\) are parameters of the RAdam optimization process. This optimizer stabilizes training and enhances convergence.  
Prioritized experience replay is employed to prioritize sampling experiences with higher temporal-difference (TD) error. The TD error is defined as follows:  
\[\delta_{t}=r_{t+1}+\gamma\max_{a}Q(s_{t+1},a;\theta)-Q(s_{t},a_{t};\theta), \tag{64}\]  
where \(Q\) is the action-value function, \(s_{t}\) is the current state, \(a_{t}\) is the current action, and \(\gamma\) is the discount factor. This prioritization accelerates learning by focusing on significant experiences.  
To improve exploration in complex environments, a curiosity module is introduced. This module provides intrinsic rewards by minimizing the prediction error of the forward dynamics model:  
\[\hat{\phi}(s_{t+1})=f(\phi(s_{t}),a_{t};\theta_{F}), \tag{65}\]  
where \(\phi(s_{t})\) is the encoded feature vector of the current state, \(a_{t}\) is the action, and \(\theta_{F}\) represents the model parameters. The intrinsic reward is calculated as follows:  
\[r_{t}^{i}=\frac{\eta}{2}\|\hat{\phi}(s_{t+1})-\phi(s_{t+1})\|^{2}, \tag{66}\]  
where \(\eta>0\) is a scaling factor.  
The total reward combines intrinsic and extrinsic rewards:  
\[r_{t}=r_{t}^{i}+r_{t}^{e}, \tag{67}\]  
where \(r_{t}^{e}\) is the external reward from the environment.  
Transfer learning is applied to initialize the policy and value networks with pre-trained weights, reducing the required training time. The combined effect of these enhancements significantly improves the convergence speed and success rate of the DDPG algorithm in dynamic environments.
# A New String Edit Distance and Applications
###### Abstract  
String edit distances have been used for decades in applications ranging from spelling correction and web search suggestions to DNA analysis. Most string edit distances are variations of the Levenshtein distance and consider only single-character edits. In forensic applications polymorphic genetic markers such as short tandem repeats (STRs) are used. At these repetitive motifs the DNA copying errors consist of more than just single base differences. More often the phenomenon of \"sutter\" is observed, where the number of repeated units differs (by whole units) from the template. To adapt the Levenshtein distance to be suitable for forensic applications where DNA sequence similarity is of interest, a generalized string edit distance is defined that accommodates the addition or deletion of whole motifs in addition to single-nucleotide edits. A dynamic programming implementation is developed for computing this distance between sequences. The novelty of this algorithm is in handling the complex interactions that arise between multiple- and single-character edits. Forensic examples illustrate the purpose and use of the Restricted Forensic Levenshtein (RFL) distance measure, but applications extend to sequence alignment and string similarity in other biological areas, as well as dynamic programming algorithms more broadly.  
Keywords:string edit distance; Levenshtein distance; short tandem repeat; dynamic programming; massively parallel sequencing; next-generation sequencing; DNA identification +
Footnote †: journal:
## 2 Materials and Methods
### SH-SYSY Cell Treatment and Cell Viability  
The SH-SYSY human neuroblastoma cells used in the experiments were obtained from the Korea Cell Line Bank (Seoul, Republic of Korea). The SH-SYSY cells were cultured in MEM medium supplemented with 10% FBS and 1% penicillin/streptomycin and incubated at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) incubator. When the cells reached 70-80% confluence, they were washed with PBS (pH 7.4), treated with trypsin/ethylenediaminetetraacetic acid to detach the cells, centrifuged at 1000 rpm for 5 min to recover the cells, and then seeded and used for experiments. To investigate the cytotoxicity of ESLC in SH-SYSY cells, cell viability was analysed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2-tetrazolzolium bromide (MTT) assay. The cells were seeded at a concentration of 6 \(\times\) 10\({}^{4}\) cells per well in 96-well plates. After 24 h of adherence, they were treated with different concentrations of ESLC (5, 10, 25, 50, 100, and 250 \(\upmu\)g/mL) and incubated for 18 h. Then, 200 \(\upmu\)L of the MTT solution at 2.5 \(\upmu\)g/mL was added and incubated for 3 h at 37 \({}^{\circ}\)C in a 5% CO\({}_{2}\) incubator. After the incubation, the culture in each well was removed and 100 \(\upmu\)L of dimethyl sulfoxide (DMSO) was added. Cell viability was determined by measuring the absorbance at 570 nm using a Tecan Multifunction (Tecan) and expressed as a percentage of the control absorbance.
## 2 Materials and Methods
### Methods
#### 2.3.5 Spatial Auto-Correlation Analysis  
Spatial auto-correlation is an important index reflecting the degree of correlation between a geographic phenomenon or an attribute value in a regional unit and the same  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Year** & **Indictors** & **PC1** & **PC2** & **PC3** & **PC4** & **PC5** \\ \hline \multirow{8}{*}{2008} & FVC & 0.73481 & 0.34362 & 0.02455 & 0.57563 & \(-\)0.10015 \\  & GPP & 0.43585 & 0.20132 & \(-\)0.03644 & \(-\)0.55731 & 0.67645 \\  & LAI & 0.27947 & 0.1872 & 0.07268 & \(-\)0.59759 & \(-\)0.72419 \\  & LST & \(-\)0.37923 & 0.74486 & 0.54284 & 0.02957 & 0.07628 \\  & WET & 0.21949 & \(-\)0.50153 & 0.83553 & \(-\)0.00846 & 0.04589 \\  & Eigenvalue & 0.04673 & 0.00551 & 0.00177 & 0.00048 & 0.00013 \\  & Percent & 85.5597 & 10.0876 & 3.2419 & 0.8798 & 0.231 \\  & eigenvalue & & & & & \\  & eigenvalue & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Principal component eigenvalues.  
phenomenon or attribute value in the adjacent regional unit. In this study, the classical global spatial auto-correlation was selected to test the spatial auto-correlation of the MODEL. When the absolute value of the global Moran's I index is close to 1, it indicates stronger spatial auto-correlation. The formulas are expressed as:  
\[\text{I}=\frac{\sum_{\text{i=1,j=1}}^{\text{n}}W_{\text{ij}}(\text{x}_{\text{i} }-\overline{\text{x}})\big{(}\text{x}_{\text{j}}-\overline{\text{x}}\big{)}}{ \text{S}^{2}\sum_{\text{i=1,j=1}}^{\text{n}}W_{\text{ij}}} \tag{10}\]  
\[\text{S}^{2}=\frac{1}{\text{n}}\ \sum_{\text{i=1}}^{\text{n}}(\text{x}_{\text{i} }-\overline{\text{x}})^{2} \tag{11}\]  
where I represents the value of the global Moran's I index; S\({}^{2}\) is the variance; x\({}_{\text{i}}\) and x\({}_{\text{j}}\) represent the attribute values of grid unit i and j, respectively; n is the number of grid cells; \((\text{x}_{\text{i}}-\overline{\text{x}})\) is the deviation between the measured value and the mean value of grid cell i; \(W_{\text{ij}}\) is the normalized spatial weight matrix. The Moran's I value is between \(-1\) and \(1\). A value greater than 0 indicates positive auto-correlation, while a value less than 0 indicates negative auto-correlation.  
Local spatial auto-correlation can measure the local spatial correlation degree and spatial differentiation between each grid and the surrounding grid [57]. In this study, the local Moran's I is selected for spatial association or difference analysis of independent variables, characterized by the Moran scatter diagram and Lisa distribution diagram. It should be noted that the Lisa distribution must satisfy the Z test (\(\alpha\) = 0.05), and the calculation formula of the local Moran's I index is as follows:  
\[\text{I}_{\text{i}}=\frac{(\text{x}_{\text{i}}-\overline{\text{x}})\sum_{ \text{j=1}}^{\text{n}}W_{\text{ij}}(\text{x}_{\text{i}}-\overline{\text{x}}) }{\text{S}^{2}} \tag{12}\]  
\[\text{S}^{2}=\frac{1}{\text{n}}\ \sum_{\text{i=1}}^{\text{n}}(\text{x}_{\text{i} }-\overline{\text{x}})^{2} \tag{13}\]
# An Enhanced Contrastive Ensemble Learning Method for Anomaly Sound Detection
## 2 Materials and Methods  
### Motivation  
As neural networks continue to evolve, neural network-based deep learning methods have increasingly become the primary approach for ASD. However, in real-life industrial manufacturing environments, it is difficult to obtain accurate data labels, making it difficult for common deep learning approaches to perform. This results in the formulation of a solution based on a self-supervised approach. The self-supervised learning process enables the model to effectively capture the global feature distribution. Self-supervised learning methods spontaneously mine feature information based on auxiliary tasks through the construction of these tasks, and generalize this to general features, thereby accomplishing the modeling of the model's global feature distribution. In the task of ASD, common self-supervised learning approaches employ auxiliary classification methods or contrastive learning techniques. For example, reference [14] collects phase and amplitude features from the data, utilizing a self-supervised classification network and attention method for auxiliary classification, thereby enabling the model to acquire feature modeling of normal sample data and realize the capability of abnormal sound detection; reference [15] employs a flow-based density estimation model, integrating self-supervised classification methods with unsupervised density likelihood estimation approaches to enhance the model's capability for ASD; reference [16] employs contrastive learning method by calculating the cosine angle between positive and negative samples, enabling the model to simultaneously learn the distributions of normal and abnormal features.  
Although these techniques may prove effective in the detection of anomalies, the resulting precision may be compromised. This is because the issue of neural network bias, which is caused by the imbalance in the amount of data, is not taken into account in the feature representation. Furthermore, the problem of model collapse, which occurs in the absence of valid anomaly samples, is also overlooked. Consequently, this paper presents a novel model that addresses these issues. It is capable of gathering more comprehensive feature data, rectifying the bias issue inherent to neural networks, and performing multi-domain fusion and adaptive weighting of multi-domain information. Furthermore, it can quickly and effectively generate high-quality anomaly samples during the training process, thereby enhancing the model's overall anomaly detection capabilities. The model has been subjected to extensive experimentation with multiple datasets, and a comparative analysis of the experimental results has demonstrated its efficacy in anomaly detection and generalization.
## 4 Discussion
In addition, SFs are not 'wavelets' that are applied to the waveform from outside as wavelets do, they come from inside; moreover, they are parts of the waveform they intend to analyze. Speaking of wavelet transform, spectral descriptions of seismograms are widely used in various areas related to seismic effects and their consequences. The prominent place of spectrum in seismic databases confirms its significance and practical applicability. However, the Fourier transform (FT), with its numerical derivatives (STFT, DFT, and FFT), still dominates the spectral world, while the WT gradually builds its place in it.
# Technology Acceptance in Healthcare: A Systematic Review
## 6 Conclusions
### Theoretical Contributions  
As per the conducted classification analysis, the study provided multiple contributions to technology acceptance models and theories, especially in healthcare. This systematic review is believed to add a significant contribution to the existing literature for several reasons. First, it analyzed all the technology acceptance models instead of focusing on one model or theory (e.g., TAM). Second, this study included only the empirically evaluated acceptance models, their extensions, and integrations. Third, the study reviewed different information technologies instead of considering only one technology (e.g., electronic medical records). Fourth, studies with different settings and types of users were included in the review. Other healthcare professionals such as nurses, pharmacists, and clinical technicians are using the information technologies and playing a critical role in the success of those technologies. Fifth, the considered studies in the review were published in the recent decade (2010-2019), which provides a fresh overview of the literature.
## 2 Results  
### Optimization of the Extraction Method  
There are many factors that can have a direct impact during the HS-SPME process. These include the sample volume, extraction time, extraction temperature, and NaCl amount. From Figure 1, the different fibers had a similar adsorption trend under different extraction conditions. The difference is mainly concentrated on adsorption capacity; the highest to lowest adsorption capacity was from divinylbenzene (DVB)/Carboxen (CAR)/polydimethylsiloxane (PDMS), CAR/PDMS, polyacrylate (PA), then PDMS.
## 3 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\omega_{i}\) & & \multicolumn{8}{c}{**Effect of Strain Energy on the Natural Frequency [Hz]**} \\ \cline{3-10} \(\omega_{i}\) & & \multicolumn{2}{c}{_L/Ih = 26_} & \multicolumn{2}{c}{_L/Ih = 28_} & \multicolumn{2}{c}{_L/Ih = 30_} & \multicolumn{2}{c}{_L/Ih = 40_} & \multicolumn{2}{c}{_L/Ih = 50_} & \multicolumn{2}{c}{_L/Ih = 60_} & \multicolumn{2}{c}{_L/Ih = 70_} \\ \hline
2 & TMM & 3046.1 & 2658.9 & 2342.9 & 1380.6 & 911.99 & 647.23 & 482.81 \\  & C\({}_{f,a}\) & 0.1023 & 0.1180 & 0.1338 & 0.2087 & 0.2701 & 0.3172 & 0.3526 \\  & C\({}_{f,b}\) & 0.7365 & 0.7189 & 0.7028 & 0.6412 & 0.6021 & 0.5766 & 0.5592 \\  & C\({}_{c}\) & 0.1612 & 0.1631 & 0.1634 & 0.1501 & 0.1278 & 0.1063 & 0.0881 \\ \hline  & Equation (36) & 3046.1 & 2658.9 & 2343.0 & 1380.6 & 912.01 & 647.24 & 482.82 \\
3 & TMM & 5720.2 & 4986.6 & 4390.3 & 2588.3 & 1717.6 & 1225.6 & 918.96 \\  & C\({}_{f,a}\) & 0.0577 & 0.0681 & 0.0791 & 0.1381 & 0.1953 & 0.2452 & 0.2866 \\  & C\({}_{f,b}\) & 0.8014 & 0.7841 & 0.7678 & 0.6997 & 0.6513 & 0.6172 & 0.5928 \\  & C\({}_{c}\) & 0.1410 & 0.1477 & 0.1531 & 0.1623 & 0.1534 & 0.1376 & 0.1206 \\ \hline  & Equation (36) & 5720.3 & 4986.7 & 4390.4 & 2588.3 & 1717.6 & 1225.6 & 918.98 \\
4 & TMM & 9180.2 & 7989.3 & 7023.9 & 4126.1 & 2739.8 & 1960.3 & 1474.8 \\  & C\({}_{f,a}\) & 0.0334 & 0.0401 & 0.0475 & 0.0909 & 0.1390 & 0.1857 & 0.2278 \\  & C\({}_{f,b}\) & 0.8496 & 0.8341 & 0.8190 & 0.7513 & 0.6984 & 0.6584 & 0.6282 \\  & C\({}_{c}\) & 0.1170 & 0.1258 & 0.1335 & 0.1578 & 0.1625 & 0.1559 & 0.1440 \\ \hline  & Equation (36) & 9180.4 & 7989.5 & 7024.1 & 4126.2 & 2739.9 & 1960.3 & 1474.8 \\
5 & TMM & 13,442.7 & 11,679.7 & 10,253.1 & 5992.0 & 3971.8 & 2843.1 & 2142.5 \\  & C\({}_{f,a}\) & 0.0211 & 0.0253 & 0.0301 & 0.0608 & 0.0989 & 0.1394 & 0.1788 \\  & C\({}_{f,b}\) & 0.8840 & 0.8708 & 0.8575 & 0.7946 & 0.7410 & 0.6977 & 0.6635 \\  & C\({}_{c}\) & 0.0948 & 0.1039 & 0.1124 & 0.1446 & 0.1601 & 0.1629 & 0.1577 \\  & Equation (36) & 13,443.0 & 11,680.0 & 10,253.3 & 5992.1 & 3971.9 & 2843.1 & 2142.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Effect of strain energies on the natural frequencies of three-layered beams with the P-P end condition depending on the variation in _L/h_.
## 4 Results and Discussion
### Numerical Results--SBD2D Energy-Efficient Algorithm  
The graph in Figure 8 compares the active current and standby current for different ratios. The results from the simulation show that an increase in the standby time results in the wastage of a lot of energy, as shown by the blue line in our graph. If the active-to-standby ratio is set to 1:1000, the current consumption reaches \(10^{4}\) uA in twenty seconds, if the active-to-standby ratio is set to 1:100, the maximum current consumption in the same time (20 s) is \(10^{3}\) uA, and if the active to standby ratio is set to 1:10, the current consumption reaches \(10^{2}\) uA in twenty seconds. This shows that an increase in the standby time leads to a high rate of current consumption. The ratio of 1:10 is selected because it seems to be the best, as, by decreasing the standby time and increasing the active time, the microcontroller will need to wake up more often, which consumes a lot of power. To optimize the power, we set the SBD2D to operate under the ratio of 1:10, taking a sample period of 60 s, according to Figure 3, as every sensor in smart beekeeping takes 2 s to active and 13 s in standby mode. The active current consumption remains the same at any level of transmission from the active to standby ratio.
## 4 Results & Discussion
### Influence of Onset and Offset of Vowels on Classification Results  
To test whether or not the onset and offset of the vowel contains important classification information, we repeated the baseline experiments with unclipped and clipped versions of Neurovoz and ItalianPVS, respectively, and obtained the results included in Figure 11. We found that in both corpora, removing the beginning and end of recordings mostly decreased accuracy in both AVCA-based and DARTH-based trials. In Neurovoz, accuracy was reduced much more using AVCA-based coefficients. Additionally, the total decrease in accuracy for clipped audios is larger for Neurovoz than ItalianPVS. The discrepancy between the Neurovoz DARTH-based and AVCA-based results may be explained by the hypothesis that the onset and offset contains relevant articulatory information. The AVCA toolkit calculates two separate perceptual coefficient families: MFCCs and PLPs; while the DARTH-VAT toolkit only calculates the former. Assuming that removing onset and offset also removes some relevant articulatory information, it makes sense that AVCA-based trials would see a larger decrease in accuracy than their DARTH-VAT counterparts. As for ItalianPVS, not much difference between the coefficient type results suggests that although articulatory information has a marked impact on the results, it may not have as much of an impact as other factors, such as dysphonia of participants' voices as discussed in Section 4.7.
## 4 Numerical Results and Discussion
### Evolution of Transient Dynamic Response of Blade off
#### 4.2.2 Transient Orbit Characteristics  
In addition to the significant impact of blade off on the vibration displacement, it also has a significant influence on the trajectory and frequency components in the response. By comparing the orbits at different times, as shown in Figure 13, it can be seen that at the moment of the blade loss, the orbit is very turbulent, with a large initial vibration amplitude, but gradually becomes a regular circle, with the vibration waveform of a simple sine wave. In addition, the motion orbits at different positions are very similar, as shown in Figure 13a-c, while the maximum response displacement and steady-state amplitude of the bearings at both ends are greater than those at the center of gravity. The reason for this is due to the dynamic characteristics of the gas generator rotor operating at 50,000 r/min. The excitation at node 14 cannot effectively excite the third-order mode (see Figure 8c), and node 11 in the excited second-order mode is much closer to the node point (see Figure 8b), so the vibration at the center of gravity is smaller than those at other positions.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### System Budgets
#### 3.6.2 Power Budget  
Regarding the power budget, the S/C is equipped with 40 solar cells in total; 16 body mounted and 24 deployable solar cells. According to GOMspace, each solar cell can generate up to 1.2 W in LEO [17; 18]. However, for the following analysis, it is assumed that a solar cell can generate 1 W when it is exposed vertically to sunlight. That way, when the S/C is in Sun-Tracking Mode and the solar arrays are fully deployed, it is assumed that 40 W can be generated.  
As seen in Figure 11, a MATLAB Day in the life simulation has been conducted to calculate the batteries' state of charge (SoC) based on the following assumptions. The EOL (End-of-Life) capacity of a single battery is 50 Wh, and since there are two batteries on board, the S/C has a total EOL battery capacity of 100 Wh. The S/C operates in the Sun-Tracking Mode, which can generate 40 W of power while exposed to sunlight. Over a 24 h period, the S/C will enter the PNT Service Mode an average of seven times, corresponding to the typical number of daily contacts with the service area. Those are separated by approximately 3 h, which is the ART to the service area. Each contact, based on STK analysis, lasts an average of 10.7 min. Additionally, the S/C will engage in the S-Band Link Mode twice within a 24 h period for 5 min each time. After completing either an S-Band Link Mode or PNT Service Mode, the S/C will return to the Sun-Tracking Mode. It is assumed that no power is generated (0 W) when the S/C is in a different mode than the Sun-Tracking Mode. Moreover, the PNT Solution Generator shall maintain a duty factor of 100% during nominal operations. The CSAC needs to be monitored and steered a couple of hours prior to operating with the required performance, and since the ART of 3 h is a shorter timeframe, it needs to remain active at all times. The power consumption breakdown for the modes mentioned above is shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component/Function** & **Power** & **Sun-Tracking** & **S-Band Link** & **PNT Service** \\  & **w/Margin (w)** & **Mode** & **Mode** & **Mode** \\ \hline Battery Heaters & 1.1 & 100\% & 100\% & 100\% \\ EPS & 0.6 & 100\% & 100\% & 100\% \\ GNSS kit & 1.5 & 100\% & 100\% & 100\% \\ CDHS & 0.3 & 100\% & 100\% & 100\% \\ ADCS-3-axis control & 2.8 & 100\% & 100\% & 100\% \\ UHF Rx & 0.5 & 90\% & 0\% & 0\% \\ UHF Tx & 3.2 & 10\% & 0\% & 0\% \\ S-Band Tx/Rx & 17.9 & 0\% & 100\% & 0\% \\ PNT Solution Generator & 3.0 & 100\% & 100\% & 100\% \\ PNT Signal Transmission & 37.5 & 0\% & 0\% & 100\% \\ \hline Mode Consumption w/Margin (W) & & 10.1 & 27.1 & 46.8 \\ Mode Consumption w/system & & 12.1 & 32.6 & 56.1 \\ margin (W) & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Nominal operation modes power breakdown.