esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Chaotic Stream Cipher Based on Compound Coupled Map Lattices
### The Improved Compound Coupled Chaotic Map Lattice Combining Different Chaotic Maps
#### 3.3.2 The Improved Compound CML Combining Logistic Map and Tent Map in an Alternate Stage  
In this subsection, we present the scheme of the improved seven stage CML, combining the logistic map and tent map in an alternate stage. Figure 14 shows the structure of the improved seven stage CML, where Key is the key, and the final throughput is output. The input key, initial values and the parameter of the chaotic map are defined as the same as Equations (4)-(6). The modified function \(Z(x_{t+1}^{n})\) is defined as Equation (12).  
\[Z(x_{t+1}^{n})=\left\{(1-\varepsilon)f(x_{t}^{n})+\frac{\varepsilon}{2}[q(x_{t }^{n+1})+q(x_{t}^{n-1})]\right\}*i\bmod 1,1\leq i\leq Q \tag{12}\]  
where \(n=1,2,\,\ldots,N,N=4\). The \(f(x)\) is the chaotic logistic map, \(q(x)\) is the chaotic tent map, \(i\) is a counter and \(Q=512\) is a fixed integer.  
Figure 15 is the simulation results of the improved compound CML, combining the logistic map and tent map in an alternate stage, where \(p(x_{t})\) is the probability density, \(t\) is the time index and \(x_{t}\) is the system output. From this figure, we can find that the probability density of the improved CML is almost uniformly distributed and reduces the weakness of the original CML.
# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 5 Experiments and Results
### Experimental Setup  
In order to comprehensively evaluate the performance ability of traditional LSTM and AB-LSTM on spoof detection, experiments were conducted in both clean and noisy environments, respectively. Firstly, the CQCC features of each utterance were extracted. Secondly, the split/padding method was used to process the utterance and to obtain segments with the same length. Traditional LSTM and AB-LSTM were then utilized to handle the frame-level features and to decide on genuine or spoof status through bagging methods. Finally, noises with different levels of signal-to-noise ratios (SNRs) were added to the original dataset, and the results on replay detection in noisy environments were obtained by repeating the above steps.
## 5 Results II: IFP family  
In this section, we show results of the application of the method described above to identify putative SDPs for the multimerization propensity of a set of intrinsically fluorescent proteins. More space is reserved to the description of the runs and of the results performed in this case, since, as described in the introduction, they represent an original set of calculations that are interesting not only as a test case for the method, but also for the biological problem that is addressed. They can also be looked at as a reference protocol for the application of SDPhound to thoroughly investigate SDP's in a family of proteins. In preparation for the calculations, 121 IFP sequences were obtained from the Entrez NCBI public database and classified according to their aggregation state. Out of these, a non-redundant subset of crystallized proteins was extracted and used in a combined structure-based sequence-based alignment procedure as described in 5.1. Section 5.2. presents the results of the standard and pigeonhole analysis for the study of the monomerization in the family. Mutagenesis experiments [27] succeeded in identifying 33 mutations among those that induce monomerization while preserving the fluorescence of the protein. These positions provide a new and quite powerful benchmark, based on experiments, rather than on spatial localization of the residues as for the majority of positions for the MIP and LacI families. We also use this application to show some results obtained with the optimal pigeonholing procedure and the use of the probability correlating the positions of two amino acids. As a final application of SDPhound, in section 5.3. we also consider the problem of identifying positions responsible for the dimeric or tetrameric form of intrinsically fluorescent proteins. In this case, less experimental information is available so we rely on an auxiliary computational tool, the MMPBSA method [10] to validate a particularly interesting mutation suggested by our method. In the SI an Excel file containing the detailed information of which amino acids occurred in each class, for each best ranking position, is provided.  
\begin{table}
\begin{tabular}{l r r r r r r r r r r} \hline Pos. & **15** & 160 & **122** & 144 & 55 & 98 & 50 & 107 & 56 & 16 \\ \cline{2-11} hyd & 6 & 15 & 5 & 9 & 14 & \(-\) & 7 & 3 & 17 & 4 \\ crg & 2 & 8 & 9 & \(-\) & 4 & 1 & 13 & 17 & 29 & 14 \\ size & 5 & 9 & 7 & 25 & 16 & 1 & 12 & \(-\) & 4 & 8 \\ \hline Pos. & 146 & 249 & 126 & 85 & 114 & 121 & 123 & 221 & 147 & 323 \\ \cline{2-11} hyd & 18 & \(-\) & 1 & 10 & \(-\) & \(-\) & \(-\) & \(-\) & 30 \\ crg & 11 & 18 & 6 & \(-\) & 15 & \(-\) & \(-\) & \(-\) & 7 & \(-\) \\ size & 14 & \(-\) & \(-\) & 3 & \(-\) & 2 & 28 & 30 & 17 & 21 \\ \hline \end{tabular}
\end{table}
Table 4: Substitution class based runs over SDPs relevant within the LacI family. Ranks and colors as in Table 2.  
Figure 1: DsRed tetramer (1 GGX). Residues relevant for the multimeric properties are shown explicitly, and colored according to the score as in Table 5. The tetramerization is here schematized as _“dimerization of dimers”_ ( dimer in cyan).
# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
###### Abstract  
Despite advances in autonomous driving technology, traffic accidents remain a problem to be solved in the transportation system. More than half of traffic accidents are due to unsafe driving. In addition, aggressive driving behavior can lead to traffic jams. To reduce this, we propose a 4-layer CNN-2 stack LSTM-based driving behavior classification and V2X sharing system that uses time-series data as an input to reflect temporal changes. The proposed system classifies driving behavior into defensive, normal, and aggressive driving using only the 3-axis acceleration of the driving vehicle and shares it with the surroundings. We collect a training dataset by composing a road that reflects various environmental factors using a driving simulator that mimics a real vehicle and IPG CarMaker, an autonomous driving simulation. Additionally, driving behavior datasets are collected by driving real-world DGIST campus to augment training data. The proposed network has the best performance compared to the state-of-the-art CNN, LSTM, and CNN-LSTM. Finally, our system shares the driving behavior classified by 4-layer CNN-2 stacked LSTM with surrounding vehicles through V2X communication. The proposed system has been validated in ACC simulations and real environments. For real world testing, we configure NVIDIA Jetson TX2, IMU, GPS, and V2X devices as one module. We performed the experiments of the driving behavior classification and V2X transmission and reception in a real world by using the prototype module. As a result of the experiment, the driving behavior classification performance was confirmed to be ~98% or more in the simulation test and 97% or more in the real-world test. In addition, the V2X communication delay through the prototype was confirmed to be an average of 4.8 ms. The proposed system can contribute to improving the safety of the transportation system by sharing the driving behaviors of each vehicle.  
CNN-LSTM; driving behavior classification; driving safety +
Footnote †: journal: Applied Sciences
## 4 Main Issues and Challenges
### Open Issues
#### 4.2.3 Lack of Decentralized Self-Adaptive Control  
Self-adaptation should be considered both at the agents executing the mission plan and at the global mission manager, both independently and coordinately. In general, self-adaption is only considered either at the vehicles, for adapting themselves while executing a mission to still be able to achieve the goal, or at the mission management, for re-planning when the outcomes of a task in a mission deviates enough to require a new plan. However, it could be interesting to explore the managing-managed approach from MAPE-K allowing the mission manager not only to react to events notified by the vehicles, but also to update the mission plan if there is new information from other sources that suggest that the update will improve the mission outcome.
## 5 Conclusions  
In this study, the sliding mode control technique was effectively applied to accomplish two main objectives in the operation of the boost inverter: (a) provide a high quality output voltage and (b) minimize the required voltage gain in the boost cells to alleviate semiconductor voltage stress. Different from all previously published works related to the control of this converter, the voltage of the capacitors are enforced to have additional optimal values of double and fourth harmonic terms. The objectives are accomplished by means of two independent multiloop controllers involving an inner loop of sliding mode control implemented using hysteresis comparators and PI compensators ensuring the tracking of harmonic enriched references. Additionally, having a degree of freedom to modify the average value of the capacitor voltages, an adaptive feed-forward loop was integrated helping to further reduce the required gain in the converter cells. A complete study of the stationary behavior of the converter was developed to obtain a simple model of its dynamics by using the harmonic balance technique. A PI controller with saturation of the output derivative allowed to ensure the sliding regime of the inner loop which is also an innovative feature of the proposed control scheme. The obtained THD is lower than 0.8% and the regulation of the RMS value is lower than 0.2% in the entire range of operation of the converter for both standards used as case study. The alleviation of the semiconductor voltage stress is very important also for both analyzed standards.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Control Method**} & \multicolumn{3}{c}{**Standard 1 (120 V @ 60 Hz)**} & \multicolumn{3}{c}{**Standard 1 (220 V @ 50 Hz)**} \\ \cline{2-7}  & **Min (V)** & **Max (V)** & **Avg (V)** & **Min (V)** & **Max (V)** & **Avg (V)** \\ \hline SS-SMC [4] & 136 & 418 & 254 & 120 & 533 & 290 \\ DS-SMC (Sine references) & 97 & 331 & 209 & 97 & 472 & 285 \\ DS-SMC (Modified references) & 97 & 331 & 182 & 97 & 472 & 235 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of control methods regarding semiconductor voltage stress.  
Figure 13: Simulated semiconductor voltages comparing SS-SMC and DS-SMC for simulation test 2.  
From the results of the present work, a new control scheme is being developed using a multiple input multiple output perspective of the problem, this allowing a better action on the coupling dynamics of the boost cells. Prospective work involves experimental validation using a laboratory prototype.  
All authors contributed equally to all the stages of the research process and the preparation of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was developed with the partial support of the Ministerio de Ciencia, Tecnologia e Innovacion de Colombia (MinCiencias) under contract 018-2016 and the HisPaliS project supported by the Agence Nationale de la Recherce de France (ANR).  
The authors declare no conflicts of interest.
## 6 Conclusions  
In this paper, we explored the system average BER and the outage probability for the Malaga turbulence channel with pointing errors and beam spreading in MIMO-UWOC. We also analyzed the relationships between the long-term beam spreading and the turbulence strength and between the diversity gain and the pointing errors. In the numerical simulation, we employed four Argo nodes' observed ocean data, including the pressure, temperature, and salinity of seawater. We illustrated the effects of the pointing errors and the ocean turbulence on the average BER, considering pointing errors with long-term beam spreading, the average received SNR, and the received aperture diameter. In addition, we analyzed their effects on the system outage performance, taking into account the received aperture diameter. The numerical results showed that the system average BER and the outage probability increased with increasing turbulence strength and pointing errors. Moreover, in the mQAM-MIMO system with MRC, the pointing errors had a larger effect on the average BER than the turbulence strength, which had a larger effect on the outage performance than the pointing errors. Therefore, increasing the system receiving aperture diameter to mitigate long-term beam spreading and ocean turbulence can better improve UWOC reliability and efficiency than adjusting the QAM order.  
Conceptualization, J.W. and H.Y.; formal analysis, J.W.; methodology, J.W. and H.Y.; software, J.W., X.J. and Y.L.; validation, [PERSON], [PERSON] and [PERSON]; writing\(-\)original draft, [PERSON]; writing\(-\)review and editing, [PERSON], X.J. and Y.L.; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON]; funding acquisition, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the National Natural Science Foundation of China (NSFC) under grants 61871418 and 61801079 and by the Science and Technology on Underwater Information and Control Laboratory under grant 6142218200408, China.  
Not applicable.  
The data presented in this paper are available upon request from the corresponding author.  
The authors would like to thank the anonymous reviewers for their careful reading and valuable comments.  
The authors declare no conflict of interest.
# Theoretical Study of Large Uncoupling Coefficient Loading for Surface Blasting
## 3 Critical Uncoupling Coefficient of Large Diameter Drilled Holes
### Impact Pressure Generated by Gas Expansion Based on Large Uncoupling Coefficient  
The radial axial uncoupling coefficient is greater than the critical uncoupling coefficient under smooth blasting. Thus, assumption 2 presented in Section 2.1 should be modified. When the critical gas volume is less than the volume of the blasting hole, the gas will continue to expand, obeying the gas state equation \(PV^{K}\) = constant (\(K\) = 1.2-1.3). The static pressure when the product of the blast fills the blast hole should be  
\[P_{b}=\left(\frac{p_{c}}{p_{K}}\right)^{\frac{K}{n}}\left(\frac{V_{c}}{V_{b}} \right)^{K}P_{K} \tag{28}\]  
For a radial uncoupled continuous charge, substituting \(V_{c}\) = \(\pi d_{c}2L_{b}/4\), \(V_{b}\) = \(\pi d_{b}2L_{b}/4\), \(K_{d}\) = \(d_{b}/d_{c}\) in Equation (28) produces  
\[P_{b2}=P_{K}\left(\frac{P_{c}}{P_{K}}\right)^{\frac{4}{3}}\left(\frac{1}{K_{d} }\right)^{\frac{8}{3}} \tag{29}\]  
Equation (16) or Equation (20), for the equivalent charge diameter, radial uncoupling coefficient \(K_{d}\), and axial uncoupling coefficient \(K_{1}(K_{1}=(l_{a}+l_{c})/l_{c})\) presented in Section 3.2,  
Figure 3: Relationship between the pressure increase factor \(N\) and air shock wave \(P_{\lambda}\).  
are substituted into Equation (28), and the quasi-static pressure acting on the blast hole wall for the axial and radial uncoupling charge is obtained as  
\[P_{b3}=P_{K}\bigg{(}\frac{P_{c}}{P_{K}}\bigg{)}^{\frac{3}{6}}\bigg{(}\frac{1}{K_{ d}}\bigg{)}^{\frac{8}{3}}\bigg{(}\frac{1}{K_{1}}\bigg{)}^{\frac{4}{3}} \tag{30}\]  
Depending on the blast gas pressure for the static condition, the stress field formed in the rock around the hole can be subjected to uniform internal pressure of the thick-walled cylinder theory solution, with radial and tangential stress of  
\[\sigma_{r(\theta)}=\mp p_{b}\Big{(}\frac{r_{b}}{r}\Big{)}^{\alpha} \tag{31}\]  
where \(\sigma_{r(\theta)}\) is the radial and tangential stress at a point in the rock; \(r_{b}\) is the radius of the blast hole; \(r\) is the radial distance from the blast point. The equation shows that the magnitude of radial stress is the same as the tangential stress; however, their directions are opposite. The absolute value of pressure generated in the blast hole wall is \(p_{b}\).
## 3 Results
### Mineral Contents
#### 3.3.3 Heavy/Toxic Metals  
One of the main concerns about the consumption of wild-harvested insect material is the possible presence of heavy metals that have bioaccumulated in the insect material from the environment over time. Heavy metals such as arsenic (0.17 mg/kg DW), vanadium (0.01 mg/kg DW), cadmium (0.04 mg/kg DW), and lead (0.56 mg/kg DW) were found in SGP (Table 2). Overall, the arsenic content of SGP was lower than the maximum allowable concentration of 2 mg/kg FW in foodstuffs [63]. According to the EFSA, the maximum allowed level of lead in plant foods such as cereals, legumes, and pulses is 0.20 mg/kg fresh weight, FW, and the maximum permitted level in meat is 0.10 mg/kg FW [64]. The maximum allowed level for lead content in insect materials destined for use in feed is 10 mg/kg DW [65], showing that SGP was within the maximum set limit. The lead content in some edible insects found in Thailand, such as the Bombay locust (0.009 mg/kg FW), scarab beetles (0.11 mg/kg FW), and mulberry silkworm (0.004 mg/kg FW) has been reported [50]. The lack of moisture or dry matter data for these insects makes it challenging to compare their lead contents with that found in SGP.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{5}{c}{**Macrominerals (mg/kg DW)**} \\ \hline  & Sodium & Magnesium & Phosphorus & Potassium & Calcium \\ \hline SGP & \(772\pm 12.5\) & \(1657\pm 46.1\) & \(2950\pm 10.0\) & \(8053\pm 290.2\) & \(477\pm 24.4\) \\ \hline \({}^{1}\)DORM-4 & \(14052\pm 1006.0\) & \(897\pm 76.0\) & \(7113\pm 497.0\) & \(13352\pm 962.0\) & \(2391\pm 181.0\) \\ \hline Recovery (\%) & 100 & 99 & 89 & 86 & 101 \\ \hline \({}^{2}\)Limit of detection & 4 & 2 & 10 & 4 & 8 \\ \hline \({}^{3}\)Limit of quantification & 12 & 6 & 30 & 12 & 24 \\ \hline \multicolumn{5}{c}{Microminerals (mg/kg DW)} \\ \hline  & Chromium & Copper & Iron & Zinc & Selenium & Manganese \\ \hline SGP & \(0.25\pm 0.03\) & \(8.3\pm 0.32\) & \(22\pm 0.97\) & \(83\pm 0.55\) & \(0.03\pm 0.003\) & \(8.8\pm 0.27\) \\ \hline \({}^{1}\)DORM-4 & \(1.75\pm 0.20\) & \(15\pm 0.90\) & \(333\pm 0.90\) & \(51\pm 4.80\) & \(3.2\pm 0.500\) & \(3.04\pm 0.20\) \\ \hline Recovery (\%) & 94 & 97 & 97 & 98 & 93 & 96 \\ \hline \({}^{2}\)Limit of detection & 0.02 & 0.06 & 0.8 & 2 & 0.02 & 0.02 \\ \hline \multicolumn{5}{c}{Heavy metals (mg/kg DW)} \\ \hline  & Arsenic & Vanadium & Lead & Caladmium & \\ \hline SGP & \(0.17\pm 0.003\) & \(0.01\pm 0.003\) & \(0.56\pm 0.013\) & \(0.04\pm 0.01\) & \\ \hline \({}^{1}\)DORM-4 & \(6.4\pm 0.330\) & \(1.52\pm 0.090\) & \(0.34\pm 0.040\) & \(0.30\pm 0.02\) & \\ \hline Recovery (\%) & 93 & 97 & 85 & 100 & \\ \hline \({}^{2}\)Limit of detection & 0.010 & 0.006 & - & - & \\ \hline \({}^{3}\)Limit of quantification & 0.06 & 0.02 & 0.03 & 0.03 & \\ \hline \end{tabular}
\end{table}
Table 2: Mineral content of SGP of macrominerals, micro/trace minerals, and heavy metals expressed in mg/kg DW.
## 4 Distinctions Between Failure Mechanisms of Dried and Saturated Rocks  
### Macroscopic Failure Mechanism  
As shown in Figure 2, single slope shear failure occurs for dried specimens. While the failure modes of saturated specimens are not unique, unlike dried rocks. Both shear failure and tensile failure (parallel to the direction of stress) were both observed for saturated rocks.
## 3 Results  
Tables 1 and 2 show the intercepts and standard errors of match movement profiles (i.e., TD and TD > 21 km\(\cdot\)h\({}^{-1}\)) with and without ball possession, considering the leagues and the five levels of opponent quality. Regarding the TD, greater values were obtained during MB5 in L1 (109,534 m), showing significant differences with respect to MB1 and MB2 (_p_ < 0.05). Conversely, the TD was greater during MB1 in L2 (108,538 m), showing significant differences to MB2, MB3, and MB4 (_p_ < 0.05). Concerning the TD with and without ball possession, the TDWP was greater during MB1 in L1 (41,580 m), showing significant differences with respect to MB2, MB3, MB4, and MB5 (_p_ < 0.05). Similarly, the TDWP was greater during MB1 in L2 (37,692 m). In contrast, the TDWOP was greater during MB5 in L1 (47,681 m), showing significant differences with respect to MB1, MB2, MB3, and MB4 (_p_ < 0.05). Meanwhile, in L2, the TDWOP was greater during MB1 (40,990 m), showing significant differences with respect to MB2. Regarding the TD > 21 km\(\cdot\)h\({}^{-1}\), greater values were obtained during MB2 and MB4 in L1 (5956 m), showing significant differences with respect to MB3 (_p_ < 0.05). Conversely, the TD > 21 km\(\cdot\)h\({}^{-1}\) was higher during MB1 in L2 (5622 m), showing significant differences with respect to MB2, MB3, MB4, and MB5 (_p_ < 0.05). Concerning the TD > 21 km\(\cdot\)h\({}^{-1}\) with and without ball possession, the TDWP > 21 km\(\cdot\)h\({}^{-1}\) was greater during MB1 in L1 (2882 m), showing significant differences with respect to MB2, MB3, MB4, and MB5 \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**L1**} & \multicolumn{4}{c}{**Between-Groups Differences**} \\ \hline  & **MB1** & **MB2** & **MB3** & **MB4** & **MB5** & **MB1** & **MB2** & **MB3** & **MB4** & **MB5** \\ \hline TD (m) & 108,807 & 109,020 & 108,858 & 109,348 & 109,534 & d,e & e & d,e & a,b & a,b \\ TDWP (m) & 41,580 & 40,163 & 39,061 & 38,486 & 37,057 & b,c,d,e & a,c,d,e & a,b,e & a,b,e & a,b,c,d \\ TDWOP (m) & 38,759 & 40,755 & 42,271 & 44,418 & 47,681 & b,c,d,e & a,c,d,e & a,b,d,e & a,b,c,e & a,b,c,d \\ TD < 21 km\(\cdot\)h\({}^{-1}\) (m) & 5918 & 5956 & 5861 & 5956 & 5914 & c & b,d & c & \\ TDWP < 21 km\(\cdot\)h\({}^{-1}\) (m) & 2882 & 2790 & 2625 & 2578 & 2433 & b,c,d,e & a,c,d,e & a,b,e & a,b,e & a,b,c,d \\ TDWOP < 21 km\(\cdot\)h\({}^{-1}\) (m) & 2901 & 3035 & 3105 & 3252 & 3360 & b,c,d,e & a,c,d,e & a,b,d,e & a,b,c,e & a,b,c,d \\ \hline \hline \end{tabular} Note.: L1 = LaLiga Santander; MB1 = Match balance 1; MB2 = Match balance 2; MB3 = Match balance 3; MB4 = Match balance 4; MB5 = Match balance 5; TD = total distance; TDWP = total distance with ball possession; TDWOP = total distance without ball possession; a = significant differences compared to Group 1; b = significant differences compared to Group 2; c = significant differences compared to Group 2; c = significant differences compared to Group 3; d = significant differences compared to Group 4; d = significant differences compared to Group 5.  
\end{table} 
Table 1: Movement profiles based on quality of the opponent in LaLiga Santander.