esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks
## 4 Simulation Results
Furthermore, according to increase of the number of relays, the BER performance is improved. Figure 8 shows the throughput of conventional and proposed scheme in multi-hop environment. The throughput performance of BHM scheme and the proposed relay selection scheme is the same. In addition, according to the increase of the number of relays, the higher throughput performance can be obtained. Furthermore, as the number of hops increases, the throughput performance converges to 1. Therefore, the proposed scheme has same throughput performance compared to the conventional BHM scheme. Figure 6: Throughput of conventional scheme and proposed scheme (two-hop). Figure 7: BER Performance of BHM and RL-based scheme (multi-hops). Figure 9 indicates the number of selected hops of the conventional and proposed scheme. The training iteration used in this simulation is 500 times. When learning is performed 500 times in an environment that satisfies the target BER, this graph shows the number of hops used in conventional and proposed scheme. When the target BER is satisfied, the conventional BHM scheme has a large number of hops compared to the proposed scheme. The proposed scheme selects fewer relays than the conventional scheme. The RL-based scheme has 3 more hops than conventional scheme and the conventional scheme has 4 and 5 more hops than the RL-based scheme. Therefore, the proposed scheme has the same performance but has fewer number of relays than the conventional scheme. Figure 10 shows the average number of selected hops of the conventional and proposed scheme. In 3 hops, average selected number of relays in proposed scheme is high compared to the conventional BHM scheme. However in 4 and 5 hops, average selected number of relays in proposed scheme is fewer than the conventional scheme. This indicates that, when the target BER is satisfied, the benefit of the latency or waste of resources can be achieved through fewer selected relays. Figure 8: Throughput of conventional scheme and proposed scheme (multi-hop). Figure 9: The number of selected hops. Figure 11 shows the number of steps in proposed RL-based scheme according to the learning episode when the target BER is satisfied. In this figure, a step represents how many relays are passed until it reaches the destination node. In the early stage of the learning process, it is shown that the number of steps is very high. However, as the learning progresses, the number of hops is decreased. Finally, after 400,000 episodes, the number of steps converges to 3. This shows that three relays have the best performance when the target BER is satisfied.
# The Design and Investigation of Hybrid a Microfluidic Micromizer
## 4 Results and Discussion
### Fluid Mixing
#### 4.2.2 Effect of Micropillar Gaps  
In this section, the effect of micropillar gaps on the mixing performance of microfluidic micromisers is investigated. Figure 14 represents the graphical representation of the variation of the mixing index by varying the micropillar gaps. It is evident that the mixing index increased linearly by decreasing the micropillar gaps. The higher mixing index can be observed for a 0.150 mm gap, while the lower mixing index can be observed for a 0.250 mm gap. The reason behind this larger value is due to the larger contact area and more boundaries to hit in the case of smaller micropillar gaps. On the other hand, among all micropillar shapes, the higher value of the mixing index can be noted in the case of blade-shaped micropillars. The reason behind this larger value in such a case is due to a larger contact area and intense microvortices near the micropillar walls compared to other shapes. Furthermore, the distribution of species concentrations with different micropillar gaps is also shown in Figure 15. The lower diffusion of species transports, as well as mixing, can be clearly observed in the case of a 0.250 mm micropillar gap. Similarly, a higher mixing and diffusion rate can be observed in the case of a 0.150 mm micropillar gap. The main reason behind this larger mixing index with small micropillar gaps is due to the larger contact area, resulting in generating more microvortices.  
In a comparative examination of the performance of different micropillar shapes and micropillar gaps, it was investigated that the presence of micropillars in the microchamber of the microfluidics system is significant in improving the mixing performance of two different heterogeneous fluids. In addition, blade-shaped micropillars with a 0.150 mm gap deliver the best outcomes and perform better as compared to the other cases. As discussed earlier, this is due to employing a hybrid actuation approach and a higher diffusion rate of species concentrations. Finally, it is concluded that the proposed hybrid microfluidic micromiser can be used for high-quality mixing and reaction kinetics.  
Figure 13: Distribution of species concentrations: (**a**) no micropillars, (**b**) circular micropillars, (**c**) hexagonal-shaped micropillars, (**d**) blade-shaped micropillars.
# Thermal Energy Storage Performance of Tetrabutylammonium Acrylate Hydrate as Phase Change Materials
## 1 Introduction
The thermodynamic properties of TBAB hydrate have been reported in the previous studies. TBAB hydrate has an equilibrium temperature at an atmospheric pressure of 285.9 K [32], a dissociation heat of 193 kJ/ kg [33], and the thermal conductivity of 0.35 W m\({}^{-1}\) K\({}^{-1}\)[34]. As reported by ASHRAE Technical Committee [18], the appropriate temperature range of DC cooling is 288 K to 305 K. This is why TBAB hydrate is not favorable for DC cooling. [PERSON] et al. [35] studied the thermodynamic properties of tetrabutylammonium acrylate (TBAAc) hydrate. According to the study, TBAAc hydrate had the highest equilibrium temperature of 291.5 K at \(w_{\text{TBAAc}}\) = 0.36, where \(w_{\text{TBAAc}}\) is the mass fraction of TBAAc. The greatest dissociation heat, a latent heat, of 195 kJ/kg was obtained at \(w_{\text{TBAAc}}\) = 0.33 under ambient pressure. As for the thermal conductivity of ionic semelathrate hydrates, [PERSON] et al. [34] studied the value of tetrabutylammonium bromide (TBAB) hydrate and tetrabutylammonium chloride (TBAC) hydrate, which have similar characteristics with TBAAc hydrate. They reported that TBAB hydrate and TBAC hydrate have thermal conductivity of approximately 0.40 W m\({}^{-1}\) K\({}^{-1}\). According to the report by ASHRAE Technical Committee [18], the cooling system for DCs is required to be set from 288 K to 305 K. From the aspect of operation temperature in DCs, it can be said that TBAAc hydrates are suitable for utilizing in DC cooling as PCMs. Also, the dissociation heat of the TBAAc is larger than that of TBAB hydrate, which has been commercialized in the industrial area [33; 35]. Given these advantages, TBAAc hydrates could be better PCMs for DC cooling. This is why it has significant meaning to practically evaluate the performance of TBAAc hydrates as PCMs. As a step for industrial use of TBAAc hydrate, we performed kinetic thermal energy storage experiments with TBAAc hydrate. Based on these results, we obtained the data of energy storage density and energy storage rate during the experiments. During the experiments, we also found the adhesion of the hydrate to the surface of the heat exchanger. As the adhered hydrate is the thermal resistance to the further hydrate formation, we used an agitator or an ultrasonic transducer to break the adhesion. By comparing the data with and without external forces, such as the agitation and ultrasonic waves, we obtained the practical data for a hydrate-based thermal energy storage system in DC cooling.
# Diversity and Functional Evolution of Terpene Synthases in Rosaceae
## 2 Results
### Chromosomal Location, Synteny Analysis of TPS Family Members in Rosaceae  
The chromosomal location maps of the TPSs in Rosaceae species were constructed by TBtools, as shown in Figures S3-S9, which indicates the diversity of distribution on chromosomes. For _P. persica_, TPSs were mainly distributed on three chromosomes (chromosomes 3, 4, and 8). Most TPSs (39) were distributed on the 4 th chromosome, while five and one TPSs were located on the 3 rd and 8 th chromosomes, respectively. For the Rosoideae and Maloidae species, TPSs were widely distributed on the 8 th and 10 th chromosomes, respectively. On the whole, plants from the same subfamily shared a similar chromosome distribution of TPSs. From the physical location maps, we observed that the tandem arrays of TPSs are quite extensive, as in _P. persica_, about 30 TPSs cluster across a stretch of 1051 kb on the 4 th chromosome, in _M. domestica_, six TPSs occur in a 200 kb stretch on 10 th chromosome. These tandem arrays are likely the consequence of duplication by unequal crossover; genes in tandem arrays are typically highly homologous to each other. For _P. persica_, _M. domestica_, and _F. vesca_, synteny analysis within genomes was conducted to determine their duplication events, as shown in Figure 4, which indicated that genome segmental and tandem duplication were together the driving force for the expansion of the TPS gene family in Rosaceae. For _M. domestica_ and _P. persica_, segmental and tandem duplication both contributed to family expansion; for _F. vesca_, tandem duplication played the most important role in the family expansion. MCScanX was used to identify possible collinear blocks between genomes in Rosaceae; the syntenic map between them was constructed, as shown in Figure 5, which showed plenty of syntenic relationships resulting from genome duplication and recombination.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**TPSs Clade** & **TPS-a** & **TPS-b** & **TPS-g** & **TPS-c** & **TPS-d** & **TPS-e** & **TPS-f** & **Total** \\ \hline Motif & DDXYD & DDVYD & DDIFD & DIDDT & DXDD, DXXD & DDFFD & DDFFD & \\ \hline _Prunus persica_ & 24 & 5 & 2 & 1 & 0 & 3 & 3 & 38 \\ _Prunus mira_ & 0 & 0 & 4 & 1 & 0 & 3 & 1 & 9 \\ _Prunus mume_ & 13 & 5 & 4 & 1 & 0 & 3 & 1 & 27 \\ _Pyrus betulifolia_ & 20 & 3 & 8 & 2 & 0 & 1 & 3 & 37 \\ _Malus x domestica_ & 17 & 4 & 6 & 3 & 0 & 3 & 3 & 36 \\ _Rosot chimensis_ & 37 & 9 & 5 & 2 & 0 & 3 & 1 & 57 \\ _Fragaria vesca_ & 26 & 7 & 4 & 4 & 0 & 2 & 0 & 43 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numbers of complete TPSs with both domains from Rosaceae species that were used for the phylogenetic tree of Figure 3.  
Figure 4: Inter-chromosomal relationships of TPSs in _P. persica_ (**A**), _M. domestica_ (**B**), and _F. vesca_ (**C**). Grey lines in the circle indicate the collinear blocks in the _P. persica_ or _M. domestica_ genome. The red lines in the circle highlight the segmental duplicated TPS gene pairs, while the red curve outside the circle indicates the tandemly duplicated TPS gene pairs produced by MCSscanX.
###### Abstract  
Fault mitigation techniques based on pure software, known as software-implemented hardware fault tolerance (SIHFT), are very attractive for use in COTS (commercial off-the-shelf) microprocessors because they do not require physical modification of the system. However, these techniques cause software overheads that may affect the efficiency and costs of the overall system. This paper presents a design method of radiation-induced fault-tolerant microprocessor-based systems with lower execution time overheads. For this purpose, approximate computing and selective fault mitigation software-based techniques are used; thus it can be used in COTS devices. The proposal is validated through a case study for the TI MSP430 microcontroller. Results show that the designer can choose among a wide spectrum of design configurations, exploring different trade-offs between reliability, performance, and accuracy of results.  
Keywords:fault tolerance; approximate computing; soft errors; hardening techniques +
Footnote †: journal: Electronics Electronics Electronics
###### Abstract  
This paper describes the synergetic use of earth observation satellites optical and radar data with a high-resolution digital elevation model (DEM) to detect flooded areas and explore the impacts of a flood event. A flash flood episode took place in May 2016, in the central-eastern part of West Thessaly (Central Greece). Landsat-7 ETM+ and a Sentinel-1 SAR images were acquired. For Landsat-7, several water indices were applied and for the Sentinel-1 a threshold method was implemented. Elevation data were also used to improve the delineation of the inundated areas, and to estimate flood water depth. Furthermore, Sentinel-2 images were utilized so as to record the land use/cover of the flooded area. The inundated areas and the affected cultivations were delineated with high precision, and the financial effects were evaluated.  
Landsat 7; Sentinel-2; Sentinel-1; GIS modeling; DEM; water indices; flash-flood mapping; damage evaluation +
Footnote †: journal: Nuclear Physics B
## 2 Methods and Data  
We selected thirteen flare events that occurred between 4 and 10 September 2017 for the current analysis. The following aspects were considered during the selection: the solar zenith angle (SZA) had to be \(<\)90\({}^{\circ}\) at the investigated ionosonde stations at the time of the flare events (thus the Sun to be over the horizon); M class flares were selected for investigation, furthermore, one C7.7 flare from 4 September, because it occurred around 12 UT and it caused a detectable impact on the absorption. Figure 1 displays the X-Ray and proton flux recorded by the GOES satellites during the analyzed period. We will use X-Ray and proton flux names during the manuscript which refer to the solar ones. The time of the selected flares with their class are indicated by vertical dashed lines. Table 1 provides a detailed overview of the key properties of the selected events.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & **Start Time** & **Peak Time** & **End Time** & **Duration** & \multirow{2}{*}{**Class**} \\  & **[UT]** & **[UT]** & **[UT]** & **[min]** & \\ \hline
4 September 2017 & 11:54 & 12:22 & 14:00 & 126 & C7.7 \\
4 September 2017 & 15:11 & 15:30 & 15:33 & 22 & M1.5 \\
5 September 2017 & 4:33 & 4:53 & 5:07 & 34 & M3.2 \\
5 September 2017 & 6:33 & 6:40 & 6:43 & 10 & M3.8 \\
6 September 2017 & 8:57 & 9:10 & 9:17 & 20 & X2.2 \\
6 September 2017 & 11:53 & 12:02 & 12:10 & 17 & X9.3 \\
7 September 2017 & 9:35 & 9:54 & 11:28 & 113 & M1.5 \\
7 September 2017 & 10:11 & 10:15 & 10:18 & 7 & M7.3 \\
7 September 2017 & 14:20 & 14:36 & 14:55 & 35 & X1.3 \\
8 September 2017 & 7:40 & 7:49 & 7:58 & 18 & M8.1 \\
8 September 2017 & 15:09 & 15:47 & 16:04 & 55 & M3.0 \\
9 September 2017 & 10:50 & 11:04 & 11:42 & 52 & M3.7 \\
10 September 2017 & 15:35 & 16:06 & 16:31 & 56 & X8.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The key parameters of the selected solar flares (based on [PERSON] et al. [18]).  
Figure 1: Solar X-Ray (panel **(a)**) and solar proton flux (panel **(b)**) during the investigated time period (4–10 September 2017). Panel **(a)** shows the X-Ray flux in the 0.1–0.8 nm wavelength band as measured by the GOES spacecrafts, whereas panel **(b)** shows the solar proton fluxes at 10, 30 and 60 MeV energies recorded by the same GOES spacecrafts. The dashed lines—magenta, blue and black—indicate the peak times of the X-, M- and the C7.7 class flare, respectively. The shaded areas indicate the time periods out of the scope of this study.
# Laminar _Ulva_ Species: A Multi-Tool for Humankind?
## 3 _Ulva_ Cultivation Methods: Towards an Innovative Future?
### Cultivation Control or Monitoring Techniques  
As previously said, any seaweed nursery or culture system requires strategies to be effective. Controlling seawater media (seawater and nutrients), temperature, and light is crucial, but so is monitoring the stage of growth.  
Regarding light intensity, in the laboratory, fluorescent lamps can provide energy. Daytime lamps have the highest colour temperature, 5000 K or higher, and are designed to mimic natural sunlight; cool white bulbs emit light primarily in the blue spectrum, typically with a colour temperature of around 4000 K. On the other hand, warm white bulbs emit light in the red portion of the spectrum, with a lower colour temperature of 3000 K or less. Quantum light meters detect the quantity of light accessible for algae growth and the photosynthetic photon flux density, which is expressed in micromoles of photons per square meter per second (\(\mu\)mol m\({}^{2}\) s\({}^{1}\)) [101].  
Water quality requires a refracometer (a portable instrument for measuring salinity) and a pH meter. These must be calibrated using buffer solutions before each use, washed before and after each use in deionized water, and kept in an electrode storage solution [101]. To facilitate the work by having the possibility of in situ analysis and obtaining data in real-time, both in the field and the laboratory, multiparametric probes can be used. These have different types of sensors that measure temperature, pH, turbidity, dissolved oxygen in the water, electrical conductivity (EC), total solids dissolved (TDS), and salinity.  
Regarding observation, a microscope is essential to determine successful spore release, spore density, and development of microscopic stages [101]. A magnifying glass can also be used to monitor the growth of inoculum. In more advanced locations, a high-powered dissecting microscope is also very useful for monitoring larger plants throughout the culture period or for isolating spores [101].  
A spectrophotometer is a spectrophotometric instrument that measures the intensity of light in relation to wavelength. It is used to detect the concentration of substances; elucidate the structure of organic compounds; identify impurities, the characteristics of a protein, classes of compounds, and dissolved oxygen content and for functional group detection and determination of the molecular weight of a specific compound. There are several types depending on the type of light used: visible light, UV/Visible, near infrared, nuclear magnetic resonance (NMR), atomic absorption, mercury, and fluorometers [111, 112].
# Enhancement of Yaw Moment Control for Drivers with Excessive Steering in Emergency Lane Changes
## 3 Control System Configuration
### Active Steering Saturation Control  
In this study, the front-wheel steering angle \(\delta_{f}\) is considered to be mechanically connected to the driver steering angle \(\delta_{d}\) through the planetary gear mechanism. After appropriate gear matching, the mechanism adjusts the front-wheel steering angle \(\delta_{f}\) by  
\[\delta_{f}=\delta_{d}-\delta_{sat} \tag{25}\]  
where the saturation angle \(\delta_{sat}\) is designed to dynamically block excessive driver steering angle \(\delta_{d}\) to keep the wheel steering angle \(\delta_{f}\) within the safety range \(\left|\delta_{f}\right|\leq\bar{\delta}_{\text{lim}}\), or specifically, to make the wheel steering angle \(\delta_{f}\) behave as  
\[\delta_{f}\cong\delta_{d,ref}(V_{0},\delta_{d}) \tag{26}\]  
A control scheme for the saturation angle \(\delta_{sat}\) to have the effect (26) is proposed as  
\[\dot{\delta}_{sat}=-\alpha\delta_{sat}+\alpha(\delta_{d}-\delta_{d,ref}(V_{0},\delta_{d})\ \ )+\omega_{d},\ \alpha>0 \tag{27}\]  
where \(\omega_{d}\) is the angular speed of the driver's steering angle \(\delta_{d}\) and a sufficiently large \(\alpha>0\) is chosen such that the saturation angle \(\delta_{sat}\) can be regarded as _much faster_ in comparison with the driver steering angle \(\delta_{d}\) and vehicle speed \(V_{0}\). Using (25) provides  
\[\dot{\delta}_{f}=\dot{\delta}_{d}-\dot{\delta}_{sat} \tag{28}\]  
By substituting (27) into (28) with \(\omega_{d}=\dot{\delta}_{d}\), we can arrive at  
\[\dot{\delta}_{f}=-\alpha\delta_{f}+\alpha\delta_{d,ref}(V_{0},\delta_{d}) \tag{29}\]  
Figure 3: The control system diagram.  
On the fast convergence condition, the wheel steering angle \(\delta_{f}\) in (29) can be viewed as  
\[\delta_{f}=\delta_{d,ref}(V_{0},\delta_{d})+v,\ v\underset{ faster}{\rightarrow}0 \tag{30}\]  
With the effect of the saturation angle (30), the relationship (24) can be rewritten as  
\[\mathbf{A}_{v}(V_{0})\mathbf{x}_{ref}+\mathbf{B}_{v}(V_{0})\delta_{f}=\mathbf{ B}_{v}v,\ v\to 0 \tag{31}\]
## 3 Results
### Effects of the Intervention on Older Adults' QOL According to Age  
No significant _group_ main effects were noted between the different age ranges in each QOL dimension and overall QOL. However, significant time main effects on death and dying (F\({}_{3,\,86}\) = 6.80, \(p\) = 0.011, and \(\eta_{\text{P}}^{2}\) = 0.07) were identified. Furthermore, significant group \(\times\) time interaction effects on past, present, and future activities (\(F_{3,\,86}\) = 4.48, \(p\) = 0.037, and \(\eta_{\text{P}}^{2}\) = 0.05) were noted. Past, present, and future activities exhibited a small effect size. (Detailed results are provided in Table 4).  
The post hoc analyses of simple main effects displayed a significant decrease from pre-test to post-test in past, present, and future activities (change: -2.75%, \(p\) = 0.009) exclusively in the CG young-old adults (Figure 4).  
Figure 3: Comparison of pre- to post-test quality of life change between the experimental and control groups of older adults.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline  & & \multicolumn{4}{c}{**Older Adults**} & \multicolumn{4}{c}{**Two-Way Repeated-Measure ANOVA**} \\
**Outcome** & \multicolumn{4}{c}{**(65-85 Years)**} & \multicolumn{4}{c}{**F-Value, \(p\), (\(\eta_{\text{P}}^{2}\))**} & \multicolumn{4}{c}{**Interaction**} \\ \cline{3-13}  & & \multicolumn{2}{c}{**G (\(n\) = 45)**} & \multicolumn{2}{c}{**CG (\(n\) = 45)**} & \multicolumn{4}{c}{**F-Value, \(p\), (\(\eta_{\text{P}}^{2}\))**} & \multicolumn{4}{c}{**Interaction**} \\  & & \multicolumn{2}{c}{**Mean \(\pm\) SD**} & \multicolumn{2}{c}{**Group**} & \multicolumn{4}{c}{**Time**} & \multicolumn{2}{c}{**Interaction**} \\ \hline Sensory abilities (score) & Pre & 10.20 \(\pm\) 2.75 & 10.36 \(\pm\) 2.85 & 6.29 & 0.014 & (0.07) & 16.13 & -0.001 & (0.16) & 26.91 & -0.001 & (0.23) \\ Autonomy & Pre & 14.51 \(\pm\) 2.24 & 14.96 \(\pm\) 2.06 & 10.06 & 0.307 & (0.01) & 1.04 & 0.311 & (0.01) & 11.66 & 0.001 & (0.12) \\ (score) & Post & 15.62 \(\pm\) 2.53 & 14.36 \(\pm\) 2.10 & & & & & & & & & \\ Past, present, and future activity (score) & Pre & 14.49 \(\pm\) 1.67 & 14.56 \(\pm\) 2.49 & & & & & & & & & \\ Post, 14.84 \(\pm\) 2.07 & 14.47 \(\pm\) 2.37 & 0.14 & 0.713 & (0.00) & 0.56 & 0.457 & (0.01) & 1.55 & 0.216 & (0.02) \\ Social participation/isolation (score) & Pre & 14.38 \(\pm\) 1.99 & 14.69 \(\pm\) 2.43 & & & & & & & & & \\ Post 15.07 \(\pm\) 1.70 & 14.53 \(\pm\) 2.27 & & & & & & & & & & \\ Death and dying (score) & Pre & 14.98 \(\pm\) 2.97 & 14.38 \(\pm\) 4.29 & & & & & & & & & \\ Post 16.07 \(\pm\) 3.00 & 14.22 \(\pm\) 4.11 & & & & & & & & & & \\ Intimacy & Pre & 14.93 \(\pm\) 2.73 & 14.58 \(\pm\) 2.61 & & & & & & & & & \\ (score) & Post & 15.60 \(\pm\) 2.77 & 14.67 \(\pm\) 2.56 & & & & & & & & & \\ Total quality of life (score) & Pre & 83.49 \(\pm\) 8.88 & 83.51 \(\pm\) 10.75 & & & & & & & & & \\ Post 89.84 \(\pm\) 10.43 & 82.29 \(\pm\) 10.13 & & & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Effects of the intervention on older adults’ quality of life.