esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### HS-SPME/GC-MS Analysis
#### 2.1.2 PDMS/DVB Fiber
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**CD 1**} & \multirow{2}{*}{**RI 2**} & \multicolumn{2}{c}{_S. romana_} & \multicolumn{1}{c}{_S. romana_} & \multicolumn{1}{c}{_S. romana_} & \multicolumn{1}{c}{_S. romana_} & \multicolumn{1}{c}{_S. montana_} \\  & & & & **K** & **B** & **M** & **P** & **J** & **M** \\ \hline
37 & \(\alpha\)-Gurjunene & & 1412 & - & - & 0.79 & - & - & - \\  & _trans-_ & & & & & & & & \\  & Caryophyllene & C11\({}^{\prime}\) & 1423 & 0.54 & 0.91 & 6.35 & 2.30 & 15.49 & 6.65 \\
39 & Aromaderendene & & 1438 & 0.25 & - & - & - & - & - \\
40 & Alloorandrendren & & 1443 & - & - & - & 0.25 & 0.06 & 0.14 \\
41 & Selina-5,11-diene 3 & & 1447 & - & 0.26 & - & - & - & - \\
42 & \(\gamma\)-Muurolene & & 1455 & - & - & - & - & 0.09 & 0.32 \\
43 & _trans-_\(\beta\)-Farnesene & C12\({}^{\prime}\) & 1461 & 4.88 & 7.71 & 3.55 & 4.90 & 4.02 & 2.17 \\
44 & Isocaryophyllene & C13\({}^{\prime}\) & 1471 & 7.10 & 6.85 & - & 5.17 & - & - \\
45 & \(\alpha\)-Amorphene & & 1480 & - & - & - & - & 0.19 & - \\
46 & Germacene D & C14\({}^{\prime}\) & 1484 & 12.18 & 2.16 & 1.77 & 1.22 & 53.63 & 51.08 \\
47 & _trans-_\(\beta\)-Ionone & & 1489 & - & 0.27 & 0.39 & 0.16 & - & - \\
48 & \(\beta\)-Cadinene & & 1494 & - & - & - & - & 0.01 & 0.01 \\
49 & Bicyclogermacrene & C15\({}^{\prime}\) & 1498 & 47.32 & 57.50 & 10.21 & 48.08 & 9.54 & 18.03 \\
50 & \(\alpha\)-Muurolene & & 1502 & - & - & - & - & 0.07 & - \\
51 & \(\gamma\)-Cadinene & & 1517 & - & - & - & - & 0.25 & 0.32 \\
52 & \(\delta\)-Cadinene & C16\({}^{\prime}\) & 1527 & 0.10 & - & - & - & 0.91 & 1.32 \\
53 & Dihydroactinidiolide & & 1532 & 0.17 & 0.26 & 0.42 & 0.22 & 0.12 & 0.20 \\
54 & Cadina-1,4-diene & & 1536 & - & - & - & - & - & 0.05 \\
55 & Spathrinolol & & 1580 & - & - & - & - & 0.34 & - \\
56 & Viridiffolololol & C17\({}^{\prime}\) & 1595 & - & - & 24.19 & - & - & - \\ \hline  & Total identified [\%] & & & 95.99 & 97.09 & 93.06 & 92.99 & 94.58 & 89.35 \\ \hline \hline \end{tabular}  
* CD: Compound designation of the major components (average percentages \(\geq\) 1.0% in at least one of the samples), which were included in the principal component analysis. \({}^{2}\) RI: Retention index determined relative to a homologous series of \(n\)-alkanes (C\({}_{\
abla}\)-C\({}_{2S}\)) on a HP-SMS column; \({}^{3}\) Tentatively identified.  
\end{table} 
Table 2: _Cont._  
Figure 2: Average relative percentages of different classes of headspace volatile organic compounds obtained from various populations of _S. romana_ and _S. montana_ using PDMS/DVB fiber.
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 6 Conclusions  
In this paper, we propose the THCS strategy to improve cache performance. Firstly, through dynamic vehicle and cluster construction, we optimize the cache location. Then, we introduce the concept of HCV in cache content selection, combining different attributes to evaluate the cache value of the content. Finally, this paper formulates a caching decision based on dynamic programming to obtain a near-optimal caching solution. The experimental results show that our proposed scheme can cache more content closer to the requester. Simulation experiments and performance results show that THCS shows good performance in terms of cache hit rate and average content delivery delay. In future  
Figure 5: The relationship between cache performance and content quantity: (**a**) The influence of different content quantity on HR; (**b**) The influence of different content quantity on ADL.  
Figure 6: The relationship between cache capacity and cache performance: (**a**) HR with different cache capacity, (**b**) ADL with different cache capacity.  
research, we will continue to investigate ways to reduce transmission latency and improve cache hit ratios in vehicle edge networks. Spatial prediction via neural graph networks or learning topological relationships between dynamic vehicles have been considered to further optimize caching strategies.  
Conceptualization, H.W. and J.J. ; investigation, H.W. and J.J.; writing--original draft preparation, writing--review and editing, H.W. and J.J.; supervision, H.M. and L.X. All authors have read and agreed to the published version of the manuscript.  
This work is fully supported by the National Natural Science Foundation of China (62272146, 62071170, 62171180, 62072158), the Program for Innovative Research Team in University of Henan Province (21 IRTSTHN015), in part by the Key Science and the Research Program in University of Henan Province (21A510001), Henan Province Science Fund for Distinguished Young Scholars (222300420006), the Science and Technology Research Project of Henan Province under Grant (222102210001), and Leading Talent in Scientific and Technological Innovation in Zhongyuan (234200510018).  
Not applicable.  
The authors wish to thank the editor and anonymous referees for their helpful comments in improving the quality of this paper.  
The authors declare that there is no conflict of interest regarding the publication of this paper.
# Diversity of Potential (Bio)Technological Applications of Amino Acid-Based Ionic Liquids
## 2 Bioapplications of AA ILs: An Overview
### Other Applications
#### Polymeric Materials for Electronic Devices and Materials for Nonlinear Optics  
AA ILs hold great promise in nonlinear optics (NLO) due to their customizable molecular properties, excellent stability, and role as sustainable alternatives for advanced photonic applications [289]. Their potential lies in bridging the gap between functional materials and green technologies.  
The advantages of AA ILs as NLO materials include their asymmetric structures, strong polarizability, high dipole moments, exceptional thermal stability, and their ability to stabilize organic and inorganic chromophores. These properties prevent chromophore degradation and enhance optical performance. Additionally, their ionic lattice structures amplify the local electric field, thereby boosting nonlinear optical interactions.  
Furthermore, AA ILs can form molecular aggregates or self-assemble, which further enhances cooperative optical effects, making them highly valuable for NLO applications.
# Empowering Adaptive Geolocation-Based Routing for UAV Networks with Reinforcement Learning
## 5 Performance Evaluation
### Network Efficiency Experiment  
When the _aDQN_ is completed, the server delivers the learning information to all UAVs in the swarm in order to apply the learned FR angle to actual routing communication. Each UAV chooses whether to forward or discard by adding routing rules that check whether it belongs to the FR when it receives the broadcast data. We compared the network bandwidth when each routing algorithm was used in all UAVs in the swarm. Figure 10 shows that AGLAN maintains a more stable network BW than LAR.  
Figure 11 is a graph depicting the packet loss that occurs when flooding, LAR, and AGLAN are executed. Datagrams lost for 300 s were measured. Flooding resulted in 1995 packet losses, 7.4% of a total of 26,753 packets, and LAR caused 408 packet losses, 1.5% of 26,158 packets. In contrast, AGLAN only incurred 169 packet losses, which is 0.63% of  
Figure 10: Comparison of BW among flooding, LAR, and AGLAN.  
Figure 9: (**a**) _aDQN_ episodic reward results; (**b**) _aDQN_ with pseudo-attention episodic reward results.  
26,753 packets. It can be seen that AGLAN is 6.77% better than flooding and 0.87% better than LAR.  
Figure 12 is a graph comparing the jitter of the GLAN system, the system with RL added to GLAN, and flooding, an existing routing algorithm. As we can see in the graph, GLAN applies the default FR angle according to the current position, so, depending on the situation, it may be worse than flooding or show similar values to the learned GLAN. The trained GLAN with RL always gives better results than flooding. AGLAN has an average jitter value that is 61% lower than that of flooding.  
Figure 13 is a bandwidth comparison graph. It can be seen that the bandwidth performance of GLAN is almost equal to or greater than that of flooding. The average bandwidth shows that AGLAN is 9% better than flooding.  
Figure 11: Comparison of packet loss among flooding, LAR, and AGLAN.  
Figure 12: Comparison of jitter among flooding, GLAN, and AGLAN.
## 4 Case Study: Reliability Analysis of a Diesel Engine
### Establishment of RDFTA for the Diesel Engine at the Component Level
In Formula (19), the availability formula of each unit within the system was obtained according to the failure rate \(\lambda\) and maintenance rate \(\mu\) of the constituent units in the system, which were then input into the simulation to generate a 0-1 evenly distributed random number array. It was then judged whether each unit had failed, according to the unit availability and the random number group. According to the minimum cut set and the minimum path set of the system and the fault condition of the system unit, it was determined whether the system was faulty. The system was simulated m times and the number of times that the system was in a successful state \(M\) was recorded. When the number of simulations \(m\) was large enough, the calculation of the reliability and availability of the system was closer to reality. \[A_{R}=\frac{\mu}{\lambda+\mu}+\frac{\lambda}{\lambda+\mu}e^{-(\lambda+\mu)t}, \tag{19}\]  
where \(t\) is the time variable (\(h\)). In \(1,2,3,\ldots,30h\), the simulation times of \(M=1\times 10^{4}\) and \(M=1\times 10^{6}\) were set per hour to obtain the simulated availability of the FTA's top events. A comparison between the RTFA and the MC-FTA is shown in Figures 11 and 12. Figures 13 and 14 show that the probability of the RDFTA and Monte Carlo calculation outputs was similar and that their reliability conclusions were basically the same. This showed that the reliability analysis method of RDFTA is feasible and correct. From the RDFTA minimum cut set algorithm (Equation (13)), the following could be established: K1 = {x1}; K2 = {x3}; K3 = {x7}; K4 = {x8}; K5 = {x16}; K6 = {x23}; K7 = {x15}; K8 = {x17}; K  
The probability importance of each minimum cut set could be obtained from the RDFTA probability importance algorithm (Equation (16)) as follows: K1 = 0.0368; K2 = 0.0392; K3 = 0.898; K4 = 0.023; K5 = 0.000806; K6 = 0.000253; K7 = 0.000944; K8 = 0.000576; K9 = 0.0000647; K10 = 0.0000216; K11 = 0.0000647; K12 = 0.0000242; K13 = 0.00000806; K14 = 0.0000242; K15 = 1.84 \(\times\) 10\({}^{-9}\); K16 = 1.62 \(\times\) 10\({}^{-9}\); K17 = 2.9 \(\times\) 10\({}^{-9}\); K18 = 1.27 \(\times\) 10\({}^{-15}\), and K19 = 4.54 \(\times\) 10\({}^{-16}\). Compared to the results of the minimum cut set importance, the K3 minimum cut set was the weak link at the diesel engine component level. The FMECA physical fault analysis was used for K3.
# HPT: A High Spatial Resolution Multispectral Sensor for Microsatellite Remote Sensing
## 4 Future Research  
Although the HPT project on the RISING-2 microsatellite was a pilot study, the results achieved were utilized for next-generation satellites. Multispectral sensors using LCTF technology have been developed further at Hokkaido University, and installed on subsequent microsatellites, e.g., the DIWATA-1, DIWATA-2, MicroDragon, and Rapid International Scientific Experiment Satellite (RISESAT). In these satellite sensors, a LCTF covering the visible spectral region is used in conjunction with that for the NIR. Over the past seven years, since the HPT project started, other related technologies have also been improved. The capacities of the volatile and non-volatile memories in the SHU have been increased up to 256 MB and 32 GB, respectively, and the downlink data rate with X-band telemetry has increased up to 10 Mbps. These technological developments have made multispectral remote sensing with LCTF technology more effective and operational. In fact, the DIWATA-1 microsatellite, which was launched on 23 March 2016, now routinely acquires multispectral images of several spectral bands over selected sites in the Philippines. The MicroDragon satellite will be launched in 2018, and will operate over Vietnam for ocean-color remote sensing, which requires at least eight spectral bands from the visible to NIR spectral regions [16]. The RISESAT [17], which  
Figure 7: **(a)** Normalized difference vegetation index (NDVI) values derived from the multispectral images in Figure 6 overlaid on a true color composite image acquired by the Landsat 8 Operational Land Imager (OLI) on 21 May 2015, (**b**) enlarged view of the area framed by the black square in (**a,c**) NDVI values derived from the Landsat 8 OLI bands for the same area.  
is intended for launch together with the MicroDragon satellite, will have 5-m resolution (or higher) imaging in both the visible and the NIR regions. The image analysis methods presented in this paper can be utilized for images acquired by the above microsatellites that will be operated by members of the Asian microsatellite consortium, which was established in 2016 to share microsatellite technologies, observation data, and data application methods between its members.
## 3 Results
### Flexibility with Smart Appliances  
On average per household, 408 h of dishwasher activity and 297 h of washing machine activity were recorded during the year of 2012. By using the equations mentioned in Table 2, the average consumption of the washing machine per household in 2012 is 130.83 kWh. This value is 10% lower than the average Dutch laundry electricity consumption and 25% lower than in the EU-15 [35], which clearly shows the efficient use of smart washing machines comparing to classical machines, despite the greater number of household residents. The same methodology is applied for the smart dishwasher. The total consumption of the dishwasher for 2012 was 215.7 kWh per household. This value is 8% lower than the average value for the EU-15 [36]. In total, these smart appliances brought 346.5 kWh of load flexibility to the grid per household, which corresponds to 10.5% of the average Dutch household electricity consumption in 2012, and to 6.8% of the average household electricity consumption in PMC. Table 6 summarizes the average consumption of smart appliances based on the equations presented in Table 2 and compares those consumptions to average values of different countries.  
Those percentages should be considered with the semi-automatic flexibility that it could contribute by users entering a latest run time. By using the same equations presented in Table 2, we considered this time how much the two smart appliances were shifted compared to their use time. The flexibility period offered by smart washing machines (WM) was similar to the usage time in median value and 60% for the smart dishwasher (DW) in comparison to the usage time (Figure 5).  
Defining peak hours as 17:00-23:00 on weekdays, we found that on average, 60% of the DW peak hours were shifted with the latest runtime option and, similarly, 20% for WM, comparing again the actual runtime in the same year.  
As providing full-automatic flexibility, the heat pumps with electric water boiler option consumed 400 kWh on average per household. Heating was mainly done by gas consumption, which is detailed in the discussion.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **PowerMatching City (2012)** & **Comparing to** \\ \hline Smart Appliance Type & Data Fraction (\%) & Annual Energy Consumption & Dutch Average [6] (\%) & EU-15 Average [7] (\%) \\ \hline Dishwasher & 97.8\% & 215.7 kWh & N.A. & \(-8\%\) \\ Washing Machine & 95.2\% & 130.8 kWh & \(-10\%\) & \(-25\%\) \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{4 out of 9 washing machine data were recorded without any activation during a considerable period, making their consumption abnormally close to zero, they were excluded from the analysis.} \\ \end{tabular}
\end{table}
Table 6: Smart appliance electrical consumption.
## 3 Proposed Improved Hybrid SSA with an EDO (ISSAEDO) Algorithm
### Proposed Improved Hybrid SSA with the EDO (ISSAEDO) Algorithm
#### 3.3.3 The Exhaustive ISSAEDO Algorithm
```
0:\(M\)--overall number of individuals (population size)
0:\(G_{max}\)--maximum number of iterations
0:\(D\)--dimensions of individuals' positions
0:\(X^{LB}\)--lower bounds of individuals' positions
0:\(X^{UB}\)--upper bounds of individuals' positions
0:\(C_{\text{g}}\)--crossover rate
0:\(W\)--weighting factor
0:\(X_{opt}\)--the global optimum position discovered while searching \(f(X_{opt})\)--the best fitness discovered, which should be decreased
1:Start
2:Initialize population \(P\) with \(n\) sparrows;
3: Evaluate fitness of each sparrow in \(P\) using the solar PV model;
3: Set best\({}_{\text{sparrow}}\) = the sparrow with the best fitness in \(P\);
4:Set generation count \(g=1\);
4:while\(g<G_{max}\)do
5: Generate new population \(Q\) using the following steps:
6: Perform SSA with probability \(p_{*}\) to generate new solutions;
7: For every solution obtained by SSA, perform EDO;
8: For every solution obtained by EDO, perform DE;
9: Randomly initialize remaining solutions in \(Q\);
10: Evaluate fitness of each sparrow in \(Q\) using the solar PV model
11: Set best\({}_{\text{sparrow}}\) = the sparrow with the best fitness in \(Q\);
12: If best\({}_{\text{sparrow}}\)\({}_{\text{O}}\) has better fitness than best sparrow, set best\({}_{\text{sparrow}}\) = best\({}_{\text{sparrow}}\)\({}_{\text{Q}}\);
13: Set \(P=Q\)
14: Increment \(g\) by 1;
15: Return the best sparrow as the optimized parameters for the solar PV model; \(f(X_{opt})\gets f(X_{opt}^{g+1})\);
16:End
```  
**Algorithm 3** ISSAEDO algorithm
# Outage Probability Analysis in Relaying Cooperative Systems with NOMA Considering Power Splitting
## 5 Conclusions  
In this paper, we studied a CR-NOMA network using PS receiver architecture. To analyze the system performance, we obtained closed-form exact and approximate expressions for \(OP\). Following that, ABEP was examined, and the expression for delay-limited throughput was also derived. With the provided simulation results, better outage and throughput performance were witnessed.  
Figure 6: ABEP versus the transmit SNR, \(\beta\).  
Figure 7: Transmission rate versus the transmit SNR, \(\beta\).  
**Author Contributions:** [PERSON] and [PERSON] conceived the idea; [PERSON] and [PERSON] designed the experiments, performed the simulation experiments and analyzed the data; [PERSON] and [PERSON] contributed to developing some mathematical analysis parts; [PERSON] and [PERSON] organized and wrote the paper; [PERSON] and [PERSON] critically reviewed the organization of the paper.  
**Acknowledgments:** This work was supported by the grant SGS reg. No. SP2019/41 conducted at VSB Technical University of Ostrava, Czech Republic and partly by The Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development, and Innovations project reg. no LM2015070.  
**Conflicts of Interest:** The authors declare that they have no competing interests.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Preparation of Photocatalytic Ceramsite Sand  
Ceramsite sand (CS) was activated by being immersed into 0.1 mol/L NaOH aqueous solution with sonication for 30 min to get more active sites. The other method for activating CS was to immerse CS into 5 mol/L NaOH at 85 \({}^{\circ}\)C in a reflux device for 4 h. Then, the activated CSs were put into vacuum saturation container at 0.1 MPa vacuum for 30 min to discharge the air in pores of CS. TiO\({}_{2}\) (P25) was firstly dispersed in 100 mL absolute alcohol with magnetic stirring in various concentrations. After 30 min of vacuum pumping and magnetic stirring, TiO\({}_{2}\) absolute alcohol dispersion solution was inhaled into the vacuum container with ultrasonic treatment for 1 h. Then, photocatalytic ceramsite sand (PCS) precursor was taken out, washed by distilled water, and dried at 105 \({}^{\circ}\)C for 3 h. PCS was thus successfully prepared. The concentrations of TiO\({}_{2}\) absolute alcohol dispersion solution were designed as 1, 2, 3, 4 and 5 g/L, respectively. The samples were denoted as mPCS-n, where m was the coating concentration of TiO\({}_{2}\) absolute alcohol dispersion (m = 1, 2, 3, 4, 5 g/L), and n was the particle size of CS (n = 0.1\(\sim\)0.6, 0.6\(\sim\)1.25, 1.25\(\sim\)2.35 mm). To compare PCM in this study with traditional photocatalytic cementitious materials, control groups were prepared in traditional ways by spraying TiO\({}_{2}\) on the surface or incorporating into cement pastes with same amount of photocatalysts (0.1 g) and same dimension compared with other samples. The photocatalytic activity of pure TiO\({}_{2}\) (P25) was also tested. The experimental devices and procedure are shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Density Grade** & **Bulk Density (kg\(\cdot\)m\({}^{-3}\))** & **1 h Water Absorption (\%)** & **Total Porosity (\%)** \\ \hline Ceramsite sand & 900 & 1100 & 5.6 & 43.57 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The physical characteristics of CS.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SiO\({}_{2}\)/\%** & **Al\({}_{2}\)O\({}_{3}\)/\%** & **Fe\({}_{2}\)O\({}_{3}\)/\%** & **CaO/\%** & **MgO/\%** & **K\({}_{2}\)O/\%** & **N\({}_{2}\)O/\%** & **TiO\({}_{2}\)/\%** \\ \hline
64.903 & 18.346 & 7.231 & 0.618 & 2.397 & 3.781 & 1.1 & 0.946 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The chemical composition of CS.