esa-sceva/satcom-chunk-collection huggingface dataset:
# Olfactory Impact of Terpene Alcohol on Terpenes Aroma Expression in Chrysanthemum Essential Oils
## 3 Results and Discussion  
### Evolution of Sample Composition during Sensory Analysis  
The evolution of the terpene alcohols in alkane solution and in terpenes recombination (TR)was evaluated in order to assess the stability of the component of samples submitted to the panel. As can be found out from Figure 1, terpene alcohols kept steady in the alkane solution for 20 min. Afterward, the concentrations of the compounds in the solution decreased obviously. After 50 min, the concentrations of certain compounds in alkane solution had decreased by up to 40%. In TR, terpene alcohols kept stable for a longer period of time and lost no more than 30% after 60 min, with the exception of borneol (Figure 2). In TR, the terpene alcohols were more stable over time than in alkane solution. Consequently, the samples presented to the panel were prepared every 50 min. [PERSON] et al. [30; 31] confirmed the better stability of demethyl sulfide in a complicated matrice than in a single matrice, and they observed that these compounds kept stable for about 10 min. He also evaluated the stability of 12 esters in wine or in aromatic reconstitution, and the results showed that the majority of esters kept more stable in wine.  
Our results verified that the matrix affects the stability of aroma components. The aromatic evolution is due to physicochemical phenomena. The composition of the headspace changes over time as some compounds evaporate, according to their affinity for the matrix. The matrix was apparently able to modulate the composition of the headspace, thus impacting the aromatic perception of the sample [32]. The interaction may be affected by the physicochemical properties of the aromatic compounds, as well as van der Waals forces or hydrogen bonding.  
Figure 1: Volatile loss of terpene alcohols over time in sensory analysis in alkane solution.
# UAV Charging Station Placement in Opportunistic Networks
## 7 Routing Protocols
### Spray and Wait Routing Protocol  
Spray and Wait (SW) [38] aims to decrease the number of transmissions and limit the number of message copies spans over the network. The algorithm consists of the spray and the wait phases. In the spray phase, each message is associated with a limit \(L\) of the number of copies the next node that is encountered is allowed to distribute. This number is decreased by the number of transfers for this packet at each node. When the number of copies reaches one, the node carrying the message enters the wait phase, as it stops distributing any more copies of the packet and waits until it either meets the destination or the lifetime of the messages expires. If the destination is not found in the spraying phase, each of the nodes carrying a copy of the message waits to perform a direct transmission which means it will forward the message only to its destination.  
A binary version of SW is also introduced in [38]. In the binary SW, a message initially originating at the source has \(L\) copies. Any node that has more than one copy of the message and encounters another node with no copies sends half of its copies to the encountered  
Figure 3: Session example in epidemic routing.  
node and keeps the other half for itself. When only one copy remains, it switches to direct transmission. The pseudo-code for Spray and Wait is shown in Algorithm 4.
```
Inputs \(u\) and \(v\)  boolean \(encounterSession\)  boolean \(messagesSpray\)  Drop messages with expired lift-time from \(u\) and \(v\); \(\mathtt{Spray}\left(m,L\right)\) for\(i\gets 1\)to\(L\)do if\(encounterSession\left(u,v_{i}\right)\)then  Host \(u\) sends \(m\) to \(v_{i}\);  end if  end for return;  Wait(\(m\)) foreach\(node\)\(u\)do foreach\(encounterNode\)\(v\)do if\(isDestination\left(v\right)\)then  forward \(m\) to \(v\); else  Keep \(m\);  end if  end for return;
```
**Algorithm 4** Spray and Wait routing protocol
## 2 Materials and Methods
### Spheroid Diameter and Aspect Measurement  
We performed the assessment of spheroid diameter and aspect by observing 100 spheroids through weekly follow ups for 4 weeks using a photomicroscope (Zeiss Axio A1). The mean diameter and aspect were measured using the Image-Pro Plus 6.0 image analysis program, where the measurement is made at intervals of two degrees joining two contour points, and the aspect was evaluated by the ratio between the major and smallest axis of the ellipse.
## 3 Results and Discussion
### Error Metrics at the All-India Scale
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**2014–2017**} & \multicolumn{6}{c}{**2010–2013**} \\ \cline{2-9}  & **IMD** & **IMERG-E V6** & **IMERG-L V6** & **IMERG-F V6** & **IMERG-F V5** & **IMD** & **IMERG-F V6** \\ \hline Mean(mm/day) & 6.63 & 7.34 & 7.29 & 6.80 & 6.78 & 7.32 & 7.74 \\ Bias (\%) & – & 10.70 & 9.88 & 2.59 & 2.18 & – & 5.73 \\ CV (\%) & 46.52 & 51.13 & 50.63 & 47.73 & 49.01 & 42.66 & 45.47 \\ Correlation & – & 0.89 & 0.92 & 0.94 & 0.93 & – & 0.92 \\ RMSE (\%) & – & 28.65 & 24.91 & 16.89 & 18.26 & – & 19.91 \\ KGE & – & 0.81 & 0.84 & 0.93 & 0.91 & – & 0.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Error statistics in the IMERG precipitation products as compared to the IMD rain-gauge-based dataset for all-India daily monsoon rainfall estimates. in error metrics in IMERG-F V6 as compared to V5. However, IMERG-F V6 exhibits better performance during the GPM period (2014-2017) than the pre-GPM period (2010-2013). Since monsoon rainfall shows prominent interannual variability, bias and RMSE in all-India monsoon rainfall from IMERG-F V6 for 2010-2017 has been computed for each year separately to investigate the reason for larger bias and error in the multi-satellite estimates during the pre-GPM period than the GPM period. Figure 8 presents yearly variations in all-India mean monsoon rainfall from both IMERG-F V6 and IMD rain-gauge-based data and bias and RMSE in the IMERG estimates. IMERG systematically overestimates all-India mean rainfall except for 2017. Bias is the largest during 2010 and 2015, whereas RMSE is the largest during 2010 and 2012. It indicates that bias and RMSE are not always linearly dependent. Much less bias during 2016 and 2017 with an opposite sign as compared to other years leads to smaller bias in IMERG-F V6 during the GPM period than the pre-GPM period. However, bias and RMSE in the IMERG-F V6 estimates do not show any clear evidence of their association with interannual variations of the all-India monsoon rainfall.
## 3 Model Approach
### Data Processing  
The dataset was collected from a monitoring system that takes samples every 2 s; however, the mean value of all collected samples is stored every 10 min.  
The entire dataset has been preprocessed before starting the training phase. Firstly, the incorrect samples were removed, which correspond to error in the communication process between the data acquisition system and sensors. Then, only the samples recorded when the panels were working have been considered (the radiation sensor value and the flow meter used in this step to avoid the samples when the system was not operating). The original dataset had 52,689 samples, but the number reduced to 26,665 samples after preprocessing.  
The data were normalized to obtain the new sample values in the range 0 to 1. The Max-Min Scaler [47; 48], Equation (1), is used to change all the samples (\(Data_{j}\)) to new values (\(Data_{j_{max}}\)).  
\[Data_{j_{max}}=\frac{Data_{j}-min(Data)}{max(Data)-min(Data)}. \tag{1}\]  
To select the best hybrid topology, 5% of the samples in each cluster have been isolated from the dataset. The isolated data have been used at a later stage to verify the performance of all the hybrid configurations and to select the best hybrid topology. Moreover, to validate the obtained model, 367 samples that represent 5 operation days were isolated. These samples were not used in the hybrid model creation process. Instead, they were used only in the final step to have a realistic prediction error measurement for the hybrid intelligent model.
## 4 Plant Materials and Methods
### Hs-Spme-Gc-Ms Detection of Volatiles  
Extraction was carried out via automatic headspace solid-phase microextraction (HS-SPME, SPME arrow, CTC Analytics AG, Zwingen, Switzerland) for gas chromatography coupled with mass spectrometry (GC-MS, Model 8890-7000D; Agilent, Santa Clara, CA, USA) analysis. Liquid nitrogen was added into each dried or fresh sample. A 0.5 g sample was accurately weighed and placed in headspace sampling bottles. A quality control (QC) sample was prepared by mixing all dried or fresh samples, and three QC samples were inserted before, during, and after the samples. Methyl 2-hydroxybenzoate-3,4,5,6-d4 (C/D/N ISOTopes, Montreal, QC, Canada) was used as an internal standard. Saturated NaCl solution and 10 \(\upmu\)L (50 \(\upmu\)g/mL) of the internal standard solution were added, respectively. The vials were sealed using crimp-top caps with TFE-silicone headspace septa. At the time of SPME analysis, each vial was placed at 60 \({}^{\circ}\)C for 5 min, then a 120 \(\upmu\)m DVB/CWR/PDMS fiber (Agilent, USA) was exposed to the headspace of the sample for 15 min at 60 \({}^{\circ}\)C, and then the fiber head was inserted into the GC-MS inlet and desorbed at 250 \({}^{\circ}\)C for 5 min.  
The capillary column was DB-5 MS (30 m \(\times\) 0.25 mm \(\times\) 0.25 um, Agilent, USA). The inlet temperature was 250 \({}^{\circ}\)C, and the non-shunt injection mode was adopted. High-purity helium (\(\geq\)99.999%) was used as carrier gas to flow at the speed of 1.2 mL/min, and the solvent delay was 3.5 min. In the heating stage, the initial temperature (40 \({}^{\circ}\)C) was kept for 3.5 min and increased to 100 \({}^{\circ}\)C at a rate of 10 \({}^{\circ}\)C/min in 6 min; then, it was raised to 180 \({}^{\circ}\)C at a rate of 7 \({}^{\circ}\)C/min and raised to 280 \({}^{\circ}\)C a rate of 25 \({}^{\circ}\)C/min (held for 5 min). The energy was 70 eV in the electron impact mode (EI). The quadrupole mass detector, ion source, and transfer line temperatures were set at 150, 230, and 280 \({}^{\circ}\)C, respectively. The ion mode selected ion monitor (SIM) was scanning mode.  
The original data are processed by MassHunter software (B.08.00, Agilent). The volatile compounds were identified by comparing the mass spectra with the local database (MWGC, Wuhan Metware Biotechnology Co., Ltd., Wuhan, China). In short, one quantitative ion and two-to-three qualitative ions were selected for each compound. If the retention time of the detected peak is consistent with the reference and all the selected ions appear after background subtraction, then the compound is determined [45]. The relative content of individual volatile components was calculated from the peak area, and the peak area was standardized according to the internal standard (IS) (Areastandardized = Areasample/Area\({}_{\text{JS}}\)).
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
## 2 Preliminary
### Teleportation  
Teleportation is a safe communication method when facing eavesdropper. It sends only measurement results in a classical channel, instead of sending the quantum state directly. The receiver can rebuild the quantum state based on the measurement results. Even if the classical channel is eavesdropped, qubit still cannot be rebuilt because of the lacking entangled state.  
Teleportation based on the Bell pair is shown in Figure 1. Nodes A and B share a Bell pair \(|\Phi^{+}\rangle\). Node A wants to send an arbitrary state \(|\psi\rangle=\alpha|0\rangle+\beta|1\rangle\) to node B, where \(|\alpha|^{2}+|\beta|^{2}=1\). The control state and the entangled state in node A are measured by the circuit shown in Figure 1. State \(|\psi\rangle\) can be reconstructed in node B by a quantum gate based on the entangled state. Quantum gate matching to the measurement results is shown in Appendix A.  
Teleportation based on GHZ states is shown in Figure 2. Node A holds two entangled states and an arbitrary state \(|\psi\rangle\).  
Figure 1: (**a**) Entangled states in teleportation model and (**b**) circuit in node A based on Bell pair. Classical channel is used to transfer measurement results, quantum channel is for entanglement distribution. Here, H represents the Hadamard gate. \(|\psi\rangle\) is the control qubit of the CNOT gate.  
Figure 2: (**a**) Teleportation model and (**b**) circuit based on GHZ states. GHZ states \(|\Psi_{1}\rangle_{i}\), \(i=1,2,3\) are shared between nodes A and B. Node A holds three qubits operated by CNOT and the H gate in the circuit, and sends measurement results to node B to rebuild \(|\Psi\rangle\) based on \(|\Psi_{1}\rangle_{3}\).
###### Abstract  
The aim of this study was to compare in vitro the protective and antioxidant properties of lutein and astaxanthin on human primary corneal epithelial cells (HCE-F). To this purpose, HCE-F cells were irradiated with a blue-violet light lamp (415-420 nm) at different energies (20 to 80 J/cm\({}^{2}\)). Lutein and astaxanthin (50 to 250 uM) were added to HCE-F right before blue-violet light irradiation at 50 J/cm\({}^{2}\). Viability was evaluated by the CKK-8 assay while the production of reactive oxygen species (ROS) by the H2 DCF-DA assay. Results have shown that the viability of HCE-F cells decreased at light energies from 20 J/cm\({}^{2}\) to 80 J/cm\({}^{2}\), while ROS production increased at 50 and 80 J/cm\({}^{2}\). The presence of lutein or astaxanthin protected the cells from phototoxicity, with lutein slightly more efficient than astaxanthin also on the blunting of ROS, prevention of apoptotic cell death and modulation of the Nrf-2 pathway. The association of lutein and astaxanthin did not give a significant advantage over the use of lutein alone. Taken together, these results suggest that the association of lutein and astaxanthin might be useful to protect cells of the ocular surface from short (lutein) and longer (astaxanthin) wavelengths, as these are the most damaging radiations hitting the eye from many different LED screens and solar light.  
blue-violet light; lutein; astaxanthin; ROS; corneal cells +
Footnote †: journal: Appl. Sci.
# Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters
## 4 Experimental Results
### Secondary Disaster Event Classification Based on CNN-CDMV Model
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Sample Scene Content**} & \multicolumn{1}{c}{**7. Fire**} & \multicolumn{1}{c}{**8. Fire**} & \multicolumn{1}{c}{**9. Fire**} & \multicolumn{1}{c}{**10. Fire**} & \multicolumn{1}{c}{**11. Fire**} & \multicolumn{1}{c}{**12. Fire**} \\ \multicolumn{2}{c}{**Sample Image**} & & & & & & & & & & & \\ \hline \multirow{8}{*}{\
\begin{tabular}{c} **Sampling Image** \\ \end{tabular} } & 1 Sunset & 0.533388000 & 0.00075778 & 0.046114620 & 0.002996096 & 0.000000587 & 0.259329680 \\  & 2 Welding & 0.001140279 & 0.60443044 & 0.046410684 & 0.492032800 & 0.007052570 & 0.110984270 \\  & 3 Strong light & 0.00000185 & 0.022143625 & 0.79002310 & 0.034514073 & 0.053048834 & 0.423480360 \\  & 4 Weak light & 0.000195662 & 0.370061600 & 0.033038203 & 0.004148835 & 0.68830400 & 0.005324376 \\  & 5 Fire & 0.465275880 & 0.002606651 & 0.08441338 & 0.466308330 & 0.251594600 & 0.200881240 \\  & Maximum value & 0.533388000 & 0.604430440 & 0.79002310 & 0.492032800 & 0.688303400 & 0.423480360 \\  & Result & Sunset & Welding & Strong light & Welding & Weak light & Strong light \\ \hline \multirow{8}{*}{
\begin{tabular}{c} **Sampling Image** \\ \end{tabular} } & 1 Sunset model & 0.319589730 & 0.27296620 & 0.113568020 & 0.025411200 & 0.229234260 & 0.119237535 \\  & 2 Welding model & 0.002117465 & 0.00110537 & 0.026911521 & 0.014517990 & 0.010538680 & 0.333856020 \\  & 3 Strong light model & 0.029371442 & 0.16198587 & 0.196045820 & 0.142457440 & 0.05358325 & 0.167525460 \\  & 4 Weak light model & 0.00220700 & 0.05212331 & 0.159808980 & 0.040366065 & 0.30094740 & 0.112992550 \\  & 6 Fire model & 0.472251260 & 0.80237630 & 0.639671400 & 0.669025800 & 0.598174770 & 0.469597970 \\  & Maximum value & 0.472251260 & 0.80237630 & 0.639671400 & 0.669025800 & 0.598174770 & 0.469597970 \\  & Result & Fire & Fire & Fire & Fire & Fire & Fire & Fire \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**-**} & \multicolumn{2}{c}{**Fire**} & \multicolumn{2}{c}{**Flammable**} & \multicolumn{2}{c}{**Explosive**} & \multicolumn{2}{c}{**Toxic**} & \multicolumn{2}{c}{**Trapped Person**} & \multicolumn{2}{c}{**Dangerous Building**} & \multicolumn{2}{c}{**-**} & \multicolumn{2}{c}{**For Example**} \\ \multicolumn{2}{c}{**PP**} & **PN** & **PP** & **PN** & **PP** & **PN** & **PP** & **PN** & **PP** & **PN** & **PP** & **PN** & **PP** & **PN** \\ \hline AP & 95 & 5 & 88 & 12 & 79 & 21 & 89 & 11 & 85 & 15 & 86 & 14 & AP & **TP** & **FN** \\ AN & 5 & 95 & 12 & 88 & 21 & 79 & 11 & 89 & 15 & 85 & 14 & 86 & AN & **FP** & **TN**
## 5 Experiemental Result and Analysis
### Impact of Multi-View Layers  
To investigate the impact of the number of multi-view layers on the presented model, we run experiments on different numbers of multi-view layers. The number varies from 1 to 10. Figure 3 exhibits the experimental results on Acc and F1. When the multi-view module consists of two layers, the model performs best. When there is only one layer, insufficient interaction between text and image information makes the model unable to obtain satisfactory features. As the number of layers increases, the model's performance gradually declines, indicating that the excessive depth weakens its learning ability. The possible reason is that the growth of model parameters leads to overfitting. Even if we increase this module's dropout rate, the model's performance is still constrained.
# An Intelligent Wireless System for Field Ecology Monitoring and Forest Fire Warning
## 2 Design of the Monitoring System  
### System Composition  
The system comprises multiple field ecological monitoring stations, a cloud server, and online system software, as shown in Figure 1.  
The field ecological monitoring station is connected with the sensor groups of atmosphere, soil, light, and plant parameters and is driven by solar energy. Two-way communication occurs between the field ecological monitoring station and the cloud server via the short message communication module of the Beidou satellite or GPRS. The system software runs on the cloud  
Figure 1: Schematic diagram of the field monitoring system.  
server provided by the Alibaba Cloud with functions such as real-time data query and download, big data analysis and decision-making, database storage and management, satellite data reception, and parsing. Multiple field ecological monitoring stations are networked for all-round monitoring of the microenvironment in forest regions. Users may inquire and download the ecology monitoring data of forest regions using a PC or a mobile phone via remote network connection.  
The field ecological monitoring stations automatically acquire data from the connected sensors for atmosphere, soil, light, and plants at an interval of 10 min, manually adjustable, according to their own clocks. In the forest region with weak mobile phone signal, the data are stored and reformatted to form a 72-byte short message communication package and sent to the Beidou Satellite through the communication module of the Beidou Satellite. The ground station of the Beidou Satellite receives the satellite data and in return sends the data to the cloud server through the Beidou Satellite. In the forest region with a stable mobile phone signal, GPRS is used for data transmission.  
The system software can receive and parse the communication package of the Beidou satellite or GPRS, and then store the monitoring data into the MySQL database relying on its functions of receiving and parsing the satellite data. The real-time data query and download function allows for real-time access to the database. PC and mobile phone users can enjoy the services, such as remote data access, curve query, and statement download, through the Internet or the mobile communication network. The database storage and management function allows for operations of the data in the MySQL database, such as addition, deletion, modification, updating, and search.  
The big data analysis and decision-making function of the system software allows for a determination of the grade of fire risk weather in forest regions, prediction and early warning of fire disasters in forests, an evaluation of the ecological environment restoration, decision-making on accurate irrigation, monitoring of growth state of stumpage, etc., according to the microclimate monitoring data acquired and the national standards or big data analysis models.
## 5 Evaluation: A Healthcare Scenario
### Healthcare Use Case Scenario  
The chosen scenario is based on a user-centred Digital Health Living lab, which provides a systematic user co-creation and co-production approach while integrating research and innovation processes in a real-life setting [25]. The residents, council, service providers, academic institutions, and technology companies are the key stakeholders within this living lab and are involved in every step of the way, from the creation of a product or service to commercialization. In particular, the related stakeholders contribute to health innovation in a new way, receive the opportunity to help individuals and society and can be key partners in inspiring health innovation based on their needs, perceptions, and user experience. It is an open innovation ecosystem where the living lab acts as a unique test bed for developing and testing prototypes or more mature digital healthcare solutions. The scenario is mainly based on Tier 3 test and trial category according to the UK National Institute for Health and Care Excellence (NICE) for Digital Health Technologies (DHTs). In particular, Tier 3 aims to help people with a diagnosed condition and provides treatment and health management. It includes tools used for treatment and diagnosis, as well as those influencing clinical management through active monitoring or calculation. This may include a symptom tracking function which records patient information and transmits this to the healthcare team for the derivation and the support of the clinical decision.  
Every involved stakeholder, such as patients, healthcare practitioners, residents, and service providers, will engage with the living lab within their own infrastructures and network connections. As such, they connect to the internet through their own Wi-Fi (routers) and communicate through emails (PCs) or their mobile devices (mobile phones, tablets, laptops). There is much critical big data involved in the scenario including patient healthcare information, personal information, device usage and connectivity with other devices. Additionally, the living lab includes various patient healthcare devices such as insulin pump, infusion pump and Internet of Things (IoT) devices for healthcare treatment. The scenario presented above is used to demonstrate the proposed methodology. The next section provides a detailed description of the implementation.