esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Particle Size Distribution and Scanning Electron Microscopy Analysis  
Dynamic light scattering (DLS) is a widely used method for determining particle size distribution in a colloidal solution. In the present study, DLS analysis revealed that the average particle size of the prepared ZnO NPs in the aqueous medium was 74.68 nm (Figure 3). The surface morphology of the biosynthesized ZnO NPs was examined using scanning electron microscopy (SEM) analysis. SEM images showed that most of ZnO NPs were spherical, with some agglomerated in shape and the particle diameter range was between 11 and 80 nm (Figure 4). SEM analysis confirmed the morphology and size of the prepared ZnO NPs [59].  
Figure 3: Particle size distribution of biosynthesized ZnO NPs using dynamic light scattering (DLS) technique.  
Figure 2: Fourier transform infrared (FTIR) spectra of zinc oxide nanoparticles biosynthesized by _Mentha spicata_ leaf extract as reducing agents.
## 2 Materials and Methods
### Skin Sampling Method and Investigation of Endogenous Skin Volatile Emissions  
Skin volatile sampling was performed using SPME fibres comprising 50/30 um divinylbenzene/carboxen/polydimethylsiloxane (DVB/Car/PDMS) Stableflex (2 cm) assemblies (Supelco Corp., Bellefonte, PA, USA). The adsorptive DVB/Car/PDMS coating is recommended for detection of a wide range of compounds comprising a broad range of volatility and polarity. Largeranalytes will be retained in the DVB layer, while smaller analytes will migrate through this to be retained by the Carboxen extending the analyte range compared to other available fibre coatings, while still enabling extraction of analytes at trace levels [25]. SPME fibres were conditioned at 250 \({}^{\circ}\)C for 25 min before use each day. A wearable pre-concentration approach [3; 23] was used for collection of human skin volatile samples. The SPME fibre was held within a glass housing (3 mL internal volume, Pyrex(r)) using 2 septa (Supelco Thermogreen LB-2 Septa plug, Sigma Aldrich, Ireland). The glass housing was affixed to the skin on the volar forearm using surgical tape (Leukosilk(r), BSN Medical GmbH, Hamburg, Germany) serving to create an enclosed area of skin headspace. SPME extraction time profiles were investigated during method development by collecting skin headspace samples for varying lengths of time (5-30 min) and the resulting volatile profiles were evaluated. Fifteen minutes were required to reach equilibrium, so this time was selected as the time for sampling herein. HS-SPME samples were collected from participants' volar forearms for 15 min and analysed immediately afterward by GC-MS to identify the major skin VOCs present. The volar forearm is an easily accessible area both to clean and dry and to sample from, and the potential for skin-mediated chemical transformation of perfume is low [26]. SPME control samples were also collected including fibre blanks, background air, and tape to exclude exogenous compounds from the analysis. All sampling in this study was performed in the same location, where the average humidity was 62% and room temperature was 21 \({}^{\circ}\)C. Tissue dielectric constants were measured on the volar forearm using a Delfin MoistureMeter D (Delfin Technologies, Kuopio, Finland) where each measurement was repeated 3 times at an effective measurement depth of 0.5 mm.
## 4 Conclusions  
Nanoemulsions comprising FSVs and CIN emulsified with PGU and TW-80 provided excellent physical stability over a storage period of one month at diverse temperatures. Different kinds of emulsifiers (PGU and TW-80), carrier oils (TFO and MCT), and CIN loading all directed toward prominent changes in the properties of bioactive compounds toward oxidation inside the nanoemulsions as well as within free oils. Herein, we investigated BC to be more sensitive as well as superior deprivation of VE, and BC was detected from nanoemulsions kept at 40 \({}^{\circ}\)C. Loading of CIN enhanced the oxidative stability and improved FSVs retention of produced nanoemulsions throughout a prolonged storage period. PGU-based nanoemulsions offered a little bit more or equivalent stability to the entrapped bioactive rather than TW-80-based nanoemulsions. Additionally, loading of CIN and VE performed dissimilar in each nanoemulsions holding dissimilar carrier oils. CIN entrapment performed being an antioxidant in each lipid phase that framed with TFO and MCT, but VE acted as an antioxidant and prooxidant in MCT and TFO, correspondingly. The changes in BC retention and oxidative stability proposed that the oxidative investigation is not enough to observe the stability of any emulsified system; however, still more studies on entrapped bioactive compounds are needed to find out the real fortune of bioactive compounds. This study could be more useful for designing oxidative stability of nano-emulsified systems in order to ensure the simultaneous incorporation of diverse lipophilic bioactive compounds as well as their application inside the food and beverage matrices.  
Investigation, A.A., A.R., S.M.J., M.M.A.N.R., Q.S., H.M.S., S.K., M.U. and W.X.; Methodology, A.A., A.R., S.M.J., M.M.A.N.R. and W.X.; Supervision, M.M.A.N.R. and W.X.; Writing--original draft, A.A., A.R., S.M.J., M.M.A.N.R., Q.S., H.M.S., S.K., M.U., P.L.K. and M.J.; Writing--review and editing, M.M.A.N.R., P.L.K. and M.J. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by China Agriculture Research System (grant 438 no. CARS-45-27), Jiangsu Agricultural Industry Technology System (grant no. JATS [2019] 467), and the National first-class discipline program of Food Science and Technology (grant no. JUFSTR20180201).  
Not applicable.  
All data generated or analyzed during this study are included in this published article.  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.
# Rural Healthcare IoT Architecture Based on Low-Energy LoRa
## 2 User Stories
### Story: Environmental Safety Monitoring  
Relatives and care providers would benefit from knowing that seniors live in a safe environment, including reduced risks. While notifying relatives or care providers in case of emergencies is essential, false alarms cause other sets of challenges [15], causing a need for a balance in the accuracy and frequency of notifications. Due to the lack of infrastructure in rural areas, common defects can have severe and even dangerous consequences. Having standalone, easy to install sensors to monitor electricity and water usage, detect water leaks or fire hazards can significantly improve the inhabitants' safety. There is great potential to equip everyday items with inexpensive sensors that will, in turn, communicate with the in-house or remote gateway. The following are some examples:  
* A compressed canister with gas used for the cooking stove can be equipped with a hall sensor to detect when it is being turned on and even have a safety shutoff valve triggered by a timer if left on beyond the expected time.
* An inductive coil placed around an electrical lead wire can induce a signal for a sensor to monitor electrical usage. Multiple devices can be monitored by monitoring the consumption of common power lead.
* A water sensor can be placed on the floor to detect any water leaking in the house, either from running water or rain.
* Magnetic sensors on the doors and pressure sensors on floor mats can detect when a person enters or leaves the house.
## 3 Results  
### Influence of the MFRO on the Desalination Efficiency in Purified Salt Water  
The desalination performance of the MFRO equipment under various salt concentrations is illustrated in Figure 4. Figure 4a shows the solute rejection, while Figure 4b represents the recovery rate. The highest solute rejection achieved for salt concentrations of 1000 mg/L, 2000 mg/L, and 3000 mg/L was 97.37%, 95.08%, and 93.46%, respectively. Meanwhile, the maximum recovery rates were 32.55%, 20.82%, and 16.34%, respectively. In general, lower salt concentrations corresponded to higher desalination and recovery rates. This can be attributed to the lower osmotic pressure and weaker ion interaction in low-salinity solutions, resulting in less concentration polarization. With an increasing ion concentration in the feedwater, the membrane pressure and surface potential changed, leading to increased concentration polarization and potential ion precipitation, which served to obstruct the membrane pores and subsequently reduce the permeate flux and solute rejection. However, beyond a certain range of salinity, where the ion concentration surpasses the membrane limit, the effect on membrane filtration selectivity becomes negligible. In such cases, the solute rejection stabilizes, and no significant changes occur [40,41].  
The research results indicate that the solute rejection and recovery trends of the MFRO equipment under different salt concentrations and operating pressures are similar to those in other scholars' studies, conforming to the principles of traditional RO systems. These results confirm the applicability of MFRO equipment for future experimental investigations.
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 4 Design of Network Coding Routing Protocol
### Network Coding Design
Here \(g_{ij}\) are chosen randomly from finite field \(F_{2i}\) and \(g_{i1},\cdots,g_{ik}\) are code vectors which are added into the packet header. Generally, \(N\) is a little larger than \(K\) to ensure that the receiver can receive enough packets for successful decoding. When the receiver obtains \(M,M\in(K,N)\) encoded packets with uncorrelated code vector, the original data packets can be recovered by matrix inversion. This scheme can keep the independence of each code vector from different nods and avoid the repetitive transmissions, but it is not appropriate for UWSNs. The reasons are listed as follows: (1) It is complex to calculate matrix inversion and each decoding round needs the participation of at least \(K\) encoded packets. The complexity makes it difficult to decode instantly. (2) The sender needs to add the code vector  
Figure 4: (**a**) The conventional approach of multi-casting; (**b**) The network coding approach of bi-direction transmission; (**c**) The network coding approach for unidirectional transmissions. into the packet header and this long code vector might reduce the transmission reliability and increase the transmission power. Based on the above analysis, we design a simple but efficient network coding scheme which is based on the fountain codes [28]. The fountain codes are a class of erasure-correcting codes. The classic fountain codes are Reed-Solomon codes, Tornado codes, LT codes [28; 29; 30] and so on. Like random linear network coding, the fountain codes encode \(K\) data packets into \(N\) encoded packets, \(N\geq K\). Considering the demands for real-time decoding and reducing the packet header length, we design a simple network coding scheme for UWSNs based on LT codes (SLT).
# Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR
###### Abstract  
The increasing accessibility of unmanned aerial vehicles (UAVs) drives the demand for reliable, easy-to-deploy surveillance systems to consolidate public security. This paper employs passive bistatic radar (PBR) based on a digital audio broadcast (DAB) satellite for UAV monitoring in applications with power density limitations on electromagnetic radiation. An advanced version of the extensive cancellation algorithm (ECA) based on data segmentation and coefficients filtering is designed to improve the efficiency of multipath clutter suppression while retaining robustness, for which the effectiveness is verified by theoretical derivation and simulation. The detectability of small UAVs with DAB satellite-based PBR is validated with experimental results, with which the influence of target altitude and bistatic geometry are also analyzed.  
passive bistatic radar; extensive cancellation algorithm (ECA); drone monitoring Article  
1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], C. Efficient Multipath Clutter Cancellation for UAV Monitoring Using DAB Satellite-Based PBR.  
1
### Sensing Platforms for the Mapping of Burned Areas
#### 1.2.1 Sentinel-2 Platform  
The launch of Sentinel-2 satellites equipped with multi-spectral instrument (MSI) sensors characterized by specific spectral bands configuration, combined with its short revisit time, has opened possibilities for the development and application of new indices for fire burnt severity assessment [45]. The sensor is equipped with the best radiance-based descriptors of chlorophyll content [46] and its wide spectral range allows for soil characterization in the VIS-NIR-SWIR range, providing sufficient accuracy to investigate soil type and cover [47]. Listed in Table 1 are Sentinel-2 spectral bands and their purpose. Different bands have different spatial resolutions because of the detail needed to analyze the objective and its interaction with the electromagnetic radiation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Nr.** & **Name** & **Start WL** & **Middle WL** & **End WL** & **Spectral Rn.** & **Spatial Res.** & **Purpose** \\ \hline
1 & 1 & 433 & 443 & 453 & 20 & 60 & Atmospheric correction (aerosol scattering) \\  & & & & & & & Sensitive to vegetation \\  & & & & & & & & senescing, carotenoid, \\  & & & & & & & browning, and soil \\  & & & & & & & background; atmospheric \\  & & & & & & & correction (aerosol scattering) \\  & & & & & & & & scattering) \\  & & & & & & & Green peak; sensitive to \\  & & & & & & & total chlorophyll in \\  & & & & & & & vegetation \\  & & & & & & & Maximum chlorophyll \\  & & & & & & & absorption \\  & & & & & & & Position of red edge; \\  & & & & & & & consolidation of \\  & & & & & & & atmospheric \\  & & & & & & & corrections–fluorescence \\  & & & & & & & baseline \\  & & & & & & & Position of red edge, \\  & & & & & & & atmospheric correction; \\  & & & & & & & retrieval of aerosol load \\  & & & & & & & LAI, edge of the \\  & & & & & & NIR plateau; sensitive to \\  & & & & & & & total chlorophyll, biomass, \\  & & & & & & & LAI, and protein; water \\  & & & & & & vapor absorption reference; \\  & & & & & & & retrieval of aerosol load \\  & & & & & & & and type \\  & & & & & & & Water vapor absorption; \\  & & & & & & atmospheric correction \\ \hline \end{tabular}
\end{table}
Table 1: Sentinel-2 MSI sensing bands range and purpose (Rn., range; Res., resolution; WL, wavelength) [20].
## 3 Methodology
### Evaluation Strategy
#### 3.2.1 Heterogeneity Quantification at the Fine-RES Scale  
The weak spatial representativeness of point-scale in situ observations at satellite resolution scales represents a severe challenge for SM validation using ground measurements. Upscaling errors can be minimized by selecting ground stations that maximally represent a larger-scale region. Over the QTP, we identified in situ measurement sites with maximal representativeness by measuring the spatial heterogeneity of the corresponding spatial grid cell for better validation of the fine-RES SM.  
To this end, coefficients of variation (CVs) and normalized information entropy (NIE) measures were utilized to analyze the degree of spatial heterogeneity present within a grid cell. CV is a normalized measure that characterizes data dispersion over a classical statistical index [83]. NIE, derived from information entropy [84], can clarify the relationship between probability and information redundancy.  
Since vegetation and SM have the greatest impacts on the total grid heterogeneity, LAI and SM were adopted in the heterogeneity analysis. Following an approach previously described by [PERSON] et al. [85], the spatial heterogeneity score of a pixel was then described by their weighted CV and NIE values:  
\[Score_{LAI} =0.5\times CV_{LAI}+0.5\times NIE_{LAI} \tag{4}\] \[Score_{SM} =0.5\times CV_{SM}+0.5\times NIE_{SM} \tag{5}\]  
where \(CV_{LAI}\) and \(NIE_{LAI}\) are the calculated CV and NIE of LAI, respectively, while \(CV_{SM}\) and \(NIE_{SM}\) are the corresponding values obtained from SM. The LAI and SM values used to obtain these statistics come from the corresponding GLASS LAI and ground-measured SM of the other four in situ measurement sites nearest to the evaluated one, considering days when in situ SM values were available.  
Different weights were designed in calculating the total space heterogeneity score. The formula is as follows:  
\[Score_{pixel}=0.25\times n(Score_{LAI})+0.75\times n(Score_{SM}) \tag{6}\]  
where \(Score_{pixel}\) is the total space heterogeneity score of the fine-RES pixel where a single node is located, while \(n(Score_{LAI})\) and \(n(Score_{SM})\) represent the CV and NIE min-max scores normalized to 0-1, respectively. A smaller value of \(Score_{pixel}\) indicates lower in situ spatial heterogeneity and, therefore, improved representativeness of the point-scale ground measurements within the pixel.  
The heterogeneity of each pixel with the corresponding in situ SM measurement sites was determined during the frozen (from October to March) and unfrozen (from April to September) seasons. The daily scores were averaged to a seasonal value to measure temporal changes in heterogeneity through the seasons. Finally, the last few in situ measurement sites within each network (Naqu, Maqu, and Ngari) with the lowest-ranked pixel scores were selected. These in situ measurement sites were determined to have low spatiotemporal heterogeneity, revealing the high capacity to represent local ground 'true' values in point scale, and a good ability to evaluate the fine-RES SM.
# Validation of Cable-Driven Experimental Setup to Assess Movements Made with Elbow Joint Assistance
## 4 Discussion
This will aid in understanding the magnitude of reaction forces for different anchor positions from the elbow joint. This can be further incorporated into the design of the cable-driven elbow exosuit. Several studies suggest that during the rehabilitation period, longer durations of training on rehabilitation robots will improve results and speed up recovery [48; 49; 50]. For cable-driven wearable devices, the wear and tear of anchor point attachments becomes an important parameter to be studied in order to have a better performance for a longer duration of operation [42; 43]. The presented results suggest that the anchor point position from the elbow will affect wear and tear at the anchor point attachment. Considering these changes in the position of anchor point placement, the moments generated at the anchor point attachment are reduced, which will reduce the wear and tear. The static model also calculates the tension in the cable, \(\overrightarrow{T_{s}}\)--refer to Equation (10)--and the length of the cable between the anchor points at all the joint positions, \(l_{s}\)--refer to Equation (14). These two variables can be further used to develop a control system for a cable-driven elbow exosuit. The presented results can be used for a cable-driven rehabilitation device for elbow actuation. However, one limitation is that while assisting a healthy subject with daily activities, the actuation is faster and so, therefore, a dynamic model should be considered. In focusing exclusively on the significance of the position of the anchor point from the elbow, this study does not consider the cable routing around the arm. In a future study, along with the distance from the anchor point, cable routing optimization will be considered. Also, for a future study, the comfort of the wearer can be studied by considering the pressure experienced by the user while wearing an exosuit device fabricated using these considerations. Future research will extend the analysis to three-dimensional spatial anchor point optimization, incorporating the internal and external rotation of the elbow joint to further enhance exosuit design.
# Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension
## 4 Proposed IRS Selection and Access Link Connection Approach with 3D Simulation Environment
### UAV Position Optimization  
The UAV can flexibly change its position in three dimensions according to the environment due to its characteristics. In this study, a simulation is conducted considering a disaster area as a use case. In order to communicate detailed damage information to disaster victims and rescue workers, the access UAV should be capable of high-capacity communication to all UEs. Therefore, UAVs are deployed to maximize the minimum SNR among all UEs. Several candidate points are prepared equally within the area, and the UE received SNR is calculated at all candidate points. The optimization problem P1 is shown below.  
Figure 5: The sequence of replication.  
\begin{tabular}{l l} P1: & \(\max\limits_{P_{\text{UAV}}}\min\gamma\) \\ s.t. C1: & \(P_{\text{UAV}}(1,1:2)\subseteq\Omega\) \\ C2: & \(z_{\min}\leq P_{\text{UAV}}(1,3)\leq z_{\max}\) \\ C3: & Neighboring BS - UAV is LoS \\ \end{tabular} where C1 is the constraint on the UAV horizontal position, C2 pertains to the constraint on the UAV vertical position, and C3 ensures that there is a LoS connection between the UAV and the nearby base station for establishing a stable backhaul link.  
```
1:Initialize: The number of UEs \(N\), The number of IRSs \(M\), IRS angle threshold \(\zeta\)
2:for\(i=1,\ldots N\)do
3:if UAV-UE(\(i\)) is LoS then
4: Connect UAV-UE(\(i\)) link directly
5:else if UAV-UE(\(i\)) is NLoS then
6:for\(j=1,\ldots M\)do
7:if UAV-IRS(\(j\)) is LoS Incident angle at IRS(\(j\)) \(\leq\zeta\)then
8:if IRS(\(j\))-UE(\(i\)) is LoS Reflection angle at IRS(\(j\)) \(\leq\zeta\)then
9: Connect UAV-UE(\(i\)) link via IRS(\(j\))
10:endif
11:endif
12:endfor
13: When some IRSs satisfy the conditions, choose one that makes sum path length the shortest
14:if UE(\(i\)) does not associate with any IRSs then
15:if Neighboring BS-UE(\(i\)) is LoS then
16: Connect neighboring BS-UE(\(i\))
17:else if Neighboring BS-UE(\(i\)) is NLoS then
18: Connect UAV-UE(\(i\)) link though it's NLoS
19:endif
20:endif
21:endif
22:endfor
```  
**Algorithm 1** Decision of communication link between UAV and UE
# A Method to Optimize Routing Paths for City-Pair Airlines on Three-Layer Air Transport Networks
## 4 Experiments
### Analysis of Aviation Networks  
Complex network theory is a powerful tool to analyse the structure and dynamics of air transportation systems. In what follows, we will analyse the three-layer network from the perspective of complex networks, such as the degree distribution, the degree-degree correlation, and the clustering coefficient, which are defined as follows.