esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Field Experients
#### 3.2.2 Verification Test of Cultivation  
The experiment utilized the lettuce cultivar 'Spanish Green', with seedlings cultivated by the Vegetable Research Center of the Beijing Academy of Agriculture and Forestry Sciences. After 20 days of artificial light seedling cultivation in a climate-controlled chamber, uniformly sized seedlings were transplanted onto NFT cultivation beds in areas A, B, and C on 2 August 2024, comprising three treatments with three replicates each (36 plants per replicate). Each area's storage tank contained an initial nutrient solution volume of 300 L, circulated between the tank and cultivation troughs at a flow rate of 750 \(\pm\) 20 mL/min using a continuous supply system. The nutrient solution followed a specialized formulation for hydroponic leafy vegetables, maintained at EC 2.0 \(\pm\) 0.1 mS/cm and pH 6.0 \(\pm\) 0.2 throughout the cultivation period. All lettuce plants were harvested on August 30, with the overall appearance of plants from each area shown in Figure 19. Fresh and dry weights of roots and leaves were measured using a precision electronic balance (JM-BL5003), with results presented in Table 5.  
As shown in Table 5, the fresh root weights in areas B and C increased by 24.74% and 31.65%, respectively, compared to area A. Similarly, fresh leaf weights in areas B and C showed increases of 33.31% and 36.14%, respectively, over area A. These results demonstrate that the designed nutrient solution temperature control system effectively mitigates heat stress in lettuce through root-zone cooling, promoting biomass accumulation. Furthermore, the superior biomass accumulation in area C compared to area B validates the practical effectiveness of the adaptive DBO-Fuzzy PID algorithm.
# GPS + BDS Network Real-Time Differential Positioning Using a Position Domain Estimation Method
## 3 Results
### Dynamic Experiment
The RMS errors of GPS + BDS solution for PD are 0.122 m, 0.133 m and 0.432 m in the N, E and U components, respectively, and the corresponding STD values are 0.117 m, 0.119 m and 0.397 m.  
Figure 9: Geographical distribution of the stations in the dynamic experiment. Black solid triangles represent the reference stations Ref1, Ref2 and Ref3. Black line denotes the trajectory of the dynamic user station. The improvements of RMS errors and STD values when replacing GPS-only solution with GPS + BDS solution are about 2.5%-10% and 13%-16% for PD, and about 14%-26% and 19%-32% for RD. The improvements of PD over RD in GPS-only solution are larger than those in GPS + BDS solution, which are consistent with the results of the static experiment. It is also demonstrated that PD is less affected by the satellite constellation. Table 7 shows the percentages of the position errors falling within predefined thresholds for the dynamic user station. About 100%, 100% and 95% of the position errors falling within \(\pm\)1 m in the N, E and U components, respectively; and at least 97% of the position errors falling within \(\pm\)0.5 m in the N and E components, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**GPS**} & \multicolumn{3}{c}{**GPS + BDS**} \\ \cline{3-8}  & & **N** & **E** & **U** & **N** & **E** & **U** \\ \hline \multirow{3}{*}{RMS} & RD (m) & 0.167 & 0.176 & 0.618 & 0.135 & 0.152 & 0.456 \\ \cline{2-8}  & PD (m) & 0.135 & 0.136 & 0.480 & 0.122 & 0.133 & 0.432 \\ \cline{2-8}  & Improvement (\%) & 18.8 & 22.7 & 22.4 & 9.8 & 12.5 & 5.4 \\ \hline \multirow{3}{*}{STD} & RD (m) & 0.166 & 0.175 & 0.610 & 0.130 & 0.142 & 0.414 \\ \cline{2-8}  & PD (m) & 0.134 & 0.136 & 0.469 & 0.117 & 0.119 & 0.397 \\ \cline{2-8}  & Improvement (\%) & 19.4 & 22.4 & 23.0 & 9.7 & 16.4 & 4.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: RMS errors and STD values of the network real-time differential positioning for the dynamic user station. Figure 10: Position errors of the network real-time differential positioning and number of satellites for the dynamic user station. Left panels: GPS-only; right panels: GPS + BDS. It can be seen that the percentages of the position errors of PD are larger than those of RD in most of the thresholds, especially in the small thresholds. For both PD and RD, the values of GPS + BDS solution are almost larger than those of GPS-only solution.
Sensors_**2011**, _11_, 7678-7709; doi:10.3390/s110807678  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**An Optical Sensor Network for Vegetation Phenology Monitoring and Satellite Data Calibration**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 1**  
1 Department of Earth and Ecosystem Sciences, Lund University, Solvegatan 12, Lund, SE-223 62, Sweden; E-Mails: [EMAIL_ADDRESS] (H.J.); [EMAIL_ADDRESS] (P.S.); [EMAIL_ADDRESS] (M.H.)  
2 Department of Geography and Geology, University of Copenhagen, Oster Voldgade 10, Copenhagen K, DK-1350, Denmark; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +46-46-222-9655; Fax: +46-46-222-0321.  
_Received: 19 June 2011; in revised form: 21 July 2011 / Accepted: 22 July 2011 / Published: 4 August 2011_  
**Abstract:** We present a network of sites across Fennoscandia for optical sampling of vegetation properties relevant for phenology monitoring and satellite data calibration. The network currently consists of five sites, distributed along an N-S gradient through Sweden and Finland. Two sites are located in coniferous forests, one in a deciduous forest, and two on peatland. The instrumentation consists of dual-beam sensors measuring incoming and reflected red, green, NIR, and PAR fluxes at 10-min intervals, year-round. The sensors are mounted on separate masts or in flux towers in order to capture radiation reflected from within the flux footprint of current eddy covariance measurements. Our computations and model simulations demonstrate the validity of using off-nadir sampling, and we show the results from the first year of measurement. NDVI is computed and compared to that of the MODIS instrument on-board Aqua and Terra satellite platforms. PAR fluxes are partitioned into reflected and absorbed components for the ground and canopy. The measurements demonstrate that the instrumentation provides detailed information about the vegetation phenology and variations in reflectance due to snow cover variations and vegetation development. Valuable information about PAR absorption of ground and canopy is obtained that may be linked to vegetation productivity.  
**Keywords:** optical sampling; Normalized Difference Vegetation Index (NDVI); remote sensing; spectral sensor; photosynthetically active radiation (PAR); phenology
## 4 Discussion
### Interaction of Water with the Anion and Cation of the Ionic Liquid  
The addition of water molecules to PYR\({}_{14}\)-DCA shifts the position of the anion vibration lines towards higher frequency. This fact, observed in other ILs [17; 18; 39], has been attributed to the interaction of water mainly with the anions by means of hydrogen bonding. The shift of the anions vibrations can be explained considering that with increasing water content the bonds in the anion strengthen as a result of the decrease of the interaction between anion and cation [18]. The present investigation indicates that cations also interact with water, as witnessed by the blue shift of the two upper frequency stretching modes of PYR14. This blue shift is consistent with the occurrence of hydrogen bonding forming between water and cations. Similar results were reported for the interaction of 5-hydroxymethylfurfurral (5-HMF) with various ILs based on the 1-butyl-3-methylimidazolium cation and different anions [39]. The addition of the solvent produces a blue shift of the vibrational bands of both anion and cations. This fact has been attributed to the occurrence of hydrogen bonding of 5-HMF with both anions and cations. It must be noted that 5-HMF acts as a hydrogen bond acceptor with respect to the anion (anion:-O-H bonds develop), while 5-HMF is a hydrogen donor for the cation (C-H:-O bonds can form) [39]. It is plausible that in the presently investigated case, water is a hydrogen bond acceptor for the DCA anion and a hydrogen bond donor for the PYR14 cation.  
In the present study, not all the vibrational bands of the cation are affected by the instauration of hydrogen bonding: the modes due to alkyl chain seem unaffected, while the vibrations due to the pyrrolinium ring and to the methyl group attached to the N atoms are clearly shifted. This is consistent with the hydrophobic nature of alkyl chains longer than four CH groups, that was suggested as the origin of microdomains and micelles in ILs with long chains [43].  
It must be pointed out that the interaction of the anions and cations with water molecules through hydrogen bonds implies a decrease of the interactions between PYR14 and DCA. Indeed, the frequency shift of the CH stretching bands of PYR14-DCA + H2O are comparable to those observed in aprotic cholinium propionate ([Chl][Pro]) upon hydration. In the latter compound, as well as in ILs based on the 1-butyl-3-methylimidazolium cation and various anions [43], when the concentration of water exceeds 4-5 molecules per ion pair, one observes a transition from contact ion pairs (CIPs) to solvent-shared ion pairs (SIPs) due to the increasing interactions of cholinium and propionate ions with water. This bond is established at the detriment of bonds between cation and anion. In this way the cation and anion tend to separate and water molecules are allowed to fill the space between them. This mechanism is more effective in aprotic than in protic ionic liquids [18].
## 2 Methods  
The influence of various factors on total dose in mobile communication networks was modeled and analyzed with the help of the Monte Carlo simulation technique. The total dose is described here as the local peak specific absorption rate (SAR) spatially averaged over any 10 g of tissue mass (psaSAR\({}_{10g}\)) over a period of 6 min. The unit psaSAR\({}_{10g}\) was chosen because it defines the governing basic restriction for wireless exposure, as the whole-body average SAR limits (wbaSAR) are intrinsically met if the limits of local exposure are satisfied. The averaging duration of 6 min constitutes the internationally accepted averaging time to prevent thermal hazards at frequencies below 6 GHz, as instantaneous values have little justification [16]. Despite new revisions of safety guidelines being available [17; 18] we focus on [16] here as this is the most widely adopted guideline internationally. However, both [17; 18] implement averaging times of 6 min for localized SAR for the considered frequency range. It should be noted that some regulators, e.g., the US Federal Communications Commission (FCC), define shorter averaging time periods of 100 s.
# Multi-User Scheduling for 6G V2X Ultra-Massive MIMO System
## 2 System Model
### Channel Model  
Due to the high-speed movement of the vehicle, the channel characteristics are time-varying, resulting in a Doppler effect. Therefore, introducing the Doppler effect into the channel model and combining it with the conventional millimeter wave channel, the channel of the \(t^{\text{th}}\) slot is expressed as a time-varying geometric channel model [14].  
\[\mathbf{H}(t)=\gamma\sum_{i=1}^{N_{c}}\sum_{l=1}^{N_{ny}}\alpha_{il}(t)\mathbf{ a}_{r}(\theta_{il}(t))\mathbf{a}_{l}^{H}(\varphi_{il}(t))e^{i2\pi f_{l}T_{l}t} \tag{5}\]  
where \(\gamma=\sqrt{N_{t}N_{r}/N_{c}N_{ray}}\) denotes the normalized correction factor, \(N_{c}\) is the number of clusters in the channel, and \(N_{ray}\) is the number of sub-channels in each cluster. The expression \(\alpha_{il}(t)\sim\mathcal{CN}(0,\,1)\) represents the gain of the \(i^{th}\) path in the \(l^{\text{th}}\) cluster. The expressions \(\mathbf{a}_{r}(\theta_{il}(t))\in\mathbb{C}^{N_{r}\times 1}\) and \(\mathbf{a}_{l}(\varphi_{il}(t))\in\mathbb{C}^{N_{l}\times 1}\) are the array steering vectors of the receiving and transmitting antennas, respectively.  
\[\mathbf{a}_{r}(\theta_{il}(t))=[1,e^{i\frac{2\pi}{4}d\sin{(\theta_{il}(t))}}, \ldots,e^{i(N_{r}-1)\frac{2\pi}{4}d\sin{(\theta_{il}(t))}}]^{T} \tag{6}\]  
\[\mathbf{a}_{r}(\varphi_{il}(t))=[1,e^{i\frac{2\pi}{4}d\sin{(\varphi_{il}(t))}}, \ldots,e^{i(N_{r}-1)\frac{2\pi}{4}d\sin{(\varphi_{il}(t))}}]^{T} \tag{7}\]  
where the variables \(\theta_{il}(t)\in[-\frac{\pi}{2},\frac{\pi}{2}]\) and \(\varphi_{il}(t)\in[-\frac{\pi}{2},\frac{\pi}{2}]\) are the arrival angle and departure angle of the \(i^{th}\) path in the \(l^{\text{th}}\) cluster, \(\lambda\) is the millimeter wave wavelength, and \(d\) is the distance between the antenna elements.  
Assuming that the power is equally distributed, the system uses the BD precoding algorithm at the transmitting end. To get the channel capacity of user \(k\),  
\[C_{k}=B\log_{2}\text{det}\bigg{(}I_{N}+\frac{P}{M\sigma^{2}}\mathbf{H}(t) \mathbf{H}^{H}(t)\bigg{)} \tag{8}\]  
The user set channel capacity can be expressed as  
\[\begin{array}{rl}\text{C}\bigg{(}\Big{[}\mathbf{H}_{l}^{H}(t)\mathbf{H}_{j} ^{H}(t)\Big{]}^{H}\bigg{)}&=B\log_{2}\text{det}(\mathbf{I}_{kN}+\\ &\frac{P}{K\sigma^{2}}\Big{[}\mathbf{H}_{i}^{H}(t)\mathbf{H}_{j}^{H}(t)\Big{]} ^{H}\Big{[}\mathbf{H}_{i}^{H}(t)\mathbf{H}_{j}^{H}(t)\Big{]}\bigg{)}\\ &=C_{i}+B\log_{2}\text{det}\Big{(}\mathbf{I}_{N}+\frac{P}{K\sigma^{2}} \mathbf{H}_{j}(t)\mathbf{H}_{j}^{H}(t)-\\ &\Big{(}\frac{P}{K\sigma^{2}}\Big{)}^{2}\mathbf{H}_{j}(t)\mathbf{H}_{i}^{H}(t )\ \left(\mathbf{I}_{N}+\frac{P}{K\sigma^{2}}\mathbf{H}_{j}(t)\mathbf{H}_{j}^{H}( t)\right)^{-1}\!\!\mathbf{H}_{i}(t)\mathbf{H}_{j}^{H}(t)\bigg{)}\end{array} \tag{9}\]
## 3 Results
### Typical Models
#### 3.2.2 Marine Model  
The marine model is depicted in Figure 9, where the first layer represents seawater, with a resistivity of 0.3 \(\Omega\cdot\)m and a thickness of 240 m. The second layer has a thickness of 590 m, exhibits axial conductive anisotropy, and is characterized by three principal resistivities: 10 \(\Omega\cdot\)m, 20 \(\Omega\cdot\)m, and 30 \(\Omega\cdot\)m, respectively. The third layer is also anisotropic conductive, with three principal resistivities: 40 \(\Omega\cdot\)m, 60 \(\Omega\cdot\)m, and 80 \(\Omega\cdot\)m, respectively; additionally, it possesses Euler's angles of 30\({}^{\circ}\), 0\({}^{\circ}\), and 0\({}^{\circ}\), respectively.  
Figure 8: The imaginary parts of the electric fields at the ground surface for the Anis1 model, the Iso1 model, the Iso2 model, and the Anis2 model.  
Figure 9: Marine model: (**a**) the cross-sectional view of the entire model; (**b**) the cross-sectional view of the second layer containing two 3D anomalies; (**c**) the top-down view of the 3D anomalies.  
The second layer contains two 3D anomalies: a higher resistive anomaly with a resistivity of 300 \(\Omega\cdot\) m, measuring 800 m \(\times\) 800 m \(\times\) 50 m in the x-, y-, and z-directions, respectively, and an upper magnetic anomaly, measuring 800 m \(\times\) 800 m \(\times\) 100 m in the x-, y-, and z-directions, respectively. The upper anomaly is characterized by varying relative permeabilities, and its resulting apparent resistivities are presented in Figure 10, while the corresponding phases at the seafloor are shown in Figure 11. These calculations were performed at a frequency of 1 Hz.  
Figure 11: The phases on the seafloor for the upper anomaly, with varying relative permeabilities.  
Figure 10: The apparent resistivities on the seafloor for the upper anomaly, with varying relative permeabilities.  
In Figure 10, the first to fourth rows correspond to the xx-, xy-, yx-, and yy-mode apparent resistivities, respectively. Meanwhile, the first to fourth columns represent the relative permeabilities of 1, 1.5, 2, and 3, respectively. In Figure 11, the first to fourth rows correspond to the xx-, xy-, yx-, and yy-mode phases, respectively. The first to fourth columns correspond to the relative permeabilities of 1, 1.5, 2, and 3, respectively.
## 4 Discussion
### FL Studies for Healthcare Applications
#### 4.6.1 Classification Task in FL for Healthcare Applications
The proposed FL algorithm showed an AUC of 69.13%. In a similar study, [PERSON] et al. [52] proposed a method to forecast future patient hospitalizations with heart-related disorders by solving the L1-regularized sparse support vector machine (SVM) classifier in a federated learning environment. The proposed FL model performed an AUC of 77.47%. [PERSON] et al. [47] proposed an MLP-based model framework to predict in-hospital mortality among patients admitted to the intensive care unit. Their findings indicate that training the model in a federated learning framework produces outcomes comparable to those obtained in a centralized learning environment with an AUC of 97.76%. [PERSON] et al. [55] demonstrated federated learning with an MLP-based model architecture to predict patient mortality with COVID-19 disease within seven days. Their experiment showed that the federated learning algorithm successfully produces a robust predictive model while preserving the patient's confidential information with an 82.9% AUC score. **Other healthcare areas**. Besides the healthcare areas mentioned above, FL also applied for sepsis disease [51] and autism spectrum disorder classification [18]. [PERSON] et al. [51] developed a fully decentralized federated framework (FDFF) that integrates a neural network model across edge devices to extract knowledge from internet-of-things for healthcare applications. The edge devices using FDFF can create a double deep Q-network (DDQN) that gives suggestions for sepsis treatment. In addition, [PERSON] et al. [18] proposed FL for multisite autism spectrum disorder (ASD) fMRI analysis.
# Experimental Study on Properties of Ultrasonic Coupling Agent with Graphene in NDT
## 1 Introduction
However, graphene has poor processing performance, and is easy to aggregate; it is not widely used in practical engineering. At present, three-dimension structured graphene has good thermal stability, mechanical strength, and other characteristics. Besides, three-dimensional graphene is an attractive structure that effectively prevents two-dimensional graphene from aggregation or accumulation. It not only maintains the intrinsic properties of graphene, but also has a unique self-supporting structure and porosity. Water-based polymer gels are a new three-dimension functional polymer material, which can swell in water but cannot dissolve in water. Hydrogels not only perform good swelling property, but also have ideal mechanical strength. Moreover, graphene is incorporated into water-based polymer gels to enhance the mechanical strength of the obtained hydrogels. [PERSON] et al. [13] determined the rheological properties of graphene-glycerin nanofluids at different mass fractions (0.25-2%) and temperatures (20-60 \({}^{\circ}\)C). The experimental results showed that the viscosity of graphene-glycerin nanofluids increased with the increase in mass fraction and decreased with the increase in temperature. At the same time, graphene-glycerin nanofluids were found to have strong shear thinning behavior. At 20 \({}^{\circ}\)C, 6.32 s\({}^{-1}\) shear rates, the viscosity of glycerol was increased by 401.49% with the addition of 2% graphene. [PERSON] et al. [14] calculated the solubility parameters of pure graphene in pure glycerin. Then he found that graphene could form stable dispersion in glycerin, and the dispersion coefficient of graphene/glycerin nanofluids decreased with the increase in graphene layers. Graphene can improve the properties of polymer hydrogels. [PERSON] et al. [15] underlined that graphene be incorporated into the mixed solution of sodium alginate and CaCO\({}_{3}\) to enhance the mechanical strength of the hydrogel. In this study, different proportions of graphene were added to hydrogel and glycerin coupling agents to prepare a hydrogel-graphene composite coupling agent and a glycerin graphene composite coupling agent. An experimental platform for ultrasonic testing was built to study the performance changes of different composite couplings after graphene was added.
## 3 Results and Discussion
### Fluorescence Quenching Mechanism
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Compound** & \(\boldsymbol{T}\) **(K)** & \(\boldsymbol{K_{\text{SV}}\) **(\(\times\)10\({}^{4}\)** M\({}^{-1}\))** & \(\boldsymbol{K_{\text{q}}\) **(\(\times\)10\({}^{12}\)** M\({}^{-1}\).s\({}^{-1}\))** & \(\boldsymbol{R^{\text{a}}}\)** & \(\boldsymbol{SD^{\text{b}}}\) \\ \hline \multirow{3}{*}{R = H} & 298 & 2.769 & 2.769 & 0.99956 & 0.00322 \\  & 307 & 2.976 & 2.976 & 0.99933 & 0.00424 \\  & 316 & 3.268 & 3.268 & 0.99919 & 0.00513 \\ \hline \multirow{3}{*}{R = CH\({}_{3}\)} & 298 & 4.228 & 4.228 & 0.99942 & 0.00562 \\  & 307 & 4.337 & 4.337 & 0.99924 & 0.00660 \\  & 316 & 4.513 & 4.513 & 0.99903 & 0.00776 \\ \hline \multirow{3}{*}{R = OCH\({}_{3}\)} & 298 & 4.062 & 4.062 & 0.99948 & 0.00512 \\  & 307 & 4.280 & 4.280 & 0.99889 & 0.00788 \\  & 316 & 4.478 & 4.478 & 0.99869 & 0.00896 \\ \hline \multirow{3}{*}{R = CN} & 298 & 4.961 & 4.961 & 0.99934 & 0.00705 \\  & 307 & 5.088 & 5.088 & 0.99847 & 0.01100 \\  & 316 & 5.214 & 5.214 & 0.99824 & 0.01209 \\ \hline \multirow{3}{*}{R = CF\({}_{3}\)} & 298 & 3.436 & 3.436 & 0.99928 & 0.00511 \\  & 307 & 3.507 & 3.507 & 0.99921 & 0.00545 \\ \cline{1-1}  & 316 & 3.623 & 3.623 & 0.99892 & 0.00658 \\ \hline \multirow{3}{*}{R = CH(CH\({}_{3}\))\({}_{2}\)} & 298 & 4.326 & 4.326 & 0.99939 & 0.00589 \\ \cline{1-1}  & 307 & 4.423 & 4.423 & 0.99894 & 0.00795 \\ \cline{1-1}  & 316 & 4.593 & 4.593 & 0.99852 & 0.00975 \\ \hline \hline \end{tabular}  
* \(R\) is the correlation coefficient; b\(SD\) is the standard deviation for the \(K_{\text{SV}}\) values.  
\end{table} 
Table 1: Stern-Volmer quenching constants for the interactions of PDQL with HSA at different temperatures. For a complex formation process, a modified Stern-Volmer Equation (2) [36] was used to calculate the affinity binding constant \(K_{\mathrm{a}}\) between PDQL and HSA. \[F_{0}/(F_{0}-F)=f_{\mathrm{a}}{}^{-1}\cdot K_{\mathrm{a}}{}^{-1}\cdot[\mathrm{ Q}]^{-1}+f_{\mathrm{a}}{}^{-1} \tag{2}\]  
where \(F_{0}\) and \(F\) are the fluorescence intensities before and after the addition of the quencher, respectively. \(K_{\mathrm{a}}\) is the effective quenching constant. \(f_{\mathrm{a}}\) represents the fraction of accessible fluorescence. Data were treated according to the modified Stern-Volmer equation to obtain the linear plots at different temperatures (Figure 4). A linear relationship exists between \(F_{0}/(F_{0}-F)\) and the reciprocal value of the quencher concentration [Q], and the slope equals the value of \(f_{\mathrm{a}}{}^{-1}\cdot K_{\mathrm{a}}{}^{-1}\). The values of \(K_{\mathrm{a}}\) in Table 2 showed that the affinity constants all increased after the introduction of a substituent on the benzene ring. Figure 3: The Stern-Volmer plots for the fluorescence quenching of HSA by PDQL.