esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## Appendix A BLE Communication Profiles  
### Generic Access Profile  
The Generic Access Profile (GAP) offers a standard structure for how Bluetooth Low-Energy (BLE) devices link. A BLE device can function as a broadcaster (advertises, but does not allow connections), an observer (sees advertisements, but does not initiate connections), a peripheral (advertises and accepts connections), or a central (sees advertisements and starts connections). BLE enables connectionless communications between broadcasters and observers through the use of advertisements (called beacons), as well as connection-oriented communications between peripheral and central devices. A BLE peripheral device, for example, broadcasts its presence at first, while the receiving device, a mobile phone acting as the central, establishes a connection with the peripheral to enable two-way communication. Following the formation of the connection, the central will operate as the master and the peripheral as the slave. To allow more complicated topologies, devices may also implement multiple roles.
## 4 Results and Discussion
### Flow around Two Stationary Cylinders
#### 4.1.2 Strouhal Number (St)  
In order to study the flow characteristics of two cylinders in detail, the \(St\) of the cylinders was analysed. From the results of the simulation, the vortex shedding frequencies of the two cylinders were equal, i.e., the number of \(St\) was equal for the flow around two fixed cylinders in tandem. Figure 6 shows the variation of the \(St\) with the spacing ratio at different Reynolds numbers. It can be seen that the frequency of vortex shedding increased with the increase of the spacing ratio. In the case of \(d=2D\) and \(Re=2000\), it was hard to capture the vortex shedding between the two cylinders--that is, there was no vortex shedding frequency. When \(d\leq 3D\), \(St\) rose sharply from 0.15 to 0.21 with the increase of the spacing ratio; when \(3D<d\leq 4.5D\), \(St\) increased slowly; when \(d>4.5D\), as the spacing ratio increased, the \(St\) gradually became stable and varied slightly around 0.225. At \(Re=2000\), the \(St\) suddenly increased at the spacing ratio \(d_{c}=3D\), and when \(Re=6000\)-14,000, the \(St\) suddenly changed at \(d_{c}=2.5D\), which was consistent with the lift and drag coefficients of the upstream and downstream cylinders found in Section 4.1.1. Overall, with the increase of gap distance, the \(St\) of the two cylinders gradually stabilized at 0.225, which was slightly larger than that of the single cylinder (0.18-0.22) [35].
## 2 Methodology
### Adjustment of Parameters: Relevance of the Direct Model  
For a given vertical profile of the extinction coefficient (\(\alpha\)), simulated lidar profile can be calculated by using the direct model (Section 2.2). However, before these simulations, it is necessary to adjust two important parameters involved in the lidar equation: the instrumental constant \(K(\lambda)\), and the backscatter to extinction coefficient \(BER\) in the canopy. They have been adjusted by comparing the simulation with the measurements for each forest site.  
\(K(\lambda)\) needs to be adjusted because of uncertainties on several optical components of the lidar (e.g., transmittance of optical lenses, laser energy, detector gain). The molecular extinction and backscatter coefficients are determined based on the polynomial approximation proposed by [PERSON] [63] as in [PERSON] _et al._[64]. The aerosol contribution has been assessed during the ascent of the ULA as in [PERSON] _et al._[41]. Note that for all the sites, the aerosol optical thickness between the ground and the ULA was less than 0.03. Hence, we found a relative correction within 5% on \(K(\lambda)\).  
The BER in the canopy previously retrieved was used for the sampling cases. Nevertheless, the simulated profiles did not exactly match the measurements by \(\sim\)30%, because of the hypotheses used in our simulations. Therefore the \(BER\) in UV was adjusted to \(0.005\pm 0.002\) sr\({}^{-1}\) by comparing measured and simulated canopy signals via the EES. Such a value is very close to the initial value and it is in the error tolerance (\(\sim\)30%) for a simulator.  
Taking into account these adjustments, the lidar profile can be well simulated as an example given in Figure 6. The ground-echo is well reproduced, as well as the response of the forest below 28 m agl. The simulated lidar signal in the atmosphere, _i.e._, above \(\sim\)28 m agl, also matches very well with the measurements. The direct model is thus found relevant for realistic simulations from airborne and spaceborne platforms.  
Figure 6: Simulated lidar signal (**Red**) superposed on measured lidar signal (**Blue**) as an example in temperate deciduous forests in winter. Lidar signal \(\mathbf{S}\) is in volt.
# Selecting a Proper Microsphere to Combine Optical Trapping with microsphere-Assisted Microscopy
## 4 Conclusions  
In summary, we have focused on selecting proper microsphere types when combing optical trapping with super-resolution microsphere-assisted microscopy. The PS and MF microspheres have been selected after considering the optical trapping performances and the super-resolution imaging ability of different microspheres simultaneously. Then, an optically trapped PS/MF microsphere in water has been used to achieve super-resolution imaging of a 139-nm line-width SNG, which cannot be discerned by conventional microscopes, demonstrating the super-resolved power of PS/MF microspheres. The image quality and the magnification are affected by the refractive index contrast between the microsphere and the immersion medium, and the difference of image quality is partly explained by the photonic nanojet. Our procedure achieves an optical manipulation which does not require direct contact with microspheres, making it friendly to various samples. This work demonstrates the optical manipulation of PS/MF microspheres for super-resolution imaging under white light illumination, making it valuable in many applications, especially in nanostructure observations.  
[PERSON] did the simulations; [PERSON], [PERSON] and [PERSON] designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] wrote the paper; [PERSON] applied for the funding and managed the project. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (61705232, 61675206, 61845201, 61604154, 61605212), the Instrument Developing Project of the Chinese Academy of Sciences (YZ201616), and in part by Sichuan science and technology program (18 ZDZX0164, 2014 GZ0113).  
Figure 6: Numerical simulations of electric field distributions around a 10-\(\upmu\)m sphere, with different refractive indices in water medium: (**a**) PS sphere, refractive index contrast (RIC) = 1.20, (**b**) MF sphere, RIC = 1.26. (**c**) Normalized intensity in the full width at half maximum (FWHM) plane for the PS and MF sphere. The white dotted line represents the location of the FWHM plane.
## 2 BDS/GNSS for Earth Observations
### GNSS Positioning
The model leverages an important characteristic of the ionospheric delay's impact on satellite observations. Specifically, it recognizes that the ionospheric delay produces effects of equal magnitude but opposite sign on two types of measurements: pseudorange and phase observations. By implementing a process within which each pseudorange measurement is averaged with its corresponding phase observation at the same frequency, the model effectively removes the ionospheric delay in the pseudorange data. This technique enables the creation of two new combined pseudorange observations that are free from the influence of the ionosphere, providing more accurate and reliable data for further analysis. The undifferenced and uncombined model uses the original observations to avoid the amplification of the observation noise caused by the data combination and the waste of some observation information. The mathematical model can make full use of the observation data information, and the observation noise is small. It can estimate both tropospheric and ionospheric delay, which is relevant for atmospheric monitoring. The undifferenced and uncombined model considers the short-term stability of the ionosphere, and the ionospheric parameters between epochs can be used as weak constraints to improve the robustness of the observation equation. In the case of accurately known ionospheric delay information, the ionospheric delay can be used as a known quantity to constrain the observation equation and improve the convergence time of the precise-point positioning.
## 3 Results
### Statistical Analysis
findings confirm the strict relationship between PM\({}_{10}\) and PM\({}_{2.5}\) about the sources, whereas similar evidence shows possible additional sources to the PM\({}_{1}\) contribution. This occurrence can be explained considering previous studies on aerosol size distributions [8; 66]. In fact, they have highlighted that the PM\({}_{2.5}\) convention includes particles from both fine and coarse modes. The aerosol size spectra showed that there is a region where the tails of the PM\({}_{10}\) and PM\({}_{2.5}\) may overlap to some extent. In this region, PM is dominated neither by anthropogenic sources (essentially fine PM) nor by natural sources (essentially coarse PM). Thus, this means that the PM2.5 fraction \"contains\" PM\({}_{10}\). For this reason, it could be stated that PM\({}_{10}\) and PM\({}_{2.5}\) have the same origin, whereas the presence of other sources (for instance, a highway close to site) is relevant for the PM\({}_{1}\) fraction. Finally, the loadings and the percentage of the variance obtained for each of the components are reported in Table 6. Variable factor loadings were used to identify pollution sources and to evaluate the anthropogenic and natural contributions at the sampling site. Only variables with factor loadings greater than 0.5 (in italics in the table) were taken into account in order to characterize the source of pollution. Factor 1 is related to elevated local non-crustal contribution, as expressed by the factor loadings in Br and Cr, whereas F3 is related to both crustal and anthropogenic contributions, by the factor loadings in Na and Zn as well as F4 by the factor loadings in Th and As. \begin{table}
\begin{tabular}{c c c c c} \hline
**Element** & **F1** & **F2** & **F3** & **F4** \\ \hline Sc & \(-0.825\) & \(-0.212\) & \(-0.158\) & \(0.052\) \\ Fe & \(-0.746\) & \(-0.408\) & \(-0.103\) & \(0.214\) \\ La & \(-0.725\) & \(-0.065\) & \(0.517\) & \(-0.032\) \\ Ba & \(-0.624\) & \(-0.125\) & \(-0.089\) & \(-0.399\) \\ Br & \(0.566\) & \(-0.613\) & \(0.212\) & \(-0.224\) \\ Cr & \(0.544\) & \(-0.485\) & \(-0.062\) & \(0.280\) \\ Na & \(-0.543\) & \(0.036\) & \(0.616\) & \(-0.130\) \\ Se & \(-0.066\) & \(-0.854\) & \(-0.163\) & \(-0.343\) \\ Sb & \(-0.276\) & \(-0.649\) & \(-0.557\) & \(-0.023\) \\ Cs & \(-0.160\) & \(-0.645\) & \(0.411\) & \(0.065\) \\ As & \(0.129\) & \(-0.544\) & \(0.345\) & \(0.506\) \\ Zn & \(0.348\) & \(0.027\) & \(0.690\) & \(-0.275\) \\ W & \(-0.213\) & \(0.236\) & \(-0.156\) & \(-0.755\) \\ Th & \(-0.372\) & \(0.329\) & \(-0.310\) & \(0.562\) \\ Co & \(-0.372\) & \(0.211\) & \(0.461\) & \(0.285\) \\ \% variance & \(24\%\) & \(19\%\) & \(14\%\) & \(1\%\) \\ \hline \end{tabular}
\end{table} 
Table 6: Results of principal component analysis (PCA) applied to the PM\({}_{10}\), PM\({}_{2.5}\), and PM\({}_{1}\) samples. In italics are presented are the factor loadings greater than 0.5. Figure 4: Airborne filter identification in two groups using the discriminant functions.
# Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks
## 4 Results  
Two empirical equations were developed in our study to predict TC using the physiomechanical properties of rocks (i.e., P-wave, porosity, density, and UCS). The first model was developed using non-linear multivariable regression (NLMR), and another model was developed using gene expression programming (GEP). To avoid overfitting during the training stages, 65% of all datasets were randomly selected for training, and the rest were used for testing the developed models. The testing results of the developed models showed how much these models could be generalized for other datasets. Furthermore, statistical indices (i.e., the coefficient of determination (R\({}^{2}\)) and the root mean square error (RMSE)) were used to evaluate the robustness of the developed models for prediction. The R-square (Equation (2)) showed how reliable a model was for future forecasts, and the RMSE (Equation (3)) showed how much the standard deviation of the residuals was (prediction errors).  
\[\mathrm{R}^{2}=1-\frac{\sum\left(y_{\mathrm{act}}-y_{\mathrm{pre}}\right)^{2}} {\sum\left(y_{\mathrm{act}}-\overline{y}_{\mathrm{act}}\right)^{2}} \tag{2}\]  
\[\mathrm{RMSE}=\sqrt{\frac{\sum_{i=1}^{n}\left(y_{\mathrm{pre}}-y_{\mathrm{act }}\right)^{2}}{n}} \tag{3}\]  
where \(y_{\mathrm{pre}}\) is the predicted value, \(y_{\mathrm{act}}\) is the measured (actual) value, and \(\overline{y}_{\mathrm{act}}\) is the average of the measured values.  
Figure 8: Flowchart of the GEP algorithm employed in our study.
# La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks
## 4 Dissection of CTP in EH-WSN
### Experiment T-I
Unfortunately, \(B\) has no choice as to parent update because it cannot choose its previous hop, \(C\), as its next hop; therefore, \(B\) has to update its route ETX according to its parent, \(A\). Similarly, \(C\) also has to update its route ETX because its parent is \(B\). The three nodes, \(A\), \(B\), and \(C\) will always stay in the loop, even though they are able to detect loops at runtime. CTP has no any back-pressure schemes to notify upstream nodes about the loop status. Noticeably, once loops are identified, the nodes will always increase their own route ETX to satisfy the child-parent gradient rule. From the most right subfigure of Figure 3, we see that the ETX values of these three nodes keep going up after node \(X\) is removed out, and that all of them move beyond five thousand at the end of experiment T-I. **Remark 1**. : _CTP can work smoothly--removing loops-- as long as the network always keeps connected. But it is nearly incapable of preventing the already-happening loops caused by the departure of recharging nodes from developing._
## 5 Measurement Details and Results  
In V2X networks, the most crucial channel load parameter is the CBR--Channel Busy Ratio. The relevant ETSI standard defines the measurement of this parameter. The V2X protocols also use this value for congestion control [60]. This work focused on this CBR parameter affecting the V2X channel scalability and measured CBR values on eight R-ITS-S nodes and all the V-ITS-S nodes within the selected highway section. With the evaluation of this CBR parameter, we wanted to highlight two different perspectives. On the one hand, we wanted to depict the CBR values on some well-defined measurement points in the network with the first approach. This approach gives us an overview of the average CBR values in the whole network and represents the R-ITS-S perspectives. We took the average of the CBR values measured on the R-ITS-S devices to measure this parameter. On the other hand, we tried to highlight the perspective of the vehicles. We wanted to show what CBR values an average vehicle will see when it participates in the traffic with this value. We calculated the average CBR value on each V-ITS-S node to measure this parameter and took the average of the calculated average CBR parameters.
## 4 Reliable and Secure Probability Analyses
### Destination's Outage Probability  
Given in (8), the definition of outage probability, denoted as \(P_{\text{out}}^{\text{D}}\), can be expressed as follows:  
\[P_{\text{out}}^{\text{D}}= \Pr\left(\delta>\min_{e_{1}^{\prime}e_{2}^{\prime}}\{e_{2}^{\prime },e_{2}^{\prime}\}\right),\] \[\text{subject to}\begin{cases}R_{\text{UR}_{1}}^{\mathscr{D}}(e_{1}^ {\prime})\hat{R}_{\text{UR}_{1}\text{-D}}&\leq C(\gamma_{\text{UR}_{1}\text{-D }})\\ R_{\text{UR}_{2}}^{\mathscr{D}}(e_{2}^{\prime})\hat{R}_{\text{UR}_{2}\text{-D}} &\leq C(\gamma_{\text{UR}_{2}\text{-D}}).\end{cases} \tag{27}\]  
In the above expression, for \(k\in(1,2)\), \(R_{\text{UR}_{k}}^{\mathscr{D}}(e_{k}^{\prime})\) illustrates the rate-distortion function at a distortion level of \(e_{k}^{\prime}\), while \(\hat{R}_{\text{UR}_{k}\text{-D}}\) stands for the aggregate combined coding rate for source-channel of the pertinent channel. The sequential decoding function, represented as \(\delta=f(\cdot,\cdot)\), is associated with the particular decoding technique employed. The sequential decoding first reconstructs \(\hat{\mathcal{U}}_{3}\) and \(\hat{\mathcal{U}}_{4}\), then makes a decision on \(\mathcal{U}\). Comprehensive details on the function \(f(\cdot,\cdot)\) can be found in the literature [42, 43].
## Sensor Bus Standards
### Wireless Sensor Networks
#### Sensor-MAC(S-MAC)  
S-MAC [11] trades energy efficiency for per-hop fairness and latency. It consists of three major components, periodic listen and sleep, collision and overhearing avoidance and message passing. In many sensor network applications, nodes are idle for a long time if no sensing event occurs. S-MAC reduces the listen time by letting nodes go into periodic sleep mode. Before each node starts its periodic listen and sleep, it needs to choose a schedule and exchange that with its neighbors. Each node maintains a schedule table that stores the schedules of all its known neighbors. To prevent long-term clock drift, a maintaining synchronization is specified in S-MAC. Each node periodically updates their schedules and keeps synchronized among neighboring nodes. For collision avoidance, S-MAC follows similar procedures as the 802.11 standard. It uses both virtual and physical carrier sense and RTS/CTS exchange to address the hidden terminal problem. In addition, S-MAC tries to avoid the overhearing problem by letting interfering nodes go to sleep after they hear an RTS or CTS packet. This prevents neighboring nodes from overhearing long packets and consequently saves the receiver energy significantly. In S-MAC, the long messages are broken into many small fragments and are transmitted in burst to reduce the cost of bit corruption in the wireless transmission. To avoid the large control overhead of sending many small packets, the medium are reserved for transmitting all the fragments. Result shows S-MAC can save energy by factor of 2 compared to the 802.11 MAC [11].
# A Review on Bioactive Compounds and Pharmacological Activities of _Citrus unshiu_
## 4 Pharmacological Activities of Satsuma Mandarin
### Neuroprotective Activities  
A few experimental investigations have shown that different mechanisms mediated the neuroprotective action of C. _unshiu_ bioactives. According to these experiments, mice treated with C. _unshiu_ extracts demonstrated reduced peripheral neuropathy, oxidative stress in astrocytes, and neurotoxicity in the hippocampus and cerebral cortex. The neuroprotective activity of C. _unshiu_ methanolic extracts (nobiletin, HMF, and tangeretin) was evaluated in astrocytes (glial cells) and Mongolian gerbils. Immature C. _unshiu_ powder (10 g) was suspended in methanol (50 mL) at room temperature. The extracts were then evaporated and suspended in distilled water. Cells were first preincubated with C. _unshiu_ extracts for 30 min and then treated with lipopolysaccharide (LPS) at a dose of 500 ng/mL for 24 h. Gerbils were fed with a commercial powder or 1% C. _unshiu_ and water ad libitum. Treatment with C. _unshiu_ extracts suppressed LPS-induced nitric oxide (NO) and iNOS expression in astrocytes through downregulating the NF-kB and p38-MAPK signaling pathways. Oral administration of C. _unshiu_ extracts to gerbils delayed ischemia/reperfusion in the hippocampal CAI region [88]. Swiss albino mice and human neuroblastoma SH-SYSY cells were used to test the antidepressant activities of an ethanolic extract of C. _unshiu_ dried peel. The peel (600 g) was extracted with 70% ethanol (6 L) at 80 \({}^{\circ}\)C for 6 h. Cells were treated with C. _unshiu_ extracts (hesperidin and narirutin) at different concentrations (10, 50, or 100 ug/mL) and a synthetic glucocorticoid dexamethasone at a concentration of 200 uM for 24 h. Mice were first injected with 40 mg/kg dexamethasone and then orally administered 30, 100, and 300 mg/kg C. _unshiu_ extracts for two weeks. In SH-SYSY cells, the extracts demonstrated reduced neurotoxicity and depressive-like behaviors induced by dexamethasone. The extracts also ameliorated dexamethasone-induced neurotoxicity in the cerebral cortex and hippocampus of mice by upregulating the expression of tropomyosin receptor kinase B (TrkB), brain-derived neurotrophic factor (BDNF) level, and cyclic AMP-response element-binding protein (CREB) [89]. The aim of an experiment was to determine the antioxidant activity of C. _unshiu_ ethanolic extracts in Neuro2A cells and preclinical mice. Cells were treated with C. _unshiu_ extracts to test the inhibition of hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)). Mice were first treated with oxaliplatin-based chemotherapy to induce peripheral neuropathy and then orally administered C. _unshiu_ extracts for four weeks. Treatment with C. _unshiu_ extracts significantly inhibited H\({}_{2}\)O\({}_{2}\) in Neuro2A cells and decreased the expression of iNOS- and NOX2-mediated oxidative stress in mice with peripheral neuropathy [90].