esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanocarriers-Assisted Nose-to-Brain Delivery of Levodopa: Current Progress and Prospects
## 4 Drug Delivery to the Brain  
The BBB is a crucial anatomical and functional feature of the brain, maintaining a stable environment for optimal neuronal function. The BBB is composed of specialized, tightly junked to each other endothelial cells creating a highly selective permeation barrier [53; 54]. The BBB is further equipped with efflux transporters (P-glycoprotein and other adenosine triphosphate-linked transporters) that actively pump back xenobiotics into the bloodstream, limiting their accumulation in the brain [55]. Beneath the endothelial cells lays a basement membrane composed of extracellular matrix proteins that impede the transition of larger exogenous and endogenous substances [56]. The brain drug distribution after enteral administration is a challenge that only lipophilic, uncharged, and small molecules of less than 400 Da can take [57; 58].  
In 1991, [PERSON] obtained a patent for introducing the technique that enables direct delivery of therapeutics to the neuronal cells via the olfactory bulb after intranasal administration [59]. This patent is considered a turning point in medical research since it was followed by numerous achievements for drug delivery to the brain through the olfactory pathway. The nasal cavity offers a non-invasive drug delivery site with a large absorption area and allows direct brain uptake of molecules under 1000 Da through the olfactory and trigeminal nerves [60; 61]. Naturally, the discovery attracted mostly the attention of scientists investigating behavioral, neurodegenerative, and cognitive disorders. As a result, the adaptation of nose-to-brain delivery to some problematic active pharmaceutical ingredients (APIs) in recent years has led to numerous advances in the therapies of many brain pathologies. For example, sumatriptan, in the form of a nasal spray (Imitrex(r), Tosymara(r)) is approved for the treatment of acute migraine complaints [62; 63]; diaazepam nasal spray and midazolam nasal spray are available for the management of intermittent, stereotypic episodes of frequent seizure activity, under the trade names Valtoco(r) and Nayzilam(r), respectively [63; 64]; nallerene nasal spray (Opvee(r)) is developed for emergency treatment of opioid overdose [65]; Spravato(r) is an esketamine-containing nasal spray for the treatment of depression and currently seeking the FDA-recognition as first and only monotherapy for adults with treatment-resistant depression [66; 67]. Many other APIs are under extensive research for direct brain administration via the nasal route, among which are several established or potential Antiparkinson drugs [68; 69] (Table 2).
# Laboratory Tests on Swelling Properties of Field-Coring Gypsum Rock in Tunnels
## 3 Experimental Investigation
### Lateral Restricted-Swelling Pressure Test  
A series of rock lateral restricted-swelling tests were designed to investigate the axial swelling pressure of rock after immersion in water with lateral constraints. Three samples with diameters of 50.0 mm and heights of 25.0 mm numbered PC-1, PC-2 and PC-3 were tested on the YYP-40 rock swelling-ratio setup. The YYP-40 rock swelling-ratio setup was produced as shown in Figure 6.  
During the test, petroleum jelly was applied on the inner side of the round metal collar. Then, the sample was fixed into the metal circle to constrain lateral deformation. Two pieces of permeable stone were placed both at the upper and lower ends of the sample, respectively. The dial indicator and the monitor were calibrated. The water was slowly poured into the container until the upper permeable stone was submerged. When the amount of deformation accumulated to more than 0.001 mm, pressure was loaded on the sample top to ensure a constant volume, thus the thickness of the test sample remained unchanged throughout the test. At the beginning, the test data were recorded every 10 min. When the difference between 2 adjacent data was smaller than 0.001 mm for 3 consecutive times, the deformation was considered to be stable and the test load was recorded. In order to further study the relationship between the swelling pressure and the immersion time, the swelling pressure was recorded at the same time points as in the swelling-ratio test. The immersion time was 2880 min. The water temperature change was smaller than \(\pm\)2.0 \({}^{\circ}\)C and the water level was kept constant during the experiment.  
The lateral restricted-swelling pressure can be expressed as  
\[p=F/A, \tag{5}\]  
where \(p\) represents lateral restricted-swelling pressure, \(F\) represents the axial load and \(A\) represents the cross-sectional area of the sample.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.11 Fuzzy-Control-Based AODV Routing (FCAR)  
The basic idea of FCAR [40] is to use the percentage of same-directional vehicles and route lifetime as a routing metric to evaluate a path using the method of fuzzy logic and fuzzy control to make routing decisions under multiple selection criteria. The proposal ensures that the route has better stability and is not easily broken.  
The basic idea is made up of a series of \"_if - then_\" as conditional declarations. The precursor of the conditional statement is the input, and the consequent is the output. The rule base is very important for the fuzzy-control system; in [40], the authors obtained it through simulation by a mass of tests and adjustments.  
Table 4 shows the corresponding six values of the two inputs, there are \(3^{2}=9\) rules (R1...R9). From the fuzzy membership functions, only four out of the nine were in simultaneous operation.
## 2 Study Area and Data
### Sample Data
#### 2.2.2 Crop Samples  
Crop samples were primarily collected during the harvest season. Grain samples were taken from multiple points within the sampling site and mixed in equal proportions to create a composite sample, and the dry weight of the final sample was maintained at no less than 200 g. For fresh samples with 80-90% water content, the fresh weight was estimated to be 5-10 times the dry weight. Field-collected fresh samples had a minimum weight of 2000 g. After threshing and homogenization, grain samples were divided into subsamples using the grid method and quartering method, resulting in a final sample weight of approximately 250 g for small grains and 500 g for larger grains.  
In total, wheat, rice, rapeseed, tea leaf, tobacco leaf, potato, and corn samples were collected, along with corresponding root soil samples. The geochemical analysis for crop samples included 21 elements: K, Fe, S, Ca, P, Mg, Cu, Zn, Mn, F, Ni, Se, Mo, Co, Cr, As, Cd, Pb, Hg, Si, and Sr. The root soil samples were analyzed for 20 elements, including K, Fe, S, Ca, P, Mg, Cu, Zn, Mn, F, Ni, Se, Mo, Co, Cr, As, Cd, Pb, Hg, and Si; besides, pH was measured as well.
## 1 Introduction
To the best of authors' knowledge, to date, no joint strategy has been proposed considering the WHT, NOMA, and IRC in HetNets, particularly under nonideal SIC conditions for NOMA. To illustrate the concept, a generic HetNet configuration with NOMA-enabled small-cells is shown in Figure 2 and the concept of a WHT-NOMA small-cell is shown in Figure 3. The advantages of the proposed joint strategy have been detailed above. In this framework, the main contributions of this work are summarized as follows:  
* Applying the WHT jointly with NOMA and IRC in HetNets will increase the constellation diversity in modulation schemes, hence achieving even better throughput performance and reduced BER compared to the conventional NOMA system. Math  
Figure 1: The interference rejection combining (IRC) concept to suppress intercell interference (ICI). ematical modeling is provided for the proposed joint strategy and the results are validated using MATLAB. * Using IRC in HetNets will combat the interference created at SUs by the MUs that are in the vicinity of the small-cell but are not able to create NOMA pairs with the SUs. In short, interference created by MUs will be suppressed, resulting in better BER and throughput at the SU. Figure 3: Walsh–Hadamard transform (WHT)-NOMA in a small-cell. Figure 2: Small-cells deployed in a macrocell with non-orthogonal multiple access (NOMA). The remainder of this paper is structured as follows. Section 2 describes the mathematical model of the system with the details of the joint strategy, i.e., WHT with NOMA and IRC, considering both ideal and nonideal SIC cases. Section 3 reports the simulation results of the proposed strategy, and final concluding remarks are provided in Section 4.
## 3 Results and Discussion
### Microbiological Contaminants
\begin{table}
\begin{tabular}{l l||c c} \hline \hline
**No.** & **Sampling Sites** & **Heterotrophic Bacteria, CFU/mL** & **Total Coliforms, MPN/100 mL** \\ \hline
1 & Ostrov ferry & 1760 & 20,000 \\ \hline
2 & Ostrov, Danube old branch & 12,000 & 5000 \\ \hline
3 & Fetesti & 2700 & 7000 \\ \hline
4 & Cernavoda bridge & 2240 & 6000 \\ \hline
5 & Cernavoda Seimeni & 2300 & 25,000 \\ \hline
6 & Braila harbor upstream & 39.0 & 1300 \\ \hline
7 & Braila harbor downstream & 111 & 7000 \\ \hline
8 & Siret R. upstream & 2150 & 60,000 \\ \hline
9 & Siret R. downstream & 5000 & 20,000 \\ \hline
10 & Galati downstream & 1900 & 2500 \\ \hline
11 & Galati shipard downstream & 250 & 11,000 \\ \hline
12 & Prut R. upstream Giurgiulesti & 1210 & 250 \\ \hline
13 & Prut R. downstream & 250 & 2500 \\ \hline
14 & Reri downstream & 505 & 7000 \\ \hline
15 & Isaccoa downstream & 2050 & 2500 \\ \hline
16 & Ceretal Chilia & 435 & 2000 \\ \hline
17 & Izmail downstream & 525 & 10 \\ \hline
18 & Ceretal Sf. Gheorghe & 531 & 1300 \\ \hline
19 & Chilia veche upstream & 2750 & 7000 \\ \hline
20 & Chilia veche downstream & 34.0 & 600 \\ \hline
21 & Sf.Gheorghe upstream & 715 & 2000 \\ \hline
22 & Musura bay mouth & 1700 & 250 \\ \hline
23 & Sulina mouth & 123 & 250 \\ \hline
24 & Sf.Gheorghe mouth & 950 & 2500 \\ \hline
25 & Sacalin & 2400 & 6000 \\ \hline
26 & Gura Portiti & 401 & 600 \\ \hline
27 & Corbu & 63 & 150 \\ \hline
28 & Manalia & 72 & 120 \\ \hline
29 & Constanta & 70 & 200 \\ \hline
30 & Coutinesti & 53 & 50 \\ \hline
31 & Mangalia & 42 & 120 \\ \hline
32 & Vama veche & 45 & 250 \\ \hline Microbiological pollution quality classes for bathing waters [60] & Indicator of organic pollution & Indicator of fecal pollution \\ \hline Class I—Low & <243 & <240 \\ \hline Class II—Moderate & 500-10,000 & 500-10,000 \\ \hline Class III—Critical & 10,000-100,000 & 10,000-100,000 \\ \hline Class IV—Strong & 100,000-750,000 & 100,000-100,000 \\ \hline Class V—Excessive & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Bacteriological contamination of surface water in Danube River and Black Sea.
## 3 Results and Discussion  
### Analysis of the Total Environmental Impacts  
Table 4 shows the results of the environmental impacts of each environmental impact indicator for the OSS, the FPS and the VDS during the construction phase (i.e., manufacturing building materials, transportation of building materials, and construction machinery). The result shows that the environmental impacts during the construction phase from OSS are the highest for all environmental impact indicators, while the total environmental impacts of the VDS are the lowest environmental impacts among the three slab systems for all environmental impact indicators. According to Table 4, the stage of manufacturing building materials demonstrates the greatest environmental impact factors in all indicators for all three slab systems of this study. The transportation of building materials from manufacturers to the construction site is the second greatest environmental impact source in all cases. The usage of construction machinery and equipment have the lowest value, compared to other stages, for the OSS, the FPS and the VDS. For the GWP in OSS, the FPS, and the VDS, the effects of the manufacture of building materials, transportation of building materials, and usage of construction machinery were approximately 69%, 23%, and 8%, respectively. Moreover, the results of other environmental impact indicators (i.e., Acidification Potential, Eutrophication Potential, Ozone Layer Depletion Potential, Photochemical Ozone Creation Potential, and Abiotic Depletion Potential) show similar results to the GWP, which for manufacturing building materials, transportation of building materials, and usage of construction machinery are about 75%, 18%, and 7%, respectively (see Figure 5).
## 3 Artificial Bee Colony Algorithm
### Modified ABC Algorithm with Powell's Method
#### 3.2.1 Modification I: Solution Updating with a Best Neighbor-Guided Strategy and a Decaying Factor  
As we all know, the solution updating strategy plays a crucial role in the optimization process. For standard ABC, a candidate solution is generated according to Equation (16) by imposing a perturbation on the original solution, where the perturbation is a product of a random number and the difference between another random solution in the population and the original solution, and it leads to good exploration but weak exploitation. In recent years, new solution search strategies have been proposed, such as _gbest_-guided search strategy [29], which takes the global best solution as the learning object and shows good exploitation performance. However, the best information has both advantages and disadvantages, as it improves the exploitation ability to speed up the convergence rate but weakens the exploration ability to be apt to fall into the local optima. Therefore, rather than using the global best solution, the best solution chosen from the neighboring solutions of the current solution is used in the solution updating equation, which is called the best neighbor-guided strategy [18]. It is noted that the neighbors are randomly selected. Moreover, a nonlinear decaying factor is employed for convergence rate control to enhance the balance between the global and local search at each generation [15]. The new solution updating strategy is formulated as follows:  
\[v_{i,j}=x_{nbest,j}+\chi_{iter}\phi_{ij}\left(x_{nbest,j}-x_{i,j}\right) \tag{19}\]  
where \(X_{nbest}\) is the best solution selected from the \(N\) neighboring solutions of the current solution \(X_{i}\), and \(N\) is the number of neighbors (with \(N\) = 5 one gets fairly good results); \(\chi_{iter}\) is a nonlinear decaying factor to make the convergence rate to change nonlinearly with the number of iteration steps, which is defined as:  
\[\chi_{iter}=1-\left|\frac{iter-\delta}{MCN}\right|^{m} \tag{20}\]  
where \(iter\) is the current iteration step number, \(MCN\) is the maximum step number, \(\delta\) is an integer ranging from 0 to \(MCN/2\), and \(m\) is an exponent. By altering \(\delta\) and \(m\), the convergence rate of the search process can be controlled properly. As suggested by [PERSON] and [PERSON] [15], the choices of \(m\) and \(\delta\) follow the rule that \(\chi_{iter}\) starts to nonlinearly decrease not any earlier than the \(1/3 MCN\) step and eventually reaches no less than 0.5. In this manner,the search will probe over the entire search space at early iterations and get into a local search phase with faster convergence towards the end of the iteration process. The choice of the _MCN_ value should ensure that no better solution occurs after more iterations. A large value can ensure that the algorithm obtains a good enough solution, but the larger the value of _MCN_, the longer the computation time required. The proposed enhanced algorithm improves the convergence speed and thus reduces the _MCN_ value.
## 3 Acoustic Emission Analysis  
### Definition of the Acoustic Energy  
Energy of an AE signal includes the energy released by the source at crack initiation and is affected by various parameters: wave propagation distance, damage attenuation, sensor/material surface coupling and sensor frequency response. Wave theory states that the energy of an acoustic wave decreases exponentially with the increase of propagation distance. Therefore, the following equation was proposed to describe the energy of recorded AE signals (for instance, at sensor 1) received from the source \(n\)[26; 27]:  
\[\mathrm{E}_{1}(n)=\mathrm{E}_{\mathrm{s}}(n)\cdot\mathrm{A}_{1}\cdot e^{- \mathrm{B}(\mathrm{L}+\mathrm{x}(n))} \tag{2}\]  
Figure 2: Schematic hysteresis loop and measure of the hysteresis loops modulus, \(E_{0}\) is the initial Young’s modulus, \(E_{i}\) is the secant elastic modulus for the unloading-reloading cycle \(i\).  
\(\mathrm{E_{s}(n)}\) is the energy released at source n in the form of elastic waves. Due to differences in coupling between sensors and material surface or in sensors frequency responses, for a source at equal distance, the sensors may record significantly different amounts of energy. Thus, \(A_{1}\) is the proportion of source energy that is recorded by sensor 1. It is a constant characteristic of sensor. \(L+x(n)\) is the propagation distance from source \(n\) to the sensor 1 (2L is the distance between two sensors). The attenuation coefficient \(B\) is linked to the propagation medium, which may change with damage evolution. Similarly, AE signal energy received at sensor 2 is expressed as:  
\[\mathrm{E_{2}(n)}=\mathrm{E_{s}(n)}\cdot\mathrm{A_{2}}\cdot e^{-\mathrm{B(L- x(n))}} \tag{3}\]  
The source energy is then defined as the square root of the product of the amounts of energy received at both sensors for each source:  
\[E(n)=\sqrt{E_{1}(n)\cdot E_{2}(n)} \tag{4}\]
## 2 Materials and Methods
### Average EPS Maps  
A self-consistent method of producing average EPS maps, also written \(\langle\text{EPS}\rangle\) maps, using a radius \(R\) has been suggested and applied to the Eastern Mediterranean area in Reference [81] and further developed by [PERSON] et al. [79]. To construct such a map, one first estimates EPS for disks of radius \(R\) at the points \((x_{ij},y_{ij})\) of a lattice to obtain \(\text{EPS}_{ij}\) and then averages for each point \((x_{i_{0}|0},y_{i_{0}|0})\) the estimated EPS values within the same radius \(R\), i.e.,  
\[\langle\text{EPS}\rangle(x_{i_{0}|0},y_{i_{0}|0})\equiv\frac{1}{N}\sum_{i,j} ^{d(x_{i_{0}|0},y_{i_{0}|0}x_{i_{0}|0}x_{i_{0}|0}y_{i_{0}|})\leq R}EPS_{ij}, \tag{9}\]  
where the summation is restricted to the lattice points whose distance \(d(x_{i_{0}|0},y_{i_{0}|0};x_{i_{0}|0},y_{i_{0}|})\) from the observation point is smaller than or equal to \(R\), and \(N\) stands for the number of lattice points included in the sum.  
It has been shown (see Figure 6 of Reference [79]) that the study of \(\langle\text{EPS}\rangle\) close to the epicenters of forthcoming strong EQs exhibits a logarithmic dependence on \(R\), reminiscent of the Green's function of the Poisson equation in two dimensions, while the mean value \(\overline{\langle\text{EPS}\rangle}\) of \(\langle\text{EPS}\rangle\) over all the lattice points scales with \(R\) as a power law with an exponent \(d_{f}\), i.e, \(\overline{\langle\text{EPS}\rangle}\propto R^{d_{f}}\), see Section 3 and Equation (12) of Reference [81]. A clear relation between such made \(\langle\text{EPS}\rangle\) maps and the epicenter of an impending strong EQ has been observed in the respective regional studies [79; 81]. Here, we first estimated EPS for disks of radius \(R\) centered at each point of a square \(1^{\circ}\times 1^{\circ}\) lattice covering the globe, and then averaged these EPS values within the same radius \(R\).
## 2 Materials and Methods
### Bioactive Component Analysis
#### 2.4.2. Tocopherol Analysis  
The quantification oftocopherol content was according to the Chinese Standard GB 5009.82 2016 [39]. Initially, approximately 0.8 g of the oil sample was placed into a 25 mL brown volumetric flask, followed by the sequential addition of 0.1 g of BHT and 10 mL of n-hexane. After vortexing to dissolve, the mixture was diluted to volume with n-hexane up to the mark on the volumetric flask. Finally, it was thoroughly vortexed and filtered using a 0.22 \(\upmu\)m PTFE disposable syringe filter.  
For the separation of tocopherols, an HPLC method employing a Zorbax Rx-SIL column (5 \(\upmu\)m, 4.6 mm \(\times\) 250 mm., Hewlett-Packard, Palo Alto, CA, USA) equipped with a 1260 Infinity II Fluorescence Detector (FLD, Agilent, Palo Alto, CA, USA) was used. A mixture of n-hexane and 1,4-dioxane (95/5, \(v/v\)) at a flow rate of 0.8 mL/min was employed as an isocratic mobile phase. The fluorescence excitation and emission wavelengths were 294 nm and 328 nm, respectively. The injection volume was 10 \(\upmu\)L. Calibration curves were used for each component based on the concentrations of the analyzed samples as follows: \(\alpha\)-tocopherol, Y = 57,370X \(-\) 9288.3 (R\({}^{2}\) = 0.9998); \(\beta\)-tocopherol, Y = 82,173X \(-\) 3842.2 (R\({}^{2}\) = 0.9998); \(\gamma\)-tocopherol, Y = 76,185X \(-\) 12,675 (R\({}^{2}\) = 0.9999); \(\delta\)-tocopherol, Y = 86,276X \(-\) 9753.5 (R\({}^{2}\) = 0.9998).
## 2 Materials and Methods
### Samples Characterization  
X-ray diffraction (XRD) analysis was carried out using an APD 2000 PRO X-Ray diffractometer with CuK\({}_{\alpha}\) radiation. The patterns of the samples were recorded between 6\({}^{\circ}\) and 60\({}^{\circ}\) 20 with a scanning speed of 0.025 degrees s\({}^{-1}\) and step time of 10 s. Scanning electron micrographs were obtained with a HITACHI S-3400N scanning electron microscope (SEM) and the chemical composition was determined by energy-dispersive X-ray spectroscopy at different points on the surface of the samples. The working conditions of the SEM-EDS were 10 kV for the energy dispersive analysis, with a current intensity of 30 pA, and work distance of 10.6 mm. Fourier transformed infrared (FTIR) spectroscopy was conducted using a Nicolet Nexus 670 FT-IR infrared spectrometer from 4000 to 400 cm\({}^{-1}\) with a resolution of 4 cm\({}^{-1}\) in a KBr (spectroGrade, Merk KGaA, Darmstadt, Germany) wafer.  
The pH\({}_{\text{PZC}}\) was obtained using batch equilibration technique [12]. The samples were introduced into Erlenmeyer flasks containing 0.01 M NaCl solution with a solid/solution ratio of 1:500. The initial pH was adjusted from 2 to 12 by adding a few drops of 0.5 M HCl or 0.1 M NaOH. The mixtures were shaken for 24 h at 24 \({}^{\circ}\)C and the final pH values of the filtered suspensions were measured using Thermo Scientific pH-meter (ORNION 3 star pH Benchtop). Plots of pH\({}_{\text{initial}}\) vs. pH\({}_{\text{final}}\) were constructed and the pH\({}_{\text{PZC}}\) was determined from the intersection of these curves.