esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Trace Element Composition
In the trace elements, there was a high quantity of phosphorus in the silver foil glass beads, which is closely related to plant ash material [18]. The gold-colored and silver-colored silver foil beads were different in their Sr, Zr, Ce, and Rh contents. This composition of the gold-colored silver foil glass was detected as being higher. It is presumed that this difference is due to the difference in the raw material used to make the glass: sand [19]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{3}{c}{**Gold-Color Beads (Amber Glass)**} & \multicolumn{3}{c}{**Silver-Color Beads (Colorless Glass)**} \\ \cline{2-6}  & **mr-13** & **mr-14** & **mr-15** & **mr-16** & **mr-17** & **mr-18** \\ \hline Co & 5 & 6 & 5 & 4 & 5 & 3 \\ Zn & 25 & 54 & 38 & 54 & 50 & 41 \\ P & 5044 & 5597 & 5477 & 4384 & 4555 & 5162 \\ Ba & 147 & 169 & 144 & 260 & 244 & 193 \\ B & 140 & 198 & 154 & 191 & 213 & 171 \\ V & 20 & 35 & 20 & 24 & 25 & 18 \\ Cr & 14 & 16 & 15 & 18 & 18 & 9 \\ Ni & 12 & 16 & 13 & 12 & 12 & 8 \\ As & 5 & 6 & 6 & 2 & 2 & 1 \\ Rb & 27 & 37 & 29 & 65 & 60 & 44 \\ Sr & 281 & 805 & 278 & 1146 & 1116 & 542 \\ Ce & 21 & 27 & 22 & 46 & 46 & 42 \\ Y & 14 & 10 & 12 & 16 & 15 & 13 \\ Zr & 75 & 89 & 64 & 137 & 120 & 114 \\ Nb & 3 & 4 & 3 & 11 & 11 & 11 \\ La & 13 & 15 & 12 & 22 & 21 & 18 \\ Nd & 10 & 12 & 10 & 19 & 18 & 17 \\ Th & 4 & 5 & 3 & 8 & 8 & 5 \\ U & 1 & 2 & 1 & 2 & 2 & 1 \\ Li & 25 & 50 & 25 & 38 & 33 & 29 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: LA-ICP-MS analytical results of silver foil glass beads excavated from the tom of [PERSON] [PERSON]. Figure 4: Composition comparison of silver foil glass beads; (**A**): Fe\({}_{2}\)O\({}_{3}\) vs. CaO, (**B**): Principal component analysis.
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 2 Materials and Methods
### Data Pre-Processing  
Environmental change is a continuous process and we need to consider how signal changes within a period for context awareness. Therefore, we introduced a sliding window that contained more descriptive information to preprocess data. Sliding window with different size covers diverse information to detect environment. Figure 4 shows the new GNSS measurements set in the collection sequence when the sliding window length \(k\) is 3. When the window size \(k\) is 1, the set of GNSS information \(\mathbf{G}\) and \(\mathbf{S}\) are used to extract features of the current environment. When the window length \(k\) is greater than 1, sliding window obtain time sequence information of GNSS measurements, we define three new sets of GNSS measurements \(\mathbf{G_{u}}\), \(\mathbf{G_{p}}\) and \(\mathbf{S_{u}}\) as follows:  
\[G_{u}\triangleq\overset{k}{\underset{i=1}{\cup}}G_{i}=\left\{g_{i}|i=0,1, \cdots,n\right\},G_{p}\triangleq\overset{k}{\underset{i=1}{\cap}}G_{i}=\left\{ g_{i}|i=0,1,\cdots,n\right\} \tag{3}\]  
\[g_{i}\triangleq\left\{ctype_{i},syid_{i},cnr_{i}^{j},azi_{i}^{j},elel_{i}^{j} \middle|j=1,2,\cdots k\right\} \tag{4}\]  
\[S_{u}=\left\{pdop_{j},hdp_{j},vdop_{j}\middle|j=1,2,\cdots k\right\} \tag{5}\]  
where \(\mathbf{G_{u}}\) represents the union set and \(\mathbf{G_{p}}\) represents the intersection set of the \(\mathbf{G}\) according to the visible satellite constellation and the ID in the constellation as the unique identification when the window length is \(k\). \(\mathbf{S_{u}}\) represents the set of all values of \(\mathbf{S}\) when the window length is \(k\).  
The sliding window plays two important roles in the data pre-proposal stage. The first is to reduce the noise fluctuation. Since the GNSS signal received by smartphones is unstable, we calculate the statistical features at the current moment in set \(\mathbf{G_{p}}\) and \(\mathbf{S_{u}}\). The other one is to obtain more GNSS information to extract time sequence features. The information of a single moment can hardly reflect the change of signal in time sequence. We utilize different sliding window size (e.g., 2, 3, 5) to smooth  
Figure 4: An example of the new GNSS measurements set in the collection sequence when the sliding window length \(k\) is three.  
the current time GNSS signal and extract time sequence features at different time intervals. In our experiments, we compared different kinds of features in different sliding window size (e.g., 2, 3, 5) in Section 3.2.1.
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
## 3 Selection of Solvent Systems Used in PCL Electrospraying  
PCL, as a biocompatible, biodegradable, and semi-crystalline polyester, is widely used in the fields of medical tissue engineering, drug delivery, control release systems, food packaging, antibacterial study, protective clothing fabrication, and biosensors. Particularly, the drug-loaded PCL microspheres or other active substances encapsulated by PCL shell obtained from electrospray have more potential and advantages in these fields. The selection of a suitable solvent system to prepare PCL electrospray solution is one of the main points to take into account since it influences not only the physico-chemical properties of the working solutions, but also the characteristics of the obtained particles such as the mean diameter, size distribution, structure, and morphology. Furthermore, the formation of the Taylor cone, the breakup of electrospray droplets, and the solidification step of the particles depend also on the solvents system. Therefore, among the various criteria, three of them are mainly considered, i.e., (i) it needs at least partially solubilize PCL, (ii) the obtained physico-chemical properties of the solution (surface tension, electric conductivity, viscosity, vapor pressure, boiling point, and dielectric constant) should be suitable to achieve a cone-jet mode; and (iii) the selection of solvent systems should take into account the desired structure and morphology of final PCL particles.
## 6 Simulation
### Error Rate of the Proposed Model 1 Using OMNeT  
The evaluation of the proposed network with the use of a physical simulation environment is achieved by using the OMNeT simulator, as shown in Figure 11a. Thus, the topology of model 1 is established by using the OMNeT project with practical channel parameters. The topology consists of the PU-TX tower, seven PU-RXs, the SU-TX tower, seven SU-RXs, the radio medium module, and the physical environment module. The radio module defines the practical parameters of the propagation media so that the minimum interference level at each user is \(-110\) dBm, the minimum background noise level  
Figure 10: BER of the PU and SU in model 1 under fading channel with \(\gamma=3\%\). (**a**) BER of the PU in model 1. (**b**) BER of the SU in model 1.  
Figure 9: BER of the PU and SU in model 1 over AWGN with \(\gamma=3\%\). (**a**) BER of the PU in model 1. (**b**) BER of the SU in model 1.  
is \(-110\) dBm, the minimum reception power level is \(-85\) dBm, and the carrier frequency is \(2.4\) GHz. Moreover, the network description file (NED) uses the \(802.11\)b standard with an OFDM modulation scheme. In addition, the initialization file (INI file) uses a two-ray reflection model, flat ground description, transmitted power of \(10\) mwatt, a message length of \(56\) bytes, a maximum coverage area of \(500\) m from the TX tower, and different application bandwidths, i.e., \(6\) MHz, \(9\) MHz, \(12\) MHz, \(18\) MHz, \(24\) MHz, \(36\) MHz, \(48\) MHz, and \(54\) MHz.  
The simulation process at the \(SU_{1}\) or the \(PU_{1}\) without any donation leads to the results of the packet error rate (PER) versus the SINR, as shown in Figure 11b. Thus, as a sample from the different implemented results, the \(SU_{1}\) of a certain application that has a bandwidth of \(36\) MHz achieves very closed performance when the CR system works alone and when the SU system donates \(10\%\) to the PU system, as shown in Figure 12a. Therefore, the PER is \(0.5\times 10^{-8}\) for both cases.
## 2 Analysis Methods  
### Numerical Techniques  
A two-dimensional unsteady Reynolds-averaged Navier-Stokes (RANS) analysis was performed to effectively simulate the generation and transition of shock reflections using symmetry wedges in the dual solution domain. The three-dimensional effect has more impact on the transition from regular reflection to Mach reflection and tends to reduce the height of Mach reflection [4], so two-dimensional studies are used for conservative simulations. Finite Volume Method (FVM) code was developed to simulate high-enthalpy flow with shock waves. According to [PERSON] et al. [20], turbulence has little effect on the generation and transition of the reflected wave, so the viscosity is not considered in this study. The inviscid convection term was calculated by applying the Advection Upstream Splitting Method (AUSMDV) scheme, and the Monotonic Upwind Scheme for Conservation Laws (MUSCL) technique was applied to the interface flux.  
Temporal discretization was computed using the fourth-order Runge-Kutta (RK) method, and the T-model proposed by [PERSON] et al. [21] was used to simulate the laser energy that was applied to the flow. This model is well known for simulating the generation of plasma by laser energy deposition and the propagation of blast waves. A high-powered laser is used to generate a blast wave and the laser operates with pulsed discharge. However, the duration of the pulse is only about 10 ns, which is much shorter than the time scale of the flow.  
The energy deposition is represented by a symmetric temperature with a Gaussian distribution and the flow density is unchanged because the deposition time is very short. The temperature change of the energy deposition region is expressed as follows:  
\[\Delta\mathrm{T}=T_{0}e^{-r^{2}/r_{0}^{2}} \tag{1}\]where \(r_{0}\) is an arbitrary value that is usually defined as the radius of the deposition area, \(r\). The deposition region is assumed to be a sphere and the radius, \(r\), is calculated from its volume. \(T_{0}\) is the peak temperature variation.  
The amount of deposited energy E is:  
\[\mathrm{E}=\int_{0}^{2\pi}\int_{0}^{\pi}\int_{0}^{\infty}r^{2}sin\theta\rho_{ \infty}c_{p}\Delta Tdrd\theta d\varphi \tag{2}\]  
where \(c_{p}\) is the volumetric specific heat, \(\rho_{\infty}\) is the freestream density, \(\theta\) is the azimuthal angle, and \(\varphi\) is the swiping angle. The following is then obtained:  
\[\Delta\mathrm{T}=\frac{\mathrm{E}}{\pi^{3/2}r_{0}^{3}\rho_{\infty}c_{p}} \tag{3}\]
## 3 Results
### Characterization of Sodium Alginate
#### 3.1.3 Proton Nuclear Magnetic Resonance (\({}^{1}\)H-NMR) Analysis  
The analysis was done according to [PERSON] et al. [25]. \({}^{1}\)H NMR spectra analysis was used to determine the Mannuronate/Guluronate (M/G) ratio of the used sodium alginate. Quantitatively, the guluronic and mannuronic mole fractions were associated with the area signal or integration of the respective peaks. The integration obtained is shown in \(\mathrm{I_{A}}\) (guluronate peak area of G\({}_{\text{1}}\)) 5.5-5.7 ppm, \(\mathrm{I_{B}}\) (mannuronate peak area of M\({}_{\text{1}}\)) 5.1-5.4 ppm, \(\mathrm{I_{C}}\) (guluronic peak area of G\({}_{\text{5}}\)) 4.9-5.1 ppm. The mole fraction of each composition of \(\mathrm{F_{M}}\) and \(\mathrm{F_{G}}\) is used to determine the M/G ratio of alginate with the equation referring to the research of [PERSON] et al. [35]. The signals of proton for alginate at \({}^{1}\)H-NMR spectrum has in range of 1-6 ppm. The anomer protons of each monosaccharide could be recognized from the \(\alpha\)-glycosides and \(\beta\)-glycosides of the residues. Based on the \({}^{1}\)H-NMR spectra (Figure 2b), the anomer protons of \(\alpha\)-guluronate and \(\beta\)-mannuronate were shown on the G\({}_{\text{1}}\) and M\({}_{\text{1}}\) proton at 8 5.6 and 5.1 ppm, respectively. Both signals are the most deshielded proton because both protons are  
Figure 1: (a) Size Exclusion Chromatography-Multiple Angle Laser Light Scattering (SEC-MALLS) profile and (b) TGA of the sodium alginate from _Sargassum duplicatum_.  
attached to anomeric carbon. The signals of 5-6 ppm belong to anomeric proton of the \(\alpha\)-glycosidic or \(\alpha\)-galuronate and the signals between in the range of 4.3-5 ppm belong to proton of \(\beta\)-glycosidic or \(\beta\)-mannuronate [36]. The proton of \(\alpha\)-glycosidic is more deshielded than \(\beta\)-glycosidic because it is located at equatorial position as compared to axial position. [PERSON] et al. [25] showed proton signals of sodium alginate from _Sargassum_ sp. that G\({}_{1}\), M\({}_{1}\), G\({}_{5}\) proton at 8 5.07, 4.68 and 4.45 ppm. The M/G ratio of the mannuronic and galvanonic acid residues can be determined from the integration of the respective peaks of the \({}^{1}\)H-NMR spectra [37]. The M/G ratio and \(\eta\) value of alginate _S. duplicatum_ were 0.91 and 0.68, respectively. The chemical structure of mannuronic acid and galvanonic acid could be shown in Figure 2c,d.
# Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks
## 4 Performance Evaluation
### Performance Trade Off  
In this section, we discuss the performance of of our propose scheme LiMHA with the selected schemes ODTS and LAEHA. The proposed scheme performs better in terms of optimizing network performance. The higher lifetime achievd by LiMHA is due to division of network into number of sectors and high energy node is present in each which increases the probability of successful transmission of data to to sink. On the other hand there is no such phenomena of super node deployment is discussed. However ODTS has better network lifetime as compared to LAEHA because ODTS uses ant colony optimization for transmission of data to the sink while LAEHA only considers commonly used transmission strategy. Looking at the energy comparison, LiMHA achieve less energy when compared with ODTS in dense area network. ODTS uses the most energy balance distance for transmission of data to the sink. It energy slightly changes with node density. In LiMHA, presence of each super node in a sector minimizes the energy of normal nodes due to selection of forwarder node at minimum distance thus normal nodes have to choose low distance nodes in their transmission range. With the deployment of super node the minimum distance level is increased. The performance of LAEHA in terms of less energy consumption is outstanding because LAEHA only considers the selection of forwarder node on minimum depth. E2E delay level of LiMHA is worst due to number of calculations involved in selecting the forwarder nodes. Normal node in a sector calculates its distance with neighboring normal node and super node. Also it calculates its distance with the down sector normal normal thus delay is increased in this scenario. In terms of achieving minimum delay level LAEHA performance is better compared to ODTS because ODTS considers the time involves in selection of best possible path for data transmission increases the packet sending time thus delays the performance of the network while there is no such phenomena is adopted in LAEHA. N-SEP achieved energy efficiency in sparse deployment and while effective network lifetime in sparse deployment, however, E2E delay is minimized due to the k-mean clustering algorithm. Similarly, P-SEP is attains minimum E2E delay and energy efficiency in sparse deployment due to the selection of cluster heads at optimal distance from sink and members having minimum distance from the respective head node. The performance trade off table is shown in Table 3.  
Figure 7: End 2 End delay.
## 1 Introduction  
Protease-activated receptors (PARs), which belong to the family of seven transmembrane G protein-coupled receptors (GPCRs), are activated by the serine protease-mediated proteolytic cleavage of the N termini [1]. Among the four known types of PARs (PAR1-4) [2], PAR2 is activated by trypsin, while PAR1, PAR3, and PAR4 are activated by thrombin [3].  
Several cells of the gastrointestinal (GI) tract, such as the smooth muscle cells, endothelial cells, enteric neurons, and immune cells, express PARs [4, 5]. PARs exhibit various physiological functions in the GI tract. In particular, PARs regulate the glandular exocrine secretion in the GI system [6, 7]. PAR2 exerts both pro-inflammatory and antiinflammatory effects in pancreatitis and related pain [8, 9] and gastrointestinal mucosal inflammation/damage [10, 11]. PAR1, PAR2, and PAR4, which are expressed in the smooth muscle cells and their adjacent cells, regulate the motility of the smooth muscles [12, 13]. The roles of PARs in GI motility regulation are complex and vary depending on the species and organs. PAR2 and PAR1 agonists elicit a strong contraction in isolated mouse gastric longitudinal smooth muscle strips, whereas they elicit a transient relaxation in carbachol-contracted muscles [13, 14]. Additionally, PAR2 and PAR1 agonists elicit a transient relaxation followed by a contraction in the isolated mouse small intestine [13]. Various fundamental and clinical studies have reported the role of PAR2 in colonic pain [15, 16].  
In contrast to receptors activated through ligand-receptor interactions, PAR2 is activated by a proteolytic mechanism in which the PAR2 agonist (i.e., trypsin) binds and cleaves the amino terminus of the receptor [3]. The activation of PAR2 by trypsin increases inositol triphosphate (IP\({}_{3}\)) production through G\(\alpha\)q/11 coupling [6; 11]. Trypsin, a serine protease, is a widely studied protease for which its role in intestinal digestion has been previously reported [1].  
Interstitial cells of Cajal (ICCs) are considered electrical pacemakers for the generation of slow waves in the GI tract. The loss of ICCs can cause severe gut diseases, including dysmotility, in both humans and animals [17; 18; 19]. Therefore, ICCs are reported to be pharmacological targets for regulating GI motility. [PERSON] et al. [20] suggested that PARs regulate the excitability of colonic muscles and activate different conductance pathways in each cell type of the smooth muscle cell-ICC-platelet-derived growth factor receptor \(\alpha\)-positive cell syncytium. Additionally, PAR1 and PAR2 modulate the pacemaker activity of colonic ICCs through the phospholipase C (PLC)-dependent [Ca\({}^{2+}\)]i release pathway [21]. However, the mechanism underlying the trypsin-PAR2 signaling-mediated regulation of small intestinal ICCs has not been elucidated.  
In this study, the effects of trypsin and PAR2 activation on the pacemaker potentials in murine small intestinal ICCs were investigated using whole-cell patch-clamp techniques.
## 4 Experiments
### Parameter Setting
For a given group of parameters and a case, the capability of GA to search for a good solution may decline as the value of \(q\) increases since increasing the value of \(q\) enlarges the solution space. In fact, as the value of \(q\) increases, the value of \(F_{\pi}\) of a local optimal solution (i.e., an optimal solution under a given \(q\)) obtained by (7) is much closer to that of a global optimal solution. This implies that the trend of \(F_{\pi}\)'s value becomes stable as the \(q\)'s value increases. Thus, the stability of GA to search a good solution is used to measure a group of parameter settings as well, and introduced next. Let \(\Psi_{1}(i,j)\) represent the stability of GA with the \(i\)-th group of parameters to search for a solution if \(q\) varies from two to 100 for Case \(j\), \(i\in\{1,2,\,\ldots,\,45\}\) and \(j\in\{1,\,2,\,\ldots,\,20\}\). Then, \(\Psi_{1}(i,j)\) is obtained as  
\[ \begin{array}{c}\Psi_{1}(i,\,j)=\left(\sum_{q=2}^{100}\left[\max(F_{\pi}(i, j,q)|q\in\{2,3,\ldots,100\})-F_{\pi}(i,j,q)\right]^{2}\right)/99,\\ \\ i\,\,\in\,\{1,\,2,\,\ldots,\,45\}\text{and}j\,\,\in\,\{1,\,2,\,\ldots,\,20\} \end{array}  \tag{9}\]  
Further, \(\Psi_{2}(i,j)\) records the ranking value of the \(i\)-th group of parameters among the 45 groups of parameter settings according to \(\Psi_{1}(i,j)\) for a given Case \(j\). Specifically, for Case \(j\) (i.e., the value of \(j\) is determined), if \(\Psi_{1}(i,j)\) is the \(f\)-th smallest one of all values of \(\Psi_{1}(i,j)\) for all \(i\in\{1,2,\,\ldots,\,45\}\), value \(f\) is given to \(\Psi_{2}(i,j)\). In this way, \(\Psi_{2}(i,j)\) can be obtained, \(i\in\{1,2,\,\ldots,\,45\}\) and \(j\in\{1,2,\,\ldots,\,20\}\). Further, \(\varphi_{i}\) is used to record the average ranking value of the \(j\)-th group of parameter settings for the 20 cases. Then, we have  
\[\varphi_{i}=\left(\sum_{j=1}^{20}(i,j)\right)/20,\,\,i\,\in\,\{1,\,2,\,\ldots, \,45\} \tag{10}\]  
Note that for the \(i\)-th group of parameters, the smaller the value of \(\varphi_{i}\) is, the more stable GA is to explore a good solution even if the solution space is enlarged, i.e., the value of \(q\) increases. Then, \(TI_{i}\) and \(\varphi_{i}\) are used to measure a group of parameter settings. After 89,100 experiments with different parameter settings and the data analysis according to Expressions (8)-(10), experimental results are summarized in Table 2. It follows from Table 2 that the parameter setting of the 36 th group has the largest value of \(Tl_{i}\) for all \(i\in\{1\), 2,..., 45\(\}\) and at the same time the value of \(\varphi_{i}\) is quite small. Thus, the parameter setting of the 36 th group is selected for the presented GA in this work.
## 2 Application of Membranes in Contamination Situations
### Groundwater Contamination
The obtained results indicate that the modified Pd/Fe-PMAA-PVDF membranes under laboratory conditions allowed for the degradation of 3,3',4',5-pentachlorophenyl (PCB-126) as a reference substance, even up to 96% in a shorter period of time than 15 s. In the case of other compounds, the reaction rate or the efficiency of removal of impurities varied, and in the case of, for example, TCE and CTC compounds, a reduction of about 90% and 85%, respectively, was obtained in 2.2 s. The verification of the membranes, carried out in the field conditions in groundwater, allowed for a significant reduction of pollutants. The purified waters remained 0.1% CTC, 12% TCE, and 18% TCE with a residence time of 2.4 s. Based on the obtained results, it was found that the degradation rate changed in the series: carbon tetrachloride \(>\) trichloroethylene \(>\) tetrachlorether \(>\) chloroform [102; 103].