esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Influence of Formulation Factors on the In Vitro Availability of Felodipine in Experimental Tablets
_Increased lactose proportion and decreased microcrystalline cellulose amount_  
The in vitro release profile of relodipine in the formulations is shown in Figure 13.  
By increasing the amount of lactose by 7% and decreasing the amount of microcrystalline cellulose by 10%, the relodipine availability was enhanced in LSNa surfactant medium. In 1% LSNa, the relodipine release was 108.1% at 2 h for F17, containing an increased amount of lactose, compared to 22.11% for F16, containing a higher amount of microcrystalline cellulose. The obtained results agree with the previous experiment, confirming lactose's role in accelerating the dissolution rate by pore formation inside the matrix. This fact must be correlated with the presence of other excipients in the formulation, such as the hydrophilic polymers, highlighting their role in release [51; 52; 53; 54]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Formulation** & **Dissolution Media** & \
\begin{tabular}{c} **Difference Factor** \\ \(f_{1}\) \\ \end{tabular} &
\begin{tabular}{c} **Similarity Factor** \\ \(f_{2}\) \\ \end{tabular} \\ \hline REF–F14 & LSNa 1.00\% & 17.06 & 53.68 \\ REF–F15 & LSNa 1.00\% & 111.07 & 16.85 \\ REF–F14 & CTAB 0.40\% & 17.90 & 53.86 \\ REF–F15 & CTAB 0.40\% & 11.51 & 63.03 \\ LSNa–CTAB & F14 & 42.59 & 40.12 \\ LSNa–CTAB & F15 & 134.18 & 15.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The results obtained when comparing the dissolution profiles of the innovative product and the formulations under study, in different dissolution media (for F14 and F15). Figure 12: Felodipine release rate for F14 and F15 compared with the reference product. _The influence of polyvinyl pyrrolidone (PVP) amount in the tablet formulation_  
The in vitro release study regarding the influence of polyvinyl pyrrolidone (PVP) amount in the tablet formulation is illustrated in Figure 14.  
In the 0.4% CTAB medium there were no important differences between F18 and F19 release profiles. In the LSNa 1% medium, the dissolution rate was superior for tablets with a higher amount of PVP. Increasing the concentration of PVP in the formulation significantly enhanced the in vitro release rate in LSNa medium but the effect was less obvious in the CTAB medium. This phenomenon is possibly due to an increased degree of hydrophilicity and thus, the PVP dissolving faster than the main matrix-forming polymer [55; 56; 57; 58; 59; 60]. _Final tablet formulation_  
The in vitro release study for the final tablet formulation is illustrated in Figure 15.  
Figure 14: Percentage of dissolved feloipine in 0.4% CTAB and 1% LSNa for two formulations with different amounts of polyvinyl pyrrolidone (PVP). Figure 13: Percentage of dissolved feloipine in 0.4% CTAB and 1% LSNa for F16 and F17 formulations with different amounts of lactose and microcrystalline cellulose. The felodipine release profile, in both media, showed a higher dissolution rate for the studied formulation compared with the reference product.
## 3 Results
### Safety Determination of CLRF  
#### 3.3.1 Chicken Embryo Chorioallantoic Membrane Test  
Vascular-rich eggs were selected for the experiment, and the shells were carefully peeled from the air chambers with forceps to expose the surface of the CAM, washed with NaCl solution, dropped into 300 \(\upmu\)L of positive control, negative control and sample, and observed for 3 min. Morphological observations and scores were made for the positive control (0.1 mol/L sodium hydroxide solution), negative control (0.9% NaCl solution), CW and CLRF before and after the addition of CAM samples. The results of the scoring are shown in Table 4, with no irritation at either CLRF or CW. It is clear from Figure 2A that the CAM vascular response of the positive control was accompanied by significant haemorrhage and vascular lysis, while the CAM vascular responses of CW and CLRF were consistent with the negative control in that there was no haemorrhage, vascular coagulation or vascular lysis, and therefore neither CW nor CLRF caused irritation.  
Figure 1: Determining the active substance content and DPPH radical scavenging capacity of three different Coix seed fermentation broths: (**A**) Total sugar, (**B**) Total phenol, (**C**) Total protein and (**D**) DPPH radical scavenging capacity of three different volume fractions of Coix seed fermentation broth. CW: Coix seed water extract; CLF: Coix seed _Lactobacillus bulgaricus_ fermentation broth; CSF: Coix seed _Saccharomyces cerevisiae_ fermentation broth; CLRF: Coix seed _Lactobacillus reuteri_ fermentation broth. Each value is expressed as mean \(\pm\) SD (\(n\) = 3). The difference analysis between other experimental groups and the CW group is indicated by *** (ns \(>\) 0.05, *: \(p\) \(<\) 0.05, ***: \(p\) \(<\) 0.01, ***: \(p\) \(<\) 0.001, compared with CW group).
## 5 Main Results
### Illustration of Lemma 1
Similarly, the functions \(\lambda_{v1}^{1}(t,\varepsilon)\) and \(\lambda_{u,v2}^{1}(t,\varepsilon)\) also are absent. This happens because \(\lambda_{v1}(t,\varepsilon)\equiv 0\) and \(\lambda_{v1}^{1}(t,\varepsilon)\equiv 0\), while \(\lambda_{u,v2}(t,\varepsilon)=-\lambda_{u,v1}(t,\varepsilon)\) and \(\lambda_{u,v2}^{1}(t,\varepsilon)=-\lambda_{u,v1}^{1}(t,\varepsilon)\)._In Figure 1, the graphs of the functions \(z_{1}(t,\varepsilon)\) and \(z_{1}^{1}(t,\varepsilon)\) are depicted for different values of \(\varepsilon>0\). Note that for \(z_{1}^{1}(t,\varepsilon)\), we have only one curve (the dashed one) because this function is independent of \(\varepsilon\) (see Equation (143)). It is seen from the figure that, for decreasing \(\varepsilon\), the graph of \(z_{1}(t,\varepsilon)\) approaches the graph of \(z_{1}^{1}(t,\varepsilon)\). In Figure 2, the graph of \(\Delta z_{1}(\varepsilon)\) (as a function of \(\varepsilon\)) is depicted. Along with this graph, the graph of the function \(2\varepsilon^{2}\) is presented, showing that \(\Delta z_{1}(\varepsilon)\) is estimated by \(2\varepsilon^{2}\), i.e., \(\Delta z_{1}(\varepsilon)\leq 2\varepsilon^{2}\) for all \(\varepsilon\in(0,0.2]\). Similar results with respect to \(z_{2}(t,\varepsilon)\), \(z_{1}^{1}(t,\varepsilon)\), and \(\Delta z_{2}(\varepsilon)\) are shown in Figures 3 and 4, the results with respect to \(\lambda_{\text{u}2}(t,\varepsilon)\), \(\lambda_{\text{u}2}^{1}(t,\varepsilon)\), \(\Delta\lambda_{\text{u}2}(\varepsilon)\) are shown in Figures 5 and 6, and the results with respect to \(\lambda_{\text{v}2}(t,\varepsilon)\), \(\lambda_{\text{v}2}^{1}(t,\varepsilon)\), \(\Delta\lambda_{\text{v}2}(\varepsilon)\) are shown in Figures 7 and 8. **Remark 13**. : _Note that the functions \(\lambda_{\text{u}1}(t,\varepsilon)\) and \(\lambda_{\text{u},\text{v}}(t,\varepsilon)\) do not appear in the optimal controls and in the optimal values of the cost functionals of the Stackelberg game (9)-(11) with Data (131). Therefore, in order not to overload the paper with additional figures, we do not present the figures with respect to \(\lambda_{\text{u}1}(t,\varepsilon)\), \(\lambda_{\text{u}1}^{1}(t,\varepsilon)\), \(\Delta\lambda_{\text{u}1}(\varepsilon)\) and \(\lambda_{\text{u},\text{v}1}(t,\varepsilon)\), \(\lambda_{\text{u},\text{v}1}^{1}  
Figure 4: Estimate of closeness of \(z_{2}(t,e)\) and \(z_{2}^{1}(t,e)\). In Table 1, the values \(\Delta z_{1}(\varepsilon)\), \(\Delta z_{2}(\varepsilon)\), \(\Delta\lambda_{\rm u1}(\varepsilon)\), \(\Delta\lambda_{\rm u2}(\varepsilon)\), \(\Delta\lambda_{\rm v2}(\varepsilon)\) and \(\Delta\lambda_{\rm u,v1}(\varepsilon)\) are presented for various values of \(\varepsilon>0\). The results of the table show that these values are estimated by \(\varepsilon^{2}\) with the coefficients which do not exceed 6.5.
## 3 Experimental Results and Analysis
### Deductions from Proposed Model  
In watermarking algorithm research, the parameters of watermark information length \(L\) and repeat times \(N\) are important parameters. For those algorithms which conform to the proposed watermarking principles, regarding finding how to set or optimize these parameters to enhance the robustness needs to be solved. Based on the proposed model, the relationships among robustness index \(R\), watermark information length \(L\) and repeat times \(N\) are deduced theoretically. Hence, the parameters of \(L\) and \(N\) can be adjusted in relation to the design or development of a watermarking algorithm, the aim of which is to enhance the robustness. This optimization is also, conducted without experiments. Detailed relationships are deduced as follows.
# A 2.4 GHz 20 W 8-Channel RF Source Module with Improved Channel Output Balance
## 2 Materials and Methods
### Power Dividing Part  
In the previous module, 2-way commercial dividers, GP2Y+, with transmission lines on FR-4 were used to design an 8-channel structure. It was also a method for uniform distribution between channels, but it could not predict the error between commercial components because the power divider was not its own design. Therefore, the power dividing part has been constructed with eight channels by combining three sections of the 2-way Wilkinson divider as shown in Figure 7[19; 20; 21]. Characteristic impedance (\(Z_{0}\)) of each port is based on 50 Ohm, and that of the dividing line is \(Z_{0}\sqrt{2}\). The length of the dividing line should be \(\lambda/4\). The shunt resistor is used as \(2Z_{0}\); in this case, 100 Ohm resistors were used. Important parameters during the multi-channel design of a plasma source include the phase and magnitude balance among channels that maintain the uniformity of the plasma generation. In the previous module, the channel spacing was 4.25 cm, but in the proposed module, the channel spacing was further reduced up to 3.2 cm, considering the cables to be connected to each channel. Moreover, the proposed RF source module considered detailed EM analysis to solve discontinuity at the intersection of the transmission lines from the beginning of the Wilkinson divider design on the Rogers RT/duroid(r) 6010.LM. To verify the output channel balance, each channel was measured from the network analyzer, ZVA 50, and the other channels were connected to 50 Ohm terminations. The measurement was based on an input power of 0 dBm. In the previous module, the phase difference occurred up to 2\({}^{\circ}\) from the center to the outer channel as shown in Figure 8a. In contrast, the proposed module proves that a phase difference within 0.35\({}^{\circ}\) can be achieved as shown in Figure 8b; thus, it was confirmed that the phase difference was reduced more than in the previous module. The difference in magnitude of the output power between the channels will be explained in Section 3.  
Figure 6: Thermal simulation results: the proposed module (**a**) without and (**b**) with water jacket implementation.
# Reconstruction of Preclinical PET Images via Chebyshev Polynomial Approximation of the Sinogram
## 3 Materials and Methods  
### Phantom Study: Simulated Micro-PET IQ Phantom via the NEMA NU 4-2008 Protocol  
For the purposes of our studies, we simulated an image quality (IQ) phantom in the open-source library STIR (Software for Tomographic Image Reconstruction) [29], following the \"NEMA NU 4-2008 standards\" protocol for the performance measurements of small animal PET [23]. The IQ phantom is a poly(methyl methacrylate) (PMMA) cylindric container with internal dimensions of 50 mm in length and 30 mm in diameter, and comprises three main regions: (i) a uniform cylindrical chamber 30 mm in diameter and 30 mm in length, (ii) a 20 mm long solid region, containing five fillable rods with diameters of 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm, respectively, and (iii) a lid supporting two, equally-sized, cold region chambers of 14 mm in length and 8 mm in diameter, filled with non-radioactive water and air, respectively. The phantom grid size employed was \(119\times 119\times 161\).
# NR Sidelink Performance Evaluation for Enhanced 5G-V2X Services
## 4 Performance Evaluation
### Performance Metrics  
* **Packet Reception Rate (PRR):** PRR is the ratio of all packets received (\(R\)) to all packets sent (\(T_{source}\)) from the sources. \[PRR(\%)=\frac{R}{T_{source}}\times 100\] (1)
* **Throughput:** The rate at which information is sent through the network. The unit is Kbps. \[Throughput=\frac{R}{Simulation\ Time}\] (2)
* **Latency:** The amount of time that passes after a data packet transfer instruction before it starts.
## 2 Related Work and Theoretical Background
### Efforts Towards Integrating Operational and Sustainability Perspectives  
Recognizing the limitations of single approaches, researchers have begun exploring ways to integrate operational analysis with sustainability considerations. Sustainable Process Mining is an area where some conceptual work and early studies have aimed to incorporate environmental indicators into process analysis [15]. This might involve annotating process models with aggregated environmental data or developing sustainability-related conformance checks. However, these initiatives often remain in the early stages, without dynamic data integration or robust optimization features. Another path is to extend Lean tools like Value Stream Mapping (VSM). Originally used to identify operational waste, VSM has been adapted to'sustainable VSM' (SVSM) by including environmental metrics such as energy use and material waste along with time and cost [16]. Although helpful for strategic decisions, these methods are typically manual and snapshot-based, lacking the automation and continuous discovery capabilities of process mining. Discrete-event simulation models have also been used to evaluate operational and environmental outcomes under different scenarios. Though useful for \"what-if\" exploration, these models may not perfectly match real-time factory conditions and do not leverage data in the same live, automatic way process mining does.  
Limitation: Despite these innovations, many integration efforts lack real-time feedback, depend on manually collected or aggregated environmental metrics, cannot easily handle multiple conflicting goals, or do not use live factory data from sensors. What is often missing is a seamless connection from real-time data gathering to integrated analysis and decision-ready recommendations.
## 2 S-L Analysis Method
### S-L Analysis Method of Multi-Power System
#### 2.4.1 Generator Set  
For generator sets, the analysis is similar to the S-L analysis of the single-supply system shown in Figure 4. The difference is that there are multiple power frequency characteristics in the left side of the frequency axis due to multiple power supply. Taking two generator sets with different frequency characteristics as an example, which is shown in Figure 7, lines L\({}_{\mathrm{G1}}\) and L\({}_{\mathrm{G2}}\) are the frequency characteristics of generator set 1 and generator set 2, respectively. The blue information indicates the initial steady state and the red information indicates the steady state after frequency regulation.  
In Figure 7, the initial power balance corresponding to the operation line o can be expressed as Equation (14).  
\[P_{\mathrm{G1}}{}^{0}+P_{\mathrm{G2}}{}^{0}\ =\ P_{\mathrm{L}}{}^{0} \tag{14}\]  
From the final power balance corresponding to the operation line o\({}^{\prime}\), Equation (15) is obtained.  
\[P_{\mathrm{G1}}{}^{1}+P_{\mathrm{G2}}{}^{1}\ =\ P_{\mathrm{L}}{}^{1} \tag{15}\]  
Taking into consideration the transitional process of two generator sets from operation line o to operation line o\({}^{\prime}\), for power, we obtain the equation below.  
\[\left\{\begin{array}{rcl}P_{\mathrm{G1}}{}^{0}+\Delta P_{\mathrm{G1}}&=&P_{ \mathrm{G1}}{}^{1}\\ P_{\mathrm{G2}}{}^{0}+\Delta P_{\mathrm{G2}}&=&P_{\mathrm{G2}}{}^{1}\end{array}\right. \tag{16}\]  
For the load, the frequency characteristic is the same as that of single-power supply.  
\[P_{\mathrm{L}}{}^{0}+\Delta P_{\mathrm{L}}{}^{0}-\Delta P_{\mathrm{L}}{}^{1} \ =\ P_{\mathrm{L}}{}^{1} \tag{17}\]  
Figure 7: S-L analysis method of double generators.  
With Equations (14)-(17), we deduce Equation (18).  
\[\Delta P_{\rm G1}+\Delta P_{\rm G2}\ =\ \Delta P_{\rm L}^{\ 0}-\Delta P_{\rm L}^{\ 1} \tag{18}\]  
From the mathematical relationship in Figure 7, we deduce the equation below.  
\[\Delta P_{\rm G1}\cdot R_{\rm G1}\ =\ \Delta P_{\rm G2}\cdot R_{\rm G2}\ =\ \Delta P_{\rm L}^{\ 1}\cdot R_{\rm L}\ =\ \left|\Delta f\right| \tag{19}\]  
It can be seen that Equation (18) satisfies the basic relationship 1 shown in Equation (3). Equation (19) satisfies the basic relationship 2 shown in Equation (7). What should be noted is that when the load suddenly decreases, the system's frequency regulation process is the inverse of the above process while the analysis above is still applicable.
## 2 Lining Void Form and Water Temperature Conditions
### Short-Period Fluctuation Model of Lining Surface Temperature  
In this study, the temperature field monitoring of high-speed railway tunnels in cold areas was carried out based on the Harbin-Mudanjiang high-speed railway, and the Hufengling Tunnel and Zhishan Tunnel, which have the highest altitude on the line, were selected for monitoring. In previous studies, the sine function with an annual cycle has been widely used to express tunnel temperature changes and has been used as a temperature load or boundary condition in model tests and numerical simulations, such as in the Hongfu Tunnel [22] and Galongla Tunnel [23], all using the temperature load function in the form of Equation (1):  
\[T=T_{m}+T_{a}\sin(2\pi t/365+\varphi) \tag{1}\]  
However, continuous measured results of the temperature field show that the diurnal temperature fluctuations caused by the alternation of day and night are very significant and widespread [24; 25; 26]. In on-site measurements, when the temperature difference in a day reaches more than 10 \({}^{\circ}\)C, it is considered that the temperature change on that day has obvious short-period temperature fluctuation characteristics. The temperature fluctuations  
Figure 1: Classification of voids behind the lining.  
at the entrance and exit of the Hufengling Tunnel reached 90 times in half a year, and at the entrance and exit of the Zhishan Tunnel the temperature fluctuated 130 times in half a year. The temperature curve of the entrance section of the Zhishan Tunnel from December 2018 to May of the following year is shown in Figure 2. When fitted by a sinusoidal function with an annual cycle, the correlation coefficient is 0.71, and it can be seen from the graph that the fitting using this function can only express the overall change trend of temperature and cannot express the characteristics of temperature fluctuations caused by the alternation of day and night.  
Therefore, a sine function with a daily period was used to characterize the short\(-\)period fluctuations of the tunnel temperature field, as shown in Equation (2):  
\[T=T_{mb}+T_{b}\cdot\sin(2\pi t/24+\varphi_{2}) \tag{2}\]  
where \(t\) is the time (h), \(T_{b}\) is the amplitude of the short-period temperature change (\({}^{\circ}\)C), \(T_{mb}\) is the daily average temperature (\({}^{\circ}\)C), and \(\varphi_{2}\) is the daily initial phase (rad).
## 2 Materials and Methods  
The beginning of the process, regardless of the cooling technique used, was the same for each sample produced--a weighed amount of ceramic Bi\({}_{2}\)O\({}_{3}\) powder (Sigma Aldrich, St. Louis, MO, USA, 99.96%) was placed in a ceramic or platinum-iridium (95% Pt, 5% Ir) crucible and inserted into a preheated chamber furnace where the batch remained under isothermal conditions in the atmospheric air for 15 min. Temperatures in a range from 900 to 1000 \({}^{\circ}\)C were used for melting to obtain the variety of samples. Then, in the case of slow cooling, the material was cooled freely with the furnace chamber to room temperature (25 \({}^{\circ}\)C). The average cooling rate was determined from the cooling time of the furnace chamber with the sample, and was approx. 10 \({}^{\circ}\)C/min. In the case of fast cooling using the twin rollers technique, after 15 min, the hot furnace was opened, and the molten batch poured onto stainless steel rollers--rotation speeds in the range of 400-800 RPM were used.  
The final products were examined by XRD at room temperature to determine the presence of amorphous and crystalline phases and the crystallographic phase, together with the calculation of the lattice constant by the Rietveld technique using GSAS-II software [22]. The measurements were performed with the Malvern Panalytical Empyrean diffractometer, equipped with a copper X-ray tube with the wavelength \(\lambda\) = 1.54 A on flat samples in Bragg-Brentano geometry. For preliminary characterization of the morphology of the samples, the average grain size was estimated using the Scherrer method. In order to investigate the thermal stability of the fabricated materials, XRD studies as a function of temperature were performed. The measurement was performed in the temperature range 25-775 \({}^{\circ}\)C with 25 \({}^{\circ}\)C increments and an angular range of 26 10-90\({}^{\circ}\). In order to further describe the morphology of the samples, SEM micrographs were taken, which were used to determine the homogeneity of the material in a certain volume, as well as to determine the average grain size by using the opensource code for MATLAB software, created by [PERSON] et al. [23]. An EDX microanalysis was used to estimate the amount of impurities that were present in the material due to the use of ceramic crucibles in the first step of the sample fabrication process.  
In order to determine the conductivity and activation energy of the obtained materials, electrical measurements were performed using the impedance spectroscopy method. Due to the form of the samples produced (a sintered batch in the case of free cooling and thin, brittle ribbons in the case of twin rollers), the materials required several stages of preparation for measurements. For detailed information about this process, see Appendix A. IS measurements were performed on the sample pellets in the temperature range 25-500 \({}^{\circ}\)C.
## 5 Data Processing
With a high overlapping between the images and ensuring that all important areas of the object are visible in at least three images, a dense point cloud with the useful 3D information will be produced. A proper strategy during capturing the image data will guarantee a geometrically accurate result without missing parts. Vectorization can be made in additional software.