esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Influence of Scaffold Structure on Bone Tissue Repair Scaffolds and Modulation
#### 3.2.2 Cell Size  
The S-0.8-0.56 and S-1.2-0.84 stents were simulated at an inlet velocity of 0.5 mm/s, and the pressure distributions were plotted in Figure 16.  
As illustrated, S-0.8-0.56 and S-1.2-0.84 scaffolds exhibit consistent pressure distribution trends at identical inlet velocities, with pressure decreasing gradually with decreasing \(Y\)-axis height. Quantitatively, smaller-aperture unit cells demonstrate larger inlet-outlet pressure differences and steeper pressure gradients along the scaffold height, attributed to increased flow resistance from the smaller pore diameters in S-0.8-0.56 scaffolds.  
Permeability calculations for S-0.8-0.56 and S-1.2-0.84 scaffolds, compared against S-0.7 scaffolds, are presented in Figure 17.  
Figure 16: Pressure distribution contour of S-0.8-0.56 and S-1.2-0.84 scaffolds.  
Figure 17: Influence of cell unit size on the permeability of scaffolds.  
It can be seen that the permeability of the three groups of scaffolds increased with the increase in the unit cell length, and the permeability increased by 37% when the cell length increased from 0.8 mm to 1.0 mm; when the cell length increased from 0.8 mm to 1.2 mm, the permeability increased by a factor of 1.1, and this trend was similar to the change in permeability with the pore size. Since the porosities of the three groups of scaffolds were almost the same (maximum difference of 1.4%), it can be hypothesized that for the square-pore shaped cell-type scaffolds, the pore size is the main influencing factor of the permeability, and the change in porosity does not aptly reflect the change in the permeability of the scaffolds.  
Statistical analysis of WSS for S-0.8-0.56 and S-1.2-0.84 scaffolds at an inlet velocity of 0.5 mm/s is presented in Figure 18.  
As shown in the figure, the two scaffolds with different cell sizes had significantly different WSS distribution trends at the same inlet velocity, but the distribution intervals were similar, and both scaffolds would induce cellular differentiation toward bone and cartilage at an inlet velocity of 0.5 mm/s. The WSS distribution of the S-0.8-0.56 scaffold with a cell length of 0.8 mm was between 0 and 20 mPa, with 74% of the WSS in the 0-10 mPa region, which would induce bone differentiation, and the rest of the region would induce chondrogenic differentiation. The WSS of S-1.2-0.84 scaffolds with a cell length of 1.2 mm was distributed between 0 and 30 mPa, with 61% of this region inducing bone differentiation and the rest of the region inducing chondrogenic differentiation. The average WSS of S-0.8-0.56 scaffold was calculated to be 9.067 mPa and that of S-1.2-0.84 scaffold was 6.009 mPa, with a difference of 50.77%, which shows that the larger the cell size, the smaller the scaffold WSS at the same inlet velocity under the same scaffold porosity, and the rate of change in the WSS is smaller than that of the permeability.
## 2 Basic Formulas
\begin{array}{l}I_{2}^{(4)}=\frac{\eta^{2}R}{2}J_{20}+\frac{\eta\sqrt{\eta^ {2}+1}\sqrt{R_{1}}}{12k_{2}^{3}}\big{\{}\left[3(a_{222}+a_{333})k_{2}^{4}-(6a_ {222}+8)k_{2}^{2}+8\right]K(k_{2})+\\ \left[(6a_{222}+4)k_{2}^{2}-8\right]E(k_{2})\big{\}}+\frac{k_{2}\eta\sqrt{\eta ^{2}+1}\sqrt{R_{1}}}{4}\Big{[}\frac{V_{2}}{1-p_{3}}\varPi(h_{3},k_{2})+\frac{ V_{4}}{1-p_{4}}\varPi(h_{4},k_{2})\Big{]}\end{array}  \tag{21}\]  
where  
\[ \begin{array}{l}I_{20}=\int\limits_{0}^{\frac{\pi}{2}}\text{arsinh}\frac{ \eta R\cos(2\beta)+\eta R_{1}+z_{Q}-z_{2}}{\sqrt{D_{10}}}d\beta\end{array} \]  
Finally, from (9), (14), (16), (19) and (21) the mutual inductance between the thin conical sheet and the circular loop can be calculated in the semi-analytical form as follows,  
\[ \begin{array}{l}M=-\frac{2\mu_{0}NR}{(z_{2}-z_{1})(\eta^{2}+1)^{\frac{3}{2} }}V_{0}\end{array}  \tag{22}\]where  
\[ \begin{array}{c}V_{0}=-\frac{\eta^{2}R}{2}J_{0}-\frac{\eta\sqrt{\eta^{2}+1}\sqrt{ RR_{1}}}{12k_{1}^{3}}\big{\{}\big{[}3(a_{22}+a_{33})k_{1}^{4}-(6a_{22}+8)k_{1}^{2}+8 \big{]}\,K(k_{1})+\\ \big{[}(6a_{22}+4)k_{1}^{2}-8\big{]}\,E(k_{1})\big{\}}-\eta\sqrt{\eta^{2}+1} \frac{\sqrt{RR_{1}}}{12k_{1}^{3}}\big{\{}\big{(}16k_{1}^{2}-16\big{)}K(k_{1})+ \\ \big{(}16-8k_{1}^{2}\big{)}E(k_{1})\big{\}}+\frac{\eta\sqrt{\eta^{2}+1}\sqrt{R _{1}}}{12k_{2}^{3}}\big{\{}\big{[}3(a_{222}+a_{333})k_{2}^{4}-(6a_{222}+8)k_{2 }^{2}+8\big{]}\,K(k_{2})+\\ \big{[}(6a_{222}+4)k_{2}^{2}-8\big{]}\,E(k_{2})\big{\}}+\eta\sqrt{\eta^{2}+1} \frac{\sqrt{RR_{1}}}{12k_{2}^{3}}\times\\ \big{\{}\big{(}16k_{2}^{2}-16\big{)}K(k_{2})+\,\big{(}16-8k_{2}^{2}\big{)}E(k_{ 2})\big{\}}-\frac{k_{1}\eta\sqrt{\eta^{2}+1}\sqrt{RR_{1}}}{4}\times\\ \Big{[}\frac{V_{1}}{1-p_{1}}\varPi(h_{1},k_{1})+\frac{V_{2}}{1-p_{2}}\varPi(h_ {2},k_{1})\Big{]}+\frac{k_{2}\eta\sqrt{\eta^{2}+1}\sqrt{R_{1}}}{4}+\\ \Big{[}\frac{V_{3}}{1-p_{3}}\varPi(h_{3},k_{2})+\frac{V_{4}}{1-p_{4}}\varPi(h_ {4},k_{2})\Big{]}\end{array}  \tag{23}\]  
\[J_{0}=J_{10}-J_{20}\]  
All parameters in (23) can be found in Appendix A. Thus, the general solution of Equation (22) with (23) is expressed by the complete elliptic integrals \(K(k)\), \(E(k)\) and \(\varPi(h,k)\) as well as one term \(J_{0}\) which must be solved numerically.
# Image Quality Assessment of Augmented Reality Glasses as Medical Display Devices (HoloLens 2)
## 2 Materials and Methods
### Methods
#### 2.2.2 Evaluation of Minimal and Maximal Luminance  
The minimum and maximum luminance were evaluated using the TG18-LN12 test series, which includes 18 grayscale images ranging from black to white (Figure 3). The black test pattern (-01) was used to determine the minimum luminance (\(L_{min}\)), while the white test pattern (-18) was employed to determine the maximum luminance (\(L_{max}\)) of conventional displays.  
For a black image, the HoloLens 2 lasers are turned off, making the luminance of the two darkest images indeterminate. Consequently, the TG18-LN12-03 test pattern was used on HoloLens 2 to ascertain \(L_{min}\), and the white test pattern TG18-LN12-18 was utilized to determine maximum luminance. These test patterns are highlighted in red in Figure 3.  
HoloLens 2 initially displayed the dark pattern, followed by the bright one. Both luminance values were measured using the contact method. The MAVO SPOT 2 luminance meter, equipped with the contact probe, was positioned vertically at the center of the screen.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & - & Is the “QUALITY CONTROL” text in the white and & Yes & Yes \\  & & gray boxes fully legible? & & \\
3 & - & Is “QUALITY CONTROL” in the black box visible to & Yes & No \\  & - & the penultimate letter & & \\
4 & - & Are the four low-contrast corners within the two & Yes & No \\
4 & - & luminance patches visible? & & \\
5 & - & Are the grid lines visible & Yes & Yes \\
6 & - & Are the 16 luminance patches distinguishable? & 16/16 & 16/16 \\
7 & - & Are the gradient bars continuous? & & Yes \\
8 & - & Are the black–white and white–black transitions & Yes & Yes \\  & & smooth? & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(Cont.\)  
Figure 3: Test pattern series TG18-LN12 for the investigation of luminance with red marking of the test patterns used to investigate the minimum and maximum luminance.  
In the test setup illustrated in Figure 4, the measuring point is located near the eye position when wearing HoloLens 2.  
For both primary and secondary medical display devices, \(L_{max}\) must be \(\geq\)300 cd/m\({}^{2}\), and the luminance ratio (\(L_{max}/L_{min}\)) must be \(\geq\)250 [4].
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 1 Introduction
Second, the total energy consumption can be greatly reduced by abridging the transmission distance between the communication pairs, assuming that the sink mobility pattern is well designed. Third, the latency of the network can be reduced and the throughput of the network can be increased by using a mobile sink. Finally, network connectivity can be ensured, even under the sparse or disconnected sensor networks. Although the introduction of sink mobility involves many merits, it also faces some challenges [14]. The location of the mobile sink should be broadcast frequently or predicted by sensors, which may increase the burden of the network. Additionally, the moving pattern of the sink should be well designed to cooperate with the local nodes for data transmission. For addressing the above problems, in this paper, we present an energy-efficient routing schema combined with clustering and mobile sink technology. We first divide the whole sensor area into several sectors with the same size. In each cluster, a CH is selected according to the weight, which is calculated using the residual energy and the distance between the source node and the CH. Source nodes communicate with the CH using single or multi-hop communication in accordance with the optimal energy consumption. Additionally, CHs are connected by a reasonable chain, which is constructed by the greedy algorithm for intercluster communication. The CH that is closest to the sink is chosen as the leader to communicate with the sink. Then, the mobile sink moves along a predefined trajectory for data gathering. Extensive simulations have been performed to compare our presented schema with some similar work. Additionally, we further study the influence of some parameters on the network and enhance the performance of the network. The rest of the paper is organized as follows. Section 2 discusses some classic routing protocols and some latest research achievement adopting mobile sink. Section 3 presents the system model, which contains the network and energy models. Section 4 describes our algorithm in detail. Large quantities of simulations are conducted for comparing our presented schema with some similar work and the results are discussed in Section 5. Section 6 analyzes the influence of different network parameters. Section 7 discusses some phenomena during the simulation and proposed some open research issues. Section 8 concludes this paper.
## 3 Research Methodology and Model Development
### Design Framework and DLA Development
#### 3.4.2 Model Structure  
The traffic model is based on the integration of the PTV VISSIM microsimulation environment with an external Python-based control logic using the COM API interface. This setup enables the implementation of DLA on the middle lane, triggered by predetermined traffic conditions. The model structure is presented in Figure 11.  
The model relies on three core components: input data, scenario design, and control logic integration within the PTV VISSIM simulation environment. Input data included geometric parameters (segment length, lane width, and signalized intersections), traffic characteristics (hourly volumes, peak/off-peak loads, and vehicle composition), and driver behavior settings (desired speed, headway, reaction time, acceleration, and deceleration profiles, with predefined values within the model for desired speed ranging from 40 to 60 km/h). The Python-based control logic, integrated via the COM API, monitors traffic flow in real time, manages the activation of DLA conditions, and orchestrates the transition between DLA scenarios to reflect realistic adaptation dynamics. To assess the model's operational impact, node-based indicators were extracted from all signalized intersections along the corridor for each movement direction. These included total vehicle throughput (Vehs(All) [vehicles/hour]), cumulative delay (VehDelay(All) [seconds]), the average queue length per lane (Qlen [meters]), numerical level of service (LOSVal(All)), and carbon monoxide emissions (EmissionsCO [grams]). Together, these indicators provide a multidimensional evaluation of system performance, combining traffic efficiency, service quality, and environmental impact across all tested configurations.
# Deep Q-Learning for Two-Hop Communications of Drone Base Stations
## 7 Conclusions  
In this work, we have proposed Q-learning and Deep Q-learning models for a drone base station to serve a mobile user. A two-hop communication scenario is considered, including the access and backhaul links. Our results show that the DBS can learn to navigate autonomously and improve the achievable throughput performance, with DQN outperforms Q-learning. Given the capabilities of DQN, the model can be upgraded to multiple DBSs and users. This upgrade might require a design of a multi-agent reinforcement algorithm model, which is left for our future work. However, for the Q-learning model, extending to a larger environment and multiple users is not practical. Additionally, for real-life deployment, the drone maneuverability limitations and the other dynamics of environments should be further considered.  
Conceptualization, A.F., M.D. and M.H.; Methodology and Software, A.F., Validation, A.F., M.D. and M.H.; Writing--Review and Editing, A.F., M.D. and M.H.; Formal analysis, A.F.; Resources and Supervision, M.D. and M.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Authors would like to acknowledge useful discussions with [PERSON] during the preparation of this manuscript.  
The authors declare no conflict of interest.
## 3 Results
### Suppressing the Production of Cytokines and PGE\({}_{2}\) for Skin Anti-Inflammation
Means with different letters (A–B) within the same sample are significantly different at \(p<\) 0.05, and means with different letters (a–b) within the same concentration are significantly different at \(p<\) 0.05. (1) EE: 70% ethanol extraction at 80 \({}^{\circ}\)C, (2) UE: ultrasonic extraction with 70% ethanol at 40 \({}^{\circ}\)C.  
Similarly, in Figure 4B, both extracts showed concentration-dependent decreases in TNF-\(\alpha\) production. Moreover, 37.2 pg/mL vs. 129.5 pg/mL of TNF-\(\alpha\) production was estimated in the addition of the maximum concentration of 0.3 mg/mL of the UE and EE, respectively. In general, the effects of vitamin D\({}_{3}\) were better or similar to that of the UE, but much better than the EE, while the effects of wedelolactone were lower than that of the UE, but higher than the EE. These results can be compared with the experimental data on Aronia, which is also known to have similar biological activities [21], where 100 pg/mL of IL-6 and 120 pg/mL of TNF-\(\alpha\) were produced in treating the same concentrations. Therefore, the UE can be considered to have good or even better anti-inflammatory activities than other natural resources. To confirm whether the high anti-inflammatory activities of the _E. prostrata_ L. extract could be attributed to skin anti-inflammatory effects, the inhibition of PGE\({}_{2}\) production from human skin fibroblasts was also observed, as PGE\({}_{2}\) plays a role in promoting the generation of inflammatory cytokine IL-6 and TNF-\(\alpha\) and in affecting the skin [9; 29; 33]. As shown in Figure 4C, the EE generated approximately 183.3 pg/mL of PGE\({}_{2}\) at a maximum concentration of 0.3 mg/mL, whereas the UE produced only 107.7 pg/mL, which is approximately 40% lower than the value from the EE. The efficacy of PGE\({}_{2}\) seems to be similar to the anti-inflammatory effects of inhibiting the cytokine productions (as shown in Figure 4A,B), and the effects of vitamin D\({}_{3}\) and wedelolactone also showed similar patterns. Study of the anti-inflammatory efficacy of Aronia extract at a concentration of 1.0 mg/mL showed a PGE\({}_{2}\) yield corresponding to that of _E. prostrata_ L. at 0.3 mg/mL [21]. According to the above results, the extract from the UE process had a higher anti-inflammatory activity than that from the 70% EE process, which is a conventional extraction process. This result could be attributed to the synergistic effects of wedelolactone and other components, which were increased through ultrasonication, and affected the anti-inflammatory activity so that the anti-inflammatory of _E. prostrata_ L. increased. In addition, as previous studies identified that wedelolactone--a component of _E. prostrata_ L.--is rapidly destroyed at temperatures higher than 60 \({}^{\circ}\)C, the low-temperature ultrasonic extraction process presented in this study could be an alternative process that minimizes the destruction of wedelolactone and enhances the extraction efficiency [25].
## 2 Materials and Methods
### Sample Characterization  
X-ray diffraction (XRD, Rigaku MiniFlex II, Rigaka, Tokyo, Japan) was used to characterize the phase structure of the synthesized precursor and its calcined products, using Cu K\(\alpha\) radiation (\(\lambda\) = 0.15406 nm) as the X-ray source. Scanning electron microscopy (SEM, JEOL JSM-6700-F, JEOL, Tokyo, Japan) was adopted to observe the morphology of the samples. Transmission electron microscopy (TEM, JEM-2010, JEOL, Tokyo, Japan) was used to observe the microstructure and morphology of the samples. X-ray photoelectron spectroscopy (XPS, ESCA-LABM250 XI, Waltham, MA, USA) was used to characterize the composition and chemical valence of the NiO sample. The surface area and porosity analyzer (ASAP2460, Micromerities, Norcross, GA, USA) was used to study the specific surface area and pore size distribution of the samples by the Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH) models, respectively.
# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 2 Related Work
### Grey Wolf Optimization Algorithm  
The GWO algorithm has been used in several research areas, including engineering design, economy, astronomy, and others. Since its introduction [18], GWO has gained widespread attention from researchers due to its effectiveness in solving complex optimization problems. The following presents some of its applications.  
[PERSON] et al. used an optimized learning algorithm model to provide optimized solutions for mathematical nonlinear equation problems [44]. The authors compared the use of two optimization algorithms, GWO and PSO, and their results showed that GWO outperformed PSO.  
In another work, the authors compared the use of several optimization algorithms including GWO, the Genetic Algorithm (GA), Ant-Lion Optimization (ALO), the Krill Herd Algorithm (KHA), and others for solving the problem of Combined Economic and Emission Dispatch (CEED) [45]. The result of this study showed that the use of GWO achieved higher performance and better solutions. [PERSON] et al. also compared the use of GWO with Particle Swarm Optimization (PSO) along with a machine learning classifier to detect far orbits through the extraction of image features [46]. The PSO-based classifier performance results were low due to the computational complexity, whereas the results of the GWO-based classifier outperformed the PSO-based classifier by 8%. The discussed research shows the effectiveness of using GWO for enhancing the performance of machine learning models.  
Researchers have also employed GWO-based models to discover optimal solutions for various engineering problems. For instance, a group of researchers applied GWO to a civil engineering problem involving the design of water distribution networks. The objective was to minimize financial costs and reduce the number of network components, including pipe sizes, pump ratings, and other elements. This approach met the established expectations for both performance and cost perspectives [47]. On the other hand, [PERSON] and [PERSON] [48] built a GWO-based model to automate the process of designing analog circuits. Through this application, they effectively showcased the utility of GWO by producing enhanced circuit designs in a minimal amount of time.  
In general, several researchers used optimization techniques to improve the results of different clustering approaches [49; 50; 51; 52]. The results of these studies support our hypothesis that using optimization with clustering techniques can help in improving their effectiveness.  
To conclude, the use of the GWO algorithm spans several research areas, including medical, engineering, astronomy, and others. The discussed studies show that the use of GWO enhances the performance and the accuracy of the results for the defined problems.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 5 O-RAN Alliance Architecture  
Besides the whole progress that the O-RAN Alliance has made, the O-RAN Alliance is also supported by OSC, so we can directly see how the standards are implemented into open-source software.  
Generally, the O-RAN Alliance's architecture for Open RAN consists of eight main parts: Service Management and Orchestration (SMO), Non-RT Ric, Near-RT Ric, O-RAN CU (O-CU), O-RAN DU (O-DU), O-RAN RU (O-RU), O-RAN eNB (O-eNB), and O-RAN Cloud (O-Cloud). Figure 9 shows the interconnections of these building blocks in O-RAN's architecture provided by WG1.  
Figure 8: Comparison between 3 GPP and O-RAN architecture.  
The reference architecture shown in Figure 9 shows the concept to enable the next generation of RAN infrastructures [7]. This architecture is the foundation of making RAN open, virtualized, and AI-embedded, which is desired by many MNOs. The O-RAN's architecture will be a standardized reference system, plus a complimentary reference system for other architectures made by other parties, such as 3 GPP and other RAN-related organizations.
## 3 Results and Discussion
### Assay of Arg in Real Samples  
In order to demonstrate the applicability of the constructed ArgO-based ABSs for testing real products containing Arg, selected samples of fruit juices and wine, as well as the pharmaceutical preparation \"Tivortin\", were analyzed. The estimation of Arg concentration in the sample of fresh-prepared apple juice is presented in Figure 6. The Arg analysis was performed using the graphical method known as \"the standard addition test\" (SAT) with different dilutions of the tested sample. The SAT is a quantitative analysis approach often used in analytical chemistry when a standard solution of the target analyte (in this case, Arg) is added directly to the aliquots of the analyzed sample. The SAT method is used in situations where sample components also contribute to the analytical signal [10]. Other samples of juices and wine were analyzed by the developed ABSs in the same manner; the estimated Arg contents in the tested samples are summarized in Table 3.  
Figure 6: Graphical SAT method for Arg assay in the real sample of freshly prepared apple juice using ArgO/nCeCu/GE: (**a**) chronoamperograms, (**b**) calibration graphs. A, B—parameters for the linear regression; N—dilution factor; R—correlation coefficient.  
The reproducibility of the proposed analytical methods is satisfactory: the coefficients of variation (CV) are less than 5%. It is worthwhile mentioning that the detected Arg contents in the juices and wine were close to those published by other authors (0-11 mM) [48; 49; 50]. Figure 14 shows that the Arg content values in the pharmaceutical product \"Tivortin\" and the apple-pear juice, estimated by both ABSs (ArgO/nCeCu/GE and ArgO/nNiPtPd/GE), were strongly correlated with each other (R = 0.995). Additionally, only a slight difference (less than 1%) was observed for \"Tivortin\" between the experimentally determined content of Arg (199.1 mM) and its value declared by the producer (199.8 mM).  
These results indicate that the developed mono-enzyme ABSs (namely, ArgO/nCeCu/ GE and ArgO/nNiPtPd/GE) can be utilized for a fast and simple assay of Arg in pharmaceutical preparations.