esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Challenges and Prospects
The capital costs of a flat-panel photobioreactor, a tubular photobioreactor, and a bubble-column photobioreactor were reported to be 42.2, 26.2, and 15.5 \(\upmu\)m\({}^{-2}\), respectively [175]. [PERSON] et al. reported that the culture stage used the majority of the operational resources and photobioreactors (PBR) and require around ten times as much energy when utilizing pumping and/or aeration [150]. The conventional method for growing photoautotrophic microalgae uses CO\({}_{2}\) as carbon source. However, procedures for purifying, storing, and distributing CO\({}_{2}\) raise the cost of producing microalgae. Cultivation using carbon dioxide in dissolved form or using bacteria to produce CO\({}_{2}\) were proposed as promising solution to overcome this challenge [7,175]. Bicarbonate could be used in the culture medium, that could result in productivity up to 50% greater than that of CO\({}_{2}\) while decreasing the cost of culture by 55% [175]. The resources recycling approach is also a strategy to reduce the cost of cultivation system [176]. In addition, another barrier to large-scale wastewater microalgae-based treatment is the downstream processing of microalgae. Numerous studies are now being conducted to develop highly effective inexpensive harvesting approaches [4,157,177,178]. Ref. [4] harvested _Chlorella_ sp. biomass using _Aspergillus_ sp., a filamentous fungal in molasses wastewater, to simplify the harvesting technique and minimize the process's production cost [4]. Coagulation (aluminum sulphate, ferricchloride, Tanfloc SG and Zetag 8185 coagulants) followed by dissolved air flotation (DAF) have shown high efficiency but the significant residual content of iron and aluminum in the final effluent necessitates prudence when using metal salts [177]. Although wastewater-grown microalgae have a significant potential, there have only been a few investigations of the value of biomass. Therefore, future studies should concentrate on finding the best wastewater sources, optimizing growing conditions, accumulation of product, and concomitantly extracting product and converting biomass. In order to provide suitable circumstances for microalgae development, it is also necessary to develop effective and affordable pretreatment techniques as well as integrated reactor systems.
# Influence of Incident Shock on Fuel Mixing in Scramjet
## 2 Numerical Method
### Numerical Simulations and Boundary Conditions  
To solve the equations, a finite volume method-based in-house code has been generated, which is a hybrid structured/unstructured Navier-Stokes flow solver. The spatial inviscid term is solved using the AUSM + UP scheme, and the second-order center scheme is used for viscous fluxes. The explicit four-step second-order Runge-Kutta method is used for time advancement. This in-house code has been validated, and the results are satisfactory [35]. The relative difference between the inflow and jet fuel and the outflow mass flux is less than 0.1%; it is considered that the flow field has reached a stable state.  
Boundary conditions must be designed to ensure compatibility with the true physical characteristics of the flow. Additionally, they need to satisfy the criteria for mathematical well-posedness. Moreover, these conditions should be crafted in such a way that they have minimal negative impact on the precision and stability of numerical solutions at internal points. Virtual grids are auxiliary grid elements that are conceptually positioned on the opposite side of the flow field boundary. The determination of the number of layers is based on the precision demands of the numerical scheme; as shown in Figure 1, the solid line represented the physical grid, while the dashed line represented the virtual grid and the number 0 indicated the boundary. Since this article adopted second-order accuracy, the virtual grid was limited to only the first layer.
# Cold Atmospheric Pressure Plasma in Wound Healing and Cancer Treatment  
[PERSON]  
1 Clinic and Polyclinic for Dermatology and Venereology, University Medical Center Rostock, 18057 Rostock, Germany; [EMAIL_ADDRESS] (M.S.); [EMAIL_ADDRESS] (T.B.); [EMAIL_ADDRESS] (M.L.S.); [EMAIL_ADDRESS] (O.J.); [EMAIL_ADDRESS] (G.O.); [EMAIL_ADDRESS] (T.F.); [EMAIL_ADDRESS] (S.E.)  
2 Department of Cell Biology, University Medical Center Rostock, 18057 Rostock, Germany; [EMAIL_ADDRESS] (K.P.); [EMAIL_ADDRESS] (B.N.)  
3 Institute for Immunology, University Medical Center Rostock, 18057 Rostock, Germany; [EMAIL_ADDRESS]  
4 Oral & Maxillofacial Surgery/Plastic Surgery, University Medicine Greifswald, 17489 Greifswald, Germany; [EMAIL_ADDRESS]  
5 ZIK Plasmatis, Leibniz Institute for Plasma Science and Technology (INP), 17489 Greifswald, Germany; [EMAIL_ADDRESS]  
5 Correspondence: [EMAIL_ADDRESS]; Tel.: +49-381-494-9760  
radiation, visible light, and electromagnetic fields [1]. The application spectrum of CAP ranges from decontamination, coagulation, surface treatment of implants, dentistry, as well as cosmetic and plastic surgery to skin diseases and cancer treatment [2,3]. A substantial number of studies have been conducted concerning different CAP applications in dermatology. While a significant portion of the early studies focused on disinfection and wound healing, CAP has also been investigated regarding its effects on reducing itch and pain, atopic eczema, scars, epidermal barrier defects (ichthyosis) [4]. An increasing number of studies focus on the use of CAP for cancer treatment [5]. While safety and the beneficial use of CAP as part of a multi-modular wound treatment has been demonstrated in several structured case reports and randomized case-control studies, the use of CAP for cancer treatment is an active field of research that warrants further investigations for its translation into clinic practice. For these two quite different applications of CAP, different properties of CAP are required. For wound healing, antiseptic and tissue regenerating properties are needed, whereas for cancer treatment, anti-proliferative and cell death-inducing properties are desired. These properties can be controlled by the parameters of treatment time and intensity. Higher intensities and treatment times are needed for cancer treatment, but pose a higher risk for adverse side effects.  
This review article provides an overview of the latest literature regarding the clinical use of CAP for wound healing and cancer treatment. It discusses possible underlying mechanisms triggered by CAP that grant CAP antiseptic and tissue regenerating or anti-proliferative and cell death-inducing properties.
# Energy and Exergy Analyses of an Existing Solar-Assisted Combined Cycle Power Plant
## 3 Thermodynamic Analysis
### Energy Balance  
The net rate of heat input to the ISCC (\(\dot{Q}_{ISCC,in}\)) is given by  
\[\dot{Q}_{ISCC,in}=\dot{Q}_{fuel}+\dot{Q}_{inc} \tag{1}\]  
where \(\dot{Q}_{fuel}\) is the rate of heat addition to the ISCC from the fuel combustion and \(\dot{Q}_{inc}\) is the absorbed incident solar radiation. The heat from the fuel combustion (\(\dot{Q}_{fuel}\)) as a function of the fuel flow rate (\(\dot{m}_{fuel}\)) and the fuel heating value (\(H_{v}\)) is given by  
\[\dot{Q}_{fuel}=\dot{m}_{fuel}*H_{v} \tag{2}\]  
and the heat from the absorbed incident solar radiation (\(\dot{Q}_{inc}\)) as a function of the direct normal insolation (\(DNI\)), incidence angle (\(\theta\)), incidence angle modifier (\(IAM\)), and solar collectors' aperture area (\(A_{mirrors}\)) is given by  
\[\dot{Q}_{inc}=DNI*cos\theta*IAM*A_{mirrors}. \tag{3}\]  
Figure 1: A flow diagram of the integrated solar combined cycle (ISCC) power plant in Kureimat with state points illustration.  
Here, the incidence angle modifier (_IAM_) is the correlation of the losses from the collectors due to additional reflection and absorption by the glass envelope, and it can be calculated as follows:  
\[IAM=1+\frac{0.000884*\theta}{\cos\theta}-\frac{0.00005369*\theta^{2}}{\cos\theta} \tag{4}\]  
and the solar collectors' aperture area (\(A_{mirrors}\)) is calculated from the number of solar field collectors (\(N_{collectors}\)) and the width (\(W_{collector}\)) and length (\(L_{collector}\)) of the collectors as follows:  
\[A_{mirrors}=N_{collectors}*W_{collector}*L_{collector} \tag{5}\]  
The electric power output of the ISCC (\(\dot{W}_{dec,ISCC}\)) is equal to the sum of the electric power outputs of the gas turbine (\(\dot{W}_{dec,GT}\)) and the steam turbine (\(\dot{W}_{dec,ST}\)) as follows:  
\[\dot{W}_{dec,ISCC}=\dot{W}_{dec,GT}+\dot{W}_{dec,ST} \tag{6}\]  
As a result, the overall first law efficiency of the ISCC power plant is  
\[\eta_{L,cycle}=\frac{\dot{W}_{dec,ISCC}}{\dot{Q}_{ISCC,in}} \tag{7}\]
# A 28 GHz 5G Phased Array Antenna with Air-Hole Slots for Beam Width Enhancement
## 5 Conclusions  
A 1 \(\times\) 8 phased array antenna for 5G applications operating at 28 GHz with enhanced HPBW is proposed in this paper. The proposed design is proven to have sufficient impedance matching, sufficient peak gain, a low sidelobe level, suitable scan coverage, and very wide HPBW in the elevation plane. The HPBW improvement over 20 degree is achieved by introducing air-hole slots, as compared to that without air-hole slots. Such enhancement of the HPBW in the elevation plane can be explained by the qualitative causality of two Maxwell's equations. Relatively good agreement between simulated and measured results can be explained by the simple structure of the proposed antenna since it has  
Figure 14: Measured and simulated radiation patterns of the proposed array antenna at 28 GHz: (**a**) XY plane with a 0 degree phase difference; (**b**) YZ plane with a 0 degree phase difference; (**c**) XY plane with a 120 degree phase difference.  
less factors for possible errors. Because of the very wide HPBW in the elevation plane along with reasonably good other radiation performances, the proposed array antenna can be a good candidate for the mm-wave 5G mobile handset applications. Future work will involve introduction of actual mobile handset model including metal chassis, circuit elements, and LCD panel. Since the performance of the antenna will be strongly affected by such inclusion, certain counter measures for minimizing the effect needs to be assessed. In addition, the effect of materials (dielectric or ferrite) that might be used to fill the air hole in actual situation, may also be taken into account.  
The presented work was conceptualized and written by [PERSON] and [PERSON], [PERSON] supervised the research.  
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (2018-0-00732, Fundamental research for solving the bottleneck technology of millimeter wave 5G cellular array antenna).  
The authors declare no conflict of interest.
# Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems
## 3 Results and Discussion
### Case V: With CVR and DG, without DLC  
In this case, the same CVR algorithm was used as in Case III. This case was simulated to test the effect of adding a PV system at the optimal location (Bus 21) on the CVR technique. Figure 12 shows the voltage profiles for the test system with/without PV in the presence of CVR. They have similar profiles, but the magnitude varies from bus to bus; it is clear that the voltage profile in the presence of DG and CVR is slightly higher in magnitude compared to the voltage profile in the presence of CVR technique alone. Under some circumstances, the integration of PV may alleviate voltage violations when CVR is applied.  
Figure 11: Case IV: Comparison of the voltage profile without DLC (Base case) and with DLC.  
Figure 12: Case V: Voltage profiles with/without CVR in the presence of DG.  
Tables 5 and 6 present the active and reactive power with/without CVR in the presence of DG. By comparing the tables for Case V with the tables for Case III, one can see that the effect the DG has on the CVR is a non-positive one. The DG decreased the saved active and reactive powers. On the other hand, the active and reactive powers consumed by the loads in the presence of the DG are slightly less. Thus, the overall effect of the DG is non-positive, but it is not that influential. The pros and cons of this case are similar to those of Case III alongside more technical, environmental and economic benefits at the system level due to the integration of the PV unit.  
The voltages on buses 10, 17 and 26 along the 24-h simulation with a varying load shape with/without CVR in the presence of DG are shown in Figure 13. The presence of small PV unit did not provide significant difference compared to the case of implementing CVR algorithm alone.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bus/Load (\#)**} & \multirow{2}{*}{\begin{tabular}{c} **Reactive Power** \\ **without CVR (kvar)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Reactive Power** \\ **with CVR (kvar)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Reactive Power** \\ **Reduction (kvar)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Reduction** \\ **Percentage (\%)** \\ \end{tabular} } \\ \hline B10 & 565.87 & 524.27 & 41.60 & 7.35 \\ B12 & 2140.27 & 1970.78 & 169.48 & 7.92 \\ B14 & 445.01 & 416.64 & 28.37 & 6.38 \\ B15 & 690.87 &
## 3 Numerical Calculation Models
### Boundary Conditions and Loading Steps  
The model utilizes three discrete rigid bodies with a radius of 10 mm as supports and loading points. The supports are fixed constraint boundaries, while the loading point supports use load-controlled loading.  
According to the experimental conditions, the loading process of the entire experiment is divided into two steps. First, the specimen is loaded to the initial stress level, and then cyclic loading is applied. Therefore, in the numerical simulation, two calculation steps are also used. The first step is to apply the initial stress level by loading the specimen to the initial stress level through an incremental step. The second step is to apply cyclic loading. In the experiment, it was assumed that if the specimen did not fracture after 200,000 cycles of loading, it would be considered to have an infinite life. Therefore, in the numerical simulation, only 200,000 cycles of cyclic loading are considered. The number of 200,000 cycles of cyclic loading is set by analyzing the steps, damage amplification factor, and loading steps together. In the cyclic loading calculation step, twenty incremental steps are set, and the cycle of cyclic loading in each incremental step is 0.1. This means that 10 cycles of loading occur in each incremental step. The damage amplification factor is set to 1000. Thus, 20 cycles of cyclic loading are simulated through 20 incremental steps. This significantly improves the computational efficiency of the software.  
Figure 6: Numerical Calculation Model.
## 5 Performance Evaluation
### Performance Evaluation of the Network-Bandwidth Prediction Model  
The performance of the network-bandwidth prediction model was evaluated using the dataset provided by Zeondo, as described in Section 4.1. The evaluation was conducted by categorizing user mobility into pedestrian, bus, and train scenarios. The pedestrian category included eight scenarios with a total of 8500 s of measurement data used for evaluation. Similarly, the bus and train categories each included eight scenarios with 6000 s of network bandwidth measurement data used for both categories.  
Figure 10 presents the root mean square error (RMSE) of the predicted network bandwidth values. The RMSE values for walking, traveling by bus, and traveling by train were approximately 0.079, 0.08 and 0.06, respectively. A lower RMSE indicates higher prediction accuracy, demonstrating that the proposed network bandwidth prediction model performed well. The average RMSE across the 24 scenarios was 0.073, indicating that the model is highly accurate, even when the environment changes as a result of user mobility in a wireless network.  
Figures 11-13 present the network bandwidth predictions for the scenarios with the best and worst RMSE values in the pedestrian, bus, and train scenarios, respectively. The scenario with the best RMSE is shown on the left, and that with the worst RMSE is shown on the right. In these figures, blue represents the actual measurement results, and yellow represents the predicted results. Figure 11 reveals that the proposed model predicted very similar bandwidths, even when the bandwidth changed abruptly at 100 s, and it handled small bandwidth changes effectively thereafter. As a result, the RMSE value is low. Figure 11 reveals that the proposed model predicted similar bandwidths, even when the bandwidth varied continually and drastically. However, the RMSE value was relatively high compared with that of the pedestrian scenarios because the bandwidth
## 4 Results and Discussions
### Models' Average Performance with and without CMAQ Data  
Tables 1-4 (see Appendix A) include the details of each model performance at every station before adding CMAQ features.  
Tables 1-4 show the average _MAE_, _RMSE_, \(R\), and _IA_ values of all stations before and after including CMAQ features. F\({}_{\text{P}}\), F\({}_{\text{m}}\), and F\({}_{\text{c}}\) represent pollutants, meteorological parameters, and CMAQ features, respectively. From the results in Tables 1-4, it is clear that, by including CMAQ features that are highly correlated with the PM\({}_{2.5}\) concentration, each model's _MAE_ and _RMSE_ values are decreased while their \(R\) and _IA_ values are improved, thus improving the models performance.
## 3 Control Schemes  
Figure 6 presents the SBBC control system. The voltage waveform for obtaining the reference waveform of AC current is taken from the electrical grid by means of Phase-Locked Loop (PLL). The control system requires the measurement of \(v_{s}\), \(i_{s}\), \(v_{o}\) and \(i_{o}\) signals. \(v_{o}\) and \(i_{o}\) are filtered to remove high frequency noise. Finally, the controller calculates the control actions and generates the \(u\) signal to trigger power switches \(Q_{1}\) and \(Q_{2}\).  
Three control strategies are evaluated: PI, hybrid PI-SMC and the proposed SMC (see Figure 7). A classical cascade PI controller is shown in Figure 7a. The external voltage loop gives the AC current amplitude reference to internal current loop and current controller actuates by means of PWM generator. The control Scheme of the hybrid PI-SMC is depicted in Figure 7b; in this case, there is an external PI control loop for DC voltage regulation that gives the reference AC current amplitude for the internal SMC; the voltage error is considered by the SMC and an AHB is used instead of a PWM generator. The proposed SBBC control with SMC is presented in Figure 7c. This system allows controlling both DC voltage and AC current; the reference current is obtained from the power balance and the adaptive hysteresis band gives the control signal. The proposed sliding surface  
Figure 5: \(u_{eq}\) for half cycle of electrical grid.  
Figure 6: Control system for SBBC.  
is used in both hybrid PI-SMC and SMC controllers in order to analyze their advantages and limitations.  
The AHB is designed based on current ripple geometry according to [PERSON] et al. [35]. It has two functions: (1) smooth the current zero crossing in order to decrease \(THD_{i}\) and (2) modify the switching time according to SBBC operation point in order to fix the switching frequency. Equation (22) presents the AHB as function of \(v_{o}\), \(v_{s}\), \(L\) and \(f_{sw}\), while its implementation is depicted in Figure 8. The AHB calculated for each point is compared with the sliding surface. Finally, the control signal is given by a RS flip-flop. The AHB does not require PWM signals for actuating over power switches; thus, sliding mode control does not have any delay in its AC current response.  
\[AHB=\frac{v_{s}(v_{o}-v_{s})}{2 Lf_{sw}v_{o}} \tag{22}\]
## 3 Results and Discussion
### Optimization  
Response surface methodology (RSM) was used to individually optimize FTT temperatures for each of the three Croatian olive varieties used in this study. The aim was to enhance the oxidative stability index (OSI), indicated by induction period (IP), antioxidant capacity (AC) and volatiles derived from the lipoxygenase pathway (\(\Sigma\) LOX), while minimizing oxidation-derived volatiles (\(\Sigma\) OX) and those associated with microbial activity (\(\Sigma\) MBA). Polynomial models were developed for each parameter and tested for suitability using ANOVA, coefficients of determination (R\({}^{2}\)), and lack-of-fit tests (Table 5).  
The best-fitting models were obtained for the Oblica variety. All models showed high statistical significance (\(p<0.05\)), with R\({}^{2}\) values ranging from 0.659 to 0.908, indicating they explained a substantial portion of the observed variability. Additionally, non-significant lack-of-fit results (\(p>0.05\)) for IP, \(\Sigma\) LOX and \(\Sigma\) OX, confirming the suitability of the models to capture the experimental data. Second-order polynomial models were also developed for the remaining two varieties. However, the ANOVA results showed statistical significance only for the IP model for Istarska Bjelica and the \(\Sigma\) MBA model for Levantinka.  
For the Istarska Bjelica variety, the cooling of the olives to 18.9 \({}^{\circ}\)C was predicted to increase the IP value by 6.4% and the AC value by 1.2%. Although a decrease in \(\Sigma\) LOX volatiles of about 15% was expected, \(\Sigma\) OX was expected to decrease by 4% and \(\Sigma\) MBA by 42%. In contrast, for the Oblica variety, a decrease in OSI and AC (2% and 10%, respectively) was predicted, but an improvement in sensory properties due to a 15% increase in \(\Sigma\) LOX volatile components and a 75% decrease in \(\Sigma\) MBA. For the Levantinka variety, an improvement in AC was predicted by applying the optimal FTT temperature, although a reduction in both IP and \(\Sigma\) LOX volatiles was also expected.  
Figure 2: (**a**) Variable importance in the projection (VIP) of antioxidant capacity—AC (% red. DPPH) with the variables \(\alpha\)-TOCO—\(\alpha\)-tocopherol, HTY—hydroxytrosol, TY—tyrosol, OLEA—oleacen, OLEC—oleacenthal, OLEU AGL—sum of oleuropein aglycones, LIG AGL—sum of ligtroside aglycones, MH OLEC—methyl hemiacetal of oleocanthal, \(p\)-CAA—\(p\)-comamaric acid, TPC—total phenols. The dashed black line represents the significance threshold for VIP score. (**b**) The predicted versus actual values of antioxidant capacity. The gray solid line represents the 95% confidence interval.
## 4 Discussion and Limitations  
The experiments in Section 3 have verified the automatic property of the proposed marker-object offset matrix calibration method. The mean relative scale error that influences the global visual perception effect is 1.9%. The mean calibration time for the 1920 \(\times\) 1080 experiment image is 12.23 s. Compared with the DOT method [24], the method directly estimates the 6D pose without any feature modeling and searching process by facilitating the normal direction prior. Compared with the existing SSD methods [15], the method replaces the continuous tracking pose prior by the estimated pose prior as the initial pose for optimization, which allows an accurate and fast pose re-initialization.  
Two limitations are revealed from the experiment results. First, the convergence speed and the accuracy of the non-linear global optimization are greatly influenced by the errors of the initially estimated offset matrix, which is caused by the DOT matching in a cluttered image. The other limitation  
Figure 15: AR registration results using the initial pose obtained by DOT matching (the left column) and the optimized pose obtained by the non-linear optimization (the right column).  
of the approach lies in the normal estimation which assumes that the supporting plane of the object coincides with one surface of the CAD model's bounding box, which does not hold true for all shapes of industrial parts such as shafts. The two limitations will be addressed in our future work.