esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Hybrid optical-wireless FiWi networks form a powerful future-proof platform that provides a number of advantages. Introducing optical fiber into broadband wireless access networks helps relieve emerging bandwidth bottlenecks in todays wireless backhaul due to increasing traffic loads generated by new applications, e.g., iPhone. By simultaneously providing wired and wireless services over the same infrastructure, FiWi networks are able to consolidate (optical) wired and wireless access networks that are usually run independently of each other, thus potentially leading to major cost savings.  
In this paper, We briefly reviewed enabling optical and wireless access-metro network technologies and their QoS provisioning techniques. We elaborated on enabling FiWi technologies and introduced new QoS provisioning techniques for future FiWi networks. The focus of this paper was on a novel FiWi  
Figure 14: Impact of path selection schemes on FiWi network performance using advanced aggregation techniques and wireless mesh networking.  
access-metro network architecture, called SuperMAN, that integrates next-generation WiFi and WiMAX networks with EPON and RPR networks. We described a novel integrated hierarchical scheduler that provides end-to-end QoS connectivity and satisfies the QoS requirements of different traffic classes and scheduling services for both wired and wireless end-users of integrated RPR and WiMAX networks. The obtained results show that deploying our proposed hierarchical scheduler at each RPR-WiMAX interface node improves the performance of the considered FiWi network in terms of mean aggregate throughput and mean delay for voice, video, and data traffic.  
Furthermore, we have shown that exploiting both advanced aggregation techniques and wireless mesh networking improves the performance of integrated EPON and next-generation high-throughput WLAN networks significantly. We presented a novel integrated dynamic path selection algorithm to enhance the throughput of integrated EPON and next-generation WLAN-based WMN using optical-wireless load balancing. The obtained results show that deploying our advanced aggregation techniques and integrated dynamic path selection algorithm improves the performance of SuperMAN in terms of not only throughput and delay but also overhead and packet loss.
## 3 Experiments
### Verification with Measured Data  
Two publicly available datasets [11, 26] were used for performance evaluation and comparison. One was constructed by [PERSON] et al. from nine subject using 10 GHz and 24 GHz Doppler radars with a sampling rate of 1000 Hz. The other was compiled by [PERSON] et al. from 30 subjects using a 24 GHz Doppler radar with a sampling rate of 2000 Hz, and only data acquired while the subjects were resting were used for the heart rate estimation. Both datasets provide synchronized reference electrocardiogram signals.  
The radar data from the two datasets were first down-sampled to 20 Hz to speed up the processing. The proposed method adopts a high-pass filter with a passband frequency of 1.8 Hz, which filters out the fundamental frequency of the heartbeat and other respiratory components. The signals from each subject in the two datasets were recorded over about 600 s, and the filtered data were compartmentalized into 60 10 s windows. The heartbeat rate ranges between 45 to 90 beats per minute (0.75 to 1.5 Hz). The estimation root-mean-square error (RMSE) is defined as  
\[\text{RMSE}=\frac{1}{N_{w}}\sqrt{\sum_{n}^{N_{w}}\Bigl{(}\hat{f}_{HR}(n)-f_{ REF}(n)\Bigr{)}^{2}}. \tag{13}\]  
where \(N_{w}\) is the number of windows in each dataset, and \(\hat{f}_{HR}(n)\) and \(f_{REF}(n)\) are the estimated heartbeat rate and reference heart rate in each window, respectively. The results for the two datasets are presented in Table 1, where the estimation RMSEs of six methods used in previous studies are listed for comparison.
## 3 Results
### Application Analysis
#### 3.5.1 Cytotoxicity Assessment  
To evaluate the potential effect of the tested serums on skin cells, in vitro cytotoxicity analyses were performed on human keratinocytes (HaCaT). For this purpose, the Alamar blue and neutral red assays were used, which allowed for the assessment of metabolic activity and damage to cell membranes of cells treated with the tested samples. The analyses performed showed that none of the tested samples had a cytotoxic effect on keratinocytes in the tested concentration range (0.01-1.0%). Additionally, it was indicated that the addition of the grapevine bud extract to the formula of the analyzed serums (both in the case of surfactant and solvent extraction) may have a positive effect on the viability of these cells (Figures 5 and 6). The cytoprotective effect of the tested grapevine bud extract was observed in both cytotoxicity tests, which indicates both a positive effect on metabolic activity, resulting in the reduction in resazurin and the possibility of retaining neutral red in cell lysosomes. The positive effect of the tested extract on keratinocytes is probably the result of the presence of biologically active compounds such as gallic acid, rutin, quinic acid, ECGC, quercetin, or trans-reveratrol, which are phytochemicals with proven cytoprotective properties [53; 54; 55; 56]. This effect, in addition to the ability to increase metabolic activity and maintain the proper structure and function of cell membranes, is probably related to the ability of grape extracts to enhance antioxidant protection and inhibit mediators of inflammation and oxidative stress [57; 58]. Thus, this work demonstrated that the developed cosmetic preparations containing the obtained extract do not cause cytotoxic effects on keratinocytes in vitro, and this extract is able to limit the cytotoxic effects of both the solvent and the surfactant used.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **L*** & **a*** & **b*** & **C*** & **h\({}^{o}\)** & **Color** \\ \hline SolvE & 53.65 \(\pm\) 0.04 \({}^{\text{a}}\) & \(-\)7.40 \(\pm\) 0.01 \({}^{\text{a}}\) & 19.29 \(\pm\) 0.03 \({}^{\text{b}}\) & 20.66 \(\pm\) 0.03 \({}^{\text{b}}\) & 111.00 \(\pm\) 0.04 \({}^{\text{b}}\) & more greener yellow \\ \hline SurfE & 45.06 \(\pm\) 0.02 \({}^{\text{b}}\) & \(-\)7.18 \(\pm\) 0.01 \({}^{\text{b}}\) & 29.52 \(\pm\) 0.01 \({}^{\text{a}}\) & 30.38 \(\pm\) 0.01 \({}^{\text{a}}\) & 103.66 \(\pm\) 0.01 \({}^{\text{a}}\) & less greener yellow \\ \hline \hline \end{tabular}
\end{table}
Table 5: Spectrophotometric data of the grapevine bud extracts. Values are means of six replicate determinations \(\pm\) standard deviation (n = 6). Different superscript letters for each extract highlight the significant differences at \(p\) < 0.05.
###### Abstract  
This paper presents the advanced control theory's original utilisation to realise a system that controls the fermentation process in batch bioreactors. Proper fermentation control is essential for quality fermentation products and the economical operation of bioreactors. Batch bioreactors are very popular due to their simple construction. However, this simplicity presents limitations in implementing control systems that would ensure a controlled fermentation process. Batch bioreactors do not allow the inflow/outflow of substances during operation. Therefore, we have developed a control system based on a stirrer drive instead of material flow. The newly developed control system ensures tracking of the fermentation product time course to the reference trajectory by changing the stirrer's speed. Firstly, the paper presents the derivation of the enhanced mathematical model suitable for developing a control system. A linearisation and eigenvalue analysis of this model were made. Due to the time-consuming determination of the fermentation model and the variation of the controlled plant during operation, the use of adaptive control is advantageous. Secondly, a comparison of different adaptive approaches was made. The model reference adaptive control was selected on this basis. The control theory is presented, and the control realisation described. Experimental results obtained with the laboratory batch bioreactor confirm the advantages of the proposed adaptive approach compared to the conventional PI-control.  
batch bioreactor; fermentation; modelling; eigenvalue analysis; model reference adaptive control; control practice
# A Communication-Efficient Secure Routing Protocol for IoT Networks +
## 6 Performance Evaluation
### Discussion  
Having performed theoretical and emulation-based analyses in terms of routing overhead, we now discuss application-specific scheme design based on our evaluation results.  
Figure 11: Effects of route known probability on the average routing time (50 relay nodes and \(p_{\text{out}}=0\)).  
Figure 10: Effects of outage probability on average routing time (30 relay nodes).  
The proposed method has lower latency when the communication distance is shorter, and the route known probability \(p_{\text{known}}\) increases. For example, Figure 10(a) shows that with \(d_{\text{max}}=50\,\text{(m)}\) and \(p_{\text{known}}=0.8\) it is more than three times faster than other methods. As \(p_{\text{known}}\) increases with the number of previous routing attempts, the proposed protocol is suitable for applications that require low latency, short communication distances, and static terminal locations, such as IoT environmental monitoring and decentralized edge learning in urban situations. A similar trend is expected for long communication distances over a wide area. Therefore, it is suitable for wide-area communication using a low-power wide-area (LPWA) network as well.  
However, when \(p_{\text{known}}\) is low, the proposed method requires end-to-end communication in many cases. Unlike the compared methods, the proposed method increases the packet length hop-by-hop. For environments in which terminals move rapidly, such as vehicular networks, it is preferable to choose other schemes, such as ISDSR+.
## 2 Materials and Methods
### Validation of Numerical Analysis  
The mesh was generated using Ansys Meshing, as shown in Figure 2c. Tetrahedral meshes were used for the tube and mainstream regions, while structured meshes were employed for the remaining areas. The grid resolution near the wall was evaluated using wall coordinates. In most regions, the y+ value is below 0.1, with a local maximum of 1.1. Further, a mesh sensitivity analysis was conducted to determine the appropriate mesh resolution [28]. Meshes with one million, four million, six million, seven million, eight million, and nine million cells were created and analyzed. The results of the j-factors for each mesh size are illustrated in Figure 3. Based on the mesh sensitivity analysis, a mesh size of seven million cells was selected, as it showed negligible error for the j-factor.  
Results obtained using the standard \(k\)-\(e\) and \(k\)-\(\omega\) SST turbulence models were compared to select the turbulence model. The j- and f-factor results for both models are compared in Figure 4. While there was no significant difference between the two turbulence models, the \(k\)-\(\omega\) the SST model was chosen for this study due to its advantages in predicting boundary layer flow near walls and shear stresses as well as its relative accuracy in predicting adverse pressure gradients and flow separation [29]. The flow structure analyzed in this study exhibits characteristics that differ significantly from the classical laminar-to-turbulent transition scenarios observed on flat plates. With a Reynolds number below 1000, the base flow remains predominantly laminar. However, the turbulence-enhancing effects of the wavy slit fin generate localized turbulence near the slits, which dissipates as the flow moves downstream.  
To accurately capture this behavior, we conducted simulations using the laminar model, the Low Reynolds \(k\)-\(e\) model, the \(k\)-\(e\) model with Enhanced Wall Treatment, and the \(k\)-\(\omega\) SST model. The comparison of results showed a relative error of only 0.2% for the \(j\)-factor and within 2% for the \(f\)-factor, indicating minimal differences between the models. These results suggest that while localized turbulence occurs, it has a negligible impact on the mean flow and overall thermal performance metrics in the region of interest.  
Figure 4: Comparison of the j- and f-factor across Reynolds numbers: independence from turbulence models.  
Figure 3: Grid sensitivity analysis: number of grids required for converged j-factor results.  
Consequently, the steady-state assumption is appropriate for the flow regime investigated in this study.  
The j- and f-factor obtained from the numerical analysis were compared with the experimental correlations from the work of [PERSON] [13], as shown in Figure 5. The comparison revealed that the j-factor exhibited an error of under 5%, while the f-factor had an error of under 10%.
## 3 GNSS Spoofing Signal Power Control Algorithm
### Power Allocation Optimization Algorithm  
The multi-objective optimization algorithm of sequential quadratic programming (SQP) is used to determine the power of each spoofing signal. SQP algorithm is an algorithm that converts the nonlinear constrained optimization problem into a relatively simple quadratic programming problem, and the quadratic programming problem is an optimization problem in which the objective function is a quadratic function, and the constraint function is a linear function. The specific process of the power allocation optimization algorithm based on SQP is as follows: \(F_{\theta^{2}}\), \(F_{P_{t}}\), \(F_{total}\) in the above formula are taken as the objective function, and the expected values of the objective function are \(F_{\theta^{2}}=0\), \(F_{P_{t}}=-1\), \(F_{total}=-N\), respectively, \(N\) indicating the number of spoofing signals. The weight of each expected value is set as: \(W_{F_{\theta^{2}}}=0\), \(W_{F_{P_{t}}}=1\), \(W_{F_{total}}=N\). Under the constraint condition in Equation (19), the smaller the values of \(F_{\theta^{2}}\), \(F_{P_{t}}\), \(F_{total}\), the better spoofing signal power allocation scheme is proved.
# Dynamic and Quantitative Assessment of Blood Coagulation Status with an Oscillatory Rheometer
## 1 Introduction
In this work, an oscillatory rheometer ('Kinexus Pro', Malvern Instruments GmbH, Herrenberg, Germany) was used to measure within the linear viscoelastic region, where a fluid can be reversibly deformed without being destroyed. Between two plates or a plate and a cone a sample is placed and sheared by oscillation. The sample properties determine the selection of the geometry. The cone/plate system is used for homogenous samples or dispersions with a certain particle size (D\({}_{\text{max}}\leq\) 0.1 d; with D\({}_{\text{max}}\) particle size and d measuring gap), otherwise the plate/plate system is used. Two measurement modes can be performed to obtain all viscoelastic parameters (complex shear modulus G\({}^{\prime}\) with elastic/storage G\({}^{\prime}\) and viscous/loss modulus G\({}^{\prime\prime}\)). The first mode applies a sinusoidal angular displacement to the sample and is called deformation-controlled measurement. The second mode (shear stress controlled) applies a sinusoidal torque. This device can measure over frequency (up to 100 Hz) or over time. At low frequencies the low torque signal-to noise level limits the measurement. At higher frequencies inertia effects dominate the measurement [14; 15; 16]. As a medical reference device, the ball coagulometer was used where a stainless steel ball is kept within a magnetic field. It provides the clotting time in seconds, due to the displacement of the ball by a fibrin thread. The magnetic sensor detects this change in position and the time measurement is stopped. Common used coagulation assays with fibrin formation as end point, such as the Prothrombin time (PT, extrinsic coagulation pathway) or the activated partial thromboplastin time (aPTT, intrinsic coagulation pathway), as well as the fibrinogen concentration can be determined by the ball coagulometer [17; 18; 19]. Similarly to the ball coagulometer the oscillatory rheometer should also be able to provide the clotting time, but additionally the change of viscous and elastic shear moduli over time during coagulation process and the clot firmness in maximum shear moduli. The purpose of the current study was to investigate the reliability and repeatability of the oscillatory rheometer and its possible limitations in detecting blood coagulation. To enable adaption of the rheometer for medical application a comparison between the oscillatory rheometer and a ball coagulometer (KC 1A, ABW Medizin und Technik GmbH, Lemgo, Germany) as a medical reference device was performed.
# Power Quality Impact and Its Assessment: A Review and a Survey of Lithuanian Industrial Companies
## 1 Introduction  
It is not a secret that poor power quality (PQ), and in particular short-duration RMS voltage variations caused by power grid faults, still remains a big problem for industry [1, 2]. Although the paradigm of the main European PQ standard EN 50160:2010 [3] consists only of voltage events, the main North American PQ standard IEEE Std 1159-2019 [4] encompasses current events as well. From a broader perspective, the PQ field is a part of electromagnetic compatibility (EMC), the core part of which consists of two essential requirements--equipment (apparatus and fixed installations) must be designed and manufactured in such a way (1) that the electromagnetic disturbances it generates do not exceed an allowed level and (2) that it has an appropriate immunity and is able to function as intended in an electromagnetic environment. In Lithuania, this is regulated by both the EMC Directive 2014/30/EU [5] and national Technical Regulation [6], and falls under the responsibility of the Communications Regulatory Authority of the Republic of Lithuania. It is understandable that the requirements for the immunity must be stricter than for the emissions. In addition, it is worth mentioning that according to IEEE Std 1159-2019, IEC standards classify causes of electromagnetic disturbances (or, in other words, causes ofelectromagnetic interference) into conducted low-frequency, conducted high-frequency, radiated low-frequency and radiated high-frequency phenomena as well as into electrostatic discharge phenomena and nuclear electromagnetic pulse. All mentioned aspects are faced in a complex manner: end-user equipment, when working in the electromagnetic environment, must be both resistant to all types of electromagnetic interference and not emit it--this can be represented by the overlapping area of Venn diagrams given in Figure 1. Moreover, the scientific literature is increasingly beginning to examine PQ of DC grids--this new PQ subject is not yet included in both EN 50160:2010 and IEEE Std 1159-2019, but is relevant and needs to be investigated in anticipation of, for example, massive advent of microgrids and Power-to-X systems.  
PQ is mostly related with conducted phenomena, but there are also some other cases, such as lightning-induced overvoltages, geomagnetic storm effects, and the coupling between electrocardiogram signal and power wires that is described in the PQ analyzer manufacturer's webpage [7]. In general, when assessing PQ impact, it is useful to analyze both voltage and current data because, for example, most voltage sags are caused by high current demand, which is also dangerous and not desirable phenomenon [2,8].  
The aim of this paper is to study the effects of poor PQ and their assessment methods. In addition to the literature review, this paper presents the survey of Lithuanian industrial companies focusing on the immunity to conducted electromagnetic interference (more precisely, to short-duration RMS voltage variations--voltage sags and supply interruptions).
## 4 Pavement Temperature-Evaluation Model
The analysis results of these parameters are presented in Figure 7, in which \(a_{1}\), \(a_{2}\), and \(a_{4}\) are the natural logarithm functions of depth and \(a_{3}\) is approximated to a fixed value--6.564 in this study. \[T_{Z}=a_{1}\times T_{atmo-trans}-a_{2}\times\sin\left(a_{3}\times\frac{t}{24} \right)+a_{4} \tag{3}\]  
Figure 8 compares the temperatures evaluated by Equation (3) (the model temperature) and the in situ temperature measurements (the measured temperature) for depths of 5.5 and 24.5 cm as examples. Table 4 summarized the results of correlation between measured and model temperature at various depths. The correlation coefficients at depths of 3.5 and 5.5 cm were 0.87 and 0.90, respectively. The correlation coefficient decreased with increasing depth; however, at a depth of 24.5 cm, the correlation coefficient between the model and measured temperatures remained high at 0.82. Moreover, the slopes of regression equation between model and measured temperatures were within \(1\pm 0.05\), and the intercepts were all within \(\pm 2\). The results show that the temperature-evaluation model using Equation (3) can reflect the temperature at various depths in the road structure. Furthermore, the model remains effective when estimating the temperature at deep depths. Hence, in order to reduce the model parameter and to have acceptable accuracy of estimated temperature at deep depths, this research employed Equation (3) as the temperature model to estimate the pavement temperature at various depths. The proposed model and obtained parameters \(a_{1}\)-\(a_{4}\) were based on the statistical analysis of temperature measurements in Taiwan. The climate of Taiwan belongs to the subtropics and the lowest and highest atmospheric temperature of the FWD test site are around 6 and 37 \({}^{\circ}\)C, respectively. More applications and validations of the model require further temperature measurements and analyses for other weather conditions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Depth (cm)** & \(\mathbf{R^{2}}\) & **Slope** & **Intercept** \\ \hline
3.5 & 0.87 & 0.96 & 1.91 \\
5.5 & 0.90 & 1.01 & 0.03 \\
7.5 & 0.89 & 1.00 & 0.18 \\
9.5 & 0.89 & 1.00 & \(-\)0.15 \\
11.5 & 0.88 & 1.00 & \(-\)0.07 \\
13.5 & 0.87 & 1.00 & \(-\)0.25 \\
15.5 & 0.86 & 1.00 & \(-\)0. Figure 7: \(Cont.\)  
Figure 8: _Cont._  
Figure 7: The relationship between the parameter of temperature-evaluation model and depth.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 5 Conclusions  
WUE is an important ecosystem indicator reflecting the carbon-water cycle of vegetation cover, which is of much potential meaning for both ecological restoration and environmental management. In this study, with the help of long-term satellite observations, the WUE and its spatial variation trends were analyzed across the Three-North region of China. The results revealed a great spatial diversity of WUE with similar variation trends of GPP, suggesting that GPP was the major control factor of WUE. In the Three-North region of China, the WUE value was lower than the national average due to the large area of barren lands, reducing the absolute value in this region. Despite that, the WUE increased slowly at a rate of 0.002 g\(\cdot\)C\(\cdot\)m\({}^{-2}\cdot\)mm\({}^{-1}\cdot\)y\({}^{-1}\). The fluctuation and sustainability analysis of WUE further indicated that the eastern and southeastern parts of the Three-North region had both few fluctuations and low sustainable improvements, while the western parts showed more fluctuation and sustainable degradation. These results are of great importance for future ecological restoration programs in this region, as the vegetation growth conditions here have great spatial heterogeneity.  
Writing, visualization, formal analysis, Q.Z.; conceptualization, methodology, W.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 41701391.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.