esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The present study studied the effectiveness of starter _L. plantarum_ SK15 on biogenic amine (PUT and SPD), pH, total acidity, alcohol content (methanol and ethanol), amino acid, reducing sugar content, bioactive compounds (TPC and antioxidant activity), and microbial loads in FMB samples with and without starter. _L. plantarum_ SK15 did not produce amino acid decarboxylase. Therefore, it was used as a starter culture in the process of FMB fermentation. PUT and SPD were slightly increased following the fermentation time. The FMB with starter culture contained a low PUT and SPD content when compared with the FMB without starter culture. The addition of starter culture in FMB had lower pH and less alcohol content than FMB without starter culture. The pH and alcohol values were acceptable, according to TCPS. The total amino acid of the FMB with _L. plantarum_ SK15 was decreased after 720 h fermentation. After 480 h, the reducing sugar content decreased in natural fermentation, while the level of reducing sugar increased in the FMB with _L. plantarum_ SK15. In addition, FMB with _L. plantarum_ SK15 had a high level of TPC and antioxidant activity compared to FMB without _L. plantarum_ SK15. The results suggested that the use of specific starter culture (_L. plantarum_ SK15) had the potential to be a starter culture in the process of FMB fermentation.  
Conceptualization C.C. and S.S.; methodology, C.C., N.M. and S.S.; validation, C.C. and S.S.; formal analysis, N.M.; investigation, N.M., C.C. and S.S.; resources, S.P.; data curation, N.M.; writing--original draft preparation, N.M.; writing--review and editing, C.C., S.S., J.S., N.M., S.P., W.C. and W.W.; visualization, S.S.; supervision, C.C.; project administration, S.P.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** N.M. was funded by Research and Researcher Development for the Industry Program (RRi) of the Thailand Research Fund (TRF) through the grant number PHD58I0046.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in the manuscript is available on request from the corresponding author.  
**Acknowledgments:** Authors gratefully acknowledge the Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand. Chiangmai University partially supported this project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Network Analysis to Identify the Risk of Epidemic Spreading
## 1 Introduction  
The emergence of a pandemic is one of the various scenarios frequently discussed as a human extinction event, and it is listed as one of the global catastrophic risks in studies regarding the future [1, 2, 3]. In particular, several pandemics, such as the Black Death [4, 5], Spanish flu [6], and those caused by smallpox [7], severe acute respiratory syndrome (SARS) [8], and Ebola [9], have affected a large population throughout history. The risk of pandemics increases with an increase in population mobility between cities, nations, and continents, thereby threatening humankind [10, 11, 12]. It is essential to analyze the epidemic spread in society to minimize the damage from epidemic disasters; however, extinctive epidemic spreading experiments have limitations in real-world situations, as they predict stochastic effects on the spread without considering the structure of human society. Network-based approaches have been proposed to overcome these limitations and perform epidemic spreading simulations by considering the network structure of numerous real-world connections [13, 14, 15]. These methods use various models of epidemic spreading, such as the susceptible-infectious-susceptible (SIS) [16, 17, 18], susceptible-infectious-recovered (SIR) [19, 20, 21], and Watts threshold models [22]. While these methods are mathematically convenient, they are epidemiologically unrealistic for various infections because they require exponentially distributed incubation and infectious periods [23, 24, 25]. Moreover, previous epidemic studies did not perform quantitative assessment of thepandemic risk depending on the network connectivity in individuals and fatality rate of various diseases [26].  
In the present study, we applied an SIR epidemic model to a scale-free network with Monte Carlo simulation to identify the quantitative relationship between infectious diseases and human existence. Our fundamental hypothesis states that when the epidemic spreads to all nodes of the network and the fatality rate is 100%, it can increase the pandemic risk. To address this, we initially constructed a scale-free network to simulate a society. Moreover, for the epidemic spreading simulation, an SIR model was applied to the network to describe the immune state of an individual after infection. From the simulation study, we found that the mean degree of a scale-free network was an essential factor in determining whether epidemics threaten humans. This approach provides important insights into epidemic spreading analysis by investigating the relationship between epidemic and scale-free network parameters. Furthermore, it highlights the necessity of determining information flow during an epidemic.
# Partial Oscillation Flow Control on Airfoil at Low Reynolds Numbers
## 1 Introduction
The advancement in high-performance piezoelectric materials technology has paved the way for a novel approach where the active oscillation of the wing's skin can be leveraged to enhance its aerodynamic performance. [PERSON] [15] was among the first to implement a high-frequency oscillation methodology to regulate the boundary layer separation phenomenon along a cylindrical surface, successfully reducing the drag force acting upon the cylinder by a significant margin of 12.4%. Regarding the application of active flow control via oscillation on airfoils, [PERSON] [16] ingeniously utilized piezoelectric composite materials to effectuate periodic deformations along the airfoil's upper surface. The research demonstrates that the frequency of oscillation exerts a substantial influence on the airflow patterns and stall attributes of the airfoil. [PERSON] [7] employed electroactive polymers to induce periodic deformation within specifically designated discrete areas on the wing surface. Through the use of Particle Image Velocimetry (PIV) in a wind tunnel setting, he investigated the effects of such periodic deformation on LSBs. His findings revealed that when the frequency of the induced deformation approximates that of the K-H instability, the tendency for laminar separation can be significantly attenuated. In comparison with the inherent self-vibrations exhibited by flexible airfoils, the active oscillation of the airfoil's surface material demonstrates enhanced controllability and broader potential for practical application. Currently, research predominantly focuses on the impact of localized oscillations at predetermined positions or full-surface oscillations on the aerodynamic attributes of wings. Most of these studies concentrate on the lift characteristics of wings under high angles of attack. Research into the effects of the placement of localized oscillations and the impact of oscillation patterns on the drag characteristics of a wing, particularly at its cruise angle of attack, remains insufficiently explored. On the basis of previous studies, a partial oscillation mechanical model with different chord positions and different oscillation frequencies is established in this study. The effects of oscillation position and frequency on the lift and drag characteristics and flow structure of the airfoil near the cruise angle of attack are studied, and the mechanism of the lift increasing and drag reducing of partial oscillation is revealed. The reasonable flow control method of partial oscillation is obtained, which can effectively improve the aerodynamic performance of the airfoil.
# Forced Circulation of Nitrogen Gas for Accelerated and Eco-Friendly Cooling of Metallic Parts
## 3 Results and Discussion
### Thermal Distortion  
Although cooling at uniform cooling rates in a circumferential direction, longitudinal direction, and inside or outside is ideal, it is practically impossible. Generally, process-induced distortion is generated by non-uniform stresses caused by a non-uniform temperature gradient during the cooling process or non-uniform deformations [49]. In this study, the dimensional accuracies after water and nitrogen cooling were measured. To estimate the degree of distortion, the deviations from the circularity are measured at three different positions. Figure 12 shows the comparison of distortions in tubular parts after water and nitrogen cooling. As shown in this figure, nitrogen gas cooling provides more uniform cooling and less distortion than water cooling, thus reducing post-quenching machining. The standard deviations of distortion for water and nitrogen cooling are 0.343 mm and 0.090 mm, respectively. In the case of water cooling, the differences in cooling rates between the inner and outer tubular surfaces and the locally different immersion times might increase the distortion. In particular, larger distortions are expected in the non-axisymmetric part, cup-shaped part, thin-flat part, and the part with a high length to diameter ratio, where uniform heat transfers cannot occur. However, the distortion in the case of nitrogen cooling is significantly smaller than in the case of water cooling, because nitrogen can cool the parts more uniformly and rapidly due to the forced circulation of the gaseous flow.  
Figure 11: Surface cleanliness: (**a**) Water cooling; (**b**) nitrogen cooling.
## 5 Optimization of LPG Distribution Routes Using Clark and Wright Heuristics
### Clark and Wright Savings Algorithm  
The [PERSON] and Wright savings algorithm operates on a graph where the vertices correspond to individual customers. The algorithm starts with the initial solution, which consists of elementary routes. An elementary route, in the form of single depot (D) \(\rightarrow\) served point \(\rightarrow\) single depot (D), is created for each served point, e.g., in Figure 10(a), the generation of two different routes (D\(\rightarrow,\ldots,i\)\(\rightarrow\)D) and (D\(\rightarrow\)\(j,\ldots,\rightarrow\)D) is shown. This initial solution is then modified, and the routes are merged to form a new route (D\(\rightarrow,\ldots,i\)\(\rightarrow\)\(j,\ldots,\rightarrow\)D), as shown in Figure 10(b), to achieve maximum savings in total cost.  
The distance or time saving obtained by such a joining can be expressed as:  
\[s_{ij}=c_{i0}+c_{0j}-c_{ij} \tag{7}\]  
since the new solution will no longer use the arcs \((i,0)\) and \((0,j)\), the arc \((i,j)\) will be added. Thus, in this algorithm, we start from an initial solution and make the unions that generate the greatest savings, provided that the problem restrictions are not violated.  
Since this algorithm is well known, it will not be discussed further in this article; however, a complete description of single depot VRP can be found in [60]. Furthermore, a simplified listing of Clark and Wright Heuristics for VRP is shown in Algorithm 1.  
Figure 11: Basic principle of the Clarke and Wright algorithm. Two different routes before and after being joined.  
```
1:Begin
2: Define the LPG filling plant \(d=0\) and the set of customers \(C=\{1,2,\dots,n\}\)
3: Assign demand \(q_{i}\) for each customer \(i\in C\)
4: Generate initial routes \(R_{i}=\{0\to i\to 0\}\), \(\forall_{i}\in C\)
5:Savings calculation
6:for each pair of customers \(i,j\in C\), with \(i\
eq j\)do
7: Calculate \(s_{ij}=c_{i0}+c_{0j}-c_{ij}\)
8:endfor
9: Sort out savings \(s_{ij}\) in descending order
10:Join routes
11:while there are savings \(s_{ij}\)do
12: Select the pair \((i,j)\) with greater savings
13: if \(R_{i}\
eq R_{j}\) and \(\Sigma q_{k}\leq Q\), join \(R_{i}\) and \(R_{j}\)
14:endwhile
15:End
16: Return the remaining routes to the set \(R_{last}\)
```  
**Algorithm 1** Clark and Wright algorithm for VRP
# Robust Control Examples Applied to a Wind Turbine Simulated Model
## 3 Data-Driven and Model-Based Control Designs
### Model-Based Adaptive Control Scheme
In the same way, a lag time constant \(\tau_{s}\) may be approximated again as a time delay:  
\[\frac{1}{\tau_{s}\,w+1}\approx e^{-\tau_{s}\,w} \tag{42}\]  
Furthermore, considering the product of Equations (41) and (42), it follows that the effective delay \(L\) of Equation (43) can be taken as the sum of the contribution from the various approximated terms \(T_{o}^{inv}\) and \(\tau_{s}\). In addition, for digital implementation with sampling period \(T_{s}\), \(L\) can consider also the contribution of the D/A converter \(T_{s}/2\). Therefore, the approximation of Equation (43) is valid, and an approximate first-order time delay stable model is obtained as already shown by Equation (43):  
\[\frac{k}{w\,\tau_{o}+1}\approx e^{-w\,L} \tag{43}\]  
where \(k\) is the model gain, \(\tau_{o}\) the dominant lag time constant, and \(L\) the effective time delay. Under these assumptions, the following rules for the tuning of the PI controller can be exploited for first-order time delay (non-minimum phase) models:  
\[\left\{ \begin{array}{rcl}\hat{K}_{p}&=&\frac{1}{k}\,\frac{\tau_{s}}{\tau_{c }+L}\\ \hat{T}_{l}&=&\min\{\tau_{o}\,,4\,(\tau_{c}+L)\}\end{array} \right. \tag{44}\]where \(\tau_{c}\) is the desired first-order closed-loop response time and represents the only tuning parameter Small values of \(\tau_{c}\) lead to response fast speed, whilst large values for stable and robust behavior. A good rule would suggest to set \(\tau_{c}=L\) for fast response with good robustness properties [36]. It is worth noting that the work [37] provided an analytical demonstration of the stability of an adaptive control scheme for wind turbines. Finally, Section 4 will show the achieved results regarding the design and the application of the adaptive controller to the data from the wind turbine benchmark.
###### Abstract  
This paper proposes voltage-stability based on congestion management (CM) for electricity market environments and considers the incorporation of wind farms into systems as well. A probabilistic voltage-stability constrained optimal power flow (P-VSCOPF) is formulated to maximize both social welfare and voltage stability. To reflect the probabilistic influence of CM in the presence of wind farms on voltage stability, Monte Carlo simulations (MCS) are used to analyze both the system load and the wind speed from their probability distribution functions. A multi-objective particle-swarm optimization (MOPSO) algorithm is implemented to solve the P-VSCOPF problem. A contingency analysis based on the voltage stability index (VSI) for line outages is employed to find the vulnerable line of congestion in power systems. The congestion distribution factor (CDF) is also used to find the optimal location of a wind farm in CM. The optimal pricing expression, which is obtained, with respect to preserving voltage stability, by calculating both the locational marginal prices (LMPs) and the nodal congestion prices (NCPs), is demonstrated in terms of congestion solutions. Simultaneously, the voltage stability margin (VSM) is considered within the CM framework. The proposed approach is implemented on a modified IEEE 24-bus system, and the results obtained are compared with the results of other optimal power flow methods.  
congestion management; probabilistic voltage-stability-constrained optimal power flow; congestion distribution factor; voltage stability margin; multi-objective particle swarm optimization; wind farm +
Footnote †: journal: Applied Sciences
# Method for Handling Massive IoT Traffic in 5G Networks
## 7 Spectrum Utilization  
Using \(N_{x,M}\) from Table 2 and \(P[X=x]\) from Equation (8), the average resource blocks used in each multiplexing process or the average resource blocks consumed by a large packet is denoted by \(k\), and determined as:  
\[k=\sum_{x=1}^{r}N_{x,M}\times P[X=x] \tag{9}\]  
The mean number of resource blocks consumed by a single multiplexed large packet, \(k\) is dissimilar from the parameter, mean number of blocks utilized per TTI. The mean number of resource blocks per TTI are considerably decreased by employing the multiplexing scheme along with the timer. However, \(k\) increases with usage of packet multiplexing scheme. The greater the value of \(k\), the better the resource utilization. The multiplexing of packets happens if a duration \(T_{max}\) has elapsed. If the packet aggregation scheme is not used at the RN and packets are sent to BS without multiplexing, then the arriving packets would be sent to BS immediately at the beginning of the next TTI, instead of waiting for the timer to expire. For this reason, the average number of resource blocks for each large packet would increase in case of multiplexing with the timer. As a result, the total number of multiplexed packets sent from RN to BS would be reduced considerably. The lesser the number of multiplexed packets, the higher the value of \(k\) and the lesser the overhead data.
## 3 Results  
### Classification by Cause of Failure  
Figure 2 illustrates the causes of failure for the total instances (\(n=359\)). The main downtime occurrence sites were the MLC (25.2%), gantry and couch motion parts (15.4%),  
-dosimetric part (11.7%), and PC (computer console either in the treatment control room or treatment room) (10.0%). Causes of failure for the PC included computer system errors, such as boot failure and unexpected shutdown. Another 7.8% of the error was a hand pendant problem with the accessory. This can be attributed to internal board failure or a problem with the pendant's body and the jack connected to the couch. A simple release of warnings accounted for 6.9% of the error resolution. That is, the problems were solved by software processing, lubricating mechanically operated parts, or cleaning contaminants without replacing parts through an engineer's visit. Additionally, failures of dosimetric or beamline parts were considered serious faults, such as the replacement of internal parts, and required additional internal quality assurance procedures.  
Figure 3 shows a cumulative downtime of 1500 h and an average downtime of 72 h \(\pm\) 33.44 for the entire dataset. To analyze the distribution of causes of failure, we analyzed the trend of the monthly average downtime for each event (instance). In particular, in 2009 and 2016, the downtime rapidly increased to 170 h and 136 h, respectively, due to the failure of the dosimetric part (electron gun, ion chamber) and beamline components. The downtime increased in those years because the part to be replaced took a long time to supply, and it entailed a complex dosimetric quality assurance (QA) procedure. However, other years saw an average downtime of 63 h \(\pm\) 20.98.  
Figure 2: LINAC causes of failure for Novalis in our institution.
## 3 Results
### Evaluation Scenario 1  
In this scenario, the configuration deployed in [9] is used. This configuration was generated by the SUMO urban mobility simulator, to simulate various levels of congestion over time. In this configuration, one hundred and fifty-eight vehicles move in the 650 m of an urban road in Manhattan (NYC). The Mininet-WiFi emulator and the SUMO simulator were integrated to implement mobility. To evaluate our proposed framework, in this scenario 42 vehicles out of the 158 vehicles generate traffic belonging to different applications simultaneously, whereas 26 vehicles generate traffic for safety applications. This scenario represents a high-traffic scenario to study the impact of a high level of interference on our proposed framework. The parameters used in the performance evaluation are summarized in Table 3. Figure 6 depicts the network setup used in simulation in case of applying the MEC concept to support safety applications prerequisites in terms of ultra-low delay and without applying the MEC concept. The values of RTT are calculated using the \"PING\" ICMP messages. Table 4 illustrates the average RTT results in the case of applying our proposed Adaptive Quality of Service (AQoS) algorithm and in the case of a QoS-free model in this high-level interference scenario. Our algorithm achieves an improvement of up to 74.63% in terms of average RTT in the case of safety applications and up to 90.88% in the case of infotainment applications. The high improvement in average RTT in the case of infotainment as the infotainment applications have a priority class 2 and are assigned a bandwidth greater than the best effort class and in case of this class is hungry for bandwidth, it collects the free bandwidth from other classes. On the other hand, the improvement in safety applications is less as in both cases the safety servers are connected directly to RSUs applying the MEC concept of 5G to support ultra-low delay application. Moreover, results revealed that the usage of MEC technology improves the average RTT by up to 98.09%. Finally, without applying the MEC technology and moving safety servers to the SDN network core, our proposed AQoS still improves the performance by up to 64% compared to the QoS-free model.
## 3 Preliminaries
### DIRECT Algorithm
#### 3.1.4 Convergence  
As shown in [28], when the performance function is continuous, the DIRECT algorithm converges to the global minimum. This is because the set of points sampled by DIRECT densely covers the unit hypercube, as the number of iterations tends to infinity. In other words, DIRECT will eventually sample a point within a distance \(\eta\) of any point \(\mathbf{x}\) within the unit hypercube for any \(\eta\geq 0\). This capability allows DIRECT to achieve high accuracy given a sufficiently large number of iterations [28; 31; 32; 33].
# A 920-MHz Dual-Mode Receiver with Energy Harvesting for UHF RFID Tag and IoT +
## 4 Proposed Dual-Mode Receiver  
### Architecture of Proposed Dual-Mode Receiver  
The architecture of the proposed receiver is shown in Figure 6. The receiver can operate in low-power (LP) mode and high-sensitivity (HS) mode. When operating in LP mode, the circuit is powered by the 3-stage rectifier with an on-chip MOSFET capacitor. When the energy provided by the PV/TE energy harvester is sufficient, the receiver operates in HS mode and the receiver's sensitivity is increased.  
Figure 4: Schematic of proposed radio frequency identification (RF) energy-harvesting circuit.  
Figure 5: Simulation results of the rectifier, (**a**) output voltage versus input power for different stages and load resistance; (**b**) efficiency versus input power for different stages and load resistance.  
The MOSFETs of the RF detector are operating in the sub-threshold region in order to reduce power consumption. It detects the envelope from the incident RF signal due to the exponential transfer function of \(M_{\mathrm{N}}\) and \(M_{\mathrm{P}}\). Then, the envelope is switched to the intermediate frequency (IF) amplifier. The first stage is buffer with high pass filter (HPF), and the gain of the second stage amplifier is 20 dB and the bandwidth is 150 kHz which is from 5 kHz to 200 kHz. The demodulator circuit is based on low-pass filter (LPF) and comparator, the hysteresis value of comparator is optimized considering mismatch and noise [9].