esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### In Vitro Antimicrobial Assay  
A preliminary evaluation of citral antimicrobial activity was performed against _R. oryzae_ ITEM 18876 by applying the disk diffusion method at two inoculum levels and different citral concentrations (from 0.5 \(\mu\)L to 4 of pure citral). At the optimum growth temperature (25 \({}^{\circ}\)C) of incubation, the results show that the lowest concentration used (0.5 \(\mu\)L) completely inhibited _R. oryzae_ ITEM 18876 growth at both levels of inocula (see Supplementary Materials Figure S1). These data are in accordance with other studies reporting the inhibitory activity of citral against fungi, including _R. stolonifer_[37; 38]; however, a higher active concentration was reported against the latter [37]. Overall, the monoterene citral exhibited a broad antimicrobial spectrum against bacteria and fungi; MIC values ranging from 50 to 1000 \(\mu\)g/mL were registered by other authors [27].  
Studies on _Penicillium digitatum_ showed that one of the fungicidal mechanisms exerted by citral is represented by the repression of five ergosterol biosynthetic genes, leading to cell membrane damage and mycelial growth inhibition [39]. Additionally, for _R Rhizopus_ spp., the fungicidal mechanism of citral was proposed to act by reducing the ergosterol content in the plasma membrane [22].  
The antimicrobial activity of citral against different bacteria and plant pathogenic fungi in functionalized coatings [27; 40; 41; 42; 43; 44], spray dried microparticles [45], and nanostructured lipid carriers was also demonstrated [46]. In particular, the incorporation of the volatile antimicrobial citral into packaging materials has the advantage that it can exert an antimicrobial activity without physical contact with the food surface; the elevated vapor pressures allow citral to be released into the headspace, subsequently, and be absorbed by the food matrix [47; 48].  
Thus, considering the previous results, the antifungal activity of citral against the target strain was evaluated in relation to the citral levels in the headspace under the experimental conditions. The results show that the MIC value for citral vapor against _R. oryzae_ ITEM 18876 was equal to 0.0125 uL/cm\({}^{3}\) for the two different assayed spore concentrations. In a previous study, a concentration equal to 0.06 uL/mL inhibited only one-third of the radial mycelial growth of _Penicillium italicum_, whereas higher concentrations (>0.5 uL/mL) caused the full inhibition or fungicidal activity of the same fungus displaying a MIC of 0.5 uL/mL [49].
## 7 Conclusions  
In the existing literature on TACRN, the wave in the tire cavity is usually assumed to be a plane wave for convenience. But up to now, no literature researched the real sound field in the tire cavity systematically. Aiming at this problem, the characteristics and evolution of traveling waves in tire acoustic cavities are investigated in this paper, and the following conclusions are reached:  
1. The acoustic wave close to the contact patch in the tire acoustic cavity contains the plane wave and the high-order evanescent waves, so it is not a real plane wave. And, the existing range of high-order waves depends on the frequency of acoustic waves, the sound velocity, and the cross-section size.  
Figure 10: Range of the high-order waves when summing the effects of forward/backward traveling waves.  
Figure 9: Variations of the existing range of the high-order waves with acoustic wave frequency for different values of tire speed.  
Based on the assumptions in this paper, for the given tire and in the frequency range (200 to 250 Hz) of tire acoustic cavity resonance, the high-order wave exists in the range of about 320 mm around the sound source (contact patch), which is about 20% of the circumference of the whole tire.
3. For different media and tire temperatures, the tire cavity resonance frequency, the cut-off frequency, the frequency range in which the wave in the tire cavity is considered a plane wave, and the range in which the plane wave exists vary considerably. Except for TACRN, the media in the cavity and tire temperatures do not affect the existing range of plane waves.
4. The existing range of high-order waves gradually increases with tire speed, but when studying the tire acoustic cavity resonance noise, the influence of tire rotation on the change of acoustic characteristics of the tire acoustic cavity can be ignored.  
Conceptualization, X.L. and X.H.; methodology, X.H.; software, X.H.; validation, X.L., X.H. and Y.S.; investigation, T.H.; data curation, X.H.; writing--original draft preparation, X.H.; writing--review and editing, X.L.; visualization, X.H.; supervision, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of China grant number 51675021.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the requirements of the university.  
The authors declare no conflict of interest.
# COVID-19 Outbreak Prediction with Machine Learning
## 2 Materials and Methods
### Machine Learning (ML)  
ML is regarded as a subset of Artificial Intelligence (AI). Using ML techniques, the computer learns to use patterns or \"training samples\" in data (processed information) to predict or make intelligent decisions without overt planning [72; 73]. In other words, ML is the scientific study of algorithms and statistical models used by computer systems that use patterns and inference to perform tasks instead of using explicit instructions [74; 75].  
Time series are data sequences collected over a period of time [76], which can be used as inputs to ML algorithms. This type of data reflects the changes that a phenomenon has undergone over time. Let X\({}^{t}\) be a time-series vector, in which \(x_{t}\) is the outbreak at time point \(t\) and \(T\) is the set of all equidistant time points. To train ML methods effectively, we defined two scenarios, listed in Table 2.  
As can be seen in Table 2, scenario 1 employs data for three weeks to predict the outbreak on day \(t\) and scenario 2 employs outbreak data for five days to predict the outbreak for day \(t\). Both of these scenarios were employed for fitting the ML methods. In the present research, two frequently used ML methods, the multi-layered perceptron (MLP) and adaptive network-based fuzzy inference system (ANFIS), were employed for the prediction of the outbreak in the five countries.
## Materials and Methods
### MS-Schmidt and MMS Criteria for the Analysis of the Size Effect
Then, Equation (11) is used for calculating the tangential stress averaged over the critical distance \(\mathrm{d_{c}}\):  
\[(1/\mathrm{d_{c}})\cdot\int_{0}{}^{\mathrm{dc}}\sigma_{\mathrm{\Theta\Theta}} \mathrm{d}r=(\sigma_{\mathrm{\Theta\Theta}})_{\mathrm{c}}=\sigma_{\mathrm{u}}, \tag{11}\]  
The final equation for \(\mathrm{d_{c}}\) when using the MMS gives:  
\[\mathrm{d_{c}}\mathrm{-MMS}=(\pi/8)\cdot([R\cdot A_{1}{}^{*}\cdot\sigma_{ \mathrm{u}}\mp((R\cdot A_{1}{}^{*}\cdot\sigma_{\mathrm{u}})^{2}-(8\cdot A_{1} {}^{*}\cdot A_{3}{}^{*}\cdot R\cdot K_{\mathrm{IC}}{}^{2}/\pi))^{1/2}]/(A_{3}{ }^{*}\cdot K_{\mathrm{IC}})]^{2}, \tag{12}\]  
In Equation (12), the minimum positive value of \(\mathrm{d_{c}}\) is physically acceptable and will be denoted as \(\mathrm{d_{c}}\mathrm{-MMS}\) for the sake of more clarity. Now, \(\mathrm{d_{c,V}}\) can be calculated according to the new proposed approach as follows:  
\[\mathrm{d_{c,V-MMS}}=r_{0}+\mathrm{d_{c,MMS}}=(\pi-2\alpha)/(2\pi-2\alpha) \cdot\rho+\mathrm{d_{c,MMS}}, \tag{13}\]  
The length of the FPZ depends on the size of the cracked specimen [15; 24; 27]. Therefore, it can be stated that the parameter \(\mathrm{d_{c}}\) in both the approaches mentioned above (i.e., \(\mathrm{d_{c}}\mathrm{-MMS}\) and \(\mathrm{d_{c}}\mathrm{-Schmidt}\)) is size-dependent. In the present study, the recent approach suggested by [PERSON] and [PERSON] [27] is used to explore the size effect on the parameter \(\mathrm{d_{c}}\):  
\[n_{\Theta\theta}(0)=q\cdot(\chi_{\rm d1}\cdot(1+\mu_{1})+\chi_{\rm c1})/\{4 \cdot(q-1)\cdot[1+\lambda_{1}+\chi_{\rm b1}\cdot(1-\lambda_{1})]\}, \tag{6}\]0  
in which A and B are fitting parameters that are determined from mode I fracture tests performed in cracked samples with different sizes. Equation (14) may be converted into Equation (15), which is more suitable for linear regression:  
\[n_{\Theta\theta}(0)=q\cdot(\chi_{\rm d1}\cdot(1+\mu_{1})+\chi_{\rm c1})/\{4 \cdot(q-1)\cdot[1+\lambda_{1}+\chi_{\rm b1}\cdot(1-\lambda_{1})]\}, \tag{6}\]1  
More details about the calculation of A and B will be described below. Now, the size-dependent values of the notch fracture toughness for notched polymeric samples can be predicted by replacing the parameter \(\mathrm{d_{c}}\) according to the size of the sample from Equations (9) or (12) into Equations (7) or (13) and then substituting the calculated values of \(\mathrm{d_{c,V}}\) into Equation (5). In the next sections, the notch fracture toughness obtained from experiments will be compared with the predictions derived from the MS criteria (MS-Schmidt's and MMS).
# A B5G Non-Terrestrial-Network (NTN) and Hybrid Constellation Based Data Collection System (DCS)
## 6 DCS Equipment Design  
### Module Design  
NTN-mMTC DCS equipment is comprehensive sensor data collection equipment. Various signaling modes should be compatible. For traditional applications, the Automatic Identification System (AIS), Automatic Dependent Surveillance-Broadcast (ADS-B), Lora, NB-IoT, Beidou text, and military sensors (MS) need to be compatible. These applications work in different operating modes, frequency bands, and power consumption levels, as shown in Table 4. Due to miniaturization and reconfigurability, the SDR architecture is suitable for the specific application. Different data collection functions can be realized on a unified SDR hardware platform. Several antennas resonating in different frequency bands are required.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item** & **Freq** & **Rate** & **Modulation** & **Protocol** & **Object** \\ \hline AIS & VHF & 9.6 Kbps & GMSK & HDLC & Ship \\ ADS-B & L & 1 Mbps & PPM & CRC & Aircraft \\ Lora & UHF or S & 0.3–50 Kbps & Spread spectrum & FEC & Ground \\ NB-IoT & S & 1 Mbps & QPSK & OFDM & Ground \\ Beidou & S/L & / & / & RDSS & Ground \\ MS & C & 10 K–1 Mbps & BPSK & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 4: Various DCS applications.  
The USRP E310 is a unified SDR platform with continuous frequency coverage from 70 MHz to 6 GHz. As shown in Figure 6, the integrated RF frontend on E310 is designed with the analog devices AD9361. A FPGA chip is responsible for the digital signal process such as digital filter and resample. Ubuntu system is embedded in it. All baseband process and application functions are realized in Ubuntu. The power supple is 5.15 v DC and an external is allowed.  
A NTN-mMTC-based DCS equipment based on the E310 platform is shown in Figure 7a. It contains an SDR module, a radio frequency (RF) front-end module, a power supply module, and a connector module. E310 is implemented as a SDR module. Other modules are self-developed. Multiple antennas are shown in Figure 7b. This CubeSat flies along the X axis and keeps the Z axis pointing to the earth. A pair of tape measure antennas in UHF and VHF bands are mounted on the top. A L-band whip antenna is mounted on the bottom. A pair of patch antenna in S-band and C-band are mounted on the body of CubeSat.  
Figure 6: Software defined radio structure.  
Figure 7: DCS equipment design: **(a)** SDR equipment; **(b)** Antenna design.  
The performance of the RF module affects the communication capability. For a 500 km orbit altitude, the detailed design parameters are shown in Table 5.
# Optimized DM-RS Configuration for Improved 5G New Radio Network Capacity and Performance.
## 5 DM-RS Optimization, Achieved Network Capacity and Performance Improvements
### DM-RS Optimization for FWA Use Case and Improved Capacity
For example, the beginnings of months are typically periods with the most load in the network, since user data buckets are full at the beginning of the measurement  
period, and users consume more data services. For that reason, network performance was observed and analyzed during the first week of three consecutive months. The standard DMRS configuration was kept during November, and new settings were deployed during December and January; the results are presented in Figure 7a. With such an approach, the impact of traffic pattern changes during the month is minimized. To minimize the impact of other seasonal traffic pattern changes between different months affecting conclusions, network performance was observed in parallel for the same period for a control group of sites where DM-RS parameters were not changed, and the results are presented in Figure 7b. It should be considered that January is a specific month, especially during the first week, due to public holidays and significant related impact on traffic changes and users' mobility, followed by an observed traffic increase in comparison with December for the control group of sites. Improvements in spectral efficiency in the range of 5-10% are observed for optimized sites. In parallel, in the control group of sites, spectral efficiency performance in December is on par with November, while degradation may be noticed in January, probably driven by seasonal traffic pattern changes. In conclusion, DM-RS optimization has undoubtedly improved spectral efficiency by 5-10%. As discussed earlier, the main motivation for DM-RS configuration optimization was to improve the spectral efficiency and capacity of congested networks. From that perspective, it is interesting to introduce another metric into the analysis and observe the behavior of the correlation function between PRB utilization presented on the x-axis and the amount of data transferred through the network presented on the y-axis, for the period before and after the changes in DM-RS configuration (the first week of November versus the first weeks of December and January). The results are presented in Figure 8, showing an analysis conducted in parallel for optimized sites (8a) and a control group of sites where initial settings were unchanged (8b). It can be noticed that when PRB utilization is high (above 30-40%), the optimized 5G NR network can deliver a larger data payload with the same PRB utilization. The gain depends on the load of a cell and increases with the load increase. For PRB utilization of 70%, we will notice 12% more data being transferred through the optimized network compared to initial settings. Figure 6: DM-RS optimization for FWA use case, capacity improvements: (**a**) spectral efficiency improvements measured by counters from 5G NR performance management (PM) system; (**b**) PRB utilization changes and data payload in the network.
# Correlation between Biogenic Amines and Their Precursors in Stored Chicken Meat
## 2 Materials and Methods  
### Experimental Scheme  
Breast and leg muscles were obtained from chickens nourished on a diet based on wheat, maize, and soya, structured in a three-stage system: 0-16 d, starter: 11.5 M] energy, 261 g crude protein (CP)/kg; 17-35 d, grower: 12.1 M] energy, 221 g CP/kg; and 36-42 d, finisher: 13.4 M] energy, 187 g CP/kg. After slaughtering and carcass cooling, 10 samples of each breast and leg muscle were obtained. Each muscle sample was individually homogenized using a meat grinder featuring 3 mm holes and was rigorously mixed to ensure homogeneity. The protein and fat contents within the procured muscles were assessed using a Food Scan(tm) analyzer (Foss Electric, Hillered, Denmark). The protein level in the breast muscles was registered at 22.23 \(\pm\) 0.39%, whereas it stood at 20.04 \(\pm\) 0.42% for the leg muscles. Concurrently, the fat level was 2.73 \(\pm\) 0.09% in the breast muscles and 7.81 \(\pm\) 0.14% in the leg muscles. The obtained homogenate was subsequently divided into five samples (each sample weighed 20 g), each encased in polyethylene (PE) film string bags of dimensions 100 \(\times\) 150 mm. Each pouch was tightly closed and stored under refrigeration at a temperature of 2.2 \(\pm\) 0.3 \({}^{\circ}\)C. Assessments of BAs and amino acids were performed on days 1, 3, 5, 7, and 10 of storage.
## 5 Rheological Properties of the Control and xGNP Modified Asphalt Models
### Comparison of Viscosity Predictions of the Control and xGNP Modified Asphalt Models  
Based on the data analysis from the MD simulation and laboratory data, the viscosity data between the control and xGNP modified asphalt binder models was compared and analyzed, as well as the experimental results. The exponential regressions were used to fit the viscosity data of the control and xGNP modified asphalt binder models. The comparisons between the control and xGNP modified asphalt binder models were conducted, as shown in Figure 8, including the reference data.  
Figure 8 shows the viscosities of the control and xGNP modified asphalt binder models. The viscosities of MD simulations were averaged from the calculations under each separate temperature. The MD simulation viscosities of the xGNP modified asphalt binder model (red line in Figure 8) are also higher than those of the control asphalt binder model. It is similar to the trend in the laboratory data. The exponential trends are observed to be fitted for the MD simulation results due to the same trend in the laboratory data [44]. In this figure, it is obvious that the viscosities of the control and xGNP modified asphalt binder models are higher than those of the reference models [19] (0.65 cp and 1.35 cp at 443.15 K) using Green-Kubo and Einstein (Ein) EMD methods. The relatively flat line is observed in the viscosity data of the control asphalt binder model, and the viscosity results (92.47 cp and 80.13 cp) at 423.15 K and 443.15 K are close to the laboratory data (155.0 cp at 423.15 K and 95.0 cp at 438.15 K in Figure 5b) at two different temperatures for the control asphalt binder model, respectively. It is noticed that there is some improvement between viscosities of the control asphalt binder model (Figure 8) and the asphalt model in the reference [24]. Furthermore, for the xGNP modified asphalt model, the viscosities (452.42 cp, 195.98 cp and 108.96 cp) at 403.15 K, 423.15 K, and 443.15 K, respectively, are very close to the laboratory data (530.0 cp, 270.0 cp and 122.5 cp). The viscosity simulation results of the xGNP modified asphalt binder system are better than those of the control asphalt binder model, and the trend in viscosity of the xGNP modified asphalt binder model is similar to that of the laboratory data. With regard to this improvement, it is caused by the increase in the molecular number in the xGNP modified asphalt model compared to the control asphalt model. It is also an improvement to use the Muller-Plathe method to calculate the viscosity of the asphalt binder model, as well as the optimization methods used in the simulations. It is expected that more molecules in the MD asphalt system improve the accuracy of data prediction. Therefore, the viscosity calculation of MD simulations in the xGNP modified asphalt system with the Amber Cornell Extension Force Field provides a better prediction using the Muller-Plathe method compared to the results of the references [19] and the control asphalt model.  
Figure 8: MD viscosity results of the control and xGNP modified asphalt binder systems.
# SRv6-Based Edge Service Continuity in 5G Mobile Networks +
## 6 Testbed and Performance Evaluation
### Implementation and Testbed Deployment
#### 6.1.3 Testbed  
We deployed a test environment comprising the six devices shown in Figure 14. Firstly, the Qotom mini-PC runs the system orchestrator and the Mininet container, which emulates the core networks of the BMv2 switches. Further information about the network topologies will be given in Section 6.2. Four additional Qotom mini-PCs are also used as follows: one is the client, while the others are MEC Hosts. Two of them are the source and target of the container migration, whereas the third one is used to run the buffer function in some of the experiments. The last device is a PC server running the ONOS controller. The Qotom mini-PCs have a Quad-core Intel i7-4600U CPU, 8 GB RAM, Ubuntu 18.04; whereas the PC server has an Octa-core Intel i7-3770 CPU, 16 GB RAM, Ubuntu 18.04.  
The ONOS configures the nodes emulated in Mininet out-of-band. To simulate the in-band network with consistent network delays, we used Linux tc- netem. In particular, we set a 1 ms one-way delay for each traversed link in the core network.
## 7 System Performance and Evaluation
### Localization Results  
Figure 9a shows the values of RSS of a moving MN plotted against its distance from SN (discussed in Section 5.4). In the same Figure, ANCi-the is the theoretically calculated RSS, while the experimental RSS values are denoted by ANCi-act, calculated for the \(j\)-th to \(i\)-th anchor. Even though MN was placed at the same height, some variation in RSS values was found for different anchor nodes. This was attributed to the change in wavelength, uneven surfaces of mine walls, and obstacles. The value of RSS decreased with an increasing distance. Again, the same figure shows a gradual change in the theoretically calculated RSS values. RSS values leveled off at a distance of 17 m, which also indicates the topology design of our system that perfectly matches the RSS values of Bluetooth. Figure 9b shows the calculated error between source anchor nodes and MNs, while varying the distance from 1 to 30 m with a step interval of 2 m. The standard deviation of the system in the main roadway of the mine was less than 1.8 m for a node 30 m away from the source anchor node. On the contrary, the error for simple RSS was found to be more than 2.4 m when the same MN was 30 m away from the source node. Thus, the proposed RSS range-based weighted centroid localization algorithm improved the accuracy by 30%. One of the key assumptions of this experiment is that it only considers the weights of anchor nodes without knowing their condition, thus, the degree of error is irrational.
# Study on Corrosion Behavior of Waterborne Polyurethane Coating with High Thermal Conductivity
## 3 Results and Discussion
### Protective Mechanism of the Composite Coating  
Based on the above analysis, it can be concluded that the corrosion resistance of the waterborne polyurethane coating could be enhanced by composite fillers, and the corresponding protective mechanism is depicted in Figure 10. During the curing process of coating, some pores are formed due to the evaporation of solvent. For the waterborne polyurethane coating, the chloride ions, water, and oxygen i.e., could readily penetrate into the coating and reach the interface between the coating and Q235 steel [36; 37] due to its larger size pores and bad shielding property (Figure 10a). Then, the electrochemical reactions occur on the Q235 steel surface and cause severe corrosion. Thus, the waterborne polyurethane coating shows a weak protective action for the substrate metal. After adding  
Figure 9: Surface Kelvin potential mapping of samples after salt spray tests with different periods, (a) 0%, 0 d; (b) 2 wt %, 0 d; (c) 0%, 3 d; (d) 2 wt %, 3 d; (e) 0%, 10 d; (f) 2 wt %, 10 d.  
2 wt % composite fillers, these fillers with different sizes are dispersed in the coating and fill some initial coating pores, which promotes the coating compactness and thus decreases the penetration channel for the corrosive ions, as is shown in Figure 10b. Moreover, the addition of fillers could affect the penetration path of the electrolyte [38] and delay the time for the corrosive ions to arrive on the Q235 surface. Therefore, it can be concluded that the incorporation of proper carbon materials could not only improve the thermal conductivity of the coating but also enhance the validly of its anticorrosion performance in the simulated marine environment.