esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Application of MeJA and GA3 on Overexpressed CpMYC2 Plants  
Following MeJA and gibberellic acid (GA3) treatment, terpenoids were detected in the inflorescence of transgenic arabidopsis plants using GC-MS. The results again indicate that the emission of linalool was more than 1.5 times higher in the plants transformed by the _CpMYC2_ gene (35S::CpMYC2), while the mutant plants Salk_083483, mutant plants transformed by the vector pCAMBIA2300S (2300S-Sk83), and the Col-0 wild-type plants showed a lower amount of linalool emission compared to the wild-type (35S::CpMYC2) plants (Figures 9 and 10). Further analysis using real-time PCR (qRT-PCR) indicated that the expression of the linalool-producing gene _At1g61680_ was about twofold higher in the plants transformed by _CpMYC2_ (35S::CpMYC2) than the other plants, while the wild-type Col-0, mutant plants Salk_083483, and mutant plants transformed by the vector pCAMBIA2300S (2300S-Sk83) showed a lower expression of the _At3g25810_ gene as compared to 35S::CpMYC2 plants (Figure 11).  
Figure 8: GC-MS analysis of \(\beta\)-careophyllene emitted from the flowers of (**A**) tobacco wild type SR1, transformed by the empty vector pCAMBIA2300S (2300S-SR1) and overexpressed _CpbHLH13_ gene (35S::CpbHLH13) plants; (**B**) arabidopsis wild type (WT-Col-0), transformed by the empty vector pCAMBIA2300S (2300S-SR1) and overexpressed _CpbHLH13_ gene (35S::CpbHLH13) plants. (**a**) GC-MS chromatogram comparison between different plants. (**b**) Mass spectrum of the product. (**c**) Product confirmation in the NIST database according to retention index and mass spectrum.  
Figure 7: GC-MS analysis of linalool emitted from the flowers of (**A**) arabidopsis wild type (WT-Col-0), mutant Salk-083483, transformed by the empty vector pCAMBIA2300S (2300S-Sk83) and overexpressed _CpbMYC2_ gene (35S::CpMYC2) plants, and (**B**) tobacco wild type SR1, transformed by the empty vector pCAMBIA2300S (2300S-SR1) and overexpressed _CpMYC2_ gene (35S::CpbMYC2) plants. (**a**) GC-MS chromatogram comparison between different plants. (**b**) Mass spectrum of the product. (**c**) Product confirmation in the NIST database according to retention index and mass spectrum.  
Figure 10: GC-MS analysis of linalool emitted after the application of MeJA and GA3 from the flowers of arabidopsis wild type (WT-Col-0), mutant (Salk_083483), mutant Salk_083483 transformed by the empty vector pCAMBIA2300S (2300S-Sk83), and _CpMYC2_ gene (355::CpMYC2) plants. (**a**) GC-MS chromatogram comparison between different plants. (**b**) Mass spectrum of the product. (**c**) Product confirmation in the NIST database according to retention index and mass spectrum.  
Figure 9: The concentration of emitted linalool (ng g-1 FW h-1) after the application of methyl jasmonic acid (MeJA) and gibberellic acid (GA3) from arabidopsis wild type (WT-Col-0), mutant (Salk_083483), mutant Salk_083483 transformed by the empty vector pCAMBIA2300S (2300S-Sk83), and _CpMYC2_ gene (355::CpMYC2) plants. The different letters on the bars show significance between the treatments by using least significant difference (LSD) at \(p<0.05\).
# Oceania's 5G Multi-Tier Fixed Wireless Access Link's Long-Term Resilience and Feasibility Analysis
## 3 5G Base Coverage Network Layout
### 5G Hybrid Beamforming for Massive MIMO
The angle of arrival, carrier frequency wavelength, and Doppler frequency component of ray \(n\), \(m\) are represented by \(\overline{\phi}_{n,m}\), \(\overline{\varphi}_{n,m}\), and \(\lambda_{0}\), \(v_{n,m}\), respectively. The location vectors for elements \(s\) and \(u\) are represented by \(\overline{r}_{tx,u}\) and \(\overline{r}_{rx,u}\), respectively. If the radio channel changes over time, the small-scale parameters also vary with time. Modern technology known as massive MIMO beamforming produces 5G signals that dramatically increase network capacity. The technique significantly boosts throughput per user and peak data rate, enabling connections to operate at up to 10 Gbps. Large antenna arrays at base stations allow for the support of a huge number of concurrent users. The technology also improves spectral efficiency and the signal-to-interference-plus-noise ratio (SINR). By transmitting tailored signal streams to specific users rather than broadcasting a signal, inter-cell interference is also decreased. Massive MIMO beamforming enables operators to improve their networks and services in the future by supporting both 4G and 5G networks.
# Incorporating Epistemic Uncertainties in Ship Operability Study
## 5 Conclusions  
This study contributes by formulating and solving limit state functions for various ship operability criteria, incorporating epistemic uncertainties in seakeeping responses and uncertainties in operability limiting values. The reliability analysis was conducted using Comrel software, employing the second-order reliability method.  
The results of this study are presented in probabilistic operability diagrams, i.e., polar plots providing significant wave heights and heading angles for which seakeeping criteria will be exceeded with certain probabilities. Probabilistic operability diagrams are more informative than \"deterministic\" diagrams, and this approach could be further extended for use in risk-based decision making, providing that consequences of exceeding seakeeping criteria are defined.  
The probabilistic approach can be used in ship design as the decision-making tool when acquiring a new ship for a specific service. For that purpose, a novel probabilistic operability index is defined, representing an extension of the commonly used operability index. Furthermore, the approach can be used as a part of an advanced, risk-based tactical decision support system, assisting ship masters in making real-time decisions on heavy weather maneuvering.  
It was also shown how the proposed system can be validated using numerical wave databases, in this case, Mediterranean Sea Wave Reanalysis and Mediterranean Sea Waves Analysis and Forecast, both part of the Copernicus Marine Service.
# Modeling a Milling Dynamometer as a 3 DOF Dynamic System by Stiffness Identification
## 5 Modal Analysis of the Dynamometer
### Frequency Response Function of the System  
Frequency response functions (denoted as FRFs) define the structural response to different applied forces as a function of frequency. FRFs can be expressed in terms of the main quantities that describe vibrations, displacement, velocity, or acceleration, and are defined as a ratio of these quantities over the input signal (force/moment) for different frequencies. Generally, the FRF describes the motion of the considered point due to an applied exciting force using an impact hammer or a shaker [14; 15; 16; 17].  
As is known, modal decoupling enables the transformation of a system with \"\(j\)\" degrees of freedom, coupled by motion equations, in \"\(j\)\" decoupled single-degree-of-freedom (SDOF) equations. Each equation defines a vibration mode, and the system is practically decomposed in the \"\(j\)\" SDOF, with each DOF being defined by a motion equation of the form:  
\[\left(-m_{j}\omega^{2}\,+\,2i\zeta_{j}\omega\omega_{j}\,+\,\omega_{j}^{2}\right) \,X(\omega)\ =\ F(\omega) \tag{20}\]  
where the index \"\(j\)\" refers to the \"\(j\)\" mode of vibration, \(m_{j}\), \(\omega_{j}\), and \(\zeta_{j}\), are the modal mass, the circular frequency, and the damping ratio, respectively, of the \"\(j\)\" mode, and \(i\) is the complex number (\(i^{2}=-1\)).  
From (20), one can write the FRF as:  
\[H(\omega)\ =\ \frac{X(\omega)}{F(\omega)} \tag{21}\]  
with X(\(\omega\)) and F(\(\omega\)) defined as the Fourier transforms of the response and excitation, respectively.  
Considering relations (20) and (21), the FRF expression becomes:  
\[\frac{X(\omega)}{F(\omega)}\ =\ \frac{1}{\left(-m_{j}\omega^{2}\,+\,2i\zeta_{j} \omega\omega_{j}\,+\,\omega_{j}^{2}\right)}\ =\ \frac{1}{m_{j}(\omega_{j}^{2}-\omega^{2}\,+\,2i\zeta_{j} \omega\omega_{j})} \tag{22}\]  
The FRF given by (22) be rewritten as:  
\[H(\omega)=\ \text{Re}(\omega)\ +\,j\text{Im}(\omega) \tag{23}\]  
where Re(\(\omega\)) is the real part and Im(\(\omega\)) is the imaginary part of the FRF. After some mathematical transformations and taking into consideration the general relationships between damping coefficient, mass, damping ratio, and circular natural frequency, the real and imaginary components of (23) are:  
\[\left\{\begin{array}{rcl}\text{Re}(\omega)&=&\frac{1-u_{j}^{2}}{k_{j}[\left( 1-u_{j}^{2}\right)^{2}+4\zeta_{j}^{2}u_{j}^{2}]}\\ \text{Im}(\omega)&=&\frac{-2i\zeta_{j}\,u_{j}}{k_{j}[\left(1-u_{j}^{2}\right) ^{2}+4\zeta_{j}^{2}u_{j}^{2}]}\end{array}\right. \tag{24}\]  
Based on the obtained values for natural circular frequencies, the model components (mass, damping, and stiffness), considering the superposition principle, are plotted as an FRF for the considered dynamometer (Figure 16).  
Figure 16: The FRF representation.
# Time Domain Analysis of NB-IoT Signals
## 6 Conclusions  
Measurements of NB-IoT signals were performed at some LTE800 cells of various network operators in Pisa and surroundings both by analyzing the sub-carriers of the PRB in the time domain and by measuring the signal with a vector analyzer using the available NB-IoT module. All the analyzed cells transmit the NB-IoT signal in guard-band mode with a configuration that involves the use of a single port and PRBs with different center band frequencies.  
The time domain analysis of the sub-carriers allows us, on the one hand, to identify the control channels (NPBCH) and the signals (NRS, NPSS, NSSS) transmitted in the NB-IoT frame and, mostly, to measure the power received during the transmission of the NRS as is done in vector analyzers equipped with specific analysis module and as required by CEI 211-7/E technical standard for extrapolation techniques.  
The comparison between the average values of the received power due to the NRS obtained in the time and code domains showed an agreement within 0.5 dB. These results support the hypothesis that the two methods are equivalent with an uncertainty less than 1 dB, which is acceptable for this kind of measurements.  
More data are necessary to confirm the preliminary results in order to confirm that time domain measurements, obtained by common spectrum analyzer, could represent an alternative method for extrapolation of the maximum electric field level produced by an NB  
Figure 12: Example of comparison between measurements of received power values for NRS: in time domain (**a**,**b**) and code domain (**c**).  
IoT cell. Further measurements should also be performed in order to verify the dependence of the results on different analyzer settings (detector and RBW filter bandwidth).  
Conceptualization, A.M.S. and A.B.; Methodology, A.M.S.; Investigation, A.M.S. and A.B.; Supervision, G.L. and B.B.; Writing--original draft, A.M.S. and G.L. All authors have read and agreed to the published version of the manuscript.  
The APC was partially founded by Associazione Italiana di Radioprotezione (AIRP), Calci, Italy.  
The authors declare no conflict of interest.
# Train Wheel Condition Monitoring via Cepstral Analysis of Axle Box Accelerations
## 3 Synthetic Data Analysis
### Synthetic Models  
The wheel-rail contact vibrations are excited by the unevenness of the wheel and rail. Here, we employ a \"moving irregularity model\", where the wheel is static and the wheel-rail surface is pulled between the wheel and rail [28]. Since the cepstrum analysis allows to bypass the transmission path, only the excitation signal is considered in the synthetic data. The impulse response of the resulting relative displacement can then be written as:  
\[y(x)=u_{w}(x)+u_{r}(x), \tag{7}\]  
where \(u_{w}\), \(u_{r}\) are the surface profile functions of the wheel and rail, respectively, with respect to the distance \(x\) covered by the train. The wheel profile function is periodic with a period of \(L=2\pi R_{w}\), where \(R_{w}\) is the wheel radius. Thus, it can be expressed by a convolution of the wheel surface profile \(p_{w}\) with an impulse train constructed from Dirac delta functions:  
\[u_{w}(x)=p_{w}(x)*\sum_{n=0}^{n=N}\delta(x-nL), \tag{8}\]  
with \(N\) the number of wheel rotations. The cepstrum of a periodic series of delta functions with a period \(L\) is also a periodic delta function with the period of \(L\). Therefore, according to Equations (2)-(6), the cepstrum of \(u_{w}(x)\) can be written as:  
\[C\{u_{w}(x)\}(\widetilde{x})=C\{p_{w}(x)\}(\widetilde{x})+\sum_{n=0}^{n=N} \delta(\widetilde{x}-nL), \tag{9}\]  
The particular contribution of the left and right term of the cepstrum in Equation (9) depends on the shape of the wheel surface profile. The smoother the wheel the higher the contribution of the left term. From Equations (8) and (9) one can see that if \(p_{w}\) is a spike with a certain amplitude, the cepstrum reduces to a series of spikes with the same amplitude. In contrast, if \(p_{w}\) is a sinusoid, as one would expect of a perfectly periodically polygonized wheel, the right term in Equation (9) would vanish. In general, the cepstrum provides a measure of the periodicity, namely the wheel circumference, and the repeatability of the ABA signal.  
In the following we consider different models with a length of 15 m (\(L=3\) m, \(N=5\), see Figure 1). The models represent a wheel with a flat spot of different length and depth. The vertical profile of the wheel flat is modelled as:  
\[p_{w}(x)=\left\{\begin{array}{cc}0,&0\leq x<\ L/2-l/2,\\ -\frac{d}{2}\big{(}1-\cos\frac{2\pi x}{l}\big{)},&L/2-l/2\leq x\leq L/2+l/2,\\ 0,&L/2+l/2<x\leq L,\end{array}\right. \tag{10}\]  
where \(l\) and \(d\) are the wheel flat length and depth, respectively. Additionally, wheel and track roughness are modeled as a Gaussian random signal with varying standard deviation (std). The parameters of the different models can be found in Table 1.
# The Influence of Accidental Overheating on the Microstructure and Hardness of the Inconel 718 Alloy
## 3 Experimental Results and Discussion
### The Influence of Thermal Shocks on the Microhardness  
Since treatment at low temperatures (400-600 \({}^{\circ}\)C) did not produce significant thermally influenced areas in relation to the thermal shock surface, the microhardness evolution was characterized by measurements on the shock surface (Figure 12). The surface microhardness measured at 400 \({}^{\circ}\)C and 500 \({}^{\circ}\)C was lower than that of the unshocked reference sample. Due to the precipitation processes occurring, the hardness increased with the shock application temperature. At 600 \({}^{\circ}\)C, which is at the lower limit of the aging treatment, the microhardness values oscillated around the reference microhardness curve.  
To evaluate the influence of overheating at temperatures higher than the recommended limit for Inconel 718, the variation in microhardness was measured along the height of the sample, from the surface opposite to the one exposed to thermal shock (Figure 13).  
Figure 12: The evolution with temperature of the microhardness measured in the surface area of thermal shock application for Inconel 718 samples treated at 400–600 \({}^{\circ}\)C with 20 cycles of thermal shock.  
The surface microhardness at the point of application of the 20 thermal shocks remains nearly identical across all shock temperatures.  
In the Heat-Affected Zone (HAZ) at 700 \({}^{\circ}\)C, the microhardness increases rapidly, reaching a maximum at approximately 4 cm from the shock-exposed surface, corresponding to the region of maximum precipitation. For samples treated at 800 \({}^{\circ}\)C and 900 \({}^{\circ}\)C, microhardness increases more gradually, indicating a more extended thermally affected zone. At 1000 \({}^{\circ}\)C, the thermal influence extends along the entire length (height) of the sample, affecting the material uniformly. The decrease in hardness near the shock application surface is explained by the diffusion and dissolution processes of the hard phases. The decrease in hardness at high temperatures is consistent with the research conducted by [PERSON], which examined the degradation of die quality in copper alloys under deformation [10], and by [PERSON] et al., who investigated the hot-working behavior of the Inconel 718 superalloy [48].
# Reputation-Based Blockchain for Spatial Crowdsourcing in Vehicular Networks
###### Abstract
The security and high efficiency of blockchain technology mainly depends on the participation of a large scale of nodes, that is, more miners involved in blockchain will provide better system performance concerning security and efficiency. However, there are still some limitations that need to be solved urgently: (1) The blockchain nodes lack motivation to become involved in the blockchain, especially weak-computational ability nodes; (2) Although the blockchain nodes are mostly low-power devices, redundant computational resources are still present; (3) The blockchain ensures that damage to system security can occur only when the computational capability reaches more than 51%; however, it cannot supervise and guarantee the honest behavior of nodes. Hence, the current research focus aims at the design of a credible blockchain incentive mechanism based on a reputation framework, so that the vehicle-crowdsourcing platform can achieve credible and reasonable resource allocation. To improve mining efficiency and incentive, we propose the utilization of cooperative pool mining to solve the proof of work puzzle, and finally achieve consensus. A mining pool refers to a resource pool where miners share their computational capabilities on the crowdsourcing blockchain framework. Miners who meet the mining pool protocols/contracts can become involved in mining by contributing computational resources, and the pool can join in computing as a cluster of miners through mining pool protocols. Vehicle miners can choose to participate in mining pools (or sidechains) according to their computational capability to obtain benefits. The benefit is the utility of pool miners, including direct and indirect rewards. The mining pool incentive distributes rewards based on the contribution of computational resources, which can directly motivate miners to participate in honest mining, referred to as direct utility; reputation is an evaluation indicator for miners' honest behavior and performance, which is generated by historical behaviors and interactions. The combination of incentive and reputation encourages the honest participation of vehicle miners to achieve high efficiency and credible crowdsourcing task deployment.
## 4 Experimental Results and Analysis
### Optimization of Hyperparameters of the CNN and ANN Models  
Hyperparameter tuning findings show that certain combination of parameters has more influence on model's performance, while others have a modest impact. We have discovered that the width of the filter and the number of layers had a substantial influence on prediction performance. The results further demonstrated that for all filter widths, excellent performance may be attained. Furthermore, utilizing many layers resulted in somewhat higher performance than a single layer, since it allows for more complexity in the model, but it also resulted in a longer training period. The number of layers and the breadth of the filter had a corresponding effect on the training time but not on the classification performance. As a result, if the number of layers is fixed, a large filter width requires less training time than a smaller filter width, despite the fact that both options give equivalent prediction results.  
The same is true for the number of filters employed: The more filters utilized, the longer the training time, with no apparent improvement to prediction performance. The number of filters has a considerable influence on the training time (bottom). The number of filters, along with the filter width and the number of layers and stacks, defines the number of trainable parameters. As a consequence, training time is reduced by using fewer filters for the same mix of filter width and layers. Adding more layers to the network increases the depth, and consequently the complexity, of the model. The number of layers influences training time but not performance. If the number of layers were fixed, a broad filter width would need fewer layers, and hence less training time than a smaller filter width, despite the fact that both would provide similar results.
# Detection of Impact Damage on PVA-ECC Beam Using Infrared Thermography
## 5 Discussion and Conclusions  
Through experimental investigation, we have demonstrated that the infrared thermography technique was feasible and effective in detecting impact damage in a PVA-ECC beam. An external heat stimulus was adopted to induce relevant thermal contrasts between defective and intact areas on the specimen. It was found that the thermal contrasts in both the warm-up and cool-down period can be used to effectively detect the impact-induced crack. On the monitored surface, the thermal contrast images were totally different before and after the crack initiation. In addition, the evolution of the impact damage can be qualitatively determined by the temperature variation analysis, and it was consistent with warm-up and cool-down period results. A key finding of this study is that it was possible to obtain the width of the crack, even to predict the expansion of the cracks, by analyzing the thermal contrast images. In conclusion, the infrared thermography-based method is feasible and effective in detecting impact-induced damage in a PVA-ECC structure.  
The success of infrared thermography in dealing with damage arising in service depends on the thermal properties of the material under test. It should be noted that the infrared images acquired from active thermography are greatly affected by many factors. For an in situ application, the signal detected may be affected by the non-uniform properties of the specimen surface. In addition, it may be affected by the ambient temperature variations. Moreover, more tests should be investigated to further verify the validity, reliability, and accuracy of the proposed approach. Thus additional building materials other than the PVA-ECC used in this study should be tested. Further calibrations are needed to deploy the proposed method to practical application. Despite this, the infrared thermography offers an additional advantage that it is a non-contact testing technique which allows for large areas to be examined and the data interpreted in a reasonably straightforward procedure. In future work, more studies on the structural impact behaviors, theoretical and experimental investigation will be performed.  
[PERSON] and [PERSON] designed the experiment, performed the experiment and collected the data; [PERSON] analyzed the data; [PERSON] wrote the manuscript; [PERSON] made major revision of the manuscript.  
This work was supported by China Earthquake Administration, Institute of Seismology Foundation (IS201726163). The first author would like to appreciate the support from China Scholarship Council and China Earthquake Administration.  
Figure 9: Temperature versus time in the defective area and nearby intact area.