esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring the Role of Attitudinal Factors in Electric Vehicle Timeshare Rentals Adoption
## 4 Results
### EVTR Travel Choice Prediction Using Two Mixed Logit Models
#### 4.2.2 EVTR Travel Selection Model with Latent Variables
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **B** & **S.E.** & **Sig.** & **Exp (B)** \\ \hline Constant & \(-\)2.700 & 1.611 & 0.014 & 0.067 \\ \(X_{gen}\) & \(-\)0.023 & 0.184 & 0.090 & 0.977 \\ \(X_{age}\) & \(-\)0.032 & 0.089 & 0.072 & 0.969 \\ \(X_{inc}\) & 0.018 & 0.102 & 0.186 & 1.018 \\ \(X_{edu}\) & \(-\)0.076 & 0.112 & 0.060 & 0.927 \\ \(X_{chi}\) & 0.096 & 0.205 & 0.064 & 1.101 \\ \(X_{car}\) & 0.499 & 0.19 & 0.009 & 1.647 \\ \(X_{sta}\) & \(-\)0.079 & 0.103 & 0.043 & 0.924 \\ \(X_{ch}\) & \(-\)0.033 & 0.083 & 0.039 & 0.968 \\ \(X_{lmo}\) & \(-\)0.144 & 0.086 & 0.020 & 0.866 \\ \(X_{lM1}\) & 0.057 & 0.133 & 0.006 & 1.059 \\ \(X_{lM2}\) & 0.057 & 0.122 & 0.016 & 1.059 \\ \(X_{lM3}\) & 0.136 & 0.112 & 0.022 & 1.146 \\ \(X_{lM4}\) & \(-\)0.097 & 0.114 & 0.040 & 0.908 \\ \(X_{lM5}\) & 0.117 & 0.114 & 0.030 & 1.124 \\ \(\eta_{AC}\) & 0.387 & 0.181 & 0.032 & 1.473 \\ \(\eta_{AQ}\) & 0.211 & 0.173 & 0.037 & 1.235 \\ \(\eta_{AW}\) & 0.165 & 0.163 & 0.031 & 1.179 \\ \(\eta_{AD}\) & 0.174 & 0.151 & 0.025 & 0.840 \\ \(\eta_{BS}\) & \(-\)0.005 & 0.137 & 0.027 & 0.995 \\ \(\eta_{BV}\) & \(-\)0.121 & 0.174 & 0.049 & 0.886 \\ \(\eta_{BU}\) & \(-\)0.277 & 0.187 & 0.014 & 0.758 \\ \(\eta_{B1}\) & \(-\)0.263 & 0.179 & 0.014 & 1.301 \\ \(\eta_{SN}\) & 0.162 & 0.178 & 0.086 & 1.176 \\ \(\eta_{CI}\) & 0.023 & 0.184 & 0.040 & 0.977 \\ n & & & 619 & \\ Log likelihood & & & 727.120 & \\ [PERSON]’s R2 & & & 0.341 & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Parameter fitting results of logit model without latent attitude variables. 0.237 and 0.341, respectively. In practice, it is considered that the accuracy of the model is higher when the value of R2 is above 0.2. Thus, this model could meet the reliability requirements of model fitting, and the analysis results of the above factors could be acceptable. Additionally, compared with the binary logit model, the goodness of fit (0.341 \(>\) 0.273) of the mixed logit model with attitude latent variables is better. This finding indicates that attitude latent factors could be well matched with the travelers' travel behavior and could better reflect travelers' travel demand.
## 3 Results
### Classification Stage and Result Analysis
#### 3.5.2 Classification Result  
Using the method described in this article, we obtained an average class-wise balanced accuracy of 76.4% for all nine subjects, as shown in Table 2. Compared with the 75.8% accuracy reported in original paper, proposed method used only one channel of data and still obtained a similar result to the benchmark method using 64 channels of data for classification. We ran the whole classification experiment on a personl computer with Intel i5-4690 CPU and 16 GB RAM. The mean classification computation time for each subject was 20.2 s, including feature extraction and classification for 2915 samples.
## 4 Results and Discussion  
Using FE analysis, maximum Von Mises stresses are calculated for the 6 \(\times\) 7, 7 \(\times\) 7 and 8 \(\times\) 7 wire ropes. This was done for wire ropes with a full cross-section area and a reduced cross-section area (98.76%, 88.89%, and 77.78% of the full area, which roughly corresponds to a one-wire, one-strand, or two-strand reduction in the rope diameter). Change in Von Mises stresses according to the reduction in cross-section area can be seen in Figure 5.  
To verify numerically obtained stress levels, stress was also calculated analytically for wire ropes with full cross-section areas according to Equation (1). Good correspondence of analytically and numerically obtained stresses for the 7 \(\times\) 7 configuration provided confidence in further use of FE analysis in calculating the stress of wire ropes with a reduced cross-section area. As for the 6 \(\times\) 7 and 8 \(\times\) 7 configurations, values are a bit more dissipated, which can be accounted for different types of core (Table 1), and this issue requires further investigation in future research. It can clearly be seen that a small reduction in cross-section area (to 98.76% of the initial area) does not cause a significant elevation in stress level; however, with further reduction (88.89% and 77.78% of the initial area), stress levels rise steeply.  
Figure 4: 6 \(\times\) 7 wire rope with (**a**) full cross-section area; (**b**) one wire suppressed; (**c**) one strand suppressed; (**d**) full cross-section and stress distribution.  
Figure 5: [PERSON] stress levels for 6 \(\times\) 7, 7 \(\times\) 7, and 8 \(\times\) 7 wire ropes with full and reduced cross-section areas.  
Using the obtained results and linear regression, the relationship between the reduction in the cross-section area and [PERSON] stress levels is established through three equations that are quoted in Figure 5.  
Using FE stress analysis results and a modified Goodman diagram Goodman (1965), the remaining fatigue life was predicted. A constant amplitude and zero-based load was employed since wire ropes can only withstand tensile load. Results (Figure 6) show that there is virtually no change in remaining life when the damage causes a reduction in cross-section area to 98.76% of the initial area. However, with further reduction (88.89% and 77.78% of the initial area), remaining life decreases significantly to about 3% and 1.5% of the remaining life of the wire rope with a full diameter.  
Furthermore, when comparing the remaining life of the 6 \(\times\) 7, 7 \(\times\) 7, and 8 \(\times\) 7 wire ropes with reduced cross-section areas, it can be noticed that the 8 \(\times\) 7 wire rope offers a duration that is about 50% longer when compared to the 6 \(\times\) 7 wire rope.  
Similar to Figure 5, using the obtained results and linear regression, the relationship between the reduction in cross-section area and fatigue life is established through the three equations in Figure 6.
# NO Separation Characteristics in Integrated Electromigration Membrane Reactor
## 3 Results and Discussion  
### Effect of Discharge Voltage on Separation Efficiency and Total Mass Transfer Coefficient  
The effect of discharge voltage on the NO separation efficiency and total mass transfer coefficient is shown in Figures 2 and 3. It can be seen from Figure 2 that the NO separation efficiency was related to the discharge voltage. When the discharge voltage increased from 8 to 18 kV, the NO separation efficiency first slightly rose and then rapidly increased. At an applied voltage of 18 kV, the separation efficiency was about 57%, which improved by 48.7% compared with the one without discharge. The formation of NO negative ions is related to the electron concentration in the reaction area [27] ([PERSON] 1996). When the discharge voltage was lower than 8 kV, the efficiency increment was lower than 1.05% compared with the efficiency without discharge, where few NO negative ions were formed due to the low electron concentration in the reaction space. While the discharge voltage exceeded the corona onset voltage, the electron amounts increased rapidly due to electron avalanche and prompted the formation of NO negative ions. Therefore, the NO separation efficiency increases rapidly with the increase in the discharge voltage [28]. When the discharge voltage was higher than 18 kV, the electric field was broken down. Therefore, the maximum discharge voltage was set at 18 kV. Similarly, the total mass transfer coefficient of NO increases with the increase in the discharge voltage (Figure 3). When the discharge voltage increases from 0 to 18 kV, the total mass transfer coefficient of NO increases from 0.91 \(\times\) 10\({}^{-4}\) to 8.81 \(\times\) 10\({}^{-4}\) m\(\cdot\)s\({}^{-1}\); the total mass transfer coefficient at discharge voltage of 18 kV is about 9.7 times than one without applied voltage. This may be because a high discharge voltage caused an increase in the amount and electromigration velocity of the NO negative ions, which not only prompted NO absorption but also reduced the mass transfer resistance in the gas phase.
## 3 One-Dimensional Seismic Fragility Analysis  
### Fundamental Theory of Seismic Fragility Analysis  
Seismic fragility refers to the conditional probability of a structure or its components exceeding a certain performance limit state under different ground motions of varying intensities [35]; it can be generally expressed by the seismic fragility curves:  
\[P_{f}=P(\,u_{c}\leq u_{d}|\text{IM})=p(u_{c}/u_{d}\leq 1|\text{IM}) \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Time** & **Earthquake** & **Log Site** & **Recording Component** & **Magnitude** & **PGA (g)** & **Rjb (km)** \\ \hline
1 & 1999 & Chi-Chi & CHY024 & RSN2159\_CHICHL02\_CHY024E & 5.9 & 0.031 & 38.33 \\
2 & 1999 & [PERSON] & CHY025 & RSN2160\_CHICHL02\_CHY025N & 5.9 & 0.074 & 46.46 \\
3 & 1999 & [PERSON] & CHY034 & RSN2167\_CHICHL02\_CHY034N & 5.9 & 0.031 & 58.40 \\
4 & 1999 & [PERSON] & CHY080 & RSN2195\_CHICHL02\_CHY080N & 5.9 & 0.043 & 42.90 \\
5 & 1999 & [PERSON] & CHY088 & RSN2199\_CHICHL02\_CHY088E & 5.9 & 0.030 & 80.62 \\
6 & 1999 & [PERSON] & HWA009 & RSN2219\_CHICHL04\_HWA009E & 5.9 & 0.036 & 49.57 \\
7 & 1999 & [PERSON] & CHY070 & RSN2732\_CHICHL04\_CHY070N & 6.2 & 0.019 & 88.91 \\
8 & 1999 & [PERSON] & CHY046 & RSN2954\_CHICHL05\_CHY046N & 6.2 & 0.119 & 65.81 \\
9 & 1999 & [PERSON] & CHY096 & RSN2986\_CHICHL05\_CHY096W & 6.2 & 0.020 & 117.62 \\
10 & 1999 & [PERSON] & HWA012 & RSN3001\_CHICHL05\_HWA012E & 6.2 & 0.031 & 57.81 \\
11 & 1999 & [PERSON] & HWA013 & RSN3002\_CHICHL05\_HWA013E & 6.2 & 0.075 & 54.21 \\
12 & 1999 & [PERSON] & TCU051 & RSN3159\_CHICHL05\_TCU008N & 6.2 & 0.015 & 124.43 \\
13 & 1999 & [PERSON] & TCU038 & RSN3449\_CHCHI06\_TCU038E & 6.3 & 0.043 & 60.21 \\
14 & 1999 & [PERSON] & TCU008 & RSN3458\_CHICHL06\_TCU051N & 6.3 & 0.048 & 32.36 \\
15 & 1999 & Chi-Chi & CHY004 & RSN3836\_CHICHL02\_CHY004N & 5.9 & 0.040 & 85.89 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detailed data for the fifteen ground motions.  
Figure 6: Acceleration response spectrum for the fifteen ground motions.  
where \(\mu_{c}\) is the seismic capacity of the structure or components and \(\mu_{d}\) is the seismic demand of the structure or components. IM is a parameter used to evaluate the seismic intensity. Because in previous papers \(\mu_{c}\) and \(\mu_{d}\) are assumed to follow the normal distribution [35], Formula (7) can be simplified further as follows:  
\[P_{f}=\Phi\left(\frac{\ln(u_{d}/u_{c})}{\sqrt{\beta_{c}{}^{2}+\beta_{d}{}^{2}}}\right) \tag{8}\]  
where \(\Phi(x)\) is the standard normal distribution function and \(\beta_{c}\) and \(\beta_{d}\) are the logarithmic standard deviations of the structure's or component's seismic capacity and seismic demand, respectively. According to HAZUS99 [36], \(\sqrt{\beta_{c}{}^{2}+\beta_{d}{}^{2}}\) should adopt a value of 0.5 when PGA is used as IM.
## 2 Material Properties of Fe-SMA  
### Introduction of Fe-Mn-Si Alloys  
Overall, Fe-SMA contains three types of crystal structures, as shown in Figure 1, which are the face-centered cubic lattice of austenite phase (fcc, \(\gamma\)-austenite), the body-centered tetragonal lattice of martensite phase (bct, \(\alpha^{\prime}\)-martensite), and the hexagonal close-packed lattice of martensite phase (hcp, \(\varepsilon\)-martensite).  
Fe-SMA is divided into two different groups based on material properties [36]. The first group of Fe-SMA is thermoelastic martensite alloys, including Fe-Pt [37], Fe-Pd [38; 39], and Fe-Ni-Co [40; 41], whose typical characteristics are similar to NiTi-SMA. The martensite transformation is the crystal lattice changes between fcc (\(\gamma\)-austenite) and bct (\(\alpha^{\prime}\)-martensite). The first group alloys possess the ability of SE (or pseudoelasticity), but with a narrow thermal hysteresis, its SME is limited by temperature when used for structural reinforcement. The second group of Fe-SMA contains alloys such as Fe-Ni-C [42] and Fe-Mn-Si [43; 44]. This group of alloys carries a large thermal hysteresis in the transformation and expresses  
Figure 1: Crystal phase of Fe-SMA [32]: (**a**) \(\gamma\)-austenite (fcc), (**b**) \(\alpha^{\prime}\)-martensite (bct), (**c**) \(\varepsilon\)-martensite (hcp).  
SME in an acceptable temperature range. For the Fe-Ni-C alloys, the crystal changes are fcc \(\rightleftharpoons\) bct (\(\gamma\)-austenite \(\rightleftharpoons\)\(\alpha^{\prime}\)-martensite) in the martensite transformation. The crystal changes in Fe-Mn-Si alloys are fcc \(\rightleftharpoons\) hcp (\(\gamma\)-austenite \(\rightleftharpoons\)\(\varepsilon\)-martensite) in the martensite transformation.  
The Fe-Mn-Si alloys show considerable SME, and its phase transformation temperature is easy to realize, so it has been widely researched and applied [45; 46]. Therefore, this paper mainly focuses on the Fe-Mn-Si alloys in Fe-SMA.  
The original Fe-Mn-Si based SMA only contains the elements Fe, Mn, and Si. The optimization of corrosion prevention, training improvement, and cyclic strengthening of materials mainly experiences two stages [47]. The optimization of alloy elements is the first improvement stage [48]. The SME and corrosion resistance of SMA are improved by changing the percentage of elements and adding new elements. The element contents of Mn and Si have an obvious influence on SME [49; 50], and the corrosion resistance can be greatly improved by adding elements of Cr, Ni, N, etc. [51; 52; 53]. At the same time, the alloys possess an excellent recovery capacity with the cyclic thermo-mechanical process of \"training\" [54]. The second optimization stage is introducing fine precipitates, such as NbC, VC, and VN, into the alloy microstructure to improve the SME of Fe-SMA without \"training\" [55; 56; 57]. The most significant alloys for the development process of Fe-SMA are presented in Table 1.
## 3 Analysis of Experimental Data and Results
### Probabilistic Model Development  
Since the results show that changes in certain loading conditions do not result in any significant influences on the linear model of AE count rate versus crack growth rate, all the experimental data from different tests could be used to develop the crack length vs. AE count rate model. To do this, the experimental data set for Al7075-T6 was divided into three different sets. One set was used for parameter estimation when defining the model, one set was used for evaluating the error term in the model, and the final data set was used for model validation. The first set was of four experiments that were selected from different loading conditions to arrive at a more generic model in the phase of model development. Those experiments were CT1, CT3, CT5 and CT7 from Table 1. The second set of data used to estimate the uncertainties and capture the error term in the model were CT2, CT4, CT6, CT10 and CT12 from Table 1. The last step was to validate the developed model. The experiments used for validation were CT9 and CT11 from Table 1. The procedure and results of model development and validation are reported in the remainder of this section.
## 3 Results
### IM-EP-Mediated Naked mRNA Vaccination Reduces SARS-CoV-2 Load in K18-hACE2 Tg Mice  
To evaluate the immunogenicity of naked mRNA vaccines delivered via IM-EP, we immunized K18-hACE2 Tg mice thrice with 5 or 25 \(\upmu\)g of SARS-CoV-2 spike mRNA at 2-week intervals. We noted significant RBD-specific antibody production and IFN-\(\gamma\) responses only in the 25 \(\upmu\)g group. Hence, we performed subsequent experiments using the 25 \(\upmu\)g mRNA vaccine dose (Supplementary Figure S2). Two weeks after the final immunization, we intranasally challenged the mice with a lethal dose of SARS-CoV-2 (1 \(\times\) 10\({}^{4}\) PFU). We then euthanized the mice at 3 dpi (Figure 4A). While body weight and body temperature remained comparable between the vaccinated and unvaccinated groups (Figure 4B,C), the lung-to-body weight ratio showed a slightly higher trend in the vaccinated group than in the unvaccinated group; the difference was not statistically significant (Figure 4D). However, viral titers in lung tissues measured by the plaque assay exhibited a significant reduction in the vaccinated mice. All unvaccinated control mice showed a detectable viral load in lung tissues. In contrast, 60% of vaccinated mice showed complete viral clearance, with no detectable infectious virus (Figure 4E). Furthermore, analysis of nasal washes showed a marked reduction in viral RNA in the vaccinated group, indicating that IM-EP-mediated mRNA vaccination effectively limited viral replication at the primary site of infection (Figure 4F).  
These findings indicate that two doses of IM-EP-mediated mRNA vaccination confer strong immune protection against SARS-CoV-2 infection, significantly reducing viral burden in lung tissues and nasal washes.
## 2 Signal Model and Pre-Processing
### Signal Pre-Processing  
For the neural network to better capture the time-domain and frequency-domain features of the signals, the time-frequency image is used as the input of the convolutional neural network [14]. After obtaining the time-domain signal, the time-frequency image of the received signal could be produced by applying short-time Fourier transform to the signal. The formula of short-time Fourier transform is [15] :  
\[\mathrm{STFT}[m,k]=\sum_{n=0}^{N-1}r[n]w^{*}[n-mL]e^{-j\frac{2\pi\mathrm{i}n}{N}} \tag{2}\]  
where \(m\) represents the discrete index of time, \(k\) represents the discrete index of frequency, \(r[n]\) represents the received time-domain signal, \(w[n]\) represents the window function, \(L\) represents the length of a time-domain signal, and \(N\) represents FFT points.  
By functioning short-time Fourier transform, we obtained eight kinds of single jamming signals and four kinds of compound jamming signals. According to the time-frequency uncertainty principle, the time resolution and frequency resolution of the spectrum are mutually restricted when the time-frequency analysis window area is certain. The highest accuracy cannot be achieved at the same time. In addition, different types of jamming signals have different requirements for resolution. Therefore, it is necessary to balance the needs of different jamming signals when selecting the window length. At the same time, there needs to be a certain overlap ratio between adjacent time windows to ensure that the time-frequency curve of non-stationary jamming becomes smooth and avoids obvious distortion [16]. As shown in Figures 2 and 3, different types of jamming signals can be clearly classified by time-frequency characteristics; similarly, other jamming in the real world can be classified by time-frequency characteristics as well.  
Figure 2: Time–frequency diagram of eight single jamming signals.
## 2 Methodology of Mkcis  
A very important application of SAR image segmentation is to partition an image into different types of non-overlapping land cover such that the pixels in the same type are more similar to each other than pixels in different types. Unfortunately, the speckle noises in SAR image usually bring much more uncertainty and ambiguity to the clustering process. Therefore, corresponding filtering technology is essentially required to suppress the speckle noise in SAR image. As it can be seen, the nonlocal means filter has been shown very attractive de-noising performance by exploring redundancy information of a pixel' gray level and geometrical configuration in its local neighborhood [15, 16, 17].  
Furthermore, over the past few decades there has been a growing interest in the use of biology as a source of inspiration for solving computational problems. The nature inspired algorithms have attracted lots of interests and they have become important parts of computational intelligence. Many of them have been proposed and applied to real-world problems. Among them, artificial immune system (AIS) inspired by the biological immune system is one representative of these kinds of new algorithms. It mimics the human immune system's distinguishing characteristics of robustness, memory, highly parallelism and adaptability. AIS-based algorithms have begun to emerge as problem solvers in image classification recently and two popular used clustering indices are designed in kernel distance mapping. The algorithm can be divided into two stages, _i.e._, the first stage attempts to remove the speckle noise as much as possible and the second stage is used to partition the smoothed image by multi-objective kernel clustering in AIS. The basic procedure of the algorithm has shown in Figure 1 and it is called MKCIS for brief description in the subsequent paragraphs.
# Empirical Evidence Regarding Few-Shot Learning for Scene Classification in Remote Sensing Images
## 3 Materials and Methods
### Research Questions  
The research questions (RQs) associated with this study were already mentioned in Section 1. They are repeated below for completeness:  
1. RQ_1--Which of the inference approaches is more suitable: inductive or transductive?
2. RQ_2--Considering classical training, how does the number of epochs used during training, based on the meta-training (base) dataset, relate to the number of unseen classes during inference?
3. RQ_3--Is relying on 5-shot tasks statistically significantly better than 1-shot ones?
4. RQ_4--Would a higher similarity between unseen classes (during inference) and some of the existing classes in the base training dataset improve technique performance?  
In Sections 1 and 2.1, the motivations for the RQs and how this study differs from others in the literature were already presented. However, the general reasoning behind the creation of these RQs is emphasised here. With RQ_1, the goal is to understand which of the two inference approaches, inductive or transductive, is the best so that the answer to this question can motivate the development/use of solutions of a certain type in the context of the RS community. Note that several previous studies claim that transductive approaches are in general better than inductive ones [43; 45], but the datasets used do not contain RS images. Thus, the idea is to see if this conclusion generalises to the RS domain. RQ_2 can be seen as a question related to the \"reduction of the reduction\" of computational demand. As discussed in Section 1, while FSL minimises the need for a large number of labelled samples, there is still a training phase (whether episodic or classical training). The aim of this question is to explore whether the training process can be made even less demanding and how this reduction relates to the number of unseen classes during the inference phase.  
It seems natural to expect a better performance from a technique with 5-shot tasks than with 1-shot ones. But is this improved performance statistically significant? RQ_3 addresses this perspective. Let us assume that the set of unseen classes during the inference phase in scenario A has a greater similarity to some classes from the base dataset (training phase) than in scenario B. Does this mean that techniques will perform better in scenario A? This is the focus of RQ_4.