esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Private Comparison Based on Four-Particle Cluster State
## 4 Security Analysis
### External Attacks
#### 4.1.1 Intercept-Measure-Resend Attack  
In the intercept-measure-resend attack, [PERSON] intercepts the quantum sequence being transmitted, measures it using her guessed bases, and then sends a fabricated sequence that aligns with her measurement outcomes to the intended recipient. However, this attack will ultimately cause the protocol to terminate, as the measurement results of the decoy photons will not match those of the prepared decoy photons. Essentially, [PERSON] is unaware of the positions and states of the decoy photons, which prevents her from differentiating between the decoy photons and the target particles, as well as from knowing the measurement bases.  
[PERSON]'s only recourse is to utilize guessed measurement bases to measure the intercepted quantum sequence. For any intercepted decoy photon, she has a 50% probability of correctly guessing the measurement basis, which enables her to avoid detection. Conversely, there is also a 50% probability that her guessed measurement basis will be incorrect, which similarly allows her to bypass detection. Consequently, the probability that [PERSON] can evade eavesdropping detection when choosing the wrong measurement basis is 25%. Therefore, the overall probability that [PERSON] can bypass eavesdropping detection for any decoy photon is \(1-50\%\times 1-50\%\times 50\%=75\%\).  
When considering \(\delta\) decoy photons, the likelihood of [PERSON] being detected while performing the intercept-measure-resend attack is given by \(1-0.75^{\delta}\). For \(\delta=27\), the detection probability is 0.9996, and this value approaches 1 as \(\delta\) increases.  
In conclusion, the high probability of detection means that [PERSON] is unable to gather any information regarding the participants' secrets.
## 2 Economy-Quality Bi-Objective Optimization Model
### Constraints  
#### 2.3.1 Constraints 1: Power Balance of ME-VPP  
* Electrical Power Balance of ME-VPP The sum of power output of DERs in ME-VPP should equate with the total electrical load at each time period. Among of all the DERs, \(P_{\text{s}}\) and \(P_{\text{g}}\) could be negative. Hence, \[\sum_{i=1}^{N_{\text{B}}}P_{\text{h}}^{i}(t)+\sum_{j=1}^{N_{\text{w}}}P_{\text {w}}^{j}(t)+\sum_{k=1}^{N_{\text{pr}}}P_{\text{pv}}^{k}(t)+\sum_{m=1}^{N_{ \text{g}}}P_{\text{c}}^{m}(t)+\sum_{n=1}^{N_{\text{s}}}P_{\text{s}}^{n}(t)+P_{ \text{g}}(t)=L_{\text{ele}}(t)+L_{\text{a}}(t),\] (9)  
Notice that \(L_{\text{a}}\) denotes electrical load of air-conditioners, which is used to satisfy heat/cooling demand.  
* Thermal Power Balance of ME-VPP The sum of heat/cooling power output of DERs in ME-VPP should equate with the total heat/cooling load at each time period. Among of all the DERs, CCHP can generate thermal power simultaneously when it generates electrical power. Rest of thermal power will generate from air-conditioner by transforming from electrical power to thermal power. Hence, \[\left\{\begin{array}{l}\left(\sum_{i=1}^{N_{\text{c}}}Q_{\text{c,he}}^{i}(t )+Q_{\text{a,he}}(t)\right)=L_{\text{co}}(t)\\ \left(\sum_{i=1}^{N_{\text{c}}}Q_{\text{c,co}}^{i}(t)+Q_{\text{a,co}}(t)\right) =L_{\text{he}}(t)\end{array},\right.\] (10)  
where \(L_{\text{co}}\) and \(L_{\text{he}}\) are total cooling load and heat load respectively; \(Q_{\text{c}}\) and \(Q_{\text{a}}\) denote thermal power generated from CCHP and air-conditioner, which consist of heat power output and cooling power output as \(Q_{\text{c,he}}\), \(Q_{\text{c,co}}\), \(Q_{\text{a,he}}\) and \(Q_{\text{a,co}}\).  
Thermal power output of CCHP:  
\[Q_{\text{c}}=\frac{P_{\text{c}}\cdot\left(1-\eta_{\text{e}}-\eta_{\text{L}} \right)}{\eta_{\text{e}}}, \tag{11}\]where \(\eta_{\rm e}\) is generating efficiency of CCHP, and \(\eta_{\rm L}\) is heat loss coefficient.  
\[\left\{\begin{array}{l}Q_{\rm c,he}=Q_{\rm c}\cdot COP_{\rm c,he}\\ Q_{\rm c,co}=Q_{\rm c}\cdot COP_{\rm c,co}\end{array}\right., \tag{12}\]  
where \(COP_{\rm c,he}\) and \(COP_{\rm c,co}\) are coefficient of CCHPs' performance with respect to heat and cooling energy efficiency.  
Thermal power output of air-conditioner:  
\[\left\{\begin{array}{l}Q_{\rm a,he}=P_{\rm a}\cdot COP_{\rm a,he}\\ Q_{\rm a,co}=P_{\rm a}\cdot COP_{\rm a,co}\end{array}\right., \tag{13}\]  
where \(COP_{\rm a,he}\) and \(COP_{\rm a,co}\) are coefficient of air-conditioners' performance with respect to heat and cooling energy efficiency.
## 3 Results  
The results obtained from the statistical data processing are represented in tables and graphs: Table 2 (multivariate analysis), Tables 3 and 4 (univariate analysis, with the identification of the effect of each independent variable on test results), Tables 5 and 6 (analysis on data pairs of mean values and the significance of differences between them), Table 7 (correlations between BMI values and results on balance tests), graphs 1-3 (comparison by gender of average performance at each BMI stage). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Effect** & \(\mathbf{\lambda}\) & **F** & **Hypothesis df** & **Error df** & **Sig.** & \(\mathbf{\eta_{\mathrm{P}}}\) & **Observed Power** \\ \hline Gender & 0.675 & 12.572 b & 7.000 & 183.000 & 0.000 & 0.325 & 1.000 \\ BMI framing steps & 0.686 & 5.425 b & 14.000 & 366.000 & 0.000 & 0.172 & 1.000 \\ Gender * BMI & 0.850 & 2.219 b & 14.000 & 366.000 & 0.007 & 0.078 & 0.970 \\ \hline \hline \end{tabular}  
* Design: Gender + BMI framing steps + Gender
* BMI framing steps.
* F—Fisher test; df—degrees of freedom; Sig—level of probability; \(\eta^{2}_{p}\)—partial eta squared.  
\end{table} 
Table 2: The results of the multivariate tests a (MANOVA). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dependent** & **Sum of** & **Mean** & **F (2, 192)** & **Sig.** & **Partial Eta** & **Noncent** & **Observed** \\
**Variable** & **Squares** & **Square** & **F (2, 192)** & **Sig.** & **Squared** & **Parameter** & **Power** \\ \hline One Standing & 617.602 & 308.801 & 8.889 & 0.000 & 0.085 & 17.779 & 0.971 \\ Functional & 33.781 & 16.891 & 0.421 & 0.657 & 0.004 & 0.841 & 0.118 \\  & 108.109 & 54.055 & 4.953 & 0.008 & 0.049 & 9.906 & 0.805 \\ Flamingo test & 975.599 & 487.800 & 36.261 & 0.000 & 0.274 & 72.522 & 1.000 \\ Bass test & 5455.519 & 2727.760 & 16.499 & 0.000 & 0.147 & 32.998 & 1.000 \\ Walk and & & & & & & \\ turn field & 8.537 & 4.268 & 15.034 & 0.000 & 0.135 & 30.068 & 0.999 \\  & 5778.505 & 2889.252 & 4.371 & 0.014 & 0.044 & 8.741 & 0.751 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Univariate test results (ANOVA)—influence/effect of the variable BMI framing steps on performance on balance tests. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dependent** & **Sum of** & **Mean** & **Sig.** & **Partial Eta** & **Noncent** & **Observed** \\
**Variable** & **Squares** & **Square** & **F (1, 193)** & **Sig.** & **Squared** & **Parameter** & **Power** \\ \hline One standing & 498.941 & 498.941 & 14.185 & 0.000 & 0.068 & 14.185 & 0.963 \\  & 157.890 & 157.890 & 4.017 & 0.046 & 0.020 & 4.017 & 0.514 \\  & 30.826 & 30.826 & 2.738 & 0.100 & 0.014 & 2.738 & 0.377 \\ Flamingo test & 659.669 & 659.669 & 43.920 & 0.000 & 0.185 & 43.920 & 1.000 \\ Bass test & 412.554 & 412.554 & 2.164 & 0.143 & 0.011 & 2.164 & 0.310 \\ Walk and & & & & & & \\  & 0.640 & 0.640 & 1.979 & 0.161 & 0.010 & 1.979 & 0.288 \\  & 0.344 & 0.344 & 0.000 & 0.982 & 0.000 & 0.000 & 0.050 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Univariate test results (ANOVA)—the influence of the gender variable on performance in the balance tests.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Architecture** & **Fold** & **Sensitivity** & **Specificity** & **Accuracy** & **B. Accuracy** & **F1 Score** \\ \hline \multirow{8}{*}{CNNs} & VGG19 & fold\_4 & 0.7833 & 0.8537 & 0.8467 & 0.8185 & 0.5054 \\  & VGG19 & fold\_5 & 0.8833 & 0.8898 & 0.8892 & 0.8866 & 0.6145 \\ \cline{2-8}  & ResNet50 & fold\_5 & 0.8083 & 0.8065 & 0.8067 & 0.8074 & 0.4554 \\  & ResNet50 & fold\_2 & 0.8417 & 0.9009 & 0.8950 & 0.8713 & 0.6159 \\ \cline{2-8}  & InceptionV3 & fold\_4 & 0.6750 & 0.9843 & 0.9533 & 0.8296 & 0.7431 \\  & InceptionV3 & fold\_3 & 0.8500 & 0.9389 & 0.9300 & 0.8944 & 0.7083 \\ \cline{2-8}  & Xception & fold\_5 & 0.9250 & 0.6898 & 0.7133 & 0.8074 & 0.3922 \\  & Xception & fold\_2 & 0.8083 & 0.8963 & 0.8875 & 0.8523 & 0.5897 \\ \hline \multirow{8}{*}{ViTs} & Original ViT & fold\_1 & 0.7000 & 0.9667 & 0.9400 & 0.8333 & 0.7000 \\  & Original ViT & fold\_4 & 0.8167 & 0.9343 & 0.9225 & 0.8755 & 0.6782 \\ \cline{2-8}  & Swin Base & fold\_2 & 0.6833 & 0.9389 & 0.9133 & 0.8111 & 0.6119 \\  & Swin Base & fold\_5 & 0.8167 & 0.9194 & 0.9092 & 0.8681 & 0.6426 \\ \cline{2-8}  & Swin Tiny & fold\_1 & 0.5500 & 0.9454 & 0.9058 & 0.7477 & 0.5388 \\  & Swin Tiny & fold\_2 & 0.7667 & 0.9315 & 0.9150 & 0.8491 & 0.6434 \\ \cline{2-8}  & Twins-PCPVT & fold\_4 & 0.7750 & 0.8546 & 0.8467 & 0.8148 & 0.5027 \\  & Twins-PCPVT & fold\_2 & 0.8417 & 0.8713 & 0.8683 & 0.8565 & 0.5611 \\ \hline \multirow{8}{*}{Hybrid models} & DeiT & fold\_5 & 0.7417 & 0.9370 & 0.9175 & 0.8394 & 0.6426 \\  & DeiT & fold\_4 & 0.7833 & 0.9352 & 0.9200 & 0.8593 & 0.6620 \\ \cline{2-8}  & CaiT\_XXS36 & fold\_3 & 0.5000 & 0.9741 & 0.9267 & 0.7370 & 0.5769 \\  & CaiT\_XXS36 & fold\_5 & 0.7917 & 0.9083 & 0.8967 & 0.8500 & 0.6051 \\ \cline{2-8}  & CaiT\_S24 & fold\_3 & 0.7083 & 0.9417 & 0.9183 & 0.8250 & 0.6343 \\  & CaiT\_S24 & fold\_1 & 0.8333 & 0.9009 & 0.8942 & 0.8671 & 0.6116 \\ \cline{2-8}  & CeiT & fold\_3 & 0.6833 & 0.8481 & 0.8317 & 0.7657 & 0.4481 \\  & CeiT & fold\_4 & 0.7917 & 0.8935 & 0.8833 & 0.8426 & 0.5758 \\ \cline{2-8}  & ConViT & fold\_1 & 0.6250 & 0.9667 & 0.9325 & 0.7958 & 0.6494 \\  & ConViT & fold\_2 & 0.8333 & 0.9435 & 0.9325 & 0.8884 & 0.7117 \\ \hline \multirow{8}{*}{Others inspired in ViT} & ResMLP\_12 & fold\_4 & 0.6417 & 0.8750 & 0.8517 & 0.7583 & 0.4639 \\  & ResMLP\_12 & fold\_1 & 0.8333 & 0.8296 & 0.8300 & 0.8315 & 0.4950 \\ \cline{1-1} \cline{2-8}  & ResMLP\_24 & fold\_2 & 0.7250 & 0.9833 & 0.9575 & 0.8542 & 0.7733 \\ \cline{1-1} \cline{2-8}  & ResMLP\_24 & fold\_3 & 0.8333 & 0.9380 & 0.9275 & 0.8856 & 0.6969 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the results obtained by evaluating the different DL models on the Refuge dataset according to different metrics. For simplicity, only the results corresponding to the folds with minimum and maximum balanced accuracy (B. Accuracy) are displayed. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Architecture** & **Fold** & **Sensitivity** & **Specificity** & **Accuracy** & **B.
## Theory
### Calculation of Modal Participant Coefficients
#### Orthogonal Decomposition Method (ODM)  
The structural modes of arbitrary model are orthogonal to each other, which means the surface integral of the product of the displacement distribution functions of any two modes is zero. This product also needs to be multiplied by a position-dependent weighting coefficient, which is the corresponding mass distribution function. For the convenience of analysis, all the cases in this paper are only under the circumstance of uniform thickness, which means that the weighting coefficient is constant in these cases. Furthermore, this constant can be incorporated into other integral constants without being considered separately.  
According to the abovementioned principle of modal superposition, the displacement field of a vibrating structure can be expressed as Equation (1). Then, a new formula can be obtained by multiplying both sides of Equation (1) by modal displacement of (\(p\),\(q\)) order mode and integrating the equation over the entire surface. According to the orthogonality between the modes, the new formula can be simplified. Finally, the symbols \(p\) and \(q\) can be replaced with symbols \(n\) and \(m\) after orthogonalization, and then the participant coefficient of (\(n\),\(m\)) order mode is  
\[W_{nm}=\frac{\iint\limits_{s}w\phi_{nm}ds}{\iint\limits_{s}\phi_{nm}{}^{2}ds} \tag{2}\]
## 3 Proposed Model
### ALBERT (ELECTRA) Nueral Topic Model  
Our neural topic model was based on the VAE [27; 29]. We first examined the training method for the proposed neural topic model. \(D\), which expresses the input document \(d\) in the form of a bag of words, passes through the encoder \(f(D)\). The resulting vector subsequently passes through the hidden layers \(h_{1}\) and \(h_{2}\) to obtain the mean \(\mu\) and variance vector \(\sigma^{2}\). The encoder \(f\) and hidden layers \(h_{1}\) and \(h_{2}\) are fully connected neural networks.  
\[\mu=h_{1}(f(D)),\quad\sigma^{2}=h_{2}(f(D)) \tag{1}\]  
The obtained mean, variance vector, and noise \(\epsilon\) generate a latent vector \(h\) with the specified number of dimensions. A softmax activation function is applied to the generated vector to obtain \(R\) using the topic ratio information of the document.  
\[\begin{split} h&=\mu+\sigma^{2}+\epsilon,\quad \epsilon\sim N(0,1)\\ R&=\text{softmax}(W_{h}h+b_{h})\end{split} \tag{2}\]  
After passing \(R\) through a decoder composed of a hidden layer, the data are reconstructed to be as similar as possible to the input value \(d\). The topic vectors \(t_{i}(i=1,2,\dots,k)\) and topic distribution \(A\) for the words are used to train the model after configuring the initial values through Xavier initialization [35].  
Next, we examine how NTM and the BERT-based models can be hybridized using the attention mechanism. Final encoder representations \((C,T_{1},T_{2},\dots,T_{SEP})\) of input tokens \(x_{n}\) are obtained through a pre-trained BERT-based model. After passing the final encoder representation through the hidden layer \(o\) to equalize its size with the topic vector \(t\), the encoder representation is combined with the topic vector by applying an attention mechanism using the topic vector \(t\) as the query. The equation below is a relation between the \(j\)-th topical attention weight, word step \(b_{j}(\text{j}\in\{1,2,\dots,\text{m}\})\), and topic vector \(t_{i}(\text{i}\in\{1,2,\dots,\text{k}\})\):  
\[\begin{split} b_{j}&=\text{tanh}(o(C,T_{1},T_{2}, \dots,T_{SEP}))\\ c_{j,i}&=\text{softmax}(b_{j}^{\top}t_{i})\\ W_{j}&=\text{concat}(c_{j,i})\end{split} \tag{3}\]  
The final topical attention weight is computed by multiplying \(R\) by the topic ratio. Additionally, we apply a threshold value, denoted as \(\alpha(0<\alpha<1)\), to ensure that each topical attention learns independently for other topics.  
\[Y_{j}=W_{j}(R_{i}-\alpha)=\sum_{i=1}^{k}c_{j,i}(R_{i}-\alpha) \tag{4}\]  
Figure 1: Structure of the ALBERT (ELECTRA) baseline model.  
The final representation \(G\) of the document is obtained by combining the topical attention weights with the representation vector of each token.  
\[G=\sum_{j=1}^{m}v_{j}=\sum_{j=1}^{m}Y_{j}b_{j} \tag{5}\]  
Finally, \(G\) is applied to the desired tasks using an activation function \(g\) to obtain the prediction \(F\).  
\[F=g(W_{F}G+b_{F}) \tag{6}\]  
A detailed structure of the model is shown in Figure 2. The two variants are designated as the ALBERT neural topic model (ANTM) and ELECTRA neural topic model (ENTM).
# Evaluation of Biosynthetic Pathway and Engineered Biosynthesis of Alkaloids
## 3 Saframycin
Thus, to establish the mechanism of how the complex core structure of **9** was biosynthesized, in vitro assays on the recombinantly prepared SfmA and SfmB were carried out [30]. As predicted from the adenylation domain amino acid sequence [30], two NRPSs, SfmA and SfmB, were shown to specifically activate L-alanine and glycine, respectively, indicating that those two enzymes were responsible for the formation of the non-tyrosine dipeptide segment of **9** (Figure 8). However, amino acid sequence analysis of SfmA also revealed the presence of an \(N\)-terminal acyl-CoA ligase (AL)-acyl carrier protein (ACP) didomain [30; 35], which is commonly found in NRPSs that form lipopeptides having a long fatty acyl chain extension at the \(N\)-terminus of the core peptide [36; 37]. This finding prompted analysis for the requirement of an \(N\)-terminal acyl chain in the L-alanyl-glycyl intermediate. In vitro assays with synthetically prepared L-alanyl-glycyl-S-CoA substrates having an \(N\)-acyl chain of various length and SfmC that was recombinantly prepared in _Escherichia coli_ clearly indicated that the \(N\)-myristoylated dipeptide was the actual substrate of SfmC [35]. No reaction took place when substrates lacking a long acyl extension were provided to SfmC. Because the final product **9** does not carry any acyl chain, the requirement of SfmC for a cryptic acyl chain in its substrate is a completely unexpected finding. Once the actual substrate for SfmC was identified, the mechanism of the formation of the pentacyclic tetrahydrooisoquinoline core was investigated. Further detailed examinations, which  
Figure 7: Chemical structures of tetrahydrooisoquinoline alkaloids, saframycin A **9**[21] and ecteinascidin 743 **10**[24]. involved the use of additional synthetic substrates and SfmC mutants [35], demonstrated that SfmC was solely responsible for the formation of the pentacyclic scaffold from two molecules of a L-tyrosine derivative, 3-hydroxy-5-methyl-O-methyltyrosine (Figure 8) [30]. Based on the results from those experiments, the reaction mechanism was suggested with the Re (reduction) domain of SfmC catalyzing three reduction reactions and the C (condensation) domain catalyzing two cycles of the Pictet-Spengler reaction to form the tetrahydroroisoquinoline core of **9**[35]. The last cyclization reaction after the third reduction step may also be catalyzed by SfmC, although the reaction could proceed spontaneously. While further investigations into the biosynthesis of **9** and related compounds are required, elucidation of this complex reaction mechanism would provide us with a novel chemoenzymatic approach toward synthesizing sufficient amount of **10** and its analogs.
# Despite for SMEs: A Systematic Review of Socio-Technical Cybersecurity Metrics
## 2 Related Work
### Adaptability  
Adaptability is crucial to any cybersecurity solution [53]. Especially when measuring cybersecurity, a rigid solution that does not adapt to a changing environment or a new use case is far from optimal [54]. It is not surprising to see, then, that adaptability is a key focus of many studies [13; 55], although operationalisation of adaptability is still a challenge [53].  
We consider adaptability to be \"the state of being able to change to work or fit better\" [18]. This definition outlines two important dimensions of adaptability. First, a solution is considered adaptable if it can change to work better. There are several reasons why a cybersecurity metric solution may not be functioning as it should. This can relate to problems with the metrics themselves, such as missing or dirty data [56]. It can also relate to a changing security landscape that invalidates an existing model. This phenomenon is known as concept drift [57]. Second, a solution is considered adaptable if it can change to fit better. Generally, cybersecurity solutions in research are made to fit their use case.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Aggregation** & **Injective** & **Idempotent** & **Prioritisation** & **Dependence** & **Weakest link** \\ \hline Weighted linear combination & ✓ & ✓ & ✓ & × & × \\ Weighted product & ✓ & × & ✓ & × & × \\ Weighted maximum & × & ✓ & ✓ * & × & ✓ * \\ Weighted complementary product & ✓ & × & ✓ * & × & ✓ * \\ Bayesian network & ✓ & × & ✓ & ✓ & × \\ \hline \hline \end{tabular}  
* Strategies within the classes of weighted maximum and weighted complementary product cannot satisfy the prioritisation and weakest link properties at the same time.  
\end{table}
Table 2: Various classes of metric aggregation strategies, and important security-related properties their strategies can possess.  
We can determine their adaptability in the \"fitting\" dimension by determining how easily the solution can be deployed at other (similar) use cases.  
Adaptability is significant in the SME context. The SME landscape is diverse [14], and SMEs often lack the knowledge and expertise to perform extensive adaptations independently [9]. In Section 6, we assimilate observations from earlier research and our results of Section 4 to provide suggestions for improving solution adaptability.
# Study on the Method of Moving Load Identification Based on Strain Influence Line
## 5 Conclusions  
In this paper, considering the load transverse distribution, a novel moving load identification method was proposed based on distributed strain sensing technique and influence line. The load identification accuracy and anti-noise performance were greatly improved, and it was more universal for a variety of bridge types. In addition, a series of numerical simulations and experiments were conducted to verify the proposed method. The main conclusions are as follows:  
1. Through the verification of numerical simulation, the method without considering the load transverse distribution was not suitable for solving the space problem, and the method considering the load transverse distribution has a high identification accuracy and excellent performance of anti-noise performance.
2. The results of the model test showed that the average relative error of the speed identification was smaller than \(\pm\)4%, which shows the great performance of the method.
3. The speed has no obvious influence on the vehicle weight identification, and the average identification error was smaller than \(\pm\)5%. In addition, it should be noted that the results obtained by using the actual speed to calculate the vehicle weight were more accurate than those obtained by using the inversion speed.
4. The relative error of the vehicle weight identification was smaller than \(\pm\)10%, and the error of more than 90% of samples was smaller than \(\pm\)5%.  
[PERSON] proposed the topic of this study and designed the experiments; [PERSON] and [PERSON] analyzed the experimental date; [PERSON] guided the research and provided advice. The paper was finally written by [PERSON] and confirmed by all the authors. All authors have read and agreed to the published version of the manuscript.  
Innovative Venture Technology Investment Project of Hunan Province (2018 GK5028). National Natural Science Foundation of China (52078122).  
Not applicable.  
The authors gratefully appreciate the financial support of Innovative Venture Technology Investment Project of Hunan Province (2018 GK5028), and National Natural Science Foundation of China (52078122).  
The authors declare no conflict of interest.  
Figure 17: The relative error of vehicle weight identification: (**a**) Two-axle vehicle, (**b**) three-axle vehicle.