esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory and Formulation
### Verification
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Axisymmetric Vibration Mode Number**} \\ \cline{3-7}  & & \multicolumn{2}{c}{**1**} & **2** & **3** & **4** \\ \hline \multirow{6}{*}{0.1} & Ref. [83] & 0.001 & 27.280 & 75.364 & 148.21 & 245.47 \\  & Present & 0.001 & 28.514 & 77.363 & 151.05 & 249.05 \\  & Ref. [83] & 0.050 & 26.534 & 71.228 & 135.24 & 215.08 \\  & Present & 0.050 & 27.679 & 72.766 & 136.36 & 214.46 \\  & Ref. [83] & 0.100 & 24.629 & 62.140 & 111.12 & 167.16 \\  & Present & 0.100 & 25.558 & 62.834 & 110.39 & 163.41 \\  & Ref. [83] & 0.150 & 22.230 & 52.762 & 90.286 & 131.35 \\  & Present & 0.150 & 22.909 & 52.835 & 88.962 & 127.51 \\  & Ref. [83] & 0.200 & 19.843 & 44.913 & 74.860 & 106.81 \\  & Present & 0.200 & 20.294 & 44.683 & 73.628 & 103.75 \\ \hline \multirow{6}{*}{0.2} & Ref. [83] & 0.001 & 34.609 & 95.738 & 188.14 & 311.40 \\  & Present & 0.001 & 35.712 & 97.395 & 190.61 & 314.62 \\  & Ref. [83] & 0.050 & 33.533 & 89.550 & 168.60 & 265.78 \\  & Present & 0.050 & 34.426 & 90.285 & 168.15 & 262.39 \\  & Ref. [83] & 0.100 & 30.841 & 76.560 & 134.71 & 200.02 \\  & Present & 0.100 & 31.274 & 75.895 & 131.87 & 193.37 \\  & Ref. [83] & 0.150 & 27.545 & 63.827 & 107.32 & 154.20 \\  & Present & 0.150 & 27.534 & 62.466 & 104.32 & 152.53 \\  & Ref. [83] & 0.200 & 24.348 & 53.574 & 87.870 & 123.93 \\  & Present & 0.200 & 24.022 & 52.122 & 85.531 & 121.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of nondimensional natural frequency of the annular plate for different axisymmetric vibration mode number, inner radius to outer radius ratio, and thickness to outer radius ratio for clamp-clamp boundary condition. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Axisymmetric Vibration Mode Number**} \\ \cline{3-7}  & & \multicolumn{2}{c}{**1**} & **2** & **3** & **4** \\ \hline \multirow{6}{*}{0.1} & Ref. [83] & 0.001 & 22.701 & 65.538 & 132.89 & 224.41 \\  & Present & 0.001 & 20.852 & 63.734 & 131.50 & 223.86 \\  & Ref. [83] & 0.050 & 22.294 & 63.052 & 123.85 & 201.54 \\  & Present &
## 3 Proposed Protocol Architecture
### Proxy Binding Message Format  
In the proposed proxy binding update (PBU) and proxy binding acknowledgement (PBA) messages in Figure 11, we have specified a flag bit S. If this S flag is set, it indicates an IP-WSN based on SMIPv6 operations. If the S bit is not set, this indicates non-SPMIPv6 operations. The meanings and descriptions of the other flags are described in different requests for comments (RFCs) [9,13-15]. The mobility options header plays an important role for the different types of mobility scenario. Some of  
Figure 8: Inter-SMAG communication under different border router.  
Figure 10: Communication from CN to IP-WSN.  
Figure 9: Communication from IP-WSN to CN.  
the mobility header options are hand-off indicator options, access technology type options, link layer identifier options, link local address options and time stamp options.
## 5 Policy Control
### Compliance to International Treaties  
It has been recognized since the mid 1990s [51] that EO has the potential to verify compliance toward international treaties [52-54], and increasingly, EO is used by governments in implementing their commitments to these treaties, while controlling and demonstrating their compliance. Remote sensing is also increasingly used by treaties to verify compliance of contracting and non-contracting parties, such as, for example, the treaty to restrict the proliferation of nuclear weapons [55]. Remotely sensed evidence of non-compliance has been used to mobilize signatories of this treaty to increase political pressure for the enforced compliance of non-compliant states.  
More recently, interest has increased in the application of EO to enforce compliance with international environmental treaties, such as, for example, the Ramsar convention [56]. The application of remote sensing to enforce compliance with international environmental treaties, although technically feasible, is not straightforward because these treaties typically lack a framework to enforce compliance. Although RS alone cannot enforce compliance, since countries are sovereign to decide whether to react, RS can be used as evidence in the case of non-compliance and in the dissemination of remote sensing information that reveals lack of enforcement. This may increase pressure on non-compliant parties to improve performance.
## 1 Introduction  
The freeway on-ramp is a special section where mainstream traffic flow merges with the incoming flow. At present, the study on the merging of mainline freeways with the on-ramp lane has been pervasive, and numerous cooperative driving strategies, such as rule-based [1, 2] and optimization-based [3, 4] strategies, have been proposed, but they usually only considered single lane freeways with one on-ramp lane. The reality is that mainline is usually multi-lanes, so this study aimed to deal with the case of multilane mainline merging areas.  
Due to unreasonable lane-changing and frequent acceleration or deceleration behavior, the on-ramp is also a high incidence of traffic accidents and congestion. Recent work has shown the application of connected and automated vehicles (CAVs) and wireless communication in the on-ramp area [5, 6]. CAVs can improve freeway traffic efficiency and eliminate the potential hazard caused by human factors, such as fatigue and response delay, while reducing vehicles' distance and increasing traffic capacity [7]. Vehicle-to-vehicle (V2V) communication enables the real-time exchange of information between vehicles, including speed, acceleration, and position, so that vehicles can coordinate to avoid rear-end and lateral collisions [8, 9], thereby improving traffic efficiency. Compared with single-lane lanes, additional considerations are required for lane-changing operations in the case of multilane mainline merging areas. A suitable lane change method is necessary to improve traffic efficiency.  
In this study, we investigated several challenges in multilane mainline merging areas according to [10] and our research work. Firstly, the model was complicated and challenging to solve when all vehicles' trajectory optimizations were performed on a multilane and ramp. Secondly, the unbalance of flow distribution between lanes could lead to the reduction of merging efficiency. For example, traffic congestion may occur when traffic flows in the outer lane are very dense in the downstream merged area. Thirdly, narrow lane-changing behavior affects the normal driving of other drivers and can cause severe congestion before a merging point. We designed a rule-based lane-changing strategy and car-following trajectories for connected and automated vehicles at freeway on-ramps to address the problems mentioned above. Additionally, the problem was modeled as a time-discrete linear system. The objective function was to maximize the input vehicle's speed as much as possible and minimize the acceleration change rate, and various constraints were imposed to avoid collisions.  
The remainder of this paper is structured as follows. Section 2 presents a brief literature review, and Section 3 elaborates on the proposed methodology. In Section 4, we discuss our simulation of three different cases under different demands and analyze the results, and Section 5 presents the conclusions to this work.
# Multi-Axis Laser Interferometer Not Affected by Installation Errors Based on Nonlinear Computation
## 2 Interferometer Measurement Model  
In this section, the structure and the mathematical model of the multi-axis laser interferometer, which avoids the two factors mentioned in Section 1, is proposed. For simplicity, the 3-DOF displacement measurement of the X-Y stage is mainly discussed, while the 6-DOF displacement measurement of the X-Y-Z stage is also mentioned, but is not the focus. Nevertheless, there is no difference in the principle between the two but there is in the computation complexity.
# Design of a Dynamic Secondary Mirror Truss Adjustment Mechanism for Large Aperture Telescopes
## 2 Kinematic Modeling of the Mechanism
### Analysis of the Model Degrees of Freedom  
The mechanism consists of four identical PUR-branched chains connected to the fixed and moving platforms. Figure 1 shows each branched chain. These are equivalent to three single DOF rotations and one moving sub, as shown in Figure 2. The intersection of \(\mathfrak{S}_{i2}\) and \(\mathfrak{S}_{i3}\) with the origin establishes the branched chain coordinate system O-\(x_{i}y_{i}z_{i}\); then, the motion spiral of the moving sub of PUR-branched chain \"\(i\)\" system is defined as follows:  
\[\begin{array}{l}\mathfrak{S}_{i1}=\left(\begin{array}{cccccc}0&0&0&0&f_{1} \\ \mathfrak{S}_{i2}=\left(\begin{array}{cccccc}1&0&0&0&0&f_{2}\\ 0&1&0&0&0&f_{3}\\ \mathfrak{S}_{i4}=\left(\begin{array}{cccccc}0&1&0&0&0&f_{4}\\ \end{array}\right)&\end{array}\right)\\ \mathfrak{S}_{i3}=\left(\begin{array}{cccccc}0&1&0&0&0&f_{3}\\ \mathfrak{S}_{i4}=\left(\begin{array}{cccccc}0&1&0&0&f_{4}\\ \end{array}\right)&\end{array}\right)\\ \end{array}, \tag{2}\]  
where \(f_{1},f_{2},f_{3},f_{4}\), and \(d_{4}\) are all non-zero real numbers.  
Using the principle that the reciprocal product of spirals is zero for the kinematic spiral system of a branch chain, the \(i\)-th term develops the inverse spiral. The matrix obtains its bound spiral system as follows:  
\[\begin{array}{l}\mathfrak{S}_{1}^{r}=\left(\begin{array}{cccccc}0&1&0;&0&0 &0\\ \mathfrak{S}_{2}^{r}=\left(\begin{array}{cccccc}0&0&0;&0&0&1\\ \end{array}\right)&\end{array}\right)\,\end{array} \tag{3}\]  
The constrained spiral system contains two constrained spirals. The \(\mathfrak{S}_{1}^{r}\) is the force constraint in the y-direction and \(\mathfrak{S}_{2}^{r}\) is a force couple constraint in the z-direction. The mechanism has four identical branch chains and \(\mathfrak{S}_{1}^{r}\) provides four force constraints in a plane parallel to the platform's plane. The mechanism has two redundant constraints, as shown by the fact that only two of the four force constraints in the same plane are linearly independent, and the four constraints restrict the movement of the moving platform in the x- and y-directions. The \(\mathfrak{S}_{2}^{r}\) restricts the rotation of the moving platform about the z-axis. Hence, the mechanism offers three DOFs for the x-, y-, and z-axis.
# An Overview of Emergency Communication Networks
## 4 Discussion
To summarize, each emergency communication network technology has its own advantages and disadvantages. Therefore, in the field of emergency communication, various networks should be used together to complement each other's advantages and cooperate with each other, so as to better complete more complex communication tasks. A typical collaborative network scenario is shown in Figure 13. The system integrates satellite communication systems, a cellular network, a wireless private network, and an ad hoc network to obtain resilient post-disaster communication services through collaboration with various systems. The accident scene and the rescue command center realize data, voice, and video services through the cellular network. When cellular communication is interrupted due to a major accident or disasters, the wireless private network can be used to continue communication. The system can expand the communication range by means of an ad hoc network. Due to the advantages of the rapid and flexible deployment of UAVs in complex environments, FANET can be used to provide wireless network access services for air platforms. The emergency network at the disaster site can be deployed by emergency communication vehicles; however, in the case of complex terrain environments, such as earthquakes and floods, satellite communication system is a better choice. In general, in the case of sudden large-scale natural disasters and public emergencies, a single communication network technology cannot guarantee communication needs, so the emergency communication network should integrate multiple communication technologies to ensure normal communication. In this paper, a broadband-narrowband integrated emergency communication system is proposed, which uses the PDT+LTE scheme to fuse information from the broadband-narrowband communication network into a unified scheduling platform. An LTE private network base station + piggyback mesh base station is adopted to expand the accident rescue area and ensure rescue communication. The system realizes instant message, audio, and video unified command and scheduling. Compared with traditional converged communication system equipment, it has the characteristics of integrating broadband and narrowband technology, portability, and a self-provided power supply. Through actual testing, it was found that the voice communication distance of the system is 15-20 km, and
# Sensitivity Estimation for Differentially Private Query Processing
## 5 Experiments
### Experimental Setup
#### 5.1.3 Queries  
The queries for the experiments are shown in Figure 5. We used queries Q1-Q3 for the TPC-H dataset and Q4-Q7 for the Facebook dataset. The queries include chain queries, acyclic queries, and cyclic queries. The orange circles represent non-private relations, and the blue circles represent the private relations.
## 1 Introduction
### Background of an \"Estimation Procedure\"
#### 1.1.3 Unsaturated Elastic Modulus
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & \begin{tabular}{c} **Type of** \\ **Equation** \\ \end{tabular} & \begin{tabular}{c} **Shear Strength Equation** \\ \end{tabular} & \begin{tabular}{c} **Parameters** \\ \end{tabular} \\ \hline \begin{tabular}{c} [PERSON], \\ [PERSON], and \\ [PERSON] [33] \\ \end{tabular} & Estimation & \(\tau=\)\(\tan\phi^{\prime}((u_{a}-u_{w})_{b}+P_{a})\ln\left[\frac{(u_{a}-u_{w})+P_{a}}{P_{a}}\right]\) & P\({}_{a}\): Atmospheric pressure \\ \hline \begin{tabular}{c} [PERSON] et al. \\ [28] \\ \end{tabular} & Estimation & \(\tau=c^{\prime}+(\sigma-u_{a})\tan\phi^{\prime}+(u_{a}-u_{w})\left[(\tan\phi^{ \prime})\left(\frac{\phi-\phi_{w}}{\phi_{w}-\phi_{w}}\right)\right]\) & \begin{tabular}{c} \(\theta:\) Water content \\ \(\theta_{z}\): Residual water content \\ \(\theta_{z}\): Saturated water content \\ k: Fitting parameter \\ \end{tabular} \\ \hline \begin{tabular}{c} [PERSON] et al. [32] \\ \end{tabular} & Fitting & \(\tau=c^{\prime}+\left\{(\sigma-u_{a})\ln\phi^{\prime}\right]+(u_{a}-u_{w}) \left[(\theta^{b})(\tan\phi^{\prime})\right]\) & \begin{tabular}{c} \(\zeta_{1},\zeta_{2},\zeta_{3}\): Fitting parameters \\ \(\zeta_{4}\): Fitting parameter related to \\ m: Fitting parameter related to \\ the material characteristics \\ S: Degree of saturation \\ S: Residual saturation \\ Po: Fitting parameter related to \\ the inflexion point of SWCC \\ \end{tabular} \\ \hline \begin{tabular}{c} [PERSON] et al. \\ [34] \\ \end{tabular} & Fitting & \begin{tabular}{c} \(\tau=c^{\prime}+(\sigma-u_{a})\tan\phi^{\prime}+(u_{a}-u_{w})[\theta(u_{a}-u_{w})]^{k} \tan\phi^{\prime}\) \\ where: \\ \(\theta=\left(\frac{\theta-\theta_{z}}{\theta_{z}}-\frac{\theta}{\theta_{z}} \right)=\frac{\theta}{\theta_{z}}\) \\ \end{tabular} & \begin{tabular}{c} k: Fitting parameter \\ \(\theta:\) Normalized water content \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Vilar [30] \\ \end{tabular} & Fitting & \(\tau=c^{\prime}+(\sigma-u_{a})\tan(\phi^{\prime})+\frac{(u_{a}-u_{w})\tan \phi^{\prime}/(\zeta_{a}-c^{\prime})}{\tau+(u_{a}-u_{w})\tan\phi^{\prime}/( \zeta_{a}-c^{\prime})}\) &
\begin{tabular}{c} \(\zeta_{a}\): Last resistance when suction \\ values tends to infinite \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: \(Cont.\)
# A Method of Supplementing Reviews to Less-Known Tourist Spots Using Geotagged Tweets
## 3 Proposed Method
### Inter-Rater Agreement  
The reliability of annotations and adequacy of assigned labels are especially important in the case of sentiment annotations. In particular, [PERSON] et al. [21], addressed the importance of evaluating the reliability between annotators for statistical accuracy. To measure the agreement between three raters, we use [PERSON]'s kappa coefficient, [PERSON] et al. [22].  
Kappa coefficient between two or more annotators can be computed by using the following formula:  
\[\kappa=1-\frac{1-P_{o}}{1-P_{h}} \tag{1}\]  
In this above equation, \(P_{o}\) is the relative observed agreement among raters, and \(P_{h}\) is the hypothetical probability of chance agreement, using the observed tweets data to calculate the probabilities of each observer randomly seeing each category.  
When kappa = 1, the annotators are in complete agreement. When the score is negative, it shows that there is no effective agreement between annotators, or the agreement is worse than random.  
In addition, the hypothetical probability of the chance of agreement can be computed using the following formula:  
\[P_{h}=\frac{1}{N^{2}}\sum_{k}n_{k1}n_{k2} \tag{2}\]  
where \(k\) represents categories, and \(N\) being the number of observations to categorize. In this study, the degree of agreement between the three annotators was calculated as 0.37. Kappa's have specific interpretations, and 0.37 can be interpreted as \"substantial\", \"fair\", \"medium\" or \"somewhat good\" depending on the interpretation ([PERSON] and [PERSON] et al. [23]). This value, however, is not high to say annotators have an agreement on the annotation results. From this observation, we can assume that the final results of our proposed model was also affected by the low level of agreement between annotators. One way to improve this is by  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Annotated Tweets** & **Unit** \\ \hline Sample of all tweets & 1273 \\ Sample of score assigned tweets & 1273 \\ Sample of On-spot annotated tweets & 974 \\ Sample of Not On-spot annotated tweets & 299 \\ Avg. length (char) of all tweets & 118 \\ Avg. length (words) of all tweets & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A summary of annotated tweets (corpus).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**id** & **Tweet** & **Label** & **Remarks** \\ \hline i & i’m at serengeti national park & 1 & at target spot \\ j & I’m at ngorongoro & 1 & at target spot \\  & wildlife lodge in ngorongoro & 1 & at target spot \\ k & I’m at serengeti park in & & \\  & hodenhagen niedersachisen & 0 & different spot \\
1 & forbookingsafariserenegeti, & & \\  & ngorongoro, mikumi national park & 0 & advert \\ \hline \hline \end{tabular}
\end{table}
Table 3: Examples of annotation made from location-clustered tweets.  
carefully removing ambiguous tweets, which will be our improvement consideration in our future work.
## 1 Introduction
### Flagship Project VINO  
VINO promotes collaboration between stakeholders, focusing on three pillars: technological development, modeling, and application. In fact, the use of remote sensing and big data proves useful for precision agriculture [5], together with sustainable and organic practices. The project addresses the impact of climate change on viticulture in North-West Italy, focusing on areas such as Oltrepo Pavese (Figure 1), Langhe, and Colli Piacentini: climate change, with projected increases in temperature and altered precipitation patterns posing significant challenges to vineyard performance [6]. The project seeks sustainable solutions to adapt viticulture to climate change, including mapping vocational features, evaluating alternative crops, and valorizing waste materials.  
Obtaining temperature maps is important to optimize viticultural strategies, as wines are sensitive to microclimatic variations. Temperature maps allow for tailored practices such as irrigation and harvest timing. This targeted approach is useful to enhance grape quality.
## 2 Mathematical Formulation of Pseudointensity Vector
### Subspace PV  
The concept of PV is extended to the SPV to take the advantage of SHs a higher order, and provide a more reliable and accurate estimate of DoA with multiple sources. The covariance of \(\mathbf{a}_{lm}\) is [15]  
\[\begin{array}{l}\mathbf{R}_{\mathbf{a}_{lm}}=\mathbb{E}\big{\{}\mathbf{a}_{lm}\mathbf{a}_{lm }\mathbf{}^{H}\big{\}}\\ =\mathbf{Y}^{H}(\mathbf{\
abla})\mathbf{R}_{s}\mathbf{Y}(\mathbf{\
abla})\end{array} \tag{11}\]  
with \(\mathbf{R}_{s}=\big{\{}\mathbf{s}\mathbf{s}^{H}\big{\}}\). The singular value decomposition (SVD) gives  
\[\mathbf{R}_{\mathbf{a}_{lm}}=\mathbf{U}\sum\mathbf{U}^{H}=[\mathbf{U}_{s}\mathbf{U}_{n}][\begin{matrix} \sum_{s}&0\\ 0&\sum_{n}\end{matrix}][\begin{matrix}\mathbf{U}_{s}^{H}\\ \mathbf{U}_{n}^{H}\end{matrix}] \tag{12}\]  
\(\mathbf{U}\) denotes unitary matrix, \(\sum\) represents the diagonal matrix housing the singular values of \(\mathbf{R}_{\mathbf{a}_{lm}}\), while \(\mathbf{U}_{s}\) and \(\mathbf{U}_{n}\) are the traditional partitioning of signal and noise subspace, respectively [42]. The plane wave \(\mathbf{U}_{s}=\big{[}\hat{a}_{00}\hat{a}_{1(-1)}\hat{a}_{10}\hat{a}_{11}\ldots\hat {a}_{LL}\big{]}^{T}\) is a column vector, corresponding to the steering vector of plane wave DoA, \(y(\mathbf{\
abla}_{n})\). Obtaining a vector pointing to the source, the SPV approach applies PV approach to a one-dimensional (1D) EM wave subspace, as follows:  
\[\widetilde{\mathbf{I}}_{\mathbf{s}\mathbf{s}}=\frac{4\pi\sqrt{4\pi}}{3}\Re\Bigg{\{}\hat{a }_{00}^{*}\begin{bmatrix}D(k,\varphi_{x},\mathbf{U}_{s})\\ D\big{(}k,\varphi_{y},\mathbf{U}_{s})\\ D(k,\varphi_{z},\mathbf{U}_{s})\end{bmatrix}\Bigg{\}} \tag{13}\]  
Equation (13) is a function of 0 and first-order components of \(\mathbf{U}_{s}\), through Equations (11) and (12); their figure is a function of a higher-order SH of \(\mathbf{a}_{lm}\). As in the PV approach, the advantage of SPV is that the direction can be directly obtained for all TF-regions, i.e., there is no need to calculate the possible directions.
## 2 Materials and Methods
### DNA Extraction and PCR Amplification of 16S rDNA  
Bacterial genomic DNA of the larval gut, brood provision, and nest soil were extracted though the QIAGEN DNeasy Power Soil Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. The purity, quality, and integrity of the extracted DNA were determined by the Qubit 2.0 Fluorimeter (Life Technologies, Carlsbad, CA, USA), NanoDrop spectro-photometer (Thermo Fisher Scientific, Inc., Waltham, MA, USA) and Agilent 2 100 bioana-lyzer (Agilent Technologies, Santa Clara, CA, USA). Then, the extracted DNA was diluted and used as the template to amplify the full-length 16S rRNA genes using the universal primer 27F (5\({}^{\prime}\)-AGGRTTTGATYNTGGCTCAG-3\({}^{\prime}\)) and 1492R (5\({}^{\prime}\)-TASGGHTACCTT GTTASGACTT-3\({}^{\prime}\)) (Weiss and Vossbrinck, 1998). PCR reactions were performed in a 50 \(\mu\)L system, containing 25\(\mu\)L KOD FX Neo Buffer (2\(\times\)), 10 \(\mu\)L dNTPs (2 mM/each), 2.5 \(\mu\)L each primer (10 \(\mu\)M), 1\(\mu\)L KOD FX Neo (TOYOBO), 2 \(\mu\)L template DNA (5 ng/\(\mu\)L) and 7 \(\mu\)L ddH2O. All reactions were performed in an ABI GeneAmp 9700 (Applied Biosystems, Carlsbad, CA, USA) with the following parameters: an initial denaturation at 95 \({}^{\circ}\)C for 5 min, followed by 30 cycles of 95 \({}^{\circ}\)C for 30 s, 50 \({}^{\circ}\)C for 30 s, 72 \({}^{\circ}\)C for 1 min and a final extension at 72 \({}^{\circ}\)C for 7 min. amplicons were extracted from 2 % agarose gels and purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA). The purified products were quantified using Qubit\({}^{\otimes}\)3.0 (Life Invitrogen, Waltham, MA, USA) and pooled in an equimolar ratio for sequencing.  
DNA libraries were constructed by SMRTbell Express Template Prep Kit 2.0 and sequenced with PacBio Sequel. The original reads were analyzed using SMRT Link v8.0 to obtain circular consensus sequence (CCS). Barcode-CCS sequences were obtained by Lima v1.7.0 with the default parameters, then effective sequences were achieved using Cutadapt v2.7. UCHIME v4.2 was used to confirm the quality of Effective-CCS sequences.