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Abstract
The optical fiber distributed strain sensor based on the optical frequency domain reflectometer (OFDR) preserves its dominant position in short-distance measurement fields with high spatial resolution, such as biomedical treatment, soft robot, etc. However, owing to the weak intensity of the Rayleigh backscattered signal (RBS) in the single-mode fiber (SMF) and complex computation, the large strain changes cannot be precisely and rapidly demodulated by the traditional cross-correlation method. In this work, the OFDR with backscattering enhanced optical fiber (BEOF) is proposed and demonstrated for fast and large strain measurement. By enhancing the RBS amplitude, the signal-to-noise ratio (SNR) is improved, resulting in a higher similarity between the reference signal and test signal, which is beneficial for the expansion of the strain measurement range. Moreover, the adaptive local feature extraction and matching (ALFEM) algorithm is presented and demonstrated, which replaces the traditional cross-correlation method for strain demodulation and fast measurement. On account of the enhancement ratio of BEOF, the dominant characteristic data segment can be extracted from whole wavelength data. In the experiments, the enhancing ratio of BEOF is designed as 10, resulting in the spatial resolution reaches 400µm and the strain measurement range is greatly increased to 4800µɛ. Further, the effectiveness of the ALFEM algorithm has been verified, in which the strain demodulation time is approximately 25% of that of the traditional method. This scheme fully exploits the enhancement characteristic of the BEOF and is also applicable to the systems based on other types of BEOF, different strain changes and sensing distances.
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1. Introduction
Owing to the dominant advantages of high sensitivity, large dynamic range, resistance to electromagnetic interference, and strong stability, optical fiber strain sensor (OFSS) has been extensively studied including quasi-distributed OFSS based on Fiber Bragg Gratings (FBGs) [1,2], and distributed OFSS based on Brillouin scattering [3,4] and Rayleigh scattering [5,6]. The distributed OFSS based on Rayleigh scattering involves the phase-sensitive optical time-domain reflectometer (φ-OTDR) and the optical frequency domain reflectometer (OFDR). The strain measurement method using OFDR technology provides high spatial resolution up to the millimeter or even micrometer level, as well as high measurement accuracy and sensitivity. Therefore, compared to the other aforementioned OFSS, the OFDR scheme is better suited for accurate strain measurement in short-distance range, such as biomedical treatment [7,8], soft robot [9], flexible aerospace equipment diagnosis [10], shape sensing [11–13], and so on. For these application fields, the accurate strain measurement with wide dynamic range and high spatial resolution is the dominant requirement. Furthermore, the fast strain demodulation and response also gradually develops some concerns.
For strain measurement based on OFDR technology, the spatial resolution, detection range, measurement sensitivity and accuracy heavily depend on the intensity of the Rayleigh backscattered signal (RBS). Typically, the intensity of RBS signal is weak in standard single-mode fiber (SMF), resulting in a low signal-to-noise ratio (SNR). Under the condition of strain loading, the measurement spectrum is shifted from the reference spectrum. Therefore, the RBS profile will introduce new spectrum in the same distance domain window. As the strain increases, the proportion of similarity between the reference signal and the test signal decreases. The low SNR of the RBS signal will also worsen the assessment of similarity [14]. Consequently, a false correlation peak may occur, leading to erroneous strain demodulation results with the traditional cross-correlation method. In order to overcome the problem of low SNR, several studies have been demonstrated to achieve SNR improvement, such as enhancing the RBS intensity, which contains the fiber with a special design in the fiber core and the fiber fabricated using an FBG inscribing system. Among them, the first type of special fiber contains silica fiber doped with various impurities [15,16] and polymer fiber with a larger cross-section [17]. However, the fabrication process of these kinds of special fibers is complex. The second special fiber is backscattering enhanced optical fiber (BEOF), which consists of discrete engraved ultra-weak FBG (UWFBG) [18,19] and continuous enhancement optical fibers [20–23]. However, these above studies cannot simultaneously achieve both high spatial resolution and large strain detection range.
For the strain demodulation process based on the BEOF-OFDR, the strain demodulation result primarily relies on the large amplitude of a small fraction of the scattering enhanced signal. However, the majority of unenhanced signals with low amplitude have a negligible effect on the demodulation process. Therefore, when using the traditional cross-correlation algorithm on the entire dataset, it will result in a significant redundant computation. To avoid this, it is necessary to reduce the amount of data to be computed or present a novel fast data demodulation method, which can shorten the strain demodulation time. Several methods have been proposed to overcome the limitations of slow demodulation of the traditional cross-correlation method in measuring strain based on OFDR technology [24,25]. For instance, a matching function based on local measurement spectrum and least-square method was used to improve calculation speed [24]. However, the key challenge with this method lies in confirming the position and length of the local spectrum, which cannot adapt to changes in strain. Currently, there are lack of methods in implementing fast strain measurement for the BEOF-OFDR system.
In this paper, we propose and demonstrate a superior distributed strain sensor with both high spatial resolution and large strain measurement capabilities. The strain sensor is based on the OFDR system and a specially designed fiber with continuously longitudinal micro-structure. Firstly, the superior performance of the designed BEOF is introduced, in which the amplitude of the RBS signal is periodically improved by 20 dB. Furthermore, we propose an adaptive local feature extraction and matching (ALFEM) algorithm that leverages the capabilities of the BEOF to accelerate the strain demodulation process. Based on the proposed system, the strain sensing spatial resolution of 400µm and strain measurement range of 4800µɛ are simultaneously realized in a 22.95 m fiber. After extracting 202 feature points from the original 5000 data points, a series of operations significantly reduced the strain calculation time to 25% of the original, in which the high spatial resolution and strain sensing resolution are maintained at the same time. By using the ALFEM method in conjunction with BEOF for the OFDR system, the high spatial resolution, accurate, fast and large strain sensing over short-distance range is realized, which can be applicable for high-precision shape sensing.
2. Operation principle
2.1 Enhancement principle of BEOF
For an OFDR system, supposing that there is a reflection point at the position z of the optical fiber, τz represents the delay between the probe light and the local oscillator. At position z, the reflection coefficient is refined as rz, and the attenuation coefficient as α. Thus, the equivalent reflection coefficient R(z) at position z can be expressed as [26]:
When the strain occurs on the optical fiber, the RBS profile in same sensing position will produce wavelength shift. To extract strain changes applied on optical fibers, the classical cross-correlation technique based on the sliding window is adopted [27]. For the same position window in Fig. 1, the RBS profile can be considered to be composed of two parts: the same RBS segment denoted as ‘brown’ line and different RBS segment between reference and test signals represented as ‘blue’ and ‘orange’ lines. The length of new RBS segment is determined by the strain change. However, when the strain change increases, the sensing position mismatch phenomenon will gradually degrade, which causes in the lower similarity between reference signal and test signal for same position window. Therefore, as the increasement of the strain change, the inaccurate wavelength shift demodulation result is gradually degraded for the SMF. In contrast, the BEOF is fabricated by inscribing the FBG array, the RBS profile of BEOF is indicated in Fig. 1(b). The RBS profile consists of the high amplitude regions induced by FBG array and unenhanced regions. The calculation of similarity mainly relies on the relative shift of high amplitude regions according to the principle of cross-correlation algorithm. Therefore, the wider position window can be replaced by the narrower high amplitude region for the BEOF. Due to the high amplitude of BEOF and high SNR, the similarity can maintain in higher degree for the BEOF, which is beneficial for strain measurement dynamic range.
2.2 Adaptive local feature extraction and matching algorithm
Due to the scattering enhancement characteristic of BEOF, the fiber exhibits a wavelength profile similar to that of FBG in the wavelength domain. To fully exploit this phenomenon, we present an adaptive local feature extraction and matching (ALFEM) method that can precisely extract valuable characteristic data from the total wavelength domain data. Figure 2 illustrates the main process for measuring strain with BEOF and ALFEM algorithm. Specifically, the local feature extraction operations on the reference and measurement wavelength domain of different window positions are separately performed. The widths of these two local feature fragments are recorded as Mref and Mmea, respectively. The positions with highest peak value of reference and measurement information are defined as Pref and Pmea, respectively. As the strain changes, the overall profile of measurement signal produces the wavelength shift compared to the reference signal. Therefore, the local feature data segments from the same window position in the reference and measurement signals are extracted and matched to obtain the wavelength shift (ws1). Meanwhile, the peak shift between the reference data and measurement data in the wavelength domain is computed and represented as ws2. Eventually, the strain distribution along the optical fiber is demodulated and demonstrated.
The specific operation diagram of ALFEM algorithm is illustrated in Fig. 3. Firstly, the amplitude of the wavelength domain signal undergoes a normalization operation, which can be adaptively applied to different wavelength domain signal. The purpose of normalization is to serve as a uniform standard for partitioning distribution statistical histograms, thereby facilitating subsequent feature signal extraction. In the broadest sense, the strength ratio of enhanced and unenhanced regions is defined as “a”. In our research, since the center wavelength of inscribing FBG is 1542.16 nm, the other wavelength amplitudes within the sweeping range are not enhanced. Consequently, according to the enhancing ratio “a”, the entire data points (N) can be divided into “a” part. The data points of each part are counted and then presented in the amplitude distribution statistical histogram. For the distribution statistical histogram, the horizontal axis represents the interval distribution such as [0, 1/a), [1/a, 2/a) …… (1-1/a, 1], while the longitudinal axis expresses the frequency of data distribution in each interval. Specifically, for reference data, the number of data points in each interval can be recorded as Nref_1, Nref_2, ….. Nref_a. And for measurement data, the number of data points in each interval can be recorded as Nmea_1, N mea_2, ….. Nmea_a. The cross-correlation result between ref and mea can be expressed as follows:
where R(j) is the cross-correlation result, j = -N, -N + 1, …, N-1, and N is the length of reference and measurement signal local window. And the ref and mea are the local wavelength domain window information under the conditions of reference and measurement, respectively. According to Eq. (3), the information with high intensity is the important source that affects the cross-correlation results. After the amplitude normalization operation, the data in the [0, 1/a) interval is definitely not enhanced and has low amplitude. Therefore, the values of RBS signal below 1/a have little impact on signal demodulation results and are considered redundant data. In contrast, these high intensity values ranging from 1/a to 1 represent the key characteristics of the BEOF, which are valuable feature points denoted as “Fref” and “Fmea”. The amounts of the feature points, Mref and Mmea are decided by the value of the enhancement ratio “a”. They are computed and expressed as Eq. (4).
Fig. 3. The specific operation process diagram of adaptive local feature extraction and matching algorithm.
Subsequently, the lengths of the Mref and Mmea are compared. If Mref < Mmea, the two groups of feature data segments with a width of Mref are extracted symmetrically, centered on the highest peak value point (Pref and Pmea). On the other hand, if Mref > Mmea, the width of data segments is Mmea. These main feature points are extracted from the original data and referred to as matching data, which is used to compute the wavelength shifts. The process of matching the reference signal and measurement signal involves two computations. The first computation result is obtained from the local feature data. The new local feature data segments from both the reference signal and measurement signal are performed the operation of cross-correlation. The number of offset points corresponding to the peak of cross-correlation is d1, that is, the wavelength shift is ws1. The wavelength shift between local feature data is expressed using Eq. (5).
Then, the second computation result is decided by the matching shift between peak values. The peak shift between the reference data and measurement data in the wavelength domain is computed and represented as d2, that is, the wavelength shift is ws2. The wavelength shift between peaks is expressed as Eq. (6).
In the Eq. (5) and Eq. (6), R(Mref, Mmea) is the meaning of cross-correlation operation and D(Pref, Pmea) denotes difference operation. Δλ is the wavelength resolution. To sum up, the actual shift results consist of two components: the wavelength shift determined by the local feature matching algorithm, and the shift between the peaks of the reference signal and test signal, which can be mathematically represented as ws = ws1 + ws2.
Before the ALFEM calculation, the time complexity is O(N2) when the traditional cross-correlation is performed on reference and measurement data. In contrast, the time complexity of cross-correlation is deduced to O(M2mea) or O(M2ref) after the ALFEM algorithm. Due to the enhancement intensity of BEOF, the number of local feature data is evidently less than that of original wavelength data. As a result, the computation time of strain demodulation will be shortened through the ALFEM algorithm. Additionally, the ALFEM is adaptable to changes in the enhancing ratio (a) of BEOF and the fiber positions.
3. Fabrication and experimental setup
3.1 Fabrication process and characteristic of BEOF
To enhance the intensity of the RBS signal, we have designed and fabricated a type of BEOF by inscribing the UWFBG arrays into the UV-transparent coating fiber using a high-precision UWFBG array automatic fabrication system. During the process of manufacturing the BEOF, a phase mask is utilized to generate a periodic interference pattern and engrave the grating structure through the UV-transparent coating into the fiber core. Specifically, the BEOF is fabricated with the power of 3.6mj, repetition of 100 Hz, the spot size of 9mm*9 mm, and the tagged fiber speed of 60 m/min. The process diagram for fabricating the BEOF is shown in Fig. 4(a). Firstly, the UV-transparent coating fiber is inserted into the laying fiber system. Next, the UV laser irradiates and inscribes the FBG on the UV-transparent coating fiber through the optical fiber transport and control system. Finally, the optical fiber is sent to the collecting fiber system and outputted as BEOF. During the fabrication process of BEOF, the online monitoring system is utilized to observe the specific characteristics of BEOF.
Fig. 4. (a) The process diagram of fabricating the BEOF; (b) detailed characteristic of the BEOF; (c) spectrum of the BEOF from 1540 nm to 1550 nm.
The detailed features of the BEOF are presented in Fig. 4(b), where we have chosen a section from 12.14 m to 12.19 m to demonstrate. In order to avoid the problems of FBG array overlap and misalignment in splicing, the continuously enhanced FBG array is replaced by the periodic FBG array. Due to the limitations in splicing process stability and manufacturing technology, the BEOF is actually designed with an enhanced region of 9 mm and interval of 1 mm. The RBS signal intensity of the BEOF is notably enhanced by 20 dB in comparison to the SMF. It is derived that the reflectivity of the FBG inscribed in the BEOF is about -45 dB, classifying it as UWFBG. The crosstalk noise induced by multi-mirror reflection can be effectively weakened by reducing the FBG reflectivity [28,29]. When the reflectivity is -45 dB and the sensing distance is 17.95 m, the power of the crosstalk noise induced by the multi-mirror reflection is far lower than that of the sensing signal. Therefore, the issue of multi-mirror reflection can be deemed negligible. The spectrum of RBS ranging from 1540 nm to 1550 nm is displayed in Fig. 4(c). By measuring the reflection spectroscopy, we can observe the center wavelength of FBG is about 1542.16 nm which is consistent with the design value of 1542 nm.
3.2 OFDR system setup based on BEOF
The configuration of the OFDR system based on BEOF is depicted in Fig. 5. The laser generated from the tunable laser (TL, TSL-710, Santec) continuously sweeps from 1540 nm to 1550 nm at a speed of 20 nm/s. Then the TL output is split into two paths by coupler1, with one path directed to the auxiliary interferometer (AI) and the other to the main interferometer (MI). On the one hand, the instantaneous frequency of TL is recorded through a Michelson interferometer with Faraday rotating mirror (FRM). The length of the delay fiber in the Michelson interferometer arm is 20 m.
On the other hand, 90% of light in MI is split into two portions as local reference light and sensing light by a 10:90 coupler3. Due to the polarization state of sweeping light is arbitrary, the polarization diversity detection method is employed. Specifically, the reference light goes through the polarization controller (PC) and is separated into “p” light and “s” light using a polarization beam splitter (PBS1). Similarly, the RBS returned from the BEOF through the circulator is also split into “p” light and “s” light using PBS2. By constantly adjusting the PC, the two groups of “p” light and “s” light components from the reference path and sensing path will respectively interfere. Then, the two groups of beat signals are collected by BPD2 and BPD3 (BPD, PDB470C, Thorlabs). Eventually, when the pulse trigger signal from TL is captured by the four-channel data acquisition card (DAQ, PCIE8586, ART Technology) and demonstrated as a high level, the signal from BPD1 and the two signals from BPD2 and BPD3 are all acquired at the same time. Due to the nonlinear sweeping phenomenon of the TL, the cubic spline interpolation algorithm is utilized to compensate for it. The signal from AI is used to resample the main beat signals at an equal interval of frequency.
4. Experimental results and discussion
4.1 Cross-correlation comparison results between BEOF and SMF
To illustrate the differences in RBS signals and cross-correlation results between SMF and BEOF, we present a typical example to display. Figures 6(a) and (b) exhibit the spectrum when the strain is 0µɛ and 1800µɛ for SMF and BEOF, respectively. In the SMF, the RBS signal power is randomly distributed within the sweeping range from 1540 nm to 1550 nm. Furthermore, the vast majority of the RBS signal intensity is below 30000. In contrast, for the BEOF, most of the power is concentrated within a narrow width of about 2 nm. And a substantial portion of the RBS signal intensity exceeds 50000. It can be observed from Fig. 6(c) that the cross-correlation peak of BEOF reaches to 0.95. In contrast, the cross-correlation similarity of the SMF is lower than 0.15, which presents false cross-correlation peak and then causes in the inaccurate strain demodulation. Therefore, the cross-correlation spectrum of BEOF is more distinct, which allows for easier obtainment of the excellent correlation peak. The high similarity of cross-correlation of the BEOF is beneficial for the accurate demodulation process of larger strains.
Fig. 6. (a),(b) The spectrums when the strain is 0µɛ and 1800µɛ for SMF and BEOF; (c) cross-correlation results with the strain of 1800µɛ for SMF and BEOF.
4.2 Strain measurement comparison results between BEOF and SMF
To verify the superior performance of BEOF over SMF for short-distance strain monitoring, the experimental investigations were conducted as shown in Fig. 7. In this OFDR system, the sweeping range is set to 10 nm. The positioning distance between two scattering points is 80µm. The local distance domain window width is 1000, resulting in a corresponding gauge length is 80 mm. To obtain more sensing sampling points and minimize spectral leakage, the sliding window step size is set to 5, leading to a strain sensing spatial resolution of 400µm. The total window width (N) was broadened to 5000 by padding zero. Therefore, the wavelength resolution is 2pm and the strain resolution is about 1.7µɛ according to the analysis in the principle part, which is adequate for most practical application fields.
Fig. 7. (a) The RBS profile; (b) specific details about RBS profile; (c) strain measurement results occurring on the SMF; (d) strain measurement results of the BEOF.
The RBS profile is shown in Fig. 7(a), in which the total length is 22.95 m, comprising 5 m SMF and 17.95 m BEOF. For the SMF, there exists an angled physical contact (APC) connector. From the perspective of the specific RBS amplitude comparison between the SMF and BEOF, the RBS signal of BEOF is enhanced more than 20 dB from Fig. 7(b). And the BEOF is designed as 9 mm FBG array and 1 mm interval. Figures 7(c)(d) respectively depict the strain measurement changes in the SMF and BEOF. The experimental parameters and sliding window width are same for the SMF and the BEOF. In Fig. 7(c), the strain change of SMF with the spatial resolution of 400µm is shown. The strain varies from 0µɛ to 2000µɛ with increments of 400µɛ, covering a strain region of 0.5 m from 7.7 m to 8.2 m. When the strain range is from 0µɛ to 1200µɛ, the strain is accurately demodulated and depicted. However, when the strain reaches 1600µɛ and 2000µɛ, there is an error in the calculation of wavelength offset. The strain demodulation results of 1600µɛ and 2000µɛ are fully illustrated in subgraph. Owing to the low similarity between reference signal and test signal, the cross-correlation peak will be false peak, leading to the strain jumps. In contrast, the strain change of BEOF with the spatial resolution of 400µm from 21.9 m to 22.4 m is demonstrated in Fig. 7(d). The strain change is still accurately recorded from 0µɛ to 4800µɛ at the interval of 800µɛ. Moreover, the measurement values remain stable and consistent for different strain changes. For the end point of the 0.5 m fiber gauge, the location area in the corresponding local distance domain window consists of a portion of the strain region and a portion of the unstrained area. Therefore, it results in the lower similarity degree between the measurement spectrum and the reference spectrum and then the strain jump phenomenon. Compared to SMF, the BEOF exhibits a significantly wider strain measurement range and fivefold improvement in spatial resolution.
4.3 Demonstration results of ALFEM algorithm
To demonstrate the ALFEM algorithm, the normalized amplitudes of the original reference signal (0µɛ) and the measurement signal (4800µɛ) in the wavelength domain are displayed in Fig. 8(a). According to the principle and demodulation process in section II.C, the amplitudes of both wavelength domain signals are normalized to [0,1]. From Fig. 8(a), the center wavelength of the reference signal is 1542.16 nm, which is consistent with the previously mentioned value. While, the center wavelength of the measurement signal is 1547.85 nm corresponding to a strain of 4800µɛ. By observing these signals, a large number of amplitudes are less than 0.1 which is redundant. Therefore, the local feature fragments (Mref and Mmea) of the reference and measurement signals with amplitudes larger than 0.1 need to be obtained. The lengths of the local feature fragments are calculated by using the amplitude distribution histogram.
Fig. 8. (a) The normalized amplitude of the original reference signal and measurement signal in the wavelength domain; (b) amplitude distribution histogram of the reference signal.
According to the enhancing ratio of BEOF, a = 10, the whole data is separated into 10 parts. The amplitude distribution histogram of the reference signal is shown in Fig. 8(b). The horizontal axis represents the segmentation range of data. And the longitudinal axis represents the frequency expressed by the number of data points within each segment. The largest number of data points falls within the range of [0,0.1), and local feature data primarily focus on the interval of [0.1,1]. By calculating the number of data points in this range, the length of Mref is determined as 202. The length of Mmea can be calculated in the same way, and its value is 226. Therefore, the Mref is adopted as the extraction length for the feature data extraction and calculation of the reference and measurement signals.
After determining the length of the local feature data, the feature data can subsequently be extracted. Referring to the result of peak searching in Fig. 9(a), the peak position (Pref) of the reference wavelength domain signal is determined to be 1083. Based on the length of Mref, the local feature data segment is then symmetrically extracted around Pref. Thus, the range of local feature data is belonged to [983,1184]. Similarly, for the measurement signal (4800µɛ), the peak position Pmea is 3923 and the range of local feature data is [3823,4024], which are exhibited in Fig. 9(a). By the cross-correlation operation, the shift point corresponding to the position with the highest similarity is found to be 0, with the peak value of 0.98, as shown in Fig. 9(b).
Fig. 9. (a),(b) The extracted local feature data of the original reference signal and measurement signal; (c) cross-correlation result between local reference and test signal.
According to Eq. (5,6) the total wavelength shift is composed of two components: the cross-correlation offset and the difference in peak locations. As an illustrative example, at the strain of 4800µɛ, the wavelength shift is 5676pm with the ALFEM algorithm, over a 0.5 m region. In contrast with the traditional method (TM), the comparison results are shown in Fig. 10(a), in which the cross-correlation similarity degree is calculated as 0.996. Therefore, it can be proved that the strain change could be precisely obtained by the ALFEM algorithm, which is equivalent to the strain obtained by the TM.
Fig. 10. For the ALFEM and TM (a) the wavelength shifts comparison (b) the computation time comparison with different fiber lengths.
To demonstrate the effectiveness of the ALFEM algorithm in accelerating data demodulation, the computation time comparison with the ALFEM and TM by using MATLAB is shown in Fig. 10(b). As the length of the BEOF increases, the data calculation amount also increases exponentially. Consequently, the data demodulation time-consuming is gradually scaled up when the length of the BEOF is increased from 1 m to 16 m. This phenomenon is observed in both the ALFEM algorithm and the TM. Under the same optical fiber length conditions, the ALFEM algorithm outperforms the TM with the computation time is approximately 3.1 to 3.9 times that of the ALFEM. As the distance of the BEOF increases, the acceleration effect becomes more and more excellent. Therefore, this approach is also suitable for long-distance strain measurement. Based on the ALFEM algorithm, the wavelength shifts and strain changes along the BEOF can be rapidly demodulated.
5. Conclusion
In summary, the characteristics of backscattering enhanced optical fiber are fully exploited and utilized in the OFDR system. This approach not only meets the requirement of large dynamic strain measurement range by improving the SNR, but also reduces the amount of data by extracting significant features. Based on the BEOF and OFDR system, the adaptive local feature extraction and matching operation on the original data is proposed, resulting in efficient and accurate demodulation process. In the experiments, the BEOF is demonstrated as the strain measurement range can reach 4800µɛ with a high spatial resolution of 400µm and a high strain resolution of 1.7µɛ, which are superior than those of SMF. Thereafter, the proposed ALFEM algorithm significantly reduces computation time without compromising measurement accuracy compared to the traditional strain demodulation method. The combination of BEOF and ALFM algorithm provides an effective tool for high-resolution and real-time strain measurement, which has significant influence for the applications such as 3D shape sensing.
Funding
National Natural Science Foundation of China (61922033, 62305124, U22A20206); China Postdoctoral Science Foundation (2023M731188); National Funded Postdoctoral Researcher Program (GZB20230237).
Disclosures
The authors declare that there are no conflicts of interest related to this article.
Data availability
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.
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