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Advances in Radar Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Radar Sensors".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 38394

Special Issue Editors


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Guest Editor
Institute of Radio Frequency Engineering and Electronics (IHE), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Interests: millimeter-wave radar and communication systems; millimeter-wave antennas; metamaterial antennas; system-in-package and system-on-chip for millimeter-wave integrated-circuits; frequency-modulated continuous-wave and phase-modulated continuous-wave radar sensors; signal processing for high-accuracy radar sensors

E-Mail Website
Guest Editor
Institute of Radio Frequency Engineering and Electronics (IHE), Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
Interests: High-power microwaves; mixed fields; mmW Packaging

Special Issue Information

Dear Colleagues,

Radar sensors offer unique characteristics that are ideal for measuring the distance, speed and trajectory of a wide range of targets in real-life working conditions. Radars use microwave signals that can handle adverse conditions, such as dust, oil, low visibility, humidity, high temperature and pressure, and still deliver reliable measurements with an adequate accuracy. In addition, they do not require complex calibration and fall under the category of medium-priced sensors. Due to these multi-faceted advantages, radars are being used in a wide range of industrial and consumer, as well as medical, applications. Their inherent advantages, combined with their rapidly increasing demand, has provided an impetus to develop innovative solutions for enhancing radar capabilities. This Special Issue is aimed at publishing the recent advancements in the field of radar sensors on topics including, but not limited to, the following:

  • Radar systems, subsystems and components: front-end, antenna, array-related circuits and components.
  • Radar modulation schemes: frequency-modulated continuous wave (FMCW), phase-modulated continuous wave (PMCW), orthogonal frequency division multiplexing (OFDM), chirp sequence.
  • Radar architecture: multiple input multiple output (MIMO), passive, multistatic, cognitive, millimeter-wave, sub-millimeter-wave, Terahertz radar systems.
  • Radar signal processing: imaging techniques, localization and tracking, cognitive sensing, waveform diversity, compressed sensing.
  • Packaging solutions for miniaturized radars: system-on-chip, system-in-package.
  • Radar applications: industrial, automotive, remote sensing, medical.

Dr. Akanksha Bhutani
Dr. Mario Pauli
Guest Editors

Manuscript Submission Information

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Published Papers (12 papers)

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Research

15 pages, 3212 KiB  
Article
PN Codes Estimation of Binary Phase Shift Keying Signal Based on Sparse Recovery for Radar Jammer
by Bo Peng and Qile Chen
Sensors 2023, 23(1), 554; https://doi.org/10.3390/s23010554 - 3 Jan 2023
Cited by 2 | Viewed by 2213
Abstract
Parameter estimation is extremely important for a radar jammer. With binary phase shift keying (BPSK) signals widely applied in radar systems, estimating the parameters of BPSK signals has attracted increasing attention. However, the BPSK signal is difficult to be processed by traditional time [...] Read more.
Parameter estimation is extremely important for a radar jammer. With binary phase shift keying (BPSK) signals widely applied in radar systems, estimating the parameters of BPSK signals has attracted increasing attention. However, the BPSK signal is difficult to be processed by traditional time frequency analysis methods due to its phase jumping and abrupt discontinuity features which makes it difficult to extract PN (PN) codes of the BPSK signal. To solve this problem, a two-step PN codes estimation method based on sparse recovery is introduced in this paper. The proposed method first pretreats the BPSK signal by estimating its center frequency and converting it to zero intermediate frequency (ZIF). The pretreatment transforms phase jumps of the BPSK signal into the level jumps of the ZIF signal. By nonconvex sparsity promoting regularization, the level jumps of the ZIF signal are extracted through an iterative algorithm. Its effectiveness is verified by numeric simulations and semiphysical tests. The corresponding results demonstrate that the proposed method is able to estimate PN codes from the BPSK signal in serious electromagnetic environments. Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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11 pages, 2538 KiB  
Communication
Machine Learning Models for Enhanced Estimation of Soil Moisture Using Wideband Radar Sensor
by Akileshwaran Uthayakumar, Manoj Prabhakar Mohan, Eng Huat Khoo, Joe Jimeno, Mohammed Yakoob Siyal and Muhammad Faeyz Karim
Sensors 2022, 22(15), 5810; https://doi.org/10.3390/s22155810 - 3 Aug 2022
Cited by 11 | Viewed by 3659
Abstract
In this paper, machine learning models for an effective estimation of soil moisture content using a microwave short-range and wideband radar sensor are proposed. The soil moisture is measured as the volumetric water content using a short-range off-the-shelf radar sensor operating at 3–10 [...] Read more.
In this paper, machine learning models for an effective estimation of soil moisture content using a microwave short-range and wideband radar sensor are proposed. The soil moisture is measured as the volumetric water content using a short-range off-the-shelf radar sensor operating at 3–10 GHz. The radar captures the reflected signals that are post processed to determine the soil moisture which is mapped to the input features extracted from the reflected signals for the training of the machine learning models. In addition, the results are compared and analyzed with a contact-based Vernier soil sensor. Different machine learning models trained using neural network, support vector machine, linear regression and k-nearest neighbor are evaluated and presented in this work. The efficiency of the model is computed using root mean square error, co-efficient of determination and mean absolute error. The RMSE and MAE values of KNN, SVM and Linear Regression are 11.51 and 9.27, 15.20 and 12.74, 3.94 and 3.54, respectively. It is observed that the neural network gives the best results with an R2 value of 0.9894. This research work has been carried out with an intention to develop cost-effective solutions for common users such as agriculturists to monitor the soil moisture conditions with improved accuracy. Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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19 pages, 8128 KiB  
Article
An Adjusted Frequency-Domain Algorithm for Arc Array Bistatic SAR Data with One-Moving Transmitter
by Pingping Huang, Lingxia Hao, Weixian Tan, Wei Xu and Yaolong Qi
Sensors 2022, 22(13), 4725; https://doi.org/10.3390/s22134725 - 23 Jun 2022
Cited by 6 | Viewed by 1513
Abstract
Arc array synthetic aperture radar (AA-SAR), which can observe the scene in all directions, breaks through the single view of traditional SAR. However, the concealment of AA-SAR is poor. To mitigate this, arc array bistatic SAR (AA-BiSAR) with the moving transmitter is proposed, [...] Read more.
Arc array synthetic aperture radar (AA-SAR), which can observe the scene in all directions, breaks through the single view of traditional SAR. However, the concealment of AA-SAR is poor. To mitigate this, arc array bistatic SAR (AA-BiSAR) with the moving transmitter is proposed, it has the advantages of good concealment and can expand the imaging scene, and improve the flexibility of the system. The imaging geometry including the signal model is established, and a range frequency-domain algorithm based on keystone transform (KT) is proposed in this paper. In the first step, the slant range equation is approximated by Taylor series expansion to compensate for the residual phase caused by the transmitter motion. In the second step, the range cell migration between the range and azimuth is eliminated through the KT method in the range frequency-domain. In the third step, using the data after range cell migration correction in step 2, an azimuth pulse compression is performed to obtain the focused image. In addition, the spatial resolution of the AA-BiSAR system is analyzed in detail. Finally, three simulation results verify the effectiveness of the proposed algorithm and the change in the spatial resolution. Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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18 pages, 3308 KiB  
Article
A Novel 65 nm Active-Inductor-Based VCO with Improved Q-Factor for 24 GHz Automotive Radar Applications
by Prangyadarsini Behera, Abrar Siddique, Tahesin Samira Delwar, Manas Ranjan Biswal, Yeji Choi and Jee-Youl Ryu
Sensors 2022, 22(13), 4701; https://doi.org/10.3390/s22134701 - 22 Jun 2022
Cited by 8 | Viewed by 2415
Abstract
The inductor was primarily developed on a low-voltage CMOS tunable active inductor (CTAI) for radar applications. Technically, the factors to be considered for VCO design are power consumption, low silicon area, high frequency with reasonable phase noise, an immense quality (Q) factor, and [...] Read more.
The inductor was primarily developed on a low-voltage CMOS tunable active inductor (CTAI) for radar applications. Technically, the factors to be considered for VCO design are power consumption, low silicon area, high frequency with reasonable phase noise, an immense quality (Q) factor, and a large frequency tuning range (FTR). We used CMOS tunable active inductor (TAI) topology relying on cascode methodology for 24 GHz frequency operation. The newly configured TAI adopts the additive capacitor (Cad) with the cascode approach, and in the subthreshold region, one of the transistors functions as the TAI. The study, simulations, and measurements were performed using 65nm CMOS technology. The assembled circuit yields a spectrum from 21.79 to 29.92 GHz output frequency that enables sustainable platforms for K-band and Ka-band operations. The proposed design of TAI demonstrates a maximum Q-factor of 6825, and desirable phase noise variations of −112.43 and −133.27 dBc/Hz at 1 and 10 MHz offset frequencies for the VCO, respectively. Further, it includes enhanced power consumption that varies from 12.61 to 23.12 mW and a noise figure (NF) of 3.28 dB for a 24 GHz radar application under a low supply voltage of 0.9 V. Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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40 pages, 12690 KiB  
Article
High-Resolution Doppler and Azimuth Estimation and Target Detection in HFSWR: Experimental Study
by Dragan Golubović, Miljko Erić and Nenad Vukmirović
Sensors 2022, 22(9), 3558; https://doi.org/10.3390/s22093558 - 7 May 2022
Cited by 5 | Viewed by 3691
Abstract
In this paper, we present a new high-resolution algorithm for primary signal processing in High Frequency Surface Wave Radar (HFSWR). The algorithm has been developed to achieve and improve primary signal processing performance in existing HFSWR radars in terms of radar target detection. [...] Read more.
In this paper, we present a new high-resolution algorithm for primary signal processing in High Frequency Surface Wave Radar (HFSWR). The algorithm has been developed to achieve and improve primary signal processing performance in existing HFSWR radars in terms of radar target detection. The proposed algorithm is based on a high-resolution estimate of the Range–Doppler (RD-HR) map using given number of frames in the selected integration period. RD-HR maps are formed at every antenna in receive antenna array. Target detection is based on an RD-HR map averaged across all the antennas. Azimuth estimation is performed by a high-resolution MUSIC-type algorithm that is executed for all detections we found in the RD-HR map. The existence of strong Bragg’s lines in the RD-HR map complicates the detection process but the contrast of the RD-HR map as well as the detectability of targets on the RD-HR map is significantly better compared to the RD-FFT map used by many existing radars, such as WERA. Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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19 pages, 3877 KiB  
Article
Development of the Romanian Radar Sensor for Space Surveillance and Tracking Activities
by Liviu Ionescu, Alexandru Rusu-Casandra, Calin Bira, Alexandru Tatomirescu, Ionut Tramandan, Roberto Scagnoli, Dan Istriteanu and Andrei-Edward Popa
Sensors 2022, 22(9), 3546; https://doi.org/10.3390/s22093546 - 6 May 2022
Cited by 10 | Viewed by 2738
Abstract
The constant increase in the number of space objects and debris orbiting the Earth poses risks to satellites and other spacecraft, both in orbit and during the launching process. Therefore, the monitoring of space hazards to assess risk and prevent collisions has become [...] Read more.
The constant increase in the number of space objects and debris orbiting the Earth poses risks to satellites and other spacecraft, both in orbit and during the launching process. Therefore, the monitoring of space hazards to assess risk and prevent collisions has become part of the European Space Policy and requires the establishment of a dedicated Framework for Space Surveillance and Tracking (EU SST) Support. This article presents the CHEIA SST Radar, a new space tracking radar sensor developed and installed in Romania with the purpose of being included in the EU SST sensor network and of contributing to the joint database of space objects orbiting the Earth. The paper describes the processes of design, simulation, and implementation of the hardware and software building blocks that make up the radar system. It emphasizes the particular case of using an already existing system of two large parabolic antennas requiring an innovative retrofitting design to include them as the basis for a new quasi-monostatic radar using LFMCW probing signals. The preliminary design was validated by extensive simulations, and the initial operational testing carried out in December 2021 demonstrated the good performance of the radar in the measuring range and radial speed of LEO space objects. Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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21 pages, 7887 KiB  
Article
Enhancing the Radar Cross-Range Resolution in Ultra-Fast Radar Scans by Utilizing Frequency Coded Sub-Channels
by Christoph Baer, Nicholas Karsch, Robin Kaesbach and Thomas Musch
Sensors 2022, 22(9), 3343; https://doi.org/10.3390/s22093343 - 27 Apr 2022
Cited by 4 | Viewed by 1979
Abstract
This contribution handles a single-channel radar method that utilizes frequency-coded sub-channels for enabling cross-range resolution. Because of the sub-channel coding, the whole area of interest (AOI) is scanned within a single radar measurement. To further enhance the cross-range resolution, the sub-channels’ antenna beams [...] Read more.
This contribution handles a single-channel radar method that utilizes frequency-coded sub-channels for enabling cross-range resolution. Because of the sub-channel coding, the whole area of interest (AOI) is scanned within a single radar measurement. To further enhance the cross-range resolution, the sub-channels’ antenna beams are overlaid in this work, resulting in multiple coding signatures. Next to the operation theory, hardware components, such as coding filters and antennas, as well as signal processing methods, are presented and discussed in detail. A final measurement campaign that investigates several radar scenarios reveals high detection properties and proves the applicability of the proposed radar method. Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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21 pages, 3626 KiB  
Article
Doppler Shift Tolerance of Typical Pseudorandom Binary Sequences in PMCW Radar
by Lucas Giroto de Oliveira, Theresa Antes, Benjamin Nuss, Elizabeth Bekker, Akanksha Bhutani, Axel Diewald, Mohamad Basim Alabd, Yueheng Li, Mario Pauli and Thomas Zwick
Sensors 2022, 22(9), 3212; https://doi.org/10.3390/s22093212 - 22 Apr 2022
Cited by 12 | Viewed by 2220
Abstract
In the context of all-digital radar systems, phase-modulated continuous wave (PMCW) based on pseudorandom binary sequences (PRBSs) appears to be a prominent candidate modulation scheme for applications such as autonomous driving. Among the reasons for its candidacy are its simplified transmitter architecture and [...] Read more.
In the context of all-digital radar systems, phase-modulated continuous wave (PMCW) based on pseudorandom binary sequences (PRBSs) appears to be a prominent candidate modulation scheme for applications such as autonomous driving. Among the reasons for its candidacy are its simplified transmitter architecture and lower linearity requirements (e.g., compared to orthogonal-frequency division multiplexing radars), as well as its high velocity unambiguity and multiple-input multiple-output operation capability, all of which are characteristic of digital radars. For appropriate operation of a PMCW radar, choosing a PRBS whose periodic autocorrelation function (PACF) has low sidelobes and high robustness to Doppler shifts is paramount. In this sense, this article performs an analysis of Doppler shift tolerance of the PACFs of typically adopted PRBSs in PMCW radar systems supported by simulation and measurement results. To accurately measure the Doppler-shift-induced degradation of PACFs, peak power loss ratio (PPLR), peak sidelobe level ratio (PSLR), and integrated-sidelobe level ratio (ISLR) were used as metrics. Furthermore, to account for effects on targets whose ranges are not multiples of the range resolution, oversampled PACFs are analyzed. Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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20 pages, 4019 KiB  
Article
mm-Wave Radar-Based Vital Signs Monitoring and Arrhythmia Detection Using Machine Learning
by Srikrishna Iyer, Leo Zhao, Manoj Prabhakar Mohan, Joe Jimeno, Mohammed Yakoob Siyal, Arokiaswami Alphones and Muhammad Faeyz Karim
Sensors 2022, 22(9), 3106; https://doi.org/10.3390/s22093106 - 19 Apr 2022
Cited by 25 | Viewed by 8371
Abstract
A non-contact, non-invasive monitoring system to measure and estimate the heart and breathing rate of humans using a frequency-modulated continuous wave (FMCW) mm-wave radar at 77 GHz is presented. A novel diagnostic system is proposed which extracts heartbeat phase signals from the FMCW [...] Read more.
A non-contact, non-invasive monitoring system to measure and estimate the heart and breathing rate of humans using a frequency-modulated continuous wave (FMCW) mm-wave radar at 77 GHz is presented. A novel diagnostic system is proposed which extracts heartbeat phase signals from the FMCW radar (reconstructed using Fourier series analysis) to test a three-layer artificial neural network model to predict the presence of arrhythmia in individuals. The effect of person orientation, distance of measurement and movement was analyzed with respect to a reference device based on statistical measures that include number of outliers, mean, mean squared error (MSE), mean absolute error (MAE), median absolute error (medAE), skewness, standard deviation (SD) and R-squared values. The individual oriented in front of the radar outperformed almost all other orientations for most distances with an expected d = 90 cm and d = 120 cm. Furthermore, it was found that the heart rate that was measured while walking and the breathing rate which was measured for a motionless individual generated results with the lowest SD and MSE. An artificial neural network (ANN) was trained using the MIT-BIH database with a training accuracy of 93.9 % and an R2 value = 0.876. The diagnostic tool was tested on 15 subjects and achieved a mean test accuracy of 75%. Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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12 pages, 1680 KiB  
Article
Improving Radar Human Activity Classification Using Synthetic Data with Image Transformation
by Rodrigo Hernangómez, Tristan Visentin, Lorenzo Servadei, Hamid Khodabakhshandeh and Sławomir Stańczak
Sensors 2022, 22(4), 1519; https://doi.org/10.3390/s22041519 - 16 Feb 2022
Cited by 6 | Viewed by 3133
Abstract
Machine Learning (ML) methods have become state of the art in radar signal processing, particularly for classification tasks (e.g., of different human activities). Radar classification can be tedious to implement, though, due to the limited size and diversity of the source dataset, i.e., [...] Read more.
Machine Learning (ML) methods have become state of the art in radar signal processing, particularly for classification tasks (e.g., of different human activities). Radar classification can be tedious to implement, though, due to the limited size and diversity of the source dataset, i.e., the data measured once for initial training of the Machine Learning algorithms. In this work, we introduce the algorithm Radar Activity Classification with Perceptual Image Transformation (RACPIT), which increases the accuracy of human activity classification while lowering the dependency on limited source data. In doing so, we focus on the augmentation of the dataset by synthetic data. We use a human radar reflection model based on the captured motion of the test subjects performing activities in the source dataset, which we recorded with a video camera. As the synthetic data generated by this model still deviates too much from the original radar data, we implement an image transformation network to bring real data close to their synthetic counterpart. We leverage these artificially generated data to train a Convolutional Neural Network for activity classification. We found that by using our approach, the classification accuracy could be increased by up to 20%, without the need of collecting more real data. Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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26 pages, 3521 KiB  
Article
Robust Beamforming Based on Covariance Matrix Reconstruction in FDA-MIMO Radar to Suppress Deceptive Jamming
by Fuhai Wan, Jingwei Xu and Zhenrong Zhang
Sensors 2022, 22(4), 1479; https://doi.org/10.3390/s22041479 - 14 Feb 2022
Cited by 10 | Viewed by 2050
Abstract
Frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radars can generate a range-angle two-dimensional transmit steering vector (SV), which is capable of suppressing mainbeam deceptive jamming in the transmit–receive frequency domain by utilizing additional degrees of freedom (DOFs) in the range dimension. However, when there [...] Read more.
Frequency diverse array (FDA)-multiple-input multiple-output (MIMO) radars can generate a range-angle two-dimensional transmit steering vector (SV), which is capable of suppressing mainbeam deceptive jamming in the transmit–receive frequency domain by utilizing additional degrees of freedom (DOFs) in the range dimension. However, when there are target SV mismatch, covariance matrix estimation error and target contamination, the jamming suppression performance degrades severely. In this paper, a robust adaptive beamforming algorithm for anti-jammer application based on covariance matrix reconstruction is proposed in FDA-MIMO radar. In this method, the residual noise is further determined by using the spatial power spectrum estimation approach, which results in improved estimation accuracy of the signal covariance matrix and the desired target SV. The jamming SV is obtained from vectors in the intersection of two subspaces (namely, the signal-jamming subspace derived from the sample covariance matrix (SCM) and the jamming subspace generated from the jamming covariance matrix) by an alternating projection algorithm. Furthermore, the jamming power is obtained by exploiting the orthogonality between the different SVs. With the obtained parameters of target and jamming, the optimal adaptive beamformer weight vector is calculated. Simulation results demonstrate that the proposed algorithm can cope with the mainbeam deceptive jamming suppression under various model mismatches and has excellent performance over a wide range of signal-to-noise ratios (SNRs). Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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17 pages, 5321 KiB  
Article
A 65 nm Duplex Transconductance Path Up-Conversion Mixer for 24 GHz Automotive Short-Range Radar Sensor Applications
by Tahesin Samira Delwar, Abrar Siddique, Manas Ranjan Biswal, Prangyadarsini Behera, Yeji Choi and Jee-Youl Ryu
Sensors 2022, 22(2), 594; https://doi.org/10.3390/s22020594 - 13 Jan 2022
Cited by 3 | Viewed by 1997
Abstract
A 24 GHz highly-linear upconversion mixer, based on a duplex transconductance path (DTP), is proposed for automotive short-range radar sensor applications using the 65-nm CMOS process. A mixer with an enhanced transconductance stage consisting of a DTP is presented to improve linearity. The [...] Read more.
A 24 GHz highly-linear upconversion mixer, based on a duplex transconductance path (DTP), is proposed for automotive short-range radar sensor applications using the 65-nm CMOS process. A mixer with an enhanced transconductance stage consisting of a DTP is presented to improve linearity. The main transconductance path (MTP) of the DTP includes a common source (CS) amplifier, while the secondary transconductance path (STP) of the DTP is implemented as an improved cross-quad transconductor (ICQT). Two inductors with a bypass capacitor are connected at the common nodes of the transconductance stage and switching stage of the mixer, which acts as a resonator and helps to improve the gain and isolation of the designed mixer. According to the measured results, at 24 GHz the proposed mixer shows that the linearity of output 1-dB compression point (OP1dB) is 3.9 dBm. And the input 1-dB compression point (IP1dB) is 0.9 dBm. Moreover, a maximum conversion gain (CG) of 2.49 dB and a noise figure (NF) of 3.9 dB is achieved in the designed mixer. When the supply voltage is 1.2 V, the power dissipation of the mixer is 3.24 mW. The mixer chip occupies an area of 0.42 mm2. Full article
(This article belongs to the Special Issue Advances in Radar Sensors)
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