Single-frequency Data Research Articles

Towards Time-Resolved Multi-Property Measurements By Single-Frequency FRS

The filtered Rayleigh scattering technique (FRS), extended by the method of frequency scanning, has historically been limited to time-averaged multi-property flow measurements. In our recently published work, we present a concept that potentially enables the combined measurement of time-resolved pressure, temperature and three-component (3C) velocity fields. It is based on the observation of the region of interest from six perspectives and a single excitation frequency. This work summarizes and expands on a follow-up publication that experimentally verifies this concept on an aspirated circular duct flow. For this purpose, the results obtained from single-frequency data processing are compared with reference pressures, temperatures and corresponding LDA velocity measurements. Overall, a very good agreement is found for all operating points with accuracies of 3.4% in pressure, 1.3% in temperature and ±2 m/s in axial velocity. Concerning precision, a newly developed multistage evaluation procedure enables values for pressure, temperature and velocity as low as 3 hPa, 2.2 K and 1.7 m/s. In a second flow configuration, an axial swirler is introduced into the duct. The resulting secondary flow structure and deformation of the axial velocity field caused by swirler geometry and support are very well captured with the single-frequency analysis. A closing discussion on the implementation challenges of a single-frequency multi-property FRS instrument with pulsed laser radiation reveals significant obstacles to overcome. Due the considerable optimization potential identified, chances are high that true time-resolved multi-property measurements by FRS will become a reality.

VARMA Models with Single- or Mixed-Frequency Data: New Conditions for Extended Yule–Walker Identification

This paper deals with the identifiability of VARMA models with VAR order greater than or equal to the MA order, in the context of mixed-frequency data (MFD) using extended Yule–Walker equations. The main contribution is that necessary and sufficient conditions for identifiability in the single-frequency data case are expressed in an original way and yield new results in the MFD case. We also provide two counterexamples that answer an open question in this topic about whether certain sufficient conditions are necessary for identifiability. Therefore, this paper expands the set of models that can be identified with MFD using extended Yule–Walker equations. The main idea is that with MFD, some autocovariance blocks are not available from observed variables and, in some cases, the new conditions in this paper can be used to reconstruct all the non-available covariance blocks from available covariance blocks.

A genome-wide association study of freezing tolerance in red clover (Trifolium pratense L.) germplasm of European origin.

Improvement of persistency is an important breeding goal in red clover (Trifolium pratense L.). In areas with cold winters, lack of persistency is often due to poor winter survival, of which low freezing tolerance (FT) is an important component. We conducted a genome wide association study (GWAS) to identify loci associated with freezing tolerance in a collection of 393 red clover accessions, mostly of European origin, and performed analyses of linkage disequilibrium and inbreeding. Accessions were genotyped as pools of individuals using genotyping-by-sequencing (pool-GBS), generating both single nucleotide polymorphism (SNP) and haplotype allele frequency data at accession level. Linkage disequilibrium was determined as a squared partial correlation between the allele frequencies of pairs of SNPs and found to decay at extremely short distances (< 1 kb). The level of inbreeding, inferred from the diagonal elements of a genomic relationship matrix, varied considerably between different groups of accessions, with the strongest inbreeding found among ecotypes from Iberia and Great Britain, and the least found among landraces. Considerable variation in FT was found, with LT50-values (temperature at which 50% of the plants are killed) ranging from -6.0°C to -11.5°C. SNP and haplotype-based GWAS identified eight and six loci significantly associated with FT (of which only one was shared), explaining 30% and 26% of the phenotypic variation, respectively. Ten of the loci were found within or at a short distance (<0.5 kb) from genes possibly involved in mechanisms affecting FT. These include a caffeoyl shikimate esterase, an inositol transporter, and other genes involved in signaling, transport, lignin synthesis and amino acid or carbohydrate metabolism. This study paves the way for a better understanding of the genetic control of FT and for the development of molecular tools for the improvement of this trait in red clover through genomics assisted breeding.

GNSS standard point positioning method based on spherical harmonic expansion of signal propagation path relating errors

The errors affecting standard point positioning mainly include the ionospheric delay error, tropospheric delay error, clock bias, and multipath effect. The errors are divided into two categories depending on the relation to the signal propagation path: errors related to the propagation path and errors unrelated to the propagation path. The ionospheric delay error, tropospheric delay error, and multipath effect are related to the satellite signal propagation path. Based on this characteristic, a new GPS standard point positioning algorithm based on spherical harmonic expansion (SPP-SH) is proposed, which uses spherical harmonic expansion to represent the errors related to the propagation path. We hope to continuously promote the further development of standard point positioning (SPP) by proposing this new algorithm and to further reduce the redundancy of SPP algorithms and improve the efficiency and reliability of the algorithms. The most important purpose of this manuscript is to validate the proposed SPP-SH algorithm based on GPS data provided by IGS stations. Pseudorange data of multiple continuous epochs are used to achieve a sequential sliding point positioning solution, and the truncated singular value decomposition and iteration method correcting characteristic values are used to solve the ill-conditioned equations in the SPP-SH algorithm. The SPP-SH algorithm is implemented and verified using IGS station data. The positioning accuracy of the SPP-SH algorithm for both dual-frequency data and single-frequency data is better than that of a traditional SPP algorithm. By analyzing the positioning results of the stations at different latitudes, when the spherical harmonic expansion is expanded to the same degree, the positioning accuracy in low latitude areas is poor because the station is seriously affected by factors such as the ionosphere and water vapor, while the positioning accuracy at high latitude stations is the best. The proposed SPP-SH algorithm is also realized in the kinematic scene based on the dual-frequency data collected dynamically. The kinematic data is collected by an Android phone which supports receiving dual-frequency GPS observations. To validate the reliability of the SPP-SH algorithm, two strategies are designed to validate the results, which proves that the newly proposed method can be used for positioning with low accuracy in kinematic conditions.

A robust cascaded deep neural network for image reconstruction of single plane wave ultrasound RF data

Reconstruction of ultrasound data from single plane wave Radio Frequency (RF) data is a challenging task. The traditional Delay and Sum (DAS) method produces an image with low resolution and contrast, if employed with RF data from only a single plane wave. A Coherent Compounding (CC) method that reconstructs the image by coherently summing the individual DAS images was proposed to enhance the image quality. However, CC relies on a large number of plane waves to accurately sum the individual DAS images, hence it produces high quality images but with low frame rate that may not be suitable for time-demanding applications. Therefore, there is a need for a method that can create a high quality image with higher frame rates. Furthermore, the method needs to be robust against the input transmission angle of the plane wave. To reduce the method’s dependence on the input angle, we propose to unify the RF data at different angles by learning a linear data transformation from different angled data to a common, 0° data. We further propose a cascade of two independent neural networks to reconstruct an image, similar in quality to CC, by making use of a single plane wave. The first network, denoted as “PixelNet”, is a fully Convolutional Neural Network (CNN) which takes in the transformed time-delayed RF data as input. PixelNet learns optimal pixel weights that get element-wise multiplied with the single angle DAS image. The second network is a conditional Generative Adversarial Network (cGAN) which is used to further enhance the image quality. Our networks were trained on the publicly available PICMUS and CPWC datasets and evaluated on a completely separate, CUBDL dataset obtained from different acquisition settings than the training dataset. The results thus obtained on the testing dataset, demonstrate the networks’ ability to generalize well on unseen data, with frame rates better than the CC method. This paves the way for applications that require high-quality images reconstructed at higher frame rates.

On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification.

The aim of this work was to test microwave brain stroke detection and classification using support vector machines (SVMs). We tested how the nature and variability of training data and system parameters impact the achieved classification accuracy. Using experimentally verified numerical models, a large database of synthetic training and test data was created. The models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head phantoms with virtually inserted strokes of arbitrary size, and different dielectric parameters in different positions. The generated synthetic data sets were used to test four different hypotheses, regarding the appropriate parameters of the training dataset, the appropriate frequency range and the number of frequency points, as well as the level of subject variability to reach the highest SVM classification accuracy. The results indicate that the SVM algorithm is able to detect the presence of the stroke and classify it (i.e., ischemic or hemorrhagic) even when trained with single-frequency data. Moreover, it is shown that data of subjects with smaller strokes appear to be the most suitable for training accurate SVM predictors with high generalization capabilities. Finally, the datasets created for this study are made available to the community for testing and developing their own algorithms.

Three-stage inversion improvement for forest height estimation using dual-PolInSAR data

This paper addresses an algorithm for forest height estimation using single frequency and single baseline dual polarization radar interferometry data. The proposed method is based on a physical two layer volume over ground model and is represented by using polarimetric synthetic aperture radar interferometry (PolInSAR) technique. The presented algorithm provides the opportunity to take advantages of the dual polarimetric data, i.e, better spatial resolution and wider swath width, in comparison with the full polarimetric data, in forest height estimation application. In this research, a polarimetric optimization method is utilized to choose the optimum volume polarization basis in order to improve the results of the three-stage inversion algorithm. For the performance analysis of the proposed approach, the L-band ESAR data of the European Space Agency from BioSAR 2007 campaign (ESA) which is acquired over the Remningstorp test site in southern Sweden, is employed. The experimental result shows the dual PolInSAR HH/HV data capability in the forest height estimation without decreasing the accuracy of the result compared with the full polarimetric data. The suggested method leads to the average root mean square error (RMSE) of 4.39 m and the determination of coefficient of 0.66 in the forest height estimation in 15 predetermined stands in comparison with the LiDAR reference heights.

A new method for identifying micro fractures and characterizing fractures of different scales

Oil and gas exploration professionals have begun to focus more on unconventional oil and gas reserves in recent years as a result of their increased efforts. Fractures have a significant impact on the permeability and connectivity of reservoirs as a crucial component of rock mechanics and hydraulics, which directly affects the production of oil and gas. The identification of fracture development zones or micro faults, as well as how to adequately define the fracturing model, have thus become crucial and pressing issues in the forecast of oil and gas reservoirs. In this study, we decompose the three-dimensional seismic data volume in a site in order to obtain the single frequency data volume that can be calculated using the ant tracking technique. We do this by taking advantage of the synchronous extrusion improvement of short time Fourier transform in time-frequency focusing. Coupled with the advanced DFN model, the extracted data are calibrated in various rock attributes to restore the morphology and characteristics of fractures. The findings demonstrate that this method is capable of providing not only a precise outline of micro fractures but also a reflection of the characteristics of fractures at various scales, including structure and associated properties. The precision and applicability of this method are confirmed in this paper, which is significant as a reference for the oil and gas exploration industry.

Revisit economic growth and CO2 emission nexus in G7 countries: mixed frequency VAR model.

Applying a mixed frequency vector autoregressive (MF-VAR) approach, we examine relationships between CO2 emissions and economic growth from 1970Q1 to 2019Q4 among G7 countries. We incorporate primary energy consumption as a control variable, to avoid any bias from an omitted variable. Our empirical results, using forecast error variance decomposition and a Granger causality check, suggest that MF-VAR exhibits better explanatory ability over the more commonly used VAR model employing single frequency data. Results from LF-VAR exhibit a feedback loop connecting economic expansion and emissions of CO2 with one-way Granger causality from primary energy consumption to growth of the economies studied. MF-VAR model results also indicate that, in G7 countries, economic expansion exhibits a one-way causal link to CO2 emissions in Canada, UK, and US cases. Interestingly, MF-VAR shows feedback between economic expansion and primary energy use in Germany, while LF-VAR quantifies the link from economic growth to CO2 emissions in Canada and from CO2 to economic growth in the UK. Results raise implications for the G7 samples' policymakers.

An improved ant-tracking workflow based on divided-frequency data for fracture detection

Abstract The conventional ant-tracking technique often results in weak transverse continuity or unobvious plane fracture patterns. It also has limited ability to identify small-scale natural fractures. To improve the image quality of fractures, a spectrum decomposition technique has been used in ant-tracking. Although it has achieved good results, it also brings a high workload and judgment difficulty. To solve these issues, we proposed an improved ant-tracking workflow based on frequency filtering to improve the accuracy of fracture detection, in which the tracking was performed on low-, medium- and high-frequency cubes. The workflow was applied in the H area of the Sichuan Basin with good results: (i) more continuous, sharper and more small-scale fractures were detected by the proposed workflow than the ones with the full-band seismic data. (ii) Compared to the ant-tracking methods based on a series of single-frequency data cubes, this not only requires less computation time, but is also easier to distinguish the differences between the three divided-frequency cubes. (iii) Comparing the ant-tracking effect, the results of medium- and high- frequency cubes are much better than the low- frequency. (iv) Compared with the traditional methods, the time of automatic fracture extraction is obviously shortened. This provided a solid data foundation for subsequent reservoir production prediction and well deployment.

An assessment of reprocessed GPS/MET observations spanning 1995–1997

Abstract. We have performed an analysis of reprocessed GPS/MET data spanning 1995–1997 generated by the COSMIC Data Analysis and Archive Center (CDAAC) in 2007. CDAAC developed modified dual-frequency processing methods for the encrypted data (anti-spoofing (AS)-on) during 1995–1997. We compared the CDAAC data set to the Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) reanalysis, separately for AS-on and AS-off, focusing on the altitude range 10–30 km. MERRA-2 did not assimilate GPS/MET data in the period 1995–1997. To gain insight into the CDAAC data set, we developed a single-frequency data set for GPS/MET, which is unaffected by the presence of encryption. We find excellent agreement between the more limited single-frequency data set and the CDAAC data set: the bias between these two data sets is consistently less than 0.25 % in refractivity, whether or not AS is on. Given the different techniques applied between the CDAAC and the new data set presented here (designated JPL), agreement suggests that the CDAAC AS-on processing and the single-frequency processing are not biased in an aggregate sense greater than 0.25 % in refractivity, which corresponds approximately to a temperature bias less than 0.5 K. Since the profiles contained in the new single-frequency data set are not a subset of the CDAAC profiles, the combination of the CDAAC data set, consisting of 9579 profiles, and the new single-frequency data set, consisting of 4729 profiles, yields a total number of 11 531 unique profiles from combining the JPL and CDAAC data sets. All numbers are after quality control has been applied by the respective processing activities.

Relative Permittivity and Loss Tangent Determination Combining Broadband S-parameter and Single-Frequency Resonator Measurements

Wideband permittivity and loss tangent curves for the dielectric laminate of a printed circuit board are obtained. For this purpose, the single-frequency data provided by the laminate manufacturer together with propagation constant experimentally obtained from stripline measurements are used. This allows for enabling the wideband simulation of interconnects while the dielectric material properties are defined respecting causality. Furthermore, using the extracted data, the dielectric and conductor effects on the signal attenuation and delay can be separated to analyze the corresponding impact on the propagation features of interconnects. This is demonstrated here through the implementation of an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RLGC</i> model up to 30 GHz.

Multi-frequency data fusion via joint weighted deconvolution for resolution enhancement

The limited bandwidth of a single source normally restricts the imaging resolution for the subsurface target. Multi-frequency acquisition is a promising way to enhance the resolution with reasonable data fusion. An Ormsby wavelet based theoretical study confirms the significance of both the low and high frequencies for improving the resolution by means of peak duration and side-lobe oscillation level. To avoid over-boosting the noise during the data fusion, we propose a joint deconvolution approach to associate the single-frequency data with the target function directly. A weighted matched filter is accordingly derived based on the signal-to-noise ratio of the multi-frequency data. Error analysis shows that the joint weighted deconvolution based data fusion achieves better noise control compared to the direct-summation based deconvolution. Both wedge model based synthetic result and ground penetrating radar acquired field data example confirm the effectiveness of the proposed data fusion approach on resolution enhancement.

An Efficient UD Factorization Implementation of Kalman Filter for RTK Based on Equivalent Principle

Real-time kinematic (RTK) is a technique frequently utilized to provide real-time highly precise positioning services for mobile Internet-of-Things (IoT)-embedded terminals from intelligence appliances and smartphones to autonomous drones and self-driving vehicles. To fully utilize hardware resources, the internal GNSS chips or modules equipped in IoT terminals should satisfy the traits of energy efficiency and low computational complexity. As the number of global navigation satellite system (GNSS) increases, the continuous accumulation of high-dimensional rounding errors, the rough system model, and seriously distorted observations will result in divergence and considerable processing burden in the conventional Kalman filter (KF) process. Computational efficiency is significant in the reduction in the power consumption and intensifies the positioning performance of GNSS receivers. Here, a new filter strategy based on UD factorization, where U stands for the unit upper-triangular factor and D indicates the diagonal factor, is proposed for RTK positioning to enhance the numerical stability and reduce the computational effort. The equivalent principle was applied to turn double-difference (DD) observations into zero-difference (ZD) observations. Then, the UD-factorization-based Kalman filter (UD-KF) is proposed as a way to sequentially provide accurate real-time estimations of the filter states and variance–covariance (VC) matrix. Both static and dynamic tests were carried out with single-frequency data from a GPS to evaluate the performance of UD-KF. The results of the zero-baseline test show that UD-KF can obtain smaller RMS of the estimated parameters as the noise of DD observations was twice that of the ZD observations. A short baseline test showed that, compared to the regular filter approach with DD observations, UD-KF achieved a shorter computation time with a higher data utilization rate for both filtering and fixing stages, with an average improvement of 32% and 18%. Finally, a dynamic test showed that the UD-KF can avoid the undesirable effect of satellite changes. Therefore, compared to KF with DD observations, the UD-KF with equivalent ZD observations can enhance the robustness as well as improve the positioning accuracy of RTK positioning.

Semi-Supervised Classification of Dual-Frequency PolSAR Image Using Joint Feature Learning and Cross Label-Information Network

Dual-frequency polarimetric synthetic aperture radar (PolSAR) data can provide more information than single-frequency data, which can effectively improve classification accuracy. However, how to obtain sufficient and non-redundant feature representation from dual-frequency PolSAR data remains to be resolved. Besides, deep learning has shown good performance in PolSAR image classification, but it often requires a large number of labeled samples to participate in the training process, which is time-consuming and labor-intensive. In this paper, we propose a novel dual-frequency PolSAR image semi-supervised classification method that combines a dual-frequency joint feature learning (DFJFL) module with a cross label-information network (CLIN). First, the DFJFL module is developed based on the consistency and complementarity of dual-frequency data. It eliminates information redundancy by feature constraint loss function, and obtains compact dual-frequency joint feature representation. Subsequently, in order to avoid the influence of speckle noise, the proposed CLIN not only applies consistency regularization under network perturbation, but also uses the scattering mechanism of PolSAR data to find similar sample pairs to complete the consistency regularization under input perturbation, thereby achieving semi-supervised classification for PolSAR data. Experiments on four real dual-frequency PolSAR datasets verify that the proposed method can effectively extract dual-frequency PolSAR information, and make full use of unlabeled samples to improve classification accuracy. At the same time, compared with several related image classification algorithms, the proposed method could achieve the best performance.

Group Sparsity Penalized Contrast Source Solution Method for 2-D Non-Linear Inverse Scattering

A group sparsity penalized CSI in the wavelet domain is proposed to alleviate ill-posedness within the framework of a contrast-source inversion (CSI) method. It is then applied to the retrieval of a large inhomogeneous dielectric scatterer from time-harmonic single-frequency data. As dependency exists between wavelet coefficients at different scales, referred to as the parent-child relationship, it enables to yield the wavelet quad tree structure. Therefore, wavelet coefficients can be regarded not only as pixel-wise sparse, but also group-wise sparse. Focus is put on using the dual-tree complex wavelet transform ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {C}$ </tex-math></inline-formula> WT) to properly achieve the sought-after group-wise sparse representation of the spatial distribution of the contrast. It provides a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2,1}$ </tex-math></inline-formula> norm which is added to the standard cost functional to enforce group sparsity onto the wavelet coefficients of the spatially-varying contrast. The replication strategy is combined with the proximal method in order to solve the overlapping group penalized problem. Simulations from synthetic data in different configurations with in particular different signal-to-noise ratios illustrate pros and cons of the proposed method. The approach is shown to overcome the standard CSI method in demanding situations. Comparisons with the discrete wavelet transform (DWT) as usually performed and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> norm confirm the advantage of the proposed methodology.

Galileo E5 AltBOC Signals: Application for Single-Frequency Total Electron Content Estimations

Global navigation satellite system signals are known to be an efficient tool to monitor the Earth ionosphere. We suggest Galileo E5 AltBOC phase and pseudorange observables—a single-frequency combination—to estimate the ionospheric total electron content (TEC). We performed a one-month campaign in September 2020 to compare the noise level for different TEC estimations based on single-frequency and dual-frequency data. Unlike GPS, GLONASS, or Galileo E5a and E5b single-frequency TEC estimations (involving signals with binary and quadrature phase-shift keying, such as BPSK and QPSK, or binary offset carrier (BOC) modulation), an extra wideband Galileo E5 AltBOC signal provided the smallest noise level, comparable to that of dual-frequency GPS. For elevation higher than 60 degrees, the 100 s root-mean-square (RMS) of TEC, an estimated TEC noise proxy, was as follows for different signals: ~0.05 TECU for Galileo E5 AltBOC, 0.09 TECU for GPS L5, ~0.1TECU for Galileo E5a/E5b BPSK, and 0.85 TECU for Galileo E1 CBOC. Dual-frequency phase combinations provided RMS values of 0.03 TECU for Galileo E1/E5, 0.03 and 0.07 TECU for GPS L1/L2 and L1/L5. At low elevations, E5 AltBOC provided at least twice less single-frequency TEC noise as compared with data obtained from E5a or E5b. The short dataset of our study could limit the obtained estimates; however, we expect that the AltBOC single-frequency TEC will still surpass the BPSK analogue in noise parameters when the solar cycle evolves and geomagnetic activity increases. Therefore, AltBOC signals could advance geoscience.

Low-cost positioning performance using remotely sensed ionosphere

Low-Cost Positioning [Single Frequency Receivers (SFR)] using Global Positioning Systems (GPS) can become a precise alternative to Dual Frequency Receivers (DFRs) in different applications. Ionospheric delay is one of the significant provenances of errors in GPS navigation and positioning. These blunders in both Phase range and pseudo range can differ according to the receiver location, solar cycle, time, and geomagnetic activity.Some ground receivers over Egypt and Europe were regarded in This publication to examine various ionosphere correction products' capability. Data from IGS stations spanning various latitudes and solar activity times (high, medium, or small) are taken into account. The findings demonstrate that the sub-meter level's precision is obtainable using single-frequency data using suitable mitigation approaches for ionospheric impacts.The implications for GNSS positioning (especially on horizontal precision) of ionospheric corrections have been studied. The findings demonstrate that all ionosphere corrections clearly enhance the accuracy of the vertical positions where the absolute vertical error is about 30 cm, 32.3 cm, and 57.2 cm for LIMs (Local Ionosphere Maps), GIMs (Global Ionosphere Maps), and BKP (Broadcasted Klobuchar Parameter). However, the horizontal enhancement is lower than the vertical, and even worse, the horizontal error can be increased with ionosphere correction. The horizontal error ranges from 2.7 to 51.8 cm by using LIMs, while ranges from 4.5 to 47.2 cm by using GIMs but the maximum horizontal error obtained by applying BKP, which ranges from 20.7 to 100.2 cm. In comparison, the north bias plays a vital role in reducing horizontal precision. If north bias were not resolved correctly with ionosphere corrections, the horizontal positioning efficiency would deteriorate.The significant efficiency of mid-latitude Egyptian ionosphere correction (estimated by LIMs) in stormy and quiet geomagnetic environments rather than GIMs and BKP, 3D RMS error improved by 16 %, and GIMs up 63 %. Furthermore, in low solar activity, 3D RMS error improved 6% compared to GIMs and 87% compared to BKP.

Does high crude oil dependence influence Chinese military expenditure decision-making?

This paper employs the mixed frequency vector autoregression (MF-VAR) to discuss the causal link between crude oil dependence and military expenditure in China. The empirical results demonstrate that the crude oil dependence Granger causes military expenditure and accounts for 66.8% of the long-run forecast error variance decomposition. That means the MF-VAR model owns stronger explanatory and asymptotic powers than conventional VAR with single frequency data. Besides, the impulse-response analysis finds time varying relationship between crude oil dependence and military expenditure across four quarters within one year period. Therefore, the Chinese People's Liberation Army, especially its Navy, is needed to take responsibility and active activities to assure crude oil transport security. Government, enterprises and institutes should increase investment in military innovation to promote the upgrading of weapons and equipment. The peaceful rise strategy should be reasserted to construct harmonious international relations, which is helpful for crude oil security, foreign energy investment and reduce unnecessary military expenditure.

SUPREME: an open-source single-frequency uncombined precise point positioning software

With the rapid development of Global Navigation Satellite Systems (GNSS), precise point positioning (PPP) has been widely used for positioning, navigation, timing (PNT), and atmospheric sensing. Currently, the demand for low-cost single-frequency hardware is increasing. However, open-source single-frequency PPP (SFPPP) software, in particular those that employ uncombined GNSS observations, is scarce. In view of this fact, we developed an open-source software, called Single-frequency Uncombined PREcise point positioning for Multi-parameter Estimation (SUPREME), which can process multi-GNSS observations collected from a low-cost single-frequency receiver. It can simultaneously estimate receiver coordinates, receiver clock offsets, zenith tropospheric delays, and ionospheric delays utilizing a least-squares filter. SUPREME is developed in the C/C++ computer language; thus, it has good portability and cross-platform capability. The formatted output is also beneficial for the post-analysis of results. We evaluated SUPREME using several experiments based on real single-frequency data, and the results indicate that it provides high accuracy and robust performance.