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A high-spatial-resolution hyperspectral image (HR-HSI) can be obtained by fusing a hyperspectral image (HSI) and a multispectral image (HSI-MSI) since it takes the advantage of combing the low-spatial-resolution HSI (LR-HSI) and the high-spatial-resolution multispectral image (HR-MSI). Currently, most HSI-MSI fusion methods decompose HSIs in both spatial and spectral domains, which destroys the correlation of the two domains and results in poor fusion results. To further explore the correlation of spatial and spectral information, an unsupervised blind spectral–spatial cross-super-resolution network (CSRNet) is proposed for HSI-MSI fusion. The network transforms the fusion into two super-resolution (SR) reconstructions for a HR-HSI to avoid the decomposition of HSIs and achieves interaction with the two reconstructions to provide strong constraints for each other. The network uses the following procedure. First, the HSI-MSI fusion is transformed into a spectral–spatial cross-SR model, which consists two branches: one for spatial and the other for spectral SR (SpeSR) reconstructions. As the basic blocks of the two branches, spatial SR (SpaSR) and the SpeSR blocks in the new network are designed via iterations unfolding from half quadratic splitting (HQS). Then their corresponding spatial constraint (SpaC) and spectral constraint (SpeC) modules are established for the mutual constraints and interactions between the two branches. The SpaSR/SpeSR and SpaC/SpeC modules are alternately connected to form the above SpaSR/SpeSR branches for reconstructing a HR-HSI. Both visual and quantitative results of experiments based on both simulated and real observation datasets showed that the proposed method outperformed seven commonly used methods, suggesting that the new method is effective for preserving the correlation of spatial and spectral information in HR-HSIs.

Precipitable water vapor (PWV), as an important representative parameter of atmospheric water vapor contents, can be obtained by means of Global Navigation Satellite Systems (GNSS) using both ground-based and space-borne observation techniques. However, the PWV prediction models currently accessible tend to be simplistic combinations or individual models. In this study, we develop a whale optimization algorithm (WOA) convolutional neural network (CNN) long short-term memory (LSTM)-Attention model to predict PWV, which takes the 16 GNSS PWV values near the King’s Park (HKKP) station as characteristic parameters and the spatial relationship between the point of interest and its neighboring GNSS stations into consideration. An optimal model via the WOA is investigated by using a wavelet analysis to separate noises, through combining CNN, LSTM neural network, and attention mechanism. Results show that considerable improvement in the prediction accuracy has been achieved through a comparison between CNN-LSTM–Attention and the conventional LSTM and CNN-LSTM models. In terms of long-term predictability, CNN-LSTM–Attention is proven to be a superior model when eight features are incorporated. The model’s root mean square error (RMSE) is 2.30 mm, which is reduced by 20.42% than in the case of 0 feature is used. As a further analysis, we also examine the prediction performance of various models for hourly PWV using 7, 15, 30, 60, and 90 days of data as different lengths of training. The results show that CNN-LSTM–Attention has a better prediction effect when the training length is 30 days, the RMSE is 0.74 mm, and the Nash-Sutcliffe efficiency (NSE) coefficient is 0.98.

This study utilizes L1B level data from reflected global navigation satellite system (GNSS) signals from the Cyclone GNSS (CYGNSS) mission to estimate sea surface significant wave height (SWH). The normalized bistatic radar cross Section (NBRCS), the leading edge slope (LES), the signal-to-noise ratio (SNR), and the delay-Doppler map average (DDMA) are used as the key variables for the SWH retrieval. Eight other parameters, including instrument gain and scatter area, are also utilized as auxiliary variables to enhance the SWH retrieval performance. A variety of multivariable regression models are investigated to clarify the relationship between the SWH and the variables by using the following five methods: stepwise linear regression, Gaussian support vector machine, artificial neural network, sparrow search algorithm–extreme learning machine, and bagging tree (BT). Results show that, among the five regression models developed, the BT model performs the best with the root mean square error (RMSE) of 0.48 m and the correlation coefficient (CC) of 0.82 when testing one million sets of data randomly selected, while the RMSE and CC of BT model are 0.44 m and 0.73 in the 4500 National Data Buoy Center (NDBC) buoy testing dataset. Meanwhile, the BT model also has the best generalization ability, which means that it performs well in practical applications. In addition, the impacts of different input variables, the size of the training dataset, and the sea surface wind speed are also investigated. These findings are anticipated to serve as helpful guides for creating future SWH retrieval algorithms that are more advanced.

Owing to the limitations of hyperspectral optical imaging, hyperspectral images (HSIs) have a dilemma between spectral and spatial resolutions. The hyperspectral and multispectral image (HSI–MSI) fusion, which combines a low-spatial-resolution HSI (LR-HSI) and a high-spatial-resolution multispectral image (HR-MSI), can generate a high-spatial-resolution HSI (HR-HSI). In existing methods for hyperspectral and multispectral fusion, correlation between spectral and spatial domains in HSIs is mostly neglected. To address this issue, an unsupervised encoder–decoder network under spatial and spectral guidance for the HSI–MSI fusion (uEDSSG) was proposed in this study. To learn more accurate abundances of an LR-HSI and an HR-MSI, multihierarchical encoders under spatial and spectral guidance were designed to extract multihierarchical fused features from the LR-HSI and HR-MSI with the guidance of the HR-MSI and LR-HSI, respectively. In the new method, deep coupling of the point spread function (PSF) or spectral response function (SRF) and edge of the HSIs was designed to maintain the spatial and spectral details of the HR-HSI; a spatial–spectral constraint was constructed to establish the relationship of the HSIs. Both visual and quantitative evaluation results of experiments based on both synthetic and real datasets showed that the proposed method outperformed seven common methods. The results suggest that the new method by maintaining the correlation between spectral and spatial domains can improve the result of HSI–MSI fusion.

•Propose a one-stage object detection model for accurate coal-gangue detection.•Use Swin-transformer as backbone to extract global multi-scale information.•Improve feature pyramid to facilitate effective cross-scale feature fusion.•Introduce task-aligned head to mitigate classification-localization misalignments.•Our STATNet model outperforms SOTA baseline models in real industrial dataset.

An artificial neural network (ANN) method for the modeling of global topside ionospheric vertical scale height (VSH) using electron density profiles retrieved from Global Navigation Satellite Systems radio occultation (RO) data is proposed in this study. The data for this study are 80,124 VSHs derived from the events randomly selected from 9 years of Constellation Observing System for Meteorology, Ionosphere, and Climate RO measurements and 144,530 VSHs derived from the events randomly selected from 16 years of topside sounder measurements of both Aluoette‐1/2 and ISIS‐1/2 satellites during 1962–1978 are used for comparison. VSHs from the International Reference Ionosphere are also used for the comparison. Results showed that: (1) the median of the relative residuals of the new ANN regression approach/model (which was based on RO measurements) was 8.5% less than that of the traditional approach/model (which was based on the topside sounder data); (2) the median of the relative residuals of the ANN model when longitude was used as a variable was 1.1% less than the one without longitude; and substantial error in the polar region was shown to be mitigated by taking the variable longitude into consideration; (3) compared to International Reference Ionosphere, the accuracy of the new ANN model was improved by around 14%; (4) the new ANN model outperforms the traditional base vector‐based least squares model by around 10% when incoherent scatter radar measurements are used as a reference; and (5) the characteristics of global VSHs generated from the new model during geomagnetic storms better agree with measurements than that of the base vector‐based least squares. A new approach for vertical scale height modeling from ionospheric radio occultation data using artificial neural network is proposedThe new approach significantly outperforms the traditional model in accuracyThe new approach agrees with the characteristic of VSH in equatorial region better than traditional approach during geomagnetic storm times

The paper presents a detailed analysis of the density‐temperature (ρ‐T) synchrony in the thermosphere using a hydrostatic general circulation model. The numerical models in general offer not only great potential for forecasting the transient response of the thermosphere but also are excellent tools for understanding the driving mechanisms of various thermospheric trends and features. This study investigates and isolates the dependency of the ρ‐Tsynchrony on the season, altitude, space weather, high‐latitude electrodynamics, and the lower atmospheric tidal spectrum. The results demonstrate that the previously reported ρ‐Tsynchrony begins around 300‐km (350‐km) altitude at the equator (high latitudes). The effect of the lower atmospheric tidal spectrum on the ρ‐Tsynchrony patterns seems to be only marginal and more noticeable during the equinox months. The study demonstrates that the ρ‐Tphase lag is larger in the high latitudes of the summer hemisphere and evolves through the day and is attributable to ion drag and temperature fluctuations via soft particle precipitation. The study provides physical insights into how the winds contribute to the ρ‐Tsynchrony. In addition, the results show that geomagnetic activity contributes significantly to the ρ‐Tsynchrony; the underlying mechanism may be related to temperature enhancements in the high latitudes via Joule heating and associated nonlinear interactions. While the ρ‐Tphase lags attributable to different solar activity levels are modest, the solar heating is the primary source that maintains the ρ‐Tsynchrony in the low/middle latitudes via upward propagating thermal tides. A detailed analysis of the neutral density‐temperature (ρ‐T) synchrony in the thermosphere is presentedThe ρ‐Tsynchrony begins around 300 km near the equator and varies significantly with latitude, height, and seasonThe ion drag and temperature fluctuations via electron precipitation alters the ρ‐Tsynchrony in the high latitudes

Two solar flares X2.2 and X9.3 erupted over the active region 2673 on 6 September 2017, and the second flare is the strongest since 2005. In order to investigate the ionospheric and thermospheric responses to the two solar flares, the global total electron content and the critical frequency of F2 layer obtained from GPS stations and ionosondes are used. The results indicate that the ionosphere in the sunlit hemisphere increased significantly with magnitudes of 0.1 and 0.5 total electron content units for the X2.2 and X9.3 solar flares, respectively. The electron density, thermospheric neutral density, and neutral temperature simulated by the Thermosphere‐Ionosphere Electrodynamics Global Circulation Model show that the behavior of ionospheric and thermospheric responses is different. The ionospheric disturbances occurred at the altitude ranges of 150−300 km, and the thermospheric responses occurred at the altitudes of 250−400 km are caused by solar extreme ultraviolet and ultraviolet photons, respectively. Both ionospheric and thermospheric responses are proportional to the height within their corresponding altitude ranges. Observations and simulations reveal that the ionospheric and thermospheric responses are nonlinearly dependent on the solar zenith angle. The disturbances caused by the X2.2 solar flare are symmetric, but the X9.3 solar flare are not. The O/N2density ratio simulated by Thermosphere‐Ionosphere Electrodynamics Global Circulation Model increases from lev0 to lev5.0 pressure surface with a magnitude of 0.1–1.8, while the ratio decreases in the American sector with a magnitude of −0.6 to −0.3. The longitudinal asymmetry of O/N2density ratio is a major contributor to the longitudinal asymmetry of ionospheric and thermospheric responses. The behaviors of ionospheric and thermospheric responses to solar flares are found to be contrastingThe ionospheric and thermospheric responses are dependent on solar zenith angle nonlinearlyThe longitudinal asymmetry of responses is attributed to the distribution of O/N2ratio

The first systematic comparison between Swarm‐C accelerometer‐derived thermospheric density and both empirical and physics‐based model results using multiple model performance metrics is presented. This comparison is performed at the satellite's high temporal 10‐s resolution, which provides a meaningful evaluation of the models' fidelity for orbit prediction and other space weather forecasting applications. The comparison against the physical model is influenced by the specification of the lower atmospheric forcing, the high‐latitude ionospheric plasma convection, and solar activity. Some insights into the model response to thermosphere‐driving mechanisms are obtained through a machine learning exercise. The results of this analysis show that the short‐timescale variations observed by Swarm‐C during periods of high solar and geomagnetic activity were better captured by the physics‐based model than the empirical models. It is concluded that Swarm‐C data agree well with the climatologies inherent within the models and are, therefore, a useful data set for further model validation and scientific research. The first systematic comparison of the thermospheric densities between Swarm‐C accelerometer‐derived densities is presented. The data are compared to two latest versions of a physics‐based general circulation model and arguably the most widely used empirical models using multiple model performance metrics. The comparison at the satellite's temporal resolution provides a useful evaluation of the models' fidelity for orbit prediction and pertinent space weather forecasting applications. The results of this study show that the short‐timescale variations observed by Swarm‐C during periods of high solar and geomagnetic activities were better captured by the physics‐based model than the empirical model. The results show the complex interconnectedness of solar activity and geomagnetic activity on model performance in terms of estimating density. The study also uses a machine learning exercise to demonstrate characteristics inherent to each model run. Swarm‐C data are consistent with the models. It is concluded that Swarm‐C data are suitable for further model validation and scientific research. Presented is the first analysis of Swarm‐C accelerometer‐derived densities using both physical and empirical modelsSwarm‐C data are consistent with models, and TIE‐GCM significantly outperforms both DTM‐2013 and NRLMSISE‐00Implications of various model‐forcing parameters are clarified for neutral density along the Swarm‐C orbit

Geomagnetic storms on 7–8 September 2017 triggered severe ionospheric disturbances that had a serious effect on satellite navigation and radio communication. Multiple observations derived from Global Navigation Satellite System receivers, Earth's Magnetic Field and Environment Explorers (SWARM) and the Thermosphere‐Ionosphere ‐Electrodynamics General Circulation Model's simulations are utilized to investigate the spatial‐temporal ionospheric behaviors under storm conditions. The results indicate that the electron density in the Asia‐Australia, Europe‐Africa and America sectors suddenly changed with the Bz southward excursion, and the ionosphere over low‐middle latitudes under the sunlit hemisphere is easily affected by the disturbed magnetic field. The SWARM observations verified the remarkable double‐peak structure of plasma enhancements over the equator and middle latitudes. The physical mechanism of low‐middle plasma disturbances can be explained by a combination effect of equatorial electrojets, vertical E × B drifts, meridional wind and thermospheric O/N2change. Besides, the severe storms triggered strong Polar plasma disturbances on both dayside and nightside hemispheres, and the Polar disturbances had a latitudinal excursion associated with the offset of geomagnetic field. Remarkable plasma enhancements at the altitudes of 100–160 km were also observed in the auroral zone and middle latitudes (>47.5°N/S). The topside polar ionospheric plasma enhancements were dominated by the O+ ions. Furthermore, the TIE‐GCM's simulations indicate that the enhanced vertical E × B drifts, cross polar cap potential and Joule heating play an important role in generating the topside plasma perturbations. Positive disturbances over low‐middle latitudes only appear on the dayside, but polar disturbances occur on both dayside and nightsideStorms trigger bottomside plasma enhancements over middle‐high latitudes (>47.5 N/S) that have a latitudinal offset with geomagnetic fieldA combination of equatorial electrojet, E × B drifts, meridional wind and O/N2change is responsible for low‐middle latitudinal disturbances Positive disturbances over low‐middle latitudes only appear on the dayside, but polar disturbances occur on both dayside and nightside Storms trigger bottomside plasma enhancements over middle‐high latitudes (>47.5 N/S) that have a latitudinal offset with geomagnetic field A combination of equatorial electrojet, E × B drifts, meridional wind and O/N2change is responsible for low‐middle latitudinal disturbances

Adenosine elicits cardioprotection through A1-receptor activation. Therapy with adenosine A1-receptor agonists, however, is limited by undesirable actions of full agonism, such as bradycardia. This study examined the effects of capadenoson (CAP), a partial adenosine A1-receptor agonist, on left ventricular (LV) function and remodeling in dogs with heart failure.

It is well known that the key problem associated with network-based real-time kinematic (RTK) positioning is the estimation of systematic errors of GPS observations, such as residual ionospheric delays, tropospheric delays, and orbit errors, particularly for medium-long baselines. Existing methods dealing with these systematic errors are either not applicable for making estimations in real-time or require additional observations in the computation. In both cases, the result is a difficulty in performing rapid positioning. We have developed a new strategy for estimating the systematic errors for near real-time applications. In this approach, only two epochs of observations are used each time to estimate the parameters. In order to overcome severe ill-conditioned problems of the normal equation, the Tikhonov regularization method is used. We suggest that the regularized matrix be constructed by combining the a priori information of the known coordinates of the reference stations, followed by the determination of the corresponding regularized parameter. A series of systematic errors estimation can be obtained using a session of GPS observations, and the new process can assist in resolving the integer ambiguities of medium-long baselines and in constructing the virtual observations for the virtual reference station. A number of tests using three medium- to long-range baselines (from tens of kilometers to longer than 1000 kilometers) are used to validate the new approach. Test results indicate that the coordinates of three baseline lengths derived are in the order of several centimeters after the systematical errors are successfully removed. Our results demonstrate that the proposed method can effectively estimate systematic errors in the near real-time for medium-long GPS baseline solutions.

The effects of the 18 August 2003 geomagnetic storm on the ionosphere over China have been first studied by combining dual-frequency Global Positioning System (GPS) observation data from the Crustal Movement Observation Network of China (CMONOC) using the computerized ionospheric tomography (CIT) technique. The temporal and spatial variations of the ionosphere are analyzed using a time series of ionospheric electron density (IED) maps, and the ionospheric storm evolution process is revealed. The tomographic results show that the main ionospheric effects of this storm over China are: (1) that positive storm phase effects usually happen in the low-latitude ionosphere, (2) that negative storm phase effects occur in the mid-latitude ionosphere, and (3) that the equatorial anomaly structure can also be found. In contrast to the quiet period of the ionosphere on 17 August 2003, the equatorial anomaly crest moved to the north in the main phase of the storm and then moved back to the original location in the recovery phase on 19 August 2003. We compared the peak density NmF2and the peak height hmF2obtained from the ionosonde observations at Wuhan station and those inverted by the CIT technique to confirm the reliability of the GPS-based tomographic technique. The tomographic results revealed that the GPS-based CIT technique can be used to monitor large-scale ionospheric disturbances during geomagnetic storms.

Satellite velocity determination using the broadcast ephemeris is discussed and it is pointed out that the conventional rotation matrix method involves a complicated process of computation. This paper proposes an alternative method using a simple differentiator to derive satellite Earth-Centred-Earth-Fixed (ECEF) velocity from the ECEF satellite positions that are calculated using the standard ICD-GPS-200 algorithm. The proposed algorithm simplifies the velocity transformation procedure, and therefore provides a good alternative. It is demonstrated that ±1 mm/s per axis ECEF satellite velocity is achievable by using the first-order central difference of a Taylor series approximation.A closed-form formula is also derived for the determination of GPS satellite ECEF acceleration using the broadcast ephemeris. This formula is capable of accuracies better than ±0·1 mm per second squared in each axis. With such a high accuracy of satellite acceleration in real-time, it is possible to detect the line-of-sight range acceleration precisely and as such it is concluded that a GPS receiver can be considered as a precise accelerometer.The success of the position differential method implies that real-time satellite ECEF velocities can be directly derived through numerical differentiation of the position polynomials. This is desirable for GPS velocity determination applications which require high output rate results in real-time. The derived closed-form formula for GPS satellite ECEF acceleration would benefit those who attempt to use a GPS receiver as an accurate accelerometer in real-time.

The Doppler effect is the apparent shift in frequency of an electromagnetic signal that is received by an observer moving relative to the source of the signal. The Doppler frequency shift relates directly to the relative speed between the receiver and the transmitter, and has thus been widely used in velocity determination. A GPS receiver-satellite pair is in the Earth’s gravity field and GPS signals travel at the speed of light, hence both Einstein’s special and general relativity theories apply. This paper establishes the relationship between a Doppler shift and a user’s ground velocity by taking both the special and general relativistic effects into consideration. A unified Doppler shift model is developed, which accommodates both the classical Doppler effect and the relativistic Doppler effect under special and general relativities. By identifying the relativistic correction terms in the model, a highly accurate GPS Doppler shift observation equation is presented. It is demonstrated that in the GPS “frequency” or “velocity” domain, the relativistic effect from satellite motion changes the receiver-satellite line-of-sight direction, and the measured Doppler shift has correction terms due to the relativistic effects of the receiver potential difference from the geoid, the orbit eccentricity, and the rotation of the Earth.The Doppler effect is the apparent shift in frequency of an electromagnetic signal that is received by an observer moving relative to the source of the signal. The Doppler frequency shift relates directly to the relative speed between the receiver and the transmitter, and has thus been widely used in velocity determination. A GPS receiver-satellite pair is in the Earth’s gravity field and GPS signals travel at the speed of light, hence both Einstein’s special and general relativity theories apply. This paper establishes the relationship between a Doppler shift and a user’s ground velocity by taking both the special and general relativistic effects into consideration. A unified Doppler shift model is developed, which accommodates both the classical Doppler effect and the relativistic Doppler effect under special and general relativities. By identifying the relativistic correction terms in the model, a highly accurate GPS Doppler shift observation equation is presented. It is demonstrated that in the GPS “frequency” or “velocity” domain, the relativistic effect from satellite motion changes the receiver-satellite line-of-sight direction, and the measured Doppler shift has correction terms due to the relativistic effects of the receiver potential difference from the geoid, the orbit eccentricity, and the rotation of the Earth.

This paper presents the current progress of the River Level Monitoring using GPS Heighting (RiGHt) project. The aim of the project is to develop an integrated system to allow the continuous monitoring of river heights using a buoy equipped with Global Positioning System (GPS) and satellite communications and using Geographical Information System (GIS) techniques. © 2000 John Wiley & Sons, Inc.

The goal of this study is to present a new model for global topside electron temperature ( Te) using a deep neural network (DNN) that is trained using measurements from incoherent scatter radars (ISRs). This study is also an investigation into whether this model can be used to generate the electron temperature in the topside ionosphere using GNSS ionospheric radio occultation (GNSS‐IRO) data as the input. ISR is one of the most reliable and long‐term sources to measure topside ionospheric electron density and plasma temperature information simultaneously. However, a drawback of ISR databases is the relatively poor spatial coverage due to the low number of ISR stations around the world. In contrast, GNSS‐IRO can be used to measure the global distributed electron density, but Teinformation is not directly detected. The relationship between the electron density and the electron temperature has been investigated by many researchers, but these studies have not explicitly considered the parameters that are known to influence the electron temperature, such as solar and geomagnetic activity level, and the features of electron density profile ( hmF2, NmF2, and scale height). This study uses a DNN technique to create a new global topside electron temperature model from three submodels that have been trained using data from three ISR stations: Arecibo (low latitude), Millstone Hill (midlatitude), and Poker Flat (high latitude). This global model is trained using electron density profile information (e.g., vertical scale height [VSH], hmF2, and NmF2) and solar and geomagnetic activity ( F10.7and Kp, respectively) in addition to traditional spatial and temporal variables (e.g., local time, month, and latitude) as the independent variables. After the Temodel is developed, Teinformation can be generated from the GNSS‐IRO electron density profiles using a newly created Ne−Temodel. This model's outputs are assessed with regard to out‐of‐sample ISR data and compared to the latest International Reference Ionosphere model. It is found that the electron temperature profiles from the DNN have a root‐mean‐square deviation of 259 K in the low‐latitude region (i.e., against Arecibo data), 254 K in the midlatitude region (i.e., against Millstone Hill data), and 314 K in high‐latitude region (i.e., against Poker Flat data), and all of them are smaller than the root‐mean‐square deviation from International Reference Ionosphere. An additional comparison between the model results versus the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model outputs is also conducted. A statistical analysis of the diurnal electron temperature profiles obtained from GNSS‐IRO is shown to agree with the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model outputs. The full codes and outputs can be found at the Zenodo (10.5281/zenodo.3637617). A new global topside Temodel using deep neural network from incoherent scatter radar (ISR) with newly added Ne‐related variables is proposedThe new model agrees better with the out‐of‐sample ISR measurements than IRI‐2016The Teresults predicted from the proposed model and ionospheric radio occultation measurements agree well with TIEGCM in the region with sufficient IRO data

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