Showing papers on "TEC published in 2022"

The 15 January 2022 Hunga Tonga Eruption History as Inferred From Ionospheric Observations

Abstract: On 15 January 2022, the Hunga Tonga-Hunga Ha’apai submarine volcano erupted violently and triggered a giant atmospheric shock wave and tsunami. The exact mechanism of this extraordinary eruptive event, its size and magnitude are not well understood yet. In this work, we analyze data from the nearest ground-based receivers of Global Navigation Satellite System to explore the ionospheric total electron content (TEC) response to this event. We show that the ionospheric response consists of a giant TEC increase followed by a strong long-lasting depletion. We observe that the explosive event of 15 January 2022 began at 04:05:54UT and consisted of at least five explosions. Based on the ionospheric TEC data, we estimate the energy released during the main major explosion to be between 9 and 37 Megatons in trinitrotoluene equivalent. This is the first detailed analysis of the eruption sequence scenario and the timeline from ionospheric TEC observations.

EC-SAGINs: Edge-Computing-Enhanced Space–Air–Ground-Integrated Networks for Internet of Vehicles

Abstract: Edge-computing-enhanced Internet of Vehicles (EC-IoV) enables ubiquitous data processing and content sharing among vehicles and terrestrial edge computing (TEC) infrastructures (e.g., 5G base stations and roadside units) with little or no human intervention, and plays a key role in the intelligent transportation systems. However, EC-IoV is heavily dependent on the connections and interactions between vehicles and TEC infrastructures, thus will break down in some remote areas where TEC infrastructures are unavailable (e.g., desert, isolated islands, and disaster-stricken areas). Driven by the ubiquitous connections and global-area coverage, space–air–ground-integrated networks (SAGINs) efficiently support seamless coverage and efficient resource management, and represent the next frontier for edge computing. In light of this, we first review the state-of-the-art edge computing research for SAGINs in this article. After discussing several existing orbital and aerial edge computing architectures, we propose a framework of edge computing-enabled SAGINs to support various Internet of Vehicles (EC-IoV) services for the vehicles in remote areas. The main objective of the framework is to minimize the task completion time and satellite resource usage. To this end, a preclassification scheme is presented to reduce the size of action space, and a deep imitation learning-driven offloading and caching algorithm is proposed to achieve real-time decision making. The simulation results show the effectiveness of our proposed scheme. Finally, we also discuss some technology challenges and future directions.

Pronounced Suppression and X‐Pattern Merging of Equatorial Ionization Anomalies After the 2022 Tonga Volcano Eruption

Abstract: Following the 2022 Tonga Volcano eruption, dramatic suppression and deformation of the equatorial ionization anomaly (EIA) crests occurred in the American sector ∼14,000 km away from the epicenter. The EIA crests variations and associated ionosphere-thermosphere disturbances were investigated using Global Navigation Satellite System total electron content data, Global-scale Observations of the Limb and Disk ultraviolet images, Ionospheric Connection Explorer wind data, and ionosonde observations. The main results are as follows: (a) Following the eastward passage of expected eruption-induced atmospheric disturbances, daytime EIA crests, especially the southern one, showed severe suppression of more than 10 TEC Unit and collapsed equatorward over 10° latitudes, forming a single band of enhanced density near the geomagnetic equator around 14–17 UT, (b) Evening EIA crests experienced a drastic deformation around 22 UT, forming a unique X-pattern in a limited longitudinal area between 20 and 40°W. (c) Thermospheric horizontal winds, especially the zonal winds, showed long-lasting quasi-periodic fluctuations between ±200 m/s for 7–8 hr after the passage of volcano-induced Lamb waves. The EIA suppression and X-pattern merging was consistent with a westward equatorial zonal dynamo electric field induced by the strong zonal wind oscillation with a westward reversal.

Significant Ionospheric Hole and Equatorial Plasma Bubbles After the 2022 Tonga Volcano Eruption

Abstract: This paper investigates the local and global ionospheric responses to the 2022 Tonga volcano eruption, using ground-based Global Navigation Satellite System (GNSS) total electron content (TEC), Swarm in-situ plasma density measurements, the Ionospheric Connection Explorer (ICON) Ion Velocity Meter (IVM) data, and ionosonde measurements. The main results are as follows: (1) A significant local ionospheric hole of more than 10 TECU depletion was observed near the epicenter ∼45 min after the eruption, comprising of several cascading TEC decreases and quasi-periodic oscillations. Such a deep local plasma hole was also observed by space-borne in-situ measurements, with an estimated horizontal radius of 10–15° and persisted for more than 10 hours in ICON-IVM ion density profiles until local sunrise. (2) Pronounced post-volcanic evening equatorial plasma bubbles (EPBs) were continuously observed across the wide Asia-Oceania area after the arrival of volcano-induced waves; these caused a Ne decrease of 2–3 orders of magnitude at Swarm/ICON altitude between 450-575 km, covered wide longitudinal ranges of more than 140°, and lasted around 12 hours. (3) Various acoustic-gravity wave modes due to volcano eruption were observed by accurate Beidou geostationary orbit (GEO) TEC, and the huge ionospheric hole was mainly caused by intense shock-acoustic impulses. TEC rate of change index revealed globally propagating ionospheric disturbances at a prevailing Lamb-wave mode of ∼315 m/s; the large-scale EPBs could be seeded by acoustic-gravity resonance and coupling to less-damped Lamb waves, under a favorable condition of volcano-induced enhancement of dusktime plasma upward E×B drift and postsunset rise of the equatorial ionospheric F-layer.

Prediction of Global Ionospheric TEC Based on Deep Learning

Abstract: The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS-TEC maps are established based on testing several different long short-term memory (LSTM) network (LSTM)-based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time. We find that a Multi-step auxiliary algorithm based prediction model performs best. It can effectively predict the global ionospheric IGS-TEC in the next 6 days (the mean absolute deviation (MAD) and root mean square error (RMSE) are 2.485 and 3.511 TECU, respectively) compared to the IRI (the MAD and RMSE are 4.248 and 5.593 TECU). The analyses of four geomagnetic storm events are completely separate from the time range of the training set, so as to further validate the performance of the model. The International Reference Ionosphere model is used as a reference for the performance of our predictive model, and a rotated persistence is estimated by time-shift algorithm of IGS-TEC. The result suggests that the Multi-step auxiliary prediction model has a good generalization performance and can have a relatively good stability and low error during a geomagnetic storm and quiet time.

An Ionospheric TEC Forecasting Model Based on a CNN-LSTM-Attention Mechanism Neural Network

Abstract: Ionospheric forecasts are critical for space-weather anomaly detection. Forecasting ionospheric total electron content (TEC) from the global navigation satellite system (GNSS) is of great significance to near-earth space environment monitoring. In this study, we propose a novel ionospheric TEC forecasting model based on deep learning, which consists of a convolutional neural network (CNN), long-short term memory (LSTM) neural network, and attention mechanism. The attention mechanism is added to the pooling layer and the fully connected layer to assign weights to improve the model. We use observation data from 24 GNSS stations from the Crustal Movement Observation Network of China (CMONOC) to model and forecast ionospheric TEC. We drive the model with six parameters of the TEC time series, Bz, Kp, Dst, and F10.7 indices and hour of day (HD). The new model is compared with the empirical model and the traditional neural network model. Experimental results show the CNN-LSTM-Attention neural network model performs well when compared to NeQuick, LSTM, and CNN-LSTM forecast models with a root mean square error (RMSE) and R2 of 1.87 TECU and 0.90, respectively. The accuracy and correlation of the prediction results remained stable in different months and under different geomagnetic conditions.

Using Support Vector Machine (SVM) with GPS Ionospheric TEC Estimations to Potentially Predict Earthquake Events

Abstract: There are significant controversies surrounding the detection of precursors that may precede earthquakes. Natural hazard signatures associated with strong earthquakes can appear in the lithosphere, troposphere, and ionosphere, where current remote sensing technologies have become valuable tools for detecting and measuring early warning signals of stress build-up deep in the Earth’s crust (presumably associated with earthquake events). Here, we propose implementing a machine learning support vector machine (SVM) technique, applied with GPS ionospheric total electron content (TEC) pre-processed time series estimations, to evaluate potential precursors caused by earthquakes and manifested as disturbances in the TEC data. After filtering and screening our data for solar or geomagnetic influences at different time scales, our results indicate that for large earthquakes (>Mw 6), true negative predictions can be achieved with 85.7% accuracy, and true positive predictions with an accuracy of 80%. We tested our method with different skill scores, such as accuracy (0.83), precision (0.85), recall (0.8), the Heidke skill score (0.66), and true skill statistics (0.66).

Individual Wave Propagations in Ionosphere and Troposphere Triggered by the Hunga Tonga-Hunga Ha'apai Underwater Volcano Eruption on 15 January 2022

Abstract: The devastating Hunga Tonga-Hunga Ha’apai underwater volcano erupted at ~04:15 UT on 15 January 2022. We captured the waves that erupted from the volcano propagating in the ionosphere by monitoring total electron content (TEC) perturbations utilizing ground-based global navigation satellite system (GNSS) receivers that receive electromagnetic signals transmitted from the geostationary satellites operated by the BeiDou Navigation Satellite System (BDS). Meanwhile, ground barometers detected unusual enhancements of air pressure traveling in the troposphere. A novel phenomenon shows that the waves can individually propagate with a speed of ~335 m/s in the ionosphere, which is faster than its’ ~305 m/s in the troposphere. We further examined multiple geophysical data at the particular site of the novel instrumental array. Analytical results show that the pressure enhancements traveling in the troposphere not only downward trigger ground vibrations mainly in the horizontal components without obvious time difference, but also upward, leading the secondary TEC perturbations with a ~12-min delay.

Comprehensive characterization of the prostate tumor microenvironment identifies CXCR4/CXCL12 crosstalk as a novel antiangiogenic therapeutic target in prostate cancer

Abstract: Crosstalk between neoplastic and stromal cells fosters prostate cancer (PCa) progression and dissemination. Insight in cell-to-cell communication networks provides new therapeutic avenues to mold processes that contribute to PCa tumor microenvironment (TME) alterations. Here we performed a detailed characterization of PCa tumor endothelial cells (TEC) to delineate intercellular crosstalk between TEC and the PCa TME.TEC isolated from 67 fresh radical prostatectomy (RP) specimens underwent multi-omic ex vivo characterization as well as orthogonal validation of both TEC functions and key markers by immunohistochemistry (IHC) and immunofluorescence (IF). To identify cell-cell interaction targets in TEC, we performed single-cell RNA sequencing (scRNA-seq) in four PCa patients who underwent a RP to catalogue cellular TME composition. Targets were cross-validated using IHC, publicly available datasets, cell culture expriments as well as a PCa xenograft mouse model.Compared to adjacent normal endothelial cells (NEC) bulk RNA-seq analysis revealed upregulation of genes associated with tumor vasculature, collagen modification and extracellular matrix remodeling in TEC. PTGIR, PLAC9, CXCL12 and VDR were identified as TEC markers and confirmed by IF and IHC in an independent patient cohort. By scRNA-seq we identified 27 cell (sub)types, including endothelial cells (EC) with arterial, venous and immature signatures, as well as angiogenic tip EC. A focused molecular analysis revealed that arterial TEC displayed highest CXCL12 mRNA expression levels when compared to all other TME cell (sub)populations and showed a negative prognostic role. Receptor-ligand interaction analysis predicted interactions between arterial TEC derived CXCL12 and its cognate receptor CXCR4 on angiogenic tip EC. CXCL12 was in vitro and in vivo validated as actionable TEC target by highlighting the vessel number- and density- reducing activity of the CXCR4-inhibitor AMD3100 in murine PCa as well as by inhibition of TEC proliferation and migration in vitro.Overall, our comprehensive analysis identified novel PCa TEC targets and highlights CXCR4/CXCL12 interaction as a potential novel target to interfere with tumor angiogenesis in PCa.

Swarm-TEC Satellite Measurements as a Potential Earthquake Precursor Together With Other Swarm and CSES Data: The Case of Mw7.6 2019 Papua New Guinea Seismic Event

Abstract: On May 14, 2019, a strong Mw = 7.6 shallow earthquake occurred in Papua New Guinea. This paper explores for the first time the analysis of total electron content (TEC) products measured for 6 months by GPS antenna onboard Swarm satellites, to detect possible seismo-ionospheric anomalies around the time and location of the above-mentioned earthquake. The night-time vertical total electron content (VTEC) time series measured using Swarm satellites Alpha and Charlie, inside the earthquake Dobrovolsky’s area show striking anomalies 31 and 35 days before the event. We successfully verified the possible presence of concomitant anomalous values of in situ electron density detected by the new Chinese satellite dedicated to search for electromagnetic earthquake precursors [China Seismo-Electromagnetic Satellite (CSES)-01]. On the other hand, the analysis of VTEC night time measured by Swarm Bravo shows gradual and abnormal increase of the VTEC parameter from about 23 days before the earthquake, which descends 3 days before the earthquake and reaches its lowest level around the earthquake day. We also analyzed the time series and tracks of other six in situ parameters measured by Swarm satellites, electron density from CSES, and also GPS-TEC measurements. As it is expected from the theory, the electron density anomalous variations acknowledge the Swarm VTEC anomalies, confirming that those anomalies are real and not an artifact of the analysis. The comparative analysis with measurements of other Swarm and CSES sensors emphasizes striking anomalies about 2.5 weeks before the event, with a clear pattern of the whole anomalies typical of a critical system as the earthquake process is for Earth. A confutation analysis outside the Dobrovolsky area and without significant seismicity shows no anomalies. Therefore based on our study, the VTEC products of Swarm satellites could be an appropriate precursor aside from the other measured plasma and magnetic parameters using Alpha, Bravo, and Charlie Swarm and CSES satellites that can be simultaneously analyzed to reduce the overall uncertainty.

Deep Recurrent Neural Networks for Ionospheric Variations Estimation Using GNSS Measurements

Abstract: Modeling ionospheric variability throughout a proper total electron content (TEC) parameter estimation is a demanding, however, crucial, process for achieving better accuracy and rapid convergence in precise point positioning (PPP). In particular, the single-frequency PPP (SF-PPP) method lacks accuracy due to the difficulty of dealing adequately with the ionospheric error sources. In order to apply ionosphere corrections in techniques, such as SF-PPP, external information of global ionosphere maps (GIMs) is crucial. In this article, we propose a deep learning model to efficiently predict TEC values and to replace the GIM-derived data that inherently have a global character, with equal or better in accuracy regional ones. The proposed model is suitable for predicting the ionosphere delay at different locations of receiver stations. The model is tested during different periods of time, under different solar and geomagnetic conditions and for stations in various latitudes, providing robust estimations of the ionospheric activity at the regional level. Our proposed model is a hybrid model comprising of a 1-D convolutional layer used for the optimal feature extraction and stacked recurrent layers used for temporal time series modeling. Thus, the model achieves good performance in TEC modeling compared to other state-of-the-art methods.

Machine Learning-Based Short-Term GPS TEC Forecasting During High Solar Activity and Magnetic Storm Periods

Abstract: Precise ionospheric total electron content (TEC) is critical for many aerospace applications, and forecasting ionospheric TEC is of great significance to it. Besides, short-term prediction of TEC values fills the gap between the TEC product latency and the precision. The machine learning-based approaches are promising in solving the nonlinear prediction issues, particularly suitable for short-term global positioning system TEC forecasting due to its complex temporal and spatial variation. In this article, four different machine learning models, i.e., artificial neural network, long short-term memory networks, adaptive neuro-fuzzy inference system based on subtractive clustering, and gradient boosting decision tree (GBDT) are applied for forecasting ionospheric TEC in three IGS GNSS monitoring stations at the low-latitude region (16°S to 10°S). The performance of these approaches in extreme conditions is investigated, including the high solar activity and magnetic storm, which are the most challenging scenario for TEC prediction. The results show that the machine learning algorithms outperform the global ionospheric map prediction model. The prediction accuracy during the high solar activity period was improved from 37.93% to 49.28%. During the magnetic storm period, the prediction accuracy was improved from 28.16% to 67.39%. Among the machine learning algorithms, the GBDT model outperforms the rest three algorithms in ionosphere prediction scenarios, which improves the prediction accuracy by 5.6% and 12.7% than the rest three approaches on average during high solar activity (2012–2015) and magnetic storm periods respectively.

An Approach for Predicting Global Ionospheric TEC Using Machine Learning

Abstract: Accurate corrections for ionospheric total electron content (TEC) and early warning information are crucial for global navigation satellite system (GNSS) applications under the influence of space weather. In this study, we propose to use a new machine learning model—the Prophet model, to predict the global ionospheric TEC by establishing a short-term ionospheric prediction model. We use 15th-order spherical harmonic coefficients provided by the Center for Orbit Determination in Europe (CODE) as the training data set. Historical spherical harmonic coefficient data from 7 days, 15 days, and 30 days are used as the training set to model and predict 256 spherical harmonic coefficients. We use the predicted coefficients to generate a global ionospheric TEC forecast map based on the spherical harmonic function model and select a year with low solar activity (63.4 < F10.7 < 81.8) and a year with the high solar activity (79.5 < F10.7 < 255.0) to carry out a sliding 2-day forecast experiment. Meanwhile, we verify the model performance by comparing the forecasting results with the CODE forecast product (COPG) and final product (CODG). The results show that we obtain the best predictions by using 15 days of historical data as the training set. Compared with the results of CODE’S 1-Day (C1PG) and CODE’S 2-Day (C2PG). The number of days with RMSE better than COPG on the first and second day of the low-solar-activity year is 151 and 158 days, respectively. This statistic for high-solar-activity year is 183 days and 135 days.

BCC: Blockchain-Based Collaborative Crowdsensing in Autonomous Vehicular Networks

Abstract: The vehicular crowdsensing, which benefits from edge computing devices (ECDs) distributedly selecting autonomous vehicles (AVs) to complete the sensing tasks and collecting the sensing results, represents a practical and promising solution to facilitate the autonomous vehicular networks (AVNs). With frequent data transaction and rewards distribution in the crowdsensing process, how to design an integrated scheme which guarantees the privacy of AVs and enables the ECDs to earn rewards securely while minimizing the task execution cost (TEC) therefore becomes a challenge. To this end, in this article, we develop a blockchain-based collaborative crowdsensing (BCC) scheme to support secure and efficient vehicular crowdsensing in AVNs. In the BCC, by considering the potential attacks in the crowdsensing process, we first develop a secure crowdsensing environment by designing a blockchain-based transaction architecture to deal with privacy and security issues. With the designed architecture, we then propose a coalition game with a transferable reward to motivate AVs to cooperatively execute the crowdsensing tasks by jointly considering the requirements of the tasks and the available sensing resources of AVs. After that, based on the merge and split rules, a coalition formation algorithm is designed to help each ECD select a group of AVs to form the optimal crowdsensing coalition (OCC) with the target of minimizing the TEC. Finally, we evaluate the TEC of the task and the rewards of the ECDs by comparing the proposed scheme with other schemes. The results show that our scheme can lead to a lower TEC for completing crowdsensing tasks and bring higher rewards to ECDs than the conventional schemes.

Deep Learning for Global Ionospheric TEC Forecasting: Different Approaches and Validation

Abstract: The application of deep learning technology to ionospheric prediction has become a new research hotspot. However, there are still some gaps, such as the prediction effect with different input solar and geomagnetic activity parameters, and the forecast accuracy with different prediction methods as well as the validation of long period data results, to be filled. We developed an ionospheric long short-term memory network (Ion-LSTM) with multiple input parameters to predict the global ionospheric total electron content (TEC). Two solutions with different ionospheric data based on Ion-LSTM were assessed, namely spherical harmonic coefficients (SHC) and vertical TEC (VTEC) prediction solution. The results show two solutions, both perform well in accuracy and stability. The input of the geomagnetic activity index improves the prediction effect of the model in the storm period. For the 1- and 2-day-predicted global ionospheric maps (GIMs) from 2015 to 2020, the root mean square error (RMSE) of SHC prediction solution is 1.69 TECU and 1.84 TECU while that of the VTEC prediction solution is 1.70 TECU and 1.84 TECU, respectively. Over 70% of the absolute residuals are within 3 TECU in high solar activity and over 96% in low solar activity. Further, by comparing the predicted results between Ion-LSTM and conventional methods (e.g., Center for Orbit Determination in Europe (CODE) predicted GIMs), the evaluation results show that the RMSE of Ion-LSTM is 0.7 TECU lower than that of CODE predicted GIMs under different solar and geomagnetic activities. Additionally, the accuracy of the Ion-LSTM prediction results decreases slightly as the input time span increases.

Comprehensive characterization of the prostate tumor microenvironment identifies CXCR4/CXCL12 crosstalk as a novel antiangiogenic therapeutic target in prostate cancer

Abstract: Crosstalk between neoplastic and stromal cells fosters prostate cancer (PCa) progression and dissemination. Insight in cell-to-cell communication networks provides new therapeutic avenues to mold processes that contribute to PCa tumor microenvironment (TME) alterations. Here we performed a detailed characterization of PCa tumor endothelial cells (TEC) to delineate intercellular crosstalk between TEC and the PCa TME.TEC isolated from 67 fresh radical prostatectomy (RP) specimens underwent multi-omic ex vivo characterization as well as orthogonal validation of both TEC functions and key markers by immunohistochemistry (IHC) and immunofluorescence (IF). To identify cell-cell interaction targets in TEC, we performed single-cell RNA sequencing (scRNA-seq) in four PCa patients who underwent a RP to catalogue cellular TME composition. Targets were cross-validated using IHC, publicly available datasets, cell culture expriments as well as a PCa xenograft mouse model.Compared to adjacent normal endothelial cells (NEC) bulk RNA-seq analysis revealed upregulation of genes associated with tumor vasculature, collagen modification and extracellular matrix remodeling in TEC. PTGIR, PLAC9, CXCL12 and VDR were identified as TEC markers and confirmed by IF and IHC in an independent patient cohort. By scRNA-seq we identified 27 cell (sub)types, including endothelial cells (EC) with arterial, venous and immature signatures, as well as angiogenic tip EC. A focused molecular analysis revealed that arterial TEC displayed highest CXCL12 mRNA expression levels when compared to all other TME cell (sub)populations and showed a negative prognostic role. Receptor-ligand interaction analysis predicted interactions between arterial TEC derived CXCL12 and its cognate receptor CXCR4 on angiogenic tip EC. CXCL12 was in vitro and in vivo validated as actionable TEC target by highlighting the vessel number- and density- reducing activity of the CXCR4-inhibitor AMD3100 in murine PCa as well as by inhibition of TEC proliferation and migration in vitro.Overall, our comprehensive analysis identified novel PCa TEC targets and highlights CXCR4/CXCL12 interaction as a potential novel target to interfere with tumor angiogenesis in PCa.

Utilizing a Long Short-Term Memory Algorithm Modified by Dwarf Mongoose Optimization to Predict Thermal Expansion of Cu-Al2O3 Nanocomposites

Abstract: This paper presents a machine learning model to predict the effect of Al2O3 nanoparticle content on the coefficient of thermal expansion in Cu-Al2O3 nanocomposites prepared using an in situ chemical technique. The model developed is a modification of Long Short-Term Memory (LSTM) using dwarf mongoose optimization (DMO), which mimics the behavior of DMO to find its food for predicting the behavior of the composite. The swarm of DMO consists of three groups, namely the alpha group, scouts, and babysitters. Each group has its own behavior to capture the food. The preparation of the nanocomposite was performed using aluminum nitrate that was added to a solution containing scattered copper nitrate. After that, the powders of CuO and Al2O3 were obtained, and the leftover liquid was removed using thermal treatment at 850 °C for 1 h. The powders were consolidated using compaction and sintering processes. The impact of Al2O3 contents on the thermal properties of the Cu-Al2O3 nanocomposite was investigated. The results showed that the Thermal Expansion Coefficient (TEC) decreases with increasing Al2O3 content due to the increased precipitation of Al2O3 nanoparticles at the grain boundaries of the Cu matrix. Moreover, the good interfacial bonding between Al2O3 and the Cu may participate in this decrease in TEC. The proposed machine learning model was able to predict the TEC of all the produced composites with different Al2O3 content and was tested at different temperatures with very good accuracy, reaching 99%.

Developing a Deep Learning-Based Detector of Magnetic, Ne, Te and TEC Anomalies from Swarm Satellites: The Case of Mw 7.1 2021 Japan Earthquake

Abstract: Since the appearance and evolution of earthquake ionospheric precursors are expected to show a nonlinear and complex behaviour, the use of nonlinear predictor models seems more appropriate. This paper proposes a new approach based on deep learning as a powerful tool for extracting the nonlinear patterns from a time series of ionospheric precursors. A Long Short-Term Memory (LSTM) network as a type of Recurrent Neural Network (RNN) was used to investigate 52 six-month time series, deduced from the three Swarm satellite (Alpha (A), Bravo (B) and Charlie (C)) measurements, including electron density (Ne), electron temperature (Te), magnetic scalar and vector (X, Y, Z) components, Slant and Vertical Total Electron Content (STEC and VTEC), for day and night periods around the time and location of a seismic event. This new approach was tested on a strong Mw = 7.1 earthquake in Japan on 13 February 2021, at 14:07:50 UTC by comparing the results with two implemented methods, i.e., Median and LSTM methods. Furthermore, clear anomalies are seen by a voting classification method 1, 6, 8, 13, 31 and 32 days before the earthquake. A comparison with atmospheric data investigation is further provided, supporting the lithosphere–atmosphere–ionosphere coupling (LAIC) mechanism as a suitable theory to explain the alteration of upper geolayers in the earthquake preparation phase. In other words, using multi-method and multi-precursor analysis applied to 52 time series and also to the orbit-by-orbit investigation, the observed anomalies on the previous day and up to 32 days before the event in normal solar and quiet geomagnetic conditions could be considered as a striking hint of the forthcoming Japan earthquake.

Geopolitics of technology: a new battleground?

Abstract: This study aims to consider the causality between global geopolitical risk (GPR) and technology (TEC) using the rolling window approach. The results reveal that GPR has a significant impact on TEC across different sub-samples. It points out that GPR increases TEC and both are highly integrated and the geopolitical constraints are pivotal to TEC development. On the other hand, TEC leads GPR due to competition between countries for geopolitical dominance and security. The results back the Becker Model, which reveals that divergence of interests between countries drive TEC competition and systematically associated with GPR in a country. The international community should create a mechanism that supports interoperability and the states should pay attention to TECs that are critical to economic and strategic interests in the long run. Moreover, the states could enhance cooperation with the private sector which is an important determinant.

Atmospheric modes excited by the 2021 August eruption of the Fukutoku-Okanoba volcano, Izu–Bonin Arc, observed as harmonic TEC oscillations by QZSS

Abstract: Abstract Continuous Plinian eruptions of volcanoes often excite atmospheric resonant oscillations with several distinct periods of a few minutes. We detected such harmonic oscillations by the 2021 August eruption of the Fukutoku-Okanoba volcano, a submarine volcano in the Izu–Bonin arc, in ionospheric total electron content (TEC) observed from Global Navigation Satellite System (GNSS) stations deployed on three nearby islands, Chichijima, Hahajima, and Iwojima. Continuous records with the geostationary satellite of Quasi-Zenith Satellite System (QZSS) presented four frequency peaks of such atmospheric modes. The harmonic TEC oscillations commenced at ~ 5:16 UT with a large amplitude but decayed in a few hours. Graphical Abstract

Range Geolocation Accuracy of C-/L-Band SAR and its Implications for Operational Stack Coregistration

Abstract: Time series analysis of synthetic aperture radar (SAR) and interferometric SAR generally starts with coregistration for the precise alignment of the stack of images. Here, we introduce a model-adjusted geometrical image coregistration (MAGIC) algorithm for stack coregistration. This algorithm corrects for atmospheric propagation delays and known surface motions using existing models and ensures simplicity and computational efficiency in the data processing systems. We validate this approach by evaluating the impact of different geolocation errors on stacks of the C-band Sentinel-1 and L-band ALOS-2 data, with a focus on the ionosphere. Our results show that the impact of the ionosphere dominates Sentinel-1 ascending (dusk-side) orbit and ALOS-2 data. After correcting for ionosphere using the JPL high-resolution global ionospheric maps, with topside total electron content (TEC) estimated from GPS receivers onboard the Sentinel-1 platforms, solid Earth tides, and troposphere, the mis-registration RMSE reduces by over a factor of four from 0.20 to 0.05 m for Sentinel-1 and from 2.66 to 0.56 m for ALOS-2. The results demonstrate that for Sentinel-1, the MAGIC approach is accurate enough in the range direction for most applications, including interferometry; while for the L-band SAR, it can be potentially accurate enough if topside TEC is available. Based on our current understanding of different error sources, we evaluate the expected range geolocation error budget for the upcoming NISAR mission with an upper bound of the relative geolocation error of 1.3 and 0.2 m for its L- and S-band SAR, respectively.

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method

Abstract: The ionospheric total electron content (TEC) is an important ionospheric parameter, and it is widely utilized in research such as space weather prediction and precise positioning. However, it is still challenging to develop an ionospheric TEC prediction model with high accuracy. In this study, a new ionospheric TEC model over China was developed using the bidirectional long short-term memory (bi-LSTM) method and observations from 257 ground-based global navigation satellite system (GNSS) stations in the Crustal Movement Observation Network of China from January 2018 to December 2021. The root mean square errors of the bi-LSTM-based model’s 1 and 2 hr ahead predictions on the test data set (from June 2021 to December 2021) are 1.12 and 1.68 TECU, respectively, which are 75/50/32% and 72/48/22% smaller than those of the IRI-2016, artificial neural network and LSTM-based models, correspondingly. The bi-LSTM-based model shows the best performance, which is most likely due to the fact that the sequence information in both forward and backward directions is taken into consideration in the new model. In addition, the diurnal variation, seasonal variation of the ionospheric TEC, and variations under geomagnetic storm conditions are successfully captured by the bi-LSTM-based model. Moreover, the TEC maps resulting from the bi-LSTM model agree well with those obtained from the final ionospheric product from the Chinese Academy of Sciences. Hence, the new model can be a good choice for the investigation of the spatiotemporal variation trend in the ionosphere and GNSS navigation.

Resident Waves in the Ionosphere Before the M6.1 Dali and M7.3 Qinghai Earthquakes of 21–22 May 2021

Abstract: Geostationary BeiDou satellites monitor the total electron content (TEC) in the ionosphere over certain locations 24 hr per day without interruption and act as ionosphere-based seismometers. The system detected perturbations in TEC before both the M6.1 Dali and M7.3 Qinghai earthquakes that occurred during the night of 21–22 May 2021. The TEC perturbations reside mainly over an area within a distance of ∼700 km from the epicenters of the earthquakes. The standing waves revealed the persistence of a subsurface wave source before the occurrences of the earthquakes, which differs from the co-seismic ionospheric distributions propagating away from the epicenters. The resident waves in TEC and ground vibrations share a frequency of ∼0.004 Hz, which can be attributed to the resonant coupling between the lithosphere and ionosphere.