Search Results (843)

On 10 May 2024, a super geomagnetic storm with a minimum Dst index of less than −400 nT occurred. It has attracted a significant amount of attention in the literature. Using total electron content (TEC) observations from a global navigation satellite system (GNSS), in situ electron density data from the Swarm satellite, and corresponding simulations from the thermosphere–ionosphere–electrodynamics general circulation model (TIEGCM), the dynamic poleward shift of the equatorial ionization anomaly (EIA) during the main phase of the super geomagnetic storm has been explored. The results show that the EIA crests moved poleward from ±15° magnetic latitude (MLat) to ±20° MLat at around 19.6 UT, to ±25° MLat at 21.2 UT, and to ±31° MLat at 22.7 UT. This poleward shift was primarily driven by the enhanced eastward electric field, neutral winds, and ambipolar diffusion. Storm-induced meridional winds can move ionospheric plasma upward/downward along geomagnetic field lines, causing the poleward movement of EIA crests, with minor contributions from zonal winds. Ambipolar diffusion contributes/prevents the formation of EIA crests at most EIA latitudes/the equatorward edge. Full article

Modern electronic devices such as smartphones, wearable devices, and robots typically integrate three-dimensional sensors to track the device’s movement in the 3D space. However, sensor measurements in three-dimensional vectors are highly sensitive to device orientation since a slight change in the device’s tilt or heading can change the vector values. To avoid complications, applications using these sensors often use only the magnitude of the vector, as in geomagnetic-based indoor positioning, or assume fixed device holding postures such as holding a smartphone in portrait mode only. However, using only the magnitude of the vector loses the directional information, while ad hoc posture assumptions work under controlled laboratory conditions but often fail in real-world scenarios. To resolve these problems, we propose a universal vector calibration algorithm that enables consistent three-dimensional vector measurements for the same physical activity, regardless of device orientation. The algorithm works in two stages. First, it transforms vector values in local coordinates to those in global coordinates by calibrating device tilting using pitch and roll angles computed from the initial vector values. Second, it additionally transforms vector values from the global coordinate to a reference coordinate when the target coordinate is different from the global coordinate by correcting yaw rotation to align with application-specific reference coordinate systems. We evaluated our algorithm on geomagnetic field-based indoor positioning and bidirectional step detection. For indoor positioning, our vector calibration achieved an 83.6% reduction in mismatches between sampled magnetic vectors and magnetic field map vectors and reduced the LSTM-based positioning error from 31.14 m to 0.66 m. For bidirectional step detection, the proposed algorithm with vector calibration improved step detection accuracy from 67.63% to 99.25% and forward/backward classification from 65.54% to 100% across various device orientations. Full article

Accurate modeling of ionospheric variability is critical for space weather forecasting and GNSS applications. While machine learning approaches have shown promise, progress is hindered by the absence of standardized benchmarking practices and narrow test periods. In this paper, we take the first step toward fostering rigorous and reproducible evaluation of AI models for ionospheric forecasting by introducing IonoBench: a benchmarking framework that employs a stratified data split, balancing solar intensity across subsets while preserving 16 high-impact geomagnetic storms (Dst ≤ $-100$ nT) for targeted stress testing. Using this framework, we benchmark a field-specific model (DCNN) against state-of-the-art spatiotemporal architectures (SwinLSTM and SimVPv2) using the climatological IRI 2020 model as a baseline reference. DCNN, though effective under quiet conditions, exhibits significant degradation during elevated solar and storm activity. SimVPv2 consistently provides the best performance, with superior evaluation metrics and stable error distributions. Compared to the C1PG baseline (the CODE 1-day forecast product), SimVPv2 achieves a notable RMSE reduction up to $32.1\%$ across various subsets under diverse solar conditions. The reported results highlight the value of cross-domain architectural transfer and comprehensive evaluation frameworks in ionospheric modeling. With IonoBench, we aim to provide an open-source foundation for reproducible comparisons, supporting more meticulous model evaluation and helping to bridge the gap between ionospheric research and modern spatiotemporal deep learning. Full article

In this cross-disciplinary investigation, we uncover a suite of previously unexamined factors and their intricate interplay that hold causal relationships with the distribution of Trachurus japonicus in the northern reaches of the South China Sea, thereby extending the existing research paradigms. Leveraging advanced machine learning algorithms and causal inference, our robust experimental design uncovered nine key global and regional factors affecting the distribution of T. japonicus density. A robust experimental design identified nine key factors significantly influencing this density: mean sea-level pressure (msl-0, msl-4), surface pressure (sp-0, sp-4), Summit ozone concentration (Ozone_sum), F10.7 solar flux index (F10.7_index), nitrate concentration at 20 m depth (N3M20), sonar-detected effective vertical range beneath the surface (Height), and survey month (Month). Crucially, stable causal relationships were identified among Ozone_sum, F10.7_index, Height, and N3M20. Variations in Ozone_sum likely impact surface UV radiation levels, influencing plankton dynamics (a primary food source) and potentially larval/juvenile fish survival. The F10.7_index, reflecting solar activity, may affect geomagnetic fields, potentially influencing the migration and orientation behavior of T. japonicus. N3M20 directly modulates primary productivity by limiting phytoplankton growth, thereby shaping the availability and distribution of prey organisms throughout the food web. Height defines the vertical habitat range acoustically detectable, intrinsically linking directly to the vertical distribution and availability of the fish stock itself. Surface pressures (msl-0/sp-0) and their lagged effects (msl-4/sp-4) significantly influence sea surface temperature profiles, ocean currents, and stratification, all critical determinants of suitable habitats and prey aggregation. The strong influence of Month predominantly reflects seasonal changes in water temperature, reproductive cycles, and associated shifts in nutrient supply and plankton blooms. Rigorous robustness checks (Data Subset and Random Common Cause Refutation) confirmed the reliability and consistency of these causal findings. This elucidation of the distinct biological and physical pathways linking these diverse factors leading to T. japonicus density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies. Full article

This study focuses on the mid- and low-midlatitude ionospheric response to the 2024 Mother’s Day superstorm, utilizing ground-based and Swarm satellite observations. The ground-based ionosonde measured F1, F2-layer, B0 and B1 parameters, as well as isodensity data, were used. The ionospheric absorption was investigated with the so-called amplitude method, which is based on ionosonde data. Auroral sporadic E-layer was the first time ever recorded at Sopron. Moreover, the auroral F-layer appeared at exceptionally low latitude (35° mlat, over San Vito) during the storm main phase. These unprecedented detections were confirmed by optical all-sky cameras. The observations revealed that these events were linked to the extreme equatorward shift of the auroral oval along with the midlatitude trough. As a result, the midlatitude ionosphere became confined to the trough itself. Three stages of F2-layer uplift were identified during the night of 10/11 May, each caused by different mechanisms: most probably by the effect of prompt penetration electric fields (PPEFs) (1), the travelling ionospheric disturbances (TIDs) (2) and the combination of electrodynamic processes and decreased O/N₂ ratio (3). After a short interval of G-condition, an unprecedented extended disappearance of the layers was observed during daytime hours on 11 May, which was further confirmed by Swarm data. This phenomenon appeared to be associated with a reduced O/N₂ along with the influence of disturbance dynamo electric fields (DDEFs) and it cannot be explained only by the increased ionospheric absorption according to the results of the amplitude method. Full article

The ionospheric response to the major geomagnetic storm (SYM-H = −518 nT) of 10–11 May 2024 was investigated using total electron content (TEC) observations from the Central Asian (CAR) and East Asian (EAR) regions. In the CAR region, shortly after the storm sudden commencement (SC) (17:05 UT on 10 May), during a rapid decrease in SYM-H, a significant TEC decrease (~70%) and a subsequent formation of a prolonged TEC depletion phase on 11 May were observed. The duration of the phase’s maximum intensity seemed to agree with the duration of southward interplanetary magnetic field (IMF) Bz activity. The total duration of the negative phase exceeded 3 days and correlated with the duration of the Auroral Electrojet (AE) index activity. The ionospheric response in the EAR region differed significantly, exhibiting a secondary, deeper TEC decrease (termed “phase 2”) on 12 May, which occurred during a period of reduced AE and IMF Bz activity. The analysis of latitudinal TEC variations in the EAR region revealed that “phase 2” occurred across a geographic latitude range of 31.4° N to 43.9° N (approximately 21° N to 34° N dipole latitude). These results are discussed in the context of potential longitudinal variations in thermospheric composition and meridional circulation during the geomagnetic storm. Full article

In complex environments where satellite signals may be interfered with, it is difficult to achieve precise positioning of high-speed aerial vehicles solely through the inertial navigation system. To overcome this challenge, this paper proposes an NGO-ELM geomagnetic matching-assisted navigation algorithm, in which the Northern Goshawk Optimization (NGO) algorithm is used to optimize the initial weights and biases of the Extreme Learning Machine (ELM). To enhance the matching performance of the NGO-ELM algorithm, three improvements are proposed to the NGO algorithm. The effectiveness of these improvements is validated using the CEC2005 benchmark function suite. Additionally, the IGRF-13 model is utilized to generate a geomagnetic matching dataset, followed by comparative testing of five geomagnetic matching models: INGO-ELM, NGO-ELM, ELM, INGO-XGBoost, and INGO-BP. The simulation results show that after the airborne equipment acquires the geomagnetic data, it only takes 0.27 µs to obtain the latitude, longitude, and altitude of the aerial vehicle through the INGO-ELM model. After unit conversion, the average absolute errors are approximately 6.38 m, 6.43 m, and 0.0137 m, respectively, which significantly outperform the results of four other models. Furthermore, when noise is introduced into the test set inputs, the positioning error of the INGO-ELM model remains within the same order of magnitude as those before the noise was added, indicating that the model exhibits excellent robustness. It has been verified that the geomagnetic matching-assisted navigation algorithm proposed in this paper can achieve real-time, accurate, and stable positioning, even in the presence of observational errors from the magnetic sensor. Full article

This study examines the impact of geomagnetic disturbances quantified by the Kp and Dst indices on the accuracy of single-frequency GPS positioning across mid-latitudes and the equatorial zone, with a focus on temporal and spatial positioning errors variability. GNSS data from a globally distributed network of 14 IGS stations were analyzed for September 2017, featuring significant geomagnetic activity. The selection of stations encompassed equatorial and mid-latitude regions (approximately ±45°), strategically aligned with the distribution of the Dst index during geomagnetic storms. Satellite navigation data were processed using RTKLIB software in standalone mode with standardized atmospheric and orbital corrections. The GPS was chosen over GLONASS following preliminary testing, which revealed a higher sensitivity of GPS positional accuracy to variations in geomagnetic indices such as Kp and Dst, despite generally lower total error magnitudes. The ECEF coordinate system calculates the total GPS error as the vector sum of deviations in the X, Y, and Z axes. Statistical evaluation was performed using One-Way Repeated Measures ANOVA to determine whether positional error variances across geomagnetic activity phases were significant. The results of the variance analysis confirm that the variation in the total GPS positioning error is non-random and can be attributed to the influence of geomagnetic storms. However, regression analysis reveals that the impact of geomagnetic storms (quantified by Kp and Dst) displays spatiotemporal variability, with no consistent correlation to GPS positioning error dynamics. The findings, as well as the developed methodology, have qualitative implications for GNSS-dependent operations in sensitive sectors such as navigation, timing services, and geospatial monitoring. Full article

We present lightning-induced ionospheric perturbations in narrowband very-low-frequency (VLF) signals from the transmitters NWC (21.82° S, 114.17° E, 19.8 kHz) and VTX (8.4° N, 77.8° E, 18.6 kHz) recorded at the low-latitude station Dehradun (DDN; 30.3° N, 78.0° E) over a 12-month period from September 2020 to October 2021. Early/slow VLF events, VLF LOREs, and step-like VLF LOREs associated with lightning were analyzed for their onset and recovery times. This study utilized data from the World Wide Lightning Location Network (WWLLN), which provides lightning locations and energy estimates. The results show that early/slow VLF events occur most frequently, accounting for approximately 68% of cases, followed by VLF LOREs at 12%, and step-like VLF LOREs at 10%. Furthermore, we observed that 100% of the VLF perturbing events occurred during the nighttime, which is not entirely consistent with previous studies. Moreover, more than 60% of VLF LOREs were associated with lightning energies of approximately 1 kJ, and about 40% were associated with lightning energies of ~10 kJ. Step-like VLF LOREs were linked to WWLLN energies between 1 and 5 kJ. The observed WWLLN energy range is somewhat lower than the energies reported in previous studies. Scattering characteristics revealed that 87.3% of events were associated with wide-angle scattering, while approximately 12.6% were linked to narrow-angle scattering. LWPC version 2.1 was used to simulate these perturbing events and to estimate the reflection height (H′, in km) and the exponential sharpness factor (β, in km⁻¹) corresponding to changes in D-region electron density. The reflection height (H′, in km) and the exponential sharpness factor (β, in km⁻¹) of the D-region varied from 83 to 87 km and from 0.42 to 0.79 km⁻¹ for early/slow VLF events, from 83 to 85 km and from 0.5 to 0.75 km⁻¹ for step-like VLF LOREs, and from 81 to 83 km and from 0.75 to 0.81 km⁻¹ for VLF LOREs, respectively. Full article

The sudden increase of fluxes of quasi-trapped energetic electrons under the Earth’s radiation belt (ERB) has remained a puzzling phenomenon for decades. It is known as enhancements of forbidden energetic electrons (FEEs). The FEE enhancements are occasionally observed by low-Earth orbit NOAA/POES satellites. Previously, no strong correlation was established between FEEs and geomagnetic activity, while external control of FEE occurrence by solar activity and interplanetary parameters was revealed on a long time-scale. Two important questions are still open: (1) key parameters of the mechanism and (2) solar wind drivers or triggers. In the present study we conducted detailed analysis of three FEE events that occurred during the greatest geomagnetic storms, which dramatically affected space weather. The FEE enhancements occurred under northward IMF and, thus, Bz and convection electric fields could have been neither driver nor trigger. We found that an abrupt and significant change in solar wind pressure is a key solar wind driver of the FEE enhancements observed. The characteristic time of FEE injection from the inner edge of the ERB at L-shell 1.2 to the forbidden zone at L < 1.1 was estimated to be 10–20 min. In the mechanism of ExB drift, this characteristic time corresponds to the radial inward transport of electrons caused by a transient electric field with the magnitude ~10 mV/m. Full article

With the widespread application of DC technology in data centers, renewable energy, electric transportation, and high-voltage direct current (HVDC) transmission, DC rectangular busbars are becoming increasingly important in power transmission systems due to their high current density and compact structure. However, space constraints make the deployment of conventional sensors challenging, highlighting the urgent need for miniaturized, non-contact current measurement technologies to meet the integration requirements of smart distribution systems. This paper proposes a field-source inversion-based contactless DC measurement method for rectangular busbars. The mathematical model of the magnetic field near the surface of the DC rectangular busbar is first established, incorporating the busbar eccentricity, rotation, and geomagnetic interference into the model framework. Subsequently, a magnetic field–current inversion model is constructed, and the DC measurement of the rectangular busbar is achieved by performing an inverse calculation. The effectiveness of the proposed method is validated by both simulation studies and physical experiments. Full article

The present study analyzes the global ionospheric response to the intense geomagnetic storm of 10–11 October 2024 (SYM—H minimum of −346 nT), using observations from COSMIC—2 and Swarm satellites, GNSS TEC, and Digisondes. Significant uplift of the F-region was observed across both Hemispheres on the dayside, primarily driven by equatorward thermospheric winds and prompt penetration electric fields (PPEFs). However, this uplift did not correspond with increases in foF2 due to enhanced molecular nitrogen-promoting recombination in sunlit regions and the F2 peak rising beyond the COSMIC—2 detection range. In contrast, in the Southern Hemisphere nightside ionosphere exhibited pronounced Ne depletion and low hmF2 values, attributed to G-conditions and thermospheric composition changes caused by storm-time circulation. Strong vertical plasma drifts exceeding 100 m/s were observed during both the main and recovery phases, particularly over Ascension Island, driven initially by southward IMF—Bz-induced PPEFs and later by disturbance dynamo electric fields (DDEFs) as IMF—Bz turned northward. Swarm data revealed a poleward expansion of the Equatorial Ionization Anomaly (EIA), with more pronounced effects in the Southern Hemisphere due to seasonal and longitudinal variations in ionospheric conductivity. Additionally, the storm excited Large-Scale Travelling Ionospheric Disturbances (LSTIDs), triggered by thermospheric perturbations and electrodynamic drivers, including PPEFs and DDEFs. These disturbances, along with enhanced westward thermospheric wind and altered zonal electric fields, modulated ionospheric irregularity intensity and distribution. The emergence of anti-Sq current systems further disrupted quiet-time electrodynamics, promoting global LSTID activity. Furthermore, storm-induced equatorial plasma bubbles (EPBs) were observed over Southeast Asia, initiated by enhanced PPEFs during the main phase and suppressed during recovery, consistent with super EPB development mechanisms. Full article

While space weather indices (e.g., F10.7, Dst index) are commonly employed to characterize ionospheric activity levels, the Global Mean Electron Content (GMEC) provides a more direct and comprehensive indicator of the global ionospheric state. This metric demonstrates greater potential than space weather indices for calibrating empirical ionospheric models such as IRI-2020. The COSMIC-2 constellation enables continuous, all-weather global ionospheric monitoring via radio occultation, unimpeded by land–sea distribution constraints, with over 8000 daily occultation events suitable for GMEC modeling. This study developed two lightweight GMEC models using COSMIC-2 data: (1) a POD GMEC model based on slant TEC (STEC) extracted from Level 1b podTc2 products and (2) a PROF GMEC model derived from vertical TEC (VTEC) calculated from electron density profiles (EDPs) in Level 2 ionPrf products. Both backpropagation neural network (BPNN)-based models generate hourly GMEC outputs as global spatial averages. Critically, GMEC serves as an essential intermediate step that addresses the challenges of utilizing spatially irregular occultation data by compressing COSMIC-2’s ionospheric information into an integrated metric. Building on this compressed representation, we implemented a convolutional neural network (CNN) that incorporates GMEC as an auxiliary feature to calibrate IRI-2020’s global ionospheric maps. This approach enables computationally efficient correction of systemic IRI TEC errors. Experimental results demonstrate (i) 48.5% higher accuracy in POD/PROF GMEC relative to IRI-2020 GMEC estimates, and (ii) the calibrated global IRI TEC model (designated GCIRI TEC) reduces errors by 50.15% during geomagnetically quiet periods and 28.5% during geomagnetic storms compared to the original IRI model. Full article

This study investigates pre-seismic ionospheric anomalies preceding the 2023 Jishishan Ms6.2 earthquake using total electron content (TEC) data derived from BDS geostationary orbit (GEO) satellites. Multi-scale analysis integrating Butterworth filtering and wavelet transforms resolved TEC disturbances into three distinct frequency regimes: (1) high-frequency perturbations (0.56–3.33 mHz) showed localized disturbances (amplitude ≤ 4 TECU, range < 300 km), potentially associated with near-field acoustic waves from crustal stress adjustments; (2) mid-frequency signals (0.28–0.56 mHz) exhibited anisotropic propagation (>1200 km) with azimuth-dependent N-shaped waveforms, consistent with the characteristics of acoustic–gravity waves (AGWs); and (3) low-frequency components (0.18–0.28 mHz) demonstrated phase reversal and power-law amplitude attenuation, suggesting possible lithosphere–atmosphere–ionosphere (LAI) coupling oscillations. The stark contrast between near-field residuals and far-field weak fluctuations highlighted the dominance of large-scale atmospheric gravity waves over localized acoustic disturbances. Geometry-based velocity inversion revealed incoherent high-frequency dynamics (5–30 min) versus anisotropic mid/low-frequency traveling ionospheric disturbance (TID) propagation (30–90 min) at 175–270 m/s, aligning with theoretical AGW behavior. During concurrent G1-class geomagnetic storm activity, spatial attenuation gradients and velocity anisotropy appear primarily consistent with seismogenic sources, providing insights for precursor discrimination and contributing to understanding multi-scale coupling in seismo-ionospheric systems. Full article

In view of the problems of poor safety, slow detection speed, and low accuracy of existing nondestructive testing (NDT) technologies, such as X-ray methods and ultrasonic detection in detecting electron beam weld defects in aluminum alloys, this study proposes a weak magnetic NDT method based on the geomagnetic field. Firstly, the finite element analysis method was used to establish a simulation model of aluminum alloy electron beam welding defects, and the distribution characteristics of the magnetic field around weld defects, such as cracks and pores, were obtained. Then, the magnetic anomaly signal at the crack weld was identified by combining the wavelet transform and the least squares method. Finally, experimental tests show that the proposed method can safely, quickly, and accurately detect the defects of aluminum alloy electron beam welds. Full article