Search Results (246)

Magnetocardiography (MCG) offers a noninvasive method for early screening and precise localization of cardiovascular diseases by measuring picotesla-level weak magnetic fields induced by cardiac electrical activity. However, in unshielded magnetic environments, geomagnetic disturbances, power-frequency electromagnetic interference, and physiological/motion artifacts can significantly overwhelm effective magnetocardiographic components. To address this challenge, this paper systematically constructs an integrated denoising framework, termed “AOA-VMD-WT”. In this approach, the Arithmetic Optimization Algorithm (AOA) adaptively optimizes the key parameters (decomposition level K and penalty factor α) of Variational Mode Decomposition (VMD). The decomposed components are then regularized based on their modal center frequencies: components with frequencies ≥50 Hz are directly suppressed; those with frequencies <50 Hz undergo wavelet threshold (WT) denoising; and those with frequencies <0.5 Hz undergo baseline correction. The purified signal is subsequently reconstructed. For quantitative evaluation, we designed performance indicators including QRS amplitude retention rate, high/low frequency suppression amount, and spectral entropy. Further comparisons are made with baseline methods such as FIR and wavelet soft/hard thresholds. Experimental results on multiple sets of measured MCG data demonstrate that the proposed method achieves an average improvement of approximately 8–15 dB in high-frequency suppression, 2–8 dB in low-frequency suppression, and a decrease in spectral entropy ranging from 0.1 to 0.6 without compromising QRS amplitude. Additionally, the parameter optimization exhibits high stability. These findings suggest that the proposed framework provides engineerable algorithmic support for stable MCG measurement in ordinary clinic scenarios. Full article

We present three dimensional particle-in-cell simulations of current-voltage characteristics of the hemispherical Langmuir probe (LAP), onboard the China Seismo-Electromagnetic Satellite (CSES). Using realistic plasma parameters and background magnetic fields obtained from the International Reference Ionosphere (IRI) and International Geomagnetic Reference Field (IGRF) models, we simulate probe–plasma interactions at three locations: the equatorial region and two magnetically conjugate mid-latitude sites: Millstone Hill (Northern Hemisphere) and Rothera (Southern Hemisphere). The simulations, performed using the PTetra PIC code, incorporate realistic LAP geometry and spacecraft motion in the ionospheric plasma. Simulated current voltage characteristics or I–V curves are compared against in-situ LAP measurements from CSES Orbit-026610, with Pearson’s correlation coefficients used to assess agreement. Our findings indicate how plasma temperature, density, and magnetization affect sheath structure and probe floating potential. The study highlights the significance of kinetic modeling in enhancing diagnostic accuracy, particularly in variable sheath regimes where classic analytical models such as the Orbital-Motion-Limited (OML) theory may be inadequate. Full article

Conventional thermospheric density models are limited in their ability to capture solar-geomagnetic coupling dynamics and lack probabilistic uncertainty estimates. We present MSIS-UN (NRLMSISE-00 with Uncertainty Quantification), an innovative framework integrating sparse principal component analysis (sPCA) with heteroscedastic neural networks. Our methodology leverages multi-satellite density measurements from the CHAMP, GRACE, and SWARM missions, coupled with MSIS-00-derived exospheric temperature (tinf) data. The technical approach features three key innovations: (1) spherical harmonic decomposition of T∞ using spatiotemporally orthogonal basis functions, (2) sPCA-based extraction of dominant modes from sparse orbital sampling data, and (3) neural network prediction of temporal coefficients with built-in uncertainty quantification. This integrated framework significantly enhances the temperature calculation module in MSIS-00 while providing probabilistic density estimates. Validation against SWARM-C measurements demonstrates superior performance, reducing mean absolute error (MAE) during quiet periods from MSIS-00’s 44.1% to 23.7%, with uncertainty bounds (1σ) achieving an MAE of 8.4%. The model’s dynamic confidence intervals enable rigorous probabilistic risk assessment for LEO satellite collision avoidance systems, representing a paradigm shift from deterministic to probabilistic modeling of thermospheric density. Full article

The formation of international networks of magnetic observatories has provided new opportunities for monitoring geomagnetic disturbances and solving problems related to space weather. On the other hand, collective processing of data from numerous observatories revealed problems with the quality of data provided by some of them. Sometimes, the reason for the decrease in data quality is associated with faulty measuring instruments. However, every year, industrial interference also increases its influence on measurement quality. INTERMAGNET suggests carrying out a mutual comparison between the values of neighboring magnetic observatories as one of the methods for solving the problem of maintaining data quality. This paper proposes a method to improve the quality of magnetograms, which makes it possible to eliminate the results of interference of local origin through the use of four spatially separated magnetic observatories located in Kazakhstan. This method is suitable for automating the magnetograms’ quality control process. Full article

In this study, we present the dating results of an ancient kiln excavated near Ceva (Northern Italy) obtained through combined archaeomagnetic and thermoluminescence approaches. For archaeomagnetic dating, the full geomagnetic field vector (both direction and intensity) was determined. The archaeomagnetic direction was defined through stepwise alternating field demagnetization of in situ-oriented samples of baked clay, and the archaeointensity value was obtained through the Thellier–Coe protocol, including corrections for magnetic anisotropy and cooling rate effects. Thermoluminescence analyses were obtained individually on three samples, using the conventional multiple-aliquot, additive dose procedure. Archaeomagnetic dating was carried out twice, once using the directional results only and once using the full geomagnetic field vector. The independent dating provided by the thermoluminescence analysis was used for comparison, examining the added value of incorporating archaeointensity measurements alongside directional data. The new archaeomagnetic and thermoluminescence results were integrated with previously available radiocarbon dating, using Bayesian modeling for chronological reconstructions. Our results show that the use of archaeointensity in archaeomagnetic dating can be advantageous, better refining the dating. This multidisciplinary strategy underscores the significance of cross-dating in establishing robust chronological frameworks and highlights the crucial role of transdisciplinary methodologies in advancing and refining dating techniques in archaeology. Full article

The geomagnetic dynamo is currently considered the most likely source of the Earth’s main dipole field. However, the radius of the current ring located in the Earth’s core is not reliably known. There are methods for indirectly estimating the radius of this current. Another method is proposed that allows one to indirectly estimate the radius of the current ring inside the Earth’s core based on measurements of the Earth’s magnetic field by observatories included in the INTERMAGNET network. The results of measurements taken on a day with low magnetic activity were compared using the least squares method with fields that could be created by ring currents of different diameters at the locations of magnetic observatories. The assumption was made that the ring current in the model used is located in the plane of the Earth’s equator with the center coinciding with the axis of rotation of the Earth. Estimates of the current radius in the range of 957–1595 km were obtained, which corresponds to the boundary between the solid and liquid cores of the Earth. These results can refine the model of the structure of the Earth’s core and Earth’s magnetism. Full article

This study examines the ionospheric response to the intense geomagnetic storm of 9–12 October 2024 over the European sector. Digisonde data from mid-latitude European stations and in situ electron density measurements from Swarm A and B satellites were used to analyze variations in key ionospheric characteristics, including the critical frequency (foF2), peak height (hmF2) and plasma drift velocities. Significant uplift of the F2 layer and a corresponding reduction in foF2 were observed across latitudes, primarily driven by prompt penetration electric fields (PPEFs) and storm-induced thermospheric winds. Horizontal and vertical ion drifts showed large asymmetries and reversals, with zonal drift velocities exceeding 1000 m/s at some stations. Swarm observations confirmed plasma density enhancements during the main phase and notable depletions during recovery, particularly after 1:00 UT on 11 October. The midlatitude ionospheric trough (MIT) intensified during the recovery phase, as can be seen from Swarm B. These variations were shaped by electrodynamic forcing, compositional changes and disturbance dynamo electric fields (DDEFs). The results emphasize the role of solar wind drivers, latitude-dependent electrodynamic coupling and thermospheric dynamics in mid-latitude ionospheric variability during geomagnetic storms. Full article

As contemporary society and the global economy become increasingly dependent on satellite-based systems, the need for reliable and resilient positioning, navigation, and timing (PNT) services has never been more critical. This study investigates the impact of the geomagnetic storm that occurred in May 2024 on the performance of global navigation satellite system (GNSS) low-cost permanent stations. The research evaluates the influence of ionospheric disturbances on both positioning performance and raw GNSS observations. Two days were analyzed: 8 May 2024 (DOY 129), representing quiet ionospheric conditions, and 11 May 2024 (DOY 132), coinciding with the peak of the geomagnetic storm. Precise Point Positioning (PPP) and static relative positioning techniques were applied to data from a low-cost GNSS station (DYVA), supported by comparative analysis using a nearby geodetic-grade station (TRDS00NOR). The results showed that while RMS positioning errors remained relatively stable over 24 h, the maximum errors increased significantly during the storm, with the 3D positioning error nearly doubling on DOY 132. Short-term analysis revealed even larger disturbances, particularly in the vertical component, which reached up to 3.39 m. Relative positioning analysis confirmed the vulnerability of single-frequency (L1) solutions to ionospheric disturbances, whereas dual-frequency (L1+L2) configurations substantially mitigated errors, highlighting the effectiveness of ionosphere-free combinations during storm events. In the second phase, raw GNSS observation quality was assessed using detrended GPS L1 carrier-phase residuals and signal strength metrics. The analysis revealed increased phase instability and signal degradation on DOY 132, with visible cycle slips occurring between epochs 19 and 21. Furthermore, the average signal-to-noise ratio (SNR) decreased by approximately 13% for satellites in the northwest sky sector, and a 5% rise in total cycle slips was recorded compared with the quiet day. These indicators confirm the elevated measurement noise and signal disruption associated with geomagnetic activity. These findings provide a quantitative assessment of low-cost GNSS receiver performance under geomagnetic storm conditions. This study emphasizes their utility for densifying GNSS infrastructure, particularly in regions lacking access to geodetic-grade equipment, while also outlining the challenges posed by space weather. Full article

This paper presents a bionic extended Kalman filter (EKF) state estimation algorithm for agricultural planters, inspired by the bionic mechanism of migratory birds navigating in complex environments, where migratory birds achieve precise localization behaviors by fusing multi-sensory information (e.g., geomagnetic field, visual landmarks, and somatosensory balance). The algorithm mimics the migratory bird’s ability to integrate multimodal information by fusing laser SLAM, inertial measurement unit (IMU), and GPS data to estimate the position, velocity, and attitude of the planter in real time. Adopting a nonlinear processing approach, the EKF effectively handles nonlinear dynamic characteristics in complex terrain, similar to the adaptive response of a biological nervous system to environmental perturbations. The algorithm demonstrates bio-inspired robustness through the derivation of the nonlinear dynamic teaching model and measurement model and is able to provide high-precision state estimation in complex environments such as mountainous or hilly terrain. Simulation results show that the algorithm significantly improves the navigation accuracy of the planter in unstructured environments. A new method of bio-inspired adaptive state estimation is provided. Full article

In aeromagnetic detection and geomagnetic navigation, the reference geomagnetic maps usually need to be continued to different altitudes. Traditionally, the geomagnetic field upward continuation is stable. Nevertheless, the downward continuation is instable near the magnetic source and sensitive to the high-frequency noise. To address the problem, this article proposes a recursive truncated Taylor expansion (RTTE) downward continuation method for geomagnetic field. This method models the geomagnetic field in the vertical direction. The coefficients of the model are calculated based on the harmonicity of the geomagnetic field to ensure stability; a recursive process is implemented to extend its effect under a large continuation distance. To demonstrate the effectiveness of the proposed method, this paper compares the effects of the traditional Landweber iteration method and the proposed method using simulation data and real measured data. Under real measurement conditions, the MAE and RMSE of the proposed RTTE method are 0.1878 nT and 0.3184 nT, respectively, representing a reduction of 90.33% and 95.75% compared to the Landweber iteration method. The results show that the proposed RTTE method significantly improves the continuation accuracy compared with traditional methods, providing support for geomagnetic navigation and aeromagnetic exploration. 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

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

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

During geomagnetic storms, a geomagnetically induced current (GIC) flows into grounding transformers, potentially causing anomalous vibrations and audible noise in internal components. This study establishes an electromagnetic–mechanical–acoustic coupling (EMAC) model to characterize the multi-physics interactions in transformers under GIC interference. Based on the measured data, the GIC is classified into fluctuating and constant components according to its fluctuation characteristics. A propagation-path-based coupling model is proposed to investigate the correlated interactions among physical fields, extracting critical parameters, including winding current, magnetic flux, electromagnetic force, vibration, and noise. Comparative simulations reveal that the fluctuating component induces more complex multi-physics variations, generating significantly higher vibration amplitudes and noise levels compared to those of the constant component. A dynamic experimental platform is built to obtain multi-physics field information in different modes, and the effectiveness of the model and the correctness of the conclusions are verified through virtual–physical consistency validation. On this basis, multimodal feature information domains are established to delineate the operational state intervals of the transformer under GIC interference. Stability threshold criteria are subsequently developed, providing a critical quantitative basis for the condition monitoring of power transformers. Full article

This study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum usable frequency of the F2 layer for a 3000 km circuit (MUF(3000)F2) based on the total electron content (TEC) observed by global navigation satellite system (GNSS) receivers. The ionospheric dataset used comprised TEC, foF2, and MUF(3000)F2 measurements from 11 stations in China during a solar activity period (2008–2020). The results indicate that all three machine learning models performed better than the IRI-2020 model, with varying levels of accuracy. For foF2 (MUF(3000)F2) estimation, the root mean square error (RMSE) values at Kunming and Xi’an stations were reduced by approximately 38% (26%) and 18% (11%), respectively, compared to IRI-2020. During geomagnetic disturbances, all three models were able to reproduce the variations in both foF2 and MUF(3000)F2 parameters. Nevertheless, the RF model showed significantly better performance in foF2 estimation compared to the SVM and BPNN models. Full article