Search

The occurrence of small‐scale and intense ionospheric currents that can contribute to geomagnetically induced currents have recently been discovered. A difficulty in their characterization is that their signatures are often only observed at single widely spaced (typically 200–400 km) ground geomagnetic stations. These small‐scale structures motivate the examination of the maximum station separation required to fully characterize these small‐scale signatures. We analyze distributions of correlation coefficients between closely spaced mid‐latitude and auroral zone ground magnetometer stations spanning day to month long intervals to assess the separation distance at which geomagnetic signatures appear in only one station. Distributions were analyzed using periods that included low and high geomagnetic activity. We used data from pairs of magnetometer stations across North America within 200 km of each other, all of which were separated primarily latitudinally. Results show that while measurements remain largely similar up to separations of 200 km, large and frequent differences appear starting at around 130 km separation. Larger differences and lower correlations are observed during high geomagnetic activity, while low geomagnetic activity leads to frequent high correlation even past 200 km separation. Small but identifiable differences can appear in magnetometer data from stations as close as 35 km during high geomagnetic activity. We recommend future magnetometer array deployment in the auroral and sub‐auroral zone to have separations of 100–150 km. This enables the monitoring of large scale effects of geomagnetic storms, better temporal and spatial resolution of substorms, and observations of small‐scale current signatures. Plain Language Summary: Magnetic field data, gathered by magnetometer stations, are used to observe currents in the ionosphere and throughout the magnetosphere. Magnetometer stations are generally separated by 200–400 km. High‐frequency (1 s) data have become the norm in recent years. These data have uncovered that this spacing is too large to fully observe small currents in the ionosphere, which can damage technology on the ground if undetected. We looked at correlations between magnetometer stations separated by < ${< } $200 km across northern North America to analyze how observations change over various distances. We found that at high latitudes (>63° ${ >} 63{}^{\circ}$ magnetic latitude) and during high geomagnetic activity, substantial differences can appear over intervals < ${< } $150 km. Because of these findings, we suggest that future magnetometer stations placed at high latitudes should be separated by < ${< } $150 km. Key Points: The conventional wisdom for ground magnetometer station separation is based on outdated assumptionsCurrent magnetometer arrays do not sufficiently measure small‐scale geomagnetic disturbancesRecommendation for spacing of future ground magnetometers in the auroral zone is 100–150 km [ABSTRACT FROM AUTHOR]

Background: Studies describing the movement of free-ranging animals often use remotely collected global positioning system (GPS) data. However, such data typically only include intermittent positional information, with a sampling frequency that is constrained by battery life, producing sub-sampling effects that have the potential to bias interpretation. GPS-enhanced 'dead-reckoning' of animal movements is an alternative approach that utilises combined information from GPS devices, tri-axial accelerometers, and tri-axial magnetometers. Continuous detailed information of animal movement, activity and habitat selection can then be inferred from finer-scale GPS-enhanced dead-reckoning. It is also a useful technique to reveal the minutiae of an animal's movements such as path tortuosity. However, examples of studies using these approaches on terrestrial species are limited. Methods: Collars equipped with GPS, tri-axial accelerometer, and tri-axial magnetometer loggers were deployed on European badgers, Meles meles, to collect data on geo-position, acceleration and magnetic compass heading, respectively. This enabled us to compare GPS data with calculated GPS-enhanced dead-reckoned data. We also examined space use, distances travelled, speed of travel, and path tortuosity in relation to habitat type. Results: Nightly distances travelled were 2.2 times greater when calculated using GPS-enhanced dead-reckoned data than when calculated using GPS data alone. The use of dead-reckoned data reduced Kernel Density Estimates (KDE) of animal ranges to approximately half the size (0.21 km2) estimated using GPS data (0.46 km2). In contrast, Minimum Convex Polygon (MCP) methods showed that use of dead reckoned data yielded larger estimates of animal ranges than use of GPS-only data (0.35 and 0.27 km2, respectively). Analyses indicated that longer periods of activity were associated with greater travel distances and increased activity-related energy expenditure. Badgers also moved greater distances when they travelled at faster speeds and when the routes that they took were less tortuous. Nightly activity-related energy expenditure was not related to average travel speed or average ambient temperature but was positively related to the length of time individuals spent outside the sett (burrow). Badger activity varied with habitat type, with greater distance, speed, track tortuosity, and activity undertaken within woodland areas. Analyses of the effects of varying GPS sampling rate indicate that assessments of distance travelled depend on the sampling interval and the tortuosity of the animal's track. Where animal paths change direction rapidly, it becomes more important to use dead-reckoned data rather than GPS data alone to determine space use and distances. Conclusions: This study demonstrates the efficacy of GPS-enhanced dead-reckoning to collect high-resolution data on animal movements, activity, and locations and thereby identify subtle differences amongst individuals. This work also shows how the temporal resolution of position fixes plays a key role in the estimation of various movement metrics, such as travel speed and track tortuosity. [ABSTRACT FROM AUTHOR]

Biologging has proven to be a powerful approach to investigate diverse questions related to movement ecology across a range of spatiotemporal scales and increasingly relies on multidisciplinary expertise. However, the variety of animal‐borne equipment, coupled with little consensus regarding analytical approaches to interpret large, complex data sets presents challenges and makes comparison between studies and study species difficult. Here, we present a combined hardware and analytical approach for standardizing the collection, analysis, and interpretation of multisensor biologging data. Here, we present (i) a custom‐designed integrated multisensor collar (IMSC), which was field tested on 71 free‐ranging wild boar (Sus scrofa) over 2 years; (ii) a machine learning behavioral classifier capable of identifying six behaviors in free‐roaming boar, validated across individuals equipped with differing collar designs; and (iii) laboratory and field‐based calibration and accuracy assessments of animal magnetic heading measurements derived from raw magnetometer data. The IMSC capacity and durability exceeded expectations, with a 94% collar recovery rate and a 75% cumulative data recording success rate, with a maximum logging duration of 421 days. The behavioral classifier had an overall accuracy of 85% in identifying the six behavioral classes when tested on multiple collar designs and improved to 90% when tested on data exclusively from the IMSC. Both laboratory and field tests of magnetic compass headings were in precise agreement with expectations, with overall median magnetic headings deviating from ground truth observations by 1.7° and 0°, respectively. Although multisensor equipment and sophisticated analyses are now commonplace in biologging studies, the IMSC hardware and analytical framework presented here provide a valuable tool for biologging researchers and will facilitate standardization of biologging data across studies. In addition, we highlight the potential of additional analyses available using this framework that can be adapted for use in future studies on terrestrial mammals. [ABSTRACT FROM AUTHOR]

This study proposes compact, highly sensitive, and stable diamond quantum sensors for a wide range of applications, including biomedical and energy electronics. For enhanced sensitivity and alignment precision within the objective field, a high‐quality, (111)‐oriented 12C‐enriched chemical vapor deposition (CVD) diamond, featuring a nitrogen‐vacancy (NV) axis in the (111) direction, is employed as the sensor. To increase the fluorescence collection efficiency, the laser beam is irradiated from the side surface of the CVD diamond, and fluorescence is detected using a compound parabolic concentrator (CPC) lens. The floor noise level of the magnetic field signal is 44 pT/Hz0.5. An Allan deviation of 1.2 pT over 1000 s of averaging demonstrates stability. This is attributable to the integration of a balancing circuit to cancel out laser noise, alongside mechanisms to compensate for temperature fluctuations and a copper housing to shield against electromagnetic field noise. [ABSTRACT FROM AUTHOR]

Copyright of Reviews of Geophysics & Planetary Physics is the property of Editorial Office of Reviews of Geophysics & Planetary Physics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract Background Studies describing the movement of free-ranging animals often use remotely collected global positioning system (GPS) data. However, such data typically only include intermittent positional information, with a sampling frequency that is constrained by battery life, producing sub-sampling effects that have the potential to bias interpretation. GPS-enhanced ‘dead-reckoning’ of animal movements is an alternative approach that utilises combined information from GPS devices, tri-axial accelerometers, and tri-axial magnetometers. Continuous detailed information of animal movement, activity and habitat selection can then be inferred from finer-scale GPS-enhanced dead-reckoning. It is also a useful technique to reveal the minutiae of an animal’s movements such as path tortuosity. However, examples of studies using these approaches on terrestrial species are limited. Methods Collars equipped with GPS, tri-axial accelerometer, and tri-axial magnetometer loggers were deployed on European badgers, Meles meles, to collect data on geo-position, acceleration and magnetic compass heading, respectively. This enabled us to compare GPS data with calculated GPS-enhanced dead-reckoned data. We also examined space use, distances travelled, speed of travel, and path tortuosity in relation to habitat type. Results Nightly distances travelled were 2.2 times greater when calculated using GPS-enhanced dead-reckoned data than when calculated using GPS data alone. The use of dead-reckoned data reduced Kernel Density Estimates (KDE) of animal ranges to approximately half the size (0.21 km2) estimated using GPS data (0.46 km2). In contrast, Minimum Convex Polygon (MCP) methods showed that use of dead reckoned data yielded larger estimates of animal ranges than use of GPS-only data (0.35 and 0.27 km2, respectively). Analyses indicated that longer periods of activity were associated with greater travel distances and increased activity-related energy expenditure. Badgers also moved greater distances when they travelled at faster speeds and when the routes that they took were less tortuous. Nightly activity-related energy expenditure was not related to average travel speed or average ambient temperature but was positively related to the length of time individuals spent outside the sett (burrow). Badger activity varied with habitat type, with greater distance, speed, track tortuosity, and activity undertaken within woodland areas. Analyses of the effects of varying GPS sampling rate indicate that assessments of distance travelled depend on the sampling interval and the tortuosity of the animal’s track. Where animal paths change direction rapidly, it becomes more important to use dead-reckoned data rather than GPS data alone to determine space use and distances. Conclusions This study demonstrates the efficacy of GPS-enhanced dead-reckoning to collect high-resolution data on animal movements, activity, and locations and thereby identify subtle differences amongst individuals. This work also shows how the temporal resolution of position fixes plays a key role in the estimation of various movement metrics, such as travel speed and track tortuosity.

Magnetic field measurements play a crucial role in deep space exploration, contributing significantly to our understanding of planetary habitability and the space plasma environment. Among the various instruments employed in space exploration missions, the fluxgate magnetometer stands out as a widely used tool. However, its zero offset undergoes gradual changes, necessitating regular in-flight calibration. This article comprehensively reviews in-flight calibration methods for spaceborne magnetometers in deep space exploration, leveraging physical phenomena inherent to the space environment. The methods for calculating the zero offset can be divided into two categories. The first group employs formulas, including the Belcher method, Hedgecock method, Davis-Smith method, and both one-dimensional and three-dimensional mirror mode methods. Notably, the Davis-Smith method emerges as the optimal choice among these approaches. The second group employs probability-based solutions, constituting the in-flight calibration technology of the new generation, which encompasses six algorithms. This innovative technology utilizes phenomena such as Alfvén waves, magnetic mirror structures, and current sheets for in-flight calibration. Moreover, this technology can leverage interplanetary magnetic field data, specifically requiring a sufficient data amount (e.g., 4 hours) to calculate the zero offset. Compared to the Davis-Smith method, the in-flight calibration technology of the new generation demonstrates good compatibility, allowing for the simultaneous use of different physical phenomena, and scalability. This technology extends its applicability beyond solar wind, enabling in-flight calibration in the Martian magnetosheath. Currently, this technology has successfully provided calibration services for the magnetic field data of the Tianwen-1 orbiter.

The SMILE (Solar wind Magnetosphere Ionosphere Link Explorer) mission is a joint space science mission between the Chinese Academy of Sciences (CAS) and the European Space Agency (ESA), aiming to understand the interaction of the solar wind with the Earth’s magnetosphere in a global manner. As of May 2024, the SMILE mission is in phase-D with an expected launch date of September 2025. This report summarizes developments in the mission during the past two years.

Abstract The navigational accuracy of sea animals and trans-ocean birds provides inspiration in using geo-magnetic field (GMF) for realizing strategic truly autonomous underwater vehicles (AUV) capable of determining their absolute position on earth, without the aid of ship-referenced acoustic baseline systems. Supervised Machine Learning algorithms are applied on the GMF intensity data obtained from NOAA World Magnetic Model for a 900 km2 within the Indian mineral exploratory area in the Central Indian Ocean, with a resolution of 50 m, considering the sensitivity of commercially available magnetometers. It is identified that, for AUVs equipped with magnetometers with a detection sensitivity of 0.1 nT, the supervisory random forest regression and decision tree algorithm trained with priori GMF data, could provide trajectory guidance to AUVs with an absolute mean position accuracy in 2D plane, with reference to the last known position from Integrated Navigation system aided initially with GPS and with acoustic positioning in underwater. Circular Error Probable (CEP 50) of 53 m and 56 m, respectively. The scalar GMF anomaly navigation demonstrated to be a viable GPS-alternative navigation system could be extended to larger areas with inclination and declination vectors, as unique identifiers.

Powerful lightning strikes generate broadband electromagnetic signals. At Extremely Low Frequencies (ELF), the signal partly leaks into the ionosphere and produces whistlers that can be detected by satellites. Indeed, the satellites of the European Space Agency (ESA) Swarm Earth Explorer mission can detect those signals during 250 Hz burst‐mode acquisition campaigns of their Absolute Scalar Magnetometers (ASM). The dispersion of these whistlers depends on their propagation path and the distribution of ionization in the ionosphere crossed along that path. In this paper, we introduce a technique to derive a new measure of ionosphere electron content, the Total square‐Root Electron Content (TREC), using the arrival times of two frequencies of the whistler signal. We validate this approach by using data from ionosondes and from in situ measurements of the electron density at Swarm location. This technique brings new opportunities for sounding the ionosphere in regions poorly observed by other techniques. Plain Language Summary: A lightning strike generates an electromagnetic impulse that propagates within Earth's atmosphere and eventually leaks out into the ionosphere. As it propagates through the ionosphere toward low‐Earth orbiting (LEO) satellites, it gets converted into a so‐called whistler, with high frequencies arriving earlier than low frequencies. This frequency dispersion depends on the state of the ionosphere. Here, we analyse such whistler waves detected by magnetometers onboard the European Space Agency Swarm satellites to recover information about the state of the ionosphere below the satellites. We first introduce a new metric, the Total Root Electron Content (TREC), which quantifies the cumulative value of the square root of electron density along the path of the whistler. We next propose a method to recover the TREC from the analysis of the whistler dispersion. We finally validate this method by using independently derived ionospheric electron density profiles to infer expected TREC values. Our results show that whistlers detected by LEO satellites can be used to locally improve the widely used empirical International Reference Ionosphere model. Such whistler inferred TREC values could be used to sound the ionosphere above places difficult to sample with conventional measuring techniques, and help better model and understand the highly dynamic ionosphere. Key Points: Total square‐Root Electron Content (TREC) is a new measure of the ionospheric electron content for electromagnetic signals in the ELF bandA method to retrieve TREC from fractional‐hop whistlers in the ELF detected by the ESA Swarm mission is proposedThe method is validated using TREC computed with independently constrained electron density profiles close to the Swarm whistler locations [ABSTRACT FROM AUTHOR]

The navigational accuracy of sea animals and trans-ocean birds provides inspiration in using geo-magnetic field (GMF) for realizing strategic truly autonomous underwater vehicles (AUV) capable of determining their absolute position on earth, without the aid of ship-referenced acoustic baseline systems. Supervised Machine Learning algorithms are applied on the GMF intensity data obtained from NOAA World Magnetic Model for a 900 km2 within the Indian mineral exploratory area in the Central Indian Ocean, with a resolution of 50 m, considering the sensitivity of commercially available magnetometers. It is identified that, for AUVs equipped with magnetometers with a detection sensitivity of 0.1 nT, the supervisory random forest regression and decision tree algorithm trained with priori GMF data, could provide trajectory guidance to AUVs with an absolute mean position accuracy in 2D plane, with reference to the last known position from Integrated Navigation system aided initially with GPS and with acoustic positioning in underwater. Circular Error Probable (CEP 50) of 53 m and 56 m, respectively. The scalar GMF anomaly navigation demonstrated to be a viable GPS-alternative navigation system could be extended to larger areas with inclination and declination vectors, as unique identifiers. [ABSTRACT FROM AUTHOR]

Polarization moments play a crucial role in measuring magnetic fields for nonlinear magneto‐optical rotation (NMOR) atomic magnetometers. However, it is challenging to distinguish between each polarization moment and evaluate its effect on the magnetic resonance response signal in an alkali vapor cell with buffer gas. To address this issue, a method is proposed to identify different polarization moments through the frequency shift of the magnetic resonance response signal. The proportion of each polarization moment is determined, and it is demonstrated that the magnetic resonance response signal is affected by the hexadecapole moment, resulting in a frequency shift and a decrease in signal amplitude. To mitigate this effect, an approach is investigated to manipulate the polarization moments by flipping the phase of the pump light. Ultimately, a 15.19% increase in response amplitude is achieved in the simulated geomagnetic environment within the magnetic shield barrel. The theory and method presented here provide strong support for the study of the polarization moments in an alkali vapor cell with buffer gas, which potentially enhance the performance of NMOR atomic magnetometers. [ABSTRACT FROM AUTHOR]

High-precision magnetometers play a crucial role in ocean exploration, geophysical prospecting, and military and security applications. Installing them on human-occupied vehicle (HOV) platforms can greatly enhance ocean exploration capabilities and efficiency. However, most existing magnetometers suffer from low sensitivity and excessively large size. This study presents a high-sensitivity, miniaturized magnetometer based on cesium optically pumped probes. The designed magnetometer utilizes a three-probe design to eliminate the detection dead zone of the cesium optically pumped probe and enable three-dimensional magnetic detection. The proposed magnetometer uses a flux gate probe to detect the three-axis magnetic field and ensure that the probe does not enter the dead zone. The three probes can automatically switch by measuring the geomagnetic elements and real-time attitude of the HOV platform. This article primarily introduces the cesium three-probe optically pump, flux gate sensor, and automatic switching scheme design of the above-mentioned magnetometer. Moreover, it is proven through testing that the core indicators, such as the accuracy and sensitivity of the cesium three-probe optically pumped and flux gate sensor, reach international standards. Finally, the effectiveness of the automatic switching scheme proposed in this study is demonstrated through drone-mounted experiments. [ABSTRACT FROM AUTHOR]

Vertical drilling is the first stage of petroleum exploitation and directional well technology. The near-vertical attitude at each survey station directly determines the whole direction accuracy of the borehole trajectory. However, the attitude measurement for near-vertical wells has poor azimuth accuracy because the poor signal-to-noise ratio of radial accelerometers hardly obtains the correct horizontal attitude, especially the roll angle. In this paper, a novel near-vertical attitude measurement method was proposed to address this issue. The redundant micro-electromechanical system (MEMS) accelerometers were employed to replace the original accelerometers from MEMS inertial measurement unit (IMU)/magnetometers for calculating horizontal attitude under near-vertical conditions. In addition, a simplified four-position calibration method for the redundant accelerometers was proposed to compensate for the installation and non-orthogonal error. We found that the redundant accelerometers enhanced the signal-to-noise ratio to upgrade the azimuth accuracy at the near-vertical well section. Compared with the traditional method, the experiment results show that the average azimuth errors and roll errors are reduced from 34.45° and 27.09° to 5.7° and 0.61°, respectively. The designed configuration scheme is conducive to the miniaturized design and low-cost requirements of wellbore measuring tools. The proposed attitude measurement method can effectively improve the attitude accuracy of near-vertical wells. [ABSTRACT FROM AUTHOR]

The magnetic field range in which a magnetic sensor operates is an important consideration for many applications. Elliptical planar Hall effect (EPHE) sensors exhibit outstanding equivalent magnetic noise (EMN) on the order of pT / Hz , which makes them promising for many applications. Unfortunately, the current field range in which EPHE sensors with pT / Hz EMN can operate is sub-mT, which limits their potential use. Here, we fabricate EPHE sensors with an increased field range and measure their EMN. The larger field range is obtained by increasing the uniaxial shape-induced anisotropy parallel to the long axis of the ellipse. We present measurements of EPHE sensors with magnetic anisotropy which ranges between 12 Oe and 120 Oe and show that their EMN at 10 Hz changes from 800 pT / Hz to 56 nT / Hz . Furthermore, we show that the EPHE sensors behave effectively as single magnetic domains with negligible hysteresis. We discuss the potential use of EPHE sensors with extended field range and compare them with sensors that are widely used in such applications. [ABSTRACT FROM AUTHOR]

A superconducting quantum magnetometer for high-sensitivity applications has been developed by exploiting the flux focusing of the superconducting loop. Unlike conventional dc SQUID magnetometers that use a superconducting flux transformer or a multiloop design, in this case, a very simple design has been employed. It consists of a bare dc SQUID with a large washer-shaped superconducting ring in order to guarantee a magnetic field sensitivity BΦ less than one nT/Φ0. The degradation of the characteristics of the device due to an inevitable high value of the inductance parameter βL was successfully compensated by damping the inductance of the dc SQUID. The size of the magnetometer, coinciding with that of the washer, is 5 × 5 mm2 and the spectral density of the magnetic field noise is 8 fT/√Hz with a low frequency noise knee of two Hz. The excellent performance of this simple magnetometer makes it usable for all high-sensitivity applications including magnetoencephalography. [ABSTRACT FROM AUTHOR]

The eruption of the Hunga Tonga Hunga Ha'apai volcano on 15 January 2022 significantly impacted the lower and upper atmosphere globally. Using multi-instrument observations, we described disturbances from the sea surface to the ionosphere associated with atmospheric waves generated by the volcanic eruption. Perturbations were detected in atmospheric pressure, horizontal magnetic field, equatorial electrojet (EEJ), ionospheric plasma drifts, total electron content (TEC), mesospheric and lower thermospheric (MLT) neutral winds, and ionospheric virtual height measured at low magnetic latitudes in the western South American sector (mainly in Peru). The eastward Lamb wave propagation was observed at the Jicamarca Radio Observatory on the day of the eruption at 13:50 UT and on its way back from the antipodal point (westward) on the next day at 07:05 UT. Perturbations in the horizontal component of the magnetic field (indicative of EEJ variations) were detected between 12:00 and 22:00 UT. During the same period, GNSS-TEC measurements of traveling ionospheric disturbances (TIDs) coincided approximately with the arrival time of Lamb and tsunami waves. On the other hand, a large westward variation of MLT winds occurred near 18:00 UT over Peru. However, MLT perturbations due to possible westward waves from the antipode have not been identified. In addition, daytime vertical plasma drifts showed an unusual downward behavior between 12:00 and 16:00 UT, followed by an upward enhancement between 16:00 and 19:00 UT. Untypical daytime eastward zonal plasma drifts were observed when westward drifts were expected. Variations in the EEJ are highly correlated with perturbations in the vertical plasma drift exhibiting a counter-equatorial electrojet (CEEJ) between 12:00 and 16:00 UT. These observations of plasma drifts and EEJ are, so far, the only ground-based radar measurements of these parameters in the western South American region after the eruption. We attributed the ion drift and EEJ perturbations to large-scale thermospheric wind variations produced by the eruption, which altered the dynamo electric field in the Hall and Pedersen regions. These types of multiple and simultaneous observations can contribute to advancing our understanding of the ionospheric processes associated with natural hazard events and the interaction with lower atmospheric layers. [ABSTRACT FROM AUTHOR]

Magnetometers are essential instruments in space physics, but their measurements are often contaminated by various external interference sources. In this work, we present a comprehensive review of existing magnetometer interference removal methods and introduce MAGPRIME (MAGnetic signal PRocessing, Interference Mitigation, and Enhancement), an open‐source Python library featuring a collection of state‐of‐the‐art interference removal algorithms. MAGPRIME streamlines the process of interference removal in magnetic field data by providing researchers with an integrated, easy‐to‐use platform. We detail the design, structure, and functionality of the library, as well as its potential to facilitate future research by enabling rapid testing and customization of interference removal methods. Using the MAGPRIME Library, we present two Monte Carlo benchmark results to compare the efficacy of interference removal algorithms in different magnetometer configurations. In Benchmark A, the Underdetermined Blind Source Separation (UBSS) and traditional gradiometry algorithms surpass the uncleaned boom‐mounted magnetometers, achieving improved correlation and reducing median error in each simulation. Benchmark B tests the efficacy of the suite of MAGPRIME algorithms in a boomless magnetometer configuration. In this configuration, the UBSS algorithm proves to significantly reduce median error, along with improvements in median correlation and signal to noise ratio. This study highlights MAGPRIME's potential in enhancing magnetic field measurement accuracy in various spacecraft designs, from traditional gradiometry setups to compact, cost‐effective alternatives like bus‐mounted CubeSat magnetometers, thus establishing it as a valuable tool for researchers and engineers in space exploration and magnetism studies. Plain Language Summary: Magnetometers, tools used to measure magnetic fields, are crucial for space exploration but often face interference from a spacecraft's own systems. This can distort measurements, making it hard to get accurate data. In order to address this, we introduce MAGPRIME, an open‐source software that helps clean up these magnetic field measurements. It is user‐friendly and integrates various techniques to reduce interference in the data. We tested MAGPRIME in two scenarios: one with magnetometers on a long mechanical arm or boom, and another without the boom. The results from these cases show that the use of MAGPRIME can make it easier and cheaper to get reliable magnetic field data, especially for smaller satellites like CubeSats. Key Points: MAGPRIME is an open‐source Python library with advanced interference removal algorithms for magnetic field measurements in space missionsThe MAGPRIME library is designed to be a community‐driven platform for magnetometer signal processing algorithmsMonte Carlo simulation results show UBSS achieves significant interference reduction in both boom and bus‐mounted magnetometer setups [ABSTRACT FROM AUTHOR]

In this study, we introduce the concept and construction of an innovative Digital Miniature Cathode Ray Magnetometer designed for the precise detection of magnetic fields. This device addresses several limitations inherent to magnetic probes such as D.C. offset, nonlinearity, temperature drift, sensor aging, and the need for frequent recalibration, while capable of operating in a wide range of magnetic fields. The core principle of this device involves the utilization of a charged particle beam as the sensitivity medium. The system leverages the interaction of an electron beam with a scintillator material, which then emits visible light that is captured by an imager. The emitted scintillation light is captured by a CMOS sensor. This sensor not only records the scintillation light but also accurately determines the position of the electron beam, providing invaluable spatial information crucial for magnetic field mapping. The key innovation lies in the combination of electron beam projection, CMOS imager scintillation-based detection, and digital image signal processing. By employing this synergy, the magnetometer achieves remarkable accuracy, sensitivity and dynamic range. The precise position registration enabled by the CMOS sensor further enhances the device's utility in capturing complex magnetic field patterns, allowing for 2D field mapping. In this work, the optimization of the probe's performance is tailored for applications related to the characterization of insertion devices in light sources, including undulators. [ABSTRACT FROM AUTHOR]

Severe space weather has the potential to cause significant socio‐economic impact and it is widely accepted that mitigating this risk requires more comprehensive observations of the Sun and heliosphere, enabling more accurate forecasting of significant events with longer lead‐times. In this context, it is now recognized that observations from the L5 Sun‐Earth Lagrange point (both remote and in situ) would offer considerable improvements in our ability to monitor and forecast space weather. Remote sensing from L5 allows for the observation of solar features earlier than at L1, providing early monitoring of active region development, as well as tracking of interplanetary coronal mass ejections through the inner heliosphere. In situ measurements at L5 characterize the solar wind's geoeffectiveness (particularly stream interaction regions), and can also be ingested into heliospheric models, improving their performance. The Vigil space weather mission is part of the ESA Space Safety Program and will provide a real‐time data stream for space weather services from L5 following its anticipated launch in the early 2030s. The interplanetary magnetic field is a key observational parameter, and here we describe the development of the Vigil magnetometer instrument for operational space weather monitoring at the L5 point. We summarize the baseline instrument capabilities, demonstrating how heritage from science missions has been leveraged to develop a low‐risk, high‐heritage instrument concept. Plain Language Summary: Severe space weather is now recognized worldwide as a major socio‐economic threat. A widely accepted strategy for improving societal resilience is to maximize the warning time to end‐users. Vigil, led by the European Space Agency and due to launch in the early 2030s, will station‐keep at the fifth Sun‐Earth Lagrange point (which trails the Earth in its orbit by 60°) and this will allow solar features to be observed before they rotate into Earth view. The payload will contain remote sensing and in situ instruments, and here we describe the magnetometer, which will measure the solar wind magnetic field at the location of Vigil. The rationale for L5 observations is briefly reviewed, as well as the specific need for in situ magnetic field data. We then examine the magnetic field measurement requirements and describe in detail the instrument concept together with the concept of operations. A particular challenge of Vigil will be the need to provide data in real time, and we discuss the implications of this in terms of availability and magnetic cleanliness. Finally, in summarizing the outlook for Vigil, we discuss how it can contribute to a virtuous circle between research and operations. Key Points: Operational in situ magnetic field measurements at the Sun Earth L5 point enable improved space weather forecastingIn the context of the Vigil mission, measurement requirements are quantified and the instrument design is presentedThe Vigil magnetometer also accounts for operational requirements to provide clean magnetic field data in real time with high reliability [ABSTRACT FROM AUTHOR]

As part of Ham Radio Science Citizen Investigation (HamSCI) Personal Space Weather Station (PSWS) project, a low-cost, commercial off-the-shelf magnetometer has been developed to provide quantitative and qualitative measurements of the geospace environment from the ground for both scientific and operational purposes at a cost that will allow for crowd-sourced data contributions. The PSWS magnetometers employ a magneto-inductive sensor technology to record three-axis magnetic field variations with a field resolution of ∼3 nT at a 1 Hz sample rate. The measurement range of the sensor is ±1.1×106 nT) and is valid over a temperature range of −40 °C to +85 °C. Data from the PSWS network will combine these magnetometer measurements with high frequency (HF, 3–30 MHz) radio observations to monitor large-scale current systems and ionospheric disturbances due to drivers from both space and the atmosphere. A densely-spaced magnetometer array, once established, will demonstrate their space weather monitoring capability to an unprecedented spatial extent. Magnetic field data obtained by the magnetometers installed at various locations in the US are presented and compared with the existing magnetometers nearby, demonstrating that the performance is very adequate for scientific investigations.

Landmines continue to pose an ongoing threat in various regions around the world, with countless buried landmines affecting numerous human lives. The detonation of these landmines results in thousands of casualties reported worldwide annually. Therefore, there is a pressing need to employ diverse landmine detection techniques for their removal. One effective approach for landmine detection is UAV (Unmanned Aerial Vehicle) based Airborne Magnetometry, which identifies magnetic anomalies in the local terrestrial magnetic field. It can generate a contour plot or heat map that visually represents the magnetic field strength. Despite the effectiveness of this approach, landmine removal remains a challenging and resource-intensive task, fraught with risks. Edge computing, on the other hand, can play a crucial role in critical drone monitoring applications like landmine detection. By processing data locally on a nearby edge server, edge computing can reduce communication latency and bandwidth requirements, allowing real-time analysis of magnetic field data. It enables faster decision-making and more efficient landmine detection, potentially saving lives and minimizing the risks involved in the process. Furthermore, edge computing can provide enhanced security and privacy by keeping sensitive data close to the source, reducing the chances of data exposure during transmission. This paper introduces the MAGnetometry Imaging based Classification System (MAGICS), a fully automated UAV-based system designed for landmine and buried object detection and localization. We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces. By simulating the proposal in various network scenarios, we have successfully detected landmine signatures present in the magnetometry images. The trained models exhibit significant performance improvements, achieving a maximum mean average precision value of 97.8%. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Jiaotong University (Science) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research proposes a magnetic field sensor with spatial orientation ability. Through the assistance of a magnetic flux concentrator, out-of-plane magnetic flux can be concentrated and guided into the planar magnetic cores of a fluxgate sensor. A printed circuit board is used to construct the basic planar structure, on which the proposed three-dimensional magnetic flux concentrator and magnetic cores are assembled. This reduces the alignment error of the coils and improves the reliability of the sensor. Three-axis sensing is achieved by using the second harmonic signals from selected sensing coil pairs. The magnetometer exhibits a linear range to 130 μT. At an excitation frequency of 50 kHz, the measured sensitivities are 257.1, 468.8, and 258.8 V/T for the X-, Y-, and Z-axis sensing modes, respectively. This sensor utilizes only one sensing mechanism for the vector field, making it suitable for IoT applications, especially for assessing mechanical posture or position. [ABSTRACT FROM AUTHOR]

Studying the spatial response of a single-axis magnetometer could be the key parameter to optimize the ultimate performances of magnetic heads of detection. Indeed, the problem of non-orthogonality, misalignment, and 3D spatial response could be improved based on the knowledge of the 3D sensor spatial response. In that way, we have investigated the latter for our giant magneto-impedance (GMI) magnetometer, as a far-field pattern, by using a three-axis Helmholtz coil system. Firstly, we calibrate our device and secondly, we apply a specific 3D magnetic field to obtain this pattern. The latter helps to observe the directional or angular dependence of the sensor sensitivity versus the applied magnetic field, as we exemplified. The results confirm the excellent directivity of our off-diagonal GMI magnetometer. The evaluation of the associated error compared to an ideal vector magnetometer is also given and discussed. [ABSTRACT FROM AUTHOR]

Abstract Powerful lightning strikes generate broadband electromagnetic signals. At Extremely Low Frequencies (ELF), the signal partly leaks into the ionosphere and produces whistlers that can be detected by satellites. Indeed, the satellites of the European Space Agency (ESA) Swarm Earth Explorer mission can detect those signals during 250 Hz burst‐mode acquisition campaigns of their Absolute Scalar Magnetometers (ASM). The dispersion of these whistlers depends on their propagation path and the distribution of ionization in the ionosphere crossed along that path. In this paper, we introduce a technique to derive a new measure of ionosphere electron content, the Total square‐Root Electron Content (TREC), using the arrival times of two frequencies of the whistler signal. We validate this approach by using data from ionosondes and from in situ measurements of the electron density at Swarm location. This technique brings new opportunities for sounding the ionosphere in regions poorly observed by other techniques.

Given the popularity of wrist-worn devices, particularly smartwatches, the identification of manual movement patterns has become of utmost interest within the research field of Human Activity Recognition (HAR) systems. In this context, by leveraging the numerous sensors natively embedded in smartwatches, the HAR functionalities that can be implemented in a watch via software and in a very cost-efficient way cover a wide variety of applications, ranging from fitness trackers to gesture detectors aimed at disabled individuals (e.g., for sending alarms), promoting behavioral activation or healthy lifestyle habits. In this regard, for the development of artificial intelligence algorithms capable of effectively discriminating these activities, it is of great importance to have repositories of movements that allow the scientific community to train, evaluate, and benchmark new proposals of movement detectors. The UMAHand dataset offers a collection of files containing the signals captured by a Shimmer 3 sensor node, which includes an accelerometer, a gyroscope, a magnetometer and a barometer, during the execution of different typical hand movements. For that purpose, the measurements from these four sensors, gathered at a sampling rate of 100 Hz, were taken from a group of 25 volunteers (16 females and 9 males), aged between 18 and 56, during the performance of 29 daily life activities involving hand mobility. Participants wore the sensor node on their dominant hand throughout the experiments.

Abstract Magnetometers are essential instruments in space physics, but their measurements are often contaminated by various external interference sources. In this work, we present a comprehensive review of existing magnetometer interference removal methods and introduce MAGPRIME (MAGnetic signal PRocessing, Interference Mitigation, and Enhancement), an open‐source Python library featuring a collection of state‐of‐the‐art interference removal algorithms. MAGPRIME streamlines the process of interference removal in magnetic field data by providing researchers with an integrated, easy‐to‐use platform. We detail the design, structure, and functionality of the library, as well as its potential to facilitate future research by enabling rapid testing and customization of interference removal methods. Using the MAGPRIME Library, we present two Monte Carlo benchmark results to compare the efficacy of interference removal algorithms in different magnetometer configurations. In Benchmark A, the Underdetermined Blind Source Separation (UBSS) and traditional gradiometry algorithms surpass the uncleaned boom‐mounted magnetometers, achieving improved correlation and reducing median error in each simulation. Benchmark B tests the efficacy of the suite of MAGPRIME algorithms in a boomless magnetometer configuration. In this configuration, the UBSS algorithm proves to significantly reduce median error, along with improvements in median correlation and signal to noise ratio. This study highlights MAGPRIME's potential in enhancing magnetic field measurement accuracy in various spacecraft designs, from traditional gradiometry setups to compact, cost‐effective alternatives like bus‐mounted CubeSat magnetometers, thus establishing it as a valuable tool for researchers and engineers in space exploration and magnetism studies.

Studying the spatial response of a single-axis magnetometer could be the key parameter to optimize the ultimate performances of magnetic heads of detection. Indeed, the problem of non-orthogonality, misalignment, and 3D spatial response could be improved based on the knowledge of the 3D sensor spatial response. In that way, we have investigated the latter for our giant magneto-impedance (GMI) magnetometer, as a far-field pattern, by using a three-axis Helmholtz coil system. Firstly, we calibrate our device and secondly, we apply a specific 3D magnetic field to obtain this pattern. The latter helps to observe the directional or angular dependence of the sensor sensitivity versus the applied magnetic field, as we exemplified. The results confirm the excellent directivity of our off-diagonal GMI magnetometer. The evaluation of the associated error compared to an ideal vector magnetometer is also given and discussed.

Unmanned aerial vehicles (UAVs), commonly known as drones, offer several advantages over traditional piloted aircraft. They are characterized by enhanced safety, cost-effectiveness, and the ability to operate in closer proximity to targeted sources. Consequently, magnetic sensors have been adapted or specifically designed for integration onto UAV platforms. However, existing sensors are burdened by issues such as weight, cost, and high power consumption. These challenges are particularly pronounced when employing aeromagnetic gradiometry, which necessitates simultaneous measurements from at least two sensors. In response to these limitations, we propose the implementation of a cost-effective, lightweight, and low-power magneto-inductive sensor with satisfactory resolution aboard a UAV. To evaluate its efficacy, a survey was conducted over a small iron ore deposit in Western Iran. To validate our approach, we compare the results with those obtained using only one sensor on the drone. This comparative analysis reveals that employing a gradiometry array leads to a pronounced steepening of magnetic anomaly margins. Specifically, the gradient of magnetic measurements on four selected profiles increases to 3.8, 4.6, 9.3, and 10 nT/m when utilizing the proposed magneto-inductive sensor, in contrast to the conventional method of gradient determination through mathematical derivatives in the z-direction. This research contributes to the advancement of efficient and economical methods for mineral exploration using UAV-based magnetic surveying techniques.

While airborne magnetometry has been used for geological surveys for decades, magnetic surveys for archaeological prospection are almost exclusively ground-based, as the detection of archaeological features requires higher spatial resolution and close proximity between sensor and object. However, the recent development of drones and magnetic sensors allows for low-altitude drone-based surveys, which are an interesting alternative for magnetic prospection of challenging areas, where vegetation, difficult terrain, access restrictions, or safety concerns hamper ground-based surveys. In this paper, we present test measurements in challenging areas in Germany and Switzerland, which demonstrate the potential as well as technical and practical concerns of drone-based magnetometry for archaeological prospection. We used a miniature total-field magnetometer, which was tethered to an octocopter drone. Although it is preferable to fly the sensor close to the ground where anomalies show the highest values, we could also detect magnetic anomalies in altitudes up to few metres above ground. Flights at different altitudes show the decay and widening of the anomalies with height. Drone-based magnetic measurements in rough and vegetated terrain require careful flight planning based on a high-resolution surface model. Further development is needed to improve positioning accuracy of the tethered magnetometer and to improve heading error correction.

Space weather phenomena like geomagnetic disturbances (GMDs) and geomagnetically induced currents (GICs) pose significant risks to critical technological infrastructure. While traditional predictive models, grounded in simulation, hold theoretical robustness, they grapple with challenges, notably the assimilation of imprecise data and extensive computational complexities. In recent years, Tiny Machine Learning (TinyML) has been adopted to develop Machine Learning (ML)-enabled magnetometer systems for predicting real-time terrestrial magnetic perturbations as a proxy measure for GIC. While TinyML offers efficient, real-time data processing, its intrinsic limitations prevent the utilization of robust methods with high computational needs. This paper developed a physics-guided TinyML framework to address the above challenges. This framework integrates physics-based regularization at the stages of model training and compression, thereby augmenting the reliability of predictions. The developed pruning scheme within the framework harnesses the inherent physical characteristics of the domain, striking a balance between model size and robustness. The study presents empirical results, drawing a comprehensive comparison between the accuracy and reliability of the developed framework and its traditional counterpart. Such a comparative analysis underscores the prospective applicability of the developed framework in conceptualizing robust, ML-enabled magnetometer systems for real-time space weather forecasting.

As part of the Chinese Tianwen-1 mission, the Zhurong Rover began its scientific investigation in the southern Utopia Planitia after its successful landing in 15 May, 2021. The Zhurong Rover magnetometer (RoMAG), one of the six payloads onboard the rover, includes two identical high-sensitivity triaxial fluxgate magnetometers and can implement mobile magnetic measurements on the surface of Mars. Although a rover magnetic compensation procedure was conducted to remove the magnetic interferences pre-launch, due to the different state of the payloads and electric power system such as the solar panel, an along-track calibration of the magnetometer is necessary to obtain a more accurate Martian magnetic field. Two methods, mast yaw rotations and Rover yaw rotations were utilized separately to determine the Martian horizontal magnetic components. Results show that the Martian horizontal magnetic components determined by the two methods are in good agreement, with the root mean square deviation less than 2.0 nT. The vertical component was also constrained through the pitch movements of the mast by assuming the interferences field distributes like a dipole field. A linear correlation between magnetic field measurements and the solar array currents was derived to calibrate the body field during the regular exploration. We conclude that more accurate measurements could be made when applying the calibration results in the magnetic survey on the surface of Mars. [ABSTRACT FROM AUTHOR]

The geomagnetic field models are presented with the external field influences taken into account. Magnetometers detect the external field, although most geomagnetic field models do not account for it. It is crucial to take into account how the external field affects the small satellite's other components. Furthermore, variations in the external field have the ability to affect the satellite's orbit and modify the spacecraft's drag, which could result in an altitude decrease and, eventually, mission failure. Consequently, to guarantee a successful mission, a comprehensive comprehension of the external field's influence on the small satellite's subsystems is required. In this work, the magnetorquers apply the magnetic control during the nanosatellite's detumbling phase. Although magnetic control cannot achieve three-axis control instantaneously, it can in near-polar trajectories due to the continuous variation of the magnetic field. In order to determine whether the geomagnetic storms may be exploited to our advantage by producing greater field variations, this study focuses on the extent to which it contributes to the magnetic control of the small satellites. [ABSTRACT FROM AUTHOR]

Featured Application: Precise close range underwater electromagnetic navigation and docking. This study introduces a groundbreaking approach for real-time 3D localization, specifically focusing on achieving seamless and precise localization during the terminal guidance phase of an autonomous underwater vehicle (AUV) as it approaches an omnidirectional docking component in an automated deployable launch and recovery system (LARS). Using the AUV's magnetometer, an economical electromagnetic beacon embedded in the docking component, and an advanced signal processing algorithm, this novel approach ensures the accurate localization of the docking component in three dimensions without the need for direct line-of-sight contact. The method's real-time capabilities were rigorously evaluated via simulations, prototype experiments in a controlled lab setting, and extensive full-scale pool experiments. These assessments consistently demonstrated an exceptional average positioning accuracy of under 3 cm, marking a significant advancement in AUV guidance systems. [ABSTRACT FROM AUTHOR]

This paper proposes a new methodology for the automatic detection of magnetic disturbances from magnetic inertial measurement unit (MIMU) sensors based on deep learning. The proposed approach considers magnetometer data as input to a long short-term memory (LSTM) neural network and obtains a labeled time series output with the posterior probabilities of magnetic disturbance. We trained our algorithm on a data set that reproduces a wide range of magnetic perturbations and MIMU motions in a repeatable and reproducible way. The model was trained and tested using 15 folds, which considered independence in sensor, disturbance direction, and signal type. On average, the network can adequately detect the disturbances in 98 % of the cases, which represents a significant improvement over current threshold-based detection algorithms. [ABSTRACT FROM AUTHOR]

The paper presents a method for estimating the inertia tensor components of a spacecraft that has expired its active life using measurement data of the Earth's magnetic field induction vector components. The implementation of this estimation method is supposed to be carried out when cleaning up space debris in the form of a clapped-out spacecraft with the help of a space tug. It is assumed that a three-component magnetometer and a transmitting device are attached on space debris. The parameters for the rotational motion of space debris are estimated using this measuring system. Then, the known controlled action from the space tug is transferred to the space debris. Next, measurements for the rotational motion parameters are carried out once again. Based on the available measurement data and parameters of the controlled action, the space debris inertia tensor components are estimated. It is assumed that the measurements of the Earth's magnetic field induction vector components are made in a coordinate system whose axes are parallel to the corresponding axes of the main body axis system. Such an estimation makes it possible to effectively solve the problem of cleaning up space debris by calculating the costs of the space tug working body and the parameters of the space debris removal orbit. Examples of numerical simulation using the measurement data of the Earth's magnetic field induction vector components on the Aist-2D small spacecraft are given. Thus, the purpose of this work is to evaluate the components of the space debris inertia tensor through measurements of the Earth's magnetic field taken using magnetometer sensors. The results of the work can be used in the development and implementation of missions to clean up space debris in the form of clapped-out spacecraft. [ABSTRACT FROM AUTHOR]

The Geostationary Operational Environmental Satellite (GOES)-18, the latest spacecraft from the NOAA GOES-R satellite series, was launched March 1, 2022. As with the previous GOES-16 and GOES-17 satellites, GOES-18 monitors sources of space weather on the Sun and its effects at Earth. NOAA uses GOES data as part of the national space weather forecasts, warnings and alerts to many customers. GOES-18 hosts new magnetometers called the Goddard magnetometers (GMAG) that replace those (called MAG) built by a different vendor on GOES-16 and GOES-17. Like the other GOES satellites, the GOES-18 GMAG provides observations of the geomagnetic field at geostationary orbit (35,786 km), a location that often provides early indication of enhanced space weather activity. In this paper, we review the capabilities of the GOES-18 GMAG along with lessons learned from the GOES-16/17 MAGs. The GOES-R series magnetometer instrument includes two magnetometer sensors (inboard and outboard) mounted along a boom extended from the spacecraft. As with the previous magnetometers, the GMAG sensors are three-axis fluxgates sampling the geomagnetic field at 10 samples/second, with the data low-pass filtered with a 2.5 Hz cutoff. On-orbit analysis demonstrates that the GOES-18 GMAG is a highly stable instrument showing little variations between the inboard and outboard sensors either diurnally or over multiple days. A nearly 2.5 months collocation between GOES-18 and GOES-17 (136.8°W and 137.2°W) allowed direct cross-satellite comparisons that was unprecedented for GOES satellites. Differences between the on orbit performance of the GMAG and MAG sensors are attributed to thermal stability issues observed on the GOES-17 MAG (also observed on the GOES-16 MAG). The cross-satellite analysis during the collocation interval, along with inboard/outboard sensor comparisons and comparisons to models, suggests that the GOES-18 GMAG meets the NOAA mission requirement of ± 1 nT accuracy, excluding arcjet firing periods. Arcjet firing periods were also excluded in performance analysis for GOES-16/17. [ABSTRACT FROM AUTHOR]

In quantum metrology researching, weak measurement has been widely applied in many aspects, providing weak-value amplification and noise suppressing ability. Recently, several modified schemes of weak measurement have been proposed, such as inverse, almost-balanced, and biased weak measurement. In this study, we enhance a spin-exchange relaxation-free (SERF) magnetometer using the biased weak measurement metrology technique. We establish and simulate a biased weak measurement (BWM)-enhanced SERF magnetometer model. By adjusting the pre-coupling process and analyzing the Fisher information, higher sensitivity to magnetic fields is verified in this study. 0.003 fT can be achieved through adjusting pre-coupling process and extinction ratio, which is higher than the best sensitivity of SERF magnetometer in existence. Our work paves the way for embedding weak measurement techniques into atomic magnetometer sensors and greatly improving their sensitivity.

Magnetic quantum sensors based on trapped ions utilize properties of quantum mechanics which have optimized precision and beat current limits in sensor technology. Trapped ions are highly sensitive in a large span of signal ranging from DC or static B-field to the radiofrequency range in 100s of MHz and can attain the sensitivity in the range of pT/Hz to sub-pT/Hz. They are tuneable to frequencies of interest and can be used as a lock-in frequency detector. This modelling and simulation based study presents an innovative design of Surface Paul Traps, enabling the use of trapped ions as ultra-sensitive sensors for magnetic field detection and precise measurement of magnetic field gradients at a sub-millimeter spatial resolution. The novel design features multiple trapping regions, allowing for the mapping of magnetic fields across various ion-trapping zones. The study demonstrates groundbreaking advancements in ion manipulation and confinement through innovative chip architecture.

Recent embedded technologies on today’s smartphone make the smartphones more comfortable to run a large number of applications which are used for people’s daily activities. Among all these applications, the location-based services (LBS) are frequently used. The LBS applications utilized location information via Smartphones technologies. For example, when outdoors, Global Navigation System (GPS) or generally Global navigation satellite system (GNSS) signals are used to retrieve the location information within enough positioning accuracy. However, when smartphone holders are entering to the urbane area or indoors, the performance of GPS service will be degraded or sometime cannot retrieve location information due to blocking the GPS signals through the roofs or walls of the buildings. Beside this, many onboard smartphones wireless chipsets or sensors’ readings can be used as alternate technologies to provide location information including Wi-Fi, Bluetooth, cellular, and inertial sensors. However, these technologies during positioning process will faced its own limitations such as: none-line-of-sight signals, multipath signals, and sensor drift or accumulated error. For these reasons, it is very difficult to provide a good positioning accuracy, when only a single technology is utilized alone. Therefore, this study proposes a new positioning solution based on hybridize two different technologies measurements including received signal strength (RSS) of the Wi-Fi access points and onboard smartphone magnetometer readings within fingerprinting positioning technique. The hybridization of these technologies is based on taking their advantaged and mitigating their drawbacks. In addition to that, this study also provided an improved version of matching algorithm of the fingerprinting technique by applying the concept of boosting-dataset records. A set of real trial experiments are conducted to prove the validity of the proposed solution. The obtained results of the experiments show that the proposed positioning solution can provide an enough positioning accuracy, up to 0.13 meter.

High-precision magnetometers play a crucial role in ocean exploration, geophysical prospecting, and military and security applications. Installing them on human-occupied vehicle (HOV) platforms can greatly enhance ocean exploration capabilities and efficiency. However, most existing magnetometers suffer from low sensitivity and excessively large size. This study presents a high-sensitivity, miniaturized magnetometer based on cesium optically pumped probes. The designed magnetometer utilizes a three-probe design to eliminate the detection dead zone of the cesium optically pumped probe and enable three-dimensional magnetic detection. The proposed magnetometer uses a flux gate probe to detect the three-axis magnetic field and ensure that the probe does not enter the dead zone. The three probes can automatically switch by measuring the geomagnetic elements and real-time attitude of the HOV platform. This article primarily introduces the cesium three-probe optically pump, flux gate sensor, and automatic switching scheme design of the above-mentioned magnetometer. Moreover, it is proven through testing that the core indicators, such as the accuracy and sensitivity of the cesium three-probe optically pumped and flux gate sensor, reach international standards. Finally, the effectiveness of the automatic switching scheme proposed in this study is demonstrated through drone-mounted experiments.