scholarly journals Inter-instrument calibration using magnetic field data from Flux Gate Magnetometer (FGM) and Electron Drift Instrument (EDI) onboard Cluster

2013 ◽  
Vol 3 (2) ◽  
pp. 459-487
Author(s):  
R. Nakamura ◽  
F. Plaschke ◽  
R. Teubenbacher ◽  
L. Giner ◽  
W. Baumjohann ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Spin Axis ◽  
Electron Drift ◽  
Magnetic Field Data ◽  
Field Magnitude ◽  
Magnetic Field Magnitude ◽  
Flux Gate ◽  
Axis Offset ◽  
The Magnetic Field

Abstract. We compare the magnetic field data obtained from the Flux-Gate Magnetometer (FGM) and the magnetic field data deduced from the gyration time of electrons measured by the Electron Drift Instrument (EDI) onboard Cluster to determine the spin axis offset of the FGM measurements. Data are used from orbits with their apogees in the magnetotail, when the magnetic field magnitude was between about 20 nT and 500 nT. Offset determination with the EDI-FGM comparison method is of particular interest for these orbits, because no data from solar wind are available in such orbits to apply the usual calibration methods using the Alfvén waves. In this paper, we examine the effects of the different measurement conditions, such as direction of the magnetic field relative to the spin plane and field magnitude in determining the FGM spin-axis offset, and also take into account the time-of-flight offset of the EDI measurements. It is shown that the method works best when the magnetic field magnitude is less than about 128 nT and when the magnetic field is aligned near the spin-axis direction. A remaining spin-axis offset of about 0.4 ~ 0.6 nT was observed between July and October 2003. Using multi-point multi-instrument measurements by Cluster we further demonstrate the importance of the accurate determination of the spin-axis offset when estimating the magnetic field gradient.

scholarly journals Interinstrument calibration using magnetic field data from the flux-gate magnetometer (FGM) and electron drift instrument (EDI) onboard Cluster

2014 ◽  
Vol 3 (1) ◽  
pp. 1-11 ◽  
Author(s):  
R. Nakamura ◽  
F. Plaschke ◽  
R. Teubenbacher ◽  
L. Giner ◽  
W. Baumjohann ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Spin Axis ◽  
Electron Drift ◽  
Magnetic Field Data ◽  
Field Magnitude ◽  
Magnetic Field Magnitude ◽  
Flux Gate ◽  
Axis Offset ◽  
The Magnetic Field

Abstract. We compare the magnetic field data obtained from the flux-gate magnetometer (FGM) and the magnetic field data deduced from the gyration time of electrons measured by the electron drift instrument (EDI) onboard Cluster to determine the spin-axis offset of the FGM measurements. Data are used from orbits with their apogees in the magnetotail, when the magnetic field magnitude was between about 20 and 500 nT. Offset determination with the EDI–FGM comparison method is of particular interest for these orbits, because no data from solar wind are available in such orbits to apply the usual calibration methods using the Alfvén waves. In this paper, we examine the effects of the different measurement conditions, such as direction of the magnetic field relative to the spin plane and field magnitude in determining the FGM spin-axis offset, and also take into account the time-of-flight offset of the EDI measurements. It is shown that the method works best when the magnetic field magnitude is less than about 128 nT and when the magnetic field is aligned near the spin-axis direction. A remaining spin-axis offset of about 0.4 ∼ 0.6 nT was observed for Cluster 1 between July and October 2003. Using multipoint multi-instrument measurements by Cluster we further demonstrate the importance of the accurate determination of the spin-axis offset when estimating the magnetic field gradient.

Using an induction coil sensor to indirectly measure the B-field response in the bandwidth of the transient electromagnetic method

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1489-1494 ◽  
Author(s):  
Richard S. Smith ◽  
A. Peter Annan
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Indirect Measurement ◽  
Rate Of Change ◽  
Induction Coil ◽  
Voltage Response ◽  
Magnetic Field Data ◽  
Transient Electromagnetic ◽  
Different Characteristics ◽  
The Magnetic Field

The traditional sensor used in transient electromagnetic (EM) systems is an induction coil. This sensor measures a voltage response proportional to the time rate of change of the magnetic field in the EM bandwidth. By simply integrating the digitized output voltage from the induction coil, it is possible to obtain an indirect measurement of the magnetic field in the same bandwidth. The simple integration methodology is validated by showing that there is good agreement between synthetic voltage data integrated to a magnetic field and synthetic magnetic‐field data calculated directly. Further experimental work compares induction‐coil magnetic‐field data collected along a profile with data measured using a SQUID magnetometer. These two electromagnetic profiles look similar, and a comparison of the decay curves at a critical point on the profile shows that the two types of measurements agree within the bounds of experimental error. Comparison of measured voltage and magnetic‐field data show that the two sets of profiles have quite different characteristics. The magnetic‐field data is better for identifying, discriminating, and interpreting good conductors, while suppressing the less conductive targets. An induction coil is therefore a suitable sensor for the indirect collection of EM magnetic‐field data.

scholarly journals Magnetic Fields in OH Maser Clouds

1974 ◽  
Vol 60 ◽  
pp. 275-292 ◽  
Author(s):  
R. D. Davies
Keyword(s):  
Magnetic Field ◽  
Angular Momentum ◽  
Magnetic Fields ◽  
Magnetic Flux ◽  
Field Data ◽  
Zeeman Splitting ◽  
Galactic Rotation ◽  
Magnetic Field Data ◽  
The Magnetic Field ◽  
Maser Sources

Observations of Class I OH maser sources show a range of features which are predicted on the basis of Zeeman splitting in a source magnetic field. Magnetic field strengths of 2 to 7 mG are derived for eight OH maser sources. The fields in all the clouds are directed in the sense of galactic rotation. A model of W3 OH is proposed which incorporates the magnetic field data. It is shown that no large amount of magnetic flux or angular momentum has been lost since the condensation from the interstellar medium began.

scholarly journals Enhancing Performance of Magnetic Field Based Indoor Localization Using Magnetic Patterns from Multiple Smartphones

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2704 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Field ◽  
Geomagnetic Field ◽  
Field Data ◽  
Performance Comparison ◽  
Magnetic Sensors ◽  
Location Based Services ◽  
Field Pattern ◽  
Magnetic Field Data ◽  
Localization Performance ◽  
The Magnetic Field

Wide expansion of smartphones triggered a rapid demand for precise localization that can meet the requirements of location-based services. Although the global positioning system is widely used for outdoor positioning, it cannot provide the same accuracy for the indoor. As a result, many alternative indoor positioning technologies like Wi-Fi, Bluetooth Low Energy (BLE), and geomagnetic field localization have been investigated during the last few years. Today smartphones possess a rich variety of embedded sensors like accelerometer, gyroscope, and magnetometer that can facilitate estimating the current location of the user. Traditional geomagnetic field-based fingerprint localization, although it shows promising results, it is limited by the fact that various smartphones have embedded magnetic sensors from different manufacturers and the magnetic field strength that is measured from these smartphones vary significantly. Consequently, the localization performance from various smartphones is different even when the same localization approach is used. So devising an approach that can provide similar performance with various smartphones is a big challenge. Contrary to previous works that build the fingerprint database from the geomagnetic field data of a single smartphone, this study proposes using the geomagnetic field data collected from multiple smartphones to make the geomagnetic field pattern (MP) database. Many experiments are carried out to analyze the performance of the proposed approach with various smartphones. Additionally, a lightweight threshold technique is proposed that can detect user motion using the acceleration data. Results demonstrate that the localization performance for four different smartphones is almost identical when tested with the database made using the magnetic field data from multiple smartphones than that of which considers the magnetic field data from only one smartphone. Moreover, the performance comparison with previous research indicates that the overall performance of smartphones is improved.

scholarly journals Flux-gate magnetometer spin axis offset calibration using the electron drift instrument

2014 ◽  
Vol 25 (10) ◽  
pp. 105008 ◽  
Author(s):  
Ferdinand Plaschke ◽  
Rumi Nakamura ◽  
Hannes K Leinweber ◽  
Mark Chutter ◽  
Hans Vaith ◽  
...  
Keyword(s):  
Spin Axis ◽  
Electron Drift ◽  
Flux Gate ◽  
Axis Offset

scholarly journals Cluster-C1 observations on the geometrical structure of linear magnetic holes in the solar wind at 1 AU

2010 ◽  
Vol 28 (9) ◽  
pp. 1695-1702 ◽  
Author(s):  
T. Xiao ◽  
Q. Q. Shi ◽  
T. L. Zhang ◽  
S. Y. Fu ◽  
L. Li ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Geometrical Structure ◽  
Occurrence Rate ◽  
Geometrical Shape ◽  
Magnetic Field Data ◽  
Magnetic Field Magnitude ◽  
The Relationship ◽  
Plasma Measurements ◽  
The Magnetic Field

Abstract. Interplanetary linear magnetic holes (LMHs) are structures in which the magnetic field magnitude decreases with little change in the field direction. They are a 10–30% subset of all interplanetary magnetic holes (MHs). Using magnetic field and plasma measurements obtained by Cluster-C1, we surveyed the LMHs in the solar wind at 1 AU. In total 567 interplanetary LMHs are identified from the magnetic field data when Cluster-C1 was in the solar wind from 2001 to 2004. We studied the relationship between the durations and the magnetic field orientations, as well as that of the scales and the field orientations of LMHs in the solar wind. It is found that the geometrical structure of the LMHs in the solar wind at 1 AU is consistent with rotational ellipsoid and the ratio of scales along and across the magnetic field is about 1.93:1. In other words, the structure is elongated along the magnetic field at 1 AU. The occurrence rate of LMHs in the solar wind at 1 AU is about 3.7 per day. It is shown that not only the occurrence rate but also the geometrical shape of interplanetary LMHs has no significant change from 0.72 AU to 1 AU in comparison with previous studies. It is thus inferred that most of interplanetary LMHs observed at 1 AU are formed and fully developed before 0.72 AU. The present results help us to study the formation mechanism of the LMHs in the solar wind.

Solar flare forecasting model using 3D magnetic field data

2020 ◽  
Author(s):  
Xin Huang
Keyword(s):  
Magnetic Field ◽  
Deep Learning ◽  
Solar Flare ◽  
Solar Flares ◽  
Field Data ◽  
Active Regions ◽  
Forecasting Model ◽  
Magnetic Field Data ◽  
The Magnetic Field

<p>Solar flares originate from the release of the energy stored in the magnetic field of solar active regions. Generally, the photospheric magnetograms of active regions are used as the input of the solar flare forecasting model. However, solar flares are considered to occur in the low corona. Therefore, the role of 3D magnetic field of active regions in the solar flare forecast should be explored. We extrapolate the 3D magnetic field using the potential model for all the active regions during 2010 to 2017, and then the deep learning method is applied to extract the precursors of solar flares in the 3D magnetic field data. We find that the 3D magnetic field of active regions is helpful to build a deep learning based forecasting model.</p>

scholarly journals New Force-Free Models of Magnetic Clouds

2005 ◽  
Vol 13 ◽  
pp. 133-133
Author(s):  
M. Vandas ◽  
E. P. Romashets ◽  
S. Watari
Keyword(s):  
Magnetic Field ◽  
Magnetic Cloud ◽  
Free Field ◽  
Flux Rope ◽  
Field Vector ◽  
Magnetic Clouds ◽  
Field Magnitude ◽  
Flux Ropes ◽  
Magnetic Field Magnitude ◽  
The Magnetic Field

AbstractMagnetic clouds are thought to be large flux ropes propagating through the heliosphere. Their twisted magnetic fields are mostly modeled by a constant-alpha force-free field in a circular cylindrical flux rope (the Lundquist solution). However, the interplanetary flux ropes are three dimensional objects. In reality they possibly have a curved shape and an oblate cross section. Recently we have found two force-free models of flux ropes which takes into account the mentioned features. These are (i) a constant-alpha force-free configuration in an elliptic flux rope (Vandas & Romashets 2003, A&A, 398, 801), and (ii) a non-constant-alpha force-free field in a toroid with arbitrary aspect ratio (Romashets & Vandas 2003, AIP Conf Ser. 679, 180). Two magnetic cloud observations were analyzed. The magnetic cloud of October 18-19, 1995 has been fitted by Lepping et al. (1997, JGR, 102, 14049) with use of the Lundquist solution. The cloud has a very flat magnetic field magnitude profile. We fitted it by the elliptic solution (i). The magnetic cloud of November 17-18, 1975 has been fitted by Marubashi (1997) with use of a toroidally adjusted Lundquist solution. The cloud has a large magnetic field vector rotation and a large magnetic field magnitude increase over the background level. We fitted it by the toroidal solution (ii). The both fits match the rotation of the magnetic field vector in a comparable quality to the former fits, but the description of the magnetic field magnitude profiles is remarkable better. It is possible to incorporate temporal effects (expansion) of magnetic clouds into the new solutions through a time-dependent alpha parameter as in Shimazu & Vandas (2002, EP&S, 54, 783).

Identification of Complex Shear Modulus of MR Layer Placed in Three-Layer Beam – Part 2: Algorithm

2015 ◽  
Vol 759 ◽  
pp. 15-25
Author(s):  
Mateusz Romaszko ◽  
Jacek Snamina ◽  
Sebastian Pakuła
Keyword(s):  
Magnetic Field ◽  
Shear Modulus ◽  
Field Data ◽  
Free Vibrations ◽  
Complex Shear Modulus ◽  
Mr Fluid ◽  
Magnetic Field Data ◽  
Complex Modes ◽  
Complex Shear ◽  
The Magnetic Field

The paper presents the procedure of identification of a complex shear modulus which describes properties of MR fluid in the pre-yield regime as a function of magnetic field. Data necessary for identification were collected basing on measurements of free vibrations of a three-layered cantilever beam at a special laboratory stand. Magnetic field exerting on MR fluid placed in the beam was generated by electromagnet. In the next step, complex modes of beam vibrations for various places of applying the magnetic field and its strength were calculated.

3-D Modeling the Magnetic Field due to Ocean Tidal Flow O1

2013 ◽  
Vol 658 ◽  
pp. 471-474
Author(s):  
Yan Xu ◽  
Jun Xu ◽  
Wei Hua Zhu ◽  
Xia Feng ◽  
Hai Yan Xie
Keyword(s):  
Magnetic Field ◽  
Field Data ◽  
Tidal Flow ◽  
Ocean Water ◽  
Magnetic Field Data ◽  
Tidal Motion ◽  
The Earth ◽  
Sea Bottom ◽  
Tidal Constituents ◽  
The Magnetic Field

The tidal motion of the ocean water through the ambient magnetic field, generates secondary electric and magnetic field. The magnetic fields generated by the diurnal (O1) ocean flow can be clearly detected. We simulate the magnetic signals for tidal constituents –diurnal (O1) tides. The idea of exploiting tidal signals for EM studies of the Earth is not new, but so far it was used only for interpretation of inland and transoceanic magnetic field data due to O1. Emphasis in this work is made on a discussion of sea bottom electric field of the same origin.

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