The Magnetic Electron Ion Spectrometer (MagEIS) Instruments Aboard the Radiation Belt Storm Probes (RBSP) Spacecraft

This paper describes the Magnetic Electron Ion Spectrometer (MagEIS) instruments aboard the RBSP spacecraft from an instrumentation and engineering point of view. There are four magnetic spectrometers aboard each of the two spacecraft, one low-energy unit (20–240 keV), two medium-energy units (80–1200 keV), and a high-energy unit (800–4800 keV). The high unit also contains a proton telescope (55 keV–20 MeV). The magnetic spectrometers focus electrons within a selected energy pass band upon a focal plane of several silicon detectors where pulse-height analysis is used to determine if the energy of the incident electron is appropriate for the electron momentum selected by the magnet. Thus each event is a two-parameter analysis, an approach leading to a greatly reduced background. The physics of these instruments are described in detail followed by the engineering implementation. The data outputs are described, and examples of the calibration results and early flight data presented.     

Scientific Planning and Commanding of the Rosetta Payload

ESA’s Rosetta mission was launched in March 2004 and is on its way to comet 67P/Churyumov-Gerasimenko, where it is scheduled to arrive in summer 2014. It comprises a payload of 12 scientific instruments and a Lander. All instruments are provided by Principal Investigators, which are responsible for their operations. As for most ESA science missions, the ground segment of the mission consists of a Mission Operations Centre (MOC) and a Science Operations Centre (SOC). While the MOC is responsible for all spacecraft-related aspects and the final uplink of all command timelines to the spacecraft, the scientific operations of the instruments and the collection of the data and ingestion into the Planetary Science Archive are coordinated by the SOC. This paper focuses on the tasks of the SOC and in particular on the methodology and constraints to convert the scientific goals of the Rosetta mission to operational timelines.     

Energetics of reconnection: A reply

In this article we address several criticisms of Petschek-type reconnection models which have recently been raised by Heikkila. We discuss features of the time-dependent Petschek-type models in the context of the solar wind-magnetosphere interaction, and point out that such models can incorporate and reproduce observed features at the magnetopause, such as plasma jets and erosion of the current sheet. We argue that some of Heikkila's criticisms can be attributed to weaknesses in the analysis due to incomplete experimental information, rather than to flaws in the concept of reconnection per se; in this category we include the question of which instability leads to the localised breakup of the magnetopause current sheet. Other criticisms are based on an adherence to steady-state models, and cannot be sustained within the extended time-dependent theory. We discuss, for example, how the time-dependent model can provide a consistent picture of how energy from the incoming solar wind is transferred and converted as it enters the magnetosphere.     

The Role of Magnetic Reconnection in CME Acceleration

Observations carried out from the coronagraphs on board space missions (LASCO/SOHO, Solar Maximum and Skylab) and ground-based facilities (HAO/Mauna Loa Observatory) show that coronal mass ejections (CMEs) can be classified into two classes based on their kinematics evolution. These two classes of CMEs are so-called fast and slow CMEs. The fast CME starts with a high initial speed that remains more or less constant; it is also called the constant-speed CME. On the other hand, the slow CME starts with a low initial speed, but shows a gradual acceleration; it is also called the accelerated and slow CME. Low and Zhang [Astrophys. J. 564, L53–L56, 2002] suggested that these two classes of CMEs could be a result of a difference in the initial topology of the magnetic fields associated with the underlying quiescent prominences. A normal prominence magnetic field topology will lead to a fast CME, while an inverse quiescent prominence results in a slow CME, because of the nature of the magnetic reconnection processes. In a recent study given by Wu et al. [Solar Phys. 225, 157–175, 2004], it was shown that an inverse quiescent prominence magnetic topology also could produce a fast CME. In this study, we perform a numerical MHD simulation for CMEs occurring in both normal and inverse quiescent prominence magnetic topology. This study demonstrates three major physical processes responsible for destabilization of these two types of prominence magnetic field topologies that can launch CMEs. These three initiation processes are identical to those used by Wu et al. [Solar Phys. 225, 157–175, 2004]. The simulations show that both fast and slow CMEs can be initiated from these two different types of magnetic topologies. However, the normal quiescent prominence magnetic topology does show the possibility for launching a reconnection island (or secondary O-line) that might be thought of as a “CME’’.     

Streamer Evaporation

Streamer evaporation is the consequence of heating in ideal MHD models because plasma is weakly contained by the magnetic field. Heating causes inflation, opening of field lines, and release of solar wind. It was discovered in simulations and, due to the absence of loss mechanisms, the ultimate end point is the complete evaporation of the streamer. Of course streamers do not behave in this way because of losses by thermal conduction and radiation. Heating is also expected to depend on ambient conditions. We use a global MHD model with thermal conduction to examine the effect of changing the heating scale height. We also extend an analytic model of streamers developed by Pneuman (1968) to show that steady streamers are unable to contain plasma for temperatures near the cusp greater than ∼ 2 × 10⁶ K. This revised version was published online in June 2006 with corrections to the Cover Date.     

Charge measurement circuit for electric vehicle batteries

Calculating the state of charge (SOC) of an electric vehicle (EV) battery is an inherently error prone process that depends on several variables. However, the accuracy of the required charge flow measurements can be greatly improved by using a voltage to frequency (V/F) converter in conjunction with a digital counter to integrate the measured battery current.