Concept of wireless sensor network for future in-situ exploration of lunar ice using wireless impedance sensor

It is known that a wireless sensor network uses some sort of sensors to detect a physical quantity of interest, in general. The wireless sensor network is a potential tool for exploring the difficult-to-access area on the earth and the concept may be extended to space applications in future. Recently, lunar water has been detected by a few lunar missions using remote sensing techniques. The lunar water is expected to be in the form of ice at very low temperatures of permanently dark regions on the moon. To support the remote observations and also to find out potential ice bearing sites on the moon, in-situ measurement of the lunar ice is essential. However, a rover may not be able to reach the permanently shadowed regions due to terrain irregularity. One possibility to access such areas is to use a wireless sensor network on the lunar surface.     

Comparison of ionospheric F2 peak parameters foF2 and hmF2 with IRI2001 at Hainan

Monthly median values of foF2, hmF2 and M(3000)F2 parameters, with quarter-hourly time interval resolution for the diurnal variation, obtained with DPS4 digisonde at Hainan (19.5°N, 109.1°E; Geomagnetic coordinates: 178.95°E, 8.1°N) are used to investigate the low-latitude ionospheric variations and comparisons with the International Reference Ionosphere (IRI) model predictions. The data used for the present study covers the period from February 2002 to April 2007, which is characterized by a wide range of solar activity, ranging from high solar activity (2002) to low solar activity (2007). The results show that (1) Generally, IRI predictions follow well the diurnal and seasonal variation patterns of the experimental values of foF2, especially in the summer of 2002. However, there are systematic deviation between experimental values and IRI predictions with either CCIR or URSI coefficients. Generally IRI model greatly underestimate the values of foF2 from about noon to sunrise of next day, especially in the afternoon, and slightly overestimate them from sunrise to about noon. It seems that there are bigger deviations between IRI Model predictions and the experimental observations for the moderate solar activity. (2) Generally the IRI-predicted hmF2 values using CCIR M(3000)F2 option shows a poor agreement with the experimental results, but there is a relatively good agreement in summer at low solar activity. The deviation between the IRI-predicted hmF2 using CCIR M(3000)F2 and observed hmF2 is bigger from noon to sunset and around sunrise especially at high solar activity. The occurrence time of hmF2 peak (about 1200 LT) of the IRI model predictions is earlier than that of observations (around 1500 LT). The agreement between the IRI hmF2 obtained with the measured M(3000)F2 and the observed hmF2 is very good except that IRI overestimates slightly hmF2 in the daytime in summer at high solar activity and underestimates it in the nighttime with lower values near sunrise at low solar activity.     

The Pre-CME Sun

The coronal mass ejection (CME) phenomenon occurs in closed magnetic field regions on the Sun such as active regions, filament regions, transequatorial interconnection regions, and complexes involving a combination of these. This chapter describes the current knowledge on these closed field structures and how they lead to CMEs. After describing the specific magnetic structures observed in the CME source region, we compare the substructures of CMEs to what is observed before eruption. Evolution of the closed magnetic structures in response to various photospheric motions over different time scales (convection, differential rotation, meridional circulation) somehow leads to the eruption. We describe this pre-eruption evolution and attempt to link them to the observed features of CMEs. Small-scale energetic signatures in the form of electron acceleration (signified by nonthermal radio bursts at metric wavelengths) and plasma heating (observed as compact soft X-ray brightening) may be indicative of impending CMEs. We survey these pre-eruptive energy releases using observations taken before and during the eruption of several CMEs. Finally, we discuss how the observations can be converted into useful inputs to numerical models that can describe the CME initiation.     

Ionization effect of solar protons in the Earth atmosphere – Case study of the 20 January 2005 SEP event

The cosmic ray ground level enhancement on January 20, 2005 is among the largest recorded events in the history of cosmic ray measurements. The solar protons of MeV energies cause an excess of ionization in the atmosphere, specifically over polar caps following major solar disturbances. The ionization effect in the Earth atmosphere is obtained for various latitudes on the basis of solar proton energy spectra, reconstructed from GOES 11 measurements and subsequent full Monte Carlo simulation of cosmic ray induced atmospheric cascade. The estimation of ionization rates is based on a numerical model for cosmic ray induced ionization. The evolution of atmospheric cascade is performed with the CORSIKA 6.52 code using FLUKA 2006b and QGSJET II hadron interaction models. The atmospheric ion rate ionization is explicitly obtained for 40°N, 60°N and 80°N latitudes. The time evolution of obtained ion rates is presented. It is demonstrated that ionization effect is negative for 40°N and small for 60°N, because of accompanying Forbush decrease. The ionization effect is significant only in sub-polar and polar atmosphere during the major ground level enhancement of 20 January 2005.     

Distributed computation for a tree network with communicationdelays

Tree networks of communicating processors are examined with the objective of solving a computational problem in a minimal amount of time. The processors in the networks may be equipped either with or without front-end processors for communicating of loading. The determination of the optimal division of processing load is discussed for the network with and the network without front-end processors. The inclusion of solution time, the time taken for sensors to report the solution back to originator, is discussed