The influence of meteoric smoke particles on the artificial heating effect in the D-region

<p>Artificial heating increases the electron temperature by transferring the energy of powerful high frequency radio waves into thermal energy of electrons. Current models most likely overestimate the effect of artificial heating in the D-region compared to observations [1, 2]. We investigate if the presence of meteoric smoke particles can explain the discrepancy between observations and model. The ionospheric D-region varies in altitude range from about 50 km to 100 km. In the D-region, the electron density is low, the neutral density is relatively high and it is here that meteors ablate. The ablated meteoric material is believed to recondense to form meteoric smoke particles (MSP). The presence of MSP in the D-region can influence plasma densities through charging of dust by electrons and ions, depending on different ionospheric conditions. Charging of dust influence the electron density mainly through electron attachment to the dust, which results in height regions with less electron density. The heating effect varies with electron density height profile [3], since the reduction in radio wave energy is due to absorption by electrons. We study artificial heating of the D-region and consider MSP by using a one-dimensional ionospheric model [4], which also includes photochemistry. In the ionospheric model, we assume that artificial heating only influences the chemical reactions that depend on electron temperature. We model the electron temperature increase during artificial heating with an electron density calculated from the ionospheric model, where we will do the modelling with and without the MSP and compare day and night condition. Our results show a difference in electron temperature increase with the MSP compared to without the MSP and between day and night condition.</p><p>References:</p><ul><li>[1] Senior, A., M. T. Rietveld, M. J. Kosch and W. Singer (2010): «Diagnosing radio plasma heating in the polar summer mesosphere using cross modulation: Theory and observations». Journal of geophysical research, Vol. 115, A09318.</li> <li>[2] Kero, A., C.-F Enell, Th. Ulich, E. Turunen, M. T. Rietveld and F. H. Honary (2007): «Statistical signature of active D-region HF heating in IRIS riometer data from 1994-2004». Ann. Geophys., 25, 407-415.</li> <li>[3] Kassa, M., O. Havnes and E. Belova (2005): «The effect of electron bite-outs on artificial electron heating and the PMSE overshoot». Annales Geophysicae, 23, 3633-3643.</li> <li>[4] Baumann, C., M. Rapp, A. Kero and C.-F. Enell (2013): «Meteor smoke influence on the D-region charge balance –review of recent in situ measurements and one-dimensional model results». Ann. Geophys., 31, 2049-2062.</li> </ul>

0289 What is the Optimal Duration to Sleep in on Weekends?

Introduction Most individuals in the workforce exhibit differing sleep/wake patterns between work days and weekends. Work days are typically characterized by shorter and earlier sleep. On weekends, sleep debt is repaid by sleeping later and longer, often due to evening events. While social jet-lag (the mismatch in work vs. free sleep timing) is associated with poor health outcomes, repaying sleep debt is beneficial to health. The degree to which individuals should sleep in on weekends is currently unknown. Methods We used a mathematical model of human sleep/wake timing, which has been validated for predicting sleep/wake patterns in a variety of field/lab conditions. Sleep timing constraints are inputs, and the model generates predicted sleep/wake patterns and alertness levels. We simulated a traditional 7-day work week, with 7am rise times on week days. Inter-individual differences in chronotype were modeled by varying intrinsic circadian period. The model was applied to two conditions: (i) free choice of sleep onset times on weekends; or (ii) late nights on weekends (2am bedtime). Weekend rise time was systematically varied to optimize predicted daytime alertness. Results Optimal weekend rise times varied as a function of chronotype. With free choice sleep onset times, the model predicted optimal rise time was later for late types than early types, ranging from 7:20 to 8:40am across individuals. Sleeping later than optimal was associated with poorer performance due to misaligned circadian phase. The same trend was observed in the late-night condition, but with later optimal rise times, ranging from 8:30 to 9:50am. Conclusion Although individuals should maintain a consistent sleep/wake pattern on all days of the week, they often do not, due to work or social commitments. Within real-world constraints, we provided the first objective recommendations for sleep timing on the weekend, finding a compromise between repaying sleep debt and avoiding circadian misalignment. Support N/A

Trends in Sighting Systems for Combat Vehicles

Search and tracking in dynamic condition, rapid re-targeting, precision pointing and long range engagement in day and night condition are core requisite of stabilised sighting systems used for combat vehicles. Complex battle field requires integrated fire control system with stabilised sighting system as its main constituent. It facilitates quick reaction to fire control system and provides vital edge in the battlefield scenario. Precision gimbal design, optics design, embedded engineering, control system, electro-optical sensors, target detection and tracking, panorama generation, auto-alerting, digital image stabilisation, image fusion and integration are important aspects of sighting system development. In this paper, design considerations for a state of art stabilised sighting system have been presented including laboratory and field evaluation methods for such systems.

The Effects of Driving Environment on the Mental Workload of Train Drivers

A study is carried out to investigate the effects of driving environment on the mental workload of train drivers while driving. The driving task is performed under three environmental conditions, i.e. clear sunny day, rainy day and rainy night driving. Electroencephalography (EEG) measurements are recorded from the Fz and Pz channels of fifteen male subjects aged between 24 to 48 years old. The mean alpha power is monitored as a function of time as this signal reflects the variations in mental workload of the drivers. The results exhibit that the signal pattern for rainy night driving condition is significantly different compared to others. This finding indicates that the train drivers show an increase in mental workload after six minutes of driving under rainy night condition. The results demonstrate a percentage difference in mean alpha power of 37% between daytime and rainy night driving conditions during the early periods of driving. This indicates that the mental workload of train drivers tends to be low with an increased level of sleepiness under such conditions, which are signs of low vigilance.

Auto-Layering Wavelet Transfer to Remove the Circumstance Effect and Noise

The accuracy of visibility measurement from night light sources image is usually affected by the circumstance light and noise. This paper presents an auto-layering wavelet transfer method to remove the circumstance effect and noise simultaneously. Firstly, the light propagation through the fog at night condition is formulized, where the model and features of night image with circumstance effect and noise is given. Secondly, we propose to use multi-scale features of wavelet transfer to decompose the image to remove the circumstance effect and noise, where an auto-layering method is used based on the energy ratio of wavelet coefficients. Experiments show that our method is able to remove the circumstance effect and noise simultaneously and to adjust the decomposed layering number automatically. Our method is not only suitable for many wavelet functions, but also preserves the light sources as well as their glows in the digital images. The relative error of using db4 is 3.16%, and the relative error of using sym2 is 2.02%.