Simultaneous observations for the total column densities of NO2,O3 and H2O were carried on using the portable Spectrometer (438-450 nm and 400-450 nm) and the visible Spectrometer (544.4-628 nm) during premonsoon thun...Simultaneous observations for the total column densities of NO2,O3 and H2O were carried on using the portable Spectrometer (438-450 nm and 400-450 nm) and the visible Spectrometer (544.4-628 nm) during premonsoon thunderstorms and embedded hail storm activity at Pune (18°32'N & 73°51'E),India.These observations confirm the fact that there is an increase in O3 and NO2 column densities during thunderstorms.The increase in O3 was observed following onset of thunderstorm,while the increase in NO2 was observed only after the thunder flashes occur.This implies that the production mechanisms for O3 and NO2 in thunderstorm are different.The observed column density of NO,value (1 to 3×1017molecules cm-2) during thunderstorm activity is 10 to 30 times higher than the value (1×10th molecules cm-2) of a normal day total column density.The spectrometric observations and observations of thunder flashes by electric field meter showed that 6.4×1025molecules/flash of NO2 are produced.The increased to-oil column density of ozone during thunderstorm period is 1.2 times higher than normal (clear) day ozone concentration.The multiple scattering in the clouds is estimated from H2O and O2 absorption bands in the visible spectral region Considering this effect the calculated amount of ozone added in the global atmosphere due to thunderstorm activity is 0.26 to 0 52 DU,and the annual production of ozone due to thunderstorm activity is of the order of 4.02×10 molecules/year The annual NO2 production may be of the order of 2.02×1035molecules/year.展开更多
Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the internet.SDN has increased the flexibility and transparency of the managed,centralized,and controlled network...Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the internet.SDN has increased the flexibility and transparency of the managed,centralized,and controlled network.On the other hand,these advantages create a more vulnerable environment with substantial risks,culminating in network difficulties,system paralysis,online banking frauds,and robberies.These issues have a significant detrimental impact on organizations,enterprises,and even economies.Accuracy,high performance,and real-time systems are necessary to achieve this goal.Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System(IDS)has stimulated the interest of numerous research investigators over the last decade.In this paper,a novel HFS-LGBM IDS is proposed for SDN.First,the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature subset.In thefirst phase,the Correlation based Feature Selection(CFS)algorithm is used to obtain the feature subset.The optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination(RF-RFE)in the second phase.A LightGBM algorithm is then used to detect and classify different types of attacks.The experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy,precision,recall and f-measure.展开更多
文摘Simultaneous observations for the total column densities of NO2,O3 and H2O were carried on using the portable Spectrometer (438-450 nm and 400-450 nm) and the visible Spectrometer (544.4-628 nm) during premonsoon thunderstorms and embedded hail storm activity at Pune (18°32'N & 73°51'E),India.These observations confirm the fact that there is an increase in O3 and NO2 column densities during thunderstorms.The increase in O3 was observed following onset of thunderstorm,while the increase in NO2 was observed only after the thunder flashes occur.This implies that the production mechanisms for O3 and NO2 in thunderstorm are different.The observed column density of NO,value (1 to 3×1017molecules cm-2) during thunderstorm activity is 10 to 30 times higher than the value (1×10th molecules cm-2) of a normal day total column density.The spectrometric observations and observations of thunder flashes by electric field meter showed that 6.4×1025molecules/flash of NO2 are produced.The increased to-oil column density of ozone during thunderstorm period is 1.2 times higher than normal (clear) day ozone concentration.The multiple scattering in the clouds is estimated from H2O and O2 absorption bands in the visible spectral region Considering this effect the calculated amount of ozone added in the global atmosphere due to thunderstorm activity is 0.26 to 0 52 DU,and the annual production of ozone due to thunderstorm activity is of the order of 4.02×10 molecules/year The annual NO2 production may be of the order of 2.02×1035molecules/year.
文摘Software Defined Networking(SDN)has emerged as a promising and exciting option for the future growth of the internet.SDN has increased the flexibility and transparency of the managed,centralized,and controlled network.On the other hand,these advantages create a more vulnerable environment with substantial risks,culminating in network difficulties,system paralysis,online banking frauds,and robberies.These issues have a significant detrimental impact on organizations,enterprises,and even economies.Accuracy,high performance,and real-time systems are necessary to achieve this goal.Using a SDN to extend intelligent machine learning methodologies in an Intrusion Detection System(IDS)has stimulated the interest of numerous research investigators over the last decade.In this paper,a novel HFS-LGBM IDS is proposed for SDN.First,the Hybrid Feature Selection algorithm consisting of two phases is applied to reduce the data dimension and to obtain an optimal feature subset.In thefirst phase,the Correlation based Feature Selection(CFS)algorithm is used to obtain the feature subset.The optimal feature set is obtained by applying the Random Forest Recursive Feature Elimination(RF-RFE)in the second phase.A LightGBM algorithm is then used to detect and classify different types of attacks.The experimental results based on NSL-KDD dataset show that the proposed system produces outstanding results compared to the existing methods in terms of accuracy,precision,recall and f-measure.
