Oblique-incidence ionospheric soundings over Central Europe and their application for testing now casting and long term prediction models

After a first oblique-incidence ionospheric sounding campaign over Central Europe performed during the period 2003–2004 over the radio links between Inskip (UK, 53.5°N, 2.5°W) and Rome (Italy, 41.8°N, 12.5°E) and between Inskip and Chania (Crete, 35.7°N, 24.0°E), new and more extensive analysis of systematic MUF measurements from January 2005 to December 2006 have been performed. MUF measurements collected during moderately disturbed days (17 ? Ap ? 32), disturbed days (32 < Ap ? 50) and very disturbed days (Ap > 50), have been used to test the long term prediction models (ASAPS, ICEPAC and SIRM&LKW), and the now casting models (SIRMUP&LKW and ISWIRM&LKW). The performances of the different prediction methods in terms of r.m.s are shown for selected range of geomagnetic activity and for each season.     

Variability of foF2 in the African equatorial ionosphere

This paper presents the impact of diurnal, seasonal and solar activity effects on the variability of ionospheric foF2 in the African equatorial latitude. Three African ionospheric stations; Dakar (14.8°N, 17.4°W, dip: 11.4°N), Ouagadougou (12.4°N, 1.5°W, dip: 2.8°N) and Djibouti (11.5°N, 42.8°E, dip: 7.2°N) were considered for the investigation. The overall aim is to provide African inputs that will be of assistance at improving existing forecasting models. The diurnal analysis revealed that the ionospheric critical frequency (foF2) is more susceptible to variability during the night-time than the day-time, with two peaks in the range; 18–38% during post-sunset hours and 35–55% during post-midnight hours. The seasonal and solar activity analyses showed a post-sunset September Equinox maximum and June Solstice maximum of foF2 variability in all the stations for all seasons. At all the stations, foF2 variability was high for low solar activity year. Overall, we concluded that equatorial foF2 variability increases with decreasing solar activity during night-time.     

某些NTO盐的热分解特性及在固体推进剂中催化特性的相关性研究

采用高压差示扫描量热仪,研究了几种ＮＴＯ（３－硝基－１,２,４－三唑－５－酮）盐在不同压力下的热分解特性,同时测定了以ＮＴＯ盐为催化剂的固体推进剂的燃速特性。结果认为,这些ＮＴＯ盐的热分解特性与催化特性间存在某种联系,热分解行为压力不敏感的ＮＴＯ盐,可能会是较好的燃速催化剂。因此热分解行为可作催化能力的判据之一。     

Operation and Application of A, B Satellites for Environment and Disaster Monitoring and Forecasting

Environment and disaster monitoring and forecasting small satellite constellation A and B satellites (HJ-1-A, B) are called "environment and disaster reduction satellites A and B' for short. The constellation adopts a 10:30 LT sun-synchronous circular orbit, with orbit altitude of 649 km. HJ-1-A and HJ-1-B are distributed with a phase difference of 180o in the same orbital plane, so as to enhance the time resolution of earth observation. The satellites have orbit maintenance capability, the lifetime is 3 years. Both satellites adopt CAST968 platforms. Two wide-coverage multispectral CCD cameras with resolution 30 m and width 700 km, a super-spectral imager with resolution 100 m and width 50 km as well as a data transmission subsystem of 120 Mbit/s are deployed on HJ-1-A, which also carries Ka communication testing equipment of Thailand. HJ-1-B has two wide-coverage multispectral CCD cameras (the same as satellite A), one infrared camera with resolution 150 m and width 720 km and a data transmission subsystem of 60 Mbit/s. The coverage period of the wide-coverage multispectral CCD camera is 48 hours. The revisit period of super-spectral imager is 96 hours and the coverage period of infrared camera is 96 hours.      

An improved cyclic multi model-eXtreme gradient boosting (CMM-XGBoost) forecasting algorithm on the GNSS vertical time series

The study of GNSS vertical coordinate time series forecasting is helpful for monitoring the crustal plate movement, dam or bridge deformation monitoring, and global or regional coordinate system maintenance. The eXtreme Gradient Boosting (XGBoost) algorithm is a machine learning algorithm that can evaluate features, and it has a great potential and stability for long-span time series forecasting. This study proposes a multi-model combined forecasting method based on the XGBoost algorithm. The method constitutes a new time series as features through the fitting and forecasting results of the forecasting model. The XGBoost model is then used for forecasting. In addition, this method can obtain higher precision forecasting results through circulation. To verify the performance of the forecasting method, 1095 epochs of data in the Up coordinate of 16 GNSS stations are selected for the forecasting test. Compared with the CNN-LSTM model, the experimental results of our forecasting method show that the mean absolute error (MAE) values are reduced by 30.23 %～52.50 % and the root mean square error (RMSE) values are reduced by 31.92 %～54.33 %. The forecasting results have higher accuracy and are highly correlated to the original time series, which can better forecast the vertical movement of the GNSS stations. Therefore, the forecasting method can be applied to the up component of the GNSS coordinate time series.