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The observations of short-duration meteor showers at different geographic locations are important for well understanding of their outbursts. Using the four all-sky interferometric meteor radars at Mohe (53.49°N, 122.34°E), Beijing (40.3°N, 116.19°E), Wuhan (30.54°N, 114.34°E), and Ledong (18.44°N, 108.97°E) in China, four outbursts (2011, 2012, 2018, and 2019) of the October Draconid meteor shower were observed between 2011 and 2021. The October Draconid meteor velocity was concentrated around 22∼23 km s−1, with an active duration of 6∼9 h and a main peak around the solar longitude 194.73°∼195.63°. When comparing the hourly rates of the October Draconid meteor shower for different years at Mohe, a maximum (minimum) of peak flux was observed in 2012 (2011). The radiants and orbit parameters of the October Draconid meteors derived from the radars at different latitudes are consistent, except that the weighted mean echo height during the peak activity at Mohe was always lower than that at other stations.

The Hunga-Tonga volcano eruption at 04:14:45 UT on 15 January 2022 produced various waves propagating globally, disturbing the background atmosphere and ionosphere. Coinciding with the arrival of perturbation waves, several equatorial plasma bubbles (EPBs) were consecutively generated at post-sunset hours over the East/Southeast Asian region, with the largest extension to middle latitudes. These EPBs caused intense L-band amplitude scintillations at middle-to-low latitudes, with signal fading depths up to ~16 dB. Considering the very rare occurrence of EPBs during this season in East/Southeast Asian sector and the significantly modulated background ionosphere, we believe that the perturbation waves launched by the volcano eruption triggered the generation of unseasonal super EPBs. The ionospheric perturbations linked with the 2022 Tonga volcano eruption propagated coincidently through the East/Southeast Asia longitude sector near sunset, modulated the equatorial F region bottomside plasma density and acted as the seeding source for the generation of unseasonal super bubbles. Our results implicate that volcano eruption could indirectly affect the satellite communication links in the region more than ten thousand kilometers away., 15 pages, 6 figures. The paper has been accepted for publication in the Journal of Space Weather and Space Climate (JSWSC)

Observational evidence is insufficient to understand how equatorial plasma bubbles (EPBs) form over low latitudes. The mechanism of plasma-density enhancement (formation of “plasma blobs”) at low latitudes is in dispute. In this paper, we use data from multiple ground-based instruments (one all-sky airglow imager, five digisondes, and one Fabry–Perot interferometer) to investigate the evolution of an EPB event that occurred at low latitudes over China on the night of 06 December 2015 (06-Dec-2015). We provide observational evidence that an enhanced equatorward wind most likely induced by a substorm could have initiated the Rayleigh–Taylor instability (RTI) that destabilized several EPB depletions in an upwelling region of a large-scale wave-like structure (LSWS) in the bottomside ionosphere. Those EPB depletions were forced to surge poleward, from nearly 10° to 19° magnetic latitude, two hours before midnight. Smaller-scale bifurcations evolved rapidly from tips of airglow depletions by a secondary E × B instability when the aforementioned substorm-induced southwestward wind blew through. During the growth phase of the EPB depletions, a westward polarization electric field inside the LSWS is likely to have compressed plasma downward, inducing the two airglow-type blobs observed in the bottomside ionosphere, by a mechanism of LSWS-blob connection that we propose. We also provide observational evidence of brightness airglow depletions. We find that an enhanced poleward wind associated with a passing-by brightness wave (BW) is likely to have transported plasma to fill the airglow depletions, which finally evolved into brightness airglow structures. This study investigates the physical processes accompanied by the EPB event and those two-airglow blobs observed at low-latitudes over China.

This paper reports the ionospheric response to Typhoon Chan-hom using Global Positioning System network data from China, combined with observations of an ionosonde chain. Two medium-scale traveling ionospheric disturbances (MSTIDs) were observed around the two landing times of Typhoon Chan-hom. The MSTID with a phase fronts aligned in the north-south direction was detected on the east coast of China at 16:50-18:40 UT on 11 July 2015 and moved westward with a mean horizontal phase velocity of 268 m/s and period of 56 min. Then the concentric MSTID appeared on the northeast of China at 01:50-05:10 UT on 13 July 2015 and propagated in the radial direction with average horizontal phase velocity of 143 m/s and period of 45 min. There may be some connections between the north-south-aligned MSTID and the electrodynamical coupling of the sporadic E layer and the F layer, while the concentric MSTID may be caused by the concentric gravity waves resulting from the bodyforces related with the typhoon.

Early and recent observations suggested that E-region field aligned irregularities (FAIs) related closely to the sporadic E (Es) layer of the ionosphere. The Sanya (18.3 ºN, 109.6 ºE) very high frequency (VHF) radar can operate at ionospheric irregularities mode for the detection of 3-m scale FAIs. The development of a portable digital ionosonde (PDI) which is collocated with the Sanya VHF radar can operate with temporal periods down to 1 minute, facilitating the capability of capturing the fast evolution of Es structures. But the low spatial resolution of the two kinds of instruments makes it difficult to depict the horizontal morphology of the Es structures and E-region FAIs. Since the capability of ground-based GNSS in strong Es detection was presented, it serves as a perfect supplement for the investigation of E region of the ionosphere. So comprehensive observation with multi kinds of instruments makes it possible to reveal the relationship and mechanisms of Es and E-region FAIs.A complex daytime sporadic E (Es) case with extremely high critical frequency (foEs) was observed over the low latitude of China on 19 May 2018. Simultaneous observational results from two very high frequency (VHF) radars, two ionosondes, and multiple Global Navigation Satellite System total electron content and scintillation receivers are analyzed to investigate the evolution of the complex Es occurrence, which consisted of a relatively weak ambient Es layer (foEs < 8 MHz) and band-like strong Es structures (foEs > 17 MHz) drifting from higher latitude. The strong Es structures elongated more than 500 km in the northwest-southeast direction, drifted southwestward at a speed of ~65 m/s. VHF radar backscatter echoes were generated when the strong Es structures passed the radar field of view, with different echo patterns due to different radar and antenna configurations. No VHF radar backscatter echo was associated with the ambient Es layer.

The occurrences of equatorial plasma bubble (EPB) irregularities over Asian and American sectors often show a different behavior due to significantly different geometries of the geomagnetic field. By using the GPS total electron content observations over equatorial and low latitudes sectors of Asian (about 120°E) and American (about 60°W) during 1997–2018, we present a comparative study of long-term occurrences of EPB kilometer-scale irregularities over the two longitude sectors. The results show that post-sunset EPB irregularities over the Asian sector were positively correlated with solar activity. However, in the American sector, such a positive dependence was not apparent in 2000–2002, which could be partly induced by limited data available during the high occurrence season of solar maximum when the GPS receivers frequently lost lock. The geomagnetic disturbance tends to inhibit the generation of EPB irregularities over both sectors during post-sunset but enhance their generation during post-midnight. The latitudinal distribution of EPB irregularities exhibited a double-peak structure centered within ± 10° (dip lat.), with the latitudinal limit below ± 25° at solar maximum in both sectors and below ± 20° and ± 10° at solar minimum in American and Asian sectors, respectively. Specifically, in the American sector, the occurrence rates were about two times higher than those in the Asian sector, and the occurrence rates in the northern hemisphere were apparently lower than those in the southern hemisphere. The enhanced ionization by particle precipitation over the South Atlantic Magnetic Anomaly region could cause the north–south asymmetry of occurrence rates of EPB irregularities. The results help in designing experiments to understand better the generation of EPB irregularities under the future international meridian circle project.

The International Reference Ionosphere (IRI) is the most frequently used community empirical model. The latest version IRI2016 provides three options for the F2 peak height hmF2: AMTB2013, SHU-2015, and BSE-1979. In this paper, we used the hmF2 data derived by the ionosondes at Mohe, Beijing, Wuhan and Sanya ranging from year 2007 to 2016 to assess the performance of these three options in the model. The results show that the variability of the observed hmF2 versus local time, seasons and levels of solar activity could be reproduced well by the three options. However, the SHU-2015 option performs best at these four stations, followed by BSE-1979, and the AMTB2013 option is worst. Generally, the AMTB2013 and BSE-1979 options overestimate the hmF2 values, while the SHU-2015 option mainly underestimates the hmF2 values. This is the first evaluations of the three IRI2016 hmF2 options by manually scaled ionosonde data over China area to our knowledge. Overall, we recommend the usage of SHU-2015 hmF2 option over China region when using IRI2016 model in terms of hmF2 calculation.

Equatorial spread-F (ESF) backscatter plumes are often observed in radar range-time-intensity (RTI) maps at low latitude. Except case studies, few statistical investigations on the onset locations of scintillation-producing ESF plumes at given sites have been conducted. In this study, a statistical analysis is carried out on onset locations of ESF backscatter plumes observed at a low-latitude location Sanya (18.4°N, 109.6°E; dip lat 12.8°N) during equinoctial months of 2013. By employing a tracing method to locate backscatter plumes, we estimate the onset longitudes of periodic plumes obtained from the Sanya VHF radar five-beam steering measurements. The results show that the inter-plume distances (in longitude) are mostly confined within 200–600 km, and the ESF plumes producing ionospheric scintillations over Sanya are almost exclusively generated at the longitudes of 94°–110°E. The results indicate the necessity to monitor ESF plume initial generation in the longitude region of 94°–110°E to better understand the day-to-day variability in the occurrence of ionospheric scintillations over Sanya.

In this paper, the AdaBoost-BP algorithm is used to construct a new model to predict the critical frequency of the ionospheric F2-layer (foF2) one hour ahead. Different indices were used to characterize ionospheric diurnal and seasonal variations and their dependence on solar and geomagnetic activity. These indices, together with the current observed foF2 value, were input into the prediction model and the foF2 value at one hour ahead was output. We analyzed twenty-two years’ foF2 data from nine ionosonde stations in the East-Asian sector in this work. The first eleven years’ data were used as a training dataset and the second eleven years’ data were used as a testing dataset. The results show that the performance of AdaBoost-BP is better than those of BP Neural Network (BPNN), Support Vector Regression (SVR) and the IRI model. For example, the AdaBoost-BP prediction absolute error of foF2 at Irkutsk station (a middle latitude station) is 0.32 MHz, which is better than 0.34 MHz from BPNN, 0.35 MHz from SVR and also significantly outperforms the IRI model whose absolute error is 0.64 MHz. Meanwhile, AdaBoost-BP prediction absolute error at Taipei station from the low latitude is 0.78 MHz, which is better than 0.81 MHz from BPNN, 0.81 MHz from SVR and 1.37 MHz from the IRI model. Finally, the variety characteristics of the AdaBoost-BP prediction error along with seasonal variation, solar activity and latitude variation were also discussed in the paper.

Currently, condition-based maintenance becomes increasingly important with additions of factory automation through the development of new technologies. For many complicated machines, it is difficult to use mathematical models to describe their conditions. Intelligent maintenance makes it possible to perform maintenance similar to that of a human being. However, conventional artificial intelligent methods such as neural network and SVM use only labeled data (feature/label pairs) for training. Labeled instances are often difficult, expensive, or time consuming to obtain. Active learning and semi-supervised learning address this problem by using a large amount of unlabeled data together with labeled data to build better models. In this paper, a new active semi-supervised procedure was proposed to perform fault classification for machine condition monitoring. The effectiveness of the procedure was verified by its application to bearing diagnosis and gear fault detection.

The chemical reactions and phases involved in the potential flux system of Li-Ca-N for the growth of bulk GaN crystals have been investigated under varying conditions. It is found that no preferential nitrification of Li or Ca by N-2 in Li-Ca melts at 500 degrees C. Only the ternary compound LiCaN is identified in the Li-Ca-N system under the present experimental conditions. Static N2 pressures are found to enhance the formation of LiCaN compared to an N2 stream. LiCaN forms from two possible pathways: one is a modified metathesis chemical reaction represented by Li3N+Ca -> LiCaN+2Li, and the other is a combination chemical reaction represented by Li3N+Ca3N2 -> 3LiCaN. The formation of LiCaN by the metathesis reaction is thermodynamically favored over the other pathway. In addition, the formation of LiCaN might benefit from a slightly larger initial amount of Li3N compared with Ca or Ca3N2. (c) 2006 Elsevier B.V. All rights reserved.

Piezoceramic-based active sensing is a useful approach to structural health monitoring. This approach often involves a large number of distributed piezoceramic transducers. It may be confusing to incorporate each sensor data. It is desired to develop an automated health monitoring approach to obtain a comprehensive and accurate health monitoring result by simultaneously interpreting data from all sensors. In this paper, an innovative data fusion enabled structural health monitoring (SHM) approach based on the Dempster-Shafer (D-S) evidence theory is proposed to obtain comprehensive SHM results for a distributed sensor network in a civil infrastructure. Considering that evidence from multiple different information sources (sensor data) has different levels of significance, not all evidence is equivalently effective for the final decision. A weighted fusion damage index (WFDI) is proposed to perform damage identification based on the authors’ recently developed piezoceramic-based smart aggregates. Experimental data of a two-story concrete frame was used to study the effectiveness of the proposed weighted fusion damage index. Analyses show that the proposed weighted fusion damage index can reveal the damage status of different areas of the frame. The results are consistent with the visual inspection of the cracks on the concrete frame.

In structural health monitoring, damage detection results have uncertainty due to the measurement noise, modeling error and environment changes. In this paper, data fusion based on the Dempster-Shafer (D-S) evidence theory was employed to decrease the uncertainty level and improve the accuracy of damage identification. A weighted fusion damage index (WFDI) was proposed to perform damage identification. A two-story concrete frame was fabricated as the object for the structural health monitoring test. Experimental results have verified the effectiveness of the proposed method, which will improve damage identification accuracy and increase the reliability of damage identification.

Currently, condition-based maintenance becomes more and more important with the addition of factory automation through the development of new technologies. For many complicated machines, it is difficult to use the mathematical model to describe their faults. Intelligent maintenance makes it possible to perform maintenance similar to that of a human being. Support vector machine (SVM) has become famous and popular in artificial intelligent community due to the excellence of generalization ability than the traditional method such as neural network. However, conventional SVMs use only labeled data (feature/label pairs) to train. Labeled instances are often difficult, expensive, or time consuming to obtain. Semi-supervised learning addresses this problem by using large amount of unlabeled data, together with the labeled data, to build better classifiers. In this paper, a multi-class semi-supervised framework is proposed to perform classification for machine condition monitoring. The effectiveness of the framework is verified by the application to the bearing diagnosis.

In structural health monitoring, damage detection results always have uncertainty because of three factors: measurement noise, modeling error and environment changes. Data fusion can lead to the improved accuracy of a classification decision as compared to a decision based on any individual data source alone. Ensemble approaches constitute a relatively new breed of algorithms used for data fusion. In this paper, we introduced a hierarchical ensemble scheme to the data fusion field. The hierarchical ensemble scheme was based on the Dempster–Shafer (DS) theory and the Rotation Forest (RF) method, it was called a hierarchical ensemble because the RF method itself was an ensemble method. The DS theory was used to combine the output of RF based on different data sources. The validation accuracy of the RF model was considered in the improvement of the performance of the hierarchical ensemble. Health monitoring of a small-scale two-story frame structure with different damages subject to shaking table tests was used as an example to validate the efficiency of the proposed scheme. The experimental results indicated that the proposed scheme will improve the identification accuracy and increase the reliability of identification.