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In the re-entry phase of a ballistic missile, decoys can be deployed as a mean to overburden enemy defenses. This results in a single track being split into multiple track-lets. Tracking of these track-lets is a critical task as any miss in the tracking procedure can become a cause of a major threat. The tracking process becomes more complicated in the presence of clutter. The low detection rate is one of the factors that may contribute to increasing the difficulty level in terms of tracking in the cluttered environment. This work introduces a new algorithm for the split event detection and target tracking under the framework of the joint integrated probabilistic data association (JIPDA) algorithm. The proposed algorithm is termed as split event-JIPDA (SE-JIPDA). This work establishes the mathematical foundation for the split target detection and tracking mechanism. The performance analysis is made under different simulation conditions to provide a clear insight into the merits of the proposed algorithm. The performance parameters in these simulations are the root mean square error (RMSE), confirmed true track rate (CTTR) and confirmed split true track rate (CSTTR).

ABSTRACTNational and regional data products of the ecosystem structure derived from airborne laser scanning (ALS) surveys with Light Detection And Ranging (LiDAR) technology are essential for ecology, biodiversity, and ecosystem monitoring. However, noises like powerlines often remain, hindering the accurate measurement of 3D ecosystem structures from LiDAR. Currently, there is a lack of studies assessing powerline noise removal in the context of generating data products of ecosystem structures from ALS point clouds. Here, we assessed the (1) performance and accuracy, (2) effectiveness, and (3) time efficiency and execution time of three powerline extraction methods (i.e. two point-based methods based on deep learning and eigenvalue decomposition, respectively, and one hybrid method) for removing powerline noise when quantifying 3D ecosystem structures in landscapes with varying canopy heights and vegetation openness. Twenty-five LiDAR metrics representing three key dimensions of the ecosystem structure (i.e. vegetation height, cover, and vertical variability) across 10 study areas in the Netherlands were used for our assessment. The deep learning method had the best performance and showed the highest accuracy of powerline removal across various landscape types (average $F1$F1score = 96%), closely followed by the hybrid method (average $F1$F1score = 95%). In contrast, the accuracy of the eigenvalue decomposition method was lower (average $F1$F1score = 82%) and depended on landscape context and vegetation composition (e.g. the $F1$F1score decreased from 96% to 63% when the average canopy height increased across landscapes). Powerline noise removal had the highest effectiveness (i.e. generating LiDAR metrics closest to those derived from manually labeled ground truth data) for LiDAR metrics capturing height and cover of low- and high-vegetation layers. Time efficiency (processed points per second) was highest for the eigenvalue decomposition method, yet the hybrid method reduced the execution time by > 50% compared to the deep learning method (ranging from 20% to 89% in study areas with different landscape composition). Based on our findings, we recommend the hybrid method for upscaling powerline removal on multi-terabyte ALS datasets to a regional or national extent because of its high accuracy and computational efficiency. Remaining misclassifications in LiDAR metrics could be further minimized by improving the training dataset for deep learning models (e.g. including various shapes of transmission towers from different datasets). Our findings provide novel insights into the performance of different powerline extraction methods, how their effectiveness varies for improving vegetation metrics and mapping the 3D ecosystem structure from LiDAR, and their computational efficiency for upscaling powerline removal in multi-terabyte ALS datasets to a national extent.

Background: Turner syndrome (TS) is characterized as the complete or partial absence of one X chromosome and is an extremely rare disease affecting approximately 1:2500 live female births. Though the prevalence of osteoporosis among women with TS is estimated to be around 55–64% and they suffer more frequently from fractures than normal, few reports concerning TS patients with osteoporosis are able to be seen due to tiny number of patients. Case presentation: Here, we report a rare case of TS with osteoporosis, who has undergone percutaneous vertebroplasty (PVP) seven times because of several vertebral compression fractures (VCFs). G-banded karyotype analysis was performed and the result was 45,X[43]/47,XXX[17], indicating that the patient was a mosaicism of TS karyotype and Trisomy X syndrome karyotype. TS is the underlying cause of low level of estrogen for this patient. The interaction of aging, estrogen deficiency and intestinal dysbacteriosis leads to her severe osteoporosis and multi-segmental VCFs. The aim of this report is to provide recommendations regarding the management of TS patients with osteoporosis by reviewing the clinical presentation of TS, the influence of estrogen deficiency in osteoporosis, etc. Conclusions: Early diagnosis and hormone replacement treatment are essential for TS patients to prevent osteoporosis and reduce the risk of fractures. This is a rare case report describing TS patient with severe osteoporosis and VCFs.