Satellite Image Compression Guided by Regions of Interest.

Small satellites empower different applications for an affordable price. By dealing with a limited capacity for using instruments with high power consumption or high data-rate requirements, small satellite missions usually focus on specific monitoring and observation tasks. Considering that multispectral and hyperspectral sensors generate a significant amount of data subjected to communication channel impairments, bandwidth constraint is an important challenge in data transmission. That issue is addressed mainly by source and channel coding techniques aiming at an effective transmission. This paper targets a significant further bandwidth reduction by proposing an on-the-fly analysis on the satellite to decide which information is effectively useful before coding and transmitting. The images are tiled and classified using a set of detection algorithms after defining the least relevant content for general remote sensing applications. The methodology makes use of the red-band, green-band, blue-band, and near-infrared-band measurements to perform the classification of the content by managing a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. Experiments for a set of typical scenarios of summer and winter days in Stockholm, Sweden, were conducted, and the results show that non-important content can be identified and discarded without compromising the predefined useful information for water and dry-land regions. For the evaluated images, only 22.3% of the information would need to be transmitted to the ground station to ensure the acquisition of all the important content, which illustrates the merits of the proposed method. Furthermore, the embedded platform's constraints regarding processing time were analyzed by running the detection algorithms on Unibap's iX10-100 space cloud platform.

A probabilistic approach for estimating infant exposure to environmental pollutants in human breast milk.

Effective risk assessment and management are often hampered by a lack of reliable exposure data. The probabilistic exposure assessment approach takes into account individual variations in exposure, and thus, overly conservative estimates based on worst case scenarios can be avoided. The aim was to provide reliable information on the intake of non-dioxinlike (NDL) and dioxinlike PCBs, PCDDs and PCDFs in breastfed infants and their mothers during 2000-2006. Hence, a probabilistic model was developed to estimate the exposure and compare it with a deterministic exposure assessment approach. The estimated probabilistic mean intake in 1, 3 and 6 months old infants was 44, 31 and 17 pg total-TEQ/kg bw per day, and 418, 294 and 165 ng NDL-PCBs/kg bw per day, respectively. Intakes differed up to 41% between the upper-bound percentiles of the probabilistic approach and the deterministic worst case scenario approach, whereas no difference in mean values was observed between the two approaches. The median cumulative intake increased during 6 months of breastfeeding to 20 ng total-TEQ and 352 µg NDL-PCBs. There was a significant temporal decrease in infant exposure during 2000-2006 (30%). Less than 4% of the mothers had an intake exceeding the TDI of 2 pg TEQ/kg bw per day (median: 1.2 pg total-TEQ/kg bw). To conclude, by use of a probabilistic approach and biomonitoring data we were able to calculate reliable estimates of infant exposure to environmental pollutants and the daily intakes of the nursing mothers using the same data.