Data-centric AI workflow based on compressed raw images

In order to extract the full potential of the high volume of image data coming from earth observation, image compression is needed for transfer and storage, and artificial intelligence (AI) is needed for analysis. The promise of AI is to perform complex operations with low programming effort, naturally shifting the focus of the development of machine learning systems from the code, i.e. the implementation of the neural network, to the training process, and in particular to the acquisition, selection and preparation of training data. Lossy compression (like many other image processing methods), however, was developed primarily to compress already processed images for visual inspection, not regarding damage to invisible image properties which play an important role in machine-learning, such as higher order statistics, correlations and bias. The Jetraw image format, in contrast, was designed to compress raw image data, preserving its statistics and embedding camera calibration profile and noise model. These features facilitate the generation of accurate raw synthetic data. They allow for “Jetraw functions” to take a Jetraw image as an argument and return another Jetraw image, complete with its newly computed calibration profile and noise model. Several of these functions can be chained to build complex operations while always maintaining metrologically correct data, i.e. values that have independent errors, are unbiased and have a well-defined noise model. Jetraw images and functions may be used in end-to-end models to generate synthetic data with statistics matching those of genuine raw images, and play an important role in data-centric AI methodologies. Here we show how these features are used for a machine-learning task: the segmentation of cars in an urban, suburban and rural environment. Starting from a drone and airship image dataset in the Jetraw format (with calibrated sensor and optics), we use an end-to-end model to emulate realistic satellite raw images with on-demand parameters. First, we study the effect of various satellite parameters on the task’s performance as well as on the compressed image size. These parameters are satellite mirror size, focal length, pixel size and pattern, exposure time and atmospheric haze. Then, we discuss characterising and improving the performance and tolerances of the neural network through the use of on-the-fly generation of data that accurately reflects the statistics of the target system

Verification of straylight rejection of optical science payloads using a pulsed laser source

editorial reviewedThe performance of astronomical space telescopes can be greatly impacted by straylight. That is why characterizing the straylight in such telescopes before they are deployed is paramount. Nowadays such characterization can be done by simulation or by test. Simulation can provide very useful information on the origin of straylight, helping devise solutions to reduce it and improve the performance of the telescope. However, simulation suffers from limitations due to processing power needed and assumptions made in the model which can lead to simulation results quite far from the actual performances. Standard straylight tests on the other hand provide accurate measurement of the straylight but without any insight about its origin, making it difficult to mitigate. Emerging technologies now offer new possibilities for straylight measurement using time-of-flight technics to help identify the origin of the straylight. Such technologies were reviewed and analysed in a first activity called TRIPP (Time-Resolved Imaging of Photon Paths). The results and outcome of this study are presented in the first chapter of this paper. A second chapter then presents the ongoing status of a second activity, SLOTT (Straylight Lidar Ogse verificaTion Tool) which aims to develop a demonstrator for such a time-resolved straylight verification system. With the development and test of such a tool, CSEM and its partners (TAS-CH, Difrotec, CSL, LusoSpace), supported by ESA, hopes to establish new methods to characterize and reduce the straylight propagation in future space-based telescopes

STE-QUEST: Space Time Explorer and QUantum Equivalence principle Space Test

As submitted to the M7 call in July 2022, except updated for the recent (Sept. 2022) MICROSCOPE results, and new section 2.5 summarizing the information provided to ESA during the September 2022 auditionAn M-class mission proposal in response to the 2021 call in ESA's science programme with a broad range of objectives in fundamental physics, which include testing the Equivalence Principle and Lorentz Invariance, searching for Ultralight Dark Matter and probing Quantum Mechanics

Pancreatic surgery outcomes: multicentre prospective snapshot study in 67 countries

Background: Pancreatic surgery remains associated with high morbidity rates. Although postoperative mortality appears to have improved with specialization, the outcomes reported in the literature reflect the activity of highly specialized centres. The aim of this study was to evaluate the outcomes following pancreatic surgery worldwide.Methods: This was an international, prospective, multicentre, cross-sectional snapshot study of consecutive patients undergoing pancreatic operations worldwide in a 3-month interval in 2021. The primary outcome was postoperative mortality within 90 days of surgery. Multivariable logistic regression was used to explore relationships with Human Development Index (HDI) and other parameters.Results: A total of 4223 patients from 67 countries were analysed. A complication of any severity was detected in 68.7 percent of patients (2901 of 4223). Major complication rates (Clavien-Dindo grade at least IIIa) were 24, 18, and 27 percent, and mortality rates were 10, 5, and 5 per cent in low-to-middle-, high-, and very high-HDI countries respectively. The 90-day postoperative mortality rate was 5.4 per cent (229 of 4223) overall, but was significantly higher in the low-to-middle-HDI group (adjusted OR 2.88, 95 per cent c.i. 1.80 to 4.48). The overall failure-to-rescue rate was 21 percent; however, it was 41 per cent in low-to-middle-compared with 19 per cent in very high-HDI countries.Conclusion: Excess mortality in low-to-middle-HDI countries could be attributable to failure to rescue of patients from severe complications. The authors call for a collaborative response from international and regional associations of pancreatic surgeons to address management related to death from postoperative complications to tackle the global disparities in the outcomes of pancreatic surgery (NCT04652271; ISRCTN95140761)