The James Webb Space Telescope Mission

Twenty-six years ago a small committee report, building on earlier studies, expounded a compelling and poetic vision for the future of astronomy, calling for an infrared-optimized space telescope with an aperture of at least $4m$. With the support of their governments in the US, Europe, and Canada, 20,000 people realized that vision as the $6.5m$ James Webb Space Telescope. A generation of astronomers will celebrate their accomplishments for the life of the mission, potentially as long as 20 years, and beyond. This report and the scientific discoveries that follow are extended thank-you notes to the 20,000 team members. The telescope is working perfectly, with much better image quality than expected. In this and accompanying papers, we give a brief history, describe the observatory, outline its objectives and current observing program, and discuss the inventions and people who made it possible. We cite detailed reports on the design and the measured performance on orbit.Comment: Accepted by PASP for the special issue on The James Webb Space Telescope Overview, 29 pages, 4 figure

SNOWED: Automatically Constructed Dataset of Satellite Imagery for Water Edge Measurements

Monitoring the shoreline over time is essential to quickly identify and mitigate environmental issues such as coastal erosion. Monitoring using satellite images has two great advantages, i.e., global coverage and frequent measurement updates; but adequate methods are needed to extract shoreline information from such images. To this purpose, there are valuable non-supervised methods, but more recent research has concentrated on deep learning because of its greater potential in terms of generality, flexibility, and measurement accuracy, which, in contrast, derive from the information contained in large datasets of labeled samples. The first problem to solve, therefore, lies in obtaining large datasets suitable for this specific measurement problem, and this is a difficult task, typically requiring human analysis of a large number of images. In this article, we propose a technique to automatically create a dataset of labeled satellite images suitable for training machine learning models for shoreline detection. The method is based on the integration of data from satellite photos and data from certified, publicly accessible shoreline data. It involves several automatic processing steps, aimed at building the best possible dataset, with images including both sea and land regions, and correct labeling also in the presence of complicated water edges (which can be open or closed curves). The use of independently certified measurements for labeling the satellite images avoids the great work required to manually annotate them by visual inspection, as is done in other works in the literature. This is especially true when convoluted shorelines are considered. In addition, possible errors due to the subjective interpretation of satellite images are also eliminated. The method is developed and used specifically to build a new dataset of Sentinel-2 images, denoted SNOWED; but is applicable to different satellite images with trivial modifications. The accuracy of labels in SNOWED is directly determined by the uncertainty of the shoreline data used, which leads to sub-pixel errors in most cases. Furthermore, the quality of the SNOWED dataset is assessed through the visual comparison of a random sample of images and their corresponding labels, and its functionality is shown by training a neural model for sea–land segmentation

First tests of the altimetric and thermal accuracy of an UAV landfill survey

The drones allow to acquire geometric and thematic information quickly and with considerable operational advantages in close range surveys. The lower flight altitude, and the possibility of rapidly replacing the sensors between panchromatic, multispectral and thermal sensors allow the detection of useful characteristics for monitoring and control, even periodically, of areas with reduced size. This paper will show the first results of testing these technologies on a landfill in the various stages of its development to verify the applicability and achievable accuracy in this specific field of application. It will show how the metric accuracy of the altimetric information obtained and the possible information that can be inferred from the thermal coverage has been verified. To this end, the surface of the site was materialized through the installation of dozens of fixed photogrammetric markers, specially prepared for the purpose. Markers were surveyed with GNSS receivers repeatedly in order to assess the displacements due to natural landfill settlement. It was possible to verify beforehand that the accuracy is comparable with that obtained from GP S. More in detail, for optical imagery the precision evaluated using SFMby Agisoft on GCPs is 5 mm, but we estimated separately the accuracy on some CPs that is around 23-55 cm. On the other hand on thermal imagery the precision evaluated by PCI Geomaticsrigorous model on GCPs is around 70 cm. while the actual accuracy is more complex to be estimated due to the little number of surely collimable CPs and so it will discussed more in detail in the paper. These preliminary results show that the technique can be properly calibrated to operate in such situations with technical and safety advantages without interrupting cultivation activities. Results could be developed in similar fields such as mining extraction and excavations of various kinds

Towards Non-Invasive Methods to Assess Population Structure and Biomass in Vulnerable Sea Pen Fields

Colonies of the endangered red sea pen Pennatula rubra (Cnidaria: Pennatulacea) sampled by trawling in the northwestern Mediterranean Sea were analyzed. Biometric parameters, such as total length, peduncle length, number of polyp leaves, fresh weight, and dry weight, were measured and related to each other by means of regression analysis. Ad hoc models for future inferencing of colonies size and biomass through visual techniques were individuated in order to allow a non-invasive study of the population structure and dynamics of P. rubra

Assessment of Position Repeatability Error in an Electromagnetic Tracking System for Surgical Navigation

In this paper we present a study of the repeatability of an innovative electromagnetic tracking system (EMTS) for surgical navigation, developed to overcome the state of the art of current commercial systems, allowing for the placement of the magnetic field generator far from the operating table. Previous studies led to the development of a preliminary EMTS prototype. Several hardware improvements are described, which result in noise reduction in both signal generation and the measurement process, as shown by experimental tests. The analysis of experimental results has highlighted the presence of drift in voltage components, whose effect has been quantified and related to the variation of the sensor position. Repeatability in the sensor position measurement is evaluated by means of the propagation of the voltage repeatability error, and the results are compared with the performance of the Aurora system (which represents the state of the art for EMTS for surgical navigation), showing a repeatability error about ten times lower. Finally, the proposed improvements aim to overcome the limited operating distance between the field generator and electromagnetic (EM) sensors provided by commercial EM tracking systems for surgical applications and seem to provide a not negligible technological advantage