Hybrid Satellite-Terrestrial Communication Networks for the Maritime Internet of Things: Key Technologies, Opportunities, and Challenges

With the rapid development of marine activities, there has been an increasing number of maritime mobile terminals, as well as a growing demand for high-speed and ultra-reliable maritime communications to keep them connected. Traditionally, the maritime Internet of Things (IoT) is enabled by maritime satellites. However, satellites are seriously restricted by their high latency and relatively low data rate. As an alternative, shore & island-based base stations (BSs) can be built to extend the coverage of terrestrial networks using fourth-generation (4G), fifth-generation (5G), and beyond 5G services. Unmanned aerial vehicles can also be exploited to serve as aerial maritime BSs. Despite of all these approaches, there are still open issues for an efficient maritime communication network (MCN). For example, due to the complicated electromagnetic propagation environment, the limited geometrically available BS sites, and rigorous service demands from mission-critical applications, conventional communication and networking theories and methods should be tailored for maritime scenarios. Towards this end, we provide a survey on the demand for maritime communications, the state-of-the-art MCNs, and key technologies for enhancing transmission efficiency, extending network coverage, and provisioning maritime-specific services. Future challenges in developing an environment-aware, service-driven, and integrated satellite-air-ground MCN to be smart enough to utilize external auxiliary information, e.g., sea state and atmosphere conditions, are also discussed

Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Oceanus.

v. 42, no. 1 (2000

Immortality Through Mind Uploading and Resurrection

Technology in the last century has flourished exponentially. Previous fantasies are becoming cutting-edge discoveries like global communications, encyclopedic knowledge at the average person’s fingertips, and even medical advances used to improve and extend one’s quality of life and life expectancy. As technology pushes the boundaries of what is possible, ambitious visionaries look to solve the arguably greatest problem known to humanity: death. Transhumanists aiming to use technology to overcome this great human limitation, mortality, present the newest proposed solutions to life’s oldest challenge. One of these solutions, mind uploading, is perhaps the most ambitious, but it is not without its own philosophical hindrances. In contrast, Christian resurrection claims to not only solve the problem of death, it claims to already have a historical model in the person of Jesus Christ

Stratus 14 : fourteenth setting of the Stratus Ocean Reference Station cruise on board RV Cabo de Hornos April 14 - 30, 2015 Valparaiso, Chile

The Ocean Reference Station at 20°S, 85°W under the stratus clouds west of northern Chile is being maintained to provide ongoing climate-quality records of surface meteorology, air-sea fluxes of heat, freshwater, and momentum, and of upper ocean temperature, salinity, and velocity variability. The Stratus Ocean Reference Station (ORS Stratus) is supported by the National Oceanic and Atmospheric Administration’s (NOAA) Climate Observation Program. It is recovered and redeployed annually, with past cruises that have come between October and January. This cruise was conducted on the Chilean research vessel Cabo de Hornos. During the 2015 cruise on the Cabo de Hornos to the ORS Stratus site, the primary activities were the recovery of the previous (Stratus 13) WHOI surface mooring, deployment of the new Stratus 14 WHOI surface mooring, in-situ calibration of the buoy meteorological sensors by comparison with instrumentation installed on the ship and CTD casts near the moorings. Surface drifters were also launched along the track.Funding was provided by the National Oceanic and Atmospheric Administration under Grant No. NA140AR432015

D6.2 Workplan for transfer of knowledge and experience

This document represents the ‘Workplan for transfer of knowledge and experience’ (deliverable D.6.2) for the EXCELSIOR project. It focuses on the scope and activities of WP6 ”Knowledge Transfer and Capacity Building”. The main objective of WP6 is to coordinate and manage the knowledge transfer and capacity building that will take place during the EXCELSIOR project with Strategic Partners. The document will provide a workplan of how knowledge transfer and capacity building will take place between the Strategic Partners via workshops, seminars and secondments. This plan relies heavily on the extensive work done at the preparation of the project in defining the seminars, workshops and secondments that will take place between the Strategic Partners. This deliverable focuses on the initial workplan developed for Capacity Building Scheme A, which runs from M26 to M44. The deliverable includes the capacity building and knowledge transfer activities that will be conducted by the Strategic Partners DLR, NOA and TROPOS. The course description and program for selected trainings can be found in the appendices. The present document constitutes the ‘Workplan for transfer of knowledge and experience’ for Capacity Building Scheme period ‘A’ in the framework of the EXCELSIOR project, dedicated to Task T6.1 ‘Personnel Mobility Scheme’ under work package WP6 ‘Knowledge Transfer and Capacity Building’. D6.2 focuses on the trainings that will take place during the Capacity Building Scheme A of the project. This document provides a guideline of the knowledge transfer activities, but it is not limited to the activities that will take place during Capacity Building Scheme A. The Strategic Partners suggested that a flexible workplan is needed in order to identify the gaps and needs of the researchers of the ECoE, especially during the first Capacity Building Scheme and adjust the workplan as needed in order to facilitate more effective knowledge transfer and capacity building. The secondments will be selected by the Strategic Partners as needed, during the knowledge transfer activities, parallel to the demonstration projects in WP7. Selected descriptions of knowledge transfer activities are featured in Appendix A and Appendix B