Hydro-meteorological influences and multimodal suspended particle size distributions in the Belgian nearshore area

Suspended particulate matter (SPM) concentration and particle size distribution (PSD) were assessed in a coastal turbidity maximum area (southern North Sea) during a composite period of 37 days in January–April 2008. PSDs were measured with a LISST 100X and classified using entropy analysis in terms of subtidal alongshore flow. The PSDs during tide-dominated conditions showed distinct multimodal behaviour due to flocculation, revealing that the building blocks of flocs consist of primary particles (<3 µm) and flocculi (15 µm). Flocculi comprise clusters of clay minerals, whereas primary particles have various compositions (calcite, clays). The PSDs during storms with a NE-directed alongshore subtidal current (NE storms, Case NEW) are typically unimodal and characterised by mainly granular material (silt, sand) re-suspended from the seabed. During storms with a SW-directed alongshore subtidal current (SW storms, Case SWW), by contrast, mainly flocculated material can be identified in the PSDs. The findings emphasise the importance of wind-induced advection, alongshore subtidal flow and high-concentrated mud suspensions (HCMSs) as regulating mechanisms of SPM concentration, as well as other SPM characteristics (cohesiveness or composition of mixed sediment particles) and size distribution in a high-turbidity area. The direction of subtidal alongshore flow during SW storm events results in an increase in cohesive SPM concentration, HCMS formation, and the armouring of sand; by contrast, there is a decrease in cohesive SPM concentration, no HCMS formation, and an increase in sand and silt in suspension during NE storms

Experimental Performance of Blind Position Estimation Using Deep Learning

Accurate indoor positioning for wireless communication systems represents an important step towards enhanced reliability and security, which are crucial aspects for realizing Industry 4.0. In this context, this paper presents an investigation on the real-world indoor positioning performance that can be obtained using a deep learning (DL)-based technique. For obtaining experimental data, we collect power measurements associated with reference positions using a wireless sensor network in an indoor scenario. The DL-based positioning scheme is modeled as a supervised learning problem, where the function that describes the relation between measured signal power values and their corresponding transmitter coordinates is approximated. We compare the DL approach to two different schemes with varying degrees of online computational complexity. Namely, maximum likelihood estimation and proximity. Furthermore, we provide a performance comparison of DL positioning trained with data generated exclusively based on a statistical path loss model and tested with experimental data.Comment: Published in: GLOBECOM 2022 - 2022 IEEE Global Communications Conferenc

Blind Transmitter Localization Using Deep Learning: A Scalability Study

This work presents an investigation on the scalability of a deep leaning (DL)-based blind transmitter positioning system for addressing the multi transmitter localization (MLT) problem. The proposed approach is able to estimate relative coordinates of non-cooperative active transmitters based solely on received signal strength measurements collected by a wireless sensor network. A performance comparison with two other solutions of the MLT problem are presented for demonstrating the benefits with respect to scalability of the DL approach. Our investigation aims at highlighting the potential of DL to be a key technique that is able to provide a low complexity, accurate and reliable transmitter positioning service for improving future wireless communications systems.Comment: Published in: 2023 IEEE Wireless Communications and Networking Conference (WCNC

Simulated Greenland Surface Mass Balance in the GISS ModelE2 GCM: Role of the Ice Sheet Surface

The rate of growth or retreat of the Greenland and Antarctic ice sheets remains a highly uncertain component of future sea level change. Here we examine the simulation of Greenland ice sheet surface mass balance (GrIS SMB) in the NASA Goddard Institute for Space Studies (GISS) ModelE2 General Circulation Model (GCM). GCMs are often limited in their ability to represent SMB compared with polarregion Regional Climate Models (RCMs). We compare ModelE2 simulated GrIS SMB for presentday (19962005) simulations with fixed ocean conditions, at a spatial resolution of 2 latitude by 2.5 longitude (~200 km), with SMB simulated by the Modle Atmosphrique Rgionale (MAR) RCM (19962005 at a 25 km resolution). ModelE2 SMB agrees well with MAR SMB on the whole, but there are distinct spatial patterns of differences and large differences in some SMB components. The impact of changes to the ModelE2 surface are tested, including a subgridscale representation of SMB with surface elevation classes. This has a minimal effect on ice sheetwide SMB, but corrects local biases. Replacing fixed surface albedo with satellitederived values and an agedependent scheme has a larger impact, increasing simulated melt by 60100%. We also find that lower surface albedo can enhance the effects of elevation classes. Reducing ModelE2 surface roughness length to values closer to MAR reduces sublimation by ~50%. Further work is required to account for meltwater refreezing in ModelE2, and to understand how differences in atmospheric processes and model resolution influence simulated SMB