Plastic pollution is becoming an increasingly prominent problem and the function of plastics determines whether they need to be recycled or not. In order to explore the possibility of using satellite imagery to classify the functionality of plastics, this study proposes a two-stage workflow: firstly, a classification map is obtained based on hyperspectral satellite imagery to generate plastic types, and then using these identified plastic coverage areas, a deep learning algorithm is used to assign functionality to these classified plastic areas based on sentinel-2 imagery. By comparing five leading-edge image classification models, classification accuracies of up to 74% were achieved, demonstrating the feasibility of using deep learning models trained on satellite images to identify plastic features.

We present system-level evaluation results to characterize the performance of eXtended Reality (XR) applications over a 5G NR system. A split-rendering framework is assumed, where the XR device uses 5G to offload computation associated with rendering and encoding of video frames to an edge server. The XR traffic model includes rendered video frames on the downlink and user pose and control updates on the uplink. We present results for both sub-6 GHz (FR1) and millimeter-wave (FR2) bands. Additionally, we also discuss potential enhancements to further improve user experience for XR applications over 5G.

Large data transfers can result in significant congestion and performance degradation for interactive end-user applications such as web browsing and streaming. While there are existing TCP congestion control algorithms for delivery of large volume data (e.g., LEDBAT, TCP-LP), our results show that these protocols are not effective in cellular networks due to variability in radio channel conditions and the use of cellular schedulers in base stations. We propose Legilimens, an agile TCP variant for cellular downlink transfers, which not only retains desirable properties of existing approaches, but also exploits the properties of the cellular schedulers to estimate load and capacity and addresses the challenges in cellular networks. As a result, Legilimens is able to deliver traffic using only the spare capacity on the downlink. We conduct extensive evaluations of Legilimens in multiple settings-in a large cellular network for real-world performance, on the PhantomNet emulator for controlled experiments, and ns-3 simulator for scaled experiments-all of which demonstrate that Legilimens is superior to existing protocols in transferring large volumes of data without interfering with regular user traffic. Compared to existing low-priority protocols, Legilimens improves the throughput of background flows by 2x on average (up to 5x) without degrading the performance of foreground flows across all the three testbeds.

A large variety of traffic - time-sensitive “foreground” traffic (e.g., web browsing) and time-insensitive “background” traffic (e.g., software updates) - compete for the scarce cellular bandwidth, especially on the downlink. While there is limited in-network support for traffic prioritization, existing endto-end, “low priority transport protocols” exhibit sub-optimal performance in cellular networks. We propose Sneaker, which yields to time-sensitive foreground traffic during periods of congestion and enables time-insensitive background traffic to efficiently utilize any spare capacity. Sneaker achieves the desired goal by randomly dropping packets coming into the base station, based on traffic type and network conditions. Our key contribution is the derivation of the optimal dropping rate and a practical dropping rate, which performs close to optimal. Further, Sneaker co-exists and performs well with existing cellular schedulers and transport protocols.