The rise of interactive real-time applications has exposed the latency limitations that current networks experience. Recently, the Low Latency, Low Loss, and Scalable Throughput (L4S) architecture emerged to address this problem across the Internet. This paper is the first to systematically review existing L4S experimental development efforts. It synthesizes the current state of research and identifies and outlines several remaining challenges. This analysis maps the primary research themes, dominant algorithms, and common evaluation methodologies, confirming that L4S can deliver on its promise for low-latency in controlled settings. Concurrently, the review summarizes the critical challenges and limitations reported in the literature, particularly concerning partial deployment, real-world wireless performance, and implementation robustness. By providing a structured overview of the technology’s maturity and key open questions, it serves as a guide for future work toward a robust, at-scale deployment of low-latency internet services.

The Synthetic Aperture Radar operating in P-band and performing a helical flight pattern promises to be a potential tool for underground tomography, featuring high resolution and penetration. Based on the success of detecting ant nests beneath the industrial forest, a new experiment was carried out to detect buried bodies. Two sets of flights were performed and validated. Six bodies buried 0.5 m to 1.5 m deep, containing the remains of cow and pork, were located with a detection rate of 100% and a false alarm rate of 0%. Also, the ability to detect change across two sets of flights was verified by locating a bucket filled up with a large tarpaulin piece in the first set of flights.

The synthetic aperture radar (SAR) has long been used from satellites for forest monitoring at global level. The boreal forests in Sweden are well described wall-to-wall from airborne laser scanning and airborne photography. Hence, in Sweden, SAR can often only provide a limited added value, due to the low resolution compared to other sensors, despite its all-weather acquisition capabilities. By accounting for interfering effects that currently degrade the useful information in SAR images, it can be extremely valuable for both vegetation mapping and belowground mapping (e.g., soil conditions and tree roots). In the current work, we present the configuration of the first drone-based SAR experiment that allows us to image the forest in 3D with very high spatial resolution. We have started the analyses by using tomography to derive reflectivity for the roots of single trees, and comparing these with reference root biomass. The linear relationship indicates a potential for using SAR to derive forest variables that were yet neglected or little researched. Moreover, extensive additional remote sensing data have been collected from both airborne and spaceborne platforms, and reference data for both the vegetation and soil have been inventoried in-situ using complementary measurements and sensors. Hence, this unique experimental setup enables many unprecedented analyses about SAR applied to boreal forests.