Accurate walking speed estimation in lower-limb prostheses is crucial for delivering biomechanically appropriate assistance across varying speeds. However, training robust models requires extensive domain-specific, user-dependent (DEP) data, which is impractical for every new prosthesis user. This study presents a transfer learning framework to simplify and enhance the training process. Convolutional neural networks were pre-trained on publicly available datasets from able-bodied (AB) individuals and transfemoral amputees using the Open Source Leg (OSL) knee-ankle prosthesis, then fine-tuned with data from a transfemoral amputee using the Power Knee (PK) prosthesis. The fine-tuned models, AB-PK and OSL-PK were trained with varying data amounts and evaluated across constant and variable walking speed trials, with performance compared to DEP models trained from scratch on PK data. Training and testing were conducted on a per-subject basis, with performance averaged across subjects (N=7). The lowest post-fine-tuning error was observed in AB-PK, with RMSE values of 0.041 m/s for constant speeds, 0.072 m/s for variable speeds, and 0.088 m/s for novel speeds not included in the original training data. Significant error reductions were observed in both fine-tuned models compared to DEP when fewer than 30 gait cycles per speed of training data were available. Notably, AB datasets appeared highly viable for this application and may even outperform OSL datasets in transfer learning for walking speed estimation, perhaps due to the much larger original training dataset. This approach highlights the potential of transfer learning across different subject populations and devices, offering insights into the data needed to achieve state-of-the-art speed estimation.

With the large-scale deployment and application of IPv6, IPv6 network measurements will become increasingly important. However, a special type of IPv6 prefix called Fully Responsive Prefix (FRP) is having a significant impact on IPv6 measurement campaigns, which is defined as all addresses under a prefix responding to scans. Obviously, there cannot be a real responder behind each of these addresses. To reveal the current status and impact of Internet-wide IPv6 FRPs, we propose for the first time an active probing method for Internet-wide IPv6 FRPs, Luori, which transforms the active probing process under IPv6 huge prefix space (potential range of prefix presence) into a dynamic search process in a tree based on reinforcement learning, achieving efficient probing of arbitrary routing prefixes. The evaluation results show that Luori found 31.7K largest FRPs in a single Internet-wide probing with 11 M budget, covering$1.5 \times 10^{30}$address space, which is$10^{6} \times$that of existing methods. More importantly, after six months of Internet-wide probing, we have found 516 K largest FRPs, which covers$1.3 \times 10^{33}$address space and 795 ASes, making it the largest publicly known FRP list. Based on this list, we screen out$20 \%$of the addresses covered by FRPs from a well-known IPv6 active address dataset. Furthermore, we further analyze and find that the distribution of these FRPs is extensive and their implementation methods are diverse, which can provide beneficial references for the practical application of FRPs. We also make this list publicly available and maintain it long-term for use and study by relevant researchers.