This study carried out an X- and C-band spaceborne synthetic aperture radar (SAR) observation synchronized with the field experiment in the Bohai Sea of China in January 2024. Based on the co-located X- and C-band SAR multipolarized data from TerraSAR-X/TanDEM-X, Gaofen-3, and Sentinel-1, we elucidated the signatures of thin ice and quantified their separability. Underlying the identified thin ice information, we further demonstrated the feasibility of separating thin ice and seawater from the ice-water mixture. Combining with the LiDAR-measured surface roughness, we investigated its correlation with different X- and C-band SAR signatures, of which the optimal parameter was used to develop the regression models for estimating thin ice surface roughness. The comprehensive application of multiband, multipolarization SAR contributes to resolve the ambiguity of observed thin ice signatures. Our case study highlights the potential role of multipolarized (or even fully polarized) X-band SAR for thin ice observations.

Distributed key-value stores provide the Multiget API, where many key-value operations are batched together, to meet the parallel requirement of applications. Correspondingly, reducing the latency of Multigets is crucial for the responsiveness of the distributed key-value stores. The latency of a Multiget depends on both which replica server its key-value operations are scheduled to, i.e., the replica selection for each key-value operation, and when these key-value operations are served, i.e., the scheduling of the service sequence at different replica servers. Existing solutions solely focus on either one of them and accordingly lead to suboptimal latency for Multigets. To address these issues, this paper proposes an Adaptive Multiget Scheduling (AMS) algorithm in this paper, and specifically, our AMS re-architectures the framework to remove the conflict between replica selection and service sequence scheduling in the existing solution Rein. Based on the new framework, a sophisticated replica selection method is designed. Furthermore, AMS guides both replica selection and service sequence scheduling by the piggybacked information of replica servers, being adaptive to the heterogeneous time-varying server performance. Consequently, AMS can respectively reduce the median,$95^{th}$, and$99^{th}$percentile latencies of Multigets by a factor of 4, 3.1, and 1.86 compared to the default FIFO algorithm and significantly outperforms Rein.

In distributed key-value stores, multiple replica servers are always available for each key-value access operation when the eventual consistency model is employed. Accordingly, the completion times of the key-value access operations generated by an end-user request at different servers may be of great difference, especially when the replica servers are heterogeneous and have time-varying performance. Accordingly, the replica selection algorithm is crucial to cut the response time of end-user requests. The main challenge of making replica selection for each light-weighted key-value access operation is to timely know the status of replica serves. Recently, the adaptive replica selection algorithm C3 suggests guiding the replica selection with the piggybacked information of replica server in the returned “value”. Although C3 has good performance, the poor timeliness of feedback information makes a large performance gap between C3 and the ideal replica selection algorithm. To narrow this gap, the Accelerated Information Dissemination (AID) mechanism is proposed in this paper. Specifically, AID removes the bottleneck of information dissemination at the “slow” servers by letting both “client” and “replica server” store the records about the status of replica servers and both “key” and “value” piggyback multiple records. AID is implemented in Cassandra and evaluated by experiments and large scale simulations. The results show AID can significantly improve the timeliness of feedback information, especially when the number of nodes is large. Accordingly, AID helps C3 to greatly reduce the latency.

Sea ice drift (SID) is crucial for understanding sea ice dynamics and for navigation safety. This study focuses on a comprehensive analysis of SID retrieval in the Arctic based on spaceborne synthetic aperture radar (SAR) data. A state-of-the-art method combining feature tracking and pattern matching techniques was applied to sequential Sentinel-1 (S1) SAR data in 2020 to derive SID from the central Arctic to the Fram Strait. The SAR retrievals were validated with drifting buoys. For temporal intervals of S1 data of approximately 24 hours, 15,254 collocations were collected from January to June and from October to December, yielding a 0.00 cm/s bias for the drift velocity magnitude and 0.27 degrees for direction with corresponding root mean square error (RMSE) of 0.47 cm/s and 4.73 degrees. Using temporal intervals of S1 data of less than 24 hours, we retrieved SID from July to September. A total of 644 collocations yields a comparison with a bias of 0.52 cm/s and 4.62 degrees for the drift magnitude and direction, respectively. The corresponding RMSE values are 1.85 cm/s and 20.73 degrees. The comparisons demonstrate better performance than the operational SAR-based SID product using the MCC method and are consistent with seasonal trends in drift velocity with the coarse-resolution product. We also analyzed the variations in SAR retrievals and further estimated appropriate temporal intervals, making it feasible to conduct long-term SID retrievals based on spaceborne SAR data at high spatial resolution in the Arctic.