arXiv subjects

Jianrong Cai

Publications and source records attributed to Jianrong Cai.

2 recordsLinked to original sources

arXiv2026

Development and characterization of a millimeter-wave cold load prototype

Superconducting transition-edge sensors (TESs) are crucial detectors for cosmic microwave background (CMB) observations and require stable and tunable millimeter-wave cold loads for optical-efficiency calibration. This work presents the design, fabrication, and preliminary characterization of a 4-20 K millimeter-wave cold load prototype intended for integration into the 1 K stage of a dilution refrigerator and subsequent 40/90 GHz CMB TES calibration experiments. Two absorber prototypes based on commercially available CR-110 and a Stycast 2850FT composite were fabricated and studied. Simulation results show that both absorber structures exhibit small predicted steady-state temperature gradients and low normal-incidence reflectance in the target frequency bands. Room-temperature S11 measurements were used only to screen low-reflectance cold load prototype, and the measured results generally agree with the electromagnetic simulations. The measured S11 of the Stycast 2850FT composite is comparable to that of the commercial absorber TK RAM. Additionally, to explore a more readily obtainable alternative absorber material, TIE280-25AB was preliminarily evaluated by measuring its electromagnetic parameters. Based on the measured parameters, the simulated S11 of the TIE280-25AB pyramidal absorber structure is comparable to those of CR-110 and the Stycast 2850FT composite over 33-110 GHz. These results identify CR-110 and the Stycast 2850FT composite as promising absorbers for subsequent cryogenic evaluation. The absolute low-temperature emissivity, effective radiation temperature, and TES calibration performance remain to be established through future cryogenic radiometric and TES based optical-power measurements.

astro-ph.IM↗

arXiv2026

Self-Supervised Mask-Aware Transformers for Fault-Tolerant FBG Force Sensing in Minimally Invasive Surgical Robotics

In minimally invasive surgical robotics, catheter-scale Fiber Bragg Grating (FBG) sensors are promising due to their ability to estimate multi-dimensional forces by multiplexing several optical channels. However, deploying these compact multi-channel sensors introduces two critical engineering challenges: inherent nonlinear cross-axis coupling during complex deformations, and intermittent channel dropouts caused by fiber fractures in constrained workspaces. These compounding issues severely degrade force estimation. Existing fault-tolerant approaches rely on combinatorial model banks, which scale exponentially with the channel count and demand prohibitively expensive per-pattern calibration. In this paper, we propose a unified, self-supervised mask-aware Transformer that explicitly models channel availability to enable graceful degradation under diverse and dynamic sensor failures. The encoder is pretrained via masked-channel reconstruction on unlabeled data streams and fine-tuned for force regression using a balanced clean-and-corrupted-view objective alongside a dynamic corruption curriculum. Furthermore, a parallel uncertainty head, trained via heteroscedastic Gaussian negative log-likelihood, predicts per-axis confidence in a single forward pass, circumventing the overhead of multi-pass ensembles. Evaluated on a catheter-scale 8-channel FBG dataset, our single unified model achieves a nominal Root Mean Square Error (RMSE) of 0.0066~N and degrades gracefully to 0.0126~N under severe 4-channel failures. This significantly outperforms a comprehensive model bank of 255 per-pattern neural networks (0.0154~N at 4-channel loss) while eliminating pattern-specific calibration.

cs.RO↗