Sensors

Sensor failures reduce the spatial information available for greenhouse microclimate monitoring, but their practical consequences depend on whether agronomic risk decisions remain reliable. We evaluated the progressive loss of paired indoor temperature–relative humidity (T/RH) sensors in a Chinese solar greenhouse using whole-sensor holdout. Longitudinally and vertically balanced subsets and transparent estimators were frozen before a sealed final test of 28 dates, four calendar blocks and six stage-aware risks. Relative to a 20-input reference, a longitudinally and vertically balanced six-input network increased the temperature mean absolute error by 0.014 °C and the relative-humidity error by 0.027 percentage points; macro-recall changed by −0.001 and support-aware macro-F1 by −0.004, while median false-negative exposure remained unchanged. The results remained within the predefined limits across 26 agricultural-threshold variants. In an exploratory validation-only comparison, spatially concentrated six-input layouts had greater temperature error (+0.080 °C, 95% block-bootstrap interval +0.058 to +0.104 °C), relative-humidity error (+0.099 percentage points), and macro-F1 loss (−0.004) than longitudinally and vertically balanced layouts. Macro-F1 did not differ consistently between the longitudinally and vertically balanced layouts and the unconstrained random layouts. Longitudinal and vertical coverage therefore functions as a robustness constraint against concentrated input loss in this system, rather than evidence of universal six-sensor sufficiency or cross-greenhouse generalisation.

3D Gaussian Splatting (3DGS) has significantly advanced the field of visual SLAM. However, outdoor monocular SLAM systems still suffer from scale ambiguity, so recovering the absolute scale remains difficult, and tracking often becomes brittle in large scenes. To address these issues, we present MoGe2-SLAM, an outdoor monocular 3DGS-SLAM framework that incrementally recovers camera trajectories and dense maps in an approximate absolute metric scale from a monocular RGB sequence with a known camera-height prior. By approximate absolute metric scale, we mean that camera poses and the reconstructed map are expressed in meters after camera-height calibration, rather than only up to an unknown global scale. Our core idea is to anchor frozen MoGe-2 geometry to a physical camera-height/ground-plane ruler, enabling approximate absolute-scale reconstruction. Specifically, we perform Absolute-Scale Initialization on the first keyframe by replacing MoGe-2’s advertised metric global scale with a known camera height and ground-plane geometry, generating the initial Gaussian scene directly in physical units. We then adopt Grid-Balanced Tracking that encourages spatially distributed feature extraction and maintains stable tracking. For mapping, we employ an ORB-anchored strategy that aligns the MoGe-2 depth prior with the rendered depth using geometric constraints, thereby mitigating scale drift. On KITTI, Waymo, and Argoverse 2, MoGe2-SLAM is competitive with existing monocular SLAM baselines in trajectory shape and novel-view fidelity while recovering trajectories and Gaussian maps in an approximate physical scale under the stated camera-height prior.

Underwater laser cutting is a promising remote segmentation method for highly activated reactor internals because it enables non-contact cutting while maintaining water shielding. Although previous studies have demonstrated the feasibility of thick-section underwater laser cutting, process monitoring remains challenging because bubbles, optical scattering, local gas-cavity behavior, water cooling, and complex melt ejection can alter the relationship between the cutting state and externally measured signals. Therefore, the monitoring characteristics of different sensing modalities during underwater thick-section cutting need to be clarified. This study synchronized a camera, a broadband photodiode, and a triaxial accelerometer during underwater laser cutting of 100 mm thick STS 304 stainless steel. Camera observations were used as the reference for identifying process states. During partial penetration, a broad lateral plume developed, followed by transient lower-lateral ejection and pronounced downward discharge at full penetration. The photodiode mean decreased by 88.8% and 93.0% from partial to full penetration in Experiments 1 and 3, respectively, and remained low afterward. Accelerometer responses around full penetration were less consistent, whereas in Experiment 2, late-stage abnormal behavior was accompanied by a 128.8% increase in the mean PSD in the 75–245 Hz band. These results indicate distinct monitoring characteristics of the three sensing modalities.