General Electric (Finland)

Addressing the challenges in quantitative 3D inspection of micro-to-nanoscale surface defects in optical components for high-energy laser systems, this paper proposes a novel, to our knowledge, inspection framework integrating bright-field and dark-field structured illumination microscopy (BDSIM) with deep learning-based 3D reconstruction. To mitigate the limitations of sparse point clouds and inherent noise caused by the low luminous flux in BDSIM imaging, we developed the Att-PU-Net model, building upon the point cloud upsampling network (PU-Net) architecture. This model incorporates a self-attention mechanism to enhance the contextual perception of local geometric abruptness and employs a multi-scale feature fusion strategy to preserve fine topological details. To ensure robust generalization from simulation to reality, a hybrid training strategy combining procedurally generated and real-world defect samples is adopted. Furthermore, a composite loss function integrating chamfer distance, repulsion loss, and curvature consistency constraints was designed to significantly improve point distribution uniformity and edge sharpness. Simulations and comparisons between Att-PU-Net and the marching cubes, contour filter algorithms demonstrate that Att-PU-Net achieves an optimal balance between geometric accuracy and uniformity (P2S: 0.5720µm, NUC: 0.3230). Experimental validation on actual optical damage reveals a reconstruction accuracy of 0.6343µm and a maximum error of only 0.79µm in defect depth compared with white light interferometry (WLI), confirming the method's effectiveness and reliability for high-precision 3D reconstruction of complex optical surface defects.

Accelerating mass loss from the Greenland Ice Sheet is strongly modulated by meltwater-driven changes of bed conditions in ice dynamics, but these processes remain poorly constrained due to limited high-resolution observations of glacier hydrological systems. Constraints on basal properties can be obtained from borehole measurements and active reflection seismic surveys; however, these methods are inherently limited by their localized spatial coverage and are time intensive. As a result, they are poorly suited to monitoring the subglacial hydrological system, which evolves over seasonal timescales and across spatial scales larger than can be practically sampled. Thus, dense seismic node arrays offer a powerful alternative, enabling passive, high-resolution imaging and continuous monitoring of englacial and subglacial processes over broad areas and extended time periods.Here, we first present a dense seismic array experiment covering approximately 2.5 km² we conducted in the ablation zone of Isunnguata Sermia, West Greenland. The experiment comprised 82–117 autonomous seismic nodes deployed during one-month long monitoring periods in spring 2023 and in summer and fall 2024, complemented by multi-week surface Distributed Acoustic Sensing (DAS) measurements conducted in 2024. We assess data quality using power spectral densities and ambient noise cross-correlations to detect sensor tilt and GPS timing desynchronization, which are critical issues for data quality in remote and highly dynamical glacial environments such as the Greenland Ice Sheet.Then, we demonstrate we can use natural icequakes to perform seismic reflection analysis for bed mapping and interface property evaluation. We locate numerous natural seismic events with meter-scale resolution using Matched Field Processing (MFP) beamforming applied to array data in the 4–6 Hz frequency band. These events reveal a large population of near-surface sources associated with crevassing that generate both surface and body waves. We extract body waves and enable event stacking following a two-step synchronization procedure first synchronizing signals in the 4–6 Hz band dominated by surface waves, and subsequently refining timing in the 40–80 Hz band where P waves dominate. By sorting and stacking waveforms from these synchronized natural sources into common midpoint gathers, we identify high signal-to-noise ratio reflected P waves from the ice–bedrock interface at offsets of up to 1 km, significantly greater than those typically achieved in traditional active reflection seismic surveys on glaciers. This extended offset range makes the passive approach better suited for estimating basal conditions using classical amplitude-versus-offset (AVO) analyses due to the stronger dependence of reflectivity to bed properties at large incidence angles.Finally, following a workflow analogous to active reflection seismology, we derive a two-dimensional map of ice thickness beneath the array with a vertical resolution of approximately 15 m, comparable to that of conventional active surveys. This ice-thickness model will serve as a critical constraint for future high-resolution surface-wave tomography, enabling improved imaging of glacier structure and basal conditions at depth.

The Taita Hills in southeastern Kenya are a critical biodiversity hotspot within the Eastern Arc Mountains, characterized by a complex mosaic of montane forest fragments, exotic plantations, and agroforestry systems transitioning into semi-arid grasslands and Acacia-Commiphora bushland. This landscape with elevations ranging from approximately 750 m to 2200 m exemplifies competing land-use interests, where a growing population and agricultural expansion have historically driven forest and tree cover loss. Accurate monitoring of these biomass dynamics is essential for quantifying carbon stocks, informing climate mitigation strategies, and guiding contemporary conservation and natural forest regeneration efforts.This study employs an extensive multi-temporal dataset to quantify aboveground biomass (AGB) changes across the Taita Hills and adjacent lowlands. We analyzed data from 38 airborne LiDAR flights conducted between 2014 and 2024, covering 1,600 km², with 650 km² of overlapping coverage for change detection. Field-measured AGB plots (2013–2018) and LiDAR data from 2014/2015 were used to generate a baseline AGB map. A Random Forest model, calibrated on this baseline and LiDAR metrics, was then applied to predict AGB from 2022/2024 acquisitions. These predictions were validated using independent field measurements collected in 2024–2025. Finally, we analyzed annual AGB change rates in relation to high-resolution canopy height model changes, elevation zones, and land cover types to characterize spatial AGB dynamics and identify drivers of gain and loss.Preliminary analysis reveals heterogeneous AGB dynamics across the landscape. The highest positive change rates were observed in young forest plantations, while agroforestry systems exhibited modest gains, indicating successful tree retention and maturation. Notably, native montane forest fragments remained relatively stable, with forest cover losses primarily concentrated within exotic plantations. Conversely, localized AGB reductions were identified in foothill areas and along riverine corridors. The multi-temporal LiDAR approach proved robust for capturing these fine-scale spatial patterns. This ongoing analysis will further refine the magnitude and drivers of decadal carbon stock fluctuations, providing critical evidence for landscape-level conservation and climate mitigation strategies in the region.

Abstract. Accurate estimates of future land carbon sinks and thus the remaining carbon budget to achieve the Paris climate goals requires rigorous modelling of the carbon sequestration potential of the terrestrial biosphere. Estimating the terrestrial carbon budget requires an accurate understanding of the interlinkages between the land carbon and nitrogen cycles, yet coupled carbon-nitrogen cycle models exhibit large uncertainties. Leaf chlorophyll, chlleaf, is an indicator of the leaf nitrogen content stored within photosynthetic nitrogen pools and is central to the exchange of carbon, water and energy between the biosphere and the atmosphere. In this work, we harness an advanced remote sensing (RS) chlleaf product to evaluate a terrestrial biosphere model (TBM), QUantifying Interactions between terrestrial Nutrient CYcles and the climate system (QUINCY), which explicitly models chlleaf. We focus on comparing the spatial and seasonal patterns of modelled and observed chlleaf, and then further assessing if modelled leaf area and productivity agree with a RS leaf area index product and in-situ eddy covariance-based gross primary production, respectively. In addition, we conduct additional simulations to test two alternative formulations of leaf-internal nitrogen allocation within QUINCY. Our analysis over a globally representative set of locations reveals that QUINCY chlleaf magnitudes are mostly in line with the RS chlleaf values. However, QUINCY chlleaf tends to show a narrower numerical range compared to RS for specific ecosystem types, such as grasslands. While the seasonal cycle of QUINCY chlleaf mostly corresponds well to the observations, for many deciduous forests, the increase in QUINCY's chlleaf predictions in spring and the decrease in autumn were delayed compared to observations. Our results also show that compared to the original leaf nitrogen allocation scheme of QUINCY, the revised scheme produced a more reasonable sensitivity of gross primary production to increases in chlleaf. However, the revised scheme did not directly lead to improvement in simulating chlleaf and gross primary production. Our study shows the value of RS products linked to nitrogen cycle that will be useful in both carbon and nitrogen modelling, and paves way for closer linking of RS and TBMs.

Efficiency and quality are a pair of everlasting contradictions in manufacturing industry. To achieve atomic surface, it usually consists of rough, precision, and ultra-precision polishing. These processes contain generally several to tens of polishing pads, slurries, and setups, which is time-consuming and expensive. These diverse pads, slurries, and setups are strictly separated, avoiding contamination, damage, and corrosion. To solve these challenges, a novel composite polishing system was developed, using a developed hyper-conjugated polishing pad and custom-made green slurry, through adjusting the temperature of slurry to control the material removal rate (MRR). The MRR was controlled between 40.99 and 133.91 nm/min, by adjusting the temperature between 40 and 80 ℃ via splitting of fibers. After pre-polishing by rare earth cerium oxide abrasive and atomic level polishing by proposed composite system, atomic surface is garnered on fused silica with surface roughness Sa of 0.117 nm at a scanning area of 50 × 50 μm2. It is unprecedented that the developed polishing system realized atomic surface integrating rough and fine polishing on a setup by a composite fiber polishing pad and a new green chemical mechanical polishing (CMP) slurry. The CMP slurry included silica, hydrogen peroxide, sodium hydroxy cellulose, and sodium carbonate. Transmission electron spectroscopy confirms that the thickness of damaged layer is 1.89 nm. Prior to and after splitting of fibers, diameter, bending length, and Shore hardness decreased from 18.33 to 2.48 μm, 13.7 to 4.6 cm, and 52.7 to 20.5 HC, respectively, reducing 86.5%, 66.4%, and 61.1% correspondingly. However, wicking height and specific surface area increased from 7.5 to 14.1 cm, and 0.028 to 1.926 m2/g, separately, enhancing 88% and 6779%, accordingly. An interactive model was built among a single fiber, abrasive, and workpiece. After splitting of fibers, the maximum stress exerted on axial and radial directions of fiber are 2.45 and 1.36 MPa, respectively, reducing 59% and 73% compared with those of prior to splitting correspondingly. A model of repeated unit cell was constructed between polishing pad and slurry. It is calculated that the stress of 51.15% units is in the range between 0.1 and 0.5 MPa, while that of 92.83% fibers varies from 0.05 to 1 MPa after splitting. A macroscale model of polishing pad was established. Peak stress of an unsplit pad is high up to 13.19 MPa, while that of split one decreases to 2.42 MPa, reducing 81.65%. At an actual polishing pressure of 30 kPa, contact area of a split polishing pad between fibers and workpiece reaches 15.38%, promoting 44.09% compared with that of an unsplit one. Our developed composite polishing system paves a new way to fabricate atomic surface using a hyper-conjugated polishing pad and a green slurry on a setup, saving several and tens of various pads, slurries, and setups, which are both time and cost-effective and has broad prospects in manufacturing industry.

Black phosphorus (BP) stands out among vdW materials for chemical sensing, with its edge sites offering the potential to enhance performance. While the nanocutting method shows promise for BP edge fabrication, the underlying mechanism remains unclear, lacking guidance for the process. In this study, molecular dynamics (MD) simulations were conducted to investigate the material removal behavior during nanocutting of BP while considering the influence of cutting thickness, tool edge radius, and tool angle. These findings indicate that the plastic removal of BP is primarily governed by interlayer slip when using a cutting tool with a relatively large rake angle. In contrast, at a small rake angle, a brittle removal state occurs due to the formation and expansion of a shear band spanning across the BP layers, leading to crack propagation. Additionally, as the cutting thickness approaches the tool edge radius, a “size effect” is observed in the BP. This phenomenon results in both decreased plastic deformation and a singular decrease in cutting force, ultimately causing an increase in chip thickness deviation. The research findings offer a comprehensive theoretical foundation for the removal behavior of BP and serve as a technical reference for the fabrication of nanostructures for chemical sensing via nanocutting.