Search Results (732)

Wildfire detection and characterization from space critically depend on accurate thermal infrared measurements across a wide dynamic range. OroraTech’s SAFIRE payloads feature mid-wave infrared (MWIR) and long-wave infrared (LWIR) bands optimized for this purpose, yet the volume and power constraints of many CubeSat platforms preclude the use of onboard calibration sources. This reflects a broader limitation of current Earth observation systems: the absence of robust calibration and validation methodologies at high brightness temperatures relevant to active fires. We evaluate the potential of using thermophysical models (TPMs) of the Moon as a vicarious calibration reference for calibration transfer between instruments. The Moon reaches surface temperatures of up to 400 K, offering a stable target in the thermal domain that is visible from many different orbits. Previous research has established the use of TPMs for Moon observations in the infrared; however, uncertainties in surface properties remain, particularly in the mid-wave infrared. We investigate these effects using SAFIRE observations of the lunar surface acquired over a wide range of lunar phase angles (from waxing $-81.5°$ to waning +122.2°). We constrain the TPMs using observations from the Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) fire channels and uncover a 5.2% inter-sensor disagreement in the MWIR between Sentinel-3A and Sentinel-3B at high brightness temperatures, corresponding to ∼2.2 K at 400 K. Applying the same methodology to observations of our SAFIRE payloads bounds the calibration transfer error at 5.8% in the MWIR and 4.2% in the LWIR for lunar phase angles within ±45°. These results establish lunar vicarious calibration as a viable approach in the thermal domain for high-temperature applications, providing a pathway toward improved fire radiative power retrievals and enhanced global wildfire monitoring. Full article

Severe conditions in hard-to-reach regions, where terrestrial networks are limited, make data backhaul from these areas challenging. In this context, advances in the Space-IoT have created new opportunities for data collection and monitoring using LEO satellites. To address this issue, this paper presents an orbit-aware S&F architecture for data collection from an intelligent ground terminal located in remote areas using a 3U CubeSat orbiting at 500 km altitude in a Sun-synchronous orbit. The designed ground terminal integrates an ESP32 microcontroller and a 433 MHz LoRa module and is enhanced with an embedded satellite pass prediction, Doppler pre-correction, and an adaptive LoRa strategy. The CubeSat utilizes a TOTEM SDR receiver, onboard data buffering, and an S-band downlink with variable coding for throughput enhancement. The orbital model for satellite prediction is validated using Ansys STK software version 12, while UHF and S-band links are evaluated using time-varying link-budget analysis. The adaptive transmission is compared with fixed configurations in terms of usable contact time and data delivered per pass. Results indicate that the proposed system demonstrates the feasibility of a Store-and-Forward mission co-design that combines precise orbit prediction, Doppler compensation, and adaptive transmission for future Space-IoT applications. Full article

Potato tuber dry rot is a major postharvest decay caused predominantly by Fusarium spp. During early infection, external symptoms may be absent even when faint internal browning, localized dehydration, and tissue structure changes have begun. Manual inspection, destructive cutting, and culture- or molecular-based assays are therefore poorly suited to rapid, nondestructive, high-throughput screening. We developed a near-infrared hyperspectral imaging method that combines spatial and spectral representations in a dual-branch ResNet12-SE network. The working dataset contained 1725 labeled records (862 healthy and 863 early-infected records); records were assigned to subsets by tuber identifier, and an infected volume ratio below 5% was used as an operational early infection threshold. The calibrated model input was a 224-band, 224 × 224 hyperspectral cube. The spatial branch used a ResNet-12 backbone with spatial squeeze-and-excitation (SE), whereas the spectral branch used spectral SE followed by bidirectional long short-term memory (BiLSTM). The two feature vectors were concatenated for binary classification. In the single-split test, the proposed model achieved 98.22% accuracy, compared with 90.67% for the ResNet-12 baseline and 97.43% for Vision Transformer. Preprocessing, principal component image, local binary pattern, and gray-level co-occurrence matrix analyses provide complementary interpretation of the spectral and spatial responses. The results support the feasibility of hyperspectral screening under the controlled laboratory protocol and define the validation work required before broader deployment. Full article

Anchors constructed in water-rich sandy-pebble strata require grout systems that can maintain material continuity and transfer tensile force effectively before groundwater-related disturbance weakens the borehole interface. This study investigated a sulphoaluminate cement-based rapid-setting grout (SAC) for PSB high-strength threaded anchors, using an ordinary Portland cement-based grout (OPC) as a reference material. Laboratory central pull-out tests were first conducted on grout cube specimens with different steel-bar diameters and bonded lengths to evaluate the steel–grout bond response. Full-scale field pull-out tests were then performed to examine the anchor–grout–ground response under water-rich ground conditions. LS-DYNA finite element models were calibrated against the laboratory and field results to interpret the governing load-transfer mechanism and to assess the influence of representative stratum resistance. The laboratory tests showed that the steel–grout bond response depended on both interfacial degradation and mortar splitting, indicating that peak bond strength should be interpreted together with failure mode and slip development. In the field tests, all anchors failed by pull-out, and the steel bar and grout body were pulled out together, showing that the full-scale response was governed mainly by the grout–ground interface rather than by steel–grout debonding. The calibrated numerical models reproduced the main load–displacement trends and supported a scale-dependent transition from steel–grout bond control at material scale to grout–ground interface control at field scale. The results provide a basis for evaluating rapid-setting grouts for high-strength anchors in water-rich sandy-pebble ground. Full article

Hyperspectral images (HSIs) are inevitably degraded by mixed noise, which hampers downstream interpretation. Recent graph-signal-processing denoisers encode the spatial–spectral structure of an HSI through a product graph over superpixel bodies, yet the adopted Kronecker (tensor) product graph retains only the joint spatial–spectral edges and discards the pure-spatial and pure-spectral edges—the two priors that govern HSI smoothness. We introduce a two-parameter weighted product-graph family that contains the Kronecker, Cartesian and strong products as exact special cases, and propose Weighted Strong Product Graph Laplacian Regularization (WSPGLR)—the strong-product branch with a tunable joint-edge weight β—embedded in a global low-rank plus sparse model solved by the Alternating Direction Method of Multipliers (ADMM) with singular-value-thresholding and soft-thresholding updates and a sparse conjugate-gradient (CG) solve. On three simulated cubes (Washington DC Mall, Pavia University, Indian Pines) under four mixed-noise scenarios, WSPGLR consistently improves Mean Peak Signal-to-Noise Ratio (MPSNR) and ERGAS (Erreur Relative Globale Adimensionnelle de Synthèse) over a matched Kronecker-product control in every tested case (and Mean Structural Similarity (MSSIM) in 11 of 12 settings)—up to 2.5 dB in MPSNR from the graph term alone—and an ablation shows that β governs a spatial–spectral fidelity trade-off (Spectral Angle Mapper, SAM). On the detailed urban scenes, WSPGLR attains the highest MPSNR against the external methods and matched control in seven of eight settings, whereas it trails LRTDTV on the smooth agricultural scene; with an optional scene-adaptive TV step it attains the best average rank across scene types; a Tucker-based variant further shows the low-rank block is modular and generally improves spectral fidelity. Tests on four no-reference real HSIs and 30 paired real-noise MEHSI samples extend the sensor coverage; on MEHSI, the native-domain RND framework remains substantially stronger, which delimits the scope of the proposed training-free regularizer. Full article

Construction material identification is important for automated construction monitoring, digital twin generation, and building information modelling. Terrestrial laser scanning (TLS) provides dense geometric information together with LiDAR intensity measurements; however, reliable material discrimination remains challenging because intensity is affected by acquisition geometry and surface texture may vary depending on the spatial scale at which it is characterised. This study proposes a TLS-only machine-learning framework combining a reflectance-related intensity–geometry regression descriptor with multiscale geometric roughness features. Plane-residual roughness and normal-variation roughness were calculated using local cube neighbourhoods with side lengths of 0.10, 0.20, and 0.30 m. To avoid ambiguity associated with surface-normal orientation, the normal-variation descriptor was calculated using orientation-invariant angular differences between neighbouring surface normals. The training dataset was balanced using distance-stratified random undersampling, while the held-out test dataset retained its original class distribution. FLAML was used for automated model selection and hyperparameter optimisation using three-fold cross-validation with macro F1-score as the optimisation metric. The final stacking classifier achieved an overall accuracy of 90.44%, balanced accuracy of 87.42%, and macro F1-score of 87.85% on 1,296,822 held-out test points. The held-out test data were acquired from different scanner positions and spatially distinct material regions from those used for training, with no shared point samples; however, both datasets originated from the same general study area, and broader cross-site generalisation therefore requires further independent validation. Most material classes showed strong discrimination, although Carpet remained challenging because of confusion with Asphalt. The results demonstrate the potential of combining TLS-derived radiometric information with multiscale geometric surface descriptors for construction material classification without relying on RGB colour information. Full article

Hyperspectral image (HSI) classification benefits from rich spectral information; however, high dimensionality of HSI data increases computational cost, noise sensitivity, and the risk of overfitting when labeled samples are limited. Most pretrained computer vision networks are designed for three-channel inputs, making direct application to hyperspectral cubes difficult. Conventional principal component analysis (PCA) ranks components by total variance without distinguishing useful signal variance from noise-related variance, which can reduce the reliability of the resulting representation when only a few components are retained. This paper proposes a data-augmented Noise-Adjusted Principal Component Analysis (DA-NAPCA) framework for deep learning-based HSI classification. By accounting for estimated noise covariance, NAPCA orders the transformed components by signal-to-noise ratio rather than total variance, while data augmentation mitigates the overfitting risk when labeled samples are limited. Unlike typical NAPCA/MNF applications, which select the number of retained components empirically, DA-NAPCA deliberately retains three noise-adjusted components to form a compact three-channel representation, enabling pretrained models designed for three-channel inputs to be fine-tuned without modifying their input layers. The framework is evaluated using a 3D convolutional neural network (3D-CNN) for spatial–spectral feature learning and a pretrained EfficientNet-B0 model for lightweight transfer learning. Although this paper uses 3D-CNN and EfficientNet-B0 as illustrative examples, the proposed DA-NAPCA framework is a representation-level preprocessing approach and does not require architecture-specific modification. Experiments conducted on the Indian Pines, University of Pavia, and Salinas datasets compare DA-NAPCA with RGB, band selection, PCA-based dimensionality reduction, and ablation variants. Across the three datasets, DA-NAPCA achieved mean overall accuracies of 93.11–94.71% with 3D-CNN and 95.93–97.44% with EfficientNet-B0. Compared with the second-best baseline method, DA-NAPCA improved overall accuracy by 2.75–7.58 percentage points with 3D-CNN and 1.28–2.12 percentage points with EfficientNet-B0. These results demonstrate that combining a compact noise-adjusted representation with spatial augmentation provides an effective input representation for deep learning-based HSI classification. Full article

This study presents a computational comparison of 24 lattice topologies over their geometrically feasible relative density ranges. Conjugate heat transfer simulations were performed in ANSYS Fluent 2024 R2 using 10 mm unit cells, inlet air at 300 K and 0.05 m/s, a constant base temperature of 312 K, and gravity acting in the negative z direction. The inlet Reynolds number was approximately 32.5. The prescribed temperature difference of 12 K gives a Grashof number of 1.68 × 10³ and a Richardson number of 1.59, indicating that buoyancy and the imposed flow are both relevant. The operating condition was therefore classified as low-speed mixed convection with perpendicular forced flow and buoyancy directions. The hydrodynamic model was benchmarked against published pressure gradient data for a body-centered cubic lattice. Thermal performance was compared using interfacial area, the magnitude of the ANSYS Fluent surface heat transfer coefficient, interfacial heat transfer rate, and interfacial thermal resistance. At 10% relative density, Auxetic gave the lowest resistance, 112.34 K/W, compared with 327.51 K/W for Cube. At 70%, FBCC reached 97.56 K/W, whereas Cube reached 1028.12 K/W. Increasing relative density improved or degraded thermal performance depending on topology. The database provides comparative guidance for lattice selection and subsequent multiscale design optimization of lightweight aerospace and electronic heatsinks. Full article

Stroke often causes persistent upper extremity (UE) motor deficits, affecting reaching and grasping and impacting daily activities. Recovery may depend on whether the dominant or non-dominant hand is affected, a distinction that remains understudied. This pilot study aimed to characterize longitudinal UE recovery post-stroke. Twenty participants post-stroke (ten with the dominant hand affected, ten with the non-dominant hand affected) performed reach-to-grasp-and-lift tasks (1-inch cube, 2.5-inch circular object), plus clinical assessments, five times over six months with the initial testing around 15 days post-stroke (mean = 14.9). Kinematic measurements included Time to Peak Velocity, Time After Peak Velocity, Reach Duration, Reaching Trajectory Smoothness, Path Linearity, and Grasp Duration. Piecewise linear mixed-effects models evaluated longitudinal trends. Clinical measures improved significantly within 45 days post-stroke, while most kinematics improved significantly in the first 30 days but plateaued thereafter. A secondary exploratory analysis examining effects of hand dominance on UE recovery showed significant clinical improvements in both groups, but significant kinematic improvements were only demonstrated in the affected dominant hand group. Findings suggest kinematic improvements may be driven by dominant hand impairment, possibly reflecting motor control differences or greater reliance on the dominant hand. These results highlight the need for targeted rehabilitation strategies tailored to the non-dominant hand’s functional role. Full article

Grouted anchors made of glass fiber-reinforced polymer (GFRP) have gained popularity in cold-region construction projects, primarily owing to their resistance to corrosion and low density. Although prior research has addressed the bond–slip characteristics of FRP-to-concrete joints, a theoretical formulation that links cumulative freeze–thaw damage of the mortar matrix to the progressive loss of bond strength at the GFRP bar–mortar interface is still lacking. This work provides a combined experimental and theoretical examination of how the bonding capacity of ribbed GFRP bars in cement mortar declines after 0, 30, 60, and 90 FTCs. Compression and splitting tension tests were carried out on mortar cubes, while pullout specimens were used to assess the interfacial bond strength. Two mortar grades commonly used in anchorage practice (M25 and M35) and two bar diameters (12 mm and 16 mm) were selected as test variables. After 90 FTCs, the maximum bond strength fell by as much as 64.1%, whereas the post-peak residual bond strength suffered an even more pronounced drop of up to 79.3%. Meanwhile, the residual-to-peak-bond-strength ratio decreased steadily with the number of FTCs, marking a shift from a mechanically interlocked interface to a friction-governed one. The higher-grade mortar (M35) experienced clearly superior resistance to freeze–thaw attack compared to M25, while the larger-diameter bars (16 mm) degraded faster. An analytical model for estimating the bond-strength degradation is proposed, where an exponential environmental factor was introduced and the decay constants were calibrated via nonlinear regression against the bond-strength retention ratios at 0, 30, 60, and 90 FTCs. The proposed models are calibrated empirical relationships that reproduce the measured degradation well within the tested parameter ranges and indicate reasonable internal stability under leave-one-group-out cross-validation. This study provides two theoretical provisions: the freeze–thaw degradation of the GFRP–mortar bond can be effectively described by a single exponential damage law whose decay constant quantifies the rate at which the interfacial capacity is exhausted, and the residual-to-peak-bond-strength ratio serves as a mechanistic indicator of the transition from mechanical interlock to friction-controlled failure. These provisions quantitatively link mortar degradation to interfacial capacity loss, thereby providing a theoretical basis for durability design of GFRP grouted anchors in cold regions. Full article

This work presents a modular, sensor-aware, end-to-end simulation framework for optical camera communication (OCC). The framework combines modified Monte Carlo ray tracing with pixel-level camera modeling, including optical blur, shutter timing, noise, digitization, and modulation-aware signal processing. It produces physically consistent synthetic images from simulated optical propagation and enables communication performance to be estimated through image-domain signal-to-noise ratio (SNR) and theoretical bit-error-rate (BER) calculations. The simulator is applied to intra-satellite optical links as a representative case study involving confined three-dimensional geometries, line-of-sight (LOS) visibility, partial occlusion, and rolling-shutter image formation. Experimental validation under LOS conditions shows good agreement in the dominant spatial-temporal characteristics of rolling-shutter imagery, with a structural similarity index measure (SSIM) of approximately $0.80$. Simulated SNR values range from approximately $20.5$ to $22.6$ dB, compared with measured values between $20.8$ and $24.4$ dB. No bit errors are observed in the experimental sequences, corresponding to finite-length BER upper confidence bounds, while the BER values derived from the simulated images are theoretical estimates obtained from the image-domain SNR under ideal receiver assumptions. Additional simulations using a detailed 12U CubeSat model demonstrate the capability to assess emitter–receiver placement and partial geometric occlusion, including cases in which the visible portion of the source remains sufficient for bitstream decoding. By jointly modeling optical propagation, camera acquisition, and communication metrics, the proposed framework supports early-stage OCC system analysis and configuration trade-offs without requiring immediate hardware implementation. The approach is applicable to other OCC scenarios in which spatial image formation and sensor dynamics influence communication performance. Full article

Pneumatic soft actuators have been drawing considerable attention in the field of soft robotics, thanks to their inherent flexibility, high power density, and safe interaction. However, the strong, intricate coupling between the material’s hyperelastic behavior and the reinforcement of anisotropic fibers creates significant challenges for both analytical modeling and numerical characterization of these actuators. In this paper, we design and fabricate a fiber-reinforced pneumatic soft actuator using Ecoflex 00-30 silicone rubber as the base material and helically wound fibers as the reinforcing layer. We set up a theoretical framework that combines the Neo-Hookean model for isotropic silicone rubber with a strain energy-based formulation for anisotropic wound fibers. This framework describes how the actuator is stretched, expanded, twisted, and bent. Finite element simulations are then carried out, focusing on three key design parameters: winding fiber density (three levels: high, medium, low), air cavity offset distance from the central axis (1, 2, 3, and 4 mm), and air cavity cross-sectional geometry (cube vs. cylindrical). The simulations reveal that a higher winding fiber density promotes more uniform stress distribution across both the strain and confinement layers. In contrast, a low fiber density can lead to local bulging and large stress variations, which ultimately compromises the bending performance. The offset distance of the air cavity from the neutral axis is directly linked to the bending curvature: a larger offset produces greater air cavity deformation and higher actuation efficiency. Furthermore, the cuboid air cavity yields a larger bending angle (experimentally validated up to 90° at 0.045 MPa) and better efficiency, while the cylindrical air cavity distributes stress more evenly across the outer surface of the strain layer and reduces stress concentration at the edges. These findings provide useful quantitative guidance for optimizing the structure of fiber-reinforced soft actuators and establish a framework for hybrid analytical–numerical prediction of their mechanical behavior. Full article

Late blight in tomato caused by Phytophthora infestans can lead to severe economic losses. Early detection is essential for effective disease management. This study investigates the spectral characteristics of healthy and late blight-infected tomato leaves and plants using non-destructive hyperspectral imaging and proposes a cost-effective machine vision system for early disease detection. Hyperspectral images from seven batches of leaf sets and six batches of whole plant sets were taken hourly over a 96 h period under both controlled and artificially infected conditions. The hyperspectral data cubes were processed with an image analysis model that identified healthy vs. infected regions. Key wavelengths (77 from leaf datasets and 24 from plant datasets) were selected using recursive feature elimination and analysed with four machine learning classifiers: k-nearest neighbour, support vector machine, random forest, and artificial neural network. The models differentiated healthy and infected tissue with high accuracy (98–99%). The hyperspectral data were simplified into a multichannel image with most informative wavelengths, using a custom spectral index and binary decision rule. Experimental limitations were addressed, and a conceptual design of practical hardware was proposed: a monochrome camera combined with a multichannel light source and polariser mounted on mobile equipment. Although further trials will be needed, this proof-of-concept study and conceptual hardware design can be adapted in other crops facing similar disease challenges. Full article

Proposed in this paper is a systematic methodology to identify cementitious paste mix design and printing parameters suitable for yielding dimensionally accurate 3D-printed specimens. Using a Box–Behnken surface response experimental design approach, the first work phase generated mathematical models to predict ink flow based on the paste water–binder ratio and dosage of superplasticising and viscosity-modifying admixtures. In the second work phase, favourable mixes were investigated further, again using a Box–Behnken surface response experimental design and corresponding mathematical models to predict printing accuracy in relation to printing parameters including ranges of print speed, extrusion multipliers and layer height. Confirmed by parallel preliminary print trials, favourable ink mix designs were efficiently identified for inks comprising either Portland cement only, or ternary blends of calcined clay, silica fume and Portland cement. For the Portland cement binder, the favourable mix design comprised a water–binder ratio of 0.26 and superplasticising and viscosity-modifying admixture dosages of 1.12% and 1.10% by mass of the binder respectively. Corresponding values for the calcined clay/silica fume/Portland cement binder ink were 0.27, 1.1% and 1.1% respectively. For both binder types, corresponding favourable values of the above listed print parameters were identified as 5 mm/s, 1.1% and 1.0 mm respectively. In the final work phase, the outputs from phases one and two were validated via print trials of hollow cube, beam, hexagonal and circular elements enabling measurements of the dimensional accuracy and buildability. With deviations in element height and width ranging from only ±0.3 to 2.6% from corresponding CAD designs, the appropriateness of the methodology was established. Full article

Background: The skin barrier plays a fundamental role in preventing transepidermal water loss (TEWL), regulating immune responses, and protecting against pathogen colonization. Disruption of this barrier underlies xerosis, sensitive skin, and clinical cracking. Heat-treated probiotics (postbiotics) represent a stable and biologically active approach to topical formulation. Objective: To evaluate the safety, molecular mechanisms, and clinical efficacy of heat-treated Lactiplantibacillus plantarum Skinbac™ SB14 (SB14) in improving skin barrier function, hydration, and the appearance of dry and cracked skin. Methods: In vitro studies assessed cell viability (MTT assay) and cytotoxicity (LDH release assay), Aquaporin-3 (AQP3) expression, Claudin-1 expression recovery following UV-induced damage (post-damage treatment model), cytokine modulation in Normal Human Epidermal Keratinocytes (NHEK) and Peripheral Blood Mononuclear Cells (PBMCs), and antipathogen activity against Staphylococcus aureus biofilm. A 30-day open-label, placebo-controlled clinical study (n = 20 healthy volunteers, both sexes, age > 18 years) evaluated an emulsion containing 1% SB14 versus placebo using instrumental measurements of superficial hydration (Corneometer^® CM825) and TEWL (Tewameter^® TM300), and clinical scoring of skin hydration (Kligman scale 1–4) and skin cracking (ODS Overall Dry Skin Score 1–5) via C-Cube imaging. Results: In vitro testing confirmed the safety of SB14 (full cell viability by MTT assay; no cytotoxicity by LDH release assay) and demonstrated significant AQP3 upregulation (p < 0.05), partial Claudin-1 recovery in UV-damaged cells following post-damage SB14 application (p < 0.1 vs. UV damage), significant reduction in pro-inflammatory IL-8 and IL-23 in NHEK (p < 0.01 and p < 0.05), strong innate immune activation in PBMCs (TNF-α and IL-6, p < 0.001), and 21% inhibition of S. aureus biofilm at 72 h. Clinically, the SB14 formulation significantly increased superficial skin hydration by +46.1% at T14 (p = 0.0451) and +33.6% at T30 (p = 0.0144) versus baseline, while TEWL decreased by −14.5% at T14 (p = 0.0205). Clinical hydration scores improved significantly from a median of 3.0 (moderate dry skin) at baseline to 2.0 (slightly dry skin) at T30 (p = 0.0073). Cracking scores improved significantly from a median of 3.5 at baseline to 2.0 at T30 (p = 0.0037), with 80% of subjects showing improvement at T30. All parameters remained non-significant in the placebo group. No adverse events were reported. Conclusions: SB14 is safe, biologically active across multiple barrier-relevant mechanisms, and clinically effective in improving hydration and reducing visible skin cracking in subjects with dry, barrier-compromised skin. Full article