Search Results (3,623)

With the rapid development of intelligent connected vehicles and autonomous driving, real-time and accurate road condition perception has become increasingly critical. Aiming at the limitations of traditional direct and indirect detection methods, this paper proposes an ultrasonic-based real-time detection system for road roughness. Most urban roads today feature asphalt pavements; therefore, this system focuses its research on asphalt pavements. Under the same pavement type (asphalt roads), there is a strong correlation between pavement roughness and the friction coefficient. By measuring the roughness of different pavements, the friction coefficient is estimated using the fuzzy processing method. Then the system through measuring ultrasonic echo amplitude and sensor–road distance, combined with software digital filtering, dual-parameter compensation (distance and temperature–humidity), probabilistic statistical analysis, and fuzzy inference, the mapping relationship among echo signals, road roughness and friction coefficient is established. The system mainly includes an ultrasonic transceiver module, a hardware signal conditioning module, and an MCU-based data processing, display and transmission module. Both simulated experiments and real asphalt pavement tests are carried out for verification. The results show that the system can effectively suppress noise, compensate distance attenuation and environmental interference, and achieve accurate real-time detection of road roughness, with a relative error less than 10% compared with the reference value. The proposed system can provide reliable data support for vehicle active safety systems and autonomous driving applications. Full article

Carbon dioxide (CO₂) corrosion affects the integrity of energy and process infrastructure, yet the field has lacked a quantitative description of its own structure and evolution. This study presents a scientometric analysis of CO₂ corrosion research published between 2005 and 2025, based on 8671 documents retrieved from Scopus and Web of Science and processed in VOSviewer for co-authorship, co-citation, and keyword co-occurrence mapping. Annual output rose from low and irregular levels in the early period to sustained growth from approximately 2013 onward, and more than 80% of cumulative citations were recorded after 2016, indicating that the recently published literature constitutes the field’s actively cited base. Ranked by publication volume, China and the United States are the leading contributors across both databases, followed by a stable group of European and other national communities; at the institutional level, energy-focused organizations predominate, and Corrosion Science is the most frequently occurring and most strongly connected source in the co-citation network. Keyword co-occurrence mapping resolves the literature into four thematic clusters: physic-chemical context, degradation quantification, electrochemical and surface-analytical methods, and industrial application. The analysis also indicates that broad CO₂-based queries retrieve substantial adjacent-field literature; corrosion-specific search terms are therefore suggested for delimiting this domain in future bibliometric studies. Full article

Public space vitality is increasingly regarded as a central issue in the conservation and renewal of traditional villages, yet existing studies rarely combine spatial pattern analysis with residents’ perceptual evaluation within a single framework. Taking Yutu Village in southeastern Hubei, China, as a case, this study develops a Space–Event analytical framework integrating GIS-based spatial statistics with Importance–Performance Analysis (IPA). Semi-structured interviews, participatory mapping, and questionnaire surveys identified 58 public event units, 34 physical public spaces, and 241 event–space coordinate points. Kernel density estimation (KDE) and Ripley’s K-function analysis indicate a composite structure of one core, multiple centers, four axes, and five clusters. Historical and cultural events cluster around heritage buildings, daily life and production events extend along everyday movement corridors up to 155 m, and ritual and folk cultural events remain concentrated at small-scale ceremonial nodes. Based on 112 valid questionnaires, IPA-based evaluation shows negative gaps between importance and satisfaction across all 23 indicators (p < 0.001 for all items), with spatial narrative showing the largest gap (Δ = −1.66), while ritual-related indicators achieve the highest satisfaction scores, suggesting a degree of cultural resilience in ceremonial spaces. By cross-referencing spatial clustering types with IPA quadrants, the study proposes differentiated strategies, including narrative-oriented design for heritage spaces, connectivity enhancement for everyday networks, and protective maintenance for ritual nodes, offering an evidence-based framework transferable to comparable traditional village contexts. Full article

Groundwater-potential assessment in karst aquifers is complicated by pronounced spatial heterogeneity driven by structural permeability, lithological variability, recharge redistribution, and unresolved subsurface conduit connectivity. Although machine-learning approaches have improved regional groundwater mapping, most existing models provide only deterministic predictions and offer limited information on predictive uncertainty and hydrogeological reliability. To address this limitation, we propose CT-TreeFlow. This probabilistic groundwater assessment framework goes beyond conventional machine-learning models by explicitly learning the full conditional probability distribution of groundwater favourability rather than a single deterministic estimate. The framework integrates sparse probabilistic environmental routing, conditional density estimation, hydrogeologically constrained pseudo-absence generation, geographically structured spatial validation, and explainability-driven interpretation within a unified modelling architecture, enabling simultaneous groundwater prediction, uncertainty quantification, and hydrogeological interpretation. The framework was applied to the Zagros karst system in Khuzestan Province, Iran, using remote-sensing-derived environmental predictors, Copernicus DEM-based morphometric variables, geological–structural datasets, and hydroclimatic indicators. Performance was evaluated against LightGBM and XGBoost using GroupKFold spatial cross-validation. CT-TreeFlow achieved a mean RMSE of 2.737 and a mean R² of 0.852, while also providing spatially explicit uncertainty estimates and probabilistic prediction intervals. Explainability analyses identified fracture density, lithology, drainage organisation, and terrain-controlled recharge conditions as the dominant controls on groundwater favourability. Predicted high-favourability zones showed strong spatial correspondence with major carbonate formations and independent spring–cave inventories, supporting the hydrogeological plausibility of the mapped patterns. These results demonstrate that probabilistic modelling can provide more reliable and physically interpretable groundwater assessments than deterministic approaches in structurally complex karst environments. CT-TreeFlow offers a transferable framework for uncertainty-aware groundwater exploration and regional hydrogeological decision support in heterogeneous aquifer systems. Full article

Coastal adaptation planning often focuses on keeping growth out of flood-prone areas, but in some coastal cities, business as usual growth may already avoid the most exposed locations while remaining concentrated near the shoreline. This problem is examined in Dalian, China, using the Land Use Evolution and Impact Assessment Model combined with connected bathtub flood exposure mapping at 30 m resolution. Existing model outputs are translated into planning support layers covering flood exposure, a coastal planning belt, and a proof-of-concept port-accessibility proxy, then used to classify growth constraint conflicts, define adaptation zones, and compare three scenarios under fixed demand inputs, spatial metrics, and sensitivity analysis across 75 parameter combinations. BAU growth concentrates within 5 km of the shoreline in high-suitability cells, indicating that coastal concentration rather than direct hazard exposure defines the main planning problem. Strong conservation, used here as a reused broad-constraint reference scenario rather than a newly designed coastal-adaptation scenario, compresses residential allocation closer to the shore, with a mean distance of 1.5 km compared with 3.1 km under BAU. Only selective adaptation redirects residential growth inland, increasing mean distance to 11.5 km and reducing residential allocation within the belt to 0%, while retaining more commercial allocation than strong conservation. Because the inundation input is based on a static connected-bathtub approximation rather than a validated hydrodynamic model, the flood-exposure component is interpreted as an exploratory screening layer rather than a flood prediction. Differentiated zoning offers a targeted pathway for reducing coastal residential exposure while recognizing commercial functions that may depend on port access. Full article

Flood risk is a wicked problem, characterized by non-linear dynamics, cross-scale interdependencies, and contested responsibilities. Disentangling who or what has the capacity to drive change in flood risk management (FRM) systems is a critical step for designing inclusive and sustainable risk-reducing interventions. Given the diverse range of actors often involved in FRM decision-making, it is necessary to consider different interpretations of what system features are important. Using participatory visualization methods this study applied a systems-thinking lens to examine how experts visualize the FRM system and its drivers of change in British Columbia (BC), Canada. Workshop one participants undertook group timeline mapping to visualize their understanding of the events and processes that have driven BC FRM system development. Workshop two participants completed open-ended concept maps, producing personal mental models of the features and relationships that make up the present-day FRM system. Data from both workshops were synthesized into a ‘master’ timeline and concept map, and the experts’ perceived drivers of system change were identified. Our results revealed decentralizing governance shifts and past flood events as drivers of historic system development in the minds of our experts. Our results also indicated leverage points that experts had included as drivers of system improvement in their concept maps, with frequently named and connected features, such as the most interconnected feature: ‘flood mapping’, offering potential opportunities for cross-sector collaboration and cascading risk-reduction action. Possible gaps in the FRM system were also revealed by system features that were acknowledged as important by participants but were represented as disconnected, including themes of ‘climate change impact’ and ‘reconciliation’. Participatory visualization methods, especially when used in combination, offer a practical approach for representing experts’ mental models of FRM systems, revealing expert-identified leverage points for practical FRM improvement that can contribute to sustainable flood risk reduction goals. Full article

Given the heterogeneity in data on heat pump performance curves across manufacturers, selecting the appropriate one for more detailed studies is complex. Machine learning (ML) techniques can be very helpful in this endeavor. In this case, four techniques were used: artificial neural networks (ANN), support vector machines (SVM), Gaussian Process (GP), and decision trees (DT), to predict HP performance maps. These four techniques were then applied to a hybrid installation consisting of an air-water HP boiler and a biomass boiler modeled with TRNSYS and connected in series. Performance maps were generated using TRNSYS type 581. Key aspects, including overall efficiency, emissions, lifetime costs, and design and control parameters, were then analyzed. The study found that the coefficient of variation of root-mean-square error (CVRMSE) was 14.9% for the DT model, 11.4% for the ANN, 11.1% for the SVM, and 10.7% for the GP model. The GP model was ultimately used to develop an HP performance map due to its highest accuracy, and comparisons with baseline data revealed significant differences in efficiency, operational costs, and emissions, among others. Full article

Power-quality resilience is an important component of sustainable industrial electricity use, as voltage sag events can cause production interruptions, equipment damage, and inefficient allocation of mitigation costs and benefits among stakeholders. However, high initial investment costs and the lack of a viable commercial operation scheme have hindered the large-scale deployment of mitigation devices. To support sustainable power-quality governance, this study proposes an integrated framework that connects the technical compensation performance of the mitigation device with the economic foundation of a tripartite commercial operation model. First, an adaptive switching compensation strategy dynamically shifts between different modes based on the real-time voltage sag depth, establishing a mapping relationship with avoided-loss benefits. Then, a bi-level Nash bargaining model is constructed to allocate costs and benefits among the government, the enterprise, and the user, deriving closed-form analytical solutions for both the upper- and lower-level games. Through pilot operations at a large public service facility, economic losses of 480,000 CNY caused by a single voltage sag can be effectively avoided. Meanwhile, under the proposed scheme, all three parties achieve positive net present values. Compared to the user self-funding mode, the user’s NPV increases by 21.9%. Furthermore, unlike bilateral or equal-sharing alternatives, the Nash bargaining solution ensures all parties remain within the strong feasible region. The government and enterprise recover their costs within 4.14 and 6.20 years, respectively. These results indicate that the proposed framework can enhance the economic sustainability of power-sensitive users, encourage shared public–private investment in power-quality improvement, and support more resilient and efficient industrial electricity use. Full article

The bearingless induction motor (BIM) relies on accurate displacement feedback to maintain stable magnetic suspension, but sensor faults, degradation, and noise can distort feedback and induce transients during branch switching. This paper proposes a confidence-fusion-based fault-tolerant displacement measurement method for the BIM suspension feedback chain. A four-channel asymmetric redundant sensor configuration is developed, and channel state evaluation functions are constructed from sampling-difference terms and geometric-consistency residuals. A decreasing Sigmoid mapping with first-order smoothing generates continuous confidence coefficients to represent channel health. Combined with discrete fault flags of the primary channels, four reconstruction branches, AB, BC, AC, and CD, are adaptively weighted to obtain the reconstructed displacement, which is connected to the original suspension controller through a smooth feedback access mechanism. A MATLAB/Simulink closed-loop suspension model is used to evaluate the method under fault-free operation, an abrupt fault of primary channel A, simultaneous and sequential faults of primary channels A and B, abrupt and gradual degradation, constant bias, intermittent signal dropouts, and noise disturbance of primary channel B. Results show that the method identifies abnormal primary channels, redistributes reconstruction weights according to sensor conditions, and maintains a fallback path through the CD branch under dual-primary-channel failure. Under channel-B degradation, the confidence coefficient tracks the deterioration and supports the subsequent AB-to-AC branch transfer, whereas under noise disturbance, the fault flag remains inactive and unnecessary branch switching is avoided. The method improves feedback continuity without changing the main suspension controller. Full article

Unc-51-like kinase 3 (ULK3) is a key member of the ULK serine/threonine kinase family. Aberrant ULK3 expression has been increasingly linked to tumorigenesis and malignant progression in multiple cancer types. However, the precise role of ULK3 in tumor initiation and progression remains incompletely understood. Leveraging integrated multi-omics data from The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) project, and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), we systematically characterized the expression of ULK3 at both the transcript and protein levels across 33 cancer types. We also evaluated genomic alterations, prognostic significance, alternative splicing, pathway enrichment, tumor stemness, immune infiltration, and immunotherapy-related biomarkers. In parallel, we investigated the function of ULK3 in prostate cancer PC-3 cells using cellular localization analysis, wound-healing assays, and MTT assays. We further applied Connectivity Map (CMap) screening and molecular docking to identify candidate ULK3 activators. ULK3 was significantly upregulated in 13 cancer types, including Bladder Urothelial Carcinoma, Breast Invasive Carcinoma, and Lung Adenocarcinoma. In contrast, ULK3 was downregulated in Cholangiocarcinoma and Head and Neck Squamous Cell Carcinoma. High ULK3 expression was associated with poor overall survival in Adrenocortical Carcinoma, Kidney Renal Clear Cell Carcinoma, and Skin Cutaneous Melanoma. Copy number amplification contributed to ULK3 overexpression. A recurrent A206V missense mutation was detected in the protein kinase (Pkinase) domain. Genes co-expressed with ULK3 were enriched in RNA splicing, methylation, oxidative phosphorylation, and energy metabolism. ULK3 expression showed positive correlations with tumor stemness indices and m1A/m5C/m6A RNA modification regulators. From an immunological perspective, high ULK3 expression was associated with lower Immune Score, increased M2 macrophage infiltration, and co-expression of PD-L1, CTLA4, and LAG3 in most cancers. ULK3 expression was also correlated with Tumor Mutational Burden in Kidney Renal Clear Cell Carcinoma and Rectum Adenocarcinoma. In addition, ULK3 expression was associated with Microsatellite Instability in Brain Lower Grade Glioma, Lung Adenocarcinoma, and Uterine Corpus Endometrial Carcinoma. ULK3 overexpression promoted proliferation and migration in PC-3 cells. Cephaeline was screened as a putative ULK3 activator. Overall, ULK3 expression and amplification were associated with poor clinical outcomes, tumor stemness, immunosuppression, and RNA dysregulation. These findings highlight the potential value of ULK3 as a pan-cancer diagnostic and prognostic biomarker and as a predictor of immunotherapy response, particularly in prostate cancer. Full article

Long-term monitoring of surface-water dynamics in hyper-arid reservoir systems requires consistent remote-sensing methods that can distinguish open water from bright desert surfaces, shallow water, wet sand, and mixed shoreline pixels. This study evaluates Landsat-derived spectral water indices for delineating surface water in Lake Nasser and the adjacent Tushka Lakes, generates a multi-decadal record of surface-water extent using Google Earth Engine, and places the resulting surface-water patterns in the context of available hydrogeological observations. Landsat TM and OLI surface reflectance imagery was used to compare seven commonly applied water indices (NDWI, EWI, NDX, WRI, AWEI_nsh, TCW, and NWI) based on mapped water area, relative area differences, and classification accuracy metrics derived from 1000 stratified reference samples. Among the tested indices, NDWI provided stable water–land separation (overall accuracy ≈ 93.6%; κ ≈ 0.898) and was selected for long-term mapping. The NDWI-based workflow was implemented in Google Earth Engine to generate quarterly composites of surface-water extent for the period 1987–2026. The resulting time series reveals stable, persistent surface water in the central and southern sectors of Lake Nasser, in contrast to pronounced seasonal and interannual variability in the shallow, intermittently connected Tushka basins. Total mapped water area increased from 2631 km² in 1987 to 8923 km² in early 2026, with Lake Nasser ranging from 2411 to 6060.7 km² and the Tushka Lakes expanding from no mapped water before 1998 to more than 3300 km² during 2025. To assess possible surface–subsurface interaction, daily lake-stage records (1965–2014) and monthly groundwater levels from 44 observation wells were used to estimate potential seepage losses from Lake Nasser to the Nubian Sandstone Aquifer System using Darcy’s law. Annual seepage estimates ranged from 15.58 × 10⁶ to 36.68 × 10⁶ m³/year, suggesting spatial variability in potential lake–aquifer seepage along the western lake margin. The combined remote-sensing and hydrogeologic results provide complementary, non-causal evidence for interpreting where surface-water persistence and estimated seepage may co-occur. Because spatial correlation analysis, calibrated ground-water modeling, full water-budget analysis, and independent field validation were not performed, the inferred seepage–surface-water relation should be regarded as a cautious hypothesis rather than proof of causality. Full article

The accurate detection of unmanned aerial vehicles (UAVs) in various sizes played an important role in the practical applications. Yet the preceding works suffered from the missing inference, the false alarms, and the poor accuracy due to the the adverse scene conditions, as well as the mutable scales. To solve the problems, a hierarchical attention promoted cross-scale learning framework was proposed in this paper. First, the hierarchical attention mechanism was introduced in the backbone to generate the multi-scale features of targets, so they can be discerned and located at different scales. The resulting features were further delivered to the neck, in which two branches of features were built, respectively. The former was obtained by the target-specific feature operator, while the latter was generated by the upsampling operation. The dual branches were further connected in the quasi-residual structure. So the content of targets can be protected well, and the detail information can be reconstructed. Finally, the dynamic focusing loss measurement was presented to regress the bounding box of the target, so the learning effectiveness of presented the architecture can be promoted. To verify the proposed method, multiple rounds of experiments were performed. The results demonstrated that small and weak drones can be detected accurately, especially in adverse lighting and weather conditions. The evaluation metric of mean average precision rate (mAP) can be improved by 18.5% (YOLO6) on the collected dataset. Full article

This paper presents a metrology-oriented review of vision-based displacement and deformation measurement for civil structural health monitoring (SHM), with an emphasis on field robustness and uncertainty quantification (UQ). The review focuses on image- and video-based methods that convert visual information into quantitative physical measurements, such as displacement, strain, or derived dynamic indicators. The literature is organized according to the main stages of the measurement chain: image formation, image-plane motion estimation, and geometric conversion to metric motion. Within this framework, measurement pipelines are interpreted through three levels of geometric mapping, namely, a scalar scale-factor model, a planar homography-based model, and a full Jacobian-based model. The review synthesizes major method families, including marker-based and markerless tracking, feature-based tracking, optical flow, digital image correlation (DIC), phase-based motion magnification, edge-based estimators, fixed- and moving-camera configurations, UAV-based acquisition with ego-motion compensation, hybrid vision–sensor fusion, and deep-learning-enhanced pipelines. A structured taxonomy of uncertainty sources is then presented along the processing chain, covering camera geometry and calibration, imaging noise and blur, quantization, timing and synchronization, environmental disturbances, optical turbulence and heat haze, platform motion, algorithmic failure modes, and reference-sensor uncertainty. The paper also compares UQ practices, including GUM-aligned analytical propagation, Monte Carlo methods, DIC-specific error budgets, bootstrap and resampling strategies, and probabilistic deep learning. The main contribution of this review is to connect computer-vision-based displacement pipelines with metrological requirements by explicitly linking measurement models, uncertainty sources, UQ methods, and field-validation evidence within a unified framework. A practical uncertainty-budget template is compiled to support traceable reporting across different pipelines and deployment scenarios. The paper concludes with prioritized research gaps and future directions, including standardized benchmarks and datasets, traceable UQ for moving-camera systems, multi-sensor fusion with end-to-end uncertainty propagation, long-term drift characterization, optical-turbulence and adverse-weather modeling, validated subpixel limits at extreme range, probabilistic deep learning–metrology integration, and standardized reporting practices. Full article

Background/Objectives: Diagnostic metabolites measured in newborn screening, inherited metabolic disease, lysosomal storage disease, oncometabolite testing and routine clinical biochemistry are direct read-outs of human metabolic state. Their mechanistic interpretation requires linking measured metabolites to enzymes, pathways, regulatory context, disease knowledge and, increasingly, AI-assisted quantitative systems pharmacology (AI-QSP) workflows. We developed EHMN2026^®T as a license-aware AI-QSP integration framework that connects the EHMN2026^® metabolic backbone with licensed geneXplain knowledge resources while keeping ownership, licensing and redistribution constraints explicit. Methods: EHMN2026^®T integrates the SBML-encoded EHMN2026^® metabolic backbone with licensed TRANSFAC^® 2025.2, TRANSPATH^® 2025.2 and HumanPSD™ 2025.2 resources. TRANSFAC^® position weight matrices were used for promoter-level analysis of EHMN metabolic genes. The resulting transcription factor (TF)–gene connections were mapped to EHMN genes, TRANSPATH^® signalling/molecular-state entries and HumanPSD™ disease/drug context. The framework is positioned as a controlled component of the IQANOVA AI-QSP environment, but only aggregate statistics, non-proprietary EHMN-derived summaries and manuscript-level examples are reported publicly unless separate permission is obtained from the relevant rightsholders. Results: Promoter analysis of 1681 EHMN2026^® metabolic genes using 1147 mapped TRANSFAC^® matrices identified 291,387 ENSG-level TF–gene regulatory-potential connections involving 398 TFs and 1,107,264 predicted binding sites. The diagnostic panel contained 80 covered genes (63.5%), including complete coverage of oncometabolite enzymes and high coverage of organic acidaemia, steroidogenesis and fatty-acid oxidation categories. Mapping to TRANSPATH^® expanded the EHMN genes into 144,529 molecular-state representations and 14,879 gene–pathway or gene–chain pairs. HumanPSD™ was used as a licensed translational context layer; EHMN-specific HumanPSD™ outputs are treated as license-controlled derived outputs and are therefore not redistributed as open detailed tables in this manuscript. Conclusions: EHMN2026^®T provides a license-aware AI-QSP integration framework for tracing a diagnostic metabolite from a measured clinical value to candidate enzyme nodes, regulatory potential, signalling/molecular-state context and disease or therapeutic interpretation. PWM-derived TF–gene links are presented as regulatory hypotheses, not proof of active regulation. Public release should be limited to aggregate statistics and non-proprietary EHMN-derived components; detailed TRANSFAC^®, TRANSPATH^® and HumanPSD™-derived edges, mappings, annotations and SBML outputs remain subject to geneXplain ownership and licensing terms. Full article

Professional UAV thermal imaging systems are widely used for inspection, environmental monitoring, search and rescue, agriculture, and technical diagnostics. However, their cost limits their use in education, preliminary field screening, rapid prototyping, and low-resource applications. This study evaluates a minimum-cost indirect UAV thermal sensing workflow based on a DJI Mini 4K consumer drone, a lightweight Servo King9000 smartphone, and a UTi260M smartphone-connected infrared thermal camera. In the proposed configuration, the smartphone displayed and recorded the thermal stream, while the onboard RGB camera of the UAV recorded the smartphone-displayed infrared video during flight. The aim was not to develop a radiometric UAV thermal imaging platform, but to determine whether such a low-cost configuration can provide qualitative presence/absence indication of clear thermal hotspots and to identify its operational limits. The system was experimentally assessed under no-payload and payload conditions, daylight and nighttime illumination, and several low-altitude operating heights. Additional motor-region thermal observations were performed using a UTi260T handheld thermal camera under loaded and unloaded operating conditions. The complete UAV–payload configuration had a measured mass of approximately 340 g, corresponding to an effective added payload of 91 g and a payload-to-UAV mass ratio of 36.5%. Payload operation reduced near-ground flight endurance from approximately 25 min to 14 min 40 s. The maximum observed motor-region temperature increased from 24.9 °C under unloaded operation to 42.0 °C under loaded operation, while motor thermal asymmetry increased from 4.8 °C to 7.6 °C. Nighttime and low-glare operation improved the readability of the smartphone-displayed thermal stream, with the most practical usability observed at approximately 10–20 m. The results show that the proposed workflow is feasible only for short-range qualitative thermal screening and clear hotspot presence/absence indication. The UAV-recorded video should not be interpreted as direct thermal data, but as an RGB recording of a smartphone display showing thermal information. Therefore, the workflow is not suitable for quantitative temperature measurement, radiometric thermal mapping, or accurate thermal shape delineation. The main operational limits are payload mass, suspended-load oscillation, display readability, reduced endurance, motor-region thermal loading, sensitivity to payload alignment, and the absence of raw radiometric data. Direct UTi260M smartphone-recorded thermal frames were additionally used for pixel-size-assisted qualitative verification of practical reference thermal targets, including a human-sized target and a vehicle-sized target, at selected low-altitude operating heights. Full article