Search Results (143,136)

Automated alert triage can reduce Security Operations Center (SOC) workload, yet the validation-tuned thresholds deployed systems rely on carry no finite-sample control of their operational error rates and degrade unpredictably under distribution shift. We present a model-agnostic dual-threshold conformal deferral architecture: high-score alerts are auto-escalated under finite-sample marginal class-conditional control of the benign-escalation probability (budget α), low-score alerts are auto-closed under matching control of the threat-miss probability (budget β), and the rest are deferred to an analyst. It needs no retraining and closes an automatic zone rather than certifying what the calibration data cannot support. We evaluate it on a reinforcement-learning investigation agent in a simulated SOC and on four classifiers trained on CIC-IDS2017 and tested on CSE-CIC-IDS2018, using stratified 25,000-flow calibration and evaluation samples, with attack-type recall computed over the full 16.2-million-flow corpus. Pooling episodes from ten trained policies across two evaluation datasets, the architecture automated 73.7% of decisions at α = β = 0.01—a figure for that predefined pooled mixture rather than a per-policy or per-dataset guarantee—realizing benign auto-escalation and threat auto-close rates of 0.0099 and 0.0101 and deferring the hardest ~26% of alerts. After recalibration on labeled target-domain data, severe cross-dataset degradation appears not as a silent error but as sharply reduced certifiable automation, with deferral rising to 79–99% for the most affected classifiers. This visibility is a property of the recalibrated layer: thresholds left un-recalibrated after a shift continue to certify nothing while still deciding, so the architecture requires periodic recalibration on labelled target-domain alerts to deliver it. Substituting open-weight language models for the analyst inside the band failed a pre-specified criterion at every scale tested from 7B to 32B across two model families, with the discriminative signal flat in model size and far below the first-stage policy’s own. Full article

Immune checkpoints (ICPs) are essential regulators of immune homeostasis, maintaining the balance between effective immune responses and tolerance to self-antigens. Dysregulation of ICP signaling may contribute to impaired immune surveillance, immune evasion, chronic inflammation, autoimmunity, persistent infections, and tumor progression. Consequently, ICP molecules are increasingly recognized not only as therapeutic targets but also as potential diagnostic, prognostic, predictive, and treatment-monitoring biomarkers. This review provides a comprehensive overview of the biological functions, signaling mechanisms, and clinical significance of major co-inhibitory and co-stimulatory ICP pathways, including PD-1/PD-L1/PD-L2, CTLA-4/CD28/CD80/CD86, LAG-3, TIM-3, TIGIT, BTLA, VISTA, ICOS, OX40, 4-1BB, GITR, CD27, CD40, and CD2, together with their corresponding ligands. Particular emphasis is placed on their biomarker potential in cancer and immune-mediated diseases. In addition, the review presents a bioinformatic characterization of ICP receptors and ligands based primarily on data available in UniProtKB and complementary bioinformatic resources. The analysis includes protein sequence length, molecular weight, theoretical isoelectric point, amino acid composition, subcellular localization, conserved and functional domains, protein family classification, post-translational modifications, isoforms, and selected structural features. Collectively, the available evidence indicates that ICPs constitute a structurally and functionally diverse group of immunoregulatory molecules with substantial biomarker potential. Integrating their molecular, structural, functional, and bioinformatic characteristics may improve disease classification, prognosis, patient stratification, treatment selection, and therapeutic monitoring. Such an integrated approach may also support the identification of novel biomarkers and therapeutic targets and contribute to the further development of precision and personalized medicine. Full article

Astrocytes are extensively interconnected via gap junction channels formed primarily by connexin 43 (Cx43) and connexin 30 (Cx30), two proteins that play central roles in intercellular signaling and homeostatic regulation in the brain. Although the functional properties of these connexins have been widely investigated, their spatial organization within astrocytes and its evolution during postnatal development remain poorly characterized. Here, we combined confocal and stimulated emission depletion (STED) super-resolution microscopy to examine the expression, distribution and colocalization of Cx43 and Cx30 immunoreactive puncta in hippocampal astrocytes from postnatal day 15 to adulthood. Quantitative analyses revealed a progressive increase in the number of both Cx43 and Cx30 puncta during development, whereas puncta size remained largely unchanged. Although connexin puncta appeared more distally distributed in mature astrocytes, this shift was fully accounted for by the growth of astrocytes during development. In addition, colocalization between Cx43 and Cx30 increased during maturation, reaching a stable level after postnatal day 30. Finally, STED super-resolution imaging revealed a diversity of connexin arrangements, including isolated puncta as well as complex assemblies composed of multiple neighboring connexin clusters. Together, these findings provide a quantitative characterization of the developmental remodeling of astroglial connexins and identify structural features that may contribute to the maturation of astrocytic networks. Full article

Background/Objective: Activated coagulation factor XII (FXIIa) is a component of the contact activation pathway and a pharmacologically relevant target in contact-system-associated processes. In this study, scaffold-aware and interpretable machine-learning QSAR models were developed for human FXIIa activity. Methods: Bioactivity records for the human single protein target CHEMBL2821 were retrieved from ChEMBL release 37. Modeling was restricted to exact IC50 measurements from assays explicitly referring to FXIIa, Factor XIIa, or activated Factor XII. Median-consolidated pIC50 values and two-dimensional Mordred descriptors were evaluated using leakage-safe preprocessing, scaffold-disjoint validation, Y-randomization, applicability domain analysis, structural similarity auditing, and SHAP interpretation. Results: The regression dataset comprised 424 compounds and 166 Bemis–Murcko scaffolds in this study. The Gradient Boosting regressor achieved R² = 0.7560, RMSE = 0.6924, and MAE = 0.4965 on the locked scaffold-disjoint test set (n = 85); across 50 repeated scaffold partitions, the mean R² was 0.6892 ± 0.1515. The classification model achieved ROC-AUC = 0.9453, PR-AUC = 0.9807, balanced accuracy = 0.7561, and MCC = 0.5972 (n = 73). Y-randomization supported nonrandom predictive signals (empirical p = 0.0099). Conclusions: The models support computational prioritization within the represented FXIIa chemical domain, while prospective evaluation of independently generated compounds remains necessary. Full article

This work presents an experimental investigation of droplet evaporation dynamics using a step-index multimode fiber-tip (MMF) interferometer. Distilled water, ethanol, isopropyl alcohol (IPA), and their binary mixtures with water were analyzed through complementary frequency- and time-domain approaches. Fast Fourier Transform (FFT) analysis was used to identify the dominant spectral components over selected evaporation intervals, while the instantaneous phase obtained from the analytic signal was used to track time-dependent variations in the optical response. For water, dominant components at 8.34 and 9.87 Hz corresponded to thickness-variation rates of −4.85 and −5.75 µm/s, respectively. Ethanol exhibited a dominant component at 15.8 Hz, corresponding to −9.01 µm/s, whereas IPA showed components at 13.2 and 34.8 Hz, associated with rates of −7.46 and −19.6 µm/s. Binary mixtures exhibited multiple spectral components and stronger temporal variability, indicating a nonstationary optical response during evaporation. The frequency- and time-domain results therefore provide complementary descriptions: the FFT identifies the dominant components over the selected interval, whereas instantaneous-phase analysis reveals their temporal evolution. Because the analysis was performed over short, selected evaporation intervals, the refractive index was assumed to remain approximately constant, and the measured phase variations were therefore attributed predominantly to changes in droplet thickness. The retrieved values are consequently interpreted as thickness-variation rates rather than direct mass-loss rates. The proposed approach provides a simple and compact method for monitoring droplet evaporation. Full article

Alcohol consumption is associated with increased breast cancer risk, partly due to the accumulation of toxic aldehydes like acetaldehyde, a carcinogenic byproduct of ethanol metabolism. Acetaldehyde Dehydrogenase 2 (ALDH2), a key mitochondrial enzyme, detoxifies acetaldehyde and other reactive aldehydes that drive oxidative stress, DNA damage, and hormonal dysregulation—processes central to carcinogenesis. Although alcohol consumption has been implicated in breast cancer, the role of ALDH2 deficiency itself, in the absence of exogenous alcohol exposure, in mammary gland biology and cancer susceptibility remains unclear. Genetic variants that impair ALDH2 activity are highly prevalent in East Asian populations, where carriers of inactive ALDH2 alleles exhibit impaired aldehyde detoxification. While such individuals are more susceptible to alcohol-related cancers, the effects of ALDH2 deficiency on mammary gland development and homeostasis without alcohol exposure remain unexplored. To investigate the effects of ALDH2 deficiency on mammary proliferation and development, we utilized a C57BL/6-based ALDH2 knockout (Aldh2−/−) mouse model. Our findings revealed that Aldh2−/− mice displayed hyperproliferative mammary glands with increased epithelial cell density, ductal expansion, and increased numbers of Ki67+ cells. Flow cytometry analysis revealed expansion of luminal and basal epithelial subpopulations, accompanied by enhanced mammary epithelial stemness, as indicated by increased mammosphere formation and colony-forming efficiency. At the molecular level, ALDH2 deficiency activated oxidative stress pathways, reflected by elevated 8-OHdG, p38 MAPK, NF-κB, and Nrf2 signaling, along with DNA damage responses involving p53 and H2A.X. We also identified a novel upregulation of RANK and RANKL in Aldh2−/− mammary glands, identifying the RANK/RANKL upregulation associated with NF-κB/p38 MAPK activation and enhanced mammary stemness. Furthermore, hormonal dysregulation was observed, with a significant increase in ERα and PR expression and phosphorylation. Dysregulated ER signaling correlated with enhanced erbB3 activation and downstream signaling, including the cyclin D1–pRb-E2F1 axis. These findings suggest that ALDH2 deficiency, possibly through accumulated endogenous aldehydes, profoundly alters mammary morphogenesis, epithelial repopulation, and stemness. These effects are associated with activation of oxidative stress and DNA damage pathways, together with upregulation of RANKL, estrogen receptor and receptor tyrosine kinase signaling. This study is the first to identify ALDH2 deficiency as a novel factor associated with mammary epithelial alterations that may create a tissue state that could predispose to oncogenic transformation. Full article

The continuous static contraction of the upper limb during overhead work often leads to progressive muscle fatigue, which is the main cause of work-related musculoskeletal disorders (WMSDs) and reduced operational safety. Traditional fatigue detection methods cannot capture subtle early fatigue characteristics under dynamic working conditions, and sensors are difficult to conveniently deploy in harsh environments. This study constructs an upper limb fatigue detection model based on wearable interface pressure signals and proposes a spatiotemporal dual-entropy fusion strategy. Sample entropy evaluates temporal muscle movement regularity, with a 10% incremental rate-of-change threshold for significant fatigue identification, superior fatigue sensitivity, and anti-interference capability. Information entropy reflects the spatial dispersion of pressure amplitude, adopting a 5% rate-of-change threshold for early fatigue warning. Combined with sEMG comparative verification and nonlinear fitting analysis, a hierarchical monitoring framework is established: single-index abnormality indicates slight fatigue, while dual-entropy synchronous elevation represents severe neuromuscular fatigue. The proposed method overcomes the limitations of single-index evaluation, accurately identifies multiple fatigue levels, and reliably monitors fatigue in real time, providing effective technical support for ergonomic monitoring and occupational safety protection in overhead operations. Full article

The cardiovascular actions of testosterone remain controversial, particularly regarding the safety of testosterone replacement therapy (TTh) in hypogonadal men. We investigated the effects of sustained supraphysiological testosterone exposure on aortic atherogenesis and cardiac remodelling in the testicular feminised (Tfm) mouse, a model of functional androgen receptor (AR) deficiency. Male Tfm mice and AR-intact XY littermate controls were fed a cholesterol-enriched diet for 28 weeks and received fortnightly intramuscular injections of saline or supraphysiological testosterone, alone or in combination with fulvestrant (oestrogen receptor antagonist) or anastrozole (aromatase inhibitor). Aortic lipid deposition was quantified by Oil Red O staining, while cardiac remodelling was assessed by heart weight, cardiomyocyte cross-sectional area and myocardial gene expression. Supraphysiological testosterone significantly reduced aortic fatty streak formation in Tfm mice compared with saline-treated controls (1.25 ± 0.36% vs 2.85 ± 0.37%, p < 0.01), an effect preserved following fulvestrant or anastrozole treatment, consistent with mechanisms that do not require classical AR or oestrogen receptor signalling. No additional reduction in aortic lipid deposition was observed in AR-intact XY littermates. In contrast, supraphysiological testosterone increased heart weight, cardiomyocyte size and expression of hypertrophic markers exclusively in XY mice, with no evidence of cardiac remodelling in Tfm mice. Collectively, these findings demonstrate divergent tissue-specific cardiovascular actions of testosterone, whereby supraphysiological exposure promotes AR-dependent cardiac remodelling without conferring additional vascular benefit, supporting maintenance of physiological testosterone concentrations during TTh. Full article

Physical sensor deployments in ground-based environmental monitoring networks are frequently constrained by high installation costs, hardware failures, and limited spatial coverage, resulting in incomplete observational datasets and degraded sensing capacity across monitoring stations. Data-driven virtual sensing offers a cost-effective alternative by estimating target environmental parameters through machine learning models trained on correlated sensor measurements, reducing dependency on dense physical infrastructure. This paper presents VS-DCFF, an applied virtual sensing framework for dual-target estimation of near-surface air temperature and relative humidity from ground-based sensor network data. VS-DCFF integrates: (i) a deterministic pipeline applying temporal encoding, rolling-window statistics, and mutual information-based feature selection to capture a linear trend/seasonal component and to select features predictive of the residual signal; (ii) a probabilistic pipeline employing a Gaussian copula model to generate statistically consistent synthetic residual samples preserving inter-variable dependencies; and (iii) an early feature-level fusion strategy feeding a copula-augmented XGBoost residual-boosting stage, whose output is combined with the linear trend component for the final prediction. Under a strict chronological evaluation protocol, VS-DCFF is benchmarked against persistence, linear and ensemble regression baselines, and a same-protocol re-implementation of the statistical core of the VSG-SGL framework, and achieves near-surface air temperature $\mathrm{RMSE}=0.7791{}^{\circ }$C, $R^2=0.9735$, and relative humidity $\mathrm{RMSE}=3.7747\%$, $R^2=0.9701$, outperforming all tested baselines. The framework is further validated through leave-one-station-out spatial generalization, robustness evaluation under simulated sensor faults and target-history loss, copula-variant and synthetic-data fidelity diagnostics, and a lightweight edge-deployment ablation. All findings, including cases where tested extensions such as spatial context features did not yield a robust improvement, are reported transparently. Results indicate that the proposed architecture provides a computationally efficient, extensively validated approach to dual-target environmental virtual sensing under realistic deployment conditions. Full article

Pseudomonas aeruginosa ST235 isolates drive a distinct macrophage inflammatory program characterized by IL-1β axis dominance and suppression of canonical NF-κB-associated cytokine responses. In THP-1-derived macrophages, ST235 isolates induce robust inflammasome activation, resulting in elevated IL-1β production and altered cell death responses, while eliciting markedly lower TNFα, IL-6, IL-8, and IL-10 responses than non-ST235 isolates. In contrast, nonST235 isolates preferentially stimulate NF-κB-dependent cytokine production with weaker inflammasome activation. Mechanistically, ST235 infection suppresses NF-κB signaling and reduces caspase-1 expression, whereas caspase-8 remains unchanged, indicating that increased IL-1β production can be due to alternative inflammasome pathways or posttranscriptional modifications. Rapid macrophage death further restricts overall cytokine production, reinforcing an IL-1β-dominant inflammatory profile. ST235 isolates also selectively reprogram M2-polarized macrophages toward an M1 phenotype, as demonstrated by increased CD86 expression, while both isolate groups promote M1 polarization in unpolarized macrophages. Importantly, pyoverdine purified from ST235 isolates reproduced aspects of these responses by inducing IL-1β production and cytotoxicity more effectively than pyoverdine from non-ST235 isolates, supporting a potential contribution of ST235-derived pyoverdine to the IL-1β-dominant inflammatory phenotype. Collectively, these findings suggest that ST235 isolates modulate macrophage immune responses through suppression of NF-κB-associated responses and promotion of IL-1β-dominant inflammation, providing insights into the virulence of this high-risk clone and highlighting potential therapeutic targets. Significant statement: ST235 isolates of P. aeruginosa uniquely modulate macrophage immune responses by robustly activating inflammasome signaling, inducing IL-1β expression while suppressing other cytokines, and driving macrophage polarization toward a pro-inflammatory M1 phenotype. These findings highlight distinct pathogenic strategies of ST235 isolates that may inform targeted therapeutic interventions. Full article

Gas-insulated switchgear (GIS) is an important component of power transmission systems. Accurate partial discharge (PD) pattern recognition is a key requirement for identifying internal insulation defects within the equipment. However, ultra-high-frequency (UHF) PD pulse sequences produced by different insulation defects usually contain complex nonlinear temporal structures and multi-scale periodic variations. These coupled characteristics are difficult to describe adequately via a single feature-mapping strategy. Thus, this paper proposes a temporal-frequency dual-branch stochastic configuration network (TF-SCN), which consists of two heterogeneous hidden-layer branches, for GIS PD pattern recognition. Specifically, in the temporal branch, the model uses a non-periodic, nonlinear activation function similar to that used in a conventional SCN to capture the nonlinear temporal characteristics. The frequency-sensitive branch introduces paired sine–cosine harmonic nodes with shared random projection parameters to capture frequency-sensitive features. The hidden outputs of the two branches are concatenated into a joint temporal-harmonic feature space, and the output weights are solved under the residual inequality constraints for GIS PD classification. To verify the superiority of the proposed model, comparative experiments are conducted on a dataset containing four PD patterns collected from the GIS PD experimental platform. Several baseline models, including 1DCNN, BPNN, SVM, KELM, RVFL, and SCN, are selected for performance comparison. The results show that, compared to 1DCNN, BPNN, SVM, KELM, RVFL, and SCN, TF-SCN effectively extracts distinguishable features in both the time and frequency domains, thereby achieving the best overall performance. Furthermore, its recognition performance remains consistently superior even on noisy data with signal-to-noise ratios ranging from 50 dB to 20 dB. By integrating highly sensitive UHF sensors with the proposed TF-SCN, this study presents a robust, AI-enhanced intelligent sensing and fault diagnosis system for continuous condition monitoring of power equipment. Full article

Obesity is associated with hypothalamic dysfunction, including altered arcuate nucleus (ARC) signaling-related impairment of energy homeostasis. However, the central metabolic effects of the specialized pro-resolving mediator Maresin-1 (Mar1) remain unclear. This study investigated whether central Mar1 administration alters ARC agouti-related peptide (AgRP), proopiomelanocortin (POMC), and metabolic hormone profiles in rats under standard diet (STD) and high-fat diet (HFD) conditions. Male Wistar albino rats were fed either an STD or an HFD for 12 weeks and assigned to untreated control, vehicle, 50 ng/kg/day Mar1, or 100 ng/kg/day Mar1 groups within each diet condition. Mar1 was administered by continuous intracerebroventricular infusion for 7 days. Body weight, food intake, serum insulin, leptin, ghrelin levels, and ARC AgRP and POMC expressions were evaluated. Central Mar1 infusion did not alter body weight or food intake. In STD-fed rats, 100 ng Mar1 significantly increased serum insulin compared to control (p = 0.025), both Mar1 doses significantly increased leptin versus control (p = 0.0003 and p = 0.0091, respectively), and 50 ng Mar1 significantly increased ghrelin compared with control and vehicle groups (p = 0.0016 and p = 0.0055, respectively). In HFD-fed rats, Mar1 did not significantly change serum hormone levels, and the difference in ghrelin between the 50 ng Mar1 and vehicle groups did not reach statistical significance (p = 0.051). Mar1 did not alter AgRP expression in the ARC under either diet condition. However, under HFD, 100 ng Mar1 increased POMC expression relative to control, vehicle, and 50 ng Mar1 groups (p = 0.0076, p = 0.0078 and p = 0.0204, respectively). Central Mar1 infusion therefore altered circulating metabolic hormones with no significant diet x treatment interaction, and increased ARC POMC expression under HFD, an effect supported by a significant diet x treatment interaction (p = 0.0091), without changing energy intake or body weight over the 7-day infusion. These findings indicate a diet-dependent modulation of hypothalamic melanocortin tone rather than a significant effect on energy balance. Full article

In recent years, the use of UAVs has become increasingly widespread, and the public safety risks posed by unauthorized UAV flights have become increasingly prominent, creating an urgent need for effective detection and identification of UAV targets. However, such targets are small in size, have low contrast, and exhibit an extremely low signal-to-noise ratio; conventional detection methods generally suffer from insufficient feature discrimination, missed detections, and false alarms in complex backgrounds. To address these challenges, this paper proposes a Frequency-Guided Cross-scale Refinement Network (FGCR-Net). Based on an encoder-decoder architecture, this network achieves end-to-end collaborative optimization through cross-layer feature fusion, side-channel prediction refinement, and frequency-domain background suppression. First, a multi-path selective cross-layer fusion module (SCFM) is designed. This module employs coordinated modeling via both channel and spatial paths, supplemented by adaptive weighting with learnable coefficients, to perform differentiated selective fusion of the encoder’s fine-grained features and the decoder’s semantic features, thereby bridging the semantic gap at jump connections; Second, we designed a Cross-Scale Adaptive Fusion Enhancement Attention Module (CAFEM), which cascades multi-receptive-field hollow convolutions, strip pooling, and a bidirectional semantic guidance mechanism to perform cross-scale refinement on the side outputs of each decoder layer, thereby alleviating the issues of blurred boundaries and false alarms caused by inconsistent quality of multi-scale prediction maps and insufficient cross-layer consistency; finally, we design a Frequency-Guided Semantic Enhancement Module (FGSEM), which uses the Fast Fourier Transform (FFT) to decouple encoder features into the frequency domain. By leveraging low-frequency energy to predict the background confidence map and applying spatially selective suppression to high-frequency components, this module distinguishes, from a frequency-domain perspective, the high-frequency responses of complex backgrounds and targets that are highly similar in the spatial domain. Experiments on MSDS-UAV, a self-built multi-scenario UAV dataset for small targets, demonstrate that our method consistently outperforms existing state-of-the-art methods across multiple performance metrics, with Pixel Accuracy, Mean Intersection over Union, and Probability of Detection reaching 92.76%, 70.91%, and 92.69%, respectively; Compared to the baseline model, these three metrics improved by 1.90, 3.20, and 3.76 percentage points, respectively, fully validating the effectiveness and superiority of the proposed method. Full article

Air pollution poses a major environmental and public health challenge in Metropolitan Lima, Peru, where complex topography, coastal meteorological conditions, and intense urbanization generate highly dynamic patterns of pollutant concentrations. Accurate multi-horizon forecasting is therefore essential for supporting environmental monitoring and early-warning systems. This study proposes a hybrid deep learning framework, called VMD-Attention-BiLSTM, for forecasting hourly PM_2.5, PM₁₀, and NO₂ concentrations using a decade of hourly air quality observations (2015–2024) collected by the National Meteorology and Hydrology Service of Peru (SENAMHI). The proposed methodology integrates a strictly causal preprocessing pipeline—including forward-only imputation, spatial-corroborated percentile-95 outlier detection, and pollutant-calibrated Variational Mode Decomposition (VMD)—with a Bidirectional Long Short-Term Memory (BiLSTM) network enhanced by a Bahdanau-style attention mechanism. All transformations are fitted exclusively on the training partitions of a five-fold TimeSeriesSplit cross-validation to prevent information leakage. A systematic benchmark of 1008 imputation experiments was conducted to justify the choice of causal linear interpolation over Kalman Filter alternatives. Model performance was evaluated at 24-, 48-, and 72-h forecasting horizons. The optimized framework achieved competitive predictive performance across the evaluated horizons, yielding best RMSE values of 9.04, 19.93, and 9.41 µg/m³ for PM_2.5, PM₁₀, and NO₂ at 24 h, degrading to 9.79, 24.56, and 11.02 µg/m³ at 72 h. All metrics are reported on the original concentration scale. VMD sensitivity analysis revealed that the optimal mode count is pollutant-dependent ($K=12$ for particulate matter; $K=4$ for NO₂). Furthermore, the ablation study showed that the complete VMD-Attention-BiLSTM configuration provided competitive and frequently improved performance relative to the baseline and partial configurations, with the magnitude of the improvement varying according to pollutant and forecasting horizon. The obtained results indicate that integrating signal decomposition with attention-based bidirectional learning can provide complementary benefits for forecasting under highly non-stationary urban conditions, particularly at shorter forecasting horizons. The proposed framework provides a reproducible and scalable solution for intelligent air-quality forecasting and serves as a valuable decision-support tool for environmental monitoring and public health protection in complex metropolitan environments. Full article

This paper describes a polarimetric, frequency-modulated continuous wave (FMCW) synthetic aperture radar (SAR) with an ultrawide bandwidth that spans 2–18 GHz. It is being developed as an airborne sensor called SKuSAR. The intent is to generate a set of sub-band images, thereby creating a multispectral SAR. This opens the prospect for new remote sensing algorithms that exploit variations in the scene’s polarization/frequency response occurring over the system’s three octaves of bandwidth. We describe the SKuSAR hardware and the processing steps applied to the FMCW data to create a multispectral SAR. The approach is practically demonstrated using data collected against calibration targets deployed in a field with the system mounted on a truck. This ground-based arrangement provided an inexpensive solution to test and fine-tune the system hardware and processing algorithms. A few complicating factors specific to the ground-based geometry were encountered, such as multipath signal contamination due to ground reflection and rather short data collections that affected attainable azimuth resolutions. Both these factors were identified and analyzed. The measured characteristics follow theoretical predictions rather well, giving confidence that the system meets its expected nominal performance once airborne, with the mentioned limiting factors absent or of reduced significance. Full article