Background noise contamination in encoder features and insufficient directional modeling on the decoder side remain central challenges in arbitrary-oriented object detection (AOOD). To address these issues, an encode–transmit–decode detector (ETD-Det) is presented, co-optimized across encoding, transmission, and decoding. On the encoding side, a spectral diffusion encoder (LapSpecEnc) is proposed. Frequency-domain diffusion is performed by LapSpecEnc to suppress high-frequency background noise and to maintain coherent, orientation-consistent features. On the decoding side, an Extended Gaussian Distribution (EGD) together with a Kullback–Leibler (KL) divergence-based loss (EGKLD) are introduced. This design provides direction-sensitive geometric representation and mitigates the angle-gradient degradation observed in standard Gaussian formulations, particularly for nearly square targets. Extensive evaluation has been conducted, demonstrating strong effectiveness and generalization: ETD-Det achieves 90.61% mAP on HRSC2016, 90.37% mAP on UCAS-AOD, 81.54% mAP on DOTA-v1.0, and an F-measure of 82.33% on ICDAR2015. These results surpass existing methods under comparable settings and support ETD-Det as a robust and accurate solution for high-precision AOOD.

Global Positioning System (GPS) spoofing poses a significant threat to the reliability of unmanned aerial vehicle (UAV) navigation systems that rely heavily on Global Navigation Satellite Systems (GNSS). To address this challenge, we propose a detection framework named PIR–PSO–XGBoost, which integrates Physics-Informed Residual (PIR) modeling with Particle Swarm Optimization (PSO) and Extreme Gradient Boosting (XGBoost). Unlike existing detection frameworks that rely on handcrafted features or deep black box models, the proposed method introduces a physically interpretable residual construction process that captures signal inconsistencies by enforcing temporal and carrier level consistency across GNSS observables. These residuals, combined with conventional navigation features, are used to train an XGBoost-based classifier, while PSO is employed to perform global hyperparameter tuning to enhance model generalization and robustness across diverse spoofing scenarios. This design improves interpretability and computational efficiency, addressing the limitations of traditional feature engineering and deep learning-based detectors. Experimental results on a real-world GPS spoofing dataset demonstrate that the proposed framework achieves a classification accuracy of 95.26% and an F1-score of 95.28%, significantly outperforming conventional learning baselines. These findings confirm that combining physics-guided feature construction with swarm optimized learning yields a robust, efficient, and deployable solution for GPS spoofing detection in UAV applications.

Conventional object detection methods face challenges in addressing the complexity of targets in optical remote sensing images (ORSIs), including multi-scale objects, high aspect ratios, and arbitrary orientations. This study proposes a novel detection framework called Progressive Self-Modulating Detector (PSMDet), which incorporates self-modulation mechanisms at the backbone, feature pyramid network (FPN), and detection head stages to address these issues. The backbone network utilizes a reparameterized large kernel network (RLK-Net) to enhance multi-scale feature extraction. At the same time, the adaptive perception network (APN) achieves accurate feature alignment through a self-attention mechanism. Additionally, a Gaussian-based bounding box representation and smooth relative entropy (${\mathrm{smooth}}_{\mathrm{RE}}$) regression loss are introduced to address traditional bounding box regression challenges, such as discontinuities and inconsistencies. Experimental validation on the HRSC2016 and UCAS-AOD datasets demonstrates the framework’s robust performance, achieving the mean Average Precision (mAP) scores of 90.69% and 89.86%, respectively. Although validated on ORSIs, the proposed framework is adaptable for broader applications, such as autonomous driving in intelligent transportation systems and defect detection in industrial vision, where high-precision object detection is essential. These contributions provide theoretical and technical support for advancing intelligent image sensor-based applications across multiple domains.

Object Detection (OD) in Remote Sensing Imagery (RSI) encounters significant challenges such as multi-scale variation, high aspect ratios, and densely distributed objects. These challenges often result in misalignments among Bounding Box (BBox) representation, Label Assignment (LA) strategies, and regression loss functions. To address these limitations, this study proposes a novel detection framework, the Gaussian Detection (GaussianDet) Framework, that integrates probabilistic modeling with dynamic sample assignment to achieve more precise OD. The core design of this framework is inspired by the theory of geometric symmetry. Specifically, the radial symmetry of a two-dimensional Gaussian distribution is employed to capture the rotational and scale-invariant properties of Remote Sensing (RS) objects. By leveraging the axial symmetry of elliptical geometry, the proposed Gaussian Elliptical Intersection over Union (GEIoU) enables rotation-aligned matching, while Omni-dimensional Adaptive Assignment (ODAA) introduces dynamic symmetric constraints to optimize the spatial distribution of training samples. Specifically, a Flexible Bounding Box (FBBox) representation based on a 2D Gaussian distribution is introduced to more accurately characterize the shape, aspect ratio, and orientation of objects. In addition, the GEIoU is designed as a scale-invariant similarity metric to align regression loss with detection accuracy. To further enhance sample quality and feature learning, the ODAA strategy adaptively selects positive samples based on object scale and geometric constraints. Experimental results on the High-Resolution Ship Collection 2016 (HRSC2016) and University of Chinese Academy of Sciences–Aerial Object Detection (UCAS-AOD) datasets demonstrate that GaussianDet achieves mean Average Precision (mAP) scores of 90.53% and 96.24%, respectively. These results significantly outperform existing Oriented Object Detection (OOD) methods, thereby validating the effectiveness of the proposed approach and providing a solid theoretical foundation for future research in Remote Sensing Object Detection (RSOD).

Function-level source-platform attribution asks which known repository a code fragment originated from; it does not infer vulnerabilities, defects, exploitability, or other security properties. This study benchmarks lexical, frozen semantic, and multi-view pipelines for five open-source UAV flight-control repositories. UAV-SAFENet (a project-specific name; the “SAFE” label does not denote safety or security assurance) is used as a controlled comparator combining character-level TF-IDF, frozen CodeBERT embeddings, and 20 lightweight count-based indicators. Exact-hash cleaning yields 170,761 functions, and a path-keyword-selected (PKS) subsystem subset contains 42,808 functions. Under the random function-level split, 32,334 of 34,153 full-dataset test functions (94.7%) and 8018 of 8562 PKS test functions (93.6%) originate from files also represented in training; the scores may therefore partly reflect file- or module-specific memorization and do not establish unseen-file generalization. Class-weighted LinearSVM remains strongest at 0.9580 Accuracy/0.9276 Macro-F1 on the full dataset and 0.9547/0.9112 on PKS. Across seeds 42, 43, and 44, UAV-SAFENet obtains 0.9349 ± 0.0036/0.8946 ± 0.0045 and 0.9342 ± 0.0030/0.8846 ± 0.0026, respectively; frozen CodeBERT + MLP obtains 0.9277 ± 0.0034/0.8820 ± 0.0059 and 0.9249 ± 0.0033/0.8691 ± 0.0032. Single-seed diagnostics show that concatenation and equal-mean fusion outperform the learned gate on PKS, that removing supervised contrastive learning does not reduce performance, and that the indicator branch receives mean gate weights near 10⁻⁵. The principal contribution is therefore the traceable benchmark, artifact-audited comparison, and diagnosis of lexical shortcut dependence rather than a claim of a new best model or a security detector.

Flow-based intrusion detection in unmanned aerial vehicle (UAV) networks can underuse protocol relationships and model complementarity. The Protocol-Semantic and Confusion-Pair-Aware Heterogeneous Ensemble (PS-CAHE) combines semantic features, training-only confusion-pair selection and two fusion weights. On 113,952 deduplicated UAVIDS-2025 flows, PS-CAHE achieved 95.58% Macro-F1 and 93.88% Matthews correlation coefficient on the fixed holdout. The full pipeline improved Macro-F1 by 0.83 percentage points over Original XGBoost; the pair-aware increment over global out-of-fold (OOF) weighting was only 0.15 points. Across 25 endpoint-pair-disjoint folds, this increment averaged 0.14 points (corrected 95% confidence interval: 0.06 to 0.22), positive in every fold. Three-base and matched two-base probability stacking scored higher in both nested protocols, with row-stratified advantages of 0.08 and 0.05 points. Strict no-port node-disjoint evaluation yielded 93.54% versus 93.78% for Original XGBoost; the corrected difference interval spanned zero, establishing no unseen-node advantage. Training-only temperature scaling reduced negative log-likelihood from 0.1097 to 0.1074 and expected calibration error from 0.88% to 0.49% without changing decisions. The small fusion increment has no demonstrated operational benefit. PS-CAHE offers directly inspectable, low-dimensional fusion on one simulator-generated benchmark; real-flight data, independent external datasets, temporal/mission shifts, open-set attacks and adversarial robustness remain unevaluated.