Search Results (227)

Evaluating intrusion detection methods at the level of individual MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) for Industrial Control System techniques requires Operational Technology traffic in which each attack sequence carries its MITRE technique identifier as ground truth. Publicly available Industrial Control System datasets either provide coarse attack-versus-benign labels (SWaT, WADI, CIC-APT-IIoT) or require ex-post technique reconstruction from CALDERA operation logs, and therefore do not support per-technique benchmarking. We describe one primary contribution and two supporting contributions, demonstrated on one Modbus/Raspberry-Pi programmable logic controller/CALDERA/convolutional bidirectional Long Short-Term Memory autoencoder (CNN-BiLSTM-AE) use case. The primary contribution is an in-orchestrator labelling methodology for per-technique-labelled Industrial Control System attack capture. Its single load-bearing property is that the campaign orchestrator owns the label primitive and writes each per-sequence technique identifier into the capture artefact at injection time, eliminating ex-post log-to-packet alignment. The first supporting contribution is a protocol-aware detection pipeline. Its load-bearing architectural choice is a priority-ordered protocol router that dispatches each labelled flow to a per-protocol detector plug-in (protocol-aware features here, with generic-flow features admissible as an alternative plug-in policy on the same router). The second supporting contribution is a suite of four reproducible CALDERA chains (three Information-Technology-to-Operational-Technology kill chains plus one enterprise-side control) that exercise the labelling methodology end-to-end and the detection pipeline along complementary detection paths. All three contributions are platform-independent: any ATT&CK-aligned emulator and any fieldbus protocol can host the labelling methodology, and any detector trained on an admissible feature space can plug into the router. The dataset contains 40,000 benign and 9997 attack Modbus sequences spanning four ATT&CK techniques (T0802 Automated Collection, T0831 Manipulation of Control, T0836 Modify Parameter, T0846 Remote System Discovery). On this dataset, the CNN-BiLSTM-AE reaches a 100% true-positive rate (TPR) at the 98th-percentile benign threshold across all four techniques and a 99.7% overall TPR at the tighter 99.5th-percentile threshold, with per-technique TPR between 96.1% (T0836 Modify Parameter) and 100% (T0802 Automated Collection, T0846 Remote System Discovery). Across the four CALDERA chains, the Modbus autoencoder produces 234 protocol-layer detections and the Security Information and Event Management (SIEM) rule set produces 30 alerts, with per-chain tactic coverage between 0.714 and 0.786 and CALDERA-ability success rates between 0.800 and 0.857. Full article

The use of surfactants as drag-reducing agents (DRAs) has received significant attention in oil–gas transportation due to their ability to enhance liquid drainage efficiency and reduce operational costs. This work experimentally examines the performance of an anionic sodium lauryl sulphate (SLS) surfactant as a DRA in horizontal two-phase flow through experimental studies focusing on three key aspects, (1) changes in flow patterns, (2) drag reduction (DR%), and (3) liquid holdup reduction (HLR%), with the aim of identifying optimal SLS concentrations for achieving stable and efficient multiphase pipeline flow. The results illustrate that adding SLS shifts the slug flow toward more stable stratified wavy and plug flow patterns, as well as a newly emerging bubbly flow pattern. This in turn significantly decreases the pressure gradient (PG), achieving a maximum DR% of 71% and 83% at 100 and 200 ppm, respectively. In addition, as the SLS concentration increases, the liquid draining efficiency increases, achieving maximum holdup reductions of 69% and 85% at 100 and 200 ppm, respectively. Full article

Point cloud semantic segmentation is a pivotal technology for realizing non-contact body measurement and refined management of livestock. However, processing sheep point clouds in smart livestock scenarios presents specific challenges, primarily due to non-rigid posture deformations and severe background interference. To address these issues, this paper proposes a novel symmetric encoder–decoder architecture named Local–Global Aggregation Network (LGA-Net), which achieves high-precision parsing of sheep point clouds by constructing a dual-scale feature aggregation mechanism. First, a Dual Attention Aggregation (DAA) module is designed to jointly encode geometric and color features, significantly enhancing the network’s ability to capture fine-grained local boundaries, such as sheep ears and hooves. Second, a Global Semantic Relation (GSR) module is introduced, utilizing spatial occupancy ratios to establish long-range dependencies, thereby effectively resolving semantic ambiguity caused by posture variations. Furthermore, a plug-and-play Dual-domain Feature Enhancement (DFE) module is proposed. By fusing bilinear interactions between explicit 3D space and implicit feature space, the DFE module constructs a high-pass filtering mechanism to suppress low-frequency background noise. Extensive experiments on a self-constructed point cloud dataset involving two semantic classes (Sheep and Fence) demonstrate that LGA-Net achieves a mIoU of 97.3%, an OA of 99.0%, and a mAcc of 97.8%. These results indicate that the proposed method outperforms existing mainstream algorithms in both segmentation accuracy and robustness. This study not only proposes a feasible solution for precise sheep extraction under the tested experimental conditions, but also provides solid technical support for subsequent automated body measurement and behavior analysis. Full article

Abandoned oil and gas wells pose significant risks to human health and the environment by emitting air pollutants, contaminating groundwater, and leaving behind hazardous debris. In the United States, approximately 3.9 million documented wells vary widely in the accuracy of their recorded locations and plugging status, creating major challenges for detection, mapping, and remediation. Existing well detection methods show some promise but often lose effectiveness under complex conditions, such as vegetation occlusion or construction without metal components. In this study, we propose a drone-based approach equipped with a highly sensitive methane sensor to identify statistical anomalies in methane concentrations around abandoned oil and gas well sites. To address the noisy and variable nature of environmental sensor data, statistical methods were developed that enable reliable anomaly detection under field conditions. Controlled release experiments with known emission points validated the method’s ability to statistically detect methane anomalies that may indicate nearby emission sources. We further tested the approach at a field site containing three abandoned wells with known locations and sparse emission profiles. The results demonstrate that the proposed drone-based sensing method can serve as a rapid survey approach to identify areas with elevated methane signals around well sites, helping to reduce the scope of the ground survey area, and supporting prioritization of follow-up ground investigations. This approach provides a practical means to support targeted monitoring and prioritization of remediation efforts, while supporting the future development of source attribution and localization methods. Full article

With the expansion of global oil and gas resource exploration and development into deep and ultra deep layers, the efficient development of deep carbonate rock fracture cave reservoirs has become the key to ensuring energy security. However, this type of reservoir commonly faces high temperatures, high salinity, and extremely strong heterogeneity, leading to increasingly severe water content spikes caused by dominant water flow channels. Although the existing traditional inorganic plugging agent has good temperature resistance, it has the defects of great brittleness and easy cracking, while the organic polymer gel is prone to degradation failure under high temperature and high salt environments. In order to solve the above problems, a new biochar-enhanced inorganic composite gel system was constructed by using biochar prepared from agricultural and forestry waste pyrolysis as a functional enhancement component. Through rheological testing, high-temperature and high-pressure mechanical experiments, long-term thermal stability evaluation, and dynamic sealing experiments of fractured rock cores, the reinforcement and toughening laws and rheological control mechanisms of biochar on inorganic matrices were systematically studied. Research has found that a biochar content of 0.5 wt% can significantly improve the micro pore structure of the matrix. By utilizing its micro aggregate filling effect and interfacial chemical bonding, the compressive strength of the solidified body can be increased to over 2 MPa, and there is no significant decline in strength after aging at 130 °C for 30 days. More importantly, the unique “adsorption slow-release” mechanism of biochar effectively stabilizes the hydration reaction kinetics at high temperatures, extending the solidification time of the system to 15 h and solving the problem of flash condensation in deep well pumping. This system exhibits excellent shear thinning characteristics and crack sealing ability, and presents a unique “yield reconstruction” toughness sealing feature. This study elucidates the multidimensional strengthening mechanism of biochar in inorganic cementitious materials, providing technical reference for stable oil and water control in deep fractured reservoirs. Full article

Surfactants play a crucial role in enhanced oil recovery by reducing interfacial tension (IFT) and promoting emulsification. However, for low-permeability reservoirs after water flooding, it remains unclear which ability is more crucial for improving recovery. To address this question, this study compared the oil displacement effects of three surfactants with different IFTs and emulsifying properties using a series of core displacement experiments. The results show that the strongly emulsifying surfactant S1# achieved an incremental oil displacement efficiency of 9.94%, which is higher than that of the ultra-low-IFT surfactant S3# (8.63%), while the composite surfactant S2# (strong emulsification + ultra-low-IFT) achieved the highest incremental oil displacement efficiency of 11.79%, representing an improvement of 3.16% compared with S3#. The three surfactants have the same effect on improving oil recovery and oil displacement efficiency in heterogeneous rock cores. Strong emulsification promotes abundant in situ emulsions, increases injection pressure (up to 0.85 MPa compared with 0.41 MPa for ultra-low-IFT flooding), and expands the swept volume, indicating that emulsification-driven sweep improvement dominates over capillary-force reduction under the investigated conditions. Furthermore, a composite displacement strategy combining emulsification-driven plugging and ultra-low-IFT oil washing achieved a 12.37% recovery improvement while maintaining relatively low injection pressure (~0.48 MPa). However, the effectiveness of this mechanism is strongly dependent on reservoir heterogeneity. When the permeability contrast increased from 5.4 to 17.3, incremental recovery decreased by up to 7.03%, demonstrating that strong heterogeneity significantly limits the effectiveness of emulsification-driven sweep expansion. This work advances the understanding of surfactant flooding mechanisms and provides guidance for designing and deploying surfactants in water-flooded low-permeability reservoirs. Full article

The understanding of the object permanence rule (the notion that an object that has disappeared from view continues to exist) is an important issue for animal cognition studies. This ability has been tested in laboratory rodents, but no studies have been conducted using wild rodent species. The aim of this study was to compare the ability to use the object permanence rule in three species of wild rodents and to identify plausible interspecific behavioral differences. The wood mouse (Sylvaemus uralensis), the striped field mouse (Apodemus agrarius), and the bank vole (Clethrionomys glareolus) were used as subjects in the puzzle-box, in which an animal is motivated to escape from a brightly lit environment into the darkness. Test stages 2, 3 and 4 required an understanding that the underpass which leads from light into the dark part of a box still exists, although it is not seen any longer. The test difficulty gradually increased: at first, the passage to the dark was unobstructed; then, it was covered with sawdust; and, finally, it was blocked using a cardboard plug. Interspecific differences were found. Wood mice and striped field mice demonstrated consistently high success rates at all stages of the test, including the most difficult one (when the passage was blocked by a plug), indicating a well-developed ability to operate the object permanence rule. In contrast, the proportion of bank voles who solved the test decreased as the test complexity increased. Bank voles were also characterized by prolonged periods of immobility and lower levels of locomotion. The data suggest that interspecies variability in object permanence task solutions is associated not only with different levels of cognitive ability per se - but also with species-specific behavioral traits, which could be linked to the ecological specialization of these species. Full article

Knowledge of the migration characteristics of polymer systems in pore throats is essential for the effective application of polymers as a profile-control oil-displacement agent for enhanced oil recovery. In this study, the effect of concentration on the viscosity and hydrodynamic radius of polymer systems was investigated using a rheometer and a dynamic light scattering instrument. Furthermore, pore-throat models, homogeneous cores, and multi-measuring-point sand-packed models were constructed to investigate pore-scale migration patterns and the effect of the throat–polymer ratio (defined as the ratio of throat size to polymer hydrodynamic radius) on the migration properties of polymers in porous media. The results showed that the transport of polymer systems in porous media is primarily related to the throat–polymer ratio. When this ratio is sufficiently small (i.e., no more than 18.94), the migration pattern of the polymer systems in the pore-throat model does not exhibit the characteristics of polymer solution flow, but rather, of discontinuous-dispersion retention, plugging-breakthrough migration, and stable-plugging retention. Upon increasing the injection rate, the polymer systems also exhibit the migration characteristics of discontinuous dispersion at a larger throat–polymer ratio. Moreover, polymer system migration resistance and improved sweep efficiency in porous media are influenced by not only the viscosity of polymer systems, but also the throat–polymer ratio. The smaller the throat–polymer ratio, the stronger the retention and plugging ability of the polymer systems. The outcomes of this study are significant for the design of polymer flooding operations in oilfields. Full article

In remote sensing and low-altitude unmanned aerial vehicle(UAV) detection scenarios, small target detection is extremely challenging due to the low pixel proportion, sparse features, and complex backgrounds of targets. The reliability of low-altitude security, in particular, is directly dependent on the accuracy of small target detection. However, current methods still face three major limitations: insufficient detection accuracy for targets smaller than 20 pixels; artifacts and false textures introduced by Generative Adversarial Network-based enhancement, which lead to increased false detection rates; and the reliance of existing approaches on specialized architectures, resulting in weak generalization capability and difficulty in adapting to multi-scenario deployment requirements. To address these issues, this paper proposes a plug-and-play dual-mechanism collaborative enhancement framework named HD-BSNet. Firstly, a High-Frequency Differential Perception mechanism is designed to enhance the detailed feature representation of small targets. Secondly, a Background Semantic Modeling mechanism is introduced to learn key features that distinguish targets from the background. Additionally, a Parallel Multi-Scale Focus Module is constructed to further reinforce target features. Extensive experiments on three small target datasets demonstrate that the proposed method effectively improves the accuracy and generalization ability of small target detection. Full article

Hydraulic fracturing is crucial for the effective development of unconventional oil and gas reservoirs. This paper systematically reviews the damage issues caused by conventional fracturing fluids in tight unconventional reservoirs, highlighting problems such as significant formation damage and high risks of scale deposition and plugging. To address these shortcomings, a phase-change self-propping fracturing fluid is proposed and compared with a guar gum fracturing fluid and slickwater fracturing fluid. The self-propping fluid offers advantages of low damage and low fluid loss. It can undergo a phase transition to form solid particles that effectively prop the fractures, thereby significantly reducing damage such as reservoir pore structure blockage. This study demonstrates that the phase-change self-propping fracturing fluid is well-suited for tight, low-permeability reservoirs due to its ability to minimize formation damage. Furthermore, the reservoir damage evaluation methodology established in this work provides an effective means for analyzing damage mechanisms and assessing effectiveness during the fracturing process. Full article

Facial Beauty Prediction (FBP) aims to develop a machine that can automatically predict facial attractiveness. Recent advances demonstrate that deep learning models have achieved promising results in FBP tasks. However, conventional deep learning models lack the efficient ability to generalize to unseen attribute domain data, as attributes cause distribution discrepancy (asymmetry) among face data. To address this issue, we propose a simple yet effective method called MixAttr, a lightweight plug-and-play module that mixes two randomly selected feature statistics with different attributes to form a newly attributed feature. In this way, the feature space is implicitly enriched by increasing the diversity of features and mitigating the model shift caused by a single attribute, which benefits the decoupling of attributes and facial beauty prediction. Extensive experiments conducted to evaluate the properties and effectiveness of our method show that MixAttr can be flexibly inserted into existing network architectures to achieve state-of-the-art performance on different FBP benchmarks (e.g., a Pearson correlation of 0.9307 on SCUT-FBP5500). This feature mixing implicitly enriches the representation space, which is key to mitigating attribute-induced asymmetry and improving generalization. Additionally, we have also extended our method to the task of facial age estimation, demonstrating through superior experimental results that our method can also be applied to other attribute prediction tasks. We propose that distribution discrepancy in FBP can be viewed as a form of asymmetry in the feature space across different demographic groups. MixAttr mitigates this asymmetry by implicitly enriching the feature space and encouraging a more symmetric and attribute-invariant feature representation. By disentangling task-irrelevant attributes from task-oriented features, our method can improve both the accuracy and generalizability of deep models on tasks involving facial attribute prediction. Full article

To address the problem of serious gas channeling during CO₂ flooding in low-permeability reservoirs, which leads to poor oil recovery, this study developed a CO₂-resistant gel using a novel CO₂-responsive polymer (ADA) for gas channel control. The ADA polymer was synthesized via free-radical copolymerization of acrylamide (AM), dimethylaminopropyl methacrylamide (DMAPMA), and 2-acrylamido-2-methylpropanesulfonic acid (AMPS), which introduced protonatable tertiary-amine groups and sulfonate moieties into the polymer backbone. Comprehensive characterizations confirmed the designed structure and adequate thermal stability of the ADA polymer. Rheological tests demonstrated that the ADA polymer solution exhibits significant CO₂-triggered viscosity enhancement and excellent shear resistance. When crosslinked with phenolic resin, the resulting ADA gel showed outstanding CO₂ tolerance under simulated reservoir conditions (110 °C, 10 MPa). After 600 s of CO₂ exposure, the ADA gel retained over 99% of its initial viscosity, whereas a conventional HPAM-based industrial gel degraded to 61% of its original viscosity. The CO₂-resistance mechanism involves protonation of tertiary amines to form quaternary ammonium salts, which electrostatically interact with sulfonate groups, creating a reinforced dual-crosslinked network that effectively protects the gel from H⁺ ion attack. Core flooding experiments confirmed its ability to enhance oil recovery by plugging high-permeability channels and diverting flow, achieving a final recovery of up to 48.5% in heterogeneous cores. This work provides a novel gel system for improving sweep efficiency and storage security during CO₂ flooding in low-permeability reservoirs. Full article

Deep learning-based industrial product surface defect detection methods are replacing manual inspection, while the issue of small object detection remains a key challenge in the current field of surface defect detection. The feature pyramid structures demonstrate great potential in improving the performance of small object detection and are one of the important current research directions. Nevertheless, traditional feature pyramid networks still suffer from problems such as imprecise focus on key features, insufficient feature discrimination capabilities, and weak correlations between features. To address these issues, this paper proposes a plug-and-play guided focus feature pyramid network, named GF-FPN. Built on the foundation of FPN, this network is designed with a bottom-up guided aggregation network (GFN): through a lightweight pyramidal attention module (LPAM), star operation, and residual connections, it establishes correlations between objects and local contextual information, as well as between shallow-level details and deep-level semantic features. This enables the feature pyramid network to focus on key features, enhance the ability to distinguish between objects and backgrounds, and thereby improve the model’s small object detection performance. Experimental results on the self-built TinyIndus dataset and NEU-DET demonstrate that the detection model based on GF-FPN exhibits more competitive advantages in object detection compared to existing models. Full article

Severe lost circulation frequently occurs in deep and ultra-deep wells under high-temperature/high-pressure (HPHT) conditions and in fracture-cavity composite loss channels. Conventional lost-circulation materials (LCMs) often fail because of premature loss of mobility, insufficient residence in loss paths, and irreversible failure after solidification. Cement-based sealing systems, owing to their ability to plug large leakage channels and their cost-effectiveness, have become the mainstream solution. To improve their performance under extreme downhole conditions, recent studies have focused on base-cement design, reinforcement phases, and property regulation strategies-including the use of granular/fibrous/nanoscale additives for bridging reinforcement, rheology and thickening control to enhance injectability and residence, and chemical/functional modifiers to improve compactness and durability of the hardened matrix. Significant progress has been achieved in terms of HPHT resistance, densification design, regulation of rheological properties and thickening behavior, and self-healing/responsive sealing functions. However, most existing studies still focus on improving individual properties and lack a cross-scale, holistic design and unified mechanistic perspective for fracture-cavity coupled flow and long-term sealing stability. Distinct from previous reviews that mainly catalogue material types or discuss single-performance optimization, this review is framed by fracture-cavity composite loss channels and long-term sealing requirements under HPHT conditions, systematically synthesizes the material design strategies, reinforcement mechanisms and applicability boundaries of cement-based plugging systems, builds cross-scale linkages among these aspects, and proposes future research directions toward sustainable plugging design. Full article

Due to the recent improvement in the additive manufacturing field, aerospike engines have been reconsidered as a possible alternative to the traditional bell-shaped nozzles. The former offer higher thrust and specific impulse during the launcher ascension phase because they are theoretically able to adapt the gas expansion ratio, reaching the optimal condition for a wide range of ambient pressure values, while bell-shaped nozzles can achieve the optimal expansion condition only at the design altitude. This capability has been proved for full-length plug nozzles, which, however, have some drawbacks, like a low thrust-to-weight ratio and challenging design of the cooling system at the spike tip. Therefore, research is moving towards truncated spike geometries, which allow the previously mentioned issues to be overcome. The aim of this work is to verify the expansion adaptation ability of a specific truncated aerospike geometry at different ambient pressures and to develop a simplified theory to estimate the upper bound of the base thrust coefficient. The analysis has been addressed by running numerical fluid dynamics simulations performed with an OpenFOAM solver. Full article