Leakage Model Research Articles

In Situ Forming Injectable Gelatin-Based Antibacterial Bioadhesives for Preventing Postoperative Leakage and Abdominal Adhesions.

Postoperative anastomotic leakage (AL) and abdominal adhesions are two major complications after intestinal surgery, with an incidence of 2-25% for AL and 93% for adhesion. Until now, there is no method addressing AL and abdominal adhesions simultaneously. In this work, Bi-PEG-succinimidyl succinate (PEG-NHS), amino-gelatin (Agel) is combined with cefoperazone-sulbactam (SCF) to prepare a multifunctional bioadhesive (SCF/SEAgel) for the postoperative leakage and adhesion prevention. SCF/SEAgel possesses a great sealing capability for tissue, with a bursting pressure of 54kPa. The loaded SCF endows the systems with good antibacterial properties. The in vivo antiadhesion experiments show that SCF/SEAgel possesses better anti-adhesion properties than the commercially used sodium hyaluronate gel. In the cecum leakage model, the SCF/SEAgel effectively seals the leakage with a survival rate of 100%, superior to commercial products (Sainaoning). Meanwhile, it significantly reduces tissue adhesion. Finally, the laparoscopic surgery with dogs shows that the SCF/SEAgel can be injected through minimally invasive surgery, demonstrating its ease of use. Combined with its great biocompatibility, SCF/SEAgel is very promising in intestinal surgery.

A Buried Gas Pipeline Leakage Model

A Buried Gas Pipeline Leakage Model

Air leakage mechanism and hole sealing technology in directional long-drilled perimeter rock-borehole composite fissures: for gas enrichment in varying coal-seam thickness change areas

The issue of gas enrichment in the tectonic zone of coal seams, which poses significant threats to coal mine safety and leads to gas disasters, is primarily addressed through borehole extraction. The quality of this extraction is notably influenced by air leakage and sealing technologies used during drilling. To tackle the problem of gas leakage in directional long boreholes within the floor, a composite fissure leakage model for the surrounding rock and borehole is developed. This model inverts the characteristics of coal seam thickness variations and abnormal gas enrichment through heterogeneous geostatistical modeling. It enables the quantitative characterization of the air leakage mechanism in directional long boreholes of the bottom plate and proposes an effective sealing process. Due to damage and disturbance, fissures and irregular plastic zones form around the excavated roadway, which are major contributors to air leakage. Over time, the air content in the coal and rock seams gradually increases while the gas content and concentration decrease continuously. Compared to the polyurethane "two plugs and one injection" sealing process, the multi-stage grouting and sealing process provides a more stable gas extraction concentration and flow rate in the sealed drill holes. This process effectively seals the fissure zones of the roadway, reduces air leakage from the boreholes, and improves gas extraction concentration.

Consequence modeling and analysis of ethanol leakage from storage tank using the ALOHA program

Chemical spills pose a significant threat to the safety of people living nearby, as well as to air quality and occupational safety. Therefore, preventing chemical spills has become a key issue in environmental protection and process safety. This study aims to evaluate the effects of ethanol released from a tank at a chemical plant in Istanbul, Türkiye. The Areal Location of Hazardous Atmospheres model (ALOHA) version 5.4.7 estimates the storage tank's leakage radius and spread risk under three scenarios. ALOHA can assess the area affected by chemical hazards. The model shows that if the spill occurs 10 centimeters from the base of the tank, the toxic concentration of ethanol exceeds the Immediately Dangerous to Life or Health (IDLH) threshold (i.e., 3300 parts per million) at a distance of 31 meters from the place of release. In addition, when the ethanol concentration exceeds 60% of the Lower Explosive Limit (LEL) (i.e., 19800 parts per million), the flammable vapor cloud extends up to 31 meters from the place of release. For a Boiling Liquid Expanding Vapor Explosion (BLEVE), the mass of the fireball is assumed to be 100%, resulting in the worst possible scenario. During BLEVE, it is estimated that the fireball's diameter and time duration, determined by the amount of Ethanol present, would be 141 meters and 10 seconds, respectively. The results show that the thermal radiation effects caused by Ethanol BLEVE are extremely dangerous.

Towards more realistic stochastic modelling of leakage in water distribution systems

ABSTRACT A new stochastic model is developed to predict leak types and sizes in water distribution systems. By linking records of actual failures in water networks with new tools for stochastic modelling, the study provides more accurate estimates of network leakage behaviour. District metered areas (DMAs) have been adopted internationally as best practice for monitoring and controlling leakage, with the infrastructure leakage index (ILI) as the main performance indicator. The leakage exponent (N1), which quantifies the relationship between leakage rate and pressure, is a key parameter in describing DMA leakage behaviour. After analysing thousands of stochastic networks for different combinations of pipe material and ILI, the study found that different pipe materials have distinct leakage exponent distributions. Iron and asbestos cement (AC) pipes display N1 values close to 0.5, polyvinyl chloride (PVC) pipes have the highest N1 values, and polyethylene (PE) lies between PVC and iron/AC. The study further showed that current guidelines linking ILI to N1 for different pipe materials depart substantially from the observed results, indicating that these guidelines may need to be updated. The study provides a tool for more realistic modelling of leakage that offers valuable insights into the behaviour of leakage in different pipe materials.

Research and clearance analysis on of steam twin-screw expander employed in indutrial waste heat recovery

In actual factories, screw expanders frequently operate under partial loads.To address the lack of theoretical research and the risks of errors in engineering design associated with steam pressure differential power generation under these conditions,a simulation of the twin-screw expander's working process was developed. The rotor profile was designed, geometric variables such as rotor mesh clearance were calculated, and models for leakage and heat transfer were established. A suction pressure correction model was proposed for the suction process under partial loads, along with correction parameters to simplify simulations.Experimental results demonstrate that the twin-screw expander operates stably under highly fluctuating conditions, demonstrating excellent variable load performance.As the load decreases, the loss of suction pressure reduces the expander's pressure differential, leading to lower leakage and improved efficiency. When the load is reduced from 100 % to 65 %, the volumetric efficiency increases from 78.87 % to 84.76 %, and the isentropic efficiency rises from 66.81 % to 74.25 %.Compared to experimental data, the flow calculation error of the suction pressure correction model presented in this study is controlled within 3 %.This model addresses performance calculations under partial loads, ensuring that theoretical models better align with real-world applications and supporting accurate selection of twin-screw expanders.This model enables appropriate selection of expanders to reduce pressure losses and enhance economic efficiency.

Simulation and experimental research on the propagation mechanisms of acoustic energy inside the pipeline to detect the leakage

Leakage from pipelines has negative impacts on property and environment. The diameters of internal acoustic detectors in pipelines are small and not easily blocked, making it suitable for pipelines under various working conditions. Studying the characteristics of pipeline leakage sound signal and the propagation mechanisms within pipelines is crucial for timely detection of small leaks. In this study, a 3D simulation model of pipeline leakage was established, and a CFD/CAA hybrid calculation method was used to obtain effective leakage flow field information via LES. The equivalent sound sources in the flow field were extracted based on the Lighthill acoustic analogy theory. Determine the characteristic frequency range of the leakage sound signal was 1–2 kHz. This study founded that an increase in the leakage pressure and aperture size both lead to an enhancement in the leakage sound signal, and the propagation of leakage sound signal in pipelines is affected by background flow. An experimental pipeline leakage system was designed, and the accuracy of the simulation results was verified experimentally.

Research on the Dynamic Leaking and Diffusion Law of Hydrogen-Blended Natural Gas under the Soil–Atmosphere Coupled Model

With the breakthrough in mixing hydrogen into natural gas pipelines for urban use, the widespread application of hydrogen-blended natural gas (HBNG) in energy delivery is imminent. However, this development also introduces significant safety concerns due to notable disparities in the physical and chemical properties between methane and hydrogen, heightening the risks associated with gas leaks. Current models that simulate the diffusion of leaked HBNG from buried pipelines into the atmosphere often employ fixed average leakage rates, which do not accurately represent the dynamic nature of gas leakage and diffusion. This study uses computational fluid dynamics (CFD) 2024R1 software to build a three-dimensional simulation model under a soil–atmosphere coupling model for HBNG leakage and diffusion. The findings reveal that, in the soil–atmosphere coupling model, the gas diffusion range under a fixed leakage rate is smaller than that under a dynamic leakage rate. Under the same influencing factors in calm wind conditions, the gas primarily diffuses in the vertical direction, whereas under the same influencing factors in windy conditions, the gas mainly diffuses in the horizontal direction.

Leakage Model-flexible Deep Learning-based Side-channel Analysis

Profiling side-channel analysis has gained widespread acceptance in both academic and industrial realms due to its robust capacity to unveil protected secrets, even in the presence of countermeasures. To harness this capability, an adversary must access a clone of the target device to acquire profiling measurements, labeling them with leakage models. The challenge of finding an effective leakage model, especially for a protected dataset with a low signal-to-noise ratio or weak correlation between actual leakages and labels, often necessitates an intuitive engineering approach, as otherwise, the attack will not perform well. In this paper, we introduce a deep learning approach with a flexible leakage model, referred to as the multi-bit model. Instead of trying to learn a pre-determined representation of the target intermediate data, we utilize the concept of the stochastic model to decompose the label into bits. Then, the deep learning model is used to classify each bit independently. This versatile multi-bit model can adjust to existing leakage models like the Hamming weight and Most Significant Bit while also possessing the flexibility to adapt to complex leakage scenarios. To further improve the attack efficiency, we extend the multi-bit model to profile all 16 subkey bytes simultaneously, which requires negligible computational effort. The experimental results show that the proposed methods can efficiently break all key bytes across four considered datasets while the conventional leakage models fail. Our work signifies a significant step forward in deep learning-based side-channel attacks, showcasing a high degree of flexibility and efficiency with the proposed leakage model.

Plaintext-based Side-channel Collision Attack

Side-channel Collision Attacks (SCCA) is a classical method that exploits information dependency leaked during cryptographic operations. Unlike collision attacks that seek instances where two different inputs to a cryptographic algorithm yield identical outputs, SCCAs specifically target the internal state, where identical outputs are more likely. Although SCCA does not rely on the pre-assumption of the leakage model, it explicitly operates on precise trace segments reflecting the target operation, which is challenging to perform when the leakage measurements are noisy. Besides, its attack performance may vary dramatically, as it relies on selecting a reference byte (and its corresponding leakages) to “collide” other bytes. A poor selection would lead to many bytes unrecoverable. These two facts make its real-world application problematic. This paper addresses these challenges by introducing a novel plaintext-based SCCA. We leverage the bijective relationship between plaintext and secret data, using plaintext as labels to train profiling models to depict leakages from varying operations. By comparing the leakage representations produced by the profiling model instead of the leakage segmentation itself, all secret key differences can be revealed simultaneously without processing leakage traces. Furthermore, we propose a novel error correction scheme to rectify false predictions further. Experimental results show that our approach significantly surpasses the state-of-the-art SCCA in both attack performance and computational complexity (e.g., training time reduced from approximately three hours to five minutes). These findings underscore our method's effectiveness and practicality in real-world attack scenarios.

Information Theoretic Evaluation of Raccoon's Side-Channel Leakage

Raccoon is a lattice-based scheme submitted to the NIST 2022 call for additional post-quantum signatures. One of its main selling points is that its design is intrinsically easy to mask against side-channel attacks. So far, Raccoon's physical security guarantees were only stated in the abstract probing model. In this paper, we discuss how these probing security results translate into guarantees in more realistic leakage models. We also highlight that this translation differs from what is usually observed (e.g., in symmetric cryptography), due to the algebraic structure of Raccoon's operations. For this purpose, we perform an in-depth information theoretic evaluation of Raccoon's most innovative part, namely the AddRepNoise function which allows generating its arithmetic shares on-the-fly. Our results are twofold. First, we show that the resulting shares do not enforce a statistical security order (i.e., the need for the side-channel adversary to estimate higher-order moments of the leakage distribution), as usually expected when masking. Second, we observe that the first-order leakage on the (large) random coefficients manipulated by Raccoon cannot be efficiently turned into leakage on the (smaller) coefficients of its long-term secret. Concretely, our information theoretic evaluations for relevant leakage functions also suggest that Raccoon's masked implementations can ensure high security with less shares than suggested by a conservative analysis in the probing model.

EP.07A.04 Risk Factor Analysis and Predictive Model for Air Leakage after Thoracoscopic Lung Wedge Resection

EP.07A.04 Risk Factor Analysis and Predictive Model for Air Leakage after Thoracoscopic Lung Wedge Resection

Analysis of hydraulic breakdown and seepage of tail sealing system in shield tunnel machines

The use of shield method in tunnel construction is limited by the engineering conditions of highwater pressure. This is mainly due to the uncertainty of the pressure-bearing capacity of the sealing chambers of the shield tail under different grades and conditions when subjected to different external water pressures. Therefore, it is crucial to determine the pressure-bearing capacity of the sealing chambers. However, there is a lack of studies on the calculating method of the pressure-bearing capacity, which requires more theoretical investigation. To explore the common patterns of multi-grade sealing-related parameters and quantify the pressure-bearing capacity of the sealing chambers, a breakdown and leakage model of the shield tail is proposed, targeting the basic sealing unit of the system. Based on non-Newtonian fluid dynamics and fractal theory of porous media, the model is used to calculate the breakdown pressure and grease seepage rate corresponding to tunneling and shutdown states. In addition, a hydraulic breakdown device of the sealing unit of the static shield tail is built to investigate the relationship between the shield tail clearance and the shield tail brush porous media area, which helps to verify the theoretical model. Finally, the analysis of sealing chamber geometry parameters, grease rheological parameters, and an environmental parameter using the proposed theoretical model shows that the pressure-bearing capacity of the shield tail can be improved by increasing the shield tail clearance and grease yield stress. It also shows that the length of the sealing chamber and the plastic viscosity of the grease do not have a significant effect on the breakdown pressure of the shield tail. The model proposed in this paper will provide ideas for the calculation of the pressure-bearing capacity of multi-grade sealing chambers in the future.

Experimental study on coal oxidation and temperature rise under different air leakage modes due to atmospheric pressure changes

ABSTRACT The spontaneous combustion of residual coal in enclosed goafs may induce coupled thermodynamic disasters with severe consequences. To study the dynamic characteristics of coal oxidation and temperature increase in an enclosed goaf under varying atmospheric pressure conditions, the air leakage model was analyzed through theoretical derivation. The resistance, temperature, and CO concentration during coal oxidation under different air leakage modes were further studied using a self-built experimental system. The results indicated three air leakage modes of coal oxidation in an enclosed goaf: constant, intermittent, and reciprocating. The resistance of the three modes increased from 5 Pa to 7 Pa with time. Compared to constant air leakage, the time of resistance change was advanced by 1428 s and 1897 s under intermittent and reciprocating air leakage, respectively. Additionally, both intermittent and reciprocating air leakage promote coal oxidation and produce higher CO levels compared to constant air leakage. However, intermittent air leakage is more likely to produce higher temperature points, while reciprocating air leakage is more likely to cause large areas of coal oxidation. The order of the promoting effect of the intermittent and reciprocating half cycle on coal oxidation is 60 s, 300 s, 600 s, and 1200 s. These results are significant for understanding coal spontaneous combustion and efficiently managing enclosed goafs.

Closing the Gap: Leakage Contracts for Processors with Transitions and Glitches

Security verification of masked software implementations of cryptographic algorithms must account for microarchitectural side-effects of CPUs. Leakage contracts were proposed to provide a formal separation between hardware and software verification, ensuring interoperability and end-to-end security for independently verified components. However, previously proposed leakage contracts did not consider a class of ephemeral hardware effects called glitches, which leaves a considerable gap between security models and the capabilities of real-world attackers. We address this issue by extending the model for leakage contracts to account for glitches and transitions. We further present the first end-to-end verification tool for transient leakage contracts. Our hardware and software verification rely on the same contract as a single source of truth, facilitating fully machine-checked verification from the hardware gate level to the software. By allowing contracts to be written in the C programming language we make power contracts more accessible and intuitive for system-level engineers. To showcase the efficacy of our approach, we apply it to the RISC-V Ibex core. We show that it is possible to write a power contract for Ibex without any modifications to the hardware design. Using this contract, we prove end-to-end security between masked software and gate-level hardware.

Physical model and Bayesian theory based hydraulic component fault diagnosis method

Hydraulic components are integral parts of hydraulic systems, and their failures directly impact the normal operation of hydraulic systems. Due to the dynamic variation of working pressure in hydraulic components with load, it is challenging to diagnose faults of hydraulic components under dynamic pressure conditions using existing methods. This paper focuses on the research of internal leakage faults in hydraulic directional control valves and proposes a fault diagnosis method based on physical model and Bayesian theory for hydraulic components. Firstly, this method investigates the working fault mechanism of hydraulic directional control valves and constructs an internal leakage model. Then, correlation analysis is introduced to select fault feature signals, and principal component analysis is adopted to construct internal leakage features. Finally, based on Bayesian model, Markov chain Monte Carlo sampling, and internal leakage features, the parameters of the internal leakage model are estimated to achieve fault diagnosis of internal leakage in hydraulic directional control valves. Experimental results demonstrate that compared to several traditional fault diagnosis methods, the proposed method exhibits higher adaptability and accuracy under dynamic pressure conditions. This method, based on the internal leakage model and Bayesian theory, realizes fault diagnosis of hydraulic components under dynamic pressure conditions, avoiding the shortcomings of deep learning methods that rely on a large amount of fault data to train fault models.

Prediction models for seal performance of a space seal considering atomic oxygen effects

A low impact docking system with advanced capabilities has been developing for future spacecrafts, and its main interface docking seal (MIDS) is both a critical component and a research topic of interest. It is a typical design for the MIDS that adopts the elastomer seal-on-seal structure made of silicone rubbers, and the atomic oxygen (AO) in space had been found to take great effects on the sealing performance by previous experimental studies, especially for the seal leak, seal adhesion, and compression set. Exposure to AO may cause obvious changes on the sealing surface of the MIDS, and some characteristic parameters of morphology are proposed to characterize the changes in this study, including of the porosity, roughness and radius of curvature. Both the leakage model and the adhesion model of the MIDS are further developed to predict the sealing performance considering AO effects, which had been confirmed by the application experiments. The experimental data are in good agreement with the calculation data. The effects of AO on the sealing performance are simulated and analyzed by the proposed prediction models. The results are beneficial to the developments of the MIDS and other space seals.

A new perspective on the diffuse gamma-ray emission excess* *Supported by the National Natural Science Foundation of China (12105292, 12175248, 12393853)

The Large High-Altitude Air Shower Observatory (LHAASO) recently published measurements of diffuse Galactic gamma-ray emission (DGE) in the 10−1000 TeV energy range. The measured DGE flux is significantly higher than the expectation from hadronic interactions between cosmic rays (CRs) and the interstellar medium. This excess has been proposed to originate from unknown extended sources produced by electron radiation, such as pulsar wind nebulae or pulsar halos (PWNe/halos). In this paper, we propose a new perspective to explain the DGE excess observed by LHAASO. The masking regions used in the LHAASO DGE measurement may not fully encompass the extended signals of PWNe/halos. By employing a two-zone diffusion model for electrons around pulsars, we find that the DGE excess in most regions of the Galactic plane can be well explained by the signal leakage model under certain parameters. Our results indicate that this signal leakage from known sources and contributions from unresolved sources should be considered as complementary in explaining the DGE excess.

Improved Hybrid Bagging Resampling Framework for Deep Learning-Based Side-Channel Analysis

As cryptographic implementations leak secret information through side-channel emissions, the Hamming weight (HW) leakage model is widely used in deep learning profiling side-channel analysis (SCA) attacks to expose the leaked model. However, imbalanced datasets often arise from the HW leakage model, increasing the attack complexity and limiting the performance of deep learning-based SCA attacks. Effective management of class imbalance is vital for training deep neural network models to achieve optimized and improved performance results. Recent works focus on either improved deep-learning methodologies or data augmentation techniques. In this work, we propose the hybrid bagging resampling framework, a two-pronged strategy for tackling class imbalance in side-channel datasets, consisting of data augmentation and ensemble learning. We show that adopting this framework can boost attack performance results in a practical setup. From our experimental results, the SMOTEENN ensemble achieved the best performance in the ASCAD dataset, and the basic ensemble performed the best in the CHES dataset, with both contributing over 70% practical improvements in performance compared to the original imbalanced dataset, and accelerating practical attack space in comparison to the classical setup of the attack.

Regularizers to the rescue: fighting overfitting in deep learning-based side-channel analysis

Despite considerable achievements of deep learning-based side-channel analysis, overfitting represents a significant obstacle in finding optimized neural network models. This issue is not unique to the side-channel domain. Regularization techniques are popular solutions to overfitting and have long been used in various domains. At the same time, the works in the side-channel domain show sporadic utilization of regularization techniques. What is more, no systematic study investigates these techniques’ effectiveness. In this paper, we aim to investigate the regularization effectiveness on a randomly selected model, by applying 4 powerful and easy-to-use regularization techniques to 8 combinations of datasets, leakage models, and deep learning topologies. The investigated techniques are L1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_1$$\\end{document}, L2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_2$$\\end{document}, dropout, and early stopping. Our results show that while all these techniques can improve performance in many cases, L1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_1$$\\end{document} and L2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_2$$\\end{document} are the most effective. Finally, if training time matters, early stopping is the best technique.