Leakage Measurements Research Articles

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.

Reducing Leakage Effects and Measurement Time in Low Frequency Noise Measurements

Reducing Leakage Effects and Measurement Time in Low Frequency Noise Measurements

Deletion within ameloblastin multitargeting domain reduces its interaction with artificial cell membrane

In human, mutations in the gene encoding the enamel matrix protein ameloblastin (Ambn) have been identified in cases of amelogenesis imperfecta. In mouse models, perturbations in the Ambn gene have caused loss of enamel and dramatic disruptions in enamel-making ameloblast cell function. Critical roles for Ambn in ameloblast cell signaling and polarization as well as adhesion to the nascent enamel matrix have been supported. Recentely, we have identified a multitargeting domain (MTD) in Ambn that interacts with cell membrane, with the majority enamel matrix protein amelogenin, and with itself. This domain includes an amphipathic helix (AH) motif that directly interacts with cell membrane. In this study, we analyzed the sequence of the MTD for evolutionary conservation and found high conservation among mammals within the MTD and particularly within the AH motif. We computationally predicted that the AH motif lost its hydrophobic moment upon deleting hydrophobic but not hydrophilic residues from the motif. Furthermore, we rationally designed peptides that encompassed the Ambn MTD and contained deletions of largely hydrophobic or hydrophilic stretches of residues. To assess their AH-forming and membrane-binding abilities, we combined those peptides with synthetic phospholipid membrane vesicles and performed circular dichroism, membrane leakage, and vesicle clearance measurements. Circular dichroism showed retention of α-helix formation in all peptides except the one with the largest deletion of eleven amino acids including seven that were hydrophobic. This same peptide variant failed to cause leakage or clearance of synthetic membranes, while smaller deletions yielded intermediate membrane interaction as measured by leakage and clearance assays. Our data revealed that deletion of key hydrophobic residues from the AH leads to the most dramatic loss of Ambn–membrane interaction. Pinpointing roles of residues within the MTD has important implications for the multifunctionality of Ambn.

A Framework for Enhancing Social Media Misinformation Detection with Topical-Tactics

Recent years have seen advancements in machine learning methods for the detection of misinformation on social media. Yet, these methods still often ignore or improperly incorporate key information on the topical-tactics used by misinformation agents. To what extent does this affect the (non)detection of misinformation? We investigate how supervised machine learning approaches can be enhanced to better detect misinformation on social media. Our aim in this regard is to enhance the abilities of academics and practitioners to understand, anticipate, and preempt the sources and impacts of misinformation on the web. To do so, this article leverages a large sample of verified Russian state-based misinformation tweets and non-misinformation tweets from Twitter. It first assesses standard supervised approaches for detecting Twitter-based misinformation both quantitatively (with respect to classification) and qualitatively (with respect to topical-tactics of Russian misinformation). It then presents a novel framework for integrating topical-tactics of misinformation into standard “bag of words”-oriented classification approaches in a manner that avoids data leakage and related measurement challenges. We find that doing so substantially improves the out-of-sample detection of Russian state-based misinformation tweets.

Failure law of hydraulic pipe joints sealing performance under vibration loads

Aviation hydraulic pipe joints are inevitably affected by vibration loads during service. Fatigue relaxation occurs in the pipe joint structure by vibration, and fretting fatigue wear occurs in its sealing interface, leading to a decline in sealing performance. In order to investigate the sealing performance of hydraulic pipe joints affected by vibration load, this paper first analyzes the modal distribution of the sealing system pipeline under different hydraulic pressures and hydraulic fluid flow rates through vibration frequency sweeping experiments. Secondly, the vibration load is applied to the pipe joints for different numbers of cycles, which reveals the microscopic wear behavior of the sealing interfaces under different tightening torques. Finally, by carrying out the leakage measurement experiment under vibration load, the evolution law of the sealing performance of pipe joints under different hydraulic fluid pressures and tightening torques was obtained.

Aggression to Biomembranes by Hydrophobic Tail Chains under the Stimulus of Reductant.

Stimulus-responsive materials hold significant promise for antitumor applications due to their variable structures and physical properties. In this paper, a series of peptides with a responsive viologen derivative, Pep-CnV (n = 1, 2, 3) were designed and synthesized. The process and mechanism of the interaction were studied and discussed. An ultraviolet-visible (UV) spectrophotometer and fluorescence spectrophotometer were used to study their redox responsiveness. Additionally, their secondary structures were measured by Circular Dichroism (CD) in the presence or absence of the reductant, Na2SO3. DPPC and DPPG liposomes were prepared to mimic normal and tumor cell membranes. The interaction between Pep-CnV and biomembranes was investigated by the measurements of surface tension and cargo leakage. Results proved Pep-CnV was more likely to interact with the DPPG liposome and destroy its biomembrane under the stimulus of the reductant. And the destruction increased with the length of the hydrophobic tail chain. Pep-CnV showed its potential as an intelligent antitumor agent.

Feasibility experimental study on plugging leakage in goaf based on two-phase foam

Air leakage in goaf often leads to coal spontaneous combustion (CSC), which not only directly affects the safety production of mines but also causes significant environmental damage. Therefore, effectively sealing the airflow in goaf is crucial for preventing CSC. Feasibility experiments on using two-phase foam to seal air leakage in goaf were conducted, leveraging the advantages of large flow rate, wide diffusion range, and good accumulation characteristics of two-phase foam. The research results indicate that continuous injection of foam into loose media with maintained ventilation can completely seal the air leakage, with the foam capable of withstanding wind pressures of nearly 600 Pa. When the foam is used for one-time sealing with a length of 2 m, it remains effective for 60 min, and the sealing effectiveness improves with longer distances sealed against air leakage. Numerical simulation analysis and field measurements of airflow leakage in mine working faces reveal that effectively sealing the airflow passage in the goaf behind the corner of the return airway is crucial for preventing CSC. Two methods are proposed for sealing external airflow during coal mining: foam injection using a point drilling method near the heading and an incremental buried pipe injection method. Finally, the feasibility of two-phase foam sealing technology for goaf airflow leakage is analyzed from multiple perspectives including sealing effectiveness, practicality, economy, foaming process, and engineering implementation. The research findings provide new insights into goaf sealing technology, aiding in addressing safety and environmental issues caused by spontaneous combustion in goaf areas.

Measurement of inward leakage of full-face masks in EN and ISO standards: comparison of gas and aerosol test agents.

In Europe, respiratory protective devices must be certified before they can be marketed. Among the parameters of interest, inward leakage (IL) characterizes the tightness between the face seal and the face, to verify that the device is well-designed. European standard EN 13274-1 (2001) and International Organization for Standardization (ISO) standard ISO 16900-1 (2019) specify that IL should be measured using sodium chloride (NaCl) aerosol or sulfur hexafluoride (SF6) gas. For reusable masks made of nonporous materials, both test agents are considered equally acceptable. However, the few studies that have compared IL values measured with various aerosols and gases have come to divergent conclusions. This work then aimed to measure IL with the test agents recommended by the standards to determine whether they are really equivalent. Since krypton (Kr) is an interesting candidate for replacing SF6 in standard tests, IL was assessed with SF6 and Kr simultaneously, and with NaCl aerosol using various calculation methods. Tests were carried out on 5 models of full-face masks donned on a headform connected to a breathing machine simulating 3 sinusoidal breathing rates of various intensities. The respirator fit on the headform was evaluated using a controlled negative pressure method to determine a manikin fit factor. Four scenarios were then tested to represent very poor, bad, good, and excellent fit. Gas concentration was measured using a mass spectrometer, and IL was calculated for SF6 and Kr. A combination of 3 devices allowed the determination of the number-based concentration of particles with diameters between 20 nm and 2 µm, and IL was calculated for each of the 33 channels, as well as using a cumulative number concentration. In addition, to comply with standards, a conversion was carried out to calculate IL using a cumulative mass concentration. The results of this work evidenced that the IL values measured with NaCl were systematically lower than those determined with gases. IL was also shown to vary with particle size, with a maximum value exceeding that calculated with cumulative concentrations (in number or mass). As part of the revision of the standards, protocols for measuring inward leakage should be redefined. On the one hand, acceptability thresholds should be re-evaluated according to the nature of the test agent (gas or aerosol), as it is clear that the 2 options do not give the same results for a given configuration. On the other hand, the aerosol leakage measurement protocol needs to be reworked to enable the measurement of a well-defined, robust, and reproducible inward leakage value.

Measurement of CO2 leakage from pipelines under CCS conditions through acoustic emission detection and data driven modeling

CO2 leakage from carbon capture and storage (CCS) networks may lead to ecological hazards, bodily injury and economic losses. In addition, captured CO2 often contains impurities which affect the leakage behavior of CO2. This paper presents a method for continuous and quantitative measurements of CO2 leakage flowrate and the volume fraction of impurities by combining data-driven models with acoustic emission (AE) and temperature sensors. Three data-driven models based on artificial neural network (ANN), random forest (RF), and least squares support vector machine (LS-SVM) algorithms are established. The outputs from the three data-driven models are then integrated to give improved results. Experimental work was conducted on a purpose-built CO2 leakage test rig under a range of conditions. N2 was injected to the CO2 gas stream as an impurity medium. Results show that the integrated model yields a relative error within ±4.0 % for leakage flowrate and ±3.4 % for volume fraction of N2.

The Simulation and Analysis of Leakage, Diffusion Behavior, and Risk Mitigation Measures in a Hydrogen‐Refueling Station

As an component of hydrogen energy utilization, hydrogen‐refueling stations require considerable attention regarding their safety. In this study, the influence of wind conditions is analyzed, specifically no wind and 10 m s−1 wind speed, on hydrogen diffusion characteristics following a 70 MPa hydrogen‐filling machine leakage. In the results, it is suggested that, during the initial stage of leakage, hydrogen exists in the form of an under‐expanded jet, later transitioning to diffusion dominated by buoyancy and wind conditions after moving upward for a certain distance. When there is no wind, hydrogen is significantly affected by buoyancy and obstacles, which leads to the formation of flammable clouds. In windy conditions, the volume fraction of hydrogen in the station is smaller than that in the no‐wind condition at the same leakage time, but the flammable clouds still predominantly distribute in the direction of the hydrogen jet. In this study, risk mitigation measures based on the distribution of flammable clouds are proposed. In the results, it is shown that after implementing these measures, the volume of flammable clouds is reduced from0.135 to 0.014 m3, manifesting the effectiveness of risk mitigation measures and minimizing the risk of hydrogen explosion.

Experimental study on flow characteristics and burning behavior for fire of flammable gases leakage underwater

The occurrence of subsea pipeline leakage incidents has gradually increased with the rapid development of the offshore oil and gas industry. Research on the fire dynamics of flammable gases leakage underwater is very limited, particularly in understanding the coupling mechanism of flow and combustion on the water surface. This paper presents a systematical investigation on flow characteristics and burning behavior for fire of flammable gases leakage underwater. A series of laboratory-scale fire behavior experiments were conducted through a self-designed experimental platform, consisting of the leakage control module, water tank and measurement apparatus. It was revealed that the leakage flow rate and water depth would influence the flow characteristics and burning behavior significantly. Two different burning conditions were found for the flame burning above the water surface (FBWS), i.e., the stable and self-extinguishing (unstable) burning states. The dimensionless analysis is conducted for the burning conditions, where the critical criterion is developed to distinguish the stable and unstable burning states. The base diameter of stable FBWS is almost 1.6 times of the diameter of bubble plume on the water surface. The flame pulsation frequency and dimensionless flame height (the ratio of height to base diameter of flame) of the stable FBWS are different from thoes of liquid pool fires and gas diffusion fires. Consequently, a correlation between the Strouhal number and the Froude number is correlated. Finally, the correlation between the dimensionless flame height and the dimensionless heat release rate is proposed for the stable FBWS.

Development and experimental evaluation of new building air leakage measurement methods: measurement of interior air leaks and comparison to conventional methods

Building air leaks (both through exterior and interior surfaces) can have a significant impact on energy consumption, indoor air quality, fire safety, and moisture accumulation affecting structural durability. Blower door testing has been used to measure leaks in buildings, but commonly used testing methods do not directly measure interior leaks. In this paper, new testing methods (guarded interior test and zonal multipoint pressure testing method) are presented that directly measure these interior leaks, utilizing common blower door equipment for both single and multi-point testing. These new methods are compared to conventional methods in terms of the information provided, limitations and time/effort needed. In addition, building leak measurement results are analyzed to reveal (a) coupling between power law model values (exponent and coefficient) for an ensemble of buildings, (b) the error in using single point testing when estimating low pressure leakage, and (c) how building power law models vary from low to high pressure ranges.

A Deep-Learning-Based Low-Cost Micro-Leakage Measurement System for Industrial Applications

Serious leakage will cause environmental pollution and safety problems, so strict leakage measurement is necessary for sealed products before leaving the factory. In this article, a semantic-segmentation-based micro-leakage measurement framework (MLMF) is proposed, and the core modules in MLMF are designed and analyzed. First, the DeepLabv3+ algorithm is improved from three aspects, including the design of the fusion layer, the introduction of the attention mechanism of the segmentation model, and the reconstruction of the loss function, which improve the precision of image segmentation tasks. Then, the volume of the intermediate cavity (ICV), which affects the accuracy of leakage measurement, is studied, and the reasonable selection interval of the ICV has been obtained. Finally, through the experimental analysis of the influencing factors of the bubble characteristics in the two-phase flow, the key parameters affecting the bubble shape are found, and the control of the initial shape, generation period, and oscillation process of the bubble is realized. The experimental analysis is carried out through the established experimental platform to verify the effectiveness of the proposed MLMF. The bubble method is innovatively combined with deep learning, and a series of problems, such as bubble shape control and liquid media selection, are solved. It provides a novel low-cost dry measurement mechanism for the field of micro-leakage measurement of sealed vessels.

Qualitative and quantitative diagnosis of internal hydrogen leakage in fuel cell based on air flowrate control and open-circuit voltage measurements

Internal hydrogen leakage faults in fuel cells can cause severe performance loss and safety risks. The aim of this study is to achieve qualitative and quantitative diagnoses of internal leakage in fuel cell based on air flowrate control and transient voltage measurements. The hydrogen crossover characteristics of a fault-free and defective fuel cell are both investigated, and the effectiveness of the diagnosis method is evaluated by comparing and analyzing the results before and after the leakage fault introduction. The results suggest that hydrogen leakage failure inside fuel cell is dominated by convection processes. In airflow interruption tests, where the fuel cell is operated under open-circuit conditions with given anode overpressures, the fall time of the cell voltage is used as a qualitative measure of internal hydrogen leakage, and fault occurrence can be recognized by a considerable decrease in this indicator. Through cyclic staircase flowrate sweep tests, we obtain good estimates of internal leakage rates in the defective fuel cell. These diagnostic methods can be performed directly under hydrogen/air conditions, and there is no need for additional power source equipment dedicated to generating excitation signals for fuel cell, giving them great potential in on-board applications.

Near-Infrared Off-Axis integrated cavity output spectroscopic sensor system for ammonia leakage measurement in cold store using embedded Multi-Core processor

A near-infrared off-axis integrated cavity output spectroscopic (OA-ICOS) sensor system for ammonia (NH3) leakage detection was developed. A distributed feedback (DFB) laser was used to target the absorption line of NH3 at 6612.167 cm−1. Experiments were conducted to evaluate the performance of the sensor system. When the averaging time is 0.8 s, the detection limit (1σ) of NH3 is ∼ 1.21 parts-per-million by volume (ppmv) using wavelength modulation spectroscopy (WMS). The 10–90 % response time of the NH3 sensor system is ∼ 7.2 s. Based on the developed local monitoring and control platform (LMCP), cloud server, remote monitoring and control end (RMCE), the unattended NH3 leakage detection experiments were carried out in a cold store located in Jilin City. The experimental results confirmed the rapid detection capability of the reported sensor system for NH3 leakage as well as the capability of remote monitoring and control.

A high-precision TMR sensor array system for detecting surface and internal defects in thin sheet of steel

The online detection of small defects within thin steel holds significant importance in the field of steel manufacturing. This paper presents a high-precision flux leakage measurement system based on tunneling magnetoresistance sensors for detecting small defects in thin steel. We optimized the probe size and magnetic circuit design through numerical simulations and used the developed instrument to detect a standard mimicking through-hole defect plate, achieving a high signal-to-noise ratio and successfully detecting artificial defects small to a diameter of 50 μm. Additionally, we detected an actual internal defect in thin steel provided by the steel plant, meeting the required industrial accuracy. The data acquisition process in this study has been optimized to achieve real-time display and defect localization. The measurement scheme proposed in this paper has great potential for online monitoring of the thin steel production process.

Measurement of Ammonia Leakage by TDLAS in Mid-Infrared Combined with an EMD-SG Filter Method

Ammonia (NH3) has been used as the main refrigerant in various types of cold storages, exceeding 90% of cooling applications. However, NH3 leakage occurs frequently due to damage of the refrigeration compressor, resulting in environmental pollution and intoxication accidents. Therefore, reliable and highly sensitive NH3 detection is a critical requirement in cold storage. In this paper, tunable diode laser absorption spectroscopy (TDLAS) is used to measure NH3 leakage, where a new denoising method combining empirical mode decomposition with the Savitzky-Golay smoothing algorithm (EMD-SG) is proposed to improve the signal-to-noise ratio (SNR) of absorbance signals. The results show that a 5.5 times improvement of SNR and 86% reduction of the linear fitting error in calibration is obtained; furthermore, this system achieves a detection limit of 0.2 ppm at an average time of 69 s by Allan variance analysis. Hence, it can be concluded that the EMD-SG algorithm can effectively improve the performance of TDLAS systems to meet the requirements for NH3 leakage detection in cold storages.

Report of the Workshop for 2023 and Future Plans of X-Ray Leakage Measurement Expert Qualification for X-Ray Examination Rooms

Report of the Workshop for 2023 and Future Plans of X-Ray Leakage Measurement Expert Qualification for X-Ray Examination Rooms

SIMULATION OF GROUNDWATER POLLUTION IN A FACTORY BASED ON VISUAL MODFLOW

The groundwater pollution situation of an industrial park in Mengzhou City, Henan province was simulated to provide reference for groundwater pollution migration and prevention. Through various experiments and hydrogeological surveys to obtain basic parameters for simulation work, this paper uses Visual Modflow software to establish groundwater numerical model in the study area, and on this basis, MT3DMS module is used to simulate solute transport of leaked pollutants COD and NH3-N. In this paper, solute transport is simulated under two working conditions: under abnormal working conditions, the influence range and maximum transport distance of pollutants in sewage transport pipeline leakage without seepage prevention measures; Sewage transport pipeline leakage but seepage prevention measures in the case of pollutant diffusion.

Genome-wide identification of the melon (Cucumis melo L.) response regulator gene family and functional analysis of CmRR6 and CmPRR3 in response to cold stress

The response regulator (RR) gene family play crucial roles in cytokinin signal transduction, plant development, and resistance to abiotic stress. However, there are no reports on the identification and functional characterization of RR genes in melon. In this study, a total of 18 CmRRs were identified and classified into type A, type B, and clock PRRs, based on phylogenetic analysis. Most of the CmRRs displayed tissue-specific expression patterns, and some were induced by cold stress according to two RNA-seq datasets. The expression patterns of CmRR2/6/11/15 and CmPRR2/3 under cold treatment were confirmed by qRT-PCR. Subcellular localization assays indicated that CmRR6 and CmPRR3 were primarily localized in the nucleus and chloroplast. Furthermore, when either CmRR6 or CmPRR3 were silenced using tobacco ringspot virus (TRSV), the cold tolerance of the virus-induced gene silencing (VIGS) melon plants were significantly enhanced, as evidenced by measurements of chlorophyll fluorescence, ion leakage, reactive oxygen, proline, and malondialdehyde levels. Additionally, the expression levels of CmCBF1, CmCBF2, and CmCBF3 were significantly increased in CmRR6-silenced and CmPRR3-silenced plants under cold treatment. Our findings suggest that CmRRs contribute to cold stress responses and provide new insights for further pursuing the molecular mechanisms underlying CmRRs-mediated cold tolerance in melon.