The Paschen law, especially in its linear approximation, is said to be useful for predicting the partial discharge inception voltage (PDIV) in insulation systems when considering different defect sizes and pressure values. Hence, it is often used for designing electrical insulation systems in aerospace applications. This paper presents a comparison between PDIV estimates provided by the Paschen law and a new model applicable to internal and surface discharges in electrical insulation systems under varying pressure and defect size or creepage distance. It is shown that the Paschen law estimates can often be very far from the measured PDIV values for both surface and internal defects and at pressures above and below standard atmospheric pressure (SAP), which can negatively affect the design and reliability of insulation systems. On the contrary, the proposed model provides accurate and consistent PDIV estimates, which are very close to those measured, for both internal and surface discharges. The lower limit of the model application/validation is 50 mbar from SAP.

High-voltage cable systems are critical components of power transmission networks, where partial discharge (PD) can cause insulation degradation and severe operational failures. Accurate detection and reliable identification of PD are therefore essential, yet conventional methods are often vulnerable to electromagnetic interference and exhibit limited recognition performance. This paper proposes a PD detection and identification framework for high-voltage cable systems based on heterogeneous sensor fusion and enhanced deep learning. A high-speed optical electric-field sensor is employed to localize potential insulation defects, while an acoustic pressure wave sensor is used to confirm PD occurrence and intensity. An improved adaptive-threshold discrete wavelet transform is applied for signal denoising, and an optimized Gramian Angular Field transformation converts one-dimensional signals into two-dimensional feature representations. A residual convolutional neural network incorporating an efficient channel attention mechanism is then developed for PD pattern identification. Experiments involving corona, void, and surface discharges demonstrate that the proposed system achieves a 100% PD detection rate and a recognition accuracy of 96.0% under laboratory conditions. Field tests on 220 kV tunnel-laid cables further verify that both detection and identification accuracies reach 100%, with superior robustness and environmental adaptability compared with conventional approaches.

ABSTRACT The problem of gas–solid interface charge accumulation is an important reason for distorting the surface electric field and then inducing surface discharge. The temperature gradient distribution formed on the spacer during the operation of a gas‐insulated transmission line (GIL) will aggravate the surface charge accumulation, further threatening the surface insulation performance of the spacer. This study proposes a thermal‐dependent conductivity coating method to regulate the surface charge and electric field distribution on DC GIL spacers under an electrothermal coupling field. Under the temperature gradient, the symmetrically ring‐shaped homocharges around the high‐voltage electrode and the randomly cloud‐shaped bipolar charges are distributed on the uncoated spacer surface. After coating TiO 2 /epoxy mixtures on the spacer surface, the surface charge distribution on the spacer is dramatically homogenised under the thermal gradient, no matter which content of TiO 2 is used. A simulation model for the surface charge and electric field on the spacer with/without TiO 2 /epoxy coating under the electrothermal coupling field is established. The results are well consistent with those of the experiment. When the content of TiO 2 in the coating material increases from 10 wt% to 30 wt%, the maximum electric field strength on the spacer surface gradually decreases and tends to be stable, from 2.7 kV/mm on the uncoated spacer surface to 1.1 kV/mm on the 20‐wt% TiO 2 /epoxy‐coated spacer. We hope that this study can provide guidance for the optimal design of DC GIL spacers under the electrothermal coupling field.

Many rural communities in Alabama’s Black Belt region lack adequate sanitation, resulting in wastewater discharges that may pose risks to residents. To understand the scope of the problem in one community, we conducted three cross-sectional surveys in a small town with limited sanitation infrastructure in 2023. We measured a range of enteric pathogens in environmental samples by multi-parallel qPCR as well as fecal indicator bacteria E. coli and Enterococcus by culture and molecular methods. We examined soil samples (n = 58) from sites near failing septic systems or suspected direct surface discharges and comparison soil (n = 10) far from potential discharges to estimate sanitation-related pathogen hazards. We examined surface water samples from community (n = 8) and localized (n = 20) sites that may have been impacted by wastewater discharges. Comparing impacted and unimpacted soil samples revealed greater fecal contamination near known or suspected discharges, compared with control samples. The mean culturable E. coli count in impacted soils was 224 MPN/g (95% CI 0-510.5 MPN/g) and in unimpacted soils was 0.5 MPN/g (95% CI 0-1.5 MPN/g). We detected several pathogens via qPCR in impacted soil and surface water, including Acanthamoeba spp., Balantidium coli , Blastocystis spp., Cryptosporidium spp., and rotavirus. In community-level surface waters, 88% of samples were positive for E. coli by culture (n = 8, mean 3.04 x 10 5 , 95% CI 0-8.96 x 10 5 MPN/100 mL); 100% were positive for Enterococcus by culture (n = 4, mean 1.10 x 10 4 , 95% CI 0-2.55 x 10 4 MPN/100 mL); and we detected Acanthamoeba spp., Blastocystis spp., Cryptosporidium spp., Plesiomonas shigelloides ., rotavirus, and Yersinia enterocolitica , suggesting community-level wastewater discharges may degrade local surface water quality. Evidence suggests sanitation failures contribute to enteric pathogen hazards in this community.

Background: Cold plasma is a non-thermal technology that has growing attention for its potential application in agriculture, particularly for seed quality enhancement and establishment of early crops. Cold plasma seed treatment is increasingly being identified as a potential alternative to conventional seed treatment practices. Methods: The present study evaluated the effect of surface discharge cold plasma treatment on seed germination and seedling vigour in rice. Three popular rice varieties, IR-64, MTU-1010 and BPT-5204, and two traditional varieties, Chitrakar and KaruppuKavuni, were subjected to different plasma treatment times to evaluate the differences among genotypes. Surface discharge cold plasma was generated by a dielectric barrier discharge system under atmospheric air conditions. The variables measured were seed germination percentage, root length, shoot length, fresh weight, dry weight, seedling vigour index I and II. Result: The plasma treatment showed increased physiological quality parameters over the untreated control, with maximum responses in the cultivated varieties after 10 min of plasma treatment and in traditional varieties after 40 min of plasma treatment. The results clearly determine the effectiveness of cold plasma seed enhancement technology for seed germination and vigour improvement in rice, thus establishing its potential application in seed technology and crop improvement.

Although wastewater surveillance for assessing community health and well-being is now mainstream, most cities in low- and middle-income countries lack conventional wastewater services. In these settings, environmental surveillance beyond conventional wastewater offers the potential to inform public health responses, design interventions intended to reduce exposures, and to evaluate infection control programs. To explore these potential use cases, we measured pathogens, source-tracking markers, and fecal indicator bacteria in wastewater treatment plant (WWTP) influent and effluent, wastewater surface discharges, impacted river water, impacted soils, open drains, stormwater, and fecal sludges from onsite sanitation in Maputo, Mozambique. We detected a wide range of pathogens by multi-parallel RT-qPCR across all matrices, revealing a nuanced picture of pathogen flows in the city and suggesting the potential for exposures beyond those typically included in studies of sanitation and health. We developed a regression model with multiple pathogens as the dependent variable and observed lower pathogen concentrations in direct wastewater discharges (mean difference -1.4 log10 per liter, 95% CI: -1.7, -1.1), WWTP effluent (-0.97 log10, 95% CI: -1.5, -0.47), water from open drains (-2.0 log10, 95% CI: -2.5, -1.6), impacted river water (-3.0 log10, 95% CI: -3.7, -2.4), and stormwater (-4.7 log10, 95% CI: -7.0, -3.3) compared to WWTP influent. We further observed that a one standard deviation increase in 7-day cumulative precipitation was associated with an increase in the pathogen concentration in all matrices (0.11 log10, [0.04, 0.19]). Despite lower concentrations of pathogens in matrices compared to WWTP influent, frequent detection of pathogens indicates clear potential to use environmental pathogen surveillance to inform public health responses in cities lacking universal conventional wastewater, with a wide range of promising applications.

Deep injection wells represent a critical infrastructure solution for municipal wastewater disposal in regions where surface discharge options are environmentally constrained. This study examines the hydrogeologic characterization and integrity verification of Class I deep injection wells used for municipal wastewater disposal in the southeastern United States. The region’s subsurface environment is defined by complex sequences of permeable aquifers and low-permeability confining units that provide natural containment for injected fluids. This research synthesizes current methodologies for subsurface characterization, well design, operational monitoring, and regulatory compliance under the U.S. Underground Injection Control program. Emphasis is placed on hydrogeologic modeling, geochemical compatibility, mechanical integrity testing, and risk assessment frameworks used to ensure long-term containment of injected wastewater. Case studies from southeastern injection sites illustrate both the effectiveness of engineered containment systems and the challenges posed by geologic heterogeneity, potential upward migration pathways, and geochemical reactions within deep formations. Advances in monitoring technologies, including fiber-optic sensing and data-driven predictive analytics, are evaluated for their potential to improve early detection of integrity failures. The analysis highlights the importance of comprehensive site-specific investigations, integrated monitoring networks, and probabilistic risk assessment approaches in ensuring environmental protection and regulatory compliance. The findings contribute to improving best practices for deep well injection operations and provide guidance for sustainable wastewater management strategies in hydrogeologically complex regions.

Abstract Vacuum surface discharge on dielectric substrates is frequently observed in vacuum electronic devices and insulation systems, spanning gap distances from the sub-micrometer to millimeter scale and beyond. Such discharges propagate through a secondary electron emission avalanche (SEEA), in which field emission electrons multiply on the surface via successive secondary electron emission. Understanding the SEEA propagation speed (v_SEEA) is essential for elucidating the underlying discharge physics and optimizing device performance. Despite extensive studies since the 1970s, no predictive theory has been available to calculate this key quantity or reveal its parametric dependencies. Here, we resolve this longstanding issue by introducing a charging-by-slice model, which yields a compact expression, v_SEEA~J_FE/E_x , linking the propagation speed to the cathode field emission current and applied electric field. Theoretical predictions are validated by particle-in-cell simulations and experimental measurements, showing excellent agreement and revealing a v_SEEA in the range of 106-107 m/s. The established theoretical framework advances both the fundamental understanding of device physics and the practical design of vacuum electronic and insulation systems.

Charging and discharging effects are among the primary factors leading to anomalies and failures of geostationary orbit (GEO) satellites. In addition to causing material degradation and the burnout of high-voltage solar arrays, the strong electromagnetic pulses (EMPs) generated during discharge can interfere with electromagnetic-sensitive onboard equipment, resulting in operational interference, performance degradation, or even damage to the equipment—and in extreme cases, complete satellite failure. To investigate the electromagnetic characteristics of satellite surface charging and discharging, as well as the associated electromagnetic radiation characteristics, during geomagnetic substorms, this study selects typical electrostatic discharge (ESD) risk sources for spacecraft. Based on a comprehensive satellite ESD test platform, seven sets of antenna sensors covering different frequency bands were deployed inside and outside a vacuum chamber to measure the key parameters of electromagnetic radiation characteristics during ESD under a simulated GEO environment. The results indicate that the energy of the discharge current on the surface of GEO satellites is concentrated in the frequency band below 30 MHz, while the energy of the discharge electromagnetic radiation field is distributed between 10 and 50 MHz. Furthermore, based on the test results of discharges with different intensities, the finite integration technique (FIT) was employed to simulate the time-domain waveforms of electromagnetic radiation pulses and the frequency characteristics of high-frequency electromagnetic wave energy distribution in both the near-field and far-field regions of satellite discharge within a 10-m free space. These simulation results provide accurate input parameters for the experimental verification and evaluation of electromagnetic coupling during satellite charging and discharging, as well as technical support for the detection and localization of discharge sources based on the spectral characteristics of discharge EMP signals.

ABSTRACT This work combines use of environmental tracers and hydrochemistry for the assessment of hydrological processes at the basin scale. We present analyses of hydrological behaviours in three arid, headwater basins, that is, Derecho, Cochiguaz and Incaguaz (29°59′ S–30°29′ S and 70°15′ W–70°32′ W) in the Andes of north‐central Chile, combining isotope data and concentration–discharge ( C – Q ) relationships. During 2 years, monthly surface water samples were analysed for stable isotopes (δ 2 H, δ 18 O), radioactive isotopes ( 222 Rn and 3 H) and chemical composition (Ca, Mg, Na, K, Si, HCO 3 , Cl, SO 4 and Fe, both total and dissolved concentrations). Even though the basins present relatively similar physiographic features (e.g., orientation, elevation distribution, dominant geology), noticeable differences in hydrological processes were revealed. Derecho presented episodic contributions of groundwater to the surface channel (inferred from 222 Rn), not observed in Cochiguaz and Incaguaz. This was also associated with longer residence times, as inferred from stable isotopes. Based on 3 H, we inferred the occurrence of an important subterranean component of some decades, which explains the permanent existence of surface discharge despite an extreme 15‐year drought in the area. Apart from seasonal snowfall, no significant contribution of glaciers or other cryogenic forms to the surface streams was found. The chemical composition of the waters reflects quite clearly the imprint of predominant igneous rocks in the three basins. Specifically, the C – Q relationships showed a consistent behaviour for both geogenic and exogenous constituents, and most of this behaviour was classified as chemostatic. This is consistent with the identification of an important older water component in the streamflow of the basins under study, associated with an important storage volume in the basins, determining that water remains in the system for long time periods, that is, longer than is required to reach water–rock equilibrium. The combined isotopic and C – Q analysis provides a reliable understanding of the basins' behaviour beyond what each approach could have provided separately.

The present work addresses the investigation of the spatio-temporal character of dielectric barrier discharge plasma actuator arrays applied to generate virtual wall oscillations. To this end, an electro-optical diagnostics approach is introduced. Optical measurements by means of intensified charge-coupled device imaging are conducted to capture the light emission of the oscillatory gas discharges, allowing observation of the discharge characteristics of the plasma actuator array for individual phase positions within both the oscillation and plasma cycle. Through simultaneous acquisition of electrical signals of the actuator limited to the captured image domain, the entire light emission is directly linked to the ignited surface discharges. On this basis, occurring discharge structures are observed throughout the plasma cycle that can be related to the applied voltage signals. A quasi-linear correlation of the integral discharge light emission and transferred charge is determined. It is shown that the present experimental assessment allows to uncover the spatio-temporal relation of opposing discharge filaments occurring for the present plasma actuator array and forcing strategy, and (as a future scope) their influence on the resulting flow manipulation upon periodically changing forcing direction can be evaluated.

Filamentary mode, as a common phenomenon that appears in dielectric barrier discharge (DBD), is realized by rod-to-rod electrodes in N2-O2 mixtures at 80 mbar. The effects of the dielectric thickness on the characteristics of filamentary DBD are investigated through experiments and simulations. The discharges are driven by a positive unipolar nanosecond pulse voltage with 15.8 kV amplitude, 9 ns rise time (Tr10–90%), and 14 ns pulse width. The characteristics of filamentary DBD are recorded with an intensified charge-coupled device and a Pearson current probe in the experiment, and a 2D axisymmetric fluid mode is established to analyze the discharge. Surface discharges occur on the anode and cathode dielectric after the breakdown, and the discharge is gradually extinguished as the applied voltage decreases. A thinner total dielectric thickness (Da + Dc) leads to larger currents, stronger discharges, and wider discharge channels. These characteristics are consistent when the total dielectric thickness is the same but anode dielectric thickness and cathode dielectric thickness are different (Da ≠ Dc ≠ 0). If the anode is a metal electrode (Da = 0), the current will be substantially large, and two discharge modes are observed: stable mono-filament discharge mode and random multi-filament discharge mode. It is found in simulations that the dielectric thickness changes the electric field configuration. The electric field is stronger with the decrease in dielectric thickness and leads to a more intense ionization which is responsible for most of the observed effects.

Pulse sequence analysis-based characterization of corona and surface discharges under DC voltages

ABSTRACT Accurate acoustic localisation of surface discharge on converter valve damping capacitors is essential for the safe operation of high‐voltage direct current (HVDC) systems. However, the stochastic nature of discharge signals, leading to frequency ambiguity, coupled with the shielding effects of near‐field obstacles, poses significant challenges to the accuracy and robustness of traditional sound source localisation algorithms. To address these issues, this study proposes a fuzzy narrowband focusing beamforming method for acoustic source localisation. Initially, the time–frequency characteristics of discharge acoustic signals are thoroughly analysed using continuous wavelet transform (CWT). Subsequently, a generalized bell‐shaped fuzzy function is introduced to focus and fuse the fuzzy narrowband, effectively mitigating problems associated with frequency drift and uneven bandwidth. Furthermore, the sound source localisation task is reformulated as a sparse signal recovery problem, and a compressed beamforming algorithm based on Block Sparse Bayesian Learning (BSBL), combined with the Expectation Maximisation (EM) method, is employed to achieve accurate estimation of the direction of arrival (DOA). Finally, the superiority of the proposed algorithm is validated through simulation experiments and simulated valve hall tests. The results demonstrate that the method achieves excellent localisation accuracy and robustness across varying signal‐to‐noise ratios (SNRs), different characteristic frequencies, and diverse microphone array configurations. In field experiments within a simulated valve hall, the proposed method achieves an average spatial angular error of 0.75 with a standard deviation of 0.92, which is significantly lower than that of conventional algorithms. Overall, this research provides an innovative technical approach for the early detection and high‐precision localisation of concealed defects in converter valves.

Metal particles in gas-insulated switchgear (GIS) may attach to the insulator surface and cause surface discharge (SD), which can lead to flashover failure and threaten the stable operation of power grid. To develop an efficient method for SD detection and risk assessment, an optical pulse measurement module based on silicon photomultiplier is designed, and SD experiments of metal particles with different sizes are carried out on the basin insulator of a real 126 kV GIS device. By analyzing the SD inception voltage, time-domain and phase characteristics of optical pulses, a novel method for SD and particle size identification is proposed by combining phase resolved optical pulse pattern and lightweight convolutional neural network. Test and comparison experiments demonstrate that the proposed method exhibits superior generalization and real-time performance. The identification accuracy of SD sample by this method is 100%, and the identification accuracy of particle size is 98.52%. The average runtime of the proposed lightweight identification model in the embedded device is 173.96 ms. Finally, various interpretability techniques are combined to analyze the inference mechanism of the identification model and the feature importance of the optical pulse phase pattern samples.

Vacuum surface flashover along solid insulator remains a major cause of high voltage device failure, therefore attracts sustainted research interest during the past a few decades. Current investigations of flashover encounter a bottleneck period due to incomplete understanding of the underlying physical mechanism. Optical diagnostics for evolution of surface flashover process can provide a new insight to its mechanism. However, the surface flashover process is a nanosecond (ns) scale transient phenomenon, and most of the existing diagnostic cameras are unable to obtain two-dimensional images with such high temporal resolution. In this paper, a four-framing camera with subnanosecond temporal-resolution is developed and tested. By adopting avalanche transistor switches in the subnanosecond gating module for photocathode in the image intensifier, a minimal gate width of ~1ns of each frame is achieved. A combination of analog and digital circuit is designed to realize a large delay range from picosecond (ps) to millisecond (ms), with a high accuracy of 150 ps within 10 ns range, therefore the time interval between consecutive frames can be adjusted flexibly according to different applications. The developed camera successfully captures the fast-evolving surface discharge processes, providing a powerful diagnostic tool for fundamental flashover mechanism research.

Surface discharge is a common type of partial discharge (PD) that puts any high-voltage (HV) system at significant risk. Accurate identification and segregation of various surface discharges is important to prevent the premature failure of HV system. To address this issue, this study proposes a data-driven clustering framework utilizing signal decomposition technique followed by nonlinear feature extraction inspired by chaos theory. To this end, four types of surface discharge defects have been emulated and for each discharge, time domain PD signals were recorded using a high-frequency current transformer (HFCT) sensor designed to operate from 300kHz–10MHz. The PD signals captured by the HFCT sensor were initially decomposed using empirical mode decomposition (EMD) to derive intrinsic mode functions (IMFs), which capture inherent oscillatory modes of the PD signals. Following this,second-order difference plot (SODP) of the extracted IMFs were generated to analyze the dynamic and chaotic behavior of PD signals. From each SODP plot, several features have been extracted and following feature selection using analysis of variance (ANOVA), top three features were used as inputs to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-means clustering algorithm for separation of different surface discharge signals into distinct clusters. In addition, experiments have been conducted on real-life insulator defects to verify the practicability of the proposed method. It has been observed that the proposed method employing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula>-means clustering and SODP plots can segregate between different surface discharge PD sources accurately. The proposed PD detection method offers a reliable solution for unsupervised PD signal categorization in HV applications.

International audience

Abstract As novel carriers of plasma-generated reactive oxygen and nitrogen species (RONS), plasma-treated hydrogels (PTHs) have broad biomedical applications. Regulating the key reactive species in PTHs is crucial for optimal biomedical effects. This study focuses on how N 2 /O 2 ratios in discharge gas of surface dielectric barrier discharge affect RONS generation and its correlation with the bactericidal effect plasma-treated AVC hydrogel (PTH AVC ). It was found that PTH AVC achieved the best bactericidal effect and contained the highest concentration of ONOO − aq /ONOOH aq when the O 2 content was 70%, indicating that ONOO − aq /ONOOH aq may play a key role in the bactericidal effect of PTH AVC . This study provides a new strategy to regulate the biological activity of PTH AVC and valuable insights into its bactericidal mechanism.

Dry Anophthalmic Socket Syndrome (DASS) is a multifactorial condition that affects roughly half of all prosthetic eye wearers and remains frequently underrecognized. It is characterised by symptoms such as dryness, discomfort, discharge, and inflammation of the socket surface. Diagnostic criteria include validated symptom questionnaires (e.g., OSDI, DEQ-5, SANDE) and at least one clinical sign such as conjunctival staining, blepharitis, or reduced tear meniscus height. This review describes the anatomical, cellular, and molecular changes associated with DASS. Meibomian gland dysfunction is common, with a significant reduction in gland density and structure. Goblet cell density is also often decreased, particularly in the tarsal and bulbar conjunctiva, although findings may be affected by topical treatments. Increased conjunctival inflammation-evidenced by immune cell infiltration and elevated markers such as MMP-9 and ICAM-1-is frequently observed, particularly in the posterior socket lining. Oxidative stress, mediated by dysregulated NOX4, KEAP1, and NRF2 expression, appears to play a contributory role. Additional factors influencing DASS include eyelid malpositions such as entropion and ectropion, prosthesis smoothness and amount of tear film production. Poor hygiene practices and environmental factors may exacerbate symptoms. Given its multifactorial aetiology, DASS requires a complex management strategy targeting inflammation, tear film instability, mechanical irritation, eyelid position and patient education. Increased awareness, standardised diagnostics, and evidence-based care protocols are critical to improving outcomes for prosthetic eye wearers.