Source Location Research Articles

High-resolution carbon emission mapping and spatial-temporal analysis based on multi-source geographic data: A case study in Xi'an City, China

Cities, contributing over 70% of global emissions, are key areas for climate change mitigation. Heterogeneity within cities determines the need for spatialized urban emissions reduction policies. However, few studies have attempted to characterize the spatial distribution of carbon emissions at the urban scale. To address this issue, a novel mapping method was proposed, using Xi'an as an example to explore the spatial distribution of carbon emissions at the city scale. Firstly, multiple geospatial open-source data, such as point of interest (POI), road networks, and land use, were utilized to identify the locations of emission sources. High-resolution carbon emission distributions were then mapped by allocating emissions based on the Intergovernmental Panel on Climate Change (IPCC) methodology. The study employed Global Moran's I to analyze the changes in spatial heterogeneity at different resolutions. Additionally, the Local Indicators of Spatial Association index (LISA) and Standard Deviation Ellipses (SDE) were adopted to examine the spatiotemporal characteristics of carbon emissions in Xi'an. The results show that carbon emissions at Xi'an City rises from 45.112 million tons to 72.701 million tons between 2010 and 2021. The construction of multi-scale carbon emissions spatial distributions, with a resolution of up to 30 m, allowed for a more detailed characterization of carbon emissions, especially in urban fringe areas. In addition, the results indicate that urban carbon emissions exhibit the strongest spatial autocorrelation at a resolution of 350 m. The study can provide a reference for the development of regional carbon emission reduction policies and spatial planning. In addition, the proposed spatialized method of city carbon emissions depends on open-source data, which allows it to have the potential for application in other cities.

Применение модифицированного метода дискретных источников в задаче дифракции импульсной волны на подповерхностном импедансном цилиндре

In contrast to the classical method of auxiliary sources (MAS) with the location of discrete sources (DS) on a closed additional contour, this article investigates the use of a complete system of functions for describing DS fields localized on an open curve in a modified MAS (MMAS). The MMAS was applied to solve a two-dimensional diffraction problem of a broadband electromagnetic impulse on an impedance cylinder located in free space and in the subsurface. The electric thread was a source of external current, excited by an impulse of 1.6 ns duration with a spectrum width from 122 MHz to 726 MHz (at the level of -6 dB). In various combinations, fresh water, thawed and frozen soil, air, and ice were used as the filling medium of the cylinder and the dielectric half-space. The complete system of functions for describing the fields of DS was constructed based on the Hankel functions of the first kind of zero order, the corresponding Green's function for the layered medium problem, and their derivatives in the direction of normal to the curve on which the DS was placed. When compared with the finite difference time domain method (FDTD), it is shown that the MMAS, using Leontovich impedance boundary conditions, can describe impulse fields, scattered by a dielectric cylinder with practically significant accuracy. It has been established that in the case of an elliptical cylinder, the MMAS requires approximately half as many auxiliary DSs compared to the classical MAS while achieving the same accuracy of the solution. In general, this article confirms the possibility of using a complete system of functions to describe the fields of DSs, localized on an open curve for the solving of impulse diffraction problems on subsurface impedance cylinders.

BP3M: Bayesian Positions, Parallaxes, and Proper Motions Derived from the Hubble Space Telescope and Gaia Data

We present a hierarchical Bayesian pipeline, BP3M, that measures positions, parallaxes, and proper motions (PMs) for cross-matched sources between Hubble Space Telescope (HST) images and Gaia—even for sparse fields (N * < 10 per image)—expanding from the recent GaiaHub tool. This technique uses Gaia-measured astrometry as priors to predict the locations of sources in HST images, and is therefore able to put the HST images onto a global reference frame without the use of background galaxies/QSOs. Testing our publicly available code in the Fornax and Draco dwarf spheroidal galaxies, we measure PMs that are a median of 8–13 times more precise than Gaia DR3 alone for 20.5 < G < 21 mag. We are able to explore the effect of observation strategies on BP3M astrometry using synthetic data, finding an optimal strategy to improve parallax and position precision at no cost to the PM uncertainty. Using 1619 HST images in the sparse COSMOS field (median nine Gaia sources per HST image), we measure BP3M PMs for 2640 unique sources in the 16 < G < 21.5 mag range, 25% of which have no Gaia PMs; the median BP3M PM uncertainty for 20.25 < G < 20.75 mag sources is 0.44 mas yr−1 compared to 1.03 mas yr−1 from Gaia, while the median BP3M PM uncertainty for sources without Gaia-measured PMs (20.75 < G < 21.5 mag) is 1.16 mas yr−1. The statistics that underpin the BP3M pipeline are a generalized way of combining position measurements from different images, epochs, and telescopes, which allows information to be shared between surveys and archives to achieve higher astrometric precision than that from each catalog alone.

The Solar Aspect System of the Hard X-ray Imager Onboard ASO-S

The ASO-S/HXI is a bi-grids modulating instrument for solar hard X-ray imaging, whose collimator contains 91 pairs of tungsten grids. Since the solar disk is invisible in hard X-rays, a Solar Aspect System (SAS) is required to provide the pointing of hard X-ray imager (HXI) for locating X-ray sources on the solar disk. In addition, the knowledge of the alignment and relative twist of the corresponding front–rear grid pairs is important for image reconstruction as well as locating flares. Therefore, the SAS system was designed to monitor the alignment status of HXI grids and to provide the pointing direction of the HXI collimator with two subsystems DM and SA during the whole life cycle of HXI. DM measures the centroids of the front frosted glasses and the solar disk. SA images the Sun and provides precise relative locations of the solar disk center. Both work in the visible light of 565–585 nm. With all the data together, we can solve with an inversion algorithm the alignment status of the front and rear grids, the relative twist, and the pointing direction. We tested and validated the SAS design with both the simulation model and ground coordinate measuring machine. Here we present the detailed system design, the testing results, the inversion algorithm, and the in-orbit status of the SAS. Currently, the SAS has realized the rotational measurement accuracy of about 4 arcsec, and a translational measurement accuracy of about 15 μm, and the SAS pointing data has been used in both imaging calibration for flare locations and imaging corrections for the platform drifting effect. The high-cadence precise measurement (better than 0.3 arcsec) of the pointing will allow the study of source locations at different energies and therefore help us to understand electron acceleration and transportation in flares.

Magma fizz: Tremor during the Kīlauea summit reservoir decompression

Typical eruptions at Kīlauea volcano involve the evacuation of magma from the summit and/or south caldera reservoirs towards the East or Southwest rift zones. The reservoir drainage provokes the summit deflation, and on extreme occasions, such as in 2018, the summit caldera collapse. Systematically, seismic tremor, often with a particular multichromatic spectral signature characterized by frequency gliding, accompanies summit deflation episodes. In 2018, this type of continuous tremor accompanied the steady subsidence stage, whereas discrete earthquakes dominated the collapse stage. In this work, we aim to understand the source mechanism of the syn-deflation tremor of 2018. To locate the seismic source, we develop a novel machine-learning-based algorithm as an alternative to the amplitude source location technique. We use a large high-resolution catalog to resolve a composite amplitude decay function. Under these conditions, our method outperforms the traditional technique. We locate the tremor source 1 km below the eastern perimeter of the Halema‘uma‘u crater, which coincides with the position of the summit magma reservoir, as determined in many other studies. Furthermore, we model the seismic source as pressure oscillations driven by gas porous flow at the roof of the reservoir. In this model, gas accumulates temporarily in many gas pockets between the magma and the roof. Our modeling shows that the gas flux is responsible for the tremor amplitude modulations, whereas the gas pocket thickness controls the frequency variations. Beyond a critical point of depressurization, the magma cannot contribute further to the tremor oscillations via decompression-driven degassing, nor support the roof above it, resulting in rock failure. This work advances our understanding of magma-degassing dynamics and tremor generation at Kilauea volcano, and provides novel seismological techniques for volcano seismology monitoring and research.

Understanding fire combustion characteristics and available safe egress time in underground metro trains: A simulation approach

Due to insufficient oxygen supply in the tunnel, incomplete combustion of the underground metro train fire easily produces toxic gases and smoke dust, causing difficulty in breathing for passengers and rescue. The experimental study of the fire combustion characteristics of metro trains in the tunnel is of great significance for designing metro fire extinguishing and fire prevention systems and providing reliable guidance for emergency evacuation in fire scenarios. A train fire model is established to study the effects of various factors on fire combustion characteristics and calculate the available safe egress time (ASET). The smoke spread characteristics under different conditions such as fire source power, fire source location, carriage layout, and train location are simulated. The simulation results show that the fire source power, location, and seat arrangement have a significant impact on the smoke spread characteristics of carriage fires and ASET.

Pulsar timing array harmonic analysis and source angular correlations

Gravitational waves (GWs) influence the arrival times of radio signals coming from pulsars. Here, we investigate the harmonic space approach to describing a pulsar’s response to GWs. We derive and discuss the “diagonalized form” of the response, which is a sum of spin-2-weighted spherical harmonics of the GW direction multiplied by normal (spin-weight 0) spherical harmonics of the pulsar direction. We show how this allows many useful objects, for example, the Hellings and Downs two-point function, to be easily calculated. The approach also provides a clear description of the gauge dependence. We then employ this harmonic approach to model the effects of angular correlations in the sky locations of GW sources (sometimes called “statistical isotropy”). To do this, we construct ensembles made up of many Gaussian subensembles. While each of the individual subsensembles breaks rotational invariance, the full ensemble is rotationally invariant. Using harmonic techniques, we compute the cosmic covariance and the total covariance of the Hellings and Downs correlation in these models. The results may be used to assess the impact of angular source correlations on the Hellings and Downs correlation, and for optimal reconstruction of the Hellings and Downs curve in models where GW sources have correlated sky locations. Published by the American Physical Society 2024

Environmental impacts and scale-up efficiency of four carbon capture and storage scenarios

Capture, transportation, and permanent storage of CO2 is considered pivotal in reaching global net-zero emission targets. Conceptualization and deployment of carbon capture and storage (CCS) value chains require substantial resources with current CCS projects trailing behind expectations. To date few studies address the environmental impacts related to introducing CCS into existing value chains, and even fewer focus on the environmental efficiency of scaling-up CCS projects, despite the need for acceleration. Four scenarios were studied, resembling CCS projects at different capture capacities of 0.3, 1.5, 3.5 and 10 million tonnes of CO2 per year (mtpa) deployed within a Northern European region. Using attributional life cycle assessment, we demonstrate that permanent carbon storage today may furnish a 47–91% CO2 net-abatement potential depending on the CO2 source and geographical location of CCS projects. Despite limited operational data, results appear robust with general low uncertainty. Our environmental assessment show that capturing, conditioning and liquefaction account for the largest emission shares of the studied CCS value chains, ranging from 70 to 80%, which is heavily fluctuated by the carbon emission intensity of regional electricity production. Impacts related to permanent storage activities, such as offshore monitoring and well drilling are limited to less than 2% across all scenarios. Moreover, our study suggests that expanding the spatial geographical scope to accumulated 5000 km has a low influence on the efficiency of CCS value chains regardless of capture rate. Freshwater eutrophication shows the highest burden across the assessed environmental impact categories caused by the electricity production for capture processes. Lastly, we identify net-abatement improvement potential for post-combustion CCS projects at capturing capacities between 1.5 and 10 million tonnes per annum ranging from 10 to 20% for transport-related activities, and 60–68% for capturing processes when assuming net-zero energy scenarios.

Reference 1D Seismic Velocity Models for Volcano Monitoring and Imaging: Methods, Models, and Applications

Abstract Seismic velocity models of the crust are an integral part of earthquake monitoring systems at volcanoes. 1D models that vary only in depth are typically used for real-time hypocenter determination and serve as critical reference models for detailed 3D imaging studies and geomechanical modeling. Such models are usually computed using seismic tomographic methods that rely on P- and S-wave arrival-time picks from numerous earthquakes recorded at receivers around the volcano. Traditional linearized tomographic methods that jointly invert for source locations, velocity structure, and station corrections depend critically on having reasonable starting values for the unknown parameters, are susceptible to local misfit minima and divergence, and often do not provide adequate uncertainty information. These issues are often exacerbated by sparse seismic networks, inadequate distributions of seismicity, and/or poor data quality common at volcanoes. In contrast, modern probabilistic global search methods avoid these issues only at the cost of increased computation time. In this article, we review both approaches and present example applications and comparisons at several volcanoes in the United States, including Mount Hood (Oregon), Mount St. Helens (Washington), the Island of Hawai’i, and Mount Cleveland (Alaska). We provide guidance on the proper usage of these methods as relevant to challenges specific to volcano monitoring and imaging. Finally, we survey-published 1D P-wave velocity models from around the world and use them to derive a generic stratovolcano velocity model, which serves as a useful reference model for comparison and when local velocity information is sparse.

A framework and generic models for quantifying surface environmental impact of VOCs emissions from the complex fractured rocks

To analyze the surface cumulative mass of VOCs from residual sources in dual-media fractured rocks and assess environmental health risks, complex 3D numerical models were constructed. These models comprehensively considered fracture-rock interactions, density-driven effects, and surface pressure fluctuations. The investigation identified the key control factors affecting surface cumulative mass, including the fracture aperture, pollutant source location, fracture density, and so on. Additionally, a regression-based general surrogate model was established using the obtained representative dataset. According to U.S. EPA's Respiratory Inhalation Reference Concentrations, the cumulative mass of CH2ClCHCl2 in one day for one-third of the model exceeds the concentration limit. Benzene and TCE concentrations reached 29 and 740 times the reference limits, significantly impacting air quality and health. Surrogate model analysis showed that in the worst-case scenario, 1 min's surface cumulative mass could cause Benzene concentrations to exceed the limit by 57 times. The implications of the study lies in reminding us that even after groundwater remediation in the saturated zone, residual VOCs in the capillary zones can still significantly impact surface environmental health risks. This investigation also presents an effective framework that integrates complex, time-consuming numerical modeling with simple, efficient statistical modeling to predict concerned variables and their uncertainties. This study provides a reference basis for the control of environmental pollution pertaining to VOCs volatilization from buried capillary zones at specific depths.

Effect of nano-metakaolin on the early-age fracture properties of cement mortar

In this paper, the fracture performance of nano-metakaolin (NMK) cement mortar during early hydration period was investigated. Different NMK contents (0 %, 3 %, 5 %, 7 %) were considered, and three-point bending tests were conducted. The digital image correlation technology and acoustic emission technology were utilized to analyze P-CMOD curves, initial fracture toughness, unstable fracture toughness, fracture energy, cumulative AE hits, and cumulative AE energy, AE source locations of cement mortar at early hydration stages. Furthermore, SEM experiments were utilized to analyze the mechanism by which NMK affects fracture performance of cement mortar. The results indicate that the incorporation of NMK enhances the fracture properties of cement mortar, with the most suitable NMK content being 5 %. The addition of NMK increases the cracking load, peak load, and the CMOD at the peak load of the cement mortar at 3 d, 7 d, and 14 d. The cracking load and fracture energy of the cement mortar with 5 % NMK increase by 22.5 % and 35.7 % respectively, compared to ordinary cement mortar at 14 d. NMK enhances the initial fracture toughness and unstable fracture toughness of the cement mortar, improving its crack propagation resistance. The unstable fracture toughness of the cement mortar is the largest when the NMK content is 5 % at 14 d, which is 31.6 % higher than that of ordinary cement mortar. Furthermore, NMK increases the cumulative AE energy and fracture energy of the cement mortar, increasing the cumulative ring counts and cumulative AE hits, causing the crack propagation path to deviate and resulting in improved crack resistance. The cumulative ring counts and AE hits for the cement mortar with 5 % NMK are 42.9 % and 66.2 % higher, respectively, than those for ordinary cement mortar at 14 d. NMK significantly modifies the cement matrix, thereby enhancing the fracture resistance of cement mortar.

High-Resolution Identification of Sound Sources Based on Sparse Bayesian Learning with Grid Adaptive Split Refinement

Sound source identification technology based on a microphone array has many application scenarios. The compressive beamforming method has attracted much attention due to its high accuracy and high-resolution performance. However, for the far-field measurement problem of large microphone arrays, existing methods based on fixed grids have the defect of basis mismatch. Due to the large number of grid points representing potential sound source locations, the identification accuracy of traditional grid adjustment methods also needs to be improved. To solve this problem, this paper proposes a sound source identification method based on adaptive grid splitting and refinement. First, the initial source locations are obtained through a sparse Bayesian learning framework. Then, higher-weight candidate grids are retained, and local regions near them are split and updated. During the iteration process, Green’s function and the source strength obtained in the previous iteration are multiplied to get the sound pressure matrix. The robust principal component analysis model of the Gaussian mixture separates and replaces the sound pressure matrix with a low-rank matrix. The actual sound source locations are gradually approximated through the dynamically adjusted sound pressure low-rank matrix and optimized grid transfer matrix. The performance of the method is verified through numerical simulations. In addition, experiments on a standard aircraft model are conducted in a wind tunnel and speakers are installed on the model, proving that the proposed method can achieve fast, high-precision imaging of low-frequency sound sources in an extensive dynamic range at long distances.

Application of artificial intelligence in coal mine ultra-deep roadway engineering—a review

The deep integration of computer field and coal mining field is the only way to coal mine intellectualization. A variety of artificial intelligence tools have been applied in open-pit and shallow coal mines. However, with the geometric increase of coal demand, the contradiction between supply and demand is becoming more and more serious, and the exploitation of resources from shallow layer (> 600 m) has become an inevitable trend. Well then, as a new engineering scene, the harsh conditions of “three high and one disturbance” seriously threaten the safety of personnel. The superposition of complex mining environment makes the number of input factors increase sharply, which leads to the application of artificial intelligence in deep coal mine roadway engineering. The guidance is not mature, the construction of various databases is missing, and there are still some problems in universality and applicability. To this end, this paper starts with the introduction of the operating characteristics of various artificial intelligence tools, and conducts a comprehensive study of the relevant high-level articles published in top journals. It systematically sorts out the research progress that has successfully solved the five directions of rock mechanics strength, surrounding rock stability, rock-burst, roof fall risks and micro-seismic events. While objectively evaluating the comprehensive performance of different tools, it also expounds its own views on key research and results. Literature review shows that whether as a development tool or a comparative model, the application of ANN in the field of coal mining is more than 98%, and it performs extremely well in the direction of surrounding rock stability and roof fall risk, with an accuracy rate of more than 90%. As the most mature direction of AI tools application, rock mechanical strength has experienced the development process from “SVM → ANN → DL → XGBoost → RF”. The dataset is from small samples (< 100) to big samples (> 1000), and the R2 of tree-based models can be stabilized at more than 95%. The research on rock-burst prediction mainly focuses on field factors and micro-seismic monitoring data. Whether it is a small sample or a large-scale data model, the accuracy of BN remains above 85%. The prediction and evaluation of micro-seismic events is a new direction in recent years. The image processing and application of CNN is extremely important. The signal recognition and classification accounts for more than 90%, and the research potential of source location needs to be further explored. In general, the nature of the rock itself is the first choice for almost all influencing factors. At the same time, the update iteration of monitoring methods (micro-seismic, ground sound, separation, deformation, etc.) expands the development of the database, making it possible to obtain the data due to threat to life and cost of equipment, which is very difficult to obtain before. In the process of parameter selection at the input end, the method of combining lithology conditions, geological environment and monitoring data will gradually become the first choice for research. Finally, in the follow-up work collation and on-the-spot investigation, it mainly focuses on the existing problems of deep coal mines, explores the application potential of artificial intelligence in deep coal mine roadway engineering, puts forward the possible research focus and challenging problems in the future, and gives its own opinions.

Acoustic scene complexity affects motion behavior during speech perception in audio-visual multi-talker virtual environments

In real-world listening situations, individuals typically utilize head and eye movements to receive and enhance sensory information while exploring acoustic scenes. However, the specific patterns of such movements have not yet been fully characterized. Here, we studied how movement behavior is influenced by scene complexity, varied in terms of reverberation and the number of concurrent talkers. Thirteen normal-hearing participants engaged in a speech comprehension and localization task, requiring them to indicate the spatial location of a spoken story in the presence of other stories in virtual audio-visual scenes. We observed delayed initial head movements when more simultaneous talkers were present in the scene. Both reverberation and a higher number of talkers extended the search period, increased the number of fixated source locations, and resulted in more gaze jumps. The period preceding the participants’ responses was prolonged when more concurrent talkers were present, and listeners continued to move their eyes in the proximity of the target talker. In scenes with more reverberation, the final head position when making the decision was farther away from the target. These findings demonstrate that the complexity of the acoustic scene influences listener behavior during speech comprehension and localization in audio-visual scenes.

Natural gas leakage: Forward and inverse method of leakage field based on multipath concentration monitoring

Natural gas leakage can easily lead to safety accidents and environmental pollution, affecting normal safe production and sustainable energy development. Among them, the leakage accident caused by the integrity failure of the process equipment is particularly noteworthy. To improve the efficiency of methane leakage monitoring and the accuracy of leakage source location, the forward prediction model of leakage concentration field, the optimization model of monitoring equipment layout, and the inversion model of leakage source location were established. To evaluate the model’s applicability, an open-air oil–gas joint station was selected for experimental simulation. The monitoring data of the open circuit laser methane sensor and the forward simulation results of the leakage concentration field were compared. It was found that at 30 % methane concentration, the monitoring distance was 0–6 m, with high monitoring accuracy. Then, by eliminating redundant monitoring sensors, three leakage field concentration monitoring paths are formed. A representative leakage source scenario set is selected for inversion localization. In the simulation of the single-point leakage experiment, the leakage location error is 1.824 m, and the average relative error is 3.6 %. Through error analysis, the results show that the inversion positioning model can also have a good adaptability to the parameter estimation of the leakage source when there are only 3 sets of observation data.

Improving source estimation of retinotopic MEG responses by combining data from multiple subjects

Abstract Magnetoencephalography (MEG) is a functional brain imaging modality, which measures the weak magnetic field arising from neuronal activity. The source amplitudes and locations are estimated from the sensor data by solving an ill-posed inverse problem. Commonly used solutions for these problems operate on data from individual subjects. Combining the measurements of multiple subjects has been suggested to increase the spatial resolution of MEG by leveraging the intersubject differences for increased information. In this article, we compare 3 multisubject analysis methods on a retinotopic mapping dataset recorded from 20 subjects. The compared methods are eLORETA with source-space averaging, minimum Wasserstein estimates (MWE), and MWE with source-space averaging. The results were quantified by the geodesic distances between early (60–100 ms) MEG peak activations and fMRI-based retinotopic target points in the primary visual cortex (V1). By increasing the subject count from 1 to 10, the median distances decreased by 6.6–9.4 mm (33–46%) compared with the single-subject median distances of around 20 mm. The observed peak activation locations with multisubject analysis also comply better with the established retinotopic maps of the primary visual cortex. Our results suggest that higher spatial accuracy can be achieved by pooling data from multiple subjects. The strength of MWE lies in individualized and sparse source estimates, but in our data, averaging eLORETA estimates across individuals in source space outperformed MWE in spatial accuracy.

The Design of Sound Source Location and Tracking System Based on TDOA

The Design of Sound Source Location and Tracking System Based on TDOA

Breaking the mold of simulation-optimization: Direct forward machine learning methods for groundwater contaminant source identification

Groundwater Contaminant Source Identification (GCSI) is important for addressing environmental concerns. Currently, it is widely achieved using the Simulation/Optimization (S/O) method. However, the utilization of optimization techniques may cause high computation costs and parameter equifinality issues. We introduce two innovative GCSI methods, Direct Forward Machine Learning (DFML) and One-Hot Machine Learning (OHML), utilizing the classical Artificial Neuro Network (ANN) model in the machine learning field. Both new methods eliminate the need for an optimization algorithm in GCSI, thus reducing the construction effort and improving efficiency. The first method, DFML can directly estimate eight parameters, providing valuable insights into the contaminant location, release history, and aquifer properties. The second method, OHML can estimate the spatial probability distribution of the contaminant location through one-hot encoding, addressing uncertainties in the contaminant source location estimations realized by DFML.Evaluations demonstrate that both methods exhibit satisfying performances. DFML can estimate contaminant location and aquifer properties with high accuracy; and estimate the release history information with moderate accuracy. The OHML correctly assigns the higher contaminant probabilities to regions containing true contaminant locations. The combination of DFML and OHML offers a comprehensive framework. This study contributes valid methodologies to the GCSI field.

Directional pre-sensitivity of the laser interferometer space antenna

The space-based Laser Interferometry Space Antenna (LISA) is a gravitational waves observatory currently under development. It comprises three spacecraft, each traveling in a heliocentric orbit that is weakly eccentric and inclined. Gravitational waves comprise two polarization components. They will be detected by conducting interferometric Doppler measurements between the LISA spacecraft. Among other factors, the signal strength of the Doppler measurements will depend on the location of the GW source, the GW polarization angle, and the orbits of the spacecraft. Thus, the signal strength of the Doppler measurements will vary over time. For given spacecraft orbits, we derive bounds on the signal strength that are functions of the source location. These bounds are simple, explicit expressions, and we refer to them as the directional pre-sensitivity. Using the directional pre-sensitivity, we construct a metric for the relative change in the signal strength depending on the source location and the spacecraft orbits. We illustrate how this formalism can be used to assess the signal strength for several examples of chosen orbits.

Advancements in Li-Ion Battery Safety: A Multiphysics and Deep Learning Approach for Thermal Runaway Prediction

The increasing adoption of Lithium-ion batteries (LIBs) in various applications necessitates an enhanced focus on safety, particularly concerning the thermal runaway (TR) phenomenon. This study introduces a novel framework combining multiphysics modeling and deep learning (DL) to predict TR in cylindrical Li-ion batteries. The primary objective is to prevent catastrophic battery failures by identifying critical thermal conditions leading to TR.Our methodology integrates state-of-the-art convolutional neural networks (CNNs) and the "You Only Look Once" (YOLO) object detection model to classify and detect the evolution of TR stages in LIBs. The CNNs, trained on simulated thermal images generated through comprehensive multiphysics modeling, classify TR into three distinct stages: safe operation, critical condition, and actual TR occurrence. The YOLO model further pinpoints the location of TR heat sources, enhancing the predictive accuracy.The multiphysics model employed captures the coupled thermal and electrochemical behavior of LIBs, focusing on the solid electrolyte interface (SEI) formation/decomposition on the anode as the primary degradation mechanism. The simulated thermal imagery, reflecting real-time battery conditions, serves as a dataset for training our DL models. This combination of physics-based and data-driven approaches allows for a detailed understanding of TR dynamics.In our study, we also explored the efficiency of other neural network architectures, notably ResNet, EfficientNet, and MobileNet for their potential in TR prediction. ResNet, known for its deep residual learning framework, demonstrated proficiency in handling the complex, layered features of thermal images. EfficientNet, optimized for efficiency and accuracy, showed promise in its scalable architecture and balanced depth, width, and resolution, making it a viable alternative for real-time TR detection. The MobileNet known for its lightweight architecture and efficiency on mobile devices, was evaluated for its capability to process thermal images in a resource-constrained environment. It demonstrated a remarkable balance between computational efficiency and predictive accuracy, making it a viable option for real-time TR monitoring in portable LIB applications.While MobileNet, ResNet, and EfficientNet each showed potential in their respective capacities, our primary CNN-YOLO framework emerged as the most effective for TR prediction in this study. Its proficiency in extracting spatial features and detecting anomalies in real-time was critical for early TR identification in LIBs.Results demonstrate high accuracy in predicting TR stages and heat source locations, emphasizing the ability of the integrated approach. The study highlights the importance of thermal management and real-time monitoring in LIBs, offering insights into the thermal behavior under various load conditions. The predictive framework not only advances the fundamental understanding of TR in LIBs but also provides practical implications for enhancing battery safety in applications like electric vehicles and consumer electronics.In conclusion, this work presents a significant stride in battery research, paving the way for safer and more reliable energy storage solutions. The combined multiphysics modeling and DL framework offer a robust tool for predicting and preventing TR in LIBs, contributing to the broader goal of advancing battery technology in the modern energy landscape.