Ground-based Detection Research Articles

Design and implementation of a portable high-altitude combustible gas alarm detection device

Abstract High-altitude operations present significant safety risks and challenges in accurately detecting combustible gases due to the inherent dangers of working at heights and the technical limitations of traditional detection methods. This study proposes a portable high-altitude combustible gas alarm detection device designed to address the safety risks and accuracy issues associated with high-altitude operations. By integrating wireless communication technology, precise gas concentration control, and automated flow adjustment, this device enables remote ground-based detection, eliminating the safety hazards of high-altitude operations while ensuring measurement accuracy. The device’s real-time monitoring of gas concentration within the calibration hood mitigates the issue of standard gas dilution in the air, and automated control further enhances detection precision. Experimental results demonstrate the device’s suitability for high-altitude alarm calibration, with a concentration measurement error of less than ±5% FS. This research offers a novel technical solution for the on-site detection of high-altitude combustible gas alarms.

Out of the Darkness: High-resolution Detection of CO Absorption on the Nightside of WASP-33b

We observed the ultrahot Jupiter WASP-33b with the SpectroPolarimètre InfraRouge on the Canada–France–Hawaii Telescope. Previous observations of the dayside of WASP-33b show evidence of CO and Fe emission indicative of a thermal inversion. We observed its nightside over five Earth nights to search for spectral signatures of CO in the planet’s thermal emission. Our three pretransit observations and two posttransit observations are sensitive to regions near the morning or evening terminators, respectively. From spectral retrievals, we detect CO molecular absorption in the planet’s emission spectrum after transit at ∼6.6σ. This is the strongest ground-based detection of nightside thermal emission from an exoplanet and only the third ever. CO appearing in absorption suggests that the nightside near the evening terminator does not have a temperature inversion; this makes sense if the dayside inversion is driven by absorption of stellar radiation. On the contrary, we do not detect CO from the morning terminator. This may be consistent with heat advection by an eastward jet. Phase-resolved high-resolution spectroscopy offers an economical alternative to space-based full-orbit spectroscopic phase curves for studying the vertical and horizontal atmospheric temperature profiles of short-period exoplanets.

Generation of squeezed vacuum state in the millihertz frequency band

The detection of gravitational waves has ushered in a new era of observing the universe. Quantum resource advantages offer significant enhancements to the sensitivity of gravitational wave observatories. While squeezed states for ground-based gravitational wave detection have received marked attention, the generation of squeezed states suitable for mid-to-low-frequency detection has remained unexplored. To address the gap in squeezed state optical fields at ultra-low frequencies, we report on the first direct observation of a squeezed vacuum field until Fourier frequency of 4 millihertz with the quantum noise reduction of up to 8.0 dB, by the employment of a multiple noise suppression scheme. Our work provides quantum resources for future gravitational wave observatories, facilitating the development of quantum precision measurement.

Precariously Balanced Rocks in Northern New York and Vermont, U.S.A.: Ground-Motion Constraints and Implications for Fault Sources

ABSTRACT Precariously balanced rocks (PBRs) and other fragile geologic features have the potential to constrain the maximum intensity of earthquake ground shaking over millennia. Such constraints may be particularly useful in the eastern United States (U.S.), where few earthquake-source faults are reliably identified, and moderate earthquakes can be felt at great distances due to low seismic attenuation. We describe five PBRs in northern New York and Vermont—a region of elevated seismic hazard associated with historical seismicity. These boulders appear to be among the most fragile PBRs in the region, based on reports from hobbyists. The PBRs are glacial erratics, best evidenced by glacial striations on bedrock pedestals. The pedestals themselves are locally high knobs, often situated on regionally high topography; this setting limits soil development and indicates that any outwash deposits were likely ephemeral. As a result, PBR ages can be reliably established by the retreat of the last continental ice sheet, ∼15–13 ka. To quantify the fragility of the PBRs, we surveyed them with ground-based light detection and ranging and calculated geometric parameters from the point clouds, field observations, and seismic responses. Preliminary validation of the 2023 time-independent U.S. National Seismic Hazard Model (NSHM) shows that the existence of PBRs is generally consistent with the median site-specific hazard curves. Only the Blue Ridge Road site suggests a modest reduction in hazard. To visualize the ensemble of data, we mapped the minimum permissible distance to potential source faults around each PBR site as a function of source magnitude by using the ground-motion models from the 2023 NSHM. Viewed in this manner, our data are consistent with potential M∼6.5 earthquake-source faults in many parts of the Lake Champlain Valley and northern Adirondack Mountains. Our work illustrates a potential pathway for better constraining earthquake-source faults in regions of cryptic faults.

Associations of Long-Term Exposure to Fine Particulate Constituents With Cardiovascular Diseases and Underlying Metabolic Mediations: A Prospective Population-Based Cohort in Southwest China.

The health effects of particulate matter with an aerodynamic diameter ≤2.5 μm (PM2.5) might differ depending on compositional variations. Little is known about the joint effect of PM2.5 constituents on metabolic syndrome and cardiovascular disease (CVD). This study aims to evaluate the combined associations of PM2.5 components with CVD, identify the most detrimental constituent, and further quantify the mediation effect of metabolic syndrome. A total of 14 427 adults were included in a cohort study in Sichuan, China, and were followed to obtain the diagnosis of CVD until 2021. Metabolic syndrome was defined by the simultaneous occurrence of multiple metabolic disorders measured at baseline. The concentrations of PM2.5 chemical constituents within a 1-km2 grid were derived based on satellite- and ground-based detection methods. Cox proportional hazard models showed that black carbon, organic matter (OM), nitrate, ammonium, chloride, and sulfate were positively associated with CVD risks, with hazard ratios (HRs) ranging from 1.24 to 2.11 (all P<0.05). Quantile g-computation showed positive associations with 4 types of CVD risks (HRs ranging from 1.48 to 2.25, all P<0.05). OM and chloride had maximum weights for CVD risks. Causal mediation analysis showed that the positive association of OM with total CVD was mediated by metabolic syndrome, with a mediation proportion of 1.3% (all P<0.05). Long-term exposure to PM2.5 chemical constituents is positively associated with CVD risks. OM and chloride appear to play the most responsible role in the positive associations between PM2.5 and CVD. OM is probably associated with CVD through metabolic-related pathways.

Integrating near-surface geophysical methods and remote sensing techniques for reconstructing fault-bounded valleys (Mellieha valley, Malta)

The island of Malta (central Mediterranean) is dissected by several WSW-trending fault-line valleys related to Miocene-Pliocene extensional tectonics. Some valleys host remnants of alluvial deposits that could provide information on possible Quaternary faulting, but the thickness of these deposits and their subsurface extent is poorly constrained. Our study aimed to investigate the structural configuration of the southern sector of the Mellieha valley, which is located in the north-west part of the island and is limited by the ENE-WSW general trending Mellieha and Ghadira faults, and their relation with a thin layer of infill sediments. We employed different near-surface geophysical techniques (electrical resistivity tomography, active and passive seismic methods, ground-penetrating radar), as well as remote sensing techniques (unmanned aerial vehicle digital photogrammetry and ground-based Light Detection and Ranging) to complement classic structural surveys. The valley structure has an asymmetric graben configuration, being bounded by a normal fault to the southeast (Mellieha Fault) and other to the north-west (Ghadira Fault). Our study provides insights of possible Quaternary fault displacements and revealed the presence of an additional fault splay, the previously unmapped NNW-dipping Mellieha Fault 2 at the south-eastern edge of the valley. Additionally, by integrating results from geophysical surveys, we estimated the thickness of the valley's recent deposits, being thicker towards the Mellieha Bay sector, with maximum values of 8–10 m. Our study aligns with the general model of horst and graben structuring with associated regional tilting since the late Miocene, and supports the hypothesis that some segments of NW-trending normal faults within the North Malta Graben exhibit Quaternary activity, although with minimal throw rates (<< 0.1 mm/yr).

Assimilation of Satellite Lightning Data in a Storm-Scale Numerical Weather Prediction System Using a 3D-EnVar Data Assimilation Scheme

Abstract The objective of this study is to prepare the assimilation of the Meteosat Third Generation Lightning Imager (MTG-LI) observations in the regional numerical weather prediction model AROME-France using a 3D-EnVar data assimilation algorithm. As opposed to the current operational configuration of AROME-France data assimilation system, the hydrometeor specific contents are added in the control variable and are thus updated during the assimilation process. Consequently, a lightning observation operator based on the specific contents of snow and graupel is used. As the first MTG satellite was launched in December 2022, the real MTG-LI observations are not available yet, and proxy spaceborne lightning observations generated from ground-based lightning detection network are used. We assess the forecast skills of experiments assimilating lightning in a close to operational configuration by comparing the results to a reference experiment that does not assimilate lightning. In the studied cases, the frequency bias and fraction skill score are improved for brightness temperatures forecasts lower than 280 K up to 4 h after the assimilation, implying a better description of the general cloud cover. The convective cores, identified by brightness temperatures lower than 220 K, are better captured in the first forecast hour but this improvement is not maintained through time and quickly fades.

Leveraging Frequency-Guided Mixer and Target-Aware Attention for Ground-Based Cloud Detection

Leveraging Frequency-Guided Mixer and Target-Aware Attention for Ground-Based Cloud Detection

Retrieval of Interior Structure of Asteroids with the Low-Frequency Telescope DART

Understanding the internal structure of asteroids is crucial for deciphering their formation and establishing defenses against potential hazards. The Daocheng Radio Telescope (DART), a recently constructed interferometric array designed for low-frequency Sun imaging, presents a promising tool for probing asteroid interiors. With a substantial 1-km array aperture and an equivalent receiving area of approximately 8,850 m 2 , DART plays a vital role in diagnosing asteroid internal structures. This study introduces an electromagnetic wave scattering model tailored to asteroids within DART’s operational frequency range (150 to 450 MHz). Ground-based radar detection can unveil multiple facets of these celestial bodies by leveraging low-frequency waves’ penetrating capabilities and capitalizing on asteroids’ rotational dynamics. Through simulations capturing the characteristics of low-frequency waves traversing a layered model and interacting with internal structures, we propose an electromagnetic scattering model of asteroids. Our results underscore DART’s potential as a crucial instrument for discerning the internal structure of near-Earth objects. We first formulate an asteroid model through celestial impact models, dimensional analysis, and data fitting to achieve this. Subsequently, we derive an electromagnetic scattering model using geometric optics and a propagation model for lossy mediums. Simulations demonstrate that morphology and internal structure dictate the distribution of scattered waves, with forward and backscattered waves providing comprehensive internal structure information over a rotation cycle. Furthermore, we observe that alterations in electromagnetic wave frequency induce changes in the scattering characteristics, prompting the convenience of employing multiple frequencies for retrieving detailed information about an asteroid’s internal medium and structure. This multidimensional approach positions DART as a promising asset in advancing our understanding of asteroid interiors, offering valuable insights for scientific inquiry and hazard mitigation strategies.

The first ground-based detection of the 752 GHz water line in local ultra-luminous infrared galaxies using APEX-SEPIA

Abstract We report the first ground-based detection of the water line p-H2O (211–202) at 752.033 GHz in three z &amp;lt; 0.08 ultra-luminous infrared galaxies (ULIRGs): IRAS 06035-7102, IRAS 17207-0014 and IRAS 09022-3615. Using the Atacama Pathfinder EXperiment (APEX), with its Swedish-ESO PI Instrument for APEX (SEPIA) band-9 receiver, we detect this H2O line with overall signal-to-noise ratios of 8–10 in all three galaxies. Notably, this is the first detection of this line in IRAS 06035-7102. Our new APEX-measured fluxes, between 145 to 705 Jy km s−1, are compared with previous values taken from Herschel SPIRE FTS. We highlight the great capabilities of APEX for resolving the H2O line profiles with high spectral resolutions while also improving by a factor of two the significance of the detection within moderate integration times. While exploring the correlation between the p-H2O(211–202) and the total infrared luminosity, our galaxies are found to follow the trend at the bright end of the local ULIRG’s distribution. The p-H2O(211–202) line spectra are compared to the mid-J CO and HCN spectra, and dust continuum previously observed with ALMA. In the complex interacting system IRAS 09022-3615, the profile of the water emission line is offset in velocity with respect to the ALMA CO(J = 4 − 3) emission. For IRAS 17207-0014 and IRAS 06035-7102, the profiles between the water line and the CO lines are spectroscopically aligned. This pilot study demonstrates the feasibility of directly conducting ground-based high-frequency observations of this key water line, opening the possibility of detailed follow-up campaigns to tackle its nature.

NIGHT: A compact, near-infrared, high-resolution spectrograph to survey helium in exoplanet systems

ABSTRACT Among highly irradiated exoplanets, some have been found to undergo significant hydrodynamic expansion traced by atmospheric escape. To better understand these processes in the context of planetary evolution, we propose NIGHT (the Near-Infrared Gatherer of Helium Transits). NIGHT is a high-resolution spectrograph dedicated to surveying and temporally monitoring He i triplet absorption at 1083 nm in stellar and planetary atmospheres. In this paper, we outline our scientific objectives, requirements, and cost-efficient design. Our simulations, based on previous detections and modelling using the current exoplanet population, determine our requirements and survey targets. With a spectral resolution of 70 000 on a 2-m telescope, NIGHT can accurately resolve the helium triplet and detect 1 per cent peak absorption in 118 known exoplanets in a single transit. Additionally, it can search for 3σ temporal variations of 0.4 per cent in 66 exoplanets in-between two transits. These are conservative estimates considering the ongoing detections of transiting planets amenable to atmospheric characterization. We find that instrumental stability at 40 m s−1, less stringent than for radial velocity monitoring, is sufficient for transmission spectroscopy in He i. As such, NIGHT can utilize mostly off-the-shelf components, ensuring cost-efficiency. A fibre-fed system allows for flexibility as a visitor instrument on a variety of telescopes, making it ideal for follow-up observations after JWST or ground-based detections. Over a few years of surveying, NIGHT could offer detailed insights into the mechanisms shaping the hot Neptune desert and close-in planet population by significantly expanding the statistical sample of planets with known evaporating atmospheres. First light is expected in 2024.

Long-Range Thermal Target Detection in Data-Limited Settings Using Restricted Receptive Fields.

Long-range target detection in thermal infrared imagery is a challenging research problem due to the low resolution and limited detail captured by thermal sensors. The limited size and variability in thermal image datasets for small target detection is also a major constraint for the development of accurate and robust detection algorithms. To address both the sensor and data constraints, we propose a novel convolutional neural network (CNN) feature extraction architecture designed for small object detection in data-limited settings. More specifically, we focus on long-range ground-based thermal vehicle detection, but also show the effectiveness of the proposed algorithm on drone and satellite aerial imagery. The design of the proposed architecture is inspired by an analysis of popular object detectors as well as custom-designed networks. We find that restricted receptive fields (rather than more globalized features, as is the trend), along with less downsampling of feature maps and attenuated processing of fine-grained features, lead to greatly improved detection rates while mitigating the model's capacity to overfit on small or poorly varied datasets. Our approach achieves state-of-the-art results on the Defense Systems Information Analysis Center (DSIAC) automated target recognition (ATR) and the Tiny Object Detection in Aerial Images (AI-TOD) datasets.

Space-based gravitational wave signal detection and extraction with deep neural network

Space-based gravitational wave (GW) detectors will be able to observe signals from sources that are otherwise nearly impossible from current ground-based detection. Consequently, the well established signal detection method, matched filtering, will require a complex template bank, leading to a computational cost that is too expensive in practice. Here, we develop a high-accuracy GW signal detection and extraction method for all space-based GW sources. As a proof of concept, we show that a science-driven and uniform multi-stage self-attention-based deep neural network can identify synthetic signals that are submerged in Gaussian noise. Our method exhibits a detection rate exceeding 99% in identifying signals from various sources, with the signal-to-noise ratio at 50, at a false alarm rate of 1%. while obtaining at least 95% similarity compared with target signals. We further demonstrate the interpretability and strong generalization behavior for several extended scenarios.

Exploring the Extremes: Characterizing a New Population of Old and Cold Brown Dwarfs

Mapping out the populations of thick disk and halo brown dwarfs is important for understanding the metallicity dependence of low-temperature atmospheres and the substellar mass function. Recently, a new population of cold and metal-poor brown dwarfs has been discovered, with T eff ≲ 1400 K and metallicity ≲−1 dex. This population includes what may be the first known “extreme T-type subdwarfs” and possibly the first Y-type subdwarf, WISEA J153429.75−104303.3. We have conducted a Gemini YJHK/Ks photometric follow-up campaign targeting potentially metal-poor T and Y dwarfs, utilizing the GNIRS and Flamingos-2 instruments. We present 14 near-infrared photometric detections of eight unique targets: six T subdwarf candidates, one moderately metal-poor Y dwarf candidate, and one Y subdwarf candidate. We have obtained the first-ever ground-based detection of the highly anomalous object WISEA J153429.75−104303.3. The F110W − J color of WISEA J153429.75−104303.3 is significantly bluer than that of other late T and Y dwarfs, indicating that WISEA J153429.75−104303.3 has an unusual spectrum in the 0.9–1.4 μm wavelength range which encompasses the J-band peak. Our J-band detection of WISEA J153429.75−104303.3 and corresponding model comparisons suggest a subsolar metallicity and temperature of 400–550 K for this object. JWST spectroscopic follow-up at near-infrared and mid-infrared wavelengths would allow us to better understand the spectral peculiarities of WISEA J153429.75−104303.3, assess its physical properties, and conclusively determine whether or not it is the first Y-type subdwarf.

Tree Species Classification Based on PointNet++ and Airborne Laser Survey Point Cloud Data Enhancement

Compared with ground-based light detection and ranging (LiDAR) data, the differential distribution of the quantity and quality of point cloud data from airborne LiDAR poses difficulties for tree species classification. To verify the feasibility of using the PointNet++ algorithm for point cloud tree species classification with airborne LiDAR data, we selected 11 tree species from the Minjiang River Estuary Wetland Park in Fuzhou City and Sanjiangkou Ecological Park. Training and testing sets were constructed through pre-processing and segmentation, and direct and enhanced down-sampling methods were used for tree species classification. Experiments were conducted to adjust the hyperparameters of the proposed algorithm. The optimal hyperparameter settings used the multi-scale sampling and grouping (MSG) method, down-sampling of the point cloud to 2048 points after enhancement, and a batch size of 16, which resulted in 91.82% classification accuracy. PointNet++ could be used for tree species classification using airborne LiDAR data with an insignificant impact on point cloud quality. Considering the differential distribution of the point cloud quantity, enhanced down-sampling yields improved the classification results compared to direct down-sampling. The MSG classification method outperformed the simplified sampling and grouping classification method, and the number of epochs and batch size did not impact the results.

Denoising of Hyperspectral Signal from Drone for Ganoderma Disease Detection in Oil Palm

Oil palm is an important crop that generates high income to Malaysia. However, the oil palm is susceptible to Ganoderma infection that reduces the productivity of the oil palm. Conventional ground-based disease detection is laborious and costly. Therefore, airborne remote sensing technology coupled with ground detection provides a more effective control of the disease. Airborne hyperspectral remote sensing utilizes narrow and contiguous bands to assist in detection of diseases in crops. Spectral responses recorded by the camera tend to suffer from interference and these noises could reduce the quality of the data. Therefore, this study presents the application of Savitzky-Golay and wavelet spectral denoising technique to improve the hyperspectral signatures for Ganoderma disease detection in oil palm

Uncovering Early Traces of Bark Beetle Induced Forest Stress via Semantically Enriched Sentinel-2 Data and Spectral Indices

Forest ecosystems are shaped by both abiotic and biotic disturbances. Unlike sudden disturbance agents, such as wind, avalanches and fire, bark beetle infestation progresses gradually. By the time infestation is observable by the human eye, trees are already in the final stages of infestation—the red- and grey-attack. In the relevant phase—the green-attack—biochemical and biophysical processes take place, which, however, are not or hardly visible. In this study, we applied a time series analysis based on semantically enriched Sentinel-2 data and spectral vegetation indices (SVIs) to detect early traces of bark beetle infestation in the Berchtesgaden National Park, Germany. Our approach used a stratified and hierarchical hybrid remote sensing image understanding system for pre-selecting candidate pixels, followed by the use of SVIs to confirm or refute the initial selection, heading towards a 'convergence of evidence approach’. Our results revealed that the near-infrared (NIR) and short-wave-infrared (SWIR) parts of the electromagnetic spectrum provided the best separability between pixels classified as healthy and early infested. Referring to vegetation indices, we found that those related to water stress have proven to be most sensitive. Compared to a SVI-only model that did not incorporate the concept of candidate pixels, our approach achieved distinctively higher producer’s accuracy (76% vs. 63%) and user’s accuracy (61% vs. 42%). The temporal accuracy of our method depends on the availability of satellite data and varies up to 3 weeks before or after the first ground-based detection in the field. Nonetheless, our method offers valuable early detection capabilities that can aid in implementing timely interventions to address bark beetle infestations in the early stage.

Cloud-to-ground lightning occurrences over India: seasonal and diurnal characteristics deduced with ground-based lightning detection sensor network (LDSN)

Cloud-to-ground lightning occurrences over India: seasonal and diurnal characteristics deduced with ground-based lightning detection sensor network (LDSN)

Monte Carlo Simulations for Evaluating the Accuracy of Geostationary Lightning Mapper Detection Efficiency and False Alarm Rate Retrievals

Abstract Performance assessments of the Geostationary Lightning Mapper (GLM) are conducted via comparisons with independent observations from both satellite-based sensors and ground-based lightning detection (reference) networks. A key limitation of this evaluation is that the performance of the reference networks is both imperfect and imperfectly known, such that the true performance of GLM can only be estimated. Key GLM performance metrics such as detection efficiency (DE) and false alarm rate (FAR) retrieved through comparison with reference networks are affected by those networks’ own DE, FAR, and spatiotemporal accuracy, as well as the flash matching criteria applied in the analysis. This study presents a Monte Carlo simulation–based inversion technique that is used to quantify how accurately the reference networks can assess GLM performance, as well as suggest the optimal matching criteria for estimating GLM performance. This is accomplished by running simulations that clarify the specific effect of reference network quality (i.e., DE, FAR, spatiotemporal accuracy, and the geographical patterns of these attributes) on the retrieved GLM performance metrics. Baseline reference network statistics are derived from the Earth Networks Global Lightning Network (ENGLN) and the Global Lightning Dataset (GLD360). Geographic simulations indicate that the retrieved GLM DE is underestimated, with absolute errors ranging from 11% to 32%, while the retrieved GLM FAR is overestimated, with absolute errors of approximately 16% to 44%. GLM performance is most severely underestimated in the South Pacific. These results help quantify and bound the actual performance of GLM and the attendant uncertainties when comparing GLM to imperfect reference networks.

Utilizing UAV-based hyperspectral imaging to detect surficial explosive ordnance

Across postconflict regions of the world, explosive ordnance (EO), which includes remnant antipersonnel land mines, antivehicle/tank mines, unexploded cluster munitions, improvised explosive devices, and explosive remnants of war (ERW) such as unexploded ordnance and abandoned explosive ordnance, remains a critical humanitarian concern. Clearance and land release efforts anchored on manual geophysical detection and mechanical probing methods remain painstakingly slow, expensive, and dangerous to operators. As a result, postconflict regions impacted by EO contamination significantly lag in social and economic development. Developing, calibrating, and field testing more efficient detection methods for surficial EO is a crucial task. Unpiloted aerial systems featuring advanced remote sensing capabilities are a key technology that may allow the tide to turn in the EO crisis. Specifically, recent advances in hardware design have allowed for effective deployment of small, light, and less power consuming hyperspectral imaging (HSI) systems from small unpiloted aerial vehicles (UAVs). Our proof-of-concept study employs UAV-based HSI to deliver a safer, faster, and more cost-efficient method of surface land mine and ERW detection compared to current ground-based detection methods. Our results indicate that analysis of HSI data sets can produce spectral profiles and derivative data products to distinguish multiple ERW and mine types in a variety of host environments.