Source Detection Research Articles

Advanced chemometric methodologies on single shot hyphenated LIBS data for rapid and reliable characterization of plastic classes

BackgroundPlastic Solid Waste (PSW) sorting is a procedure of paramount importance in the mechanical recycling process of plastics waste. The major limitation of the techniques relying on physical properties of plastics is the time taken for analysis and poor accuracy. Spectroscopy has been shown to be a suitable method in plastic sorting due to its atomic and molecular characterization capabilities, and ability to give results in very short time scales. However, for practical applications it is essential to translate this technique into an automatic technology, by combining it with advanced chemometric tools which can give observer independent judgement. ResultsThe indigenously developed bi-model Laser Induced Breakdown Spectroscopy (LIBS)-Raman system with single source and single detector can record the LIBS-Raman spectral signals in single-shot mode in a total time frame of 20 ms. Out of the combinations of Principal Component Analysis (PCA) and Partial Least Squares (PLS) with Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Partial Least Squares-Discriminant Analysis (PLS-DA) based classifiers, the PLS-DA based model showed the maximum classification accuracy with 95 % based on LIBS data and 100 % based on Raman data. The reliability of the model was assessed using 4-fold cross-validation which showed a sensitivity of 90.28 % and specificity of 98.29 % for predictions based on LIBS data, and 99 % sensitivity and 99.82 % specificity for predictions relying on Raman data. SignificanceThe results show how the combination of multimodal spectroscopy with chemometric analysis enhances the applicability of spectroscopic techniques for plastic sorting. The classification model successfully classified seven types of post-consumer plastic samples based on combined LIBS and Raman data. With the home-built software for automated prediction, the system takes less than a second to predict the plastic type illustrating the potential of the method for translation to regular routine industrial applications.

Clinical usefulness and acceptability of small‐bowel capsule endoscopy with panoramic imaging compared with axial imaging in Japanese patients

AbstractObjectivesWe aimed to evaluate the usefulness and acceptability of CapsoCam Plus (CapsoCam) in Japanese patients.MethodsThis retrospective single‐center study enrolled 930 patients with suspected small‐bowel bleeding (SSBB) who underwent capsule endoscopy. Thirty‐three patients using CapsoCam and PillCam SB3 (SB3) were matched using propensity score matching. The diagnostic yield and the acceptability of CapsoCam were evaluated.ResultsThere was no SSBB case where capsule endoscopy was performed within 48 h of bleeding. CapsoCam had a significantly higher observation rate of the entire small bowel (97% vs. 73%, p = 0.006) and Vater's papilla (82% vs. 15%, p < 0.001) than SB3. The reading time of CapsoCam was significantly longer than that of SB3 (30 vs. 25 min, p < 0.001), and CapsoCam's time from the capsule endoscopy swallowing to read completion was longer than that of SB3 (37 vs. 12 h, p < 0.001). The two groups showed no difference in the capsule endoscopy findings according to the P classification. Notably, 85% of the patients using CapsoCam reported examination distress as “not at all” or “almost not,” and 94% reported swallowing difficulty as “very easy” or “easy.”ConclusionsCapsoCam took time to read; however, it is a well‐tolerated examination with a high observation rate of Vater's papilla and entire small‐bowel mucosa. Detectability of bleeding sources was comparable in both modalities for cases of occult SSBB and overt SSBB more than 48 h after bleeding. CapsoCam is a useful modality for patients with SSBB.

Controlled Discovery and Localization of Signals via Bayesian Linear Programming

Scientists often must simultaneously localize and discover signals. For instance, in genetic fine-mapping, high correlations between nearby genetic variants make it hard to identify the exact locations of causal variants. So the statistical task is to output as many disjoint regions containing a signal as possible, each as small as possible, while controlling false positives. Similar problems arise, for example, when locating stars in astronomical surveys and in changepoint detection. Common Bayesian approaches to these problems involve computing a posterior distribution over signal locations. However, existing procedures to translate these posteriors into credible regions for the signals fail to capture all the information in the posterior, leading to lower power and (sometimes) inflated false discoveries. We introduce Bayesian Linear Programming (BLiP), which can efficiently convert any posterior distribution over signals into credible regions for signals. BLiP overcomes an extremely high-dimensional and nonconvex problem to verifiably nearly maximize expected power while controlling false positives. Applying BLiP to existing state-of-the-art analyses of UK Biobank data (for genetic fine-mapping) and the Sloan Digital Sky Survey (for astronomical point source detection) increased power by 30%–120% in just a few minutes of additional computation. BLiP is implemented in pyblip (Python) and blipr (R). Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Exploring the bias: how skin color influences oxygen saturation readings via Monte Carlo simulations.

Our goal is to understand the root cause of reported oxygen saturation ( ) overestimation in heavily pigmented skin types to devise solutions toward enabling equity in pulse oximeter designs. We aim to gain theoretical insights into the effect of skin tone on curves using a three-dimensional, four-layer tissue model representing a finger. A finger tissue model, comprising the epidermis, dermis, two arteries, and a bone, was developed using a Monte Carlo-based approach in the MCmatlab software. Two skin tones-light and dark-were simulated by adjusting the absorption and scattering properties within the epidermal layer. Following this, curves were generated in various tissue configurations, including transmission and reflection modes using red and infrared wavelengths. In addition, the influence of source-detector (SD) separation distances on both light and dark skin tissue models was studied. In transmission mode, curves did not deviate with changes in skin tones because both pulsatile and non-pulsatile terms experienced equal attenuation at red and infrared wavelengths. However, in reflection mode, measurable variations in curves were evident. This was due to differential attenuation of the red components, which resulted in a lower perfusion index at the red wavelength in darker skin. As the SD separation increased, the effect of skin tone on curves in reflection mode became less pronounced, with the largest SD separation exhibiting effects similar to those observed in transmission mode. Monte Carlo simulations have demonstrated that different light pathlengths within the tissue contribute to the overestimation of in people with darker skin in reflection mode pulse oximetry. Increasing the SD separation may mitigate the effect of skin tone on readings. These trends were not observed in transmission mode; however, further planned research using more complex models of the tissue is essential.

An Attention-Based Convolutional Network Framework for Detection and Localization of GNSS Interference Sources

An Attention-Based Convolutional Network Framework for Detection and Localization of GNSS Interference Sources

Hetero-core fiber-optic surface plasmon resonance sensor with ionic liquid gel coating for CO2 sensing

Fiber-optic CO2 sensors are free of electromagnetic interference and highly resistant to corrosion but often require expensive measurement systems or sophisticated preparation procedures. To address this challenge, we developed a CO2 sensor comprising a hetero-core fiber-optic surface plasmon resonance sensor coated with an ionic liquid (IL) gel as a cost-effective CO2 sensing system. Among the various sensors tested, the sensor using ethyl-3-methylimidazolium tetrafluoroborate ([EMIM][BF4]) as the IL, prepared at a pull-up speed of 4.5 mm s−1, showed the best CO2 sensing performance. A hybrid membrane of [EMIM][BF4] and a CO2-absorbing polymer (monoethanolamine) demonstrated higher sensitivity for CO2 concentrations ranging from 0 to 100%. The highlight of the proposed system is its low-cost configuration comprising an 850 nm-wavelength LED and a photodiode as the light source and signal detection device, respectively. Along with facile preparation and low-cost system configuration, the proposed system exhibited significant potential for CO2 sensing in various fields.

Uncovering an Excess of X-Ray Point Sources in the Halos of Virgo Late-type Galaxies

We present a systematic search for extraplanar X-ray point sources around 19 late-type, highly inclined disk galaxies residing in the Virgo cluster, based on archival Chandra observations reaching a source detection sensitivity of L(0.5–8 keV) ∼ 1038 erg s−1. Based on the cumulative source surface density distribution as a function of projected vertical distance from the disk midplane, we identify a statistically significant (∼3.3σ) excess of ∼20 X-ray sources within a projected vertical off-disk distance of 0.′92–2.′5 (∼4.4–12 kpc), the presence of which cannot be explained by the bulk stellar content of the individual galaxies, nor by the cosmic X-ray background. On the other hand, there is no significant evidence for an excess of extraplanar X-ray sources in a comparison sample of field late-type edge-on galaxies, for which Chandra observations reaching a similar source detection sensitivity are available. We discuss the possible origins for the observed excess, including low-mass X-ray binaries (LMXBs) associated with globular clusters, supernova-kicked LMXBs, and high-mass X-ray binaries born in recent star formation induced by ram pressure stripping of the disk gas, as well as a class of intracluster X-ray sources previously identified around early-type member galaxies of Virgo. We find that none of these X-ray populations can naturally dominate the observed extraplanar excess, although supernova-kicked LMXBs and the effect of ram pressure are most likely relevant.

Modeling sub‐resolution porosity of a heterogeneous carbonate rock sample

AbstractAccurately estimating the petrophysical properties of heterogeneous carbonate rocks across various scales poses significant challenges, particularly within the context of water and hydrocarbon reservoir studies. Digital rock analysis techniques, such as X‐ray computed microtomography and synchrotron‐light‐based imaging, are increasingly employed to study the complex pore structure of carbonate rocks. However, several technical limitations remain, notably the need to balance the volume of interest with the maximum achievable resolution, which is influenced by geometric properties of the source–detector distance in each apparatus. Typically, higher resolutions necessitate smaller sample volumes, leading to a portion of the pore structure (the sub‐resolution or unresolved porosity), that remain undetected. In this study, X‐ray microtomography is used to infer the fluid flow properties of a carbonate rock sample having a substantial fraction of porosity below the imaging resolution. The existence of unresolved porosity is verified by comparisons with nuclear magnetic resonance (NMR) data. We introduce a methodology for modeling the sub‐resolution pore structure within images by accounting for unresolved pore bodies and pore throats derived from a predetermined distribution of pore throat radii. The process identifies preferential pathways between visible pores using the shortest distance and establishes connections between these pores by allocating pore bodies and throats along these paths, while ensuring compatibility with the NMR measurements. Single‐phase flow simulations are conducted on the full volume of a selected heterogeneous rock sample by using the developed pore network model. Results are then compared with petrophysical data obtained from laboratory measurements.

Study of Secondary Cosmic Rays and Astronomical X-Ray Sources using Small Stratospheric Balloons

The X-ray sources of the universe are extraterrestrial in nature which emit X-ray photons. The closest strong X-ray source is the Sun, which is followed by various compact sources such as neutron stars, black holes, the Crab pulsar, etc. In this paper, we analyze the data received from several low-cost lightweight meteorological balloon-borne missions launched by the Indian Centre for Space Physics. Our main interest is to study the variation of the vertical intensity of secondary cosmic rays, the detection of strong X-ray sources, and their spectra in the energy band of ∼10–80 keV during the complete flights. Due to the lack of an onboard pointing system, low exposure time, achieving a maximum altitude of only ∼42 km, and freely rotating the payload about its axis, we modeled the background radiation flux for the X-ray detector using physical assumptions. We also present the source detection method, observation of the pulsation of the Crab (∼33 Hz), and spectra of some sources such as the quiet Sun and the Crab pulsar.

In-fiber illumination-detection-synchronized confocal microscopy for high-precision fast axial scanning

Confocal microscopy remains a major workhorse in the 3D surface topography measurement owing to its reliability and compatibility for workpieces with various reflectance, which, however, suffers from slow scanning speed and limited axial resolution. Commercial confocal microscopy primarily relies on the axial movement of a sample or objective lens to create a series of 2D image stacks, and then reconstruct 3D map. Nevertheless, most samples and objective lens are too bulky to scan rapidly, let alone that multiple scans are required to increase the signal-to-noise ratio (SNR). To address this issue, an original approach named illumination-detection-synchronized confocal microscopy was developed, which enabled high-precision fast axial scanning via a compact fiber-multiplexed port with the high-speed synchronous motion of point source and point detection. The approach can effectively improve the peak localization precision while not requiring a high-precision motion stage. Theoretically, via controlling the magnification of the optical system M > 1, the total peak localization precision of axial light intensity response curve can be improved by M3 times. Experiments show that the single-point repeat measurement precision by this approach is improved by 4 times, and the profile measurement precision is enhanced by 2.3 times. Moreover, this approach can effectively avoid collisions between the sample and objective lens, and has the potential to design a low-cost single-point confocal sensor or confocal profilometer.

TRANSLIENT: Detecting Transients Resulting from Point-source Motion or Astrometric Errors

Detection of moving sources over a complicated background is important for several reasons. First is measuring the astrophysical motion of the source. Second is that such motion resulting from atmospheric scintillation, color refraction, or astrophysical reasons is a major source of false alarms for image-subtraction methods. We extend the Zackay, Ofek, and Gal-Yam image-subtraction formalism to deal with moving sources. The new method, named the translient (translational transient) detector, applies hypothesis testing between the hypothesis that the source is stationary and that the source is moving. It can be used to detect source motion or to distinguish between stellar variability and motion. For moving source detection, we show the superiority of translient over the proper image subtraction, using the improvement in the receiver-operating characteristic curve. We show that in the small translation limit, translient is an optimal detector of point-source motion in any direction. Furthermore, it is numerically stable, fast to calculate, and presented in a closed form. Efficient transient detection requires both the proper image-subtraction statistics and the translient statistics: When the translient statistic is higher, then the subtraction residual is likely due to motion. We test our algorithm both on simulated data and on real images obtained by the Large Array Survey Telescope. We demonstrate the ability of translient to distinguish between motion and variability, which has the potential to reduce the number of false alarms in transients detection. We provide the translient implementation in Python and MATLAB.

Double debiased transfer learning for adaptive Huber regression

AbstractThrough exploiting information from the source data to improve the fit performance on the target data, transfer learning estimations for high‐dimensional linear regression models have drawn much attention recently, but few studies focus on statistical inference and robust learning in the presence of heavy‐tailed/asymmetric errors. Using adaptive Huber regression (AHR) to achieve the bias and robustness tradeoff, in this paper we propose a robust transfer learning algorithm with high‐dimensional covariates, then construct valid confidence intervals and hypothesis tests based on the debiased lasso approach. When the transferable sources are known, a two‐step ‐penalized transfer AHR estimator is firstly proposed and the error bounds are established. To correct the biases caused by the lasso penalty, a unified debiasing framework based on the decorrelated score equations is considered to establish asymptotic normality of the debiased lasso transfer AHR estimator. Confidence intervals and hypothesis tests for each component can be constructed. When the transferable sources are unknown, a data‐driven source detection algorithm is proposed with theoretical guarantee. Numerical studies verify the performance of our proposed estimator and confidence intervals, and an application to Genotype‐Tissue Expression data is also presented.

Constrained Iterative Adaptive Algorithm for the Detection and Localization of RFI Sources Based on the SMAP L-Band Microwave Radiometer

The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas.

Multi unmanned aerial vehicle odor source location based on improved artificial fish swarm algorithm

Odor source location technology has important application value in environmental monitoring, safety emergency and search and rescue operations. For example, it can be used in post-disaster search and rescue, detection of hazardous gas leakage, and fire source detection. Existing odor source location methods have problems such as low search efficiency, inability to adapt to complex environments, and inaccurate odor source location. In this study, based on unmanned aerial vehicle technology and using swarm intelligence optimization algorithm, an improved artificial fish swarm algorithm (IAFSA) is proposed by combining curiosity in psychology on the basis of retaining the good optimization performance of the artificial fish swarm algorithm. The algorithm quantifies the curiosity of artificial fish searching high-concentration areas through a model, dynamically adjusts the artificial fish's field of vision and step length with the calculated curiosity factor, and avoids the oscillation phenomenon in the later stage of the algorithm. Simulation results show that the IAFSA has a higher success rate and smaller location error. Finally, odor source location experiments were carried out in an indoor physical environment, the feasibility of the odor source location method proposed in this study is verified in actual scenarios.

U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers

Abstract. Current methods for detecting atmospheric plumes and inferring point-source rates from high-resolution satellite imagery are labor-intensive and not scalable with regard to the growing satellite dataset available for methane point sources. Here, we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a U-Net machine learning architecture for image segmentation. The second step quantifies the point-source rate from the masked plume using wind speed information and either a convolutional neural network (CNN) or a physics-based integrated mass enhancement (IME) method. The algorithm can process 62 images (each measuring 128 pixels × 128 pixels) per second on a single 2.6 GHz Intel Core i7-9750H CPU. We train the algorithm using large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point-source observability, Ops=Q/(UWΔB), as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate Q, wind speed U, instrument pixel size W, and instrument-dependent background noise ΔB. We show that Ops can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h−1 in GHGSat-C1 images over surfaces with low background noise and successfully handles larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates, while the CNN method is biased towards the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2–4 m s−1 is optimal for detection and quantification of point sources from satellite data.

Bottom‐bounce ray angle subspace detector in the shadow zone of deep water

AbstractPassive source detection is a challenging problem in the shadow zone, where the sound is contributed primarily by bottom‐bounce rays. The conventional beamforming detector (CBFD), which utilises the sound energy from a single direction, suffers potential significant performance degradation in the multipath‐signal scenario. The matched field detector (MFD) offers optimal performance by exploiting full‐wave field characteristics but is limited due to its reliance on prior ocean environmental knowledge. The authors demonstrate that the incident sound on a near‐surface vertical line array in the shadow zone can be approximated as a coherent sum of two plane waves that share a symmetric arrival angle about the horizontal. This leads to the signal subspace depending only on the arrival angle, which the authors call the bottom‐bounce ray angle subspace (BRAS). With generalised likelihood ratio test theory, the authors further derive the BRAS detector (BRASD). It can utilise the full signal energy under the premise of weak environmental knowledge requirements and is generally superior to the CBFD. Simulation results in a typical deep‐water channel under different source‐position and array configuration conditions demonstrate the effectiveness of the BRASD and suggest that it can even offer a performance comparable to that of the MFD under specific conditions.

Diversity of Sarcocystis parasites in southeastern Baltic Sea catchment ecosystems.

Currently, research on apicomplexan Sarcocystis parasites is mainly carried out by analyzing animal carcasses. However, environmental studies would not only allow faster detection of possible sources of infection but also avoid the use of animals for investigations. Therefore, in the current study, we aimed to identify tested Sarcocystis species in sediment collected from water bodies located in the southeastern Baltic countries. A total of 99 sediment samples were collected during the summer from different types of water bodies in Estonia, Latvia, Lithuania, and Poland. Species-specific nested PCR targeting cox1 gene was used for the detection of selected Sarcocystis species (S. cruzi, S. bovifelis, S. hirsuta, S. arieticanis, S. tenella, S. capracanis, S. miescheriana, and S. bertrami) infecting livestock. The results showed a statistically lower (p < 0.05) occurrence of Sarcocystis parasites in Estonia (50%) compared to three countries, where the detection rate of Sarcocystis spp. DNA was remarkably higher, ranging from 88 to 100%. Among Sarcocystis species tested, S. cruzi (83.8%) and S. arieticanis (55.6%) using cattle and sheep as their intermediate hosts were most commonly identified. The detection rates of some of the analyzed Sarcocystis species were significantly different in southeastern Baltic countries. It is discussed that the detection rates of certain Sarcocystis species depend not only on the number of animals per 1 km2 but also on various ecological factors and farming practices that differ in the amount of contact domestic animals have with predators and the potential for animals to become infected through natural water or food sources.

ConvLSTM-based Sound Source Localization in a manufacturing workplace

In this paper, Sound Source Localization (SSL) is explored as an approach to localize both human operators and machines emitting sound signals in a manufacturing workplace. In particular, a comprehensive analysis of the source localization ability of a state-of-the-art deep learning architecture in environments of increasing complexity is presented. Scenarios including single, dual, and multiple sound sources, in the form of both human and Computerized Numerical Control (CNC) machines, are investigated, as well as configurations with a mix of stationary and moving sources. Our work contributes to the extant literature by enriching previous research findings primarily devoted to single stationary sources. Furthermore, by focusing on the simultaneous and centralized detection of sources of different nature and type, it diverges from traditional SSL studies in manufacturing, which emphasize the localization of humans by robots in human–robot interaction, and presents a localization approach which enables a broader control over the workspace. For the localization task, a Convolutional LSTM architecture able to capture both spatial and temporal sound characteristics is also proposed, with each source assigned a dedicated model.Extensive experiments were carried out for each scenario in a simulated environment, where different levels of noise were also applied. The results showed the remarkable accuracy and robustness of the deep learning models when it comes to localizing single and dual stationary sources, as well as single moving sources. For multiple stationary and moving sources a general decline in the detection performance was observed, alongside a heightened sensitivity to noise.

Next Generation Seismic Source Detection by Computer Vision: Untangling the Complexity of the 2016 Kaikōura Earthquake Sequence

AbstractSeismic source locations are fundamental to many fields of Earth and planetary sciences, such as seismology, volcanology and tectonics. However, seismic source detection and location are challenging when events cluster closely in space and time with signals tangling together at observing stations, such as they often do in major aftershock sequences. Though emerging algorithms and artificial intelligence (AI) models have made processing high volumes of seismic data easier, their performance is still limited, especially for complex aftershock sequences. In this study, we propose a novel approach that utilizes three‐dimensional image segmentation—a computer vision technique—to detect and locate seismic sources, and develop this into a complete workflow, Source Untangler Guided by Artificial intelligence image Recognition (SUGAR). In our synthetic and real data tests, SUGAR can handle complex, energetic earthquake sequences in near real time better than skillful analysts and other AI and non‐AI based algorithms. We apply SUGAR to the 2016 Kaikōura, New Zealand sequence and obtain five times more events than the analyst‐based GeoNet catalog. The improved aftershock distribution illuminates a continuous fault system with extensive fracture zones beneath the segmented, discontinuous surface ruptures. Our method has broader applicability to non‐earthquake sources and other time series image data sets.

The SRG/eROSITA All-Sky Survey

Clusters of galaxies can be used as powerful probes to study astrophysical processes on large scales, test theories of the growth of structure, and constrain cosmological models. The driving science goal of the SRG/eROSITA All-Sky Survey is to assemble a large sample of X-ray clusters with a well-defined selection function to determine the evolution of the mass function and, hence, the cosmological parameters. We present here a catalog of 12 247 optically confirmed galaxy groups and clusters detected in the 0.2–2.3 keV as extended X-ray sources in a 13 116 deg2 region in the western Galactic half of the sky, which eROSITA surveyed in its first six months of operation. The clusters in the sample span the redshift range 0.003 &lt; z &lt; 1.32. The majority (68%) of these clusters, 8361 sources, represent new discoveries without known counterparts in the literature. The mass range of the sample covers three orders of magnitude from 5 × 1012 Msun to 2 × 1015Msun. We construct a sample for cosmology with a higher purity level (~95%) than the primary sample, comprising 5259 securely detected and confirmed clusters in the 12791 deg2 common footprint of eRASS1 and the DESI Legacy Survey DR10. We characterize the X-ray properties of each cluster, including their flux, luminosity and temperature, the total mass, gas mass, gas mass fraction, and mass proxy YX. These are determined within two apertures, 300 kpc, and the overdensity radius R500, and are calculated by applying a forward modeling approach with a rigorous X-ray background treatment, K-factor, and the Galactic absorption corrections. Population studies utilizing log N-log S, the number of clusters detected above a given flux limit, and the luminosity function show overall agreement with the previous X-ray surveys after accounting for the survey completeness and purity through the selection function. The first eROSITA All-Sky Survey provides an unprecedented sample of galaxy groups and clusters selected in the X-ray band. The eRASS1 cluster catalog demonstrates the excellent performance of eROSITA for extended source detection, consistent with the pre-launch expectations for the final all-sky survey, eRASS:8.