Real Observational Data Research Articles

Simplified models of wind-wave relationships in China's shallow-water coasts based on SWAN+ADCIRC simulations

The study of the shallow-water wind-wave relationship holds considerable importance in the realm of coastal engineering construction. However, existing empirical formulas for China's coastal areas face predictive accuracy challenges due to the discontinuity and insufficient temporal and spatial resolution of conventional observations, such as buoy and satellite data. Therefore, this study emphasizes two representative shallow-water areas along China's coast, the Pearl River Estuary and Bohai Bay. Based on the advanced SWAN + ADCIRC wave-current coupling model, numerical models for calculating wave evolution are established, calibrated and validated by real observation data. From these numerical foundations, a new model capturing shallow-water wind-wave relationship is proposed, demonstrating substantive applicability specifically tailored to the estuary and bay regions. The accuracy and applicability of the model are validated by comparing with real observation data. In addition, probabilistic models are introduced in this study accounting for the randomness of the significant wave height. These enhancements allow for refined quantification of significant wave height occurrence probabilities relative to specified wind speeds, offering informed predictive parameters, including upper and lower limits.

A Data-driven Approach for Mitigating Dark Current Noise and Bad Pixels in Complementary Metal Oxide Semiconductor Cameras for Space-based Telescopes

In recent years, there has been a gradual increase in the performance of complementary metal oxide semiconductor (CMOS) cameras. These cameras have gained popularity as a viable alternative to charge-coupled device cameras in a wide range of applications. One particular application is the CMOS camera installed in small space telescopes. However, the limited power and spatial resources available on satellites present challenges in maintaining ideal observation conditions, including temperature and radiation environment. Consequently, images captured by CMOS cameras are susceptible to issues such as dark current noise and defective pixels. In this paper, we introduce a data-driven framework for mitigating dark current noise and bad pixels for CMOS cameras. Our approach involves two key steps: pixel clustering and function fitting. During the pixel clustering step, we identify and group pixels exhibiting similar dark current noise properties. Subsequently, in the function fitting step, we formulate functions that capture the relationship between dark current and temperature, as dictated by the Arrhenius law. Our framework leverages ground-based test data to establish distinct temperature–dark current relations for pixels within different clusters. The cluster results could then be utilized to estimate the dark current noise level and detect bad pixels from real observational data. To assess the effectiveness of our approach, we have conducted tests using real observation data obtained from the Yangwang-1 satellite, equipped with a near-ultraviolet telescope and an optical telescope. The results show a considerable improvement in the detection efficiency of space-based telescopes.

Accuracy of ALMA estimates of young disk radii and masses

Context. Protoplanetary disks, which are the natural consequence of the gravitational collapse of the dense molecular cloud cores, host the formation of the known planetary systems in our universe. Substantial efforts have been dedicated to investigating the properties of these disks in the more mature Class II stage, either via numerical simulations of disk evolution from a limited range of initial conditions or observations of their dust continuum and line emission from specific molecular tracers. The results coming from these two standpoints have been used to draw comparisons. However, few studies have investigated the main limitations at work when measuring the embedded Class 0/I disk properties from observations, especially in a statistical fashion. Aims. In this study, we provide a first attempt to compare the accuracy of some critical disk parameters in Class 0/I systems, as derived on real ALMA observational data, with the corresponding physical parameters that can be directly defined by theoreticians and modellers in numerical simulations. The approach we follow here is to provide full post-processing of the numerical simulations and apply it to the synthetic observations the same techniques used by observers to derive the physical parameters. Methods. We performed 3D Monte Carlo radiative transfer and mock interferometric observations of the disk populations formed in a magnetohydrodynamic (MHD) simulation model of disk formation through the collapse of massive clumps with the tools RADMC-3D and CASA, respectively, to obtain their synthetic observations. With these observations, we re-employed the techniques commonly used in disk modelling from their continuum emissions to infer the properties that would most likely be obtained with real interferometers. We then demonstrated how these properties may vary with respect to the gas kinematics analyses and dust continuum modelling. Results. Our modelling procedure, based on a two-component model for the disk and the envelope, shows that the disk sizes can be properly recovered from observations with sufficient angular resolutions, with an uncertainty of a factor ≈1.6–2.2, whereas their masses cannot be accurately measured. Overall, the masses are predominantly underestimated for larger, more massive disks by a median factor of ≈2.5, and even up to 10 in extreme cases, with the conversion from flux to dust mass under the optically thin assumption. We also find that the single Gaussian fittings are not a reliable modelling technique for young, embedded disks characterised by a strong presence of the envelopes. Thus, such an approach is to be used with caution. Conclusions. The radiative transfer post-processing and synthetic observations of MHD simulations offer genuine help in linking important observable properties of young planet-forming disks to their intrinsic values in simulations. Further extended investigations that tackle the caveats of this study, such as the lack of variation in the dust composition and distribution, dust-to-gas ratio, and other shortcomings in the numerical models, would be essential for setting constraints on our understanding of disk and planet formations.

Identifying Exoplanet Candidates Using WaveCeptionNet

In this study, we propose a wavelet-transform-based light curve representation method and a CNN model based on Inception-v3 for fast classification of light curves, enabling the quick discovery of potentially interesting targets from massive data. Experimental results on real observation data from the TESS showed that our wavelet processing method achieved about a 32-fold dimension reduction, while largely removing noise. We fed the wavelet-decomposed components of light curves into our improved Inception-v3 CNN model, achieving an accuracy of about 95%. Furthermore, our model achieves F1-scores of 95.63%, 95.93%, 95.65%, and 89.60% for eclipsing binaries, planet candidates, variable stars, and instrument noise, respectively. The precision rate of planet candidates identification reaches 96.49%, and the recall rate reaches 95.38% in the test set. The results demonstrate the effectiveness of our method for light curve.

Constructing the three-dimensional extinction density maps using V-net

ABSTRACT One of the major challenges we face is how to quickly and accurately create the three-dimensional (3D) density distributions of interstellar dust in the Milky Way using extinction and distance measurements of large samples of stars. In this study, we introduce a novel machine-learning approach that utilizes a convolution neural network, specifically a V-net, to infer the 3D distribution of dust density. Experiments are performed within two regions located towards the Galactic anticentre. The neural network is trained and tested using 10 000 simulations of dust density and line-of-sight extinction maps. Evaluation of the test sample confirms the successful generation of dust density maps from extinction maps by our model. Additionally, the performance of the trained network is evaluated using data from the literature. Our results demonstrate that our model is capable of capturing detailed dust density variations and can recover dust density maps while reducing the ‘fingers of god’ effect. Moving forward, we plan to apply this model to real observational data to obtain the fine distribution of dust at large and small scales in the Milky Way.

Synchronous Retrieval of Wheat Cab and LAI from UAV Remote Sensing: Application of the Optimized Estimation Inversion Framework

Chlorophyll a and b content (Cab) and leaf area index (LAI) are two key parameters of crops, and their quantitative inversions are important for growth monitoring and the field management of wheat. However, due to the close correlation between the spectral signals of these two parameters and the effects of soil and atmospheric conditions, as well as modeling errors, synchronous retrieval of LAI and Cab from remote sensing data is still a challenging task. In a previous study, we introduced the optimal estimation theory and established the inversion framework by coupling the PROSAIL (PROSPECT + SAIL) model with the unified linearized vector radiative transfer model (UNL-VRTM). The framework fully utilizes the simulated radiance spectra for synchronous retrieval of Cab and LAI at the UAV observation scale and has good convergence and self-consistency. In this study, based on this inversion framework, synchronized retrieval of Cab and LAI was carried out by real wheat UAV observation data and validated with the ground-measured data. By comparing with the empirical statistical model constructed by the PROSAIL model and coupled model, least squares support vector machine (LSSVM), and random forest (RF), the proposed method has the highest accuracy of Cab and LAI estimated from UAV multispectral data (for Cab, R2 = 0.835, RMSE = 14.357; for LAI, R2 = 0.892, RMSE = 0.564). Our proposed method enables the fast and efficient estimation of Cab and LAI in multispectral data without prior measurements and training.

Toward Effective Aircraft Call Sign Detection Using Fuzzy String-Matching between ASR and ADS-B Data

Recently, artificial intelligence and data science have witnessed dramatic progress and rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). Consequently, new end-to-end Recurrent Neural Network (RNN) toolkits were developed with higher speed and accuracy that can often achieve a Word Error Rate (WER) below 10%. These toolkits can nowadays be deployed, for instance, within aircraft cockpits and Air Traffic Control (ATC) systems in order to identify aircraft and display recognized voice messages related to flight data, especially for airports not equipped with radar. Hence, the performance of air traffic controllers and pilots can ultimately be improved by reducing workload and stress and enforcing safety standards. Our experiment conducted at Tangier’s International Airport ATC aimed to build an ASR model that is able to recognize aircraft call signs in a fast and accurate way. The acoustic and linguistic models were trained on the Ibn Battouta Speech Corpus (IBSC), resulting in an unprecedented speech dataset with approved transcription that includes real weather aerodrome observation data and flight information with a call sign captured by an ADS-B receiver. All of these data were synchronized with voice recordings in a structured format. We calculated the WER to evaluate the model’s accuracy and compared different methods of dataset training for model building and adaptation. Despite the high interference in the VHF radio communication channel and fast-speaking conditions that increased the WER level to 20%, our standalone and low-cost ASR system with a trained RNN model, supported by the Deep Speech toolkit, was able to achieve call sign detection rate scores up to 96% in air traffic controller messages and 90% in pilot messages while displaying related flight information from ADS-B data using the Fuzzy string-matching algorithm.

PNet—A Deep Learning Based Photometry and Astrometry Bayesian Framework

Time-domain astronomy has emerged as a vibrant research field in recent years, focusing on celestial objects that exhibit variable magnitudes or positions. Given the urgency of conducting follow-up observations for such objects, the development of an algorithm capable of detecting them and determining their magnitudes and positions has become imperative. Leveraging the advancements in deep neural networks, we present PNet, an end-to-end framework designed not only to detect celestial objects and extract their magnitudes and positions, but also to estimate the photometric uncertainty. PNet comprises two essential steps. First, it detects stars and retrieves their positions, magnitudes, and calibrated magnitudes. Subsequently, in the second phase, PNet estimates the uncertainty associated with the photometry results, serving as a valuable reference for the light-curve classification algorithm. Our algorithm has been tested using both simulated and real observation data, demonstrating the ability of PNet to deliver consistent and reliable outcomes. Integration of PNet into data-processing pipelines for time-domain astronomy holds significant potential for enhancing response speed and improving the detection capabilities for celestial objects with variable positions and magnitudes.

Impact of cardiovascular events and major bleeding in mortality in patients with atrial fibrillation and active lung cancer: data from a multicentre, retrospective, observational study

Abstract Background Lung cancer has a poor prognosis for most patients, as it is frequently diagnosed in advanced tumour stages. Real world observational data on the impact of major cardiovascular events (MACE) and major bleedings (MB) in the prognosis of patients with atrial fibrillation (AF) and active lung cancer are very limited. Purpose Our aim was to investigate the impact of MACE and MB in mortality in this population. Methods We used data from CANAC-FA Registry (Active Cancer and AF, in Spanish, CÁNcer ACtivo y Fibrilación Auricular), an observational, multicentre, retrospective study. The medical records of all subjects attended at the outpatient oncology clinics solely or mainly attending lung cancer patients from January 1st, 2017 to December 31st, 2019 in five tertiary university hospitals in the south of Spain were reviewed. The first visit to the oncology clinic with AF diagnosis during the first year after the lung cancer detection was considered the basal visit. Follow up period ended at December 31st, 2021. MACE (hospital admission for cardiovascular causes) and MB (International Society of Thrombosis and Haemostasis definition) were registered, and impact on survival was assessed for the whole series and according to tumour stage. Results Among 6984 patients, 269 presented active lung cancer and AF (3.9%). Mean age was 71±8 years, and 91% were male. Tumour stage was I, II, III and IV in 11%, 11%, 34% and 45% of the study sample, respectively. Anticoagulants were prescribed to 84% of the patients. After up to 46 months of maximum follow-up, 33 patients presented 40 MACE (13 heart failure admissions, 9 pulmonary embolisms, 5 strokes, 5 severe symptomatic arrhythmias, 4 deep vein thrombosis, 2 transient ischemic attacks, 2 systemic embolisms and 1 acute coronary syndrome), 16 patients had a MB and 186 died. However, two years’ mortality was similar in those patients with MACE or MB in follow-up versus those without them, in the whole of series (79% versus 73%, p=0.79, figure A), and in those with advanced cancer stages (III-IV, 89% versus 85%, p=0.39, figure B). In spite of that, in those patients with early tumour stages (I-II), two years’ mortality was significantly higher in those who suffered MACE or MB than in those free of both of them (85% versus 25%, p=0.01, figure C), and this difference remained after adjusting by other independent predictors of mortality (Hazard Ratio 11.08 [2.69-45.58], p=0.001). Conclusions In patients with AF and active lung cancer, patients with MACE and MB in follow up had similar mortality than those without them in the subgroup with advanced cancer stages. However, mortality was significantly higher in patients with these complications versus those without them in the subgroup with early cancer stages. This information could be useful for individualizing therapeutic efforts in this population.Impact of events in survival

Image restoration with point-spread function regularization and active learning

ABSTRACT Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal the intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point-spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep-learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can restore images obtained by the telescope directly, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by the Large Synoptic Survey Telescope (LSST), Euclid, and the Chinese Space Station Telescope (CSST), to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research.

A multifidelity approach to robust orbit determination

This paper presents an algorithm for the preprocessing of observation data aimed at improving the robustness of orbit determination tools. Two objectives are fulfilled: obtain a refined solution to the initial orbit determination problem and detect possible outliers in the processed measurements. The uncertainty on the initial estimate is propagated forward in time and progressively reduced by exploiting sensor data available in said propagation window. Differential algebra techniques and a novel automatic domain splitting algorithm for second-order Taylor expansions are used to efficiently propagate uncertainties over time. A multifidelity approach is employed to minimize the computational effort while retaining the accuracy of the propagated estimate. At each observation epoch, a polynomial map is obtained by projecting the propagated states onto the observable space. Domains that do no overlap with the actual measurement are pruned thus reducing the uncertainty to be further propagated. Measurement outliers are also detected in this step. The refined estimate and retained observations are then used to improve the robustness of batch orbit determination tools. The effectiveness of the algorithm is demonstrated for a geostationary transfer orbit object using synthetic and real observation data from the TAROT network.

Application of Sparse Regularization in Spherical Radial Basis Functions-Based Regional Geoid Modeling in Colorado

Spherical radial basis function (SRBF) is an effective method for calculating regional gravity field models. Calculating gravity field models with high accuracy and resolution requires dense basis functions, resulting in complex models. This study investigated the application of sparse regularization in SRBFs-based regional gravity field modeling. L1-norm regularization, also known as the least absolute shrinkage selection operator (LASSO), was employed in the parameter estimation procedure. LASSO differs from L2-norm regularization in that the solution obtained by LASSO is sparse, specifically with a portion of the parameters being zero. A sparse model would be advantageous for improving the numerical efficiency by reducing the number of SRBFs. The optimization problem of the LASSO was solved using the fast iterative shrinkage threshold algorithm, which is known for its high efficiency. The regularization parameter was selected using the Akaike information criterion. It was specifically tailored to the L1-norm regularization problem. An approximate covariance matrix of the estimated parameters in the sparse solution was analytically constructed from a Bayesian viewpoint. Based on the remove–compute–restore technique, a regional geoid model of Colorado (USA) was calculated. The numerical results suggest that the LASSO adopted in this study provided competitive results compared to Tikhonov regularization; however, the number of basis functions in the final model was less than 25% of the Tikhonov regularization. Without significantly reducing model accuracy, the LASSO solution provides a very simple model. This is the first study to apply the LASSO to SRBFs-based modeling of the regional gravity field in real gravity observation data.

Using public clinical trial reports to probe non-experimental causal inference methods

BackgroundNon-experimental studies (also known as observational studies) are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in non-experimental studies require untestable assumptions. This lack of intrinsic verifiability makes it difficult both to compare different non-experimental study methods and to trust the results of any particular non-experimental study.MethodsWe introduce TrialProbe, a data resource and statistical framework for the evaluation of non-experimental methods. We first collect a dataset of pseudo “ground truths” about the relative effects of drugs by using empirical Bayesian techniques to analyze adverse events recorded in public clinical trial reports. We then develop a framework for evaluating non-experimental methods against that ground truth by measuring concordance between the non-experimental effect estimates and the estimates derived from clinical trials. As a demonstration of our approach, we also perform an example methods evaluation between propensity score matching, inverse propensity score weighting, and an unadjusted approach on a large national insurance claims dataset.ResultsFrom the 33,701 clinical trial records in our version of the ClinicalTrials.gov dataset, we are able to extract 12,967 unique drug/drug adverse event comparisons to form a ground truth set. During our corresponding methods evaluation, we are able to use that reference set to demonstrate that both propensity score matching and inverse propensity score weighting can produce estimates that have high concordance with clinical trial results and substantially outperform an unadjusted baseline.ConclusionsWe find that TrialProbe is an effective approach for probing non-experimental study methods, being able to generate large ground truth sets that are able to distinguish how well non-experimental methods perform in real world observational data.

Uncertainty propagation of turbulence parameters for typhoon and Non-typhoon winds in buffeting analysis of Long-span bridges

Wind turbulence-induced buffeting vibration affects the serviceability of long-span bridges and lead to fatigue problems. The randomness of turbulence parameters in the power spectrum density function is always disregarded, failing to well reproduce the dispersion of structural buffeting responses and achieve the extreme vibration amplitude. This study proposes a probabilistic buffeting analysis approach based on multi-point estimation, dimension reduction techniques, and the principle of maximum entropy accounting for the uncertainty of turbulence parameters. The uncertainties in turbulence power spectral density functions are quantified based on the long-term real observational wind data captured by the sensors installed at Xihoumen Bridge. These uncertainties are propagated into the buffeting response of the bridge using the proposed method. The results are compared with those of Monte Carlo simulation, suggesting a good agreement with substantially lower computational costs can be attained by the proposed method. Then, the probability density evolution of buffeting response versus wind speed is calculated. Results reveal that typhoon-induced buffeting response is greater than non-typhoon’s under low mean wind speeds (e.g., 25 m/s), whereas it is the opposite at high wind speeds (e.g., 30 m/s). The predicted probability density evolution of buffeting response exhibits a great consistency with real observations. By introducing the mean wind speed hazard curves for both typhoon and non-typhoon winds, the exceeding probability of vibration within a given period can be easily attained. It allows the estimation of return periods of structural responses and development of the uniform-risk approach for structure design rather than the conventional uniform-hazard design method.

Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation

The fast motion of low Earth orbit (LEO) satellites provides rapid geometric changes in a short time, which can accelerate the initialization of precise point positioning (PPP). The rapid convergence of ambiguity parameters is conducive to the rapid success of ambiguity fixing. This paper presents the performance of single- and four-system combined PPP Ambiguity Resolution (AR), enhanced with an ambiguity-float solution LEO. Two LEO constellations were designed: L was a typical polar orbit constellation, with a higher number of visible satellites at high latitudes than at low and middle latitudes; and M was designed to compensate for the lack of visible satellites at low and middle latitudes. The ground observation data of the LEO satellites at the MGEX stations were simulated. Because the global navigation satellite systems (GNSSs) were fully operational, the GNSS data were real observation data from the MGEX stations. Based on the daily observation datasets collected at 258 stations in the global MGEX observation network over three days (from January 1 to January 3 2022), in addition to the LEO simulation data, we evaluated the positioning performance of LEO ambiguity-float solution-enhanced PPP ambiguity resolution and compared it with LEO-enhanced PPP. The L+M mixed constellation was able to reduce the time to first fix (TTFF) of the four-system combined PPP-AR to 5 min, and four LEO satellites were sufficient to achieve this. L+M mixed constellation was able to redu ce the convergence time of the four-system combined PPP to 2 min. Unlike PPP-AR, PPP required more LEO satellites for augmentation to saturate.

Comparative study of typhoon wind hazard estimation in coastal region of China using different wind field parameter models

Radius to maximum wind speed (Rmax) and Holland pressure profile parameter (B) are two key parameters for parametric typhoon or hurricane wind field modelling and hazard estimation. They are difficult to be observed or estimated for all historical storms and therefore always unavailable in most best track datasets. The statistical models were developed to correlate these two parameters with other available parameters based on limited real observation data and widely employed in typhoon hazard estimation. The applicability of these models to different ocean regions remains questionable. In this study, series of statistical models of Rmax and B are revisited and classified based on their regions of application, i.e., Gulf of Mexico and Atlantic Basin (GM&AB), South Pacific (SP), Bay of Bengal (BB) and Western North Pacific (WNP, 0°N-60°N, 100°E−180°E). Moreover, recursive models of Rmax and B in WNP are also introduced. The comparison of variation characteristics of different models for these two key parameters along with the change of typhoon surface central pressure deficit ΔPs are conducted. The stochastic typhoon tracking and intensity models are then coupled with an analytical wind field model to estimate the typhoon wind hazard of five coastal cities of China. The probability of exceedance and return period curves of wind speed are compared using different Rmax and B parameter models. The use of different Rmax and B models is shown to have significant effects on wind hazard curves. The extreme wind speeds estimated using parameter models from GM&AB and wind field model in this study are almost consistent with each other. Different models in WNP produce significant differences of wind hazard curve. The new findings in this study provide a forward step to the rational estimation of typhoon wind hazard in WNP by using the appropriate wind field parameter models.

Disentangling Modified Gravity and Massive Neutrinos with Intrinsic Shape Alignments of Massive Halos

We present two new diagnostics based on the intrinsic shape alignments of group/cluster size dark matter halos to disentangle the effect of f(R) gravity from that of massive neutrinos. Using snapshot data from a series of the DUSTGRAIN-pathfinder N-body simulations for a Planck ΛCDM cosmology and three f(R) gravity models with massive neutrinos (ν), we first determine the probability density functions of the alignment angles between the shape orientations of massive halos and the minor principal axes of the local tidal fields. The numerically obtained results turn out to agree very well with the analytic formula derived under the assumption that the anisotropic merging along the cosmic web induces the halo shape alignments. The four cosmologies, which several standard diagnostics failed to discriminate, are found to yield significantly different best-fit values of the single parameter that characterizes their analytic formulae. We also numerically determine the spatial cross-correlations between the shape orientations of neighbor group/cluster halos, and find them to be in good agreements with a fitting formula characterized by two parameters, whose best-fit values are found to differ substantially among the four models. We also discuss the limitations and caveats of these new diagnostics that must be overcome for their application to real observational data.

Data-driven and physics-informed Bayesian learning of spatiotemporally varying consolidation settlement from sparse site investigation and settlement monitoring data

A digital twin of a geotechnical project (e.g., a reclamation or ground improvement project) is a virtual model that aims to continuously learn from actual observations (e.g., site investigation and monitoring data) and improve model prediction (e.g., spatiotemporally varying consolidation settlement). However, real geotechnical observation data obtained from a site are often spatially sparse (e.g., site investigation data) and spatiotemporally varying (e.g., settlement monitoring data). The sparse and spatiotemporally varying data pose great challenges for continuous learning of data and improvement in model prediction. To address these challenges, this study proposes a novel data-driven and physics-informed Bayesian learning framework that automatically develops ground models from spatially sparse site investigation data, performs geotechnical analysis, and integrates geotechnical analysis results with limited, but spatiotemporally varying, settlement monitoring data to improve model prediction in a systematic and quantitative manner. The proposed method contains three key components, (1) data-driven ground modeling by Bayesian compressive sampling (BCS) using sparse site investigation data as input, (2) finite element modeling (FEM) of consolidation settlement that incorporates domain knowledge, and (3) Bayesian sparse dictionary learning of settlement monitoring data together with FEM results. The proposed method is illustrated using a real ground improvement project, and the results show that the proposed approach performs well.

Hydra II: Characterisation of Aegean, Caesar, ProFound, PyBDSF, and Selavy source finders

Abstract We present a comparison between the performance of a selection of source finders (SFs) using a new software tool called Hydra. The companion paper, Paper I, introduced the Hydra tool and demonstrated its performance using simulated data. Here we apply Hydra to assess the performance of different source finders by analysing real observational data taken from the Evolutionary Map of the Universe (EMU) Pilot Survey. EMU is a wide-field radio continuum survey whose primary goal is to make a deep ( $20\mu$ Jy/beam RMS noise), intermediate angular resolution ( $15^{\prime\prime}$ ), 1 GHz survey of the entire sky south of $+30^{\circ}$ declination, and expecting to detect and catalogue up to 40 million sources. With the main EMU survey it is highly desirable to understand the performance of radio image SF software and to identify an approach that optimises source detection capabilities. Hydra has been developed to refine this process, as well as to deliver a range of metrics and source finding data products from multiple SFs. We present the performance of the five SFs tested here in terms of their completeness and reliability statistics, their flux density and source size measurements, and an exploration of case studies to highlight finder-specific limitations.

Modeling land subsidence near the shaft pillars at the Upper Kama potassiummagnesium salt deposit based on the geological and geomechanical model

Introduction. Land subsidence is a visible consequence of deformation processes in the rock mass as a result mining. Deformation processes originate at producing horizons and propagate in the rock mass, usually upwards. The presence of natural physical and mechanically weakened zones in the rock mass complicates the situation creating risks of rock fracture propagation. The main problem is that subsidence of the earth's surface can lead to negative consequences for facilities within the undermining zone. Research objective is to carry out geomechanical modeling to adequately display the current situation on the earth's surface in accordance with real actual observational data. The created geological and geomechanical model can later be used to analyze and predict potential fracture zones in the rock within the rated section. Methods of research. The geological and geomechanical model is based on strata geological description, salt and oil wells geophysical exploration data, current position of workings, as well as physical and mechanical properties of rocks building up the rock mass. Results. The paper presents the results of geomechanical modeling of land subsidence at a site of the Upper Kama potassium-magnesium salt deposit in the vicinity of shaft pillars. Calculations were based on a geomechanical model of salt rock deformation and the developed geological and geomechanical model of an orefield site, including shaft barrier pillars regulated by the relevant reference documents. Timely measures for preventing negative consequences are recommended.