The satellite radar monitoring for anthropogenic and natural geological hazards mapping within the Solotvyno mining area (Transcarpathia, Ukraine)

<p>Satellite images have been interpreted to establish the basic patterns of land surface deformations and predict the development of hazardous geological processes within the Solotvyno salt dome structure and the adjacent territories (Transcarpathia, Ukraine). Solotvyno rock salt deposit is one of the largest in Ukraine. Geotechnical and hydro-geological problems at the deposit have started to accumulate since the mid-90s and have led to a dangerous environmental technogenic situation that was given the national emergency status in 2010. This multi-hazard geo-ecological situation is a result of overlapping both anthropogenic “post-mining” (karst, subsidence, sinkhole formation, ground surface collapses, mine flooding, slope mass movements) and natural (flooding, landslides, etc.) hazardous geological processes, in particular, the disturbance of land surface, which is the sign of the uncontrolled development of salt karst, flooding and could result in transboundary pollution of the Tysa River, etc.</p><p>To forecast the development of hazardous geological processes, monitoring was implemented by using innovative techniques for processing satellite radar data, such as “Persistent Scatterers (PS)”, “Small Baseline Subset (SBAS)”. Due to the use of long-time series of images obtained with a synthetic aperture radar (SAR), the errors of orbital data and the effects of atmospheric phenomena have been effectively suppressed. The results of processing are digital maps, with the accuracy of evaluating the average vertical displacement rate of the objects being 2–4 mm/year when using the “PS” technique and 6–15 mm/year if using the “SBAS”.</p><p>A highly accurate evaluation of the vertical displacements of objects and land surface has been carried out using interferometric processing of satellite radar monitoring data by means of new satellite constellations including Sentinel-1A and 1B (DInSAR analysis data for 2016–2020, SBAS approach, Copernicus EMSN-030, EMSN-064; PS+SBAS approach, Center of the Special Information Receiving and Processing and the Navigating Field Control, Ukraine). The research area was 33 sq. km. Information end products (raster and vector) have been created, which permitted the changes in spatial and temporal dimensions to be analyzed. The values and areas of concentrated land surface deformations have been determined within the zone of anthropogenic and natural karst development. The areas of land surface subsidence with the average rate of vertical displacements from -6 to -94 mm/year have been digitized using GIS tools.</p><p>The assessment of anthropogenic hazards for the Solotvyno salt dome structure and adjacent territories has been provided. It has been determined that mines No7, 8, and 9 pose an anthropogenic threat to the safety of Solotvyno community inhabitants.</p><p>The reconstruction of land surface vertical displacements in time has been carried out within the studies performed. In order to ensure life safety in Solotvyno, the results will be used in territory development and in setting up the system of monitoring. In view of the complicated geo-ecological situation, the development and functioning of a permanent geo-ecological monitoring system for the Solotvyno mining area and the adjacent territories is the top-priority objective.</p><p>The research has been carried out with the EU financial support: projects REVITAL 1 (HURSKOVA/1702/6.1/0072) and ImProDiReT (No. 783232).</p>

A COVID-19 Non-contact Screening System Based on XGBoost and Logistic Regression (Preprint)

BACKGROUND COVID-19 is a new infectious disease with high infectivity. At present, body temperature detection is the main method for primary screening, but this single detection method has poor accuracy and is easy to miss detection. OBJECTIVE The objective of our study was to propose a non-contact, high-precision COVID-19 screening system. METHODS We used impulse-radio ultra-wideband (IR-UWB) radar to detect the respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital, and compared them with 144 radar monitoring data of healthy controls. Then XGBoost and logistic regression(XGBoost+LR) algorithm was used to classify the data of patients and healthy people; feature selection was performed by SHAP value; using ten-fold cross-validation, XGBoost+LR algorithm was compared with five other classic classification algorithms, and the classification performance was evaluated by precision, recall, and the area under the ROC curve( AUC ). RESULTS The XGBoost+LR algorithm demonstrate excellent discrimination (precision=99.1 %, recall rate = 94.1 %, AUC=98.7 %), which is superior to several other single machine learning algorithms. In addition, the SHAP value indicate that number of apnea during REM(‘ REMSATims’) and mean heart rate(‘meanHR’) are important features for classification. CONCLUSIONS The COVID-19 non-contact screening system based on XGBoost+LR algorithm can accurately predict COVID-19 patients and can be applied in isolation wards to effectively help medical staff.

Vintage Radar Film Tracks What’s Beneath Antarctic Ice

The newly digitized data double the timescale of ice-penetrating radar monitoring in some of the fastest changing areas of Antarctica.