Automatic Stations Research Articles

ИСПОЛЬЗОВАНИЕ МЕТОДА TDOA ДЛЯ ПОДТВЕРЖДЕНИЯ ДОСТОВЕРНОСТИ ИНФОРМАЦИИ РАДИОВЕЩАТЕЛЬНОГО АВТОМАТИЧЕСКОГО ЗАВИСИМОГО НАБЛЮДЕНИЯ

The paper considers the possibility of using the Time Difference of Arrival (TDOA) method, which can be applied to confirm the reliability of information coming from multiple sources. TDOA method can be applied to validate the information received from several ground automatic dependent surveillance stations of broadcast type (ADS-B). The paper is aimed at studying the TDOA method and its application to ADS-B stations. This method makes it possible to use the ADS-B as the only means of surveillance at the airfield. Currently, according to ICAO recommendations, ADS-B stations are not allowed to be used as a stand-alone surveillance aid due to the problems related to the possibility of distorting a signal or introducing some false information into messages, so the operation of this type of stations is possible only together with other means of surveillance, such as multi-position surveillance systems (MLAT) or secondary surveillance radar systems (SSR) thus increasing the cost of organizing surveillance on the airfield. Thus, the relevance of the work is related to the need to find an alternative method of confirming the validity of an ADS-B message.

ИЗМЕНЧИВОСТЬ БИОХИМИЧЕСКИХ ПОКАЗАТЕЛЕЙ КРОВИ СВИНЕЙ ПОРОДЫ ДЮРОК НА ОСНОВЕ МОДЕЛИ СМЕШАННОГО УРАВНЕНИЯ

Our article presents the biochemical parameters of the blood of Duroc pigs, obtained by the classical approach. We used the results of a biochemical blood test of 78 clinically healthy boars undergoing test fattening at automatic feed stations, at the age of 5 months, with an average live weight of about 100 kg. The main biochemical parameters (creatinine, urea, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, cholesterol, triglycerides, total protein, albumin, glucose, total bilirubin) were studied and genetic and phenotypic correlations were calculated using the RENUMF90, REMLF90 programs.

Ares novos para a primeira infância: as crianças são o futuro do planeta

Abstract In early childhood, air pollution leads to an increased risk of disease, premature death, and development of disruptions. Fine particulate matter (PM2.5) is considered the classical pollutant of major concern. However, in Brazil, fine particulate monitoring is still limited and restricted to a few cities. Particularly, in the city of Rio de Janeiro, only one automatic station obtains PM2.5 concentrations and publishes the air quality indexes daily. This study is part of the project “Clean air for a good start” managed by the Horizon Citizen Foundation, with the goal of obtaining evidence to set a plan to reduce children exposition to air pollution. Data is shared on a global web platform. Results showed that PM2.5 levels are high when compared with WHO guidelines and coordinated actions would be needed to decrease emissions of primary pollutants and secondary aerosol’s precursors.

Contaminants in the Lunar Regolith

The lunar regolith delivered to Earth in the 1970s by the Soviet automatic stations Luna-16, -20, and -24, despite the small amount of material, is of great value for science and requires careful study. How ever, since soil samples have gone quite a long way from the moment of extraction to direct examination, there is a danger of possible contamination of the samples with foreign material, both man-made and terrestrial natural minerals. The probability of contamination of the finest fraction of regolith, the particle size of which is less than 100 microns, is especially high, since their detection is possible only by electron microscopy. Using the methods of analytical scanning electron microscopy in regolith samples, contaminant phases that con taminated the original preparations were found, and the sources of these artifacts were shown. The examples of contamination of regolith samples given in this paper and the methods for their identification will make it possible in the future to more accurately diagnose the phases of lunar origin.

DESIGN AND RESEARCH OF AUTOMATIC STATION BUFR DATA EMERGENCY BACKUP PLATFORM BASED ON HYBRID PROGRAMMING

Relying on the domestic meteorological transmission and communication system and NAS backup mechanism, based on the relevant standards and specifications of the meteorological big data cloud platform, in the case that the “Tianqing” interface cannot obtain data in a timely and effective manner, detailed requirements, detailed research, and clear implementation steps are made to provide users with reliable meteorological data services. Through the design and research of automatic station BUFR data rain express emergency backup system in this paper, the BUFR data for cell turn decoding, the specific factor value processing, can provide users with continuous data service, for business personnel in flood season normal work to provide basic support. The application shows that the sustainability of the data service is enhanced, and it provides a strong guarantee for the acquisition of rainfall elements in flood season.

Математическая модель очистки кожного покрова коров для повышения интенсивности теплового обмена при тепловых стрессах

Automatic brushing devices that improve the sanitary-hygienic condition of animal skin and create comfortable conditions are becoming widespread in modern dairy farms. To verify the developed mathematical model of cow skin cleaning, the authors carried out experimental studies on ten animals on a dairy farm. On the lateral surface of each animal three zones with the area of 10×10 cm2 were selected: the first area was not cleaned; the second area was cleaned “dry” with a brush; the third area was subjected to “wet” cleaning with a brush and detergent (soap solution). The temperature of the skin areas was determined for 15 minutes by the non-contact method using a pyrometer. The study results confirmed the effect of skin cleaning on the intensity of animal heat exchange due to increased convection and evaporation under heat stress. It was found that wet cleaning is the most effective method, when the skin temperature decreases by 4.1°C due to the increase in heat transfer by evaporation, while after dry cleaning it is 0.7°C. The developed mathematical model of cow skin cleaning establishes the relationship between the parameters of the brush bristle impact on the contaminated skin surface, the brush device and cleaning modes. The received dependences determine technical requirements and rational design and mode parameters of the brushing device to develop an automatic station of cow skin cleaning on farms.

SEISMISITY of the VOLCANIC AREAS of KAMCHATKA in 2016–2017

The results of near real-time monitoring of the active Kamchatka volcanoes are described. Continuous monitoring was carried out using three remote methods: 1) seismic monitoring according to automatic telemetric seismic stations; 2) visual and video observation; 3) satellite observation of the thermal anomalies and the ash clouds. Daily information about volcanic activity is published in the Internet (http://www.emsd.ru/~ssl/monitoring/main.htm) since February 2000. The results of seismic activity of the Northern (Shiveluch, Kluchevskoy, Bezymianny, Krestovsky and Ushkovsky), Avacha (Avachinsky and Koryaksky), Mutnovsky-Gorely volcano group and Kizimen volcano for 2016–2017 are presented. Within two years 8152 earthquakes with KS=1.6–8.5 were located for Northern volcano group, 616 earthquakes with KS=1.6–7.2 – for Avacha volcano group, 357 earthquakes with KS=2.0–6.2 – Mutnovsky-Gorely volcano group, 144 earthquakes with KS=2.3–9.4 for Kizimen volcano, 322 earthquakes with KS=1.8–8.1 for Zhupanovsky volcano and 90 earthquakes with KS=5.0–8.6 for Kambalny volcano. Maps of epicenters, quantities of seismic energy and earthquake distribution according to class are given. All periods of activity were fixed and investigated by remote methods in 2016–2017: intensive volcanic activity of Sheveluch volcano associated with new cone, the summit explosive-effusive eruption of Kluchevskoy volcano from April 2016 till September 2017, explosive activity of Zhupanovsky volcano and seismic preparation and volcanic eruption of Kambalny volcano observed for the first time in historical period.

SEISMICITY of YAKUTIA in 2016–2017

According to the data of 23 seismic stations of the Yakutia branch of GS RAS, the seismicity monitoring of the territory of the Republic of Sakha (Yakutia) was carried out for 2016–2017. During this period, more than 800 earthquakes with an energy class of KR≥7.1 (magnitude M≥1.7) were registered. This is somewhat more than in 2015 (N=567) and is due to the extension of the time interval of work up to 2 years. The epicenters were located in the zones of interaction between large lithospheric plates: the Eurasian and North American in the Northeastern and Arctic regions, as well as the Eurasian and Amur in the southern regions of Yakutia. The clusters of these events comprised two extended (Arctic-Asian and Baikal-Stanovoy) seismic belts. On the map of epicenters, these zones are displayed as a continuous strip of earthquakes from the Olekma River to the Sea of Okhotsk in the south and in the form of separate groups and clusters of earthquakes between the Laptev and Okhotsk Seas – in the northeast and in the Arctic. The highest seismic activity was noted in the Olekma area, the Stanovoy Ridge and the Aldan Highlands (Southern Yakutia) and the Chersky Ridge and Laptev Sea (in the North-East). Information on the manifestations of local seismicity in the Lena River Delta, where from August 2016 to December 2017 is given. 25 temporary field automatic stations under the joint Russian-German project "Seismicity and neotectonics of the Laptev Sea region" (SIOLA) in 2016–2018 operated. The first results of the distribution of Arctic earthquakes in the lower reaches of the Lena River in space, time, by depth and their connection with active tectonics are considered.

Application of Data Science for the reconstruction of time series of meteorological variables in the Islas del Rosario (Colombian Caribbean), between the years 2013-2021

This study reviews two time series of meteorological variables measured by an automatic station located in Islas del Rosario (Colombian Caribbean), belonging to the Network for Measurement of Oceanographic Parameters and Marine Meteorology (RedMpomm) of the General Maritime Directorate (Dimar). The time series correspond to data of air temperature and wind magnitude in the period 2013-2021, which present some missing values. The objective of the study was to develop a model that would automatically reconstruct missing values in the time series, using the advantages of data science to complete information with estimated values. The importance of obtaining reconstructed series lies in having more solid databases to be used in the research and academic work carried out by Dimar. The methodology developed consisted of the use of imputation of medians from existing data on dates and times associated with missing values, all this through the use of data lags and complementary information such as periodicity relationships on the data set. The results showed that it was possible to implement a reliable methodology capable of estimating the most appropriate value to complete the different time series, which constitutes a first approximation for reconstruction of meteorological data.

A system based on an artificial neural network of the second generation for decision support in especially significant situations

Currently, specialized system models implemented on the basis of decision support in exceptional (emergency) situations (states) using machine learning, artificial intelligence (including using neural networks) to reproduce, predict and prevent (or minimize the risk of consequences) in exceptional situations are useful and are becoming increasingly popular. Floods also fall under such exceptional situations and states, on the basis of which the task of early forecasting of an exceptional situation arises, using the example of rising water levels at stationary hydrological posts in order to prevent (or minimize the risk) the transition of the territory management system under consideration to an exceptional state (emergency situation). To solve this problem, a decision support system is proposed for early prediction of water rise levels, based on a neural network (intelligent) analysis of retrospective data (code of a stationary hydrological post / automatic station, date, water level at a stationary hydrological post / automatic station, atmospheric pressure, wind speed, snow cover thickness, amount of precipitation, time and air temperature) in order to calculate the values of water levels for 5 days in advance. The artificial neural network itself is based on the freely distributed TensorFlow machine learning software library, and a modified error backpropagation method is used as training, the main difference of which is the addition of an artificial neural network (ANN) learning rate increase factor. A mathematical model of the subject area is constructed in the form of a multidimensional space and an integrated multidimensional data model. A single integrated model describes the subject area quite fully and can be used as a single source of information, which provides system information support, defined as a set of distributed databases. All this makes it possible to improve the accuracy of forecast values of water levels at stationary hydrological posts and automatic stations. The main difficulty in early forecasting is a sharp rise in water levels over a short period of time (usually 1 day) for various reasons. Our neural network model takes this into account and allows us to give more accurate results. The results of the analysis of the effectiveness of the method showed that the proposed decision support system is more accurate: the average error does not exceed 55.68 cm for the entire flood period, the average error between the real and calculated values in the framework of forecasting for 5 days does not exceed 2.10 % compared to existing common methods/systems (8.36 %). This will give the necessary time to special services to carry out anti–flood measures to prepare for the protection of technical facilities of enterprises.

Water Quality Predictions Based on Grey Relation Analysis Enhanced LSTM Algorithms

With the growth of industrialization in recent years, the quality of drinking water has been a great concern due to increasing water pollution from industries and industrial farming. Many monitoring stations are constructed near drinking water sources for the purpose of fast reactions to water pollution. Due to the relatively low sampling frequencies in practice, mathematic prediction models are clearly needed for such monitoring stations to reduce the delay between the time points of pollution occurrences and water quality assessments. In this work, 2190 sets of monitoring data from automatic water quality monitoring stations in the Qiandao Lake, China from 2019 to 2020 were collected, and served as training samples for prediction models. A grey relation analysis-enhanced long short-term memory (GRA-LSTM) algorithm was used to predict the key parameters of drinking water quality. In comparison with conventional LSTM models, the mean absolute errors (MAEs) to predict the four parameters of water quality, i.e., dissolved oxygen (DO), permanganate index (COD), total phosphorus (TP), and potential of hydrogen (pH), were reduced by 23.03%, 10.71%, 7.54%, and 43.06%, respectively, using our GRA-LSTM algorithm, while the corresponding root mean square errors (RMSEs) were reduced by 24.47%, 5.28%, 6.92%, and 35.89%, respectively. Such an algorithm applies to predictions of events with small amounts of data, but with high parametric dimensions. The GRA-LSTM algorithm offers data support for subsequent water quality monitoring and early warnings of polluting water sources, making significant contributions to real-time water management in basins.

High Throughput Isolation and Data Independent Acquisition Mass Spectrometry (DIA-MS) of Urinary Extracellular Vesicles to Improve Prostate Cancer Diagnosis.

Proteomic profiling of extracellular vesicles (EVs) represents a promising approach for early detection and therapeutic monitoring of diseases such as cancer. The focus of this study was to apply robust EV isolation and subsequent data-independent acquisition mass spectrometry (DIA-MS) for urinary EV proteomics of prostate cancer and prostate inflammation patients. Urinary EVs were isolated by functionalized magnetic beads through chemical affinity on an automatic station, and EV proteins were analyzed by integrating three library-base analyses (Direct-DIA, GPF-DIA, and Fractionated DDA-base DIA) to improve the coverage and quantitation. We assessed the levels of urinary EV-associated proteins based on 40 samples consisting of 20 cases and 20 controls, where 18 EV proteins were identified to be differentiated in prostate cancer outcome, of which three (i.e., SERPINA3, LRG1, and SCGB3A1) were shown to be consistently upregulated. We also observed 6 out of the 18 (33%) EV proteins that had been developed as drug targets, while some of them showed protein-protein interactions. Moreover, the potential mechanistic pathways of 18 significantly different EV proteins were enriched in metabolic, immune, and inflammatory activities. These results showed consistency in an independent cohort with 20 participants. Using a random forest algorithm for classification assessment, including the identified EV proteins, we found that SERPINA3, LRG1, or SCGB3A1 add predictable value in addition to age, prostate size, body mass index (BMI), and prostate-specific antigen (PSA). In summary, the current study demonstrates a translational workflow to identify EV proteins as molecular markers to improve the clinical diagnosis of prostate cancer.

Sensor Actuator Network for In Situ Studies of Antarctic Plants Physiology.

This article documents a custom sensor-actuator network designed and implemented as a part of experimental setup, where a long-term phenological response of antarctic plants is studied. The first part of our work presents the context of the study, reports experimental methods used in antarctic plant field studies, and characterizes the environmental conditions and logistics facilities available on the measurement spot. After contextualization of the research, we present, in detail, both the network itself and some results obtained during the Antarctic summer seasons between 2019 and 2022 on the King George Island, South Shetlands. The results collected with our network and correlated with selected data registered with a reference automatic meteorological station reveal the thermal plants response. The groups of plants individuals, which were actively warmed using thermal actuators, show the nighttime temperature difference, in reference to the air temperature, of 5 ∘C, which complements the daytime difference caused by the passive method of open top chamber (OTC) used in previous studies carried out in the same localization.

Evolution of tunnel uplift induced by adjacent long and collinear excavation and an effective protective measure

Tunnel uplift control is an important issue for excavation construction near an existing tunnel, especially for long and collinear excavations. In this study, an effective anti-floating measure combining shaft excavation with the frictional pile slab (FPS) was proposed based on a long and collinear foundation pit above existing metro tunnels. The convergence of the shield segments was carefully measured using automatic total stations over 800 days. Numerical simulation was applied to analyze the effect of the width of the shaft excavation and geometric dimension of the frictional pile on the tunnel uplift. Furthermore, a multiobjective optimization procedure was proposed to optimize anti-floating design considering both safety and economy. A comparison between the FPS method and the high-pressure jet-grouting pile (HJP) method shows that the FPS method is a more effective way to control the heave of the underlying tunnel induced by long and colinear excavation due to its small-scale graded unloading and integral portal anti-floating structure. Combining the FPS method with sectional excavation, all the heaves of the underlying tunnel can be controlled to be less than 9 mm. For granite residual strata, the width of shaft excavation is advised to be lower than 8 m, and the length of the frictional pile is suggested to be longer than the distance between the pit bottom and tunnel invert. The heave of the tunnel crown is suggested as the key index to evaluate the excavation-induced disturbance on the underlying tunnel. The unloading ratio and construction time are the key factors that influence the magnitude of tunnel heave. Compared with the actual design, an optimal design with a lower cost and better anti-floating effect is obtained with the proposed multiobjective optimization procedure. The presented tunnel deformation data, advised anti-floating method and construction sequence can provide useful guidance for similar scenarios.

An empirical study of the key factors affecting herders' purchasing decision on weather index insurance—A case study from inner Mongolia autonomous region, China

The weather index insurance for mutton sheep (WIMS) is the first weather index insurance product that addresses the increased feeding cost-related risk of grassland animal husbandry herders in China. In 2019, the WIMS was implemented as a pilot project in 11 county regions of Xilin Gol League, Inner Mongolia, China. Since the implementation of this product, the purchasing rate has varied from region to region, as the highest purchasing rate was 84.3%, while the lowest was only 1.8%. This research uses the double hurdle model to identify the factors affecting herders' purchasing behavior. This paper reports a survey study of 535 participants herders in the Xilin Gol League. The result indicates that herders' age, disaster risk evaluation, insurance awareness, pasture area, and neighborhood purchasing decision significantly impact herders' purchasing propensity. Disaster evaluation, insurance awareness, pasture area and location significantly impact the purchasing degree of herders. The study suggests that young herders and herders with large pasture areas should be motivated to purchase insurance. In addition, new methods should be applied for the publicity of this insurance product; Investments should be focused on the automatic weather monitoring station projects in pastoral areas to reduce the basis risk of the insurance.

Племенная ценность кормового поведения свиней

Abstract. The purpose of the research is to develop a comprehensive assessment system with new indicators of feed behavior and feed conversion in Duroc pigs based on a selection index. Research methods. The Herdsman database was used, which includes the processing of data on fattening characteristics and feeding behavior of 764 heads of Duroc pigs that have passed test fattening at automatic feeding stations. 13 indicators were evaluated, of which 7 indicators of feeding behavior and 6 – fattening characteristics. The evaluation of breeding value animals according to studied traits was carried out using the BLUP Animal Model methodology. Results. When determining the weight coefficients of final index equation, which included all the studied traits, we relied on the chosen principle, according to which 50 % was given to weight coefficients of feeding behavior traits and 50 % to the weights of the fattening indicators. Also, in accordance with this principle, in feeding behavior indicators, 25 % was allocated for feed conversion, and the same amount in fattening indicators – for the average daily weight gain. The obtained values of the coefficients presented the general structure of the selection index: Itotal = 0.5I1 + 0.5I2 = 0.0241 (TPD) + 0.0439 (ADFI) – 0.0376 (NVD) – 0.0322 (TPV) – 0.0074 (FR) + 0.0376 (FPV) – 0.5343 (FCR) + 0.0531 (Age1) + 0.0778 (Age0) + 0.1217 (BW1) + 0.1027 (BW0) + 0.0331 (BWG) + 0.4557 (ADG). The results obtained showed the difference between the minimum and maximum index, which was 76 sigma points. Also, according to distribution according the categories of RBV (relative breeding value) among 14 pigs with an index greater than 120 units, 30.8 % have the category “unconditional improver” and 25.8 % – the category “improver”. Scientific novelty. These results show the direct impact of feeding behavior and feed conversion on the refinement of animal’s genetic potential, which will improve the system of selection and genomic evaluation of animals.

DESCRIPTION OF THE AIR QUALITY MONITORING NETWORK OF TBILISI

Among the challenges facing the world community at the modern stage, one of the leading ones is the issue of the qualitative state of the environment, where one of the leading links is the creation of a proper state control system. The small number of automatic air monitoring stations in Tbilisi cannot reflect the full state of ambient air pollution. Based on the above, it is necessary to rehabilitate and modernize the air pollution quality monitoring network to achieve a safe condition for human health and the natural environment.

Integration of Satellite-Derived and Ground-Based Soil Moisture Observations for a Precipitation Product over the Upper Heihe River Basin, China

Precipitation monitoring is important for earth system modeling and environmental management. Low spatial representativeness limits gauge measurements of rainfall and low spatial resolution limits satellite-derived rainfall. SM2RAIN-based products, which exploit the inversion of the water balance equation to derive rainfall from soil moisture (SM) observations, can be an alternative. However, the quality of SM data limits the accuracy of rainfall. The goal of this work was to improve the accuracy of rainfall estimation through merging multiple soil moisture (SM) datasets. This study proposed an integration framework, which consists of multiple machine learning methods, to use satellite and ground-based soil moisture observations to derive a precipitation product. First, three machine learning (ML) methods (random forest (RF), long short-term memory (LSTM), and convolutional neural network (CNN)) were used, respectively to generate three SM datasets (RF-SM, LSTM-SM, and CNN-SM) by merging satellite (SMOS, SMAP, and ASCAT) and ground-based SM observations. Then, these SM datasets were merged using the Bayesian model averaging method and validated by wireless sensor network (WSN) observations. Finally, the merged SM data were used to produce a rainfall dataset (SM2R) using SM2RAIN. The SM2R dataset was validated using automatic meteorological station (AMS) rainfall observations recorded throughout the Upper Heihe River Basin (China) during 2014–2015 and compared with other rainfall datasets. Our results revealed that the quality of the SM2R data outperforms that of GPM-SM2RAIN, Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ERA5-Land (ERA5) and multi-source weighted-ensemble Precipitation (MSWEP). Triple-collocation analysis revealed that SM2R outperformed China Meteorological Data and the China Meteorological Forcing Dataset. Ultimately, the SM2R rainfall product was considered successful with acceptably low spatiotemporal errors (RMSE = 3.5 mm, R = 0.59, and bias = −1.6 mm).

Design of robotic hydrogen-filling system for hydrogen-powered vehicles

Background: The application of hydrogen-powered vehicles is increasingly widespread, however, the hydrogen-filling process can be dangerous, to ensure both safety and efficiency. A new robotic hydrogen-filling system whose consisting of a hybrid robot combined with an automatic guided vehicle and robotic arm is designed. Methods: An analysis of functional composition of the system was performed, and the hardware scheme was designed. A dual-differential drive AGV and an end effector including a holding jaw and a sucker were designed. According to the system workflow, the control system is divided into four modules. A path planning simulation considering obstacle avoidance is carried out based on improved artificial potential field method and a trajectory planning of the operating arm is completed using source code written in MATLAB. Results: The simulation results show that the automatic guided vehicle can avoid obstacles and move to the specified position. The planed trajectory for robotic arm has certain smoothness, which can be proved that the operating arm can complete the process of grasping the hydrogenation gun. Conclusions: The robotic hydrogen-filling system can replace human beings in most of the work of the hydrogen-filling process, which provides a theoretical basis for automatic hydrogen refueling station.

Creation of More Efficient Work Environment through the New Design of the Automatic Robotic Assembly Station

Creation of More Efficient Work Environment through the New Design of the Automatic Robotic Assembly Station