Network Of Stations Research Articles

Assimilation of GNSS tropospheric gradients into the Weather Research and Forecasting (WRF) model version 4.4.1

Abstract. In this study, we have incorporated tropospheric gradient observations from a Global Navigation Satellite Systems (GNSS) ground station network into the Weather Research and Forecasting (WRF) model through a newly developed observation operator. The experiments aim at testing the functionality of the developed observation operator and at analyzing the impact of tropospheric gradients on the sophisticated data assimilation (DA) system. The model was configured for a 0.1° mesh over Germany with 50 vertical levels up to 50 hPa. Our initial conditions were obtained from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data at 0.25° resolution, and conventional observations were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF), restricted to mainly surface stations and radiosondes. We selected approximately 100 GNSS stations with high data quality and availability covering Germany. We performed DA every 6 h for June and July 2021. Four experiments were conducted: (1) a control run assimilating only conventional observations; (2) an impact run assimilating zenith total delays (ZTDs) on top of the control run; (3) an impact gradient run assimilating ZTDs and gradients on top of the control run; and (4) a gradient run assimilating only gradients on top of the control run. The error for the impact run was reduced by 32 % and 10 % for ZTDs and gradients, whereas the error for the impact gradient run was reduced by 35 % and 18 %, respectively. The gradient errors for the gradient run were nearly equal to those of the impact gradient. Overall, the newly developed operator for the WRFDA system works as intended. In particular, the combined assimilation of gradients and the ZTDs led to a notable improvement in the humidity field at altitudes above 2.5 km. With the operator codes developed and freely available to the WRF users, we aim to trigger further GNSS tropospheric gradient assimilation studies.

Deep Neural Networks Based Denoising of Regional Seismic Waveforms and Impact on Analysis of North Korean Nuclear Tests

AbstractWe test a deep learning based denoising autoencoder algorithm on regional and teleseismic seismological and hydroacoustic datasets, which we compile from the International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organisation. We focus on stations which can be relevant to investigate North Korean nuclear tests. Denoising of waveform records using autoencoder techniques potentially enables improved signal detection and processing due to lowered signal-to-noise ratios. We train and compare the performance of several different denoising autoencoder models, for short- and long waveform periods, trained on the complete station network as well as on individual stations. We investigate if the denoised waveform signals are useful for seismic source analysis and if they can still be reliably used in downstream analysis for further inferences on the seismic source type, i.e. seismic moment tensor analysis. The declared North Korean nuclear tests are a suitable benchmark test set, as they have extensively been researched and their source type and location might be assumed known. Verification of the source type is of particular interest for potential nuclear tests under international law. We find that care needs to be taken using the denoised waveform data, as a slight bias is introduced in the seismic moment tensor analysis. However we also find promising results hinting at possible future use of the technique for standard analyses, as it improves the investigation of smaller events. Autoencoder based denoising techniques could be employed in future routine frameworks to increase earthquake catalog completeness and possibly aid in detecting smaller potential treaty relevant events.

Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned for its computational efficacy in forecasting streamflow events with a forecast horizon of 7 days. The novel integration of the groundwater level, precipitation, and river discharge as predictive variables offers a holistic view of the hydrological cycle, enhancing the model’s accuracy. Our findings reveal that for a 7-day forecasting period, the STA-GRU model demonstrates superior performance, with a notable improvement in mean absolute percentage error (MAPE) values and R-square (R2) alongside reductions in the root mean squared error (RMSE) and mean absolute error (MAE) metrics, underscoring the model’s generalizability and reliability. Comparative analysis with seven conventional deep learning models, including the Long Short-Term Memory (LSTM), the Convolutional Neural Network LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the Spatio-Temporal Attention LSTM (STA-LSTM), the Gated Recurrent Unit (GRU), the Convolutional Neural Network GRU (CNNGRU), and the STA-GRU, confirms the superior predictive power of the STA-LSTM and STA-GRU models when faced with long-term prediction. This research marks a significant shift towards an integrated network of real-time monitoring stations with advanced deep-learning algorithms for streamflow forecasting, emphasizing the importance of spatially and temporally encompassing streamflow variability within an urban watershed’s stream network.

Analysis of Hydrogen Value Chain Events: Implications for Hydrogen Refueling Stations’ Safety

Renewable hydrogen is emerging as the key to a sustainable energy transition with multiple applications and uses. In the field of transport, in addition to fuel cell vehicles, it is necessary to develop an extensive network of hydrogen refueling stations (hereafter HRSs). The characteristics and properties of hydrogen make ensuring the safe operation of these facilities a crucial element for their successful deployment and implementation. This paper shows the outcomes of an analysis of hydrogen incidents and accidents considering their potential application to HRSs. For this purpose, the HIAD 2.0 was reviewed and a total of 224 events that could be repeated in any of the major industrial processes related to hydrogen refueling stations were analyzed. This analysis was carried out using a mixed methodology of quantitative and qualitative techniques, considering the following hydrogen value chain: production, storage, delivery and industrial use. The results provide general information segmented by event frequency, damage classes and failure typology. The analysis shows the main processes of the value chain allow the identification of key aspects for the safety management of refueling facilities.

Analysis of Multipath Changes in the Polish Permanent GNSS Stations Network

This study examines the influence of multipath errors on Global Navigation Satellite System (GNSS) measurements collected at ASG-EUPOS reference stations between 2010 and 2021. Multipath occurs when GNSS signals reflect off surrounding objects before reaching the receiver antenna, leading to positioning errors. In the case of reference stations, all available mitigation techniques were used to minimize the impact of multipath. However, it is still detectable and affects the measurement results. For carrier phase differential positioning, it increases the ambiguous search space, which results in a decrease in determining rover—reference station vector accuracy. The study employs two linear combinations (Code-Minus-Carrier and Multipath Pseudorange Observable) to quantify the multipath effect on both pseudorange and carrier phase measurements. Based on the research, it was found that the multipath values changed depending on the change of the receiver and the terrain around the reference stations. The study observed a gradual decrease in multipath errors from 2010 to 2021, likely due to technological advancements in receiver design. No significant increase in multipath errors was observed due to environmental changes around the stations, suggesting a minimal influence from new reflecting objects nearby. Based on the analyses conducted, it is also recommended to perform periodic tests to detect incorrect receiver configuration or operation.

Vegetation dieback in the Mississippi River Delta triggered by acute drought and chronic relative sea-level rise

Vegetation dieback and recovery may be dependent on the interplay between infrequent acute disturbances and underlying chronic stresses. Coastal wetlands are vulnerable to the chronic stress of sea-level rise, which may affect their susceptibility to acute disturbance events. Here, we show that a large-scale vegetation dieback in the Mississippi River Delta was precipitated by salt-water incursion during an extreme drought in the summer of 2012 and was most severe in areas exposed to greater flooding. Using 16 years of data (2007–2022) from a coastwide network of monitoring stations, we show that the impacts of the dieback lasted five years and that recovery was only partial in areas exposed to greater inundation. Dieback marshes experienced an increase in percent time flooded from 43% in 2007 to 75% in 2022 and a decline in vegetation cover and species richness over the same period. Thus, while drought-induced high salinities and soil saturation triggered a significant dieback event, the chronic increase in inundation is causing a longer-term decline in cover, more widespread losses, and reduced capacity to recover from acute stressors. Overall, our findings point to the importance of mitigating the underlying stresses to foster resilience to both acute and persistent causes of vegetation loss.

FEASIBILITY STUDY OF GNSS-ENABLED GCP MARKER FOR CONTROL ESTABLISHMENT FOR DRONE SURVEYING

Abstract. This study explores the potential of GNSS-enabled Ground Control Point (GCP) markers as an alternative to traditional survey-grade receivers in observing GCPs for drone surveys. This is because an average drone flight and the observation period for rapid static are both 15 minutes. A GNSS-enabled GCP is already in the market through Propeller Aeropoints but is not available in the Philippines. Previous independent studies on the accuracy of such markers are limited as it is only focused on the accuracy. There are other considerations in using this technique such as the practicality of the marker, its usability with public Continuously Operating Reference Station (CORS) networks such as PAGeNet, and whether using it in conjunction with passive monuments will significantly affect the accuracy. The researchers designed and assembled a low-cost alternative using a u-blox module to determine the markers’ applicability and effectiveness. This study was limited to using three (3) GCPs to simulate the minimum number required to georeference in 2D. Accuracy remains an important criterion so observations will be compared with known positions. Three drone surveys were conducted to further evaluate its performance. The marker achieved 3rd Order geodetic control and demonstrated up to one standard deviation of error using statistical analysis. However, the lack of control points affected the accuracy observed in the checkpoints. This study focused on low-cost GNSS-enabled GCP marker since the commercial Aeropoints markers are not vailable in the country. Implementing this methodology in drone surveying could potentially increase survey efficiency in the Philippines and aid in the country's cadastral mapping efforts.

A GIS-based Study of The Impact of HSR Network Distribution on Firms' Total Factor Productivity: Evidence from Listed Manufacturing Firms in China

With the rapid development of the economy, enterprises are increasingly pursuing efficient division of labor and communication, and in order to achieve high-quality economic growth and sustainable development, high speed rail (HSR) has been born. Therefore, the study of the impact of HSR accessibility on the total factor productivity (TFP) of enterprises has become a hot topic of research in today's academia. In this paper, firstly, based on GIS network analysis, the routes and stations after opening are matched with the locations of enterprises to describe the spatial correlation between HSR and the locations of enterprises as well as the distribution of HSR among enterprises; secondly, a multi-period difference-in-difference model (DID) is utilized to study the impact of HSR opening on TFP of enterprises on the basis of the data of China's listed manufacturing enterprises in the period of 2000-2017; and The PSM-DID model to test the robustness of the empirical results; finally, the path of the impact of HSR on the TFP is explored. The empirical results are as follows: (1) The distribution of HSR and the location of listed enterprises are highly correlated in terms of geographic distribution information; (2) HSR has a significant impact on the TFP, and the impact effect is different in different periods; (3) HSR mainly affects the TFP of enterprises by using the innovative behavior of enterprises and industrial agglomeration as the mediating variables.

Added value of amateur observational network for high-resolution climatological analysis: a case study in the aterno valley, abruzzo, Italy

The ecological transition calls for an increasing need for local climate services. A fine spatial characterization of atmospheric relevant quantities (temperature, precipitation, humidity, wind, solar radiation, etc.) at long-term climatological scales is typically based on observational networks run by public entities such as the European Union (e.g. Copernicus services) and national and regional Agencies (e.g. National Met Office, Hydrographic Offices). The aim of this work is to verify if the density of these networks is adequate to represent the variability over the territory, with particular regard to a complex terrain area such as the Aterno river Valley in Abruzzo, Central Italy. We use a combination of public networks and the available dense amateur network of weather stations. We subject the database to careful data quality check both in terms of temporal and spatial anomalies. We found that the public network is generally adequate to represent the spatial and temporal variability over the area in terms of temperature and precipitation, but this is not the case for wind and relative humidity for the lack of sensors. We suggest that an integration of public and non-institutional observational networks is desirable for a finer climatological characterization of a complex territory and for allowing the description of more phenomena, in order to better inform adaptation measures with respect to climate change.

Local and Regional Diurnal Variability of Aerosol Properties Retrieved by DSCOVR/EPIC UV Algorithm

AbstractThe hour‐to‐hour variability of 388 nm aerosol optical depth (AOD) and single scattering albedo (SSA) derived from near UV observations by the Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory has been evaluated at multiple locations around the world. AOD retrievals by the EPIC near UV algorithm (EPICAERUV) have been compared to ground based AOD measurements at 16 Aerosol Robotic Network (AERONET) stations representative of the most commonly observed aerosol types over geographic regions in three continents. Obtained results show that, in general, the EPICAERUV algorithm reproduces closely the hour‐to‐hour AOD variability reported by AERONET ground‐truth observations. Although most sites in the analysis show high correlation between the AOD hourly measurements by the ground‐based and space‐borne measuring techniques. Best algorithm performance is observed in the presence of carbonaceous and desert dust aerosols. The diurnal cycle of the retrieved SSA product was also analyzed. Although, a direct comparison of hourly EPICAERUV retrievals to equivalent ground‐based observations was not possible, the satellite result shows that diurnal SSA variability as large as 0.05 can be observed mostly associated with carbonaceous aerosols. EPICAERUV observed diurnal cycle of retrieved AOD on a regional basis was examined for the unusually active seasons of aerosol production of Saharan desert dust aerosols in 2020, and during the 2023 Canadian wildfires. Results presented in this study confirm the EPIC near UV aerosol product is well suited for observing diurnal variability of aerosols and, therefore, it is an important resource for climate and air quality studies.

EVALUATION OF THE PERFORMANCE OF MULTI-GNSS ADVANCED ORBIT AND CLOCK AUGMENTATION – PRECISE POINT POSITIONING (MADOCA-PPP) IN JAPAN REGION

For users of Precise Point Positioning (PPP), Multi-GNSS Advanced Orbit and Clock Augmentation PPP signals provide corrective data. When using the PPP approach and/or PPP-Ambiguity Resolution (AR) method, the QZSS signal provides globally applicable error corrections on satellite orbit, clock offset, and code/phase biases. In addition, from FY2024, as a part of the MADOCA-PPP technology demonstration, wide-area ionospheric correction data will be provided for the Asia-Oceania region. A software estimator of precise satellite information developed by JAXA, Multi-GNSS Advanced Demonstration Tool for Orbit and Clock Analysis (MADOCA), allows u-blox CO99-ZED-F9P and MSJ 3008-GM4-QZS utilizing MADOCA-PPP to be used in GNSS applications that need sub-decimetre precision but don’t have to be expensive. Errors caused by positioning satellites are computed by using observation data from domestic and overseas GNSS monitoring station networks such as IGS and MIRAI, and obtained correction data is transmitted from QZSS signal to provide highly precise positioning augmentation services that can be used in the Asia-Oceania Region. Users may utilize the PPP technique for high-precision locating by employing a GNSS receiver that supports the QZSS signals. This paper describes an experiment carried out with the static method to combine GPS, GLONASS, and QZSS signals in the project site (ISHI, USUD and MIZU stations in Japan). This paper examines the GPS/GLONASS/QZSS obtainable accuracy. These obtained results indicate that integrating GPS system with GLONASS and QZSS is favoured for surveying applications. It appears that integrating GPS/GLONASS/QZSS (MADOCA precise ephemeris file) static measurements in the study area between 0–4 millimetres accuracy can be guaranteed on all occasions.

Spatial pattern of bias in areal rainfall estimations and its impact on hydrological modeling: a comparative analysis of estimating areal rainfall based on radar and weather station networks in South Korea

Spatial pattern of bias in areal rainfall estimations and its impact on hydrological modeling: a comparative analysis of estimating areal rainfall based on radar and weather station networks in South Korea

Denoising Seismic Waveforms Using a Wavelet-Transform-Based Machine-Learning Method

ABSTRACT Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency-based filtering methods for waveform denoising do not result in desired outcomes when the targeted signal and noise occupy similar frequency bands. Recently, denoising techniques based on deep-learning convolutional neural networks (CNNs), in which a recorded waveform is decomposed into signal and noise components, have led to improved results. These CNN methods, which use short-time Fourier transform representations of the time series, provide signal and noise masks for the input waveform. These masks are used to create denoised signal and designaled noise waveforms, respectively. However, advancements in the field of image denoising have shown the benefits of incorporating discrete wavelet transforms (DWTs) into CNN architectures to create multilevel wavelet CNN (MWCNN) models. The MWCNN model preserves the details of the input due to the good time–frequency localization of the DWT. Here, we use a data set of over 382,000 constructed seismograms recorded by the University of Utah Seismograph Stations network to compare the performance of CNN and MWCNN-based denoising models. Evaluation of both models on constructed test data shows that the MWCNN model outperforms the CNN model in the ability to recover the ground-truth signal component in terms of both waveform similarity and preservation of amplitude information. Model evaluation of real-world data shows that both the CNN and MWCNN models outperform standard band-pass filtering (BPF; average improvement in signal-to-noise ratio of 9.6 and 19.7 dB, respectively, with respect to BPF). Evaluation of continuous data suggests the MWCNN denoiser can improve both signal detection capabilities and phase arrival time estimates.

Music Curriculum Research Using a Large Language Model, Cloud Computing and Data Mining Technologies

This paper presents a method to enhance the scientific nature of the music curriculum model by integrating a large language model, cloud computing and data mining technology for the analysis of the music teaching curriculum model. To maintain the integrity of the mixing matrix while employing the frequency hopping frequency, the paper suggests dividing the mixing matrix into a series of sub-matrices along the vertical time axis. This approach transforms wideband music signal processing into a narrowband processing problem. Additionally, two hybrid matrix estimation algorithms are proposed in this paper using underdetermined conditions. Furthermore, utilizing the estimated mixing matrix and the detected time-frequency support domain, the paper employs the subspace projection algorithm for underdetermined blind separation of music signals in the time-frequency domain. This procedure, along with the integration of the estimated direction of arrival (DoA), enables the completion of frequency-hopping network station music signal sorting. Extensive simulation teaching demonstrates that the music curriculum model proposed in this paper, based on a large language model, cloud computing and data mining technologies, significantly enhances the quality of modern music teaching.

Modeling Geomagnetically Induced Currents in the Alberta Power Network: Comparison and Validation Using Hall Probe Measurements During a Magnetic Storm

AbstractDuring space weather events, geomagnetic disturbances (GMDs) induce geoelectric fields which drive geomagnetically induced currents (GICs) through electrically‐grounded power transmission lines. Alberta, Canada—located near the auroral zone and thus prone to large GMDs—has a dense network of magnetometer stations and surface impedance measurements to better characterize the GMD and ground conductivity, respectively. GIC monitoring devices were recently installed at five substation transformer neutrals, providing a unique opportunity to compare data to modeled GICs. GICs are modeled across the >240 kV provincial power transmission network during a moderate GMD event on 24 April 2023. GIC monitoring devices measured larger neutral‐to‐ground currents than expected up to 117 Amps during peak storm time, providing unequivocal evidence linking network GICs with GMDs. The model performs reasonably well (correlation coefficients >0.5; performance parameter >0.15) at four of five substations, but generally underestimates peak GIC values (sometimes by a factor >2), suggesting that the present model underrepresents overall network risk. The model performs poorly at one of the five substations (correlation = 0.46; performance parameter = 0.10), the reasons for which may be due to simplifications and/or unknowns in network parameters. Despite these underestimates, during this GMD, the model predicts the largest GIC at substations located in the northeastern part of the province (240 kV) or around Edmonton (500 kV)—regions which have significant electrical and industrial infrastructure. Further refinement of the network model with transformer resistances, more line and earthing resistances, and/or including lower voltage levels is necessary to improve data fit.

Optimizing multimodal timetable synchronization of intercity railway and metro for the first service period during holidays

Metro plays a vital role in managing passenger distribution at intercity railway (IR) stations, particularly during holidays when there is a surge in tourist traffic. To efficiently accommodate the high demand for intercity travel, it becomes imperative for metro agencies to optimize holiday timetables. This paper focuses on designing holiday timetables of the first service period for the metro network that connects to an IR station, aiming to enhance multimodal collaboration with IR timetables while ensuring seamless coordination among various metro lines at the network level. A bi-objective model is proposed to maximize the temporal availability of metro network and minimize transfer waiting times for IR passengers traveling in early morning. To solve the model, an improved Artificial Bee Colony algorithm is designed, incorporating adaptive neighbour search and simulated annealing techniques. The effectiveness of the model and algorithm is verified using the Shanghai Metro network and Hongqiao Railway Station. Results indicate a 9.46% increase in the temporal availability of metro network for IR passengers, coupled with a 9.68% reduction in passenger transfer waiting times. Notably, the study reveals that solely advancing operations of the IR-connected metro lines is inefficient. Instead, optimizing train timetables for the entire metro network proves to be a cost-effective approach to enhancing the overall service level of early-morning operations. Furthermore, the study emphasizes the significance of even-numbered train headways in reducing passenger transfer waiting times.

Projected wind and solar energy potential in the eastern Mediterranean and Middle East in 2050

The ongoing energy transition from conventional fuels to renewable energy sources (RES) has given nations the potential to achieve levels of energy self-sufficiency previously thought unattainable. RES in the form of utility-scale solar and wind energy are currently the leading alternatives to fossil-fuel generation. Precise location siting that factors in efficiency limitations related to current and future climate variables is essential for enabling the green energy transition envisioned for 2050. In this context, understanding and mapping the intermittency of RES provides insights to energy system operators for their seamless integration into the grid. The Eastern Mediterranean and Middle East (EMME) region has the potential to harness vast amounts of RES. The scarcity of observations from weather station networks and the lack of private sector incentives for transitioning to RES mean that relevant, supporting weather and climate studies have been limited. This study employs the Weather Research and Forecasting model with Chemistry to estimate the RES technical potential of EMME countries and map the hourly generation profiles per source and country, simulated for the reference year 2015 and considering future conditions. The findings indicate that by 2050, seven countries within the region could transform into net energy exporters, while the remaining nine might remain reliant on energy imports or fossil fuels. Egypt emerges as a “powerhouse”, potentially enjoying a potential surplus energy generation of 76 GW per hour, whereas the United Arab Emirates may face an annual deficit of 955 TWh. Further, we derived the hourly generation profiles for wind and solar during different seasons. Four dominant patterns were identified. We find a complementary relationship for six countries, and for four countries, a substitute relationship between solar and wind energy generation. Greece stands out with a near-constant wind energy source, which would facilitate its integration into the national grid.

The driving force: organizational resilience of OKKO network

Learning outcomes After completion of the case study, the students would be able to identify and evaluate organizational culture as a critical element of organizational resilience and assess its fit to the business context, evaluate different elements of organizational resilience and their contribution to business adaptation and develop leadership approaches that help adapt and leverage organizational culture to foster individual, team and organizational resilience. Case overview/synopsis This teaching case covers topics of organizational leadership, including organizational culture and organizational resilience. This case study is appropriate for the postgraduate and executive education programmes. This case study covers the approach to organizational leadership and resilience of the OKKO, a Ukrainian retail petrol station network. The dilemmas considered by top managers of the company emerged in February–April 2022 amid the unfolding Russian invasion of Ukraine. The case study protagonists solved multiple business and organizational dilemmas to continue efficient business operations while allowing the organization to adapt to a complex and fast-changing environment. They leveraged a distinct corporate culture, strong employee engagement and established business processes and management practices to ensure the viability of the business. Complexity academic level This case study is appropriate for postgraduate and executive education programmes. The level of difficulty is light to medium. Recommended pre-requisites are understanding human resources management terminology and reviewing preparation materials. The case study is suitable for teaching courses in leadership, people management and organizational development that cover corporate culture, leadership and organizational resilience. Subject code CSS 6: Human resource management Supplementary materials Teaching notes are available for educators only.

2019年中国1 km格网河网密度数据集

Drainage density is an outcome of the combined influence of watershed landform, climate, soil, and vegetation. As a comprehensive characterization index, the measurement and monitoring of drainage density is of great significance to environmental protection and water resources allocation in regional watersheds. This paper uses river water surface vector data from the third National Land Survey, extracting the river center line. Then, the kilometer grid network production software is employed to generate the dataset of 1 km grid drainage density in China. Rigorous quality control and quality inspection measures throughout all stages of data processing have been implemented, including river surface vector extraction, river skeleton line extraction, and kilometer grid density calculation, to ensure the correctness, integrity and consistency of the data. This dataset can be used for national and regional hydrogeological analysis, soil erosion estimation, planning and layout of basic monitoring station network, etc.

Станция подводного обслуживания гибридного необитаемого подводного аппарата системы мониторинга подводного добычного комплекса

The use of support vessels significantly increases the cost of monitoring objects of underwater production complexes (UPC), and the presence of a cable communication line significantly limits the possibility of using remote-controlled uninhabited underwater vehicles (ROV), which is reduced to zero in conditions of continuous ice coverage of the work area. An obvious alternative to the use of ROVs are autonomous uninhabited underwater vehicles (AUVs), equipped with equipment for conducting contact work with monitoring objects and communicating with the shore control post. At the same time, the necessary autonomy for the use of such hybrid autonomous uninhabited underwater vehicles (HAUVs) can be ensured by a network of bottom stations equipped with systems for contactless charging and information exchange with the shore UPC control post. The work proposes the concept of an underwater service station (USS) for a HAUV, which allows, in long-term underwater deployment conditions, to use the device not only in an autonomous mode, but also in a remote control mode from a shore control post. A model for using the station for its intended purpose and the composition of the equipment necessary for its implementation have been determined, the design of the technical solution of the station has been presented, determined taking into account the features of installation on a complex bottom surface and the requirements for docking the HAUV for contactless charging and information exchange, including in the mode of remote control from the SCP. Basic technical solutions for the USS have been developed that provide automatic adjustment of the horizontal position of the docking unit, reliable docking and holding of the HAUV charging and information exchange terminals, and control of the vehicle’s pointing process using a set of rotary video cameras with lamps and a sector-view sonar. Algorithms for interaction between the station and the HAUV have been developed for autonomous and controlled via optical communication cable operating modes of the vehicle.