Robotic Network Research Articles

Particulate matter estimation using satellite datasets: a machine learning approach.

In the present work, it is the first time an interpretable machine learning model has been developed for the estimation of Particulate Matter 10µm (PM10) concentrations over India using Aerosol Optical Depth (AOD) from two different satellites, i.e. INSAT-3D and Moderate Resolution Imaging Spectroradiometer (MODIS) for the period of 7years (2014 to 2020). Ground datasets of AOD are taken from the Aerosol Robotic Network (AERONET) for the validation of satellite-retrieved AOD. The observation of particulate matter (PM) data is acquired from the Central Pollution Control Board (CPCB) station across India. Analysis has been performed on a monthly basis for the given time period. The result shows that AOD products of MODIS exhibit good correlation with AERONET AOD whereas INSAT-3D AOD is not well correlated with AERONET AOD. However, after applying an error envelope and threshold-based filtering technique, we have found that INSAT-3D shows significant correlation with ground-level AOD with approximate correlation of 0.66 for Jaipur and 0.57 for Kanpur exhibiting almost similar performance as MODIS-derived AOD. Satellite AOD data together with ground PM concentration data is used to train the machine learning model (random forest) for the estimation of the PM distribution across India for the year 2020. An encouraging correlation of R-squared (R2) value 0.78 has been observed between the estimated and observed PM10 concentrations. The model demonstrates effective training, mitigating huge overestimation and underestimation. However, despite closely tracking the trends of estimated PM10 with observed PM10, few instances of overestimation persist. This suggests the need for an expanded training dataset to further refine and enhance the model's accuracy. Finally, the machine learning model used for PM10 estimation is found to be optimal for a calibrated satellite AOD product.

First impressions of Telesurgery robotic-assisted radical prostatectomy using the Edge medical robotic platform.

We reported, as a referral center in prostate cancer, our perspectives and experience performing Telesurgery using robotic surgery and 5G network. We described and illustrated the Telesurgery applications and outcomes to treat a patient with prostate cancer located 1300 kilometers away from the surgeon (Beijing-Harbin) in China. We used the Edge Medical Robot (MP1000) in November 2023 in a 71-year-old patient with Gleason 6 (ISUP 1) in 8 cores from 13, PSA of 14 ng/dL, and clinical stage cT2a. MRI described a PIRADS 5 nodule on the left peripheral zone at the base, and 20gr prostate. We described details about the connection between centers, perioperative outcomes, and our perspectives as a referral center in prostate cancer. We had no delays, or problems with network connection between the centers. The procedure was performed in 60 minutes, with no intra- or postoperative complications. Estimated blood loss was 100 mL. The patient was ambulating soon after anesthesia recovery. Final pathology described a Gleason 6 (ISUP 1) involving the left base and left seminal vesicle, negative surgical margins, and no lymph node involvement (pT3bN0). The patient was continent soon after catheter removal (7 days). As technological progress introduced novel robotic platforms and high-speed networks, the concept of Telesurgery became a tangible reality while 5G technology solved latency and transmission concerns. However, with these advancements, ethical considerations and regulatory frameworks should underline the importance of transparency and patient safety with responsible innovation in the field.

Global evaluation of NOAA-20 VIIRS dark target aerosol products over land and ocean

The Dark Target (DT) algorithm has been applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi-NPP (SNPP) and NOAA-20 satellites to extend the aerosol data record from the Moderate Resolution Imaging Spectrometer (MODIS). The newly released VIIRS DT Version 2.0 (V2.0) dataset includes NOAA-20 aerosol products for the first time. This study provides the first evaluation of NOAA-20 VIIRS DT aerosol products, including aerosol optical depth (AOD) and Ångstrom exponent (AE), for the period 2018–2022 using Aerosol Robotic Network (AERONET) and Maritime Aerosol Network (MAN) measurements. In addition, the operational DT aerosol products from SNPP VIIRS (V2.0) and MODIS (Collection 6.1) are also used for comparison purposes. Overall, NOAA-20 AODs over land and ocean exhibit good validation metrics globally, showing comparability to MODIS products and superiority over overestimated SNPP products. Nevertheless, it is worth noting that in South Africa specifically, NOAA-20 land AODs tends to display more pronounced negative biases, despite all products being underestimated. Furthermore, all ocean AE products demonstrate high correlation coefficients (>0.83) with the ground-based data, meeting the fraction of expected accuracy (>80%). Encouragingly, NOAA-20 AE product has significantly improved the persistent issue of overestimation at low-value in MODIS product, making it a more preferable choice for usage. Error analysis reveals that the performance of all three land products decreases in sparsely vegetated areas and when the solar zenith angle is small. However, NOAA-20 AOD exhibits relatively stable performance and is less affected by variations in aerosol loading, observation geometry, and surface vegetation cover. In addition, the performance of AOD and AE over ocean is significantly influenced by scattering angle and wind speed. This research is anticipated to serve as a reference for the utilization of operational VIIRS DT aerosol products and possible algorithm optimization.

Influence of changes in anthropogenic and natural sources on global aerosol optical depth during COVID-19 lockdown: Ground-based observations, satellites, models

We analyze, in this study, for the first time, multi-source data - high-quality ground-based observations (Aerosol Robotic Network (AERONET)) and satellite (Moderate Resolution Imaging Spectroradiometer (MODIS) and Ozone Monitoring Instrument (OMI)) data in combination with two high resolution model (Modern-Era Retrospective Analysis for Research and Applications-2 (MERRA-2) and Copernicus Atmosphere Monitoring Service (CAMS)) outputs, and examine the changes in aerosol optical depth (AOD) across the globe during Corona Virus Disease-2019 (COVID-19) lockdown (2020) period. Statistical significance of the change in aerosol characteristics (AOD and species AODs) from normal (2017–2019 mean) to COVID (2020) period is calculated by two-tailed Student's t-test. AERONET AODs decreased by 20–40% during peak lockdown period in spring and summer of 2020 in most locations across the globe, and these changes are statistically significant. The observed magnitude of changes (increase/decrease) are consistent between AERONET and MODIS. Both global models (MERRA-2 and CAMS) capture well, observed AOD changes. OMI absorbing aerosol index captures increases in carbonaceous aerosols due to wildfires and dust. Global analysis reveals that changes in total AOD and species AODs are highest, and statistically significant during spring over Middle East, South and East Asia. Among all regions, change in dust AOD is maximum over South Asia during spring (>75%; −0.04 decrease in dust AOD in −0.05 change in total AOD) which is statistically significant. Black carbon (BC) and sea salt contribute ≤10% to AOD across the globe. Decrease in BC AOD is maximum over South Asia (50%) during spring 2020 providing a direct evidence for the impact of applied restrictions on anthropogenic activities. Decrease in AOD over North America, Europe, Russia, Middle East, South Asia, East Asia, and South East Asia led to global decrease in AOD. This quantitative documentation of changes in AODs due to reduction in aerosol components/species, their regional and seasonal variations are crucial in planning and supporting future mitigation efforts.

Spatial Gap-Filling of Himawari-8 Hourly AOD Products Using Machine Learning with Model-Based AOD and Meteorological Data: A Focus on the Korean Peninsula

Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps that limit comprehensive environmental analysis. This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. Developed and validated over South Korea from 1 January to 31 December 2019, the model effectively improved data coverage from 6% to 100%. The approach demonstrated high performance in blind tests, achieving a root mean square error (RMSE) of 0.064 and a correlation coefficient (CC) of 0.966. Meteorological analysis indicated optimal model performance under cold, dry conditions (RMSE: 0.047, CC: 0.956), compared to humid conditions (RMSE: 0.105, CC: 0.921). Validation against Aerosol Robotic Network (AERONET) ground observations showed that, while the original Himawari-8 data exhibited higher accuracy (RMSE: 0.189, CC: 0.815, n = 346), the gap-filled dataset maintained reasonable precision (RMSE: 0.208, CC: 0.711) and significantly increased the number of valid data points (n = 4149). Furthermore, the gap-filled dataset successfully captured seasonal AOD patterns, with values ranging from 0.245–0.300 in winter to 0.381–0.391 in summer, providing a comprehensive view of aerosol dynamics across South Korea.

High spatial–temporal image fusion model for retrieving aerosol optical depth based on top-of-atmosphere reflectance

With the growth in industrialization and urban development, air pollution has become an increasing serious health concern. Although ground stations can effectively monitor air quality, they generally observe only located phenomena and limited in the spatial distribution. Remote-sensing approaches have thus been employed by many scholars for air quality monitoring in an entire region. However, no single satellite equips with sufficient spatial and temporal resolutions for detecting rapidly changing local phenomena, such as air quality variations. A top-of-atmosphere reflectance–based spatial–temporal image fusion model (TOA-STFM) is proposed in this paper to solve this problem. The proposed TOA-STFM is modified based on the spatial–temporal adaptive reflectance fusion model (STARFM) and yields fused images in which atmospheric properties are retained. A key process in the TOA-STFM is blurring effect adjustment (BEA), which is performed to match the atmospheric effects caused by aerosols in images with different spatial resolutions. The feasibility of fusing Himawari-8 images with SPOT-6 images was evaluated in this study. We used the proposed model to extract aerosol optical depths (AODs) from images produced by fusing Himawari-8 and SPOT-6 images and compared the extracted AODs with corresponding in-situ observations made by the AErosol RObotic NETwork (AERONET). The AOD relative errors of the proposed TOA-STFM were 2.3%–7.6%, which is a significant improvement comparing to a relative error of 8.4%–13.5% from Himawari-8 images and existing AOD products.

Enhancing the performance of a resonance-based sensor network for soft robots using binary excitation

Embedded, flexible, multi-sensor sensing networks have shown the potential to provide soft robots with reliable feedback while navigating unstructured environments. Time delay associated with extracting information from these sensing networks and the complexity of constructing them are significant obstacles to their development. This paper presents a novel enhancement to an existing class of embedded sensor network with the potential to overcome these challenges. In its original version, this sensor network extracts information from multiple reactive sensors on a two-wire electrical circuit simultaneously. This paper proposes to change the excitation signal applied to this sensor network to a binary signal. This change offers two key advantages: it provides the ability to employ small, inexpensive microcontrollers and results in a faster data extraction process. The potential of this enhanced system is demonstrated here with a proof of concept implementation. The self-inductance of all inductance-based sensors within this proof of concept sensor network can be measured at a rate of over 5000 times per second with an average measurement error of less than 2%.

Three-dimensional distribution of aerosols of multiple types at daily scale using TROPOMI spaceborne observations

We present new satellite-based daily observations of the 3D distribution of aerosols from most common types and origins, both over land and ocean, derived from TROpospheric Ozone Monitoring Instrument (TROPOMI) measurements. This is done using the so-called AEROS5P (AEROsol Sentinel 5 Precursor) approach which retrieves vertical profiles of aerosol extinction from passive measurements of high-resolution spectral reflectance in the visible and near infrared for cloud-free conditions. Previous work demonstrated the potential of this approach, but it was initially applicable only to biomass burning aerosols with limited spatial coverage over ocean. The new methodology presented here generalizes the method for the most common aerosol types and improves spatial coverage. We take advantage of an operational aerosol type product derived from Visible Infrared Imaging Radiometer Suite (VIIRS) observations, combined with those from TROPOMI, to choose a priori aerosol set of intensive properties (particle size and refractive index). AEROS5P shows good agreement in aerosol optical depth, when compared to observations from AErosol RObotic NETwork and other widely used satellite measurements. Additionally, the vertical distribution of aerosol plumes of different types derived from AEROS5P agrees well with lidar coincident observations from the Advanced Topographic Laser Altimeter System spaceborne sensor and ground-based measurements. We utilize AEROS5P to analyze the three-dimensional distribution of aerosols for four different scenarios. It traces the three-dimensional pathways of smoke from Canadian wildfires reaching the Eastern USA in July 2023, desert dust from the Namibian deserts over the southeast Atlantic Ocean confined near the surface during the same month, fine aerosol pollution over Western Europe during June 2019, and an extreme dust event mixed with fine aerosol pollution over Northern China in March 2021. These observations provide valuable insights into aerosol transport patterns, surface-level air quality impacts, and can potentially be used to assess the radiative effects at various atmospheric layers.

Neural Network Algorithm for Training a Mobile Robot in the Task of Following a Target

The article is devoted to the research, development and implementation of an artificial neural network with reinforcement for controlling a mobile robot with a differential drive to solve the task of following a target. The authors of the study present a detailed description of the architecture of the neural network, its training using a deep deterministic gradient policy algorithm, and integration with the robot control system. A distributed neural network structure was selected for specialized generation of controls, for which the authors chose the angular and linear speed of the robot. A system of rules has been synthesized that takes into account changes in the distance between the robot and the target object and tracking of the heading angle in real time. A mathematical model of a robot with a differential drive is considered, on the basis of which a simulation program is implemented for intermediate training of a neural network, as a result of which the initial weighting coefficients are formed. Using this program, you can get by with less energy and time costs at the initial stage of the study. Experiments to test the effectiveness of the developed neural network were carried out in the Gazebo simulation environment using the ROS2 communication interface. The article describes the process of integrating a neural network with a robot control system in a simulation environment, as well as test results and analysis of the obtained data. The results of experiments are presented for three scenarios in which the initial position of the robot is the same, the remaining parameters are generated according to the rules described by the authors. The effectiveness of using such a solution for trajectory planning has been confirmed. The research contributes to the field of autonomous systems and demonstrates the potential of artificial reinforcement neural networks in robotics.

Aerosol loading in the guinea coast climate region of Nigeria: comparison of MODIS and AERONET data sources

This study compares aerosol optical depth (AOD) measurements from satellite data with that from a ground-based station. It aims at validating monthly AOD data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) collection 6.1 (Aqua and Terra) at 550 nm against AOD data from the Aerosol Robotic Network (AERONET) station at 500 nm in Ilorin over the period 2003–2022. The analysis focuses on eight selected cities/locations within the Guinea Coast region of Nigeria. Descriptive and inferential statistical methods, including correlation, regression, standard deviation, coefficient of variation, the Mann–Kendall trend test, root mean square error (RMSE), mean absolute error (MAE), and relative mean bias (RMB), were employed. The results indicate a positive correlation in most locations, with correlation values ranging from 0.3 to 0.7. The correlation between the datasets in Ilorin showed a positive and significant relationship while the other seven locations presented a weak and insignificant relationship. Additionally, the period from 2013–2022 showed better correlation compared to 2003–2012. AOD levels were highest during the harmattan season (DJF), exceeding 0.8, and lowest during the monsoon (JJA) and post-monsoon (SON) seasons, with values below 0.5, reflecting the effects of wet scavenging. The highest agreement between MODIS Aqua and AERONET data was observed during the harmattan season. The study also found that MODIS tends to overestimate AOD compared to AERONET and that MODIS-AOD exhibits greater spread and variability than AERONET-AOD.

Retrieving hourly aerosol optical depth for geostationary satellite FY-4B/AGRI by surface-related dynamic spectral reflectance ratio method

The Advanced Geostationary Radiation Imager (AGRI) on board Fengyun-4B (FY-4B) has been found to have significant advantages in aerosol dynamic monitoring. This study proposed a surface-related dynamic spectral reflectance ratio (SDSRR) method for FY-4B AGRI to solve the problem of inaccurate surface reflectance estimation in Aerosol Optical Depth (AOD) retrieval. This method introduced Moderate-resolution Imaging Spectroradiometer (MODIS) aerosol product to assist in calculating the surface reflectance of the AGRI blue channel and the spectral reflectance ratio between the shortwave infrared (SWIR) channel and the blue channel, then constructed a surface-related dynamic spectral reflectance ratio series by combining the spectral reflectance ratio, Normalized Difference Vegetation Index (NDVI) and Scattering Angle (SCA) to obtain aerosol retrieval results. To verify the accuracy of the SDSRR method, the AOD dataset of the SDSRR method, the official aerosol products of AGRI (LDA) and Advanced Himawari Imager (AHI) AOD datasets were compared with the ground-based observations of Aerosol Robotic Network (AERONET) and Sun-shy radiometer Observation Network (SONET) in East Asia. The results indicate that the SDSRR method performs more consistently in East Asia compared to the official ARGI aerosol products. The root-mean-square-error (RMSE), mean error (ME), and correlation coefficient (R) between SDSRR AOD and ground-based measurements are 0.286, 0.180 and 0.70, which is better than that of LDA AOD (RMSE = 0.508, ME = 0.292, R = 0.69). Additionally, the RMSE, ME, and R of AHI AOD were 0.253, 0.168, and 0.74, respectively.

Aerosol Components Derived from Global AERONET Measurements by GRASP: A New Value-Added Aerosol Component Global Dataset and Its Application

Abstract Aerosols affect Earth’s climate both directly and indirectly, which is the largest uncertainty in the assessment of radiative forcings affecting anthropogenic climate change. The standard Aerosol Robotic Network (AERONET) aerosol products have been widely used for more than 30 years. Currently, there is strong community interest in the possibility of determining aerosol composition directly from remote sensing observations. This work presents the results of applying such a recently developed approach by Li et al. to extended datasets of the directional sky radiances and spectral aerosol optical depth (AOD) measured by AERONET for the retrievals of aerosol components. First, the validation of aerosol optical properties retrieved by this component approach with AERONET standard products shows good agreement. Then, spatiotemporal variations of the obtained aerosol component concentration are characterized globally, especially the absorbing aerosol species (black carbon, brown carbon, and iron oxides) and scattering aerosol species (organic carbon, quartz, and inorganic salts). Finally, we compared the black carbon (BC) and dust column concentration retrievals to the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), products in several regions of interest (Amazon zone, Indo-China Peninsula, North India, southern Africa, sub-Sahel, Gobi Desert, Middle East, Sahara Desert, and Taklamakan Desert) for new insights on the quantitative assessment of MERRA-2 aerosol composition products (R = 0.60–0.85 for BC; R = 0.75–0.90 for dust). The new value-added and long-term aerosol composition product globally is available online (https://doi.org/10.6084/m9.figshare.25415239.v1), which provides important measurements for the improvement and optimization of aerosol modeling to enhance estimation of the aerosol radiative forcing.

Fault Tolerant Metric Dimension of Arithmetic Graphs

For a graph G , two vertices x , y ∈ G are said to be resolved by a vertex s ∈ G , if d ( x | s ) ≠ d ( y | s ) . The minimum cardinality of such a resolving set R in G is called its metric dimension. A resolving set R is said to be fault-tolerant, if for every p ∈ R , R − p preserves the property of being a resolving set. A fault-tolerant metric dimension of G is a minimal possible order fault-tolerant resolving set. A wide variety of situations, in which connection, distance, and connectivity are important aspects, call for the utilisation of metric dimension. The structure and dynamics of complex networks, as well as difficulties connected to robotics network design, navigation, optimisation, and facility positioning, are easier to comprehend as a result of this. As a result of the relevant concept of metric dimension, robots are able to optimise their methods of localization and navigation by making use of a limited number of reference locations. As a consequence of this, numerous applications of robotics, including collaborative robotics, autonomous navigation, and environment mapping, have become more precise, efficient, and resilient. The arithmetic graph A l is defined as the graph with its vertex set as the set of all divisors of l , where l is a composite number and l = p γ 1 1 p η 2 2 , … , p n n , where p n ≥ 2 and the p i ’s are distinct primes. Two distinct divisors x , y of l are said to have the same parity if they have the same prime factors (i.e., x = p 1 p 2 and y = p 2 1 p 3 2 have the same parity). Further, two distinct vertices x , y ∈ A l are adjacent if and only if they have different parity and gcd ( x , y ) = p i (greatest common divisor) for some i ∈ { 1 , 2 , … , t } . This article is dedicated to the investigation of the arithmetic graph of a composite number l , which will be referred to throughout the text as A l . In this study, we compute the fault-tolerant resolving set and the fault-tolerant metric dimension of the arithmetic graph A l , where l is a composite number.

Changes in Anthropogenic Aerosols during the First Wave of COVID-19 Lockdowns in the Context of Long-Term Historical Trends at 51 AERONET Stations

A quasi-consensus has steadily formed in the scientific literature on the fact that the prevention measures implemented by most countries to curb the 2020 COVID-19 pandemic have led to significant reductions in pollution levels around the world, especially in urban environments. Fewer studies have looked at how these reductions at the ground level translate into variations in the whole atmosphere. In this study, we examine the columnar values of aerosols at 51 mainland European stations of the Aerosol Robotic Network (AERONET). We show that when considered in the context of the long-term trend over the last decade, the columnar aerosol levels for 2020, at the regional level, do not appear exceptional. Both the yearly means and the number of episodes with extreme values for this period are within one standard deviation of the long-term trends. We conclude that the spatially and temporally localized reductions do not add up to statistically significant reductions at the global levels of aerosols. Furthermore, considering that pandemic lockdowns can be thought of as a simulation of a climate change mitigation scenario, we conclude that such lifestyle-based changes present a very low potential as a global climate change mitigation strategy.

Chaotic Encryption-Based Network Robot for Indoor Security and Remote Video Monitoring

Chaotic Encryption-Based Network Robot for Indoor Security and Remote Video Monitoring

Lognormal mode dissociation method based on intrinsic characteristics of aerosol size distribution.

Aerosols significantly affect the transmission of optical signals in the atmosphere, necessitating accurate atmospheric models for the performance evaluation of electro-optic devices. Aerosol size distribution is a critical parameter in these models, and the lognormal function is commonly used to mathematically represent it. This study aims to handle the lack of a solid criterion for determining the number of lognormal modes and introduces an improved scheme that leverages the characteristics of the second derivative (SD) of the Gaussian curve to identify the mode amount and to initialize mode parameters for fitting. The optimization problem is solved using a genetic algorithm, incorporating a goodness-of-fit index to determine the presence of spurious modes. For aerosol size distributions characterized by a single Gaussian peak, mode parameters such as mode radius and width can be straightforwardly identified through the positions of peaks and roots on the SD curve. However, the original mode dissociation method may overlook potential modes in distributions composed of superimposed Gaussian peaks. Numerical tests indicate that such oversights can result in substantial errors in calculating the aerosol extinction coefficient, with relative errors exceeding 100%. The proposed scheme significantly enhances the accuracy of mode dissociation in aerosol size distribution, reducing errors in aerosol extinction coefficient calculations by approximately 40% when applied to data from the Aerosol Robotic Network (AERONET). An additional benefit of this method is its ability to constrain the number of lognormal modes in an aerosol size distribution. Results from applying this scheme to data from selected AERONET sites reveal that over half of the size distributions consist of more than two lognormal modes, highlighting the effectiveness of the proposed approach in capturing complex aerosol behaviors.

Deep Learning-Driven Robot Arm Control Fusing Convolutional Visual Perception and Predictive Modeling for Motion Planning

The wide application of robotic technology in various industries from industrial automation to medical assistance is gradually changing our production and lifestyle, and has attracted widespread attention in many fields. However, existing robotic control systems often grapple with limited flexibility and poor adaptability to complex environments, particularly in highly dynamic operational contexts. Against this backdrop, the integration of deep learning technologies offers new possibilities in enhancing robotic perception and decision-making, especially in visual perception and motion planning. To address these challenges, we have introduced a novel robotic arm control network model, MPC-WGAN-Faster R-CNN, which combines Model Predictive Control concepts with Wasserstein Generative Adversarial Networks and Faster R-CNN visual recognition technology. This integration aims to improve the precision and adaptability of robotic arm operations in complex environments.

Reliable and Energy-Efficient Communications in Mobile Robotic Networks by Collaborative Beamforming

For mobile robotic networks in industrial scenarios, reliable and energy-efficient communications are crucial yet challenging. Fortunately, collaborative beamforming (CB) emerges as a promising solution, which can increase the transmission gain and reduce the transmit power of robots by constructing a mobile robot-enabled virtual antenna array (MRVAA). The performance of CB is tightly related to robot positions, necessitating proper robot selection. However, robot selection may expose the network to the risk of unbalanced energy distribution, reducing network lifetime. Additionally, the mobility and variable numbers of robots require flexible and scalable robot selection algorithms. To tackle these challenges, we first formulate a multi-objective optimization problem to reduce the maximum sidelobe level (MSLL) of MRVAA while minimizing the standard deviation of the network energy distribution (SDNED) by selecting robots for CB. Then, based on distributed multi-agent learning (MARL), we propose an effective and scalable robot selection algorithm with energy considered (RoSE) to solve the problem, where difference-rewards function (DRF) and policy sharing are designed for enhancing convergence rate and policy stability. Simulation results show that the RoSE has the scalability to positions and numbers of robots. Furthermore, RoSE surpasses existing selection algorithms in network lifetime and time efficiency, while still maintaining comparable MSLL.

Assessment of the impact of NO2 contribution on aerosol-optical-depth measurements at several sites worldwide

Abstract. This work aims at investigating the effect of NO2 absorption on aerosol-optical-depth (AOD) measurements and Ångström exponent (AE) retrievals of sun photometers by the synergistic use of accurate NO2 characterization for optical-depth estimation from co-located ground-based measurements. The analysis was performed for ∼ 7 years (2017–2023) at several sites worldwide for the AOD measurements and AE retrievals by Aerosol Robotic Network (AERONET) sun photometers which use OMI (Ozone Monitoring Instrument) climatology for NO2 representation. The differences in AOD and AE retrievals by NO2 absorption are accounted for using high-frequency columnar NO2 measurements by a co-located Pandora spectroradiometer belonging to the Pandonia Global Network (PGN). NO2 absorption affects the AOD measurements in UV-Vis (visible) range, and we found that the AOD bias is the most affected at 380 nm by NO2 differences, followed by 440, 340, and 500 nm, respectively. AERONET AOD was found to be overestimated in half of the cases, while also underestimated in other cases as an impact of the NO2 difference from “real” (PGN NO2) values. Overestimations or underestimations are relatively low. About one-third of these stations showed a mean difference in NO2 and AOD (at 380 and 440 nm) above 0.5 × 10−4 mol m−2 and 0.002, respectively, which can be considered a systematic contribution to the uncertainties in the AOD measurements that are reported to be of the order of 0.01. However, under extreme NO2 loading scenarios (i.e. 10 % highest differences) at highly urbanized/industrialized locations, even higher AOD differences were observed that were at the limit of or higher than the reported 0.01 uncertainty in the AOD measurement. PGN NO2-based sensitivity analysis of AOD difference suggested that for PGN NO2 varying between 2 × 10−4 and 8 × 10−4 mol m−2, the median AOD differences were found to rise above 0.01 (even above 0.02) with the increase in NO2 threshold (i.e. the lower limit from 2 × 10−4 to 8 × 10−4 mol m−2). The AOD-derivative product, AE, was also affected by the NO2 correction (discrepancies between the AERONET OMI climatological representation of NO2 values and the real PGN NO2 measurements) on the spectral AOD. Normalized frequency distribution of AE (at 440–870 and 340–440 nm wavelength pair) was found to be narrower for a broader AOD distribution for some stations, and vice versa for other stations, and a higher relative error at the shorter wavelength (among the wavelength pairs used for AE estimation) led to a shift in the peak of the AE difference distribution towards a higher positive value, while a higher relative error at a lower wavelength shifted the AE difference distribution to a negative value for the AOD overestimation case, and vice versa for the AOD underestimation case. For rural locations, the mean NO2 differences were found to be mostly below 0.50 × 10−4 mol m−2, with the corresponding AOD differences being below 0.002, and in extreme NO2 loading scenarios, it went above this value and reached above 1.00 × 10−4 mol m−2 for some stations, leading to higher AOD differences but below 0.005. Finally, AOD and AE trends were calculated based on the original AERONET AOD (based on AERONET OMI climatological NO2), and its comparison with the mean differences in the AERONET and PGN NO2-corrected AOD was indicative of how NO2 correction could potentially affect realistic AOD trends.

Increasing aerosol optical depth spatial and temporal availability by merging datasets from geostationary and sun-synchronous satellites

Abstract. This comprehensive study analyzed aerosol products from six low-Earth orbit (LEO) and geostationary Earth orbit (GEO) sensors. LEO sensors like the MODerate resolution Imaging Spectroradiometer (MODIS) and VIsible InfraRed Suite (VIIRS) provide one to two daily global measurements, while GEO sensors (Advanced Himawari Imager: AHI, Advanced Baseline Imager: ABI) offer high-frequency data (∼ 10 min) over specific regions. The combination of LEO and GEO capabilities offers expanded coverage of the global aerosol system if aerosol retrievals are applied consistently across all sensors and packaged in an easy-to-use product. The Dark Target aerosol retrieval algorithm was applied to the six sensors, and the resulting Level 2 aerosol optical depth (AOD) products were gridded and merged into a Level 3 quarter-degree latitude–longitude grid with a 30 min temporal resolution, providing the necessary consistency and packaging. Validation of this packaged Level 3 AOD product against Aerosol Robotics NETwork (AERONET) measurements across global locations showcased the merged product's robustness with a correlation coefficient of 0.83, revealing a global mean bias of approximately ±0.05, with 65.5 % of retrievals falling within an expected uncertainty range, underlining the reliability of the dataset. The new gridded Level 3 dataset significantly improved daily global coverage to nearly 45 %, overcoming the limitations of individual sensors, which typically range from 12 % to 25 %. Furthermore, this merged dataset approximates the diurnal cycle of AOD observed by AERONET, thus offering insights into diurnal signatures retrieved elsewhere. The resulting dataset's high spatiotemporal resolution and improved global coverage, especially in regions covered by GEO sensors (Americas and Asia), make it a valuable tool for diverse applications. Tracking aerosol transport from phenomena like wildfires and dust storms is gaining precision, enabling enhanced air quality forecasting and hindcasting. Additionally, the study positions the merged dataset as a significant asset for evaluating and intercomparing regional or global model simulations, which was previously unattainable in such a gridded format. The dataset and fusion framework layout in this study have the potential to include data from recently (future) launched other GEO (FCI, AMI) and LEO (PACE, VIIRS-JPSS) sensors.