Static Sensor Research Articles

A 3D Coverage Method Involving Dynamic Underwater Wireless Sensor Networks for Marine Ranching Monitoring

In view of the poor adaptability and uneven coverage of static underwater wireless sensor networks (UWSNs) to environmental changes and the need for dynamic monitoring, a three-dimensional coverage method involving a dynamic UWSNs for marine ranching, based on an improved sparrow search algorithm (ISSA), is proposed. Firstly, the reverse learning strategy was introduced to generate the reverse sparrow individuals and fuse with the initial population, and the individual sparrows with high fitness were selected to improve the search range. Secondly, Levy flight was introduced to optimize the location update of the producer, which effectively expanded the local search capability of the algorithm. Finally, the Cauchy mutation perturbation mechanism was introduced into the scrounger location to update the optimal solution, which enhanced the ability of the algorithm to obtain the global optimal solution. When deploying UWSNs nodes, an autonomous underwater vehicle (AUV) was used as a mobile node to assist the deployment. In the case of underwater obstacles, the coverage hole in the UWSNs was covered by an AUV at specific times. The experimental results show that compared with other algorithms, the ISSA has a shorter mobile path and achieves a higher coverage rate, with lower node energy consumption.

An overview of outdoor low-cost gas-phase air quality sensor deployments: current efforts, trends, and limitations

Abstract. We reviewed 60 sensor networks and 17 related efforts (sensor review papers and data accessibility projects) to better understand the landscape of stationary low-cost gas-phase sensor networks deployed in outdoor environments worldwide. This study is not exhaustive of every gas-phase sensor network on the globe but rather exists to categorize types of sensor networks by their key characteristics and explore general trends. This also exposes gaps in monitoring efforts to date, especially regarding the availability of gas-phase measurements compared to particulate matter (PM) and geographic coverage gaps (the Global South, rural areas). We categorize ground-based networks that measure gas-phase air pollutants into two main subsets based on their deployment type – quasi-permanent (long term) and campaign (short to medium term) – and explore commonplace practices, strengths, and weaknesses of stationary monitoring networks. We conclude with a summary of cross-network unification and quality control efforts. This work aims to help scientists looking to build a sensor network explore best practices and common pathways and aid end users in finding low-cost sensor datasets that meet their needs.

Modeling of Static Stress Identification Using Electromechanical Impedance of Embedded Piezoelectric Plate.

Working stress is an important indicator reflecting the health status of structures. Passive-monitoring technology using the piezoelectric effect can effectively monitor the dynamic stress of structures. However, under static loads, the charge generated by the piezoelectric devices can only be preserved when the external circuit impedance is infinitely large, which means passive-monitoring techniques are unable to monitor static and quasi-static stress caused by slow-changing actions. In current studies, experimental observations have shown that the impedance characteristics of piezoelectric devices are affected by external static loads, yet the underlying mechanisms remain inadequately explained. This is because the impedance characteristics of piezoelectric devices are actually dynamic characteristics under alternating voltage. Most existing impedance analysis models are based on linear elastic dynamics. Within this framework, the impact of static stress on dynamic characteristics, including impedance characteristics, cannot be addressed. Accounting for static stress in impedance modeling is a challenging problem. In this study, the static stress applied on an embedded piezoelectric plate is abstracted as the initial stress of the piezoelectric plate. Based on nonlinear elastic dynamic governing equations, using the displacement method, an impedance analysis model of an embedded piezoelectric plate considering initial stress is established and verified through a fundamental experiment and a finite element analysis. Based on this, the explicit analytical relation between initial stress and impedance characterizations is provided, the mechanism of the effect of initial stress on the impedance characterizations is revealed, and procedures to identify static stress using impedance characterizations is proposed. Moreover, the sensitivities of the impedance characterizations in response to the initial stress are thoroughly discussed. This study mainly provides a theoretical basis for monitoring static stress using the electromechanical impedance of an embedded piezoelectric plate. And the results of the present study can help with the performance prediction and design optimization of piezoelectric-based static stress sensors.

Adaptive Control for Hydronic Radiant Heating System Using Occupant Behaviors in Residential Building

This study proposes an occupant-centric control strategy for residential heating systems, aiming to enhance thermal comfort and reduce energy consumption. A sensor station utilizing a frequency-modulated continuous wave radar sensor was developed to detect occupancy and infer activities within residential spaces. By analyzing field measurement data, schedules for occupancy and activities were established. These schedules were then used to implement a variable control strategy for the hydronic radiant heating system, adjusting its operating characteristics based on the identified activities. The proposed control strategy, which includes resetting the indoor set temperature during unoccupied periods and adjusting it during sleep to account for changes in metabolic rate and clothing insulation, resulted in significant energy savings. Compared to continuous operation, the hydronic radiant heating system’s energy consumption was reduced by approximately 21% on peak load days and up to 34% over three winter months. This study demonstrates the potential of occupant-centric control for achieving substantial energy savings in residential buildings while maintaining occupant thermal comfort.

How Connected Cars Could Capture Cloud Dynamics for Solar Forecasting—Evidence from Two Simulation Scenarios

The rapidly increasing share of fluctuating electricity from photovoltaics calls for accurate approaches to estimate cloud motion, the primary source for the varying power supply. While local sensor networks are prominent in targeting forecast horizons too short for image‐based methods, they have minimal spatial coverage. This work presents the first step towards expanding those approaches to spatially scalable sensor networks: With the motivation of using automotive light sensors as a sensor network, two excerpts from a microscopic traffic simulation serve as simulative sensor networks. A fractal‐based cloud shadow pattern passes the sensor network areas with defined velocities and directions, which shall be estimated using the cumulative mean absolute error method. The evaluation results indicate that the more extensive observation areas compensate for the dynamics in the sensor network when compared to a reference work with a static sensor grid. Furthermore, this work shows how the estimates deteriorate with lower vehicle penetration rates (PR) and longer building shadows due to a lower solar elevation angle. At a penetration rate of 40%, the root mean square errors for both sensor networks are still below 5 ms−1. In conclusion, the spatiotemporal characteristics of a vehicle network offer a potential for estimating cloud movements.

Bridge monitoring using mobile sensing data with traditional system identification techniques

AbstractMobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode shapes (the absolute value of mode shapes, i.e., mode shapes without phase information) can be estimated. However, time‐synchronized data and improved system identification techniques are necessary to estimate frequencies, full mode shapes, and damping ratios within the same context. This paper presents a framework that uses only two time‐synchronous mobile sensors to estimate a spatially dense frequency response matrix. Subsequently, this matrix can be integrated into existing system identification methods and structural health monitoring platforms, including the natural excitation technique eigensystem realization algorithm and frequency domain decomposition. The methodology was tested numerically and using a lab‐scale experiment for long‐span bridges. In the lab‐scale experiment, synchronized smartphones atop carts traverse a model bridge. The resulting cross‐spectrum was analyzed with two system identification methods, and the efficacy of the proposed framework was demonstrated, yielding high accuracy (modal assurance criterion values above 0.94) for the first six modes, including both vertical and torsional. This novel framework combines the monitoring scalability of mobile sensing with user familiarity with traditional system identification techniques.

Performance Study of Collection Tree Protocol in Mobile Wireless Sensor Network

The Mobile Wireless Sensor Networks (MWSN) are defined as the successor of Wireless Sensor Networks (WSN); geographically distributed autonomous mobile sensors for physical and environmental conditions monitoring. Mobile sensor networks are more flexible than static sensor networks as they can cope with rapid topology changes. Sensor networks are deployed by having their sensors collect information reliably and then relay the sensor readings to a central base station using wireless multi-hop transmission. Multi-hop routes within a rapid-changed topology requires a routing protocol that can adapt to network changes with an efficient communication mechanism among the nodes; to comply with the energy-constraint nature of the wireless sensor networks. Through significant simulation on this study using the AVRora simulator, the Collection Tree Protocol’s (CTP) capacity to respond to MWSN’s changing network topology was assessed by analysing the performance indicators; specifically, the average of packet loss and energy use of mobile nodes with different circumstances considering varied quantities of mobile nodes in the simulation region, with different velocities and network density. In several circumstances, the implementation of CTP in MWSN indicates a rise in energy usage because of broken linkages along with regular tree regeneration brought on by node mobility.

ANALYSIS AND ASSESSMENT OF AIR QUALITY IN ASTANA: COMPARISON OF POLLUTANT LEVELS AND THEIR IMPACT ON HEALTH

This study presents an in-depth analysis of air quality in Astana, Kazakhstan, utilizing both mobile and stationary air monitoring systems over a two-year period. The research focuses on tracking key air pollutants, namely carbon monoxide (CO), nitrogen dioxide (NO₂), particulate matter (PM2.5 and PM10), and sulfur dioxide (SO₂), providing a comparative assessment of seasonal trends and the sources of pollution, which include transportation, industrial emissions, and domestic heating during the cold season. The study emphasizes the significance of monitoring systems in urban environments to understand better the impact of air pollution on public health and the effectiveness of sustainable interventions. One of the major insights from this research is the comparison between seasonal variations in pollutant levels and the city's transition toward sustainable energy practices, such as increased gasification and the use of electric transportation, which has already demonstrated a positive impact on reducing emissions during peak heating periods. The results show that while Astana has improved air quality, air pollution remains a concern, especially in winter due to the increased use of solid fuel. This paper emphasizes the importance of real-time data from mobile sensors and suggests their wider use to complement stationary sensors for better monitoring. In addition to pollutant tracking, the study delves into the health implications of prolonged exposure to air pollutants, particularly in urban areas. The study concludes by advocating for expanded use of mobile monitoring systems and advanced data analytics to provide actionable insights for policymakers, urban planners, and public health officials.

A second-generation URANS model (STRUCT-$$\epsilon $$) applied to a generic side mirror and its impact on sound generation

AbstractA generic side mirror can be approximated to the combination of a half cylinder topped with a quarter of sphere. The flow structure in the wake of the side mirror is highly transient and the turbulence plays an important role affecting aeroacoustics through pressure fluctuation. Thus, this geometry is one of the test cases object of several numerical studies in recent years to assess the aerodynamic and aeroacoustic capabilities of the turbulence models. In this context, this study presents how the second-generation URANS closure STRUCT-$$\epsilon $$ ϵ is able to properly predict the expected stagnation, flow separation and vortex shedding phenomena. Besides, the predictive accuracy for the noise generation mechanism is evaluated by comparing the spectra of the sound pressure level measured at several static pressure sensors with the numerical results obtained with the STRUCT-$$\epsilon $$ ϵ . The response of this turbulence model has exceeded that from other hybrid methods and is in good agreement with the results from Large-Eddy Simulations or the experiments. To conclude the paper, the applicability of STRUCT-$$\epsilon $$ ϵ to construct a Spectral Proper Orthogonal Decomposition method that helps identifying the most energetic modes to appropriately capture the dominant flow structures is also introduced.

Biomechanics on Ultra-Sensitivity of Venus Flytrap's Micronewton Trigger Hairs.

Numerous plants evolve ingeniously microcantilever-based hairs to ultra-sensitively detect out-of-plane quasi-static tactile loads, providing a natural blueprint for upgrading the industrial static mode microcantilever sensors, but how do the biological sensory hairs work mechanically? Here, the action potential-producing trigger hairs of carnivorous Venus flytraps (Dionaea muscipula) are investigated in detail from biomechanical perspective. Under tiny mechanical stimulation, the deformable trigger hair, composed of distal stiff lever and proximal flexible podium, will lead to rapid trap closure and prey capture. The multiple features determining the sensitivity such as conical morphology, multi-scale functional structures, kidney-shaped sensory cells, and combined deformation under tiny mechanical stimulation are comprehensively researched. Based on materials mechanics, finite element simulation, and bio-inspired original artificial sensors, it is verified that the omnidirectional ultra-sensitivity of trigger hair is attributed to the stiff-flexible coupling of material, the double stress concentration, the circular distribution of sensory cells, and the positive local buckling. Also, the balance strategy of slender hair between sensitivity and structural stability (i.e., avoiding disastrous collapse) is detailed revealed. The unique basic biomechanical mechanism underlying trigger hairs is essential for significantly enhancing the performance of the traditional industrial static mode microcantilever sensors, and ensure the stability of arbitrary load perception.

Horizontal Test Stand for Bone Screw Insertion

Screws are a versatile method of fixation and are often used in orthopaedic surgery. Various specialised geometries are often used for bone screws to optimise their fixation strengths in limited spaces at the expense of manufacturing costs. Additionally, ongoing research is looking to develop systems/models to automatically optimise bone screw tightening torques. For both applications, it is desirable to have a test rig for inserting screws in a regulated, instrumented, and repeatable manner. This work presents such a test rig primarily used for the validation of optimal torque models; however, other applications like the above are easily foreseeable. Key features include controllable insertion velocity profiles, and a high rate measurement of screw torque, angular displacement, and linear displacement. The test rig is constructed from mostly inexpensive components, with the primary costs being the rotational torque sensor (approx. 2000 €), and the remainder being approximately 1000 €. This is in comparison to a biaxial universal testing machine which may exceed 100,000 €. Additionally, the firmware and interface software are designed to be easily extendable. The angular velocity profiling and linear measurement repeatability of the test rig is tested and the torque readings are compared to an off-the-shelf static torque sensor.

Enhancing explainability of deep learning models for point cloud analysis: a focus on semantic segmentation

ABSTRACT Semantic segmentation of point clouds plays a critical role in various applications, such as urban planning, infrastructure management, environmental analyses and autonomous navigation. Understanding the behaviour of deep neural networks (DNNs) in analysing point cloud data is essential for improving segmentation accuracy and developing effective network architectures and acquisition strategies. In this paper, we investigate the traits of some state-of-the-art neural networks using indoor and urban outdoor point cloud datasets. We compare PointNet, DGCNN, and BAAF-Net on specifically selected datasets, including synthetic and real-world environments. The chosen datasets are S3DIS, SynthCity, Semantic3D, and KITTI. We analyse the impact of different factors such as dataset type (synthetic vs. real), scene type (indoor vs. outdoor), and acquisition system (static vs. mobile sensors). Through detailed analyses and comparisons, we provide insights into the strengths and limitations not only of different network architectures in handling urban point clouds but also of their data structure. This study contributes to going beyond the mere and unconditional use of AI algorithms, trying to explain DNNs behaviour in point cloud analysis and paving the way for future research to enhance segmentation accuracy and develop possible guidelines both for network design and data acquisition in the geomatics field.

Micro-Urban Heatmapping: A Multi-Modal and Multi-Temporal Data Collection Framework

Monitoring microclimate variables within cities with high resolution and accuracy is crucial for enhancing urban resilience to climate change. Assessing intra-urban characteristics is essential for ensuring satisfactory living standards. This paper presents a comprehensive methodology for studying urban heat islands (UHIs) on a university campus, emphasizing the importance of multi-modal and multi-temporal data collection. The methodology integrates mobile surveys, stationary sensor networks, and drone-based thermal imaging, providing a detailed analysis of temperature variations within urban microenvironments. The preliminary findings confirm the presence of a UHI on the campus and identify several hotspots. This comprehensive approach enhances the accuracy and reliability of UHI assessments, offering a cost-effective, fine-resolution approach that facilitates more effective urban planning and heat mitigation strategies.

Assessing bias in personal exposure estimates when indoor air quality is ignored: A comparison between GPS-enabled mobile air sensor data and stationary outdoor sensor data

Neglecting indoor air quality in exposure assessments may lead to biased exposure estimates and erroneous conclusions about the health impacts of exposure and environmental health disparities. This study assessed these biases by comparing two types of personal exposure estimates for 100 individuals: one derived from real-time particulate matter (PM2.5) measurements collected both indoors and outdoors using a low-cost portable air monitor (GeoAir2.0) and the other from PurpleAir sensor network data collected exclusively outdoors. The PurpleAir measurement data were used to create smooth air pollution surfaces using geostatistical methods. To obtain mobility-based exposure estimates, both sets of air pollution data were combined with the individuals' GPS tracking data. Paired-sample t-tests were then performed to examine the differences between these two estimates. This study also investigated whether GeoAir2.0- and PurpleAir-based estimates yielded consistent conclusions about gender and economic disparities in exposure by performing Welch's t-tests and ANOVAs and comparing their t-values and F-values. The study revealed significant discrepancies between GeoAir2.0- and PurpleAir-based estimates, with PurpleAir data consistently overestimating exposure (t = 5.94; p < 0.001). It also found that females displayed a higher average exposure than males (15.65 versus. 8.55 μg/m3) according to GeoAir2.0 data (t = 4.654; p = 0.055), potentially due to greater time spent indoors engaging in pollution-generating activities traditionally associated with females, such as cooking. This contrasted with the PurpleAir data, which indicated higher exposure for males (43.78 versus. 46.26 μg/m3) (t = 3.793; p = 0.821). Additionally, GeoAir2.0 data revealed significant economic disparities (F = 7.512; p < 0.002), with lower-income groups experiencing higher exposure—a disparity not captured by PurpleAir data (F = 0.756; p < 0.474). These findings highlight the importance of considering both indoor and outdoor air quality to reduce bias in exposure estimates and more accurately represent environmental disparities.

Flaw Detection by SHM with Static Sensors Approach: State of the Art and Perspectives

AbstractStructural health monitoring (SHM) is a technology aimed to monitor the soundness of the structures. Applications for aircraft structures are largely investigated. The goal is to utilize the information acquired during the monitoring to save maintenance costs, improve flight safety, and design lighter structures. Different issues, like load monitoring and impact detection, are investigated by SHM research. The most largely investigated issue is damage detection, i.e., developing a system that utilizes sensors to detect the damage in the structure. Damage detection can be performed with two sensors: dynamic and static. Dynamic sensors work by transmission of waves through the structures. Defects are identified by deviation and reflection of the waves by damages. The SHM approach described in this work is based on static sensors, like strain gages or fiber optics. The strain fields under load in pristine and damaged conditions are compared; the evaluation of the change in the strain field identifies damages. Two different algorithms for damage detection have been developed and patented in Leonardo Aircraft and are described in this work; they are based on the reverse finite element model (FEM) and neural network. The issues related to the strain field measurement are also briefly described.

Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges

ABSTRACT Air pollution affects not only the air in cities but also extends to all indoor environments (homes, offices, schools, public places, transportation, etc.), where we spend between 80% and 90% of our time. Both indoor and outdoor air quality have emerged as significant health concerns and are integral to national strategies implemented by health and environmental institutes in each country. Recently, complaints regarding outdoor air quality have risen in cities, primarily due to automobile traffic and industrial activities in urban areas, and also indoors within homes, offices, and schools. The following paper presents a methodology for the calibration of low-cost monitoring stations based on measurements in a couple of cities in Colombia as part of the development of a project to reduce the environmental awareness gap in urban areas for the estimation of the air quality through low-cost, flexible, modular, and mobile air quality monitoring station design that could be used to assess air pollution in different indoor and outdoor environments. With the implementation of the low-cost stations, we have calibrated and evaluated the performance of the stations using usual linear regression methods, but we have also explored the use of unsupervised estimation with the help of machine learning algorithms, specifically with Random Forest estimators. We have found a significant improvement with using Random Forest for station calibration compared with those found using simple linear regressions for calibration effects. We have found that all the models offer a significant improvement in terms of RMSE. The regression model improves RMSE by up to 70%, while the multiple regression model does so by up to 73%. However, it is the Random Forest that shows the most remarkable improvement, with a reduction in RMSE of up to 86%.

Information-Theoretic Bayesian Inference for Multi-Agent Localization and Tracking of an Radio Frequency Target With Unknown Waveform

Abstract Information-theoretic motion planning and machine learning through Bayesian inference are exploited to localize and track a dynamic radio frequency (RF) emitter with unknown waveform (uncooperative target). A target-state estimator handles non-Gaussian distributions, while mutual information is utilized to coordinate the motion control of a network of mobile sensors (agents) to minimize measurement uncertainty. The mutual information is computed for pairs of sensors through a four-permutation-with-replacement process. The information surfaces are combined to create a composite map, which is then used by agents to plan their motion for more efficient and effective target estimation and tracking. Simulations and physical experiments involving micro-aerial vehicles with time difference of arrival (TDOA) measurements are performed to evaluate the performance of the algorithm. Results show that when two or three agents are used, the algorithm outperforms state-of-the-art methods. Results also show that for four or more agents, the performance is as competitive as an idealized static sensor network.

5G as Communication Platform for Solar Tower Plants

Wiring of heliostat fields for solar tower plants is a cost factor that becomes more important as the overall cost target is decreasing. Wireless heliostats with radio communication and autarchic energy supply have therefore been proposed in the past. But none of the communication solutions investigated so far could realistically scale to commercial size plants with tens of thousands of heliostats. Moreover, the digitalization of CSP plants with numerous mobile and stationary sensor systems requires a suitable data communication, too. The new generation of mobile radio communication (5G) is capable of handling the heterogenous communication profile portfolio comprising large numbers of units with low data rates – like heliostats - and few units with very large data volume – like e.g. drone-based camera systems. The communication requirements of a typical solar tower installation are assessed in this work and a data traffic model is created for the most relevant communication channels. The various existing 5G implementations are assessed to find the most suitable solution. Different operator models for 5G are considered and their applicability in CSP target countries is discussed. A simulation test case is presented that models the radio communication traffic of a heliostat field control during a cloud passage. Finally, the experimental 5G campus network is introduced that is currently installed at the Solar Tower Jülich research plant and will be operated in the upcoming months to demonstrate the technical feasibility of 5G radio for communication in solar fields.

Multi-scale assessment of a cosmic-ray neutron probe observation of soil moisture for surface layer applications in a mountainous forest environment

Soil moisture is critical to the Earth's water cycle as it influences water exchange between the land, atmosphere, and oceans. This study assessed the potential of a cosmic-ray neutron probe (CRNP) to monitor soil moisture dynamics in a forested mountain region, which is critical for water resource management and ecosystem health. Accurate soil moisture monitoring is essential for effectively managing forested mountainous areas, but various limitations exist in such environments. To overcome the complexities of soil moisture monitoring, an observatory consisting of a CRNP, automatic weather sensors, and dataloggers was established. Fifteen capacitance sensor (CS) stations were placed strategically within the CRNP's footprint to ensure strict evaluation. Although each CS measurement revealed the heterogeneity of the experimental site, temporal stability analysis determined the representative soil moisture. The validation of the CRNP and the average results of the 15 surface CS sensors revealed that they were strongly correlated (R = 0.94). It means that the CRNP can describe the dynamics of soil moisture that fluctuate according to physical properties, underscoring its reliability in estimating intermediate scale soil moisture. This correlation remained robust despite differences in rainfall patterns over three years. These findings indicate that the CRNP may be a valuable tool for monitoring soil moisture in a challenging environment. Additionally, the evaluation of the Advanced Microwave Scanning Radiometer-2 (AMSR2), Advanced Scatterometer (ASCAT), and Global Land Data Assimilation System (GLDAS) demonstrated the usefulness of CRNP in supplying reference data. The AMSR2 showed an inadequate temporal pattern (R = 0.35), and the ASCAT achieved the highest correlation agreement (R = 0.67) with the CRNP. The GLDAS also showed close agreement, although with some discrepancies (R = 0.52). The results of this study support the utilization of CRNP and provide insight into establishing and expanding a soil moisture network.

Malicious replica quarantining protocol for Mobile Wireless Sensor Networks using replica detection and identification

Since sensor networks are deployed in harsh and unattended environments, and the nodes are not tamper-resistant, security is crucial for all the impending sensor network applications. When physically compromised, confidential credential of sensor nodes may be revealed to adversaries. Upon obtaining the credentials, an adversary can produce a number of replicas of a compromised legitimate node. These replicas interfere with or prevent the normal operation of the network through attacks called replica or imposterattacks. Hence detection, identification and quarantining of replicas attacks are crucial. Existing schemes mostly focus on static sensor networks while solutions for MWSN’s are also available. Upon discovering that an id is used by multiple nodes, these schemes quarantine all nodes with the same id without distinguishing the legitimate node from its replicas. In this paper, we propose a novel distributed light-weight and memory efficient signature-based protocol for quarantining malicious replicas in the presence of Byzantine replicas in MWSN’s. This protocol quarantines only the replicas that present wrong sensed data in the presence of Byzantine replicas that modify and communicate its data and send false claims. The proposed protocol uses a signature generation, exchange and verification scheme to detect, uniquely identify and distinguish replicas with the same id among multiple replicas for a single legitimate node. The protocol quarantines only the malicious replicas that present wrong sensed data among all the detected replicas of each legitimate node by uniquely identifying the malicious replicas using their unique signatures.