Large-scale Measurements Research Articles

Comparison of COVID and influenza vaccine hesitancy among clients of the San Antonio Food Bank

Background
 The COVID-19 pandemic has continued despite large-scale public health measures. Some people are still hesitant to receive the COVID vaccine. The San Antonio Food Bank (SAFB) is an organization that wants to gather opinions of clientele regarding vaccinations. A prior study at the SAFB captured reasons for influenza vaccine hesitancy. 
 Objectives
 
 Identify reasons for COVID vaccine hesitancy.
 Compare reasons for COVID vaccine hesitancy from this study to reasons for influenza vaccine hesitancy from a prior study.
 
 Methods
 Student pharmacists partnered with SAFB leaders to create and administer English and Spanish surveys regarding client demographics, COVID vaccination status, and COVID vaccine hesitancy in those who had not yet received the COVID vaccine. Investigators completed paper surveys with clients during verbal interviews. Information from this COVID vaccine hesitancy study was compared to previous information from a 2019-2020 influenza vaccine hesitancy study also conducted at the SAFB. 
 Outcomes
 COVID vaccination rates in this study were higher than influenza vaccination rates observed in a prior study at the SAFB. When comparing the reasons for hesitancy, several differences were identified regarding what clients said about the COVID and influenza vaccines: too expensive, not knowing where to get it, and afraid of getting sick.
 Conclusion
 This study indicates that reasons for vaccine hesitancy differ between COVID and influenza vaccines, suggesting a need to provide accurate information about each vaccine. Healthcare providers can make an impact by educating the community regarding COVID vaccine misconceptions and helping them navigate where and how to receive influenza vaccines.

Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology.

In the large-scale measurement field, deployment planning usually uses the Monte Carlo method for simulation analysis, which has high algorithm complexity. At the same time, traditional station planning is inefficient and unable to calculate overall accessibility due to the occlusion of tooling. To solve this problem, in this study, we first introduced a Poisson-like randomness strategy and an enhanced randomness strategy to improve the remora optimization algorithm (ROA), i.e., the PROA. Simultaneously, its convergence speed and robustness were verified in different dimensions using the CEC benchmark function. The convergence speed of 67.5-74% of the results is better than the ROA, and the robustness results of 66.67-75% are better than those of the ROA. Second, a deployment model was established for the large-scale measurement field to obtain the maximum visible area of the target to be measured. Finally, the PROA was used as the optimizer to solve optimal deployment planning; the performance of the PROA was verified by simulation analysis. In the case of six stations, the maximum visible area of the PROA reaches 83.02%, which is 18.07% higher than that of the ROA. Compared with the traditional method, this model shortens the deployment time and calculates the overall accessibility, which is of practical significance for improving assembly efficiency in large-size measurement field environments.

Abnormal structural and functional network topological properties associated with left prefrontal, parietal, and occipital cortices significantly predict childhood TBI-related attention deficits: A semi-supervised deep learning study.

Traumatic brain injury (TBI) is a major public health concern in children. Children with TBI have elevated risk in developing attention deficits. Existing studies have found that structural and functional alterations in multiple brain regions were linked to TBI-related attention deficits in children. Most of these existing studies have utilized conventional parametric models for group comparisons, which have limited capacity in dealing with large-scale and high dimensional neuroimaging measures that have unknown nonlinear relationships. Nevertheless, none of these existing findings have been successfully implemented to clinical practice for guiding diagnoses and interventions of TBI-related attention problems. Machine learning techniques, especially deep learning techniques, are able to handle the multi-dimensional and nonlinear information to generate more robust predictions. Therefore, the current research proposed to construct a deep learning model, semi-supervised autoencoder, to investigate the topological alterations in both structural and functional brain networks in children with TBI and their predictive power for post-TBI attention deficits. Functional magnetic resonance imaging data during sustained attention processing task and diffusion tensor imaging data from 110 subjects (55 children with TBI and 55 group-matched controls) were used to construct the functional and structural brain networks, respectively. A total of 60 topological properties were selected as brain features for building the model. The model was able to differentiate children with TBI and controls with an average accuracy of 82.86%. Functional and structural nodal topological properties associated with left frontal, inferior temporal, postcentral, and medial occipitotemporal regions served as the most important brain features for accurate classification of the two subject groups. Post hoc regression-based machine learning analyses in the whole study sample showed that among these most important neuroimaging features, those associated with left postcentral area, superior frontal region, and medial occipitotemporal regions had significant value for predicting the elevated inattentive and hyperactive/impulsive symptoms. Findings of this study suggested that deep learning techniques may have the potential to help identifying robust neurobiological markers for post-TBI attention deficits; and the left superior frontal, postcentral, and medial occipitotemporal regions may serve as reliable targets for diagnosis and interventions of TBI-related attention problems in children.

Environmental information-aided electromagnetic propagation testbed for maritime communication

Maritime channel modeling can be affected by some key time-varying environmental factors. The ducting effect is one of the thorniest factors since it causes anomalous propagation enhancement and severe co-channel interference. Moreover, the atmospheric attenuation is much more severe in the ocean environment, resulting in shorter coverage distance and more link outage. In this paper, we propose an environmental information-aided electromagnetic propagation testbed. It is based on complex refractivity estimation and improved parabolic equation propagation model, taking into account both ducting effect and atmospheric attenuation. A large-scale temporal and spatial propagation measurement was conducted with meteorological acquisition. We consider practical path loss and ducting conditions to verify the testbed feasibility in these long-distance radio links. The simulation results are in good agreement with the measured data, which further reveal the basic temporal and spatial distribution of ducting effect at 3.5 GHz band.

Summary of underwater ambient sound from wind and rain in the northeast Pacific continental margin

Analyzing underwater ambient sound from various sources such as ships, marine mammals, rain, and wind is crucial for characterizing the ocean environment. While efforts to analyze ocean ambient sounds have been ongoing since the 1940s, networks such as the Ocean Observatories Initiative (OOI) provide modern large-scale recording setups for a more in-depth analysis. Here we will summarize results from analyzing over 11,000h of wind generated ambient sound and 280 h of ambient sound during rain collected between 2015 and 2019 by two OOI hydrophones deployed in the northeast Pacific continental margin. The hydrophones record continuously at depths of 81 and 581 m with a sample rate of 64 kHz. Meteorological data are provided by surface buoys deployed near the hydrophones. We compare our results to data obtained from a large-scale recording setup in the tropical Pacific Ocean (Ma et al., 2005). In contrast to their results, we found that sound levels during rain in the northeast Pacific Ocean are highly dependent on the wind speed over a wide frequency range. This implies that large-scale distributed sound measurements are necessary to accurately characterize underwater ambient sound from wind and rain across the globe. [Work supported by ONR.]

A Dataset for Radar Scattering Characteristics of Vehicles Under Real-World Driving Conditions: Major Findings for Sensor Simulation

In the virtual validation of automated driving, trustworthy simulation models of perception sensors are required. Radar sensors are particularly hard to model, as their measurements are notoriously difficult to interpret. This is due to their complex measurement principle, involving multipath propagation of mm-waves, varying backscattering characteristics of objects, and further factors such as limited measurement ranges and resolutions that introduce uncertainty to the measurements. This work presents a method for studying the backscatter characteristics of vehicles under real-world driving conditions. A slalom-like driving scenario, which is representative of road driving where the vehicle is visible under different aspect angles, has been designed. It aims at a high level of reproducibility of the trajectories driven by the vehicles, hence reducing additional sources of uncertainty that were otherwise present in the measurements. In a large-scale measurement campaign, 13 vehicles have been studied. The vehicles under test are observed by multiple radars, mounted at different heights, and carry reference sensors for obtaining their positions. In this article, we present the measurement campaign and show major findings from our measurement results. Our focus lies on drawing conclusions for trustworthy sensor simulation. Both sensor measurement data and MATLAB code for data analysis are made publicly available alongside this article.

ISSUES OF IMPROVING THE PROCEDURE FOR GRANTING TAX BENEFITS

One of the important tasks is to increase the volume of market services by introducing new approaches to the tax legislation of the Republic of Uzbekistan in 2023 and to improve the procedure for granting tax benefits. In recent years, with the aim of rapid socio-economic development of the country, large-scale measures have been taken to liberalize the economy, support entrepreneurship, and create a single economic space with equal competitive conditions. Also, despite the fact that tax benefits and other reliefs are directed to specific goals, a system for evaluating their effectiveness has not been developed.

STUDY OF METHODS OF MEASURING SOCIAL RESPONSIBILITY OF BUSINESS IN THE SYSTEM OF SUSTAINABLE DEVELOPMENT OF THE REGION

The article proves that in modern conditions the sustainable development of regions is possible on the basis of the creation and development of institutions of coordination of interests between all the main participants of market relations: business, state, society. Business should assume responsibility for many areas of society development directly related to its economic activity (ecology, social programmes in the field of territorial development, improvement of the quality of life of the entire population: health care, labour protection, education, culture, sports). Analysis of the mechanisms of social responsibility implementation in practice shows that at the current stage of development socially responsible business behaviour should be elevated to the rank of the main direction of state policy. To achieve political, economic and social goals facing society, it is necessary to consistently implement large-scale measures to create social infrastructure in industrial regions, protect and expand the reproduction of natural resources, and create conditions for sustainable socio-economic development. The interrelationships of business social responsibility and sustainable development are studied, and priorities on the part of business and the territories of presence are identified. The experience is generalised and methodological approaches to macroeconomic assessment of damage and losses of the region's economy due to the environmental factor in accordance with different development scenarios are proposed. The theoretical foundations of the social responsibility of business as a system that naturally ensures the increase of socio-economic development of regions on the basis of the principles of sustainable development, necessity and sufficiency for sustainable development of the territory are developed; it is determined that the nature of compensation measures depends largely on the structure, functions and main directions of the implementation of social responsibility of business. A set of methodological approaches to the social assessment of business in the system of sustainable socio-economic development of the region

Real-time high-precision FMCW laser range extraction method based on a hardware multiplier array.

Frequency-modulated continuous wave (FMCW) laser interferometry is an ideal large-scale absolute distance measurement method. It has advantages of high precision and noncooperative target measurement capability, with no blind spot for ranging. To meet the requirements of high-precision, high-speed 3D topography measurement technologies, a faster measurement speed of FMCW LiDAR at each measurement point is required. To solve the shortcomings of the existing technology, a real-time high-precision hardware solution method (including but not limited to FPGA and GPU) for lidar beat frequency signals is provided here based on hardware multiplier arrays to reduce lidar beat frequency signal processing time and to save energy and resource consumption during processing. A high-speed FPGA architecture was also designed for the frequency-modulated continuous wave lidar range extraction algorithm. The whole algorithm was designed and implemented in real time based on the principle of full-pipelines and parallelism. The results show that the processing speed of the FPGA system is faster than that of current top-performing software implementations.

Formation and development characteristics of women's entrepreneurship in the Republic of Azerbaijan

Expanding the role of women in the social, economic and socio-political life of the country is one of the priority directions of the state. In this regard, in recent years, large-scale measures have been implemented in the direction of the development of small and medium-sized entrepreneurship in the non-oil sector, especially in Baku and the regions, and various projects in accordance with advanced international practice have been implemented, and comprehensive support has been provided to the development of women's entrepreneurship: "Increasing attention to the development of women's entrepreneurship in our country makes us even more proud. and encourages. In order to develop female entrepreneurship in our country, it is planned to develop stimulating mechanisms for female entrepreneurs. These mechanisms will significantly increase the opportunities for realizing women's economic initiatives. In the mechanisms, the issue of women's employment, especially in rural areas, will be focused on, more promising directions in this field will be determined and relevant measures will be taken. As a result of joint efforts and measures to be taken, a more favorable environment for women's entrepreneurial activity will be created, and the number of women among small and medium entrepreneurs will increase."

A unified model for large-scale thickness measurement of lubricating film based on ultrasonic lag phase slope

A unified model based on the ultrasonic lag phase slope is developed for measuring the lubricating film thickness at a large scale. The ultrasonic lag phase of adjacent waves instead of the phase of overlapped waves is calculated as a function of the ultrasonic frequency and film thickness. The slope of the ultrasonic lag phase is determined correspondingly, which is linearly proportional to the lubricating film thickness. Both the finite element analysis and tests on the lubricating film thickness are performed to verify the proposed method. The results show that despite the fluctuations of the lag phase, the lag phase slope can be used for measuring the lubricating film thickness at a large scale from 0.1 μm to 170 μm.

Geophysical Survey and Changes in the Use of the Cultural Landscape

The results of detailed and large-scale geophysical measurements in archaeology have been steadily increasing for years. The growth in measured data has also increased the need for processing and interpretation; in archaeology, this primarily means the archaeological interpretation of the measured data. However, the information contained in geophysical data includes a substantial volume or area of data of varying size or thickness of some different natural or modern anthropogenic origin (beyond archaeological interest). Like the archaeological situations themselves, these must also be identified and demarcated. The presented article consists of a wide range of case studies in which the result of a specific applied geophysical method includes both the desired interpretations of archaeological features and the differentiation and warning of other anomalies, the origin of which may or may not be unambiguous or related to the post-deposition processes of archaeological features. The purpose of selecting several different examples of results in our paper is to point out that there are many more consequences of anthropogenic activity hidden beneath the surface of the terrain of the contemporary cultural landscape than just those that archaeologists have in their viewfinder. Other anomalies in specific environments may be of natural origin or related to various geological, pedological or hydrological changes in a site’s natural environment. This should be dealt with by the alternative differentiation of anomalies of various probable origins; the interpretive descriptions, diagrams or maps should not just focus strictly on the anticipated subsurface relics of the archaeological features and situations, as these are not there alone.

Integrating technology in mental healthcare practice: A repeated cross-sectional survey study on professionals' adoption of Digital Mental Health before and during COVID-19.

As a consequence of the outbreak of the COVID-19 global pandemic in the spring of 2020, large-scale social distancing measures were implemented, resulting in the forced adoption of online or digital forms of psychological treatment. This sudden transition to digital care offered a unique opportunity to investigate if and how this experience impacted mental healthcare professionals' perceptions and use of Digital Mental Health tools. The current paper presents findings of a repeated cross-sectional study consisting of three iterations of a national online survey in the Netherlands. This survey contained open and closed questions on professionals' adoption readiness, frequency of use, perceived competency, and perceived value of Digital Mental Health collected in 2019 (before the pandemic), in 2020 (after the first wave), and in 2021 (after the second wave). The inclusion of data gathered prior to the COVID-19 pandemic offers a unique window to assess how professionals' adoption has developed through this transition from voluntary to mandatory use of Digital Mental Health tools. Our study also re-assesses the drivers, barriers, and needs of mental healthcare professionals after having gained experience with Digital Mental Health. In total, 1,039 practitioners completed the surveys (Survey 1: n = 432, Survey 2: n = 363, and Survey 3: n = 244). Results indicate that compared to the period before the pandemic, there was a particularly large increase in use, competency, and perceived value regarding videoconferencing. Small differences were also found for some other basic tools that were crucial to ensure the continuation of care, such as e-mail, text messaging, and online screening, but not for more innovative technologies, such as virtual reality and biofeedback. Many practitioners reported to have gained skills regarding Digital Mental Health and experienced several benefits of it. They expressed the intention to continue with a blended approach, using Digital Mental Health tools in combination with face-to-face care, focused on situations in which they found it to have specific added value, such as when clients are unable to travel. Others were less satisfied with the technology-mediated interactions and remained more reluctant to future use of DMH. Implications for broader implementation of Digital Mental Health and future research are discussed.

Generalized anxiety disorder among rural primary and middle school students during the outbreak of COVID-19: a multicenter study in three southern Chinese cities

BackgroundThe major public health crisis caused by the rapid spread of the coronavirus disease 2019 (COVID-19) and the large-scale public health measures such as social isolation and school closures enforced by some countries have severely affected on the physical and mental wellbeing of children and adolescents globally. This study aimed to estimate the prevalence of the psychological impact and investigate the similarities and differences in the influential factors for generalized anxiety disorder among rural adolescents as a relatively lesser noticed population the outbreak of COVID-19.MethodsFrom May 11 to 22, 2020, a total of 1,179 adolescents, including Grade 5–6 in primary school and Grade 7–8 in middle school, were selected by multistage sampling in three Southern Chinese cities (Shantou, Guangdong Province; Hezhou, Guangxi Province; Nanchong, Sichuan Province), and completed the questionnaires including sociodemographic, generalized anxiety disorder, academic stress, coronaphobia, knowledge of COVID-19, and precautionary measures. ANOVA, Chi-square test, Kruskalwallis H test and multivariate linear regression were performed in the statistical analysis.ResultsThe average scores of generalized anxiety disorder during the past two weeks were 3.43 (SD 4.46), 4.47 (SD 5.15), and 4.10 (SD 4.94) in Shantou, Hezhou and Nanchong, respectively. For the pooled data, academic stress (P < 0.001), coronaphobia (P < 0.001), and precautionary measures (P = 0.002) contributed to the prediction of anxiety scores. Academic stress was significantly associated to anxiety symptoms in all cities (P all < 0.001). Coronaphobia was also significantly associated to anxiety symptoms in all cities (P all < 0.001).ConclusionThis study highlights the urgent need for researchers and policymakers to focus on the mental health of rural children and adolescents during the COVID-19 epidemic. The adolescents with academic stress and coronaphobia, the greater the risk that adolescents will suffer from anxiety, suggesting mental health counseling and professional family support are needed.

Consumption and tax gains attributable to Covid-19 vaccinations in 12 EU countries with low vaccination rates.

The Covid-19 pandemic is an economic and a health crisis. Households reduced consumption expenditures as large-scale physical distancing measures, lower disposable incomes and fear of infection when engaging in many types of economic activity took hold. This, in turn, reduced domestic tax revenues at a time when governments were facing increased financial pressures to strengthen and sustain welfare states. We developed a simulation model, the Covid-19 Taxination Simulator, to estimate potential economic gains and tax revenues attributable to vaccine rollouts. We apply the model to 12 European Union countries which had low vaccination rates at the beginning of 2022. The highest growth in aggregate personal consumption expenditure attributable to Covid-19 vaccines administered as of January 2022 is in Greece (10.8%), Slovenia (8.6%) and Czechia (8.6%), while the lowest is in Bulgaria (2.2%) and Slovakia (2.1%). If countries had vaccinated 85% of their adult population, the largest gains in consumption tax revenues would be expected in Romania (830 million Euros) and Poland (738 million Euros). Consumption tax revenues generated by meeting the 85% of the adult population target would, on their own, be large enough to fully cover the costs of expanding the vaccine rollout itself in Estonia, Latvia, Slovenia, Croatia, Czechia, Hungary and Greece. Covid-19 vaccination rollouts not only save lives and relieve pressures on health systems, they also support economic growth and generate additional tax revenues. These revenues can partially offset the costs of vaccines programmes themselves.

Transport-limited kinetics of phosphate retention on iron-coated sand and practical implications

Iron-coated sand (ICS) is a by-product from drinking water treatment made of sand coated with ferric iron (hydr)oxides. It is considered a suitable material for large-scale measures for phosphate removal from natural and agricultural waters to prevent eutrophication. Previous studies demonstrated that the residence time of water must be very long to reach equilibrium partitioning between phosphate and ICS but specifics for application are missing. First, SEM-EDX images were used to support the conceptual assumption that P adsorption inside the coating is a transport-limited process. Second, a conceptual model of phosphate adsorption was proposed considering two types of sites: one type with fast adsorption kinetics and reaching equilibrium with the percolating solution, and another type for which adsorption is also reversible but described by pseudo-first-order kinetics. The latter is conceived to account for transport-limited adsorption in the interior of the coating while the former fraction of sites is assumed to be easily accessible and located close to the grain surface. Third, the kinetics of phosphate adsorption on ICS were quantitatively determined to describe and predict phosphate retention in filters under various flow conditions. The model was calibrated and validated with long-term column experiments, which lasted for 3500 h to approach equilibrium on the slowly reacting sites. The model reproduced the outflowing phosphate concentrations: the pronounced increase after a few pore volumes and the slow increase over the remaining part of the experiment. The parameterized model was also able to predict the time evolution of phosphate concentrations in the outflow of column experiments with different flow velocities, flow interruption, and in desorption experiments. The equilibrium partition coefficient for the experimental conditions was identified as 28.1 L/g-Fe at pH 6.8 and a phosphate concentration of 1.7 mg-P / L. The optimized first-order mass transfer coefficient for the slow adsorption process was 1.56 10−4 h−1, implying that the slow adsorption process has a time scale of several months. However, based on the parameterized model, the slow adsorption process accounted for 95.5% of the equilibrium adsorption capacity, emphasizing the potential relevance of this process for practical applications. The implications for the design, operation, and lifespan of ICS filters are exemplarily illustrated for different scenarios.

Maize Ear Height and Ear–Plant Height Ratio Estimation with LiDAR Data and Vertical Leaf Area Profile

Ear height (EH) and ear–plant height ratio (ER) are important agronomic traits in maize that directly affect nutrient utilization efficiency and lodging resistance and ultimately relate to maize yield. However, challenges in executing large-scale EH and ER measurements severely limit maize breeding programs. In this paper, we propose a novel, simple method for field monitoring of EH and ER based on the relationship between ear position and vertical leaf area profile. The vertical leaf area profile was estimated from Terrestrial Laser Scanner (TLS) and Drone Laser Scanner (DLS) data by applying the voxel-based point cloud method. The method was validated using two years of data collected from 128 field plots. The main factors affecting the accuracy were investigated, including the LiDAR platform, voxel size, and point cloud density. The EH using TLS data yielded R2 = 0.59 and RMSE = 16.90 cm for 2019, R2 = 0.39 and RMSE = 18.40 cm for 2021. In contrast, the EH using DLS data had an R2 = 0.54 and RMSE = 18.00 cm for 2019, R2 = 0.46 and RMSE = 26.50 cm for 2021 when the planting density was 67,500 plants/ha and below. The ER estimated using 2019 TLS data has R2 = 0.45 and RMSE = 0.06. In summary, this paper proposed a simple method for measuring maize EH and ER in the field, the results will also offer insights into the structure-related traits of maize cultivars, further aiding selection in molecular breeding.

Leaf functional traits have more contributions than climate to the variations of leaf stable carbon isotope of different plant functional types on the eastern Qinghai–Tibetan Plateau

Elucidating the mechanisms that control the leaf stable carbon isotope values (δ13Cleaf) is the prerequisite for the widespread application of δ13Cleaf. However, the competing effects of physiological and environmental factors on δ13Cleaf variations of the different plant functional types (PFTs) have not been disentangled, and the corresponding mechanisms remain unclear. Based on large-scale δ13Cleaf measurements on the eastern Qinghai–Tibetan Plateau, the relative contributions and regulatory pathways of leaf functional traits (LFTs) and climatic factors to δ13Cleaf variations of the different PFTs were investigated. We found that δ13Cleaf of the different PFTs was correlated with annual mean precipitation negatively, but not a simple linear relationship with annual mean temperature and varied by PFTs. Leaf nitrogen content per unit area and leaf mass per area (correlated with δ13Cleaf positively) had more substantial effects on the δ13Cleaf variations of the different PFTs than other LFTs. The relative contributions of LFTs to the δ13Cleaf variations were greater than that of climatic factors, and the direct and indirect effects of climatic factors on δ13Cleaf variations varied by PFTs. Our findings provide new insights into understanding key drivers of δ13Cleaf variations at the PFT level on a regional scale.

Development and characterization of a passive, bio-inspired flow-tracking sensor

The effective natural transport of seeds in turbulent atmospheric flows is found across a myriad of shapes and sizes. However, to develop a sensitive passive sensor required for large-scale (in situ) flow tracking measurements, systems suffer from inertial lag due to the increased size and mass needed for optical visibility, or by carrying a sensor payload, such as an inertial measurement unit (IMU). While IMU-based flow sensing is promising for beyond visual line-of-sight applications, the size and mass of the sensor platform results in reduced flow fidelity and, hence, measurement error. Thus, to extract otherwise inaccessible flow information, a flow-physics-based tracer correction is developed through the application of a low-order unsteady aerodynamic model, inspired by the added-mass concept. The technique is evaluated using a sensor equipped with an IMU and magnetometer. A spherical sensor platform, selected for its symmetric geometry, was subject to two canonical test cases including an axial gust as well as the vortex shedding generated behind a cylinder. Using the measured sensor velocity and acceleration as inputs, an energized-mass-based dynamic model is used to back-calculate the instantaneous flow velocity from the sensor measurements. The sensor is also tracked optically via a high-speed camera while collecting the inertial data onboard. For the 1D test case (axial gust), the true (local) wind speed was estimated from the energized-mass-based model and validated against particle image velocimetry measurements, exhibiting good agreement with a maximum error of 10%. For the cylinder wake (second test case), the model-based correction enabled the extraction of the velocity oscillation amplitude and vortex-shedding frequency, which would have otherwise been inaccessible. The results of this study suggest that inertial (i.e. large and heavy) IMU-based flow sensors are viable for the extraction of Lagrangian tracking at large atmospheric scales and within highly-transient (turbulent) environments when coupled with a robust dynamic model for inertial correction.

A large-scale, long-term experimental campaign for the investigation of wind turbine noise fluctuations and amplitude modulation phenomena.

The understanding and prediction of wind turbine noise (WTN) remain a subject on which progress is needed to better address the concerns of residents of some wind farms and to help wind farm operators to better optimize their wind farms. A large-scale and long-term measurement campaign was conducted by partners of the PIBE project (https://www.anr-pibe.com/en) near a French wind farm during 430 days, in order to study the emission and propagation of WTN. The campaign provided 100ms sound levels and acoustic spectra, together with periodically recorded 2 min audio samples, at 5 locations ranging from 350m to 1.3km from the wind farm, in different propagation directions. Meteorological data were simultaneously collected by a 80m meteorological mast, one or two Lidars and sonic anemometers, in order to characterize vertical profiles of wind speed, wind direction, temperature, and wind turbulence. 2 distinct weeks of intensive measurements (one in summer and one in winter) completed the data by including 15 additional acoustic points at other locations. The database will mainly be used within the project for the investigation of an uncertainty model of WTN, and of WTN amplitude modulations. This communication presents the database and first results about temporal variabilities of WTN.