Small Unmanned Aircraft Research Articles

Large-Scale Solar-Powered UAV Attitude Control Using Deep Reinforcement Learning in Hardware-in-Loop Verification

Large-scale solar-powered unmanned aerial vehicles possess the capacity to perform long-term missions at different altitudes from near-ground to near-space, and the huge spatial span brings strict disciplines for its attitude control such as aerodynamic nonlinearity and environmental disturbances. The design efficiency and control performance are limited by the gain scheduling of linear methods in a way, which are widely used on such aircraft at present. So far, deep reinforcement learning has been demonstrated to be a promising approach for training attitude controllers for small unmanned aircraft. In this work, a low-level attitude control method based on deep reinforcement learning is proposed for solar-powered unmanned aerial vehicles, which is able to interact with high-fidelity nonlinear systems to discover optimal control laws and can receive and track the target attitude input with an arbitrary high-level control module. Considering the risks of field flight experiments, a hardware-in-loop simulation platform is established that connects the on-board avionics stack with the neural network controller trained in a digital environment. Through flight missions under different altitudes and parameter perturbation, the results show that the controller without re-training has comparable performance with the traditional PID controller, even despite physical delays and mechanical backlash.

UAS‐based multispectral imaging for detecting iron chlorosis in grain sorghum

AbstractThis study uses a small unmanned aircraft system equipped with a multispectral sensor to assess various vegetation indices (VIs) for their potential to monitor iron deficiency chlorosis (IDC) in a grain sorghum (Sorghum bicolor L.) crop. IDC is a nutritional disorder that stunts a plants’ growth and causes its leaves to yellow due to an iron deficit. The objective of this project is to find the best VI to detect and monitor IDC. A series of flights were completed over the course of the growing season and processed using Structure‐from‐Motion photogrammetry to create orthorectified, multispectral reflectance maps in the red, green, red‐edge, and near‐infrared wavelengths. Ground data collection methods were used to analyze stress, chlorophyll levels, and grain yield, correlating them to the multispectral imagery for ground control and precise crop examination. The reflectance maps and soil‐removed reflectance maps were used to calculate 25 VIs whose separability was then calculated using a two‐class distance measure, determining which contained the largest separation between the pixels representing IDC and healthy vegetation. The field‐acquired data were used to conclude which VIs achieved the best results for the dataset as a whole and at each level of IDC (low, moderate, and severe). It was concluded that the MERIS terrestrial chlorophyll index, normalized difference red‐edge, and normalized green (NG) indices achieved the highest amount of separation between plants with IDC and healthy vegetation, with the NG reaching the highest levels of separability for both soil‐included and soil‐removed VIs.

Detecting sorghum aphid infestation in grain sorghum using leaf spectral response

Sorghum aphid, Melanaphis sorghi (Theobald) have become a major economic pest in sorghum causing 70% yield loss without timely insecticide applications. The overarching goal is to develop a monitoring system for sorghum aphids using remote sensing technologies to detect changes in plant-aphid density interactions, thereby reducing scouting time. We studied the effect of aphid density on sorghum spectral responses near the feeding site and on distal leaves from infestation and quantified potential systemic effects to determine if aphid feeding can be detected. A leaf spectrometer at 400–1000 nm range was used to measure reflectance changes by varying levels of sorghum aphid density on lower leaves and those distant to the caged infestation. Our study results demonstrate that sorghum aphid infestation can be determined by changes in reflected light, especially between the green–red range (550–650 nm), and sorghum plants respond systemically. This study serves as an essential first step in developing more effective pest monitoring systems for sorghum aphids, as leaf reflection sensors can be used to identify aphid feeding regardless of infestation location on the plant. Future research should address whether such reflectance signatures can be detected autonomously using small unmanned aircraft systems or sUAS equipped with comparable sensor technologies.

Monitoring of a rockfill embankment dam using TLS and sUAS point clouds

Abstract Terrestrial laser scanning (TLS) and camera-equipped small unmanned aircraft systems (sUAS) are two methods that are often used to produce dense point clouds for several monitoring applications. This paper compares the two methods in their ability to provide accurate monitoring information for rockfill embankment dams. We compare the two methods in terms of their uncertainty, data completeness, and field data acquisition/processing challenges. For both datasets, we derive an error budget that considers registration and measurement uncertainty. We also proceed to merge the TLS and sUAS data and leverage the advantages of each method. Furthermore, we conduct an analysis of the multiscale model-to-model cloud comparison (M3C2) input parameters, namely projection scale, normal scale, and sub-sampling of the reference point cloud, to show their effect on the M3C2 distance estimation. The theoretical methodologies and practical considerations of this paper can assist surveyors, who conduct monitoring of rockfill embankment dams using point clouds, in establishing reliable change/deformation estimations.

Using machine learning models to estimate Escherichia coli concentration in an irrigation pond from water quality and drone-based RGB imagery data

The rapid and efficient quantification of Escherichia coli concentrations is crucial for monitoring water quality. Remote sensing techniques and machine learning algorithms have been used to detect E. coli in water and estimate its concentrations. The application of these approaches, however, is challenged by limited sample availability and unbalanced water quality datasets. In this study, we estimated the E. coli concentration in an irrigation pond in Maryland, USA, during the summer season using demosaiced natural color (red, green, and blue: RGB) imagery in the visible and infrared spectral ranges, and a set of 14 water quality parameters. We did this by deploying four machine learning models – Random Forest (RF), Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGB), and K-nearest Neighbor (KNN) – under three data utilization scenarios: water quality parameters only, combined water quality and small unmanned aircraft system (sUAS)-based RGB data, and RGB data only. To select the training and test datasets, we applied two data-splitting methods: ordinary and quantile data splitting. These methods provided a constant splitting ratio in each decile of the E. coli concentration distribution. Quantile data splitting resulted in better model performance metrics and smaller differences between the metrics for both the training and testing datasets. When trained with quantile data splitting after hyperparameter optimization, models RF, GBM, and XGB had R2 values above 0.847 for the training dataset and above 0.689 for the test dataset. The combination of water quality and RGB imagery data resulted in a higher R2 value (>0.896) for the test dataset. Shapley additive explanations (SHAP) of the relative importance of variables revealed that the visible blue spectrum intensity and water temperature were the most influential parameters in the RF model. Demosaiced RGB imagery served as a useful predictor of E. coli concentration in the studied irrigation pond

Morning Glory Flower Detection in Aerial Images Using Semi-Supervised Segmentation with Gaussian Mixture Models

The invasive morning glory, Ipomoea purpurea (Convolvulaceae), poses a mounting challenge in vineyards by hindering grape harvest and as a secondary host of disease pathogens, necessitating advanced detection and control strategies. This study introduces a novel automated image analysis framework using aerial images obtained from a small fixed-wing unmanned aircraft system (UAS) and an RGB camera for the large-scale detection of I. purpurea flowers. This study aimed to assess the sampling fidelity of aerial detection in comparison with the actual infestation measured by ground validation surveys. The UAS was systematically operated over 16 vineyard plots infested with I. purpurea and another 16 plots without I. purpurea infestation. We used a semi-supervised segmentation model incorporating a Gaussian Mixture Model (GMM) with the Expectation-Maximization algorithm to detect and count I. purpurea flowers. The flower detectability of the GMM was compared with that of conventional K-means methods. The results of this study showed that the GMM detected the presence of I. purpurea flowers in all 16 infested plots with 0% for both type I and type II errors, while the K-means method had 0% and 6.3% for type I and type II errors, respectively. The GMM and K-means methods detected 76% and 65% of the flowers, respectively. These results underscore the effectiveness of the GMM-based segmentation model in accurately detecting and quantifying I. purpurea flowers compared with a conventional approach. This study demonstrated the efficiency of a fixed-wing UAS coupled with automated image analysis for I. purpurea flower detection in vineyards, achieving success without relying on data-driven deep-learning models.

Detecting Floral Resource Availability Using Small Unmanned Aircraft Systems

Floral resources for native pollinators that live in wildland settings are diverse and vary across and within growing seasons. Understanding floral resource dynamics and management is becoming increasingly important as honeybee farms seek public land for summer pasture. Small Unmanned Aircraft Systems (sUASs) present a viable approach for accurate broad floristic surveys and present an additional solution to more traditional alternative methods of vegetation assessment. This methodology was designed as a simplified approach using tools frequently available to land managers. The images of three subalpine meadows were captured from a DJI Phantom 4 Pro drone platform three times over the growing season in 2019 in Sanpete County, Utah. The images were composited using Pix4D software 4.5.6 and classified using a simple supervised approach in ENVI 4.8 and ArcGIS Pro 2.4.3 These same meadows were assessed using two traditional ocular methods of vegetation cover–meter-squared quadrats and macroplot estimation. The areas assessed with these methods were compared side by side with their classified counterparts from drone imagery. Classified images were not only found to be highly accurate when detecting overall floral cover and floral color groups (76–100%), but they were also strongly correlated with quadrat estimations, suggesting that these methods used in tandem may be a conducive strategy toward increased accuracy and efficiency when determining floral cover at broad spatial scales.

Development of a project to create a road transport infrastructure using small unmanned aircraft

The infrastructure for small unmanned aircraft has not yet been formed: there are no organized ground-based stations, there are no clear operating regulations for performing specialized types of work. It is proposed that when creating projects for an updated road transport infrastructure using ground-based autonomous vehicles, take into account the ability to maintain and maintain the operability of unmanned aircraft systems. Unmanned aerial vehicles can be used not only when conducting an inspection of the accident site, but also when checking on-site readings, investigative experiments, determining visibility on the road, etc. In the course of these investigative and procedural actions. The use of traffic control methods using small unmanned aircraft will make a significant contribution to ensuring road safety and reducing the level of accidents on highways. It is proposed that when creating projects for an updated road transport infrastructure using ground-based autonomous vehicles, take into account the ability to maintain and maintain the operability of unmanned aircraft systems.

Evaluation of Dicamba Drift Injury and Yield Loss on Soybean Using Small Unmanned Aircraft Systems (sUAS) and Multispectral Imaging Technologies

Highlights The response of dicamba-susceptible soybean to three simulated rates and growth stages has been studied. Soybean physiological data, such as plant height and phototoxicity, was collected at the selected growth stage. Aerial images of the experiment field using UAV were collected on specific days after treatment. Soybean yield, biological response, and the correlation of vegetative indices in predicting soybean yield were achieved. Abstract. Many dicotyledonous weeds can be selectively controlled by dicamba application during the growing season. However, vapor or spray drift from dicamba application could cause significant yield loss on non-target species such as soybean [Glycine max (L.) Merrill] that do not contain the dicamba resistance trait. In this study, the impact of simulated dicamba drift was investigated on dicamba-susceptible soybeans during different growth stages. A field experiment was conducted employing small unmanned aircraft systems (sUAS) and multispectral image sensors to assess the dose-response relationship between dicamba drift and soybean yield. The experiment followed a randomized complete block design, with main plots representing three distinct soybean growth stages (third trifoliate, sixth trifoliate, and early reproductive stage) and sub-plots encompassing various simulated dicamba drift rates. The analysis, involving non-linear regression and normalized difference vegetation index (NDVI) regression, revealed that visible soybean injuries were observed at minimal dicamba rates. However, significant yield loss occurred during the V6 growth stage at a dicamba rate of 98 g.ai ha-1, emphasizing the critical growth stage sensitivity. Moreover, a moderate yet meaningful relationship (R2 = 0.46) was established between sUAS multispectral NDVI data and soybean yield, highlighting the potential for sUAS with multispectral sensors to predict soybean yield losses. These results contribute valuable insights into the impact of dicamba drift on soybeans and the efficacy of remote sensing technology in assessing yield outcomes. Keywords: Dicamba-drift injury, Multispectral image, Precision agriculture, Soybean yield, sUAS, Yield prediction.

A Method for Evaluating Global Navigation Satellite System Position Accuracy in Small Unmanned Aircraft Systems

Highlights A novel UAS-based GNSS testing system was developed to assess both horizontal and vertical accuracy components. Recommendations are provided for further standard development related to UAS GNSS testing protocols. Future research can utilize this framework to enhance understanding of UAS GNSS accuracy. Abstract. Current standards for Global Navigation Satellite Systems (GNSS) testing are primarily designed for assessing horizontal positioning accuracy in ground-based applications. However, there is a notable absence of guidelines specifically addressing vertical accuracy assessment and applications that extend beyond ground-based scenarios, including those involving small unmanned aircraft systems (UAS). This effort aimed to develop a UAS-based GNSS testing system, tailored for the evaluation of GNSS receiver performance in dynamic low-altitude scenarios. The main objectives included (1) designing a GNSS testing system for UAS applications and (2) validating the system through field testing. The system development consisted of GNSS receiver configuration, data acquisition system design, processing procedures, and UAS payload and base station component design. The UAS payload featured multiple GNSS receivers operating in different modes, and data were collected using a custom Python script on a Raspberry Pi computer. For in-field validation, dynamic flight testing with vertical profiles was conducted. A robotic tracking total station (TTS) was used as the reference instrument for measuring the location of the UAS payload. The findings underscore the system's capabilities, primarily emphasizing its functionality rather than conducting an extensive assessment of the individual GNSS receiver performance. The UAS-based testing system that was developed was capable of satisfying most of the criteria for existing GNSS test standards. The insights derived from this process offer recommendations for standardizing UAS-based GNSS testing methods, with a particular focus on mission design, battery management, data processing, and environmental adaptability. Keywords: Dynamic Error, Global Navigation Satellite Systems (GNSS), Positioning Accuracy, Tracking Total Station (TTS), Unmanned Aircraft Systems (UAS).

ANALISIS CFD PADA SAYAP MINI GLIDER DENGAN PENAMPANG LOW REYNOLD NUMBER AIRFOIL DAN FOLDED FLAT PLATE AIRFOIL

The purpose of this research is to make a very small unmanned aircraft as a teaching aid in aerospace engineering workshops, because of its very small size, this type of unmanned aircraft is abbreviated as Micro Air Vehicle, this MAV is only operated at low Reynolds numbers. MAV development research for smaller sizes continues to be carried out such as the Micro Glider. In this study, the author uses the Computational Fluid Dynamic method and the software used ANSYS Fluent and compares the existing DATCOM data, the author simulates the 4 types of airfoils into a wing object. The results of this study can be seen that based on the average deviation diff value that has been obtained in a population to determine the error from the comparison of the reference airfoil lift coefficient and its FFP by comparing the two methods, namely the IISHI airfoil reference geometry is 6.22% while the FFP is is 11.25% and the SD 7037 airfoil reference geometry is 3.71% while the FFP is 2.11%. Then for the comparison of the maximum lift to drag ratio values, it is obtained that the airfoil reference geometry (SD 7037) has a value of = 9.96 while the airfoil reference geometry (IISHI) has a value of = 8.81. In the Folded Flat Plate airfoil geometry (SD 7037) it has a value of = 7.61 while the Folded Flat Plate airfoil (IISHI) has a value of = 7.37. Then for the visualization of fluid flow can be obtained, namely the results of visualization of fluid flow in several parts of the angle of view / plane of the wing geometry when the AoA is high, namely the root plane to the half wing plane. early, while the geometry of SD 7037 (airfoil reference) is not easy to occur early turbulence flow. Second, that in general the fluid flow pattern seen in the FFP airfoil geometry (FFP IISHI) has the most dominant turbulence flow pattern (very much/large) while the FFP airfoil geometry (FFP SD 7037) has very little/small turbulence flow pattern..

Gust response of an elasto-flexible morphing wing using fluid–structure interaction simulations

Small and micro unmanned aircraft are the focus of scientific interest due to their wide range of applications. They often operate in a highly unstable flight environment where the application of new morphing wing technologies offers the opportunity to improve flight characteristics. The investigated concept comprises port and starboard adjustable wings, and an adaptive elasto-flexible membrane serves as the lifting surface. The focus is on the benefits of the deforming membrane during the impact of a one-minus-cosine type gust. At a low Reynolds number of Re = 264000, the morphing wing model is investigated numerically by unsteady fluid–structure interaction simulations. First, the numerical results are validated by experimental data from force and moment, flow field, and deformation measurements. Second, with the rigid wing as the baseline, the flexible case is investigated, focusing on the advantages of the elastic membrane. For all configurations studied, the maximum amplitude of the lift coefficient under gust load shows good agreement between the experimental and numerical results. During the decay of the gust, they differ more the higher the aspect ratio of the wing. When considering the flow field, the main differences are due to the separation behavior on the upper side of the wing. The flow reattaches earlier in the experiments than in the simulations, which explains the higher lift values observed in the former. Only at one intermediate configuration does the lift amplitude of the rigid configuration exceeds that of the flexible by about 12%, with the elastic membrane resulting in a smaller and more uniform peak load, which is also evident in the wing loading and hence in the root bending moment.

Issue of VHF Continuous Emission Radars Coordinate Measurement Discrepancy

The research delves into the significance of continuous emission radars of very high frequency (VHF) range in detecting small unmanned aircraft and marine targets for navigation safety. Recognising the need for improved radars and reduced coordinate measurement errors, the study aims to analyse discrepancies in azimuth and range determination in radars with fixed-phased antenna arrays. By developing effective tools with low computational complexity for target azimuth determination, the research seeks to enhance radar performance. Employing analytical, classification, functional, and statistical methods, the study comprehensively examines the peculiarities and differences of radars. It meticulously analyses coordinate measurement errors and investigates their causes. The impact of these discrepancies on radar performance and their relevance in various applications, particularly maritime navigation, is carefully evaluated. The findings emphasise the critical role of continuous emission radars in ensuring shipping safety and economic efficiency. The recommendations derived from the study offer valuable insights for improving radar effectiveness, addressing operational limitations, and enhancing overall functionality. By tackling coordinate measurement errors and providing accurate azimuth determination tools, this research contributes to advancing continuous emission radar technology and its practical applications. Through its findings and recommendations, the study aims to optimise radar performance, enhance navigation safety, and improve economic efficiency in diverse sectors.

Construction of interpolated source from dynamic flights (UAV/AAM Special Issue)

A previous comparison of far-field predictions from equivalent acoustic sources generated from dynamic flight measurements of small unmanned aircraft systems demonstrated that a single pass over an acoustic array was insufficient to accurately predict far-field acoustic levels. In effort to reduce the uncertainty in the current study, measurements of multiple passes of dynamic flight within finite engine speed/airspeed bins were combined. From these measurements, equivalent acoustic sources were decomposed using spherical harmonics fitted to levels depropagated on a spherical surface. Two source descriptions, one at a low speed and one at high speed, represent end points on the noise-power-distance curve, which was similar to a technique demonstrated in a previous study of static measurements. Interpolation of spherical harmonic coefficients between the two sources permitted the determination of intermediate speed conditions. The resulting equivalent acoustic sources removed more than 10 dB of uncertainty from the dynamic flight definitions.

Small Unmanned Aircraft Systems and Agro-Terrestrial Surveys Comparison for Generating Digital Elevation Surfaces for Irrigation and Precision Grading

Advances in remote sensing and small unmanned aircraft systems (sUAS) have been applied to various precision agriculture applications. However, there has been limited research on the accuracy of real-time kinematic (RTK) sUAS photogrammetric elevation surveys, especially in preparation for precision agriculture practices that require precise topographic surfaces, such as increasing irrigation system efficiency. These practices include, but are not limited to, precision land grading, placement of levees, multiple inlet rice irrigation, and computerized hole size selection for furrow irrigation. All such practices rely, in some way, on the characterization of surface topography. While agro-terrestrial (ground-based) surveying is the dominant method of agricultural surveying, aerial surveying is emerging and attracting potential early adopters. This is the first study of its kind to assess the accuracy, precision, time, and cost efficiency of RTK sUAS surveying in comparison to traditional agro-terrestrial techniques. Our findings suggest sUAS are superior to ground survey methods in terms of relative elevation and produce much more precise raster surfaces than ground-based methods. We also showed that this emergent technology reduces costs and the time it takes to generate agricultural elevation surveys.

Cancer Care During the COVID-19 Pandemic: The African Narrative and Prospects.

The COVID-19 pandemic has had a significant impact on healthcare services globally. Whilst it has been particularly disruptive for cancer care in low-resource settings, a few African countries have been able to adapt strategies to enable continued delivery of medical care to persons with cancer. This study seeks to highlight how much effect the coronavirus pandemic has had on oncological care in Africa and to indicate the way forward. For this narrative review, PubMed and Google Scholar were used to search for literature addressing the effect of the coronavirus pandemic on the care of patients with cancer in Africa with ensuing coping strategies. Selection criteria were manuscripts published since the onset of the pandemic in 2019 and written in the English language with Africa being the focus. In total, 52 research papers involving up to 21 African nations were found and reviewed. Across the board, the COVID-19 pandemic resulted in the deferral of oncological screening programs and a halt in immunization activities routinely scheduled for preventable cancers. It caused a colossal shortage in the availability of appropriately trained medical personnel, reduced frequency and duration of outpatient consultations, and a delay in cancer investigations and diagnosis. It also stirred up the substandard modification of chemotherapy regimens and radiotherapy due to the scarcity of anticancer medications and radioisotopes and engendered the cancellation of cancer surgical procedures. Palliative care for patients with locally advanced and metastatic disease was in many cases interrupted and cancer research activities were abruptly deferred. Ultimately, these led to poor patient outcomes and increased cancer-related fatalities. However, a few African countries - Rwanda, Ghana, and Tunisia - have continued to adapt telemedicine, small unmanned aircraft systems (sUAS), and home therapy to facilitate cancer care. To date, there is a paucity of data concerning the successesand cost-effectiveness of these relatively new methods recently adapted to cater to the medical needs of cancer patients in Africa. The pandemic has presented the African communityan opportunity to advance her healthcare systems, especially as it pertains to the delivery of medical care to persons with cancer. The need of the hour is to study further the alternative cancer care delivery systems initiated during the pandemic in order to determine their sustainability in Africa at large.

Policy Learning in Nascent Industries’ Venue Shifting: A Study of the U.S. Small Unmanned Aircraft Systems (UAS) Industry

Industry groups engage in venue shifting when they seek to overturn or alter restrictive regulations imposed by one political venue through another. A critical step in this process is resolving uncertainties surrounding the preference of the targeted venue and the nature of the relevant policy proposal. While existing studies emphasize a long-term trial-and-error process of policy learning, we focus on nascent industries and argue that ventures seek other information sources to resolve these uncertainties quickly. In particular, nascent industry groups are likely to perceive that the targeted venue will support their policy proposal if the targeted venue is ideologically distant from the venue that has enacted the restrictive regulations, if the targeted venue has recently supported other nascent industry groups’ similar policy proposals, or if the industry groups themselves are more exposed to industry peers’ success in promoting the same policy proposal in other jurisdictions. Under these conditions, the industry groups invest more to influence the targeted venue in response to restrictive regulations enacted by other venues. We find support for our theory by examining how from 2013 to 2019 the small unmanned aircraft systems industry trade associations in the United States lobbied state governments to nullify local regulations.

Safety Assessment of an Unmanned Air Vehicle Flight Control System in accordance with SAE ARP 4761

Certification Technologies of Small Unmanned Aircraft System (CTsUAS) is an Unmanned Air Vehicle, which is on development based on KC-100. KC-100 is a four-seater civil aircraft developed by the Korea Aerospace Industries (KAI) with the type certification of the US Federal Aviation Administration (FAA). For the type certification of CTsUAS, system safety assessment and analysis have been conducted in accordance with SAE ARP 4761. This paper presents the partial results of safety assessment of the flight control system of CTsUAS, focusing on the fault detection and recovery functions. Through the lessons from the results, additional considerations needed to support safety assessment and safety compliance determination of an unmanned aircraft system have been derived.

Policy Learning in Nascent Industries’ Venue Shifting: A Study of the U.S. Small Unmanned Aircraft S

Policy Learning in Nascent Industries’ Venue Shifting: A Study of the U.S. Small Unmanned Aircraft S

The Influence Of Using The SIDOPI GO Website On The Issuance Of Small Unmanned Aircraft Certificates

The Effect of Using the SIDOPI GO website on Issuance Services for Small Unmanned Aircraft Certificates is a quantitative analysis using data taken from the results of distributing questionnaires to small unmanned aircraft users. The data taken is the response of small unmanned aircraft users to the SIDOPI GO website which can assist in the process of applying for the issuance of small unmanned aircraft license certificates in June 2023. Respondents belong to a small unmanned aircraft community called the Indonesian Drone Pilots Association with various types of work, especially in the Jabodetabek region. A systematic literature review was conducted in June 2023 at the Curug Indonesian Aviation Polytechnic using a descriptive analysis study. Descriptive analysis is a research method by collecting data in accordance with the actual data then compiled, processed and analyzed to be able to provide an overview of the existing problems. The purpose of this research is to find out whether the existence of this SIDOPI GO website can make it easier for small unmanned aircraft users to apply for small unmanned aircraft license certificates or also complaints from users if any. for other researchers.