Data Collection Campaign Research Articles

Inventory and Spatial Distribution of Invasive Aquatic Plants in the Lower Part of the Nyong River at Lokoundje in the Ocean Department (South-Cameroon)

The study, conducted from February to June 2020, presents a specific inventory of aquatic invasive plants and a mapping of their distribution gained from field observations and data collection campaigns on the lower part of the Nyong River. Specifically, it was a question of inventorying plant species and geo-referencing their biotopes. Through the results obtained, 12 taxa of aquatic invasive species are present (10 species upstream, 08 in transition, and 02 downstream). Of the 12 species, 10 have an ornamental mode of introduction and 01 is endemic to the environment. In addition, it also emerges from this study that the spatial distribution of the species of aquatic plants designated in the study area presents clusters mainly around the islets represented and on certain parts of the banks of the river.

Environmentally sustainable food: an analysis of consumer practices and their diffusion in France

PurposeThis aim of this study is to identify the ways of helping public authorities bring about change to environmentally sustainable household food practices.Design/methodology/approachThe authors identified the practices involved in this concept from the consumer perspective and measured their diffusion among French households. The analyses were conducted following two successive data collection campaigns comprising 571 and 501 respondents in France. The methodology involved two complementary scaling techniques: factor analysis and item response theory.FindingsThe results show that consumers understand sustainable food through five food practices: buying and cooking products with sustainable attributes, anti-waste storage, self-production, plant protein consumption and anti-waste cooking.Originality/valueThe findings suggest that while at the individual level people appear to have incorporated anti-waste practices into their daily lives, at the household level, there is still work to be done for improving diets and stimulating the production of home-grown food. It is also worth noting that the emerging vision typically involves sustainable foods that are organic, locally grown, seasonal, based on fair trade and packaging-free.

A comparative analysis of surface and canopy layer urban heat island at the micro level using a data-driven approach

The Urban Heat Island (UHI) phenomenon, which is defined as the temperature differential between inner cities and surrounding areas, has been extensively studied over the past few years to unravel its mechanism and develop mitigation strategies. Nonetheless, two distinct types of temperature can be used to measure UHI, namely, (1) air temperatures, which refer to air temperatures at the canopy layer (CUHI), and (2) surface temperature, which refers to the temperatures at the surface layer (SUHI). The two types of UHI can have different deriving mechanisms and any effective mitigation strategies should be able to mitigate both concurrently. While efforts have been made to compare these two types of UHI, the studies so far seldom used consistent data. This is because SUHI is mostly based on remote sensing data, whereas CUHI commonly requires field measurements. These data are often not consistent with respect to spatial resolution, frequency, and accuracy. To address this gap, the present research aims to perform a comprehensive comparative analysis of CUHI and SUHI using data collected by a tailor-made mobile data collection unit at a high frequency and resolution at the micro-level (i.e., street-level). Data was collected from a fixed 8 km-long route in the city of Apeldoorn, the Netherlands, for the period of one year. Two different machine learning techniques, i.e., random forest and neural network, were used to study SUHI and CUHI. The results indicated a considerable variability between air and surface temperatures during the data collection campaign. Air temperatures ranged from -0,3 to 35,3°C, while surface temperatures fluctuated over a wider range, from -12,0 to 48,4°C at a micro-level. This variability of temperatures translated into an average of 0,10°C for CUHI, and -0,48°C SUHI. More importantly, however, the results highlighted the importance of investigating simultaneously the two types of UHI. This is because while urban features do not change dramatically in short periods of time, the impact of these same features on the CUHI and SUHI is different, therefore urban-heat resilience strategies planned for one type of UHI alone could have a different impact on the other type.

Generalisable Dialogue-based Approach for Active Learning of Activities of Daily Living

While Human Activity Recognition systems may benefit from Active Learning by allowing users to self-annotate their Activities of Daily Living (ADLs), many proposed methods for collecting such annotations are for short-term data collection campaigns for specific datasets. We present a reusable dialogue-based approach to user interaction for active learning in activity recognition systems, which utilises semantic similarity measures and a dataset of natural language descriptions of common activities (which we make publicly available). Our approach involves system-initiated dialogue, including follow-up questions to reduce ambiguity in user responses where appropriate. We apply this approach to two active learning scenarios: (i) using an existing CASAS dataset, demonstrating long-term usage; and (ii) using an online activity recognition system, which tackles the issue of online segmentation and labelling. We demonstrate our work in context, in which a natural language interface provides knowledge that can help interpret other multi-modal sensor data. We provide results highlighting the potential of our dialogue- and semantic similarity-based approach. We evaluate our work: (i) quantitatively, as an efficient way to seek users’ input for active learning of ADLs; and (ii) qualitatively, through a user study in which users were asked to compare our approach and an established method. Results show the potential of our approach as a hands-free interface for annotation of sensor data as part of an active learning system. We provide insights into the challenges of active learning for activity recognition under real-world conditions and identify potential ways to address them.

Site selection in Switzerland: parallelization of implementer data collection and technical–political decision-making

Abstract. The ambition of the Swiss radioactive waste program is to store waste at the best-suited, safest site in Switzerland. In September 2022 the announcement of Nördlich Lägern as the best-suited site was a major milestone of this site selection process. The full documentation of the site selection and the safety assessment will be submitted with the general license application at the end of 2024. The total time of the site selection process from its formal initiation in 2008 until submission of the general license application will be 16 years. The three-phase site selection process consisted of (1) initial screening, (2) de-selection of less favourable sites and (3) identification of the best-suited region. The database was refined for each selection step, but detailed site characterization was restricted to the final phase. The selection criteria were kept unchanged in the entire process. Four groups were used: (1) properties of the host rock; (2) long-term evolution of the geological situation; (3) quality of the geological findings; (4) engineering feasibility. The requirements for the criteria were re-adjusted in each phase to honour the available data and to adopt the requirements to the increasing quality of the sites as less-suited regions were continuously screened out. Initial estimates for the duration of the site selection were in the range of 8–10 years. Soon it became clear that the technical work and the decision-making process for each phase would take additional time. To minimize phases of low activity, NAGRA started data collection campaigns for each step as soon as possible. This led to overlapping of regulator reviews and decision-making by the government for the previous step and data collection by NAGRA for the next step. Step-1 data collection started as soon as enough information about the process was available. This allowed the proposal of the initial selection of sites a few months later. Data collection for step 2 with seismic data reprocessing, outcrop work, investigations of third-party boreholes, regional seismic and geodetic monitoring and 2D fill-in lines began in 2009/10, well before the decision on step 1 by the federal government in 2011. This overlap allowed the proposal for the detailed sited investigations in 2014 to be prepared. Step 3 data collection with 3D seismic campaigns started in 2015. To minimize the delay between the 3D seismic investigations and the start of deep drillings, drill sites were planned on available 2D seismic information. In 2017 initial 3D seismic data became available, the drilling sequence was adopted accordingly and the first applications for the drill sites were handed in. The licensing of the drill sites was continuously adjusted together with the involved authorities. The decision of the federal government on completion of the second phase, confirming three sites for the third and last phase, was taken in 2018. The data collection in the third and final step was completed with the ninth deep borehole in early 2022. Close monitoring of the incoming data allowed the announcement of the best-suited site in September of the same year.

In-situ ice strength distribution of anchor ice dams

Ice runs and ice jams can cause significant damage to infrastructure and other human interests. Accurate modelling and prediction of these events rely on data and assumptions about river ice properties. This paper seeks to address the lack of data on ice strength distributions and associated properties in small, steep rivers and to obtain a better understanding of those. To this end, an ice data collection campaign was carried out in the Sokna river in central Norway during the 2020–2021 winter field season. The following types of data were collected for anchor ice dams and level ice sections: Borehole Jack (BHJ) in-situ ice strength, ice thickness, ice density, thin section imagery and associated depth measurements. A total of 27 ice cores and 68 thin sections were successfully collected and processed. Furthermore, a survey was carried out recording GPS coordinates of anchor ice dams in the river. Meteorological and hydrological data was also collated, and large boulder location data was obtained through the analysis of aerial photography. Ice strength distributions and trends on how ice strength varies in small, steep rivers were obtained from these data. A binomial generalized linear model for predicting ice dam locations was developed using field measurements and aerial photography data. This model describes how river width, slope and to a lesser degree large boulder density affect ice dam location. Sinuosity was shown to have no effect on ice dam location. This work highlights the significant variation in microstructure and large-scale structure of ice in small, steep rivers.

Measuring, representing and analysing indoor soundscapes: A data collection campaign in residential buildings with natural and mechanical ventilation in England

A model for the assessment of perceived affective quality of indoor residential soundscapes is first applied in a field survey carried out in 61 dwellings in England. Objectives are i) to investigate measurement and indoor soundscape data representation, ii) to characterize soundscape appropriateness for home working and relaxation, iii) to test differences based on the ventilation strategy, and iv) to identify factors predicting indoor soundscapes. In dwellings with natural ventilation (N = 34) the survey was carried out with windows open, while in those with mechanical ventilation (N = 27) with windows closed and the system in operation. Parallel to the administration of the questionnaire, monaural and binaural measurements of the acoustic environments were performed. The study provides examples of data representation in the circumplex space defined by comfort and content dimensions. Soundscapes which are appropriate for work and relaxation are characterized by high comfort and low content, i.e., perceived as private and under control. Indoor soundscapes are strongly related to the perception of traffic noise, window opening, and sound pressure level, especially in the energy content of interference in speech perception through the Speech Interference Level parameter (SIL), factors that lead to reduced comfort and increased saturation of the environment. Moreover, aspects related to psychoacoustics (sharpness), contextual and building-related factors (ownership status and floor level) and cross-modal effects from other sensory modalities (perceived air quality and air temperature) have an impact. A threshold value of 32.7 dB for SIL was identified at which a neutral comfort is attained.

Unsupervised clustering of ambient seismic noise in an urban environment

In this study, a semi-automated unsupervised clustering workflow is developed to classify ambient seismic noise from datasets acquired in an urban region, specifically using a pair of Tromino3G+ seismographs simultaneously recording high-gain velocity along two axes (north-south and east-west). A multi-day acquisition is used as a reference ambient seismic noise characterization of the area and to assess clusterability of the dataset. Additional 20-min datasets, acquired during and after anomalous urban activities are analyzed for a case study. The anomalous urban activity is due to street parties (large gatherings of people) during the Queen's University Homecoming celebrations in Kingston, Ontario. Unsupervised clustering methods are applied to classify ambient seismic events (coherent amplitude peaks in time-frequency space caused by uncontrolled natural and anthropogenic sources) directly from statistical properties of the event features (amplitude, frequency, bandwidth, duration, and azimuth relative to source). For urban ambient seismic noise, the signal sources may be numerous and unspecified a-priori and therefore unsupervised clustering methods are sought. Three clustering algorithms (k-Means, spectral, and agglomerative) are applied to characterize the urban region during the data collection campaigns. A probability density-based threshold modeled from the baseline dataset leads to the identification of event anomalies resulting from street party activity. The results provide insight into the utility of unsupervised clustering of ambient seismic noise for urban activity monitoring and indicate future work should include assessing the dependency of the workflow on the number of clusters and the selection of clustering methods.

Hyperlocal environmental data with a mobile platform in urban environments

Environmental data with a high spatio-temporal resolution is vital in informing actions toward tackling urban sustainability challenges. Yet, access to hyperlocal environmental data sources is limited due to the lack of monitoring infrastructure, consistent data quality, and data availability to the public. This paper reports environmental data (PM, NO2, temperature, and relative humidity) collected from 2020 to 2022 and calibrated in four deployments in three global cities. Each data collection campaign targeted a specific urban environmental problem related to air quality, such as tree diversity, community exposure disparities, and excess fossil fuel usage. Firstly, we introduce the mobile platform design and its deployment in Boston (US), NYC (US), and Beirut (Lebanon). Secondly, we present the data cleaning and validation process, for the air quality data. Lastly, we explain the data format and how hyperlocal environmental datasets can be used standalone and with other data to assist evidence-based decision-making. Our mobile environmental sensing datasets include cities of varying scales, aiming to address data scarcity in developing regions and support evidence-based environmental policymaking.

Collecting volunteered geographic information from the Global Navigation Satellite System (GNSS): experiences from the CAMALIOT project

ABSTRACT Raw observations (carrier-phase and code observations) from the Global Navigation Satellite System (GNSS) can now be accessed from Android mobile phones (Version 7.0 onwards). This paves the way for GNSS data to be utilized for low-cost precise positioning or in ionospheric or tropospheric applications. This paper presents results from data collection campaigns using the CAMALIOT mobile app. In the first campaign, 116.3 billion measurements from 11,828 mobile devices were collected from all continents. Although participation decreased during the second campaign, data are still being collected globally. In this contribution, we demonstrate the potential of volunteered geographic information (VGI) from mobile phones to fill data gaps in geodetic station networks that collect GNSS data, e.g. in Brazil, but also how the data can provide a denser set of observations than current networks in countries across Europe. We also show that mobile phones capable of dual-frequency reception, which is an emerging technology that can provide a richer source of GNSS data, are contributing in a substantial way. Finally, we present the results from a survey of participants to indicate that participation is diverse in terms of backgrounds and geography, where the dominant motivation for participation is to contribute to scientific research.

The impact of fatigue on shipyard welding workers’ occupational health and safety and performance

Welding processes are critical for shipbuilding operations in shipyards. Welders’ performance is critical for the quality and speed of the welding; on the other hand, welding requires awkward and repetitive body postures for long durations, which has a negative impact on the Occupational Health and Safety (OHS) of the welding workers. This study, therefore, investigates the long-term impact of welding workload on the body for different age groups and experience levels, as well as the long-term impact on chronic fatigue and inter-shift recovery. In order to determine the impact, this study conducted a comprehensive data collection campaign in the shipyard through observation and questionnaires. Results indicate that the eyes, knee, neck and waist are the most discomforted body parts for investigated welding positions. The age of welders was an important parameter of the most affected body part.Moreover, the short-term fatigue impact on welding performance was also investigated to identify the impact on productivity. The Discrete event simulation (Rockwell ARENA) demonstrated that efficiency loss from short-term daily fatigue is around 22,9% compared to the rested condition. The main contribution of this study was investigating fatigue impact on OHS and productivity for selected shipbuilding tasks. The findings of this study can be utilised for shipyard production capacity and resource planning and OHS improvements. The results can also be used further as a coefficient of performance in the production simulation analysis when studying shipyard efficiency. This is a valuable contribution to the literature on shipyard productivity.

Depth profiles of Jerlov water types

AbstractTypical depth profiles of Jerlov water types have been derived to characterize the clarity of the world's oceans. Measured values of the downwelling diffuse attenuation coefficient, taken from a world‐wide database, were quantitatively analyzed. Depth profiles were extracted from more than 2500 data collection campaigns, consisting of the closest‐matching Jerlov water types at the uppermost layer (0–10 m) and the corresponding Jerlov water types at deeper depth layers down to 200 m. A table of “typical” depth profiles for the 10 Jerlov water types was generated based on the maximum campaign count at each depth layer. This new ocean classification will find use in applications where modeling of typical ocean waters requires the depth‐dependence of clarity to be accounted for.

ON THE CO-REGISTRATION OF ASYNCHRONOUS MULTI-SPECTRAL AND THERMAL IMAGES

Abstract. Plastic pollution has a severe impact on the ecosystem, altering its natural equilibrium and causing serious health issues to both flora and fauna. Several actions have already been undertaken in order to reduce the plastic litter dispersion in the environment, both in terms of changing the human behavior, reducing the use of plastics and avoiding their dispersion, and of implementing methods for detecting and collecting the already dispersed ones. This paper focuses on the latter, and, in particular, on plastic litter detection on the fluvial environment. To this aim, an Unmanned Aerial Vehicle, provided with a multi-spectral and a thermal camera, have been used, in order to: (i) allow affordable periodic monitoring of relatively long river reaches, (ii) detect even quite small macro-plastics, based on their spectral signature. More specifically, since the cameras deployed in our data collection campaigns are not synchronized, this work aims at presenting the developed strategy for the co-registration of the acquired imagery, which results to be quite challenging given the few amount of visual features recognizable on the images acquired flying at a limited altitude over a river. The proposed methodology, which is based on the correlation maximization between multi-spectral and thermal images, provided reasonable results on the considered case study. The obtained values of normalized intersection over union of plastic areas are over 80%.

GEODETIC KINEMATIC TERRESTRIAL NAVIGATION USING A MMS – MULTI-CONSTELLATION AND MULTI-FREQUENCY GALILEO/GPS/GLONASS PERFORMANCE COMPARISONS

Abstract. This paper presents a part of the research activities performed from 2019 to 2022 in the framework of WP 5.3 “Field campaign” and in particular, the activities “Land urban and extra-urban data measurement campaigns” of the European GRC-MS (Galileo Reference Center – Member States) Project - GSA/GRANT/04/2016.The dynamic data-collection campaigns were conducted by three participating member states (Portugal, Italy, and Romania), in three different environments: aerial, terrestrial, and maritime, respectively.This article presents a comparison of the results obtained for the terrestrial environment, considering the performance of Galileo used in conjunction with other GNSS systems, such as GPS, using different geodetic and navigational multi-constellation and multi-frequency receivers/devices experimental dynamic activities. Starting from the user’s needs and requirements, the experimental setup and the methodology for the estimation of the reference («actual») trajectory are provided. These are complemented by numerical results and reasons for possible performance degradations analyses.The processed rover trajectories and the produced outputs were compared to the reference trajectory obtained from GNSS/INS (INertial System) integrated high-performance solution.Galileo-only and GPS-only solutions, so as multi-system solutions (GPS+Galileo, GPS+GLONASS and GPS+GLONASS+Galileo) were analysed. PVT (Position Velocity Time) solutions were computed using post-processing geodetic interferential techniques.From the performed analyses, some preliminary conclusions relative to the Galileo performances applied to terrestrial urban and extra-urban navigation were derived.These activities need to be continued in the near future, taking into consideration the Galileo constellation improvements and the ITS (Intelligent Transport Systems) applications in order to improve the offered services and the road safety.

Smart Multi-Sensor Calibration of Low-Cost Particulate Matter Monitors.

A variety of low-cost sensors have recently appeared to measure air quality, making it feasible to face the challenge of monitoring the air of large urban conglomerates at high spatial resolution. However, these sensors require a careful calibration process to ensure the quality of the data they provide, which frequently involves expensive and time-consuming field data collection campaigns with high-end instruments. In this paper, we propose machine-learning-based approaches to generate calibration models for new Particulate Matter (PM) sensors, leveraging available field data and models from existing sensors to facilitate rapid incorporation of the candidate sensor into the network and ensure the quality of its data. In a series of experiments with two sets of well-known PM sensor manufacturers, we found that one of our approaches can produce calibration models for new candidate PM sensors with as few as four days of field data, but with a performance close to the best calibration model adjusted with field data from periods ten times longer.

Deep Learning at the Physical Layer for Adaptive Terahertz Communications

Wireless communications in the terahertz (THz) band will become a cornerstone of sixth-generation (6G) networks. The THz channel, however, presents several challenges, such as distance-dependent absorption coefficients that can change the bandwidth significantly in case of mobility. Thus, future THz transmitters will have to switch modulation and bandwidth almost continuously. Moreover, using the same transmission scheme can enable adversaries to leverage smart interfering to inflict more damage with less energy expense. To help enable adaptive and secure THz communications, this article presents the first ever experimental study of modulation and bandwidth classification (MBC) at THz frequencies through deep learning (DL) techniques. We have performed an extensive experimental data collection campaign at 120 GHz with different modulation schemes, signal bandwidth (up to 20 GHz), and different signal-to-noise ratio (SNR) levels. We prove for the first time the feasibility and effectiveness of MBC at THz frequencies, with our DL models reaching accuracy up to 78% and 90% in low- and high-SNR conditions. Furthermore, we investigate the memory and latency constraints that need to be satisfied as a function of the signal bandwidth, and propose a boosting technique to improve the inference quality by trading off latency for accuracy. Finally, we experimentally evaluate the latency of our CNN models through FPGA implementation.

Assessing the effect of a classroom IEQ on student satisfaction, engagement and performance

Inappropriate indoor environmental quality (IEQ) conditions are shown to reduce occupants’ satisfaction, wellbeing and performance. Therefore, it is crucial to provide an excellent classroom IEQ in order to minimize learning loss among students. This study determines the effect of different thermal and indoor air quality (IAQ) conditions in a classroom on students’ satisfaction, study engagement and cognitive performance. Three data collection campaigns were performed in a university classroom. Data collection consisted of (1) continuous IEQ monitoring (i.e., temperature, relative humidity, CO2, …), (2) frequent assessments of students’ satisfaction with IEQ and study engagement via here-and-now surveys, and (3) evaluations of students’ memory and processing speed. The IEQ conditions were varied by adapting (1) a room temperature setpoint between 18°C to 26°C and (2) a CO2-setpoint between 500 ppm and 1000 ppm. A mixed-effects regression analysis on the collected data showed statistically significant relationships between students’ study engagement and the room temperature trend, air enthalpy, relative humidity and TVOC-concentrations in the classroom. The addition of data on students’ satisfaction with IEQ only improved a minority of model fits. A statistically significant difference in students’ memory was found between test conditions. However, a randomized experimental design is needed to determine the relationship between the classroom IEQ and students’ cognitive performance.

Spectral Acoustic Fingerprints of Sand and Sandstone Sea Bottoms

Modern studies which dealt with the frequency domain analysis showed that a frequency-domain approach has an essential advantage and mentioned an inner qualitative relationship between the subsurface structure and its frequency spectra. This paper deals with the acoustic spectral response of sand and sandstone sediments at the sea bottom. An acoustic data collection campaign was conducted over two sand sites and two sandstone sites. The analysis of the results shows that reflections of acoustic signals from sand and sandstone sea bottom are characterized by various spectral features in the 2.75–6.75 kHz range. The differences in acoustic response of sand and sandstone can be quantified by examining the maximal normalized reflected power, the mean frequency, and the number of crossings at different power levels. The statistical value distribution of these potential classifiers was calculated and analyzed. These classifiers, and especially the roughness of the spectrum quantified by the number of crossings parameter can give information to assess the probability for sand or sandstone based on the reflected spectra and be used for actual distinction between sand and sandstone in sub bottom profiler data collection campaigns.

Application of Geospatial Techniques in Evaluating Spatial Variability of Commercially Harvested Mangoes in Bangladesh

Mango is widely known as a popular fruit in South Asia, including Bangladesh. The country is a significant producer of different local and exotic varieties of mangoes in different geographic locations. Therefore, a study of fruit maturity at diverse locations and climatic conditions becomes critical for a sustainable mango production. In responding to this need, this study evaluates the variability of a few selected commercial mango (Mangifera indica L.) varieties and their maturity timeline with respect to spatial extent (longitudinal-latitudinal variations), elevation profile, and time. Remote sensing technology has been widely used for horticultural applications to study fruit phenology, maturity, harvesting time, and for mapping locational differences. In doing so, we have employed remotely sensed data, such as the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) of 30 m spatial resolution, GPM IMERGM precipitation datasets (0.1 × 0.1 degree), NASA GLDAS (Global Land Data Assimilation System) surface skin temperature (0.25 × 0.25 degree), and Noah Land Surface Model L4 3-hourly soil moisture content datasets (0.25 × 0.25 degree). Alongside these, an intensive field data collection campaign has been carried out for 2019 and 2020. It was found that 1° locational variations may result in approximately 2–5 days delay of mango harvesting. The outcome of this study may enhance the appropriate planning of harvesting, production, and the commercialization of mango selection in specific geographic setting for a sustainable harvest and production system in the country.

Machine Learning and Analytical Power Consumption Models for 5G Base Stations

The energy consumption of the fifth generation (5G) of mobile networks is one of the major concerns of the telecom industry. However, there is not currently an accurate and tractable approach to evaluate 5G base stations' (BSs') power consumption. In this article, we propose a novel model for a realistic characterization of the power consumption of 5G multi-carrier BSs, which builds on a large data collection campaign. At first, we define a machine learning architecture that allows modeling multiple 5G BS products. Then we exploit the knowledge gathered by this framework to derive a realistic and analytically tractable power consumption model, which can help driving both theoretical analyses as well as feature standardization, development, and optimization frameworks. Notably, we demonstrate that this model has high precision, and it is able to capture the benefits of energy saving mechanisms. We believe this analytical model represents a fundamental tool for understanding 5G BSs' power consumption and accurately optimizing the network energy efficiency.