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Today's technological developments make it possible to use machines to perform specific tasks instead of humans. However, the challenge for such autonomous devices is to precisely move and navigate in constantly changing external environments. In this paper, the influence of varying weather conditions (air temperature, humidity, wind speed, atmospheric pressure, type of satellite systems used/satellites visible, and solar activity) on the accuracy of position determination was analyzed. To reach the receiver, a satellite signal must travel a great distance and pass through all layers of the Earth's atmosphere, the variability of which causes errors and delays. Moreover, the weather conditions for receiving data from satellites are not always favorable. In order to investigate the impact of delays and errors on position determination, the measurements of the satellite signal were conducted, the motion trajectories were determined, and the standard deviations of these trajectories were compared. The results obtained show that it is possible to achieve high precision in determining the position, but varying conditions, such as solar flares or satellites' visibility, meant that not all measurements are able to achieve the required accuracy. The use of the absolute method of satellite signal measurements contributed to this to a large extent. To increase the accuracy of positioning by GNSS systems, it is first of all proposed to use a dual-frequency receiver that eliminates ionospheric refractions. [ABSTRACT FROM AUTHOR]

A compact thermometer for air temperature based on the measurement of the speed of sound was developed at INRIM. This paper focuses on the comparison of this instrument with platinum resistance thermometers in a climatic chamber over a temperature range (−30 ÷ +55) °C, relative humidity (10 ÷ 90)%rh, and irradiation (>1 kW/m2) values similar to those of surface atmospheric conditions. Overall uncertainty values of 0.2 °C over the range from −30 °C to +30 °C, and from 0.6 °C to +55 °C, were found. Moreover, the instrument proved to be immune to irradiation errors and free from the need for temperature calibration. [ABSTRACT FROM AUTHOR]

Energy harvesting is an effective technique for prolonging the lifetime of Internet of Things devices and Wireless Sensor Networks. In applications such as environmental sensing, which demands a deploy-and-forget architecture, energy harvesting is an unavoidable technology. Thermal energy is one of the most widely used sources for energy harvesting. A thermal energy harvester can convert a thermal gradient into electrical energy. Thus, the temperature difference between the soil and air could act as a vital source of energy for an environmental sensing device. In this paper, we present a proof-of-concept design of an environmental sensing node that harvests energy from soil temperature and uses the DASH7 communication protocol for connectivity. We evaluate the soil temperature and air temperature based on the data collected from two locations: one in Belgium and the other in Iceland. Using these datasets, we calculate the amount of energy that is producible from both of these sites. We further design power management and monitoring circuit and use a supercapacitor as the energy storage element, hence making it battery-less. Finally, we deploy the proof-of-concept prototype in the field and evaluate its performance. We demonstrate that the system can harvest, on average, 178.74 mJ and is enough to perform at least 5 DASH7 transmissions and 100 sensing tasks per day. [ABSTRACT FROM AUTHOR]

Due to the coupling impacts of solar radiation, wind, air temperature and other environmental parameters, the temperature field of steel structures is significantly non-uniform during their construction and service stages. Corrugated web steel beams have gained popularity in structural engineering during the last few decades, while their thermal actions are barely investigated. In this paper, both experimental and numerical investigations were conducted to reveal the non-uniform features and time variation of the corrugated web steel beams under various environmental conditions. The heat-transfer simulation model was established and verified using the experimental temperature data. Both the experiment and simulation results demonstrate that the steel beam has a complicated and non-uniform temperature field. Moreover, 2-year continuous numerical simulations of steel beams' thermal actions regarding eight different cities were carried out to investigate the long-term temperature variations. Finally, based on the long-term simulation results and extreme value analysis (EVA), the representative values of steel beams' daily temperature difference with a 50-year return period were determined. The extreme temperature difference of the steel beam in Harbin reached up to 46.9 °C, while the extreme temperature difference in Haikou was 28.8 °C. The extreme temperature difference is highly associated with the steel beam's location and surrounding climate. Ideally, the outcomes will provide some contributions for the structural design regarding the corrugated web steel beam. [ABSTRACT FROM AUTHOR]

This paper describes the design, construction and validation of a mobile sensory platform for greenhouse monitoring. The complete system consists of a sensory system on board a small quadrotor (i.e., a four rotor mini-UAV). The goals of this system include taking measures of temperature, humidity, luminosity and CO2 concentration and plotting maps of these variables. These features could potentially allow for climate control, crop monitoring or failure detection (e.g., a break in a plastic cover). The sensors have been selected by considering the climate and plant growth models and the requirements for their integration onboard the quadrotor. The sensors layout and placement have been determined through a study of quadrotor aerodynamics and the influence of the airflows from its rotors. All components of the system have been developed, integrated and tested through a set of field experiments in a real greenhouse. The primary contributions of this paper are the validation of the quadrotor as a platform for measuring environmental variables and the determination of the optimal location of sensors on a quadrotor. [ABSTRACT FROM AUTHOR]

The increasing applications of low-cost air sensors promises more convenient and cost-effective systems for air monitoring in many places and under many conditions. However, the data quality from such systems has not been fully characterized and may not meet user expectations in research and regulatory uses, or for use in citizen science. In our study, electrochemical sensors (Alphasense B4 series) for carbon monoxide (CO), nitric oxide (NO), nitrogen dioxide (NO2), and oxidants (Ox) were evaluated under controlled laboratory conditions to identify the influencing factors and quantify their relation with sensor outputs. Based on the laboratory tests, we developed different correction methods to compensate for the impact of ambient conditions. Further, the sensors were assembled into a monitoring system and tested in ambient conditions in Hong Kong side-by-side with regulatory reference monitors, and data from these tests were used to evaluate the performance of the models, to refine them, and validate their applicability in variable ambient conditions in the field. The more comprehensive correction models demonstrated enhanced performance when compared with uncorrected data. One over-arching observation of this study is that the low-cost sensors may promise excellent sensitivity and performance, but it is essential for users to understand and account for several key factors that may strongly affect the nature of sensor data. In this paper, we also evaluated factors of multi-month stability, temperature, and humidity, and considered the interaction of oxidant gases NO2 and ozone on a newly introduced oxidant sensor. [ABSTRACT FROM AUTHOR]

An understanding of the changes of the rammed earth temperature of earthen ruins is important for protection of such ruins. To predict the rammed earth temperature pattern using the air temperature pattern of the monitoring data of earthen ruins, a pattern prediction method based on interesting pattern mining and correlation, called PPER, is proposed in this paper. PPER first finds the interesting patterns in the air temperature sequence and the rammed earth temperature sequence. To reduce the processing time, two pruning rules and a new data structure based on an R-tree are also proposed. Correlation rules between the air temperature patterns and the rammed earth temperature patterns are then mined. The correlation rules are merged into predictive rules for the rammed earth temperature pattern. Experiments were conducted to show the accuracy of the presented method and the power of the pruning rules. Moreover, the Ming Dynasty Great Wall dataset was used to examine the algorithm, and six predictive rules from the air temperature to rammed earth temperature based on the interesting patterns were obtained, with the average hit rate reaching 89.8%. The PPER and predictive rules will be useful for rammed earth temperature prediction in protection of earthen ruins. [ABSTRACT FROM AUTHOR]

nEMoS (nano Environmental Monitoring System) is a 3D-printed device built following the Do-It-Yourself (DIY) approach. It can be connected to the web and it can be used to assess indoor environmental quality (IEQ). It is built using some low-cost sensors connected to an Arduino microcontroller board. The device is assembled in a small-sized case and both thermohygrometric sensors used to measure the air temperature and relative humidity, and the globe thermometer used to measure the radiant temperature, can be subject to thermal effects due to overheating of some nearby components. A thermographic analysis was made to rule out this possibility. The paper shows how the pervasive technique of additive manufacturing can be combined with the more traditional thermographic techniques to redesign the case and to verify the accuracy of the optimized system in order to prevent instrumental systematic errors in terms of the difference between experimental and actual values of the above-mentioned environmental parameters. [ABSTRACT FROM AUTHOR]

Temperature in an urban area exhibits a complicated pattern due to complexity of infrastructure. Despite geographical proximity, structures of a group of buildings and streets affect changes in temperature. To investigate the pattern of fine-grained distribution of temperature, we installed a densely distributed sensor network called UScan. In this paper, we describe the system architecture of UScan as well as experience learned from installing 200 sensors in downtown Tokyo. The field experiment of UScan system operated for two months to collect long-term urban temperature data. To analyze the collected data in an efficient manner, we propose a lightweight clustering methodology to study the correlation between the pattern of temperature and various environmental factors including the amount of sunshine, the width of streets, and the existence of trees. The analysis reveals meaningful results and asserts the necessity of fine-grained deployment of sensors in an urban area. [ABSTRACT FROM AUTHOR]

Indoor air temperature belongs to the most important climatic variables in indoor climate research, affecting thermal comfort, energy balance, and air movement in buildings. This paper focuses on measurement errors when using thermocouples in indoor temperature measurements, with special attention on measurements of air temperature. We briefly discuss errors in thermocouple measurements, noting that, for temperatures restricted to indoor temperature ranges, a thermocouple Type T performs much better than stated in "standards". When thermocouples are described in the literature, industrial applications are primarily considered, involving temperatures up to several hundred degrees and with moderate demands on accuracy. In indoor applications, the temperature difference between the measuring and the reference junction is often only a few degrees. Thus, the error contribution from the thermocouple itself is almost immeasurable, while the dominant error source is in the internal reference temperature compensation in the measuring instrument. It was shown that using an external reference junction can decrease the measurement error substantially (i.e., down to a few hundredths of a degree) in room temperature measurements. One example of how such a device may be assembled is provided. A special application of room temperature measurements involves measuring indoor air temperature. Here, errors, due to radiation influence on the sensor from surrounding surfaces, were surprisingly high. The means to estimate the radiative influence on typical thermocouples are presented, along with suggestions for modification of thermocouple sensors to lower the radiation impact and thereby improve the measurement accuracy. [ABSTRACT FROM AUTHOR]

The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely manner to suppress their spread. Occurrences and numbers of insects can be monitored through insect traps, which include their permanent touring and checking of their condition. Another more efficient way is to set up sensor devices with a camera at the traps that will photograph the traps and forward the images to the Internet, where the pest insect's appearance will be predicted by image analysis. Weather conditions, temperature and relative humidity are the parameters that affect the appearance of some pests, such as Helicoverpa armigera. This paper presents a model of machine learning that can predict the appearance of insects during a season on a daily basis, taking into account the air temperature and relative humidity. Several machine learning algorithms for classification were applied and their accuracy for the prediction of insect occurrence was presented (up to 76.5%). Since the data used for testing were given in chronological order according to the days when the measurement was performed, the existing model was expanded to take into account the periods of three and five days. The extended method showed better accuracy of prediction and a lower percentage of false detections. In the case of a period of five days, the accuracy of the affected detections was 86.3%, while the percentage of false detections was 11%. The proposed model of machine learning can help farmers to detect the occurrence of pests and save the time and resources needed to check the fields. [ABSTRACT FROM AUTHOR]

With the advent of the Smart Agriculture, the joint utilization of Internet of Things (IoT) and Machine Learning (ML) holds the promise to significantly improve agricultural production and sustainability. In this paper, the design of a Neural Network (NN)-based prediction model of a greenhouse's internal air temperature, to be deployed and run on an edge device with constrained capabilities, is investigated. The model relies on a time series-oriented approach, taking as input variables the past and present values of the air temperature to forecast the future ones. In detail, we evaluate three different NN architecture types—namely, Long Short-Term Memory (LSTM) networks, Recurrent NNs (RNNs) and Artificial NNs (ANNs)—with various values of the sliding window associated with input data. Experimental results show that the three best-performing models have a Root Mean Squared Error (RMSE) value in the range 0.289 ÷ 0.402 ∘ C , a Mean Absolute Percentage Error (MAPE) in the range of 0.87 ÷ 1.04 %, and a coefficient of determination (R 2 ) not smaller than 0.997 . The overall best performing model, based on an ANN, has a good prediction performance together with low computational and architectural complexities (evaluated on the basis of the NetScore metric), making its deployment on an edge device feasible. [ABSTRACT FROM AUTHOR]

Internal body temperature is the gold standard for the fever of pigs, however non-contact infrared imaging technology (IRT) can only measure the skin temperature of regions of interest (ROI). Therefore, using IRT to detect the internal body temperature should be based on a correlation model between the ROI temperature and the internal temperature. When heat exchange between the ROI and the surroundings makes the ROI temperature more correlated with the environment, merely depending on the ROI to predict the internal temperature is unreliable. To ensure a high prediction accuracy, this paper investigated the influence of air temperature and humidity on ROI temperature, then built a prediction model incorporating them. The animal test includes 18 swine. IRT was employed to collect the temperatures of the backside, eye, vulva, and ear root ROIs; meanwhile, the air temperature and humidity were recorded. Body temperature prediction models incorporating environmental factors and the ROI temperature were constructed based on Back Propagate Neural Net (BPNN), Random Forest (RF), and Support Vector Regression (SVR). All three models yielded better results regarding the maximum error, minimum error, and mean square error (MSE) when the environmental factors were considered. When environmental factors were incorporated, SVR produced the best outcome, with the maximum error at 0.478 °C, the minimum error at 0.124 °C, and the MSE at 0.159 °C. The result demonstrated the accuracy and applicability of SVR as a prediction model of pigs′ internal body temperature. [ABSTRACT FROM AUTHOR]

The successful launch of the Landsat 8 satellite provides important data for the monitoring of urban heat island effects. Since the Landsat 8 TIRS data has two thermal infrared bands, it is suitable for many algorithms to retrieve the land surface temperature (LST). However, the selection of algorithms for retrieving the LST, the acquisition of algorithm input parameters, and the verification of the results are problems without obvious solutions. Taking Changchun City as an example, this paper used the mono-window algorithm (MWA), the split window algorithm (SWA), and the single-channel (SC) method to extract the LST from the Landsat 8 image and compared the three algorithms in terms of input parameters, accuracy, and sensitivity. The results show that all three algorithms can achieve good results in retrieving the LST. The SWA is the least sensitive to the error of the input parameters. The MWA and the SC method are sensitive to the error of the input parameters, and compared with the error of the LSE, these two algorithms are more sensitive to the error of atmospheric water vapor content. In addition, the MWA is also very sensitive to the error of the effective mean atmospheric temperature. [ABSTRACT FROM AUTHOR]

An increasing number of individuals and institutions own or operate meteorological stations, but the resulting data are not yet commonly used in the Czech Republic. One of the main difficulties is the heterogeneity of measuring systems that puts in question the quality of outcoming data. Only after a thorough quality control of recorded data is it possible to proceed with for example a specific survey of variability of a chosen meteorological parameter in an urban or suburban region. The most commonly researched element in the given environment is air temperature. In the first phase, this paper focuses on the quality of data provided by amateur and institutional stations. The following analyses consequently work with already amended time series. Due to the nature of analyzed data and their potential use in the future it is opportune to assess the appropriateness of chronological and possibly spatial interpolation of missing values. The evaluation of seasonal variability of air temperature in the scale of Brno city and surrounding area in 2015–2017 demonstrates, that the enrichment of network of standard (professional) stations with new stations may significantly refine or even revise the current state of knowledge, for example in the case of urban heat island phenomena. A cluster analysis was applied in order to assess the impact of localization circumstances (station environment, exposition, etc.) as well as typological classification of the set of meteorological stations. [ABSTRACT FROM AUTHOR]

Weigh-in-motion systems are installed in pavements or on bridges to identify and reduce the number of overloaded vehicles and minimise their adverse effect on road infrastructure. Moreover, the collected traffic data are used to obtain axle load characteristics, which are very useful in road infrastructure design. Practical application of data from weigh-in-motion has become more common recently, which calls for adequate attention to data quality. This issue is addressed in the presented paper. The aim of the article is to investigate the accuracy of 77 operative weigh-in-motion stations by analysing steering axle load spectra. The proposed methodology and analysis enabled the identification of scale and source of errors that occur in measurements delivered from weigh-in-motion systems. For this purpose, selected factors were investigated, including the type of axle load sensor, air temperature and vehicle speed. The results of the analysis indicated the obvious effect of the axle load sensor type on the measurement results. It was noted that systematic error increases during winter, causing underestimation of axle loads by 5% to 10% for quartz piezoelectric and bending beam load sensors, respectively. A deterioration of system accuracy is also visible when vehicle speed decreases to 30 km/h. For 25% to 35% of cases, depending on the type of sensor, random error increases for lower speeds, while it remains at a constant level at higher speeds. The analysis also delivered a standard steering axle load distribution, which can have practical meaning in the improvement of weigh-in-motion accuracy and traffic data quality. [ABSTRACT FROM AUTHOR]

In the context of global warming, the air temperature of the Heihe basin in Northeast China has increased significantly, resulting in the degradation of the island permafrost. In this paper, we used an elaborated time-series Interferometric Synthetic Aperture Radar (InSAR) strategy to monitor the ground deformation in the Heihe area (Heilongjiang Province, China) and then analyzed the permafrost deformation characteristics from June 2007 to December 2010. The results showed that the region presented island permafrost surface deformation, and the deformation rate along the line of sight mainly varied from –70 to 70 mm/a. Based on the analysis of remote sensing and topological measurements, we found that the deformation area generally occurred at lower altitudes and on shady slopes, which is consistent with the distribution characteristics of permafrost islands. Additionally, the deformation of permafrost is highly correlated with the increase of annual minimum temperature, with an average correlation value of –0.80. The accelerated degradation of permafrost in the study area led to the settlement, threatening the infrastructure safety. Our results reveal accelerated degradation characteristics for the island permafrost under the background of rising air temperature, and provide a reference for future relevant research. [ABSTRACT FROM AUTHOR]