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The main goals of the following paper are to evaluate the performance of two multispectral airborne sensors and compare their image data with in situ spectral measurements. Moreover, the authors aim to present an enhanced workflow for processing multitemporal image data using both commercial and open-source solutions. The research was provoked by the need for a relevant comparison between airborne and ground sensors for vegetation analysis and monitoring. The research team used an eBee fixed-wing platform and the multiSPEC 4c and Sequoia sensors. The authors carried out field measurements using a handheld spectrometer by Trimble—GreenSeeker. There were two flight campaigns which took place near the village of Tuhan in the Czech Republic. The results from the first campaign were discouraging, showing less possibility in the correlation between the aerial and field data. The second campaign resulted in a very high percentage of correlation between both types of data. The researchers present the image processing steps and their enhanced photogrammetric workflow for multitemporal data which helps experts and nonprofessionals to reduce their processing time. [ABSTRACT FROM AUTHOR]

Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostěnice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories. [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) technology has established itself as an effective tool for identifying various objects in all human and business areas. There are many studies describing the use of this technology. However, scientific articles only marginally address the issue of recycling or reusing radio frequency identifiers. Radio frequency identifiers are defined as electronic waste by European Union legislation. This article deals with the environmental burden resulting from the use of radio frequency identifiers in a selected logistics centre and courier company in the Slovak and Czech Republic territories. The research and its relevance have become topical in the context of pandemics and with the increasing demand for products and courier services. In order to access the level of the above-mentioned environmental burden in a relevant way, an analysis of the circulation of transport units (pallets) and radio frequency identifiers in the selected logistics centre was carried out. The research results showed that the selected logistics centre generated annually 5.7 t of the e-waste from radio frequency identifiers placed on received pallets. The amount of 139 kg of metal was present in the e-waste quantity. The partial results of the research were applied to the e-commerce area. This article's conclusion is dedicated to the characterization of possibilities of reducing the environmental burden from the use of radio frequency identifiers in logistics. [ABSTRACT FROM AUTHOR]

In the Czech part of the Upper Silesian Coal Basin (Moravian-Silesian region, Czech Republic), there are many deposits of endogenous combustion (e.g., localized burning soil bodies, landfills containing industrial waste, or slag rocks caused by mining processes). The Hedwig mining dump represents such an example of these sites where, besides the temperature and the concentrations of toxic gases, electric and non-electric quantities are also monitored within the frame of experimentally proposed and patented technology for heat collection (the so-called "Pershing" system). Based on these quantities, this paper deals with the determination and evaluation of negative heat sources and the optimization of the positive heat source dependent on measured temperatures within evaluation points or on a thermal profile. The optimization problem is defined based on a balance of the heat sources in the steady state while searching for a local minimum of the objective function for the heat source. From an implementation point of view, it is the interconnection of the numerical model of the heat collector in COMSOL with a user optimization algorithm in MATLAB using the LiveLink for MATLAB. The results are elaborated in five case studies based on the susceptibility testing of the numerical model by input data from the evaluation points. The tests were focused on the model behavior in terms of preprocessing for measurement data from each chamber of the heat collector and for the estimated value of temperature differences at 90% and 110% of the nominal value. It turned out that the numerical model is more sensitive to the estimates in comparison with the measured data of the chambers, and this finding does not depend on the type optimization algorithm. The validation of the model by the use of the mean-square error led to the finding of optimal value, also valid with respect to the other evaluation. [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]

Bipolar Plates (BPPs) are the most crucial component of the Polymer Electrolyte Membrane (PEM) fuel cell system. To improve fuel cell stack performance and lifetime, corrosion resistance and Interfacial Contact Resistance (ICR) enhancement are two essential factors for metallic BPPs. One of the most effective methods to achieve this purpose is adding a thin solid film of conductive coating on the surfaces of these plates. In the present study, 410 Stainless Steel (SS) was selected as a metallic bipolar plate. The coating process was performed using titanium nitride and chromium nitride by the Cathodic Arc Evaporation (CAE) method. The main focus of this study was to select the best coating among CrN and TiN on the proposed alloy as a substrate of PEM fuel cells through the comparison technique with simultaneous consideration of corrosion resistance and ICR value. After verifying the TiN and CrN coating compound, the electrochemical assessment was conducted by the potentiodynamic polarization (PDP) and electrochemical impedance spectroscopy (EIS) tests. The results of PDP show that all coated samples have an increase in the polarization resistance ( R p ) values (ranging from 410.2 to 690.6 Ω · cm 2 ) compared to substrate 410 SS (230.1   Ω · cm 2 ). Corrosion rate values for bare 410 SS, CrN, and TiN coatings were measured as 0.096, 0.032, and 0.060 mpy , respectively. Facilities for X-ray Diffraction (XRD), Scanning Electron Microscope (FE-SEM, TeScan-Mira III model and made in the Czech Republic), and Energy Dispersive X-ray Spectroscopy (EDXS) were utilized to perform phase, corrosion behavior, and microstructure analysis. Furthermore, ICR tests were performed on both coated and uncoated specimens. However, the ICR of the coated samples increased slightly compared to uncoated samples. Finally, according to corrosion performance results and ICR values, it can be concluded that the CrN layer is a suitable choice for deposition on 410 SS with the aim of being used in a BPP fuel cell system. [ABSTRACT FROM AUTHOR]

This work outlines an approach for localizing anomalies in nuclear reactor cores during their steady state operation, employing deep, one-dimensional, convolutional neural networks. Anomalies are characterized by the application of perturbation diagnostic techniques, based on the analysis of the so-called "neutron-noise" signals: that is, fluctuations of the neutron flux around the mean value observed in a steady-state power level. The proposed methodology is comprised of three steps: initially, certain reactor core perturbations scenarios are simulated in software, creating the respective perturbation datasets, which are specific to a given reactor geometry; then, the said datasets are used to train deep learning models that learn to identify and locate the given perturbations within the nuclear reactor core; lastly, the models are tested on actual plant measurements. The overall methodology is validated on hexagonal, pre-Konvoi, pressurized water, and VVER-1000 type nuclear reactors. The simulated data are generated by the FEMFFUSION code, which is extended in order to deal with the hexagonal geometry in the time and frequency domains. The examined perturbations are absorbers of variable strength, and the trained models are tested on actual plant data acquired by the in-core detectors of the Temelín VVER-1000 Power Plant in the Czech Republic. The whole approach is realized in the framework of Euratom's CORTEX project. [ABSTRACT FROM AUTHOR]

Soil contamination by potentially toxic elements (PTEs) is intensifying under increasing industrialization. Thus, the ability to efficiently delineate contaminated sites is crucial. Visible–near infrared (vis–NIR: 350–2500 nm) and X-ray fluorescence (XRF: 0.02–41.08 keV) spectroscopic techniques have attracted tremendous attention for the assessment of PTEs. Recently, the application of fused vis–NIR and XRF spectroscopy, which is based on the complementary effect of data fusion, is also increasing. Moreover, different data manipulation methods, including feature selection approaches, affect the prediction performance. This study investigated the feasibility of using single and fused vis–NIR and XRF spectra while exploring feature selection algorithms for the assessment of key soil PTEs. The soil samples were collected from one of the most heavily polluted areas of the Czech Republic and scanned using laboratory vis–NIR and XRF spectrometers. Univariate filter (UF) and genetic algorithm (GA) were used to select the bands of greater importance for the PTE prediction. Support vector machine (SVM) was then used to train the models using the full-range and feature-selected spectra of single sensors and their fusion. It was found that XRF spectra alone (primarily GA-selected) performed better than single vis–NIR and fused spectral data for predictions of PTEs. Moreover, the prediction models that were derived from the fused data set (particularly the GA-selected) enhanced the models' accuracies as compared with the single vis–NIR spectra. In general, the results suggest that the GA-selected spectra obtained from the single XRF spectrometer (for As and Pb) and from the fusion of vis–NIR and XRF (for Pb) are promising for accurate quantitative estimation detection of the mentioned PTEs. [ABSTRACT FROM AUTHOR]

This pilot study focuses on the design, implementation, optimization and verification of a novel solution of smart measuring of water consumption and crisis detection leading to a smart water management platform. The system implemented consists of a modular IoT platform based on a PCB (Printed Circuit Board) design using the M2.COM standard, a LoraWAN modem and a LoraWAN gateway based on the Raspberry Pi platform. The prototype is modular, low-cost, low-power, low-complex and it fully reflects the requirements of strategic technological concepts of Smart City and Industry 4.0, i.e., data integration, interoperability, (I)IoT, etc. The study was produced in cooperation with M.I.S Protivanov and VODARENSKA AKCIOVA SPOLECNOST, a.s. (industry partners distributing drinking water in the Olomouc and South-Moravian regions) to depict the current situation in the Czech Republic, characterized by extreme weather fluctuations and increasingly frequent periods of drought. These drinking water distributors are also constantly placing new demands on these smart solutions. These requirements include, above all, reliability of data transmission, modularity and, last but not least, low cost. However, smart water management (water consumption, distribution, system identification, equipment maintenance, etc.) is becoming an important topic worldwide. The functionality of the system was first verified in laboratory conditions and, then, in real operation. The study also includes checking signal propagation in the municipal area of the village of Zdarna, where the radius of the proposed measuring system was tested. A laboratory test with simulation of water leakage is also part of this work. Subsequently, the system was tested in a residential unit by means of water leakage detection using the MNF method (minimum night flow); the detection success rate was 95%. [ABSTRACT FROM AUTHOR]

A large number of rock formations in the Czech Republic and abroad directly threaten to damage objects or traffic along the roads located beneath these formations. For this reason, many such rock formations are stabilized using protective fences or dynamic barriers. There are several special sensors available on the market. However, there is no comprehensive monitoring system, including remote threshold settings, data processing, and alarm conditions. This statement is supported by extensive research in this area as well as information from major geotechnical companies that are interested in such a system and want to include it in their portfolio. The aim of the article is to describe the unique wireless monitoring system used to measure the geotechnical quantities we have developed. The design and implementation of systems used to measure protective fence states with accelerometers and slope shift with load anchor cells are presented. Wireless accelerometric sensors and load anchor cell sensors are proposed for both systems. To transfer data from the accelerometer sensor to a superior system, IQRF® technology is applied for the communication between the wireless nodes and the network coordinator under the IQMESH topology. The article includes a detailed description of the development of the accelerometric wireless sensor node and load anchor cell wireless sensor node. Three case studies are also discussed. The first case study focuses on the data implementation and assessment at a testing polygon at the village of Málkov. The second case study describes the data implementation and an assessment of the measuring system under operating conditions in Zbraslav, a municipality near Prague. The third case study describes the implementation and assessment of data from load anchor cell wireless nodes installed in realistic conditions on a supporting gabion wall next to a road. All communication between the sensors and with the IQMESH network coordinator and database was executed wirelessly. The data were archived in a MySQL database and it provides a data source for the assessment and visualizations using the Grafana SW system. [ABSTRACT FROM AUTHOR]