Abstract
The occurrence of the first illnesses of the inhabitants of Poland and Portugal caused decision-makers to introduce many changes in the functioning of economic units in various areas. This document aims to answer the questions of whether the changes related to the occurrence of COVID-19 had a significant impact on the housing situation of students by answering two questions: (1) How has the pandemic affected the change in the form of residence? (2) What changes in the provisions of the contract do students expect after returning to the full-time form? The empirical study was conducted based on data obtained from a survey. The research was conducted in May and June 2021 on a sample of 599 students at the University of Warmia and Mazury in Olsztyn and the School of Technology and Management of Porto Polytechnic in Portugal. The analysis related to the determination of statistically significant interdependencies of socio-demographic characteristics of respondents with their attitudes, and a multidimensional method of comparative analysis was used, known as correspondence analysis. As a method of recording data in the analysis of correspondence, the Burt matrix was used. The result of the statistical analysis was the identification of structural relationships between variables and objects (respondents). The results showed different behaviours related to housing conditions in Poland and Portugal. Polish students, due to the epidemiological situation, were mostly forced to change their place of residence, which was usually associated with returning to their family home. This trend was not observed for students in Portugal (median response: Housing had not been affected in any way by the pandemic).

Abstract
The goal for a virtual reality (VR) training system is to enable trainees to acquire all the knowledge they need to perform effectively in a real environment. Such a system should provide an experience so authentic that no further real-world training is necessary, meaning that it is sufficient to train in VR. We evaluate the impact of a haptic thermal stimulus, which is of paramount importance to decision making, on trainees performance and knowledge acquisition. A thermal device was created to deliver the stimulus. As a proof of concept, a procedure from firefighter training is selected, in which sensing the temperature of a door with one's hand is essential. The sample consisted of 48 subjects divided among three experimental scenarios: one in which a virtual thermometer is used (visual stimulus), another in which the temperature is felt with the hand (thermal stimulus) and a third in which both methods are used (visual + thermal stimuli). For the performance evaluation, we measured the total time taken, the numbers of correctly executed procedures and identified neutral planes, the deviation from the target height, and the responses to a knowledge transfer questionnaire. Presence, cybersickness, and usability are measured to evaluate the impact of the haptic thermal stimulus. Considering the thermal stimulus condition as the baseline, we conclude that the significantly different results in the performance among the conditions indicate that the better performance in the visual-only condition is not representative of the real-life performance. Consequently, VR training applications need to deliver the correct stimuli for decision making.

Abstract
This paper presents a collaborative mobile manipulator assembly station, which uses force control to surpass the positional uncertainties arising from unstructured work environments and positional errors of the mobile platform. For this purpose, the use case of an internal combustion engine for the automotive industry was considered. Several force control heuristics relying on blind searches using oscillations and/or environment exploration were developed and implemented. Particular attention was given to the orientation errors of the mobile platform, as it was proved that they have a significant impact on the assembly task. The proposed heuristics showed great potential for the use case at hand. Particularly, when the orientation error of the platform is limited to +/- 2 degrees, the oscillation method complemented by environment exploration was able to surpass a maximum translation error of 32.3mm, whereas the environment exploration complemented by orientation correction was able to surpass an error of 73.3mm. Moreover, a generalization strategy was proposed, intending to expand the scope of the developed heuristics to other assembly applications.

Abstract
Radon gas is the largest source of public exposure to naturally occurring radioactivity. However, radon is also a useful tracer for understanding atmospheric processes, assessing the accuracy of chemical transport models, and enabling integrated emissions estimates of greenhouse gases. A sound metrological system for low level atmospheric radon observations is therefore needed for the benefit of the atmospheric, climate and radiation protection research communities. To this end, here we present a new calibration method for activity concentrations below 20 Bq m(-3) and a prototype of the first portable radon monitor capable of achieving uncertainties of 5% (at k = 2) at these concentrations. Compliance checking of policy-driven regulations regarding greenhouse gas (GHG) emissions is an essential component of climate change mitigation efforts. Independent, reliable 'top down' methods that can be applied consistently for estimating local- to regional-scale GHG emissions (such as the radon tracer method (RTM)) are an essential part of this process. The RTM relies upon observed radon and GHG concentrations and measured or modeled radon fluxes. Reliable radon flux maps could also significantly aid EU member states comply with European COUNCIL DIRECTIVE 2013/59/EURATOM. This article also introduces the traceRadon project, key aims of which include outlining a standardized approach for application of the RTM, creating infrastructure with a traceability chain for radon concentration and radon flux measurements, and developing tools for the validation of radon flux models. Since radon progeny dominate the terrestrial gamma dose rate, the planned traceRadon activities are also expected to improve the sensitivity of radiation protection early warning networks because of the correlation known to exist between radon flux and ambient equivalent dose rates.

Abstract
In this article, we investigate the secure spectral efficiency of an ultrareliable low-latency communication system, where communications occur with short packets due to delay constraints, so that a finite blocklength formulation is considered. In addition, we assume that no feedback channel is available to implement automatic repeat request schemes, so that packet replication (PR) and interface diversity (ID) strategies are used to improve performance, which are then compared in terms of physical-layer security while considering a Nakagami-m fading channel. Furthermore, we assume no knowledge of the instantaneous channel state information at Alice, neither with respect to Bob nor Eves, while the position of multiple colluding eavesdroppers are specified according to a Poisson point process. Numerical results show that the joint optimization of the blocklength, the transmit power, and the amount of information bits per codeword are crucial to maximize the secure spectral efficiency. In addition, we also show that ID outperforms the PR strategy in most scenarios when the number of replications/interfaces increases.

Abstract
Li-ion battery State-of-Charge (SOC) estimation is a complex challenge for battery management systems designers, due to the battery's non-linear behaviour at different operating conditions and ageing levels. As a possible solution, multiple machine learning models have been proposed for SOC estimation throughout the years. These provide an advantage over model-based methods, as they do not require a deep knowledge and study of the battery's internal behaviour. However, many of these proposed models could not be considered due to their complexity. The high number of required stored parameters and/or elevated memory consumption during estimation may pose challenges to the application of these methods. Therefore, in this paper, several feedforward neural network models are proposed for SOC estimation, with an efficient method for online input preprocessing and low parameter requirement in storage. These models are simulated and validated using battery data, taken at different temperatures with several driving cycles and charge cycles, achieving lowest estimation Root Mean Squared Error (RMSE) of 1.096% over the whole validation dataset.