Tool developed as part of the EngD thesis: DaylightGuide - A tool for personalized daylight recommendations for the home office

The DaylightGuide tool (1) asks for input about the user’s home office setup , (2) provides insights in the daylighting conditions in the home office, and (3) presents recommendations for optimizing daylight in the home office. (Day)light metrics that are presented are melanopic Equivalent Daylight Illuminance (m-EDI), Daylight Glare Probability (DGP), spatial Daylight Autonomy (sDA), and Useful Daylight Illuminance (UDI). In the tool, these metrics are referred to as the daylight targets. The tool also provides recommendations for optimizing the daylight targets. The tool is developed for use in The Netherlands. To establish this tool, three steps were taken. First, a questionnaire study was conducted to make an inventory of Dutch home office space characteristics, with a special focus on characteristics that contribute to the indoor daylight distribution. Second, based on these office characteristics, parametric simulation models were developed and annual daylight simulations were conducted using Radiance. The parameters used in the simulations were: the type of window, the orientation of the window, the density of the outside area surrounding the home office, the glazing area of the window, the color of the walls in the home office space, the placement of the window with respect to the occupant, and the distance between the workplace and the window. In specific, simulations were conducted to retrieve m-EDI, DGP, sDA, and UDI values. Third, the simulation results were processed such that the annual data were averaged per month for daytime working hours (i.e., 08.00-18.00). The data were processed to show, for each month of the year, the percentage of work hours during which a certain target was met. This analysis was done for each daylight metric. A user interface was built for the processed simulation data, which is the developed DaylightGuide tool. Through the interface, anyone working (occasionally) from a home office in The Netherlands can gain insight into the daylighting conditions in their home office as well as recommendations on any changes they could make to their home office to optimize the daylight targets

Audio Files - Driving simulator soundscapes

To prevent drivers from taking over control in the vehicle from an autopilot when there is no need to do so, the degradation of experience may need to be compensated for. One way to do so maybe by offering proper soundscapes. A study was conducted in a driving simulator, in which the influence of two different soundscapes on the driving experience was investigated, one giving a more thrilling experience, the other giving a more relaxing experience

Viewing behavior and vertical eye-level light for non-image-forming effects

When considering non-image-forming (NIF) light effects on people, knowing the light vertically at eye-level is necessary. However, people are dynamic in their behavior and constantly change their viewing direction. This means that light measured vertically towards a constant direction might differ from the actual light that reaches people’s eyes. If the difference is large, viewing behavior might need to be included in lighting design measurements and simulations predicting the potential of the light to induce NIF light effects. This dataset was collected during an experiment on the difference between the actual dynamic eye-level light of office workers while seated at a desk (dynamic condition) and light measured statically towards a computer screen (static condition). The dataset was collected to test the hypothesis: "There is a significant and relevant difference between simultaneously measured static and dynamic light conditions in an office environment occupied by one user." It includes measured and simulated light quantities (illuminance, alpha-opic quantities according to CIE S026 and light-driven alertness according to the non-visual direct response model) together with participants' measured face orientation (horizontal and vertical) in an office environment with a single user

Investigation of Dose-Response Relationships for Effects of White Light Exposure on Correlates of Alertness and Executive Control during Regular Daytime Working Hours

To date, it is largely unknown which light settings define the optimum to steer alertness and cognitive control during regular daytime working hours. In the current article, we used a multimeasure approach combined with a relatively large sample size (N = 60) and a large range of intensity levels (20-2000 lux at eye level) to investigate the dose-dependent relationship between light and correlates of alertness and executive control during regular working hours in the morning and afternoon. Each participant was exposed to a single-intensity light level for 1 h after a 30-min baseline phase (100 lux at the eye) in the morning and afternoon (on separate days) during their daily routine. Results revealed no clear dose-dependent relationships between 1-h daytime light exposure and correlates of alertness or executive control. Subjective correlates showed only very modest linear relationships with the log-transformed illuminance, and we found no significant effects of light intensity on the behavioral and physiological indicators. Overall, these results suggest that daytime exposure to more intense light, at least for 1 h of exposure, may not systematically benefit alertness or executive functioning. However, future research is required to investigate effects of longer exposure durations and potential moderations by prior light exposure, personal characteristics, and spectrum

Fluctuations in pedestrian dynamics routing choices

Set of pedestrian trajectories recorded during the GLOW festival in Eindhoven (The Netherlands), between November 9th and 16th 2019. The data has been used in the analysis described in the paper "Fluctuations in pedestrian dynamics routing choices". PNAS Nexus, pgac16

26 GHz OFDM and 77 GHz FMCW Radar Dataset for Domain Shift Invariant Blockage Prediction

This dataset is the radar and groundtruth dataset linked to the paper "26 GHz OFDM and 77 GHz FMCW Radar Dataset for Domain Shift Invariant Blockage Prediction". The abstract of this paper is blow. The infor of the other part of the communication OFDM dataset in this paper can be found in the paper that is openly accessible. This paper presents a novel millimeter wave communication (comms) and radar sensing co-existing dataset. The measurement campaign was performed for blockage prediction with diverse human activities. 26 GHz Orthogonal Frequency Division Multiplexing (OFDM) multi-beam communication testbed and 77 GHz Frequency-Modulated Continuous-Wave (FMCW) multiple input, multiple output (MIMO) radar multi-monostatic set-up were configured. The corresponding bistatic channel state information and multi-monostatic backscattered channels are pre-processed for preliminary domain shift analysis by means of visual pre-processed sample inspection. Domain shift inside a blockage prediction model occurs when measurement circumstances under which model training data was collected significantly differ from the model inference measurement circumstances. Domain shifts cause model performance deterioration in the inference phase. No previous millimeter wave blockage prediction research considers mitigating domain shift in prediction models. We argue that this is caused by no millimeter wave blockage prediction datasets being available with samples collected under a large number of different measurement circumstances. Analysis results indicate presence of different signature presence levels in pre-processed radar backscattered channel samples and different doppler bin energy magnitudes and locations in pre-processed OFDM testbed channel state information samples captured under varying measurement circumstances. Therefore, creating a large enough blockage prediction dataset with samples captured under varying measurement circumstances that induce hard enough domain shifts between model train and inference situations is important to allow model domain shift mitigation research

MetTLM 20NRM01 TU/e Dataset: Dependence of Temporal Frequency and Chromaticity on the Visibility of the Phantom Array Effect

The dataset has the following format: 20 (Participants) by 18 (= 3 Chromaticities × 6 Temporal Frequencies) Chromaticities: Red (R); Green (G); Warm White (W) Temporal Frequencies: F1 = 80 Hz; F2 = 300 Hz; F3 = 600 Hz; F4 = 900 Hz; F5 = 1200 Hz; F6 = 1800 Hz. R F1 R F2 R F3 R F4 R F5 R F6 G F1 G F2 G F3 G F4 G F5 G F6 W F1 W F2 W F3 W F4 W F5 W F6 P01 P02 P03 P04 ... P19 P20 Due to the fractional factorial 3 (colour) × 6 (temporal frequency) mixed design, there are some empty cells. The details are described in Table 2 of the publication. The values in the table represent the visibility thresholds in Modulation Depth (MD). For example, the values of 0.05, 0.1, and 1 means a MD of 5%, 10% and 100% respectively

Representation of crowd accidents in popular media

This repository contains results related to the analysis of a corpus of news reports covering the topic of crowd accidents. To facilitate online visualization and offline analysis, the files are organized by assigning a number to each. The number system and the details of each set of files are described as follows: Class 0 – This contains the same files provided in this repository, but they are organized into folders to make analysis easier. If you intend to analyze the data from our lexical analysis, we suggest using this file since it is better organized and can be directly downloaded. Class 1 – This contains the sources and relevant information for people who are interested in replicating our dataset or accessing the news reports used in our analysis. Please note that due to copyright regulations, the texts cannot be shared. However, you can refer to the links provided in these files to access the news articles and Wikipedia pages. Some links have stopped working during the time we were working on this study, and others may be unreachable in the future. Class 2 – This contains the results from a lexical analysis of the corpus. The HTML page allows you to visualize each result interactively through the online VOSviewer app (you need to download the file and open it using a browser since Zenodo does not recognize this as a link). It is possible that this service (VOSviewer app) may be discontinued at some point in the future. PNG images of lexical maps are, therefore, available for download through the ZIP archive, although they do not allow interactive access. If you plan to read our results using the offline VOSviewer software or perform a more systematic analysis, JSON files are available for each category (time period, geographical area of the reporting institution, and purpose of gathering). The same files can be also find in the ZIP archive in class 0. Class 3 – These are the results of the sentiment analysis. For each report, a single result is generated for the title. However, for the body, the text is divided into parts, which are analyzed independently. The format of CSV and JSON files should be self-explanatory after reading our publication. For specific questions or queries, please contact one of the authors, and we will try to assist you

MetTLM 20NRM01 TU/e Dataset: Dependence of Temporal Frequency and Chromaticity on the Visibility of the Phantom Array Effect

The dataset has the following format: 20 (Participants) by 18 (= 3 Chromaticities × 6 Temporal Frequencies) Chromaticities: Red (R); Green (G); Warm White (W) Temporal Frequencies: F1 = 80 Hz; F2 = 300 Hz; F3 = 600 Hz; F4 = 900 Hz; F5 = 1200 Hz; F6 = 1800 Hz. R F1 R F2 R F3 R F4 R F5 R F6 G F1 G F2 G F3 G F4 G F5 G F6 W F1 W F2 W F3 W F4 W F5 W F6 P01 P02 P03 P04 ... P19 P20 Due to the fractional factorial 3 (colour) × 6 (temporal frequency) mixed design, there are some empty cells. The details are described in Table 2 of the publication. The values in the table represent the visibility thresholds in Modulation Depth (MD). For example, the values of 0.05, 0.1, and 1 means a MD of 5%, 10% and 100% respectively

Data-driven physics-based modeling of pedestrian dynamics

Python package to create physics-based pedestrian models from crowd measurement