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The automatic identification system (AIS) can improve situational awareness at sea, but its protocol is simple and does not guarantee message integrity, authentication, and proper use. For example, the lack of safety measures creates problems when AIS messages are used for tracking a target or predicting a target trajectory. We present a methodology for the validation of AIS messages, a prerequisite for their safe use in maritime situational awareness applications. The validation method relies on target trackers that fuse AIS data and exteroceptive sensor data, and it detects errors in position, speed and course, and rate of turn. We use radar data to exemplify exteroceptive sensor data. With the use of simulated data, we show that the proposed methods effectively detect errors in the position and velocity data received through AIS messages and are also able to detect errors in turn-rate data. The effectiveness of the methods is demonstrated on a real-world dataset with injected false AIS data. [ABSTRACT FROM AUTHOR]

Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans by the means of language are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the role of emotion in the prediction of two important subjective goals in a negotiation – outcome satisfaction and partner perception. We devise ways to measure and compare different degrees of emotion expression in negotiation dialogues, consisting of emoticon, lexical, and contextual variables. Through an extensive analysis of a large-scale dataset in chat-based negotiations, we find that incorporating emotion expression explains significantly more variance, above and beyond the demographics and personality traits of the participants. Further, our temporal analysis reveals that emotive information from both early and later stages of the negotiation contributes to this prediction, indicating the need for a continual learning model of capturing emotion for automated agents. Finally, we extend our analysis to another dataset, showing promise that our findings generalize to more complex scenarios. We conclude by discussing our insights, which will be helpful for designing adaptive negotiation agents that interact through realistic communication interfaces. [ABSTRACT FROM AUTHOR]

Spain’s Comunitat Valenciana (Valencian Community) () has a rich agricultural history, with long traditions of resilience and innovation. This tradition, along with the community’s deep ethic of respect for the land, remains at the core of the region’s approach to climate change and sustainability. [ABSTRACT FROM AUTHOR]

This paper investigates the technical aspects of the 1550 nm free space optical (FSO) link in extreme Arctic weather, specifically in falling snow, which is currently understudied. We measured the time variation of the received optical power in many snowfall events and derived attenuation statistics. Continuous measurements allowed us to estimate attenuation under varying temperatures and humidity to examine the after-snow channel impairments. Our results show several shortcomings in the International Telecommunication Union-Radio Communication (ITU-R) snow attenuation model and verify that a correlation exists between snow features, snowfall rate, temperature, and humidity in determining the total snow attenuation. We demonstrated the relationship between snow properties, temperature, humidity, and attenuation under both in-snow and post-snow environments. Furthermore, based on the experimental results, we recommend several mitigation techniques to realize FSO communication during challenging Arctic weather conditions. Lastly, we present a feasibility study of FSO in Svalbard at 78°N latitude.

The Absorbing Aerosol Index (AAI) determines the presence and approximate amount of absorbing aerosol particles. The Absorbing Aerosol Sensor (AAS) equipped on the Gaofen-5B satellite was successfully launched and deployed in orbit on September 7, 2021. Following a period of initial on-orbit testing, reliable AAI observational results were collected. The nadir of the AAS had a design spatial resolution of 4 km × 4 km and a current spatial resolution of 2 km × 4 km, which presented significant benefits in the spatial resolution and clear distribution characteristics of pollutants. The results of AAS were validated by comparing with the observed data of Global Ozone Monitoring Experiment-2 (GOME-2) in the Sahara Desert and surrounding areas. In 2022 and 2023, we utilized AAS observations to study the typical pollution processes. These included the spring pollution distribution around the Bohai Sea, the observation and tracking of long-term dust pollution in China in March and April 2023, the distribution of spring pollution in the Indochina Peninsula in April 2023, and the distribution of wildfires in Canada on June 22, 2023. The results were validated and analyzed using data from Tropospheric Monitoring Instrument (TROPOMI), GOME-2, AErosol RObotic NETwork (AERONET) and Moderate-resolution Imaging Spectroradiometer (MODIS). The results of the observation and evaluation revealed that the AAS AAI can provide reliable information for precise observation of air pollutants at a high resolution.

The atmospheric electric current, "air–earth current", flows between the low ionosphere and Earth's surface. The source of this current is the potential difference between the global equalizing layer called the ionosphere and the ground surface. According to Wilson's concept of the Earth's Global Electric Circuit, in the areas of so-called fair weather, based on current measurements at the Earth's surface, it is possible to conclude the global electrical processes in the ionosphere and higher layers. The theoretical basis for this inference is the law of continuity of electric current or the principle of conservation of electric charge. We present the results of simulations of the distribution of electric field lines for sensors with different geometries placed in a uniform electric field, representing the atmospheric electric field. The sensors are metal surfaces on which electric charges are induced or deposited. In the external measuring circuit to which the sensor is connected, an electric current [A] will flow, related to the air–earth current density [A/m2], but their relationship may be challenging to interpret. We analyze the impact of sensor geometry on the possibility of interpreting the atmospheric electric conduction and atmospheric displacement current based on the current measured in the external circuit. This present method can be used for the geometric construction of new sensors at the stage of determining the electrical characteristics of the sensor (e.g., effective collecting area). It can support the comprehensive design of a measurement system at the interface between an atmosphere, sensor, and electronic equipment. [ABSTRACT FROM AUTHOR]

The common saying, that information is power, takes a rigorous form in stochastic thermodynamics, where a quantitative equivalence between the two helps explain the paradox of Maxwell's demon in its ability to reduce entropy. In this letter, we build on earlier work on the interplay between the relative cost and benefits of information in producing work in cyclic operation of thermodynamic engines (by Sandberg et al. 2014). Specifically, we study the general case of overdamped particles in a time-varying potential (control action) in feedback that utilizes continuous measurements (nonlinear filtering) of a thermodynamic ensemble, to produce suitable adaptations of the second law of thermodynamics that involve information.

The quality of daily spontaneous conversations is of importance towards both our well-being as well as the development of interactive social agents. Prior research directly studying the quality of social conversations has operationalized it in narrow terms, associating greater quality to less small talk. Other works taking a broader perspective of interaction experience have indirectly studied quality through one of the several overlapping constructs such as rapport or engagement, in isolation. In this work we bridge this gap by proposing a holistic conceptualization of conversation quality, building upon the collaborative attributes of cooperative conversation floors. Taking a multilevel perspective of conversation, we develop and validate two instruments for perceived conversation quality (PCQ) at the individual and group levels. Specifically, we motivate capturing external raters’ gestalt impressions of participant experiences from thin slices of behavior, and collect annotations of PCQ on the publicly available MatchNMingle dataset of in-the-wild mingling conversations. Finally, we present an analysis of behavioral features that are predictive of PCQ. We find that for the conversations in MatchNMingle, raters tend to associate smaller group sizes, equitable speaking turns with fewer interruptions, and time taken for synchronous bodily coordination with higher PCQ. [ABSTRACT FROM AUTHOR]

Two people's physiological responses become more similar as those people talk or cooperate, a phenomenon called physiological synchrony. The degree of synchrony correlates with conversation engagement and cooperation quality, and could thus be used to characterize interpersonal interaction. In this study, we used a combination of physiological synchrony metrics and pattern recognition algorithms to automatically classify four different dyadic conversation scenarios: two-sided positive conversation, two-sided negative conversation, and two one-sided scenarios. Heart rate, skin conductance, respiration and peripheral skin temperature were measured from 16 dyads in all four scenarios, and individual as well as synchrony features were extracted from them. A two-stage classifier based on stepwise feature selection and linear discriminant analysis achieved a four-class classification accuracy of 75.0% in leave-dyad-out crossvalidation. Removing synchrony features reduced accuracy to 65.6%, indicating that synchrony is informative. In the future, such classification algorithms may be used to, e.g., provide real-time feedback about conversation mood to participants, with applications in areas such as mental health counseling and education. The approach may also generalize to group scenarios and adjacent areas such as cooperation and competition. [ABSTRACT FROM AUTHOR]

Spatial navigation is an important aspect of everyday life but may be negatively impacted by both cognitive and affective load. Cognitive and affective load may be measured via autonomic arousal and increased load may lead to reduced navigational abilities. 53 college students (64.0% female; M age = 19.62) participated in the Virtual Reality Paced Serial Auditory Addition Task (VR-PASAT). Participants followed guides through different areas of the virtual environment (VE). In some areas participants completed the PASAT, high load, and in other areas they simply followed the guides, low load. Some participants were instructed beforehand they would perform a navigate task, increasing load. Results suggested that several psychophysiological measures including skin conductance and inter-beat intervals were impacted by increased load while others were related to the interactions between load and zone order. Awareness of the navigation task led to worse performance on the VR-PASAT, and high load decreased navigational performance. The VR-PASAT was used to implement a VE to manipulate cognitive load. This study may be useful for the creation of adaptive systems because it demonstrates that psychophysiological metrics can assess cognitive and affective load, which may impact navigation within a VE, and navigational task awareness may interact with load. [ABSTRACT FROM AUTHOR]

Measurements of the vertical structure of the lower atmosphere are important to the understanding of air quality. Unmanned Aerial Systems (UASs, drones) can provide low cost, repeatable measurements of the temperature, pressure, and relative humidity. A set of inexpensive sensors controlled with an Arduino microprocessor board were tested on a UAS against a meteorology grade sensor. Two modes of operation for sampling were tested: a forward moving sampler and a vertical ascent sampler. A small particle sensor (Sensiron SPS30) was integrated and was capable of retrieving vertical aerosol distributions during an inversion event. The thermocouple-based temperature probe and the relative humidity measurement on the Bosch BME280 sensor correlated well with the meteorological sensor. The temperature and relative humidity sensors were then deployed on a rocket sounding platform. The rocket sounding system performed well up to a height of 400 m. The inexpensive sensors were found to perform adequately for low-cost development and uses in education and research.

The harmful consequences of fossil fuels use has resulted in the promotion of clean and renewable energies. During the past decades, green technologies have experienced a strong development, paying especial attention to wind energy, that covers a significant share of the electric energy demand. In this context, the main efforts are focused on the optimization of wind generator facilities, not only in the mechanic design but also in the energy management. Then, the present work deals with the prediction of the energy generated in a small wind turbine placed in a bioclimatic house located on the north west region of Spain. This includes an analysis of the characteristics of the atmospheric variables registered during the turbine operation for a period of one year and an exploratory examination of a range of regression techniques in order to assess the suitability of using the registered information to predict the installation's power generation levels on the short term. The study detailed in this work proves that this objective is an attainable one with a good degree of accuracy. [ABSTRACT FROM AUTHOR]

Space exploration is evolving with the recent increase in interest and investment. For the success of planned long-duration crewed missions, good interpersonal interactions between crew members are crucial. In this study, we evaluate the use of wearables for detection and estimation of the quality of each social interaction participants have throughout a long mission rather than aggregate measures of interactions. Our proposed method utilizes Temporal Convolutional Networks(TCNs) for extracting individual representations from acceleration and audio streams and learnable pooling layers(NetVLAD) to aggregate these representations into fixed-size representations. Use of NetVLAD layers provides an intelligent alternative to simple aggregation for handling variable-sized interactions and interactions with missing data. We evaluate our method on a 4-month simulated space mission where 5 participants wore Sociometric Badges and provided reports on their interactions in terms of effectiveness, frustration, and satisfaction. Our method provides an average ROC-AUC score of 0.64. Since we are not aware of any comparable baselines, we compare our method to hand-crafted features formerly utilized for cohesion estimation in similar scenarios and show it significantly outperforms them. We also present ablation studies where we replace the components in our approach with well-known alternatives and show that they provide better performance than their respective counterparts. [ABSTRACT FROM AUTHOR]

We evaluated whether a vibrotactile breathing pacer would influence two measures of affect during a cognitive stressor. In particular, we examined whether changes in breathing would be evident, and if so, whether these would mediate the effects of breathing pacer on self-report anxiety and skin conductance. Our results were surprising: although we observed the expected effects on breathing, we were unable to demonstrate that changes in breathing parameters were responsible for the observed changes in self-report anxiety. In addition, we failed to show that the pacer had an impact on skin conductance. In this paper, we investigate why we did not observe the expected effects. We believe our negative results have implications for evaluating technological interventions for affect regulation. [ABSTRACT FROM AUTHOR]

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) onboard the International Space Station (ISS) provides standard land surface temperature (LST) products to meet community concerns on water stress and evapotranspiration. In 2019, an anomaly on the mass storage unit (MSU) changed the data acquisition mode of ECOSTRESS to a direct streaming one, eliminating three bands centered at 1.60, 8.29, and $9.20~\mu \text{m}$. This letter analyzes the feasibility of retrieving LST from ECOSTRESS data using split-window (SW) techniques which need only two thermal infrared (TIR) bands to serve as an alternative to produce LST products. Three different spilt-window algorithms have been developed and analyzed for all possible TIR band combinations using the simulated top-of-atmosphere (TOA) radiance dataset. Besides, the selected SW algorithms were validated using the intercomparison method with ECOSTRESS LST product data and the temperature-based method with surface radiation budget (SURFRAD) ground-based measurements. The result showed that SW algorithms provided higher accuracies than the temperature-emissivity separation (TES) algorithm, with the main advantage that the atmospheric profile data is not demanded prior. [ABSTRACT FROM AUTHOR]

In remote sensing, plenty of multispectral images are publicly available from various landcover satellite missions. Contrastive self-supervised learning is commonly applied to unlabeled data but relies on domain-specific transformations used for learning. When focusing on vegetation, standard transformations from image processing cannot be applied to the near-infrared (NIR) channel, which carries valuable information about the vegetation state. Therefore, we use contrastive learning, relying on different views of unlabeled, multispectral images to obtain a pretrained model to improve the accuracy scores on small-sized remote sensing datasets. This study presents the generation of additional views tailored to remote sensing images using atmospheric correction as an alternative transformation to color jittering. The purpose of the atmospheric transformation is to provide a physically consistent transformation. The proposed transformation can be easily integrated with multiple channels to exploit spectral signatures of objects. Our approach can be applied to other remote sensing tasks. Using this transformation leads to improved classification accuracy of up to 6%. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is an essential parameter in the water cycle and surface energy balance. Accurate estimation of daytime or daily ET is of great significance for many activities in human economic and social development. This study aims to propose a novel method for estimating daytime average ET by using temporal measurements of land surface temperature (LST) and net surface shortwave radiation (NSSR), following a previously developed elliptical relationship between diurnal cycles of LST and NSSR under cloud-free days. The method was primarily developed from the simulated data of a physics-based Atmosphere–Land Exchange (ALEX) model under different underlying surfaces and atmospheric conditions. Based on the simulated data, the proposed method showed considerable accuracy with the overall coefficient of determination ($R^{2}$) of 0.958 and the root mean square error (RMSE) of 25.3 Wm $^{-2}$. In addition, ground ET measurements at four Ameriflux sites (US-ARM, US-SRM, US-Whs, and US-Wkg) during the 2018 growing season were collected to assess the estimated daytime average ET. Results show an overall RMSE of 64.7 Wm $^{-2}$ for the estimated ET at the four sites, and the US-Whs site reveals the best accuracy ($R^{2}$ = 0.825 and RMSE = 44.4 Wm $^{-2}$). These results indicated a potential for generating daytime ET with geostationary satellite observations at regional scales in future development. [ABSTRACT FROM AUTHOR]

A back propagation neural network (BPNN)-based calibration method is developed to enhance the all-weather quality of official integrated water vapor (IWV) products from near-infrared (NIR) observations of the Ocean and Land Color Instrument (OLCI) onboard Sentinel-3A and Sentinel-3B satellites. The model utilizes multiple variables that link with the derivation of satellite NIR IWV products, including official OLCI NIR IWV, latitude, land quality and science flag, month, and solar zenith angle. The in situ IWV observations, collected from 100 Global Positioning System (GPS) stations, are employed as the desired IWV estimates. The model is evaluated using one-year water vapor data at additional 114 GPS stations and 97 radiosonde stations from June 1, 2019 to May 31, 2020 in China and its surrounding regions. The results show that the BPNN-based calibration model reduces the root-mean-square error (RMSE) of official OLCI NIR all-weather IWV products by 24.52% from 3.10 to 2.34 mm for Sentinel-3A and by 25.00% from 3.44 to 2.58 mm for Sentinel-3B, when compared with in situ GPS-observed reference IWV. When compared with in situ radiosonde-observed IWV, the RMSE reduces 21.01% from 4.57 to 3.61 mm and 20.81% from 5.19 to 4.11 mm for Sentinel-3A and Sentinel-3B, respectively. [ABSTRACT FROM AUTHOR]

Temperature and humidity profiles in the atmospheric boundary layer are essential for climate studies. The ground-based infrared hyperspectral spectrometer has the advantage of measuring radiances emitted from the atmosphere at a high temporal and moderate vertical resolution. In this article, the retrieval of temperature and humidity profiles from ground-based infrared hyperspectral observations is exploited. Although existing inversion algorithms based on physical models or statistical learning have made some progress, they still suffer from high computational complexity or poor performance. Motivated by the strength of the deep learning, we present a deep retrieval architecture (DReA) by skillfully designing a light-weight one-dimensional convolution neural network (CNN) to retrieve the temperature and humidity profiles. Experiments were conducted using atmospheric emitted radiance interferometer (AERI) and radiosonde data to demonstrate the superiority of the proposed DReA. The validation of the DReA with the radiosonde, using 802 profiles with 37 layers below 3 km, presents an excellent retrieval ability with a root mean square error (RMSE) of 0.87 K for the temperature and 1.06 g/kg for the water vapor mixing ratio. Furthermore, a thorough comparison with commonly used inversion methods such as the traditional back propagation (BP) and the eigenvector (EV) regression method, shows that our proposed DReA method obtains a leading solution in retrieving temperature and humidity profiles. [ABSTRACT FROM AUTHOR]

Assessment of cognitive load is a major step towards adaptive interfaces. However, non-invasive assessment is rather subjective as well as task specific and generalizes poorly, mainly due to methodological limitations. Additionally, it heavily relies on performance data like game scores or test results. In this study, we present an eye-tracking approach that circumvents these shortcomings and allows for effective generalizing across participants and tasks. First, we established classifiers for predicting cognitive load individually for a typical working memory task (n-back), which we then applied to an emergency simulation game by considering the similar ones and weighting their predictions. Standardization steps helped achieve high levels of cross-task and cross-participant classification accuracy between 63.78 and 67.25 percent for the distinction between easy and hard levels of the emergency simulation game. These very promising results could pave the way for novel adaptive computer-human interaction across domains and particularly for gaming and learning environments. [ABSTRACT FROM AUTHOR]

In this article we introduce Mementos: the first multimodal corpus for computational modeling of affect and memory processing in response to video content. It was collected online via crowdsourcing and captures 1995 individual responses collected from 297 unique viewers responding to 42 different segments of music videos. Apart from webcam recordings of their upper-body behavior (totaling 2012 minutes) and self-reports of their emotional experience, it contains detailed descriptions of the occurrence and content of 989 personal memories triggered by the video content. Finally, the dataset includes self-report measures related to individual differences in participants’ background and situation (Demographics, Personality, and Mood), thereby facilitating the exploration of important contextual factors in research using the dataset. We describe 1) the construction and contents of the corpus itself, 2) analyse the validity of its content by investigating biases and consistency with existing research on affect and memory processing, 3) review previously published work that demonstrates the usefulness of the multimodal data in the corpus for research on automated detection and prediction tasks, and 4) provide suggestions for how the dataset can be used in future research on modeling Video-Induced Emotions, Memory-Associated Affect, and Memory Evocation. [ABSTRACT FROM AUTHOR]

The atmospheric electric current, “air–earth current”, flows between the low ionosphere and Earth’s surface. The source of this current is the potential difference between the global equalizing layer called the ionosphere and the ground surface. According to Wilson’s concept of the Earth’s Global Electric Circuit, in the areas of so-called fair weather, based on current measurements at the Earth’s surface, it is possible to conclude the global electrical processes in the ionosphere and higher layers. The theoretical basis for this inference is the law of continuity of electric current or the principle of conservation of electric charge. We present the results of simulations of the distribution of electric field lines for sensors with different geometries placed in a uniform electric field, representing the atmospheric electric field. The sensors are metal surfaces on which electric charges are induced or deposited. In the external measuring circuit to which the sensor is connected, an electric current [A] will flow, related to the air–earth current density [A/m2], but their relationship may be challenging to interpret. We analyze the impact of sensor geometry on the possibility of interpreting the atmospheric electric conduction and atmospheric displacement current based on the current measured in the external circuit. This present method can be used for the geometric construction of new sensors at the stage of determining the electrical characteristics of the sensor (e.g., effective collecting area). It can support the comprehensive design of a measurement system at the interface between an atmosphere, sensor, and electronic equipment.

To our knowledge, it has been widely studied in Screen-2D modality for the six basic emotions proposed by Professor Paul Ekman, but there are only studies on their positive and negative valence in VR-3D modality. In this study, we will investigate whether the six basic emotions have stronger brain activation states in VR-3D modality than in Screen-2D modality. We designed an emotion-inducing experiment with six basic emotions (happiness, surprise, sadness, fear, anger, and disgust) to record the electroencephalogram (EEG) signals during watching VR-3D and Screen-2D videos. The power spectral density (PSD) was calculated to compare the brain activation differences between VR-3D and Screen-2D modalities during the induction of the six basic emotions. The results of statistical analysis of the relative power differences between VR-3D and Screen-2D modalities for each emotion revealed that both happiness and surprise presented greater differences in the $\alpha $ and $\gamma $ frequency bands, while sad, fear, disgust and anger all presented greater differences in the $\alpha $ and $\theta $ frequency bands, which are mainly observed in the frontal and occipital regions. On the other hand, the six emotions all yielded satisfactory classification accuracy (above 85%) by classification from a subset of power feature of the brain activation states in the same emotion between the two modalities. Overall, there are significant differences in the induction of same discrete emotions in VR-3D and Screen-2D modalities, with greater brain activation in VR-3D modalities. These findings provide a better understanding about the neural activity of discrete emotional tasks assessed in VR environments. [ABSTRACT FROM AUTHOR]

The prevalence and impact of balance impairments and falls in older adults have motivated several studies on the characterization of human balance. This study aimed to determine the ability of recurrence quantification analysis (RQA) measures to characterize balance control during quiet standing in young and older adults and to discriminate between different fall risk groups. We analyze center pressure trajectories in the medial-lateral and anterior-posterior directions from a publicly available static posturography dataset that contains tests acquired under four vision-surface testing conditions. Participants were retrospectively classified as young adults (age< 60, n=85), non-fallers (age≥60, falls=0, n=56), and fallers (age≥60, falls≥1, n=18). Mixed ANOVA and post hoc analyzes were performed to test for differences between groups. For CoP fluctuations in the anterior-posterior direction, all RQA measures showed significantly higher values for young than older adults when standing on a compliant surface, indicating less predictable and stable balance control among seniors under testing conditions where sensory information is restricted or altered. However, no significant differences between non-fallers and fallers were observed. These results support the use of RQA to characterize balance control in young and old adults, but not to discriminate between different fall risk groups. [ABSTRACT FROM AUTHOR]

Mental imagery (MI) is gaining attention as a strategy towards endogenous brain stimulation for improving motor skill. Neurofeedback (NF) is commonly used to guide MI in order to activate the relevant brain networks. The current study investigates an individualized EEG-based method for NF through broad consideration of interactions between different brain networks. We selected the change in brain functional connectivity (FC) as an objective neurophysiological measure of change in motor skill during a longitudinal physical training (PT) program. Digital tracing tasks were developed for skill training and the spatial error in tracing was used to gauge the change in skill. We used partial least squares algorithms to find the most robust contributing networks towards correlation between the resting state FC and the acquired motor skill. We used the network with the largest margin for increasing FC as the candidate for NF training while experimenting with MI during a neurofeedback training program. The participant was informed of the changes in instantaneous FC through real-time audio feedback to help guide the MI. We showed over 20% reduction in tracing error through neurofeedback training alone, without any additional PT. We also showed retention of improvement in skill for several days after the completion of neurofeedback training. Our proposed methodology shows promise for a highly individualized approach towards improvement in motor skill. Given that EEG is an accessible health and wellness technology, such a method could provide a practical complementary option towards personalized therapeutic strategies to improve motor function. [ABSTRACT FROM AUTHOR]

As humans, we experience social stress in countless everyday-life situations. Giving a speech in front of an audience, passing a job interview, and similar experiences all lead us to go through stress states that impact both our psychological and physiological states. Therefore, studying the link between stress and physiological responses had become a critical societal issue, and recently, research in this field has grown in popularity. However, publicly available datasets have limitations. In this article, we propose a new dataset, UBFC-Phys, collected with and without contact from participants living social stress situations. A wristband was used to measure contact blood volume pulse (BVP) and electrodermal activity (EDA) signals. Video recordings allowed to compute remote pulse signals, using remote photoplethysmography (RPPG), and facial expression features. Pulse rate variability (PRV) was extracted from BVP and RPPG signals. Our dataset permits to evaluate the possibility of using video-based physiological measures compared to more conventional contact-based modalities. The goal of this article is to present both the dataset, which we make publicly available, and experimental results of contact and non-contact data comparison, as well as stress recognition. We obtained a stress state recognition accuracy of 85.48 percent, achieved by remote PRV features. [ABSTRACT FROM AUTHOR]

Changes in emotions affect our physiological responses, and perhaps vice versa. We investigate a new game interaction system that uses false heart rate (fHR) feedback to improve the player experience (PX). The fHR feedback presents false HR information to players so that they perceive changes in the presented HR as being a result of alteration in PX. We introduced auditory fHR feedback into game interaction and investigated its effects through an experiment. Participants repeated gameplay of an action game while hearing heartbeat-like sounds and answered questionnaires regarding PX. Some participants heard the heartbeat-like sounds synchronized with their actual HR, whereas others heard the heartbeat-like sounds whose tempo became gradually faster or slower than their actual HR. The results indicated that an accelerating fHR feedback pattern with +5 bpm/min was appropriate for improving PX; participants were able to maintain their motivation to continue the game. The experiment also indicated that it is necessary for participants to perceive the presented heartbeat-like sounds as reflecting their actual HR. Participants did not maintain their motivation when they were told that the presented sounds were not correlated with their actual HR. The present work provides new principles for video game interaction design based on physiological measurements. [ABSTRACT FROM AUTHOR]

We trained a model to distinguish an extreme high arousal, unpleasant drink from regular drinks based on a range of implicit behavioral and physiological responses to naturalistic tasting. The trained model predicted arousal ratings of regular drinks, highlighting the possibility to estimate affective experience without having to rely on subjective ratings. [ABSTRACT FROM AUTHOR]

As password distribution lays the foundation for various password research, accurately characterizing it receives considerable attention. At IEEE TIFS’17, Wang et al. proposed the CDF-Zipf distribution model with the golden-section-search (GSS) fitting method to find the optimal parameters. Their model has been adopted by over 120 password-related studies. In this paper, we address their remaining, fundamental goodness-of-fit issue of password distribution in a principled approach. First, we prove that the confidence level of the state-of-the-art Monte Carlo approach (MCA, for the goodness-of-fit test) converges asymptotically to 0. By experimenting on 228.92 million real-world passwords, we confirm Wang et al.’s conjecture on the effect of sample size that minor deviations would lead to statistical significance for large-scale datasets. We propose both absolute and relative deviation metrics, and find that 1% random deviations in both metrics suffice to reject CDF-Zipf. Second, we attempt to reduce the non-negligible gap between the empirical and fitted distributions (with the maximum deviation of cumulative distribution function (CDF) being 1.91% on average). We explore eight alternative distribution models in two coordinate systems, and find that three models are more accurate than CDF-Zipf, but none can pass MCA. Particularly, we reveal that stretched-exponential, a variant of CDF-Zipf, can on average reduce the maximum CDF deviation from 1.91% to 1.25%. Third, to replace MCA, we introduce a new goodness-of-fit measure based on log-likelihoods. We find that stretched-exponential constantly has a larger log-likelihood than its counterparts. In all, stretched-exponential fits passwords better and further supports Zipf’s law in passwords. [ABSTRACT FROM AUTHOR]

Cyber challenges faced by organizations today involve malicious inside actors, often labeled insider threats (ITs). These present a difficult challenge in that the most well-designed cybersecurity apparatus is vulnerable to those inside the organization who have privileged access to information systems. Innovative methods must be developed to help security analysts narrow the large pool of potential ITs in large organizations to a more manageable number. The purpose of this article is to develop and validate eye-tracking metrics that are diagnostic of IT behavior. Key stimuli, or called active indicator probes, were embedded into a simulated workflow to elicit diagnostic eye-tracking responses. Two environments were simulated: financial and intelligence analysis. We evaluated participants performing as regular workers relative to ITs to identify metrics that distinguished between the two groups. Detection of illicit eye gaze behavior while using chat programs was possible when conversations with accomplices occurred in a separate chat window from normal permissible chat conversations. Validation of results in real work environments is necessary for practical application. However, if the approach proves to translate successfully, automated monitoring of eye-tracking responses may augment existing insider detection methods, within frameworks for best practices in organizational security and cyber defense. [ABSTRACT FROM AUTHOR]

Spatial co-location pattern mining (SCPM) aims to discover subsets of spatial features frequently located together in proximate areas. Previous studies of SCPM solely concern the inter-features association of a pattern, but neglect the interesting intra-feature behavior. In this paper, we propose the task of spatial co-location pattern mining with coupling relation consideration (SCPM-CR) to capture complex relations embedded in a co-location. Specifically, InterPCI measure is designed to capture the inter-features coupling by considering the comprehensive interaction of objects for the features in a pattern, and luckily it possesses the anti-monotone property. Another measure, IntraCAI, is proposed to capture the congregating behavior of intra-feature objects under the restriction of a co-location. A general framework is designed for SCPM-CR task and experiments show that a large fraction of computation time is devoted to identifying the participating objects of a candidate pattern. To tackle this calculation bottleneck, a novel candidate-and-search algorithm is suggested, CS-HBS, equipped with heuristic backtracking search. Extensive experiments are conducted on real and synthetic datasets to demonstrate the superiority of SCPM-CR compared with traditional SCPM methods, and also to validate the efficiency and scalability of CS-HBS. Experimental results show that CS-HBS outperforms the baselines by several times or even orders of magnitude. [ABSTRACT FROM AUTHOR]

This research investigates how experiencing virtual embodiment in a wheelchair affects implicit bias towards people who use wheelchairs. We also investigate how receiving information from a virtual instructor who uses a wheelchair affects implicit bias towards people who use wheelchairs. Implicit biases are actions or judgments of people towards various concepts or stereotypes (e.g., races). We hypothesized that experiencing a Disability Simulation (DS) through an avatar in a wheelchair and receiving information from an instructor with a disability will have a significant effect on participants’ ability to recall disability-related information and will reduce implicit biases towards people who use wheelchairs. To investigate this hypothesis, a 2x2 between-subjects user study was conducted where participants experienced an immersive VR DS that presents information about Multiple Sclerosis (MS) with factors of instructor (i.e., instructor with a disability versus instructor without a disability) and locomotion interface (i.e., without a disability – locomotion through in-place-walking, with a disability – locomotion in a wheelchair). Participants took a disability-focused Implicit Association Test two times, once before and once after experiencing the DS. They also took a test of knowledge retention about MS. The primary result is: experiencing the DS through locomotion in a wheelchair was better for both the disability-related information recall task and reducing implicit bias towards people who use wheelchairs. [ABSTRACT FROM AUTHOR]

• TD-GC with dual detection FID/MS method developed for 60 VOCs quantification in air. • Instrument performance were calculated, showing complementarity between detectors. • Algorithm developed to select the most accurate concentration between FID and MS. • Automated data processing validated during indoor and outdoor measurement campaigns. Because of their major role in indoor and outdoor air pollution, even at trace levels, VOCs are of great interest, and their monitoring requires sensitive analytical instruments. Several techniques are commonly used, such as portable sensors, Proton Transfer Reaction Mass Spectrometry (PTR-MS) and Thermal Desorption Gas Chromatography (TD-GC). The latter is widely used off- and on-line with Flame Ionization Detectors (FID) or Mass Spectrometers (MS). Given the large number of molecules detected per chromatogram, the data generated by these monitoring techniques are usually checked and reprocessed manually. This process is extremely time consuming and could result in human error. The challenge is to provide reliable results as quickly as possible. In this study, the performances of an on-line TD-GC system with dual detection FID and MS were tested. The Method Detection Limits (MDL), linearities and accuracies of 60 VOCs (alkanes, aromatics, oxygenated and halogenated) were calculated both for FID and MS detectors. The MDLs and accuracies ranged from 0.006 to 0.618 ppbv and from 77 % to 100 % for FID, and from 0.018 to 0.760 ppbv and from 80 % to 100 % for MS. Both detectors showed good complementarity and allowed the development of two programs to facilitate data analysis. These algorithms were designed to autonomously select optimal results between FID and MS detectors, and were evaluated for outdoor and indoor measurement conditions. Measuring VOCs in field campaigns is challenging, and it is anticipated that these programs could be extended to other types of dual-detector systems or for the comparison of data from different calibrated instruments. [ABSTRACT FROM AUTHOR]

The Meteor-M No. 2-2 meteorological satellite with the microwave radiometer MTVZA-GY on board was launched into a circular sun-synchronous orbit on July 5, 2019. The radiometer conducts a conical scanning at 65-degree incidence angle and receives the Earth's outgoing radiation in the frequency range ν &approx; 6–190 GHz. The swath width is 2500 km on ascending orbits and 1500 km on descending orbits. The parameters of the ocean, land surface, and troposphere are extracted from brightness temperatures TB(ν) measured at imager frequencies of 6.9, 10.65, 18.7, 23.8, 31.5, 36.5, 42, 48, and 91.65 GHz in vertical and horizontal polarizations. Measurements at the sounder frequencies (ten channels in the 52–58 GHz oxygen absorption band and three channels in the strong water vapor resonance line region centered at 183.31 GHz) provide information on air temperature and humidity in the troposphere and stratosphere. The structure and development of dynamic atmospheric phenomena of synoptic scale are imprinted on the global TB(ν) maps at imager frequencies. The TB(ν) time series at the sounder frequencies allowed us to detect and trace the evolution of a rare phenomenon — a sudden stratospheric warming over Antarctica in August–September 2019.

Vector quantization is a viable solution to the problem as to full utilization of randomness provided by statistically dependent channel measurements in secret key extraction from randomly fluctuating channels. By aiming at atmospheric optical wireless channels, new vector quantization schemes based on sample grouping (SG) are proposed by using different ways, specifically, the $K\text {-dimensional}$ tree (KDT) partition and random placement, to separate samples of the dimension-reduced channel coefficient vector into a given number of groups, with each containing equal number of samples. Use of the KDT partition leads to both the basic KDT-partition-based quantization (B-KDTPQ) and improved KDT-partition-based quantization (I-KDTPQ) schemes. On the other hand, utilization of the random placement brings forth the random equisized grouping based quantization (REGQ) scheme. Performance evaluation shows that, due to use of quantization symbol modification, the I-KDTPQ scheme always has lower quantization symbol disagreement rate (QSDR) than the B-KDTPQ scheme; compared with the I-KDTPQ scheme, the REGQ scheme has more flexibility in accommodating itself to various conditions of channel coefficient fluctuations and noise levels. [ABSTRACT FROM AUTHOR]

For the safe operation of the power grid, online monitoring of insulator pollution degree is of great significance to the power system. Considering that the insulator contamination mostly is accumulated in the dry season with little rain, it is very valuable to assess the pollution degree with the small leakage current signal measured at low humidity. To this end, this article analyzed the moisture characteristics of contaminated insulators when the insulator temperature is lower than the ambient temperature and found that the water absorption rate of the contamination layer is proportional to the “apparent humidity” at the interface. Then, an assessment method for insulator pollution degree based on contamination moisture at unsaturated humidity (RH < 100%) was proposed, which has application prospects in arid areas. [ABSTRACT FROM AUTHOR]

Prior research in depth perception and perceptuo-motor calibration have primarily focused on participants completing experiments in single sessions and therefore do not empirically evaluate changes over time. Further, these studies do not typically take into account the amount of experience that the participants have in virtual reality (VR) prior to participation, the role of experience during participation, or calibration that may occur throughout the experiment session. In this contribution, we conducted a novel empirical evaluation of how calibration affects perception-action coordination over time. We recruited novice VR users and they completed eight sessions of a depth perception reaching experiment over the course of 12 weeks. During these experiments, we examined how participants' ability to estimate depth in a virtual environment changed as they gradually gained experience. While previous literature has shown that participants tend to underestimate distances, we found that this underestimation diminished over time as they gained experience in the virtual environment. Our study highlights the need for carrying out VR studies over time and the influence that longitudinal calibration can have on spatial perception in long-term VR experiences. [ABSTRACT FROM AUTHOR]

Prior studies suggest that emotional empathy is one of the components of intercultural sensitivity - the affective dimension under the concept of intercultural communication competence. Based on existing theories and findings, this paper reports a randomised parallel longitudinal study investigating the use of virtual reality (VR) exposure to enhance intercultural sensitivity. A total of 80 participants (36 females and 44 males) joined the study and were included in the data analysis. The participants were randomly assigned to the VR group, the video group, and the control group. Their intercultural sensitivity was measured three times: one week before the exposure ($T_{1}$), right after the exposure ($T_{2}$), and three weeks after the exposure ($T_{3}$). The results suggested that (1) the intercultural sensitivity of the VR group was significantly enhanced in both within-subject comparisons and between-subject comparisons, (2) there were no significant differences in intercultural sensitivity between the VR group and the video group at $T_{2}$, but the VR group retained the enhancement better at $T_{3}$, and (3) the sense of presence and emotional empathy well predicted the change in intercultural sensitivity of the VR group. The results, together with the participants' feedback and comments, provide new insights into the practice of using VR for intercultural sensitivity training and encourage future research on exploring the contributing factors of the results. [ABSTRACT FROM AUTHOR]

Physiological sensing often complements studies of human behavior in virtual reality (VR) to detect users' affective and cognitive states. Some psychological states, such as fear and frustration, can be particularly hard to differentiate from a physiological perspective as they are close in the arousal and valence emotional space. Moreover, it is largely unclear how users' physiological reactions are expressed in response to transient psychological states such as fear, frustration, and insight—especially since these are rich indicators for characterizing users' responses to dynamic systems but are hard to capture in highly interactive settings. We conducted a study ($N=24$) to analyze participants' pulmonary, electrodermal, cardiac, and pupillary responses to moments of fear, frustration, and insight in immersive settings. Participants interacted in five VR environments, throughout which we measured their physiological reactions and analyzed the patterns we observed. We also measured subjective fear and frustration using questionnaires. We found differences between fear and frustration pupillary, respiratory, and electrodermal responses, as well as between the pupillary changes that followed fear in a horror game and those that followed fear in a vertigo experiment. We present the relationships between fear levels, frustration levels, and their physiological responses. To detect these affective events and states, we introduce user-independent binary classification models that achieved an average micro $F_{1}$ score of 71% for detecting fear in a horror game, 75% for fear of vertigo, 76% for frustration, and 75% for insight, showing the promise for detecting these states from passive and objective signals. [ABSTRACT FROM AUTHOR]

We are experiencing an explosion in the amount of sensors measuring our activities and the world around us. These sensors are spread throughout the built environment and can help us perform state estimation and control of related systems, but they are often built and/or maintained by third parties or system users. As a result, by outsourcing system measurement to third parties, the controller must accept their measurements without being able to directly verify the sensors' correct operation. Instead, detection and rejection of measurements from faulty sensors must be done with the raw data only. Towards this goal, we present a method of detecting possibly faulty behavior of sensors. The method does not require that the control designer have any model of faulty sensor behavior. As we discuss, it turns out that the widely-used particle filter state estimation algorithm provides the ingredients necessary for a hypothesis test against all ranges of correct operating behavior, obviating the need for a fault model to compare measurements. We demonstrate the applicability of our method by showing its ability to reject faulty measurements and accuracy in state estimation of a nonlinear vehicle traffic model, without information of generated faulty measurements' characteristics. In our test, we correctly identify nearly 90% of measurements as faulty or non-faulty without having any fault model. This leads to only a 3% increase in state estimation error over a theoretical 100%-accurate fault detector.

Exploration of the Red Planet is essential on the way through both human colonization and establishing a habitat on the planet. Due to the high costs of space missions, the use of distributed sensor networks has been investigated to make in situ explorations affordable. Along with this, the devices with ultralow-power receivers, which are called zero-energy (ZE) devices, can pave the way to further discoveries for the environment of Mars. This article focuses on wireless power transmission to provide the power required by ZE devices on the Martian surface. The main motivation of this study is to investigate whether conventional harvesters and communication units can supply the required power for a long distance. The numerical results show that it is possible to deliver power to ZE devices without utilizing any sophisticated hardware. In addition, the effects of pointing error and dust storms on harvesting performance are investigated. Comprehensive simulation results reveal that harvester selection and design should be done by considering propagation channel and transmitter characteristics. [ABSTRACT FROM AUTHOR]

We extend upper bounds on the quantum independence number and the quantum Shannon capacity of graphs to their counterparts in the commuting operator model. We introduce a von Neumann algebraic generalization of the fractional Haemers bound (over $\mathbb {C}$) and prove that the generalization upper bounds the commuting quantum independence number. We call our bound the tracial Haemers bound, and we prove that it is multiplicative with respect to the strong product. In particular, this makes it an upper bound on the Shannon capacity. The tracial Haemers bound is incomparable with the Lovász theta function, another well-known upper bound on the Shannon capacity. We show that separating the tracial and fractional Haemers bounds would refute Connes’ embedding conjecture. Along the way, we prove that the tracial rank and tracial Haemers bound are elements of the (commuting quantum) asymptotic spectrum of graphs (Zuiddam, Combinatorica, 2019). We also show that the inertia bound (an upper bound on the quantum independence number) upper bounds the commuting quantum independence number. [ABSTRACT FROM AUTHOR]

Infrared thermography is a widely used noncontact temperature measurement method. However, the infrared temperature measurement result is susceptible to interference factors. To overcome the infrared temperature measurement errors caused by the simultaneous influence of multiple interference factors, this article takes the measuring distance and dust as specific interference factors and proposes an intelligent compensation method for the infrared temperature measurement errors caused by the measuring distance and dust. First, the influence of the measuring distance and dust is analyzed from the perspective of the infrared temperature measurement principle. Second, a weighted ensemble stacked denoising autoencoder (WE-SDAE) is proposed and used to construct an intelligent compensation model. Then, the measuring distance, dust transmittance, and measured temperature are used as the input of the compensation model to estimate the errors caused by the measuring distance and dust. Finally, the estimated error is used to compensate for the original infrared temperature measurement results under multifactor interference. Experimental results in two cases show that the proposed method is effective in compensating for the infrared temperature measurement errors caused by the measuring distance and dust, which is beneficial to ensure the accuracy of the infrared thermal imager in complex temperature measurement scenarios. [ABSTRACT FROM AUTHOR]

Functional corticomuscular coupling (FCMC) between the cerebral motor cortex and muscle activity reflects multi-layer and nonlinear interactions in the sensorimotor system. Considering the inherent multiscale characteristics of physiological signals, we proposed multiscale transfer spectral entropy (MSTSE) and introduced the unidirectionally coupled Hénon maps model to verify the effectiveness of MSTSE. We recorded electroencephalogram (EEG) and surface electromyography (sEMG) in steady-state grip tasks of 29 healthy participants and 27 patients. Then, we used MSTSE to analyze the FCMC base on EEG of the bilateral motor areas and the sEMG of the flexor digitorum superficialis (FDS). The results show that MSTSE is superior to transfer spectral entropy (TSE) method in restraining the spurious coupling and detecting the coupling more accurately. The coupling strength was higher in the ${\boldsymbol{\beta}}$ 1, ${\boldsymbol{\beta}}$ 2, and $\ {\boldsymbol{\gamma}}$ 2 bands, among which, it was highest in the ${\boldsymbol{\beta}}$ 1 band, and reached its maximum at the 22–30 scale. On the directional characteristics of FCMC, the coupling strength of EEG→sEMG is superior to the opposite direction in most cases. In addition, the coupling strength of the stroke-affected side was lower than that of healthy controls’ right hand in the ${\boldsymbol{\beta}}$ 1 and ${\boldsymbol{\beta}}$ 2 bands and the stroke-unaffected side in the ${\boldsymbol{\beta}}$ 1 band. The coupling strength of the stroke-affected side was higher than that of the stroke-unaffected side and the right hand of healthy controls in the sEMG→EEG direction of ${\boldsymbol{\gamma}}$ 2 band. This study provides a new perspective and lays a foundation for analyzing FCMC and motor dysfunction. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) has been deployed in a variety of fields including wideband spectrum sensing, active user detection, and antenna arrays. In massive multiple input multiple output (MIMO) arrays, CS has been applied to reduce the number of measurements required to verify the arrays excitation. To date, the literature has concentrated on the various methods of achieving CS and applying them to both linear and 2-D arrays, and aimed at detecting fully failed elements in an array, offering simple pass/fail testing. All follow the general approach of creating the sparsity needed for CS by subtracting the measured far field or near field of the test array from that of a “gold standard” array measured under exactly identical conditions. This article extended this work to the need for rapid but accurate reconstruction of element excitation in a production testing environment for massive MIMO arrays. The aim is to demonstrate that CS can offer accurate reconstruction of array excitation. Particularly, the work addresses the issues of optimal sampling, measurement noise, accuracy of faulty element detection, effects of beam scanning, and physical alignment of the gold standard array with the test array. We have restricted ourselves to considering production standard arrays with failure rates up to around 5% and conclude with a set of proposed modifications to the basic CS process as applied to array excitations that achieve a near 20 dB improvement in the accuracy of the reconstructed array excitation offering mean square errors (MSEs) near to −40 dB, with a sampling strategy of just 1.4% of the Nyquist rate. This is achieved with the number of measurements to array element size ratio of approximately 0.2. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for the data-driven characterization of power system dynamics. The developed method of Extended Subspace Identification (ESI) is suitable for systems with output measurements when all the dynamics states are not observable. It is particularly applicable for power systems dynamic identification using Phasor Measurement Units (PMUs) measurements. As in the case of power systems, it is often expensive or impossible to measure all the internal dynamic states of system components such as generators, controllers and loads. PMU measurements capture voltages, currents, power injection and frequencies, which can be considered as the outputs of system dynamics. The ESI method is suitable for system identification, capturing nonlinear modes, computing participation factor of output measurements in system modes and identifying system parameters such as system inertia. The proposed method is suitable for measurements with a noise similar to realistic system measurements. The developed method addresses some of the known deficiencies of existing data-driven dynamic system characterization methods. The approach is validated for multiple network models and dynamic event scenarios with synthetic PMU measurements. [ABSTRACT FROM AUTHOR]

Accurate calculation of neutron dose in the nuclear radiation field is of great significance to radiation detection, radiation protection, and radiation effect research. The distribution of neutron dose field can be affected by neutron spectrum, air density, and atmospheric humidity. In order to quantitatively investigate the influence of atmospheric humidity on the physical process of neutron transport and the distribution of neutron dose field, the Monte Carlo (MC) simulation model of neutron transport under different humidity conditions is presented in this article, and the parameters of neutron dose at different transport distances under typical humidity conditions for a scenario of an explosion of a nuclear device (ordinary hydrogen bomb) are calculated and analyzed. The quantitative relationship between the neutron dose and atmospheric water content under different mass distances is obtained. Based on this relationship, a fast-parameterized calculation method of neutron dose under different humidity conditions is presented. The relative deviation between the results of fast algorithm and MC simulation is within 8%. The results show that the neutron dose decreases with the increase of atmospheric water content at the same mass distance, and the change of atmospheric humidity can lead to a change of neutron dose up to about 10%–30%. For other types of nuclear devices, in which the source neutrons produced by them are different from that by hydrogen bombs, the influence of atmospheric humidity on the neutron dose field can also be studied by using the method presented here. [ABSTRACT FROM AUTHOR]