PCO and hardware delay calibration for LEO satellite antenna downlinking navigation signals

Augmentation of the Global Navigation Satellite System by low earth orbit (LEO) satellites is a promising approach benefiting from the advantages of LEO satellites. This, however, requires errors and biases in the satellite downlink navigation signals to be calibrated, modeled, or eliminated. This contribution introduces an approach for in-orbit calibration of the phase center offsets (PCOs) and code hardware delays of the LEO downlink navigation signal transmitter/antenna. Using the satellite geometries of Sentinel-3B and Sentinel-6A as examples, the study analyzed the formal precision and bias influences for potential downlink antenna PCOs and hardware delays of LEO satellites under different ground network distributions, and processing periods. It was found that increasing the number of tracking stations and processing periods can improve the formal precision of PCOs and hardware delay. Less than 3.5 mm and 3 cm, respectively, can be achieved with 10 stations and 6 processing days. The bias projections of the real-time LEO satellite orbital and clock errors can reach below 3 mm in such a case. For near-polar LEO satellites, stations in polar areas are essential for strengthening the observation model

Multi-colour light emission based on pixel-array phosphor layer in LEDs

Phosphor layers are of vital importance for the development of advanced phosphor-converted light-emitting diodes (LEDs). However, owing to the fixed ratio of red-green-blue (RGB) phosphors, it has been difficult for the RGB phosphor layers with conventional structure to tune light emission colours. Herein, we have experimentally fabricated nine types of phosphor layers with patterned RGB pixel array, which consist of tuneable RGB ratio in a planar configuration. Moreover, we carried out optical simulation based on Monte-Carlo theory to assist in adjusting the light-emission colours and the corresponding chromaticity coordinates. The simulations were further verified by the experimental results via samples fabricated by the stencil printing technique. In accordance with the nine types of phosphor layers with patterned pixel arrays in various RGB ratios, we have finally obtained corresponding nine light-emission colours for the applications of LED light emission decoration. These designed advanced pixel-array phosphor layers demonstrate great potential for applications in decorated light emission and display devices with significant implications for industrial improvement in these research areas

From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models

Passively collected behavioral health data from ubiquitous sensors holds significant promise to provide mental health professionals insights from patient's daily lives; however, developing analysis tools to use this data in clinical practice requires addressing challenges of generalization across devices and weak or ambiguous correlations between the measured signals and an individual's mental health. To address these challenges, we take a novel approach that leverages large language models (LLMs) to synthesize clinically useful insights from multi-sensor data. We develop chain of thought prompting methods that use LLMs to generate reasoning about how trends in data such as step count and sleep relate to conditions like depression and anxiety. We first demonstrate binary depression classification with LLMs achieving accuracies of 61.1% which exceed the state of the art. While it is not robust for clinical use, this leads us to our key finding: even more impactful and valued than classification is a new human-AI collaboration approach in which clinician experts interactively query these tools and combine their domain expertise and context about the patient with AI generated reasoning to support clinical decision-making. We find models like GPT-4 correctly reference numerical data 75% of the time, and clinician participants express strong interest in using this approach to interpret self-tracking data

MindShift: Leveraging Large Language Models for Mental-States-Based Problematic Smartphone Use Intervention

Problematic smartphone use negatively affects physical and mental health. Despite the wide range of prior research, existing persuasive techniques are not flexible enough to provide dynamic persuasion content based on users' physical contexts and mental states. We first conduct a Wizard-of-Oz study (N=12) and an interview study (N=10) to summarize the mental states behind problematic smartphone use: boredom, stress, and inertia. This informs our design of four persuasion strategies: understanding, comforting, evoking, and scaffolding habits. We leverage large language models (LLMs) to enable the automatic and dynamic generation of effective persuasion content. We develop MindShift, a novel LLM-powered problematic smartphone use intervention technique. MindShift takes users' in-the-moment physical contexts, mental states, app usage behaviors, users' goals & habits as input, and generates high-quality and flexible persuasive content with appropriate persuasion strategies. We conduct a 5-week field experiment (N=25) to compare MindShift with baseline techniques. The results show that MindShift significantly improves intervention acceptance rates by 17.8-22.5% and reduces smartphone use frequency by 12.1-14.4%. Moreover, users have a significant drop in smartphone addiction scale scores and a rise in self-efficacy. Our study sheds light on the potential of leveraging LLMs for context-aware persuasion in other behavior change domains

GLOBEM Dataset: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization

Recent research has demonstrated the capability of behavior signals captured by smartphones and wearables for longitudinal behavior modeling. However, there is a lack of a comprehensive public dataset that serves as an open testbed for fair comparison among algorithms. Moreover, prior studies mainly evaluate algorithms using data from a single population within a short period, without measuring the cross-dataset generalizability of these algorithms. We present the first multi-year passive sensing datasets, containing over 700 user-years and 497 unique users' data collected from mobile and wearable sensors, together with a wide range of well-being metrics. Our datasets can support multiple cross-dataset evaluations of behavior modeling algorithms' generalizability across different users and years. As a starting point, we provide the benchmark results of 18 algorithms on the task of depression detection. Our results indicate that both prior depression detection algorithms and domain generalization techniques show potential but need further research to achieve adequate cross-dataset generalizability. We envision our multi-year datasets can support the ML community in developing generalizable longitudinal behavior modeling algorithms.Comment: Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Trac

Emotional Intensity Modulates the Integration of Bimodal Angry Expressions: ERP Evidence

Integration of information from face and voice plays a central role in social interactions. The present study investigated the modulation of emotional intensity on the integration of facial-vocal emotional cues by recording EEG for participants while they were performing emotion identification task on facial, vocal, and bimodal angry expressions varying in emotional intensity. Behavioral results showed the rates of anger and reaction speed increased as emotional intensity across modalities. Critically, the P2 amplitudes were larger for bimodal expressions than for the sum of facial and vocal expressions for low emotional intensity stimuli, but not for middle and high emotional intensity stimuli. These findings suggested that emotional intensity modulates the integration of facial-vocal angry expressions, following the principle of Inverse Effectiveness (IE) in multimodal sensory integration

Hybrid silver pastes with synergistic effect of multi-scale silver fillers and the application in flexible circuits

Silver circuits prepared by new hybrid pastes exhibit high electrical conductivity and mechanical properties, which are ideal for modern flexible electronics. Herein, the hybrid silver pastes composed of different silver nanoparticle content (0, 10, 20, 30 wt%), micron-sized silver flakes and epoxy-based binders were prepared. The corresponding electrical conductivity and bending resistance of the screen-printed silver circuits were studied. The experimental results demonstrated that the synergistic effect of micro-size silver flakes and nanoparticles can greatly improve the electrical conductivity and bending resistance of flexible circuits. Specifically, the silver circuits with 46 wt% micro-size silver flakes and 20 wt% silver nanoparticles incorporation exhibit a lower electrical resistivity of 8.1 × 10 ^−5 Ω·cm. Moreover, 10 wt% silver nanoparticles can be applied to significantly reduce the resistance change of flexible circuit, indicting a superior bending property. Our designed hybrid silver pastes with excellent performance might enable valuable applications in advanced electronic devices

Use of NTRIP for Optimizing the Decoding Algorithm for Real-Time Data Streams

As a network transmission protocol, Networked Transport of RTCM via Internet Protocol (NTRIP) is widely used in GPS and Global Orbiting Navigational Satellite System (GLONASS) Augmentation systems, such as Continuous Operational Reference System (CORS), Wide Area Augmentation System (WAAS) and Satellite Based Augmentation Systems (SBAS). With the deployment of BeiDou Navigation Satellite system(BDS) to serve the Asia-Pacific region, there are increasing needs for ground monitoring of the BeiDou Navigation Satellite system and the development of the high-precision real-time BeiDou products. This paper aims to optimize the decoding algorithm of NTRIP Client data streams and the user authentication strategies of the NTRIP Caster based on NTRIP. The proposed method greatly enhances the handling efficiency and significantly reduces the data transmission delay compared with the Federal Agency for Cartography and Geodesy (BKG) NTRIP. Meanwhile, a transcoding method is proposed to facilitate the data transformation from the BINary EXchange (BINEX) format to the RTCM format. The transformation scheme thus solves the problem of handing real-time data streams from Trimble receivers in the BeiDou Navigation Satellite System indigenously developed by China

Study on the Law of Influence of Seepage Field Anomalies on Displacement Field Induced by Leakage of Enclosure Structure

The leakage of an enclosure structure will cause abnormal changes in the seepage flow field, which in turn can lead to the deformation of the enclosure structure and affect the surrounding geotechnical body. In this paper, a fiber-optic temperature measurement system is used to detect the location of the seepage points in a station of the Qingdao subway during open pit excavation, and the abnormal variation of the seepage field caused by the seepage points is obtained by numerical calculation and field measurement. Then, numerical simulation is performed to analyze the effects of seepage field anomalies on the deformation of the enclosure structure and surface settlement. It is found that the seepage flow caused by the leakage point has a significant influence on the surface settlement and the deformation of the enclosure structure. With the increase of excavation depth, the deformation of the enclosure structure increases and the maximum deformation position shifts downward. The deformation of the enclosure structure decreases when the leakage point exists. The surface volume also increases gradually with the excavation, and the maximum surface settlement position shifts outward significantly. The settlement range becomes larger when the leakage point exists