Virtual Reality (VR) co-manipulation enables multiple users to collaboratively interact with shared virtual objects. However, existing research treats objects as monolithic entities, overlooking scenarios where users need to manipulate different sub-components simultaneously. This work addresses conflict resolution when users select overlapping vertices (non-disjoint sets) during co-manipulation. We present a comprehensive framework comprising preventive strategies (Object-level and Action-level Restrictions) and reactive strategies (computational conflict resolution). Through two user studies with 76 participants (38 pairs), we evaluated these approaches in collaborative wireframe editing tasks. Study 1 identified Averaging as the optimal computational method, balancing task efficiency with user experience. Study 2 highlighted that Action-level Restriction, which permits overlapping selections but restricts concurrent identical operations, achieved better performance compared to exclusive object locking. Reactive strategies using averaging provided smooth collaboration for experienced users, while second-user priority enabled quick corrections. Our findings indicate that optimal strategy selection depends on task requirements, user expertise, and collaboration patterns. Based on the findings, we provide design implications for developing VR collaboration systems that support flexible sub-components manipulation while maintaining collaborative awareness and minimizing conflicts.

Mixed reality (MR) notification systems currently display all messages in fixed central locations regardless of urgency, leading to unnecessary interruptions and cognitive overload. Drawing from previous MR/Virtual Reality (VR) notification design work and calm technology principles, we developed an adaptive notification system that adjusts spatial placement based on urgency levels: non-urgent notifications appear as peripheral icons accessible via head movement, moderately urgent messages anchor to the user's hand, and very urgent notifications transition progressively from peripheral to central view. Through a within-subjects study (N=18), we evaluated our adaptive system against the default centralised approach. Results demonstrate that the adaptive system significantly reduces mental workload $(p=0.041)$, temporal workload $(p=0.008)$, and frustration $(p=0.004)$ while maintaining comparable notification awareness. Logistic regression analysis reveals that users prefer the adaptive system even with classification errors, provided the combined misclassification rate (disruptiveness + omission errors) remains below a determinable threshold. Our findings establish the first empirical evidence that urgency-based spatial notification distribution effectively addresses core MR usability challenges, offering practical design guidelines for immersive notification systems that balance user attention management with information accessibility.

Mixed Reality (MR) is increasingly integrated into daily life, providing enhanced capabilities across various domains. However, users face growing notification streams that disrupt their immersive experience. We present PersoNo, a personalised notification urgency classifier for MR that intelligently classifies notifications based on individual user preferences. Through a user study ($\mathrm{N}=18$), we created the first MR notification dataset containing both selflabelled and interaction-based data across activities with varying cognitive demands. Our thematic analysis revealed that, unlike in mobiles, the activity context is equally important as the content and the sender in determining notification urgency in MR. Leveraging these insights, we developed PersoNo using large language models that analyse users' replying behaviour patterns. Our multi-agent approach achieved 81.5% accuracy and significantly reduced false negative rates (0.381) compared to baseline models. PersoNo has the potential not only to reduce unnecessary interruptions but also to offer users understanding and control of the system, adhering to Human-Centered Artificial Intelligence design principles.

Satellite microwave remote sensing is acknowledged as the primary method for obtaining global-scale surface soil moisture (SSM) data. Typically, spaceborne microwave sensors aboard polar-orbiting satellites yield SSM estimates with a native spatial resolution of several tens of kilometers and a temporal resolution of about once or twice daily. This indicates considerable potential for enhancing both of their spatial and temporal resolutions. Although spatial downscaling of spaceborne microwave SSM has garnered significant attention recently, the enhancement of their temporal resolution has received less focus. This study pioneers a methodology for generating hourly-scale satellite SSM estimates. The developed approach integrates a novel blending module that combines geostationary satellite observations with a spatially downscaled SSM dataset derived from traditional fusion among microwave and optical observations on polar-orbiting platforms. This blending module leverages land surface temperature (LST) data from geostationary satellites, which effectively quantify SSM variations on an hourly interval upon the thermal inertia theory. Consequently, a comprehensive spatio-temporal integrated framework for SSM downscaling is established to produce hourly-scale SSM at a resolution of 6 km, enhancing upon the daily and 36-km resolutions of existing microwave SSM datasets. Validation of the downscaled hourly SSM estimates was conducted through an established ground soil moisture observatory network in North China, revealing an unbiased root mean square error (ubRMSE) of no higher than 0.04 cm³/cm³. This result confirms preservation of the fundamental accuracy of original microwave SSM retrievals and demonstrates the effectiveness of the developed framework in improving both spatial and temporal representativeness of SSM data.

The impact of the initial spatial resolution of soil moisture maps on the quality of downscaled maps by merging with a higher resolution dataset was addressed. Soil moisture maps acquired with airborne sensors in four different campaigns in different climate regions with resolutions of 500 m to 1 km were aggregated to 4-5 km, 8-10 km, 18-20 km and 36-40 km before applying a downscaling algorithm to compute 1 km maps. These maps were compared to the original maps at 1 km resolution. Using different quality metrics, it is shown that the downscaled maps are 30%-75% more accurate when the initial resolution is in the range of 5-10 km with respect to initial resolutions of 36-40 km.

Global surface soil moisture (SSM) mapping at a 9-km intermediate resolution from microwave remote sensing could play a pivotal role in advancing detailed global hydrological investigations. Despite the wide recognition of the soil moisture active passive (SMAP) mission’s 9-km SSM products based on oversampling of the L-band radiometry, concerns persist regarding its ability to capture higher SSM heterogeneity at finer resolutions. For addressing this concern, a novel methodological framework was proposed in this study. This framework advocates the downscaling of the SMAP 36-km dataset through a fusion with high-frequency (Ka-band) passive microwave (PMW) observations. The resultant 9-km all-weather SSM product, derived from this novel approach, is evaluated using ground-based measurements worldwide. The findings reveal a significantly enhanced accuracy compared to the SMAP conventional oversampling-based one, especially in areas exhibiting pronounced local variations in SSM patterns. The study therefore represents a substantial step forward, providing new insights into the design and use of a multifrequency satellite radiometer for global SSM mapping.

Integrated hand-tracking on modern virtual reality (VR) headsets can be readily exploited to deliver mid-air virtual input surfaces for text entry. These virtual input surfaces can closely replicate the experience of typing on a Qwerty keyboard on a physical touchscreen, thereby allowing users to leverage their pre-existing typing skills. However, the lack of passive haptic feedback, unconstrained user motion, and potential tracking inaccuracies or observability issues encountered in this interaction setting typically degrades the accuracy of user articulations. We present a comprehensive exploration of error-tolerant probabilistic hand-based input methods to support effective text input on a mid-air virtual Qwerty keyboard. Over three user studies we examine the performance potential of hand-based text input under both gesture and touch typing paradigms. We demonstrate typical entry rates in the range of 20 to 30 wpm and average peak entry rates of 40 to 45 wpm.

The FengYun-3 (FY-3) series satellite is the second generation of Chinese polar-orbiting meteorological satellite missions. The FY-3D satellite was launched on November 2017 and has been providing valuable data for meteorological applications, including brightness temperature ( TB) data from the MicroWave Radiation Imager (MWRI). In this study, we developed a global soil moisture retrieval algorithm, based on the radiative transfer equation (RTE) for using the FY-3D MWRI TBto continue the soil moisture record from FY-3 satellites. We adopted a new empirical model to compute vegetation water content (VWC) based on the leaf area index (LAI) and canopy height ( H) for vegetation effects correction. The Qpmodel, which addresses the soil surface roughness effects using dual-polarization information, is then used for soil moisture retrieval. Validation of the FY-3D soil moisture was conducted with the in-situ data and the validation results showed encouraging accuracy over a variety of landcovers, with bias and unbiased root-mean-squared difference (ubRMSE) at or below the level of 0.06 m3·m-3. Monthly averaged soil moisture products generated from FY-3D could represent the seasonal changes in soil moisture and show reasonable spatial distribution of soil moisture at a global scale.