Long-distance relationships (LDRs) struggle to sustain intimacy without physical touch. Existing mediated social touch systems rely on designer-authored haptic patterns, which limit opportunities for personalization and shared meaning-making. We present Onni, a haptic interface that lets couples collaboratively define and experience a shared library of haptic interactions. In Study 1, we conducted co-creation workshops (n = 20) to examine how couples negotiate and align meanings in haptic interactions. In Study 2, we deployed Onni in everyday routines (n = 6) to explore how these interactions are adopted, adapted, and ritualized. Our findings illustrate that couples co-create and personalize haptic interactions through continuous exploration, negotiation, and situational adaptation. By integrating a dyadic co-design approach, an end-user authoring interface for a shared action–feedback haptic repertoire, and a longitudinal view of how meanings evolve in everyday LDR routines, this work advances the understanding of haptic meaning-making as a collaboratively constructed and ritualized process. It offers concrete design implications for building personalized, evolving haptic systems that support intimacy in LDRs.

Publisher Copyright: © 2026 Copyright held by the owner/author(s)

The multiepoch interferometry synthetic aperture radar (InSAR) technique is a widely applied geodetic tool for measuring surface displacement. Yet, traditional interpretations of InSAR results have primarily centered on linear displacement velocities, often neglecting the rich insights that InSAR displacement time-series data can offer. This study innovatively addresses this gap by proposing a deep learning (DL)-based method for interpreting InSAR deformation time series in urban environments. We first introduce six canonical deformation patterns: stable, linear, stepwise, piecewise linear, power-law, and undefined. A novel postprocessing approach integrates DL models—bidirectional long short-term memory (BiLSTM), temporal convolutional network (TCN), and Transformer—with transfer learning techniques. The strategy involves pretraining models on simulated data and fine-tuning with real-world data, significantly reducing dependence on extensive labeled datasets. This study demonstrates the effectiveness of these DL models in processing InSAR deformation sequences, illustrating how transfer learning can tackle the challenge of limited labeled InSAR datasets. The experimental results reveal that the TCN model achieves the best performance, with an accuracy of 91%. Tested on InSAR data from Kunming City, the proposed approach effectively classified deformation sequences into predefined categories. The findings demonstrate that time-series analysis reveals more detailed deformation insights—particularly in regions with low deformation rates—than traditional velocity-based methods. Furthermore, incorporating transfer learning significantly reduces the dependency on extensive real-world datasets, enhancing overall model performance and facilitating future advancements in the field.

Persistent scatterers (PSs) are coherent measurement points obtained from time series of satellite radar images, which are used to detect and estimate millimeter-scale displacements of the terrain or man-made structures. However, associating these measurement points with specific physical objects is not straightforward, which hampers the exploitation of the full potential of the data. We have investigated the potential for predicting the occurrence and location of PSs using generic 3-D city models and ray-tracing methods, and proposed a methodology to match PSs to the pointlike scatterers predicted using RaySAR, a ray-tracing synthetic aperture radar simulator. We also investigate the impact of the level of detail (LOD) of the city models. For our test area in Rotterdam, we find that 10% and 37% of the PSs detected in a stack of TerraSAR-X data can be matched with point scatterers identified by ray tracing using LOD1 and LOD2 models, respectively. In the LOD1 case, most matched scatterers are at street level while LOD2 allows the identification of many scatterers on the buildings. Over half of the identified scatterers easily correspond to identify double or triple-bounce scatterers. However, a significant fraction corresponds to higher bounce levels, with approximately 25% being fivefold-bounce scatterers.

High-speed railways play important roles in connecting populated cities. Strict attention to ground subsidence is applied to ensure its transportation safety. Shanghai-Hangzhou high-speed railway is an important part of China's “Four Vertical and Four Horizontal” Passenger Line Network. In this study, TerraSAR-X and ASAR images were employed to recover the surface evolution along the high-speed railway using advanced InSAR technique. A spatial-temporal criterion is updated for PS identification. Nonlinear deformation is modeled and removed by a linear least squares fit. Measurements from TSX and ASAR during a closer period as well as the transverse subsidence profiles showing a fairly consistent agreement. According to the longitudinal subsidence profiles, although the subsidence gradient now is within the guidelines of high-speed railway, the increasing cumulative subsidence can still be a potential threat for the line.