Explore the vast range of titles available.

One important feature of the Greenland Ice Sheet (GrIS) change is its strong seasonal fluctuation. Taking advantage of deployed seismographic stations in Greenland, we apply cross‐component auto‐correlation of seismic ambient noise to measure in‐situ near surface relative velocity change (dv/v) in different regions of Greenland. Our results demonstrate that dv/v measurements for most stations have less than 3 months lag times in comparison to the surface mass change. These various lag times may provide us constraints for the thickness of the subglacial till layer over different regions in Greenland. Moreover, in southwest Greenland, we observe a change in the long‐term trend of dv/v for three stations, which might be consistent with the mass change rate (dM/dt) due to the “2012–2013 warm‐cold transition.” These observations suggest that seismic noise auto‐correlation technique may be used to monitor both seasonal and long‐term changes of the GrIS. Plain Language Summary The changes of the Greenland Ice Sheet (GrIS) have important implications for both scientific research and human society. Due to its large size, the change of the GrIS varies at different regions. Here, we apply a seismic monitoring technique to study the GrIS mass change by using seismographic stations deployed in Greenland. The advantage of using this technique is that we are able to monitor different locations in a relatively simple, low‐cost and in‐situ way, and obtain indicative information about the ice mass change over time. We specify the seasonal fluctuations of seismic signals and connect them with the subglacial setting at different regions in Greenland, and explore the potential of using these seismic techniques to monitor the long‐term changes of the GrIS. Key Points The seasonal variations of relative velocity change (dv/v) have <3 months lag with respect to the surface Greenland Ice Sheet (GrIS) change Larger lag times of dv/v may provide constrains for thicker subglacial till layers in the central Greenland dv/v may be used for monitoring long‐term GrIS mass change

Accurate 1‐day global total electron content (TEC) forecasting is essential for ionospheric monitoring and satellite communications. However, it faces challenges due to limited data and difficulty in modeling long‐term dependencies. This study develops a highly accurate model for 1‐day global TEC forecasting. We utilized generative TEC data augmentation based on the International Global Navigation Satellite Service (IGS) data set from 1998 to 2017 to enhance the model's prediction ability. Our model takes the TEC sequence of the previous 2 days as input and predicts the global TEC value for each hourly step of the next day. We compared the performance of our model with 1‐day predicted ionospheric products provided by both the Center for Orbit Determination in Europe (C1PG) and Beihang University (B1PG). We proposed a two‐step framework: (a) a time series generative model to produce realistic synthetic TEC data for training, and (b) an auto‐correlation‐based transformer model designed to capture long‐range dependencies in the TEC sequence. Experiments demonstrate that our model significantly improves 1‐day forecast accuracy over prior approaches. On the 2018 benchmark data set, the global root mean squared error (RMSE) of our model is reduced to 1.17 TEC units (TECU), while the RMSE of the C1PG model is 2.07 TECU. Reliability is higher in middle and high latitudes but lower in low latitudes (RMSE < 2.5 TECU), indicating room for improvement. This study highlights the potential of using data augmentation and auto‐correlation‐based transformer models trained on synthetic data to achieve high‐quality 1‐day global TEC forecasting.

To estimate the phase velocity using the array observations of microtremors, some algorithms for the estimation include constraints on the array shape, such as equilateral triangles or the placement of receivers on a circle, in order to reduce the estimation error of the phase velocity. In the present study, a direct estimation technique is introduced for the phase velocity using records obtained through an array with an arbitrary shape based on a complex coherency function (CCF), where CCF is defined as the normalized cross spectrum of the microtremor records observed simultaneously by two receivers. The particle swarm optimization (PSO) method, one of metaheuristic optimization methods, is applied and optimal values are provided for the phase velocity and other unknown parameters. Approximate representations of the stochastic properties for the unknown variables are analytically derived based on the discrete representation of the CCF, for a case where the arrival directions of microtremors are treated as random variables following a uniform distribution. Furthermore, the validity of the proposed method is confirmed using numerical simulations and actual observation records.

BACKGROUND AND OBJECTIVES: Climate variability and changing weather patterns pose increasing challenges to environmental sustainability and resource management. Understanding long-term rainfall trends at the semi-arid regional scale is crucial for developing effective adaptation strategies. This study evaluates precipitation records from 1980 to 2018 to address the limited analysis of rainfall variability in the districts of Madurai and Theni. It identifies temporal trends at seasonal, monthly, and annual scales to improve forecasting and support adaptive climate planning. Data from 21 strategically placed rain gauge stations were analyzed using statistical methods, including the Mann-Kendall test and Spearmans Rank Correlation, to detect shifts in rainfall behaviour and produce actionable insights for local decision-making and climate resilience. METHODS: Daily rainfall data from Madurai and Theni districts, located between 9°32\" and 10°18 north latitude and 77°00\" and 78°30\" east longitude for Madurai, and between 9°53 and 10°22 north latitude and 77°17\" and 77°45 east longitude for Theni, were analyzed over thirty-nine years from 1980 to 2018. Following extensive quality checks, data from twenty-one sites were analyzed for homogeneity and serial correlation. The Cumulative Sum Control Chart and the Modified Mann-Kendall Test, which were supported by the AnClim and trendchange software packages, were used to identify rainfall trends and shifts. FINDINGS: Rainfall analysis from 1980 to 2018 in Madurai and Theni found 17 of 357 series with serial correlation at 90 percent confidence, notably at Tirumangalam, Usilampatti, Gudalore, and Periyakulam. Annual rainfall increased by 8.56 millimeters per year at Veerapandi, 7.21 millimeters per year at Tiruppuvanam, 5.43 millimeters per year at Edayapatti, 4.87 millimeters per year at Andipatti, 4.52 millimeters per year at Bodi, and 4.11 millimeters per year at Uthamapalayam, but declined by 6.74 millimeters per year at Usilampatti. The corrected Mann-Kendall test showed the strongest trend at Veerapandi (9.02), with weaker trends at Tirumangalam and Usilampatti. Although there were some localized changes, no noteworthy turning points were discovered. CONCLUSION: This investigation revealed significant seasonal rainfall variability and trend shifts across Madurai and Theni districts. Serial correlation and season-specific trends were identified, with distinct change-points highlighting climatic sensitivity. The study addresses gaps in previous research by providing localized, season-specific insights through integrated trend and change-point analysis. Policy recommendations include promoting drought-resilient crops, enhancing rainwater harvesting, modernizing irrigation. Additionally, incorporating localized rainfall trends into district-level agricultural and water management planning can support adaptive responses to evolving rainfall patterns in southern Tamil Nadu.

Generally, GNSS‐based indoor navigation systems use repeaters or pseudolites. However, these methods are vulnerable to multipath errors and require additional information, including the repeater or pseudolite position. In this study, we propose a novel one‐way indoor positioning system using GNSS signal transmitters. Our system uses paired transmitters, each of which broadcasts the same set of satellite signals. The autocorrelation functions of the combined signals are analyzed as the overlap of each individual autocorrelation function. The estimated position can be determined along the track between the transmitters. The multipath error is absorbed by the clock bias and does not cause position bias error. Furthermore, the proposed system can be applied in current commercial GNSS receivers directly. A theoretical analysis of the pseudorange, user position, multipath error, and signal power is included and supported by simulation results. A field test was conducted to confirm the feasibility of the proposed system.

We investigated temporal changes in seismic velocity associated with active seismic swarms and crustal deformations that occurred in 2006 and 2009 in the eastern Izu peninsula of Japan. To detect changes in seismic velocity during and after these swarm episodes, we used a passive image interferometry technique, by estimating the phase shift of auto‐correlation functions of continuous seismic waveforms in a frequency band of 1–3 Hz. During the 2006 and 2009 swarm episodes, a significant velocity decrease (>0.3%) was detected at the station nearest the swarm area. This velocity decrease occurred when the volumetric strain change at the surface due to magma intrusion was larger than 10−6. We could not, however, find a clear correlation between the velocity decrease and the peak ground acceleration caused by moderate earthquakes (M ∼ 5) that occurred during the swarms. We also found that during the interseismic period the velocity decrease gradually recovered, and appears to be proportional to displacement at the surface measured by GPS stations. These results suggest that the change in the subsurface velocity structure in the eastern Izu peninsula strongly reflects strain changes caused by magma intrusion into the crust. Key Points Two times seismic velocity reductions during seismic swarms The volumetric strain change larger than 10^‐6 and the velocity reduction Velocity recoveries associated with surface displacement

We develop a quantitative methodology to characterize vulnerability among 132 US urban centres (‘cities’) to terrorist events, applying a place-based vulnerability index to a database of terrorist incidents and related human casualties. A centred autologistic regression model is employed to relate urban vulnerability to terrorist outcomes and also to adjust for auto-correlation in the geospatial data. Risk analytic ‘benchmark’ techniques are then incorporated in the modelling framework, wherein levels of high and low urban vulnerability to terrorism are identified. This new translational adaptation of the risk benchmark approach, including its ability to account for geospatial auto-correlation, is seen to operate quite flexibly in this sociogeographic setting.

The social network literature on network dependences has largely ignored other sources of dependence, such as the school that a student attends, or the area in which an individual lives. The multilevel modelling literature on school and area dependences has, in turn, largely ignored social networks. To bridge this divide, a multiple-membership multipleclassification modelling approach for jointly investigating social network and group dependences is presented. This allows social network and group dependences on individual responses to be investigated and compared. The approach is used to analyse a subsample of the Adolescent Health Study data set from the USA, where the response variable of interest is individual level educational attainment, and the three individual level covariates are sex, ethnic group and age. Individual, network, school and area dependences are accounted for in the analysis. The network dependences can be accounted for by including the network as a classification in the model, using various network configurations, such as ego-nets and cliques. The results suggest that ignoring the network affects the estimates of variation for the classifications that are included in the random part of the model (school, area and individual), as well as having some influence on the point estimates and standard errors of the estimates of regression coefficients for covariates in the fixed part of the model. From a substantive perspective, this approach provides a flexible and practical way of investigating variation in an individual level response due to social network dependences, and estimating the share of variation of an individual response for network, school and area classifications.

This paper presents a new framework for human action recognition from depth sequences. An effective depth feature representation is developed based on the fusion of 2D and 3D auto-correlation of gradients features. Specifically, depth motion maps (DMMs) are first employed to transform a depth sequence into three images capturing shape and motion cues. A feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is introduced to extract features from DMMs. Space-time auto-correlation of gradients features are also extracted from depth sequences as complementary features to cope with the temporal information loss in the DMMs generation. Each set of features is used as input to two extreme learning machine classifiers to generate probability outputs. A weighted fusion strategy is proposed to assign different weights to the classifier probability outputs associated with different features, thereby providing more flexibility in the final decision making. The proposed method is evaluated on two depth action datasets (MSR Action 3D and MSR Gesture 3D) and obtains the state-of-the-art recognition performance (94.87 % for the MSR Action 3D and 98.50 % for the MSR Gesture 3D).

The estimation of time-varying instantaneous frequency (IF) for monocomponent signals with an incomplete set of samples is considered. A suitable time–frequency distribution (TFD) reduces the non-stationary signal into a local sinusoid over the lag variable prior to the Fourier transform. Accordingly, the observed spectral content becomes sparse and suitable for compressive sensing reconstruction in the case of missing samples. Although the local bilinear or higher order auto-correlation functions will increase the number of the missing samples, the analysis shows that an accurate IF estimation can be achieved even if we deal with only few samples, as long as the auto-correlation function is properly chosen to coincide with the signals phase non-linearity. In addition, by employing the sparse signal reconstruction algorithms, ideal time–frequency representations are obtained. The presented theory is illustrated on several examples dealing with different auto-correlation functions and corresponding TFDs.