Large Trajectory Research Articles

Low-Thrust Fuel-Optimal Guidance with Automatic Control Sequence Detection and Separation

This paper develops a novel fuel-optimal guidance scheme using differential algebraic techniques that is capable of handling changes in the optimal thrust sequence, in addition to a large domain of uncertainty. Taylor polynomials of the solution flow exhibit large approximation errors when the control sequence changes within the domain of operation. This property is leveraged through automatic domain splitting, a technique that tracks the approximation error and splits the domain into smaller subdomains when high errors are observed. After multiple splits, the domain is autonomously separated along the boundaries between different optimal control sequences. Crucially, each resulting subdomain encompasses only a single control sequence, ensuring that any guidance updates obtained from the final polynomial map provide the corresponding optimal control sequence. To ensure high accuracy of the guidance updates, this work also develops an efficient and iterative-less differential-algebra-based refinement algorithm, which is applied to the outputs of the polynomial map. The overall guidance scheme provides highly accurate guidance updates in response to large trajectory deviations, including new optimal control sequences, as demonstrated in an Earth–Psyche transfer.

Determination of Hydrogen Bonds in GROMACS: A New Implementation to Overcome Memory Limitation.

This work introduces a new faster implementation of the hydrogen bond network (complex arrangement of hydrogen bonds between or within molecules) search algorithm in biomacromolecules and their environment. Existing implementation of such an algorithm in GROMACS [Abraham et al. GROMACS 2024.2 Manual. 2024.] has limitations in the analysis of large structures and trajectories. The new implementation, in the form of a native GROMACS trajectory analysis module, allows for quick analysis of molecular dynamics trajectories without restrictions, thus overcoming the limitations of the original algorithm. The application of the developed method enabled the acquisition and analysis of hydrogen bond networks in the studied defensin-like protein Pentadiplandra brazzeana, as well as the study of hydrogen bond occupancies between the protein's residues and water molecules. The data obtained using the new implementation coincided with the experimental data.

A Novel Head-following Algorithm for Multi-Joint Articulated Driven Continuum Robots

Abstract Head-following (tracking) issue is a challenge in developing multi-joint continuum robots. However various approaches have been developed in head-following algorithm for articulated-driven mechanism (ADM) continuum robots, problems still exist such as low end-accuracy, large trajectory deviation, and low computational efficiency. This paper presents a novel head-following algorithm(NHF) with high precision, small trajectory deviation, and high computational efficiency for multi-joint ADM continuum robots. The proposed algorithm first uses the follow-the-leader (FTL) method to search for planning points. Secondly, the end-effector errors are calculated, split, and adjusted. Thirdly, the error judgment set is assigned based on the error rate of the end-effector, and also the joints that need to be adjusted are determined. Finally, the joint angles are iteratively adjusted. In this paper, the NHF algorithm is simulated on ADM continuum robots with saparately 10, 20 and 31 joints. The result shows that, compareing with other FTL algorithms, NHF algorithm has the highest end accuracy, and the smallest trajectory deviation.

Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition

Vibrational spectroscopy allows us to understand complex physical and chemical interactions of molecular crystals and liquids such as ammonia, which has recently emerged as a strong hydrogen fuel candidate to support a sustainable society. We report inelastic neutron scattering measurement of vibrational properties of ammonia along the solid-to-liquid phase transition with high enough resolution for direct comparisons to ab-initio simulations. Theoretical analysis reveals the essential role of nuclear quantum effects (NQEs) for correctly describing the intermolecular spectrum as well as high energy intramolecular N-H stretching modes. This is achieved by training neural network models using ab-initio path-integral molecular dynamics (PIMD) simulations, thereby encompassing large spatiotemporal trajectories required to resolve low energy dynamics while retaining NQEs. Our results not only establish the role of NQEs in ammonia but also provide general computational frameworks to study complex molecular systems with NQEs.

Trajectory Deviation Estimation Method for UAV-Borne Through-Wall Radar

Mini–unmanned aerial vehicles (mini-UAVs) are emerging as a promising platform for through-wall radar to sense the enclosed space in cities, especially high-rise buildings, due to their excellent maneuverability. However, due to unavoidable environmental interference such as airflow, mini-UAVs are prone to trajectory deviation thus degrading their sensing accuracy. Most of the existing approaches model the impact of trajectory deviation into a polynomial phase error on the received signal, which cannot fit the space-variant motion error well. Moreover, the large trajectory deviations of UAVs introduce the unavoidable envelope error. This article proposes an autofocusing algorithm based on the back projection (BP) image, which directly estimates the trajectory deviations between the actual and measured track. Thus, the problem of the 2D space variability of the motion error can be circumvented. The proposed method mainly consists of two steps. First, we estimate the trajectory deviation in the line-of-sight (LOS) direction by exploring the underlying linear property of the wall embedded in the BP imaging result. Then, the estimated trajectory deviation in the LOS direction is compensated for to obtain an updated BP image, followed by a Particle Swarm Optimization (PSO) approach to estimate the trajectory deviation along the track through focusing targets behind the wall. Simulations and practical experiments show that the proposed algorithm can accurately estimate the serious trajectory deviations larger than the range resolution, improving the sensing robustness of UAV-borne through-wall radar greatly.

BMI Growth Profiles Among Black Children from Immigrant and US-Born Families.

A large body of research has documented racial/ethnic disparities in childhood obesity in the United States (US) but less work has sought to understand differences within racial groups. Longitudinal studies are needed to describe BMI trajectories across development, particularly for Black children from immigrant families who have been underrepresented in childhood obesity research. The current study utilizes BMI data collected longitudinally from ages 5 to 8 years and growth mixture modeling to (1) identify and visualize growth patterns among Black children from primarily Caribbean immigrant families, and (2) to compare these patterns to growth trajectories among Black children from US-born families. First, we identified four classes or trajectories of growth for Black children from immigrant families. The largest trajectory (70% of the sample) maintained non-overweight throughout the study period. A second trajectory developed overweight by age 8 (25%). Two small trajectory groups demonstrated high rates of moderate and severe obesity-i.e., specifically, a trajectory of accelerated weight gain ending in moderate/severe obesity (3%), and a trajectory of early severe obesity with BMI decreasing slightly with age (2%). We identified a very similar four class/trajectory model among Black children from US-born families, and compared the model to the one for children from immigrant families using multi-group growth mixture modeling. We found that the patterns of growth did not differ significantly between the populations, with two notable exceptions. Among Black children from immigrant families, ∼ 5% were classified into the two heavier BMI trajectories, compared to ∼ 11% of children from US-born families. Additionally, among children with an accelerated weight gain trajectory, children from immigrant families had lower BMIs on average at each time point than children from US-born families. These findings describe the multiple trajectories of weight gain among Black children from immigrant families and demonstrate that although these trajectories are largely similar to those of Black children from US-born families, the differences provide some evidence for lower obesity risk among Black children from immigrant families compared to Black children from US-born families. As this study is the first to describe BMI trajectories for Black children from immigrant families across early and middle childhood, future work is needed to replicate these results and to explore differences in heavier weight trajectories between children from immigrant and US-born families.

Retrieving Similar Trajectories from Cellular Data of Multiple Carriers at City Scale

Retrieving similar trajectories aims to search for the trajectories that are close to a query trajectory in spatio-temporal domain from a large trajectory dataset. This is critical for a variety of applications, like transportation planning and mobility analysis. Unlike previous studies that perform similar trajectory retrieval on fine-grained GPS data or single cellular carrier, we investigate the feasibility of finding similar trajectories from cellular data of multiple carriers, which provide more comprehensive coverage of population and space. To handle the issues of spatial bias of cellular data from multiple carriers, coarse spatial granularity, and irregular sparse temporal sampling, we develop a holistic system cellSim . Specifically, to avoid the issue of spatial bias, we first propose a novel map matching approach, which transforms the cell tower sequences from multiple carriers to routes on a unified road map. Then, to address the issue of temporal sparse sampling, we generate multiple routes with different confidences to increase the probability of finding truly similar trajectories. Finally, a new trajectory similarity measure is developed for similar trajectory search by calculating the similarities between the irregularly-sampled trajectories. Extensive experiments on a large-scale cellular dataset from two carriers and real-world 1,701 km query trajectories reveal that cellSim provides state-of-the-art performance for similar trajectory retrieval.

Drilling technology of long horizontal section of shale oil

As an important supplementary energy resource, shale oil has attracted worldwide attention due to its huge reserves and rich comprehensive utilization levels. The world is rich in oil shale resources. According to statistics, if the oil shale of 33 countries and regions in the world is converted into shale oil, it will be about 400 billion tons. With the continuous maturity of horizontal well drilling and completion technology in shale oil gas reservoirs and the application of other advanced technologies in horizontal well drilling, mature shale oil gas drilling and completion matching technologies have gradually formed at home and abroad after years of research and drilling practice. In terms of measurement technology, China has been able to produce single-point, multi-point, wired, wireless inclinometers and MWD measuring instruments while drilling and has mastered their usage methods. In terms of control technology, various specifications and types of fixed-angle and ground-adjustable curved shell screw drilling tools have been produced in China. Large deformation analysis and trajectory prediction methods for lower drilling tool assemblies have been completed. Various lower drilling tool assemblies and their usage processes required for wellbore trajectory control have been improved and mastered. In the construction of inclined and horizontal sections, attention should be paid to the response law of drilling tool combinations to drilling pressure in different formations, especially the response law of composite drilling. Reasonable drilling tool combinations should be selected for the control of wellbore trajectory in stable and horizontal sections, especially in the long stable and horizontal sections.

AttentionTTE: a deep learning model for estimated time of arrival.

Estimating travel time (ETA) for arbitrary paths is crucial in urban intelligent transportation systems. Previous studies primarily focus on constructing complex feature systems for individual road segments or sub-segments, which fail to effectively model the influence of each road segment on others. To address this issue, we propose an end-to-end model, AttentionTTE. It utilizes a self-attention mechanism to capture global spatial correlations and a recurrent neural network to capture temporal dependencies from local spatial correlations. Additionally, a multi-task learning module integrates global spatial correlations and temporal dependencies to estimate the travel time for both the entire path and each local path. We evaluate our model on a large trajectory dataset, and extensive experimental results demonstrate that AttentionTTE achieves state-of-the-art performance compared to other methods.

A Privacy-Preserving Trajectory Publishing Method Based on Multi-Dimensional Sub-Trajectory Similarities.

With the popularity of location services and the widespread use of trajectory data, trajectory privacy protection has become a popular research area. k-anonymity technology is a common method for achieving privacy-preserved trajectory publishing. When constructing virtual trajectories, most existing trajectory k-anonymity methods just consider point similarity, which results in a large dummy trajectory space. Suppose there are n similar point sets, each consisting of m points. The size of the space is then mn. Furthermore, to choose suitable k- 1 dummy trajectories for a given real trajectory, these methods need to evaluate the similarity between each trajectory in the space and the real trajectory, leading to a large performance overhead. To address these challenges, this paper proposes a k-anonymity trajectory privacy protection method based on the similarity of sub-trajectories. This method not only considers the multidimensional similarity of points, but also synthetically considers the area between the historic sub-trajectories and the real sub-trajectories to more fully describe the similarity between sub-trajectories. By quantifying the area enclosed by sub-trajectories, we can more accurately capture the spatial relationship between trajectories. Finally, our approach generates k-1 dummy trajectories that are indistinguishable from real trajectories, effectively achieving k-anonymity for a given trajectory. Furthermore, our proposed method utilizes real historic sub-trajectories to generate dummy trajectories, making them more authentic and providing better privacy protection for real trajectories. In comparison to other frequently employed trajectory privacy protection methods, our method has a better privacy protection effect, higher data quality, and better performance.

A new paradigm for molecular dynamics databases: the COVID-19 database, the legacy of a titanic community effort.

Molecular dynamics (MD) simulations are keeping computers busy around the world, generating a huge amount of data that is typically not open to the scientific community. Pioneering efforts to ensure the safety and reusability of MD data have been based on the use of simple databases providing a limited set of standard analyses on single-short trajectories. Despite their value, these databases do not offer a true solution for the current community of MD users, who want a flexible analysis pipeline and the possibility to address huge non-Markovian ensembles of large systems. Here we present a new paradigm for MD databases, resilient to large systems and long trajectories, and designed to be compatible with modern MD simulations. The data are offered to the community through a web-based graphical user interface (GUI), implemented with state-of-the-art technology, which incorporates system-specific analysis designed by the trajectory providers. A REST API and associated Jupyter Notebooks are integrated into the platform, allowing fully customized meta-analysis by final users. The new technology is illustrated using a collection of trajectories obtained by the community in the context of the effort to fight the COVID-19 pandemic. The server is accessible at https://bioexcel-cv19.bsc.es/#/. It is free and open to all users and there are no login requirements. It is also integrated into the simulations section of the BioExcel-MolSSI COVID-19 Molecular Structure and Therapeutics Hub: https://covid.molssi.org/simulations/ and is part of the MDDB effort (https://mddbr.eu).

PPDF-FedTMI: A Federated Learning-based Transport Mode Inference Model with Privacy-Preserving Data Fusion

Mobile Crowd-Sensing (MCS) represents a distributed data fusion system that aggregates diverse sources to enhance inference capabilities beyond any single input. As an essential application in MCS, intelligent transportation system deploys a large number of Transport Mode Inference (TMI) models. However, robust TMI models necessitates large trajectories datasets, engendering significant privacy risks. Existing solutions fail to adequately preserve trajectory privacy in collaborative MCS. To enable secure distributed modeling while safeguarding user anonymity, a Federated Learning-based Transport Mode Inference Model with Privacy-Preserving Data Fusion (PPDF-FedTMI) is proposed in this paper. Firstly, federated learning is incorporated into the TMI architecture to mitigate isolated data silos in MCS. Subsequently, a fuzzy fusion algorithm leverages fuzzy clustering of labels to facilitate stable and effective training under non-independent and non-identically distributed decentralized data. Furthermore, a local differential privacy-based joint training technique is devised to enhance gradient privacy, preventing exposure of original user data through model updates. Finally, the experiments demonstrate PPDF-FedTMI provides superior inference accuracy and effective privacy-preserving compared to current models.

In situ skid estimation for mobile robots in outdoor environments

AbstractSkidding is a surface hazard for mobile robots' navigation and traction control systems when operating in outdoor environments and uneven terrains due to the wheel–terrain interaction. It could lead to large trajectory tracking errors, losing the robot's controllability, and mission failure occurring. Despite research in this field, the development of a real‐world feasible in situ skid estimation system with the capability of operating in harsh and unforeseen environments using low‐cost/power and ease of integrating sensors is still an open problem in terramechanics. This paper presents a novel velocity‐based definition for skidding that enables a real‐world feasible estimation for the mobile robot's undesired skidding at the vehicle‐level in outdoor environments. The proposed technique estimates the undesired skidding using a combination of two proprioceptive sensors (e.g., inertial measurement unit and wheel encoder) and deep learning. The practicality of a velocity‐based definition and the performance of the proposed undesired skid estimation technique are evaluated experimentally in various outdoor terrains. The results show that the proposed technique performs with less than 11.79 mm/s mean absolute error and estimates the direction of undesired skidding with approximately 98% accuracy.

Comment on "Exact large deviation statistics and trajectory phase transition of a deterministic boundary driven cellular automaton".

Buča etal. [Phys. Rev. E 100, 020103(R) (2019)2470-004510.1103/PhysRevE.100.020103] study the dynamical large deviations of a boundary-driven cellular automaton. They take a double limit in which first time and then space is made infinite, and interpret the resulting large-deviation singularity as evidence of a first-order phase transition and the accompanying coexistence of two distinct dynamical phases. This view is characteristic of an approach to dynamical large deviations in which time is interpreted as if it were a spatial coordinate of a thermodynamic system [Jack, Eur. Phys. J. B 93, 74 (2020)1434-602810.1140/epjb/e2020-100605-3]. Here, I argue that the large-deviation function produced in this double limit is not consistent with the basic features of the model of Buča etal. I show that a modified limiting procedure results in a nonsingular large-deviation function consistent with those features, and that neither supports the idea of coexisting dynamical phases.

Automatic Clustering of Excited-State Trajectories: Application to Photoexcited Dynamics.

We introduce automatic clustering as a computationally efficient tool for classifying and interpreting trajectories from simulations of photo-excited dynamics. Trajectories are treated as time-series data, with the features for clustering selected by variance mapping of normalized data. The L2-norm and dynamic time warping are proposed as suitable similarity measures for calculating the distance matrices, and these are clustered using the unsupervised density-based DBSCAN algorithm. The silhouette coefficient and the number of trajectories classified as noise are used as quality measures for the clustering. The ability of clustering to provide rapid overview of large and complex trajectory data sets, and its utility for extracting chemical and physical insight, is demonstrated on trajectories corresponding to the photochemical ring-opening reaction of 1,3-cyclohexadiene, noting that the clustering can be used to generate reduced dimensionality representations in an unbiased manner.

Reply to "Comment on 'Exact large deviation statistics and trajectory phase transition of a deterministic boundary driven cellular automaton'".

We reply to Whitelam's Comment [Phys. Rev. E 108, 036105 (2023)2470-004510.1103/PhysRevE.108.036105] on our paper [Phys. Rev. E 100, 020103(R) (2019)2470-004510.1103/PhysRevE.100.020103] where we compute the exact large deviation (LD) statistics of a wide class of observables in the rule 54 cellular automaton. Using some heuristic arguments, Whitelam states that despite the fact that the LD functions we compute display singular behavior, this is not indicative of a LD phase transition or of dynamical phase coexistence. Here, we refute this observation and confirm that the (standard) interpretation of our exact results stands.

Structural Dynamics Descriptors for Metal Halide Perovskites.

Metal halide perovskites have shown extraordinary performance in solar energy conversion technologies. They have been classified as "soft semiconductors" due to their flexible corner-sharing octahedral networks and polymorphous nature. Understanding the local and average structures continues to be challenging for both modeling and experiments. Here, we report the quantitative analysis of structural dynamics in time and space from molecular dynamics simulations of perovskite crystals. The compact descriptors provided cover a wide variety of structural properties, including octahedral tilting and distortion, local lattice parameters, molecular orientations, as well as their spatial correlation. To validate our methods, we have trained a machine learning force field (MLFF) for methylammonium lead bromide (CH3NH3PbBr3) using an on-the-fly training approach with Gaussian process regression. The known stable phases are reproduced, and we find an additional symmetry-breaking effect in the cubic and tetragonal phases close to the phase-transition temperature. To test the implementation for large trajectories, we also apply it to 69,120 atom simulations for CsPbI3 based on an MLFF developed using the atomic cluster expansion formalism. The structural dynamics descriptors and Python toolkit are general to perovskites and readily transferable to more complex compositions.

Revisiting Universal Variables for Robust, Analytical Orbit Propagation Under the Vinti Potential

To meet the growing complexity and demands of modern spacecraft missions, analytical solutions to initial value problems see continued use, typically supporting global searches of large trajectory design spaces. These efforts often employ universal two-body orbit propagators for their recognized speed and robustness, but many applications, like active space debris removal, would benefit from a comparable propagator with greater accuracy. Vinti propagators, which consider planetary oblateness, may serve this purpose, but existing Vinti solutions possess computational difficulties in certain orbital regimes. To mitigate these deficiencies, the present study develops and validates an analytical, third-order universal Vinti propagator free of computational difficulties by leveraging standard, oblate spheroidal (OS) universal variables and OS equinoctial orbital elements. Accuracy of the third-order approximation is assessed for multiple examples across an array of orbital regimes. Computational runtime is also evaluated, and performance is directly compared to the benchmark Vinti6 algorithm. On average, the Vinti propagator implemented in this work is only slower than a typical universal Kepler propagator by a factor of 4.0 and slower than Vinti6 by a factor of 1.8, but with greater robustness than the benchmark. The new form of the equations of motion also has favorable implications for the associated boundary value problem.

Efficient Large-Scale GPS Trajectory Compression on Spark: A Pipeline-Based Approach

Every day, hundreds of thousands of vehicles, including buses, taxis, and ride-hailing cars, continuously generate GPS positioning records. Simultaneously, the traffic big data platform of urban transportation systems has already collected a large amount of GPS trajectory datasets. These incremental and historical GPS datasets require more and more storage space, placing unprecedented cost pressure on the big data platform. Therefore, it is imperative to efficiently compress these large-scale GPS trajectory datasets, saving storage cost and subsequent computing cost. However, a set of classical trajectory compression algorithms can only be executed in a single-threaded manner and are limited to running in a single-node environment. Therefore, these trajectory compression algorithms are insufficient to compress this incremental data, which often amounts to hundreds of gigabytes, within an acceptable time frame. This paper utilizes Spark, a popular big data processing engine, to parallelize a set of classical trajectory compression algorithms. These algorithms consist of the DP (Douglas–Peucker), the TD-TR (Top-Down Time-Ratio), the SW (Sliding Window), SQUISH (Spatial Quality Simplification Heuristic), and the V-DP (Velocity-Aware Douglas–Peucker). We systematically evaluate these parallelized algorithms on a very large GPS trajectory dataset, which contains 117.5 GB of data produced by 20,000 taxis. The experimental results show that: (1) It takes only 438 s to compress this dataset in a Spark cluster with 14 nodes; (2) These parallelized algorithms can save an average of 26% on storage cost, and up to 40%. In addition, we design and implement a pipeline-based solution that automatically performs preprocessing and compression for continuous GPS trajectories on the Spark platform.

Profiles of Low-Income Help-Seeking Couples and Implications for Intervention Gains: A Couple-Centered Approach

Using a couple-centered approach, the current study seeks to understand (a) the specific ways in which help-seeking couples vary in how their relationship satisfaction changes over time, (b) whether there are important differences in relationship characteristics at the beginning of the interventions, and (c) whether couples with distinct relationship characteristics benefit equally from effective online relationship programs. Mixed-gender low-income couples (Ncouple = 659) seeking help for their relationship were randomly assigned to one of two online relationship programs (n = 432) or the wait-list control group (n = 227). Latent profile analyses were conducted to identify (a) trajectory profiles with both partners’ relationship satisfaction assessed at baseline, during, and postprogram, and at 2- and 4-month follow-ups; and (b) baseline couple profiles with indicators of baseline communication, commitment, emotional support, and sexual satisfaction reported by both partners. Four unique satisfaction trajectories were identified: women-small–men-medium improvement (39%), men-only decline (25%), large improvement (19%), and women-only improvement (17%). Five unique baseline couple profiles were identified: conflictual passionate (30%), companionate (22%), men-committed languishing (22%), satisfied (16%), and languishing (10%). Compared to control couples, intervention couples’ odds of following the large improvement trajectory increased and their odds of following the men-only decline trajectory decreased; the odds of following the other two intermediate trajectories did not differ by intervention status. Moreover, couples with more distressed baseline profiles were more likely to follow trajectories characterized by greater satisfaction gains regardless of their intervention status. However, program effects did not differ based on baseline couple profiles, suggesting that a universal approach may be sufficient for delivering online relationship programs to improve relationship satisfaction in this population.