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In the existing CFD computation program, the non-inertial reference frame method is used to achieve quasi-steady numerical calculations for special motions such as quasi-steady climbing and level flight at constant altitudes. Using the SDM model as an example, the non-inertial frame method is applied to calculate damping dynamic derivatives. The results gained by using the non-inertial frame method for the calculation of dynamic derivatives match well with the data from the wind tunnel test, reference results, and other numerical results. The non-inertial frame method can reduce the computation time and enhance efficiency.

The problem of static seal leakage in a non-inertial reference system is different from that in a traditional inertial coordinate system in several ways. Although the seals and grooves remain relatively stationary, the overall motion of the equipment leads to an increase in the variance of the load fluctuations on the contact surfaces, and thus, leakage is more likely to occur at the sealing interfaces in a non-inertial reference system. This paper reviews and analyses the current related work from four aspects, namely, long-term modulus analysis of materials, analysis of loading spectral values, analysis of surface morphology characteristics, and the basis for leakage judgement. Based on these analyses, a new framework for leakage judgment specific to non-inertial systems is proposed. This framework addresses the increased variance in load distribution caused by non-inertial conditions, offering a targeted solution to the problem of seal leakage. Ultimately, the findings in this paper serve to enhance our understanding of the sealing behavior in dynamic systems, contributing to improved sealing performance and reliability in various applications.

Background Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection, foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture, 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 h in the real-world, using a single wearable device worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the single wearable device. We assessed and validated three algorithms for gait sequence detection, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results We identified two cohort-specific top performing algorithms for gait sequence detection and CAD, and a single best for ICD and SL. Best gait sequence detection algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (proximal femoral fracture). Algorithms’ performances were lower for short walking bouts; slower gait speeds (< 0.5 m/s) resulted in reduced performance of the CAD and SL algorithms. Conclusions Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findings showed that the choice of algorithm for estimation of gait sequence detection and CAD should be cohort-specific (e.g., slow walkers and with gait impairments). Short walking bout length and slow walking speed worsened algorithms’ performances. Trial registration ISRCTN – 12246987.

In a recent paper we discussed when it is possible to define reference frames nonrotating with respect to distant inertial reference objects (extension of the IAU reference systems to exact general relativity), and how to construct them. We briefly review the construction, illustrating it with further examples, and caution against the recent misuse of zero angular momentum observers (ZAMOs).

Refusal to use inertial reference frames in favor of non-inertial reference frames means significant changes in the axiomatics of both classical and quantum physics. Taking into account the influences of random small forces and fields makes the equations of classical physics, expanding, subject not only to deterministic laws, but also expands the equations of classical physics with additional variables that have an indeterministic, probabilistic behavior. Extending the scope of consideration of physical systems to non-inertial frames of reference leads to the already well-known new field of study known as quantum correction of Newton’s laws of motion. In this case, not only stable deterministic trajectories are considered, but also unstable, random ones, which usually constitute the measurement error in experiments in cases of applying classical physics and the probability distribution of quantum mechanics. Such a quantum correction can extend the action of Newton’s laws to the microworld by abandoning the basic idea of classical physics that physical systems should be described by differential equations of no higher than second order in favor of supplementing them with higher derivative variables that describe unstable random trajectories.

Gaia used a large sample of photometrically selected active galactic nuclei (AGNs) and quasars to remove the residual spin of its global proper motion system in order to achieve a maximally inertial reference frame. A small fraction of these reference objects have statistically significant astrometric proper motions in Gaia EDR3. We compile a source sample of 105,593 high-fidelity AGNs with accurate spectroscopically determined redshifts above 0.5 from the SDSS and normalized proper motions below 4. The rate of genuinely perturbed proper motions is at least 0.17%. A smaller high completeness sample of 152 quasars with excess proper motions at a confidence level of 0.9995 is examined in detail. Pan-STARRS images and Gaia-resolved pairs reveal that 29% of the sample are either double sources or gravitationally lensed quasars. An Anderson–Darling test on parameters of a smaller high-reliability sample and their statistical controls reveals 17 significant factors that favor multiplicity and multi-source structure as the main cause of perturbed astrometry. Using a nearest-neighbor distance statistical analysis and counts of close companions in Gaia on a much larger initial sample of AGNs, an excess of closely separated sources in Gaia is detected. At least 0.33% of all optical quasars are genuinely double or multiply imaged. We provide a list of 44 candidate double or multiple AGNs and four previously known gravitational lenses. Many proper motion quasars may be more closely separated, unresolved doubles exhibiting the variability imposed motion effect, and a smaller fraction may be chance alignments with foreground stars causing weak gravitational lensing.

We show that topological order and vibrational edge modes can exist in a classical mechanical system consisting of a two-dimensional honeycomb lattice of masses and springs. The band structure shows the existence of Dirac cones and unconventional edge states that are similar to the vibrational modes in graphene. Interestingly, as the system is placed on a constantly rotational coordinate system, the Coriolis force resulting from the non-inertial reference frame introduces time-reversal symmetry breaking and leads to topologically nontrivial band gaps. The nontrivial topological orders are further verified by the calculation of Chern numbers for corresponding bands.

We present proper motion (PM) measurements for Draco II, an ultrafaint dwarf satellite of the Milky Way. These PMs are measured using two epochs of Hubble Space Telescope (HST)/Advanced Camera for Surveys imaging separated by a 7 yr temporal baseline. Measuring the PMs of low-luminosity systems is difficult due to the low number of member stars, requiring a precise inertial reference frame. We construct reference frames using three different sets of external sources: (1) stars with Gaia Data Release 3 data, (2) stationary background galaxies, and (3) a combination of the two. We show that all three reference frames give consistent PM results. We find that for this sparse, low-luminosity regime including background galaxies into the reference frame improves our measurement by up to ∼2× versus using only Gaia astrometric data. Using 301 background galaxies as a reference frame, we find that Draco II’s systemic PM is (μα*,μδ)=(1.043±0.029,0.879±0.028) mas yr−1, which is the most precise measurement of the three we present in this paper.

Aquatic animals are much better at underwater navigation than robotic vehicles. Robots face major challenges in deep water because of their limited access to global positioning signals and flow maps. These limitations, and the changing nature of water currents, support the use of reinforcement learning approaches, where the navigator learns through trial-and-error interactions with the flow environment. But is it feasible to learn underwater navigation in the agent’s Umwelt , without any land references? Here, we tasked an artificial swimmer with learning to reach a specific destination in unsteady flows by relying solely on egocentric observations, collected through on-board flow sensors in the agent’s body frame, with no reference to a geocentric inertial frame. We found that while sensing local flow velocities is sufficient for geocentric navigation, successful egocentric navigation requires additional information of local flow gradients. Importantly, egocentric navigation strategies obey rotational symmetry and are more robust in unfamiliar conditions and flows not experienced during training. Our work expands underwater robot-centric learning, helps explain why aquatic organisms have arrays of flow sensors that detect gradients, and provides physics-based guidelines for transfer learning of learned policies to unfamiliar and diverse flow environments. Aquatic animals outperform robotic vehicles in underwater navigation due to robots’ limited access to GPS and flow maps in deep water. The authors report that to successfully learn navigation, an agent must sense both local flows and flow gradients, enabling adaptable and robust policies under unfamiliar conditions.

For unmanned aerial vehicle (UAV) navigation in global satellite navigation system (GNSS)-denied environments, an image matching-based visual-inertial integrated navigation system is proposed. Deep learning-based methods are used for image matching to address the challenges of multi-modal image matching. A feature mismatch removal method using reference visual data and inertial navigation prior pose is proposed to improve the accuracy and robustness of image matching. Error-state Kalman filtering (ESKF) is applied to fuse the outputs of visual navigation and inertial navigation and calibrate the inertial navigation system. In addition, an image mismatch detection method based on Kalman innovation detection is applied to avoid severe errors caused by image mismatch. Finally, the proposed integrated navigation system is validated by Airsim simulation and a public dataset.