The exponential growth of mega-constellation satellites, typified by SpaceX’s Starlink, poses unprecedented challenges for existing space surveillance, particularly when tracking uncooperative spacecrafts executing continuous orbit-raising/deorbiting maneuvers. This situation makes the conventional orbit determination (OD) and prediction (OP) struggle with three critical issues: insufficient observational data, unknown maneuvering parameters, and the cumulative effects of unmodeled thrust. To address these issues, this study proposes a piecewise estimation-based OD method by developing a semi-analytical thrust acceleration (TA) model. The TA model employs time-explicit polynomial expansions with state-dependent coefficients to characterize the continuous low-thrust effect. The OD system integrates a piecewise least-squares estimation algorithm with dynamic compensation, enabling accurate TA resolution within one-day observation windows. Specifically, the OP system incorporates the latest TA estimate to account for the future continuous low-thrust effect. Experiments with sparse radar observations of Starlink satellites demonstrate the effectiveness of the proposed method. The TA estimation errors remain below 0.5% relative to the reference obtained from precise ephemerides. The OP capabilities maintain one-day and two-day position accuracy below 2 and 4 km, respectively, improving by more than 60% compared to the unified OD method. More importantly, the approach exhibits operational robustness, achieving OD convergence with initial TA errors up to 35%. These advantages make the proposed approach a practicable solution for autonomous catalog maintenance of maneuvering spacecraft.

With the rapid increase in low Earth orbit space objects, accurate orbit prediction (OP) is becoming critical for satellite safety and space environment management. Traditional physics-based OP methods, especially those relying on publicly available two-line element (TLE) data, often struggle with accuracy due to complex orbital perturbations. However, the availability of long-term, large-scale TLE datasets enables the analysis of underlying orbital error patterns. This study proposes a high-precision OP error compensation method solely based on TLE data. A bidirectional long short-term memory (BiLSTM) neural network is used to model time-varying orbital error trends and correct TLE/simplified general perturbation model 4-based predictions. Experiments on 340 low Earth orbit objects show that the BiLSTM framework effectively captures temporal error patterns, achieving an average accuracy improvement of 82% and a compensation effectiveness exceeding 94% across all targets. To assess the model’s robustness, the impact of training sample size and sampling intervals is examined, revealing that proper data configurations markedly influence compensation performance. Comparative analysis with other deep learning models (recurrent neural network, gated recurrent unit, long short-term memory, and convolutional neural network) further confirms the superior fitting and correction ability of BiLSTM. This study highlights the potential of BiLSTM networks to enhance the accuracy and reliability of TLE-based OP, providing a valuable approach for improving space situational awareness.

The radar-optical mixed track-to-track association (mT2TA) problem is inevitable in the task of cataloging space objects with a radar-optical integrated surveillance system. However, the classic track-to-track association (T2TA) methods mainly handle the association problem of the same measurement type, thus not suitable for the mT2TA problem when cataloging unknown space objects, leading to the observation waste and even object loss. This paper proposes a novel and efficient method to deal with the mT2TA problem by converting it into the “radar-radar” T2TA problem and then solving it by integrating the general perturbation correction technique and Lambert’s orbit determination method. Experiments of closely-spaced low-Earth orbit (LEO) and highly-elliptical orbit (HEO) objects demonstrate that the proposed method can achieve a True Positive rate of 94.3% and a False Positive rate of 0.93% with extensive radar and optical tracks. The semi-major axis estimation accuracy in the mT2TA is as high as around 88 m for LEO objects and 233 m for HEO objects. In addition, the effectiveness of the method is further verified by testing massive LEO objects consistent with the real spatial distribution. Besides, this analytical mT2TA method is computationally very efficient, with around 5000 radar-optical track-pairs being processed in one minute. We believe the proposed method is a very effective solution to the radar-optical mT2TA problem and shows great potential for cataloging new space objects in the radar and optical space surveillance. Nevertheless, the time interval of two tracks should be considered in practical applications to limit the degradation of the association reliability.

An initial orbit determination (IOD) solution from angles-only observations of a single short orbit arc is often required for applications such as tracklet association and fast reacquisition of a newly detected space object. Modern optical observations can collect tens or even hundreds of data points over a short arc, thus enabling a large number of IOD solutions to be determined when using an IOD algorithm of 3 lines of sight (3-LOSs), such as the Gooding algorithm. It is necessary but difficult to find an optimal solution from a solution pool, particularly in the case of too short arc (TSA). Another issue in using 3-LOSs IOD methods is the neglect of perturbation effects on the observations. That is, 3-LOSs IOD methods are developed in the 2-body frame, but the observations are perturbed. Thus, the IOD solutions may have additional errors if the observations are not corrected for perturbation effects. In this study, we investigate the distribution of the semi-major axis and eccentricity of IOD solutions in a pool and find that choosing the solution with the maximum kernel density in the distribution is a much better way to determine the final solution from the pool. We also propose a technique to correct J 2 secular effects on observed angle data. We use the Gooding algorithm as the basic 3-LOSs IOD algorithm to demonstrate the effectiveness of the proposed techniques in improving the IOD accuracy in the cases of short-arc ground-based observations and space-based simulation data.

This work presents a model predictive uncalibrated visual servoing scheme for an underwater vehicle manipulator system (UVMS). Visual information can be used to improve efficiency and accuracy in UVMS intervention tasks, traditional underwater visual servoing schemes suffer from uncertainties of UVMS kinematics parameters and camera calibration results, besides, systems physical constraints are seldom considered. In this work, a model predictive visual servoing scheme is used to deal with constraints of system states, Broyden's method with recursive least square is utilized to approximate the composite Jacobian matrix without prior knowledge of system parameters, which is used to predict future states of the UVMS. Simulations are carried out to testify the effectiveness of the proposed model predictive uncalibrated visual servoing scheme.

High accuracy orbit determination (OD) and prediction (OP) are essential for high-precision spatial applications, the avoidance of collision risks for space objects and space situational awareness. Two-line element (TLE) orbit parameter database is most comprehensive. However, when conducting orbital calculations using TLE, the spatiotemporal variation characteristics of OP errors are complex. The existing modeling methods struggle to accurately reveal the OP errors evolutionary patterns. Traditional numerical methods calculation speed is slow and the calculation efficiency is low. In this study, six datasets of OP errors are constructed based on the long-term historical TLE data for six space objects at different orbit altitude. The Back Propagation Neural Network (BPNN) algorithm is employed to establish a model for OP errors, aiming to enhance OP accuracy. The results show that compared with using the SGP4/SDP4 model directly for OP, the OP accuracy is improved by at least 50% after the errors processing model designed in this study. These findings provide theoretical and technical support for space object collision warning systems. Future research will focus on the investigation of input variable importance, model generalization capabilities, and selection of data capacities for different space objects.

The space surveillance network collects significant quantities of space object monitoring data on a daily basis, which varies in duration and contain observation errors. Cataloguing space objects based on these data may result in a large number of very short arcs (VSAs) being wasted due to cataloguing flaws, poor data quality, data precessing, and so on. To address this problem, an effective data mining method based on tracklet-to-object matching is proposed to improve the data utilization in new object cataloguing. The method can enhance orbital constraints based on useful track information in mined tracklets, improve the accuracy of catalogued orbits, and achieve the transformation of omitted observations into “treasures”. The performance of VSAs is evaluated in tracklet-to-object matching, which is less sensitive to tracklet duration and separation time than initial orbit determination (IOD) and track association. Further, the data mining method is applied to new space object cataloguing based on radar tracklets and achieved significant improvements. The 5-day data utilization increased by 9.5%, and the orbit determination and prediction accuracy increased by 11.1% and 23.6%, respectively, validating the effectiveness of our method in improving the accuracy of space object orbit cataloguing. The method shows promising potential for the space object cataloguing and relevant applications.

Covariance of the orbital state of a resident space object (RSO) is a necessary requirement for various space situational awareness tasks, like the space collision warning. It describes an accuracy envelope of the RSO's location. However, in current space surveillance, the tracking data of an individual RSO is often found insufficiently accurate and sparsely distributed, making the predicted covariance (PC) derived from the tracking data and classical orbit dynamic system usually unrealistic in describing the error characterization of orbit predictions. Given the fact that the tracking data of an RSO from a single station or a fixed network share a similar temporal and spatial distribution, the evolution of PC could share a hidden relationship with that data distribution. This study proposes a novel method to generate accurate PC by combining the classical covariance propagation method and the data-driven approach. Two popular machine learning algorithms are applied to model the inconsistency between the orbit prediction error and the PC from historical observations, and then this inconsistency model is used for the future PC. Experimental results with the Swarm constellation satellites demonstrate that the trained Random Forest models can capture more than 95% of the underlying inconsistency in a tracking scenario of sparse observations. More importantly, the trained models show great generalization capability in correcting the PC of future epochs and other RSOs with similar orbit characteristics and observation conditions. Besides, a deep analysis of generalization performance is carried out to describe the temporal and spatial similarities of two data sets, in which the Jaccard similarity is used. It demonstrates that the higher the Jaccard similarity is, the better the generalization performance will be, which may be used as a guide to whether to apply the trained models of a satellite to other satellites. Further, the generalization performance is also evaluated by the classical Cramer von Misses test, which also shows that trained models have encouraging generalization performance.

With the undergoing and planned implementations of mega constellations of thousands of Low Earth Orbiting (LEO) satellites, space will become even more congested for satellite operations. The enduring effects on the long-term space environment have been investigated by various researchers using debris environment models. This paper is focused on the imminent short-term effects of LEO mega constellations on the space operation environment concerned by satellite owners and operators. The effects are measured in terms of the Close Approaches (CAs) and overall collision probability. Instead of using debris environment models, the CAs are determined from integrated orbit positions, and the collision probability is computed for each CA considering the sizes and position covariance of the involving objects. The obtained results thus present a clearer picture of the space operation safety environment when LEO mega constellations are deployed. Many mega constellations are simulated, including a Starlink-like constellation of 1584 satellites, four possible generic constellations at altitudes between 1110 km and 1325 km, and three constellations of 1584 satellites each at the altitudes of 650 km, 800 km, and 950 km, respectively, where the Resident Space Object (RSO) spatial density is the highest. The increases in the number of CAs and overall collision probability caused by them are really alarming. The results suggest that highly frequent orbital maneuvers are required to avoid collisions between existing RSOs and constellation satellites, and between satellites from two constellations at a close altitude, as such the constellation operation burden would be very heavy. The study is not only useful for satellite operators but a powerful signal for various stakeholders to pay serious attention to the development of LEO mega constellations.

环形截面是工程结构中常见的截面形式,但混凝土环形截面配筋计算存在双重非线性(材料和截面宽度变化的非线性).《混凝土结构设计规范》中仅给出计算均匀配筋的超越方程组,需编程和迭代求解,不能手算,极为不便.另外,一些环形截面构件(如高桥墩、预制管桩等)长度长、截面尺寸大、钢筋用量大,若采用均匀配筋,中性轴附近钢筋应力小,经济性不好.若采用非对称配筋,将受力钢筋布置在远离中性轴的外围区域,可充分利用混凝土和钢筋强度,提高经济效益.为此,根据混凝土和钢筋的本构关系确定应变变化的范围和边界,从应变出发,利用解析方法由应变求解应力,进而计算内力,不需迭代,最终将计算结果绘制成便于手算配筋的诺谟图,计算快速方便.该方法适用于C50及以下强度混凝土和任意直径大小的环形截面.

Wide-area space surveillance sensors are the backbone to cataloging of Earth orbiting objects. Their core capability should be to efficiently detect as many space objects as possible over a large space domain. As such, the question of how to quantitively evaluate the object detection performance of the sensors is critical. The evaluation is traditionally performed by means of infield static tests and out-field calibration satellite tests. However, this simplified method is flawed in terms of its representativeness in spatial-temporal coverage and object types, because space objects vary greatly in orbit type, size, and shape, and thus the evaluation results may be overoptimistic. This paper proposes a practically implementable procedure to quickly and reliably evaluate the object detection performance of space surveillance sensors in which a catalog containing a vast number of on-orbit objects is used as a reference. It first constructs a unified model to estimate the size of objects from its radar cross section (RCS) data, then it presents a hierarchy scheme to efficiently compute object visibility, and finally, it makes the sensor performance evaluation through a data point matching technique. Experiments with two simulated sensors demonstrate that the realized performance is always inferior to the designed one, and in some cases the difference is significant and concerning. The presented approach could be routinely applied to evaluate the performance of any operational surveillance sensors and provide insight on how the sensor performance could be improved through refined design, manufacture, and operation.

钢铁行业高炉工艺中无组织排放粉尘严重污染周围环境,而无组织排放治理技术研究方面还有待加强.本文论述了高炉工艺中粉尘无组织排放的特征,针对原料堆场、运输和生产的各个环节,研究了抑尘效果、影响参数、运行成本等因素,比较了抑尘剂、抑尘网、密封技术、除尘器等技术的优缺点,综述了各环节控制无组织排放粉尘的可行技术与控制参数,提出了超低排放的可行的技术路线,并对高炉工艺无组织粉尘的重点关注方向进行了评述.

The advances in the optical space surveillance technology and the possibly more deployments of space-based optical sensors enable more and more space objects detectable and make the catalog expansion more realistic. The cataloging of an optically detected new object requires finding three or more tracklets over a few days from the object. In this process, the tracklet-to-tracklet association is a critical step, but associating short-arc optical tracklets remains a difficult task. This paper develops a novel orbit determination approach to the optical tracklet association. The main idea is to determine accurate orbit elements from two tracklets of the same object by exclusively using the Gooding angles-only initial orbit determination (IOD) method. Before applying the Gooding IOD method to two tracklets apart by a few days, the dominant secular effects on the angular observations have to be considered. The significant short-periodic effects may also need consideration if tracklet-to-tracklet IOD is to achieve high accuracy. In this paper, the secular effects from the J(2) and lunisolar gravitational perturbations, as well as the short-periodic effects due to J(2) and J(2,2), are accounted for in the tracklet-to-tracklet IOD. Extensive tests using tracklets from low Earth orbit (LEO), high elliptical orbit (HEO), medium Earth orbit (MEO), geosynchronous Earth orbit (GEO), and Molniya orbit are performed to assess the performance of the developed method. It is shown that the true positive (TP) rate for associating two tracklets from the same object within 48 h is 98.3% for LEO objects and higher than 89.8% for HEO and Molniya objects. The TP rate is remarkably high at around 99.0% for MEO and GEO objects. The analytic approach is also computationally very efficient. The results demonstrate the strong applicability of the developed method to associate optical tracklets of uncatalogued objects of all orbit types.

为分析EPS板的减荷机理与效果,通过室内模型试验,研究了不同密度、不同厚度的EPS板对高填方涵洞卸载效果的影响.结果表明:①马斯顿效应会对涵洞顶部产生较大的附加应力,在涵洞顶部铺设EPS板可以有效地减小涵洞顶部土压力;②沉降过程中土体内部形成了土拱效应,造成了土体内部应力重分配;③EPS板密度越小、厚度越大,对涵洞顶部应力的减小效果越明显;④EPS板有明显的蠕变效应,可为涵洞提供长期减荷环境.

Concerns for the collision risk involving Starlink satellites have motivated the interest in obtaining their accurate orbit knowledge. However, accurate orbit determination (OD) and prediction (OP) of Starlink satellites confront two main challenges: mismatching or missed matching of sparse tracklets to maneuvering satellites, and unknown or unmodeled orbit maneuvers. How to exactly associate a tracklet to the right satellite is the primary issue, since a maneuvering satellite does not follow the naturally evolving orbit during the maneuvering, while more tracklets are needed for developing an accurate orbit maneuver model. If these two challenges are not well addressed, it may lead to catalog maintenance failure or even loss of objects. This paper proposes a method to correctly match tracklets to the climbing Starlink satellites. It is based on the recursive OD and OP, in which the orbit maneuver is modeled and the thrust is estimated, such that the subsequent OP accuracy guarantees the correct match of tracklets shortly after the OD time. Experiments with climbing Starlink satellites demonstrate that the tracklets within three days of the last TLE (two-line element) are all correctly matched to the right satellites. With the matched tracklets, the thrust accelerations of climbing Starlink satellites can be precisely estimated through an orbit control approach, and the position prediction accuracy over 48 hours is at the level of a few kilometers, providing accurate orbit knowledge for reliable collision warning involving Starlink satellites.

电工电子技术基础课程在为理工科大学生传授电气科学知识、培养专业技能的同时,结合双一流建设和新工科发展需要,在课程目标、课程内容组织、课程资源和教学模式上,借助信息化手段将人文素质教育与工程类基础性课程教学有机结合,从而培养学生的人文素养和科学精神.

箱形和工字形截面是工程中常用的截面形式,但按《混凝土结构设计规范》计算此类截面的配筋公式繁多,还需事先判断,然后选择相应的计算公式,比如拉弯时,要判断拉力合力点是否在大小两侧钢筋之间,区分小偏拉或大偏拉;压弯时,要判断大小偏心,除此之外,还需判断中性轴是否在翼缘或在腹板内等.为了方便快速计算,本文不采用规范中的等效矩形应力换算,直接由混凝土和钢筋的应变求应力,进而计算内力.确定其可能的应变范围,由此应变可变成已知量,并将最终的计算结果绘制成了计算配筋的诺模图,该图为无量纲形式,可用于任意宽度和高度的截面尺寸和C50及以下混凝土强度等级.

Accurate orbit prediction (OP) of space debris is vital in space situation awareness (SSA) related tasks, such as space collision warnings. However, owing to the sparse and low precision observations, unknown geometrical and physical features of debris, and effects of incomplete force models, OP based on the orbital mechanics theory or physics-based OP of space debris suffers from rapid error growth over a long duration, limiting the period of validity of debris OP for precise space applications. Considering that the tracking arcs of a debris object over a single station often share a similar temporal and spatial distribution in the inertial space, the resultant OP errors possibly have a coherent relationship with the temporal and spatial distribution of tracking arcs. This article proposes a machine learning (ML)-based approach to model the underlying pattern of debris OP errors from historical observations and apply it to modify the future physics-based OP results. The approach includes three steps: constructing a historical OP error set, training an ML model to fit the historical OP error set, and correcting the future physics-based OP with ML-predicted orbital errors. The ensemble learning algorithm of boosting tree is studied as the primary ML method for the error modeling and predicting process. Experiments with three low-Earth-orbit objects, tracked by a single radar station, demonstrate that the trained ML models can capture more than 80% of the underlying pattern of the historical OP errors. More importantly, the errors of physics-based OP over the future seven days reduce from thousands of meters to hundreds or even tens of meters through the error correction with the learned error pattern, achieving at least 50% accuracy improvement. Such dramatic OP improvements show the promising potential of ML for enhanced SSA capability.

The choice of orbit propagation method is essential for orbit prediction (OP) and determination (OD) of space debris, requiring both high accuracy and computational efficiency. This paper presents a semi-analytic method using the multiscaling technique. The 7-day OP errors are less than 200 m for orbits above 800 km. The 5-year semi-analytic solutions are well fitted to the numerically propagated orbit. OD performance of the semi-analytic method is examined using real data, and the determined position accuracy is at dozens of metres. The computational efficiency of the semi-analytic method against the numerical method is improved by about 95 percent.