The I/O cost, defined as the amount of data accessed at helper nodes during the repair process, is a crucial metric for repair efficiency of Reed-Solomon (RS) codes. Recently, a formula that relates the I/O cost to the Hamming weight of some linear spaces was proposed by Liu&Zhang in TCOM-2025. In this work, we introduce an effective method for calculating the Hamming weight of such linear spaces using exponential sums. With this method, we derive lower bounds on the I/O cost for RS codes evaluated on ad-dimensional subspace of Fqℓwithr= 2 or 3 parities.We further design repair schemes for RS codes evaluated on subspaces with any parities. Whenr= 2, ℓ −d+ 1 | ℓ andr= 3,d= ℓ or ℓ−d+2 | ℓ, the I/O cost of our schemes matches the lower bound established in this work. We refer to a scheme that matches the I/O cost lower bound as an I/O-optimal repair scheme. Additionally, forr= 2 withd= ℓ, we fully determine the minimum repair bandwidth of I/O-optimal repair schemes, while forr= 3 withd= ℓ, we construct an I/O-optimal repair scheme achieving a lower repair bandwidth than previous schemes.

This paper presents a comprehensive study on the asymptotically optimal repair of Reed-Solomon (RS) codes with small sub-packetization, specifically tailored for rack-aware distributed storage systems. Through the utilization of multibase expansion, we introduce a novel approach that leverages monomials to construct linear repair schemes for RS codes. Our repair schemes which adapt to all admissible parameters achieve asymptotically optimal repair bandwidth while significantly reducing the sub-packetization compared with existing schemes. Furthermore, our approach is capable of repairing RS codes with asymptotically optimal repair bandwidth under the homogeneous storage model, achieving smaller sub-packetization than existing methods.

Node repair is a crucial problem in erasure-code-based distributed storage systems. An important metric for repair efficiency is the I/O cost, which equals the total amount of data accessed at helper nodes to repair a failed node. In this work, a general formula for computing the I/O cost of linear repair schemes is derived from a new perspective, i.e., by investigating the Hamming weight of a related linear space. Applying the formula to Reed-Solomon (RS) codes, we obtain lower bounds on the I/O cost for full-length RS codes with two and three parities. Furthermore, we build linear repair schemes for the RS codes with improved I/O cost. For full-length RS codes with two parities, our scheme meets the lower bound on the I/O cost.

Aerial geomagnetic measurement has high strategic significance and application value in geological exploration, unexploded ordnance (UXO) detection, geomagnetic navigation, etc. The magnetic interference of aircraft carrier structure materials and electronic equipment inside the cabin seriously reduces the accuracy of geomagnetic survey. The key to carrier magnetic interference compensation is to realize high-precision estimation of the interference model parameters. However, the estimation accuracy has been limited by the strong interference noises which are not described in the interference model. At the same time, the carrier magnetic interference is dynamically changing. To solve the above problems, a real-time carrier magnetic interference compensation method based on constrained Rayleigh quotient recursive total least squares (RTLSs) is proposed in this article. By considering the input and output errors in the carrier magnetic interference, we established an enhanced interference model, and designed a constrained function on basis of Rayleigh quotient (c-RQ) to acquire an unbiased adaptive solution of the compensation parameters estimation. Using this method, the parameters of carrier magnetic interference compensation model can be calculated in real time based on the prior compensation model parameters obtained by maneuvering calibration flight and the currently updated acquisition data during mission flight. To evaluate the performance of this method, simulation and experimental verification were carried out. Simulation and experimental results show that this method can effectively realize high-precision compensation of carrier magnetic interference compared with traditional methods and machine learning methods. In addition, RTLS has more efficient computing power than traditional methods and machine learning methods.

In the context of tracking multiple targets on a novel mobile robot, it is essential to obtain the three-dimensional coordinates of specified targets based on tracking boxes. Most existing multi-target tracking algorithms neglect the inherent constraints of the novel mobile robot, such as insufficient computational power, dynamically complex working environments, and irregularly occluded targets. To address these limitations, we propose a robust tracking algorithm with occlusion resistance (hereinafter referred to as ROTrack). ROTrack compensates for the predictions of Kalman filter (KF) by incorporating Inertial Measurement Unit (IMU) information, enabling the tracker to achieve more accurate tracking in dynamic environments. Additionally, MobileSAM is employed to handle occlusion issues and obtain the correct three-dimensional coordinates of the targets. At the same time, a depth-triggered segmentation strategy is proposed to reduce computational resource consumption. The effect of ROTrack is demonstrated through alignment between IMU signals and Camera Motion Compensation (CMC) data in BoT-SORT. Real-world tracking tests validate the robustness and real-time capability of ROTrack.

Node repair is a crucial problem in erasurecode-based distributed storage systems. An important metric for repair efficiency is the I/O cost which equals the total amount of data accessed at helper nodes to repair a failed node. In this work, a general formula for computing the I/O cost of linear repair schemes is derived from a new perspective, i.e., by investigating the Hamming weight of a related linear space. Applying the formula to Reed-Solomon (RS) codes, we obtain lower bounds on the I/O cost for full-length RS codes with two and three parities. Furthermore, we build linear repair schemes for the RS codes with improved I/O cost. For full-length RS codes with two parities, our scheme meets the lower bound on the I/O cost.

Efficient node repair is a crucial problem in erasure-code-based distributed storage systems. An important metric for repair efficiency is the$\mathbf{I}/\mathbf{O}$cost which means the amount of data accessed at helper nodes during the repair process. However, minimizing the cost might not directly correspond to an optimized unless the data reads are sequential. In this work, we construct a family of$(n,\ n-r;r^{2})$MDS array codes where each node has an uncoded and sequential-access repair scheme. We call MDS codes possessing this property as access-friendly ones which are of practical importance. Since the sequential-access repair is realized at a higher sub-packetization level, our codes reach lower cost (or bandwidth) ratio than existing access-friendly MDS codes.

Terahertz (THz) massive multiple-input multiple-output (MIMO) is considered as a key technology for future sixth-generation (6G) wireless communications, in which hybrid precoding facilitates an important trade-off of hardware cost and spectrum efficiency. However, the performance of traditional schemes is limited owing to the beam split effect and the non-convex optimization problem as well as the inter-user interference under imperfect channel state information (CSI) in THz massive multi-user (MU)-MIMO systems. To overcome these challenging problems, we propose an unsupervised convolutional neural network (CNN)-based hybrid precoding scheme with attention mechanism. Specifically, we first adopt the true-time-delay (TTD) structure to mitigate beam splitting. Then, to solve the non-convex optimization problem of TTD hybrid precoding and to further mitigate inter-user interference, we propose a robust hybrid precoding scheme by applying the attention mechanism and CNN, which can be trained to generate an optimal analog precoder targeting at an achievable rate maximization under imperfect CSI. Simulation results show that the proposed algorithm has good robustness and can maintain excellent achievable rate performance in the case of imperfect CSI.

Aeromagnetic compensation is important in aeromagnetic detection. Traditionally, a calibration flight would be implemented to estimate the interference model. However, the magnetic interference is not constant during the mission flight, which makes the interference model constructed in the calibration flight inapplicable in the mission. To solve that problem, this paper proposes an evolutionary aeromagnetic compensation method based on Woodbury equation. With this method, the magnetic interference model parameters can be evolved during the mission flight on basis of the previous interference model and the updated acquired data. To evaluate the performance of the evolutionary method, both simulation and the experiment were conducted. The results indicate that the proposed method can effectively reduce the influence of changing magnetic interference. In the experiment, the standard deviation (STD) of measured data before compensation in the last mission flight is 1.2070nT, the traditional compensation can reduce the STD to 0.0863nT, while our evolutionary aeromagnetic compensation method can reduce STD to 0.0450nT. The results show that increasing changes of magnetic interference will make evolutionary aeromagnetic compensation vital for isolating signals created by geologic features from signals created by the aircraft.

Abstract A question title's function is to generate readable titles and describe a problem encountered by the code. Previous studies often used an end‐to‐end sequence‐to‐sequence system to generate question title's from source code. However, long‐term dependencies are often difficult to capture, and this may result in an incomplete source code representation. To address this issue, we propose a Transformer for Generating Code Title (hereinafter referred to as TGCT) model. Specifically, the TGCT model uses the position coding mechanism to model paired relationships between source terms by applying relative position representations. Multiple self‐attention mechanism components are also used to capture long‐term dependencies of the code. Comprehensive experiments on datasets from five coding languages, namely Python, Java, JavaScript, C#, and SQL, are conducted, and the results show that TGCT outperforms state‐of‐the‐art models based on the measurements of BLEU and ROUGE in general. In addition, a cross‐sectional comparison experiment was conducted to verify the effects of different model parameters, different data set sizes, position coding mechanism, and self‐attention mechanism on model results.

Traditional geomagnetic vector matching methods are mainly based on a certain correlation criterion to filter the optimal track, that the optimal track selection function is single and unable to distinguish the nonlinear mapping of the geomagnetic field and geographical position. Since the geomagnetic matching process is similar to pattern recognition, a vector pattern recognition matching method based on a probabilistic neural network (PNN) is proposed to realize geomagnetic navigation. The neural network input is geomagnetic vector elements, and the genetic algorithm is used to optimize the PNN’s smooth parameter to classify better. The comparison of VICCP, VMAGCOM and the proposed method is carried out in simulation in two kinds of areas with significant and insignificant geomagnetic features. Simulation results show that the proposed method has the highest matching rates of 94% and 100% in two kinds of regions. The matching accuracy is also significantly better than traditional algorithms. The experiment is carried out to verify the effectiveness and robustness of the proposed method at last.

During the geomagnetic vector navigation, there are three attitude angles required for coordinate transformation in the process of geomagnetic vector elements’ calculation. However, the yaw angle provided by inertial navigation system (INS) has accumulated error which is inevitable and will seriously reduce navigation accuracy. An improved geomagnetic navigation method based on two-component gradient weighting (TCGW) is proposed in this paper. The horizontal and vertical geomagnetic components can be calculated with roll angle and pitch angle based on the implicit trigonometric theorem in the process of coordinate transformation. And the gradient weights are used as the coefficients to improve the accuracy and universality. The simulations based on four typical types have been performed, and the Monte Carlo simulation results indicated that the proposed method has higher matching accuracy than traditional vector ICCP (VICCP) method. Furthermore, the airborne navigation experiments have been carried out to verify the accuracy and effectiveness of the proposed method.

Magnetic anomaly detection (MAD) is widely applied in the fields of resource exploration, hidden target detection, and explosive ordnance disposal. Traditional methods, such as orthonormal basis functions (OBFs), are proposed to extract anomaly signals from ambient noises and device noises. Due to the weakness of the signal, the detection probability has always been limited by a low signal-to-noise ratio (SNR). To surmount the limitation, a full connected neural network (FCN) with OBF features is trained to do the detection. Nonetheless, its effect is not reliable enough under a low SNR, and it is sensitive to the orientations. This letter introduces a multifeature fusion-based neural network with three subclassifiers to conduct MAD. The first subclassifier uses the time–frequency feature, the second uses the statistical feature, and the last concentrates on the magnetic moment feature. The outputs of the subclassifiers are analyzed synthetically by weighted voting, and the optimized weights are picked on the basis of individual performance. The real noise is recorded by experiments to test the performance of our network. The result indicates that a multifeature-based neural network shows a higher detection probability than the ordinary FCN by 5% medially. At very low SNR, the multifeature-based neural network can achieve a detection probability 13% higher than FCN. Sensitivity to orientations is also improved by the multifeature-based neural network.

Potential-field downward continuation is a crucial tool to process gravity and magnetic data, which is capable of effectively enhancing weak anomalies and identifying overlapped ones. However, available methods in this procedure do not include analysis about the impact from different frequencies of the observed magnetic data on the continuation effect. Besides, these methods contain quite a few iteration processes that fix the filter operator and result in their poor performance in reality. In this article, a new downward continuation method is presented, which is based on adaptive filtering within the iteration framework. It finds out the wavenumber distribution of observed data by analyzing its power spectrum, followed by adaptive adjustments on the passband of the filter operator, so as to effectively enhance the convergence speed and continuation accuracy. The simulation results indicated that this method could achieve adaptive adjustments as desired, and it could produce results with higher accuracy and faster convergence rate than the Landweber iterative method can. In addition, tests with actual data revealed a fast and stable downward continuation effect with the proposed method.

The decomposition of magnetic dipole signal in orthonormal basis functions (OBFs) is widely used in underwater target detection. Traditionally, only the squared sum of three coefficients is constructed as a magnetic anomaly detector, but the space distribution characteristic of coefficients is not fully utilized. In this letter, the constraint of coefficients is analyzed, and their space distribution constraint that located on an ellipsoid surface is obtained. Moreover, when the magnetization direction of target is nearly horizontal position, the constraint relationship of coefficients in the signal space can be compressed into an elliptical plane. Then, we project the decomposed coefficients of the measured signal onto the elliptical plane to further reduce the noise by about 1/3, and the squared sum of renewed coefficients is constructed as a detector for anomaly judgment. The simulation is conducted, compared with the traditional OBF method, and it can achieve an incremental detection probability of about 6% in the case of low signal-to-noise ratio.

Magnetic anomaly detection is an effective approach for detecting the visually obscured ferromagnetic target, and its performance is mainly limited by background geomagnetic noise. In contrast to the traditional detection methods that rely on several a priori assumptions regarding the target or the probability of magnetic noise consisting of external geomagnetic noise and intrinsic sensor noise, we present, in this letter, a new estimator of information entropy for differential signal acquired by a pair of magnetic sensors to detect any changes in the magnetic noise pattern. First, the magnetic noise probability density function (PDF) of differential signal is estimated by using the kernel smoothing method. Then, the minimum entropy detector based on the magnetic noise PDF of differential signal is used to detect the magnetic anomaly target. Finally, according to the probabilities of false alarm, the detection threshold can be obtained to be used for abnormal judgment. In order to verify the effectiveness of the proposed method, the experiment is conducted, and the results demonstrate that the proposed method has better detection performance than that of traditional methods.

The magnetic interferential fields, such as soft-iron and hard-iron interferences, will seriously affect the accuracy of geomagnetic vector measurement system, and thus should be compensated. In this letter, a new compensation method using a 3-D Helmholtz coil is proposed. As a first step, the geomagnetic vector measurement system is exposed to different directions and the magnitudes of magnetic field generated by a 3-D Helmholtz coil to construct the equations of error model, and soft-iron parameters can be estimated by solving linear equations. Then, hard-iron parameters are estimated by changing the fixation direction of the three-axis magnetometer. Finally, all the estimated parameters are used for compensating distortion magnetic fields. In order to verify the effectiveness of the proposed method, the experiment is conducted, and the results demonstrate that the proposed method contributes to the accuracy improvement of geomagnetic vector measurement system.

The magnetometer error and distortion magnetic field of an inertial navigation system are the major two factors influencing the measurement accuracy of geomagnetic vector information measurement systems. The calibration and compensation methods are proposed in this letter. As a first step, a calibration model for a three-axis magnetometer is established, and a nonlinear least square algorithm is used to estimate thoroughly the error parameters. Then, a distortion compensation model based on relative attitude information is proposed, in which only four groups of attitude information are needed to estimate precisely the distortion parameters. Finally, the whole system is used for magnetic anomaly detection. Experimental results suggest that the magnetometer error can be accurately calibrated, and the magnetic field distortion can be suppressed significantly. After calibration and compensation, the intensity and magnetic anomaly vectors such as west vector, south vector, and vertical vector can be accurately measured by the geomagnetic vector measurement system. It demonstrates that the proposed method can effectively improve the accuracy of the geomagnetic vector measurement system.