Conventional routing protocols proposed for Vehicular Ad-hoc Network (VANET) are usually inefficient and vulnerable for multi-hop data forwarding due to the unavailability of global information and inefficiencies in their route discovering schemes. However, with the recently emerged software defined vehicular network (SDVN) technologies, link stability can be better improved through the availability of global network information. Thus, in this paper, we present a novel software-defined network (SDN) based routing scheme for P2P connection under urban inter-vehicle networks that can find a global optimal route between source and destination. This is a cross-layer routing protocol in VANETs, which utilizes metrics not only considering the position and velocity of vehicles, but also channel allocation and link duration when selecting the relay vehicles. Consequently, it starts a route discovery process which can improve the network performance in terms of end-to-end delay and low overhead. Furthermore, packet loss is largely minimized by the relatively stable paths. With the help of realistic simulation, we show that the proposed routing framework performs better than other three latest SDVN and conventional VANET protocols in routing overhead, average end-to-end delay, packet drop ratio, and average throughput. Therefore, our routing scheme is more suitable for 5G-enabled vehicular ad-hoc networks in future.

The overall temporal and spectral characteristics (e.g., duration, fluence, peak flux, peak count rate, and total counts) of X-ray bursts (XRBs) originating from magnetars have been extensively studied to confirm their self-organized criticality (SOC) behaviors. However, for the pulses in XRBs, which are an important part of unraveling radiation processes such as magnetic reconnection and crustal rupture, their variable statistical characteristics are still unclear. In this work, we investigate the distributions of fluctuations in duration, waiting time, peak count rate, and total counts of pulses within bursts from two active and prolific magnetars, SGR J1935+2154 and SGR J1550-5418, observed by Fermi’s Gamma-ray Burst Monitor, Insight-HXMT, and GECAM. We report that the waiting time for pulses follows SOC, whereas previous studies indicated for bursts do not, possibly due to incomplete observations. Besides, the duration, peak count rate, and total counts for pulses are also SOC behaviors. The α-values for the duration of pulses for both magnetars are ∼2, and the inferred Euclidean dimension S is 3. Our findings provide evidence for the SOC of each radiation process during a burst, for example, multiple pulses produced by multiple magnetic reconnections triggered by a single starquake. Finally, the similar statistical characteristics for pulses from both magnetars lend support to their shared radiation processes.

Careful scrutiny of the single pulse emissions from PSR B2110+27 has been conducted through highly sensitive observations using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) at a central frequency of 1250 MHz. Our investigation revealed significant subpulse drift behavior and nulling in this pulsar. Moreover, we observed that the nulling events tend to be of short duration, with an estimated overall nulling fraction of approximately 27% ± 3%. It is noteworthy that the drift direction of the subpulses exhibits abrupt changes, occasionally transitioning into a steady state or displaying a low drift rate. Analysis using longitude resolved fluctuation spectra indicates the presence of two distinct repetition periods for the pulsar: P ₃ = (10.8 ± 2.5)P and P ₃ = (31.6 ± 4.2)P, where P denotes the pulsar period. Our investigation revealed that the subpulse separation remains consistent across different drift patterns, with P ₂ = 2.°3 ± 0.°2. A more comprehensive analysis indicates that the unique drift behavior observed can be explained by a carousel model of the dipole field. Minor changes in P ₂ and drift rate caused significant variations in the apparent P ₃ and abrupt shifts in the drift direction, while the true repetition period (assuming first-order aliasing) changed by only ∼10%. We observe a drift band memory and apparent phase memory across the null state in this pulsar, as well as variations in the drift rate and drift direction across the null state, though we have not detected significant periodicity of the nulling itself. This suggests that these phenomena may arise from random null pulses intersecting with the frequently aliased drift bands.

We report detailed polarization and single-pulse studies of PSR B0950+08 with the Five-hundred-meter Aperture Spherical Radio Telescope (FAST) at 1250 MHz. Significant bridge emission was observed between the inter-pulse and the main pulse and the height of the bridge decreased with increase in frequency. Our results support the interpretation that both the inter-pulse and the main pulse of this pulsar are from the same magnetic pole. From the relative peak flux density and the relative energy distribution, we conclude that no giant pulse was detected in PSR B0950+08. Our results provide opportunities to study the origin of the emission from PSR B0950+08 and offer new insights into the origins of pulsar emission and bridge emission.

This study focuses on the crucial aspect of lossless compression for FAST pulsar search data. The deep generative model PixelCNN, stacking multiple masked convolutional layers, achieves neural network autoregressive modeling, making it one of the most excellent image density estimators. However, the local nature of convolutional networks causes PixelCNN to concentrate only on nearby information, neglecting important information at greater distances. Although deepening the network can broaden the receptive field, excessive depth can compromise model stability, leading to issues like gradient degradation. To address these challenges, this study combines causal attention modules with residual connections, proposing a residual causal attention module to enhance the PixelCNN model. This innovation not only resolves convergence problems arising from network deepening but also expands the receptive field. It facilitates the extraction of crucial image details while capturing the global structural information of the image, significantly enhancing the modeling capabilities for pulsar data. In the experiments, the model is trained and validated using the HTRU1 dataset. This study compares the average negative log-likelihood score with baseline models like the GMM, STM, and PixelCNN. The results demonstrate the superior performance of our model over other models. Finally, this study introduces the practical compression encoding process by combining the proposed model with arithmetic coding.

Pulsar candidate sifting is an essential part of pulsar analysis pipelines for discovering new pulsars. To solve the problem of data mining of a large number of pulsar data using a Five-hundred-meter Aperture Spherical radio Telescope (FAST), a parallel pulsar candidate sifting algorithm based on semi-supervised clustering is proposed, which adopts a hybrid clustering scheme based on density hierarchy and the partition method, combined with a Spark-based parallel model and a sliding window-based partition strategy. Experiments on the two datasets, HTRU (The High Time-Resolution Universe Survey) 2 and AOD-FAST (Actual Observation Data from FAST), show that the algorithm can excellently identify the pulsars with high performance: On HTRU2, the Precision and Recall rates are 0.946 and 0.905, and those on AOD-FAST are 0.787 and 0.994, respectively; the running time on both datasets is also significantly reduced compared with its serial execution mode. It can be concluded that the proposed algorithm provides a feasible idea for astronomical data mining of FAST observation.