Multi-secret sharing scheme has been well studied in recent years. In most multi-secret sharing schemes, all secrets must be recovered synchronously; the shares cannot be reused any more. In 2017, Harn and Hsu proposed a novel and reasonable feature in multiple secret sharing, such that the multiple secrets should be reconstructed asynchronously and the recovering of previous secrets do not leak any information on unrecovered secrets. Harn and Hsu also proposed a (t,n) multi-secret sharing scheme that satisfies this feature. However, the analysis on Harn-Hsu’s scheme is wrong, and their scheme fails to satisfy this feature. If one secret is reconstructed, all the other unrecovered secrets can be computed by any t − 1 shareholders illegitimately. Another problem in Harn-Hsu’s work is that the parameters are unreasonable which will be shown as follows. In this paper, we prove the incorrectness of Harn-Hsu’s scheme and propose a new (t,n) multi-secret sharing scheme which is extended from Harn-Hsu’s scheme; our proposed scheme satisfies the feature introduced by Harn and Hsu.

In recent years, various network attacks have emerged. These attacks are often recorded in the form of Pcap data, which contains many attack details and characteristics that cannot be analyzed through traditional methods alone. Therefore, restoring the network attack scenario through scene reconstruction to achieve data regeneration has become an important entry point for detecting and defending against network attacks. However, current network attack scenarios mainly reproduce the attacker’s attack steps by building a sequence collection of attack scenarios, constructing an attack behavior diagram, or simply replaying the captured network traffic. These methods still have shortcomings in terms of traffic regeneration. To address this limitation, this paper proposes an SDN-based network attack scenario recovery method. By parsing Pcap data and utilizing network topology reconstruction, probability, and packet sequence models, network traffic data can be regenerated. The experimental results show that the proposed method is closer to the real network, with a higher similarity between the reconstructed and actual attack scenarios. Additionally, this method allows for adjusting the intensity of the network attack and the generated topology nodes, which helps network defenders better understand the attackers’ posture and analyze and formulate corresponding security strategies.

In recent years, with the rapid development of the global demand and scale for deep underground space utilization, deep space has gradually transitioned from single-purpose uses such as underground transportation, civil defense, and commerce to a comprehensive, livable, and disaster-resistant underground ecosystem. This shift has brought increasing attention to the safety of personnel flow in deep spaces. In addressing challenges in deep space passenger flow prediction, such as irregular flow patterns, surges in extreme conditions, large data dimensions, and redundant features complicating the model, this paper proposes a deep space passenger flow prediction model that integrates a Temporal Convolutional Network (TCN) and Long Short-Term Memory (LSTM) network. The model first employs a dual-layer LSTM network structure with a Dropout layer to capture complex temporal dynamics while preventing overfitting. Then, a Self-Attention mechanism and TCN network are introduced to reduce redundant feature data and enhance the model’s performance and speed. Finally, the Beluga Whale Optimization (BWO) algorithm is used to optimize hyperparameters, further improving the prediction accuracy of the network. Experimental results demonstrate that the BWO-TCLS-Self-Attention model proposed in this paper achieves an R2 value of 96.94%, with MAE and RMSE values of 118.464 and 218.118, respectively. Compared with some mainstream prediction models, the R2 value has increased, while both MAE and RMSE values have decreased, indicating its ability to accurately predict passenger flow in deep underground spaces.

The modeling of light propagation in scattering media is an important topic that has attracted considerable attention in recent decades. Coupling microscopic parameters such as particle concentration, particle size, and the refractive index of the medium can broaden the applicability of the model and improve simulation accuracy. In this work, these parameters are used to regulate the anisotropy factor and the mean free path. They are then integrated into a random phase screen model constructed using the Monte Carlo and the Gerchberg–Saxton algorithm. An optical experimental setup was established, in which a Laguerre–Gaussian beam was employed as the incident light source and diffusers with mesh numbers of 1500, 600, and 220 were used as the scattering media. The model was validated through comparative analysis between simulated and experimental results. Correlation coefficients between the simulated and experimental beam profiles exceeded 0.73, and the maximum relative error in power-in-the-bucket was only 4.9%, confirming the model’s accuracy and reliability. Numerical simulations were performed based on the established model to investigate beam propagation behavior. The results indicate that increasing particle concentration and particle size both lead to enhanced beam centroid shift and beam broadening. This modeling method provides a useful tool for analyzing beam propagation in complex scattering media and holds potential applications in wavefront correction and structured beam recognition.