Low Earth orbit (LEO) Mega-constellations have emerged as a transformative approach to realize enhanced system capacity and improved coverage to satisfy the ever-increasing global demand for data services. Subsequently, the high density of satellites in a confined orbital region poses challenges, including potential interference among neighboring satellites. Further, it is vital to adequately address the impacts of safety distances in satellite communication systems on ensuring proper operation, collision avoidance, and interference management. Inspired by these observations, this work proposes a novel analysis tool, the Poisson arc hardcore point process (PAHPP), by extending the traditional Poisson line hardcore point process to characterize the unique orbiting properties of the satellites in LEO mega-constellations, accounting for factors such as the orbit, the satellite density, and spatial distribution. Specifically, this paper presents the PAHPP by enforcing a minimum separation between satellites operating in the same circular orbit to reflect the practical LEO mega-constellations. The imposed minimum inter-satellite separation in the proposed PAHPP model has also been applied to multi-orbit multi-satellite communication cases. Moreover, the discretization approximation technique is employed to analyze system performance, focusing on serving distance and outage probability. Numerical results provide valuable insights and conclusions for uncovering and recognizing LEO mega-constellations.

Data distillation is a technique to reduce the training data for machine learning models, by which a large training dataset can be reduced to a smaller, more streamlined dataset while retaining the key information of the original dataset and the model's learning capability. Previous research on the security of data distillation has only been done on image data, and the security of text data distillation has not yet been studied. In this paper, we conduct a systematic research on backdoor attack methods for text data in the process of data distillation. Specifically, this paper embeds a backdoor and implements a backdoor attack before and during data distillation of text data. The experimental results show that, relative to embedding the backdoor before the data distillation operation, embedding the backdoor during the distillation process can make the backdoor information more effectively retained in the distilled data, and can achieve a 100% success rate of the attack under the premise of guaranteeing good concealment. These results show that although the data distillation technique can improve the efficiency of model training, its potential security risk should not be ignored.

Nowadays, satellite and aerial platforms are playing an important role in realizing global seamless wireless coverage. In this paper, a cooperative satellite-aerial-terrestrial network (SATN) is considered, in which two kinds of relaying links, satellite and aerial relaying links, are used to assist a group of aerial terminals to forward their information to a remote terrestrial destination (D). Specifically, we model these aerial platforms sharing the same frequency band as a Matérn hard-core point process type-II. Also, a group of aerial jammers at D’s side is modeled as a Poisson point process. To demonstrate the end-to-end (e2e) performance of the two relaying links, the statistical characteristics of the received signal-to-interference are characterized and then a closed-form expression for the outage probability (OP) over the uplink from the aerial source to the satellite/the aerial relay, the downlink from the satellite/the aerial relay to D, and the inter-aerial relay link are derived. Numerical results are presented to verify the proposed analysis models and compare the outage performance of the considered cooperative SATN with the two relay links under numerous scenarios.

Few-shot Text Classification predicts the semantic label of a given text with a handful of supporting instances.Current meta-learning methods have achieved satisfying results in various few-shot situations.Still, they often require a large amount of data to construct many few-shot tasks for meta-training, which is not practical in real-world few-shot scenarios.Prompt-tuning has recently proved to be another effective few-shot learner by bridging the gap between pre-train and downstream tasks.In this work, we closely combine the two promising few-shot learning methodologies in structure and propose a Prompt-Based Meta-Learning (PBML) model to overcome the above meta-learning problem by adding the prompting mechanism.PBML assigns label word learning to base-learners and template learning to meta-learner, respectively.Experimental results show state-of-the-art performance on four text classification datasets under few-shot settings, with higher accuracy and good robustness.We demonstrate through lowresource experiments that our method alleviates the shortcoming that meta-learning requires too much data for meta-training.In the end, we use the visualization to interpret and verify that the meta-learning framework can help the prompting method converge better.We release our code to reproduce our experiments 1 .

Satellite-aerial communication (SAC) is the most necessary and reliable way to bridge the aerial terminals with their remote command and control center when terrestrial relay infrastructure is unavailable. However, as an inescapable obstacle, adjacent space interference from neighboring satellites unavoidably leads to the loss of the performance of SACs. To uncover the impacts of space interference, in this paper, a SAC system including a satellite (S), an aerial platform (D), and a space interference (I) is considered. Specifically, S operates in a circular orbit and I, which is randomly distributed in the neighboring space around S, produces a jamming signal to interfere with the information delivery between S and D. Considering the randomness of S and I and employing geometric probability theory, an approximate and asymptotic outage probabilities of the considered SAC system are studied. Moreover, the multi-interference scenario is investigated accordingly. Finally, numerical results are presented to validate our proposed analysis and some insightful conclusions are achieved to reveal the effects of spatially random space interference on SAC.

Due to the considerable fading arising from the long-distance transmission between satellites and terrestrial terminals as well as the complex geographic environments on the earth, aerial relays have been designed to facilitate the information delivery between the satellites and terrestrial terminals when reliable direct data transmissions cannot be achieved. In this paper, a cooperative satellite-aerial-terrestrial network (CSATN) system including a satellite (S), an aerial relay (R), and a terrestrial receiver (D) is considered, while decode-and-forward relay scheme is adopted. Specifically, inspired by the beam shape of the transmission signal from S, we model the operation space of R as a circular truncated cone to accurately reflect the practical large-scale transmission scenarios, the height of which is decided by the upper and lower limitations of the operation height of R. Thus, considering the randomness of R and D and employing geometric probability theory, the exact and asymptotic outage probability (OP) of S-R, S-D, and R-D links is studied, and then the end-to-end outage performance of CSATN is investigated accordingly by using approximation method. Finally, numerical results are presented to validate our proposed analysis.