###### Abstract
The study presented in this abstract [3] \"A Study on Pine Larva Detection System Using Swin Transformer and Cascade R-CNN Hybrid Model\" aimed to build an intelligent system for parcel storage by studying parcel box recognition using AI's deep learning technology. The researchers used the YOLOv5 model for parcel recognition, which is faster and has better object recognition than previous models. They compared and analyzed four different YOLOv5 models and found that the YOLOv5 large model showed the strongest performance with precision, recall, and F1 scores of 0.966, 0.899, and 0.932, respectively. The YOLOv5 large model is also half the size of the YOLOv5 xlarge model, making it more efficient. The researchers concluded that the YOLOv5 large model could be used to build an intelligent parcel storage system that shows optimal efficiency in real-time object recognition. The paper aimed to study parcel box recognition using the YOLOv5 model to develop an intelligent parcel storage system. Four different versions of the model were analyzed based on their performance and size, and it was found that the YOLOv5I model showed the strongest performance in terms of precision, recall, and F1 score. The paper \"Parcel Classification and Positioning of Intelligent Parcel Storage System Based on YOLOv5\" [4] proposes a novel feature selection and extraction approach for anomaly-based intrusion detection systems in the Internet of Things (IoT) environment. The approach uses entropy-based methods to select and extract relevant features, followed by mathematical set theory to extract the best features. The proposed model framework is trained and tested on the IoT intrusion dataset 2020 (IoTID20) and NSL-KDD dataset using four machine learning algorithms. The approach resulted in 11 and 28 relevant features. This study on parcel recognition using the YOLOv5 model for building an intelligent parcel storage system has identified several limitations and future research tasks. The need for conducting experiments in more diverse environments and datasets by securing additional data was identified. The study also highlighted the need to explore the latest model (YOLOv7) and investigate the potential of model transformation through pruning to optimize parcel recognition. Additionally, future research will focus on applying the object recognition model to the parcel storage system under development to confirm its performance in the real environment. These proposed future works will help to improve the performance and efficiency of the intelligent parcel storage system, making it a more reliable and effective solution for last-mile delivery services. This paper [5] presents a novel high-gain THz antenna array that can be utilized for various THz band space communication applications. The proposed antenna is designed in four steps and operates at two frequencies with good bandwidths and gain. The multiple-order antenna arrays are designed to achieve a high gain, and their performance is analyzed in different simulators, resulting in strong agreement. Moreover, the parametric analysis is done for the proposed type C antenna array with different separations among the radiating elements, which shows that the 128 \(\times\) 1 antenna array provides a high gain and directivity with good efficiency at both operating frequencies. Overall, the results obtained in this study demonstrate that the proposed antenna array design is a feasible option for high-speed and free-space wireless communication systems in the THz band. The results show that the proposed THz array antenna has a directivity and gain of 12.5 and 11.23 dB as well as 12.532 and 11.625 dBi at 0.714 and 0.7412 THz, respectively, with high radiation efficiency. The simulated type C antenna's minimal return loss is less than \(-\)37 dB at a resonant frequency of 0.7123 THz with a corresponding separation distance of 533 \(\upmu\)m. The study also concludes that the antenna bandwidth decreases significantly with the increase in the number of radiation elements.