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Abstract Trees can differ enormously in their crown architectural traits, such as the scaling relationships between tree height, crown width and stem diameter. Yet despite the importance of crown architecture in shaping the structure and function of terrestrial ecosystems, we lack a complete picture of what drives this incredible diversity in crown shapes. Using data from 374,888 globally distributed trees, we explore how climate, disturbance, competition, functional traits, and evolutionary history constrain the height and crown width scaling relationships of 1914 tree species. We find that variation in height–diameter scaling relationships is primarily controlled by water availability and light competition. Conversely, crown width is predominantly shaped by exposure to wind and fire, while also covarying with functional traits related to mechanical stability and photosynthesis. Additionally, we identify several plant lineages with highly distinctive stem and crown forms, such as the exceedingly slender dipterocarps of Southeast Asia, or the extremely wide crowns of legume trees in African savannas. Our study charts the global spectrum of tree crown architecture and pinpoints the processes that shape the 3D structure of woody ecosystems. 

Millimeter wave (mmWave) technology is gaining momentum because of its ability to provide high data rates. However, in addition to other challenges in the operation of mmWave systems, developing cell search algorithms is a challenge due to high path loss, directional transmission, and excessive sensitivity to blockage at mmWave frequencies. Thus, the cell search schemes of long term evolution (LTE) cannot be used with mmWave networks. Exhaustive and iterative search algorithms have been proposed in literature for carrying out cell search in mmWave systems. The exhaustive search offers high probability of detection with high discovery delay while the iterative approach offers low probability of detection with low discovery delay. In this paper, we propose a hybrid algorithm that combines the strengths of exhaustive and iterative methods. We compare the three algorithms in terms of misdetection probability and discovery delay and show that hybrid search is a smarter algorithm that achieves a desired balance between probability of detection performance and discovery delay. 

Residual self-interference cancellation is an important practical requirement for realizing the full potential of full-duplex (FD) communication. Traditionally, the residual self-interference is cancelled via digital processing at the baseband, which requires accurate knowledge of channel estimates of the desired and self-interference channels. In this work, we consider point-to-point FD communication and propose a superimposed signaling technique to cancel the residual self-interference and detect the data without estimating the unknown channels. We show that when the channel estimates are not available, data detection in FD communication results in ambiguity if the modulation constellation is symmetric around the origin. We demonstrate that this ambiguity can be resolved by superimposed signalling, i.e., by shifting the modulation constellation away from the origin, to create an asymmetric modulation constellation. We compare the performance of the proposed detection method to that of the conventional channel estimation-based detection method, where the unknown channels are first estimated and then the data signal is detected. Simulations show that for the same average energy over a transmission block, the bit error rate performance of the proposed detection method is better than that of the conventional method. The proposed method does not require any channel estimates and is bandwidth efficient.