Abstract: A battery pack, a vehicle, and an energy storage device are provided. The battery pack includes a cell array and a support member, where the cell array includes a plurality of cells, the cell has a first dimension, and the first dimension is a maximum spacing between two imaginary parallel planes sandwiching the cell; and at least one of the cells 600 mm?first dimension?2500 mm the at least one cell includes a casing and a core located inside the casing, a supporting region is formed on the casing, and the cell is connected to the support member through the supporting region and is supported by the support member. The support member is connected to the supporting region to support the cell.

Abstract: A power battery pack includes: a pack body; a plurality of cells, disposed in the pack body; the cell having a length L0, a width H0, and a thickness D0, where at least one cell meets: L0>H0?D0, a length direction of the cell is arranged along a width direction of a vehicle body of the electric vehicle, and in the width direction of the electric vehicle, the length L0 of the cell and a size W of the vehicle body of the electric vehicle in the width direction meet: 46%?L0/W?76%; or at least one cell meets: L0>H0?D0, a length direction of the cell is arranged along a length direction of a vehicle body of the electric vehicle, and in the length direction of the electric vehicle, the length L0 of the cell and a size X of the vehicle body of the electric vehicle in the length direction meet: 40%?L0/W?76%.

Abstract: A method of training a model, a method of predicting a trajectory, and an electronic device, which relate to fields of artificial intelligence, deep learning, autonomous driving and intelligent transportation technologies. The method includes: adjusting a model parameter of a to-be-trained model for an nth round according to a first action selection strategy, so as to obtain an intermediate network model, where n=1, . . . N, and N is an integer greater than 1; performing, by using the intermediate network model, at least one trajectory prediction action based on training sample data indicated by the first action selection strategy, so as to obtain a trajectory prediction result; determining a second action selection strategy according to the trajectory prediction result and the first action selection strategy; and adjusting the model parameter of the to-be-trained model for an (n+1)th round according to the second action selection strategy.

Abstract: An apparatus is configured to be employed within a base station. The apparatus comprises baseband circuitry which includes a radio frequency (RF) interface and one or more processors. The one or more processors are configured to arrange phase tracking reference signal (PT-RS) resource elements (REs) and data REs as an arrangement for a transmission based on one or more diversity factors. The one or more diversity factors include a time domain and a frequency domain. The one or more processors are also configured to provide the transmission having the PT-RS REs to the RF interface for transmission to a user equipment (UE) device.

Abstract: An apparatus is configured to be employed within a base station. The apparatus comprises baseband circuitry which includes a radio frequency (RF) interface and one or more processors. The one or more processors are configured to arrange phase tracking reference signal (PT-RS) resource elements (REs) and data REs as an arrangement for a transmission based on one or more diversity factors. The one or more diversity factors include a time domain and a frequency domain. The one or more processors are also configured to provide the transmission having the PT-RS REs to the RF interface for transmission to a user equipment (UE) device.