## 4 Numerical Results  
### Simulation Settings  
To evaluate the effectiveness of the proposed ARTHF in addressing the handover problem, we conducted simulations in an idle\(-\)mode TTG handover scenario within a large\(-\)scale satellite network. The simulations were performed using a custom\(-\)built platform developed with Satellite Tool Kit (STK) 12.2 and Python 3.8. To ensure realism and reliability, satellite radio frequency (RF) and TTG parameters were configured in accordance with the NTN RF system specifications outlined in 3 GPP TR 38.821. Specifically, the Ka\(-\)band (20 GHz) was adopted in the simulation, as is commonly used in LEO satellite systems, such as Starlink, to support high\(-\)bandwidth data transmission. For the satellite RF parameters, a system bandwidth of 400 MHz was configured, along with a wave Effective Isotropic Radiated Power (EIRP) density of 4 dBW/MHz. The satellites were equipped with directional antennas, featuring a maximum gain of 38.5 dBi and a 3 dB Half\(-\)power Beamwidth (HPBW) of 1.7647\({}^{\circ}\). The satellite beam diameter was set as 20 km, with an antenna equivalent aperture of 0.25 m. A minimum communication elevation angle of 40\({}^{\circ}\) was maintained. For the user RF parameters, the frequency was also set to 20 GHz. The TTGs were likewise equipped with directional antennas, which featured a receiver gain of 39.7 dBi. The antenna noise temperature for the TTGs was 150 K, and the noise factor was 1.2 dB.  
The simulation modeled a typical large\(-\)scale LEO satellite constellation, based on the design of SpaceX's Starlink, consisting of multiple sub\(-\)constellations, as outlined in Table 1. The TTGs were arranged in a \(20\times 20\) grid, scaled to a 25 km grid size, with the traffic distribution derived from the Milan traffic dataset [22] due to its realistic representation of temporal traffic variations, which are critical for assessing handover strategies in dynamic satellite networks, enabling the simulation of diverse traffic conditions, including high- and low\(-\)demand periods. Within each 15 s interval (three time slots), the dataset captured dynamic traffic patterns that ranged from peak loads to lower loads that reflected realistic user demand fluctuations. The simulation platform generated link relationship data at each time slot, where each slot lasted 5 s. Traffic data for each TTG, sourced from the Milan dataset, was updated every three slots (15 s). To align with this granularity, the handover trigger interval was also set as three slots, and traffic conditions were adjusted accordingly.  
The ARTHF integrates two neural network modules. The LSTM\(-\)based traffic predictor adopts a two\(-\)layer stacked architecture that processes historical data with dimensions [\(T_{l}=3\), \(F_{l}=3\)], representing three time steps and three feature channels per TTG. Each LSTM layer comprises three units, followed by fully connected layers (128, 64, 32, 1) with ReLU activations to generate traffic predictions. The attention\(-\)enhanced rainbow DQN for handover decision\(-\)making accepts a zero\(-\)padded input state matrix \(\mathbf{S}_{\epsilon}\in\mathbb{R}^{M_{\text{data}}^{\text{train}}-8\times 7}\). The model includes a self\(-\)attention layer, followed by three fully connected layers (256, 128, 64), and adopts a dueling architecture that separates the state\(-\)value and advantage streams. The resulting Q\(-\)values guide the action selection process. Both networks are trained with a learning rate of 0.001 and a discount factor \(\gamma=0.95\) for 1000 episodes.