## 4 Discussion  
Space weather forecasting usually involves a substantial amount of data observed by multiple satellites. In a satellite observation for space weather forecasting, transmission of the observed data to the ground is a critical problem because of the bandwidth for transmitting observed data is limited. The proposed model resolves the transmission bandwidth of observed data in space weather by transferring the data between satellites instead of sending data to ground stations. More importantly, using sensor systems involving sensor data from multiple satellites (hence from distinct positions) allows intelligent _in-situ_ sensing by rearranging positions and directions of sensors. The two satellites of STEREO-A and STEREO-B orbit and monitor the Sun from various positions. Their positions are symmetrical relative to the Earth, following an elliptic curve. The two satellites  
Figure 11: Time development of sensor data for five days from 30 April 2008 at 00:00:00 UTC. This plot contains an event of high-speed solar wind flowing from a coronal hole. In the first and second rows, the plots of the columns correspond to the satellites of ACE, STEREO-A, and STEREO-B, respectively. In the third row, the plot corresponds to GOES-10. detect the distinct solar wind parameter because the solar wind is expanded spirally owing to rotation of the Sun. The proposed forecasting system takes advantage of spatiotemporally distinct solar wind parameters. The proposed forecasting system allows the prediction of high-energy electron flux involving more than three sensors. The proposed model successfully forecasts the high-energy electron flux 24 h ahead. Whereas mutual evaluation with only two sensors leads to an oscillation in the credibility when the high-energy electron flux approaches the warning level, evaluation with more than three sensors prevents credibility fluctuation. The three satellites in the proposed model observe the solar wind speed and interplanetary magnetic field. The credibility of the high-energy electron flux converges to zero in this evaluation, leading to accurate forecasting for typical examples of a coronal hole and CMEs. The proposed model forecasts the binary status of whether the flux level is higher than the threshold. The forecast is conducted by evaluating the credibility of the sensors. The credibility is calculated by evaluating the data obtained from multiple sensors observing the same kinds of data but at distinct locations. The observation is done spatiotemporally from multiple satellites at distinct locations. The previous model involves only two sensors: a solar wind parameter and a high-energy electron flux [13]. The proposed model involves multiple sensors located at distinct locations for forecasting, thereby allowing more reliable forecasting than that of the previous model. Our proposed model is different approach compared with ones such as statistical analysis [2], neural networks [6, 10, 11, 12, 13] and physics-based model [25]. The proposed model expands _in-situ_ sensing that not only monitors space environmental data but also processes (evaluates) observed data. In the statistical analysis and neural networks, the prediction of space weather can be done on a computer. These two approaches need to collect the observed data from the satellites into ground stations of one space. In our proposed model, sensors on the satellites exchange the observed data and they diagnose each other based on their predefined profiles. The proposed model suggests the way for space weather forecasting without transferring huge data to ground stations. The demonstrations, in this paper, suggest that the space weather forecasting based on the satellite network would be capable in real-time in space. We are able to extend the dynamic relational network by adding links between sensors of the satellites in the proposed model. The proposed model is coped with a new arrangement of a satellite for the satellite network.