## 2 Data and Sensor System
### Profiling: Identifying Events and Their Characteristics  
Since the mission of sensor systems is detection of events, we define profiles as information that can be used to detect events. Profiles may be structured hierarchically from specific events to an intermediate category of collections of events, or events up to the most general kind of abnormal event (and its opposite, the normal event). Profiles are created from statistical data using an SVM [14]: the time-averaged value for the past three days and its standard deviation for each sensor (Figure 2). Both the time-averaged value and its standard deviation are sensitive to the event of interest. For instance, high-speed solar wind is observed during an appearance of a coronal hole of the Sun. In this situation, the time-averaged value increases rapidly and its standard deviation is large. The quiet condition (low-speed solar wind) is quite different from the condition during observation of high-speed solar wind. The event of high-energy electron flux can be considered similarly. Figure 2: Training data generation process. Training data are created from observed data of two sensors on satellites. The training data consists of averaged values, the standard deviations, and the desired output. The profile is described as the parameters of the SVM. The parameters of the SVM are generated from training dataset. The input data for training is created from two kinds of time-series data recorded by the sensors on the satellites. Each time-series data represents the hourly time-averaged values. The time-averaged values and their standard deviations are calculated from the time sequences, and then the input data for training is represented as a vector consisting of four values. The desired output expresses present high-energy electron flux or 24 h ahead also is determined. The normal and abnormal conditions of high-energy electron flux are determined by space weather criteria [24]. The space condition is usually regarded as safe when the high-energy electron flux is smaller than \(10^{3}\) and at the warning level otherwise. For training data generation process, the training data are finally determined with the desired output and input data. The training data for a relation between two sensor nodes consist of four values: time-averaged values for the past three days and their standard deviations for each type of sensor data. The profiles are respectively generated for a tuple of the two sensors (Figure 3). Figure 3 shows an example in that the learning process creates the relation between the desired output and the input training data. The training dataset is prepared and used for learning of the parameters for the SVM. In the learning process, the parameters of the SVM are optimized to make the relation between the input  
Figure 4: Test of observed data recorded by two sensors. The test data are input to an SVM based on the profile. The SVM classifies the condition of the space environment as binary values 1 (normal) or \(-1\) (abnormal). Figure 3: Training process of a profile between two sensors. The training data are input to an SVM. The SVM learns desired outputs from the training data set. data and desired output. The optimized parameters obtained from the learning process are saved as the profile. The dynamic relational network for detecting the events called _appearance of a coronal hole at the Sun_ and CMEs are considered, and the observed data are given to the sensor system. Six sensors from three satellites monitor the solar wind parameters. These sensors monitor the same kind of physical values; however, the spatiotemporal pattern is different owing to their distinct locations. The relation between the two sensors is tested based on the generated profile (Figure 4). The two sensors record the observed data, which are used for the test data for the SVM. The sensor A/B act as a source node and another one does as the target node. In order to create the test data, the time-averaged and its standard deviation are calculated for each sensor.