## 1 Introduction  
Satellites play an important role in social infrastructure. Satellites provide numerous services: Telecommunications, global positioning system, broadcasting and weather observations and are equipped with sensors. Such sensors, mounted on satellites and spacecrafts could become unavailable due to scheduled satellite operations, technical failures, external disturbances and internal deficiency [1; 2]. As a result, failures in sensors can lead to the degraded performance of satellites and their networks including related services as space weather forecasting [2; 3].  
This paper attempts to extend a dynamic relational network [3; 4; 5; 6] which supports missing of observations. The dynamic relational network is composed of the sensors capable of monitoring particular subjects and processing the data. Each sensor diagnoses other sensors based on relations among them. The faulty sensors are detected by a distributed manner within the dynamic relational network. The dynamic relational network is applied to various kinds of fields: Combustion systems [4], detection of human behaviors [6], space weather forecasting [3] and a plant system [5]. However, the faulty or unavailable sensors affect to a diagnosis of the sensors since some sensors are irresponsible for communications. In the sensor networks, the crucial sensors would fail due to troubles of their electrical circuits or technical reasons. The compromised sensors are needed to be complemented with other surviving sensors.  
Numerous studies have tackled construction of resilient computer networks or sensor networks [7; 8; 9; 10; 11]. The definition of resilience is discussed in various papers [8] and is different according to their objectives. In these studies, the functions of the missing nodes are recovered or compensated in two ways: replication and complement. For the first approach [10], the functions of the nodes are fully or partially replicated by other nodes. For the second approach [7; 11] the functions of the nodes are compensated by actions of other nodes. In the second approach, the networks allow normal sensors to be faulted sensors or malicious states of the sensors, because, their functions are compensated by other nodes.  
This paper attempts to compensate the missing data and functions of the faulted nodes in the sensor networks by information processing [12]. The functions of the sensor nodes are virtually created by interactions of neighbor sensors. Further, the missing data of observations by the faulty sensors are also interpolated with information of other sensors. In the proposed model, normal sensors do not require the real sensors for substituting the faulted sensors. In simulations, this paper considers a possibility of the proposed model for the construction of the resilient sensor network. As an example, the demonstration of the proposed model shows a prediction performance for space weather forecasting involving spatiotemporal interpolation. The sensor network for space weather forecasting predicts the high-energy electron flux at geostationary orbit (GEO). The prediction of the high-energy flux at GEO is vital for satellite operation since \"hot\" (\(\sim\)keV electron temperatures) plasmas and energetic ( MeV) electrons can lead to electrostatic charging [13] and therefore to satellite failures [1].  
The remainder of this paper is organized as follows. Section 2 describes the extension of a dynamic relational network for spatiotemporal interpolation. Further, it also explains profiling statistical properties of observation data by a Support Vector Machine (SVM) [14]. Section 3 examines the performances of the proposed model for missing observations. The sensor network for space weather forecasting is chosen as an example for evaluation. This paper compares simulation results with the observed data. Section 4 discussed a significance of our proposed model and remained issues. Section 5 provides a summary and concludes this paper.