Survey of Inter-satellite Communication for Small Satellite Systems: Physical Layer to Network Layer View

Small satellite systems enable whole new class of missions for navigation, communications, remote sensing and scientific research for both civilian and military purposes. As individual spacecraft are limited by the size, mass and power constraints, mass-produced small satellites in large constellations or clusters could be useful in many science missions such as gravity mapping, tracking of forest fires, finding water resources, etc. Constellation of satellites provide improved spatial and temporal resolution of the target. Small satellite constellations contribute innovative applications by replacing a single asset with several very capable spacecraft which opens the door to new applications. With increasing levels of autonomy, there will be a need for remote communication networks to enable communication between spacecraft. These space based networks will need to configure and maintain dynamic routes, manage intermediate nodes, and reconfigure themselves to achieve mission objectives. Hence, inter-satellite communication is a key aspect when satellites fly in formation. In this paper, we present the various researches being conducted in the small satellite community for implementing inter-satellite communications based on the Open System Interconnection (OSI) model. This paper also reviews the various design parameters applicable to the first three layers of the OSI model, i.e., physical, data link and network layer. Based on the survey, we also present a comprehensive list of design parameters useful for achieving inter-satellite communications for multiple small satellite missions. Specific topics include proposed solutions for some of the challenges faced by small satellite systems, enabling operations using a network of small satellites, and some examples of small satellite missions involving formation flying aspects.Comment: 51 pages, 21 Figures, 11 Tables, accepted in IEEE Communications Surveys and Tutorial

Gravitational microlensing and dark matter in the galactic halo

We present the basics of microlensing and give an overview of the results obtained so far. We also describe a scenario in which dark clusters of MACHOs (Massive Astrophysical Compact Halo Objects) and cold molecular clouds (mainly of $H_2$) naturally form in the halo at galactocentric distances larger than 10-20 kpc. Moreover, we discuss various experimental tests of this picture in particular a $\gamma$-ray emission from the clouds due to the scattering of high-energy cosmic-ray protons. Our estimate for the $\gamma$-ray flux turns out to be in remarkably good agreement with the recent discovery by Dixon et al. of a possible $\gamma$-ray emission from the halo using EGRET data.Comment: 14 pages, to appear in the proceedings of the 3K Cosmology Conference (Rome, october 1998), added references and minor change

Wireless Sensor Networks:A case study for Energy Efficient Environmental Monitoring

Energy efficiency is a key issue for wireless sensor networks, since sensors nodes can often be powered by non-renewable batteries. In this paper, we examine four MAC protocols in terms of energy consumption, throughput and energy efficiency. A forest fire detection application has been simulated using the well-known ns-2 in order to fully evaluate these protocols

The Cloudy Universe

Modelling of Extreme Scattering Events suggests that the Galaxy's dark matter is an undetected population of cold, AU-sized, planetary-mass gas clouds. None of the direct observational constraints on this picture -- thermal/non-thermal emission, extinction and lensing -- are problematic. The theoretical situation is less comfortable, but still satisfactory. Galactic clouds can survive in their current condition for billions of years, but we do not have a firm description for either their origin or their evolution to the present epoch. We hypothesise that the proto-clouds formed during the quark-hadron phase transition, thereby introducing the inhomogeneity necessary for compatibility with light element nucleosynthesis in a purely baryonic universe. We outline the prospects for directly detecting the inferred cloud population. The most promising signatures are cosmic-ray-induced H-alpha emission from clouds in the solar neighbourhood, optical flashes arising from cloud-cloud collisions, ultraviolet extinction, and three varieties of lensing phenomena.Comment: 16 pages, LaTeX, no figures, to appear in Pub. Ast. Soc. Au

The FireBird Mission – A Scientific Mission for Earth Observation and Hot SpotDetection

More than 10 years ago the first specialized small satellite for hot spot recognition and fire observation was designed, built and operated by several DLR departments. This BIRD (Bi-spectral Infra Red Detection) satellite demonstrated the capability of fire monitoring from space by using a dedicated small satellite and sensor system. On the other hand it has shown that DLR is capable to manage nearly a complete space mission “in house”. The comparison of typical BIRD data with the well-known MODIS fire products led to the label “fire zoom” for BIRD data. It is due to the high geometric and radiometric resolution of BIRD fire products. Typically small fires with a diameter of 4m could be detected. The precise estimation of fire parameters was successfully shown without problems like false alarms. The success of BIRD opened the doors for next steps. The scientific DLR Earth observation mission “FireBird” will continue the fire monitoring topic by using two small satellites (TET-1, launched June 2012, BIROS launch planed 2014). The paper shall present this mission. It will finally be focused on possible interfaces for a desired worldwide international scientific cooperation within this running space mission

High-Resolution Road Vehicle Collision Prediction for the City of Montreal

Road accidents are an important issue of our modern societies, responsible for millions of deaths and injuries every year in the world. In Quebec only, in 2018, road accidents are responsible for 359 deaths and 33 thousands of injuries. In this paper, we show how one can leverage open datasets of a city like Montreal, Canada, to create high-resolution accident prediction models, using big data analytics. Compared to other studies in road accident prediction, we have a much higher prediction resolution, i.e., our models predict the occurrence of an accident within an hour, on road segments defined by intersections. Such models could be used in the context of road accident prevention, but also to identify key factors that can lead to a road accident, and consequently, help elaborate new policies. We tested various machine learning methods to deal with the severe class imbalance inherent to accident prediction problems. In particular, we implemented the Balanced Random Forest algorithm, a variant of the Random Forest machine learning algorithm in Apache Spark. Interestingly, we found that in our case, Balanced Random Forest does not perform significantly better than Random Forest. Experimental results show that 85% of road vehicle collisions are detected by our model with a false positive rate of 13%. The examples identified as positive are likely to correspond to high-risk situations. In addition, we identify the most important predictors of vehicle collisions for the area of Montreal: the count of accidents on the same road segment during previous years, the temperature, the day of the year, the hour and the visibility