Technologies for Deviation of Asteroids and Cleaning of Earth Orbit by Space Debris

The present chapter presents the advanced design and technology of special equipment (SECSL) which uses concentrated solar light for deviation of asteroids and cleaning the space of debris. The elliptical orbit of any cosmic body as presented in Einstein’s general theory of relativity is rotating around the ellipse center. The trajectory of a cosmic body is permanently affected by the gravity of other moving cosmic bodies. In the case of asteroids (relatively small masses), orbit changes can lead to a collision with the Earth. At this very moment, our civilization has no efficient and reliable mean to destroy or divert a cosmic body heading toward the Earth. This new idea represents a “light canon” which uses concentrated solar light for deviation or vaporization of dangerous asteroids. The equipment is composed out of two parabolic mirrors (one large and one small) with the same focal point and coincident axes. The mirrors reflect the sunlight between them hence the term “concentrated solar light.” Next, a similar idea to the SECSL equipment is presented but applied to space debris caused mostly by humans and a new way of disintegrating satellites, spent rocket boosters, thrust chambers, etc. in the Earth’s atmosphere during reentry

Sonic Boom Mitigation through Shock Wave Dispersion

Lately, the interest for passenger space planes, supersonic passenger aircraft, and supersonic business jets has greatly increased. In order to mitigate the sonic boom effects at ground level, some aerospace companies proposed airplanes that have a very small transversal fuselage section or that have a curved (“shaped”) fuselage. Obviously, shaping the fuselage leads to the increase of dynamic drag and manufacturing cost. Reducing the fuselage transverse section leads to reducing the useful volume inside fuselage and increases the landing distance of aircraft. The solution presented in this chapter shows that it is theoretically and technologically possible as the shock wave to be dispersed through mechanical or electrical means. The shock wave is in fact a stationary effect generated by the move of aircraft with constant speed relatively to surrounding air. If this feature is in a way or another canceled, the shock wave is dispersing. Due to dispersion of the shock wave the ‘N’ wave at the ground is tens of times larger and the sonic boom is correspondingly lower. The shock wave dispersion system of the future could be mechanical or electrical is activated only when the supersonic aircraft/space plane is flying horizontally over community

Lameness Detection as a Service: Application of Machine Learning to an Internet of Cattle

Lameness is a big problem in the dairy industry, farmers are not yet able to adequately solve it because of the high initial setup costs and complex equipment in currently available solutions, and as a result, we propose an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to identify lame dairy cattle. As part of a real world trial in Waterford, Ireland, 150 dairy cows were each fitted with a long range pedometer. The mobility data from the sensors attached to the front leg of each cow is aggregated at the fog node to form time series of behavioral activities (e.g. step count, lying time and swaps per hour). These are analyzed in the cloud and lameness anomalies are sent to farmer’s mobile device using push notifications. The application and model automatically measure and can gather data continuously such that cows can be monitored daily. This means there is no need for herding the cows, furthermore the clustering technique employed proposes a new approach of having a different model for subsets of animals with similar activity levels as opposed to a one size fits all approach. It also ensures that the custom models dynamically adjust as weather and farm condition change as the application scales. The initial results indicate that we can predict lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness. Index Terms—Lameness, Internet of Things (IoT), Data Analytics, Smart Agriculture, Machine Learning, Micro services, Fog Computing. I

Fog assisted application support for animal behaviour analysis and health monitoring in dairy farming

With the exponential growth rate of technology, the future of all activities, including dairy farming involves an omnipresence of widely connected devices. Internet of things (IoT), fog computing, cloud computing and data analytics together offer a great opportunity to increase productivity in the dairy industry. In this paper, we present a fog computing assisted application system for animal behaviour analysis and health monitoring in a dairy farming scenario. The sensed data from sensors is sent to a fog based platform for data classification and analysis, which includes decision making capabilities. The solution aims towards keeping track of the animals' well-being by delivering early warning alerts generated through behavioural analytics, thus aiding the farmer to monitor the health of their livestock and the capability to identify potential diseases at an early stage, thereby also helping in increasing milk yield and productivity. The proposed system follows a service based model, avoids vendor lock-in, and is also scalable to add new features such as the detection of calving, heat, and issues like lameness

SmartHerd Management: A Microservices Based Fog Computing Assisted IoT Platform towards Data Driven Smart Dairy Farming

Internet of things (IoT), fog computing, cloud computing and data driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing assisted end-to-end IoT platform for animal behaviour analysis and health monitoring in a dairy farming scenario. The platform follows a microservices oriented design to assist the distributed computing paradigm, and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6 month mature real-world deployment, wherein the data from wearables on cows is sent to a fog based platform for data classification and analysis, which includes decision making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog based computational assistance in the SmartHerd setup, we see an 84\% reduction in amount of data transferred to the cloud as compared to the conventional cloud based approach

Experimental Investigations on the Possibility to Apply the Corrugated Sheet Metal Used in the Past with Junkers Aircraft to Reduce Noise for Future European Aircraft. Other Noise Reduction Experiments for Future European Aircraft

This paper shows that corrugated skin used in the past with Junkers aircraft to increase the fuselage and wing rigidity can lead to noise reduction for future European aircraft. If the pressure side of wing which is placed above the engine is corrugated, the jet noise reflected by wing will be scattered. This way, the diffuse acoustic field has a lower intensity at ground level and correspondingly, a lower impact on community. Similarly, it is shown that if the underside of fuselage is corrugated, the noise emitted by the nose landing-gear and main landing-gear is also scattered. The existence of this effect is demonstrated by some recent measurements done inside auto-tunnels covered inside with corrugated sheet metal which indicated a reduction of maximum noise level by up to 30%. Some experiments done by the authors at low scale on an Airbus A380 wing model (scale 1:375) showed that the jet-noise reflected by the corrugated skin of wing is reduced by 4 dB in the near field. Reintroducing corrugated skin in the manufacturing process of modern aircraft is beneficial because, on the one hand, it reduces the jet and the landing-gear noise discomfort and, on the other hand, it permits manufacturing stronger frames for passenger aircraft/airliners

Connected Cows: Utilizing Fog and Cloud Analytics toward Data-Driven Decisions for Smart Dairy Farming

The Internet of Things (IoT) is about connecting people, processes, data, and things, and is changing the way we monitor and interact with things. An active incorporation of information and communication technology coupled with sophisticated data analytics approaches has the potential to transform some of the oldest industries in the world, including dairy farming. It presents a great opportunity for verticals such as the dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. Dairy farms have all the constraints of a modern business -- they have a fixed production capacity, a herd to manage, expensive farm labor, and other varied farm-related processes to take care of. In this technology-driven era farmers look for assistance from smart solutions to increase profitability and to help manage their farms well. We present an end-to-end IoT application system with fog assistance and cloud support that analyzes data generated from wearables on cows' feet to detect anomalies in animal behavior that relate to illness such as lameness. The solution leverages behavioral analytics to generate early alerts toward the animals' well being, thus assisting the farmer in livestock monitoring. This in turn also helps in increasing productivity and milk yield by identifying potential diseases early on. The project specializes in detecting lameness in dairy cattle at an early stage, before visible signs appear to the farmer or an animal expert. Our trial results in a real-world smart dairy farm setup, consisting of a dairy herd of 150 cows in Ireland, demonstrate that the designed system delivers a lameness detection alert up to three days in advance of manual observation

Machine learning based fog computing assisted data-driven approach for early lameness detection in dairy cattle

Timely lameness detection is one of the major and costliest health problems in dairy cattle that farmers and practitioners haven't yet solved adequately. The primary reason behind this is the high initial setup costs, complex equipment and lack of multi-vendor interoperability in currently available solutions. On the other hand, human observation based solutions relying on visual inspections are prone to late detection with possible human error, and are not scalable. This poses a concern with increasing herd sizes, as prolonged or undetected lameness severely compromises cows' health and welfare, and ultimately affects the milk productivity of the farm. To tackle this, we have developed an end-to-end IoT application that leverages advanced machine learning and data analytics techniques to monitor the cattle in real-time and identify lame cattle at an early stage. The proposed approach has been validated on a real world smart dairy farm setup consisting of a dairy herd of 150 cows in Waterford, Ireland. Using long-range pedometers specifically designed for use in dairy cattle, we monitor the activity of each cow in the herd. The accelerometric data from these sensors is aggregated at the fog node to form a time series of behavioral activities, which are further analyzed in the cloud. Our hybrid clustering and classification model identifies each cow as either Active, Normal or Dormant, and further, Lame or Non-Lame. The detected lameness anomalies are further sent to farmer's mobile device by way of push notifications. The results indicate that we can detect lameness 3 days before it can be visually captured by the farmer with an overall accuracy of 87%. This means that the animal can either be isolated or treated immediately to avoid any further effects of lameness. Moreover, with fog based computational assistance in the setup, we see an 84% reduction in amount of data transferred to the cloud as compared to the conventional cloud based approach