Fault Management Architecture Based on a Digital Twin Approach

Fault management of systems is a key component in mission/operation success of each system or technology. Fault management can be implemented into various different applications, power generation, industrial processing, aviation and transportation, and electrical grids with combinations of renewable energy sources. As the complexity of the overall system design increases, reliance on just pure physics-based or pure data-based modeling is shown to be deficient in the accuracy of fault management. This work shows the potential of a combination of digital twin and a fault management algorithm. The algorithm is designed to be robust, accurate, reliable, and fast; it is based on both, physics and data-based model modeling. The algorithm compares physical and data-based approaches to provide the most reliable fault management solution, through a digital twin. The fault management algorithm is designed to use physics-based model validated on real/synthetic data (data-based model).

Afghanistan Post US Withdrawal: Security Challenges and Future Prospects

After 9/11, the mission “Operation Enduring Freedom” was to root out terrorists from Afghanistan and to establish good governance in shape of democracy. However, the current internal law and order situation is worse even after years of US forces staying in Afghanistan. While President Trump has directed Pentagon to decrease nearly half of the more than 14,000 troops stationed in Afghanistan. All the regional and global powers have their interest in Afghanistan, have no clear policies for peace and development, but everyone is in struggle to solve the Afghan problem according to their own interest. To establish democracy, improve governance, rule of law and security situation in Afghanistan, the US withdrawal without proper homework will create more problems and lead to chaos. It is believed that the abrupt withdrawal may further strengthen the Taliban and increase violence in Afghanistan. This paper aims to investigate those internal and external factors which may become the cause of insecurity for Afghanistan. Furthermore, the research work enlightens over the solution of security problems in Afghanistan and suggests possible option.

Human Adaptations to Multiday Saturation on NASA NEEMO

Human adaptation to extreme environments has been explored for over a century to understand human psychology, integrated physiology, comparative pathologies, and exploratory potential. It has been demonstrated that these environments can provide multiple external stimuli and stressors, which are sufficient to disrupt internal homeostasis and induce adaptation processes. Multiday hyperbaric and/or saturated (HBS) environments represent the most understudied of environmental extremes due to inherent experimental, analytical, technical, temporal, and safety limitations. National Aeronautic Space Agency (NASA) Extreme Environment Mission Operation (NEEMO) is a space-flight analog mission conducted within Florida International University’s Aquarius Undersea Research Laboratory (AURL), the only existing operational and habitable undersea saturated environment. To investigate human objective and subjective adaptations to multiday HBS, we evaluated aquanauts living at saturation for 9–10 days via NASA NEEMO 22 and 23, across psychologic, cardiac, respiratory, autonomic, thermic, hemodynamic, sleep, and body composition parameters. We found that aquanauts exposed to saturation over 9–10 days experienced intrapersonal physical and mental burden, sustained good mood and work satisfaction, decreased heart and respiratory rates, increased parasympathetic and reduced sympathetic modulation, lower cerebral blood flow velocity, intact cerebral autoregulation and maintenance of baroreflex functionality, as well as losses in systemic bodyweight and adipose tissue. Together, these findings illustrate novel insights into human adaptation across multiple body systems in response to multiday hyperbaric saturation.

RGB Image Prioritization Using Convolutional Neural Network on a Microprocessor for Nanosatellites

Nanosatellites are being widely used in various missions, including remote sensing applications. However, the difficulty lies in mission operation due to downlink speed limitation in nanosatellites. Considering the global cloud fraction of 67%, retrieving clear images through the limited downlink capacity becomes a larger issue. In order to solve this problem, we propose an image prioritization method based on cloud coverage using CNN. The CNN is designed to be lightweight and to be able to prioritize RGB images for nanosatellite application. As previous CNNs are too heavy for onboard processing, new strategies are introduced to lighten the network. The input size is reduced, and patch decomposition is implemented for reduced memory usage. Replication padding is applied on the first block to suppress border ambiguity in the patches. The depth of the network is reduced for small input size adaptation, and the number of kernels is reduced to decrease the total number of parameters. Lastly, a multi-stream architecture is implemented to suppress the network from optimizing on color features. As a result, the number of parameters was reduced down to 0.4%, and the inference time was reduced down to 4.3% of the original network while maintaining approximately 70% precision. We expect that the proposed method will enhance the downlink capability of clear images in nanosatellites by 112%.

Ivory Coast-Burkina Faso border areas pose fresh risks

Significance The attack, which killed at least twelve members of the security forces, was an apparent act of revenge for the recent joint Ivorian-Burkinabe counterterrorist mission ‘Operation Comoe’. The attack shows a worrying similarity to jihadist operations in Burkina Faso, where militants regularly raid rural security posts. Impacts Facing potential sporadic attacks, Ivorian security forces may struggle initially to attune to counterinsurgency tactics in the border area. Given the more potent insurgencies in northern and eastern Burkina Faso, Ouagadougou will not consider the south-west a top priority. Rumours over the alleged involvement of dissident Ivorian politicians in the latest attack could elevate tensions in the electoral arena.

Epidemic may shake Chad's government

Subject COVID-19 impact on Chad. Significance Chad has a relatively low number of confirmed COVID-19 cases but appears quite vulnerable to the impact of the pandemic, especially the economic impact. The country’s highly rural and youthful demography may help to slow the spread and keep the death rate low. Yet low oil prices, a return to recession and a new wave of sector-specific protests could pose major challenges for the government. Impacts Chad's epidemic appears unlikely to affect France’s Sahel counterterrorism mission Operation Barkhane, headquartered in Chad. A bottom-up revolution appears unlikely, and no major rebel challengers appear poised to take advantage of COVID-19 and associated crises. President Idriss Deby's government appears unlikely to fall in the short term -- French backing will continue to ensure his survival.

Model-Based Fault Detection and Diagnosis for Spacecraft with an Application for the SONATE Triple Cube Nano-Satellite

The correct behavior of spacecraft components is the foundation of unhindered mission operation. However, no technical system is free of wear and degradation. A malfunction of one single component might significantly alter the behavior of the whole spacecraft and may even lead to a complete mission failure. Therefore, abnormal component behavior must be detected early in order to be able to perform counter measures. A dedicated fault detection system can be employed, as opposed to classical health monitoring, performed by human operators, to decrease the response time to a malfunction. In this paper, we present a generic model-based diagnosis system, which detects faults by analyzing the spacecraft’s housekeeping data. The observed behavior of the spacecraft components, given by the housekeeping data is compared to their expected behavior, obtained through simulation. Each discrepancy between the observed and the expected behavior of a component generates a so-called symptom. Given the symptoms, the diagnoses are derived by computing sets of components whose malfunction might cause the observed discrepancies. We demonstrate the applicability of the diagnosis system by using modified housekeeping data of the qualification model of an actual spacecraft and outline the advantages and drawbacks of our approach.