local storage
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Currently, when satellite data volumes grow rapidly and exceed petabyte values and their quality provides reliable analysis of long-term time series, traditional data handling methods assuming local storage and processing may be impossible to implement for small or distributed research teams. Thus, new methods based on modern web technologies providing access to very large distributed data archives are gaining increasing importance. Furthermore, these new data handling solutions should provide not just access but also analysis and processing features, similar to desktop solutions. This paper describes the VEGA-Science web GIS—an open-access novel tool for satellite data processing and analysis. The overview of its architecture and basic technical components is given, but most attention is paid to examples of actual system application for various applied and research tasks. In addition, an overview of projects using the system is given to illustrate its versatility and further development directions are considered.

A system and method for facilitating a visually impaired person for identifying a person. The method includes the step of storing a plurality of instructions for facilitating the visually impaired person identify the person in front of them by their face and/or voice characteristics by updating our project with Mask Detection using OpenCv and keras. It includes the step of receiving voice signals from the person present in surrounding of the visually impaired person and includes the step of capturing the pictures of a particular person and their surroundings of the visually impaired person and storing the processed data into the database or any storage devices. The data will be processed to AWS server or any local storage for processing and determining the person with the help of the database we already have. After processing and identifying the person with the help of face and voice recognition modules the name is sent to the visually impaired user’s phone in the form of a text message which will be read aloud by his phone’s virtual assistant.

Abstract Aiming at the large-scale, compatibility and reliability problems faced by the data acquisition engineering for power consumption in the industrial field, for reducing the complexity and cost of the data acquisition engineering, and improving the quality, maintainability, scalability and manageability of the data acquisition, an engineering-oriented, intrusive, low-frequency data acquisition scheme for power consumption is proposed. The scheme leverages multi-agent networking technology to solve the large-scale problem, utilizes the communication driver of the dynamic loading adapter mode to tackle the compatibility trouble. Furthermore, the local storage, breakpoint retransmission, flow control, automatic recovery, automatic connection, and time synchronization are combined to solve the reliability issue. Engineering tests show that the proposed scheme possesses the significant backward compatibility characteristics, which can effectively reduce the complexity and cost of data acquisition engineering for power consumption, and significantly improve the quality, maintainability, scalability and manageability of data acquisition.

Abstract The nature of Oil and Gas production presents numerous challenges for creating and maintaining reliable electrical infrastructure. Production assets are often widely dispersed over remote geographical areas, leaving them vulnerable to wildlife, storms and poor load management. Unfortunately for ESPs, even an extremely short interruption in electrical service can lead to significant downtime, stress on equipment, and additional field labor costs. The economic and operational advantages to providing a readily available, alternative source of power during these short duration events is significant. This paper describes an effective, economical approach that is based on locally situated energy storage with the appropriate control circuitry. The existing Variable Speed Drive (VSD) architecture allows for the storage system to be constantly connected to the ESP's electrical system, allowing for an ever-present power supply without the need for mechanical switching. The ride-through system was designed to keep a fully loaded ESP system, of corresponding size, operational during most temporary power disturbances. At the conclusion of rigorous laboratory testing, the ride-through system was installed in the field and was subjected to simulated outages. Precision electrical monitoring equipment was installed to record voltage and current during genuine disruptions and interruptions. Records from field testing and genuine events confirmed the viability of local storage systems utilizing supercapacitors. The energy storage system provided a steady supply of power and consistently prevented shutdowns during common power system disruptions. Due to its capacity to store a considerable amount of power, the system demonstrated an ability to ride-through both multiple disturbances in short succession as well as outages lasting as long as 8 seconds, even under full load.

The global climate change mitigation efforts have increased the efforts of national governments to incentivize local households in adopting PV panels for local electricity generation. Since PV generation is available during the daytime, at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of local generation to a later time. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation conditions in the network. This paper proposes a metaheuristic approach based on a genetic algorithm that considers three different scenarios of using energy storage for reducing the energy losses in the network. Two cases considers the battery placement and operation under the direct control of the network operator, with single and multiple bus and phase placement locations. Here, the aim was to maximize the benefit for the whole network. The third case considers selfish prosumer battery management, where the storage owner uses the batteries only for their own benefit. The optimal design of the genetic algorithm and of the solution encoding allows for a comparative study of the results, highlighting the important strengths and weaknesses of each scenario. A case study is performed in a real distribution system.

P2P network enables users to share resources effectively. However, with the advent of the big data era, the sensitive data of users in P2P network are also increasing dramatically. In order to solve the contradiction between the huge amount of sensitive data and the limited local storage space, an increasing number of users choose to encrypt their sensitive data and store them in the cloud server. For the problem of the secure storage and flexible access of large amounts of user data in P2P networks, an edge-cloud-assisted multiuser forward secure searchable encryption scheme is proposed. The scheme uses the proxy reencryption technique to optimize the multiuser searchable encryption and prevent the decryption key from being directly transmitted between users. By introducing an edge-cloud architecture, the system achieves efficient communication and timely response capabilities. The security analysis proves that our scheme achieves the CPA (chosen-plaintext attack) security based on DBDH assumption and the forward privacy. Finally, the theoretical and experimental comparisons between this scheme and other schemes show that our scheme has high efficiency in the process of data update, search, and trapdoor generation. In addition, due to the use of edge-cloud architecture, our scheme reduces about 90% and 75% of the user’s consumption in the encryption and token generation process.

The global climate change mitigation efforts have increased the efforts of national government to incentivize local households in adopting individual renewable energy as a mean to help reduce the usage of electricity generated using fossil fuels and to gain independence from the grid. Since the majority of residential generation is made by PV panels that generate electricity at off-peak hours, the optimal management of such installations often considers local storage that can defer the use of locally generated electricity at later times. On the other hand, the presence of distributed generation can affect negatively the operating conditions of low-voltage distribution networks. The energy stored in batteries located in optimal places in the network can be used by the utility to improve the operation of the network. This paper proposes a metaheuristic approach based on a Genetic Algorithm that considers three different scenarios of using energy storage for reducing the losses in the network. Prosumer and network operator priorities can be considered in different scenarios inside the same algorithm, to provide a comparative study of different priorities in storage placement. A case study performed on a real distribution network provides insightful results.

In the past years, the Internet of Things (IoT) used in different applications and very wide range of fields (e.g. cloud services, smart environments, logistics, social and personal domains, and healthcare services). The IoT included a variety of components and devices such as RFID devices, wireless sensors, actuators, and wireless networks. Also, the IoT with smart devices adopted in various companies, organizations and public services systems. For instance, some devices like Notebooks and smartphones have been used to perform different management activities and duties. These smart devices relied on data exchange and data storage resources in clouds computing services. In this context, the saved data and exchanged data required protection against hacking operations, transferred with more secure communications channels, and safe storage environment in the clouds and local storage systems. In this paper, we proposed some encryption algorithms to tackle the issue of data confidentiality in the IoT applications. This research provided analysis and investigation of these encryption algorithms in light of components of the designs, versions of these algorithms, encryption keys, block size, round functions and the techniques used in the designs.

AbstractElectrified railways are becoming a popular transport medium and these consume a large amount of electrical energy. Environmental concerns demand reduction in energy use and peak power demand of railway systems. Furthermore, high transmission losses in DC railway systems make local storage of energy an increasingly attractive option. An optimisation framework based on genetic algorithms is developed to optimise a DC electric rail network in terms of a comprehensive set of decision variables including storage size, charge/discharge power limits, timetable and train driving style/trajectory to maximise benefits of energy storage in reducing railway peak power and energy consumption. Experimental results for the considered real-world networks show a reduction of energy consumption in the range 15%–30% depending on the train driving style, and reduced power peaks.