Seamless Access to the PL-Grid e-Infrastructure Using UNICORE Middleware

Fundamentals of Infrastructure Management

This paper presents the architecture of a multimodal real-time drone classification system that integrates visual and audio modalities on edge devices. Most existing vision-only or audio-only systems tend to fail under adverse weather or high background noise. We present an architectural framework that integrates synchronized imagery and propeller sound through a dual-stream deep learning pipeline to address this deficiency. Visual data is processed using a lightweight object detector, and sound data is converted into spectrograms and analyzed using a lightweight convolutional neural network. Both modalities are combined in a fusion layer to enhance robustness and precision, while ensuring computational lightness. The model is optimized for deployment on low-resource devices, such as NVIDIA Jetson and Raspberry Pi, ensuring it does not rely on cloud connectivity and supports autonomous, real-time deployment. While the present work focuses on system design, ongoing work involves developing a particular multimodal dataset for training and testing purposes. Such a framework provides a foundation for low-cost, scalable, resilient drone surveillance solutions for defense, critical infrastructure, and urban airspace monitoring.

Blockchain is considered to be the key technology to lead the transformation of Information Internet to Value Internet. More and more organizations are exploring the industry applications of blockchain, and how to apply blockchain in data management has become one of the focuses of discussion. This article focuses on blockchain and provides a detailed analysis on its core components, technologies and applications. Then, combs the problems faced by data management in quality, security, sharing and so on, and analyzes the application advantages of blockchain technology in data management, and put forwards a data collaborative management model based on blockchain, which has the characteristics of decentralization, collective maintenance, automatic execution, and non-tamperable. The model covers user authentication, data verification, data logging, data sharing and other processes, and is equipped with a data management incentive system, which can achieve convenient, secure and fast data management. Applying blockchain technology to data management can further improve the effectiveness of data management and improve the quality of data, and create a positive data sharing environment.

The geosciences are a data-heavy discipline, and a wide range of data types and formats are commonly used, even within the same sub-discipline or working group. For example, in hydrology or geomorphology, geospatial data (e.g., satellite imagery, maps, sample locations) are routinely paired with time-series data (e.g., discharge or precipitation monitoring) and laboratory-derived data from individual samples (e.g., isotope chemistry from water samples). For some data types, widely-used community standards exist (e.g., seismic or satellite remote sensing data), stipulating data formats, file types, and relevant metadata. These are known as short-tail data types. Yet, for many data types, either such standards do not exist at all, or several competing standards are used in parallel. These are known as long-tail data types. As a result, research and monitoring data are often not managed and archived according to the FAIR principles or even get lost as researchers move between positions. Yet, many funding agencies require a data management plan and a commitment to open data principles already at the proposal stage. We require a flexible digital infrastructure for data management, that (1) can handle the entire data management chain from upload to publication, (2) is modular and scalable in the sense that it can be set up for individual projects, a workgroup or unit, or entire institutes, (3) is customizable in the sense that it can be set up for different types of data, environments, and tasks, (4) allows for the automation of data management tasks, and (5) can associate rich metadata with individual data files. Here, we introduce SmartLake, a datalake application that integrates a storage environment with a modular metadata catalog and a workflow engine. We describe the concept and architecture of SmartLake, and demonstrate that it can handle a broad range of data management tasks in a flexible way. The workflow engine allows the integration of customizable workflows to retrieve data and metadata, perform quality checks, file type conversions, and standard analysis, transform the data into a form necessary for machine learning, and generate data publications. Once set up, SmartLake can, in principle, automatically handle the entire data management pipeline, thereby minimizing the efforts required for data management, metadata enrichment, archiving, and publication.

According to the requirements of smart electricity meters test data management service system, on the basis of analyzing the insufficient of the traditional smart electricity meters quality inspection tests. A set of mobile phone APP software and smart electricity meters test data management service system based on the B/ S architecture is developed. A universal data management system platform is designed and implemented. The system architecture, system database and the design ideas of each component module in detail is introduced and introduced the technical tools and methods used in each part. Key technologies such as large data volume database management, data backup and recovery are researched. By partitioning smart electricity meters test data, load data and retrieval test on the system platform. Data management efficiency has been greatly improved compared to traditional methods, which meet the requirements for large data volume data management.

In recent years, innovative technologies for monitoring and managing civil infrastructure have been widely used by stakeholders and managing bodies, to ensure the correct and efficient maintenance of these critical elements. In this context, great attention is given to the environment surrounding these assets. Indeed, the environment plays a crucial role in altering the conditions of the civil infrastructures, either as a result of catastrophic natural events or due to the gradual morphological changes over time. Understanding the surrounding conditions of critical infrastructures is therefore essential to ensure accurate predictions regarding the changing conditions of the infrastructures over time, to prevent damages, and to intervene promptly and efficiently when necessary. In addition, comprehending the environmental conditions surrounding critical infrastructures is crucial for ensuring accurate long-term monitoring procedures, mitigating potential damages proactively, and facilitating efficient and timely interventions when necessary. Moreover, several international studies and research projects focused on these topics have been increasingly supported by industry and infrastructure managers in handling data from innovative monitoring technologies. Among these technologies, remote sensing techniques, including satellite analysis using MT-InSAR methods for assessing structural subsidence [1], and change detection techniques to evaluate temporal variations are gaining momentum. Furthermore, the use of Unmanned Aerial Vehicles (UAVs), to integrate information derived from other remote surveys, stands as a crucial topic to be more investigated. This research aims to identify a methodology for managing multi-sensor and multi-scale survey information integrating satellite remote sensing and ground-based Non-Destructive Testing for Digital Twin-based infrastructure monitoring. However, the interpretation of data derived from satellite remote sensing [1] and ground-based Non-Destructive Testing (NDT) [2] techniques remain an area awaiting comprehensive exploration within the realm of transport infrastructure monitoring. This approach is aimed at the definition of a Digital Twin of the analyzed infrastructure and the environment in which it is located. An experimental application was developed selecting a bridge, located in Italy, identified as a case study. Several data obtained from inspections performed by UAVs and satellite remote sensing were implemented, as well as a digital modeling process, specifically developed for integrating such database to create a Digital Twin of the bridge and the environment. This application stands as a starting point for defining a broader integrated monitoring methodology for the management of critical transport infrastructures. AcknowledgementsThis research is supported by the Project “SIMICOM”  accepted and funded by the Lazio Region, ItalyReferences[1] Gagliardi V., et Al. Digital twin implementation by multisensors data for smart evaluation of transport infrastructure. SPIE Optical Metrology. Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, Munich, 2023. [2] Tosti F., et Al "Integration of Remote Sensing and Ground-Based Non-Destructive Methods in Transport Infrastructure Monitoring: Advances, Challenges and Perspectives," 2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS), Jakarta Pusat, Indonesia, 2021, pp. 1-7, 

디지털 정보기술과 인터넷의 발달로 인해 데이터는 여러 학문과 사회 분야에서 의미 있는 결과를 도출하고 의사결정과 정책을 수립하는 데 있어서 중요하다. 따라서 대규모의 데이터를 축적하고 관리하는 업무를 담당하는 데이터 큐레이터는 데이터 관리의 효율성과 활용성을 높이는데 필수적인 역할을 수행한다. 본 연구는 데이터 큐레이터의 핵심 직무 요건을 규명하고자 한다. 이를 위해 두 종류의 데이터를 수집하여 분석하였다. 첫째는 2011년부터 2014년까지 총 255건의 국외 데이터 큐레이터 구인 공고를 수집하였으며, 둘째는 국내 데이터 관리 실무자 5명과의 심층면담을 실시했다. 수집된 두 가지 데이터는 선행연구에서 제시한 7가지 핵심 직무 요건을 바탕으로 범주화하여 분석하였다. 데이터 분석 결과 데이터 큐레이터의 핵심 직무 요건을 4가지 범주로 구분할 수 있다. 본 연구의 결과 데이터 큐레이터의 핵심 직무 요건은 효율적인 데이터 관리 직무 수행을 위해 이해관계자와 원활하게 소통할 수 있어야 하며, 이와 함께 정보기술 지식을 기반으로 데이터 관리 시스템을 구축하고 운영할 역량이 필요하다. 또한 데이터 관리 지식 및 전략을 이해하고 마지막으로 데이터 관리 이용자 교육 및 데이터 관련 서비스와 도구를 제공할 수 있어야 한다. 이를 통해 데이터 관리에 있어 4가지 범주로 구분되는 다양한 측면의 요건이 요구되고 있음을 도출하였으며, 이를 위한 데이터 관리에 대한 통합된 전문적인 교육과정의 필요성을 제안했다. As the digital technologies and internet have advanced, data have centered in the process of meaningful scientific ramifications and policy making in a wide variety of fields. Data curator in charge of managing data plays a significant role in terms of improving the effectiveness and efficiency of data management and re-uses. The purpose of this study is to identify the core competencies for data curator. For achieving the purpose of this study, two sets of data were collected. First, a total of 255 job descriptions were collected from the web sites including ARL, Digital Curation Exchange, Code4lib, ASIS&T JobLine for the period of 2011-2014. Second, in-depth interviews with five data curators from four diverse organizations were collected. The two sets of data were analyzed into seven categories identified from the related studies. Findings of this study showed that core competencies for data curator were identified into four categories, communication skills, data management techniques, knowledge and strategies for data management, and instructions and service provisions for users. The implications of this study can be considered as integrated and professional curriculum developments for data curator with core competencies.

Information management methods of oil production plant of the traditional way are backward. All the departments are in the stand-alone mode, lacking a unified information network platform, generating large amounts of information islands. This backward working model has seriously hindered the development of oil production plant. This paper uses LC-40DR-A-type multiphase flow monitor as remote data acquisition terminal, focusing on three aspects of data acquisition, data management, and data application, to establish the remote data acquisition and control system improvement. The realization of the five major functions—real-time monitoring, automatic alarm, the user management, report management, and equipment management—can not only improve the measurement accuracy, but also provide strong support for the daily production management in oil production plant.

Case study describing the pillars, personnel, and process of developing the TARDIS:REFLUX smartphone app

Frequently, offshore oil spills have a significant impact on the marine environment and fishery resources, resulting in huge economic losses. Due to a lot of projects are involved in oil spill and the process is complex, the injured party lacks the evidence to support the evaluation of the oil spill impact on the environment. Therefore, many oil spill claims event had not been properly resolved. In this paper, we designed an environmental impact assessment system of offshore oil spill for improving the above-mentioned situation. The major functions of this system are data management, query analysis, spatial modeling analysis, evaluation of forecast, user management and so on. Environmental impact assessment system of oil spill can predict and evaluate the oil spill’s impact on environment. It’s significant for the protection of the environment and planning. In addition, it also has a realistic significance for providing some basis for claims issues.

Technology advancements blend to masses with diverse and heterogeneous environments. The Internet of Things (IoT) and Cloud Computing has broken through reachability and availability of services. The security, privacy requirements and challenges manifest with the heterogeneity and the wide range of application domains posing a major concern in adoption of IoT. The literature addresses these changing requirements individually. The paper presents a top-down survey starting with general security challenges in IoT moving to security and privacy challenges, and later exploring security frameworks of IoT. The millions of wide range of devices produce a huge amount of data. Identifying and authenticating these devices and allowing them to process their data locally improves the performance, reduces the bandwidth usage, increases the battery usage and reduces the security risk of attacks making the devices self reliant. The survey identifies the less explored research gaps to group them and address the concerns related to device management, user management, key management, trust management and data management. The surveyed secured frameworks in IoT address the multiple dimensions of security concerns and their aspects.

Security policy and security techniques have long been major research topics, but little work has been reported on management of distributed security applications other than Intrusion Detection Systems (IDS). We believe management of secure applications will see dramatic growth as adequate tools become available. This paper reviews security management issues and suggests management objects for use by Simple Network Management Protocol (SNMP) tools to collect, log, and display events and alert operators to possible trouble. We focus on the issues of adapting the necessary status and control mechanisms (management infrastructure and agents) to accommodate security management needs. To make a complete security Management Information Base (MIB), additional MIB modules must be defined for firewalls (packet filtering and proxy servers), security audit trails and IDS management parameters. Security firewalls and IDS applications are assessed for management via SNMP and are proposed as future case studies. The Packet Filter Information Protocol (PFIP) is presented as a method to allow propagation of packet filter information among compatible firewall hosts and routers in an IP-based network. We use a scenario of corporate firewalls to assess concepts of correctness, sufficiency and completeness. The properties of SNMP, version 2 that support secure management operations are reviewed and we propose further work toward an SNMP-based Security Management prototype.KeywordsFirewallsManagement Information Base (MIB)Network ManagementSNMPPacket Filter Information Protocol (PFIP)Security Application ManagementSecurity Management.

With the rapid development of Internet of Things (IoT) technology, the number of sensors in IoT and the data collected from them are increasing. If these data are not managed and stored directly, it will occupy a large amount of storage space. Therefore, studying the data management mechanism of multi‐sensor fusion in IoT is of great significance. Multi‐sensor fusion has achieved a series of successes in the field of data management. Based on this, this article proposes a secure and controllable data management mechanism for multi‐sensor fusion in IoT, including a multi‐sensor fusion model, compression of data using linear regression algorithm, and data storage based on sorted string table algorithm. Then this article analyzes the effectiveness of the model proposed for multi‐sensor fusion data management. Through comparative experiments under different data sets, it is verified that the management mechanism has higher data fusion rate, lower data lossy compression rate and higher data lossless compression rate under different data sets. And it has high data storage security, large reconstruction probability, strong management controllability, and can significantly save storage space. It is a superior secure and controllable data management mechanism for multi‐sensor fusion in IoT.

Smart Grid concept substantially increases the power measurement need in the future for efficient and guaranteed power delivery. The medium voltage (MV) cable is an important asset of a distribution network and it must guarantee a stable operation of the supply. With the increasing age of the underground MV cables in power grids, the accident and failure arising from insulation degradation is becoming one of the main challenges against power system reliability. It is also essential economically to extend the life span of the medium voltage cable. Continuous on-line partial discharge (PD) monitoring is an excellent way to determine the overall health of the MV components and to detect incipient faults in underground cables. However, continuous on-line PD monitoring is not widely used primarily because no adequate cost-effective solution is available for permanent installation. This paper presents the development of a versatile solution for continuous on-line PD monitoring of MV cables at secondary substation. The laboratory tests and data analysis exhibit the capability of the proposed system to detect PD signals successfully.

This article argues that from an organizational diffusion perspective, information infrastructure capability has the potential to increase organizational competitive advantage. As diverse knowledge management research perspectives have obscured the understanding of information infrastructure capability, we reconcile these observations for the benefit of business managers and practitioners. Knowledge management is defined operationally as the management of knowledge management activities and the activities that are performed using information infrastructure capability in order to achieve competitive advantage. Underpinned by prior studies, our research framework of information infrastructure capability includes the following key categories: dynamic, integrating, data management, security, utility, and collaborating capabilities. Data were collected from 295 randomly selected multimedia super corridor Malaysian organizations from which a conceptual model of knowledge management, information infrastructure capability, and organizational competitive advantage was developed. Based on the findings, dynamic and utility capabilities are the key significant capabilities for achieving organizational competitive advantage. Security and data management capabilities were inconsequential in achieving organizational competitive advantage. Utilizing knowledge was among the top knowledge management activities leading to competitive advantage. Our findings can be used by business managers in Malaysia and countries with similar settings to prioritize their investment in information infrastructure capability to gain increased competitiveness in the market.