Search Results (20)

Traceability plays a critical role in ensuring the quality, safety, and transparency of supply chains, where transportation stakeholders are fundamental to the efficient movement of goods. However, the diversity of actors involved poses significant challenges to achieving these goals. Each organization typically operates its own information system, tailored to manage internal data, but often lacks the ability to communicate effectively with external systems. Moreover, when data exchange between different systems is required, it becomes critical to maintain full control over the shared data and to manage access rights precisely. In this work, we propose the concept of interoperable traceability. We present a model that enables the seamless integration of data from sensors, IoT devices, data management platforms, and distributed ledger technologies (DLT) within a newly designed data space architecture. We also demonstrate a practical implementation of this concept by applying it to real-world scenarios in the agri-food sector, with direct implications for transportation systems and all stakeholders in a supply chain. Our demonstrator supports the secure exchange of traceability data between existing systems, providing stakeholders with a novel approach to managing and auditing data with increased transparency and efficiency. Full article

Gaia-X, a European initiative, aims to create a digital sovereignty framework for service ecosystems in the future Internet. Its applicability to the health domain was explored in the Gaia-X-Med project, which aimed to establish a common dataspace for various medical use cases based on Gaia-X principles. This paper presents a trust- and consent-based approach to the secure authentication and digital contract negotiation central to this endeavor and discusses the challenges that arose during the adoption of the Gaia-X framework, particularly relating to the strict requirements of the European healthcare domain with regards to privacy and consent regulations. By exploring the practical implications of Gaia-X in the healthcare context, this paper aims to contribute to the ongoing discussions surrounding the digital sovereignty of both citizens and corporations, as well as its realization via future Internet technologies. Full article

This study addresses the need for enhanced sustainability in the nonwovens industry by developing a data ecosystem that improves data transparency, interoperability, and decision-making across the value chain. The research focuses on two conceptual models, including the Digital Product Passport (DPP) for tracking sustainability information and a holistic data management system for production environments. The research involved identifying key stakeholders, their tasks, and challenges related to sustainability and applying digital tools to meet these needs. The results demonstrate that integrating these data-space use cases can significantly enhance the availability and verifiability of sustainability data, aligning with European Union objectives such as those in the Gaia-X initiative. However, the proposed concepts have not yet been validated in real-world settings, highlighting the need for further research to assess their effectiveness and scalability. These findings suggest that digital ecosystems have the potential to drive sustainable transformation and foster collaboration in the nonwovens sector, offering a pathway towards more circular and resource-efficient practices. Full article

At present, the majority of deep learning-based ship object detection algorithms concentrate predominantly on enhancing recognition accuracy, often overlooking the complexity of the algorithm. These complex algorithms demand significant computational resources, making them unsuitable for deployment on resource-constrained edge devices, such as airborne and spaceborne platforms, thereby limiting their practicality. With the purpose of alleviating this problem, a lightweight and high-accuracy synthetic aperture radar (SAR) ship image detection network (LHSDNet) is proposed. Initially, GhostHGNetV2 was utilized as the feature extraction network, and the calculation amount of the network was reduced by GhostConv. Next, a lightweight feature fusion network was designed to combine shallow and deep features through lightweight convolutions, effectively preserving more information while minimizing computational requirements. Lastly, the feature extraction module was integrated through parameter sharing, and the detection head was lightweight to save computing resources further. The results from our experiments demonstrate that the proposed LHSDNet model increases mAP50 by 0.7% in comparison to the baseline model. Additionally, it illustrates a pronounced decrease in parameter count, computational demand, and model file size by 48.33%, 51.85%, and 41.26%, respectively, when contrasted with the baseline model. LHSDNet achieves a balance between precision and computing resources, rendering it more appropriate for edge device implementation. Full article

Data sovereignty has emerged as a critical concern for enterprises, cloud service providers (hyperscalers), end-users, and policymakers at both national and international levels. In response, cloud-based distributed computing models have been proposed as frameworks to enforce data sovereignty requirements. This study aims to evaluate and enhance data sovereignty practices within public cloud environments. Through a comprehensive literature review, we analyze existing reference architectures and solutions that address data sovereignty, identifying the technological and economic constraints they impose, such as increased computational costs associated with specific frameworks and cryptographic measures. To address these challenges, we propose an abstract data sovereignty model designed to aid system designers and architects in developing compliant cloud-based systems. Additionally, we conduct computational experiments assessing the performance of the IDS connector, a key data sovereignty tool, deployed on the Google Cloud Platform and Microsoft Azure. Results reveal that while the geographic location of the software significantly impacts performance, the choice of cloud platform minimally influences the IDS connector’s efficiency. These findings offer insights into optimizing data sovereignty strategies for cloud solutions, with implications for future system design and policy development. Full article

The cyber–physical systems (CPSs) of various stakeholders from the mobility, logistics, and security sectors are needed to enable smart and secure situational awareness operations in a port environment. The motivation for this research arises from the challenges caused by some unexpected events, such as accidents, in such a multi-stakeholder critical environment. Due to the scale, complexity, and cost and safety challenges, a simulation-based approach was selected as the basis for the study. Prototype-level experimental solutions for dataspaces for secure data sharing and visualization of situational awareness were developed. The secure data-sharing solution relies on the application of verifiable credentials (VCs) to ensure that data consumers have the required access rights to the data/information shared by the data prosumer. A 3D virtual digital twin model is applied for visualizing situational awareness for people in the port. The solutions were evaluated in a simulation-based execution of an accident scenario where a forklift catches fire while loading a docked ship in a port environment. The simulation-based approach and the provided solutions proved to be practical and enabled the smooth study of disaster-type situations. The realized concept of dataspaces is successfully applied here for both daily routine operations and information sharing during accidents in the simulation-based environment. During the evaluation, needs for future research related to perception, comprehension, projection, trust, and security as well as performance and quality of experience were detected. Especially, distributed and secure viewpoints of objects and stakeholders toward real-time situational awareness seem to require further studies. Full article

In the digital age, data transmission has become a key component of globalization and international cooperation. However, it faces several challenges in protecting the privacy and security of data, such as the risk of information disclosure on third-party platforms. Moreover, there are few solutions for personal data protection in cross-border transmission scenarios due to the difficulty of handling sensitive information between different countries and regions. In this paper, we propose an approach, personal data protection based on homomorphic encryption (PDPHE), to creatively apply the privacy computing technology homomorphic encryption (HE) to cross-border personal data protection. Specifically, PDPHE reconstructs the classical full homomorphic encryption (FHE) algorithm, DGHV, by adding support for multi-bit encryption and security level classification to ensure consistency with current data protection regulations. Then, PDPHE applies the reconstructed algorithm to the novel cross-border data protection scenario. To evaluate PDPHE in actual cross-border data transfer scenarios, we construct a prototype model based on PDPHE and manually construct a data corpus called PDPBench. Our evaluation results on PDPBench demonstrate that PDPHE cannot only effectively solve privacy protection issues in cross-border data transmission but also promote international data exchange and cooperation, bringing significant improvements for personal data protection during cross-border data sharing. Full article

Millions of people worldwide currently suffer from chronic kidney disease (CKD), requiring kidney replacement therapy at the end stage. Endeavors to better understand CKD pathophysiology from an omics perspective have revealed major molecular players in several sample sources. Focusing on non-invasive sources, gut microbial communities appear to be disturbed in CKD, while numerous human urinary peptides are also dysregulated. Nevertheless, studies often focus on isolated omics techniques, thus potentially missing the complementary pathophysiological information that multidisciplinary approaches could provide. To this end, human urinary peptidome was analyzed and integrated with clinical and fecal microbiome (16S sequencing) data collected from 110 Non-CKD or CKD individuals (Early, Moderate, or Advanced CKD stage) that were not undergoing dialysis. Participants were visualized in a three-dimensional space using different combinations of clinical and molecular data. The most impactful clinical variables to discriminate patient groups in the reduced dataspace were, among others, serum urea, haemoglobin, total blood protein, urinary albumin, urinary erythrocytes, blood pressure, cholesterol measures, body mass index, Bristol stool score, and smoking; relevant variables were also microbial taxa, including Roseburia, Butyricicoccus, Flavonifractor, Burkholderiales, Holdemania, Synergistaceae, Enterorhabdus, and Senegalimassilia; urinary peptidome fragments were predominantly derived from proteins of collagen origin; among the non-collagen parental proteins were FXYD2, MGP, FGA, APOA1, and CD99. The urinary peptidome appeared to capture substantial variation in the CKD context. Integrating clinical and molecular data contributed to an improved cohort separation compared to clinical data alone, indicating, once again, the added value of this combined information in clinical practice. Full article

Collaborative Model-based Systems Engineering between companies is becoming increasingly important. The utilization of the modeling possibilities of the standard language SysML v2 and the multilateral data exchange via Dataspaces open new possibilities for efficient collaboration. Based on systemic approaches, a modeling concept for decomposing the system into sub-systems is developed as a basis for the exchange. In addition, based on the analysis of collaboration processes in the context of Systems Engineering, an architectural approach with a SysML editor and Dataspace for the exchange is elaborated. The architecture is implemented on the basis of open-source solutions. The investigations are based on an application example from precision engineering. The potential and challenges are discussed. Full article

The popularity of blockchain technology stems largely from its association with cryptocurrencies, but its potential applications extend beyond this. Fungible tokens, which are interchangeable, can facilitate value transactions, while smart contracts using non-fungible tokens enable the exchange of digital assets. Utilizing blockchain technology, tokenized platforms can create virtual markets that operate without the need for a central authority. In principle, blockchain technology provides these markets with a high degree of security, trustworthiness, and dependability. This article surveys recent developments in these areas, including examples of architectures, designs, challenges, and best practices (case studies) for the design and implementation of tokenized platforms for exchanging digital assets. Full article

Semantic models are utilized to add context information to datasets and make data accessible and understandable in applications such as dataspaces. Since the creation of such models is a time-consuming task that has to be performed by a human expert, different approaches to automate or support this process exist. A recurring problem is the task of link prediction, i.e., the automatic prediction of links between nodes in a graph, in this case semantic models, usually based on machine learning techniques. While, in general, semantic models are trained and evaluated on large reference datasets, these conditions often do not match the domain-specific real-world applications wherein only a small amount of existing data is available (the cold-start problem). In this study, we evaluated the performance of link prediction algorithms when datasets of a smaller size were used for training (few-shot scenarios). Based on the reported performance evaluation, we first selected algorithms for link prediction and then evaluated the performance of the selected subset using multiple reduced datasets. The results showed that two of the three selected algorithms were suitable for the task of link prediction in few-shot scenarios. Full article

The reuse and recycling of critical raw materials is limited, as waste electrical and electronic recycling focuses on base and precious metals, and device component reuse is in its infancy. To help to address this issue this paper provides the conceptual design of a Digital Product Passport based circular supply management system. To enable the recovery of critical raw materials at component and material levels for reuse and recycling. The works include an assessment of existing critical raw materials information management and an information needs identification survey, with 10 manufacturers, producer responsibility organisations, collectors and recyclers. The needs were used to generate 14 key product information management processes and exchanges that when implemented form a Digital Product Passport based circular supply management system. Information managed via a physical-digital linkage through individual product tags includes product registrations, materials declarations, life cycle status updates, the sorting of products at collection points based on critical raw material contents, and flagging of products for critical raw materials component extraction. A dataspace-based IT systems architecture is proposed for the implementation of the supply management system taking into account global and European information standards. Finally, key challenges to implement such an IT architecture are discussed. Full article

Building energy simulation plays a significant role in buildings, with applications such as building performance evaluation, retrofit decisions and the optimization of building operations. However, the wide range of model inputs has limited much research into empirically customized case studies due to the insufficient availability of data inputs or the lack of systematic feature selection of key inputs. To address this gap, this study proposes the concept of minimum variable sets (MVSs) for building energy-prediction models to improve the general applicability of building energy prediction using forward simulation. An MVS, in this paper, refers to a variable set that contains the most indispensable energy-related variables/features for annual building energy prediction. This study developed MVSs for office buildings by applying feature engineering algorithms to a Building Performance Database (BPD), which was established by integrating the design of experiments (DoE) method with high-dimensional data-space metrics, as well as parallel simulation. Supervised feature dimension reduction methods and multiple statistical criteria were adopted to choose different numbers of indispensable variables from the primary 16 building variables. The hierarchical MVSs that consist of the selected variables are characterized by the most influential features for building energy prediction, with certain requirements for prediction accuracy. To further improve the feasibility of MVSs, this study utilized two separate office buildings located in Shanghai and California as validation cases and provided comparable prediction accuracies across different sizes of MVS. The results showed that the MVS that has 12 variables has higher prediction accuracy than that which has 9 variables, followed by that which has 7 variables. Finally, the quantitatively hierarchical correlations between different sizes of MVS with different prediction accuracies for annual building energy could provide potential support for reasonable decision-making regarding building energy model variables, especially when comprehensive consideration is needed of the limited cost and data availability, and the acceptable accuracy of building energy. Full article

This paper proposes to combine the concept of digital twins with the concept of dataspaces to fulfill the original expectation that a digital twin is a comprehensive virtual representation of physical assets. Based upon a terminological and conceptual discussion of digital twins and dataspaces, this paper claims that a systemic approach towards digital twin Systems is required. The key conceptual approach consists of a Reference Model for Digital Twin Systems (DTS-RM) and a hypothesis regarding a symbiotic evolution. The DTS-RM distinguishes between a digital twin back-end platform comprising the access and management of comprehensive digital twin instances and digital twin-related services, and digital twin front-end services that are tailored to the demands of applications and users. The main purpose of the back-end platform is to decouple the digital twin’s generation and management from the usage of the digital twin for applications. Full article

The interpretation of three-dimensional (3-D) magnetotelluric (MT) data is usually based on the isotropic assumption of the subsurface structures, and this assumption could lead to erroneous interpretation in the area with considerable electrical anisotropy. Although arbitrary anisotropy is much closer to the ground truth, it is generally more challenging to recover full anisotropy parameters from 3-D inversion. In this paper, we present a 3-D triaxial anisotropic inversion framework using the edge-based finite element method with a tetrahedral mesh. The 3-D inverse problem is solved by the Gauss-Newton (GN) method which shows fast convergence behavior. The computation cost of the data-space method depends on the size of data, which is usually smaller than the size of model; therefore, we transform the inversion algorithm from the model space to the data space for memory efficiency. We validate the effectiveness and applicability of the developed algorithm using several synthetic model studies. Full article