Abstract

Abstract
This paper discusses the problem of information sharing and data interoperability in a B2B context. Therefore, this paper presents a case study on the scope of data-sharing in collaborative networks in an industrial cluster. It explores the feasibility of International Data Spaces in the context of the footwear industry cluster. This work also discusses how the adoption of digital processes might contribute to support data-based management to optimize the production planning of a footwear industry. As a result, it is defined and specified the foundations for the development and implementation of an dataspace oriented IIoT architecture, following a fully compliant Industry 4.0 solution for the footwear industry cluster. This paper discusses the problem of information sharing and data interoperability in a B2B context. Therefore, this paper presents a case study on the scope of data-sharing in collaborative networks in an industrial cluster. It explores the feasibility of International Data Spaces in the context of the footwear industry cluster. This work also discusses how the adoption of digital processes might contribute to support data-based management to optimize the production planning of a footwear industry. As a result, it is defined and specified the foundations for the development and implementation of an dataspace oriented IIoT architecture, following a fully compliant Industry 4.0 solution for the footwear industry cluster.

Abstract
This research evaluates the performance of water supply utilities operating at the retail level in Portugal concerning asset management practices. The study's main innovative feature is identifying peers and targets to guide improvements in the sector. Reliable data collected by the regulatory authority for water and waste services in Portugal (ERSAR) are employed to design two composite indicators reflecting different dimensions of asset management: operational conditions and management systems. Based on the Data Envelopment Analysis technique, the Benefit-of-the-Doubt model is employed in robust and conditional formulations. The role of the context on utilities' performance is also investigated. The results show that the direct management model is unfavourable concerning developing structured management systems, whilst urban environments favour managerial advancement. Rural and semi-urban environments favour goodoperational results in infrastructures. The pool of peers obtained for each utility and the quantification of targets based on the observed achievements by those peers facilitates the search for industry best practices and promotes continuous improvement. Given the high heterogeneity in asset management performance within the sector, the utility-specific target-setting approach illustrated in this paper can support a regulatory policy review for determining more realistic goals.

Abstract
Blindness and visual impairment are commonly associated with social and functional limitations, almost 45 million people in the world have blindness, and 135 million have any visual impairment. This condition has a significant impact on the quality of life and brings many challenges to the individual, one of which is the navigation and positioning tasks. Although there are already apps capable of helping visually impaired people (VIP) for mobility purposes, most of them focus on detecting obstacles and, therefore, on avoiding dangerous situations. However, mobility of VIP involves many more tasks, such as knowing their exact position and staying informed along an entire route. For this purpose, a standalone and customizable solution is proposed that uses traditional visual recognition of landmarks to process the surroundings of the current location of the visually impaired person using a smartphone and informing about the nearby places assuring the user a sense of the site. For feature detection, it used the oriented features from accelerated segment test (FAST) and rotated binary robust-independent elementary feature (BRIEF) (ORB) algorithm, and for feature matching, it used the brute-force method with the k-nearest neighbor (KNN) algorithm. Results show that the proposed solution can analyze pictures in fractions of a second with satisfactory accuracy.

Abstract
To develop a full battery model in view to accurate battery management, Li-ion cell dynamics is modelled by a capacitor in series with a simplified Randles circuit. The open circuit voltage is the voltage at the capacitor terminals, allowing, in this way, for the dependence of the open circuit voltage on the state-of-charge to be embedded in its capacitance. The Randles circuit is recognised as a trusty description of a cell dynamics. It contains a semi-integrator of the current, known as the Warburg impedance, that is a special case of a fractional integrator. To enable the formulation of a time-domain system identification algorithm, the Warburg impedance impulse response was calculated and normalised, in order to derive a finite order state-space approximation, using the Ho-Kalman algorithm. Thus, this Warburg impedance LTI model, with known parameters (normalised impedance) in series with a gain block, is suitable for system identification, since it has only one unknown parameter. A LTI System identification Algorithm was formulated to estimate the model parameters and the initial values of both the open circuit voltage and the states of the normalised Warburg impedance. The performance of the algorithm was very satisfactory on the whole state-of-charge region and when compared with low order Thevenin models. Once it is understood the parameters variability on the state-of-charge, temperature and ageing, we envisage to continue the work using parameter-varying algorithms.

Abstract
It is well known that conditional heteroscedasticity is exhibited by many economic and financial time series such as stock prices or returns. Empirical analysis is often based on a subseries obtained through systematically sampling from an underlying time series and we analyze how that can affect testing for heteroscedasticity. The results show the distribution of the test statistics is changed by systematic sampling, causing a serious power loss that increases with the sampling interval. Consequently, the tests often fail to reject the hypothesis of no conditional heteroscedasticity, leading to the wrong decision and missing the true nature of the data-generating process.