Abstract
The biomass industry is growing due to the current search for greener and more sustainable alternatives to fossil energy sources. However, this industry, due to its singularity, presents several challenges and disadvantages related to the transportation of raw materials, with the large volumes that are usually involved. This project aimed to address this internal logistics situation in torrefied biomass pellets production with two different biomass storage parks, located in Portugal. The main park receives raw material coming directly from the source and stores it in large amounts as a backup and strategic storage park. The second park, with smaller dimensions, precedes the production unit and must be stocked daily. Therefore, a fleet of transport units with self-unloading cranes is required to help to unload the biomass at the main park and transport the raw material from this park to the one preceding the production unit. Thus, the main goal was to determine the dimensions of the fleet used in internal transportation operations to minimize the idle time of the transport units using a methodology already in use in the mining and quarrying industry. This methodology was analyzed and adapted to the situation presented here. The implementation of this study allows the elimination of unnecessary costs in an industry where the profit margins are low.

Abstract
The Internet of Things (IoT) is rapidly becoming an integral component of the industrial market in areas such as automation and analytics, giving rise to what is termed as the Industrial IoT (IIoT). The IIoT promises innovative business models in various industrial domains by providing ubiquitous connectivity, efficient data analytics tools, and better decision support systems for a better market competitiveness. However, IIoT deployments are vulnerable to a variety of security threats at various levels of the connectivity and communications infrastructure. The complex nature of the IIoT infrastructure means that availability, confidentiality and integrity are difficult to guarantee, leading to a potential distrust in the network operations and concerns of loss of critical infrastructure, compromised safety of network end-users and privacy breaches on sensitive information. This work attempts to look at the requirements currently specified for a secure IIoT ecosystem in industry standards, such as Industrial Internet Consortium (IIC) and OpenFog Consortium, and to what extent current IIoT connectivity protocols and platforms hold up to the standards with regard to security and privacy. The paper also discusses possible future research directions to enhance the security, privacy and safety of the IIoT.

Abstract
The reconstitution of road traffic accidents scenes is a contemporary and important issue, addressed both by private and public entities in different countries around the world. However, the task of collecting data on site is not generally focused on with the same orientation and relevance. Addressing this type of accident scenario requires a balance between two fundamental yet competing concerns: (1) information collecting, which is a thorough and lengthy process and (2) the need to allow traffic to flow again as quickly as possible. This technical note proposes a novel methodology that aims to support road traffic authorities/professionals in activities involving the collection of data/evidences of motor vehicle collision scenarios by exploring the potential of using low-cost, small-sized and light-weight unmanned aerial vehicles (UAV). A high number of experimental tests and evaluations were conducted in various working conditions and in cooperation with the Portuguese law enforcement authorities responsible for investigating road traffic accidents. The tests allowed for concluding that the proposed method gathers all the conditions to be adopted as a near future approach for reconstituting road traffic accidents and proved to be: faster, more rigorous and safer than the current manual methodologies used not only in Portugal but also in many countries worldwide.

Abstract
Typical polar digital power amplifiers (DPAs) employ unit-cells operated in class-E or D-1, denoting a switched-resistance operation which degrades linearity. Besides introducing higher demand on digital predistortion (DPD), it also requires extra quantization bits, impacting the overall efficiency and system complexity. To address this, the present work makes use of an optimized constant-current cascode unit-cell which is combined with reduced conduction angle to achieve linear and efficient operation, while minimizing the effort on DPD and/or calibration. A design strategy is developed which focuses on the cascode bias voltage and transistor relative dimensions as design parameters, allowing cascode efficiency optimization without compromising linearity or reliability. A single-ended polar switched constant-current DPA is implemented in 180-nm standard CMOS. Continuous-wave measurements performed at 800 MHz demonstrate an output power of 24 dBm with a PAE of 47%. The DPA dynamic behavior was tested with a 64-QAM signal with 10 MS/s, achieving an average PAE of 20.9% with a peak-to-average power ratio (PAPR) of 8.7 dB and adjacent-channel leakage ratio (ACLR) = 40.34 dB. These results demonstrate comparable performance with the prior art while using only 6-bits clocked at 100 MHz baseband sampling frequency.

Abstract
Low-code application development as proposed by the OutSystems Platform enables fast mobile and desktop application development and deployment. It hinges on visual development of the interface and business logic but also on easy integration with data stores and services while delivering robust applications that scale. Data integration increasingly means accessing a variety of NoSQL stores. Unfortunately, the diversity of data and processing models, that make them useful in the first place, is difficult to reconcile with the simplification of abstractions exposed to developers in a low-code platform. Moreover, NoSQL data stores also rely on a variety of general purpose and custom scripting languages as their main interfaces. In this paper we report on building a polyglot data access layer for the OutSystems Platform that uses SQL with optional embedded script snippets to bridge the gap between low-code and full access to NoSQL stores. © IFIP International Federation for Information Processing 2020.

Abstract
This work presents some approaches to overcome current Reinforcement Learning limits. We implement a simple virtual environment and some state-of-the-art Reinforcement Learning algorithms for testing and producing a baseline for comparison. Then we implement a Relational Reinforcement Learning algorithm that shows superior performance to the baseline but requires introducing human knowledge. We also propose that Model-based Reinforcement Learning can help us overcome some of the barriers. For better World models, we explore Inductive Logic Programming methods, such as First-Order Inductive Learner, and develop an improved version of it, more adequate to Reinforcement Learning environments. Finally we develop a novel Neural Network architecture, the Inductive Logic Neural Network, to fill the gaps of the previous implementations, that shows great promise. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020.