Abstract
Satellite gravimetry requires sub-ng acceleration measurement at frequencies below 100mHz. To bring the performance of a MEMS accelerometer closer to this level, one must decrease noise sources and maximize sensitivity (to decrease input-referred electronic noise). Electrostatic pull-in based operation has great potential for high sensitivity since it relies on time transduction. Devices were fabricated with maximized proof mass (170mg over a 13x14mm2 footprint) and tuned damping coefficient (trade-off between noise and sensitivity – pull-in operation requires low Q-factors). Novel stopper designs and caps limit both in-plane and out-of-plane displacements. Devices tested using pull-in voltage-based transduction showed sensitivity of 218 V/g. © 2022 TRF.

Abstract
Background: Alzheimer's disease (AD) has paramount importance due to its rising prevalence, the impact on the patient and society, and the related healthcare costs. However, current diagnostic techniques are not designed for frequent mass screening, delaying therapeutic intervention and worsening prognoses. To be able to detect AD at an early stage, ideally at a pre-clinical stage, speech analysis emerges as a simple low-cost non-invasive procedure. Objectives: In this work it is our objective to do a systematic review about speech-based detection and classification of Alzheimer's Disease with the purpose of identifying the most effective algorithms and best practices. Methods: A systematic literature search was performed from Jan 2015 up to May 2020 using ScienceDirect, PubMed and DBLP. Articles were screened by title, abstract and full text as needed. A manual complementary search among the references of the included papers was also performed. Inclusion criteria and search strategies were defined a priori. Results: We were able: to identify the main resources that can support the development of decision support systems for AD, to list speech features that are correlated with the linguistic and acoustic footprint of the disease, to recognize the data models that can provide robust results and to observe the performance indicators that were reported. Discussion: A computational system with the adequate elements combination, based on the identified best-practices, can point to a whole new diagnostic approach, leading to better insights about AD symptoms and its disease patterns, creating conditions to promote a longer life span as well as an improvement in patient quality of life. The clinically relevant results that were identified can be used to establish a reference system and help to define research guidelines for future developments.

Abstract
The scoping review reported by this article aimed to identify (i) the purposes of the studies using crowdsourcing technologies in the context of the smart cities’ implementations, (ii) the characteristics of the crowdsourcing technologies being used, and (iii) the maturity level of the solutions being proposed. An electronic search was conducted, and 29 studies were included in the review after the selection process. The results show a current interest in crowdsourcing campaigns using participatory reporting and participatory sensing to (i) support urban infrastructures’ maintenance, (ii) facilitate urban mobility, (iii) monitor the environment, (iv) manage crowds, (v) aggregate geographical information, and (vi) collect citizens’ perspectives about the cities. However, the results also show low maturity level of the proposed solutions and lack of consolidated evidence about their effectiveness, which difficulties their dissemination.

Abstract
The Internet of Things (IoT) has changed how we interact with the world around us. Many devices are moving from offline to online mode, connecting between them and the Internet, offering more functionality to users. Despite the increase in the quality of life for users provided by IoT devices, it is also necessary to establish trust in the privacy and security of end-users. With this level of connectivity, the amount of data exchanged between devices also increases, inducing malicious activities. One of the main problems is the lack of regulation in the IoT industry, especially between different manufacturers. There are no formal security rules, and manufacturers may not choose to install security mechanisms. Therefore, it is necessary to promote the adoption of security measures. One way to do this is by using IoT devices and systems certification. In recent years, IoT certifications have emerged. Meanwhile, the European Union has passed the Cyber Security Act to unify and regulate security certifications in member states. Our work collects the requirements that different IoT environments and application scenarios impose on certifications and discusses the current certifications' status according to those requirements. In addition, we also explored how EU measures apply to IoT and, where applicable, how certifications implement them, highlighting future research challenges.

Abstract
Nontechnical losses in electricity distribution networks are often associated with a countries' socioeconomic situation. Although the amount of global losses is usually known, the separation between technical and commercial (nontechnical) losses will remain one of the main challenges for DSO until smart grids become fully implemented and operational. The most common origins of commercial losses are energy theft and deliberate or accidental failures of energy measuring equipment. In any case, the consequences can be regarded as consumption anomalies. The work described in this paper aims to answer a request from a DSO, for the development of tools to detect consumption anomalies at end-customer facilities (HV, MV and LV), invoking two types of assessment. The first consists of the identification of typical patterns in the set of consumption profiles of a given group or zone and the detection of atypical consumers (outliers) within it. The second assessment involves the exploration of the load diagram evolution of each specific consumer to detect changes in the consumption pattern that could represent situations of probable irregularities. After a representative period, typically 12 months, these assessments are repeated, and the results are compared to the initial ones. The eventual changes in the typical classes or consumption scales are used to build a classifier indicating the risk of anomaly.

Abstract
Soil Moisture (SM) is one of the most critical factors for a crop's growth, yield, and quality. Although Ground-Penetrating RADAR (GPR) is commonly used in satelite observation to analyze soil moisture, it is not cost-effective for agricultural applications. Automotive RADAR uses the concept of Frequency-Modulated Continuous Wave (FMCW) and is more competitive in terms of price. This paper evaluates the viability of using a cost-effective RADAR as a substitute for GPR for soil moisture content estimation. The research consisted of four experiments, and the results show that the RADAR's output signal and the soil moisture sensor SEN0193 have a high correlation with values as high as 0.93 when the SM is below 15%. Such results show that the tested sensor (and its cost-effective working principle) are able to determine soil water content (with certain limitations) in a non-intrusive, proximal sensing manner.