Abstract
The ionosphere is a fundamental component of the Earth's atmosphere, impacting human activities such as communication transmissions, navigation systems, satellite functions, power network systems, and natural gas pipelines, even endangering human life or health. As technology moves forward, understanding the impact of the ionosphere on our daily lives becomes increasingly important. CubeSats are a promising way to increase understanding of this important atmospheric layer. This paper reviews the state of the art of CubeSat missions designed for ionospheric studies. Their main instrumentation payload and orbits are also analyzed from the point of view of their importance for the missions. It also focuses on the importance of data and metadata, and makes an approach to the aspects that need to be improved.

Abstract
Background: Blended treatments, combining digital components with face-to-face (FTF) therapy, are starting to find their way into mental health care. Knowledge on how blended treatments should be set up is, however, still limited. To further explore and optimize blended treatment protocols, it is important to obtain a full picture of what actually happens during treatments when applied in routine mental health care. Objective: The aims of this study were to gain insight into the usage of the different components of a blended cognitive behavioral therapy (bCBT) for depression and reflect on actual engagement as compared with intended application, compare bCBT usage between primary and specialized care, and explore different usage patterns. Methods: Data used were collected from participants of the European Comparative Effectiveness Research on Internet-Based Depression Treatment project, a European multisite randomized controlled trial comparing bCBT with regular care for depression. Patients were recruited in primary and specialized routine mental health care settings between February 2015 and December 2017. Analyses were performed on the group of participants allocated to the bCBT condition who made use of the Moodbuster platform and for whom data from all blended components were available (n=200). Included patients were from Germany, Poland, the Netherlands, and France; 64.5% (129/200) were female and the average age was 42 years (range 18-74 years). Results: Overall, there was a large variability in the usage of the blended treatment. A clear distinction between care settings was observed, with longer treatment duration and more FTF sessions in specialized care and a more active and intensive usage of the Web-based component by the patients in primary care. Of the patients who started the bCBT, 89.5% (179/200) also continued with this treatment format. Treatment preference, educational level, and the number of comorbid disorders were associated with bCBT engagement. Conclusions: Blended treatments can be applied to a group of patients being treated for depression in routine mental health care. Rather than striving for an optimal blend, a more personalized blended care approach seems to be the most suitable. The next step is to gain more insight into the clinical and cost-effectiveness of blended treatments and to further facilitate uptake in routine mental health care.

Abstract
This work presents a screening and evaluation platform for depression and suicidality that has been tested in the scope of primary healthcare. The main objective is to improve the efficiency and effectiveness of screening processes. A web-based, decision support platform was provided for qualified healthcare professionals. The platform provides several assessment tools for patient evaluation and monitoring of their treatment, along with follow up appointment management. A preliminary evaluation process was carried out to understand the health professional's satisfaction. This revealed there was general satisfaction with its integrated functions and all the provided methods of assessment. In conclusion, the project sustains the goal of improving the treatment outcomes for clinical depression by refining the screening methods and consequently increase the screening effectiveness and efficiency.

Abstract
Depression is expected to be the disorder with the highest disease burden in high-income countries by the year 2030. ICT4Depression (ICT4D) is a European FP7 project, which aims to contribute to the alleviation of this burden by making use of depression treatment and ICT innovations. In this project we developed an ICT-based system for use in primary care that aims to improve access as well as actual care delivery for depressed adults. Innovative technologies within the ICT4D system include 1) flexible self-help treatments for depression, 2) automatic assessment of the patient using mobile phone and web-based communication 3) wearable biomedical sensor devices for monitoring activities and electrophysiological indicators, 4) computational methods for reasoning about the state of a patient and the risk of relapse (reasoning engine) and 5) a flexible system architecture for monitoring and supporting people using continuous observations and feedback via mobile phone and the web. The general objective of the ICT4D project is to test the feasibility and acceptability of the ICT4D system within a pilot study in the Netherlands and in Sweden during 2012 and 2013. © 2012 Interactive Media Institute and IOS Press.

Abstract
FP7 ICT4Depression project aims at providing a set of tools to,further improve both patient outcome and increase of access to treatment of the patients suffering from major depression. This article describes the Information Systems (IS) architecture used in the project. ICT4Depression uses a service oriented architecture as means of bringing together different kinds of information concerning the patient, the therapeutic modules he is advised to follow and the sensors used to assess his status.