Data management study, volume 5. Appendix K - Contractor data package data management /DM/ Final report

Contractor data management system for Voyager projec

Enhancing wireless local area networks by leveraging diverse frequency resources

In this thesis, signal propagation variations that are experience over the frequency resources of IEEE 802.11 Wireless Local Area Networks (WLANs) are studied. It is found that exploitation of these variations can improve several aspects of wireless communication systems. To this aim, frequency varying behavior is addressed at two different levels. First, the intra-channel scale is considered, i.e. variations over the continuous frequency block that a device uses for a cohesive transmission. Variations at this level are well known but current wireless systems restrict to basic equalization techniques to balance the received signal. In contrast, this work shows that more fine grained adaptation to these differences can accomplish throughput and connection range gains. Second, multi-frequency band enabled devices that access widely differing frequency resources in the millimeter wave range as well as in the microwave range are analyzed. These devices that are expected to follow the IEEE 802.11ad specification experience intense propagation variations over their frequency resources. Thus, a part of this thesis revises, the theoretical specification of the IEEE 802.11ad standard and complements it by a measurement study of first generation millimeter wave devices. This study reveals deficiencies of first generation millimeter wave systems, whose improvement will pose new challenges to the protocol design of future generation systems. These challenges are than addressed by novel methods that leverage from frequency varying propagation characteristics. The first method, improves the beam training process of millimeter wave networks, that need highly directional, though electronically steered, transmissions to overcome increased free space attenuation. By leveraging from omni-directional signal propagation at the microwave bands, efficient direction interference is utilized to provide information to millimeter wave interfaces and replace brute force direction testing. Second, deafness effects at the millimeter wave band, which impact IEEE 802.11 channel access methods are addressed. As directional communication on these bands complicates sensing the medium to be busy or idle, inefficiencies and unfairness are implied. By using coordination message exchange on the legacyWi-Fi frequencies with omnidirectional communication properties, these effects are countered. The millimeter wave bands can thus unfold their full potential, being exclusively used for high speed data frame transmission.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Ralf Steinmetz.- Secretario: Albert Banchs Roca.- Vocal: Kyle Jamieso

Which factors influence the psychological distress among relatives of patients with chronic functional psychoses? An exploratory study in a community mental health care setting

Aim: This research aimed to assess the contribution of the five core areas of the transactional stress model to the relatives' psychological distress (PD) when informally taking care of patients with functional psychoses treated in community mental health care. Subjects and methods: Cross-sectional data from 163 relatives were collected in interviews, while data on 158 patients were collected by analyzing clinical charts. The following areas were assessed: socio-demographic and illness-related features of the patients, socio-demographic features of the relatives (environmental variables); sense of coherence, mastery, causal attributions and opinions of relatives about mental disorders (person variables); interpersonal problems with the patients as well as the assessment of their symptoms by the relatives themselves (primary appraisal); support received, critical life events and burden of relatives caused by their own illnesses (secondary appraisal); control behavior and efforts of relatives to engage the patients in activities (coping). PD was assessed with the 12-item version of the General Health Questionnaire. Bi-variate correlation analysis and a multiple linear regression model were the main test statistical approaches. Results: Correlation analysis showed that differences between diagnostic groups referred to primary and secondary appraisal processes, in particular. Results of the statistical model provided evidence for the importance of primary appraisal and person variables for influencing PD, and for the lack of importance of coping and environmental variables. Conclusion: The study enhanced the validity of the transactional stress model to demonstrate the influence of salutogenetic concepts such as sense of coherence

AB2CD: AI for Building Climate Damage Classification and Detection

We explore the implementation of deep learning techniques for precise building damage assessment in the context of natural hazards, utilizing remote sensing data. The xBD dataset, comprising diverse disaster events from across the globe, serves as the primary focus, facilitating the evaluation of deep learning models. We tackle the challenges of generalization to novel disasters and regions while accounting for the influence of low-quality and noisy labels inherent in natural hazard data. Furthermore, our investigation quantitatively establishes that the minimum satellite imagery resolution essential for effective building damage detection is 3 meters and below 1 meter for classification using symmetric and asymmetric resolution perturbation analyses. To achieve robust and accurate evaluations of building damage detection and classification, we evaluated different deep learning models with residual, squeeze and excitation, and dual path network backbones, as well as ensemble techniques. Overall, the U-Net Siamese network ensemble with F-1 score of 0.812 performed the best against the xView2 challenge benchmark. Additionally, we evaluate a Universal model trained on all hazards against a flood expert model and investigate generalization gaps across events, and out of distribution from field data in the Ahr Valley. Our research findings showcase the potential and limitations of advanced AI solutions in enhancing the impact assessment of climate change-induced extreme weather events, such as floods and hurricanes. These insights have implications for disaster impact assessment in the face of escalating climate challenges.Comment: 9 pages, 4 figure

Site-selective tagging of proteins by pnictogen-mediated self-assembly

Site-selective chemical protein modification is achieved by self-assembly of a specific di-cysteine motif, trivalent pnictogens (As, Sb or Bi) and an aromatic mercaptomethyl-based probe. The strategy is demonstrated with a quaternary complex involving Zika virus protease and a lanthanide ion, enabling paramagnetic nuclear magnetic resonance spectroscopy and luminescence measurements.Financial support by the Australian Research Council is gratefully acknowledge