Abstract
Autonomous UAVs offer advantages in industrial, agriculture, environment inspection, and logistics applications. Sometimes the use of cooperative UAVs is important to solve specific demands or achieve productivity gain in these applications. An important technical challenge is the precise positioning between two or more UAVs in a cooperative task flight. Some techniques provide solutions, like the GNSS positioning, visual and LIDAR slam, and computer vision intelligent algorithms, but all these techniques present limitations that must be solved to work properly in specific environments. The proposal of new cooperative position methods is important to face these challenges. The present work proposes an evaluation of a visual relative positioning architecture between two small UAV multi-rotor aircraft working in a master-slave operation, based on an Augmented Reality tag tool. The simulation results obtained absolute error measurements lower than 0.2 cm mean and 0.01 standard deviation for X, Y and Z directions. Yaw measurements presented an absolute error lower than 0.5 degrees C with a 0.02-5 degrees C standard deviation. The real-world experiments executing autonomous flight with the slave UAV commanded by the master UAV achieved success in 8 of 10 experiment rounds, proving that the proposed architecture is a good approach to building cooperative master-slave UAV applications.

Abstract
The recent increasing interest in the application of radiology contrasting agents to create transparency in biological tissues implies that the diffusion properties of those agents need evaluation. The comparison of those properties with the ones obtained for other optical clearing agents allows to perform an optimized agent selection to create optimized transparency in clinical applications. In this study, the evaluation and comparison of the diffusion properties of gadobutrol and glycerol in skeletal muscle was made, showing that although gadobutrol has a higher molar mass than glycerol, its low viscosity allows for a faster diffusion in the muscle. The characterization of the tissue dehydration and refractive index matching mechanisms of optical clearing was made in skeletal muscle, namely by the estimation of the diffusion coefficients for water, glycerol and gadobutrol. The estimated tortuosity values of glycerol (2.2) and of gadobutrol (1.7) showed a longer path-length for glycerol in the muscle.

Abstract
This paper proposes a novel probabilistic model for quantifying the impact of demand flexibility (DF) on the long-term generation system adequacy via Sequential Monte Carlo Simulation (SMCS) method. Unlike load shedding, DF can be considered an important instrument to postpone bulk consumption from periods with limited reserves to periods with more generating capacity available, avoiding load shedding and increasing the integration of variable renewable generation, such as wind power. DF has been widely studied in terms of its contribution to the system's social welfare, resulting in numerous innovative approaches ranging from the flexibility modeling of individual electric loads to the definition of aggregation strategies for optimally deploying this lever in competitive markets. To add to the current state-of-the-art, a new model is proposed to quantify DF impact on the traditional reliability indices, such as the Loss of Load Expectation (LOLE) and the Expected Energy Not Supplied (EENS), enabling a new perspective for the DF value. Given the diverse mechanisms associated with DF of different consumer types, the model considers the uncertainties associated with the demand flexibility available in each hour of the year and with the rebound effect, i.e., the subsequent change of consumption patterns following a DF mobilization event. Case studies based on a configuration of the IEEE-RTS 79 test system with wind power demonstrate that the DF can substantially improve the reliability indices of the static and operational reserve while decreasing the curtailment of variable generation cause by unit scheduling priorities or by short-term generation/demand imbalances.

Abstract
Modern I/O applications that run on HPC infrastructures are increasingly becoming read and metadata intensive. However, having multiple applications submitting large amounts of metadata operations can easily saturate the shared parallel file system's metadata resources, leading to overall performance degradation and I/O unfairness. We present PADLL, an application and file system agnostic storage middleware that enables QoS control of data and metadata workflows in HPC storage systems. It adopts ideas from Software-Defined Storage, building data plane stages that mediate and rate limit POSIX requests submitted to the shared file system, and a control plane that holistically coordinates how all I/O workflows are handled. We demonstrate its performance and feasibility under multiple QoS policies using synthetic benchmarks, real-world applications, and traces collected from a production file system. Results show that PADLL can enforce complex storage QoS policies over concurrent metadata-aggressive jobs, ensuring fairness and prioritization.

Abstract
Aggregators are acknowledged as key agents to enable the active participation of household and commercial distributed energy resources (DER) in electricity markets. In recent years, many researchers and practitioners have been working on the development of diverse network-secure and network-insecure bidding strategies to support the participation of DER aggregators in electricity markets. An example of this is the extensive work developed by the authors of this paper in various R&D projects with industry. This paper builds on the experience gained in previous works and its main contribution is a thorough comparison between these strategies, resulting in an extensive discussion of their pros and cons. The discussion compares the economic and network security performance of the strategies, as well as their communication, computational, and data privacy requirements. To discuss and quantify these aspects, we formulate, implement, and test various bidding strategies on a real-world MV-LV distribution network with 2 aggregators and 522 customers for multiple DER scenarios. The discussion of the results provides realistic and valuable information on the pros and cons of each strategy, helping energy system stakeholders to understand which strategy may better fit their needs.

Abstract
We consider a nondurable good monopolist that collects data on its customers in order to profile them and subsequently practice price discrimination on returning cus-tomers. The monopolist's price discrimination scheme is leaky in the sense that an endogenous fraction of consumers choose to incur a privacy cost to conceal their identity when they return in the following periods. We characterize the Markov perfect equili-brium of the game under two alternative customer profiling regimes: full information acquisition (FIA) and purchase history information (PHI). In both cases, we find that, contrary to what could be expected, the monopolist's aggregate profit is not monotoni-cally increasing in the level of the privacy cost, but a U-shaped function of it, leading to ambiguous profit effects: a reduction in privacy costs increases the fraction of customers who choose to be anonymous (detrimental profit effect), but it also softens the firm's introductory price, reducing the pace at which prices targeted to new customers fall over time (positive profit effect). When comparing results under FIA and PHI, we find that market expansion is faster, and more customers conceal their identity under FIA than under PHI. Equilibrium profits are also higher in the FIA case. Although equili-brium profits are U-shaped functions of the privacy cost in both profiling regimes, they tend to be globally decreasing with the privacy cost under PHI and globally increasing under FIA.