Abstract
LoRa (along with its upper layers definition-LoRaWAN) is one of the most promising Low Power Wide Area Network (LPWAN) technologies for implementing Internet of Things (IoT)-based applications. Although being a popular technology, several works in the literature have revealed vulnerabilities and risks regarding the security of LoRaWAN v1.0 (the official 1st specification draft). The LoRa-Alliance has built upon these findings and introduced several improvements in the security and architecture of LoRa. The result of these efforts resulted in LoRaWAN v1.1, released on 11 October 2017. This work aims at reviewing and clarifying the security aspects of LoRaWAN v1.1. By following ETSI guidelines, we provide a comprehensive Security Risk Analysis of the protocol and discuss several remedies to the security risks described. A threat catalog is presented, along with discussions and analysis in view of the scale, impact, and likelihood of each threat. To the best of the authors' knowledge, this work is one of the first of its kind, by providing a detailed security risk analysis related to the latest version of LoRaWAN. Our analysis highlights important practical threats, such as end-device physical capture, rogue gateway and self-replay, which require particular attention by developers and organizations implementing LoRa networks.

Abstract
Recent Low Power Wide Area Networks (LPWAN) protocols are receiving increased attention from industry and academia to offer accessibility for Internet of Things (IoT) connected remote sensors and actuators. In this work, we present a formal study of LoRaWAN security, an increasingly popular technology, which defines the structure and operation of LPWAN networks based on the LoRa physical layer. There are previously known security vulnerabilities in LoRaWAN that lead to the proposal of several improvements, some already incorporated into the latest protocol specification. Our analysis of LoRaWAN security uses Scyther, a formal security analysis tool and focuses on the key exchange portion of versions 1.0 (released in 2015) and 1.1 (the latest, released in 2017). For version 1.0, which is still the most widely deployed version of LoRaWAN, we show that our formal model allowed to uncover weaknesses that can be related to previously reported vulnerabilities. Our model did not find weaknesses in the latest version of the protocol (v1.1), and we discuss what this means in practice for the security of LoRaWAN as well as important aspects of our model and tools employed that should be considered. The Scyther model developed provides realistic models for LoRaWAN v1.0 and v1.1 that can be used and extended to formally analyze, inspect, and explore the security features of the protocols. This, in turn, can clarify the methodology for achieving secrecy, integrity, and authentication for designers and developers interested in these LPWAN standards. We believe that our model and discussion of the protocols security properties are beneficial for both researchers and practitioners. To the best of our knowledge, this is the first work that presents a formal security analysis of LoRaWAN.

Abstract
Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard - LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRa networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova's (SoTa), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova's, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

Abstract
Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology which uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard - LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRa networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, considering realistic scenarios. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 30% in DER, and a number of collisions 17 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 10.5% and 4% of DER, and a number of collisions 13.5 and 7.5 times smaller than equal-distribution and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption 3.6 and 2.74 times lower than the equal-distribution and random dynamic allocation policies, respectively. Furthermore, we approach the practical aspects on how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

Abstract
Laser diagnostics and treatment procedures are commonly performed for visible and near-IR wavelengths. The knowledge of the wavelength dependences for the optical properties of various biological tissues in this spectral range is useful for clinical applications. Since the optical properties of human liver have been previously known only for near-IR wavelengths, the aim is to estimate their wavelength dependences between 400 and 1000 nm. Using spectral measurements from liver samples in this range, we determine their optical properties with the inverse adding-doubling method. The obtained results indicate the presence of bile, oxyhaemoglobin and deoxyhaemoglobin in human liver. The combination of these biological components results in strong absorption for wavelengths between 400 and 600 nm, with peaks at unusual wavelengths. For wavelengths above 600 nm, the wavelength dependences for all optical properties present the typical behavior, but strong and shifted absorption observed for wavelengths below 600 nm has been previously unknown and can be useful for clinical procedures with lasers working in this range.

Abstract
This book describes the Optical Immersion Clearing method and its application to acquire information with importance for clinical practice and various fields of biomedical engineering. The method has proved to be a reliable means of increasing tissue transparency, allowing the investigator or surgeon to reach deeper tissue layers for improved imaging and laser surgery. This result is obtained by partial replacement of tissue water with an active optical clearing agent (OCA) that has a higher refractive index and is a better match for the refractive index of other tissue components. Natural tissue scattering is thereby reduced. An exponential increase in research using this method has occurred in recent years, and new applications have emerged, both in clinical practice and in some areas of biomedical engineering. Recent research has revealed that treating ex vivo tissues with solutions containing active OCAs in different concentrations produces experimental data to characterize drug delivery or to discriminate between normal and pathological tissues. The obtained drug diffusion properties are of interest for the pharmaceutical and organ preservation industry. Similar data can be estimated with particular interest for food preservation. The free water content evaluation is also of great interest since it facilitates the characterization of tissues to discriminate pathologies. An interesting new application that is presented in the book regards the creation of two optical windows in the ultraviolet spectral range through the application of the immersion method. These induced transparency windows open the possibility to diagnose and treat pathologies with ultraviolet light. This book presents photographs from the tissues we have studied and figures that represent the experimental setups used. Graphs and tables are also included to show the numerical results obtained in the sequential calculations performed.