Self-consistent 2-phase AGN torus models: SED library for observers

We assume that dust near active galactic nuclei (AGN) is distributed in a torus-like geometry, which may be described by a clumpy medium or a homogeneous disk or as a combination of the two (i.e. a 2-phase medium). The dust particles considered are fluffy and have higher submillimeter emissivities than grains in the diffuse ISM. The dust-photon interaction is treated in a fully self-consistent three dimensional radiative transfer code. We provide an AGN library of spectral energy distributions (SEDs). Its purpose is to quickly obtain estimates of the basic parameters of the AGN, such as the intrinsic luminosity of the central source, the viewing angle, the inner radius, the volume filling factor and optical depth of the clouds, and the optical depth of the disk midplane, and to predict the flux at yet unobserved wavelengths. The procedure is simple and consists of finding an element in the library that matches the observations. We discuss the general properties of the models and in particular the 10mic. silicate band. The AGN library accounts well for the observed scatter of the feature strengths and wavelengths of the peak emission. AGN extinction curves are discussed and we find that there is no direct one-to-one link between the observed extinction and the wavelength dependence of the dust cross sections. We show that objects of the library cover the observed range of mid IR colors of known AGN. The validity of the approach is demonstrated by matching the SEDs of a number of representative objects: Four Seyferts and two quasars for which we present new Herschel photometry, two radio galaxies, and one hyperluminous infrared galaxy. Strikingly, for the five luminous objects we find pure AGN models fit the SED without a need to postulate starburst activity.Comment: A&A accepted by referee, AGN library available at http://www.eso.org/~rsiebenm/agn_models/index.htm

Machine Learning-Assisted Anomaly Detection in Maritime Navigation Using AIS Data

The automatic identification system (AIS) reports vessels' static and dynamic information, which are essential for maritime traffic situation awareness. However, AIS transponders can be switched off to hide suspicious activities, such as illegal fishing, or piracy. Therefore, this paper uses real world AIS data to analyze the possibility of successful detection of various anomalies in the maritime domain. We propose a multi-class artificial neural network (ANN)-based anomaly detection framework to classify intentional and non-intentional AIS on-off switching anomalies. The multi-class anomaly framework captures AIS message dropouts due to various reasons, e.g., channel effects or intentional one for carrying illegal activities. We extract position, speed, course and timing information from real world AIS data, and use them to train a 2-class (normal and anomaly) and a 3-class (normal, power outage and anomaly) anomaly detection models. Our results show that the models achieve around 99.9% overall accuracy, and are able to classify a test sample in the order of microseconds.Comment: This conference paper is uploaded here for non-comercial purpose

Rapid Variations of the Static Data Transmitted within AIS Message 5

For over a decade now the Automatic Identification System (AIS) has been considered an important improvement of both the watchkeeping duties at sea and the vessel traffic surveillance activities worldwide. The on-board AIS equipment is used for broadcasting the dynamic data describing the vessel movement vector as well as the static parameters related to her voyage or hull dimension. The reporting intervals of the AIS transmissions depend on the data validity periods which are shorter for continuously changing dynamic AIS parameters and longer in case of less frequently altering static AIS variables. This work focuses on cases of static AIS parameters like the GNSS reference point which were detected to be changing at a high rate, despite the fact that the settings like this are only allowed to be modified during the configuration phase of an AIS transponder. The AIS data received at the DLR reference station in Rostock are analysed and provide a statistical overview of this phenomenon

Interdependencies between Evaluation of Collision Risks and Performance of Shipborne PNT Data Provision

The highest priority for safe ship navigation is the avoidance of collisions and groundings. For this purpose the concept of ship domain has been introduced to describe the surrounding effective waters which should be kept clear of other ships and obstacles. In the last decades a large variety of ship domains have been developed differing in the applied method of their determination as well as in the modelled shape, size, and safety areas. However, a ship domain should be adjusted in real time to enable a reliable evaluation of collision risks by the officers of the watch. Until today in the discussions about modelling and utilization of ship domains it has been mostly unnoticed that the performance of vessel’s position (P), navigation (N), and timing data (T) ultimately determines the accuracy and integrity of indicated ship domain. This paper addresses this question and starts with a comprehensive analysis of AIS data to prove the violation of ship domains in the maritime practice. A simulation system has been developed to enable for the first time to investigate how far inaccuracies in PNT data result into a fault evaluation of collision risks. The simulation results have shown that there is a non-negligible risk of not detecting a collision, if inaccuracies of sensor data remain unnoticed

Role of IL-17 and Th17 Cells in Liver Diseases

Unbalanced Th1/Th2 T-cell responses in the liver are a characteristic of hepatic inflammation and subsequent liver fibrosis. The recently discovered Th17 cells, a subtype of CD4+ T-helper cells mainly producing IL-17 and IL-22, have initially been linked to host defense against infections and to autoimmunity. Their preferred differentiation upon TGFβ and IL-6, two cytokines abundantly present in injured liver, makes a contribution of Th17 cells to hepatic inflammation very likely. Indeed, initial studies in humans revealed activated Th17 cells and Th17-related cytokines in various liver diseases. However, functional experiments in mouse models are not fully conclusive at present, and the pathogenic contribution of Th17 cells to liver inflammation might vary upon the disease etiology, for example, between infectious and autoimmune disorders. Understanding the chemokines and chemokine receptors promoting hepatic Th17 cell recruitment (possibly CCR6 or CCR4) might reveal new therapeutic targets interfering with Th17 migration or differentiation in liver disease

Occurrence of Unknown Sensor Data within AIS Dynamic Messages

For more than a decade, the Automatic Identification System (AIS) has contributed to increasing the safety of navigation at sea. Despite the benefits of the system, AIS messages shared between vessels and the AIS dynamic data transferred to the Portable Pilot Units may contain unknown values of sensor data if the sensor data on board becomes either unavailable or undeliverable for any reason. In this paper, an experiment is conducted to analyse the performance of an AIS transponder during a virtual sea voyage. By altering the sensor data rate it is possible to cause the AIS transponder to output AIS messages with unknown sensor data. After performing the experiment, a generic approach is used in order to establish a correlation between the sensor data rate and the relative occurrence frequency of AIS unknown values. This leads to the formulation of a simple equation which describes the interdependence between the interval of sensor data provision, the age of the sensor data allowed by the AIS transponder and the percentage of unknown data within the AIS data output

EKF Based Trajectory Tracking and Integrity Monitoring of AIS Data

This work presents a novel approach for integrity monitoring of AIS data. Currently, the AIS is a valuable source for maritime traffic situation assessment but not suited for collision avoidance, as it is prone to failures and not capable of indicating the level of data integrity. To tackle this, an EKF was designed to track vessel trajectories, which allows for failure detection based on residual monitoring. For the latter, two methods for hypotheses testing were implemented, namely chi-squared and GLR tests. In addition, the IMM framework was adopted for mixing the state estimates of two different process models, the CV and CTRV. The designed filter will be validated on behalf of simulated and real-world AIS data

Role of IL-17 and Th17 Cells in Liver Diseases

Unbalanced Th1/Th2 T-cell responses in the liver are a characteristic of hepatic inflammation and subsequent liver fibrosis. The recently discovered Th17 cells, a subtype of CD4 + T-helper cells mainly producing IL-17 and IL-22, have initially been linked to host defense against infections and to autoimmunity. Their preferred differentiation upon TGFβ and IL-6, two cytokines abundantly present in injured liver, makes a contribution of Th17 cells to hepatic inflammation very likely. Indeed, initial studies in humans revealed activated Th17 cells and Th17-related cytokines in various liver diseases. However, functional experiments in mouse models are not fully conclusive at present, and the pathogenic contribution of Th17 cells to liver inflammation might vary upon the disease etiology, for example, between infectious and autoimmune disorders. Understanding the chemokines and chemokine receptors promoting hepatic Th17 cell recruitment (possibly CCR6 or CCR4) might reveal new therapeutic targets interfering with Th17 migration or differentiation in liver disease