Ground and satellite-based observations of ionospheric plasma bubbles and blobs at 5.65° latitude in the Brazilian sector

This investigation uses simultaneous observations from all-sky imager system and an ionosonde collocated at Araguatins (5.65° S, 48.07° W and dip-latitude of 4.17° S), a near-equatorial region in Brazil. These simultaneous observations were used to investigate the occurrence of plasma bubbles and blobs in the field of the imaging system and their association with atypical range Spread-F signature in ionograms. Also, in-situ observation of plasma density from Swarm satellites were used to support the ground-based observations. Using a few cases, a methodology will be established to identify in the plasma blobs (atypical ESF) in the ionograms when there is the simultaneous observation of plasma bubbles and blobs in the field of view of the ionosonde. For this purpose, simultaneous sequence of OI 630.0 nm nightglow images and ionograms are presented for different case studies; 1. when there is the absence of a plasma bubble or blob, 2. when there is only the occurrence of plasma bubbles and 3. when there is the occurrence of plasma bubbles and blobs, in order to compare traces in the ionogram in all these case studies. With these we can cover all kinds of signatures in the ionograms corresponding to no irregularities, plasma bubbles only and plasma bubbles-blobs. These OI 630.0 nm nightglow and ionograms recorded simultaneously make it possible to establish a novel methodology to recognize in ionograms cases when there is the occurrence of Spread-F signature associated with bubble-blob in the FOV of the ionosonde.     

Semi-automatic determination of the Azores Current axis using satellite altimetry: Application to the study of the current variability during 1995–2006

Satellite altimetry has been widely used to study the variability of the ocean currents such as the Azores Current (AzC) in the North Atlantic. Most analyses are performed over the region that encloses the current, thus being somehow affected by other oceanographic signals, e.g., eddies. In this study, a new approach for extracting the axis of a zonal current solely based on satellite altimetry is presented. This is a semi-automatic procedure that searches for the maximum values of the gradient of absolute dynamic topography (ADT), using the geostrophic velocity as auxiliary information. The advantage of this approach is to allow the analyses to be performed over a buffer centered on the current axis instead of using a wider region. It is here applied to the AzC for the period June 1995–October 2006.     

Extrasolar solar-sail trajectories and dark matter

Hyper-thin, high-speed solar-photon sail space probes exploring the Sun?s Oort comet cloud could also be used to set an upper bound to the concentration of WIMPS (weakly interacting massive particles), one of the suggested (but unconfirmed) forms of dark matter within the vicinity of the solar system. Newton?s Shell Theorem would be applied to determine variations in apparent solar mass as the probe moves further out from the Sun. Application of this technique to the trajectories of Pioneer 10/11 reveals that the upper limit to WIMP concentration within ~60 AU of the Sun is ~0.2 Earth masses, as revealed in studies of the Pioneer Anomaly. If the published accuracy of the Pioneer acceleration measurements can be increased by an order of magnitude, probe trajectory measurements out to ~10,000 AU may confirm or falsify the hypothesis that WIMP mass within the solar vicinity is ~3X star mass. It is shown that a space-manufactured ~40-nm thick beryllium hollow-body solar sail deployed from a ~0.07 AU perihelion is a candidate spacecraft for such a mission. Possible science-team organization strategy for a ~100-year mission to ~10,000 AU is discussed.     

Variations of geomagnetic cutoff rigidity in the southern hemisphere close to 70°W (South-Atlantic Anomaly and Antarctic zones) in the period 1975–2010

We report the existence of rapid variations in (effective) geomagnetic cutoff rigidity (Rc) between the equatorial and Antarctic zones adjacent to the Andes Mountains, revealed by the variation rate of geomagnetic cutoff rigidity (VRc) in the period 1975–2010. Our analysis is based on empirical records and theoretical models of the variations in cosmic rays and on the structure of geomagnetic fields. These have given us a different view of variations in Rc in time and space along the 70°W meridian, where secular variations in the geomagnetic field are strongly influenced by the proximity of the South Atlantic Magnetic Anomaly (SAMA), one of the most important characteristics of the terrestrial magnetic field that affects our planet, close from the equator to the 50°S parallel and from South America to South Africa. The VRc presents rapid changes in mid-latitudes where SAMA exerts its influence despite the existence of smooth changes in the geomagnetic field. This shows that these changes occur mainly in the spatial configuration, rather than in the temporal evolution of Rc. The analysis was performed using measurements from the Chilean Network of Cosmic Rays and Geomagnetism Observatories, equipped with BF-3 and latest generation He-3 neutron monitors, Fluxgate magnetometers, geomagnetic reference field (IGRF) and Tsyganenko 2001 model (just for completeness).     

An Adaptive Network-based Fuzzy Inference System for the detection of thermal and TEC anomalies around the time of the Varzeghan,Iran, (Mw = 6.4) earthquake of 11 August 2012

Anomaly detection is extremely important for forecasting the date, location and magnitude of an impending earthquake. In this paper, an Adaptive Network-based Fuzzy Inference System (ANFIS) has been proposed to detect the thermal and Total Electron Content (TEC) anomalies around the time of the Varzeghan, Iran, (M_w = 6.4) earthquake jolted in 11 August 2012 NW Iran. ANFIS is the famous hybrid neuro-fuzzy network for modeling the non-linear complex systems. In this study, also the detected thermal and TEC anomalies using the proposed method are compared to the results dealing with the observed anomalies by applying the classical and intelligent methods including Interquartile, Auto-Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The duration of the dataset which is comprised from Aqua-MODIS Land Surface Temperature (LST) night-time snapshot images and also Global Ionospheric Maps (GIM), is 62 days. It can be shown that, if the difference between the predicted value using the ANFIS method and the observed value, exceeds the pre-defined threshold value, then the observed precursor value in the absence of non seismic effective parameters could be regarded as precursory anomaly. For two precursors of LST and TEC, the ANFIS method shows very good agreement with the other implemented classical and intelligent methods and this indicates that ANFIS is capable of detecting earthquake anomalies. The applied methods detected anomalous occurrences 1 and 2 days before the earthquake. This paper indicates that the detection of the thermal and TEC anomalies derive their credibility from the overall efficiencies and potentialities of the five integrated methods.     

Fronts and thermohaline structure of the Brazil–Malvinas Confluence System

Fronts and thermohaline structure of the Brazil–Malvinas Confluence System (BMCS) are studied from climatic data, “Marathon Exp. Leg.8, 1984” data, and Sea surface temperature (SST) data base “ds277-Reynolds” (1981–2000). The South Atlantic Central Water (SACW) is divided in two main types: tropical (TW) and subtropical water (ST). Water masses, Fronts, Inter-Frontal and Frontal Zones are analysed and classified: (a) the water masses: Tropical Low-Salinity Water, Tropical Surface Water, Tropical Tropospheric Water, Subtropical Low-Salinity Water, Subtropical Surface Water, Subtropical Tropospheric Water. T,S characteristics of intermediate, deep and bottom water defined by different authors are confirmed and completed; (b) the Inter-Frontal Zones: Tropical/Brazil Current Zone, Subtropical Zone and Subantarctic Zone; (c) the Frontal Zones: Subtropical, Subantarctic and Polar, and (d) the Fronts: Subtropical Front of the Brazil Current, Principal Subtropical Front, North Subtropical Front, Subtropical Surface Front, South Subtropical Front, Subantarctic Surface Front, Subantarctic Front and Polar Front. Several stable T–S relationships are found below the friction layer and at the Fronts. The maximum gradient of the oceanographic characteristics occurs at the Brazil Current Front, which can be any of the subtropical Fronts, depending on season. Minimum mean depth of the pycnocline coincides with the Fronts of the BMCS, indicating the paths of low-salinity shelf waters into the open ocean. In the work it is shown how to recover the horizontal and vertical thermohaline structure of waters from satellite data RSMAS SST.     

Hipparchus vs. Ptolemy and the Antikythera Mechanism: Pin–Slot device models lunar motions

The ancient Greek astronomical calculator known as the Antikythera Mechanism has been analyzed using geometrical, calculus, trigonometric and complex variable methods. This analysis demonstrates that the Mechanism modeled the variations in the Moon’s angular velocity as seen from the Earth, to better than 1 part in 200. A major implication of this analysis is that the Antikythera Mechanism of the 2nd century BCE modeled the anomalistic motion of the Moon more accurately than Ptolemy’s account of Hipparchus’s theory of the 2nd century CE. In the present work, mathematics, astronomy, history and methodology of the sciences combine in the study of a unique artifact, preserved for posterity in an ancient ship that sank in the Mediterranean 2100 years ago and recovered by Greek sponge divers at the dawn of the 20th century.     

一种基于迁移学习的遥测数据异常检测方法

为解决卫星遥测数据异常检测面临的数据不平衡且缺乏有标签样本的问题，提出一种基于一维卷积神经网络(1dCNN)迁移学习的异常检测方法。首先利用源域卫星的遥测数据对1dCNN进行预训练，使得模型的卷积层具有卫星状态特征的提取能力；然后将训练好的模型迁移到缺乏标签数据的目标域卫星中；利用目标域有标签样本对预训练模型进行微调，从而实现了对目标域测试集样本的异常检测。为了使1dCNN能够适应遥测数据样本的不平衡性，引入了代价敏感训练策略，建立动态损失函数，从而提升代价敏感一维卷积神经网络(cs 1dCNN)对于异常样本的识别能力。以某两个卫星的电源分系统遥测数据进行了验证，实验结果表明该异常检测迁移方法具有较好的有效性和鲁棒性。     

Increase in fish abundance during two typhoons in the South China Sea

A fish monitoring program was conducted in the northern region of the South China Sea from March 2009 to December 2010. During this period, two typhoons, GONI and Koppu, hit this region consecutively in August and September 2009. The fish and satellite data were analyzed to understand the influence of the typhoons on fish activities. The results showed that the fish species number (FSN) increased by approximately 14.29% and 14.81% after the two typhoons, GONI and Koppu, respectively. The five increased fish species included three estuarine species and two shallow sea species. However, one shallow sea species was also absent. In the nearshore (near the Pearl River Estuary) and offshore (along the typhoon’s track) regions after GONI, the FSN increased by approximately 24% (nearshore) and 52.63% (offshore), with estuarine species accounting for 42.86% (nearshore) and 33.33% (offshore) of the fish species; after Koppu, the FSN increased by approximately 15.38% (nearshore) and 163.64% (offshore), with estuarine species accounting for 60% (nearshore) and 26.32% (offshore) of the fish species. In the increased records, small and medium-sized fish species were dominant nearshore, and small fish species were dominant offshore. The FSN increased to a maximum value between the 5th and the 10th days after the typhoon nearshore and between the 3rd and 8th days after the typhoon offshore. The results indicated that river discharge, triggered by the typhoon’s nearshore rainfall, as well as offshore upwelling nutrients, also triggered by the typhoons, and may have played important roles in the variability of fish species. This research found that the increase in the FSN was associated with the typhoons in the northern South China Sea.     

Aircraft engine fault detection based on grouped convolutional denoising autoencoders

Many existing aircraft engine fault detection methods are highly dependent on performance deviation data that are provided by the original equipment manufacturer. To improve the independent engine fault detection ability, Aircraft Communications Addressing and Reporting System (ACARS) data can be used. However, owing to the characteristics of high dimension, complex correlations between parameters, and large noise content, it is difficult for existing methods to detect faults effectively by using ACARS data. To solve this problem, a novel engine fault detection method based on original ACARS data is proposed. First, inspired by computer vision methods, all variables were divided into separated groups according to their correlations. Then, an improved convolutional denoising autoencoder was used to extract the features of each group. Finally, all of the extracted features were fused to form feature vectors. Thereby, fault samples could be identified based on these feature vectors. Experiments were conducted to validate the effectiveness and efficiency of our method and other competing methods by considering real ACARS data as the data source. The results reveal the good performance of our method with regard to comprehensive fault detection and robustness. Additionally, the computational and time costs of our method are shown to be relatively low.