Accurate characterization of seawater composition is required to understand chemical changes occurring in the ocean and their impact on marine ecosystems. In this work, three different seawater samples collected from the Portuguese Coast were analyzed by Ion Chromatography. For matrix comparison purposes, the same procedure was performed in two samples collected in two different locations in the Baltic Sea, known to be composed essentially by brackish water with a salinity gradient typical of estuaries. Special focus on calcium and magnesium ions is presented since both ions are essential for biological processes in the ocean s environment. The uncertainty associated with the results of ionic composition were evaluated to further use in the ionic balance and to assess the constancy of the proportion of major ionic components in the analysed seawaters. The collected information is of key usefulness for characterizing major components and for determining long-term trends or spatial variations of seawater composition.

Measuring marine biodiversity is essential to proper management of biological resources. Monitoring networks and observatory systems are adopting conceptual frameworks to agree on the fundamental variables describing the biodiversity change and to meaningfully inform policy makers. Biological and ecosystem Essential Ocean Variables (bio-eco EOVs) and Essential Biodiversity Variables (EBVs) both help to harmonize observations of different providers to essential measurements. Conceptual relationships between the two frameworks have been recently outlined to enrich data with complementary information. However, even if bio-eco EOVs and EBVs are adopted among research communities, they represent theoretical approaches whose implementations are often limited and still uncoupled. To evaluate operative reuse of biodiversity measurements, in this paper we identify differences and common aspects between the management recommendations established by the two frameworks in a synoptic comparison. Also, we provide evidences for applying the proposed approach to the marine component of the Long-Term Ecological Research (LTER) networks, by individuating in the Italian network (LTER-Italy) the Northern Adriatic Sea as a case study where compare, in further work, the data handling practices with the identified recommendations.

A method of calibrating the integrated circuit ATE (Automatic Test Equipment) by synchronizing and measuring the signal on the DUT(Device Under Test) side while test is presented. This method connects the measuring instruments to the pin of the DUT while calibration. The computer controls the measuring instrument to capture the changes and values of the voltage and current on each pin of the DUT during the test. The specifications of the ATE can be calculated though the measured data.Therefore ,the ATE is calibrated via compare it’s primitive specifications and the measured one.

In order to solve the problem of the dynamic error analysis of the current transformer, firstly, the mathematical model of the current transformer is established. Secondly, based on the working principle of the current transformer, the causes of the dynamic error of the current transformer are studied. On this basis, the important parameters affecting the dynamic error of the current transformer are analyzed theoretically. Thirdly, different dynamic test signals under complex working conditions are selected. After that, a composite error analysis algorithm is proposed to give the dynamic error quantitatively. Finally, the influence of the above parameters on the dynamic error is given, and the relationship between these parameters and the dynamic error is discussed. The results show that, according to the composite error analysis algorithm adopted in this paper, the dynamic error of different dynamic test signals under complex working conditions can be calculated effectively and the specific influence of each parameter on the dynamic error can be obtained.

This work presents a way of reducing the power consumption of the existing wireless sensor network used for monitoring of water quality. The improvement of sensor nodes energy efficiency is achieved by utilizing compressed sensing and lowering the average sampling rate. A novel continuous model based reconstruction method is proposed, utilizing a water parameter signal model rather than a discrete dictionary. No trained dictionary and hence no signal database is required a priori to compressed sensing application.