A wireless sensor system is an essential aspect in many fields. It consists of a great deal of sensor nodes. These sensor networks carry out a number of tasks, including interaction, distribution, recognition, and power supply. Data is transmitted from source to destination and plays an important role. Congestion may occur during data transmission from one node to another and also at cluster head locations. Congestion will arise as a result of either traffic division or resource allocation. Energy will be wasted due to traffic division congestion, which causes packet loss and retransmission of removed packets. As a result, it must be simplified; hence there are a few Wireless sensor networks with various protocols that will handle Congestion Control. The Deterministic Energy Efficient Clustering (DEC) protocol, which is fully based on residual energy and the token bucket method, is being investigated as a way to increase the energy efficiency. In the event of congestion, our proposal provides a way to cope with it and solves it using this method to improve lifespan of the sensor networks. Experiments in simulation show that the proposed strategy can significantly enhance lifetime, energy, throughput, and packet loss.

In this paper, we continue a topic of modeling measuring processes by perceiving them as a kind of signal sampling. And, in this respect, note that an ideal model was developed in a previous work. Whereas here, we present its nonideal version. This extended model takes into account an effect, which is called averaging of a measured signal. And, we show here that it is similar to smearing of signal samples arising in nonideal signal sampling. Furthermore, we demonstrate in this paper that signal averaging and signal smearing mean principally the same, under the conditions given. So, they can be modeled in the same way. A thorough analysis of errors related to the signal averaging in a measuring process is given and illustrated with equivalent schemes of the relationships derived. Furthermore, the results obtained are compared with the corresponding ones that were achieved analyzing amplitude quantization effects of sampled signals used in digital techniques. Also, we show here that modeling of errors related to signal averaging through the so-called quantization noise, assumed to be a uniform distributed random signal, is rather a bad choice. In this paper, an upper bound for the above error is derived. Moreover, conditions for occurrence of hidden aliasing effects in a measured signal are given.

Results of an oceanographic survey along the edge of drifting pack ice in the area between Elephant Island and the South Orkney Islands are reported. The influence of sea ice on hydrological factors was very weak. It was not possible to develop oceanographic features characteristic for marginal sea-ice zones in the areas with well marked surface currents and dynamic hydrological conditions. The spatial distribution of chlorophyll was governed by water stability, although during our survey, areas with enhanced vertical stability could not be described in terms of a sea-ice edge influence.

The corrosion features of 20# carbon steel, the base material of sour water stripper, in NH₄Cl solution were examined in this research. The anatomical samples of the corroded parts were characterized by an X-ray diffractometer and other analytical tools. It was found to be a localized perforation caused by pitting, and the corrosion product was FeCl₂. 20# steel was subjected to electrochemical testing using Tafel polarization and Electrochemical impedance spectroscopy. The studies showed that the 20# steel’s cathodic depolarization reaction rate speeds as density rises, and that the concentration polarization gets more marked as temperature rises. The corrosion rate of 20# steel is the fastest when temperature hits its highest point of 80°C, measuring 4.7142 mm/a. When density rose from 5 to 20%, corrosion rate grew from 1.6378 to 7.2430 mm/a.