A tombolo is a narrow belt connecting a mainland with an island lying near to the shore, formed as a result of sand and gravel being deposited by sea currents, most often created as a result of natural phenomena. However, it can also be caused by human activity, as is the case with the Sopot pier—a town located on the southern coast of the Baltic Sea in northern Poland (φ = 54°26’N, λ = 018°33’E). As a result, the seafloor rises constantly and the shoreline moves towards the sea. Moreover, there is the additional disturbing phenomenon consisting of the rising seafloor sand covering over the waterbody’s vegetation and threatening the city's spa character. Removal of the sand to another place has already been undertaken several times. There is a lack of precise geospatial data about the tombolo’s seafloor course, its size and spatial shape caused by only lowering the seafloor in random places, and the ongoing environmental degradation process. This article presents the results of extensive and integrated geodetic and hydrographic measurements, the purpose of which was to make a 3D model of the phenomena developing in Sopot. The measurements will help determine the size and speed of the geospatial changes. Most of the modern geodetic and hydrographic methods were used in the study of these phenomena. For the construction of the land part of geospatial model, the following were used: photos from the photogrammetric flight pass (unmanned aerial vehicle—UAV), laser scanning of the beach and piers, and satellite orthophotomaps for analysis of the coastline changes. In the sea part, bathymetric measurements were carried out with an unmanned surface vehicle (USV).

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An acceptable quality of electrical energy is seen today as an important component of ecology. Several instruments for estimating the quality of electrical power have been elaborated. Each supplier assures that the instrument meets the applicable standards and that the uncertainty of the measurement results obtained using the instrument does not exceed the established levels. The accuracy of the measurement results depends on a couple of things, e.g., the correctness of the measurement algorithms implemented in the instrument and the quality of its calibration. In this paper, the basic features of an "estimator/analyzer" (E/A) instrument, as well as the calibration methods of the instrument, the verification of its measurement algorithms, and also the obtained exemplary results, are shown. The proposal of the strategy of the reliable validation of embedded measurement algorithms for the identification of parameters characterizing electrical power quality in the power grid is discussed.

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Tombolo is a narrow belt connecting the mainland with an island lying near the shore. It is formed as a result of sand and gravel being deposited by sea currents. In consequence, the seabed constantly rises and the shoreline moves towards the sea. This paper deals with accuracy analysis of the undertaken tombolo effect investigation, namely estimation of uncertainty of the measurement results. The aforementioned analysis concerns two methods used for creating a 3D beach model: Firstly, based on geodetic laser scanning (TLS—terrestrial laser scanning) and secondly, using images from unmanned aerial vehicles (UAV). The presented exemplary estimation of uncertainty of the measurement of coordinates X-Y-Z is based on the Polish case study.

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The contribution of this paper is to show the opportunities for using the compressive sensing (CS) technique for detecting harmonics in a frequency sparse signal. The signal in a ship’s electrical network, polluted by harmonic distortions, can be modeled as a superposition of a small number of sinusoids and the discrete Fourier transform (DFT) basis forms its sparse domain. According to the theory of CS, a signal may be reconstructed from under-sampled incoherent linear measurements. This paper highlights the use of the discrete Radon transform (DRT) techniques in the CS scheme. In the reconstruction algorithm section, a fast algorithm based on the inverse DRT is presented, in which a few randomly sampled projections of the input signal are used to correctly reconstruct the original signal. However, DRT requires a very large set of measurements that can defeat the purpose of compressive data acquisition. To acquire the wideband data below the Nyquist frequency, the K-rank-order filter is applied in the sparse transform domain to extract the most significant components and accelerate the convergence of the solution. While most CS research efforts focus on random Gaussian measurements, the Bernoulli matrix with different values of the probability of ones is applied in the presented algorithm. Preliminary results of numerical simulation confirm the effectiveness of the algorithm used, but also indicate its limitations. A significant advantage of the proposed approach is the speed of analysis, which uses fast Fourier transform (FFT) and inverse FFT (IFFT) algorithms widely available in programming environments. Moreover, the data processing algorithm is quite simple, and therefore memory usage and burden of the data processing load are relatively low.

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