There has been enormous growth in the energy sector in the new millennium, and it has enhanced energy demand, creating an exponential rise in the capital investment in the energy industry in the last few years. Regular monitoring of the health of industrial equipment is necessary, and thus, the concept of structural health monitoring (SHM) comes into play. In this paper, the purpose is to highlight the importance of SHM systems and various techniques primarily used in pipelining industries. There have been several advancements in SHM systems over the years such as Point OFS (optical fiber sensor) for Corrosion, Distributed OFS for physical and chemical sensing, etc. However, these advanced SHM technologies are at their nascent stages of development, and thus, there are several challenges that exist in the industries. The techniques based on acoustic, UAVs (Unmanned Aerial Vehicles), etc. bring in various challenges, as it becomes daunting to monitor the deformations from both sides by employing only one technique. In order to determine the damages well in advance, it is necessary that the sensor is positioned inside the pipes and gives the operators enough time to carry out the troubleshooting. However, the mentioned technologies have been unable to indicate the errors, and thus, there is the requirement for a newer technology to be developed. The purpose of this review manuscript is to enlighten the readers about the importance of structural health monitoring in pipeline and wind turbine industries.

Abstract. Cell phones have become an inherent part of human life and have grown rapidly in the last decade. In India, there are nearly 120 crore cell phone users which require setting up of cell phone tower at an appropriate location to transmit the signals. A signal strength that is measured in (dBm) keeps on varying from one location to another. Over the decades, there has been a great deal of concern about placing a cell phone tower to manage adequate signal strength for an area. During transmission, the signals get affected by the position of building, ground and the distances the signals need to travel before reaching any receiver or user location. Existing researches focus on the requirement of a suitable number of cell phone towers for a big area in a GIS environment. Depending on the building and other infrastructure present in an area an optimal location can be determined for setting up the cell phone tower. However, the detailed 3D data is required for it. In this paper, a LiDAR-based technique is attempted. Using the point cloud data of the RGIPT campus, features like building, ground, obstruction points, etc are extracted. To determine the transmission paths for the signal, building/object boundary(es), etc. coming in the path(s) between the cell phone tower and the receiver location are determined. Once the detailed paths for the signal transmission i.e, direct path, or path after diffraction (around the buildings), and/or reflection (from the wall and ground) are determined, terrain parameters (distance, path difference, attenuation, etc) are ascertained. These are then used to model and determine the relative signal strength for any receiver location. The position of cell phone tower is then tested for optimal XY, and Z position to ascertain the best location for setting up the cell phone tower. The method is verified against various path determination algorithms. A centroid and viewshed based approach is adopted here. The technique is generic, novel and essentially work with LiDAR point data without needing DEM and can be applied for any terrain condition.

Noise is a universal problem that is particularly prominent in developing nations like India. Short-term noise-sensitive events like New Year’s Eve, derby matches, DJ night, Diwali night (celebration with firecracker) in India, etc. create lots of noise in a short period. There is a need to come up with a system that can predict the noise level for an area for a short period indicating its detailed variations. GIS (Geographic Information System)-based google maps for terrain data and crowd-sourced or indirect collection of noise data can overcome this challenge to a great extent. Authors have tried to map the highly noisy Diwali night for Lucknow, a northern city of India. The mapping was done by collecting the data from 100 points using the noise capture app (30% were close to the source and 70% were away from the source (receiver). Noise data were predicted for 750 data points using the modeling interpolation technique. A noise map is generated for this Diwali night using the crowd-sourcing technique for Diwali night. The results were also varied with 50 test points and are found to be within ±4.4 dB. Further, a noise map is also developed for the same site using indirect data of noise produced from the air pollution open-sourced data. The produced noise map is also verified with 50 test points and found to be ±6.2 dB. The results are also corroborated with the health assessment survey report of the residents of nearby areas.

The world's ecosystem and environment are rapidly deteriorating with an increase in the depletion of forest conditions due to forest fires. In recent past years, wildfire incidents in Sikkim have increased due to severe climatic changes such as turbulent rainfall, untimely summers, extreme droughts in winters, and a reduction in the percentage of yearly rainfall. Forest fires are one of the numerous kinds of disasters that impose disastrous changes on the entire environment and disrupt the complex correspondence of the flora and fauna. The research's goal is to examine the vegetation indices based on different climates to know why forest vegetation is decreasing day by day from 2000 to 2023. The frequent changes in forest vegetation are extensively studied by using satellite images. This data has been collected by three satellites Landsat-5, Landsat-8, and Landsat-9 on different vegetation indices NDVI, EVI, and NDWI. East Sikkim area is chosen to compute forest vegetation indices based on the heap's landmass this region is unexplored yet and also studied about the forest changes by using different spatial temporal indices in the range of the entire district in the future. The authors of this paper have used Landsat multi-spectral data to assess changes in the area of vegetation in a sub-tropical region like a dense forest region in east Sikkim. The analysis depicts space images, computes vegetation indices (NDVI, EVI, NDWI), and accomplishes mathematical computation of findings. The proposed method will be helpful to discuss the variance of vegetation in the entire East Sikkim region at the time span of 2000-2023. In the analysis, we find that mean and standard deviation values change over the years in all indices. Later, we also calculated changes by using a classification model and find a total 10% change in forest areas in approximately 22 years.

Abstract. In recent years, the data science and remote sensing communities have started to align due to user-friendly programming tools, access to high-end consumer computing power, and the availability of free satellite data. In particular, publicly available data from the European Space Agency’s “Sentinel” and American Earth observation satellite” landsat” missions have been used in various remote sensing applications.Google Earth Engine (GEE) is such a tool that publicly allow the use of these available datasets, there is a large amount of available data in GEE, which are being used for computing and analysing purpose. In this article, we compare the classification performance of four supervised machine learning algorithms: Classification and Regression Tree (CART), Random forests (RF), Gradient tree boosting (GTB), Support vector machines (SVM). The study area is located at 30.3165° N, 78.0322° E near the Himalayan foothills, with four land-use land-cover (LULC) classes. The satellite imagery used for the classification were multi-temporal scenes from Sentinel-2 and LANDSAT-8 covering spring, summer, autumn, and winter conditions. Here we collected a total of 2084 sample points in which 536, 506, 505, 540 points belong to urban, water, forest and agriculture points respectively. which were divided into training (70%) and evaluation (30%) subsets. Accuracy was assessed through metrics derived from an error matrix, for accuracy measurement we use confusion and Cohen’s kappa calculation method.We have calculated CART (Accuracy 93.52% and Kappa coefficient 91.36%), Random Forest (Accuracy 95.86% and Kappa coefficient 94.48%),Gradient Tree Boost (Accuracy 95.33% and Kappa coefficient 93.37%),Support Vector Machine (Accuracy 73.54% and Kappa coefficient 76.28%) for Landsat 8 data sets and CART (Accuracy 89.24% and Kappa coefficient 85.64%), Random Forest (Accuracy 91.45% and Kappa coefficient 88.59%),Gradient Tree Boost (Accuracy 87.71% and Kappa coefficient 83.58%),Support Vector Machine (Accuracy 84.96% and Kappa coefficient 79.99%) for Sentinel2 data sets. Further analysis for accuracy and machine learning algorithm are discussed in result section.