Station Point Research Articles

Study on TLS Point Cloud Registration Algorithm for Large-Scale Outdoor Weak Geometric Features

With the development of societies, the exploitation of mountains and forests is increasing to meet the needs of tourism, mineral resources, and environmental protection. The point cloud registration, 3D modeling, and deformation monitoring that are involved in surveying large scenes in the field have become a research focus for many scholars. At present, there are two major problems with outdoor terrestrial laser scanning (TLS) point cloud registration. First, compared with strong geometric conditions with obvious angle changes or symmetric structures, such as houses and roads, which are commonly found in cities and villages, outdoor TLS point cloud registration mostly collects data on weak geometric conditions with rough surfaces and irregular shapes, such as mountains, rocks, and forests. This makes the algorithm that set the geometric features as the main registration parameter invalid with uncontrollable alignment errors. Second, outdoor TLS point cloud registration is often characterized by its large scanning range of a single station and enormous point cloud data, which reduce the efficiency of point cloud registration. To address the above problems, we used the NARF + SIFT algorithm in this paper to extract key points with stronger expression, expanded the use of multi-view convolutional neural networks (MVCNN) in point cloud registration, and adopted GPU to accelerate the matrix calculation. The experimental results have demonstrated that this method has greatly improved registration efficiency while ensuring registration accuracy in the registration of point cloud data with weak geometric features.

How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

The fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts (ERA5) is the latest reanalysis product. However, the reliability of ERA5 to capture extreme temperatures is still unclear over China. Hence, based on conventional meteorological station data, a new criterion (DISO) was used to validate the ERA5 capturing extreme temperature indices derived from the Expert Team on Climate Change Detection and Indices (ETCCDI) across the six subregions of China on different timescales. The conclusions are as follows: the original daily temperatures (mean temperature, maximum temperature, and minimum temperature) can be well reproduced by ERA5 reanalysis over China. ERA5 tends to exhibit more misdetection for the duration of extreme temperature events than extreme temperature intensity and frequency. In addition, ERA5 performed best in the summer and worst in the winter, respectively. The trend of absolute indices (e.g., TXx and TNx), percentile-based indices (e.g., TX90p, TX10p, TN90p, and TN10p), and duration indices (e.g., WSDI, CSDI, and GSL) can be captured by ERA5, but ERA5 failed to capture the tendency of the diurnal temperature range (DTR) over China. Spatially, ERA5 performs well in southeastern China. However, it remains challenging to accurately recreate the extreme temperature events in the Tibetan Plateau. The elevation difference between the station and ERA5 grid point contributes to the main bias of reanalysis temperatures. The accuracy of ERA5 decreases with the increase in elevation discrepancy.

Assessment of Climate Models Performance and Associated Uncertainties in Rainfall Projection from CORDEX over the Eastern Nile Basin, Ethiopia

The adverse impact of climate change on different regionally important sectors such as agriculture and hydropower is a serious concern and is currently at the epicentre of global interest. Despite the extensive efforts to project the future climate and assess its potential impact, it is surrounded by uncertainties. This study aimed to assess climate models’ performance and associated uncertainties in rainfall projection over the eastern Nile basin, Ethiopia. Seventeen climate models from Coordinated Regional Climate Downscaling Experiment (CORDEX) and their four ensemble models were evaluated in terms of their historical prediction performance (1986–2005) and future simulation skill (2006–2016) at rainfall station (point location), grid-scale (0.44° × 0.44°) and basin scale. Station-based and spatially interpolated observed rainfall data were used as a reference during climate model performance evaluation. In addition, CRU data was used as an alternative reference data to check the effect of the reference data source on the climate models evaluation process. As the results showed, climate models have a large discrepancy in their projected rainfall and hence prior evaluation of their performance is necessary. For instance, the bias in historical mean annual rainfall averaged over the basin ranges from +760 mm (wet bias) to −582 mm (dry bias). The spatial pattern correlation (r) of climate models output and observed rainfall ranges from −0.1 to 0.7. The ensemble formed with selected (performance-based) member models outperforms the widely used multi-model ensemble in most of the evaluation metrics. This showed the need for reconsidering the widely used multi-model approach in most climate model-based studies. The use of CRU data as a reference resulted in a change in the magnitude of climate model bias. To conclude, each climate model has a certain degree of uncertainty in the rainfall projection, which potentially affects the studies on climate change and its impact (e.g., on water resources). Therefore, climate-related studies have to consider uncertainties in climate projections, which will help end-users (decision-makers) at least to be aware of the potential range of deviation in the future projected outcomes of interest.

Segmental variability of precipitation in the Mahanadi river basin from 1901 to 2017

An Eigen-based technique for sequential spatial pattern analysis, an application of PCA (Principal component analysis), is presented here. The analysis examines the spatial distribution of precipitation in the Mahanadi River basin. The spatial (S) mode of sequential spatial pattern analysis with the application of the maximum loading value of retained rotated principal component (also referred to as the maximum loading value approach) to assess gridded monthly rainfall data with a resolution of 0.25° × 0.25° and a record length of 117 years (1901–2017). Meteorological records have a sequential spatial k field for the spatial and temporal mode that is used to recognize the area of precipitation variability and regime. The identified patterns of the different timeslot segments were then analysed for their dispersions of the annual precipitation observed at different station points using similarities and dissimilarities characteristics of inter-cluster and between-clusters, respectively. Validation of the regionalized pattern for distinctness and pairwise comparison of CDF's using the Kolmogorov-Smirnov ‘D’ statistical test. The statistics result in 6 of 8 core origins being located within the upper and middle sub-division of the Mahanadi River basin, suggesting any variation in the basin is due to the influence of core points. Furthermore, the spatial pattern variability of summer is not observed in the middle Mahanadi, while high variability is observed in the lower Mahanadi.

ANALYSIS OF THE STATUS OF THE GIRIAN WATERSHED IN BITUNG CITY DUE TO HOUSEHOLD WASTE POLLUTION

The Girian watershed with a length of 17.5 km is the longest river in Bitung City which flows through a densely populated area as well as various industries such as smoked fish, tofu and tempeh, animal husbandry, agriculture, and industrial fish factories whose waste goes directly to the Girian watershed. The great deal of population activities can have an impact on water quality and the Girian watershed ecosystem. This study aims to analyse the water quality of the Girian watershed caused by domestic waste. This study uses a quantitative descriptive approach and the method used in this study is the Indonesian National Standard (SNI) according to the water quality parameters that have been determined. The results of the analysis of water quality upstream to downstream show that there is a decrease in water quality. Upstream is classified as good when compared to the parameters that have been determined. Densely populated areas begin to experience a decrease in water quality, namely with DO and Oil and Fat that exceeds the predetermined quality standard. As well as estuaries that have concentrations of TSS, NH3-N, Oils and Fats, COD, and BOD that exceed the specified quality standards. The results of the calculation of the pollution index show that station point 1 upstream is classified as good in 4 groups, station point 2 in densely populated areas shows lightly polluted results in group 2, and The Station 3 estuary shows moderate pollution results in group 3

KUALITAS DAN TINGKAT PENCEMARAN AIR WADUK TITAB, BULELENG

Monitoring water sources is essential to ensure the quality and quantity, one of which is in the Titab Reservoir. The purpose of the study was to assess the water quality and amount of pollution in the Titab reservoir in terms of physical, chemical, and microbiological characteristics, as well as the NVC of Tawes (Barbonymus gonionotus) fish. There are five station points for water sampling and 10 test parameters related to water quality. Data analysis compares test results with second-class water quality standards (Bali Governor Regulation No. 16/2016), IP value (pollution index), and NVC with Furton's equation. The test results obtained that the water in the Titab reservoir has a temperature of 28.97oC, TSS (0.18 mg/L), pH (8.82), BOD (3.22 mg/L), COD (16.70 mg/L), DO (6.77 mg/L), Total Phosphate (0.12 mg/L), Nitrate (0.19 mg/L) and Fecal Coliform (960 MPN/100 mL), the IP value was 1.39, and the NVC was 1.65. Thus, the pH, BOD, and Fecal Coliform levels of the Titab reservoir water did not match the specified limits, although the other parameters satisfied the water quality norms. The Titab reservoir's water is classed as mildly contaminated with the NVC value of tawes fish, referring to the Titab reservoir's polluted water.
 Keywords: Tawes Fish, Titab Reservoir, Water pollution, Water quality.

Turbidity Characteristics of The Waters on The Padelegan Beach, Pademawu District, Pamekasan Regency, East Java

Padelegan Beach is one of the beaches used for tourism and seaweed cultivation activities, which is located in Pademawu District, Pamekasan Regency. The distribution of water turbidity plays an important role in the success of seaweed cultivation activities and salt ponds along the Jumiang coast. The purpose of this research is to know the condition of turbidities distribution in Jumiang waters. The sampling method used the purposive sampling method with the location of nine sampling stations. The location of the turbidity measurement is based on thereare near the coast (points 1, 2, 3), beaches (points 4, 5, 6), and the high seas (7, 8, 9). Turbidity was measured using the Lutron TU-2016 Turbidity Meter. The results show that the turbidity distribution at station points near the coast is higher in value than at high sea station points. The highest turbidity value is 29,82 NTU in the coastal area, while the lowest is 4,35 NTU the offshore. The turbidity value in Jumiang coastal waters shows a value below the threshold for turbidity that is intended for cultivation activities.Keywords: characteristics, turbidity, The Padelegan Beach

Site Effects Study in the Peshawar District using Seismic Noise

Surficial geology plays an important role during earthquakes in terms of its impact on the resulting ground shaking, referred to as site effects. Site effects influence the level of earthquake hazard and risk in a region. Peshawar, the provincial capital of Khyber Pakhtunkhwa, is one of the most seismically active cities in Pakistan. However, it lacks any such seismic characterization. In this work, a seismic ambient noise study was performed in Peshawar using observations from a total of 92 stations. The seismic noise data is recorded using a broadband seismometer for a period of about 30 minutes at every station point. The fundamental resonance frequency and a first-order amplification based on the seismic noise recording are estimated using the method of Nakamura. First-order estimates of the amplification range from 1.0 to 7.0, whereas the resonance frequency ranges between 0.2 to 2.5 Hz. The results suggest a complex geological structure with low seismic impedance and deep soil deposits in Peshawar. The depth of soil deposits is estimated to be more than 300 m, leading to a potential amplification of low-frequency seismic waves. These findings are crucial for tall structures with fundamental vibration periods greater than 0.40 sec. Doi: 10.28991/CEJ-2022-08-04-010 Full Text: PDF

Projections of rainfall erosivity in climate change scenarios for the largest watershed within Brazilian territory

Global climate change can potentially threaten agricultural production due to endangered natural resources, such as rainfall patterns. Thus, extreme rainfall events can cause greater rainfall erosivity, consequently, greater soil erosion. Conversely, a reduction in rainfall amount can lead to water scarcity for the agriculture production process. This way, it is a foremost need to model climatic conditions under global climate change scenarios, particularly in places where rainfall data tends to increase. This work aimed to project rainfall erosivity in the major Brazilian watershed, the Tocantins-Araguaia river basin, throughout the 21st century under two Intergovernmental Panel for Climate Change Fifth Assessment Report (IPCC AR5) Scenarios, the Representative Concentration Pathways, RCP4.5 and RCP8.5 scenarios. This study uses the downscaling of four global climate models of the Coupled Model Intercomparison Project (CMIP5) by the Eta regional climate model, used by the Brazilian National Institute for Space Research. The average rainfall erosivity was calculated based on the Modified Fournier Index in three periods of 30-year length throughout the 21st century. Time series of R-factor were analyzed at rain gauge station points overlapping regional model grid cells over the basin for the 1961–2099 period. Projections indicated lower annual average rainfall erosivity values in comparison with historical data. Estimated mean rainfall erosivity values were 10,977.69 ± 526 MJ mm ha−1 h−1 yr−1 for the RCP4.5 scenario, and 10,379.71 ± 723 MJ mm ha−1 h−1 yr−1 for the most pessimistic climate change scenario, RCP8.5. The largest reductions of the mean R-factor reached 5,5% for the multi-model ensemble projections for near future, and 15.4% for the ensemble projections models for long-term, with the greatest decreasing trends under RCP8.5. Reductions greater than 2,000 MJ mm ha−1 h−1 are expected throughout the 21st century according to multi-model ensemble projections models under RCP8.5 scenario in most of the watershed. Decreasing rainfall erosivity factor in both RCP scenarios was due to a lower rainfall depth. However, the value of rainfall erosivity is still considered high and should be taken into account in soil conservation practices. Furthermore, the smaller rainfall amount indicates a possible reduction in water availability for crops of longer cycle, and increase in spatial variability of less intense rainfall.

3D-Trajectory and Phase-Shift Design for RIS-Assisted UAV Systems Using Deep Reinforcement Learning

Unmanned aerial vehicle (UAV) can effectively work as temporary base station or access point in the air to transfer/receive data to/from ground terminals (GTs). However, UAV-GT links might be blocked by ground obstacles, like buildings in urban area, leading to a poor performance on data transferring rate. To address this problem, reconfigurable intelligent surface (RIS), as a promising technique, can intelligently reflect the received signals between UAV and GT to significantly enhance the communication quality. Under this deployment of RIS-assisted UAV, we intend to jointly optimize the 3D-space of the UAV and the phase-shift of the RIS to maximize the data transferring rate of the UAV, while minimizing the UAV propulsion energy. The joint problem is non-convex in its original form and difficult to be timely solved by using traditional method, like successive convex approximation (SCA). Therefore, to facilitate the online decision making to this joint problem, we leverage deep reinforcement learning (DRL) to learn the near-optimal solution, and the well known Double Deep Q-Network (DDQN) and Deep Deterministic Policy Gradient (DDPG) algorithms are ultilized. Numerical results show that DRL can effectively improve the energy-efficiency performance of the RIS-Assisted UAV system, compared with benchmark solutions.

Empirically derived Coloured Dissolved Organic Matter absorption coefficient using in-situ and Sentinel 3/OLCI in coastal waters of India

ABSTRACT Chromophoric Dissolved Organic Matter (CDOM) is the fraction of dissolved organic matter that absorbs sun light in ultra-violet and visible region of the electromagnetic radiation. CDOM is abundantly found in marine and coastal waters and is very important to studying optical properties of water columns and biogeochemistry of the water. It is a proxy to study dissolved organic carbon pools in marine and freshwater pools. This research aims to establish a relation between CDOM absorption and remote sensing reflectance in coastal waters of India using a large pool of in-situ data set. This study is very significant to studying CDOM distribution, its variability and the coastal processes that influence its distribution on larger time scales (seasonal to decadal changes) through ocean colour sensors. This study presents a new empirical algorithm for estimation of CDOM absorption (412nm) in Indian coastal waters using remote sensing reflectance that has not been reported so far. The study also presents its slope and variability in different regions of coastal waters of India using in-situ data. A large pool of 409 datasets has been generated using NASA bio-Optical Marine Algorithm Dataset/SeaWiFS Bio-Optical Archive and Storage System (NOMAD/SeaBASS) datasets and Indian coastal waters datasets collected during 2014-18 exclusively for coastal waters of India. A power equation is obtained using the ratio of remote sensing reflectance (Rrs412/Rrs555) to derive CDOM absorption with R2=0.74. The retrieved absorption coefficient is further validated with independent in-situ data set using another 58 station points with R2=0.84. The root mean square error is 0.03 m−1 (with 20% standard error in modelled data). CDOM slope (S412-443) is also estimated from in-situ data and it varies from 0.010-0.020 (units nm−1) with a mean value of 0.015 (nm−1) for Indian coastal waters. CDOM absorption coefficient (412 nm) varies between 0.01-0.8 m−1 for 90% of the stations whereas remaining 10% of the stations show high CDOM absorption coefficient between 1.2- 2.0 m−1 in the study area. This empirically derived algorithm is then applied on Sentinel-3/OLCI derived remote sensing reflectance time series data of whole year 2019 and CDOM absorption coefficient has been retrieved in different regions of coastal waters off Gujarat (India). The seasonal and spatial variability in these regions show that this algorithm is having great potential to retrieve CDOM absorption in coastal areas using high-resolution ocean colour monitors like Sentinel-3/OLCI. This algorithm is highly useful in retrieving CDOM absorption in coastal waters through existing and future ocean colour monitors from space-borne platform.

Optimal Base Station Network Based on Topological Data Analysis

The decision of which base stations need to be removed due to the cost is always a difficult problem, because the influence on the cover rate of the network caused by the removal should be kept to a minimum. However, the common methods to solve this problem such as K-means Clustering show a low accuracy. Barcode, which belongs to TDA, has the possibility to show the result by identifying the Persistent Homology of base station network. This essay mainly illustrates the specific problem of optimal base station network, which applies the TDA(Topological Data Analysis) methods to find which base stations need removing due to the cost K-means Clustering and Topological Data Analysis methods were mainly used. With the simulated distribution of telecommunication users, K-means Clustering algorithm was used to locate 30 best base stations. By comparing the minimum distance between the results (K=25 and K=30), K-means Clustering was used again to decide base station points to be removed. Then TDA was used to select which 5 base stations should be removed through observing barcode. By repeating above steps five times, Finally the average and variance of cover area in original network, K-means Clustering and TDA were compared. The experiment showed that the average cover rate of original network was 81.20% while the result of TDA and K-means Clustering were 92.13% and 89.87%. It was proved by simulation that it is more efficient to use TDA methods to construct the optimal base station network.

Species Composition and Assessment of Sphingidae in Mt. Agad-Agad, Iligan City, Philippines

Sphingidae (Hawkmoth or Sphinxmoth) is a known bioindicator that is naturally affected by environmental issues such as deforestation and pollution. Members of thisfamily are widely distributed in Southeast Asia and play vital roles as indicators ofhabitat quality and biomass degradation. This study highlights the diversity and assessment of hawkmoths of Mt. Agad-Agad (Sitio Langinlanon, Barangay Puga-an)on Iligan City. Two sampling stations were established, the upper and lower Mt.Agad-Agad. Light trap sampling using 2 × 3 m2 white silk cloth and 500 watts lightbulb was established in selected points of the sampling stations. Six species belonging to four genera were recorded of which two species, Acosmeryx socrates and Polyptychus trilineatus philippinensis, were found very rare in the area. Station 2 (lowerMt. Agad-Agad) had the highest number of species recorded compared to Station 1(upper Mt. Agad-Agad). However, there is no significant difference between the two(t=0.59, critical t=2.2281, p=0.56) sampling stations in terms of species composition. Moreover, two species from the genus Ambulyx are the endemic species observed, viz, A. bakeri and A. wilemani.

MACHINE LEARNING APPROACH FOR TENAGA NASIONAL BERHAD (TNB) OVERHEAD POWERLINE AND ELECTRICITY POLE INVENTORY USING MOBILE LASER SCANNING DATA

Abstract. Electricity assets recognition and inventory is a fundamental task in the geospatial-based electrical power distribution management. In Malaysia, Tenaga Nasional Berhad (TNB) aims to complete their assets inventory throughout the country by 2022. Previous research has shown that a method for assets detection especially for TNB is still at an early stage, which mainly relied on manual extraction of the assets from different data sources including mobile laser scanner (MLS). This research aims at evaluating a geospatial method based on machine learning to classify the TNB assets using high density MLS data. The MLS data was collected using Riegl VMQ-1 HA scanner and supported by the base station and control points for point cloud registration purpose. In the first stage the point clouds were classified into ground and non-ground objects. The non-ground points were further classified into different landcover types i.e. vegetation, building, and other classes. The points classified as other classes were used for overhead powerline and electricity poles classification using random forest-based Machine Learning (ML) approach in LiDAR 360 software. Based on the classified point clouds, detailed characteristics of electricity poles (i.e. number of poles, height, diameter and inclination from ground) and overhead powerlines (number of cable segments) were estimated. This information was validated using field collected reference data. The results show that the detection accuracy for electricity poles and overhead power line are 65% and 63% respectively. The estimation of length, diameter and height of the spun pole from point clouds has produced Root Mean Square Error (RMSE) value of 0.081cm, 0.263 cm and 0.372 cm respectively. Meanwhile for the concrete pole, the length, diameter and height has been successfully estimated with the value of RMSE of 0.034 cm, 0.029 cm and 0.331 cm respectively. The length of overhead powerline was estimated with 59.02 cm RMSE. In conclusion, the MLS data had show promising results for a semi-automatic detection and characterization of TNB overhead powerlines and poles in the sub-urban area. Such outcome can be used to support the inventory and maintenance process of the TNB assets.

ASSESSMENT OF PROBABLE FOUNDATION PROBLEMS USING GEOPHYSICAL AND REMOTELY SENSED DATA IN A TYPICAL BASEMENT COMPLEX, SOUTHWESTERN NIGERIA.

This research work asses the problems of building foundation within Sagari Estate, Akure, Southwestern Nigeria, using remotely sensed data and geophysical methods. The slope, lineament, hill, and contour map were generated using terrain analysis in ArcGIS 10.5 environment. Electrical Resistivity (ER) and Very Low Frequency (EM-VLF) Electromagnetic methods were used in this study. The ER method involves the use of Vertical Electrical Sounding (VES) with Schlumberger configuration. The initial geoelectric parameters obtained from the fifty three (53) VES station points interpretation (layer resistivities and thicknesses) were used to generate geoelectric maps. The variation of strata and physical strength information within the investigated area were determined from the integrated results of the three methods. Results revealed that substantial portion of the areas studied is relatively unsuitable for high rise civil engineering foundations. However, it was showed that the northern and southern parts of the investigated area were more suitable for foundations. The dominant presence of lineaments, steep slopes , fractures, streams, and thick clayey topsoil as well as weathered layers within the western, eastern, and the central portions reveals susceptibility to subsidence if adequate structural supports mitigate the effect of the persistent foundation failure are not provided.

GEOPHYSICAL INVESTIGATION FOR POST-FOUNDATION ASSESSMENT WITHIN EKITI STATE UNIVERSITY, ADO EKITI, SOUTHWESTERN NIGERIA

This study has evaluated the post-construction subsoil integrity of a distressed building in Ado-Ekiti, Southwestern Nigeria, to decipher the in-situ lithology and structural disposition. Four traverses of 100 m length were established in approximately E-W direction with an inter-station spacing of 10 m. Two Geophysical methods were deployed including the ground magnetic method using Proton Precession Magnetometer and Electrical Resistivity method utilizing 2D Electrical Imaging and Vertical Electrical Sounding (VES) techniques. Twelve (20) VES station points were acquired within the investigated area using Schlumberger configuration which gave relevant information on layer stratification and geoelectric parameters. The obtained results from the VES showed four geo-electric layers comprises topsoil, clayey layer, weathered layer, fractured bedrock, and fresh basement. Low apparent resistivity was delineated from the 2D imaging exhibiting unfit materials at distance between 50 to 75 m and 80 to 100m. The magnetic results showed a series of bedrock ridges and depressions. The integration of results revealed that the majority of the study areas are unsuitable except for central parts which indicate appreciable competence and stability.

Water Quality Analysis Relation To Phytoplankton Community And Fish Resources in Teluk Gelam Lake, Ogan Komering Ilir

This study aimed to analyze the quality of the waters and its relationship with the structure of phytoplankton communities and fish resources. This research was conducted in March 2021 at lake Teluk Gelam Ogan Komering Ilir. Phytoplankton sampling has been done once with 3 replays and there are 5 station points. Measurement of water quality parameters physical and chemical waters include temperature, pH, dissolved oxygen (DO), ammonia, brightness, turbidity, depth, nitrite, nitrate, and phosphate. Water quality analysis was calculated using the pollution index method (IP) and evaluated based on environmental quality standards according to the Decree of the State Minister for the Environment Number 115 of 2003. Based on the results of research that have been conducted in the lake Teluk Gelam district Ogan Komering Ilir that the overall average parameters of water quality in the lake were still an adequate condition for the life of organisms, especially phytoplankton. And based on the observations on the composition of phytoplankton structures in the waters of the lake Teluk Gelam found 3 classes of phytoplankton consisting of Bacillariophyceae, Chlorophyceae, and Cyanophyceae with a varied number of genera, while the fertility level of Teluk Gelam lake waters is included in the eutrophic category.

Condition of mangrove in the waters of Pangkil Village, Teluk Bintan District, Bintan Regency

Pangkil Village is one of the areas located on the coast of Bintan Island, Teluk Bintan District. This island has an aquatic ecosystem with abundant marine resources, one of which is the mangrove ecosystem. Mangroves are the main supporting ecosystem for life activities in coastal areas that play an important role in maintaining the balance of environmental biological cycles. This study aims to determine the condition, distribution and density level of mangroves in the waters of Pangkil Village. This research was conducted in February-March 2021. The research method is purposive sampling, which means that the research station points are chosen intentionally based on the consideration of the condition of the existence of mangroves or the density of mangroves. The research station points were determined as many as 8 stations, at each station consisting of transects and at each transect consisting of 3 sample plot plots (Transect Line Plot). The density level data was collected using a line transect measuring 10x102 which was drawn from the sea to the land and the distribution of mangroves using lansat-8 imagery. The level of mangrove density in the waters of Pangkil Village is still quite good. The results of the research on the level of mangrove density in the waters of Pangkil Village are classified as good with the distribution of mangrove areas in the waters of Pangkil Village covering an area of 40.23 ha, with an average density level of 1,033 ind/ha per station to 1,733 ind/ha. with medium and solid status based on the Decree of the Minister of the Environment No. 201 year 2004.

Towards Understanding and Enhancing Association and Long Sleep in Low-Power WiFi IoT Systems

Enhancing the energy efficiency of WiFi IoT stations introduces unique challenges compared to 802.15.4 and BLE. The four essential operations performed to ensure connectivity between stations and the access point in a WiFi network are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">association</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">periodic beacon reception</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maintaining association</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">station wake up</i> . Understanding and enhancing these operations are essential for building energy-efficient and dependable IoT systems. However, it is unclear how the software and hardware configuration of station and access point, concurrent traffic, power management, and security protocols affect the reliability and energy efficiency of these operations. In this paper, first, we present a thorough analysis of the association cost of WPA2 and WPA3 and mitigate the effect of key computation on association overhead. Second, we prove that increasing listen interval to reduce beacon reception wake-up duration may negatively impact energy efficiency. We identify the primary causes of this problem subject to link quality estimation algorithm and beacon delay. Third, we show that maintaining association by relying on access-point-based polling is not reliable. In particular, we confirm the wake-up delay of low-power stations is highly affected by factors such as channel utilization and beacon listen interval. We also confirm that key renewal aggravates the chance of disassociation.

Analytical strategies to sense water stress level: An analysis of ground water fluctuations sensing SDGs under pandemic scenario

Groundwater fluctuation is directly linked with the consumption and wastage of water sources during the pandemic interval. That is why water resource planners directly target water resource and sanitation systems in line with the sustainable development goals (SDGs) concept. In this study, District Multan is designated as a study area with 85 distinct station points data sets from four zones taken to pursue this massive investigation. The data sets are studied analytically and graphically to explore the relationships among critical variables like population, average water consumption, groundwater elevation, water table depth, total consumption, wastage of water during the pandemic days, etc. For in-depth analysis, the statistical approaches are employed on these massive data sets to reveal the trend among each dataset point to generate predictive models. The results revealed that groundwater reservoirs and levels are continuously declining on an annual basis in the meantime, the water consumption and extraction are increasing simultaneously. The consumption during pandemic days has been increased so much at the same time the wastage and total consumption of water is rising a lot in contrast to previous daily consumption and water demand. The coefficient of determination (R-square) values vary from 0.41 to 0.93 in this investigation. It will help the utilization of developed models and water-providing organizations to forecast groundwater instabilities for the future. Moreover, the situation in the study area is very alarming in terms of water stress conditions. This study will help the decision-making agencies to produce a policy following the SDGs concept to control water consumption and higher extraction.