Resolution Of Sensor Research Articles

Importance-based distributed persistent coverage strategy over environmental hotspots using quadrotors: Algorithm and experiments

This paper proposes a novel importance-based persistent coverage for environments with hotspots. Multiple quadrotors are utilized to maintain coverage while acquiring high-quality information over prioritized regions by lowering the altitude. To achieve this, the approach utilizes distributed communication protocols and cost functions to address the tradeoff between wider coverage and better quality information. The cost functions determine three-dimensional waypoints based on coverage levels, sensor resolution, and Euclidean distance. Voronoi tessellations are used locally for efficient computation and collision avoidance. Trajectories of the quadrotors are generated based on quadrotor dynamics using error functions that take into account state information and desired waypoints. The proposed approach is validated through simulations, comparisons, and experiments for multi-agent systems under quadrotor dynamics. Further, metrics of performance analysis are formulated that show consistent coverage levels and differences to assess the novel importance-based approach. The simulations and experiments demonstrate the feasibility and effectiveness of the approach, with prioritized regions receiving higher sensor resolution while maintaining persistent coverage of the environment.

A tunable star-shaped highly sensitive microwave sensor for solid and liquid sensing

This paper presents the development of an innovative, tunable star-shaped microwave sensor operating at two distinct frequency bands C and X band, optimized for high-sensitivity sensing of both solid and liquid mediums. The sensor's innovative star-shaped geometry augments the surface interaction with the target substances, significantly enhancing the sensor's response and sensitivity. Furthermore, tunability allows the sensor to adapt to different sensing environments, maximizing the detection potential at both frequencies. This research delves into the sensor's performance when using four different substrates for solid sensing containing FR-4, RO-3035, RO-5880, and RO-6202. Each substrate, characterized by unique electromagnetic properties, contributes differently to the sensor's sensitivity and resolution. The extensive experimental results highlight the sensor's proficiency in detecting and differentiating between these substrates, revealing the impact of material selection on sensor performance. The sensor's ability to distinguish between various liquids, including water, milk, oil, and energy drinks, is evaluated in the liquid sensing scenario. The sensor successfully detects the subtle variances in the electromagnetic properties of these liquids, suggesting its capability for high-resolution liquid sensing. The proposed MTM-based sensor resonates at 4.46 GHz and 11.48 GHz, respectively. Numerical software CST was used to analyze all the parameters. A 3 × 3 array was used in the prototype design, and it was used to measure the C-band resonance frequency. On the other hand, a compact unit cell was used to measure the X-band resonance frequency. The outcomes suggest a substantial potential for this sensor design in various industries and scientific fields, including food quality control, the oil industry, biomedical applications, and environmental monitoring. The unique combination of star-shaped design, tunable frequencies, and various substrates offers unprecedented flexibility and sensitivity in solid and liquid sensing on microwave frequency ranges.

Аналіз стану і тенденції розвитку орбітальних угруповань малих космічних апаратів дистанційного зондування Землі для вирішення завдань землероб-ства

The goal of this paper is to assess the existing capabilities of orbital constellations of agriculture-oriented Earth remote sensing spacecraft and the prospects for their development in the nearest future. The paper analyzes the state of the art and the trends in the development of modern orbital constellations of private companies’ small Earth remote sensing satellites used in solving agricultural problems. Important sources of information in the space industry, such as NewSpace Index, Gunter's Space Page, and WMO OSCAR, were analyzed. 52 constellations of spacecraft for optical multispectral and hyperspectral, thermal, and microwave (radar) imaging are considered. It is shown that they provide a survey frequency of several times a day. The minimum spatial resolution of optoelectronic surveillance sensors is 1 m (in the panchromatic channel). This allows one to solve problems at an intrafield level, quasi-continuously monitor the plant condition, and use spectral patterns and texture features in solving applied agricultural problems. All the constellations of radar satellites use the X-band, except for the radars of the Spacety constellation (China), which use the C-band. It is shown that the number of constellations of small remote sensing spacecraft in the period 2021–2025 is to be increased by more than three times compared to the period 2016–2020. The number of countries developing constellations of Earth remote sensing satellites has increased significantly. The leading countries and companies in the number and size of constellations are the United States and China and Planet (USA) and Chang Guang Satellite Technology Corporation (China), respectively. Private developers are particularly interested in thermal infrared imaging and hyperspectral optical imaging data. A new line in the private space industry, which is expected to facilitate the solution of agricultural problems, is the deployment of constellations of small meteorological satellites.

Machine learning driven optimization and parameter selection of multi-surface HTS Maglev

This research aims to tackle the challenges posed by precise force measurement for high temperature superconducting (HTS) Maglev systems, including mechanical constraints, step motor limitations, and sensor resolutions. For this aim, six machine learning (ML) models namely Support Vector Machine (SVM), Gaussian Process Regression (GPR), Extreme Gradient Boosting (XGB), Long Short-Term Memory (LSTM), Extreme Machine Learning (EML), and Convolutional Neural Network (CNN) were developed to predict levitation force (Fz) and lateral force (Fx) based on process parameters including permanent magnet width (PMW), field cooling height (FCH), the movement in the z-axis (vertical distance), and the movement in the x-axis (lateral distance). Among six ML models, CNN emerged as the most accurate model, demonstrating smaller root mean square deviation (RMSD) without compromising correlation coefficients. Furthermore, an innovative process window approach was introduced to select process parameters that simultaneously meet the minimum value of Fz and maximum value of Fx, named β1 and β2, set at 90 N and 0 N, respectively. Within this window, PMW of 30 mm and z values less than 10 mm were found to be consistent for all FCH and x values. The novelty of this study is to formulate the optimisation problem in HTS Maglev using the developed ML model by addressing two specific objectives one of which focuses on maximizing Fz while ensuring Fx remains within a defined tolerance (β3), representing the minimum allowable ratio of the levitation force to the total force, and the second problem aims to maximize Fz while obtaining zero Fx. The optimum PMW, FCH, x, and z values were obtained at 30 mm, 30 mm, 4 mm and 5 mm, corresponding to Fz and Fx values of 224.2 N and -53.8 N for option 1. As for option 2, the process parameters were obtained as 28.6 mm, 25.9 mm, 0 mm, and 5 mm, corresponding to Fz and Fx values of 194.2 N and 0 N. It was obtained both experimentally and by the optimization that Fz reaches close its maximum as the Fx gains attractive character. Hence, it is expected that the outcomes of this study will significantly benefit the design of HTS Maglev systems and find valuable applications across various transportation engineering projects.

Angular surface plasmon resonance-based sensor with a silver nanocomposite layer for effective water pollution detection

For sensing various samples of polluted water and various sodium chloride concentrations using an angular surface plasmon resonance (ASPR), we have introduced a conventional structure and a hybrid heterostructure in the current research. The suggested structures are composed of silver metal, dielectric layers, silver nanocomposite, and a sensing medium. The reflectance spectra of all structures in the visible region were obtained through the utilization of the transfer matrix method by using the angular interrogation method depending on the Kretschmann configuration. Through our findings, five substrate parameters have been optimized to attain the utmost level of sensitivity across all structures: the thickness of Ag-metal, the type and thickness of dielectric materials, the host material type and the volume fraction of nanoparticles for the nanocomposite layer. In this regard, the suggested sensor provides excellent performance with a sensitivity of 448.1°/RIU\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$/\ ext{RIU}$$\\end{document}, signal-to-noise ratio of 0.787, sensor resolution of 0.284°, and figure of merit of 78.766 RIU−1. Therefore, we believe that the introduced design of our ASPR sensor presents a good candidate for an accurate and efficient detection of low concentrations of contaminated water and sodium chloride as well.

Hydrophobically Associated Hydrogel for High Sensitivity and Resolution of an Interdigital Electrode Pressure Sensor.

Hydrogel-based flexible strain sensors have been known for their excellent ability to convert different motions of humans into electrical signals, thus enabling real-time monitoring of various human health parameters. In this work, a composite hydrogel with hydrophobic association and hybrid cross-linking was fabricated by using polyacrylamide (PAm), surfactant sodium dodecyl sulfate (SDS), lauryl methacrylate (LMA), and polypyrrole (PPy). The dynamic dissociation-conjugation among LMA, SDS, and PPy could dissipate energy to improve the toughness of hydrogels. The SDS/PPy/LMPAm composite hydrogel with a toughness of 1.44 MJ/m3, tensile fracture stress of 345 kPa, tensile strain of 1021%, and electrical conductivity of 0.57 S/m was obtained. Furthermore, an interdigital electrode flexible pressure sensor was designed to replace the bipolar electrode flexible pressure sensor, which greatly improved the sensitivity and resolution of the pressure sensor. The SDS/PPy/LMPAm composite hydrogel-based interdigital electrode flexible pressure sensor showed extraordinary stability and identified different hand gestures as well as monitored the pulse signal of humans. Moreover, the characteristic systolic and diastolic peaks were clearly observed. The pulse frequency (65 times/min) and the radial artery augmentation index (0.57) were calculated, which are very important in evaluating the arterial vessel wall and function of human arteries.

Remote Sensing Analysis for Vegetation Assessment of a Large-Scale Constructed Wetland Treating Produced Water Polluted with Oil Hydrocarbons

The identification and assessment of plant stress using wetland satellite images is a major task in remote sensing. In this study, one of the largest constructed wetlands (CWs) in the world, located in the Sultanate of Oman, was examined, assessed, and evaluated using remote sensor data from Sentinel-2. This CW system treats produced water generated during oil exploration activities in a desert environment; thus, CW vegetation is subjected to stress induced by oil hydrocarbons and water salinity. This study examined the plant stress and detected changes between the years of 2017 and 2019. Sentinel satellite images were evaluated for vegetation status extraction. The Normalized Difference Vegetation Index (NDVI), Modified Soil-Adjusted Vegetation Index (MSAVI), and Normalized Difference Salinity Index (NDSI) were used to evaluate the vegetation change. The results showed a comprehensive mapping identification of the plant stress and water flow parameter factors including oil in water contamination (OIW), dissolved oxygen (DO), water temperature (WT), and water conductivity (COND). Among the three indices, it was found that the NDVI showed a very good correlation with all parameters in both years with average R2 = 0.78, 0.67, 0.75, and 0.60 for OIW, DO, WT, and COND, respectively. The same trend was found for MSAVI but with R2 = 0.59, 0.48, 0.55, and 0.56 for OIW, DO, WT, and COND, respectively. This shows that the NDVI performed better than the MSAVI in evaluating the water flow parameters. On the other hand, the NDSI showed a strong correlation with one flow parameter, that is, water conductivity, especially at the outlet cells of the CW with R2 = 0.86 and 0.82 for winter time and summer time, respectively. The synchronization and correlation between the water flow parameters and remote sensing vegetation indices in this study lead to a new approach to large-scale landscape wetland monitoring that improves and helps predict any degradation or stress on vegetation growth. Furthermore, the results of this work can help decision makers potentially modify the wetland design and water flow path to improve future expansion phases. The mapping of such a critical and massive industrial CW should consider the use of high spatial resolution sensors where identifications and classifications are further improved. In summary, this research demonstrates that it is feasible to estimate vegetation stress within the constructed wetland using remote sensing techniques across extensive regions when an ample dataset comprising field data, satellite imagery, and supporting information is accessible.

Development of capacitive sensors for measuring vibrations and small displacements of a high-speed rotating machines for use in active vibration control systems

This work focuses on the development of custom capacitive sensors for measuring vibration and small displacements of high-speed rotating machines. The main criteria for this development are to use cheap and available resources and to optimize these resources to achieve the best possible performance. The aim was to design a solution that could be implemented within an Active Vibration Control (AVC) system, where the focus is mainly on high measurement frequency. The paper also includes a theoretical description of the measurement methodology with capacitive sensors and the use of the FDC2214 capacitive-digital converter (CDC), which works on the principle of a resonant LC circuit. The last part of the paper is devoted to the comparison of different designs of sensitive parts and also the concepts themselves. In addition to the calibration, the static characteristics are also described, and a measurement range error analysis is performed. The result of this work is a solution with a maximum measurement frequency of 3.8 MHz, maximum relative error of about 0.7% and sensor resolution from about 700 nm to 14 µm. The work also includes the deployment of the resulting smart sensors on the high-speed rotating machine to prove the capability to use them in the already mentioned AVC system.

Ultra low loss and dual polarized SPR-PCF sensor based on refractive index

In this paper presents a numerical simulation using the finite element method (FEM) to analyze the performance of a photonic crystal fiber (PCF) integrated with plasmonic material sensor components. The sensor comprises silica and Au layers with a thickness of 45 nm, arranged in a simple geometric structure. Our proposed sensor component exhibits ultra-low loss, distinguishing it from previous studies that have focused on wavelength-sensitive (WS) and amplitude-sensitive (AS) measurement techniques. The refractive index (RI) range of the sensor component spans from 1.32 to 1.38 RIU. The maximum WS and AS values achieved are 6,000 nm/RIU, -373.4 1/RIU (x-polarization), and -385.4 1/RIU (y-polarization), respectively. Moreover, we demonstrate an ultra-low loss of 0.00117 dB/cm (x-polarized) and 0.00307 dB/cm (ypolarized). In terms of sensor resolution, this design achieves a remarkable resolution of 1.6×10-7 RIU for both x-and y-polarized measurements

Dynamic Low-Pressure Measurement Using a Fiber Optic-based Fabry-Perot Interferometer

A dynamic low-pressure measurement using a fiber optic-based Fabry-Perot Interferometer (FFPI) has been demonstrated in this work. The developed system has been divided into 2 main parts: pressure source and sensing system. The former is a chamber comprised of an elastic diaphragm, which proportionally deflects according to input pressure from an air pump. The FFPI, consequently, detects the material deflection and demodulates the parameter into useful pressure value via the fringe counting technique and Kirchhoff-Love’s plate theory. To validate the performance of the developed system, a reference pressure instrument is utilized while the air pump feeds pressure of 0.34–6.57 mbar with 10 times repeatability into the system. The experimental results indicated that the FFPI can measure the pressure of 0.343–6.568 mbar, while the reference instrument showed the output values from 0.343–6.471 mbar, respectively. Moreover, the average and maximum percentage error in measurement is 1.27% and 2.67%, respectively. The resolution of the FFPI sensor is also analyzed to be approximately 0.05% or 0.0382 mbar/μm over all measurement ranges. Therefore, we conclude that the FFPI has high accuracy, resolution, linearity, and reliability in dynamic low-pressure measurements.

Burned area mapping in Different Data Products for the Southwest of the Brazilian Amazon

Fires affect the Amazon rainforest and cause various socio-environmental problems. Analyses of forest fire dynamics supporting actions to combat and prevent forest fires. However, many studies have reported discrepancies in the quantification of fire, especially in the tropics. We evaluated four operational products for estimating burned areas (MAPBIOMAS, MCD64A1, GABAM, and GWIS) in a part of the southwestern Brazilian Amazon. We used the year 2019 as a reference to assess the relative performance of each product through stratification by forest and non-forest areas. Statistical (Kolmogorov–Smirnov test) and geospatial analyses were performed using fuzzy similarity analysis and mapping of burned areas for forest and non-forest classes. The four products showed a divergence of up to 90.6% in the total area burned. MAPBIOMAS was the product with the largest area burned (3379 km²), and MCD64A1 detected the smallest area (325 km²). MAPBIOMAS and GABAM generally overestimates burn scars in forest areas compared to MCD64A1 and GWIS. Factors that influence the mapping of burned areas include cloud shadow, the spatial resolution of sensors, and external noises (drought and decomposition of bamboo forests). We highlight the importance of field validation when mapping imagery to differentiate the truly burned areas from targets with similar spectral behavior.

A Selective-Response Hypersensitive Bio-Inspired Strain Sensor Enabled by Hysteresis Effect and Parallel Through-Slits Structures

HighlightsA bio-inspired flexible strain sensor with hypersensitivity and highly selective frequency response is prepared by styrene–isoprene–styrene combined with monolayer graphene.Benefiting from the structural design inspired by nature and hysteresis of viscoelastic materials, bio-inspired structures, and original materials' properties complement each other.The frequency recognition resolution of bio-inspired flexible strain sensor reaches 0.2 Hz, making it ideal for human–computer interaction and mechanical equipment health inspection.

Spatial structure of in situ reflectance in coastal and inland waters: implications for satellite validation

Validation of satellite-derived aquatic reflectance involves relating meter-scale in situ observations to satellite pixels with typical spatial resolution ∼ 10–100 m within a temporal “match-up window” of an overpass. Due to sub-pixel variation these discrepancies in measurement scale are a source of uncertainty in the validation result. Additionally, validation protocols and statistics do not normally account for spatial autocorrelation when pairing in situ data from moving platforms with satellite pixels. Here, using high-frequency autonomous mobile radiometers deployed on ships, we characterize the spatial structure of in situRrs in inland and coastal waters (Lake Balaton, Western English Channel, Tagus Estuary). Using variogram analysis, we partition Rrs variability into spatial and intrinsic (non-spatial) components. We then demonstrate the capacity of mobile radiometers to spatially sample in situRrs within a temporal window broadly representative of satellite validation and provide spatial statistics to aid satellite validation practice. At a length scale typical of a medium resolution sensor (300 m) between 5% and 35% (median values across spectral bands and deployments) of the variation in in situRrs was due to spatial separation. This result illustrates the extent to which mobile radiometers can reduce validation uncertainty due to spatial discrepancy via sub-pixel sampling. The length scale at which in situRrs became spatially decorrelated ranged from ∼ 100–1,000 m. This information serves as a guideline for selection of spatially independent in situRrs when matching with a satellite image, emphasizing the need for either downsampling or using modified statistics when selecting data to validate high resolution sensors (sub 100 m pixel size).

Preparation and controllable tuning of a high-quality factor phonon-laser in the optomechanical microsphere cavity

Low-threshold, narrow linewidth phonon lasers can greatly improve the detection resolution of sensors and have enormous potential for development in classical and quantum sensing fields, as well as information processing. However, its development is limited due to unfavorable factors such as the complex process, low quality factor (Q-factor), difficult tuning, and harsh environments requirements. Here, we report an easy-to-excite phonon laser with an ultra-narrow linewidth in silica whispering gallery mode microsphere optomechanical resonators at room temperature and ambient pressure. The microsphere cavity is fabricated by high-temperature melting with a CO2 laser and designed by controlling the proportion of the sphere to the stem (sphere-to-stem ratio) to reduce mechanical damping. By using a single-frequency laser as the pump source, the microsphere optomechanical resonator exhibited multiple breathing mode phonon lasers with ultra-high optical Q-factor (1.78 × 109), mechanical Q-factor (3.1 × 107), and low threshold (2.4 μW). It is the first time to achieve such a high mechanical Q-factor in the microsphere cavity system of this kind to the best of our knowledge, which opens up an avenue to develop highly sensitive sensors.

Superpixel linear independent preprocessing for endmember extraction

ABSTRACT One of the limitations of remote sensing is the low spatial resolution of the open-access multispectral sensors, generating a mixture of spatial information. The mixed pixels can be modelled as the linear combination of fundamental components, called endmember, with a weighted contribution or abundance. The development of linear unmixing algorithms considering spatial and spectral information has recently increased. Some unmixing methods have relied on segmentation to integrate spatial data, and one of the most used is superpixel-based segmentation. However, previous work in superpixel-based unmixing focuses on using superpixels as uniform regions. Commonly, linear unmixing is used on hyperspectral imagery, and limited literature is found with multispectral images. This paper aims to propose a new preprocessing approach for multispectral linear unmixing called Superpixel Linear Independent Preprocessing. The proposed approach generates a set of candidates to endmembers based on spatial-spectral information; these are the input of traditional endmember extraction methods for multispectral unmixing. Experimental results show that the proposed preprocessing improves the performance of endmember extraction.

Enhancing Oxygen Sensing Precision Through Gate-Controlled TiO2 Field Effect Transistors Under Ultraviolet Excitation

We report on the fabrication and characterization of a novel oxygen sensor based on a TiO2 thin film field effect transistor (FET) deposited on a silicon substrate with an oxide layer by magnetron sputtering. TiO2 is a n-type semiconductor with a wide band gap, which allows the formation of oxygen vacancies or adsorbed oxygen species on its surface under ambient conditions. These oxygen-related defects act as electron traps that modulate the electrical conductivity of the TiO2 film. Under ultraviolet (UV) irradiation, at 310 nm wavelength, the photogenerated carriers in the TiO2 film are captured by the oxygen defects, resulting in a decrease of the film resistance that depends on the oxygen concentration. We demonstrate that the sensitivity and resolution of the oxygen sensor can be enhanced by applying a positive gate voltage to the FET device. The photocurrent variation per unit of oxygen concentration (ΔIphoto/ΔCPO) increases from 1.08 at VG =0 V to 2.5 at VG= 20 V in the range of 5%–20% oxygen concentration. The gate voltage also extends the controllable range of oxygen defects and photocurrent. Our study provides a new insight into the design and optimization of gas sensors based on TiO2 thin film FETs.

Updates in Diagnostic Imaging for Infectious Keratitis: A Review.

Infectious keratitis (IK) is among the top five leading causes of blindness globally. Early diagnosis is needed to guide appropriate therapy to avoid complications such as vision impairment and blindness. Slit lamp microscopy and culture of corneal scrapes are key to diagnosing IK. Slit lamp photography was transformed when digital cameras and smartphones were invented. The digital camera or smartphone camera sensor's resolution, the resolution of the slit lamp and the focal length of the smartphone camera system are key to a high-quality slit lamp image. Alternative diagnostic tools include imaging, such as optical coherence tomography (OCT) and in vivo confocal microscopy (IVCM). OCT's advantage is its ability to accurately determine the depth and extent of the corneal ulceration, infiltrates and haze, therefore characterizing the severity and progression of the infection. However, OCT is not a preferred choice in the diagnostic tool package for infectious keratitis. Rather, IVCM is a great aid in the diagnosis of fungal and Acanthamoeba keratitis with overall sensitivities of 66-74% and 80-100% and specificity of 78-100% and 84-100%, respectively. Recently, deep learning (DL) models have been shown to be promising aids for the diagnosis of IK via image recognition. Most of the studies that have developed DL models to diagnose the different types of IK have utilised slit lamp photographs. Some studies have used extremely efficient single convolutional neural network algorithms to train their models, and others used ensemble approaches with variable results. Limitations of DL models include the need for large image datasets to train the models, the difficulty in finding special features of the different types of IK, the imbalance of training models, the lack of image protocols and misclassification bias, which need to be overcome to apply these models into real-world settings. Newer artificial intelligence technology that generates synthetic data, such as generative adversarial networks, may assist in overcoming some of these limitations of CNN models.

Highly sensitive dual core photonic crystal fiber-based LSPR sensor with simultaneous multi-analyte detection of a wide refractive index range

This manuscript introduces an anisotropic highly sensitive dual core photonic crystal fiber sensor based on localized surface plasmon resonance, possessing the ability to detect multiple analytes simultaneously. Liquid analytes having a refractive index (RI) within the range of 1.25 to 1.5 RIU have been investigated using the proposed sensor. The numerical analysis of the sensing parameters for the proposed sensor shows a maximum wavelength sensitivity of 47,500 nm/RIU for x-polarization (Channel 1) and 18,050 nm/RIU for y-polarization (Channel 2), respectively using the wavelength interrogation technique. Maximum amplitude sensitivity of 625 RIU−1 and 437 RIU−1 is obtained for x−pol. and y−pol., respectively using the amplitude interrogation technique. Maximum sensor resolution of 2.11 × 10−6 RIU and 5.01 × 10−6 RIU is obtained for x−pol. and y−pol., respectively. The linear relationship of the resonant wavelength with the RI produces R2 = 0.9976 and R2 = 0.9979, corresponding to a degree (3) for x−pol. and y−pol., respectively. The figure of merit for x−pol. and y−pol. are 84 RIU−1 and 123 RIU−1.

A High-Sensitivity Fiber Biosensor Based on PVDF-Excited Surface Plasmon Resonance in the Terahertz Band

In this paper, a D-type photonic crystal fiber (PCF) with Zeonex material as the substrate and polyvinylidene fluoride (PVDF) material as the surface plasmon resonance (SPR) excitation layer is proposed for biosensing in the terahertz (THz) band. Analyzed with a finite element method, the proposed biosensor has shown excellent sensing properties for analyte refractive indices ranging from 1.32 to 1.45. With a maximum sensor resolution of 8.40 × 10−7 refractive index unit (RIU) and a figure of merit of 39.42 RIU−1, the maximum wavelength sensitivity and amplitude sensitivity can reach 335.00 μm/RIU and −66.01 RIU−1, respectively. A ±2% fabrication tolerance analysis is also performed on the biosensor to prove its practical feasibility. We conclude that our proposed PCF biosensor utilizing PVDF-excited SPR can provide high sensitivity, and thus a compact, label-free, and convenient solution for biomedical liquid sensing in the THz band.

A review and uncertainty analysis of Lake Urmia's hypsometric relationships

Lake Urmia, once the second-largest saline lake worldwide, has almost dried. Several studies have provided bathymetric maps for the lake using different data and techniques but rarely identified the associated uncertainties. This paper provides a comprehensive review of Lake Urmia bathymetric studies and discusses the underlying uncertainties. We developed an updated hybrid bathymetry and hypsometric relationships for Lake Urmia using in-situ and satellite data. The proposed hybrid bathymetry was validated against independent in-situ depth data (with an RMSE < 20 cm) and applied as a baseline for comparison with other available bathymetry models. Results revealed that uncertainties in lake depth measurement, water delineation algorithms from satellite data, spatial interpolation method, and sensor resolution have the highest uncertainty, respectively. Furthermore, the width of the uncertainty bands in the lake level-area relationships is not uniform and peaks at lake levels between 1270 and 1272 m, which are critical for conserving crucial ecosystem services of Lake Urmia, such as island conservation and dust control. Our synthesis indicated that the application of various Lake Urmia bathymetries could lead to about 0.5 and 1 km3differences in the estimated lake volumes for obtaining the ecological and dust control levels, respectively. Our findings provide useful insights for Lake Urmia managers, also applicable for other saline lakes, into considering thedynamic of lake topography and associated uncertainties in lake conservation/restoration programs.