Indian Institute of Space Science and Technology

Astronomy, of all the sciences, is possibly the one with the most public appeal across all age groups. This is also evidenced by the existence of a large number of planetaria and amateur astronomy societies, which are unique to the field. Astronomy is known as a ‘gateway science’, with the ability to attract students who then proceed to explore their interest in other STEM fields. Astronomy’s link to society is, therefore, substantive and diverse. This white paper analyses six key areas: outreach and communication, astronomy education, history and heritage, astronomy for development, diversity, and hiring practices for outreach personnel. The current status of each of these areas is described, followed by an analysis of what is needed for the future. A set of recommendations for institutions, funding agencies, and individuals are evolved for each specific area. This work outlines how the future astronomy-society connection should take shape and provides a road map for the various stakeholders involved.

Glypican1 and Mucin1 antigens are prominent biomarkers for the prognosis and diagnosis of pancreatic cancer. Their presence within the extracellular vesicles (EV) opens the possibilities for oncology care through the development of minimally invasive biomarker-assisted screening tools. Traditionally, EV antigen quantification relies on ultracentrifugation and chemical lysis, which are time-consuming, equipment-dependent, and often compromise EV integrity, damaging surface intact biomarkers. This study integrates EV isolation and electric field (EF) lysis into a unified platform. The lysates were then analyzed using an electrochemical impedance spectroscopy (EIS) -based sensor to detect GPC1 and MUC1. ELISA confirms the EF lysis of the immobilized EV and shows an increase in the antigen concentration by 2.5 times (compared to pre-lysed sample). Hence, EF lysis makes the sensor more sensitive than traditional methods. To enhance the electric lysis process, we applied varying voltages of a sinusoidal signal to the SPE-immobilized EVs. The lysate was subsequently used to quantify the GPC1 and MUC1 antigens through EIS. The results indicate that a 50 mV sinusoidal signal is sufficient to effectively lyse EVs, confirmed by western blotting. The NTA results showed the successful isolation of 109 EVs from 100 μL of serum using CD63 antibody. The developed EIS sensor can detect GPC1 and MUC1 with a LOD of 0.053 pg/mL and 0.033 pg/mL respectively from EV lysate, showing minimal non-specific binding in the negative control. Beyond GPC1 and MUC1, the approach is adaptable for detecting other EV-associated biomarkers, enabling broader applications in early cancer detection and disease monitoring.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Meteorites, remnants of asteroids that successfully survive their passage through the Earth's atmosphere, hold critical information about the evolution and history of the solar system. Traditional methods of analyzing these rare and precious specimens often involve destructive geochemical techniques, which deplete the sample and limit subsequent analyses. The accurate classification of meteorites, typically determined through petrological examination, is crucial before any further analytical steps. Reflectance spectroscopy, which interprets a sample's characteristics by analyzing reflected light, has emerged as a nondestructive alternative with significant potential for meteorite classification. In this technique, apparently, sometimes we do not need to process the sample. This technique allows for the examination of spectral features such as absorption bands, symmetry, band centers, inflection points, and overall slope. In this study, we employed spectral reflectance data from 1781 meteorite samples to develop and fine‐tune a deep learning model capable of accurate classification. The model was trained on 75% of the dataset and validated on the remaining 25%, achieving a validation accuracy of 93%. These results demonstrate the efficiency of using deep learning and reflectance spectroscopy for meteorite classification, offering a nondestructive and accurate alternative to traditional methods.

Hyperspectral Imaging (HSI) is an advanced imaging technique, and Deep Learning (DL) networks have been widely used for classifying them. Transfer learning approaches rely on pre-trained networks developed from public imagery datasets, which predominantly capture spatial features while underutilizing spectral information unique to HSI. This study evaluates the extent of spatial knowledge transfer in pre-trained and application-specific CNN models across agricultural, urban and mixed landcover environments. Experimental results indicate that while pre-trained DL networks achieve reasonable classification accuracy, they primarily rely on spatial feature abstraction, limiting spectral discrimination. However, introducing just 5% supplication-specific training data significantly enhances spectral feature utilization, enabling better functional classification. The findings highlight the potential for integrating minimal spectral domain knowledge into transfer learning frameworks to improve HSI classification. This study contributes to developing more efficient, semi-automated and expert-free HSI analysis techniques by optimizing the balance between spatial and spectral feature learning in DL models.

Cloud Condensation Nuclei (CCN) are the fraction of the aerosol that participate in the formation and modification of clouds. Comprehensive knowledge of CCN is key to understanding aerosol-cloud interactions, and climate change. The ability of aerosols to act as CCN depends on their size and composition. However, various location-specific factors also determine the CCN activity. To investigate this, long-term observations of CCN, along with collocated measurements of aerosols, gases and meteorological parameters, were analyzed from a rural location, Gadanki (13.5 0 N, 79.2 0 E, 375 m AMSL) in India. The results suggest biomass burning activities, represented by high values of BC, and CO, contribute to the highest CCN during March-April. Conversely, chemical transformations involving O 3 , SO 2 , NH 3 , and VOCs, driven by solar radiation, are linked to high CCN levels observed during April as well as daytime (post 10:00 h). The similarity between CCN and small particle concentrations (< 0.5 µm, CN) suggests the dominant role of small particles in CCN activation. Furthermore, a detailed multiple linear regression (MLR) analysis was conducted to quantify the factors influencing CCN. Findings indicate that CCN is influenced by a combination of factors rather than a single one, with T, and RH having indirect effects. Although the MLR results support the observed variations in CCN, they exhibit bias due to multi-collinearity in the dataset. Therefore, a machine learning-based framework is proposed for future research. Nevertheless, the results emphasize the role of chemical transformations and biomass burning in CCN activity in the rural location, Gadanki.

The effects of substituting Zn²⁺ and Ba²⁺ for Ca²⁺ on the crystal structure, sintering temperature, microstructure, and microwave dielectric properties of (Ca1 − x Mx)VO6 (M = Zn, Ba) [x = 0.025–0.10] have been studied. X-ray diffraction, along with refinement results, indicate that all the samples had a monoclinic crystal structure with C2/m space group, and the cationic substitutions affect the cell volume of the ceramics while retaining the crystal structure. Both Zn²⁺ and Ba²⁺ at Ca²⁺ in small percentages improved the density and dielectric properties. The (Ca0.95 Zn0.05)V2O6 ceramic sintered at 650 °C achieved εr = 10.6, Q × f = 42,400 GHz and τf = − 60 ppm/°C, while the (Ca0.95 Ba0.05)V2O6 ceramic sintered at 600 °C showed εr = 10.1, Q × f = 46,100 GHz, and τf = − 59 ppm/°C. All the (Ca1 − x Mx) VO6 ceramics exhibited a negative value of τf. The chemical compatibility with metal electrode was investigated by cofiring both ceramics with Aluminium (Al) metal at the optimised sintering temperature. XRD and EDS analysis confirmed the non-existence of reaction between ceramic and metal and formation of any other secondary phases, indicating significant potential of the ceramics for use in ULTCC applications. Furthermore, a 2-section Wilkinson power divider operating in the L band has been designed and fabricated onto the ULTCC ceramic by screen printing techniques using low temperature curable silver epoxy paste. The fabricated power divider showed a maximum insertion loss of 4.8 dB and a minimum isolation of 14 dB in 1–1.7 GHz matching with the simulation.

A verifiable and regional level method for mapping crops cultivated under organic practices holds significant promise for certifying and ensuring the quality of farm products marketed as organic. The prevailing method for the identification of organic crops involves labor-intensive manual inspections, detailed record-keeping of crop stages, and certification. Hyperspectral remote sensing is an evolving general sensing technique for extracting crop information across various scales. High-resolution hyperspectral data theoretically can distinguish numerous crops unambiguously at various levels of detail. The aim of this study is to investigate the possibility of spectral discrimination of a few vegetable crops (brinjal (Solanum melongena) and red spinach (Amaranthus dubius)) grown under organic and conventional cultivation practices and assess the inclusion of numerous landscape-level co-occurring crop species in the discrimination analysis. We acquired high-resolution in situ hyperspectral measurements on the research farms of the College of Agriculture, Kerala Agricultural University, Thiruvananthapuram, India, in the 2022 crop-growing season. Methodologically, quantifying the spectral discrimination as the multi-crop classification problem, we applied 12 different machine learning algorithms to assess the spectral discrimination and evaluated their relative performance across the diverse range of the crops considered. The results reveal intricate patterns of spectral discrimination. Vegetable crops grown under both organic and conventional chemical inputs-based practices indicate a high level (accuracy: 85–95%) of spectral discrimination. The effectiveness of the discrimination observed is significantly influenced, with a reduction in the accuracy of discrimination by 10%, by choice of the machine learning model and the presence of several co-occurring crop species. We advocate for coordinated, multi-site, and multi-phenology-based crop discrimination studies to ensure the stability of observed discrimination across different spatial and temporal contexts. The findings indicate that, due to physiological and biochemical differences, organically cultivated crops exhibit distinct spectral features than conventionally cultivated crops, and with a suitable ML method, it is plausible to map crops over geographically extant areas using hyperspectral remote sensing.

This letter introduces an efficient linearizing demodulator circuit for Linear Variable Differential Transformers (LVDTs). A key innovation of this circuit lies in the integration of linearization and demodulation functionalities within simple electronics, while effectively addressing the limitations of traditional LVDT interfaces. It uses a simple saw-tooth excitation for LVDT and incorporates an enhanced inverse synthesis circuit as the core signal conditioner for processing the LVDT's secondary outputs. Further, this approach offers other unique features, including (1) compensation for transients, rise-time, and other common LVDT errors, (2) low component count, (3) non-requirement of precision oscillator and demodulators, and (4) a broad range. The efficacy of the proposed method was evaluated through simulation and experimental studies, showing a remarkable improvement in linearity (over six times) across a 100 mm span. The developed system achieved a non-linearity error of 0.9%, a signal-to-noise ratio of 52 dB, and a repeatability error of 0.014%.

This paper presents an investigation of the X-ray emission associated with the Wolf–Rayet star, WR 48-6, using observations from the XMM-Newton and Chandra X-ray telescopes covering two epochs separated by eleven months. The X-ray spectrum of WR 48-6 is well explained by a two-temperature plasma model, with cool and hot plasma temperatures of $0.8_{-0.2}^{+0.1}$ and $2.86_{-0.66}^{+1.01}$ keV. No significant X-ray variability is observed during these two epochs of observations. However, an increase in the local hydrogen column density accompanied by a decrease in the intrinsic X-ray flux between two epochs of observations is seen. Additionally, the intrinsic X-ray luminosity is found to be more than $10^{33}$ erg $\hbox {s}^{-1}$ during both epochs of observations. Based on the analysis presented, WR 48-6 is a promising colliding wind binary candidate with a possible companion of spectral type O5–O6.

Deforestation and forest degradation are significant threats, leading to a decline in forest cover change, biomass and carbon storage, a crucial factor in mitigating climate change. Remote sensing techniques using satellite imagery offer a valuable tool for efficiently monitoring forest cover and biomass over different areas. This study aimed to map and quantify the forest cover change, biomass and carbon stored in the Alemsaga forest, Ethiopia. The study employed Landsat satellite images from four different periods (1992, 2003, 2013, and 2022) to track changes in forest cover and construct carbon storage maps for the Alemsaga forest. The findings from this study can be used to develop better forest conservation and management strategies. The study revealed a significant increase in dense forest cover in Alemsaga (35.34%) between 1992 and 2022, now encompassing 48.25% of the total forest area. Notably, satellite-derived vegetation indices (NDVI & DVI) exhibited a strong correlation with ground observations (R² = 0.80), and statistical analysis confirmed this relation with above-ground carbon levels (R² = 0.84). This enabled the creation of carbon storage maps, revealing a substantial increase from 159.31 t/ha in 1992 to 323.84 t/ha by 2022. It’s important to acknowledge that while NDVI/DVI proved effective, other factors might influence carbon storage. However, the study clearly shows that satellite imaging has the capacity to map forest cover change, biomass and estimating carbon stock accurately, which is an important first step toward a better understanding of how forests contribute to climate change.

Field-verifiable technologies for the detection and mapping of diseases in vegetable crops are vital for undertaking precision agriculture practices. The evolving hyperspectral sensors have the capacity to offer plant referenceable spectral data required to map and monitor crop diseases. Tomato is one of the most widely grown vegetable crops in India. Fusarium wilt is a fungal infection that causes severe damage to the growth and yield of tomato crops. As part of the research efforts on developing a regional-level remote sensing system for crop disease surveillance and monitoring, we have undertaken multiple studies pertaining to theoretical modelling, reference spectral data acquisition, and methods for the analyses of hyperspectral data. The objective of this work is the assessment of the spectral discrimination and classification of healthy and Fusarium wilt-infected tomato plants using hyperspectral data. In-situ reflectance spectra of healthy and infected plants over a tomato-growing region (Tumakuru, India) were measured, processed, and analyzed to differentiate between diseased and healthy tomato plants spectrally. We applied nine different methods belonging to machine learning, statistical, and spectral matching approaches considering five different levels of disease severity. Results suggest the existence of stable spectral features which differentiate healthy and diseased plants at distinct levels of disease severity. The prospect of discriminating healthy tomato plants against infected plants with different infection levels is reasonable, as indicated by the different accuracy metrics indicating about 80% accuracy. It is apparent that not all levels of disease severity can be identified spectrally. The detection of disease in plants with invisible symptoms is moderate and is substantially influenced by the method used. With an appropriate combination of methods and disease severity, hyperspectral data-based approaches enable large-scale mapping of Fusarium wilt disease in tomato crop.

Conservative discretizations of transport equations are based on integral formulations that include the finite volume method (FVM) and conservative finite difference methods (CFDMs). The FVM is used by most fluid dynamics simulation packages and requires smoothly shifting grids for better convergence. This motivates the study of the order of accuracy and rate of convergence of the FVM on non-uniform grids. It is difficult to do such an analysis of the FVM on an unstructured grid; however, the FVM is reduced to a CFDM on a Cartesian grid. The effect of the order of accuracy and the rate of convergence of higher-order CFDMs on arbitrarily varying grids are investigated. It is shown that higher-order conservative discretizations on arbitrarily varying non-uniform grids need some smoothness in the grid transition to be first-order accurate. The condition to achieve first-order accuracy is also presented. If the grid is replaced by a gradually varying grid, it is shown that conservative discretizations yield a better rate of convergence. In this situation, a rate of convergence between one and the theoretical maximum can be achieved in dependence on the grid stretch/contraction ratio. Numerical examples, including the linear convection-diffusion equation, the lid-driven cavity problem, and the Taylor-Green vortex problem, are presented.

Exosomes that contain TSG101 biomarkers are synthesized by both healthy and malignant cells and have the potential to accurately diagnose a wide range of diseases, including cancer. For exosomal protein quantification, exosomes must be isolated from serum and then used for protein extraction. Ultracentrifugation is the most common way to isolate. Although detergents are commonly employed to extract the encapsulated exosomal proteins, they may compromise their protein integrity. The present work involves two detailed studies: lysing of exosomes immobilized on the Au Screen Printed Electrode (SPE) and development of non-faradaic electrochemical sensor by utilizing SPE to quantity TSG101 protein. To lyse exosomes attached to the SPE surface, we applied different amplitudes of square signals to the SPE to disrupt the exosomes and facilitate the release of their contents. The lysate solution was utilized for electrochemical impedance spectroscopy (EIS) by faradic and Non-faradic techniques. Results of both types of EIS were similar, showing that non-faradaic sensing could be an effective alternative. Hence, we employed Non-faradaic EIS to quantify the TSG101 protein released by electric lysis and validated the result with ELISA. We achieved a linear response, specifically at concentrations ranging from 0.125 to 8 ng/mL, with a detection limit of 0.10 ng/mL for human serum. Cross-reactivity analysis demonstrated selectivity to TSG101 with minimal interaction with non-specific biomolecules.

In this study, we present a non-destructive experimental platform employing total internal reflection digital holographic microscopy to precisely assess the degradation of optics used in active ring resonators, a crucial component in advanced navigation systems like Ring Laser Gyroscopes. Refractive optics-based ring resonators typically rely on high-quality fused silica optics due to its exceptional optical and mechanical properties. The developed method characterizes degradation through spatial distribution of refractive index changes in the affected regions, comparing them with undamaged areas. In total internal reflection mode, the reflected phase is influenced by the incident angle at the TIR interface and the refractive indices of the interacting media. The experimental setup integrates a custom-designed precision Right-Angle Prism made from Astrositall material, combined with an index-matching liquid at the total internal reflection interface for seamless contact with the optics, enabling single-shot measurements. Our findings on a damaged sample reveal a degradation pattern with a near-Gaussian profile, showing a maximum refractive index increase of approximately $5.7 \times 10^{-3}$ compared to the undamaged region. These results are further validated through complementary measurements using photo thermal spectroscopy and fluorescence spectroscopy.