bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Fabrication and Parameters of a Flexible Fiber Optic Pressure Sensor with High Sensitivity
Authors: Zhang Huixin , Wu Jing , Gao Chencheng
Published: September 2024

In recent years, flexible pressure sensors have garnered significant attention. However, the development of large-area, low-cost, and easily fabricated flexible pressure sensors remains challenging. We designed a flexible fiber optic pressure sensor for contact force detection based on the principle of backward Rayleigh scattering using a single-mode optical fiber as the sensing element and polymer PDMS as the encapsulation material. To enhance the sensor’s sensitivity and stability, we optimized its structural design, parameters, and fabrication process and measured the fiber strain using an optical frequency domain reflectometer (OFDR). The results showed that the sensor achieved a high sensitivity of 6.93247 με/kPa with a PDMS concentration ratio of 10:1, a curing time of 2 h, and a substrate thickness of 5 mm. The sensor demonstrated excellent linearity and repeatability in static performance tests and was successfully used to monitor the plantar pressure distribution in real time. This flexible fiber optic pressure sensor can be developed via a simple fabrication process, has a low cost, and has high sensitivity, highlighting its potential applications in smart wearables and medical diagnostics.
Experimental Evaluation of Geometric and Environmental Effects on Mechanically Fastened Non-Crimp Fabric Composites
Authors: Kim Dong-Uk , Jang Ho-Yun , Seo Hyoung-Seock
Published: September 2024

Corresponding to marine environmental regulations is important in shipbuilding and marine industries. The application of lightweight composite materials on ships is an effective approach to reducing the emission of greenhouse gases. The mechanical fastening method is a good candidate to assemble composites and conventional metals. The joint geometric and environmental effects are two important factors in mechanically fastened ship and marine structures. In this study, we evaluated the W/D (hole diameter to width ratio) and environmental effects on the bearing strength and failure mode of a mechanically fastened non-crimp fabric (NCF) composite material. To consider the effect of joint geometry, wherein hole diameters of 5, 6, 8, and 10 mm were machined. Further, by selecting three environmental conditions (UV, saltwater and low temperature), we evaluated environmental effects on bearing strength and failure modes of NCF composite specimens. The bearing strength increased as W/D decreased, and the bearing strength of the specimen exposed to low temperature and UV environments increased, while that of the specimen exposed to saltwater remained the same. From the failure mode analysis, the specimen that was exposed to salt fog showed the same failure mode as the unaged specimen. It was observed that the changes in the transition section and new failure mode in the xenon arc and low-temperature specimens.
Colloidal Characteristics of Poly(L-Lactic Acid)-b-Poly (ε-Caprolactone) Block Copolymer-Based Nanoparticles Obtained by an Emulsification/Evaporation Method
Authors: Cucoveica Oana , Stadoleanu Carmen , Bertsch Christelle , Triaud Romain , Condriuc Iustina Petra , Atanase Leonard Ionut , Delaite Christelle
Published: September 2024

Poly(L-lactic acid) (PLLA) and poly(ε-caprolactone) (PCL), two biodegradable and biocompatible polymers that are commonly used for biomedical applications, are, respectively, the result of the ring-opening polymerization of LA and ε-CL, cyclic esters, which can be produced according to several mechanisms (cationic, monomer-activated cationic, anionic, and coordination-insertion), except for L-lactide, which is polymerized only by anionic, cationic, or coordination-insertion polymerization. A series of well-defined PLLA-b-PCL block copolymers have been obtained starting from the same PLLA homopolymer, having a molar mass of 2500 g·mol−1, and being synthesized by coordination-insertion in the presence of tin octoate. PCL blocks were obtained via a cationic-activated monomer mechanism to limit transesterification reactions, and their molar masses varied from 1800 to 18,500 g·mol−1. The physicochemical properties of the copolymers were determined by 1H NMR, SEC, and DSC. Moreover, a series of nanoparticles (NPs) were prepared starting from these polyester-based copolymers by an emulsification/evaporation method. The sizes of the obtained NPs varied between 140 and 150 nm, as a function of the molar mass of the copolymers. Monomodal distribution curves with PDI values under 0.1 were obtained by Dynamic Light Scattering (DLS) and their spherical shape was confirmed by TEM. The increase in the temperature from 25 to 37 °C induced only a very slight decrease in the NP sizes. The results obtained in this preliminary study indicate that NPs have a temperature stability, allowing us to consider their use as drug-loaded nanocarriers for biomedical applications.
Study on the Effect of Electron/Hole Injection on the Energy-Storage Properties of Polymer Dielectrics
Authors: Liu Guang , Chen Yuhao , Cui Yang , Shen Lifang , Wu Taiquan , Chen Chen , Luo Yunxia , Yan Shubin
Published: September 2024

As a critical component of electrostatic capacitors, the polymer dielectric directly affects the performance of the capacitor. In this work, Polycarbonate (PC)/Polyvinylidene fluoride (PVDF) asymmetric bilayer polymer dielectrics were prepared, and the influence of different polymer materials’ barrier characteristics on various electrical properties of composite dielectrics was studied by changing the direction of applied electric fields. Research has found that the dielectric constant of a composite dielectric is between PVDF and PC (approximately 4.8 at 10 Hz) and is independent of the relative position of PVDF and PC in the dielectric. However, the relative position of PC and PVDF has a significant impact on the energy-storage characteristics of composite dielectrics. When PVDF comes into contact with the negative electrode, even though PC has a higher hole barrier, the composite dielectric can only withstand a maximum electric-field strength of 400 MV/m, which is much lower than the maximum electric-field strength that pure PC can withstand (520 MV/m), and it only achieves an energy-storage density of 3.7 J/cm3. When the PC comes into contact with the negative electrode, the high electron barrier of the PC effectively suppresses the injection of electrons at the electrode. It can withstand the same electric-field strength as PC (520 MV/m), achieving an energy-storage density of 5.48 J/cm3, which is 1.46 times that of pure PC and 1.64 times that of PVDF. This experiment effectively combined the advantages of PC and PVDF by utilizing the electron/hole barrier of polymer materials to obtain a fully organic dielectric with excellent energy-storage performance.
Correction: Nimbalkar et al. A Review of Polymer Dielectrics for Redistribution Layers in Interposers and Package Substrates. Polymers 2023, 15, 3895
Authors: Nimbalkar Pratik , Bhaskar Pragna , Kathaperumal Mohanalingam , Swaminathan Madhavan , Tummala Rao R.
Published: September 2024

In the original publication [...]
Aromatic Biobased Polymeric Materials Using Plant Polyphenols as Sustainable Alternative Raw Materials: A Review
Authors: Liu Yang , Wang Junsheng , Sun Zhe
Published: September 2024

In the foreseeable future, the development of petroleum-based polymeric materials may be limited, owing to the gradual consumption of disposable resources and the increasing emphasis on environmental protection policies. Therefore, it is necessary to focus on introducing environmentally friendly renewable biobased materials as a substitute for petroleum-based feed stocks in the preparation of different types of industrially important polymers. Plant polyphenols, a kind of natural aromatic biomolecule, exist widely in some plant species. Benefiting from their special macromolecular structure, high reactivity, and broad abundance, plant polyphenols are potent candidates to replace the dwindling aromatic monomers derived from petroleum-based resources in synthesizing high-quality polymeric materials. In this review, the most related and innovative methods for elaborating novel polymeric materials from plant polyphenols are addressed. After a brief historical overview, the classification, structural characteristics, and reactivity of plant polyphenols are summarized in detail. In addition, some interesting and innovative works concerning the chemical modifications and polymerization techniques of plant polyphenols are also discussed. Importantly, the main chemical pathways to create plant polyphenol-based organic/organic–inorganic polymeric materials as well as their properties and possible applications are systematically described. We believe that this review could offer helpful references for designing multifunctional polyphenolic materials.
Highly Transparent, Mechanically Robust, and Conductive Eutectogel Based on Oligoethylene Glycol and Deep Eutectic Solvent for Reliable Human Motions Sensing
Authors: Huang Zhenkai , Xie Jiahuan , Li Tonggen , Xu Liguo , Liu Peijiang , Peng Jianping
Published: September 2024

Recently, eutectogels have emerged as ideal candidates for flexible wearable strain sensors. However, the development of eutectogels with robust mechanical strength, high stretchability, excellent transparency, and desirable conductivity remains a challenge. Herein, a covalently cross-linked eutectogel was prepared by exploiting the high solubility of oligoethylene glycol in a polymerizable deep eutectic solvent (DES) form of acrylic acid (AA) and choline chloride (ChCl). The resulting eutectogel exhibited high transparency (90%), robust mechanical strength (up to 1.5 MPa), high stretchability (up to 962%), and desirable ionic conductivity (up to 1.22 mS cm−1). The resistive strain sensor fabricated from the eutectogel exhibits desirable linear sensitivity (GF: 1.66), wide response range (1–200%), and reliable stability (over 1000 cycles), enabling accurate monitoring of human motions (fingers, wrists, and footsteps). We believe that our DES-based eutectogel has great potential for applications in wearable strain sensors with high sensitivity and reliability.
Dual-Responsive Alginate/PNIPAM Microspheres Fabricated by Microemulsion-Based Electrospray
Authors: Ciarleglio Gianluca , Placido Monica , Toto Elisa , Santonicola Maria Gabriella
Published: September 2024

Smart materials for drug delivery are designed to offer a precise and controlled release of therapeutic agents. By responding to specific physiological stimuli, such as changes in temperature and pH, these materials improve treatment efficacy and minimize side effects, paving the way for personalized therapeutic solutions. In this study, we present the fabrication of dual-responsive alginate/poly(N-isopropylacrylamide) (PNIPAM) microspheres, having the ability to respond to both pH and temperature variations and embedding the lipophilic bioactive compound Ozoile. Ozoile® Stable Ozonides is obtained from extra virgin olive oil and acts as an inducer, interacting with major biological pathways by means of modulating the systemic redox balance. The dual-responsive microspheres are prepared by electrospray technique without the use of organic solvents. PNIPAM is synthesized by radical polymerization using the APS/TEMED redox initiators. The microspheres are further optimized with a chitosan coating to enhance their stability and modulate the degradation kinetics of the gel matrix. A comprehensive morphological analysis, Fourier transform infrared (FTIR) spectroscopy, and degradation assays are conducted to confirm the structural stability and pH-responsive behavior of the hydrogel microspheres. A study of the volume phase transition temperature (VPTT) by differential scanning calorimetry (DSC) is used to assess the microsphere thermal response. This research introduces a promising methodology for the development of targeted drug delivery systems, which are particularly useful in the context of oxidative stress modulation and inflammation management.
The Influence of Printing Speed and Temperature on the Mechanical, Absorptive, and Morphological Properties of PLA-Based Hybrid Materials Produced with an FDM-Type 3D Printer
Authors: İncesu Rumeysa , Akderya Tarkan
Published: September 2024

Composite materials are used in many engineering applications and industrial fields due to their superior properties, such as high strength, lightweight, and stiffness. These outstanding properties have made these materials an alternative to metallic materials. The vital need for new lightweight and inexpensive materials with superior strength properties has led to research on “hybridisation”. Hybrid composites with more than one type of polymer in the same structure are needed to achieve a better balance of properties and to combine many desired properties in a single material. Many researchers have studied the hybrid effect and contributed to the understanding and modelling of the subject. Studies to explain the primary mechanism of the hybrid effect are limited and insufficient to explain the complex interaction. In this study, a three-dimensional printer using fused deposition modelling technique was used to produce hybrid materials, and the influence of printing parameters on the mechanical, absorptive, and morphological properties of poly (lactic acid) (PLA), Tough PLA, and PLA/Tough PLA hybrid materials were investigated. The hybrid material form exhibited superior properties when selecting specific production parameters from individual raw elements. It can be said that the mechanical properties of the PLA/Tough PLA hybrid material increased with the increase in production temperature.
Chitosan-Zinc-Ligated Hydroxychloroquine: Molecular Docking, Synthesis, Characterization, and Trypanocidal Activity against Trypanosoma evansi
Authors: Manuja Anju , Rani Ruma , Devi Nisha , Sihag Monika , Rani Swati , Prasad Minakshi , Kumar Rajender , Bhattacharya Tarun , Kumar Balvinder
Published: September 2024

The existing treatments against Trypanosoma evansi are faced with several drawbacks, such as limited drug options, resistance, the relapse of infection, toxicity, etc., which emphasizes the necessity for new alternatives. We synthesized novel metal-based antiparasitic compounds using chitosan, hydroxychloroquine (HC), and ZnO nanoparticles (NPs) and characterized them for size, morphology, chemical interactions, etc. Molecular docking and protein interaction studies were performed in silico to investigate the inhibitory effects of HC, zinc-ligated hydroxychloroquine (HCZnONPs), and chitosan-zinc-ligated hydroxychloroquine (CsHCZnONPs) for two key proteins, i.e., heat shock protein 90 (Hsp90) and trypanothione reductase associated with T. evansi. In vitro trypanocidal activity and the uptake of zinc ions by T. evansi parasites were observed. The formulation was successfully synthesized, as indicated by its size, stability, morphology, elemental analysis, and functional groups. CsHCZnO nanoparticles strongly inhibit both Hsp90 and trypanothione reductase proteins. The inhibition of Hsp90 by these nanoparticles is even stronger than that of trypanothione reductase when compared to HC and HCZnONPs. This suggests that the presence of polymer chitosan enhances the nanoparticles’ effectiveness against the parasite. For the first time, CsHCZnO nanoparticles exhibited trypanocidal activity against T. evansi, with complete growth inhibition being observed at various concentrations after 72 h of treatment. Fluorescent microscopy using FluoZin-3 on T. evansi culture confirmed the presence of zinc on the surface of parasites. This innovative approach has shown promising results in the quest to develop improved antiparasitic compounds against T. evansi with enhanced effectiveness and safety, highlighting their potential as therapeutic agents against trypanosomiasis.
Valorisation of Blackcurrant Pomace by Extraction of Pectin-Rich Fractions: Structural Characterization and Evaluation as Multifunctional Cosmetic Ingredient
Authors: Ćorović Marija , Petrov Ivanković Anja , Milivojević Ana , Veljković Milica , Simović Milica , López-Revenga Paula , Montilla Antonia , Moreno Francisco Javier , Bezbradica Dejan
Published: September 2024

Blackcurrant pomace is a widely available waste stream derived from the industrial production of juice rich in pectin and unextracted polyphenols. Since pectin, an emerging class of gastrointestinal prebiotics, is also a common cosmetic ingredient, the aim of this work was to evaluate blackcurrant pomace as a source of pectin-rich fractions suitable for application in prebiotic cosmetics. Hereby, this raw material was valorised by sequential extraction of acid-soluble (by citric acid, CAP) and Ca-bound (by ammonium oxalate, AOPP) pectic polysaccharides. Both fractions had favourable physicochemical features and a similar degree of methyl-esterification between low- and high-methoxyl pectin (approx. 50%), but CAP had significantly higher galacturonic acid content (72.3%), branching, and purity. Regardless of that, both had very high oil (18.96 mL/g for CAP and 19.32 mL/g for AOPP) and water (9.97 mL/g for CAP and 7.32 mL/g for AOPP)-holding capacities and excellent emulsifying properties, making them promising cosmetic ingredients. The polyphenol content was 10 times higher in CAP, while corresponding antioxidant activity was 3-fold higher. Finally, the influence of varying CAP and AOPP concentrations on common skin pathogen, Staphylococcus aureus, and beneficial skin bacteria, Staphylococcus epidermidis, was examined. The results show significant prebiotic potential of two pectic fractions since they were capable of selectively stimulating S. epidermidis, while S. aureus growth was inhibited, whereas CAP demonstrated a particularly high capacity of up to 2.2, even with methicillin-resistant S. aureus.
Proposal for a New Method for Evaluating Polymer-Modified Bitumen Fatigue and Self-Restoration Performances Considering the Whole Damage Characteristic Curve
Authors: Lv Songtao , Ge Dongdong , Cao Shihao , Liu Dingyuan , Zhang Wenhui , Li Cheng-Hui , Cabrera Milkos Borges
Published: September 2024

Fatigue performance and self-repairing activity of asphalt binders are two properties that highly influence the fatigue cracking response of asphalt pavement. There are still numerous gaps in knowledge to fill linked with these two characteristics. For instance, current parameters fail to accommodate these two bitumen phenomena fully. This study aims to propose a new procedure to address this issue utilizing the linear amplitude sweep (LAS) test, LAS with rest period (RP) (LASH) test, and simplified viscoelastic continuum damage (S-VECD) model. This research work used four different types of asphalt binders: neat asphalt (NA), self-healing thermoplastic polyurethane (STPU)-modified bitumen (STPB), self-healing poly (dimethyl siloxane) crosslinked with urea bond (IPA1w)-modified bitumen (IPAB), and styrene–butadiene–styrene (SBS)-modified bitumen (SBSB). Before the testing process, all the materials were subjected to short-term and long-term aging. The new procedure showed a superior capacity to analyze and accommodate all bitumen fatigue performances and self-repairing activities compared to the current method. Another finding proved that asphalt binders with a higher self-restoration behavior failed to show a better fatigue performance. Moreover, the higher fatigue performance increments produced by STPU and IPA1w in NA concerning the control bitumen were 123.7% and 143.7%, respectively. Those values were obtained with 1.0% STPU and 0.5% IPA1w in NA. A breakthrough finding demonstrated that asphalt binder fatigue response is augmented when the RP was applied at a higher damage intensity (S) value. STPB and IPAB reached their highest increments of fatigue response, containing 1.0% of STPU and 0.5% of IPA1w, respectively. Those augmentations were 207.54% and 232.64%, respectively.
Composition of New York State (United States) Disposed Waste and Recyclables in 2021: An Advanced Analysis of Waste Sort Data
Authors: Tonjes David J. , Manzur Sameena , Wang Yiyi , Firmansyah Firman , Rahman Mohammad , Walker Griffin , Lee Sera , Thomas Tania , Johnston Matthew , Ly Michelle , Medina Luis , Shi Randy , Srivastava Sagarika , Tucker Clara C. , Maung Htet Z. , Thyberg Krista L. , Hewitt Elizabeth
Published: September 2024

Understanding the composition of disposed municipal solid waste (MSW) and recyclables can lead to better waste management. New York State (United States) has never had a state-wide waste characterization sorting program. In 2021, sampling was conducted at 11 locations, representing 25% of the state population outside of New York City. Twenty-three tonnes from 173 discrete samples were sorted into 41 categories. The resulting data were analyzed by single constituent approaches and more novel multivariate distance techniques. The analyses found that disposed MSW was 22.8% paper, 20.5% food, and 16.8% plastics. Recyclable paper and glass–metal–plastic containers were 18.2% (11.7% paper, 6.5% containers) and yard waste was 6.5%, meaning about 25% of the disposed MSW could have been recovered. Multivariate analysis determined that the disposed MSW was similar to that from other United States jurisdictions such as Wisconsin, Pennsylvania, New York City, and Syracuse (NY), and different from California and United States Environmental Protection Agency model data. Recyclables composition was different from disposed MSW composition. Dual-stream recyclables were sorted better than single-stream recyclables. Corrugated cardboard was the most common paper recyclable and plastics were the most common container recyclable. The data are being used to help guide planning for an expected packaging extended producer responsibility law for the State.
Modifying Sequential Monte Carlo Optimisation for Index Tracking to Allow for Transaction Costs
Authors: Hamilton-Russell Leila , O’Callaghan Thomas Malan , Savin Dmitrii , Schlögl Erik
Published: September 2024

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested that this problem be solved using sequential Monte Carlo (SMC, also known as particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper modifies the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.
Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction
Authors: Orozco-Castañeda Johanna M. , Alzate-Vargas Sebastián , Bedoya-Valencia Danilo
Published: September 2024

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns.
Complex Discontinuity Structure Beneath the Changbaishan-Tianchi Volcano Revealed by the P-Wave Coda Autocorrelation Method Based on Dense Seismic Array
Authors: Wen Hao , Tian You , Liu Cai , Li Hongli
Published: September 2024

The Changbai volcano, a globally recognized hotspot of volcanic activity, has garnered significant attention due to its persistent seismicity and ongoing magma activity. The volcano’s discontinuities and magma dynamics have raised concerns about the likelihood of future eruptions, which would likely result in substantial ecological, climatic, and economic impacts. Consequently, a comprehensive understanding of the Changbai volcanic system is essential for mitigating the risks associated with volcanic activity. In recent years, the P-wave coda autocorrelation method has gained popularity in lithosphere exploration as a reliable technique for detecting reflection coefficients. Additionally, the Common Reflection Point stacking approach has been employed to superimpose reflection signals in a spatial grid, enabling continuous observation of reflection coefficients in the study area. However, the accuracy of this approach is heavily reliant on better spatial data coverage. To better understand the internal dynamics of the Changbai volcano, we applied this approach to a densely packed short-period seismic array with an average station spacing of less than 1 km. Our results were constrained using waveform data of reflection coefficients and Moho dip angles. Our findings revealed a discontinuity in the Moho, which may indicate a conduit for mantle magma entering the crust. Furthermore, we identified two low-velocity anomalies within the crust, likely representing a magma chamber comprising molten and crystallized magma. Notably, our results also provided a clear definition of the lithosphere–asthenosphere boundary.
Multi-Source Data-Driven Extraction of Urban Residential Space: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area Urban Agglomeration
Authors: Yuan Xiaodie , Dai Xiangjun , Zou Zeduo , He Xiong , Sun Yucong , Zhou Chunshan
Published: September 2024

The accurate extraction of urban residential space (URS) is of great significance for recognizing the spatial structure of urban function, understanding the complex urban operating system, and scientific allocation and management of urban resources. The traditional URS identification process is generally conducted through statistical analysis or a manual field survey. Currently, there are also superpixel segmentation and wavelet transform (WT) processes to extract urban spatial information, but these methods have shortcomings in extraction efficiency and accuracy. The superpixel wavelet fusion (SWF) method proposed in this paper is a convenient method to extract URS by integrating multi-source data such as Point of Interest (POI) data, Nighttime Light (NTL) data, LandScan (LDS) data, and High-resolution Image (HRI) data. This method fully considers the distribution law of image information in HRI and imparts the spatial information of URS into the WT so as to obtain the recognition results of URS based on multi-source data fusion under the perception of spatial structure. The steps of this study are as follows: Firstly, the SLIC algorithm is used to segment HRI in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration. Then, the discrete cosine wavelet transform (DCWT) is applied to POI–NTL, POI–LDS, and POI–NTL–LDS data sets, and the SWF is carried out based on different superpixel scale perspectives. Finally, the OSTU adaptive threshold algorithm is used to extract URS. The results show that the extraction accuracy of the NLT–POI data set is 81.52%, that of the LDS–POI data set is 77.70%, and that of the NLT–LDS–POI data set is 90.40%. The method proposed in this paper not only improves the accuracy of the extraction of URS, but also has good practical value for the optimal layout of residential space and regional planning of urban agglomerations.
A New Robust Lunar Landing Selection Method Using the Bayesian Optimization of Extreme Gradient Boosting Model (BO-XGBoost)
Authors: Wen Shibo , Wang Yongzhi , Gong Qizhou , Liu Jianzhong , Kang Xiaoxi , Liu Hengxi , Chen Rui , Zhu Kai , Zhang Sheng
Published: September 2024

The safety of lunar landing sites directly impacts the success of lunar exploration missions. This study develops a data-driven predictive model based on machine learning, focusing on engineering safety to assess the suitability of lunar landing sites and provide insights into key factors and feature representations. Six critical engineering factors were selected as constraints for evaluation: slope, elevation, roughness, hillshade, optical maturity, and rock abundance. The XGBoost model was employed to simulate and predict the characteristics of landing areas and Bayesian optimization was used to fine-tune the model’s key hyperparameters, enhancing its predictive performance. The results demonstrate that this method effectively extracts relevant features from multi-source remote sensing data and quantifies the suitability of landing zones, achieving an accuracy of 96% in identifying landing sites (at a resolution of 0.1° × 0.1°), with AUC values exceeding 95%. Notably, slope was recognized as the most critical factor affecting safety. Compared to assessment processes based on Convolutional Neural Networks (CNNs) and Random Forest (RF) models, XGBoost showed superior performance in handling missing values and evaluating feature importance accuracy. The findings suggest that the BO-XGBoost model shows notable classification performance in evaluating the suitability of lunar landing sites, which may provide valuable support for future landing missions and contribute to optimizing lunar exploration efforts.
A Representation-Learning-Based Graph and Generative Network for Hyperspectral Small Target Detection
Authors: Li Yunsong , Zhong Jiaping , Xie Weiying , Gamba Paolo
Published: September 2024

Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalanced number between the target and the background spatially and the high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation-learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and spatial–spectral features to improve its representation ability. Firstly, a Graph Convolutional Network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing the detection and background suppression capabilities. The performance of the proposed approach is evaluated using the average scores of AUCD,F, AUCF,τ, AUCBS, and AUCSNPR. The corresponding values are 0.99660, 0.00078, 0.99587, and 333.629, respectively. These results demonstrate the accuracy of the model in different evaluation metrics, with AUCD,F achieving the highest score, indicating strong detection performance across varying thresholds. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.
Characterizing the Supercooled Cloud over the TP Eastern Slope in 2016 via Himawari-8 Products
Authors: Wu Qiuyu , Chen Jinghua , Yin Yan
Published: September 2024

Supercooled liquid water (SLW) refers to droplets in clouds that remain unfrozen at temperatures below 0 °C. SLW is an important intermediate hydrometeor in the processes of snowfall and rainfall that can modulate the radiation budget. This study investigates the distribution of supercooled cloud water over mainland China using the East Asia–Pacific cloud macro- and microphysical properties dataset (2016), derived from Himawari-8 observations. The results show that the highest frequency of SLW in liquid-phase stratus clouds occur at the eastern slope of the Tibetan Plateau, the western side of the Sichuan Basin. Additional SLW is mostly found in liquid-phase clouds over the Sichuan Basin and its adjacent areas in southern China. In the region with the highest frequency of SLW, the mechanical forcing of the Tibetan Plateau causes the convergence of low-level airflow within the basin, which also carries moisture that is forced to ascend stably, creating a favorable condition for the formation of supercooled clouds. As the airflow continues to ascend, it encounters the mid-to-upper-level westerlies and temperature inversion. At the mid-to-upper level, the westerlies exhibit stronger wind speeds, directing flow towards the basin. Concurrently, the temperature inversion stabilizes the atmospheric stratification, limiting the further ascent of airflow. This inversion can also restrain convection and upward motion within the clouds, allowing for SLW to exist and persist for an extended period.
Advancing CubeSats Capabilities: Ground-Based Calibration of Uvsq-Sat NG Satellite’s NIR Spectrometer and Determination of the Extraterrestrial Solar Spectrum
Authors: Meftah Mustapha , Dufour Christophe , Bolsée David , Van Laeken Lionel , Clavier Cannelle , Chandran Amal , Chang Loren , Sarkissian Alain , Galopeau Patrick , Hauchecorne Alain , Dahoo Pierre-Richard , Damé Luc , Vieau André-Jean , Bertran Emmanuel , Gilbert Pierre , Ferreira Fréderic , Engler Jean-Luc , Montaron Christophe , Mangin Antoine , Hembise Fanton d’Andon Odile , Caignard Nicolas , Minet Angèle , Maso Pierre , Pereira Nuno , Brodu Étienne , Bekki Slimane , Billard Catherine , Keckhut Philippe
Published: September 2024

Uvsq-Sat NG is a French 6U CubeSat (10 × 20 × 30 cm) of the International Satellite Program in Research and Education (INSPIRE) designed primarily for observing greenhouse gases (GHG) such as CO2 and CH4, measuring the Earth’s radiation budget (ERB), and monitoring solar spectral irradiance (SSI) at the top-of-atmosphere (TOA). It epitomizes an advancement in CubeSat technology, showcasing its enhanced capabilities for comprehensive Earth observation. Scheduled for launch in 2025, the satellite carries a compact and miniaturized near-infrared (NIR) spectrometer capable of performing observations in both nadir and solar directions within the wavelength range of 1100 to 2000 nm, with a spectral resolution of 7 nm and a 0.15° field of view. This study outlines the preflight calibration process of the Uvsq-Sat NG NIR spectrometer (UNIS), with a focus on the spectral response function and the absolute calibration of the instrument. The absolute scale of the UNIS spectrometer was accurately calibrated with a quartz-halogen lamp featuring a coiled-coil tungsten filament, certified by the National Institute of Standards and Technology (NIST) as a standard of spectral irradiance. Furthermore, this study details the ground-based measurements of direct SSI through atmospheric NIR windows conducted with the UNIS spectrometer. The measurements were obtained at the Pommier site (45.54°N, 0.83°W) in Charentes–Maritimes (France) on 9 May 2024. The objective of these measurements was to verify the absolute calibration of the UNIS spectrometer conducted in the laboratory and to provide an extraterrestrial solar spectrum using the Langley-plot technique. By extrapolating the data to AirMass Zero (AM0), we obtained high-precision results that show excellent agreement with SOLAR-HRS and TSIS-1 HSRS solar spectra. At 1.6 μm, the SSI was determined to be 238.59 ± 3.39 mW.m−2.nm−1 (k = 2). These results demonstrate the accuracy and reliability of the UNIS spectrometer for both SSI observations and GHG measurements, providing a solid foundation for future orbital data collection and analysis.
Evaluating Flood Damage to Paddy Rice Fields Using PlanetScope and Sentinel-1 Data in North-Western Nigeria: Towards Potential Climate Adaptation Strategies
Authors: Ibrahim Sa’ad , Balzter Heiko
Published: September 2024

Floods are significant global disasters, but their impact in developing countries is greater due to the lower shock tolerance, many subsistence farmers, land fragmentation, poor adaptation strategies, and low technical capacity, which worsen food security and livelihoods. Therefore, accurate and timely monitoring of flooded crop areas is crucial for both disaster impact assessments and adaptation strategies. However, most existing methods for monitoring flooded crops using remote sensing focus solely on estimating the flood damage, neglecting the need for adaptation decisions. To address these issues, we have developed an approach to mapping flooded rice fields using Earth observation and machine learning. This approach integrates high-resolution multispectral satellite images with Sentinel-1 data. We have demonstrated the reliability and applicability of this approach by using a manually labelled dataset related to a devastating flood event in north-western Nigeria. Additionally, we have developed a land suitability model to evaluate potential areas for paddy rice cultivation. Our crop extent and land use/land cover classifications achieved an overall accuracy of between 93% and 95%, while our flood mapping achieved an overall accuracy of 99%. Our findings indicate that the flood event caused damage to almost 60% of the paddy rice fields. Based on the land suitability assessment, our results indicate that more land is suitable for cultivation during natural floods than is currently being used. We propose several recommendations as adaptation measures for stakeholders to improve livelihoods and mitigate flood disasters. This study highlights the importance of integrating multispectral and synthetic aperture radar (SAR) data for flood crop mapping using machine learning. Decision-makers will benefit from the flood crop mapping framework developed in this study in a number of spatial planning applications.
Advanced 3D Face Reconstruction from Single 2D Images Using Enhanced Adversarial Neural Networks and Graph Neural Networks
Authors: Fathallah Mohamed , Eletriby Sherif , Alsabaan Maazen , Ibrahem Mohamed I. , Farok Gamal
Published: September 2024

This paper presents a novel framework for 3D face reconstruction from single 2D images and addresses critical limitations in existing methods. Our approach integrates modified adversarial neural networks with graph neural networks to achieve state-of-the-art performance. Key innovations include (1) a generator architecture based on Graph Convolutional Networks (GCNs) with a novel loss function and identity blocks, mitigating mode collapse and instability; (2) the integration of facial landmarks and a non-parametric efficient-net decoder for enhanced feature capture; and (3) a lightweight GCN-based discriminator for improved accuracy and stability. Evaluated on the 300W-LP and AFLW2000-3D datasets, our method outperforms existing approaches, reducing Chamfer Distance by 62.7% and Earth Mover’s Distance by 57.1% on 300W-LP. Moreover, our framework demonstrates superior robustness to variations in head positioning, occlusion, noise, and lighting conditions while achieving significantly faster processing times.
Noise Analysis and Suppression Methods for the Front-End Readout Circuit of a Microelectromechanical Systems Gyroscope
Authors: He Chunhua , Xu Yingyu , Wang Xiaoman , Wu Heng , Cheng Lianglun , Yan Guizhen , Huang Qinwen
Published: September 2024

Circuit noise is a critical factor that affects the performances of an MEMS gyroscope. Therefore, it is essential to analyze and suppress the noises in the key analog circuits, which are the main noise sources. This study presents an optimized front-end readout circuit and noise suppression methods. First, the noise analysis of the front-end readout circuit is carried out with theoretical derivation to clarify the main noise contributors. To suppress the output noise, an improved readout circuit based on the T-resistor networks is proposed, and the corresponding noise equation is derived in detail. In addition, the noise analysis of the critical circuits of the detection and control system, such as the inverting amplifiers, the first-order low-pass filters, and the first-order high-pass filters, is carried out, and the noise suppression strategy with the optimization of the resistances and is proposed. Taking the inverting amplifier as an example, the theoretical derivation is verified by measuring and comparing the output noises of different resistance schemes. In addition, the output noises of the gyroscope before and after circuit optimization are measured. Experimental results demonstrate that the output noise with the circuit optimization is reduced from 60 μV/Hz1/2 to 30 μV/Hz1/2 and the bias instability is reduced from 3.8 deg/h to 1.38 deg/h. In addition, the ARW is significantly improved from 0.035 deg/h1/2 to 0.018 deg/h1/2, which indicates that the proposed noise analysis and suppression methods are effective and feasible.
Optimized Design of Direct Digital Frequency Synthesizer Based on Hermite Interpolation
Authors: Zhou Kunpeng , Xu Qiaoyu , Zhang Tianle
Published: September 2024

To address the issue of suboptimal spectral purity in Direct Digital Frequency Synthesis (DDFS) within resource-constrained environments, this paper proposes an optimized DDFS technique based on cubic Hermite interpolation. Initially, a DDFS hardware architecture is implemented on a Field-Programmable Gate Array (FPGA); subsequently, essential interpolation parameters are extracted by combining the derivative relations of sine and cosine functions with a dual-port Read-Only Memory (ROM) structure using the cubic Hermite interpolation method to reconstruct high-fidelity target waveforms. This approach effectively mitigates spurious issues caused by amplitude quantization during the DDFS digitalization process while reducing data node storage units. Moreover, this paper introduces single-quadrant ROM compression technology to further diminish the required storage space. Experimental results indicate that, compared to traditional DDFS methods, the optimization scheme proposed in this work achieves a ROM resource compression ratio of 1792:1 and a 14-bit output Spurious-Free Dynamic Range (SFDR) of −88.134 dBc, effectively enhancing amplitude quantization precision and significantly lowering spurious levels. This significantly improves amplitude quantization precision and reduces spurious levels. The proposed scheme demonstrates notable advantages in both spectral performance and resource utilization efficiency, making it highly suitable for resource-constrained embedded systems and high-performance applications such as radar and communication systems.
Improving Indoor WiFi Localization by Using Machine Learning Techniques
Authors: Esmaeili Gorjan Hanieh , Gil Jiménez Víctor P.
Published: September 2024

Accurate and robust positioning has become increasingly essential for emerging applications and services. While GPS (global positioning system) is widely used for outdoor environments, indoor positioning remains a challenging task. This paper presents a novel architecture for indoor positioning, leveraging machine learning techniques and a divide-and-conquer strategy to achieve low error estimates. The proposed method achieves an MAE (mean absolute error) of approximately 1 m for latitude and longitude. Our approach provides a precise and practical solution for indoor positioning. Additionally, some insights on the best machine learning techniques for these tasks are also envisaged.
Editorial to the Special Issue “Acoustic Sensing and Monitoring in Urban and Natural Environments”
Authors: Roman Hector Eduardo
Published: September 2024

During the last decades, the great advances achieved in sensor technology and monitoring strategies have been instrumental to accurately quantify anthropogenic noise pollution in both urban and natural environments [...]
SecureVision: Advanced Cybersecurity Deepfake Detection with Big Data Analytics
Authors: Kumar Naresh , Kundu Ankit
Published: September 2024

SecureVision is an advanced and trustworthy deepfake detection system created to tackle the growing threat of ‘deepfake’ movies that tamper with media, undermine public trust, and jeopardize cybersecurity. We present a novel approach that combines big data analytics with state-of-the-art deep learning algorithms to detect altered information in both audio and visual domains. One of SecureVision’s primary innovations is the use of multi-modal analysis, which improves detection capabilities by concurrently analyzing many media forms and strengthening resistance against advanced deepfake techniques. The system’s efficacy is further enhanced by its capacity to manage large datasets and integrate self-supervised learning, which guarantees its flexibility in the ever-changing field of digital deception. In the end, this study helps to protect digital integrity by providing a proactive, scalable, and efficient defense against the ubiquitous threat of deepfakes, thereby establishing a new benchmark for privacy and security measures in the digital era.
System for the Acquisition and Monitoring of Energy Consumption in the Context of Industry 4.0
Authors: Covrig Tudor , Ciobotaru Alexandru , Bejan Pavel-Alexandru , Farmathy-Pop Lucian , Oltean Stelian , Dumitru Cristian-Dragoș , Miclea Liviu-Cristian
Published: September 2024

This paper examines methodologies for monitoring energy consumption and operational parameters of an industrial assembly line, with a particular emphasis on the utilization of virtual tools, augmented reality, and real-time data visualization. The research examines the integration of an interface and the implementation of a data acquisition system, employing augmented reality to facilitate interaction with the collected data. The proposed system employs the Node-RED interface to facilitate the establishment of connections between constituent elements. The data is stored in a database, which provides support for decision-making in the analysis of energy consumption and operational parameters of the industrial assembly line. The results demonstrate that the solution is effective in enhancing energy resource management, identifying inefficiencies, and optimizing the performance of industrial equipment. The findings indicate that the implementation of such a system can markedly enhance the industrial process.