Remote sensing of air pollution incorporating integrated-path differential-absorption and coherent-Doppler lidar

An innovative complex lidar system deployed on an airborne rotorcraft platform for remote sensing of atmospheric pollution is proposed and demonstrated.The system incorporates integrated-path differential absorption lidar(DIAL) and coherent-doppler lidar(CDL) techniques using a dual tunable TEA CO_(2)laser in the 9—11 μm band and a 1.55 μm fiber laser.By combining the principles of differential absorption detection and pulsed coherent detection,the system enables agile and remote sensing of atmospheric pollution.Extensive static tests validate the system’s real-time detection capabilities,including the measurement of concentration-path-length product(CL),front distance,and path wind speed of air pollution plumes over long distances exceeding 4 km.Flight experiments is conducted with the helicopter.Scanning of the pollutant concentration and the wind field is carried out in an approximately 1 km slant range over scanning angle ranges from 45°to 65°,with a radial resolution of 30 m and10 s.The test results demonstrate the system’s ability to spatially map atmospheric pollution plumes and predict their motion and dispersion patterns,thereby ensuring the protection of public safety.

Building Feedback-Regulation System Through Atomic Design for Highly Active SO_(2)Sensing

Reasonably constructing an atomic interface is pronouncedly essential for surface-related gas-sensing reaction.Herein,we present an ingen-ious feedback-regulation system by changing the interactional mode between single Pt atoms and adjacent S species for high-efficiency SO_(2)sensing.We found that the single Pt sites on the MoS_(2)surface can induce easier volatiliza-tion of adjacent S species to activate the whole inert S plane.Reversely,the activated S species can provide a feedback role in tailoring the antibonding-orbital electronic occupancy state of Pt atoms,thus creating a combined system involving S vacancy-assisted single Pt sites(Pt-Vs)to synergistically improve the adsorption ability of SO_(2)gas molecules.Further-more,in situ Raman,ex situ X-ray photoelectron spectroscopy testing and density functional theory analysis demonstrate the intact feedback-regulation system can expand the electron transfer path from single Pt sites to whole Pt-MoS_(2)supports in SO_(2)gas atmosphere.Equipped with wireless-sensing modules,the final Pt1-MoS_(2)-def sensors array can further realize real-time monitoring of SO_(2)levels and cloud-data storage for plant growth.Such a fundamental understanding of the intrinsic link between atomic interface and sensing mechanism is thus expected to broaden the rational design of highly effective gas sensors.

Tailoring MXene Thickness and Functionalization for Enhanced Room‑Temperature Trace NO_(2) Sensing

In this study,precise control over the thickness and termination of Ti3C2TX MXene flakes is achieved to enhance their electrical properties,environmental stability,and gas-sensing performance.Utilizing a hybrid method involving high-pressure processing,stirring,and immiscible solutions,sub-100 nm MXene flake thickness is achieved within the MXene film on the Si-wafer.Functionalization control is achieved by defunctionalizing MXene at 650℃ under vacuum and H2 gas in a CVD furnace,followed by refunctionalization with iodine and bromine vaporization from a bubbler attached to the CVD.Notably,the introduction of iodine,which has a larger atomic size,lower electronegativity,reduce shielding effect,and lower hydrophilicity(contact angle:99°),profoundly affecting MXene.It improves the surface area(36.2 cm^(2) g^(-1)),oxidation stability in aqueous/ambient environments(21 days/80 days),and film conductivity(749 S m^(-1)).Additionally,it significantly enhances the gas-sensing performance,including the sensitivity(0.1119Ωppm^(-1)),response(0.2% and 23%to 50 ppb and 200 ppm NO_(2)),and response/recovery times(90/100 s).The reduced shielding effect of the–I-terminals and the metallic characteristics of MXene enhance the selectivity of I-MXene toward NO2.This approach paves the way for the development of stable and high-performance gas-sensing two-dimensional materials with promising prospects for future studies.

Preparation of single atom catalysts for high sensitive gas sensing

Single atom catalysts(SACs)have garnered significant attention in the field of catalysis over the past decade due to their exceptional atom utilization efficiency and distinct physical and chemical properties.For the semiconductor-based electrical gas sensor,the core is the catalysis process of target gas molecules on the sensitive materials.In this context,the SACs offer great potential for highly sensitive and selective gas sensing,however,only some of the bubbles come to the surface.To facilitate practical applications,we present a comprehensive review of the preparation strategies for SACs,with a focus on overcoming the challenges of aggregation and low loading.Extensive research efforts have been devoted to investigating the gas sensing mechanism,exploring sensitive materials,optimizing device structures,and refining signal post-processing techniques.Finally,the challenges and future perspectives on the SACs based gas sensing are presented.

Temperature and Salinity Dual-parameter Sensing Based on Forward Brillouin Scattering in 1060-XP SMF

A novel temperature and salinity discriminative sensing method based on forward Brillouin scattering(FBS)in 1060-XP single-mode fiber(SMF)is proposed.The measured frequency shifts corresponding to different radial acoustic modes in 1060-XP SMF show different sensitivities to temperature and salinity.Based on the new phenomenon that different radial acoustic modes have different frequency shift-temperature and frequency shift-salinity coefficients,we propose a novel method for simultaneously measuring temperature and salinity by measuring the frequency shift changes of two FBS scattering peaks.In a proof-of-concept experiment,the temperature and salinity measurement errors are 0.12℃and 0.29%,respectively.The proposed method for simultaneously measuring temperature and salinity has the potential applications such as ocean surveying,food manufacturing and pharmaceutical engineering.

CrossFormer Embedding DeepLabv3+ for Remote Sensing Images Semantic Segmentation

High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presenceof occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficultyof segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scalefeatures based onDeepLabv3+is designed to address the difficulties of small object segmentation and blurred targetedge segmentation. First,we use CrossFormer as the backbone feature extraction network to achieve the interactionbetween large- and small-scale features, and establish self-attention associations between features at both large andsmall scales to capture global contextual feature information. Next, an improved atrous spatial pyramid poolingmodule is introduced to establish multi-scale feature maps with large- and small-scale feature associations, andattention vectors are added in the channel direction to enable adaptive adjustment of multi-scale channel features.The proposed networkmodel is validated using the PotsdamandVaihingen datasets. The experimental results showthat, compared with existing techniques, the network model designed in this paper can extract and fuse multiscaleinformation, more clearly extract edge information and small-scale information, and segment boundariesmore smoothly. Experimental results on public datasets demonstrate the superiority of ourmethod compared withseveral state-of-the-art networks.

On-chip quantum NOON state sensing for temperature and humidity

A maximal photon number entangled state,namely NOON state,can be adopted for sensing with a quantum enhancedprecision.In this work,we designed silicon quantum photonic chips containing two types of Mach-Zehnder interferometerswherein the two-photon NOON state,sensing element for temperature or humidity,is generated.Compared with classicallight or single photon case,two-photon NOON state sensing shows a solid enhancement in the sensing resolution andprecision.As the first demonstration of on-chip quantum photonic sensing,it reveals the advantages of photonic chips forhigh integration density,small-size,stability for multiple-parameter sensing serviceability.A higher sensing precision isexpected to beat the standard quantum limit with a higher photon number NOON state.

Chaotic CS Encryption:An Efficient Image Encryption Algorithm Based on Chebyshev Chaotic System and Compressive Sensing

Images are the most important carrier of human information. Moreover, how to safely transmit digital imagesthrough public channels has become an urgent problem. In this paper, we propose a novel image encryptionalgorithm, called chaotic compressive sensing (CS) encryption (CCSE), which can not only improve the efficiencyof image transmission but also introduce the high security of the chaotic system. Specifically, the proposed CCSEcan fully leverage the advantages of the Chebyshev chaotic system and CS, enabling it to withstand various attacks,such as differential attacks, and exhibit robustness. First, we use a sparse trans-form to sparse the plaintext imageand then use theArnold transformto perturb the image pixels. After that,we elaborate aChebyshev Toeplitz chaoticsensing matrix for CCSE. By using this Toeplitz matrix, the perturbed image is compressed and sampled to reducethe transmission bandwidth and the amount of data. Finally, a bilateral diffusion operator and a chaotic encryptionoperator are used to perturb and expand the image pixels to change the pixel position and value of the compressedimage, and ultimately obtain an encrypted image. Experimental results show that our method can be resistant tovarious attacks, such as the statistical attack and noise attack, and can outperform its current competitors.

Remote sensing of quality traits in cereal and arable production systems:A review

Cereal is an essential source of calories and protein for the global population.Accurately predicting cereal quality before harvest is highly desirable in order to optimise management for farmers,grading harvest and categorised storage for enterprises,future trading prices,and policy planning.The use of remote sensing data with extensive spatial coverage demonstrates some potential in predicting crop quality traits.Many studies have also proposed models and methods for predicting such traits based on multiplatform remote sensing data.In this paper,the key quality traits that are of interest to producers and consumers are introduced.The literature related to grain quality prediction was analyzed in detail,and a review was conducted on remote sensing platforms,commonly used methods,potential gaps,and future trends in crop quality prediction.This review recommends new research directions that go beyond the traditional methods and discusses grain quality retrieval and the associated challenges from the perspective of remote sensing data.

Multidomain Correlation-Based Multidimensional CSI Tensor Generation for Device-FreeWi-Fi Sensing

Due to the fine-grained communication scenarios characterization and stability,Wi-Fi channel state information(CSI)has been increasingly applied to indoor sensing tasks recently.Although spatial variations are explicitlyreflected in CSI measurements,the representation differences caused by small contextual changes are easilysubmerged in the fluctuations of multipath effects,especially in device-free Wi-Fi sensing.Most existing datasolutions cannot fully exploit the temporal,spatial,and frequency information carried by CSI,which results ininsufficient sensing resolution for indoor scenario changes.As a result,the well-liked machine learning(ML)-based CSI sensing models still struggling with stable performance.This paper formulates a time-frequency matrixon the premise of demonstrating that the CSI has low-rank potential and then proposes a distributed factorizationalgorithm to effectively separate the stable structured information and context fluctuations in the CSI matrix.Finally,a multidimensional tensor is generated by combining the time-frequency gradients of CSI,which containsrich and fine-grained real-time contextual information.Extensive evaluations and case studies highlight thesuperiority of the proposal.

Using ontology and rules to retrieve the semantics of disaster remote sensing data

Remote sensing data plays an important role in natural disaster management.However,with the increase of the variety and quantity of remote sensors,the problem of“knowledge barriers”arises when data users in disaster field retrieve remote sensing data.To improve this problem,this paper proposes an ontology and rule based retrieval(ORR)method to retrieve disaster remote sensing data,and this method introduces ontology technology to express earthquake disaster and remote sensing knowledge,on this basis,and realizes the task suitability reasoning of earthquake disaster remote sensing data,mining the semantic relationship between remote sensing metadata and disasters.The prototype system is built according to the ORR method,which is compared with the traditional method,using the ORR method to retrieve disaster remote sensing data can reduce the knowledge requirements of data users in the retrieval process and improve data retrieval efficiency.

Color Image Compression and Encryption Algorithm Based on 2D Compressed Sensing and Hyperchaotic System

With the advent of the information security era,it is necessary to guarantee the privacy,accuracy,and dependable transfer of pictures.This study presents a new approach to the encryption and compression of color images.It is predicated on 2D compressed sensing(CS)and the hyperchaotic system.First,an optimized Arnold scrambling algorithm is applied to the initial color images to ensure strong security.Then,the processed images are con-currently encrypted and compressed using 2D CS.Among them,chaotic sequences replace traditional random measurement matrices to increase the system’s security.Third,the processed images are re-encrypted using a combination of permutation and diffusion algorithms.In addition,the 2D projected gradient with an embedding decryption(2DPG-ED)algorithm is used to reconstruct images.Compared with the traditional reconstruction algorithm,the 2DPG-ED algorithm can improve security and reduce computational complexity.Furthermore,it has better robustness.The experimental outcome and the performance analysis indicate that this algorithm can withstand malicious attacks and prove the method is effective.

Determining the planting year of navel orange trees in mountainous and hilly areas of southern China:a remote sensing based method

Remote sensing has demonstrated validity in determining the planting year of deciduous fruit trees;however,its effectiveness in ascertaining the planting year of evergreen fruit trees remains unverified.Furthermore,the sources of error associated with using remote sensing to determine the planting year of fruit trees remain unclear.This study investigates several cultivated sweet orange(Citrus sinensis)varieties,which are extensively cultivated throughout subtropical China.We analyzed Landsat time series data from 132 navel orange orchards in Gannan,covering the period from 1993 to 2021.For each orchard,Google Earth Engine was employed to extract three vegetation indices—Enhanced Vegetation Index(EVI),Normalized Difference Vegetation Index(NDVI),and Normalized Burn Ratio(NBR)—for each available date,thereby generating three distinct vegetation index time series.The planting year of navel orange trees was identified based on abrupt changes observed in these time series.The principal sources of error in determining the planting year were investigated using the Wilcoxon signed-rank test,Spearman's correlation analysis,and Kruskal-Wallis H test.Key findings include:(1)Following the planting of navel orange trees,EVI,NDVI,and NBR exhibited fluctuations and a gradual increase over time,peaking approximately 10 to 15 years later.(2)The vegetation index time series derived from Landsat imagery effectively determined the planting year of evergreen navel orange trees in orchards,even within highly fragmented landscapes.Among these indices,NDVI and NBR time series outperformed the EVI time series.Specifically,the average determination errors for EVI,NDVI,and NBR time series were 6.4,1.8,and 2.8 years,respectively.(3)Major sources of error included the methods used to construct the time series,the selection of vegetation indices,and the orchard management practices.Overall,this study provides a viable method for determining the planting year of evergreen navel orange trees in fragmented landscapes and offers insights into factors contributing to uncertainty in planting year determination.

Gel-Based Triboelectric Nanogenerators for Flexible Sensing:Principles,Properties,and Applications

The rapid development of the Internet of Things and artificial intelligence technologies has increased the need for wearable,portable,and self-powered flexible sensing devices.Triboelectric nanogenerators(TENGs)based on gel materials(with excellent conductivity,mechanical tunability,environmental adaptability,and biocompatibility)are considered an advanced approach for developing a new generation of flexible sensors.This review comprehensively summarizes the recent advances in gel-based TENGs for flexible sensors,covering their principles,properties,and applications.Based on the development requirements for flexible sensors,the working mechanism of gel-based TENGs and the characteristic advantages of gels are introduced.Design strategies for the performance optimization of hydrogel-,organogel-,and aerogel-based TENGs are systematically summarized.In addition,the applications of gel-based TENGs in human motion sensing,tactile sensing,health monitoring,environmental monitoring,human-machine interaction,and other related fields are summarized.Finally,the challenges of gel-based TENGs for flexible sensing are discussed,and feasible strategies are proposed to guide future research.

Waste Cotton-Derived Fiber-Based Thermoelectric Aerogel for Wearable and Self-Powered Temperature-Compression Strain Dual-Parameter Sensing

The rapid development of the global economy and population growth are accompanied by the production of numerous waste textiles.This leads to a waste of limited resources and serious environmental pollution problems caused by improper disposal.The rational recycling of wasted textiles and their transformation into high-value-added emerging products,such as smart wearable devices,is fascinating.Here,we propose a novel roadmap for turning waste cotton fabrics into three-dimensional elastic fiber-based thermoelectric aerogels by a one-step lyophilization process with decoupled self-powered temperature-compression strain dual-parameter sensing properties.The thermoelectric aerogel exhibits a fast compression response time of 0.2 s,a relatively high Seebeck coefficient of 43μV·K^(-1),and an ultralow thermal conductivity of less than 0.04 W·m^(-1)·K^(-1).The cross-linking of trimethoxy(methyl)silane(MTMS)and cellulose endowed the aerogel with excellent elasticity,allowing it to be used as a compressive strain sensor for guessing games and facial expression recognition.In addition,based on the thermoelectric effect,the aerogel can perform temperature detection and differentiation in self-powered mode with the output thermal voltage as the stimulus signal.Furthermore,the wearable system,prepared by connecting the aerogel-prepared array device with a wireless transmission module,allows for temperature alerts in a mobile phone application without signal interference due to the compressive strains generated during gripping.Hence,our strategy is significant for reducing global environmental pollution and provides a revelatory path for transforming waste textiles into high-value-added smart wearable devices.

CCM-FL:Covert communication mechanisms for federated learning in crowd sensing IoT

The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized services.Meanwhile,how to protect the private information of users in federated learning has become an important research topic.Compared with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning models.In this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things networks.Different from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal solution.Secondly,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning agents.Theoretical analysis and nu-merical simulations are presented to show the performance of our covert communication mechanisms.We hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.

Fast compressed sensing spectral measurement with adaptive gradient multiscale resolution

We propose a fast,adaptive multiscale resolution spectral measurement method based on compressed sensing.The method can apply variable measurement resolution over the entire spectral range to reduce the measurement time by over 75%compared to a global high-resolution measurement.Mimicking the characteristics of the human retina system,the resolution distribution follows the principle of gradually decreasing.The system allows the spectral peaks of interest to be captured dynamically or to be specified a priori by a user.The system was tested by measuring single and dual spectral peaks,and the results of spectral peaks are consistent with those of global high-resolution measurements.

Sensing-Assisted Accurate and Fast Beam Management for Cellular-Connected mmWave UAV Network

Beam management,including initial access(IA)and beam tracking,is essential to the millimeter-wave Unmanned Aerial Vehicle(UAV)network.However,the conventional communicationonly and feedback-based schemes suffer a high delay and low accuracy of beam alignment,since they only enable the receiver to passively“hear”the information of the transmitter from the radio domain.This paper presents a novel sensing-assisted beam management approach,the first solution that fully utilizes the information from the visual domain to improve communication performance.We employ both integrated sensing and communication and computer vision techniques and design an extended Kalman filtering method for beam tracking and prediction.Besides,we also propose a novel dual identity association solution to distinguish multiple UAVs in dynamic environments.Real-world experiments and numerical results show that the proposed solution outperforms the conventional methods in IA delay,association accuracy,tracking error,and communication performance.

Quantifying glacier surging and associated lake dynamics in Amu Darya river basin using UAV and remote sensing data

Glaciers in the Pamir region are experiencing rapid melting and receding due to climate change,which has a significant implication for the Amu Darya river basin.Predominantly,surging glaciers,which undergo unpredictable advances,are potentially leading to the obstruction of high-altitude river channels and also glacial lake outburst floods.decrease of-703.5±30.0 m.There is a substantial increase in the number(from 19 to 75)and area(from 4889.7±0.6 m2 to 15345.5±0.6 m2)of RGS lakes along with supra-glacier ponds based on a comparison of ArcGIS base map in 2011 and high-resolution UAV data in 2023.For M glacier,number of lakes increased from 4 to 22 but the lake area declined from 10715.2±0.6 to 365.6±0.6 m2.It was noted that the largest lake in 2011 with an area of 10406.4±0.6 m2 at the southeastern portion of the glacier was not observed in 2023 due to outburst.Both the glaciers have substantially impacted the river flow(Abdukahor river)by obstructing a significant proportion of river channel in recent years and might cause outburst floods.These findings enhance the understanding of glacier dynamics and their impacts on the surrounding areas,emphasizing the urgent need for continued monitoring and appropriate management strategies,with a specific focus on surging glaciers and the associated risks.

ConvNeXt-UperNet-Based Deep Learning Model for Road Extraction from High-Resolution Remote Sensing Images

When existing deep learning models are used for road extraction tasks from high-resolution images,they are easily affected by noise factors such as tree and building occlusion and complex backgrounds,resulting in incomplete road extraction and low accuracy.We propose the introduction of spatial and channel attention modules to the convolutional neural network ConvNeXt.Then,ConvNeXt is used as the backbone network,which cooperates with the perceptual analysis network UPerNet,retains the detection head of the semantic segmentation,and builds a new model ConvNeXt-UPerNet to suppress noise interference.Training on the open-source DeepGlobe and CHN6-CUG datasets and introducing the DiceLoss on the basis of CrossEntropyLoss solves the problem of positive and negative sample imbalance.Experimental results show that the new network model can achieve the following performance on the DeepGlobe dataset:79.40%for precision(Pre),97.93% for accuracy(Acc),69.28% for intersection over union(IoU),and 83.56% for mean intersection over union(MIoU).On the CHN6-CUG dataset,the model achieves the respective values of 78.17%for Pre,97.63%for Acc,65.4% for IoU,and 81.46% for MIoU.Compared with other network models,the fused ConvNeXt-UPerNet model can extract road information better when faced with the influence of noise contained in high-resolution remote sensing images.It also achieves multiscale image feature information with unified perception,ultimately improving the generalization ability of deep learning technology in extracting complex roads from high-resolution remote sensing images.