Analysis of the joint detection capability of the SMILE satellite and EISCAT-3D radar

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.

Rail-Pillar Net:A 3D Detection Network for Railway Foreign Object Based on LiDAR

Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.

Depth-Guided Vision Transformer With Normalizing Flows for Monocular 3D Object Detection

Monocular 3D object detection is challenging due to the lack of accurate depth information.Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB images.Depth-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion learning.However,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with convolutions.Different from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth information.Then we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each other.Furthermore,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input tokens.Our method is the first Swin-Transformer-based backbone architecture for monocular 3D object detection.The experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.

Surface Defect Detection and Evaluation Method of Large Wind Turbine Blades Based on an Improved Deeplabv3+Deep Learning Model

The accumulation of defects on wind turbine blade surfaces can lead to irreversible damage,impacting the aero-dynamic performance of the blades.To address the challenge of detecting and quantifying surface defects on wind turbine blades,a blade surface defect detection and quantification method based on an improved Deeplabv3+deep learning model is proposed.Firstly,an improved method for wind turbine blade surface defect detection,utilizing Mobilenetv2 as the backbone feature extraction network,is proposed based on an original Deeplabv3+deep learning model to address the issue of limited robustness.Secondly,through integrating the concept of pre-trained weights from transfer learning and implementing a freeze training strategy,significant improvements have been made to enhance both the training speed and model training accuracy of this deep learning model.Finally,based on segmented blade surface defect images,a method for quantifying blade defects is proposed.This method combines image stitching algorithms to achieve overall quantification and risk assessment of the entire blade.Test results show that the improved Deeplabv3+deep learning model reduces training time by approximately 43.03%compared to the original model,while achieving mAP and MIoU values of 96.87%and 96.93%,respectively.Moreover,it demonstrates robustness in detecting different surface defects on blades across different back-grounds.The application of a blade surface defect quantification method enables the precise quantification of dif-ferent defects and facilitates the assessment of risk levels associated with defect measurements across the entire blade.This method enables non-contact,long-distance,high-precision detection and quantification of surface defects on the blades,providing a reference for assessing surface defects on wind turbine blades.

Fe^(3+)对细粒菱锌矿和方解石分散行为的影响

方解石矿泥的覆盖和高价金属阳离子的存在是影响粒径−20μm细粒菱锌矿浮选效果的关键因素,两者通过改变菱锌矿的表面性质而恶化其分散性和浮选环境。本文选择菱锌矿浮选中常见的Fe^(3+)为研究对象,通过吸附试验、Zeta电位、XPS分析,DLVO理论计算等考察Fe^(3+)对菱锌矿和方解石的分散行为规律和作用机理。结果表明:两种矿物对Fe^(3+)的吸附行为基本一致,但Fe^(3+)对菱锌矿分散行为的影响程度大于Fe^(3+)对方解石分散行为的影响。当Fe^(3+)浓度为5×10^(−4)mol/L时,菱锌矿分散行为被完全破坏。溶液化学及DLVO理论计算结果表明,Fe^(3+)吸附在矿物表面形成了羟基络合物或Fe(OH)3,导致菱锌矿颗粒间作用力小于方解石的,这是Fe^(3+)破坏菱锌矿分散行为、影响粒径−20μm细粒菱锌矿浮选的主要原因。

不同形态聚乙烯微塑料对Fe^(3+)的吸附性能

为探究聚乙烯(PE)微塑料的不同形态对Fe^(3+)吸附性能的影响,分析两者之间的相互作用。选用低密度聚乙烯(LDPE)和线型低密度聚乙烯(LLDPE)作为不同形态PE的代表,利用SEM、FTIR、吸附比表面测试法及Zeta电位表征其结构形貌,并通过静态吸附实验探讨不同形态PE对Fe^(3+)的吸附效果影响。结果表明LDPE相较于LLDPE具有更优的颗粒分散性、比表面积和表面负电性,其对Fe^(3+)的最大吸附量达到5.01 mg·L^(-1),PE对Fe^(3+)的吸附量随着微塑料投加量的增加先增大后减小,升温有利于吸附反应的进行。LDPE和LLDPE对Fe^(3+)的吸附均符合准一级动力学和朗缪尔模型,这表明吸附过程以单层物理吸附为主;颗粒内扩散模型表明吸附过程以表面吸附为主,且内扩散不是吸附速率控制的唯一步骤。此外,扩散力和静电力是LDPE和LLDPE吸附Fe^(3+)的主要作用机制,并且不受其线型结构的影响。

一例Eu-MOF材料的构筑及对Fe^(3+)与硝基芳香族爆炸物的荧光检测性能

金属阳离子和硝基芳香族爆炸物的大量排放对环境和人体健康造成了严重威胁,对它们的高效检测具有重要的研究意义和挑战性,金属-有机骨架(MOFs)是一类新型的荧光传感检测材料.本文采用溶剂热法合成了一例具有fcu拓扑结构的Eu-MOF材料,[(CH_(3))_(2)NH_(2)]_(2)[Eu_(6)(μ_(3)-OH)_(8)(EDDC)_(6)]·8DMA·3MeOH·6H_(2)O[JLUMOF128,H_(2)EDDC=(E)-4,4′-(乙烯-1,2-取代基)二苯甲酸],并通过单晶X射线衍射、粉末X射线衍射、X射线光电子能谱、红外光谱、元素分析和热重分析对其结构及组成进行了表征.结果表明,由于荧光配体H_(2)EDDC的引入,JLU-MOF128表现出显著的荧光性能,在DMF溶液中对Fe^(3+)、2,4,6-三硝基苯酚(TNP)和2,4-二硝基苯酚(2,4-DNP)具有较好的检测效果,Ksv值分别为2.09×10^(4),8.49×10^(4)和5.75×10^(4)L/mol,检测限分别为5.99,1.51和1.93μmol/L.在金属阳离子和硝基芳香族爆炸物的检测方面,JLU-MOF128是一种理想的多感应荧光传感材料.

含酚类煤化工废水自还原Fe^(3+)类芬顿体系研究

煤化工废水水质复杂,难降解有机物及氨氮含量高,给废水处理带来较大难度。现有的煤化工废水处理技术(混凝法、吸附法、膜生物反应法等)具有成本高、运行不稳定、预处理效果差等缺陷,难以满足煤化工行业发展的需要。为了高效处理煤化工废水,本文利用煤化工废水中酚类有机物的还原性促进Fe^(3+)/Fe^(2+)的循环,提出了一种利用Fe^(3+)/H_(2)O_(2)类芬顿体系处理煤化工废水的方法。实验结果表明:Fe^(3+)/H_(2)O_(2)体系中COD、TOC、TN、NH3-N的去除率可以达到74.63%、52.62%、10.46%、15.11%;相比于其他体系,Fe^(3+)/H_(2)O_(2)体系出水色度明显降低,UV-Vis光谱下降幅度最大,铁泥量也明显减少。Q-TOF分析结果表明:废水中主要的8种有机物为酚类或具有醛基、羰基、羧基、碳碳双键或者酯基等还原性的官能团。通过测定COD去除率和pH、Fe^(3+)/Fe^(2+)、H_(2)O_(2)等含量随时间的变化趋势,提出了Fe^(3+)/H_(2)O_(2)体系去除有机物的机理:废水中的还原性有机物将Fe^(3+)还原为Fe^(2+),促进Fe^(3+)/Fe^(2+)循环,生成的Fe^(2+)与H_(2)O_(2)发生芬顿反应,实现废水中有机污染物的去除。利用控制变量法,确定了最佳运行工况为:Fe_(2)(SO_(4))_(3)添加量为1.0 g/L、H_(2)O_(2)添加量为50 mmol/L、反应温度为30℃、初始pH为6.8。在此工况下,反应60 min后,煤化工废水的COD、TOC、TN、NH_(3)-N去除效果良好,色度明显降低,BOD_(5)和COD的比值(B/C)从0.17提升至0.47,可生化性大幅提高。本文证实了利用含酚类煤化工废水自还原Fe^(3+)/H_(2)O_(2)体系的可行性,降低了运行成本,可为后续研究及工程应用提供理论基础。

Fe^(3+)对浮钨尾矿中受抑萤石的活化作用及其与方解石浮选分离的影响

白钨矿、萤石和方解石的浮选中,抑制剂的加入往往会增加后续萤石、方解石浮选分离的难度。通过浮选实验、吸附量测试、Zeta电位测量及溶液化学计算,研究了Fe^(3+)对浮钨尾矿中受抑萤石的活化作用及其与方解石浮选分离的影响及机理。单矿物浮选实验结果表明,Fe^(3+)单独添加对方解石的抑制作用远大于萤石。水玻璃单独添加时,两种矿物同时被抑制。在pH为8.0、水玻璃用量为300 mg/L、油酸钠用量为1.5×10^(-4)mol/L的条件下,萤石、方解石浮选回收率分别为13.49%和16.83%。水玻璃体系中引入Fe^(3+),在pH为8.0、水玻璃用量为75 mg/L、Fe^(3+)用量为3×10^(-4)mol/L、油酸钠用量为1.5×10^(-4)mol/L的条件下,萤石、方解石浮选回收率分别为82.01%和15.64%,Fe^(3+)的加入提高了水玻璃体系中受抑萤石的可浮性,选择性活化了萤石,机理分析表明,Fe^(3+)更容易在方解石表面发生吸附,阻碍了油酸钠的吸附。水玻璃体系中加入Fe^(3+)后,溶液中Fe^(3+)的水解组分Fe(OH)^(2+)、Fe(OH)_(4)^(-)选择性地与萤石表面水玻璃的水解组分Si(OH)_(4)、SiO(OH)_(3)^(-)发生化学反应,生成Fe^(+)-水玻璃聚合物,使得萤石表面的水玻璃水解组分含量减少,恢复了萤石的可浮性,而Fe^(+)-水玻璃聚合物则较多地在方解石表面发生吸附,更加抑制了方解石的浮选。

养殖废水中Fe^(3+)累积对藻菌共生体EPS分泌及膜污染特征的影响

通过在养殖废水中分别投加不同浓度的Fe^(3+)进行藻菌共生体的培养,探究Fe^(3+)对藻菌共生体胞外聚合物(EPS)分泌的影响,并将提取的EPS各组分拆分,结合膜污染测试实验考察藻菌共生体混合液和其各组分导致的膜污染特征变化情况。实验结果表明,Fe^(3+)的加入通过影响EPS各组分的含量可有效减缓膜污染,在其投加质量浓度为150 mg/L时,膜污染最轻。采用Hermia经典模型对膜通量衰减过程进行拟合分析,结果发现,滤饼层的形成是膜污染形成的主要原因之一,此外Fe^(3+)增加会引起微生物絮体粒径变化,进而对膜污染产生显著影响。膜面污染物成分分析发现,其主要组成成分为色氨酸类蛋白质、酪氨酸类蛋白质和微生物代谢副产物。

绿萝制备蓝色荧光碳量子点及对Fe^(3+)的检测

以绿萝为原料,通过水热法合成了蓝色荧光碳量子点(CQDs),考察了原料质量浓度、水热温度、CQDs浓度对CQDs荧光强度的影响,确定了最佳工艺条件:原料质量浓度3.3g/L、反应温度260℃、反应时间4h,此条件下制备的CQDs荧光强度最高。同时研究了盐离子浓度、紫外灯光照射时间、溶液pH对CQDs荧光强度的影响,结果表明CQDs有较好的盐稳定性和光稳定性,对pH有一定的依赖性,酸性条件下CQDs荧光强度相对较高。傅里叶变换红外光谱、X射线光电子能谱分析表明CQDs表面含有羟基、羧基等官能团,在水中有良好的溶解性。Fe^(3+)对CQDs的荧光有明显的猝灭作用,其他金属离子对其干扰性小,基于荧光强度与Fe^(3+)浓度之间的线性关系,CQDs能快速地检测水溶液中Fe^(3+)浓度,最低检测限为0.77μmol/L。

基于Fe^(3+)-DA-APS自催化体系的Fe_(3)O_(4)/PAA水凝胶的制备及性能研究

以Fe_(3)O_(4)纳米粒子为磁性组分,基于AA(丙烯酸)与部分Fe3O4反应产生的Fe3+、多巴胺(DA)构建双重自催化过硫酸铵(APS)的自由基聚合体系,在低温下制备了Fe_(3)O_(4)/聚丙烯酸(PAA)水凝胶,并对其进行表征。研究结果表明:Fe_(3)O_(4)/PAA水凝胶具有良好的力学性能,断裂伸长率、拉伸强度分别为900%、251.1 kPa;同时,其可较好粘附不同基材,在钢材上粘附-剥离循环20次后粘附强度仍稳定在30.7 kPa左右;此外,其还可感应极小形变,并在166 ms内快速响应。该Fe_(3)O_(4)/PAA水凝胶综合性能良好,具备应用于柔性传感器等领域的潜力。

LiLu(MoO_(4))_(2):Eu^(3+)的制备及其对Fe^(3+)离子的荧光检测研究

Fe^(3+)在生命过程中扮演着重要的角色,开发一种能够快速定量检测Fe^(3+)离子的荧光探针有着重要意义。采用溶胶-凝胶法成功制备了四方相白钨矿结构的LiLu(MoO_(4))_(2):Eu^(3+)荧光粉,粒径为1~2μm;通过荧光光谱研究了所得荧光粉的光学性质,确定了Eu^(3+)的最佳发光掺杂摩尔浓度为15%。所得LiLu(MoO_(4))_(2):15%Eu^(3+)对Fe^(3+)离子的检测下限为14.4μmol/L,并且具有高选择性和抗干扰性。

A highly sensitive ratiometric near-infrared nanosensor based on erbium-hyperdoped silicon quantum dots for iron(Ⅲ) detection

Ratiometric fluorescent detection of iron(Ⅲ)(Fe^(3+))offers inherent self-calibration and contactless analytic capabilities.However,realizing a dual-emission near-infrared(NIR)nanosensor with a low limit of detection(LOD)is rather challenging.In this work,we report the synthesis of water-dispersible erbium-hyperdoped silicon quantum dots(Si QDs:Er),which emit NIR light at the wavelengths of 810 and 1540 nm.A dual-emission NIR nanosensor based on water-dispersible Si QDs:Er enables ratiometric Fe^(3+)detection with a very low LOD(0.06μM).The effects of pH,recyclability,and the interplay between static and dynamic quenching mechanisms for Fe^(3+)detection have been systematically studied.In addition,we demonstrate that the nanosensor may be used to construct a sequential logic circuit with memory functions.

学科育人视角下“Fe^(3+)、Fe^(2+)的检验与转化”教学

在学科育人的视角下,以“新打开的铁肥和久置的铁肥哪个效果更好”为驱动型问题,在实验探究过程中开展“Fe^(3+)、Fe^(2+)的检验与转化”教学。通过“了解植物叶片变黄的原因、探究新打开的铁肥和久置的铁肥中铁的存在形式、提出减缓铁肥变质的方法”等三个核心活动,落实Fe^(3+)、Fe^(2+)的检验与转化等核心知识;在减缓铁肥变质的实际问题中培养学生的创新精神、严谨的科学态度和社会责任感。

NPCl-CDs/Fe^(3+)荧光探针的制备及对L-Cys的传感检测

以葡萄糖、乙二胺、浓盐酸和浓磷酸作为反应前体,采用一步水热法合成了一种蓝绿色的荧光碳点NPCl-CDs,并以此构建了NPCl-CDs/Fe^(3+)荧光探针用于实际样品中L-半胱氨酸(L-Cys)的定量检测.实验结果表明,在NPCl-CDs中加入Fe^(3+)可使NPCl-CDs的荧光猝灭,当向NPCl-CDs/Fe^(3+)猝灭体系中引入L-Cys时,体系的荧光强度得以恢复.由此构建了一种用于L-Cys定量检测的新型“开-关-开”NPCl-CDs/Fe^(3+)-L-Cys荧光传感系统.该方法在5.8~60.0μmol/L浓度范围内呈现较宽的线性区域,检出限为0.052μmol/L.该荧光传感系统对L-Cys表现出良好的选择性,对实际样品中L-Cys的检测具有潜在的应用价值.

Heterogeneous Cu_(x)O Nano‑Skeletons from Waste Electronics for Enhanced Glucose Detection

Electronic waste(e-waste)and diabetes are global challenges to modern societies.However,solving these two challenges together has been challenging until now.Herein,we propose a laser-induced transfer method to fabricate portable glucose sensors by recycling copper from e-waste.We bring up a laser-induced full-automatic fabrication method for synthesizing continuous heterogeneous Cu_(x)O(h-Cu_(x)O)nano-skeletons electrode for glucose sensing,offering rapid(<1 min),clean,air-compatible,and continuous fabrication,applicable to a wide range of Cu-containing substrates.Leveraging this approach,h-Cu_(x)O nanoskeletons,with an inner core predominantly composed of Cu_(2)O with lower oxygen content,juxtaposed with an outer layer rich in amorphous Cu_(x)O(a-Cu_(x)O)with higher oxygen content,are derived from discarded printed circuit boards.When employed in glucose detection,the h-Cu_(x)O nano-skeletons undergo a structural evolution process,transitioning into rigid Cu_(2)O@CuO nano-skeletons prompted by electrochemical activation.This transformation yields exceptional glucose-sensing performance(sensitivity:9.893 mA mM^(-1) cm^(-2);detection limit:0.34μM),outperforming most previously reported glucose sensors.Density functional theory analysis elucidates that the heterogeneous structure facilitates gluconolactone desorption.This glucose detection device has also been downsized to optimize its scalability and portability for convenient integration into people’s everyday lives.

不同因素对Fe^(3+)-TiO_(2)/ACF降解NH_(3)的影响

为了探究不同因素对Fe^(3+)-TiO_(2)/活性炭纤维(ACF)降解氨气(NH_(3))的影响规律,采用Fe^(3+)-TiO_(2)/ACF复合材料,以NH_(3)为目标降解物,研究初始浓度、流速、光催化剂负载量以及光照强度对Fe^(3+)-TiO_(2)/ACF降解NH_(3)的影响,并进行了相应的反应动力学分析。结果表明:随着初始浓度的增加,NH_(3)降解率呈现略微降低的趋势,光催化过程基本符合L-H一级反应动力学模型;随着流速的增加,NH_(3)降解率表现为先升高后降低,其光催化过程也基本符合L-H一级反应动力学模型;随着光催化剂负载量以及光照强度的增加,NH_(3)降解率表现为略微增加的趋势。

B位Fe^(3+)掺杂Na_(0.5)Bi_(0.49)TiO_(3)陶瓷的电导性能

采用固相合成法制备了x=0,0.02,0.04和0.06的Na_(0.5)Bi_(0.49)Ti_(1-x)Fe_(x)O_(3-δ)(NBT)粉末材料,经压制成型并在1000~1150℃烧结制备了片状陶瓷样品。通过XRD,SEM和EIS测试研究了Fe^(3+)掺杂量x对NBT陶瓷性能的影响。结果表明,NBT材料在掺杂Fe^(3+)后出现了新相NaBiTi_(6)O_(14),且含量随x的增加而变多。材料在掺杂后的烧结温度降低了100~150℃,同时材料的晶粒变得细小。在300~700℃测试温度范围内,x=0.02,0.04和0.06的样品的电导率在相同温度下都处于同一数量级,相对于x=0的样品的电导率提升了约1000倍。x=0.04的样品具有最高的电导率,在600℃和700℃的测试温度下,电导率分别达到9.8 mS/cm和17.0 mS/cm。

Mural Anomaly Region Detection Algorithm Based on Hyperspectral Multiscale Residual Attention Network

Mural paintings hold significant historical information and possess substantial artistic and cultural value.However,murals are inevitably damaged by natural environmental factors such as wind and sunlight,as well as by human activities.For this reason,the study of damaged areas is crucial for mural restoration.These damaged regions differ significantly from undamaged areas and can be considered abnormal targets.Traditional manual visual processing lacks strong characterization capabilities and is prone to omissions and false detections.Hyperspectral imaging can reflect the material properties more effectively than visual characterization methods.Thus,this study employs hyperspectral imaging to obtain mural information and proposes a mural anomaly detection algorithm based on a hyperspectral multi-scale residual attention network(HM-MRANet).The innovations of this paper include:(1)Constructing mural painting hyperspectral datasets.(2)Proposing a multi-scale residual spectral-spatial feature extraction module based on a 3D CNN(Convolutional Neural Networks)network to better capture multiscale information and improve performance on small-sample hyperspectral datasets.(3)Proposing the Enhanced Residual Attention Module(ERAM)to address the feature redundancy problem,enhance the network’s feature discrimination ability,and further improve abnormal area detection accuracy.The experimental results show that the AUC(Area Under Curve),Specificity,and Accuracy of this paper’s algorithm reach 85.42%,88.84%,and 87.65%,respectively,on this dataset.These results represent improvements of 3.07%,1.11%and 2.68%compared to the SSRN algorithm,demonstrating the effectiveness of this method for mural anomaly detection.