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On the heels of orthogonal time frequency space (OTFS) modulation, the recently discovered affine frequency division multiplexing (AFDM) is a promising waveform for the sixth-generation wireless network. In this paper, we study the widely-used embedded pilot-aided (EPA) channel estimation in multiple-input multiple-output AFDM (MIMO-AFDM) system with fractional Doppler shifts. We first formulate the vectorized input-output relationship of MIMO-AFDM, and theoretically prove that MIMO-AFDM can achieve full diversity in doubly selective channels. Then we illustrate the implementation of EPA channel estimation in MIMO-AFDM and unveil that serious inter-Doppler interference (IDoI) occurs if we try to estimate the channel gain, delay shift, and Doppler shift of each propagation path. To address this issue, the diagonal reconstructability of AFDM subchannel matrix is studied and a low-complexity embedded pilot-aided diagonal reconstruction (EPA-DR) channel estimation scheme is proposed. The EPA-DR scheme calculates the AFDM effective channel matrix directly without estimating the three channel parameters, eliminating the severe IDoI inherently. Since the effective channel matrix is necessary for MIMO-AFDM receive processing, we believe this is an important step to bring AFDM towards practical communication systems. Simulation results validate the effectiveness of the proposed EPA-DR scheme.

The split energy storage-embedded modular multilevel converter has emerged as a promising solution to address energy efficiency and power quality concerns in electrified railways. However, the intricate system topology and coupled control objectives of the existing ones aggravate the reliability and flexibility. To address these challenges, a star-connection supercapacitor-embedded cascaded H-bridge multilevel converter (SCCHMC) STATCOM is proposed in this article. The proposed configuration offers advantages as it requires fewer power cells, enhancing reliability while reducing costs. To facilitate the flexible control of multiobjective, including regenerative braking energy utilization, negative sequence current suppression, and reactive compensation, a sequence components-based decoupling control is developed for the SCCHMC-STATCOM. The proposed method features straightforwardness and flexibility, with its effectiveness validated through experimental results. Additionally, a comprehensive discussion regarding fault protection and a comprehensive comparison between the proposed SCCHMC-STATCOM and the state-of-the-art energy storage-based solution for railway applications are conducted.

Adhesives that can in situ adhere to substrates and long-term maintain robust adhesion at extremely low temperatures are highly desired in fields such as aerospace, polar regions, and biological industries. Unfortunately, developing such adhesives has been a daunting task so far, as most polymers tend to freeze in such an environment, leading to poor interfacial wetting, serious deformation, and significant fragileness. To address this challenge, a series of poly(dimethylsiloxane)-based polyurea adhesives were synthesized through a non-isocyanate route. This adhesive exhibited exceptional segmental dynamics and toughness at extremely low temperatures, as demonstrated by the continuous evolution of ordered and disordered hydrogen bonds associated with C═O until the test limit of −170 °C and unprecedented bendability in liquid nitrogen (LN, −196 °C). It could adhere firmly and reversibly to various substrates in LN, achieving an in situ adhesion strength of as high as 1.6 MPa on steel. Evenly important, this adhesive exhibited outstanding long-term tolerance in LN with an ultrahigh adhesion strength of 18.3 MPa after 30 days of immersion. Surprisingly, long-persistent phosphorescence lasting for ∼7s was also observed in LN. This study reduced the adhesion temperature of adhesives to −196 °C while enhancing the in situ adhesion strength in LN to the MPa level.

Rate splitting multiple access (RSMA) empowered simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided networks have been extensively researched to enhance spectrum efficiency and coverage capability without considering eavesdropping. However, the 360° service coverage provided by STAR-RIS inevitably leads to comprehensive eavesdropping, posing more significant security risks for private information transmission compared to traditional reflection-only RIS. In this letter, we propose a secure transmission scheme for STAR-RIS aided downlink communication systems, which is empowered by a novel multiple access paradigm called RSMA. To address the security challenge, we study the proposed security transmission scheme by jointly designing active beamforming, passive beamforming, and common rate allocation to maximize the sum secrecy rate. To tackle the highly coupled non-convex optimization problem, we propose an efficient and low-complexity successive convex approximation-based penalization RSMA-STAR (PE-RSSTA) algorithm. The numerical results demonstrate that: i) the algorithm converges through iterations; ii)the proposed algorithm improves secrecy rate performance by 5.22% compared to the PE space division multiple access (SDMA) STAR algorithm and achieves a notable 27.37% improvement over the conventional RIS algorithm.

The synergistic effect of pre-intercalated Mn2+and H2O enlarge the interlayer spacing and improve the stability of MVOH for aqueous zinc-ion batteries. Zn2+/H+co–(de)intercalation mechanism is also validated through in-situ and ex-situ characterizations.

We propose and experimentally demonstrate a spatial-light-modulator (SLM) based optical wireless communication (OWC) system using non-orthogonal multiple access realizing four-layer of successive interference cancellation (SIC) iterations for the first time. This can be attributed by our two contributions. First, an iterative phase correction algorithm (PCA) is proposed for addressing the SLM non-ideal effects. Second, low-density parity check is utilized to provide coding gain for the SIC. Experimental results show that 3.5-dB improvement at a steering angle of (5o, 5o) is achieved by the PCA, and >54-Gbps total data rate is achieved in the 4-user OWC system.

The advent of wearable sensors heralds a transformation in the continuous, noninvasive analysis of biomarkers critical for disease diagnosis and fitness management. Yet, their advancement is hindered by the functional challenges affiliated with their active sensing analysis layer. Predominantly due to suboptimal intrinsic material properties and inconsistent dispersion leading to aggregation, thus compromising sensor repeatability and performance. Herein, an innovative approach to the functionalization of wearable electrochemical sensors was introduced, specifically addressing these limitations. The method involves a proton-induced self-assembly technique at the organic–water (O/W) interface, facilitating the generation of biomarker-responsive films. This research offers flexible, breathable sensor capable of real-time precision tracking l-cysteine (l-Cys) precision tracking. Utilizing an activation mechanism for Prussian blue nanoparticles by hydrogen peroxide, the catalytic core exhibits a specific response to l-Cys. The implications of this study refine the fabrication of film-based analysis electrodes for wearable sensing applications and the broader utilization of two-dimensional materials in functional-specific response films. Findings illuminate the feasibility of this novel strategy for precise biomarker tracking and extend to pave the way for constructing high-performance electrocatalytic analytical interfaces.

The effective transmission of signals from high-temperature sensors in harsh environments is crucial in aerospace applications. However, the rapid fabrication of thin/thick film conductive lines (TFCLs) while avoiding damage to the substrate and minimizing disturbance remains challenging. Here, we present a strategy for the direct conformal printing of silver-palladium (AgPd) TFCLs, enabling the stable lead-out of signals from high-temperature sensors across corners without drilling the substrate, solving the problem of traditional perforated leads damaging the substrate structure. The fabricated AgPd TFCL exhibits excellent electrical performance with a low-temperature coefficient of resistance (TCR) of 197 ppm/°C (from 200 °C to 800 °C) and high long-term stability, as evidenced by a resistance drift rate (DR) of 0.71%/h (during 24 h of isothermal aging at 800 °C). Furthermore, the TFCL demonstrates outstanding scouring resistance, high bond strength, and exceptional process compatibility. As a proof of concept, a high-temperature probe was prepared, and cross-corner TFCLs enabled the nondestructive extraction of signals from a resistance temperature detector (RTD), spanning from the front to the back of the substrate. This technology establishes a foundation for signal lead-outs from sensors on specially shaped curved surfaces in extreme environments.

This letter explores potential of a segment anything model (SAM), the first promptable image segmentation system, in detecting wireless interference based on time-frequency images (TFI). Since the original SAM is pre-trained with natural images, it struggles to segment interferences within bandwidth of legitimate communication signals. To address this, we propose a novel prompting and tuning enhanced SAM to effectively segment in-band interferences, especially in the presence of blurred boundaries on TFIs. We first exploit the difference in single-channel luminance among gray-scaled TFI pixels and propose a mean shift clustering on this basis which helps to prompt the interference regions for segmentation. Then the lightweight mask decoder of SAM is fine-tuned using an augmented dataset containing the refined examples of in-band interferences. Compared to the state-of-the-art, experiments show that the proposed method significantly improves the segmentation accuracy as indicated by dice coefficients and intersection-over-union in a wide rang of jamming-to-signal ratio.

A sub discipline of artificial intelligence (AI), deep learning (DL) has become a breakout technology in highprofile market sectors such as retail and high tech. Manufacturing has been slow to [...]

F-44 phenolic epoxy fibers were produced through high-temperature dry spinning utilizing F-44 phenolic epoxy resin as the base material, combined with n-butanol and ethylenediamine (EDA) as the curing agent. The fibers were subsequently analyzed for their structural, thermal stability, microstructural, and mechanical properties using techniques such as Fourier transform infrared spectroscopy, micro-infrared imaging, thermogravimetric analysis, nuclear magnetic resonance, scanning electron microscopy, and fiber strength testing. The limitations of phenolic resins, including high brittleness, poor toughness, and low elongation at break, restrict their potential applications, necessitating modifications to broaden their utility. Research findings indicate that modifying EDA induces a ring-opening reaction of epoxy groups, thereby enhancing the resin’s structure and improving the thermal stability and mechanical properties of fibers. The thermal stability and mechanical strength of the fibers were optimized at an EDA concentration of 2.0 wt% and curing time of 30 min, resulting in a tensile strength of 105 MPa and an elongation at break of 27.6 %.

Graphical Abstract:

The shrinkage and collapse of wood cell walls during carbonization make it challenging to control the size and shape of carbonized wood (CW) through pre- or postprocessing (e.g., sawing, cutting, and milling). Herein, a shape-adaptive MXene shell (MS) is created on the surface of the wood cell walls. The MS limits the deformation of wood cell walls by spatial confinement and traction effects, which is supported by the inherent dimensional stability of the MS and the formation of new C–O–Ti covalent bonds between the wood cell wall and MS. Consequently, the volumetric shrinkage ratio of CW encapsulated by the MS (CW-MS) is significantly reduced from 54.8% for CW to 2.6% for CW-MS even at 800 °C. The harnessing of this collapse enables the production of CW-MS with prolonged stability and high electric conductivity (384 S m–1). These properties make CW-MS suitable for energy storage devices with various designed shapes, matching the increasingly compact and complex structures of electronic devices.

In this work, we employed a one-step hydrothermal method, assisted by PVP and LAA additives, to synthesize NiFe-LDH@Ti3C2Txin situ with a three-dimensional network structure. The crumpled NiFe-LDH nanoflakes were anchored on the Ti3C2Txsurface in close face-to-face contact by heterogeneous nucleation and growth with oxygen-containing functional groups as active sites. More importantly, an oxygen bridge was simultaneously formed at the interface between NiFe-LDH and Ti3C2Tx, which, as an electron migration channel to form a connected conductive network, significantly enhanced the structural stability and electron migration rate. Meanwhile, the open 3D network (SBET: 125.6 m2g–1) facilitates the exposure of active sites and accelerates the ion migration rate, leading to excellent diffusion kinetics. As a consequence, NiFe-LDH@Ti3C2Txdemonstrates outstanding electrochemical stability as an anode for LIBs (987 mAh g–1at 0.5 A g–1up to 200 cycles and 912 mAh g–1at 1 A g–1up to 600 cycles) and SIBs (280 mAh g–1at 0.5 A g–1up to 200 cycles and 211 mAh g–1at 1 A g–1up to 2000 cycles).

The morphological features of materials significantly influence their interactions with cells, consequently affecting the cellular uptake of these materials. In this study, we examine the cellular uptake behavior of spherical metal–organic frameworks (MOFs) and petaloid MOFs, both possessing similar sizes and compositions. In comparison to spherical MOFs, dendritic cells (DCs) and macrophages exhibit superior phagocytic uptake of petaloid MOFs. Next, the results demonstrate that R848@petaloid MOFs more effectively promote the repolarization of tumor-associated macrophages (TAMs) from the M2 to M1 phenotype and the maturation of DCs. More importantly, the R848-loaded petaloid MOFs are found to significantly enhance the therapeutic effects of radiotherapy (RT) by eliciting antitumor responses. Furthermore, R848@petaloid MOFs combined with RT and αPD-L1 elicit a potent abscopal effect, effectively suppressing tumor metastasis. Therefore, this work proposes a new strategy to enhance the uptake of immunomodulators by immune cells through modulating the morphology of drug delivery carriers.

Hierarchical biobased micro/nanomaterials offer great potential as the next-generation building blocks for robust films or macroscopic fibers with high strength, while their capability in suppressing crack propagation when subject to damage is hindered by their limited length. Herein, we employed an approach to directly convert bulk wood into fibers with a high aspect ratio and nanosized branching structures. Particularly, the length of microfibers surpassed 1 mm with that of the nanosized branches reaching up to 300 μm. The presence of both interwoven micro- and nanofibers endowed the product with substantially improved tensile strength (393.99 MPa) and toughness (19.07 MJ m–3). The unique mechanical properties arose from mutual filling and the hierarchical deformation facilitated by branched nanofibers, which collectively contributed to effective energy dissipation. Hence, the nanotransformation strategy opens the door toward a facial, scalable method for building high-strength film or macroscopic fibers available in various advanced applications.

MXenes, represented by Ti3C2Tx, have been widely studied in the electrochemical energy storage fields, including lithium-ion batteries, for their unique two-dimensional structure, tunable surface chemistry, and excellent electrical conductivity. Recently, Nb2CTx, as a new type of MXene, has attracted more and more attention due to its high theoretical specific capacity of 542 mAh g–1. However, the preparation of few-layer Nb2CTxnanosheets with high-quality remains a challenge, which limits their research and application. In this work, high-quality few-layer Nb2CTxnanosheets with a large lateral size and a high conductivity of up to 500 S cm–1were prepared by a simple HCl-LiF hydrothermal etching method, which is 2 orders of magnitude higher than that of previously reported Nb2CTx. Furthermore, from its aqueous ink, the viscosity-tunable organic few-layer Nb2CTxink was prepared by HCl-induced flocculation and N-methyl-2-pyrrolidone treatment. When using the organic few-layer Nb2CTxink as an additive-free anode of lithium-ion batteries, it showed excellent cycling performance with a reversible specific capacity of 524.0 mAh g–1after 500 cycles at 0.5 A g–1and 444.0 mAh g–1after 5000 cycles at 1 A g–1. For rate performance, a specific capacity of 159.8 mAh g–1was obtained at a high current density of 5 A g–1, and an excellent capacity retention rate of about 95.65% was achieved when the current density returned to 0.5 A g–1. This work presents a simple and scalable process for the preparation of high-quality Nb2CTxand its aqueous/organic ink, which demonstrates important application potential as electrodes for electrochemical energy storage devices.

C16H14Cl2O5, orthorhombic, P212121(no. 19), a= 8.6429 (4) Å, b= 11.3361 (6) Å, c= 33.9029 (11) Å, V= 3321.7 (3) Å3, Z= 8, Rgt(F) = 0.0635, wRref(F2) = 0.1630, T= 293(2) K.

C45H33Cl2F3N4, monoclinic, P21/c(no. 14), a= 11.7076(13) Å, b= 11.8186(13) Å, c= 27.473(3) Å, β= 91.188(2)°, V= 3800.6(7) Å3, Z= 4, Rgt(F) = 0.0591, wRref(F2) = 0.1856, T = 293 K.

Ultralow temperature-tolerant electronic skins (e-skins) can endow polar robots with tactile feedback for exploring in extremely cold polar environments. However, it remains a challenge to develop e-skins that enable sensitive touch sensation and self-healing at ultralow temperatures. Herein, we describe the development of a sensitive robotic hand e-skin that can stretch, self-heal, and sense at temperatures as low as −78 °C. The elastomeric substrate of this e-skin is based on poly(dimethylsiloxane) supramolecular polymers and multistrength dynamic H-bonds, in particular with quadruple H-bonding motifs (UPy). The structure–performance relationship of the elastomer at ultralow temperatures is investigated. The results show that elastomers with side-chain UPy units exhibit higher stretchability (∼3257%) and self-healing efficiency compared to those with main-chain UPy units. This is attributed to the lower binding energy variation and lower potential well. Based on the elastomer with side-chain UPy and man-made electric ink, a sensitive robotic hand e-skin for usage at −78 °C is constructed to precisely sense the shape of objects and specific symbols, and its sensation can completely self-recover after being damaged. The findings of this study contribute to the concept of using robotic hands with e-skins in polar environments that make human involvement limited, dangerous, or impossible.

In the framework of Global Warming strategies, the safe utilization of carbon dioxide (CO2) is an important component of the Carbon Capture, Utilization, and Storage (CCUS) process. In the analysis of the jetting process of supercritical CO2(S-CO2), it is of paramount importance to accurately predict the temperature and pressure characteristics of the jetting zone and the structure of the Mach disc. This is crucial for enhancing the utilization of S-CO2in coal breaking. In this study, an experimental platform featuring an exceptionally large pipeline, measuring 258 m in length and 233 mm in diameter, was first time employed to acquire data on coal-breaking characteristics in the jetting zone of S-CO2and images depicting the development process of Mach discs. A model for accurate predict Mach discs were developed and applied to optimum jet distance. The experimental data suggested that the jetting zone comprises a significant amount of three-phase flow consisting of gas, liquid, and solid phases. Moreover, maintaining a sufficiently long duration of jetting pressure in the jetting zone is advantageous for coal-breaking utilization. The Mach disc prediction model was optimized based on the measured pressure data downstream of the Mach disc. The optimized predictive model relies on parameters representing the actual pressure ratio for the Mach disk position, as follows: 0.65 for diameter, 0.29 for boundary layer thickness, and 1.36 for Mach disk location. This model, derived from experimental measurements of S-CO2jet flows, exhibits enhanced suitability for the quantitative prediction of CO2Mach disk phenomena. The findings presented in this thesis add to our understanding the prediction of S-CO2Mach Disk. These data support further CO2development of the determination of the optimal jetting distance is crucial in jet utilization processes.

The textile industry has been weaving polymer optical fibers (POFs) into plane fabric for many years for lighting and decoration. To apply POF-incorporated fabrics in a larger field of application, it is necessary to improve the side illumination of POF-incorporated fabrics. It has been reported that the chemical etching method is one method to enhance the illumination of POFs, while there is little research related to the application of chemical etching to enhance the illumination of POF-incorporated fabrics. In this work, the end emitting POFs (EEPOFs) were used as weft yarns, and polyethylene terephthalate (PET) yarns were used as warp yarns. The POF-incorporated woven PET fabrics were successfully fabricated with a 1/3 twill structure and then treated with a mixture of acetone and methanol (volume ratio: 1:1) for 1 min. The morphology and side illumination of etched POF-incorporated PET fabrics were investigated. As a result, the acetone/methanol mixture destroyed the cladding layer of EEPOFs, and the luminance of etched POF-incorporated PET fabrics was increased by more than 50 %. Besides, acetone/methanol etching resulted in a higher side illumination attenuation behavior.

The cellular endocytosis of nanoparticles (NPs) is a fundamental biological process with significant potential in biomedical applications. However, a comprehensive understanding of the mechanistic aspects of endocytosis and the impact of particle properties on this process remains elusive. In this study, we investigated the membrane-wrapping behavior of soft NPs (SNPs) with varying rigidities using theoretical calculations. Our findings reveal that the membrane-wrapping process of SNPs involves a complex energy change including the possible existence of an energy barrier; moreover, it is found that the location and height of this barrier strongly depend on the mechanistic properties of the NPs and membranes. Additionally, by considering force balance in the membrane-wrapping process, we calculated the speed at which NP is internalized by the membrane, showing a nonmonotonic dependence on particle rigidity and/or wrapping degree. These phenomena can be attributed to competition between different energy components associated with NP–membrane binding, membrane tension, and deformations occurring during SNP–membrane interaction processes. Our results contribute to a deeper understanding of cellular-level endocytosis mechanisms and offer potential applications for soft NPs in biomedicine.

In this study, four novel aromatic polyimides (PIs) with twisted backbone and bulky side substituents were prepared via chemical imidization reaction and two‐step condensation polymerization at reduced reaction temperature. The number‐average and weight‐average molecular weights of the resulting PIs are in the range of 1.98 × 104–4.10 × 104and 2.66 × 104–5.32 × 104g mol−1, respectively, while maintaining polydispersity indices consistently below 2.0. In addition, the obtained PIs showed excellent solubility in most common solvents, even in low‐boiling solvents, such as chloroform, dichloromethane, and tetrahydrofuran. The films casted from these PIs also displayed optical transparency with a cutoff wavelength in the range of 335–360 nm. Among the PIs, PAPOT‐OPDA, derived from 3,3′‐Diisopropyl‐4,4′‐diaminodiphenyl‐3″‐phenoxybenzylmethane (PAPOT) and 3,3′,4,4′‐diphenylethertetracarboxylic dianhydride (OPDA), exhibited the highest transparency of T450= 81% and T500= 85%, and the best solubility without compromising its thermal properties.

AbstractSpatiotemporal data are common in practice. Such data often have complicated structures that are difficult to describe by parametric statistical models. Thus, it is often challenging to analyze spatiotemporal data effectively since most existing statistical methods and software packages in the literature are based on parametric modeling and cannot handle certain applications properly. This paper introduces the new Rpackage SpTe2M, which was developed for implementing some recent nonparametric methods for modeling and monitoring spatiotemporal data. This package provides analytic tools for modeling spatiotemporal data nonparametrically and for monitoring dynamic spatial processes sequentially over time. It can be used for different applications, including disease surveillance and environmental monitoring. The use of the package is demonstrated using the Florida influenza-like illness data observed during 2012–2014 and the PM2.5concentration data in China collected during 2014–2016.

Coupling Ni-rich layered oxide cathodes with Si-based anodes is one of the most promising strategies to realize high-energy-density Li-ion batteries. However, unstable interfaces on both cathode and anode sides cause continuous parasitic reactions, resulting in structural degradation and capacity fading of full cells. Herein, lithium tetrafluoro(oxalato) phosphate is synthesized and applied as a multifunctional electrolyte additive to mitigate irreversible volume swing of the SiOxanode and suppress undesirable interfacial evolution of the LiNi0.83Co0.12Mn0.05O2(NCM) cathode simultaneously, resulting in improved cycle life. Benefiting from its desirable redox thermodynamics and kinetics, the molecularly tailored additive facilitates matching interphases consisting of LiF, Li3PO4, and P-containing macromolecular polymer on both the NCM cathode and SiOxanode, respectively, modulating interfacial chemo-mechanical stability as well as charge transfer kinetics. More encouragingly, the proposed strategy enables 4.4 V 21700 cylindrical batteries (5 Ah) with excellent cycling stability (92.9% capacity retention after 300 cycles) under practical conditions. The key finding points out a fresh perspective on interfacial optimization for high-energy-density battery systems.

Recent studies have demonstrated the crucial role of cholesterol (Chol) in regulating the mechanical properties and biological functions of cell membranes. Methyl-β-cyclodextrin (MeβCD) is commonly utilized to modulate the Chol content in cell membranes, but there remains a lack of a comprehensive understanding. In this study, using a range of different techniques, we find that the optimal ratio of MeβCD to Chol for complete removal of Chol from a phosphocholine (PC)/Chol mixed membrane with a 1:1 mol ratio is 4.5:1, while the critical MeβCD-to-Chol ratio for membrane permeation falls within the range between 1.5 and 2.4. MeβCD at elevated concentrations induces the formation of fibrils or tubes from a PC membrane. Single lipid tracking reveals that removing Chol restores the diffusion of lipid molecules in the PC/Chol membrane to levels observed in pure PC membranes. Exposure to 5 mM MeβCD for 30 min effectively eliminates Chol from various cell lines, leading to an up to 8-fold enhancement in melittin cytotoxicity over Hela cells and an up to 3.5-fold augmentation of T cell cytotoxicity against B16F10-OVA cells. This study presents a diagram that delineates the concentration- and time-dependent distribution of MeβCD-induced Chol depletion and membrane deformation, which holds significant potential for modulating the mechanical properties of cellular membranes in prospective biomedical applications.

The electric field sensing technology based on the Rydberg atom has attracted widespread attention due to its powerful detection ability for the amplitude, phase, and polarization of the electric field. In this work, based on the electromagnetically induced transparency (EIT) of the Rydberg atom and combined with a heterodyne Rydberg atom-based mixer, we measured the binary array composed of the Rydberg Field Probes (RFPs). The measured phase difference of the electric field at two different RFPs is related to the direction of incidence. We digitally transform the received signal at 2.63 GHz and conduct digital beamforming (DBF) processing to obtain its array pattern and beam direction. The measurement accuracy and reliability are greatly improved compared with the phase comparison method, and the angle-of-arrival (AOA) variance is less than 1.5°. The effectiveness of DBF based on the Rydberg atomic sensor is verified, which lays the foundation for the subsequent linear and planar array reception of the Rydberg atomic sensor. In addition, the occlusion based on RFPs is also measured, and its weak occlusion characteristics are confirmed. This unique feature provides feasible support for the 3-D receiving array based on the Rydberg atomic sensor.

In recent years, artificial intelligence (AI)-driven computer technology has become popular in many practical applications in the retail industry. In particular, the development of face recognition, mobile payment, cloud computing and other technologies has given birth to three new forms of consumption related to “unmanned retail”, i.e., unmanned convenience stores, unmanned vending machines and unmanned vending racks. Due to the sudden outbreak of COVID-19, the characteristics of unmanned retail such as intelligent operation, convenience and a lack of contact have allowed the public to gradually become used to this approach. An unmanned vending machine (UVM) can be reconfigured from a refrigerated cabinet for selling commodities such as beverages. It can take photos of the goods purchased by customers, and can recognize these commodities based on information from the collected images. However, some commercial UVMs are subject to strong restrictions on the types and placement of goods, making them difficult to achieve data collection and convenient operation. In this research, we therefore design a system for automated data collection and annotation. The mechanical structure of our system is designed to mimic the placement of drinks and the scene of each drink with other different types of drinks around it. We construct a large-scale benchmark dataset called UVM++, which is composed of several sub-datasets collected from different scenes. Extensive experimental results are presented and the performance of popular object detection methods on this task is examined. In particular, we perform an empirical study of cross-scene detection by various object detectors. Our findings suggest that the proposed data annotation system and the constructed benchmark dataset, UVM++, can be useful for performing various tasks in the environment of a UVM, thus driving development in the field of new retail.

Recent anchor-free trackers that leverage the remarkably expressive capacity of the fully convolutional network have drawn considerable attention within the field of tracking. However, the independence of feature extraction and feature fusion in existing anchor-free trackers limit the representation ability of the network. To address this issue, we present a new anchor-free tracker, named box adaptive network with iterative mixed attention (BAN-ima), that adopts Vision Transformer (ViT) as its backbone. In particular, we introduce a multi-level feature fusion framework which can effectively incorporate multiple network branches to produce precise predictions of target bounding boxes. To further enhance the tracking precision, we propose a probabilistic branch to replace the traditional classification branch in the tracking head. Furthermore, we introduce an improved Intersection over Union (IoU) loss function, denoted by $\alpha $ -CIoU, which adaptively reweights the loss and gradients of objects with high and low IoUs. This scheme enhances the object localization and regression precision for object tracking. Extensive experiments were carried out on well-established benchmarks for visual tracking, including TrackingNet, GOT-10k, LaSOT, UAV123 and NFS30. The results demonstrate that our developed BAN-ima tracker achieves comparable performance to state-of-the-art trackers while maintaining a real-time speed of 35 frames per second (FPS). In Particular, it achieves 84.7% AUC and 89.1% normalized precision score on the TrackingNet dataset.

As wireless communication evolves into millimeter wave (mmWave) frequencies, it becomes extremely challenging to realize compact and integrated radio frequency (RF) front-end components with wide bandwidth and high energy efficiency. Here we propose a new fin-mounted acoustic resonator design based on nanosheet (thickness < 100 nm) Z-cut single-crystal lithium niobate (LiNbO3) thin film for large-bandwidth filtering applications in the 5G NR Frequency Range 2 (FR2) band. The fabricated fin-mounted acoustic resonator operating at 25.03 GHz exhibits a high electromechanical coupling coefficient ( ${k}^{{2}}{)}$ of 16% and quality factor ( ${Q}_{p}{)}$ of 132 at parallel resonant frequency (without any de-embedding), which achieves the highest Figure of Merit (FoM = ${k}^{{2}} \times {Q}{)}$ in the 5G FR2 band among all reported resonators to date. This demonstration provides a new solution to the internal stress problem of nanosheet piezoelectric thin film devices and opens up more possibilities in enabling piezoelectric nanostructures for filtering, sensing, and timing applications.

Autonomous driving confronts great challenges in complex traffic scenarios, where the SOTIF risk can be triggered by the dynamic operational environment and system insufficiencies. The SOTIF risk is reflected not only intuitively in the collision risk with objects outside the autonomous vehicles, but also inherently in the performance limitation risk of the implemented algorithms. How to minimize the SOTIF risk for autonomous driving is currently a critical, difficult, and unresolved issue. Therefore, this paper proposes the “Self-Surveillance and Self-Adaption System” as a systematic approach to online minimize the SOTIF risk, which aims to provide a systematic solution for monitoring, quantification, and mitigation of inherent and external risks. As a demonstration of the system, the risk monitoring of the perception algorithm is highlighted. Moreover, the inherent perception algorithm risk and external collision risk are jointly quantified via SOTIF entropy, which is then propagated downstream to the decision-making module and mitigated. Finally, Hardware-in-the-Loop experiments are conducted to verify the efficiency and effectiveness of the system. The results demonstrate that the system enables dependable online monitoring, quantification, and mitigation of SOTIF risk in real-time critical traffic environments.

The geometry of retinal layers is an important imaging feature for the diagnosis of some ophthalmic diseases. In recent years, retinal layer segmentation methods for optical coherence tomography (OCT) images have emerged one after another, and huge progress has been achieved. However, challenges due to interference factors such as noise, blurring, fundus effusion, and tissue artifacts remain in existing methods, primarily manifesting as intra-layer false positives and inter-layer boundary deviation. To solve these problems, we propose a method called Tightly combined Cross-Convolution and Transformer with Boundary regression and feature Polarization (TCCT-BP). This method uses a hybrid architecture of CNN and lightweight Transformer to improve the perception of retinal layers. In addition, a feature grouping and sampling method and the corresponding polarization loss function are designed to maximize the differentiation of the feature vectors of different retinal layers, and a boundary regression loss function is devised to constrain the retinal boundary distribution for a better fit to the ground truth. Extensive experiments on four benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance in dealing with problems of false positives and boundary distortion. The proposed method ranked first in the OCT Layer Segmentation task of GOALS challenge held by MICCAI 2022. The source code is available at https://www.github.com/tyb311/TCCT.

The d–q axis inductance is the crucial parameter for the interior permanent magnet (IPM) motor. In order to facilitate the design, analysis, and optimization of IPM motors, it is of great significance to develop an accurate and simplified inductance analytical calculation method. Among the existing calculation methods, the magnetic equivalent circuit (MEC) has advantages in the inductance calculation of the IPM motor due to the simple modeling, the fine processing of the saturation region and the visualization of the magnetic field distribution. However, the traditional MEC method is only applicable to the IPM motor with integer-slot distributed winding (ISDW) or fractional-slot concentrated winding (FSCW), while lacking of in-depth research on fractional-slot distributed winding (FSDW). The magnetic circuit topology of FSDW IPM motors is not as single as that of FSCW/ISDW IPM motors. It leads to problems in the traditional MEC method when dividing the d/q axis flux path in FSDW motors. To solve this problem and expand the applications of MEC method, a big-small pole space (BSPS) method is proposed, which divides the d-axis flux path according to the asymmetry of the d-axis magnetomotive force (MMF). Based on the divided flux paths, an improved nonlinear MEC model that considers the saturation of stator and rotor is established to calculate d- and q-axis inductances. The results of finite element analysis (FEA) verify the validity of the proposed MEC model with different geometry parameters. Experimental results of an 84-pole/360-slot FSDW IPM wind generator further confirm the validity of the proposed MEC model.

In this study, a joint blind frame synchronization and decoding method is proposed based on the normalized syndrome satisfaction probability (NSSP) provided by the soft Low-Density Parity-Check (LDPC) codes decoder. To reduce complexity, a stopping criterion is introduced into the iterative process through the convergence of NSSP evolution patterns. In addition to achieving an excellent synchronization performance, this method also eliminates the redundancy caused by the pilot sequence. Furthermore, it is compared with the optimal pilot-based and other code-aided frame synchronization algorithms. According to analytical and simulation results, the proposed technique outperforms other advanced code-aided frame synchronization solutions in synchronization, which makes it applicable to achieve comparable performance to the pilot-based methods in specific coding gains. Due to the introduction of a novel stopping criterion, the average number of joint detection & decoding is reduced by up to 90%. Analog probability processing technology integrates sub-threshold region circuits with probability domain-based iterative message-passing algorithms inspired by high energy efficiency and low complexity. Hence, hardware implementation is presented based on the analog LDPC decoder chip as fabricated in a 0.35- $\boldsymbol \mu {m}$ CMOS technology for our proposed algorithms. The experimental results demonstrate the effectiveness of the proposed method and the realization loss is within 0.2 dB compared with the theoretical circuit simulation results.

ABSTRACT In most studies concerning the influence of external loads on transport properties of concrete, concrete is assumed to be homogeneous and hence, differences in the transport properties of its [...]

ABSTRACT An in situ air permeability test method that does not require assumptions for the often-difficult unidirectional flow has been developed to calculate the air permeability coefficient of the near [...]

Lithium hexafluorophosphate (LiPF6) has been the dominant conducting salt in lithium-ion battery (LIB) electrolytes for decades; however, it is extremely unstable in even trace water (ppm level). Interestingly, in pure water, PF6–does not undergo hydrolysis. Hereby, we present a fresh understanding of the mechanism involved in PF6–hydrolysis through theoretical and experimental explorations. In water, PF6–is found to be solvated by water, and this solvation greatly improved its hydrolytic stability; while in the electrolyte, it is forced to “float” due to the dissociation of its counterbalance ions. Its hydrolytic susceptibility arises from insufficient solvation-induced charge accumulation and high activity in electrophilic reactions with acidic species. Tuning the solvation environment, even by counterintuitively adding more water, could suppress PF6–hydrolysis. The undesired solvation of PF6–anions was attributed to the perennial LIB electrolyte system, and our findings are expected to inspire new thoughts regarding its design.

Calculating the first-arrival traveltimes of quasi-compressional (qP) waves has important applications in geophysics. In practice, geophysical problems often involve extensive calculations of traveltimes, especially when dealing with a large number of source-receiver pairs in complex geological structures. This necessitates a high level of computational efficiency. However, it is challenging to solve the anisotropic eikonal equation due to its complex nonlinearity. Conventional approaches to solving this equation in tilted transversely isotropic (TTI) media involve the use of various iterative algorithms combined with a fast sweeping method (FSM). When tackling large-scale geophysical problems, optimizing efficiency and minimizing the number of iterations is a crucial challenge. To address this problem, we developed a fitting algorithm to solve the anisotropic eikonal equation. We analyze the corresponding slowness surface equations of the qP wave in a local solver and observe a small difference between the slowness function and its quadratic fitting function. Therefore, we propose using the quadratic function to approximate the slowness equation based on three known traveltime points in the local solver. Subsequently, we obtain two roots from the fitting quadratic equation as approximate solutions. After obtaining the traveltime solutions, we check the traveltime causality to preserve the solution satisfying this condition. Three numerical tests are used to further illustrate the validity and computational efficiency of the proposed algorithm in computing traveltimes for 2-D/3-D weakly TTI models, which has been improved by more than three times compared with the conventional method.

Easy access to and advances in satellite remote sensing data has enabled enhanced analysis of ocean fronts, physical and ecologically important areas where water masses converge. Recent development of higher-resolution satellite imagery to detect ocean fronts provides the potential to better capture patterns and trends of ocean change and improve modelling and prediction efforts. This study examines the relationship between satellite data spatial resolution and its influence on the quantification of frontal characteristics, frontal quantity, length, strength and density. We also examine the relationship between Finite-Size Lyapunov Exponents and image resolution. We found higher spatial resolution leads to increased frontal quantity and decreased frontal length. Also, both strength and spatial density of fronts differ at various resolutions. The Finite-Size Lyapunov Exponent value does not change significantly with resolution. Knowledge of the impact of resolution on the quantification of frontal characteristics is crucial as it enables the exploration of novel experimental design to further facilitate the development of improved parameterization and uncertainties in ocean modelling/studies.

Implementing Fresnel zones-based stacking is a pursuit in various seismic imaging methods. However, the estimation of Fresnel zones remains a challenge in 3-D migration. Migrated dip-angle gathers provide a visible domain for estimating Fresnel zones. An analytical estimation (i.e., a model-driven method) faces limitations in automatically estimating Fresnel zones in migrated dip-angle gathers in real-world situations due to complex reflections, nonuniform coverage, and noises. Human interaction remains necessary for precisely estimating Fresnel zones in migrated dip-angle gathers. Due to the high number of Fresnel zones in 3-D cases, interpolation is necessary to fill in the gaps between manually estimated zones. Aiming to reduce the workload of human interaction and mitigate interpolation errors, we propose a semantic segmentation model (i.e., a deep learning-based data-driven method) to estimate Fresnel zones in migrated dip-angle gathers automatically. We transform the estimation of Fresnel zones into a binary classification task of each pixel in dip-angle gathers. Instead of training the network using the entire dip-angle gather images, we train the network using patches to make the network focus on learning the detailed and general features within the patches. Our proposed network, named deep-supervised attention-UNet, is trained using a deep-supervised method along with a hierarchical hybrid loss function to segment the dip-angle gather on different scales. This approach yields superior segmentation results compared to the UNet model in qualitative and quantitative aspects. We test the efficiency and practicability of our method using a marine field dataset. The signal-to-noise ratio (SNR) of migration results obtained using the Fresnel zones estimated by our method is improved significantly.

The evaluation and validation of climate data records (CDRs) derived from remote sensing play crucial roles in their generation and applications. However, many existing evaluation schemes rely on simplistic, spatiotemporally invariant metrics to assess the products’ overall quality, which leads to the long-term neglect of intra-product inconsistencies stemming from observation conditions, algorithmic differences, and sensor degradation. Leaf area index (LAI) is a crucial variable for land surface and climate modeling, and the intra-product inconsistency will increase the uncertainty in related studies. In order to improve the evaluation scheme of LAI products and ensure their reliability, we propose a new perspective for evaluating global LAI time series. In this study, we utilize the Moderate Resolution Imaging Spectroradiometer (MODIS) C6.1 LAI product as an example to infer its internal consistency through cross-comparisons among different sensors and spatiotemporal correlations between two adjacent years of the product. We found that compared to the main algorithm, the backup algorithm of the MODIS LAI product tends to underestimate the retrieval results. This inconsistency is particularly pronounced in tropical regions but relatively minor in most other areas. Additionally, these inconsistencies can lead to unusual fluctuations in the LAI time series, impacting the magnitude and direction of short-term vegetation monitoring. However, the influence on long-term trend analyses is negligible. Therefore, special attention should be given to the intra-product consistency in certain studies. In conclusion, the evaluation perspective proposed in this study is of great significance for improving the LAI evaluation scheme and ensuring the use and improvement of remote sensing products.