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Mapping urban top of canopy height (UTCH) is essential for quantifying urban vegetation carbon storage and developing effective vegetation management strategies. However, the scarcity and uneven distribution of urban measurement samples pose significant challenges to accurately estimating UTCH on a large scale in complex urban environments. To address this issue, this study utilized ICESat-2 photon spot height data as reference samples, in conjunction with high-resolution GF-2 remote sensing data, to estimate UTCH. To achieve UTCH mapping at a resolution of 4 m, a synergistic model integrating data from the GF-2 and ICESat-2 grid-based canopy height was constructed using the Random Forest technique. The model’s performance was evaluated using 111 urban tree canopy height samples collected across different urban areas. The experimental results demonstrated a moderate correlation between estimated and actual canopy heights, with a coefficient of determination (R) = 0.53, root mean square error (RMSE) = 2.9 m, and mean absolute error (MAE) = 2.04 m. Texture information, the red band, and MNDVI are key indicators for determining UTCH, with contribution percentages of 25.29 %, 13.7 %, and 25.75 %, respectively. As a result, the UTCH model created by fusing remote sensing spectral data with satellite-based lidar data can accurately estimate UTCH and offer a practical solution for predicting UTCH on a regional or even global scale.

Abstract Surface acoustic waves (SAWs) have shown great potential for developing sensors for structural health monitoring (SHM) and lab‐on‐a‐chip (LOC) applications. Existing SAW sensors mainly rely on measuring the frequency shifts of high‐frequency (e.g., >0.1 GHz) resonance peaks. This study presents frequency‐locked wireless multifunctional SAW sensors that enable multiple wireless sensing functions, including strain sensing, temperature measurement, water presence detection, and vibration sensing. These sensors leverage SAW resonators on piezoelectric chips, inductive coupling‐based wireless power transmission, and, particularly, a frequency‐locked wireless sensing mechanism that works at low frequencies (e.g.,

Object detection on drone-view images is vital for applications like intelligent transportation, abnormal behavior detection, and urban surveillance. However, the diverse perspectives and altitudes from which Unmanned Aerial Vehicle (UAV) capture scenes pose challenges due to multi-scale differences and small object sizes. To address this, we propose CSSDet, emphasizing multi-scale object detection and small object enhancement. CSSDet integrates an additional detection head for small objects and employs a bidirectional weighted feature pyramid network for effective cross-scale feature fusion, enhancing global perceptual capability. Additionally, we introduce coordinate attention and Involution modules to mitigate information loss. Experiments are conducted on two publicly available datasets, VisDrone and UAVDT. Quantitative results from the experiments indicate that the proposed CSSDet achieves optimal performance across a wide range of evaluation metrics, with particular strengths demonstrated in multi-scale object detection and small-size object detection. Qualitative results showcase its remarkable effectiveness in various aspects of object detection, including small object detection, objects at different scales and categories, multi-perspective scenarios, and complex scenes. The substantial experimental findings confirm that CSSDet attains comprehensive object detection performance on drone-view images. The source code is available at https://github.com/Gui-Cheng/CSSDet.

Integrating multiple hyperspectral linear-array cameras into a Linear-array Multi-camera System (LMS) enables an expanded Field of View (FOV) while preserving high spatial resolution. However, the constrained capabilities of tie points are weakened by the limited overlap and short baseline between sub-cameras, hindering the attainment of reliable solutions for the theoretically ideal relative calibration model. Additionally, the absence of stereoscopic imaging in the LMS introduces the challenge of acquiring dense Ground Control Points (GCPs) throughout the FOV during calibration. In this paper, for calibrating an advanced airborne hyperspectral LMS named Arial Hyperspectral System-1000 (AHS-1000), we propose an equivalent geometric model that transforms the three-camera configuration of the LMS into a single-camera representation on the master camera’s focal plane, which was successfully applied to the data processing system of AHS-1000. We extracted dense GCPs by matching each Level-1 sub-image of the LMS with the bundled images of an existing three-line-scanner aerial image dataset, whose orientation parameters have been accurately solved by bundle adjustment with sparse GCPs; the matched points of each sub-image were consistently projected onto the master focal plane as image observations for our equivalent model. Finally, we established a collinearity equation system and performed a combined strip adjustment to calibrate the distortion terms for each pixel of the equivalent camera. The results demonstrate the effectiveness and robustness of our equivalent modeling method, which outperforms the classic modeling method by 2.83 pixels in interior accuracy and 0.86 pixels in relative accuracy, as well as outperforming another similar equivalent modeling method by 1.22 pixels in interior accuracy and 0.18 pixels in relative accuracy. Additionally, our comparison utilizing bundled images, public DOM (Digital Orthophoto Maps) /DEM (Digital Elevation Models), and high-precision DOM/DEM from aerial three-line-scanner demonstrates that bundled images can achieve higher calibration accuracy.

Fatigue is an important cause of traffic crashes, and effective fatigue detection models can reduce these crashes. Research has found large differences in fatigued driving performance from driver to driver, as well as a significant cumulative effect of fatigue on a given driver over time. Both sources of variation can decrease the accuracy of detection systems, but previous studies have not done enough to evaluate these differences. The purpose of this study is therefore to develop a fatigue detection model that considers individual differences and the time cumulative effect of fatigue. Data on the lateral position of the car in its lane, steering wheel movement, speed, and eye movement were collected from 22 drivers using a driving simulator with an eye-tracking system. Drivers’ subjective fatigue scores were collected using the Karolinska Sleepiness Scale. State space models (SSMs) were built to detect fatigue in each driver, considering his or her individual features. As a time series model, the SSM can also address the time cumulative effect of fatigue, and it does not require a large dataset to achieve high levels of accuracy. The differences in SSM results confirm that diversity does exist among drivers’ fatigued driving performance, so the ability of the SSM to take into account driver-specific information from each individual driver suggests that it is more suitable for fatigue detection than models that use aggregated driver data. Results show that the fatigue detection accuracy of the SSM (77.73%) is higher than that of artificial neural network models (61.37%). The advantages of accuracy, high interpretability, and flexibility make the SSM a comprehensive and valuable individualized fatigue detection model for commercial use.

Abstract In this paper, a micro‐bare‐tube heat exchanger structure with the tube bundle diameter varying gradually in the range of 1.0–0.4 mm is proposed. The airside friction and heat transfer performance correlations for this structure are given. This paper applies CFD simulation to develop and verify the correlation of the diameter gradually varied micro‐bare‐tube heat exchangers' performance. Compared with the traditional same diameter micro‐bare‐tube heat exchanger, the new structure not only reduces the pressure drop, but also improves the thermal hydraulic performance. The friction coefficient f is decreased by 8.59%, the pressure drop is reduced by 43.14%, the heat transfer coefficient j is increased by 12.07% compared to the constant diameter micro‐bare‐tube heat exchanger. At the same time, it reduces the void volume by 47.32% and the tube bundle manufacturing material by 28.57%. The proposed airside performance prediction equation can make the error of more than 70% source data within 10%, and the error of more than 90% source data within 20%. It is suggested to apply this empirical equation to 7–29 rows.

Traditionally, traffic characteristics such as speed, volume, and travel time are obtained from a range of sensors and systems such as inductive loop detectors (ILDs), automatic number plate recognition cameras (ANPR), and GPS-equipped floating cars. However, many issues associated with these data have been identified in the existing literature. Although roadside surveillance cameras cover most road segments, especially on freeways, existing techniques to extract traffic data (e.g., speed measurements of individual vehicles) from video are not accurate enough to be employed in a proactive traffic management system. Therefore, this paper aims to develop a technique for estimating traffic data from video captured by surveillance cameras. This paper then develops a deep learning-based video processing algorithm for detecting, tracking, and predicting highly disaggregated vehicle-based data, such as trajectories and speed, and transforms such data into aggregated traffic characteristics such as speed variance, average speed, and flow. By taking traffic observations from a high-quality LiDAR sensor as ‘ground truth’, the results indicate that the developed technique estimates lane-based traffic volume with an accuracy of 97%. With the application of the deep learning model, the computer vision technique can estimate individual vehicle-based speed calculations with an accuracy of 90–95% for different angles when the objects are within 50 m of the camera. The developed algorithm was then utilised to obtain dynamic traffic characteristics from a freeway in southern China and employed in a statistical model to predict monthly crashes.

Abstract Bi2Te3‐based alloys have great market demand in miniaturized thermoelectric (TE) devices for solid‐state refrigeration and power generation. However, their poor mechanical properties increase the fabrication cost and decrease the service durability. Here, this work reports on strengthened mechanical robustness in Bi2Te3‐based alloys due to thermodynamic Gibbs adsorption and kinetic Zener pinning at grain boundaries enabled by MgB2 decomposition. These effects result in much‐refined grain size and twofold enhancement of the compressive strength and Vickers hardness in (Bi0.5Sb1.5Te3)0.97(MgB2)0.03 compared with that of traditional powder‐metallurgy‐derived Bi0.5Sb1.5Te3. High mechanical properties enable excellent cutting machinability in the MgB2‐added samples, showing no missing corners or cracks. Moreover, adding MgB2 facilitates the simultaneous optimization of electron and phonon transport for enhancing the TE figure of merit (ZT). By further optimizing the Bi/Sb ratio, the sample (Bi0.4Sb1.6Te3)0.97(MgB2)0.03 shows a maximum ZT of ≈1.3 at 350 K and an average ZT of 1.1 within 300–473 K. As a consequence, robust TE devices with an energy conversion efficiency of 4.2% at a temperature difference of 215 K are fabricated. This work paves a new way for enhancing the machinability and durability of TE materials, which is especially promising for miniature devices.

Accurately mapping building height at a fine scale is crucial for comprehending urban systems. However, existing methods suffer from limitations such as coarse resolutions, long delays, and limited applicability for large-scale mapping. This challenge is particularly significant in China, where rapid urbanization has led to complex urban scenario. To address this issue, we propose a novel approach that capitalizes on publicly available Sentinel-1/-2 and crowdsourced data. Our method employs a dual-branch structure building height estimation network (BHE-NET) and an improved multi-modal Selective-Kernel (MSK) module to fuse optical and SAR features. The validation results, derived from building height data across 63 cities, demonstrate strong performance with a root mean square error (RMSE) of 4.65 m. We further test the scalability of our approach by mapping three most developed urban agglomerations in China. In comparison with four recent studies, our method captures finer details of building height while mitigating the overestimation in urban high-density building clusters. Moreover, we further investigate the relationship between population and mean building height as well as the building volume at city level. Our work opens up new possibilities for producing fine-scale building height map of China at a 10-m resolution using remote sensing data.

BackgroundExtrahepatic metastasis of hepatocellular carcinoma (HCC) is common. However, pituitary metastasis of HCC is extremely rare. Our case may be helpful to extend the understanding of the disease.Case presentationA 65-year-old man presented to the outpatient department for unexplained headache and ptosis for 1 month. Brain imaging showed a slight enhancement tumor in the pituitary fossa, and the endocrinological assessment showed normal results. We considered the tumor as a non-functioning pituitary adenoma before surgery. Then, the tumor was resected by an endonasal endoscopic transsphenoidal approach. The histopathological examination results revealed the pituitary metastasis of HCC. Additional abdominal imaging revealed tumors were located in the left and right liver lobes with portal vein invasion and bilateral ilium metastases. After multidisciplinary cooperation, the patient chose chemotherapy.ConclusionWe report a case of HCC metastasis to the pituitary gland that initially presented with neurological symptoms. We should consider the possibility of pituitary metastasis in HCC patients.

D2D communication is a promising technology for enhancing spectral efficiency (SE) in cellular networks, and full-duplex (FD) has the potential to double SE. Due to D2D’s short-distance communication and low transmittance power, it is natural to integrate FD into D2D, creating FD-D2D to underlay a cellular network to further improve SE. However, the residual self-interference (RSI) resulting from FD-D2D and interference arising from spectrum sharing between D2D users (DUs) and cellular users (CUs) can restrict D2D link performance. Therefore, we propose an FD-D2D underlying cellular system in which DUs jointly share uplink and downlink spectral resources with CUs. Moreover, we present two algorithms to enhance the performance experience of DUs while improving the system’s SE. For the first algorithm, we tackle an optimization problem aimed at maximizing the sum rate of FD-DUs in the system while adhering to transmittance power constraints. This problem is formulated as a mixed-integer nonlinear programming problem (MINLP), known for its mathematical complexity and NP-hard nature. In order to address this MINLP, our first algorithm decomposes it into two subproblems: power control and spectral resource allocation. The power control aspect is treated as a nonlinear problem, which we solve through one-dimensional searching. Meanwhile, spectral resource allocation is achieved using the Kuhn–Munkres algorithm, determining the pairing of CUs and DUs sharing the same spectrum. As for the second algorithm, our objective is to enhance the individual performance of FD-DUs by maximizing the minimum rate among them, ensuring more uniform rate performance across all FD-DUs. In order to solve this optimization problem, we propose a novel spectral resource allocation algorithm based on bisection searching and the Kuhn–Munkres algorithm, whereas the power control aspect remains the same as in the first algorithm. The numerical results demonstrate that our proposed algorithm effectively enhances the performance of DUs in an FD-D2D underlying cellular network when compared to the sum rate maximization design.

The problem of accurately measuring the motion parameters of low radar cross-section (RCS) maneuvering targets has long been a hurdle in the radar technology landscape. Small targets, due to their elusive characteristics, are particularly difficult to detect with conventional radar systems. In this paper, we investigate the capabilities of dechirp-receiving stepped-frequency radar, a modern system using a linear frequency modulation signal for downconversion. This permits the radar to function at reduced sampling rates while maintaining the transmission of large-bandwidth signals and achieving synthetic imaging. Our primary contribution is introducing the stepped dechirp generalized Radon–Fourier transform (stepped DGRFT) algorithm. This novel approach allows the radar system to perform coherent accumulation, enhancing the accuracy of motion parameter estimates for low-RCS maneuvering targets. Results from our simulations and measured data analysis validate the effectiveness of our proposed algorithm, demonstrating its superiority over other methods.

Abstract Background The estimation of total iron content at the regional scale is of much significance as iron deficiency has become a routine problem for many crops. Methods In this study, a novel method for estimating total iron content in soil (TICS) was proposed using harmonic analysis (HA) and back propagation (BP) neural network model. Several data preprocessing methods of first derivative (FD), wavelet packet transform (WPT), and HA were conducted to improve the correlation between the soil spectra and TICS. The principal component analysis (PCA) was exploited to obtained three kinds of characteristic variables (FD, WPT-FD, and WPT-FD-HA) for TICS estimation. Furthermore, the estimated accuracy of three BP models based on these variables was compared. Results The results showed that the BP models of different soil types based on WPT-FD-HA had better estimation accuracy, with the highest R2 value of 0.95, and the RMSE of 0.68 for the loessial soil. It was proved that the characteristic variable obtained by harmonic decomposition improved the validity of the input variables and the estimation accuracy of the TICS models. Meanwhile, it was identified that the WPT-FD-HA-BP model can not only estimate the total iron content of a single soil type with high accuracy but also demonstrate a good effect on the estimation of TICS of mixed soil. Conclusion The HA method and BP neural network combined with WPT and FD have great potential in TICS estimation under the conditions of single soil and mixed soil. This method can be expected to be applied to the prediction of crop biochemical parameters.

Abstract Isobaric specific heat capacity (Cp) is an important parameter not only in physics but also for most materials. Its accurate measurement is particularly critical for performance evaluation of thermoelectric materials, but the experiments by differential scanning calorimetry (DSC) often lead to large uncertainties in the measurements, especially at elevated temperatures. In this study, we propose a simple method to determine Cp by measuring the sound velocity (υ) based on lattice vibration and expansion theory. The relative standard error of the υ is smaller than 1%, showing good accuracy and repeatability. The calculated Cp at elevated temperature (>300 K) increases slightly with increasing temperature due to the lattice expansion, which is more reasonable than the Dulong–Petit value.

Abstract Realizing high average thermoelectric figure of merit (ZTave) and power factor (PFave) has been the utmost task in thermoelectrics. Here the new strategy to independently improve constituent factors in ZT is reported, giving exceptionally high ZTave and PFave in n‐type PbSe. The nonstoichiometric, alloyed composition and resulting defect structures in new Pb1+xSe0.8Te0.2 (x = 0–0.125) system is key to this achievement. First, incorporating excess Pb unusually increases carrier mobility (µH) and concentration (nH) simultaneously in contrast to the general physics rule, thereby raising electrical conductivity (σ). Second, modifying charge scattering mechanism by the authors’ synthesis process boosts a magnitude of Seebeck coefficient (S) above theoretical expectations. Detouring the innate inverse proportionality between nH and µH; and σ and S enables independent control over them and change the typical trend of PF to temperature, giving remarkably high PFave ≈20 µW cm−1 K−2 from 300 to 823 K. The dual incorporation of Te and excess Pb generates unusual antisite Pb at the anionic site and displaced Pb from the ideal position, consequently suppressing lattice thermal conductivity. The best composition exhibits a ZTave of ≈1.2 from 400 to 823 K, one of the highest reported for all n‐type PbQ (Q = chalcogens) materials.

Extremely low lattice thermal conductivity is always the pursuit of thermoelectric materials research. In this work, we reported an exceptional effect of Ag2S addition in MnTe, an emerging promising mid-temperature thermoelectric material, to enable the realization of minimum lattice thermal conductivity, namely ∼0.4 Wm−1K−1. Such a low lattice thermal conductivity is guaranteed by the incorporation of in-situ formed Ag rich phase (Ag2Te) with ultralow lattice thermal conductivity and further scattering of phonons from the partial doping effects induced point defects and boundaries between various phases. Apart from the dramatically decreased lattice thermal conductivity, the partial doping of Ag and S simultaneously enhance the electrical conductivity, further contributing to enhanced thermoelectric performance. Meanwhile, an inverse sign of Seebeck and Hall coefficient was observed and rationalized by the influence of highly electron-conductive Ag2Te phase. Thanks to the synergetic modulation of electrical and thermal transport properties by in-situ formed composite, a high ZT value of 1.1 was achieved in MnTe based thermoelectric materials, which also demonstrates the importance of compositing approaches to design state-of-the-art thermoelectric materials.

It is well-known that optimizing the wheel system of lunar rovers is essential. However, this is a difficult task due to the complex terrain of the moon and limited resources onboard lunar rovers. In this study, an experimental prototype was set up to analyze the existing mechanical design of a lunar rover and improve its performance. First, a new vane-telescopic walking wheel was proposed for the lunar rover with a positive and negative quadrangle suspension, considering the complex terrain of the moon. Next, the performance was optimized under the limitations of preserving the slope passage and minimizing power consumption. This was achieved via analysis of the wheel force during movement. Finally, the effectiveness of the proposed method was demonstrated by several simulation experiments. The newly designed wheel can protrude on demand and reduce energy consumption; it can be used as a reference for lunar rover development engineering in China.

Abstract Background Cushing’s syndrome has been described as a complex endocrine disorder characterized with high cortisol concentration. Correct and early diagnosis of Cushing’s syndrome is challenging. According to the latest guideline, bilateral inferior petrosal sinus sampling (BIPSS) is considered to be the gold standard for the differential diagnosis. However, in some unusual cases, this method may be false positive. Here, we presented a rare case of orbital neuroendocrine tumor secreting adrenocorticotrophic hormone with false positive inferior petrosal sinus sampling. Case presentation A 48-year-old woman was admitted to West China Hospital of Sichuan University, presenting with fatigue, whole body edema for 1 year, alopecia and skin pigmentation for 5 months. Hormonal profiles including plasma cortisol and adrenocorticotrophic hormone (ACTH) measurements and low-dose dexamethasone inhibition test suggested that the patient had Cushing’s syndrome. However, during tumor location phase, the results of high-dose dexamethasone inhibition test (HDDST) contradicted desmopressin (DDAVP) stimulation test. Thus, BIPSS was employed, and its results indicated a pituitary origin. Interestingly, MRI of sellar region showed an innocent pituitary but caught a serendipitous lesion in the lateral rectus muscle of left eye, which was later proved to be an orbital neuroendocrine tumor secreting ACTH by pathological and immunohistochemical results. ACTH level of the patients was

Net Primary Productivity (NPP) has been widely used to estimate the productivity of the farmland ecosystem (FES) and the carbon budget of the terrestrial ecosystem. Changes in the NPP of FES have knock-on effects on food security, sustainable agricultural development, carbon sequestration, and environmental changes. Existing studies mainly focused on the impact of climate change and urbanization on the spatial-temporal pattern of NPP, largely ignoring the roles of soil condition and agricultural management practices (AMPs). At present, the joint impact of “climate-urbanization-soil-AMPs” on the NPP of FES remains unknown. To fill this knowledge gap, we use the NPP dataset retrieved by the vegetation photosynthesis model (VPM), daily meteorological records, field-collected soil data, AMPs data collected from the Government Statistical Yearbook, and land cover dataset to study the joint mechanism of “climate-urbanization-soil” that drives spatial-temporal variations of NPP. Taking the northern slope of Tianshan Mountains as the study area, our results indicate that NPP had increased by 3.86 Tg C from 2000 to 2015 (11.93 Tg C in 2000 and 15.79 Tg C in 2015). From the proposed conceptual framework, we found that climate variables (accumulated temperature, precipitation, evapotranspiration) play a major role in driving the growth of NPP (+4.01 Tg C). The AMPs (i.e., mulching filming, drip irrigation, and fertilizer application) make a positive contribution to NPP increase (+0.98 Tg C). We also found that soil salinization (-1.07 Tg C) weakens the growth of NPP more significantly than urbanization (−0.16 Tg C). This study provides new insights on the mechanism of climate change, urbanization, and soil conditions on crops, benefiting stakeholders in designing better management plans for sustainable agriculture, ecosystem cycling, and food security.

Due to the characteristics of global coverage, on-demand access, and large capacity, the low earth orbit (LEO) satellite communication (SatCom) has become one promising technology to support the Internet-of-Things (IoT). However, due to the scarcity of satellite spectrum and the high cost of designing satellites, it is difficult to launch a dedicated satellite for IoT communications. To facilitate IoT communications over LEO SatCom, in this paper, we propose the cognitive LEO satellite system, where the IoT users act as the secondary user to access the legacy LEO satellites and cognitively use the spectrum of the legacy LEO users. Due to the flexibility of code division multiple access (CDMA) in multiple access and the wide use of CDMA in LEO SatCom, we apply CDMA to support cognitive satellite IoT communications. For the cognitive LEO satellite system, we are interested in the achievable rate analysis and resource allocation. Specifically, considering the randomness of spreading codes, we use the random matrix theory to analyze the asymptotic signal-to-interference-plus-noise ratios (SINRs) and accordingly obtain the achievable rates for both legacy and IoT systems. The power of the legacy and IoT transmissions at the receiver are jointly allocated to maximize the sum rate of the IoT transmission subject to the legacy satellite system performance requirement and the maximum received power constraints. We prove that the sum rate of the IoT users is quasi-concave over the satellite terminal receive power, based on which the optimal receive powers for these two systems are derived. Finally, the resource allocation scheme proposed in this paper has been verified by extensive simulations.