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Spatial variations in atmospheric parameters, including pressure, temperature, and humidity, significantly impact the precision of interferometric synthetic aperture radar (InSAR) measurements. This significantly limits the applicability of InSAR in the fields, such as terrain inversion and deformation monitoring. Fortunately, the evolving numerical weather models (NWMs) could offer a viable tropospheric correction solution. However, given the influence of complex terrain and areas with sparse atmospheric observation, the effectiveness of tropospheric correction with different NWMs’ resolutions remains to be evaluated. This requires the examination of different NWMs tropospheric delay correction in detail with sufficient metrics. A total of 36 Sentinel-1 interferograms of 2023, which cover Berlin, Paris, and Milan, respectively, are used as examples. Tropospheric correction is carried out using ICOsahedral nonhydrostatic D2 (ICON-D2), the fifth-generation European Centre for Medium-Range Weather Forecast atmospheric reanalysis, and modern-era retrospective analysis for research and applications version 2. To assess the correction efficacy of various resolutions, standard deviation, semivariogram function, and phase–elevation correlation coefficient served as the evaluation method. Results show that the ICON-D2 model outperforms the other models in these metrics, especially in regions with significant topographic relief. Among them, the standard deviation of the corrected interferogram decreased by 21.6%–35.8%. NWMs have demonstrated effectiveness in mitigating altitude-related tropospheric delays without needing altitude assimilation. Overall, the present study underscores that despite potential uncalibrated atmospheric effects, high-resolution NWMs are anticipated to provide a more precise solution for InSAR, especially in regions exhibiting intricate and challenging terrain features.

Non terrestrial network (NTN) has been regarded as an important component of beyond 5G communication. The 3rd Generation Partnership Project (3GPP) has officially started studies on NTN for the purpose of integrating satellites with New Radio (NR) terrestrial networks. However, the NR defined orthogonal frequency division multiplexing (OFDM) waveform is susceptible to doubly selective fading and nonlinear distortions in the NTN uplink, thus leading to severe performance loss. In this paper, a type of block scalable OFDM (BS-OFDM) waveform is proposed, which has modified subcarrier and sub-symbol segmentation when compared to the conventional NR defined OFDM. With our proposed framework, block scalable discrete Fourier transform spread OFDM (BS-DFT-s-OFDM) and block scalable orthogonal time frequency space (BS-OTFS) can also be implemented. To obtain a comprehensive understanding of different waveforms, performance metrics concerning peak to average power ratio (PAPR), total degradation (TD), bit error rate (BER), and complexity are presented. The simulation results demonstrate that the proposed block scalable waveforms can obtain better performance and lower complexity than existing NR waveforms in different NTN uplink scenarios, and can therefore be promising candidate waveforms for NR NTN.

High-resolution remote sensing technology is an efficient and low-cost space-to-earth observation strategy, which can carry out simultaneous monitoring of large-scale areas. It has incomparable advantages over ground monitoring solutions. Traditional road extraction methods are mainly based on image processing techniques. These methods usually only use one or a few features of images, which is difficult to fully deal with the real situation of roads. This work proposes a two-steps network for the road extraction. First, we optimize a pix2pix model for image translation to obtain the required map style image. Images output by the optimized model is full of road features and can relief the occlusion issues. It can intuitively reflect information such as the position, shape and size of the road. After that, we propose a new FusionLinkNet model, which has a strong stability in the road information by fusing the DenseNet, ResNet and LinkNet. Experiments show that our accuracy and learning rate have been improved. The MIOU (Mean Intersection Over Union) value of the proposed model in road extraction is over 80% in both DeepGlobe and Massachusetts road dataset. The figures are available from https://github.com/jsit-luwei/training-dataset.

Active reconfigurable intelligent surface (ARIS) has sparked more attention due to its capability to overcome the impact of double fading. This paper introduces using an ARIS to aid non-orthogonal multiple access (NOMA) communications over cascade Rician fading channels, where the direct links between the base station and users are seriously blocked. By applying ARIS, it can amplify the superposed signals to overcome the double pass loss effect caused by passive RIS. Both ARIS and NOMA can have synergistic impacts on sixth-generation communication systems. New approximated and asymptotic expressions in terms of outage probability and ergodic data rate of the k-th user are deduced for ARIS-NOMA systems. Based on asymptotic analytical results, we further calculate the diversity order and high signal-to-noise ratio slope of the k-th user. Finally, the system throughput of ARIS-NOMA is discussed in the delay-constrained transmission mode. Monte Carlo numerical results are performed to verify that: (1) the outage behaviors of ARIS-NOMA are better than that of ARIS-assisted orthogonal multiple access (OMA); (2) as the impact of thermal noise caused by ARIS becomes larger, the communication performance from the base station to ARIS, then to users, becomes worse; (3) the ARIS-NOMA systems have the ability to provide the improved ergodic data rate relative to ARIS-OMA.

LoRa has been considered as a key enabler for the next generation Internet of Things (IoT) networks. However, the low spectral efficiency (SE) of chirp spread spectrum (CSS) modulation used in LoRa is a fatal drawback for its extensive applications in the sixth generation (6G) enabled high-data-rate IoT era. In this paper, we propose SSK PSK-LoRa (slope-shift-keying and phase-shift-keying LoRa) modulation, which can achieve higher SE and better energy efficiency (EE) than the conventional LoRa modulation. In particular, the transceiver architecture of our proposed scheme is presented along with both coherent and semi-coherent detection methods. Moreover, the orthogonality of SSK PSK-LoRa symbols is analyzed and the closed-form approximations for bit error rate (BER) in both additive white Gaussian noise (AWGN) and Rayleigh fading channels are derived. Numerical results demonstrate the superiority of our proposed modulation scheme, which outperforms most classical counterparts in terms of BER and effective throughput.

Depending on the asymptotical independence of periodograms, exponential tilted (ET) likelihood, as an effective nonparametric statistical method, is developed to deal with time series in this paper. Similar to empirical likelihood (EL), it still suffers from two drawbacks: the non-definition problem of the likelihood function and the under-coverage probability of confidence region. To overcome these two problems, we further proposed the adjusted ET (AET) likelihood. With a specific adjustment level, our simulation studies indicate that the AET method achieves a higher-order coverage precision than the unadjusted ET method. In addition, due to the good performance of ET under moment model misspecification [Schennach, S. M. (2007). Point estimation with exponentially tilted empirical likelihood. The Annals of Statistics, 35(2), 634–672. https://doi.org/10.1214/009053606000001208], we show that the one-order property of point estimate is preserved for the misspecified spectral estimating equations of the autoregressive coefficient of AR(1). The simulation results illustrate that the point estimates of the ET outperform those of the EL and their hybrid in terms of standard deviation. A real data set is analyzed for illustration purpose.

Abstract Reconfigurable intelligent surface (RIS) has stimulated their potential applications for improving the performance of wireless communication networks. In this paper, the performance of RIS aided non‐orthogonal multiple access (NOMA) networks with hardware impairments over Rician fading channels is investigated. More specifically, the exact and asymptotic expressions of outage probability for a pair of users, that is, the nearby user n and distant user m are derived, where the imperfect successive interference cancellation (ipSIC) and perfect SIC (pSIC) are taken into consideration. According to the approximate analyses, the diversity orders of user n with ipSIC/pSIC and user m are obtained in the high signal‐to‐noise radio regime. It indicates that the diversity orders are in connection with reflecting elements and Rician factors except the channel ordering. The impact of these parameters on outage behaviors of RIS‐NOMA networks is also analysed. In addition, the system throughput of RIS‐NOMA networks with ipSIC/pSIC is surveyed in detail. Monte Carlo simulations are present to verify the correctness of theoretical analyses that: (1) The outage probability of user n with pSIC is superior to that of orthogonal user, while the outage probability of user m is inferior to that of orthogonal user and (2) as the number of reflecting elements and Rician factors increases, the outage behaviors of RIS‐NOMA networks are enhanced carefully.

Background To investigate the differences between physicians in target delineation in intensity-modulated radiation therapy for nasopharyngeal carcinoma as well as their impact on target dose coverage. Methods Ninety-nine in-hospital patients were randomly selected for retrospective analysis, and the target volumes were delineated by 2 physicians. The target volumes were integrated with the original plans, and the differential parameters, including the Dice similarity coefficient (DSC), Hausdorff distance (HD), and Jaccard similarity coefficient (JSC) were recorded. The dose–volume parameters to evaluate target dose coverage were analyzed by superimposing the same original plan to the 2 sets of images on which the target volumes were contoured by the 2 physicians. The significance of differences in target volumes and dose coverage were evaluated using statistical analysis. Results The target dose coverage for different sets of target volumes showed statistically significant differences, while the similarity metrics to evaluate geometric target volume differences did not. More specifically, for PGTVnx, the median DSC, JSC, and HD were 0.85, 0.74, and 11.73, respectively; for PCTV1, the median values were 0.87, 0.77, and 11.78, respectively; for PCTV2, the median values were 0.90, 0.82, and 16.12, respectively. For patients in stages T3-4, DSC, and JSC were reduced but HD was increased compared to those in stages T1-2. Dosimetric analysis indicated that, for the target volumes, significant differences between the 2 physicians were found in D95, D99, and V100 for all the target volumes (ie, PGTVnx, PCTV1, and PCTV2) across the whole group of patients, as well as in patients with disease stages T3-4 and T1-2. Conclusions The target volumes delineated by the 2 physicians had a high similarity, but the maximal distances between the outer contours of the 2 sets were significantly different. In patients with advanced T stages, significant differences in dose distributions were found, stemming from the deviations of target delineation.

Millimeter wave (MMW) communication, noted for its merit of wide bandwidth and high-speed transmission, is also a competitive implementation of the Internet of Everything (IoE). In an always-connected world, mutual data transmission and localization are the primary issues, such as the application of MMW application in autonomous vehicles and intelligent robots. Recently, artificial intelligence technologies have been adopted for the issues in the MMW communication domain. In this paper, MLP-mmWP, a deep learning method, is proposed to localize the user with respect to MMW communication information. The proposed method employs seven sequences of beamformed fingerprints (BFFs) to estimate localization, which includes line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions. As far as we know, MLP-mmWP is the first method to apply the MLP-Mixer neural network to the task of MMW positioning. Moreover, experimental results in a public dataset demonstrate that MLP-mmWP outperforms the existing state-of-the-art methods. Specifically, in a simulation area of 400 × 400 m2, the positioning mean absolute error is 1.78 m, and the 95th percentile prediction error is 3.96 m, representing improvements of 11.8% and 8.2%, respectively.

Abstract The atmosphere weighted mean temperature, Tm, is an essential parameter for retrieving precipitable water from the ground‐based Global Navigation Satellite Systems (GNSS). The accuracy of high temporal resolution GNSS precipitable water vapor (PWV) estimation requires wideband Tm information and its magnitude. However, existing Tm empirical models use trigonometric functions with only fixed amplitude and low‐frequency for time fitting, which limits real‐time or near real‐time PWV retrieval from GNSS observation. Thus, an improved Tm model for China, LTCm, containing more frequency information of Tm, based on the antileakage least‐squares spectrum analysis by utilizing the ERA5 pressure‐level products during the years 2015–2019, is developed. Both Tm data from ERA5 pressure‐level products and radiosonde stations distributed in China over 2020 are selected as reference values to verify the performance of the LCTm model. The results show that the LCTm model yields significant performance against other models in Tm estimation over China, especially in marine regions and high‐altitude areas. Furthermore, the LCTm model can generally achieve a mean Bias/root mean square (RMS) of −0.33 K/2.06 K in contrast to ERA5 pressure‐level products and 0.03 K/3.47 K in comparison with radiosonde, which corresponds to a 7.2%–13.8% improvement against GPT2w, GTm‐III, and Bevis. Moreover, LCTm has σ pwv and σ pwv /PWV values of 0.27 mm and 1.25% when used to retrieve GNSS‐PWV, respectively. Consequently, the LCTm model that considers high‐frequency information of Tm can obtain more reliable Tm values. Therefore, the LCTm model can be applied to real‐time or near real‐time GNSS PWV retrieval, which is of great significance for GNSS meteorological research.

This article develops two new empirical likelihood methods for long-memory time series models based on adjusted empirical likelihood and mean empirical likelihood. By application of Whittle likelihood, one obtains a score function that can be viewed as the estimating equation of the parameters of the long-memory time series model. An empirical likelihood ratio is obtained which is shown to be asymptotically chi-square distributed. It can be used to construct confidence regions. By adding pseudo samples, we simultaneously eliminate the non-definition of the original empirical likelihood and enhance the coverage probability. Finite sample properties of the empirical likelihood confidence regions are explored through Monte Carlo simulation, and some real data applications are carried out.

The multi-constellation, multi-frequency Global Navigation Satellite System (GNSS) has the potential to empower precise real-time kinematics (RTK) with higher accuracy, availability, continuity, and integrity. However, to enhance the robustness of the nonlinear filter, both the measurement quality and efficiency of parameter estimation need consideration, especially for GNSS challenging or denied environments where outliers and non-Gaussian noise exist. This study proposes a nonlinear Kalman filter with adaptive kernel bandwidth (KBW) based on the maximum correntropy criterion (AMC-KF). The proposed method excavates data features of higher order moments to enhance the robustness against noise. With the wide-lane and ionosphere-free combination, a dual frequency (DF) data-aided ambiguity resolution (AR) method is also derived to improve the measurement quality. The filtering strategy based on the DF data-aided AR method and AMC-KF is applied for multi-GNSS and DF RTK. To evaluate the proposed method, the short baseline test, long baseline test, and triangle network closure test are conducted with DF data from GPS and Galileo. For the short baseline test, the proposed filter strategy could improve the positioning accuracy by more than 30% on E and N components, and 60% on U. The superiority of the proposed adaptive KBW is validated both in efficiency and accuracy. The triangle network closure test shows that the proposed DF data-aided AR method could achieve a success rate of more than 93%. For the long baseline test, the integration of the above methods gains more than 40% positioning accuracy improvement on ENU components. This study shows that the proposed nonlinear strategy could enhance both robustness and accuracy without the assistance of external sensors and is applicable for multi-GNSS and dual-frequency RTK.

After implementing CGCS2000, establishing grid models for high-accuracy coordinate transformation which are mainly used to transform border lines and coordinate grids of topographic maps becomes an important issue in mapping applications. Consequently, a grid model for high-accuracy coordinate transformation of CGCS2000 is proposed. Specifically, we firstly analyze a minimum curvature equation of coordinate transformation, which possesses the characteristics of both the global and local smoothness, achieving better consistency with the consecutive smoothness for the coordinate transformation of map’s linear feature. Then an iterative calculation method of grid nodes and an approach for establishing regional grid models based on collocation by two-step minimization are proposed. Meanwhile, a data structure of grid model is constructed. Finally we give the optimized grid interval and transformation accuracy in China corresponding to the proposed grid model. Using 48433 points of 2000 National Geodetic Control Network of China, we take the proposed model into practice by constructing grid models for coordinate transformation from BJS54 and XAS80 to CGCS2000, and the external positional accuracies for both models are 0.26m and 0.03m respectively.

Cryptococcal endocarditis has rarely been reported. Most patients with this condition are associated with risk factors, such as structural heart disease/valve replacement, immunodeficiency/immunosuppression or drug abuse. We report a case of cryptococcal endocarditis of the native valves without any risk factors. A 50-year-old Chinese man was admitted to hospital with fever for 1 month without any underlying heart disease, immunodeficiency, or drug use. He was diagnosed as having Cryptococcus neoformans infective endocarditis and was discharged after valve replacement surgery and long-term antifungal therapy.

(1) Background: Personalized product customization is an important direction in the development of the chair industry. This paper studies an intelligent design method for the rapid realization of personalized office chair customization; (2) Methods: based on the case-based reasoning (CBR) method, the characteristic attributes of office chair patterns are analyzed, and an attribute model is established. According to office chair data and customer demand, an intelligent design model using multi-layer weighted k-nearest neighbor (K-NN) for chair patterns is developed using the entropy weight method and an analytic hierarchy process. In addition, an example is employed for verification of the K-NN and multi-layer weighted K-NN retrieval models; (3) Results: both models are able to effectively retrieve chair type cases that meet the target requirements from the office chair pattern base; the case matching similarity of the multi-layer weighted K-NN retrieval model was higher, with an average increase of about 3.9%, and the chair pattern case results obtained by setting different customer needs are different, indicating that the case can be selected according to different customer preferences, which is more conducive to personalized product customization design; (4) Conclusions: The multi-layer weighted K-NN model for intelligent chair pattern design proposed in this paper is more conducive to personalized product customization design.

At 21:48 on 21 May 2021, an Ms 6.4 earthquake occurred in Yangbi County, Dali Prefecture, Yunnan Province. At present, uncertainty remains regarding the source parameters and deformation mechanism of the Yangbi earthquake. In this study, we determine fault geometry and slip distribution of the earthquake by InSAR analysis. Then, the Coulomb stress loading caused by the Yangbi earthquake is further analyzed. The results show that the moment magnitude of the Yangbi earthquake was Mw 6.14. The slip mainly occurred at depths of 3–13 km, with a maximum slip of approximately 61 cm at a depth of 6.98 km. The Yangbi earthquake was triggered by a blind fault in the NW-SE in the west parallel to the Weixi-Weishan Fault and its seismogenic fault exhibits strike-slip displacement. A large number of aftershocks were distributed along the fault rupture surface where the Coulomb stress increases. As the depth of the crust increases, the area where the Coulomb stress increases in the Yangbi earthquake, decreases. The occurrence of this earthquake also caused a significant increase in the Coulomb stress in the southeastern section of the Weixi-Weishan Faul. We should pay more attention to its seismic hazards.

Based on the ERA-5 meteorological data from 2015 to 2019, we establish the global tropospheric delay spherical harmonic (SH) coefficients set called the SH_set and develop the global tropospheric delay SH coefficients empirical model called EGtrop using the empirical orthogonal function (EOF) method and periodic functions. We apply tropospheric delay derived from IGS stations not involved in modeling as reference data for validating the dataset, and statistical results indicate that the global mean Bias of the SH_set is 0.08 cm, while the average global root mean square error (RMSE) is 2.61 cm, which meets the requirements of the tropospheric delay model applied in the wide-area augmentation system (WAAS), indicating the feasibility of the product strategy. The tropospheric delay calculated with global sounding station and tropospheric delay products of IGS stations in 2020 are employed to validate the new product model. It is verified that the EGtrop model has high accuracy with Bias and RMSE of −0.25 cm and 3.79 cm, respectively, with respect to the sounding station, and with Bias and RMSE of 0.42 cm and 3.65 cm, respectively, with respect to IGS products. The EGtrop model is applicable not only at the global scale but also at the regional scale and exhibits the advantage of local enhancement.

Intrathoracic endotracheal metastasis from a very distant site is extremely rare. We report the first case of such a disease in a 68-year-old man with nasopharyngeal carcinoma who presented with a cough and hemoptysis 34 months after finishing radiotherapy. Prior to tracheal metastasis, he developed a solitary metastasis in the lung and underwent chemotherapy followed by radiotherapy. Computed tomography showed the presence of an enlarged lymph node in the para-aortic arch. Fiberoptic bronchoscopy revealed an endotracheal tumor 1 cm above the carina. Histological and immunohistochemical analyses confirmed its nasopharyngeal origin. He was treated with conventional radiotherapy and three-dimensional conformal radiotherapy; complete tumor remission was achieved. He died of nonmalignant disease with no signs of tumor recurrence 2 years after treatment completion. Radiotherapy may be an appropriate management approach to achieve long-term tumor control for this disease.

Urban green space whose soil permeability is the main factor affecting hydrological cycle plays a very important role in promoting rainwater infiltration, replenishing groundwater and reducing peak flow. In order to enhance the storage and infiltration ability of soil, different types and contents of conditioner are added to study the laws of the changes in permeability of various soils. The results showed that straw and sawdust can effectively increase the permeability coefficient of soil. According to the comparison under the same conditions, the improvement effects of straw is slightly better than sawdust, but no order of magnitude difference. The sandy soil reformed by 3.6% Straw or 4.2% Sawdust and the loam reformed by 29.5% Straw or 30.2% Sawdust can meet infiltration requires of rainwater whose rainstorm recurrence period is 10 years. The clay is not suitable for urban green space soils with higher rainwater storage and infiltration requirements. The reformed green space can effectively reduce the runoff peak flow and delay the generated time of the runoff. The higher the content of the modifier, the more obvious the advantage of promoting the hydrological cycle and reducing the runoff.