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This work aims to reveal the effect of rock bridge length (RBL), i.e., 10, 20, 30, or 40 mm, on the fatigue mechanical responses and fracture evolution of marble samples containing stepped fissures under multilevel cyclic loading paths. Comprehensive investigations were conducted on fatigue strength, deformation, damping evolution, and damage propagation. The test results demonstrate that fatigue strength, volumetric deformation, and fatigue lifetime increase as rock bridge length increases. The energy dissipation reflected by the damping ratio indicates that much energy is consumed to drive crack propagation, especially for rock with larger rock bridge segments at the final cyclic loading stage (CLS). An index of strain incremental rate is proposed to predict rock failure development. It is found that volumetric strain rate is a better early warning sign than axial strain rate. Warning time decreases with increasing rock bridge length; it is suggested that rock with large segments has good ability to resist external fatigue loading.

Building façade elements are an important foundation for smart cities. As buildings exhibit an array of textures and geometric forms, the process of image acquisition is easily affected, although the robustness of texture in scenes (e.g., dilapidated buildings) is poor, with high point cloud data, and low recognition efficiency; therefore, the accuracy of building element extraction based on a single data source remains limited. In this research, a method for building façade element extraction based on multidimensional virtual semantic feature map ensemble learning and hierarchical clustering is proposed. Point clouds were obtained by multi-view images, and then the multidimensional virtual semantic feature maps, including color, texture, orientation, and curvature semantics, were acquired via reprojection. The multi-semantic feature block pre-segmentation, considering multiple features, was obtained by ensemble learning, and a hierarchical clustering strategy was established for to achieve fine extraction of building façade elements. Experiments were conducted across multiple building types, and the results showed that: 1) The method can use different virtual semantic feature map and clustering strategies to achieve accurate extraction of diverse building façade elements; 2) The method achieved joint learning tasks in both 2D and 3D space; and, 3) The proposed method achieved fine extraction of building elements with pixel accuracy (PA) over 70% in all experiments and mean intersection over union (mIoU) up to 95%, which were better than the image based method. In summary, this method offers a novel, more reliable method for segmenting and extracting building façade elements, which has important theoretical and practical significance.

In the construction of large-scale water conservancy and hydropower transportation projects, the rock mass structural information is often used to evaluate and analyze various engineering geological problems such as high and steep slope stability, dam abutment stability, and natural rock landslide geological disasters. The complex shape and extremely irregular distribution of the structural planes make it challenging to identify and extract automatically. This study proposes a method for extracting structural planes from UAV images based on Geo-AINet ensemble learning. The UAV images of the slope are first used to generate a dense point cloud through a pipeline of SfM and PMVS; then, the multiple geological semantics, including color and texture from the image and local geological occurrence and surface roughness from the dense point cloud, are integrated with Geo-AINet for ensemble learning to obtain a set of semantic blocks; finally, the accurate extraction of structural planes is achieved through a multi-semantic hierarchical clustering strategy. Experimental results show that the structural planes extracted by the proposed method perform better integrity and edge adherence than that extracted by the AINet algorithm. In comparison with the results from the laser point cloud, the geological occurrence differences are less than three degrees, which proves the reliability of the results. This study widens the scope for surveying and mapping using remote sensing in engineering geological applications.

In order to reveal the instability law of open pit mine slope in high-cold and high-altitude area. Firstly, the slope structural plane is scanned by three-dimensional (3D) laser scanning technology, and the point cloud data is obtained to realize the intelligent identification of rock mass structural plane. The geometric parameters of rock mass are counted, and the physical structure model is established. Then, we carry out freeze-thaw cyclic tests on granite to obtain the corresponding mechanical parameters. Finally, according to the obtained mechanical parameters, we use RS2 finite element software to calculate the shear strength of structural plane and joint by generalized Hoek-Brown criterion and Barton-Bandis criterion, respectively, establish the geomechanical model, and use the finite element strength reduction method to calculate the safety factor of slope and judge the instability of slope. The results show that the physical and mechanical properties of granite deteriorate with the increase of the freeze-thaw cycle. Under the action of the freeze-thaw cycle, the pore water in the rock mass freezes and forms frost heaving force. The expansion of volume leads to the further development of joint fissures. The strength of rock slope decreases gradually with the increase of freeze-thaw cycle times, and the safety factor of slope decreases continuously. It shows that repeated freeze and thaw alternation makes the stability of mine slope worse and worse. The research results are helpful to prevent the occurrence of slope disasters in advance and are of great significance to effectively and safely manage the stability of slope, the treatment of open pit, and environmental treatment.

This work is aimed at investigating the mechanical behaviors of a fault unwelded bimrock subjected to coupled freeze-thaw (FT) and fatigue loads. The influence of FT cycle on rock strength, deformation, damage evolution, and failure morphology was detailed investigated. The testing results show that the soil-rock interface is sensitive to freeze-thaw condition. Interface damage and cracking contribute a lot to sample strength, volumetric deformation, and damage evolution. In addition, a sudden increase of stress amplitude results in the increase of damage, afterwards, the coupling degree of bimrock improves with the increase of loading cycles. A coupling freeze-thaw and mechanical damage evolution model was proposed using the damage index defined by ultrasonic velocity and axial strain. It is found that the evolution trend of the model is strongly related to the previous freeze-thaw damage. Moreover, failure mode of bimrock changes from shear failure to bulging failure with increasing FT cycles. The failure process of bimrock is the rearrangement of the fine soil particles and rock blocks, the soil matrix and rock block bear axial cyclic loads by their interactions.

The natural fractures in rock mass are susceptible to damage evolution when subjecting to repeated freeze-thaw (F-T) weathering in cold regions, which can lead to the instability of rock engineering and even occurrence of geological hazards. Knowledge of how natural fracture impacts the overall fracture evolution of freeze-thawed rock is important to predict the stability of rock structure. In this work, we reported uniaxial experimental measurements of the changes in strength, deformation, acoustic emission (AE) pattern, and Felicity effect during increasing amplitude stress-cycling conditions on granite. The results show that the change of fracture aperture is related to the fracture openness and filling characteristics, open-type fracture is sensitive to F-T treatment, and its aperture increases faster than the close-type and fill-type fracture. In addition, strength decreases, and the damping characteristics first decrease and then increase with increasing natural fracture volume. AE activities also present different responses during sample deformation. The proportion of AE signals having low-frequency characteristics increases with increasing natural fracture volume, and the shear sliding along natural fracture results in the surge of AE activities. Moreover, the Felicity effect indicates that the Felicity ratio presents a fluctuation decreasing trend, and the preexisting fractures alter the stress memory characteristics of rock. It is suggested that the changes of the geomechanical and AE pattern are the interactions between the natural fracture and the newly stimulated fracture. The testing results are expected to improve the understanding of the influence of natural fractures on rock fracture evolution and can be helpful to predict the stability of rock structures and rock mass in cold regions.

This paper aims to reveal the mechanical properties, energy evolution characteristics, and dynamic rupture process of preflawed granite under impact loading with different rock bridge angles and strain rates. A series of dynamic impact experiments were conducted along with the separate Hopkinson press bar (SHPB) testing system to analyze and study the overall rock fracture process. Under the impact load, the peak stress of granite increases with the increase of rock bridge angle and strain rate, but the increase gradually decreases. The peak strain also increases gradually with the increase of rock bridge angle, but there is an upper limit value; the total input strain energy increases with the increase of strain rate and rock bridge angle. It is shown that the higher the strain rate, the higher the unit dissipation energy, and the greater the degree of rock fragmentation. For rock under impact loads, the crack first initiates from the wing end of the prefabricated flaw, the preflaw closes gradually, and finally the crack propagates at the locking section leading to the coalescence of rock bridge. With the increase of strain rate, the fragmentation degree of the specimen increases asymptotically, and the average fragmentation size of the specimen decreases with the increase of strain rate. It is suggested that the stability of large rocked slopes is controlled by the locked section, and understanding the fracture evolution of the rock bridge is the key to slope instability prediction.

Now, most existing dynamic RGB-D SLAM methods are based on deep learning or mathematical models. Abundant training sample data is necessary for deep learning, and the selection diversity of semantic samples and camera motion modes are closely related to the robust detection of moving targets. Furthermore, the mathematical models are implemented at the feature-level of segmentation, which is likely to cause sub or over-segmentation of dynamic features. To address this problem, different from most feature-level dynamic segmentation based on mathematical models, a non-prior semantic dynamic segmentation based on a particle filter is proposed in this paper, which aims to attain the motion object segmentation. Firstly, GMS and optical flow are used to calculate an inter-frame difference image, which is considered an observation measurement of posterior estimation. Then, a motion equation of a particle filter is established using Gaussian distribution. Finally, our proposed segmentation method is integrated into the front end of visual SLAM and establishes a new dynamic SLAM, PFD-SLAM. Extensive experiments on the public TUM datasets and real dynamic scenes are conducted to verify location accuracy and practical performances of PFD-SLAM. Furthermore, we also compare experimental results with several state-of-the-art dynamic SLAM methods in terms of two evaluation indexes, RPE and ATE. Still, we provide visual comparisons between the camera estimation trajectories and ground truth. The comprehensive verification and testing experiments demonstrate that our PFD-SLAM can achieve better dynamic segmentation results and robust performances.

RGB-D SLAM (Simultaneous Localization and Mapping) generally performs smoothly in a static environment. However, in dynamic scenes, dynamic features often cause wrong data associations, which degrade accuracy and robustness. To address this problem, in this paper, a new RGB-D dynamic SLAM method, PLD-SLAM, which is based on point and line features for dynamic scenes, is proposed. First, to avoid under-over segmentation caused by deep learning, PLD-SLAM combines deep learning for semantic information segmentation with the K-Means clustering algorithm considering depth information to detect the underlying dynamic features. Next, two consistency check strategies are utilized to check and filter out the dynamic features more reasonably. Then, to obtain a better practical performance, point and line features are utilized to calculate camera pose in the dynamic SLAM, which is also different from most published dynamic SLAM algorithms based merely on point features. The optimization model with point and line features is constructed and utilized to calculate the camera pose with higher accuracy. Third, enough experiments on the public TUM RGB-D dataset and the real-world scenes are conducted to verify the location accuracy and performance of PLD-SLAM. We compare our experimental results with several state-of-the-art dynamic SLAM methods in terms of average localization errors and the visual difference between the estimation trajectories and the ground-truth trajectories. Through the comprehensive comparisons with these dynamic SLAM schemes, it can be fully demonstrated that PLD-SLAM can achieve comparable or better performances in dynamic scenes. Moreover, the feasibility of camera pose estimation based on both point features and line features has been proven by the corresponding experiments from a comparison with our proposed PLD-SLAM only based on point features.

The proteasome beta-4 subunit is required for the assembly of 20S proteasome complex, forming a pivotal component for the ubiquitin–proteasome system. Emerging evidence indicates that proteasome beta-4 subunit may be involved in underlying progression and mechanisms of malignancies. However, the role of proteasome beta-4 subunit in melanoma is currently unknown. Here, we reported that proteasome beta-4 subunit was markedly upregulated in human melanoma tissues and cells, compared with normal skin samples. High proteasome beta-4 subunit levels were significantly associated with poor overall survival in patients with melanoma. Proteasome beta-4 subunit knockdown strongly decreased melanoma cell growth in vitro and in vivo. We further identified miR-148b as a negative regulator of proteasome beta-4 subunit. Enforced expression of miR-148b resulted in vitro growth inhibition of melanoma cells, whereas this inhibition was further abolished by enforced expression of proteasome beta-4 subunit. Our findings, for the first time, indicated that the miR-148b/proteasome beta-4 subunit axis contributed to the development of melanoma, revealing novel therapeutic targets for the treatment of melanoma.

Multi-Source RS data integration is a crucial technology for rock surface extraction in geology. Both Terrestrial laser scanning (TLS) and Photogrammetry are primary non-contact active measurement techniques. In order to extract comprehensive and accurate rock surface information by the integration of TLS point cloud and digital images, the segmentation based on the integrated results generated by registration is the crux. This paper presents a Multi-Features Fusion for Simple Linear Iterative Clustering (MFF-SLIC) hybrid superpixel segmentation algorithm to extract the rock surface accurately. The MFF-SLIC algorithm mainly includes three contents: (1) Mapping relationship construction for TLS point cloud and digital images; (2) Distance measure model establishment with multi-features for initial superpixel segmentation; (3) Hierarchical and optimized clustering for superpixels. The proposed method was verified with the columnar basalt data, which is acquired in Guabushan Geopark in China. The results demonstrate that the segmentation method could be used for rock surface extraction with high precision and efficiency, the result of which would be prepared for further geological statistics and analysis.

This work presents an experimental study that focused on the meso-damage evolution of cemented waste rock-tailing backfill (CWRTB) under triaxial compression using the in situ X-ray computed tomography (CT) technique. Although numerous investigations have studied the magnitude of the strength of CWRTB material, the mesoscopic damage evolution mechanisms under triaxial deformation are still poorly understood. Artificial CWRTB samples with a waste rock proportion of 30% were prepared by mixing tailings, waste rock, cement, and water. A specific self-developed loading device was used to match the CT machine to real-time CT scanning for the CWRTB sample. A series of 2D CT images were obtained by performing CT imaging at five key points throughout the test and from three positions in the sample. The CT values, for the purpose of meso-damage evolution in CWRTB, were identified. The results showed that the axial stress–strain curve presented strain hardening characteristics. The CT data revealed the inhomogeneous damage field inside the CWRTB sample and the most severely damaged regions that were usually located at the waste block-tailings paste interfaces. The changes in CT values for the different regions of interest (ROI) revealed the complicated interactions between the waste blocks and the tailings paste matrix. The meso-structural changes, formation of the localized bands, and the associated stress dilatancy phenomenon were strongly influenced by the interactions between the waste blocks and tailing paste.

Cemented waste rock backfill (CWRB), which is a mixture of tailings, waste rock, cement, and water, is subjected to combination actions in underground mining operations and has been widely used in deep resource mining. While the strength requirement and macroscopic deformation behaviors of CWRB have been well studied, the mesoscopic damage evolution mechanisms are still not well understood. In this work, a CWRB sample with a waste rock proportion of 30% was studied with a uniaxial compression test under tomographic monitoring, using a 450 kV industrial X-ray computed tomography (CT). Clear CT images, CT value analysis, crack identification, and extraction reveal that CWRB damage evolution is extremely inhomogeneous and affected by the waste rock size, shape, and distribution. Furthermore, the crack initiation, propagation, and coalescence behaviors are limited to the existing waste rocks. When deformation grows to a certain extent, the cracks demonstrate an interlocking phenomenon and their propagation paths are affected by the waste rocks, which may improve the ability to resist compressive deformation. Volumetric dilatancy caused by the damage and cracking behavior has closed a link with the meso-structural changes, which are controlled by the interactions between the waste rocks and the cemented tailing paste.

This study employed mixed bacterial strains to ferment seabuckthorn seed meal into peptides, and conducted a comprehensive evaluation of the growth adaptive conditions, molecular weight distribution, volatile compounds, and in vitro hypoglycemic activity required for fermentation. Results showed that when the amount of maltose was 1.1% and MgSO4·7H2O was added at 0.15 g/L, the peptide yield reached 43.85% with a mixed fermentation of Lactobacillus fermentum, Bacillus subtilis, Lactobacillus casei, Lactobacillus rhamnosus, and Lactobacillus acidophilus. Components with a molecular weight below 1 kDa were found to be more effective in inhibiting the activity of α-amylase and α-glucosidase, with the identified sequence being FYLPKM. Finally, SPME/GC–MS results showed that 86 volatile components were detected during the fermentation of seabuckthorn seed meal, including 22 alcohols, 9 acids, 7 ketones, 14 alkanes, 20 esters, and 14 other compounds. With prolonged fermentation time, the content of acids and esters increased significantly.

This study assessed the impact of milk on the bioactive compounds, physicochemical properties, antioxidant activity, ROS inhibition, and volatile flavor compounds of fermented black mulberry juice (FBMJ). Firstly, the results showed that 25% concentration of milk was the most suitable for preparing FBMJ-Milk. Compared to the control group, the addition of milk significantly increased the SOD activity and antioxidant capacity, as well as enhanced the total phenolic content (TPC) and SOD storage stability. Secondly, HS-SPME-GC–MS combined with OPLS-DA analysis identified 49 compounds in FBMJM, including 12 esters, 6 acids, 1 ketone, 2 aldehydes, 19 alcohols and 9 other compounds. During the storage, the levels of ethyl ester compounds decreased significantly, while the degradation of ester produced some acid and alcohol compounds. The findings revealed that the addition of milk was beneficial for maintaining the antioxidant stability of FBMJM during storage and enhancing the richness of product flavor.

To enhance comprehension of the impact of mixed fermentation using Torulaspora delbrueckii Bio-119667 (TD) on the aroma composition of Munage grape base-wine (MGBW), we analysed the aroma composition of MGBW using HS-SPME-GC-MS widely targeted metabolomics. The levels of volatile aroma components, including terpenes, higher alcohols, aldehydes, heterocyclic compounds, and esters, were significantly higher in MGBW produced by mixed fermentation compared to the pure Saccharomyces yeast control fermentation. The study found that the content of esters increased by 26.3% after mixed fermentation, indicating the contribution of TD to the formation of ester flavour components during the fermentation of MGBW. After analysing aroma activity values, we discovered that 49 out of 115 esters (25.5% of the total) significantly contributed to the aroma profile of MGBW (rOAV > 1). Of these esters, 16 were identified as key aroma compounds (rOAV > 1, VIP > 1) produced by mixed fermentation with the participation of TD. This finding further supports the contribution of TD to the improvement of MGBW’s aroma composition. This study reveals the role of non-Saccharomyces yeast strain Torulaspora delbrueckii Bio-119667 in improving the aroma composition of MGBW produced by mixed culture fermentation and the biosynthetic pathways of key aroma components therein.

The presence of silicon in non-wood raw materials is known to interfere with the causticization of green liquor and the calcination of white mud, which has provided motivation for advances in alkali recovery technology. Therefore, reducing the silicon content of green liquor is the fundamental way to solve the problem. However, there are certain defects in conventional green liquor desilication, so this study provides a basis for desilication from green liquor via black liquor combustion and studies the desilication effect of a compound desilication agent on bamboo pulping black liquor. The results show that the desilication effect was better and the desilication rate could reach 84% when the Mg/Al proportion in compound desilication agent was 1:0.6. Meanwhile, the content of residual aluminum in green liquor decreased, which can avoid the influence of residual desilication agent on subsequent stages. Scanning electron microscopic images show that green-liquor-silicon-insoluble (GLSI) substance is a granular precipitate, and the X-ray diffraction analysis shows that GLSI substance has a complex structure similar to 3Al2O3•2SiO2•MgO. This implies that the desilication mechanism consists of chemical precipitation and physical adsorption.

Liquid crystals (LC) have been found to have many unique characteristics during the last few decades. The liquid crystal phase is ubiquitous in the biological world, as well as in cellulose and its derivatives. Cellulose nanocrystals (CNC), which can obtain the chiral LC phase in aqueous suspension, have been attracting much attention. The unique size and properties of CNC, such as their light weight, special optical properties, non-toxicity, and biocompatibility, etc., have made them widely applicable in diverse fields. This brief literature review introduces the relationship between the LC phase and CNC. Advantages of CNC as a liquid crystal forming material and the preparation of CNC are discussed. The left-handed cholesteric phase structure and the corresponding unique optical properties of CNC-based LC are described in detail. The CNC-based LC aqueous suspension and three stages of forming LC phase are also described. Then, the main reasons accounting for the brittleness and non-uniformity of pristine CNC-based LC are summarized, as well as ways to overcome these problems. Finally, some optical applications of CNC-based LC films in anti-counterfeiting, colorimetric sensors, and composite devices are considered.

Propagation graph (PG) is a stochastic channel simulation method for scattering propagation. In this article, an embedded-PG (EPG) approach, an extension of conventional PG, is proposed to simulate reflection and scattering multi-path behaviors in wireless channels. In this method, multiple propagation paths are categorized into scattering-path, reflecting-path, and scattering-reflecting mixed paths among reflectors and scatterers. The matrix recursive formula of conventional PG modeling is used to calculate scattering-path, a recursive mathematical transformation is applied to adapt reflecting-path into the recursive formula, and an embedded graph method is used to decompose mixed-path into scattering effects and reflection effects. The proposed simulation approach is validated by comparison with conventional PG and measurement in 39 GHz millimeter-wave (mm-wave) time-variant corridor scenario. Power delay profiles (PDPs) and spatial consistency of multiple paths observed in concatenated-PDPs (CPDPs) obtained by EPG are more consistent with measurement than conventional PG, differences of mean delay and delay spread between simulations and measurement in typical snapshots are within 3 ns and 1.5 ns, respectively.

The demand for ever-increasing wireless data transmission rates and throughput area-densities, especially with regard to microcellular networks, internet access, back-hauling, inter-device transmission, and sensing applications, has spurred the exploration of new spectra in the millimeter-wave (30–300 GHz) and terahertz bands (0.1–10 THz), and the study of techniques for multi-Gigabit transmission based on very high-gain antennas [items 1) and 2) in the Appendix].

Massive multiple-input multiple-output (MIMO) attracts considerable interest by increasing the spectral efficiency, and which may be adopted in future communication system. Both base station (BS) and user equipment (UE) can be equipped with large-scale antennas using frequency division duplex (FDD) schemes. The major obstacle that reduces the performance is the overhead due to the reference signal. Moreover, the channel state information (CSI) of the uplink (UL) channel cannot be simply used for downlink (DL) pre-coding. In this paper, we focus on reconstructing wireless channels on a frequency band by observing the channel response on neighboring frequency band. The inference of DL channel is conducted by utilizing parameters of multipath components extracted from UL observations, using high resolution estimation algorithms, e.g. the Space-Alternating Generalized Expectation-maximization (SAGE). Four calibration methods are proposed to improve the quality of the inferred DL channel response. Those four methods take into account the channel composition based on the availability of the initial channel observations. Their performance is demonstrated by using real massive MIMO measurements. The results show these calibration methods have their individual application scenarios and all outperform in Line-of-Sight (LoS) scenarios. These calibration methods prove a methodological way to improve the correlation of neighboring channel.

Cluster tracking is a mainstream approach for the study of time-variant channel characteristics. In the paper, we propose a power spectrum based sequential tracker (PSBST) to compensate for the disadvantages of existing cluster tracking algorithms. The proposed tracker identifies clusters via simple three-stage power spectrum processing. Furthermore, fuzzy c-means (FCM) algorithm is incorporated to separate clusters considering the overlapped clusters which may appear in the power spectrum. In terms of tracking, we implement Kalman filter to sequentially predict candidate ranges of tracked clusters in consecutive snapshots and simultaneously a novel gradient-based histogram of power (GBHOP) method is devised to determine the evolution of clusters. We also investigate the performance of the tracker by synthetic channel simulation. It demonstrates high accuracy and less computational time compared with the results derived from the existing cluster tracking algorithms. Besides, we verify applicability of the tracker by analyzing the field measurement conducted in vehicle-to-everything (V2X) scenario, and preliminary statistical characteristics for intra-cluster and inter-cluster parameters can be readily obtained in the sequel.

This paper presents a measurement-based analysis of wideband 39 GHz millimeter wave (mm-wave) dual-polarized propagation channel under the impact of foliage presence between a transmitter (Tx) and a receiver (Rx). The measurements were conducted in a rich-vegetation area, and the so-called direction-scan-sounding (DSS) method which rotates a horn antenna in angular domains was applied, aiming at investigating the direction-of-arrival (DoA)-dependent characteristics of polarimetric channels. Four Tx-to-Rx polarization configurations were considered, including co-polarization scenarios with vertical Tx-polarization to vertical Rx-polarization (VV) and horizontal to horizontal (HH), as well as cross-polarization with vertical to horizontal (VH) and horizontal to vertical (HV), which allow scrutinizing the differences in delay-direction dispersion for usually-encountered scenarios. A foliage loss model for various vegetation depths in VV polarization configuration, was also presented in this paper. The results show that the foliage-loss DoA spectra for VH and HV are similar, while the spectra exhibit less penetration loss in most directions for VV than for the HH. Furthermore, the presence of vegetation between the Tx and the Rx leads to larger dispersion in delay compared to the clear line-of-sight (LoS) scenario, particularly for vertical polarization in the Tx side, and additionally, the foliage presence also results in evident DoA dispersion, specially in the HV scenario. Selectivity in directions caused by foliage is more significant in vertically-polarized Tx scenarios than in the horizontally-polarized Tx scenarios. A statistical model is established summarizing these comparison details.

The fully insulated busbar has been extensively used in power and shipboard applications due to its favorable economic efficiency and excellent performance. Because of contact resistance and larger insulation thermal resistance, the joint, in certain circumstances, form a hotspot in the circuit. For hotspot temperature monitoring, therefore, this paper proposed an indirect approach which consists of radial direction temperature calculation (RDTC) in the busbar and axial direction temperature calculation (ADTC) in the conductor. The RDTC is to calculate the conductor temperatures from the surface temperatures of busbar in two different spots near the joint through the transient thermal network. The ADTC is to estimate the hotspot temperature from the conductor temperatures obtained in the RDTC using a fitted functional expression. The influence of solar radiation on the temperature distribution is greatly reduced by averaging the surface temperature in the RDTC and adjusting the functional form in the ADTC. A temperature-rise test on a practical insulated busbar taped joint was performed outdoors in hot summer to validate this approach. The calculated hotspot temperature agrees well with the measured result with a maximum error of only 3.8 K, indicating the high model precision and strong robustness to the solar radiation impact. Some factors in the applications of temperature monitoring are also discussed. This method is expected to be applied to the engineering application, thus improving the condition monitoring of the fully insulated busbar.

This paper introduces a measurement campaign to investigate the characteristics of massive multiple-input multiple-output (MIMO) channels in both line-of-sight (LoS) and obstructed LoS suburban outdoor environments. The measurements are conducted at the center frequency of 3.5 GHz with a bandwidth of 160 MHz. The transmitter is equipped with a 4 × 8 planar patch antenna array and the receiver is equipped with a 4 × 8 cylindrical patch antenna array. Each patch consists of two antenna ports which have main polarizations of +45° and -45°, respectively, resulting in a total of 64 × 64 sub-channels. We examined the influence of different polarization combinations on channel characteristics such as power delay profile (PDP), K-factor and delay spread for both LoS and obstructed LoS scenarios. Results show that the characteristics are more dependent on the Rx antennas rather than the Tx antennas. When the Rx antennas are facing the Tx antenna array, the received power is higher, the K-factor is lower and the delay spread is lower in cross-polarization cases with respect to the co-polarization ones in general. When the Rx antennas are back-facing the Tx antenna array, lower powers, higher K-factors and higher delay spreads can be observed in cross-polarization cases with respect to the co-polarization cases. In conclusion, the polarimetric influence on the channel characteristics depends on the antenna structure as well as the environment.