Orthogonal Components Research Articles

Fundamental studies on tactile feedback in robotic Striploin fat trimming task

This study explores a tactile perception approach for guiding a robotic cutting tool in beef processing. A 6-axis manipulator with a static knife and a 6-axis force sensor performed fifteen cutting paths on five striploin steak pieces. Unique force transients from the knife tip and sides were used to determine the knife's position relative to tissue features. Cross-correlation analysis between the orthogonal force components revealed a high coefficient above 0.95 for paths away from the interface, dropping below 0.88 near air gaps or fat-lean interfaces due to disturbances and tissue breakdown. Force transients were also identified when the knife exited the fat layer using a programmatic method, achieving less than a 1-s deviation from manual measurements. The lateral force component showed sensitivity to the natural cutting path and surrounding tissue behaviour. These tactile perception techniques could be integrated into real-time control for a robotic cutting knife in beef processing.

A confirmation of the predictive utility of the Antibiotic Use Questionnaire

BackgroundThe change in the efficacy of antimicrobial agents due to their misuse is implicated in extensive health and mortality related concerns. The Antibiotics Use Questionnaire (AUQ) is a theory driven measure based on the Theory of Planned Behaviour (TpB) factors that is designed to investigate drivers of antibiotic use behaviour. The objective of this study is to replicate the factor structure from the pilot study within a similar Australian confirmation cohort, and to extend this through investigating if the factor structure holds in a Chinese-identifying cohort.MethodsThe AUQ was disseminated to two cohorts: a confirmation cohort similar to the original study, and a Chinese identifying cohort. Data analysis was completed on the two data sets independently, and on a combined data set. An orthogonal principal components analysis with varimax rotation was used to assess the factor structure, followed by general linear models to determine the influence of the TpB factors on reported antibiotic use.Results370 participant responses from the confirmation cohort, and 384 responses from the Chinese-identifying cohort were retained for analysis following review of the data. Results showed modest but acceptable levels of internal reliability across both cohorts. Social norms, and the interaction between attitudes and beliefs and knowledge were significant predictors of self-reported antibiotic use in both cohorts. In the confirmation cohort healthcare training was a significant predictor, and in the Chinese-identifying cohort education was a significant predictor. All other predictors tested produced a nonsignificant relationship with the outcome variable of self-reported antibiotic use.ConclusionsThis study successfully replicated the factor structure of the AUQ in a confirmation cohort, as well as a cohort that identified as culturally or legally Chinese, determining that the factor structure is retained when investigated across cultures. The research additionally highlights the need for a measure such as the AUQ, which can identify how differing social, cultural, and community factors can influence what predicts indiscriminate antibiotic use. Future research will be required to determine the full extent to which this tool can be used to guide bespoke community level interventions to assist in the management of antimicrobial resistance.

Random walks in the high-dimensional limit II: The crinkled subordinator

A crinkled subordinator is an ℓ2-valued random process which can be thought of as a version of the usual one-dimensional subordinator with each out of countably many jumps being in a direction orthogonal to the directions of all other jumps. We show that the path of a d-dimensional random walk with n independent identically distributed steps with heavy-tailed distribution of the radial components and asymptotically orthogonal angular components converges in distribution in the Hausdorff distance up to isometry and also in the Gromov–Hausdorff sense, if viewed as a random metric space, to the closed range of a crinkled subordinator, as d,n→∞.

Interplanted crops and rainfall variability regulate soil erosion in rainfed maize of the Indian Himalayas

AbstractSoil erosion is a serious threat to agriculture and environmental sustainability in the risk‐prone rainfed Himalayan ecosystem. Hence, runoff and soil loss mitigation ability of four maize‐based intensified systems; maize (Zea mays) + turmeric (Curcuma longa), maize + ginger (Zingiber officinale), maize + colocasia (Colocasia esculenta), and maize + sweet potato (Ipomoea batatas) were tested against the sole maize. The study recorded 41 erosive events during the 2020–2022 monsoon season. A principal component analysis (PCA) was performed to transform correlated rainfall parameters into orthogonal principal components. A two‐dimensional biplot analysis examined the relationship between PCs, rainfall events, and rainfall parameters. The rainfall events were divided into three regimes based on cluster analysis using PC. Rainfall regime 1 was characterized by low rainfall, I30, rainfall erosivity (E), and moderate rainfall duration (D), regime2 with moderate rainfall, short duration, high I30, and E, and regime3 with high D, I30, and rainfall erosivity. The soil loss of regime‐3 was 9.29 and 3.24 times higher than regime‐1 and regime‐2, respectively. Cover crops reduced runoff by 21.5%–69.3% and soil loss by 54.1%–77.0% over control. Redundancy analysis (RDA) showed that rainfall parameters (I30, I60, D, and E) had a significant direct influence across the systems, explaining 37.7%–54.9% of response variable variance, with E being the most influential (47.4%–54.9%), indicating varied impacts on runoff and soil loss. The sweet potato was best suited with maize as an intercrop to minimize soil erosion and maximize the system's profitability.

Double vision: 2D and 3D mosquito trajectories can be as valuable for behaviour analysis via machine learning

BackgroundMosquitoes are carriers of tropical diseases, thus demanding a comprehensive understanding of their behaviour to devise effective disease control strategies. In this article we show that machine learning can provide a performance assessment of 2D and 3D machine vision techniques and thereby guide entomologists towards appropriate experimental approaches for behaviour assessment. Behaviours are best characterised via tracking—giving a full time series of information. However, tracking systems vary in complexity. Single-camera imaging yields two-component position data which generally are a function of all three orthogonal components due to perspective; however, a telecentric imaging setup gives constant magnification with respect to depth and thereby measures two orthogonal position components. Multi-camera or holographic techniques quantify all three components.MethodsIn this study a 3D mosquito mating swarm dataset was used to generate equivalent 2D data via telecentric imaging and a single camera at various imaging distances. The performance of the tracking systems was assessed through an established machine learning classifier that differentiates male and non-male mosquito tracks. SHAPs analysis has been used to explore the trajectory feature values for each model.ResultsThe results reveal that both telecentric and single-camera models, when placed at large distances from the flying mosquitoes, can produce equivalent accuracy from a classifier as well as preserve characteristic features without resorting to more complex 3D tracking techniques.ConclusionsCaution should be exercised when employing a single camera at short distances as classifier balanced accuracy is reduced compared to that from 3D or telecentric imaging; the trajectory features also deviate compared to those from the other datasets. It is postulated that measurement of two orthogonal motion components is necessary to optimise the accuracy of machine learning classifiers based on trajectory data. The study increases the evidence base for using machine learning to determine behaviours from insect trajectory data.Graphical

Empirical Model of Equatorial ElectroJet (EEJ) Using Long‐Term Observations From the Indian Sector

AbstractThe Equatorial Electrojet (EEJ) is one of the important near‐earth space weather phenomena which exhibits significant diurnal, seasonal and solar activity variations. This paper investigates the EEJ variations at diurnal, seasonal and solar cycle time scales from the Indian sector and portrays a new empirical EEJ field model developed using the observations spanning over nearly two solar cycles. The Method of Naturally Orthogonal Components (MNOC), also known as Principal Component Analysis (PCA), was employed to extract the dominant patterns of principal diurnal, semi‐diurnal, and ter‐diurnal components contributing to the EEJ variation. The amplitudes of these diurnal, semi‐diurnal, and ter‐diurnal components in EEJ are found to vary significantly with the season and solar activity. The seasonal and solar activity dependencies of these principal components are modeled using suitable bimodal distribution functions. Finally, the empirical model for EEJ field was built by combining the principal components with their corresponding modeled amplitudes. This model accurately reproduces the diurnal, seasonal and solar activity variations of EEJ. The modeled monthly mean variations of EEJ field at ground exhibit excellent correlation of 0.96 with the observations with the root mean square error <5 nT. It also successfully captures the seasonal and solar activity variations of Counter Electrojet (CEJ). Finally, this model named “Indian Equatorial Electrojet (IEEJ) Model” is made publicly available for interested scientific users (https://iigm.res.in/system/files/IEEJ_model.html).

Non-mechanical steering of the optical beam in spectral-domain optical coherence tomography

We demonstrate in a proof-of-concept experiment spectral-domain optical coherence tomography where steering of the optical beam that probes the sample in a transverse scan does not make use of any mechanical element. Steering is done with the help of a phase-only spatial light modulator, that introduces a spatially-dependent phase between the two orthogonal polarization components of an optical beam, and some optical elements that control the polarization of light. We demonstrate that making use of the non-mechanical beam steering system considered here, we can reproduce the main traits of imaging with standard OCT that makes use of mechanical-assisted optical beam steering.

Second Harmonic Generation of Twisted Vector Vortex Beams Using aβ-BaB2O4 Crystal

In this study, we demonstrate both theoretically and experimentally second harmonic generation (SHG) of a twisted vector vortex optical field (TVVOF) using a nonlinear type-II phase-matched β-BaB2O4 (BBO) crystal. Our study introduces a novel method to manipulate SHG by independently modulating the two orthogonal polarization components of a TVVOF. This flexibility in controlling SHG can be achieved through accurate experimental adjustments of the polarization components. Furthermore, we reveal that the SHG can be dynamically tuned by varying the angle between the polarization direction of the optical field and the principal axis of the BBO crystal via rotation. These findings provide a new approach for the flexible manipulation of SHG in structured vector optical fields, which have potential applications in optical communication, quantum optics, and photonic device engineering.

USE OF GPR TECHNOLOGIES IN THE ROAD CONSTRUCTION INDUSTRY

The article continues the study on determining the possibility of applying ground penetrating radar (GPR) technologies in the road construction industry. The authors conduct research using an antenna unit that registers the cross-polarisation component of the reflected signal. In this case, processing the obtained radar images comes down to subtracting two mutually orthogonal components of the signal, which allows the determination of areas of the environment with anisotropic properties. Identifying and positioning extraneous inclusions, finding communications, and determining the location of reinforcement in roadway and bridge structures are relevant tasks. Their solution lies in studying the phenomenon of wave diffraction on extraneous inclusions of the investigated environment. Metal inclusions of different diameters are distinctly visible on radar images obtained during laboratory research. The laboratory studies conducted by the authors showed that the shape and nature of the hyperbola of the diffracted wave significantly depend on the object’s depth, diameter, and material. Therefore, further research in this direction should aim at studying the shape of the diffracted wave, which ultimately allows for judging the depth of occurrence and the diameter of a local extraneous inclusion. When finding and identifying reinforcement in structural layers of road surfaces and elements of bridges, difficulties arise due to the spacing of the reinforcing mesh. With a small reinforcing mesh spacing, when the wavelength is longer than the spacing, the reflections from the adjacent reinforcement bars merge into a common continuous boundary and make the lower layers of the structure almost indistinguishable. Solving the mentioned difficulties is possible by improving the equipment base and the algorithms for processing and interpreting the received radar images. Completed theoretical studies and computational experiments allow us to state that the development of road surface thickness measurement and defect detection techniques will become the basis of the road surface monitoring system using the subsurface geolocation method. Keywords: ground penetrating radar, radar image, road surface, dielectric constant, defect detection, thickness measurement, local inclusions.

Reconfigurable Chiral Spintronic THz Emitters

AbstractCollective spin arrangements manifest diverse spin textures, encompassing ferromagnetism, antiferromagnetism, and chiral vortices. However, mapping these spin textures in ultrathin magnetic multilayers has remained elusive. A reconfigurable chiral spintronic terahertz emission method is introduced through investigations on a model system of synthetic antiferromagnet and the spin information of individual ferromagnet (FM) layers is extracted. Upon femtosecond photoexcitation of the synthetic antiferromagnet (FM1/Ru/FM2), the ferromagnets generate a pair of linearly polarized ultrafast spin currents, which, after relaxation at the FM/Ru interface, emit corresponding THz pulses. The Ruderman–Kittel–Kasuya–Yosida interactions between the two FMs give rise to magnetic‐field‐controlled spin textures causing a spin relaxation imbalance at the interfaces and induce a phase shift between the orthogonal components of the emitted terahertz fields, consequently reconfiguring the polarization of the emitted terahertz field from linear to circular. This approach offers a novel means of investigating electronic and magnetic states in ultrathin spintronic heterostructures, low‐dimensional quantum materials, topological insulators, and Weyl semimetals. Furthermore, it enables the exploration of spin textures, including skyrmions, merons, and solitons.

Borehole‐Based Interval Kriging for 3D Lithofacies Modeling

AbstractDeveloping a three‐dimensional (3D) lithofacies model from boreholes is critical for providing a coherent understanding of complex subsurface geology, which is essential for groundwater studies. This study aims to introduce a new geostatistical method—interval kriging—to efficiently conduct 3D borehole‐based lithological modeling with sand/non‐sand binary indicators. Interval kriging is a best linear unbiased estimator for irregular interval supports. Interval kriging considers 3D anisotropies between two orthogonal components—a horizontal plane and a vertical axis. A new 3D interval semivariogram is developed. To cope with the nonconvexity of estimation variance, the minimization of estimation variance is regulated with an additional regularization term. The minimization problem is solved by a global‐local genetic algorithm embedded with quadratic programming and Brent's method to obtain kriging weights and kriging length. Four numerical and real‐world case studies demonstrate that interval kriging is more computationally efficient than 3D kriging because the covariance matrix is largely reduced without sacrificing borehole data. Moreover, interval kriging produces more realistic geologic characteristics than 2.5D kriging, while conditional to spatial borehole data. Compared to the multiple‐point statistics (MPS) algorithm—SNESIM, interval kriging can reproduce the geological architecture and spatial connectivity of channel‐type features, meanwhile producing tabular‐type features with better connectivity. Because the regularization term constrains kriged value toward 0 or 1, interval kriging produces more certainty in sand/non‐sand classification than 2.5D kriging, 3D kriging, and SNESIM. In conclusion, interval kriging is an effective and efficient 3D geostatistical algorithm that can capture the 3D structural complexity while significantly reducing computational time.

Evaluation of nutritional composition, biochemical, and quality attributes of different varieties of tomato (Solanum lycopersicum L.)

Due to the wide variety and great quality differences, it is very confusing for consumers and growers to choose the best variety. For this purpose, we conducted a greenhouse test to focus on the nutrients and bioactive substances of diverse varieties of tomato. Using high-performance liquid chromatography (HPLC), we analyzed 28 quality traits across the nine tomato varieties. The results revealed significant variations in quality attributes of nine varieties, such as naringenin and cinnamic acid content, ranging from 0 to 2.02 and 0.97 μg/g, respectively. Similarly, sucrose, malic acid, lycopene, β-carotene, and gentisic acid exhibited high coefficients of variation of 31.60 %, 74.82 %, 30.94 %, 39.59 %, and 86.32 %, respectively. For the comprehensive evaluation of quality and difference among tomato varieties, we used Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) and Principal Component Analysis (PCA) combined with membership function methods. Notably, strawberry tomato (S2) and Yuanwei No.1 (S1) exhibited the highest quality, and Pink Belle No.2 (S7) showed lowest. The above metabolites show different accumulations potential among different varieties. In addition, we explored the nutritional variations in tomatoes different parts at various growth stages of two varieties. Overall, study result investigated metabolic differences of nutrients at different growth stages, especially ripening stage affecting fruit quality. Thus, these useful findings will provide useful information for consumers and researchers.

Phase retrieval in inverse ghost diffraction using Sagnac interferometer

Ghost diffraction (GD) involves the use of non-local spatial correlations to image objects with light, which has not interacted with them. Here, we propose and experimentally demonstrate a new technique for first-order correlation measurement and retrieval of two-dimensional phase objects in the GD from inversion of the experimentally measured two-point complex correlation function in a first order interferometer. The GD scheme is experimentally implemented by a specially designed experimental setup wherein one of the orthogonal polarization components of the transversely polarized light interacts with the object and the other polarization component of the light remains intact and directly reaches the detector. The Fourier spectrum of the object is encoded into the two-point spatial correlation of these two orthogonal polarization components which is experimentally detected in an interferometer with a radial shearing in the Sagnac geometry. We experimentally demonstrated imaging of spatially varying phase objects and results are presented for three different cases.

An improved IPT-PLL technology for single-phase grid-connected inverters in complex power grid conditions

Aiming at the common problems of frequency variations and harmonics in complex power grids, an improved inverse Park transform phase locked loop (IPT-PLL) technology for single-phase converters suitable for micro grid systems is proposed. Firstly, in the phase detection of PLL, the α component of Park transformation is selected as the reference voltage, and its orthogonal component is constructed using a 1/4 fundamental period delay method. Secondly, Lagrange interpolation polynomials are introduced to approximate fractional delay to solve the problem of delay calculation errors caused by frequency changes. Thirdly, in order to compensate for the poor ability of traditional proportional integral (PI) regulators, multi resonant controllers are superimposed to suppress low order harmonic disturbances. Finally, the design method and system performance of the PI regulator and each resonant controller are analyzed theoretically. The experimental results show that the proposed improved IPT-PLL method has strong adaptability to complex power grids. It can significantly improve the tracking performance of power grid frequency, suppress the interference of low order harmonics and DC bias. And it has good dynamic and static performance.

Inferring arsenic anomalies indirectly using airborne hyperspectral imaging – Implication for gold prospecting along the Rise and Shine Shear Zone in New Zealand

Well-exposed mineral deposits are scarce at a global level and presently potential mineral-rich sites are underlying vegetation cover and topsoil, which are suboptimal for direct remote sensing based exploration techniques. This study aims to implement an indirect approach to arsenic (As) distribution mapping using the surface manifestations of the subsurface geology and link it to the known gold mineralisation in the study area. Rise and Shine Shear Zone (RSSZ) in New Zealand is broadly a part of the Otago schist hosting lower to upper green-schist facies rocks manifesting mesothermal gold mineralisation. The area has several surficial geological imprints separating mineralised and non-mineralised zones, but these are dominated by topographic ruggedness, soil moisture and vegetation (mainly grass/tussock) spectra in the hyperspectral data. Initially, a band selection using Recursive Feature Elimination (RFE) was executed. The bands generated were tallied with the field and geological understanding of the area. The resultant 85 bands were then further put through Orthogonal Total Variation Component Analysis (OTVCA) to concise the information in 10 bands. OTVCA output was then classified using Random Forest classifier to map three levels of As concentration (<20 ppm, between 20 and 100 ppm and >100 ppm). The potentially high As concentration zones are likely to be related to the gold mineralisation. The geology of the area correlates with soil exposure which is captured by the classification in some parts, this increases the accuracy but also makes the classification analysis challenging.

Shaking a container full of perfect liquid; a tractable case, a torus shell, exhibits a virtual wall

Manipulation (‘shaking’) of a rigid container filled with incompressible liquid starting from stationary generally results in some displacement, or mixing, of the liquid within it. If the liquid also has zero viscosity, a ‘perfect’ or ‘Euler’ liquid, Kelvin’s theorems dramatically simplify the flow analysis. Response is instantaneous; stop the container and all liquid motion stops. In fact an arbitrary manipulation can be considered as alternating infinitesimal translations and rotations of the container. Relative to the container, the liquid is stationary during every translation. Infinitesimal rotations (an infinitesimal vector along the rotation axis) resolve into three orthogonal components in the container frame. Each generates its own infinitesimal liquid displacement vector field. Their combined consequences are usually obscure. Rather than a volume flow, a surface flow in 3D is considerably easier, the liquid slipping freely in a shell, sandwiched between two nested closed surfaces with constant infinitesimal gap. The closedness avoids extra boundaries. The two dimensionality admits a scalar streamfunction determined by the container angular velocity vector. Manipulation of the container angular velocity at will, usually leads to an infinitely rich variety of area preserving re-configurations of the liquid. In particular, any chosen point of the liquid can be moved, in the shell container frame, to any other chosen point. However, for a torus (shell) with a small enough hole (diameter < 0.195 torus diameter), there exists a virtual wall, a hypothetical axial cylinder intersecting the torus. No matter how the torus is manipulated, liquid inside the cylinder stays inside; outside stays outside. The analysis, solving for the streamfunction, is based on the relative vorticity, and the conformal mapping of a torus to a (periodic) rectangle, which lead to a fairly simple convolution integral formula for the flow.

Performance degradation assessment of rolling bearings based on the comprehensive characteristic index and improved SVDD

Rolling bearing is one of the most critical parts of mechanical equipment, so the performance degradation assessment of rolling bearing is vital to ensure the normal operation of the whole mechanical equipment. Aiming at the problems that a single degradation characteristic can only contain limited performance degradation information of rolling bearings, a large number of redundant characteristics exist in the high-dimension characteristic set resulting in the inability to effectively mine the characteristic information of rolling bearings, and that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings, a performance degradation assessment method of rolling bearings based on the comprehensive characteristic index and improved support vector data description (SVDD) is proposed in this paper. Firstly, to solve the parameter selection problem of variational mode decomposition (VMD), a parameter-adaptive VMD method based on salp swarm algorithm based on mixed strategy (MSSSA) is proposed. Secondly, to extract the performance degradation information of rolling bearings more comprehensively and fully, the comprehensive characteristic index is proposed. Then, a kernel locality preserving projection orthogonal kernel principal component analysis (KLPPOKPCA) method is proposed to reduce the dimensionality of the extracted multi-domain characteristic set of the rolling bearing. Finally, a support vector data description with negative samples (NSSVDD) is proposed and optimized by MSSSA to solve the problem that traditional degradation assessment models are not suitable for the shortage of fault data during the actual operation of rolling bearings and improve the detection performance of abnormal data. The experimental results show that the proposed method can accurately divide the performance degradation process of the rolling bearing. Moreover, the comparison with other methods further highlights the superiority of the proposed method in determining the point in time of early fault of the rolling bearing.

Identifying genes associated with disease outcomes using joint sparse canonical correlation analysis-An application in renal clear cell carcinoma.

Somatic changes like copy number aberrations (CNAs) and epigenetic alterations like methylation have pivotal effects on disease outcomes and prognosis in cancer, by regulating gene expressions, that drive critical biological processes. To identify potential biomarkers and molecular targets and understand how they impact disease outcomes, it is important to identify key groups of CNAs, the associated methylation, and the gene expressions they impact, through a joint integrative analysis. Here, we propose a novel analysis pipeline, the joint sparse canonical correlation analysis (jsCCA), an extension of sCCA, to effectively identify an ensemble of CNAs, methylation sites and gene (expression) components in the context of disease endpoints, especially tumor characteristics. Our approach detects potentially orthogonal gene components that are highly correlated with sets of methylation sites which in turn are correlated with sets of CNA sites. It then identifies the genes within these components that are associated with the outcome. Further, we aggregate the effect of each gene expression set on tumor stage by constructing "gene component scores" and test its interaction with traditional risk factors. Analyzing clinical and genomic data on 515 renal clear cell carcinoma (ccRCC) patients from the TCGA-KIRC, we found eight gene components to be associated with methylation sites, regulated by groups of proximally located CNA sites. Association analysis with tumor stage at diagnosis identified a novel association of expression of ASAH1 gene trans-regulated by methylation of several genes including SIX5 and by CNAs in the 10q25 region including TCF7L2. Further analysis to quantify the overall effect of gene sets on tumor stage, revealed that two of the eight gene components have significant interaction with smoking in relation to tumor stage. These gene components represent distinct biological functions including immune function, inflammatory responses, and hypoxia-regulated pathways. Our findings suggest that jsCCA analysis can identify interpretable and important genes, regulatory structures, and clinically consequential pathways. Such methods are warranted for comprehensive analysis of multimodal data especially in cancer genomics.

Longitudinal manipulation of local nonseparability in vector beams.

The inherent nonseparability of vector beams presents a unique opportunity to explore novel optical functionalities, expanding new degrees of freedom for optical information processing. In this Letter, we introduce a novel, to the best of our knowledge, method for tailoring the local nonseparability along the propagation axis of vector beams. Employing higher-order Bessel vector beams, the longitudinal control over the local nonseparability is achieved through targeted amplitude modulation of constituent orthogonal polarization components within the main ring region. Experimental demonstrations of diverse longitudinal nonseparability profiles corroborate the efficacy and versatility of our approach, opening avenues for further exploration of the nonseparability manipulation in vector beams.

Transformation of Longitudinally Customizable Curved Vector Vortex Beams Using Dielectric Metasurface

AbstractIn recent years, the emergence of metasurfaces has brought revolutionary changes to the generation and processing of vortex beams, triggering widespread research interest. Meanwhile, the longitudinally varying features of propagating beams provide new design freedom for realizing multi‐dimensional optical manipulation and promote the advancements of related areas such as microscopic detection, microfabrication, and biomedical applications. In addition, self‐accelerating Bessel beams are promising for a wide range of applications such as particle manipulation and medicine due to their nondiffracting, self‐healing as well as obstacle avoidance properties. In this paper, a novel kind of curved transmitted high‐order Bessel beams with longitudinally varying features based on form‐birefringent metasurface, by simultaneously manipulating the phase profiles of output orthogonal polarization components is demonstrated. Multiple dimensions of the beam, including the propagation trajectory, polarization state, and orbital angular momentum, can be tailored arbitrarily. For verifying the feasibility of the demonstrated method, two samples with different propagation trajectories, as well as different variations of orbital angular momentum, are designed and experimentally demonstrated. Such a novel approach can open new doors for the manipulation of vortex beams and can be used for depth sensing and distance measurement in complex environments.