Influence Of Edges Research Articles

Edge Influence in a Semi‐Deciduous Tropical Forest: An Investigation of Soil Organic Carbon Fractions

ABSTRACTRecent studies have proven the sensitivity of soil organic carbon (SOC) in responding to variations imposed by tropical forest fragmentation, which may reflect changes in both its quantity and quality. Despite this, studies evaluating soil carbon fractions as an edge effect indicator are still non‐existent. We developed this work asking: How do the oxidizable fractions of soil organic carbon (OFSOC) respond to the edge effect in fragments of semi‐deciduous seasonal forest? Is this response influenced by the size of the forest fragment? The study was conducted in Vitória da Conquista, Bahia, Brazil, in three forest fragments with different sizes. Thus, three sampling sections were defined in each fragment: edge (0–10 m), transition (40–50 m), and interior. Soil samples were collected at a depth of 0–10 cm. The SOC was divided into three fractions (LC—labile carbon, MLC—moderately labile carbon, and LLC—low labile carbon), adopting the chemical method based on oxidation degrees. Reductions in the SOC (28.2%), LC (17.4%), and MLC content (66.7%) were observed at the edge in the small fragment. There was a reduction of SOC (21.3%) and LLC (39.0%) at the edge in the medium fragment, while MLC showed an increase at the edge (70.4%). The OFSOC were able to respond to the changes imposed by the edge effect, with emphasis on the LC and MLC fractions in smaller fragments. The forest fragment size influenced the level of changes caused by the edge effect in OFSOC, showing greater susceptibility in smaller fragments.

A study on the classification of complexly shaped cultivated land considering multi-scale features and edge priors.

Obtaining accurate cultivated land distribution data is crucial for sustainable agricultural development. The current cultivated land extraction studies mainly analyze crops on a regular shape and a small block scale. Aiming at the problem of fragmentation of plots in complexly shaped cultivated land leads to variable scales and blurred edges and the difficulty of extracting the context information by kernel convolution operation of the CNN-based model. We propose a complexly shaped farmland extraction network considering multi-scale features and edge priors (MFEPNet). Specifically, we design a context cross-attention fusion module to couple the local-global features extracted by the two-terminal path CNN-transformer network, which obtains more accurate cultivated land plot representations. This paper constructs the relation maps through a multi-scale feature reconstruction module to realize multi-scale information compensates by combining the gated weight parameter based on information entropy. Additionally, we design a texture-enhanced edge module, which uses the attention mechanism to fuse the edge information of texture feature extraction and the reconstructed feature map to enhance the edge features. In general, the network effectively reduces the influence of variable scale, blurred edges, and limited global field of view. The novel model proposed in this paper is compared with classical deep learning models such as UNet, DeeplabV3 +, DANet, PSPNet, RefineNet, SegNet, ACFNet, and OCRNet on the regular and irregular farmland datasets divided by IFLYTEK and Netherlands datasets. The experimental results show that MFEPNet achieves 92.40 and 91.65 MIoU on regular and irregular farmland datasets, which is better than the benchmark experimental model.

Infrared low-altitude and slow-speed small target detection via fusion of target sparsity and motion saliency

Infrared (IR) small target detection exerts a significant role in IR early warning and UAV surveillance. However, in the low-altitude slow-speed small (LSS) target detection scene, the existing algorithms cannot effectively suppress high-contrast corners and sparse edges in the low-altitude background, resulting in many false alarms. To solve this problem, we propose an IR LSS target detection method based on fusion of target sparsity and motion saliency (TSMS). In the low-rank sparse model, we introduce a robust dual-window gradient operator to construct a fine local prior, which avoids the influence of highlighted edges and corners; The Geman norm is used to approximate the background rank to accurately estimate the background and effectively extract sparse targets. Then, a motion saliency model based on inter-frame local matching is constructed to accurately extract the inter-frame features of small target. Finally, the real LSS target is obtained by fusing target sparsity and motion saliency. Experiments indicate that, compared with existing advanced methods, the proposed method has stronger robustness and can effectively detect LSS targets under complex low-altitude background.

Comprehensive Understanding of TGO Morphology Effect on the Thermal Barrier Coatings Failure Under Free Edges

Abstract The growth stresses induced by the thermally grown oxide (TGO) will be amplified at the free-edge site, making the free-edge site a weak part of the thermal barrier coatings (TBCs). In this study, the TBCs failure behavior is investigated based on different TGO morphologies under free edges. The thermomechanical model is established by creating straight lines and simplified sinusoidal curves, respectively. Dynamic TGO growth is realized by the secondary development of the subroutine. The cohesive element is inserted at the TC/TGO interface to simulate the delamination. The stress evolution near different TGO morphologies under the influence of the free edge are examined. In addition, the interfacial cracking behavior near the free edge is also explored. The results show that the appearance of the free edge will deteriorate the stress condition in the nearby area, change the preferred cracking area, and induce the earlier failure behavior. The straight line morphology has the most “friendly” stress distribution. The sinusoidal curves have peaks and valleys, and different areas of the TGO shape are different under the influence of the free edge, but all of them have the effect of stress “convergence”. These results can provide significant guidance to develop the next-generation advanced TBCs.

Influence of Edges and Interlayer Electron-phonon Coupling in WS2/h-BN Heterostructure.

The semiconducting layered transition metal dichalcogenides (e.g., WS2) are excellent candidates for the realization of optoelectronic and nanophotonic applications on account of their band gap tunability, high binding energy and oscillator strength of the excitons, strong light-matter interaction, appreciable charge carrier mobility, and valleytronic properties. However, the photoluminescence (PL) emissions are reported to show a nonuniform spatial distribution, with the edges emitting features like defect-bound excitons and biexcitons at low temperatures in addition to the typical excitons and trions. The appearance of these additional PL features has been shown in the literature to have a strong dependence on the presence of S-vacancies and excess charge carriers. We demonstrate an enhancement of the defect-bound excitons and biexcitons by creating a heterostructure of WS2 with h-BN where the coupling between the charge carriers in WS2 with the polar phonons in h-BN governs the enhancement. Furthermore, we have performed a comprehensive resonant Raman study with varying polarization and magnetic field which not only confirms the presence of electron-phonon coupling in WS2/h-BN heterostructure, it further demonstrates a thermally induced differential resonance behavior with the excitonic level and the defect-induced midgap states (due to S-vacancies at the edge of WS2) exhibited by a dome-shaped behavior of the Raman intensities with temperature for the normal and defect-induced phonon modes. The defect-bound Raman modes exhibit maximum resonance at ∼240 K while normal Raman modes show at ∼280 K owing to a thermal variation of the electronic states.

Edge history modulates the depth of edge influence: Evidence from ground beetles with different feeding traits

Anthropogenic habitat loss and fragmentation are major concerns to conservationists, since these processes lead to species decline and extinction. Edge effect is one of the most important causes of biodiversity losses in fragmented habitats. The depth of edge influence, the distance over which the edge effect penetrates into the adjacent habitat is a central issue, as it fundamentally determines whether the habitat fragment has any remaining core habitat, which is essential for the survival of habitat specialist species. We reviewed 204 edge studies on ground beetles (Coleoptera: Carabidae) of different feeding habits, in forests, one of the habitats most impacted by humans. Meta-analysing 1814 abundance data of 351 ground beetle species from forest edges and their adjoining interiors, we showed that forest edges had significantly higher abundance of herbivorous, omnivorous, and predatory ground beetles than their interiors. Edge history considerably affected the depth of edge influence which on all trophic groups was similar in natural edges and those maintained by agriculture (≤ 10–20 m), while it was wider (> 300 m) when created by forestry or other anthropogenic activities. Consequently, the minimum area of a forest fragment should be ∼ 330 ha in order to keep half of it as core habitat to preserve forest-interior species.

Thermal transport characterization of monolayer GaN nanoribbon doped with group IV materials: An equilibrium molecular dynamics study

Bulk gallium nitride (GaN) has been considered as one of the prominent materials for a long time in the application of laser, LED and transistors. Due to its superior electrical characteristics, its low-dimensional hexagonal counterpart is currently drawing attention like graphene. In this study, we have investigated the effect of impurity atoms on the heat energy conduction within this honeycomb nanostructure of GaN to explore its scope of applications. The room temperature thermal conductivity of zigzag GaN nanoribbon (ZGaNNR) is found to be ∼ 11 Wm−1K−1 using the molecular dynamics approach with the Green-Kubo formulation. Various group IV elements namely- carbon, silicon, germanium and tin, have been incorporated within the pristine structure in four distinct doping patterns; like- point (Type-I and Type-II), pair and delta doping, to explore the thermal transport in chemically doped nanostructures. A gradual enhancement in the thermal conductivity is observed for the increasing concentration of carbon and silicon as dopants. On the other hand, selection of germanium and tin as impurity atoms suppresses the heat conduction within the crystal due to the increased low frequency phonon scattering. In all cases, Type-I point doping affects the thermal transport of ZGaNNR significantly. The narrower ribbons impede the phonon transportation due to the strong boundary effects resulting in a lower thermal conductivity. Moreover, phonon transportation is largely suppressed, and heat energy is observed to flow at a slower rate as the temperature of the ribbon rises. We have also studied these doped structures under the influence of point, edge and pair vacancies and observed a decreasing trend in thermal conductivity for each type of vacancy defect patterns introduced in the crystal. Such a study would promote the rising interest in properly characterizing the heat transport of low dimensional nanostructures and it can be an effective technique to tune the thermal conductivity of ZGaNNR for improving thermoelectric performance and thermal management in nanoscale devices.

The evolutionary prisoner’s dilemma game in continuous signed networks

Cooperative evolutionary games have been a focus in various subjects, but the discussion on signed networks is lacking, especially when players are assumed to have different degree of closeness with others, i.e. continuous signed network. To fill this gap, we introduce a negativity coefficient that responds to the effect of negative relationships and a new strategy imitation method that applies to continuous networks. The simulation results show the necessity of negative relations that promotes cooperation, and the increase of the influence of negative relations facilitates the generation of cooperative behaviors. The edge updating in adaptive networks promote cooperation but has little impact when negative edge influence is small. The experiments also prove that structural balance is helpful for increasing the share of cooperators in the evolution.

Sparse Graph Transformer With Contrastive Learning

Information aggregation and propagation over networks via graph neural networks (GNNs) plays an important role in node or graph representation learning, which currently depend on the calculation with a fixed adjacency matrix, facing over-smoothing problem, and difficulty to stack multiple layers for high-level representations. In contrast, Transformer calculates an importance score for each node to learn its embedding via the attention mechanism and has achieved great successes in many natural language processing (NLP) and computer vision (CV) tasks. However, Transformer is inflexible to extend to graphs, as its input and output must have the same dimension. It will also become intractable to allocate attention over a large-scale graph due to distractions. Moreover, most graph Transformers are trained in supervised ways, which consume additional resources to annotate samples with potentially wrong labels and have limited generalization of representations. Therefore, this article attempts to build a new Sparse Graph Transformer with Contrastive learning for graph representation learning, called SGTC. Specifically, we first employ centrality measures to remove the redundant topological information from input graph according to the influences of nodes and edges, then disturb the pruned graph to get two different augmentation views, and learn node representations in a contrastive manner. Besides, a novel sparse attention mechanism is also proposed to capture structural features of graphs, which effectively save memory and training time. SGTC can produce low-dimensional and high-order node representations, which have better generalization for multiple tasks. The proposed model is evaluated on three downstream tasks over six networks, and experimental results confirm its superior performance against the state-of-the-art baselines.

Grassland vegetation height affects bird responses to forest edges in Mediterranean open farmland

Afforestation affects Mediterranean farmland biodiversity due to loss and fragmentation of grassland habitats. While the influence of landscape context and plantation edges on farmland bird responses to afforestation is well-documented, less is known about the influence of grassland vegetation height and how it interacts with afforestation to influence bird communities. Here, we examined how changes in grassland vegetation height affect bird responses to afforestation in a farmland region in southern Portugal, and how these are affected by plantation type and edge. This region has experienced afforestation with eucalyptus, pine and oak stands, agricultural intensification, and frequent dry periods. To capture local and landscape-level changes, we collected data in two periods (2005 and 2014–15). Grassland vegetation height varied between sampling periods, emerging as a key factor affecting changes observed. Ground-nesting and cereal-associated species increased in abundance with taller vegetation in 2014–15, while in 2005, with drier weather and shorter vegetation, the species associated with ploughed fields were more abundant. Vegetation height effects on bird assemblages depended on plantation type and distance to plantation edges. Farmland bird abundance, including ground-nesting and cereal crops-associated species, increased with taller vegetation, particularly near oak and pine plantations. Conversely, species associated with ploughed fields declined with taller vegetation, especially near eucalyptus plantations. Results highlight complex interactions between vegetation height, plantation type, and edge proximity shaping avian assemblages. This study supports the importance of field and landscape-level management with special focus on grassland vegetation height and landscape heterogeneity for preserving open-farmland birds in fast-changing Mediterranean farmland landscapes.

The effects of edge influence on the microhabitat, diversity and life-history traits of amphibians in western Ecuador

Abstract Edge effects change biodiversity patterns and ecological processes, particularly in tropical forests. To understand the synergistic impact of multiple edges, this study examines how edge influence (EI) is associated with life-history traits (snout-vent length and body temperature), diversity and microhabitat of amphibians as well as habitat characteristics in a tropical forest in Ecuador. We used EI, a metric that calculates cumulative effects across all nearby edges, in combination with five environmental variables that are part of the amphibians’ microhabitat (temperature, humidity, slope, canopy cover and leaf litter depth) to understand how their biodiversity patterns are impacted. Our results show that most amphibian species tend to be habitat specialists, and many had an affinity for forest edges and warmer habitats. We do not find significant correlations between EI and amphibian life-history traits and diversity. Our findings corroborate previous results that many amphibian species tend to be positively associated with habitat fragmentation and show that this association is likely driven by thermal regulation.

Infrared Dim and Small Target Detection Based on Superpixel Segmentation and Spatiotemporal Cluster 4D Fully-Connected Tensor Network Decomposition

The detection of infrared dim and small targets in complex backgrounds is very challenging because of the low signal-to-noise ratio of targets and the drastic change in background. Low-rank sparse decomposition based on the structural characteristics of infrared images has attracted the attention of many scholars because of its good interpretability. In order to improve the sensitivity of sliding window size, insufficient utilization of time series information, and inaccurate tensor rank in existing methods, a four-dimensional tensor model based on superpixel segmentation and statistical clustering is proposed for infrared dim and small target detection (ISTD). First, the idea of superpixel segmentation is proposed to eliminate the dependence of the algorithm on selection of the sliding window size. Second, based on the improved structure tensor theory, the image pixels are statistically clustered into three types: corner region, flat region, and edge region, and are assigned different weights to reduce the influence of background edges. Next, in order to better use spatiotemporal correlation, a Four-Dimensional Fully-Connected Tensor Network (4D-FCTN) model is proposed in which 3D patches with the same feature types are rearranged into the same group to form a four-dimensional tensor. Finally, the FCTN decomposition method is used to decompose the clustered tensor into low-dimensional tensors, with the alternating direction multiplier method (ADMM) used to decompose the low-rank background part and sparse target part. We validated our model across various datasets, employing cutting-edge methodologies to assess its effectiveness in terms of detection precision and reduction of background interference. A comparative analysis corroborated the superiority of our proposed approach over prevailing techniques.

Analysis of urban visitor walkability based on mobile data: The case of Daejeon, Korea

The walkability of destinations plays a decisive role in obtaining a sense of place for urban visitors. For an improved walkability-based visit experience, the city structure can be analyzed by dividing it into small units, starting with the destination. However, existing studies focus on the walkability of residents by administrative district and use limited contextual information and structural analysis tools. Therefore, this study aims to structurally understand walkability through network analysis with mobile-based visitor data collected from 822 point-of-interest (POI) in Daejeon. In this study, the actual walking distance between 95,817 cells with visitors and POIs was measured to calculate the edge weight and influence of cells on POI. We analyzed the POI-cell network through edge weight and extracted nine communities to understand the city context through POI analysis and derived the following findings. First, it is possible to determine the potential demand by visitors' walkability-based division. Second, our walkability measurement method follows a bottom-up approach, starting with a small unit cell, understanding the entire city as a community extraction model, and possibly extending to other cities in Korea. Third, POI-centered structural analysis is possible using the number of visitors and distance in the proposed network analysis method.

Extended concrete capacity design method for anchors close to non‐rectangular edges

AbstractFastenings in concrete are widely applicable in construction. A significant benefit of fastening technology lies in the fact that it can adapt to various geometries and construction sequences. As a result, the need to design and realize a fastening in structural elements with unconventional geometric configurations can be encountered. Existing design standards rely on the concrete capacity design (CCD) method for concrete‐related failures, which typically covers detailing situations with rectangular configurations of the fastenings and the concrete members. However, the lack of guidelines for a wider range of applications may lead to over‐conservative dimensioning or design constraints. This study investigates the influence of beveled concrete edges on the load‐bearing behavior of anchors. As part of the research, a series of experiments and numerical simulations were conducted. The results derived from the investigations are used to develop an analytical concept that extends the area of applicability of the CCD method and can be used for reliable dimensioning of fastenings close to non‐rectangular concrete edges and geometrical boundaries.

Laminar hypersonic boundary layer flow over planar compression ramp with sharp leading edge and upstream influence

The present work aims to study the effects of shock wave — boundary layer interaction in 2D hypersonic flow at the sharp leading edge of a flat plate and a flat plate followed by a compression ramp. A key parameter in this work is the hypersonic interaction parameter χ, defined as χ≡CM∞3/Re∞1/2. This flow is modeled as a perfect gas and its behavior is studied over various compression ramp configurations, ranging from 0°–6°, using a semi-analytic reduced-order model. In this shock-layer analysis we focus on characterizing the viscous boundary layer for an adiabatic flow over an insulated wall, improving on existing semi-analytic methods by obtaining a non-singular solution, and using the Tangent Wedge approximation to match the pressure on the boundary layer edge. Upstream influence resulting from the compression ramp is also investigated with newly developed correlations.

Model Inversion Attacks Against Graph Neural Networks

Many data mining tasks rely on graphs to model relational structures among individuals (nodes). Since relational data are often sensitive, there is an urgent need to evaluate the privacy risks in graph data. One famous privacy attack against data analysis models is the model inversion attack, which aims to infer sensitive data in the training dataset and leads to great privacy concerns. Despite its success in grid-like domains, directly applying model inversion attacks on non-grid domains such as graph leads to poor attack performance. This is mainly due to the failure to consider the unique properties of graphs. To bridge this gap, we conduct a systematic study on model inversion attacks against Graph Neural Networks (GNNs), one of the state-of-the-art graph analysis tools in this paper. Firstly, in the white-box setting where the attacker has full access to the target GNN model, we present GraphMI to infer the private training graph data. Specifically in GraphMI, a projected gradient module is proposed to tackle the discreteness of graph edges and preserve the sparsity and smoothness of graph features; a graph auto-encoder module is used to efficiently exploit graph topology, node attributes, and target model parameters for edge inference; a random sampling module can finally sample discrete edges. Furthermore, in the hard-label black-box setting where the attacker can only query the GNN API and receive the classification results, we propose two methods based on gradient estimation and reinforcement learning (RL-GraphMI). With the proposed methods, we study the connection between model inversion risk and edge influence and show that edges with greater influence are more likely to be recovered. Extensive experiments over several public datasets demonstrate the effectiveness of our methods. We also evaluate our attacks under two defenses: one is the well-designed differential private training, and the other is graph preprocessing. Our experimental results show that such defenses are not sufficiently effective and call for more advanced defenses against privacy attacks.

Research on the influence of single-crystal germanium cutting parameters based on molecular dynamics

As a metalloid with unique properties, single crystal germanium is widely used in semiconductors, infrared optics, and other fields. To ensure the processing accuracy of single-crystal germanium, it is necessary to optimise the selection of parameters during processing. This paper uses molecular dynamics methods to study the cutting process of single-crystal germanium with the cutting depth and cutting speed as parameters and uses nanoindentation method to test the mechanical properties of the processed single-crystal germanium surface. The study found that with the increase of cutting depth, the cutting force and cutting heat increased significantly, the depth of the deformed layer increased significantly, and the stiffness of single-crystal germanium decreased significantly; with the increase of cutting speed, there was no built-up edge Influence, the cutting force is reduced, and more cutting heat is generated, and the depth of the deformation layer is higher, and the HCP structure increases, but the cutting speed has no obvious relationship with the mechanical properties of the machined surface.

The Factors Affecting the Quality of the Temperature Vegetation Dryness Index (TVDI) and the Spatial–Temporal Variations in Drought from 2011 to 2020 in Regions Affected by Climate Change

The temperature vegetation dryness index (TVDI) is widely used for the monitoring of global or regional drought because of its strong drought-monitoring capabilities and ease of implementation. However, the temporal errors in the land surface temperature (LST) and normalized difference vegetation index (NDVI) can affect warm and cold edges, thus determining the quality of the TVDI, especially in regions affected by climate change, such as Shandong Province. This paper explores this issue in the region in 2011, using daily MODIS MOD09GA and MOD11A1 data products. For each image acquisition time, the warm and cold edges of the NDVI–LST were extracted based on the NDVI, derived from red and near-infrared reflectance data, and the LST, derived from the MOD11A1 dataset. Then, the variations in the warm and cold edges with the LST and NDVI were analyzed. Subsequently, the influence of warm and cold edges, based on the daily values of the temperature, NDVI and precipitation during the observed period, was assessed using a linear regression. The soil moisture (SM) data obtained from the Global Land Data Assimilation System (GLDAS) datasets and the crop water stress index (CWSI) obtained from the MOD16A2 products were used for the assessment. The spatial and temporal variations in drought in Shandong Province from 2011 to 2020 were measured based on Theil–Sen median trend analysis and the Mann–Kendall test. The results show that apparently random variations were evident in the temporal evolution of the slope of the warm edge, indicating that daily data were appropriate to determine the boundary of the warm edge. Daily data were also appropriate to determine the boundary of the cold edge in a similar way. Additionally, the temperature, NDVI and precipitation in this region affected by climate change had a negative correlation with the slope and a positive correlation with the intercept. The validation results show that there was a significant negative correlation between the observed TVDI and GLDAS soil moisture values (R2 > 0.62) in 12 scatter plots. Therefore, we deduced that the monthly or yearly TVDI product produced by the daily MODIS data has a higher precision than that produced by 8-day or monthly data in regions affected by climate change. The spatial and temporal variations show that the trend of slight and moderate droughts first increased and then decreased, and, in particular, some areas presented severe drought from 2011 to 2015. The results obtained in this study are important for the scheduling of irrigation and drought warnings.

Population connectivity across east Australia's bioregions and larval duration of the range‐extending sea starMeridiastra calcar

AbstractThe diversity and distribution of marine species in eastern Australia is influenced by one of the world's strongest western boundary currents, the East Australia Current, which propels water and propagules poleward, a flow intensifying due to climate change.Population genetic structure of the asterinid sea starMeridiastra calcarwas investigated across its range in eastern Australia (12° of latitude, 2,500 km) from northern New South Wales to its poleward‐extending range in Tasmania at the southern edge influence of the East Australia Current.Population structure and connectivity ofM. calcarwere examined across six bioregions using six microsatellite loci (nuclear DNA) and the control region (mitochondrial DNA). The potential influence of the extent ofM. calcar's intertidal rock platform habitat was also assessed.Genetic structure analysis indicated that the Hawkesbury Shelf contained distinct genetic clusters, whereas the two sites in the Batemans Shelf differed from each other, with Jervis Bay Marine Park having just one genetic cluster. The Manning Shelf, Twofold Shelf, and Bruny bioregions all had similar genetic composition.Strong self‐seeding (68–98%) was indicated by microsatellite loci for all bioregions, with lower (0.3–6.5%) migration between bioregions. Poleward (New South Wales to Tasmania) migration was low except from the Manning Shelf (30%).Contemporary population connectivity and genetic structure ofM. calcarappear to be influenced by ocean currents, habitat distribution, and its short planktonic larval duration, which was a minimum of 12–14 days, depending on availability of a settlement cue.The dominance of unique genetic groups in the Hawkesbury bioregion shows the importance of this region forM. calcarand possibly a diversity of co‐distributed rock platform species. This highlights how important it is to have a large marine park in the Hawkesbury bioregion, which is presently lacking.

Radiation pattern reshaping designs for slot antenna based on the third-order mode

Based on radiation pattern reshaping technology, two slot antennas (SAs) operating on the third-order mode are designed. Both of them obtain high-gain characteristic and stable bidirectional radiation. The radiation characteristic of the SA three-order mode is analyzed. Then, the influence of the finite ground edge’s diffraction on radiation pattern of SA is considered. By using the finite ground with a suitable size, the main lobe in radiation direction can be effectively regulated. On this basis, according to the equivalent magnetic current distribution in slot, this paper proposes two radiation pattern reshaping design schemes (a. Folding rectangular slot; b. Loading C-shaped slots). Both design schemes can suppress the side lobes and regulate the main lobe in the radiation pattern. Two proposed SAs can achieve stable bidirectional radiation and obtain high-gain response. Finally, two SAs are fabricated and measured. The measured results show that two SAs achieve bidirectional radiation. The peak gains of two SAs are 6.88 dBi and 7.51 dBi, respectively.