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+# Artificial Intelligence for 6G Networks: Technology Advancement and Standardization 
+
+Muhammad K. Shehzad, Luca Rose, M. Majid Butt, István Z. Kovács, Mohamad Assaad, and Mohsen Guizani
+
+Abstract—With the deployment of 5G networks, standards organizations have started working on the design phase for sixth-generation ( $6 \mathrm{G}$ ) networks. $6 \mathrm{G}$ networks will be immensely complex, requiring more deployment time, cost and management efforts. On the other hand, mobile network operators demand these networks to be intelligent, self-organizing, and cost-effective to reduce operating expenses (OPEX). Machine learning (ML), a branch of artificial intelligence (AI), is the answer to many of these challenges providing pragmatic solutions, which can entirely change the future of wireless network technologies. By using some case study examples, we briefly examine the most compelling problems, particularly at the physical (PHY) and link layers in cellular networks where ML can bring significant gains. We also review standardization activities in relation to the use of ML in wireless networks and future timeline on readiness of standardization bodies to adapt to these changes. Finally, we highlight major issues in ML use in the wireless technology, and provide potential directions to mitigate some of them in $6 \mathrm{G}$ wireless networks.
+
+Index Terms-AI, ML, Wireless networks, 3GPP, 6G.
+
+## I. INTRODUCTION
+
+Unprecedented growth in the global cellular traffic (as shown in Fig. 1) and immense data rate demands have become a challenge, leading wireless industry to the next-generation, called 6G. 6G-era will bring digital, physical and biological worlds together with the goal to improve human experience and well-being. $6 \mathrm{G}$ will be operating in TeraHertz $(\mathrm{THz})$ frequencies $(0.1-10 \mathrm{THz})$, hence beneficial for multiple use cases in industrial applications, providing immense data rates $(\approx 1 \mathrm{~Tb} / \mathrm{s})$, accelerating internet-of-things, and wider network coverage. AI/ML will pave the way for $\mathrm{THz}$ communications at different layers [2], e.g., supporting channel acquisition [3] and modulation classification [4] at PHY. Similarly, at the link layer, beamforming design and channel allocation can exploit ML [2]. In $\mathrm{THz}$ systems, a channel can significantly vary at a micrometer scale, resulting in a tremendous increase in channel estimation frequency and corresponding overhead. ML algorithms can counter this issue by using, e.g., improved channel prediction techniques [3], [5].
+
+
+
+Fig. 1. Estimation of global mobile subscriptions in machine-to-machine (M2M) and mobile broadband (MBB) from 2020 to 2030. Source: ITU-R Report M. $2370-0$ [1].
+
+Recently, fast-growing deployment of $5 \mathrm{G}$ has opened up many challenges, including massive complexity in network architecture, low latency, high cost, power consumption, and deployment of hybrid Long-Term Evolution (LTE) new radio $(\mathrm{NR})$, leading to difficulties in network optimization. In such a complex scenario, the network intelligence has become a major focus as it will play a pivotal role in complex problem solving [6], e.g., self-healing, self-optimization, and self-configuration of a network [7].
+
+Future networks will become "cognitive" in a way that many aspects such as spectrum sensing/sharing, slicing, radio resource management (RRM), and mobility management, will be ML-based. Further, it is expected that ML will impact 6G air interface fundamentally and it will be designed to support ML natively [8]. Several recent research attempts, e.g., [9], propose different road maps for 6G, but they do not address standardization timeline and related issues regarding application of ML in 6G. Albeit, to some extent, [10] gives an overview of ML and standardization; nevertheless, ML-related technical challenges and its applications from an industrial and standardization perspective are not addressed.
+
+Reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are two key technologies for 6G [11]. RIS can re-engineer electromagnetic waves, hence beneficial to deliver the information where obstacles block the destination. RIS can be integrated with ML, allowing RIS to acquire envi-ronmental information by configuring various sensors, while ML can learn dynamic parameters intelligently, reducing the computation cost of RIS-based networks. Similarly, NOMA is a promising access technique for $6 \mathrm{G}$. In ML-empowered NOMA-based networks, gNodeBs ( $\mathrm{gNB}$ ) can intelligently define their control policy and improve decision-making ability.
+
+
+
+Fig. 2. An overview of ML paradigms, major tools, and applications in wireless networks.
+
+Today's networks use model-based methods to optimize various network functions providing characteristics of the process involved. However, these models might be too complex to be implemented in a realistic time frame or they include a great level of abstraction to function in a general environment. In contrast, ML-based solutions can adapt to real-time (RT) scenario changes and localized characteristics, learning the specific environment around the transceivers. The contributions of this article are twofold:
+
+- We look at the above-mentioned problems from an industrial perspective and outline the gap between research and practice.
+- We review standardization activities in the context of adopting ML in various aspects of wireless communications, e.g., channel acquisition, positioning. Furthermore, we highlight major issues and possible research directions in relation to the use of ML in wireless networks.
+
+## II. OVERVIEW OF ML TECHNIQUES IN WIRELESS NETWORKS
+
+ML is a process of training machines through data without explicit programming. Broadly speaking, ML consists of three paradigms: unsupervised learning, supervised learning, and reinforcement learning (RL). All these paradigms have a training/exploration phase to optimize a learning algorithm that later can be used in prediction/exploitation phase to infer on unknown inputs. As shown in Fig. 2, we briefly summarize them by providing some use cases in wireless networks.
+
+1) Supervised Learning: Supervised learning exploits a labelled data set to learn a (hidden) function that maps an input to an expected output based on the examples. The standard techniques used to solve supervised learning-based problems are artificial neural networks (ANNs), support vector machines (SVMs), Bayesian networks, recurrent neural networks (RNNs), and convolutional neural networks (CNNs).
+2) Unsupervised Learning: Unsupervised learning does not learn from labelled data, instead, training is based on an unlabelled data set. K-means and principal component analysis (PCA) are examples of two major tools used for clustering and dimensionality reduction, respectively.
+3) Reinforcement Learning: RL is not based on training but rather the agent/decision-maker learns and decides online, maximizing a long-term reward. RL is beneficial in control problems where the agent adapts to changing environmental conditions, e.g., uplink power control.
+
+Motivated by the considerable benefits of ML in various fields, its applications have also been considered in wireless networks almost at all layers of communication. Here, we focus on its impact on radio access networks (RAN), particularly PHY and link layers. Based on ML tools, given in Fig.2, some case studies will be explained later in Section III.
+
+## A. Machine Learning at PHY
+
+At PHY, many optimization problems are non-convex, e.g., sum-rate maximization. ML is a powerful tool to find good solution(s) for such non-convex optimization problems. Based on advanced learning algorithms, 6G networks provide the following major advantages by using ML.
+
+- ML can be effective to deal with network complexity. 6G networks will be more complex due to numerous network topologies, immense growth in the cellular users, staggering data rate demands, complex air interface, vast network coordination methods, etc. Forecasting considerable complexity of $6 \mathrm{G}$ networks, the derivation of optimum performance solutions is nearly infeasible without ML.
+- ML can play a vital role to deal with model deficit problems. Current cellular networks are amenable for mathematical derivation, for instance, information theory gives closed-form expressions for various problems such as Shannon theorem. However, the inherent complexity of $6 \mathrm{G}$ networks hinders the possibility of exploiting closed-form analytical expression(s), which can be due, for instance, to non-linearities either in the channel or network devices. ML offers an efficient way to deal with non-linearities, providing feasible solution(s) in a tractable manner.
+- ML can cope with algorithm deficit problems. In current cellular networks, many optimal algorithms, although well-characterized, are impractical to be implemented. Considering the example of multiple-input multipleoutput (MIMO) systems where optimal solutions are known (e.g., dirty paper coding), they are overlooked in favour of linear solutions, e.g., linear minimum meansquared error. It is envisaged that ML can pave the way to implement more efficient yet practical solutions.
+
+ML has been used to study various PHY issues, and without being exhaustive, some of the recent areas include:
+
+- CNNs are used for modulation classification in [4].
+- An RNN-based wireless channel predictor [5] is used in [3], explained in Section III-C to deal with inaccurate channel state information (CSI).
+
+## III. Wireless Networks: Case Studies
+
+In this section, we present three use cases to demonstrate the use of ML techniques in industrial wireless networks. ML tools utilized for these use cases are depicted in Fig. 2.
+
+## A. UE Positioning
+
+Highly accurate user equipment (UE) positioning is one of the prime considerations for Third Generation Partnership Project (3GPP) studies beyond Release 15. Various angle and time-of-arrival-based methods are used to determine UE positioning in today's cellular networks. All of these methods require triangulation techniques to resolve UE position and suffer from time synchronization errors.
+
+We studied UE position by using radio frequency (RF) fingerprinting and two ML techniques, namely deep learning and decision tree, for an outdoor scenario [12]. Serving cell Reference Signal Received Power (RSRP) as well as neighbor cell RSRP values were used as features to train a deep neural network (DNN). As shown in Fig. 3, nearly $5 \mathrm{~m}$ accuracy is achieved for DNN when only 4 serving cell RSRP values and corresponding beam IDs are considered as a feature input, while it improves to nearly $1 \mathrm{~m}$ when 2 more RSRP values from the strongest neighboring cells, respective cell and beam IDs are added to the input feature set. The decision tree, a less complex algorithm as compared to DNN, provides about $2 \mathrm{~m}$ accuracy using data from both serving and neighboring cell beams as an input feature. The mean accuracy of nearly $1 \mathrm{~m}$ obtained from DNN is comparable to the accuracy level achieved with traditional methods without requiring triangulation and does not suffer from signal timing synchronization issues.
+
+## B. ML-Assisted Proactive Mobility
+
+For seamless and efficient mobility, a well optimized network should reduce the number of Handover (HO) events while avoiding Handover Failures (HOF) and Radio Link Failures (RLF). An emerging approach is to utilize ML-based algorithms, which enable proactive and UE specific mobility actions in the gNB. A relatively simple approach to this is to design an ML-based estimator of the radio measurements, such as RSRP of serving and neighbor cells, with a certain minimum accuracy and within a certain time horizon. Radio measurements are traditionally performed at the UEs side and reported to the serving $\mathrm{gNB}$ (or gNB-Centralized Unit) according to specific Radio Resource Control (RRC) configurations. For ML-based prediction purposes, time-traces of RSRP, or Reference Signal Received Quality (RSRQ) values need to be collected either in the UE and/or serving the gNB.
+
+
+
+Fig. 3. Comparison of UE position for both DNN and decision tree techniques. The system level parameters for the network includes 8 sites with Inter-site distance $110 \mathrm{~m}$ and carrier frequency $28 \mathrm{GHz}$. For details of the parameters, please refer to [12].
+
+For example, collected time-series of RSRP values are used as input to the ML-based predictor, which provides at the UE, and/or at the serving $\mathrm{gNB}$, a set of sufficiently accurately estimated RSRP values within a given future time horizon. Then, these signal estimations are used for predictive evaluation of possible $\mathrm{HO}$ conditions, thus can trigger proactive measurement reports from the UE and/or proactive $\mathrm{HO}$ actions at the serving $\mathrm{gNB}$. These two steps are repeated with a time periodicity given, e.g., by the sampling rate and time filtering of the input RSRP measurements [13], or alternatively, the steps can also be triggered by the serving $\mathrm{gNB}$ when certain traffic or mobility Quality-of-Service (QoS) conditions are met.
+
+The outlined ML-based mobility algorithm can be implemented in either the UE or gNB or both, depending on the available ML assistance capabilities in each node. Furthermore, the mechanism can be integrated in self-organizing network-based Mobility Robustness Optimization solutions.
+
+## C. CSI Feedback
+
+CSI feedback in the downlink channel is a major challenge in Release 17 and beyond. Currently, CSI precision is affected by compressing the measurements imposed by the standard.
+
+In our study, summarized in Section II-A, we assumed two RNN-based twin channel predictors at the $\mathrm{gNB}$ and UE [3]. The past CSI is utilized for training the RNN at both ends of the communication system. UE's feedback is evaluated with respect to the predicted channel. Fig. 4 depicts the meansquared error (MSE) between the actual channel versus the acquired channel at the $\mathrm{gNB}$ and the precoding gain when different quantization bits are used to feedback the CSI from the UE. The results are compared with and without using ML for the CSI feedback. A clear benefit of using ML can be observed. We believe that ML-based solutions will improve current performance without increasing signaling overhead.
+
+
+
+(a) Trend of MSE.
+
+
+
+(b) Trend of precoding gain.
+
+Fig. 4. Performance of MSE and precoding gain. $2 \times 1$ MIMO configuration is considered, and RNN is composed of 1 hidden layer. For parameters' details, refer to [3].
+
+## IV. Role of ML in Standardization
+
+The potential of ML for $5 \mathrm{G}$ has been widely acknowledged in the literature and applications made it even in the standard at higher levels, e.g., for networking and security [7]. 3GPP has introduced a specification, named network data analytics function (NWDAF), in Release 15 and 16, as part of the $5 \mathrm{G}$ Core $(5 \mathrm{GC})$ architecture [7]. NWDAF is responsible for providing network analytics when requested by a network function (NF). Data is collected via application function (AF), operation, administration, and maintenance (OAM), NF, and data repositories. The specifications have also addressed the problem of inter-working for automation and data collection, which analytics vendors previously faced. 3GPP NWDAF framework for $5 \mathrm{G}$ systems is depicted in Fig.55. This automation gives leverage to network vendors for the deployment and testing of non-RT ML-related use cases. In Fig. 5. inward interfaces aggregate data from different network sources, where communication occurs using existing service-based interfaces. Outward interfaces provide decisions (analytics-based, algorithmic) to AF and NF.
+
+
+
+Fig. 5. A generalized framework for 5G network automation in Release 16, representing that NWDAF should be able to collect data from the operator OAM, AFs and $5 \mathrm{GC}$ network functions $[7]$.
+
+Regarding PHY, ML techniques lag behind, due to a number of issues. First, PHY makes use of abstractions and mathematical models that are inferred from the physical reality and electromagnetic principles. As long as such models describe the real-world precisely, there is no need for ML. Nevertheless, in practice, models and fixed algorithms are inefficient when facing rapidly changing and heterogeneous environments. For example, using the same channel acquisition scheme to acquire CSI from a laptop in line-of-sight with a $\mathrm{gNB}$, a tablet on a fast train, or a mobile quickly moving in a super densely covered area might not be optimal. Consequently, the standardization efforts of intelligent techniques have gained momentum, and while 3GPP is ready to begin a study item on ML implementations, open-radio access network (O-RAN) will be ML-native, defining a RAN intelligent controller (RIC), which will enhance several RAN functions.
+
+3GPP has started studying the implications of the ML use at layer-1 and a study item on ML for NR air interface has been agreed upon. After the RAN-1 working group studies, protocol aspects will be studied in RAN-2 and subsequently, interoperability and testability aspects will be considered in RAN-4 working group. The remaining part of this section summarizes the status of the standardization of ML techniques for PHY for both 3GPP and O-RAN.
+
+## A. CSI Feedback
+
+CSI feedback for downlink channel in Release 17 is a complex issue in which UE-based beam selection is followed by CSI reference symbols (RS) training and precoding matrix index (PMI) reporting, and lastly by Demodulation Reference Signal (DMRS) and consequent estimation of the precoded channel. Broadly, beam selection aims to establish a sufficiently strong link budget between the UEs and the gNB. The CSI-RS is used for fine channel estimation, which is then fed back to the gNB to compute a precoder (eventually multiuser); finally, DMRS are precoded pilots that the UEs use to implement coherent demodulation. Currently, each of these phases is created following pre-established rules, with little to none room for intelligent behaviour. ML has been envisioned to possibly enhance each phase in a different way. Beam selection can be improved by intelligently correlating the beams with position or identity of the UEs. This would allow for a smart selection of the beams from the gNB side, thus avoiding brute-force selection. The CSI-RS can be enhanced by compressing the pilots and the PMI feedback exploiting ad hoc ML compressors. Furthermore, channel prediction techniques [5] can be used in order to pre-establish a baseline for the CSI feedback [3]. Other aspects that can be improved include frequency of pilots in both CSI-RS and DMRS, power and timing and CSI-RS port selection.
+
+## B. $R S-D M R S$
+
+Roughly speaking, DMRS are RS used for channel estimation to perform coherent demodulation. The correct estimation of the channel using such pilots have a strong impact on the performance in terms of bit-error-rate and thus block-errorrate. The role of the ML in such domain is twofold. First, it can be used to improve the performance of the channel estimation. Second, the ML can provide a smarter positioning of DMRS in order to reduce their frequency, hence reducing the overhead footprint in $6 \mathrm{G}$.
+
+## C. Positioning
+
+A precise positioning is one of the aspects that sees the largest improvement with respect to LTE's observed time difference of arrival (OTDOA) and uplink time difference of arrival (UTDOA), defined in Release 9 onward. Various aspects of $6 \mathrm{G}$ allow for precise positioning of the UE, such as large number of antenna elements at the $\mathrm{gNB}$, millimeter wave transmissions, dense network deployment. However, the methods based on angle-of-arrival and time-of-arrival fall short when non-line-of-sight scenarios are considered, in interference-limited scenarios. ML techniques, see Fig.2, are expected to help in improving the position by exploiting channel charting, hence learning the likely position of a UE based on a report, and multiplexing together information that carries positioning information but are hard to exploit in a classical way, such as CSI report and sounding reference signal maps.
+
+## D. Mobility Enhancements
+
+In 6G, frequent cell-selection, and frequent RSRP measurement could impact UEs' battery life. Furthermore, load balancing algorithms can use intelligent techniques that exploit the UE specific channel prediction, movement trajectory prediction and traffic demands prediction. Furthermore, the scenarios like fast-trains or non-terrestrial networks, will pose challenges to $\mathrm{HO}$ and conditional-HO operations. Novel solutions envisaged, compared to current 3GPP Release 17, include the use of UE specific ML-based predictive algorithms, addressed in Section III-B, designed to reduce paging errors and HO failures; thus, improve the overall QoS.
+
+## E. Standardization for ML Data Collection
+
+3GPP has started working on data collection for running ML algorithms in 5G networks [14]. The scope of such studies include identifying mechanisms to collect data from the network through minimization of drive test framework or further advanced enhancements. Furthermore, studies will focus on discussing hosting of ML models both for training as well as inference purposes at various network entities for various use cases and defining any new interfaces required for transporting data to the models.
+
+## F. Federated Learning Model Collection
+
+Training and prediction based on ML models will put an extra load on networks already transporting a large volume of data. Therefore, it is important to estimate the effect of model training and inference on network traffic, particularly for federated learning (FL) where UEs will act as distributed hosts [15]. The latency in collecting locally trained models is bounded in FL and network links should be able to meet delay budgets. This is particularly challenging in today's networks where a UE's own QoS requirements are already demanding and the FL model training and collection will further incur an extra burden on the network. Similarly, the split inference, where UEs cooperate with each other to perform joint inference, results in increasing the network traffic. 3GPP studies in Release 18 [15] will focus on the above mentioned issues to support training and inference for ML/FL models over wireless links.
+
+## G. O-RAN-RIC
+
+O-RAN alliance, aims to define a RAN network that is non-vendor specific, and that has an innate support for ML as an enabler for automation and OPEX savings. O-RAN alliance has defined interfaces for exchange of information in the protocol stack. To this end, in the O-RAN architecture, ML-assisted RAN intelligent controller (RIC) is included for network automation, for both scenarios, i.e., non-RT and RT. In the non-RT RIC, ML algorithms' training is done by using the data obtained at lower layers. However, the learning process remains slow; therefore, it is called non-RT RIC. Later, the learner is fed into the RT RIC, which utilizes the RT captured data to perform decisions online. Additionally, the functionality of non-RT includes policy management and higher layer procedure optimization. Therefore, the RAN or core-network can deploy such a mechanism based on the collected data.
+
+## V. Open Challenges and Roadmap for Deploying ML TECHNIQUES
+
+Though ML is a potential technology and enabler for nextgeneration wireless networks, several challenges related to its practical use are addressed below.
+
+## A. Data Availability and Benchmarking
+
+One of the foremost challenges in wireless networks is data availability. Data availability concerns the problem of identifying a common and accepted set of data (e.g., channel realizations) with the goal of testing and benchmarking ML algorithms. Such a problem is of a pivotal importance for standardization, where normally algorithms and proposals are tested using agreed underlying physical models (e.g., urban macrocells/microcells channel models), evaluation methodologies and calibrated simulators. Contrary to other fields, cellular networks have no standard data set to train and benchmark an ML algorithm. Therefore, a synthetic data set or software generated data set is of a predominant importance to train and benchmark ML algorithm(s), and to agree on a common evaluation methodology to rank proposition and standard algorithms.
+
+Identifying a set of key performance indicators in wireless networks is another crucial task for ML standardization. It is necessary to design a set of metrics to classify and rank ML algorithms and their performance. Classic approaches such as throughput and signal-to-interference-plus-noise ratio (SINR) might not be sufficient since a small improvement in these values might come at the cost of large complexity augmentation and exacerbated energy consumption.
+
+
+
+Fig. 6. Model collection for FL in a wireless network when some of the UEs have large blockage and use D2D communication for model transfer. Cluster-based UE selection is another solution for asynchronous model collection to meet network QoS requirements.
+
+## B. Selection of ML versus Non-ML Solutions
+
+ML tools are regarded as an implementation-oriented tool rather than a standard relevant aspect. The idea behind this relies on the fact that each vendor has the freedom to efficiently implement each aspect of the standard as long as the external interfaces are respected. A simple example of this is given in the CSI feedback, where a UE needs to select a specific PMI, but the standard does not specify any specific way in which this selection is performed. Recently, however, the idea of having ML dedicated message exchanges and performance that only an ML-aided algorithm can achieve has paved the way for standardization of ML algorithms [3]. This opens the door for several issues, e.g., will the standard impose a specific ML structure, classifying minimum performance and implementation structure, or will it remain far from the implementation? With regards to NNs, it is still open if hyperparameters are going to be left to vendor-specific implementation or will they be set by the standard.
+
+## C. Complexity of ML Algorithms
+
+Considering the limited battery life, storage, computational capability, and limited communication bandwidth in most cellular network entities, an ML model's cost-performance tradeoff becomes a fundamental issue. Another issue is the speed/time-steps at which the training and inference needs to be performed. Whereas hard-wired gNB have sufficient computational power to run complex ML algorithms, UEs need to face battery, heating and stringent complexity limits. Possible solutions to such issue include, but not limited to implementation of substitute rule-based algorithms at the UE side, migrating the load all on the $\mathrm{gNB}$ side.
+
+## D. Communication-aware Federated Learning
+
+Traditional ML models support centralized learning. Due to difficulties in collecting large amount of training data from the UEs, privacy issues and bandwidth bottleneck, FL has emerged as a promising solution. In FL, training is performed distributively over network devices, called local model hosts, and an application server on the network side acts as a central host to aggregate local models transmitted by the local learners. Typically, an application server host aggregates models only when updates are available from all the local learners, called synchronous model transfer. However, this is highly inefficient in wireless networks where links are unpredictable, local learners (UEs) are energy limited and have their own QoS requirements. Asynchronous model collection is the most viable solution for FL in wireless networks, where a subset of UEs is selected for a local model update in each round of model collection. However, UE selection in each round is a complex problem because UEs are energy limited and the network bandwidth is scarce, hindering collection of local models from all the UEs to represent independently and identically data collection. These mechanisms are usually vendor proprietary, but standardization still needs to define some common mechanisms for efficient model collection. As shown in Fig. 6. UE clustering and local device-to-device (D2D) communication for asynchronous model collection are possible solutions to decrease network communication and will require standardization support.
+
+## E. Stability and Adaptability of ML Techniques
+
+ML algorithms applied to wireless networks must be adaptive as they will have to deal with parameters that change dynamically. Particularly, the weights of the NN are evaluated online based on the trained data. However, this approach may not be applicable in wireless, and specifically in a standard, where coordination among entities belonging to different operators and provided by different vendors have to coexist, and in which the need for quick response could prevent one or the other solution. Possible solutions include: pre-trained $\mathrm{NN}$, or partially trained $\mathrm{NN}$ (i.e., $\mathrm{NN}$ in which the starting point is pre-set); cloud-based downloadable data set for $\mathrm{NN}$ training; codebook-based $\mathrm{NN}$, in which a codebook of different NNs is used and agreed upon between the gNB and UEs. Another related problem is to detect an outdated ML model with high inference error and replace it. Replacing an outdated model with a new model incurs further delay. Thus, there must be a proactive mechanism to adapt the ML model to network conditions such that network functions suffer minimum performance loss.
+
+## VI. Conclusion
+
+Motivated by the promise of the use of ML algorithms, we presented an overview of ML techniques to be used in 5G-Advanced and 6G wireless networks. Furthermore, we discussed the key roles of ML-based solutions from industrial and standardization perspectives. We also highlighted the practical challenges of deploying ML techniques in wireless networks and how to deal with them. Non-RT and higher layer ML-based solutions can be, and are, applied already in today's networks. Implementing RT ML solutions at PHY/MAC in 6G networks are the next big challenge in the research community. We believe that overcoming these challenges, both in research as well as at standardization levels, will pave the way for next-generation wireless communication to be effective and sustainable.
+
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+
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+
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+
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+
+[15] -, "5G System (5GS); Study on traffic characteristics and performance requirements for AI/ML model transfer (Release 18)," https://portal.3gpp.org/desktopmodules/Specifications/ SpecificationDetails.aspx?specificationId=3721 , Technical report (TR) $22.874,032021$.
+
+
+
+Muhammad K. Shehzad [S'21] is working as a Research Engineer and Ph.D. student at Nokia Bell-Labs and CentraleSupelec, Paris, France, respectively. He received his B.Eng. (Hons.) degree in Electrical and Electronic Engineering from the University of Bradford, Bradford, U.K., in 2016, and M.S. in Electrical Engineering from the National University of Sciences \& Technology (NUST), Islamabad, Pakistan, in 2019. His major research interest is in MIMO communication using Artificial Intelligence (AI)/Machine Learning (ML).
+
+
+
+Luca Rose [M'11] is Senior research and standard-ization expert with Nokia Bell-labs. He received his M.Sc. from university of Pisa, Italy, and his Ph.D. in Physics from Centrale-Supelec. He worked with Huawei France research center and Thales Communications and Security, contributing to several standard organizations. He is currently an ITU-R and ETSI delegate and the lead editor of IEEE Communication magazine series on IoT. His interests span from the field of AI/ML to Game theory.
+
+
+
+M. Majid Butt [SM'15] is a Senior Specialist at Nokia Bell-Labs, France, and an adjunct Professor at Trinity College Dublin, Ireland. He has authored more than 70 peer-reviewed conference and journal articles and filed over 30 patents. He is IEEE Comsoc distinguished lecturer for the class 2022-23. He frequently gives invited and technical tutorial talks on various topics in IEEE conferences and serves as an associate editor for IEEE Communication Magazine, IEEE Open Journal of the Communication Society and IEEE Open Journal of Vehicular Technology.
+
+
+
+István Z. Kovács [M’00] received his B.Sc. from "Politehnica" Technical University of Timişoara, Romania in 1989, his M.Sc.E.E. from École Nationale Supérieure des Télécommunications de Bretagne, France in 1996, and his Ph.D.E.E. in Wireless Communications from Aalborg University, Denmark in 2002. Currently he is senior research engineer at Nokia, Aalborg, Denmark, where he conducts research on machine learning-driven radio resource management and radio connectivity enhancements for non-terrestrial and aerial vehicle communications, in LTE and 5G networks.
+
+
+
+Mohamad Assaad [SM'15] is a Professor at CentraleSupelec, France and a researcher at the Laboratory of Signals and Systems (CNRS). He has coauthored 1 book and more than 120 journal and conference papers and serves regularly as TPC cochair for top-tier international conferences. He is currently an Editor for the IEEE Wireless Communications Letters and Journal of Communications and Information Networks. His research interests include 5G and beyond systems, and Machine Learning in wireless networks.
+
+
+
+Mohsen Guizani [F'09] is currently a Professor at the Machine Learning Department at the Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE. His main research interests are wireless communications and IoT security. He was elevated to the IEEE Fellow in 2009. He was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2019, 2020 and 2021. Dr. Guizani has won several research awards. He is the author of ten books and more than 800 publications.

tests/test_cli/pdf_dev/annotations/cleaned/cleaned_ordinary_textbook_1d9a847603a5e37e379738316820850d.md

+# 数学新星问题征解 
+
+第十五期 (2016.06)
+
+主持: 牟晓生
+
+第一题. 设 $z_{1}, z_{2}, z_{3}$ 是单位复数. 证明存在单位复数 $z$ 使得:
+
+$$
+\frac{1}{\left|z-z_{1}\right|^{2}}+\frac{1}{\left|z-z_{2}\right|^{2}}+\frac{1}{\left|z-z_{3}\right|^{2}} \leq \frac{9}{4}
+$$
+
+(湖北武钢三中学生 王逸轩, 上海大学冷岗松 供题)
+
+第二题. 如图, $D$ 是正三角形 $A B C$ 的边 $B C$ 上一点, $B D>C D$. 记 $O_{1}, I_{1}$ 为 $\triangle A B D$ 的外心与内心, $O_{2}, I_{2}$ 为 $\triangle A C D$ 的外心与内心. 圆 $I_{1}$ 与圆 $I_{2}$ 除 $B C$外的另一条外公切线交 $A B, A C$ 于 $P, Q$. 设直线 $P I_{1}$与 $Q I_{2}$ 交于 $R$, 而直线 $O_{1} I_{1}$ 与 $O_{2} I_{2}$ 交于 $T$. 证明: $A T^{2}=A R^{2}+A D \cdot B C$.
+
+(广西钦州 卢圣 供题)
+
+
+
+第三题. 给定正整数 $m, n$, 考虑在 $m \times n$ 白棋盘上先将一些格染成黑色. 在之后的每一时刻, 若存在一个白格至少与两个黑格相邻, 则可将它也染成黑色. 求最初至少要染多少个黑色格才能在某一时刻染黑整个棋盘?
+
+(哈佛大学 牟晓生 供题)
+
+第四题. $A B C$ 是一个三角形, 而 $P, Q, R$ 分别是 $B C, C A, A B$ 上的点。证明 $\triangle P Q R$ 的周长不小于 $\triangle A Q R, \triangle B R P, \triangle C P Q$ 周长的最小值.
+
+(哈佛大学 牟晓生 供题)
+

tests/test_cli/pdf_dev/annotations/cleaned/cleaned_research_report_1f978cd81fb7260c8f7644039ec2c054.md

+## 增持（维持）
+
+所属行业：机械设备
+
+当前价格(元): 82.42
+
+## 证券分析师
+
+倪正洋
+
+资格编号：S0120521020003
+
+邮箱: nizy@tebon.com.cn
+
+## 研究助理
+
+杨云道
+
+邮箱: yangyx@tebon.com.cn
+
+
+
+| 沪深 300 对比 | $1 \mathrm{M}$ | $2 \mathrm{M}$ | $3 \mathrm{M}$ |
+| :--- | ---: | ---: | ---: |
+| 绝对涨幅(\%) | 7.18 | 32.88 | 80.86 |
+| 相对涨幅(\%) | 8.10 | 25.93 | 78.39 |
+
+资料来源: 德邦研究所, 聚源数据
+
+## 相关研究
+
+1.《高测股份 (688556): 光伏金刚线及硅片切割代工业务推动公司 22Q1 业绩大超预期》, 2022.4.29
+
+2.《光伏设备: 光伏高效电池扩产提速,关键设备商各领风骚》, 2022.4.10 3. 《高测股份 (688556.SH): 再签建湖 10GW 硅片切割代工产能，强化代工业务成长逻辑》, 2022.4.7
+
+3.《高测股份 (688556.SH): 签订晶澳曲靖 2.2 亿元切割设备合同，看好 22 年代工业绩释放+HJT 切割工艺进步》, 2022.3.9
+
+4.《高测股份 (688556.SH): 21 年业绩预告超市场预期，关注切片代工利润释放》, 2022.1.24
+
+# 高测股份 $(688556.5 H):$ 扩产 4000 万公里金刚线，强化光伏碰片切割三元布局
+
+## 投资要点
+
+- 事件：公司拟与蓝关县人民政府签署的《壶关年产 12000 万千米金刚线项目投资协议书》，项目一期计划建设年产 4,000万千米金刚线产能，预计一期总投资额约 6.66 亿元; 后续年产 8,000 万千米金刚线项目尚未具体约定，存在较大不确定性。
+- 顺应下游需求扩张, 金刚线产能快速扩产, 保证公司内供+外销。光伏金刚线需求 22 年提升源于两方面：1）2022 年光伏产业链景气度高涨，1-5 月光伏装机同比 $+24.4 \%$, 带动产业链各环节开工率提升, 硅片前期扩产产能逐步落地, 金刚线需求释放；2）由于多晶硅料价格持续维持高位，细线化、薄片化趋势加速，其中细线化要求金刚线线径由 40 线、 38 线向 36 线、 35 线进步, 带动单 GW 切割线耗不断提升。目前 36 线单 GW 切割线耗约 50 万公里, 较 38 线提升约 $30 \%$ 。公司于 2021 年对金刚线进行 “ 1 机 12 线” 技改，技改完成后，公司 22 年 1 季度产能 712 万公里, 年化产能超 2500 万公里。公司目前切片代工产能约 47GW, 对应远期金刚线产能超 2300 万公里。本次扩产再一次扩充公司金刚线产能, 强化金刚线产能内供+外销布局。
+- 依托萦关低成本电价提升金刚线盈利能力, 顺应硅料节约持续推动细线化布局。公司在山西长治金刚线生产厂区采购电力的平均单价较青岛金刚线生产厂区采购电力的平均单价低, 2020 年度公司陆续将青岛的金刚线生产线搬迁到山西长治並关厂区，随着山西长治金刚线生产厂区金刚线产量增加，公司采购电力的平均单价呈下降趋势。目前公司电力采购单价从 2019 年 0.8 元/kwh 降低到 2022 年 Q1 的 0.39 元/kwh，並关后续拓展有望进一步降低公司金刚线电价成本。金刚线线径越细，锯㖓越小，切割时产生的锯㖓硅料损失越少，同样一根硅棒可切割加工出的硅片数量越多，制造硅片所需的硅材料越少。相同切割工艺下，金刚线越细，固结在钢线基体上的金刚石微粉颗粒越小，切割加工时对硅片的表面损伤越小，硅片表面质量越好，砝片 TTV 等质量指标表现也就越好。金刚线母线直径已由 2016 年的 80um 降至 2022 年上半年的 36、38、40um，此外高线速、柔性化和智能化等均是金刚线及切片技术进步方向, 公司在薄片、细线化、高线速、柔性智能化方面均有领先布局, 推动切割工艺持续进步。
+- 切割工艺的持续进步领先, 是保障公司利润释放的核心壁垒。公司光伏硅片切割三元布局包括硅片切割及机加工设备、砝片切割耗材 (金刚线) 以及切割代工业务。公司 2021 年依托前期设备+耗材布局切割代工业务, 目前已公布 47GW 产能 (乐山5GW 示范基地、乐山 20GW 大硅片及配套项目、建湖一期 10GW 项目,建湖二期 $12 \mathrm{GW}$ 项目), 客户包括通威、京运通、美科及建湖周边电池企业。22 年底公司有望实现超 20GW 切割代工产能, 且当前终端客户主要为下游电池企业。客户选择切割代工模式的核心在于凭借高测的专业化服务实现快速上产, 同时可获得较自建硅片切割产能或购买硅片更多的超额利润。超额利润的核心在于高测股份的切割代工技术领先, 可实现更多的硅片切割红利, 并与客户共享。未来随着金刚线扩产和切割技术进步, 公司光伏硅片切割代工利润弹性有望持续释放。
+- 盈利预测与投资建议：预计公司 2022-2024 年归母净利润 4.7、7.2、9.3 亿元,对应 PE 30、20、15 倍，维持 “增持” 评级。
+- 风险提示：硅片扩产不及预期，公司代工业务利润波动风险，市场竞争加剧。
+
+<table><thead><tr><th>股票数据</th><th></th></tr></thead><tr><td>总股本(百万股):</td><td>227.92</td></tr><tr><td>流通 A 股(百万股):</td><td>167.01</td></tr><tr><td>52 周内股价区间(元):</td><td>21.60-97.40</td></tr><tr><td>总市值(百万元):</td><td>18,785.44</td></tr><tr><td>总资产(百万元):</td><td>3,508.81</td></tr><tr><td>每股净资产(元):</td><td>5.50</td></tr><tr><td>咨料来源，公司公告</td><td></td></tr></table>
+
+<table><thead><tr><th>主要财务数据及预测</th><th></th><th></th><th></th><th></th><th></th></tr></thead><tr><td></td><td>2020</td><td>2021</td><td>2022E</td><td>2023E</td><td>2024E</td></tr><tr><td>营业收入(百万元)</td><td>746</td><td>1,567</td><td>3,684</td><td>5,056</td><td>5,752</td></tr><tr><td>(+/-)YOY(%)</td><td>4.5\%</td><td>110.0\%</td><td>135.1\%</td><td>37.2\%</td><td>13.8\%</td></tr><tr><td>净利润(百万元)</td><td>59</td><td>173</td><td>471</td><td>717</td><td>933</td></tr><tr><td>(+/-)YOY(%)</td><td>83.8\%</td><td>193.4\%</td><td>172.8\%</td><td>52.2\%</td><td>30.1\%</td></tr><tr><td>全面摊薄 EPS(元)</td><td>0.43</td><td>1.07</td><td>2.91</td><td>4.43</td><td>5.77</td></tr><tr><td>毛利率(\%)</td><td>35.3\%</td><td>33.7\%</td><td>35.0\%</td><td>36.0\%</td><td>38.0\%</td></tr><tr><td>净资产收益率(\%)</td><td>6.0\%</td><td>15.0\%</td><td>27.9\%</td><td>28.8\%</td><td>26.5\%</td></tr></table>
+
+资料来源: 公司年报 (2020-2021)，德邦研究所
+
+备注: 净利润为归属母公司所有者的净利润
+
+## 财务报表分析和预测
+
+| 主要财务指标 | 2021 | $2022 E$ | $2023 E$ | $2024 E$ |
+| :--- | ---: | ---: | ---: | ---: |
+| 每股指标(元) |  |  |  |  |
+| 每股收益 | 1.07 | 2.91 | 4.43 | 5.77 |
+| 每股净资产 | 7.13 | 10.43 | 15.39 | 21.76 |
+| 每股经营现金流 | 0.47 | 1.27 | 4.07 | 5.02 |
+| 每股股利 | 0.11 | 0.11 | 0.11 | 0.11 |
+| 价值评估(倍) |  |  |  |  |
+| P/E | 82.90 | 30.47 | 20.02 | 15.38 |
+| P/B | 12.44 | 8.50 | 5.76 | 4.08 |
+| P/S | 8.52 | 3.62 | 2.64 | 2.32 |
+| EV/EBITDA | 49.85 | 24.12 | 15.68 | 11.46 |
+| 股息率\% | $0.1 \%$ | $0.1 \%$ | $0.1 \%$ | $0.1 \%$ |
+| 盈利能力指标(\%) |  |  |  |  |
+| 毛利率 | $33.7 \%$ | $35.0 \%$ | $36.0 \%$ | $38.0 \%$ |
+| 净利润率 | $11.0 \%$ | $12.8 \%$ | $14.2 \%$ | $16.2 \%$ |
+| 净资产收益率 | $15.0 \%$ | $27.9 \%$ | $28.8 \%$ | $26.5 \%$ |
+| 资产回报率 | $5.3 \%$ | $7.9 \%$ | $8.5 \%$ | $9.2 \%$ |
+| 投资回报率 | $15.3 \%$ | $25.9 \%$ | $24.6 \%$ | $23.7 \%$ |
+| 盈利增长(\%) |  |  |  |  |
+| 营业收入增长率 | $110.0 \%$ | $135.1 \%$ | $37.2 \%$ | $13.8 \%$ |
+| EBIT 增长率 | $233.7 \%$ | $150.7 \%$ | $52.3 \%$ | $31.9 \%$ |
+| 净利润增长率 | $193.4 \%$ | $172.8 \%$ | $52.2 \%$ | $30.1 \%$ |
+| 偿倩能力指标 |  |  |  |  |
+| 资产负债率 | $64.3 \%$ | $71.5 \%$ | $70.6 \%$ | $65.3 \%$ |
+| 流动比率 | 1.2 | 1.2 | 1.3 | 1.4 |
+| 速动比率 | 0.9 | 0.9 | 1.0 | 1.1 |
+| 现金比率 | 0.2 | 0.1 | 0.2 | 0.3 |
+| 经营效率指标 |  |  |  |  |
+| 应收怅款周转天数 | 161.7 | 165.1 | 164.9 | 164.4 |
+| 存货周转天数 | 196.1 | 170.0 | 180.0 | 190.0 |
+| 总资产周转率 | 0.5 | 0.6 | 0.6 | 0.6 |
+| 固定资产周转率 | 4.2 | 8.6 | 10.3 | 11.1 |
+
+| 现金流量表(百万元) | 2021 | $2022 E$ | 2023E | 2024E |
+| :--- | ---: | ---: | ---: | ---: |
+| 净利润 | 173 | 471 | 717 | 933 |
+| 少数股东损益 | 0 | 0 | 0 | 0 |
+| 非现金支出 | 107 | 114 | 133 | 147 |
+| 非经营收益 | 17 | 1 | 4 | 14 |
+| 营运资金变动 | -220 | -382 | -195 | -283 |
+| 经营活动现金流 | 76 | 205 | 658 | 812 |
+| 资产 | -83 | -184 | -203 | -169 |
+| 投资 | 229 | 0 | 0 | 0 |
+| 其他 | 6 | 9 | 13 | 14 |
+| 投资活动现金流 | 151 | -175 | -190 | -155 |
+| 债权募资 | -80 | 39 | 321 | 64 |
+| 股权募资 | 0 | 0 | 0 | 0 |
+| 其他活 | -21 | -3 | -14 | -25 |
+| 融资活动现金流 | -101 | 36 | 307 | 39 |
+| 现金净流量 | 127 | 66 | 775 | 696 |
+
+备注: 表中计算估值指标的收盘价日期为 7 月 19 日
+
+资料来源: 公司年报 (2020-2021), 德邦研究所
+
+| 利润表(百万元) | 2021 | 2022E | 2023E | 2024E |
+| :---: | :---: | :---: | :---: | :---: |
+| 营业总收入 | 1,567 | 3,684 | 5,056 | 5,752 |
+| 营业成本 | 1,038 | 2,394 | 3,236 | 3,567 |
+| 毛利率\% | $33.7 \%$ | $35.0 \%$ | $36.0 \%$ | $38.0 \%$ |
+| 营业税金及附加 | 6 | 18 | 25 | 29 |
+| 营业税金率\% | $0.4 \%$ | $0.5 \%$ | $0.5 \%$ | $0.5 \%$ |
+| 营业费用 | 63 | 147 | 193 | 209 |
+| 营业费用率\% | $4.0 \%$ | $4.0 \%$ | $3.8 \%$ | $3.6 \%$ |
+| 管理费用 | 131 | 313 | 409 | 444 |
+| 管理费用率\% | $8.4 \%$ | $8.5 \%$ | $8.1 \%$ | $7.7 \%$ |
+| 研发费用 | 117 | 276 | 379 | 431 |
+| 研发费用率\% | $7.5 \%$ | $7.5 \%$ | $7.5 \%$ | $7.5 \%$ |
+| EBIT | 213 | 534 | 814 | 1,074 |
+| 财务费用 | 7 | 1 | 11 | 19 |
+| 财务费用率\% | $0.4 \%$ | $0.0 \%$ | $0.2 \%$ | $0.3 \%$ |
+| 资产减值损失 | -33 | -63 | -86 | -98 |
+| 投资收益 | 5 | 9 | 13 | 14 |
+| 营业利润 | 212 | 531 | 800 | 1,040 |
+| 营业外收支 | -25 | -8 | -3 | -3 |
+| 利润总额 | 187 | 523 | 797 | 1,037 |
+| EBITDA | 282 | 582 | 865 | 1,129 |
+| 所得税 | 14 | 52 | 80 | 104 |
+| 有效所得税率\% | $7.7 \%$ | $10.0 \%$ | $10.0 \%$ | $10.0 \%$ |
+| 少数股东损益 | 0 | 0 | 0 | $\mathbf{0}-1-2$ |
+| 归属母公司所有者净利润 | 173 | 471 | 717 | 933 |
+
+| 资产负债表(百万元) | 2021 | 2022E | 2023E | $2024 E$ |
+| :---: | :---: | :---: | :---: | :---: |
+| 货币资金 | 427 | 494 | 1,269 | 1,965 |
+| 应收账款及应收票据 | 1,173 | 2,806 | 3,798 | 4,344 |
+| 存货 | 558 | 1,115 | 1,596 | 1,857 |
+| 其它流动资产 | 266 | 578 | 736 | 778 |
+| 流动资产合计 | 2,424 | 4,992 | 7,400 | 8,943 |
+| 长期股权投资 | 0 | 0 | 0 | 0 |
+| 固定资产 | 370 | 429 | 491 | 516 |
+| 在建工程 | 169 | 183 | 205 | 226 |
+| 无形资产 | 42 | 56 | 69 | 80 |
+| 非流动资产合计 | 811 | 940 | 1,087 | 1,198 |
+| 资产总计 | 3,235 | 5,932 | 8,487 | 10,141 |
+| 短期借款 | 28 | 68 | 388 | 452 |
+| 应付票据及应付账款 | 1,401 | 3,197 | 4,302 | 4,760 |
+| 预收账款 | 0 | 0 | 0 | 0 |
+| 其它流动负债 | 560 | 887 | 1,214 | 1,314 |
+| 流动负债合计 | 1,989 | 4,152 | 5,904 | 6,527 |
+| 长期借款 | 0 | 0 | 0 | 0 |
+| 其它长期负债 | 92 | 92 | 92 | 92 |
+| 非流动负债合计 | 92 | 92 | 92 | 92 |
+| 负债总计 | 2,081 | 4,243 | 5,996 | 6,619 |
+| 实收资本 | 162 | 162 | 162 | 162 |
+| 普通股股东权益 | 1,154 | 1,688 | 2,491 | 3,522 |
+| 少数股东权益 | 0 | 0 | 0 | 0 |
+| 负债和所有者权益合计 | 3,235 | 5,932 | 8,487 | 10,141 |
+
+## 信息披露
+
+## 分析师与研究助理简介
+
+倪正洋，2021 年加入德邦证券，任研究所大制造组组长、机械行业首席分析师，拥有 5 年机械研究经验，1 年高端装备产业经验，南京大学材料学学士、上海交通大学材料学硕士。2020 年获得 iFinD 机械行业最具人气分析师, 所在团队曾获机械行业 2019 年新财富第三名，2017 年新财富第二名，2017 年金牛奖第二名，2016 年新财富第四名。
+
+## 分析师声明
+
+本人具有中国证券业协会授予的证券投资咨询执业资格，以勤勉的职业态度，独立、客观地出具本报告。本报告所采用的数据和信息均来自市场公开信息, 本人不保证该等信息的准确性或完整性。分析逻辑基于作者的职业理解，清晰准确地反映了作者的研究观点，结论不受任何第三方的授意或影响，特此声明。
+
+## 投资评级说明
+
+1.投资评级的比较和评级标准:
+
+以报告发布后的 6 个月内的市场表现为比较标准，报告发布日后 6 个月内的公司股价（或行业指数）的张跌幅相对同期市场基准指数的涨跌幅;
+
+2.市场基准指数的比较标准:
+
+A 股市场以上证综指或深证成指为基准；香港市场以恒生指数为基准；美国市场以标普 500 或纳斯达克综合指数为基准。
+
+<table>
+    <tr>
+        <td rowspan="11">1. 投资评级的比较和评级标准: 以报告发布后的 6 个月内的市场表 现为比较标准，报告发布日后 6 个 月内的公司股价(或行业指数)的 涨跌幅相对同期市场基准指数的涨 跌幅：<br> 2. 市场基准指数的比较标准: A股市场以上证综指或深证成指为基 准; 香港市场以恒生指数为基准; 美 国市场以标普500或纳斯达克综合指 数为基准。</td>
+    </tr>
+    <tr>
+        <td>类型</td>
+        <td>评级</td>
+        <td>说明</td>
+    </tr>
+        <td rowspan="5">股票评级</td>
+    </tr>
+    <tr>
+        <td>买入</td>
+        <td>相对强于市场表现 20%以上;</td>
+    </tr>
+    <tr>
+        <td>增持</td>
+        <td>相对强于市场表现 5% 20%;</td>
+    </tr>
+    <tr>
+        <td>中性</td>
+        <td>相对市场表现在-5% +5%之间波动；</td>
+    </tr>
+    <tr>
+        <td>减持</td>
+        <td>相对弱于市场表现 5%以下。</td>
+    </tr>
+    <tr>
+        <td rowspan="4">行业投资评级</td>
+    </tr>
+    <tr>
+        <td>优于大市</td>
+        <td>预期行业整体回报高于基准指数整体水平10%以上;</td>
+    </tr>
+    <tr>
+        <td>中性</td>
+        <td>预期行业整体回报介于基准指数整体水平-10%与 10%之间;</td>
+    </tr>
+    <tr>
+        <td>弱于大市</td>
+        <td>预期行业整体回报低于基准指数整体水平 10%以下。</td>
+    </tr>
+    <tr>
+</table>
+
+## 法律声明
+
+本报告仅供德邦证券股份有限公司（以下简称 “本公司”）的客户使用。本公司不会因接收人收到本报告而视其为客户。在任何情况下，本报告中的信息或所表述的意见并不构成对任何人的投资建议。在任何情况下，本公司不对任何人因使用本报告中的任何内容所引致的任何损失负任何责任。
+
+本报告所载的资料、意见及推测仅反映本公司于发布本报告当日的判断，本报告所指的证券或投资标的的价格、价值及投资收入可能会波动。在不同时期，本公司可发出与本报告所载资料、意见及推测不一致的报告。
+
+市场有风险，投资需谨慎。本报告所载的信息、材料及结论只提供特定客户作参考，不构成投资建议，也没有考虑到个别客户特殊的投资目标、财务状况或需要。客户应考虑本报告中的任何意见或建议是否符合其特定状况。在法律许可的情况下，德邦证券及其所属关联机构可能会持有报告中提到的公司所发行的证券并进行交易，还可能为这些公司提供投资银行服务或其他服务。
+
+本报告仅向特定客户传送，未经德邦证券研究所书面授权，本研究报告的任何部分均不得以任何方式制作任何形式的拷贝、复印件或复制品，或再次分发给任何其他人，或以任何侵犯本公司版权的其他方式使用。所有本报告中使用的商标、服务标记及标记均为本公司的商标、服务标记及标记。如欲引用或转载本文内容, 务必联络德邦证券研究所并获得许可, 并需注明出处为德邦证券研究所，且不得对本文进行有悖原意的引用和删改。
+
+根据中国证监会核发的经营证券业务许可，德邦证券股份有限公司的经营范围包括证券投资咨询业务。
\ No newline at end of file

tests/test_cli/pdf_dev/b80cbc13-6655-42a8-a3a1-fe2db6eff883.html.json

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"2Faculty of Sciences, Manipal University Jaipur, Jaipur 302007, India"}, {"category_id": 15, "poly": [89.0, 205.0, 804.0, 207.0, 804.0, 241.0, 89.0, 239.0], "score": 0.99, "text": "3Department of Pure & Applied Physics, University of Kota, Kota 324010, India"}, {"category_id": 15, "poly": [89.0, 261.0, 549.0, 261.0, 549.0, 295.0, 89.0, 295.0], "score": 0.98, "text": "Received 25 May 2013; Accepted 24 August 2013"}, {"category_id": 15, "poly": [89.0, 314.0, 374.0, 314.0, 374.0, 346.0, 89.0, 346.0], "score": 0.97, "text": "Academic Editor: Dilip Kanhere"}, {"category_id": 15, "poly": [89.0, 395.0, 873.0, 395.0, 873.0, 429.0, 89.0, 429.0], "score": 0.98, "text": "distribution, and reproduction in any medium, provided the original work is properly cited."}, {"category_id": 15, "poly": [89.0, 453.0, 180.0, 453.0, 180.0, 487.0, 89.0, 487.0], "score": 1.0, "text": "Abstract"}, {"category_id": 15, "poly": [89.0, 514.0, 1493.0, 514.0, 1493.0, 546.0, 89.0, 546.0], "score": 0.99, "text": "The electronic structure and electron momentum density distribution in BaO and BaS are presented using Compton spectroscopy. The frst-ever Compton profle"}, {"category_id": 15, "poly": [89.0, 570.0, 1520.0, 570.0, 1520.0, 604.0, 89.0, 604.0], "score": 0.98, "text": "Compton profles of BaO and BaS using the linear combination of atomic orbitals method. In the present computation, the correlation scheme proposed by Perdew-"}, {"category_id": 15, "poly": [89.0, 599.0, 1535.0, 599.0, 1535.0, 633.0, 89.0, 633.0], "score": 0.98, "text": "Burke-Ernzerhof and the exchange scheme of Becke were considered. The hybrid B3PW and Hartree-Fock based profles were also computed for both compounds."}, {"category_id": 15, "poly": [89.0, 629.0, 1503.0, 629.0, 1503.0, 663.0, 89.0, 663.0], "score": 0.99, "text": "The ionic configurations are performed to estimate the charge transfer on compound formation, and the present study suggests charge transfer from Ba to O and S"}, {"category_id": 15, "poly": [89.0, 658.0, 1119.0, 658.0, 1119.0, 692.0, 89.0, 692.0], "score": 0.98, "text": "atoms. On the basis of equal-valence-electron-density profles,itis found that BaO is more ionic as compared to BaS."}, {"category_id": 15, "poly": [91.0, 721.0, 236.0, 721.0, 236.0, 748.0, 91.0, 748.0], "score": 1.0, "text": "1. Introduction"}, {"category_id": 15, "poly": [89.0, 772.0, 1550.0, 775.0, 1550.0, 809.0, 89.0, 806.0], "score": 0.98, "text": "The I-VI alkaline earth compounds have interesting bond characteristics and simple crystal structures. BaO and BaS have potential applications in ight-emiting diodes"}, {"category_id": 15, "poly": [89.0, 806.0, 1550.0, 806.0, 1550.0, 841.0, 89.0, 841.0], "score": 0.98, "text": "(LEDs), laser diodes (LDs), and magnetooptical devices [14]. BaO is an indirect bandgap, whereas BaS is a direct bandgap material. At normal conditions, BaO and"}, {"category_id": 15, "poly": [89.0, 836.0, 1488.0, 836.0, 1488.0, 867.0, 89.0, 867.0], "score": 0.96, "text": "BaS crystallize in NaCl (B1) structure, but under pressure, they show structural phase transition from B1 to B2 structure [5, 6]. Using the full-potential linearized "}, {"category_id": 15, "poly": [86.0, 860.0, 1500.0, 858.0, 1501.0, 899.0, 86.0, 902.0], "score": 0.81, "text": "augmented plae wave F-LAPWhd, Drablat al  rpoted the ltronic andoptcal prpertis fBa and Baincubc phase at maland "}, {"category_id": 15, "poly": [89.0, 894.0, 1515.0, 894.0, 1515.0, 926.0, 89.0, 926.0], "score": 0.99, "text": "hydrostatic pressure. Lin et al. [8] observed that the electronic structure ofthese compounds containing oxygen atoms always obeys a different relationship from the"}, {"category_id": 15, "poly": [86.0, 923.0, 780.0, 921.0, 780.0, 955.0, 86.0, 958.0], "score": 0.98, "text": " compounds not containing oxygen atoms using density functional theory (DFT)."}, {"category_id": 15, "poly": [91.0, 972.0, 1493.0, 972.0, 1493.0, 1006.0, 91.0, 1006.0], "score": 0.97, "text": "Most ofthe earlier studies, both experimental and theoretical, involve the electronic, optical, and structural properties ofBaO and BaS [515]. To the best ofour"}, {"category_id": 15, "poly": [86.0, 999.0, 1496.0, 996.0, 1496.0, 1038.0, 86.0, 1040.0], "score": 0.8, "text": "knowlede, nne attdt ltronic strutue and mnmdnsityfBaand Ba usng Coto ptropy It swellstablhed that Ctn"}, {"category_id": 15, "poly": [89.0, 1033.0, 1550.0, 1031.0, 1550.0, 1065.0, 89.0, 1067.0], "score": 0.98, "text": "spectroscopy provides a useful test to examine the bonding in solids [16, 17]. Thus, we found it worth to study the electronic structure in BaO and BaS using Compton "}, {"category_id": 15, "poly": [1402.0, 1096.0, 1555.0, 1096.0, 1555.0, 1123.0, 1402.0, 1123.0], "score": 0.97, "text": "where integration"}, {"category_id": 15, "poly": [86.0, 1191.0, 1481.0, 1189.0, 1481.0, 1230.0, 86.0, 1233.0], "score": 0.79, "text": "wave ftionand suatonxts oeralloccpid statIn this paper, te reuts ofCoptnscattrng studya and Ba are prentd.F t"}, {"category_id": 15, "poly": [91.0, 1223.0, 1547.0, 1223.0, 1547.0, 1257.0, 91.0, 1257.0], "score": 0.97, "text": "theoretical Compton profles, first-principles calculations based on inear combination ofatomic orbitals (LCAO) method are performed using CRYSTALO6 code [18]."}, {"category_id": 15, "poly": [89.0, 1250.0, 1471.0, 1250.0, 1471.0, 1284.0, 89.0, 1284.0], "score": 0.98, "text": "The ionic model has been applied to estimate the charge transfer in these compounds. The nature ofbonding in isostructural and isovalent BaO and BaS is also"}, {"category_id": 15, "poly": [89.0, 1279.0, 1505.0, 1279.0, 1505.0, 1313.0, 89.0, 1313.0], "score": 0.98, "text": "compared using equal-valence-electron-density (EVED) profles. The paper is organized as follows. Section 2 gives the experimental details and data analysis. The"}, {"category_id": 15, "poly": [89.0, 1372.0, 504.0, 1372.0, 504.0, 1403.0, 89.0, 1403.0], "score": 0.97, "text": "2. Expe rime ntal Details and Data Analysis"}, {"category_id": 15, "poly": [91.0, 1525.0, 1515.0, 1525.0, 1515.0, 1559.0, 91.0, 1559.0], "score": 0.98, "text": "which was cooled with liquid nitrogen providing overall momentum resolution of 0.6 a.u. The spectra were recorded with a multichannel analyzer (MCA) with 4096"}, {"category_id": 15, "poly": [84.0, 1618.0, 1505.0, 1615.0, 1505.0, 1657.0, 84.0, 1659.0], "score": 0.89, "text": "processed for several systematic corrections likebackground, instrumental resoution sanple absorption, scattering crosssection, and mutipe scattering using the"}, {"category_id": 15, "poly": [89.0, 1679.0, 1562.0, 1679.0, 1562.0, 1713.0, 89.0, 1713.0], "score": 0.98, "text": "a.u., being the area of free atom Compton profle [22] in the given range. The 1 s electrons of Ba were neglected for both compounds since these do not contribute in the"}, {"category_id": 15, "poly": [86.0, 1708.0, 329.0, 1705.0, 330.0, 1742.0, 86.0, 1745.0], "score": 1.0, "text": "present experimental setup."}, {"category_id": 15, "poly": [86.0, 1769.0, 303.0, 1769.0, 303.0, 1801.0, 86.0, 1801.0], "score": 1.0, "text": "3. Theoretical Details"}, {"category_id": 15, "poly": [86.0, 1825.0, 285.0, 1825.0, 285.0, 1857.0, 86.0, 1857.0], "score": 0.98, "text": "3.1. DFT-LCAO Method"}, {"category_id": 15, "poly": [89.0, 1883.0, 1547.0, 1883.0, 1547.0, 1917.0, 89.0, 1917.0], "score": 0.98, "text": "To compute the theoretical Compton profles of BaO and BaS, the LCAO method embodied in the CRYSTAL06 code [18, 23] was employed. This code provides a"}, {"category_id": 15, "poly": [89.0, 1913.0, 1508.0, 1913.0, 1508.0, 1947.0, 89.0, 1947.0], "score": 0.96, "text": "platformto calculate electronic structure of periodic systems considering Gaussian basis sets. In the LCAO technique, each crystallne orbital is built from the inear"}, {"category_id": 15, "poly": [89.0, 1942.0, 1564.0, 1942.0, 1564.0, 1974.0, 89.0, 1974.0], "score": 0.98, "text": "combination of Bloch functions. The Bloch functions are defined in terms of local functions constructed from the atom-centered certain number of Gaussian functions. For"}, {"category_id": 15, "poly": [86.0, 1971.0, 1540.0, 1971.0, 1540.0, 2005.0, 86.0, 2005.0], "score": 0.98, "text": "Ba, O, and S, the local functions were constructed from the Gaussian type basis sets [24]. In the present DFT calculation, the crystal Hamitonian was generated using"}, {"category_id": 15, "poly": [89.0, 2000.0, 1523.0, 2000.0, 1523.0, 2034.0, 89.0, 2034.0], "score": 0.97, "text": "the correlation functional proposed by Perdew et al. [25] and exchange scheme of Becke [26]. The hybrid B3PW and Hartree-Fock (HF) based profles were also"}, {"category_id": 15, "poly": [89.0, 2056.0, 175.0, 2056.0, 175.0, 2090.0, 89.0, 2090.0], "score": 0.98, "text": "and BaS."}, {"category_id": 15, "poly": [89.0, 2117.0, 216.0, 2117.0, 216.0, 2144.0, 89.0, 2144.0], "score": 0.98, "text": "3.2. Ionic Model"}, {"category_id": 15, "poly": [84.0, 2166.0, 1525.0, 2168.0, 1525.0, 2210.0, 84.0, 2207.0], "score": 0.95, "text": "The theoretical ionic profles ofBaO and BaS for various charge transfer confgurations were calculated from thefee atom Compton profle of Ba, O, and S atoms"}, {"category_id": 15, "poly": [1407.0, 1159.0, 1540.0, 1165.0, 1539.0, 1199.0, 1407.0, 1193.0], "score": 0.93, "text": " is the electron"}, {"category_id": 15, "poly": [89.0, 1094.0, 349.0, 1094.0, 349.0, 1126.0, 89.0, 1126.0], "score": 0.97, "text": "profile. The Compton profle,"}, {"category_id": 15, "poly": [91.0, 2203.0, 417.0, 2203.0, 417.0, 2237.0, 91.0, 2237.0], "score": 0.98, "text": "[22]. The valence profles for various"}, {"category_id": 15, "poly": [792.0, 1591.0, 1542.0, 1591.0, 1542.0, 1625.0, 792.0, 1625.0], "score": 0.99, "text": "counts at the Compton peak. To deduce the true Compton profle, the raw data were"}, {"category_id": 15, "poly": [89.0, 1167.0, 347.0, 1167.0, 347.0, 1199.0, 89.0, 1199.0], "score": 0.97, "text": "is performed over a constant-"}, {"category_id": 15, "poly": [789.0, 1167.0, 888.0, 1167.0, 888.0, 1199.0, 789.0, 1199.0], "score": 0.99, "text": "is given as"}, {"category_id": 15, "poly": [684.0, 2232.0, 725.0, 2232.0, 725.0, 2266.0, 684.0, 2266.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [839.0, 2232.0, 1537.0, 2232.0, 1537.0, 2266.0, 839.0, 2266.0], "score": 0.97, "text": " configurations were then added to the core contribution to get total profles. All"}, {"category_id": 15, "poly": [539.0, 2203.0, 580.0, 2203.0, 580.0, 2237.0, 539.0, 2237.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [89.0, 1462.0, 336.0, 1462.0, 336.0, 1496.0, 89.0, 1496.0], "score": 1.0, "text": "The incident gamma-rays of"}, {"category_id": 15, "poly": [437.0, 1462.0, 694.0, 1462.0, 694.0, 1496.0, 437.0, 1496.0], "score": 0.98, "text": "were scattered at an angle of"}, {"category_id": 15, "poly": [713.0, 1494.0, 1530.0, 1491.0, 1530.0, 1525.0, 713.0, 1528.0], "score": 0.98, "text": ". The scattered radiation was analyzed using an HPGe detector (Canberra model, GLO110S)"}, {"category_id": 15, "poly": [89.0, 2030.0, 958.0, 2030.0, 958.0, 2061.0, 89.0, 2061.0], "score": 1.0, "text": "computed for both compounds [18]. The computations were performed by taking B1 structure and"}, {"category_id": 15, "poly": [1014.0, 2030.0, 1542.0, 2030.0, 1542.0, 2061.0, 1014.0, 2061.0], "score": 0.98, "text": "points in the irreducible wedge ofthe Brillouin zone for BaO"}, {"category_id": 15, "poly": [91.0, 2232.0, 119.0, 2232.0, 119.0, 2266.0, 91.0, 2266.0], "score": 0.92, "text": "the"}, {"category_id": 15, "poly": [140.0, 2232.0, 561.0, 2232.0, 561.0, 2266.0, 140.0, 2266.0], "score": 0.98, "text": "shell ofO and S atoms. The valence profles for"}, {"category_id": 15, "poly": [89.0, 1649.0, 1528.0, 1649.0, 1528.0, 1684.0, 89.0, 1684.0], "score": 0.98, "text": "computer code ofthe Warwick Group [20. 21]. Finally, the corrected profles were normalized to 23.200 for BaO and 26.434 for BaS electrons in the range of0 to"}, {"category_id": 15, "poly": [374.0, 1167.0, 432.0, 1167.0, 432.0, 1199.0, 374.0, 1199.0], "score": 1.0, "text": "plane,"}, {"category_id": 15, "poly": [450.0, 1167.0, 744.0, 1167.0, 744.0, 1199.0, 450.0, 1199.0], "score": 0.98, "text": "is scattering vector direction, and"}, {"category_id": 15, "poly": [89.0, 543.0, 698.0, 543.0, 698.0, 575.0, 89.0, 575.0], "score": 0.98, "text": "measurements on polycrystalline BaO and BaS were performed using"}, {"category_id": 15, "poly": [800.0, 543.0, 1525.0, 543.0, 1525.0, 575.0, 800.0, 575.0], "score": 0.98, "text": "gamma-rays. To interpret the experimental data, we have computed the theoretical"}, {"category_id": 15, "poly": [89.0, 365.0, 180.0, 365.0, 180.0, 397.0, 89.0, 397.0], "score": 1.0, "text": "Copyright"}, {"category_id": 15, "poly": [838.0, 2203.0, 1239.0, 2203.0, 1239.0, 2237.0, 838.0, 2237.0], "score": 0.99, "text": " configurations were computed by transferring"}, {"category_id": 15, "poly": [1258.0, 2203.0, 1557.0, 2203.0, 1557.0, 2237.0, 1258.0, 2237.0], "score": 0.95, "text": "electrons from the s shellof Ba to"}, {"category_id": 15, "poly": [405.0, 1094.0, 908.0, 1094.0, 908.0, 1126.0, 405.0, 1126.0], "score": 0.99, "text": ", is related to the ground state electron momentum density"}, {"category_id": 15, "poly": [86.0, 1494.0, 340.0, 1491.0, 340.0, 1525.0, 86.0, 1528.0], "score": 0.98, "text": "and effective density for BaC"}, {"category_id": 15, "poly": [489.0, 1494.0, 528.0, 1491.0, 528.0, 1525.0, 489.0, 1528.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [807.0, 1462.0, 1399.0, 1462.0, 1399.0, 1496.0, 807.0, 1496.0], "score": 0.97, "text": "by the polycrystalline sample of BaO and BaS (pellet of 18 mm dia,"}, {"category_id": 15, "poly": [1473.0, 1462.0, 1562.0, 1462.0, 1562.0, 1496.0, 1473.0, 1496.0], "score": 1.0, "text": "thickness,"}, {"category_id": 15, "poly": [86.0, 1428.0, 617.0, 1428.0, 617.0, 1469.0, 86.0, 1469.0], "score": 0.98, "text": "The measurements on BaO and BaS were performed using "}, {"category_id": 15, "poly": [718.0, 1428.0, 1552.0, 1428.0, 1552.0, 1469.0, 718.0, 1469.0], "score": 0.94, "text": "gamma-rays Compton spectrometer. The details ofthe experimental setup are available in [19]."}, {"category_id": 15, "poly": [174.0, 1591.0, 541.0, 1591.0, 541.0, 1625.0, 174.0, 1625.0], "score": 0.97, "text": "to collect 2 \u00d7 104 counts and BaS around"}, {"category_id": 15, "poly": [616.0, 1591.0, 699.0, 1591.0, 699.0, 1625.0, 616.0, 1625.0], "score": 0.89, "text": "to collect"}, {"category_id": 15, "poly": [281.0, 365.0, 1464.0, 365.0, 1464.0, 397.0, 281.0, 397.0], "score": 0.98, "text": "Kumar et al. This is an open access article distributed under the Creative Commons Atribution License, which permits unrestricted use,"}, {"category_id": 15, "poly": [89.0, 1311.0, 504.0, 1311.0, 504.0, 1345.0, 89.0, 1345.0], "score": 0.99, "text": "theoretical calculations are presented in Section"}, {"category_id": 15, "poly": [523.0, 1311.0, 1412.0, 1311.0, 1412.0, 1345.0, 523.0, 1345.0], "score": 0.98, "text": "and Section 4 is devoted to the resuts and discussion Finally, the conclusions are drawn in Section 5."}], "page_info": {"page_no": 1, "height": 2339, "width": 1653}}, {"layout_dets": [{"category_id": 0, "poly": [91.0196304321289, 1884.4053955078125, 231.04054260253906, 1884.4053955078125, 231.04054260253906, 1912.9195556640625, 91.0196304321289, 1912.9195556640625], "score": 0.9999994039535522}, {"category_id": 1, "poly": [91.3698959350586, 754.431884765625, 1535.784912109375, 754.431884765625, 1535.784912109375, 810.5221557617188, 91.3698959350586, 810.5221557617188], "score": 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13, "poly": [278, 756, 322, 756, 322, 783, 278, 783], "score": 0.62, "latex": "(\\Delta J)"}, {"category_id": 13, "poly": [768, 484, 837, 484, 837, 510, 768, 510], "score": 0.43, "latex": "3.0\\,\\mathrm{a.u.}"}, {"category_id": 13, "poly": [739, 514, 806, 514, 806, 539, 739, 539], "score": 0.34, "latex": "3.0\\,\\mathrm{a.u.}"}, {"category_id": 13, "poly": [1333, 1243, 1400, 1243, 1400, 1269, 1333, 1269], "score": 0.33, "latex": "0.6\\,\\mathrm{a.u.}"}, {"category_id": 13, "poly": [278, 663, 321, 663, 321, 690, 278, 690], "score": 0.32, "latex": "(\\Delta J)"}, {"category_id": 13, "poly": [777, 867, 809, 867, 809, 891, 777, 891], "score": 0.27, "latex": "\\mathrm{HF}"}, {"category_id": 15, "poly": [89.0, 141.0, 342.0, 141.0, 342.0, 173.0, 89.0, 173.0], "score": 0.98, "text": "4. Results and Discussion"}, {"category_id": 15, "poly": [89.0, 200.0, 1545.0, 200.0, 1545.0, 231.0, 89.0, 231.0], "score": 0.98, "text": "The numerical values of unconvoluted spherically averaged theoretical Compton profiles (DFT-PBE, B3PW, HF, and Ionic) of BaO and BaS are presented in Table 1."}, {"category_id": 15, "poly": [91.0, 229.0, 1191.0, 229.0, 1191.0, 263.0, 91.0, 263.0], "score": 0.98, "text": "The experimental Compton profles of BaO and BaS are also given in the table including experimental errors at selected points."}, {"category_id": 15, "poly": [91.0, 317.0, 1547.0, 317.0, 1547.0, 348.0, 91.0, 348.0], "score": 0.98, "text": "Table 1: The unconvoluted theoretical (DFT-PBE, DFT-B3PW, HF, and Ionic) and experimental Compton profles for BaO and BaS. The experimental errors at few"}, {"category_id": 15, "poly": [89.0, 346.0, 772.0, 346.0, 772.0, 380.0, 89.0, 380.0], "score": 0.98, "text": "points are also presented, and all profles are normalized to the fee atom area."}, {"category_id": 15, "poly": [89.0, 397.0, 1552.0, 397.0, 1552.0, 431.0, 89.0, 431.0], "score": 0.98, "text": "In Figures 1 and 2, the experimental Compton profles ofBaO and BaS are compared with various ionic arrangements to estimate the charge transfer. For a quantitative"}, {"category_id": 15, "poly": [89.0, 487.0, 1560.0, 487.0, 1560.0, 521.0, 89.0, 521.0], "score": 0.97, "text": "the efect of charge transfer from Ba to O atoms is largely visible within 0.0 to 3.0 a.u. for BaO. The best agreement is found for x = 1.0. Figure 2 shows that the charge"}, {"category_id": 15, "poly": [89.0, 694.0, 556.0, 694.0, 556.0, 729.0, 89.0, 729.0], "score": 0.95, "text": "are convohuted with the Gaussian of 0.6 a.u. FWHM."}, {"category_id": 15, "poly": [86.0, 787.0, 558.0, 784.0, 558.0, 819.0, 86.0, 821.0], "score": 0.96, "text": " are convoluted with the Gaussian of 0.6 a.u. FWHM."}, {"category_id": 15, "poly": [89.0, 926.0, 1540.0, 923.0, 1540.0, 958.0, 89.0, 960.0], "score": 0.96, "text": "momentum region, because the contrbution in this region is mostly due to core elctrons, which remain unafected in the compound formation. Similar features are also"}, {"category_id": 15, "poly": [89.0, 953.0, 795.0, 955.0, 794.0, 989.0, 89.0, 987.0], "score": 0.96, "text": "visible in Figure 4, but the effect of exchange and correlation is not seen for BaS."}, {"category_id": 15, "poly": [91.0, 1043.0, 1535.0, 1043.0, 1535.0, 1077.0, 91.0, 1077.0], "score": 0.96, "text": "Figure 3: The difference of DFT-PBE, B3PW, and HF with experimental Compton profle of BaO. All profles are convoluted with the Gaussian of 0.6 a.u. FWHM."}, {"category_id": 15, "poly": [91.0, 1106.0, 1530.0, 1106.0, 1530.0, 1140.0, 91.0, 1140.0], "score": 0.97, "text": "Figure 4: The difference of DFT-PBE, B3PW, and HF with experimental Compton profle of BaS. Alprofiles are convoluted with the Gaussian of0.6 a.u. FWHM."}, {"category_id": 15, "poly": [89.0, 1155.0, 1523.0, 1157.0, 1523.0, 1191.0, 89.0, 1189.0], "score": 0.98, "text": "The directional Compton profles ofBaO and BaS along [100], [110], and [111] directions have been computed to examine the anisotropies [100]-[110] , [110]-"}, {"category_id": 15, "poly": [91.0, 1274.0, 1547.0, 1274.0, 1547.0, 1308.0, 91.0, 1308.0], "score": 0.98, "text": "anisotropies are visible up to 3.0 a.u. In Figure 6, the anisotropies are plotted for BaS. This figure shows similarity with BaO, but all anisotropies are diminished beyond"}, {"category_id": 15, "poly": [86.0, 1301.0, 1304.0, 1304.0, 1304.0, 1338.0, 86.0, 1335.0], "score": 0.98, "text": " 2.0 a.u. Measurements on single crystallne samples of BaO and BaS along principal directions would be valuable to examine these findings."}, {"category_id": 15, "poly": [91.0, 1508.0, 1520.0, 1508.0, 1520.0, 1540.0, 91.0, 1540.0], "score": 0.98, "text": "The nature of bonding in isostructural and isovalent BaO and BaS has been compared and plotted in Figure 7. In this figure, the experimental EVED profles of these"}, {"category_id": 15, "poly": [89.0, 1537.0, 1518.0, 1537.0, 1518.0, 1569.0, 89.0, 1569.0], "score": 0.97, "text": "compounds are considered. We also plot the theoretical EVED profles in the inset. The EVED profles were derived by normalizing valence electron profiles to 4.0"}, {"category_id": 15, "poly": [91.0, 1654.0, 1540.0, 1654.0, 1540.0, 1688.0, 91.0, 1688.0], "score": 0.98, "text": "localization of charges in BaS in the bonding direction as compared to BaO. It is worth mentioning here that the covalent bonding is a result of sharing ofelectrons, and"}, {"category_id": 15, "poly": [93.0, 1684.0, 1540.0, 1684.0, 1540.0, 1715.0, 93.0, 1715.0], "score": 0.99, "text": "hence, it increases localization ofcharge in bonding direction which results in a sharper Compton line shape [32, 33]. Therefore, we conclude that the ionic character is"}, {"category_id": 15, "poly": [91.0, 1713.0, 1505.0, 1713.0, 1505.0, 1745.0, 91.0, 1745.0], "score": 0.98, "text": "higher for BaO as compared to BaS. The larger ionic character of BaO as compared to BaS is well supported by the bulk modulus and cohesive energy data [34]"}, {"category_id": 15, "poly": [91.0, 1827.0, 1547.0, 1827.0, 1547.0, 1861.0, 91.0, 1861.0], "score": 0.98, "text": "valence profles. The inset shows the EVED profles derived from the theoretical valence profiles of these compounds. All these profles are normalized to 4.0 electrons."}, {"category_id": 15, "poly": [85.0, 1881.0, 234.0, 1886.0, 233.0, 1920.0, 83.0, 1915.0], "score": 0.97, "text": " 5. Conclusions"}, {"category_id": 15, "poly": [91.0, 1947.0, 1483.0, 1947.0, 1483.0, 1981.0, 91.0, 1981.0], "score": 0.97, "text": "Electronic structure and momentum density in BaO and BaS using Compton scattering technique are reported. The experimental values of Compton profles are"}, {"category_id": 15, "poly": [89.0, 1976.0, 1508.0, 1976.0, 1508.0, 2010.0, 89.0, 2010.0], "score": 0.98, "text": "compared with the LCAO based values for both compounds. The anisotropies in momentum densities depict larger occupied states along [1oo] direction with low"}, {"category_id": 15, "poly": [89.0, 2005.0, 1537.0, 2005.0, 1537.0, 2037.0, 89.0, 2037.0], "score": 0.99, "text": "momentum In addition, the ionic model based calculations have also been used to estimate the charge transfer in the compounds, and the model suggests a transfer of"}, {"category_id": 15, "poly": [91.0, 2064.0, 155.0, 2064.0, 155.0, 2093.0, 91.0, 2093.0], "score": 0.95, "text": "to BaS."}, {"category_id": 15, "poly": [89.0, 2119.0, 266.0, 2125.0, 265.0, 2157.0, 88.0, 2151.0], "score": 1.0, "text": "Acknowledgments"}, {"category_id": 15, "poly": [91.0, 2181.0, 1532.0, 2181.0, 1532.0, 2215.0, 91.0, 2215.0], "score": 0.97, "text": "This work is financially supported by the CSIR, New Delhi, through the Grant no. 03(1205/12EMR-II). G. Sharma is also thankful to the Head ofthe Department of"}, {"category_id": 15, "poly": [86.0, 2207.0, 891.0, 2210.0, 890.0, 2244.0, 86.0, 2242.0], "score": 0.97, "text": "Pure & Applied Physics, University ofKota, Kota, for providing the computational facities."}, {"category_id": 15, "poly": [89.0, 426.0, 639.0, 426.0, 639.0, 460.0, 89.0, 460.0], "score": 0.97, "text": "comparison of the ionic and experiment, the difference profiles "}, {"category_id": 15, "poly": [903.0, 426.0, 1520.0, 426.0, 1520.0, 460.0, 903.0, 460.0], "score": 0.94, "text": " have been deduced after convoluting alionic profles with a Gaussian"}, {"category_id": 15, "poly": [86.0, 833.0, 234.0, 833.0, 234.0, 875.0, 86.0, 875.0], "score": 0.96, "text": "The diferences"}, {"category_id": 15, "poly": [91.0, 1391.0, 397.0, 1391.0, 397.0, 1425.0, 91.0, 1425.0], "score": 0.99, "text": "Figure 5: Directional anisotropies,"}, {"category_id": 15, "poly": [468.0, 1391.0, 1188.0, 1391.0, 1188.0, 1425.0, 468.0, 1425.0], "score": 0.96, "text": ", for BaO for the pair ofdirections [100]-[110] , [110]-[111], and [100]-[111] ."}, {"category_id": 15, "poly": [91.0, 1623.0, 722.0, 1623.0, 722.0, 1657.0, 91.0, 1657.0], "score": 0.99, "text": "and isostructural compounds [27-31]. It is seen from Figure 7 that, near"}, {"category_id": 15, "poly": [789.0, 1623.0, 1476.0, 1623.0, 1476.0, 1657.0, 789.0, 1657.0], "score": 0.98, "text": "a.u., the sharpness ofCompton profles is higher for BaS, which suggests more"}, {"category_id": 15, "poly": [91.0, 1457.0, 397.0, 1457.0, 397.0, 1489.0, 91.0, 1489.0], "score": 1.0, "text": "Figure 6: Directional anisotropies,"}, {"category_id": 15, "poly": [467.0, 1457.0, 1186.0, 1457.0, 1186.0, 1489.0, 467.0, 1489.0], "score": 0.97, "text": ", for BaS for the pair ofdirections [100]-[110] , [110]-[111], and [100]-[111] ."}, {"category_id": 15, "poly": [84.0, 541.0, 514.0, 546.0, 514.0, 585.0, 84.0, 580.0], "score": 0.94, "text": " checks and from Figures 1 and 2, it isfound that"}, {"category_id": 15, "poly": [91.0, 1798.0, 803.0, 1798.0, 803.0, 1832.0, 91.0, 1832.0], "score": 0.97, "text": "Figure 7: The equal valence-electron-density (EVED) profiles of BaO and BaS ("}, {"category_id": 15, "poly": [911.0, 1798.0, 1552.0, 1798.0, 1552.0, 1832.0, 911.0, 1832.0], "score": 0.96, "text": " and 0.813 a.u., resp.). These profles are deduced from the experimental"}, {"category_id": 15, "poly": [91.0, 517.0, 277.0, 517.0, 277.0, 548.0, 91.0, 548.0], "score": 0.98, "text": "transfer configuration"}, {"category_id": 15, "poly": [626.0, 541.0, 663.0, 546.0, 663.0, 585.0, 626.0, 580.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [771.0, 541.0, 1537.0, 546.0, 1537.0, 585.0, 771.0, 580.0], "score": 0.98, "text": "configurations give the best agreement for BaO and BaS, respectively. Thus, the model"}, {"category_id": 15, "poly": [823.0, 577.0, 1259.0, 577.0, 1259.0, 612.0, 823.0, 612.0], "score": 1.0, "text": "states ofO and S for BaO and BaS, respectively."}, {"category_id": 15, "poly": [926.0, 1567.0, 1564.0, 1567.0, 1564.0, 1601.0, 926.0, 1601.0], "score": 0.98, "text": "tuned out to be 0.938 and 0.813 a.u., respectively, using the expression"}, {"category_id": 15, "poly": [283.0, 892.0, 742.0, 894.0, 742.0, 936.0, 283.0, 933.0], "score": 0.88, "text": "-, whilethe trend reversed in the momentum range of"}, {"category_id": 15, "poly": [907.0, 892.0, 1537.0, 894.0, 1537.0, 936.0, 907.0, 933.0], "score": 0.94, "text": ". The diference between theory and experiment is negigibe in the high"}, {"category_id": 15, "poly": [89.0, 1213.0, 894.0, 1213.0, 894.0, 1255.0, 89.0, 1255.0], "score": 0.7, "text": "forBa aBa retivFdits tat alant are psitiv"}, {"category_id": 15, "poly": [1015.0, 1213.0, 1505.0, 1213.0, 1505.0, 1255.0, 1015.0, 1255.0], "score": 0.96, "text": "for BaO. It ndicates larger occupied states along [100]"}, {"category_id": 15, "poly": [86.0, 2027.0, 513.0, 2030.0, 513.0, 2071.0, 86.0, 2069.0], "score": 0.95, "text": "1.0 and 1.5 electrons froms state ofBa atomto"}, {"category_id": 15, "poly": [532.0, 2027.0, 1552.0, 2030.0, 1552.0, 2071.0, 532.0, 2069.0], "score": 0.97, "text": "state ofO and S atoms. The EVED profles for the compounds conclude higher ionic character in BaO as compared"}, {"category_id": 15, "poly": [180.0, 1593.0, 246.0, 1593.0, 246.0, 1628.0, 180.0, 1628.0], "score": 0.95, "text": ", where"}, {"category_id": 15, "poly": [264.0, 1593.0, 1547.0, 1593.0, 1547.0, 1628.0, 264.0, 1628.0], "score": 0.99, "text": "is the valence electron density. A number ofresearchers have proved that this scheme offers a way to understand the nature ofbonding in isovalent "}, {"category_id": 15, "poly": [91.0, 1567.0, 673.0, 1567.0, 673.0, 1601.0, 91.0, 1601.0], "score": 0.95, "text": "electrons and scaling the resulting profiles by the Fermi momentum"}, {"category_id": 15, "poly": [716.0, 1567.0, 895.0, 1567.0, 895.0, 1601.0, 716.0, 1601.0], "score": 0.95, "text": ". For BaO and BaS,"}, {"category_id": 15, "poly": [1162.0, 755.0, 1532.0, 758.0, 1532.0, 792.0, 1162.0, 789.0], "score": 0.97, "text": " are also shown at points. All ionic profles"}, {"category_id": 15, "poly": [513.0, 833.0, 1322.0, 833.0, 1322.0, 875.0, 513.0, 875.0], "score": 0.88, "text": "betwenexperimental and CAO schee based Compton profle are presened nFigu"}, {"category_id": 15, "poly": [1341.0, 833.0, 1562.0, 833.0, 1562.0, 875.0, 1341.0, 875.0], "score": 0.96, "text": "and 4 for BaO and BaS,"}, {"category_id": 15, "poly": [1164.0, 665.0, 1532.0, 665.0, 1532.0, 699.0, 1164.0, 699.0], "score": 0.97, "text": "are also shown at points. All ionic profles"}, {"category_id": 15, "poly": [89.0, 867.0, 262.0, 870.0, 262.0, 904.0, 89.0, 901.0], "score": 0.96, "text": "respectively. Figure"}, {"category_id": 15, "poly": [89.0, 577.0, 633.0, 577.0, 633.0, 612.0, 89.0, 612.0], "score": 0.99, "text": "suggests the transfer of 1.0 and 1.5 electrons from the valence"}, {"category_id": 15, "poly": [648.0, 577.0, 803.0, 577.0, 803.0, 612.0, 648.0, 612.0], "score": 0.97, "text": "state of Ba to the"}, {"category_id": 15, "poly": [93.0, 1189.0, 1474.0, 1189.0, 1474.0, 1221.0, 93.0, 1221.0], "score": 0.98, "text": "[111], and [100]-[111] in the electron momentum density. Allthese anisotropies are derived from convoluted B3PW hybrid scheme and presented in Figures"}, {"category_id": 15, "poly": [1493.0, 1189.0, 1550.0, 1189.0, 1550.0, 1221.0, 1493.0, 1221.0], "score": 1.0, "text": "and 6"}, {"category_id": 15, "poly": [91.0, 458.0, 1308.0, 458.0, 1308.0, 490.0, 91.0, 490.0], "score": 0.98, "text": "function of0.6 a.u. FWHM. All ionic profles are normalized to 23.200 electrons for BaO and 26.434 electrons for BaS in the range of0 to"}, {"category_id": 15, "poly": [1339.0, 458.0, 1560.0, 458.0, 1560.0, 490.0, 1339.0, 490.0], "score": 0.96, "text": "a.u. Figure 1 depicts that"}, {"category_id": 15, "poly": [86.0, 755.0, 277.0, 758.0, 277.0, 792.0, 86.0, 789.0], "score": 0.99, "text": "Figure 2: Difference"}, {"category_id": 15, "poly": [323.0, 755.0, 1115.0, 758.0, 1115.0, 792.0, 323.0, 789.0], "score": 0.99, "text": "between convoluted ionic and experimental Compton profles of BaS. Experimental errors"}, {"category_id": 15, "poly": [353.0, 517.0, 738.0, 517.0, 738.0, 548.0, 353.0, 548.0], "score": 0.98, "text": "is closet to the experiment for BaS. Beyond"}, {"category_id": 15, "poly": [807.0, 517.0, 1504.0, 517.0, 1504.0, 548.0, 807.0, 548.0], "score": 0.98, "text": ", all configurations show identical behavior for both compounds. On the basis of"}, {"category_id": 15, "poly": [89.0, 1245.0, 1332.0, 1245.0, 1332.0, 1279.0, 89.0, 1279.0], "score": 0.98, "text": "direction with low momentum A close inspection ofthis fgure reveals that maximum anisotropy is seen between [100] and [111] directions at"}, {"category_id": 15, "poly": [1401.0, 1245.0, 1545.0, 1245.0, 1545.0, 1279.0, 1401.0, 1279.0], "score": 0.93, "text": " and 1.2 a.u. All"}, {"category_id": 15, "poly": [89.0, 665.0, 277.0, 665.0, 277.0, 699.0, 89.0, 699.0], "score": 1.0, "text": "Figure 1: Difference"}, {"category_id": 15, "poly": [322.0, 665.0, 1118.0, 665.0, 1118.0, 699.0, 322.0, 699.0], "score": 0.98, "text": "between convoluted ionic and experimental Compton profles of BaO. Experimental errors"}, {"category_id": 15, "poly": [281.0, 867.0, 776.0, 870.0, 776.0, 904.0, 281.0, 901.0], "score": 0.98, "text": "shows that for BaO all theories DFT-PBE, B3PW, and"}, {"category_id": 15, "poly": [810.0, 867.0, 1560.0, 870.0, 1559.0, 904.0, 810.0, 901.0], "score": 0.98, "text": ") predict lower momentum density as compared to experiment in the momentum range"}], "page_info": {"page_no": 2, "height": 2339, "width": 1653}}, {"layout_dets": [{"category_id": 2, "poly": [89.0936279296875, 80.74409484863281, 204.3162078857422, 80.74409484863281, 204.3162078857422, 106.50306701660156, 89.0936279296875, 106.50306701660156], "score": 0.9999979734420776}, {"category_id": 1, "poly": [103.02957153320312, 134.86175537109375, 1563.1484375, 134.86175537109375, 1563.1484375, 2057.353515625, 103.02957153320312, 2057.353515625], "score": 0.999991774559021}, {"category_id": 13, "poly": [1065, 1590, 1114, 1590, 1114, 1614, 1065, 1614], "score": 0.46, "latex": "\\mathrm{{X}}\\!=\\!\\mathbf{\\mathcal{C}}"}, {"category_id": 13, "poly": [608, 1045, 622, 1045, 622, 1061, 608, 1061], "score": 0.42, "latex": "\\cdot"}, {"category_id": 13, "poly": [231, 1131, 245, 1131, 245, 1147, 231, 1147], "score": 0.38, "latex": "\\cdot"}, {"category_id": 13, "poly": [524, 871, 537, 871, 537, 886, 524, 886], "score": 0.36, "latex": "\\cdot"}, {"category_id": 13, "poly": [872, 1914, 886, 1914, 886, 1931, 872, 1931], "score": 0.36, "latex": "\\cdot"}, {"category_id": 13, "poly": [743, 928, 757, 928, 757, 944, 743, 944], "score": 0.34, "latex": "\\cdot"}, {"category_id": 13, "poly": [795, 318, 809, 318, 809, 335, 795, 335], "score": 0.34, "latex": "\\cdot"}, {"category_id": 13, "poly": [582, 1973, 596, 1973, 596, 1988, 582, 1988], "score": 0.33, "latex": "\\cdot"}, {"category_id": 13, "poly": [305, 260, 319, 260, 319, 276, 305, 276], "score": 0.32, "latex": "\\cdot"}, {"category_id": 13, "poly": [514, 929, 527, 929, 527, 944, 514, 944], "score": 0.32, "latex": "\\cdot"}, {"category_id": 13, "poly": [756, 812, 770, 812, 770, 828, 756, 828], "score": 0.3, "latex": "\\cdot"}, {"category_id": 13, "poly": [811, 1422, 825, 1422, 825, 1437, 811, 1437], "score": 0.29, "latex": "\\cdot"}, {"category_id": 13, "poly": [1023, 319, 1038, 319, 1038, 334, 1023, 334], "score": 0.29, "latex": "\\cdot"}, {"category_id": 13, "poly": [305, 1567, 319, 1567, 319, 1582, 305, 1582], "score": 0.28, "latex": "\\cdot"}, {"category_id": 13, "poly": [940, 987, 954, 987, 954, 1002, 940, 1002], "score": 0.28, "latex": "\\cdot"}, {"category_id": 13, "poly": [456, 1740, 469, 1740, 469, 1756, 456, 1756], "score": 0.27, "latex": "\\cdot"}, {"category_id": 13, "poly": [549, 1683, 562, 1683, 562, 1698, 549, 1698], "score": 0.26, "latex": "\\cdot"}, {"category_id": 13, "poly": [685, 1740, 699, 1740, 699, 1756, 685, 1756], "score": 0.25, "latex": "\\cdot"}, {"category_id": 15, "poly": [111.0, 134.0, 1471.0, 139.0, 1471.0, 173.0, 111.0, 168.0], "score": 0.97, "text": "1. 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M. Srivastava,2 Hossein Jafari 3 and Xiao-Jun Yang"}, {"category_id": 15, "poly": [86.0, 200.0, 718.0, 205.0, 718.0, 246.0, 86.0, 241.0], "score": 0.93, "text": "1College of Science, Yanshan Universiy, Qinhuangdao 066004, China"}, {"category_id": 15, "poly": [84.0, 234.0, 1085.0, 236.0, 1085.0, 278.0, 84.0, 275.0], "score": 0.96, "text": " 2Department of Mathematics and Statistics, Universiyof Victora, Victoria, British Columbia, Canada V8W 3R4"}, {"category_id": 15, "poly": [86.0, 270.0, 1134.0, 273.0, 1134.0, 307.0, 86.0, 305.0], "score": 0.98, "text": "3Department of Mathematics, Faculty of Mathematical Sciences, University of Mazandaran, Babolsar 47415-416, Iran"}, {"category_id": 15, "poly": [89.0, 305.0, 1164.0, 309.0, 1163.0, 344.0, 88.0, 339.0], "score": 0.98, "text": "4Department of Mathematics and Mechanics, China University ofMining and Technology, Jangsu, Xuzhou 221008, China"}, {"category_id": 15, "poly": [89.0, 361.0, 497.0, 361.0, 497.0, 392.0, 89.0, 392.0], "score": 0.98, "text": "Received 9 June 2013; Accepted 7 July 2013"}, {"category_id": 15, "poly": [89.0, 414.0, 465.0, 414.0, 465.0, 446.0, 89.0, 446.0], "score": 0.98, "text": "Academic Editor: J. A. Tenreiro Machado"}, {"category_id": 15, "poly": [89.0, 495.0, 873.0, 495.0, 873.0, 529.0, 89.0, 529.0], "score": 0.99, "text": "distribution, and reproduction in any medium, provided the original work is properly cited."}, {"category_id": 15, "poly": [91.0, 558.0, 177.0, 558.0, 177.0, 585.0, 91.0, 585.0], "score": 1.0, "text": "Abstract"}, {"category_id": 15, "poly": [89.0, 614.0, 1532.0, 614.0, 1532.0, 646.0, 89.0, 646.0], "score": 0.98, "text": "The main object of this paper is to investigate the Helmholtz and difusion equations on the Cantor sets involving local fractional derivative operators. The Cantor-type"}, {"category_id": 15, "poly": [89.0, 641.0, 1510.0, 641.0, 1510.0, 672.0, 89.0, 672.0], "score": 0.99, "text": "cylindrical-coordinate method is applied to handle the corresponding local fractional differential equations. 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1405, 690, 1432, 661, 1432], "score": 0.35, "latex": "(\\underline{{1}})"}, {"category_id": 13, "poly": [479, 1970, 596, 1970, 596, 2000, 479, 2000], "score": 0.35, "latex": "W_{2}=7\\,\\mathrm{mm},"}, {"category_id": 13, "poly": [601, 1970, 727, 1970, 727, 2003, 601, 2003], "score": 0.34, "latex": "L_{g}=40\\,\\mathrm{mm},"}, {"category_id": 15, "poly": [86.0, 134.0, 1055.0, 139.0, 1055.0, 180.0, 86.0, 175.0], "score": 0.97, "text": "Centre for Space Science (ANGKASA), Universi Kebangsaan Malaysia, 43600 Bangi Selangor, Malaysia"}, {"category_id": 15, "poly": [91.0, 175.0, 1230.0, 175.0, 1230.0, 210.0, 91.0, 210.0], "score": 0.97, "text": "Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, 43600 Bangi Selangor, Malaysia"}, {"category_id": 15, "poly": [86.0, 224.0, 841.0, 227.0, 841.0, 261.0, 86.0, 258.0], "score": 0.99, "text": " Received 25 October 2013; Accepted 17 November 2013; Published 27 April 2014"}, {"category_id": 15, "poly": [91.0, 280.0, 364.0, 280.0, 364.0, 312.0, 91.0, 312.0], "score": 0.99, "text": "Academic Editor: Rezaul Azim"}, {"category_id": 15, "poly": [89.0, 363.0, 873.0, 363.0, 873.0, 395.0, 89.0, 395.0], "score": 0.98, "text": "distribution, and reproduction in any medium, provided the original work is properly cited."}, {"category_id": 15, "poly": [91.0, 424.0, 177.0, 424.0, 177.0, 451.0, 91.0, 451.0], "score": 1.0, "text": "Abstract"}, {"category_id": 15, "poly": [91.0, 480.0, 1547.0, 480.0, 1547.0, 512.0, 91.0, 512.0], "score": 0.98, "text": "A double inverted F-shape patch antenna is presented for dual-band operation. The proposed antenna is comprised ofcircular and rectangular slots on a printed circuit"}, {"category_id": 15, "poly": [89.0, 597.0, 1045.0, 597.0, 1045.0, 629.0, 89.0, 629.0], "score": 0.99, "text": "bands. Moreover, numerical simulations have been indicated as an important uniformity with measured results."}, {"category_id": 15, "poly": [91.0, 658.0, 236.0, 658.0, 236.0, 685.0, 91.0, 685.0], "score": 1.0, "text": "1. Introduction"}, {"category_id": 15, "poly": [89.0, 714.0, 1498.0, 714.0, 1498.0, 748.0, 89.0, 748.0], "score": 0.98, "text": "The raising demands of wireless communication systems enforce the improvement of dual band antennas that have abilities to operate under different standards in"}, {"category_id": 15, "poly": [84.0, 738.0, 1550.0, 741.0, 1550.0, 782.0, 84.0, 780.0], "score": 0.79, "text": "different fqucyba. dan nwre catons he ducd tr ds in the antea tlgy It als pavedt wayfrwidesa"}, {"category_id": 15, "poly": [84.0, 767.0, 1483.0, 770.0, 1483.0, 811.0, 84.0, 809.0], "score": 0.83, "text": "ofmobile phnes nmde socity reulting n mounng concems surodng its hamul radiation [1 2]. Mrostrip patch anta plays an iortant role a a"}, {"category_id": 15, "poly": [89.0, 802.0, 1528.0, 802.0, 1528.0, 836.0, 89.0, 836.0], "score": 0.98, "text": "harbinger in wireless communication systems and is gradually carrying out to face the changing demands ofupdate antena technology. Microstrip patch antennas are"}, {"category_id": 15, "poly": [91.0, 831.0, 1542.0, 831.0, 1542.0, 865.0, 91.0, 865.0], "score": 0.98, "text": "presently under concern for using in broadband comunication systems due to their attractive characteristics, such as low profle, low cost, ightweight, wide frequency"}, {"category_id": 15, "poly": [91.0, 860.0, 1532.0, 860.0, 1532.0, 892.0, 91.0, 892.0], "score": 0.97, "text": "bandwidth, ease of fabrication, and easy integration with monolithic microwave integrated circuits [3, 4]. However, the limitations ofthe microstrip patch antennas are"}, {"category_id": 15, "poly": [91.0, 889.0, 1505.0, 889.0, 1505.0, 921.0, 91.0, 921.0], "score": 0.98, "text": "having narrow bandwidth, and for that reason the demand of the bandwidth enhancement is gradualy rising in the practical applications [5]. In order to enhance its"}, {"category_id": 15, "poly": [89.0, 919.0, 1513.0, 919.0, 1513.0, 953.0, 89.0, 953.0], "score": 0.97, "text": "bandwidth, many approaches have been applied conventionaly, such as using thick substrates with low dielectrics constant, impedance matching network, parasitic"}, {"category_id": 15, "poly": [89.0, 948.0, 1341.0, 948.0, 1341.0, 979.0, 89.0, 979.0], "score": 0.98, "text": "patches stacked on the top ofthe main patch [6], slots lbaded on the patch, high dielectric constant substrate, and adopting short-circuit pin [Z]."}, {"category_id": 15, "poly": [89.0, 997.0, 1552.0, 997.0, 1552.0, 1031.0, 89.0, 1031.0], "score": 0.98, "text": "In [8], a rectangular slot antenna has been stated for dual frequency operation Reference [9] has narrated a printed dipole antenna to cover dual band with U-slot arms."}, {"category_id": 15, "poly": [89.0, 1028.0, 1486.0, 1028.0, 1486.0, 1060.0, 89.0, 1060.0], "score": 0.99, "text": "Reference [10] has been reported a low cost microstrip dipole antenna for wireless communications. In [11], a PIFA antenna has been presented for dual band "}, {"category_id": 15, "poly": [86.0, 1053.0, 1562.0, 1053.0, 1562.0, 1094.0, 86.0, 1094.0], "score": 0.96, "text": "operation with U-slot. Reference [12] has been mentioned a dual lop antea for 2.4/5 GHz wireless LAN. A monopole antema with double-T has been stated in [13]"}, {"category_id": 15, "poly": [86.0, 1082.0, 1555.0, 1084.0, 1555.0, 1118.0, 86.0, 1116.0], "score": 0.99, "text": " for 2.4/5.2 GHz WLAN operations. A dual polarized antemna has been mentioned in [14] for Ku-band application. Microstrip antennas on FR4 substrate material were"}, {"category_id": 15, "poly": [89.0, 1113.0, 475.0, 1113.0, 475.0, 1148.0, 89.0, 1148.0], "score": 0.98, "text": "discussed for UWB applications in [15, 16]."}, {"category_id": 15, "poly": [89.0, 1172.0, 1513.0, 1172.0, 1513.0, 1206.0, 89.0, 1206.0], "score": 0.99, "text": "In this research, a double inverted F-shape 40 \u00d7 40 mm? patch antenna for dual-band operation has been proposed and investigated to increase the bandwidth and"}, {"category_id": 15, "poly": [91.0, 1201.0, 1562.0, 1201.0, 1562.0, 1233.0, 91.0, 1233.0], "score": 0.99, "text": "reduce the size at the same time. The effect on antenna resonances and other antenna parameters is concentrated due to slots on the patch and ground plane to design the"}, {"category_id": 15, "poly": [89.0, 1230.0, 1537.0, 1228.0, 1537.0, 1262.0, 89.0, 1265.0], "score": 0.97, "text": "proposed dual-band double inverted F-shape antenna with enhanced bandwidth and effciency. Some techniques are employed such as increasing substrate thickness,"}, {"category_id": 15, "poly": [89.0, 1260.0, 1547.0, 1260.0, 1547.0, 1294.0, 89.0, 1294.0], "score": 0.98, "text": "changing patch by cutting rectangular slots, and cutting ground plane to achieve the resultant parameters such as impedance matching, gain, radiation pattern, and return"}, {"category_id": 15, "poly": [89.0, 1289.0, 1129.0, 1289.0, 1129.0, 1323.0, 89.0, 1323.0], "score": 0.97, "text": "loss. The results have been given a hint that the proposed antemna is appropriate for X-band and Ku-band applications."}, {"category_id": 15, "poly": [89.0, 1345.0, 470.0, 1345.0, 470.0, 1377.0, 89.0, 1377.0], "score": 0.99, "text": "2. Antenna Geometry and Optimization"}, {"category_id": 15, "poly": [89.0, 1606.0, 1488.0, 1606.0, 1488.0, 1640.0, 89.0, 1640.0], "score": 0.99, "text": "The geometry ofthe proposed antenna is as shown in Figure 1. The antenna consists ofrectangular conducting slots on patch and two on the ground. The design"}, {"category_id": 15, "poly": [89.0, 1667.0, 1523.0, 1667.0, 1523.0, 1701.0, 89.0, 1701.0], "score": 0.97, "text": "relative permittity 4.60, relative permeabilty 1, and dielectric loss tangent 0.02. A rectangular slot is cut fromone side of the copper patch to another. Another two"}, {"category_id": 15, "poly": [86.0, 1693.0, 1154.0, 1696.0, 1154.0, 1730.0, 86.0, 1727.0], "score": 0.98, "text": "rectangular slots are also cut from the middle ofthe patch. Five lateral rectangular slots are also cut from the ground plane."}, {"category_id": 15, "poly": [86.0, 1781.0, 684.0, 1779.0, 684.0, 1813.0, 86.0, 1815.0], "score": 0.98, "text": " Figure 1: The proposed antenna (a) top view and (b) Bottom view."}, {"category_id": 15, "poly": [93.0, 1864.0, 1168.0, 1864.0, 1168.0, 1898.0, 93.0, 1898.0], "score": 0.97, "text": "width, and slots of the proposed antenna endlessly. Here, microstrip line is used to provide feeding to the proposed antenna."}, {"category_id": 15, "poly": [89.0, 2207.0, 1542.0, 2207.0, 1542.0, 2239.0, 89.0, 2239.0], "score": 0.98, "text": "The return loss of simulation with different substrate materials is demonstrated in Figure 4. Teflon is a fuorine plastic that is very slippery with physical properties. It is a"}, {"category_id": 15, "poly": [89.0, 1552.0, 396.0, 1552.0, 396.0, 1584.0, 89.0, 1584.0], "score": 0.97, "text": "the dielectric constant of substrate,"}, {"category_id": 15, "poly": [91.0, 568.0, 826.0, 568.0, 826.0, 602.0, 91.0, 602.0], "score": 0.98, "text": "upper band, respectively. It has achieved stable radiation effciencies of 79. 76% and"}, {"category_id": 15, "poly": [904.0, 568.0, 1525.0, 568.0, 1525.0, 602.0, 904.0, 602.0], "score": 0.97, "text": "with average gains of 7.82 dBi and 5.66 dBi in the operating frequency"}, {"category_id": 15, "poly": [1489.0, 1966.0, 1535.0, 1969.0, 1535.0, 2010.0, 1489.0, 2008.0], "score": 1.0, "text": "The"}, {"category_id": 15, "poly": [422.0, 1552.0, 724.0, 1552.0, 724.0, 1584.0, 422.0, 1584.0], "score": 0.99, "text": " is the target center frequency, and"}, {"category_id": 15, "poly": [748.0, 1552.0, 1132.0, 1552.0, 1132.0, 1584.0, 748.0, 1584.0], "score": 0.99, "text": "is the effective dielectric constant. Consider"}, {"category_id": 15, "poly": [89.0, 509.0, 168.0, 509.0, 168.0, 541.0, 89.0, 541.0], "score": 1.0, "text": "board of"}, {"category_id": 15, "poly": [1481.0, 538.0, 1540.0, 538.0, 1540.0, 570.0, 1481.0, 570.0], "score": 0.96, "text": "on the"}, {"category_id": 15, "poly": [89.0, 538.0, 1149.0, 538.0, 1149.0, 570.0, 89.0, 570.0], "score": 0.99, "text": "element method (FEM) has been adopted in this investigation. It has a measured impedance bandwidths (2 : 1 VSWR) of"}, {"category_id": 15, "poly": [1226.0, 538.0, 1426.0, 538.0, 1426.0, 570.0, 1226.0, 570.0], "score": 0.98, "text": "on the lower band and"}, {"category_id": 15, "poly": [1544.0, 1406.0, 1564.0, 1406.0, 1564.0, 1440.0, 1544.0, 1440.0], "score": 0.71, "text": ".s"}, {"category_id": 15, "poly": [89.0, 1915.0, 608.0, 1915.0, 608.0, 1947.0, 89.0, 1947.0], "score": 1.0, "text": "The subminiature version A (SMA) connector that contains"}, {"category_id": 15, "poly": [660.0, 1915.0, 1476.0, 1915.0, 1476.0, 1947.0, 660.0, 1947.0], "score": 0.98, "text": "is conducted at the end of antenna feeding line for input RF signal. The input impedance of the"}, {"category_id": 15, "poly": [915.0, 1406.0, 1311.0, 1406.0, 1311.0, 1440.0, 915.0, 1440.0], "score": 0.95, "text": "and W are the length and width ofthe patch,"}, {"category_id": 15, "poly": [1327.0, 1406.0, 1520.0, 1406.0, 1520.0, 1440.0, 1327.0, 1440.0], "score": 0.92, "text": "is the velocity of ight,"}, {"category_id": 15, "poly": [91.0, 1835.0, 1186.0, 1835.0, 1186.0, 1866.0, 91.0, 1866.0], "score": 0.99, "text": "Thus, the proposed double inverted F-shape microstrip patch antenna is achieved. Two resonant frequencies, 11.32 GHz and"}, {"category_id": 15, "poly": [1293.0, 1835.0, 1552.0, 1835.0, 1552.0, 1866.0, 1293.0, 1866.0], "score": 0.98, "text": " are obtained adjusting length,"}, {"category_id": 15, "poly": [404.0, 509.0, 460.0, 509.0, 460.0, 541.0, 404.0, 541.0], "score": 0.94, "text": "with a"}, {"category_id": 15, "poly": [512.0, 509.0, 1508.0, 509.0, 1508.0, 541.0, 512.0, 541.0], "score": 0.97, "text": "microstrip transmission line. Commercially available high frequency structural simulator (HFSS) based on the finite "}, {"category_id": 15, "poly": [89.0, 1637.0, 1230.0, 1637.0, 1230.0, 1669.0, 89.0, 1669.0], "score": 0.97, "text": "procedure begins with the radiating patch with substrate, ground plane, and a feed line. It has been printed on a FR4 substrate with"}, {"category_id": 15, "poly": [1302.0, 1637.0, 1500.0, 1637.0, 1500.0, 1669.0, 1302.0, 1669.0], "score": 1.0, "text": "thickness that contains"}, {"category_id": 15, "poly": [1328.0, 1966.0, 1369.0, 1969.0, 1369.0, 2010.0, 1328.0, 2008.0], "score": 0.99, "text": "and"}, {"category_id": 15, "poly": [89.0, 331.0, 180.0, 331.0, 180.0, 365.0, 89.0, 365.0], "score": 1.0, "text": "Copyright"}, {"category_id": 15, "poly": [205.0, 331.0, 1488.0, 331.0, 1488.0, 365.0, 205.0, 365.0], "score": 0.97, "text": "2014 M. M. Islam et al This is an open access article distributed under the Creative Commons Atribution License, which permits unrestricted use,"}, {"category_id": 15, "poly": [89.0, 1944.0, 1018.0, 1942.0, 1018.0, 1976.0, 89.0, 1978.0], "score": 0.98, "text": "proposed antenna is as shown in Figure 2. Finally the optimal dimensions have been determined as follows:"}, {"category_id": 15, "poly": [89.0, 2005.0, 558.0, 2005.0, 558.0, 2039.0, 89.0, 2039.0], "score": 0.97, "text": "proposed prototype of the antenna is shown in Figure"}, {"category_id": 15, "poly": [89.0, 1406.0, 660.0, 1406.0, 660.0, 1440.0, 89.0, 1440.0], "score": 0.98, "text": "The length and width ofthe patch antenna can be calculated fom"}, {"category_id": 15, "poly": [691.0, 1406.0, 895.0, 1406.0, 895.0, 1440.0, 691.0, 1440.0], "score": 0.97, "text": "narrated in [17]. 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{"category_id": 13, "poly": [608, 444, 709, 444, 709, 470, 608, 470], "score": 0.52, "latex": "14.40\\,\\mathrm{GHz}"}, {"category_id": 13, "poly": [837, 1166, 1551, 1166, 1551, 1196, 837, 1196], "score": 0.52, "latex": "L=40\\,\\mathrm{mm},W=40\\,\\mathrm{mm},R=6\\,\\mathrm{mm},S_{1}=10\\,\\mathrm{mm},S_{2}=16.5\\,\\mathrm{mm},S_{3}=12"}, {"category_id": 13, "poly": [634, 886, 653, 886, 653, 909, 634, 909], "score": 0.5, "latex": "R"}, {"category_id": 13, "poly": [967, 333, 1059, 333, 1059, 361, 967, 361], "score": 0.5, "latex": "1.36\\,\\mathrm{GHz}"}, {"category_id": 13, "poly": [1190, 1167, 1309, 1167, 1309, 1196, 1190, 1196], "score": 0.5, "latex": "S_{1}=10\\,\\mathrm{mm}"}, {"category_id": 13, "poly": [1085, 1167, 1186, 1167, 1186, 1195, 1085, 1195], "score": 0.49, "latex": "R=6\\,\\mathrm{mm},"}, {"category_id": 13, "poly": [376, 1196, 502, 1196, 502, 1229, 376, 1229], "score": 0.48, "latex": "L_{g}=40\\,\\mathrm{mm},"}, {"category_id": 13, "poly": [754, 946, 812, 946, 812, 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{"category_id": 13, "poly": [1023, 1196, 1098, 1196, 1098, 1225, 1023, 1225], "score": 0.42, "latex": "W_{7}=7"}, {"category_id": 13, "poly": [980, 915, 1055, 915, 1055, 943, 980, 943], "score": 0.42, "latex": "W_{7}=7"}, {"category_id": 13, "poly": [254, 1195, 369, 1195, 369, 1226, 254, 1226], "score": 0.41, "latex": "W_{2}=7\\,\\mathrm{mm}"}, {"category_id": 13, "poly": [1394, 886, 1513, 886, 1513, 914, 1394, 914], "score": 0.41, "latex": "W_{1}=4\\,\\mathrm{mm},"}, {"category_id": 13, "poly": [563, 1396, 938, 1396, 938, 1426, 563, 1426], "score": 0.41, "latex": "W_{3}=18\\;\\mathrm{mm},W_{4}=6\\;\\mathrm{mm},W_{5}=8\\;\\mathrm{mm},"}, {"category_id": 13, "poly": [588, 584, 661, 584, 661, 609, 588, 609], "score": 0.39, "latex": "-10\\,\\mathrm{dB}"}, {"category_id": 13, "poly": [576, 914, 691, 914, 691, 944, 576, 944], "score": 0.39, "latex": "W_{4}=6\\,\\mathrm{{mm}}"}, {"category_id": 13, "poly": [620, 1921, 722, 1921, 722, 1949, 620, 1949], "score": 0.39, "latex": "15.48\\,\\mathrm{GHz}"}, {"category_id": 13, "poly": [767, 1367, 1121, 1367, 1121, 1394, 767, 1394], "score": 0.38, "latex": "L=40\\,\\mathrm{mm},W=40\\,\\mathrm{mm},R=6\\,\\mathrm{mm},"}, {"category_id": 13, "poly": [756, 107, 850, 107, 850, 135, 756, 135], "score": 0.37, "latex": "1.36\\,\\mathrm{GHz}"}, {"category_id": 13, "poly": [132, 1195, 250, 1195, 250, 1225, 132, 1225], "score": 0.35, "latex": "W_{1}=4\\,\\mathrm{mm},"}, {"category_id": 13, "poly": [441, 914, 573, 914, 573, 944, 441, 944], "score": 0.35, "latex": "W_{3}=18\\;\\mathrm{mm},"}, {"category_id": 13, "poly": [512, 1980, 604, 1980, 604, 2007, 512, 2007], "score": 0.34, "latex": "1.08\\,\\mathrm{GHz}"}, {"category_id": 13, "poly": [873, 886, 996, 886, 996, 912, 873, 912], "score": 0.34, "latex": "W=40\\,\\mathrm{mm},"}, {"category_id": 13, "poly": [606, 1195, 981, 1195, 981, 1225, 606, 1225], "score": 0.33, "latex": "W_{3}=18\\,\\mathrm{mm},W_{4}=6\\,\\mathrm{mm},W_{6}=4\\,\\mathrm{mm},"}, {"category_id": 13, "poly": [88, 914, 207, 914, 207, 944, 88, 944], "score": 0.32, "latex": "W_{2}=7\\,\\mathrm{mm},"}, {"category_id": 13, "poly": [1269, 885, 1387, 885, 1387, 914, 1269, 914], "score": 0.32, "latex": "S_{3}=12\\:\\mathrm{mm}"}, {"category_id": 13, "poly": [767, 1367, 880, 1367, 880, 1393, 767, 1393], "score": 0.3, "latex": "L=40\\,\\mathrm{mm},"}, {"category_id": 13, "poly": [634, 1034, 653, 1034, 653, 1058, 634, 1058], "score": 0.3, "latex": "R"}, {"category_id": 13, "poly": [131, 1195, 993, 1195, 993, 1228, 131, 1228], "score": 0.3, "latex": "W_{1}=4\\,\\mathrm{mm},W_{2}=7\\,\\mathrm{mm},L_{g}=40\\,\\mathrm{mm},W_{g}=40,W_{3}=18\\,\\mathrm{mm},W_{4}=6\\,\\mathrm{mm},W_{6}=4\\,\\mathrm{mm},a=1\\,\\mathrm{mm},W_{5}=7\\,\\mathrm{mm},W_{6}=4\\,\\mathrm{mm},a=1\\,\\mathrm{mm},W_{7}=1\\,\\mathrm{mm},W_{8}=40\\,\\mathrm{mm},W_{9}=40,W_{10}=4\\,\\mathrm{mm},W_{11}=1\\,\\mathrm{mm},W_{12}=4\\,\\mathrm{mm},W_{13}=1\\,\\mathrm{mm},W_{14}=1\\,\\mathrm{mm},W_{15}=3\\,\\mathrm{mm},W_{16}=4\\,\\mathrm{mm},"}, {"category_id": 13, "poly": [566, 108, 607, 108, 607, 133, 566, 133], "score": 0.29, "latex": "-10"}, {"category_id": 13, "poly": [252, 2148, 357, 2148, 357, 2174, 252, 2174], "score": 0.29, "latex": "14.84\\,\\mathrm{GHz}"}, {"category_id": 13, "poly": [840, 1167, 1190, 1167, 1190, 1194, 840, 1194], "score": 0.28, "latex": "L=40\\,\\mathrm{mm},W=40\\,\\mathrm{mm},R=6\\,\\mathrm{mm},"}, {"category_id": 13, "poly": [820, 914, 937, 914, 937, 943, 820, 943], "score": 0.28, "latex": "W_{6}=4\\:\\mathrm{mm},"}, {"category_id": 13, "poly": [661, 947, 716, 947, 716, 971, 661, 971], "score": 0.26, "latex": "5\\,\\mathrm{mm}"}, {"category_id": 13, "poly": [1528, 1086, 1548, 1086, 1548, 1109, 1528, 1109], "score": 0.25, "latex": "R"}, {"category_id": 15, "poly": [91.0, 78.0, 1532.0, 78.0, 1532.0, 112.0, 91.0, 112.0], "score": 0.98, "text": "brand for polytetrafluoroethylene (PTFE). When we used Teflon (tm) as a substrate material, there was no resonance on the lower band. But, resonance was found at"}, {"category_id": 15, "poly": [89.0, 195.0, 713.0, 195.0, 713.0, 227.0, 89.0, 227.0], "score": 0.99, "text": "Figure 4: Comparisons of simulated return loss with different materials."}, {"category_id": 15, "poly": [91.0, 249.0, 1550.0, 249.0, 1550.0, 283.0, 91.0, 283.0], "score": 0.97, "text": "Duroid (tm) is a circuit material with high frequency that is flled with PTFE composite. There are many benefts ofDuroid (tm) substrate material such as low outgassing"}, {"category_id": 15, "poly": [91.0, 278.0, 1537.0, 278.0, 1537.0, 312.0, 91.0, 312.0], "score": 0.98, "text": "for space applications, low moisture absorption, and low electrical loss. This material is extensively used in space satelite transceivers, radar systems based on ground"}, {"category_id": 15, "poly": [91.0, 307.0, 1545.0, 307.0, 1545.0, 341.0, 91.0, 341.0], "score": 0.98, "text": "and airbome, missile guidance systems, and military radar systems. When we used Duroid (tm) as a substrate material there was no resonance on the upper band. But,"}, {"category_id": 15, "poly": [91.0, 387.0, 1532.0, 387.0, 1532.0, 422.0, 91.0, 422.0], "score": 0.96, "text": "Bakelite is one type ofplastic that is a thermosetting phenol formaldehyde resin. It is made from synthetic components achieved from an elmination reaction of phenol"}, {"category_id": 15, "poly": [91.0, 417.0, 1486.0, 417.0, 1486.0, 451.0, 91.0, 451.0], "score": 0.98, "text": "with formaldehyde. It has several uses such as in radio, electrical insulators, and telephone casings. When we used Bakelite as a substrate material, there was no"}, {"category_id": 15, "poly": [89.0, 478.0, 145.0, 478.0, 145.0, 507.0, 89.0, 507.0], "score": 0.99, "text": "GHz)."}, {"category_id": 15, "poly": [91.0, 529.0, 1515.0, 529.0, 1515.0, 563.0, 91.0, 563.0], "score": 0.98, "text": "Aluminum is a silvery soft, white ductle material. Alminium is extensively used in transportation, aerospace industry, and structural materials. It has low density and"}, {"category_id": 15, "poly": [91.0, 558.0, 1550.0, 558.0, 1550.0, 592.0, 91.0, 592.0], "score": 0.98, "text": "abilty to resist corrosion for which it is popular. When we used aluminum as a substrate material there was no resonance on the lower band. But,resonance was found"}, {"category_id": 15, "poly": [89.0, 638.0, 1528.0, 638.0, 1528.0, 672.0, 89.0, 672.0], "score": 0.98, "text": "Finaly, FR4 has been used in the proposed design as substrate material. One resonance was achieved at 11.32 GHz center frequency on the lower band and another"}, {"category_id": 15, "poly": [91.0, 668.0, 1072.0, 668.0, 1072.0, 702.0, 91.0, 702.0], "score": 0.97, "text": "resonance at 14.96 GHz on the upper band. The dielectric properties of the materials have been listed in Table 1."}, {"category_id": 15, "poly": [89.0, 755.0, 549.0, 755.0, 549.0, 787.0, 89.0, 787.0], "score": 0.99, "text": "Table 1: Dielectric properties of substrate materials."}, {"category_id": 15, "poly": [91.0, 806.0, 1562.0, 806.0, 1562.0, 841.0, 91.0, 841.0], "score": 0.96, "text": "A parametric study has been done to observe the effects ofthe proposed antenna parameters. Mainly, the effects ofthe difeent parameters on the return loss have been"}, {"category_id": 15, "poly": [91.0, 838.0, 182.0, 838.0, 182.0, 865.0, 91.0, 865.0], "score": 1.0, "text": "observed."}, {"category_id": 15, "poly": [84.0, 1574.0, 342.0, 1574.0, 342.0, 1606.0, 84.0, 1606.0], "score": 0.97, "text": " 3. Results and Discussion"}, {"category_id": 15, "poly": [89.0, 1637.0, 1518.0, 1637.0, 1518.0, 1669.0, 89.0, 1669.0], "score": 0.98, "text": "The anechoic chamber used in this study which was conducted by the microwave laboratory, at the Institute of Space Science (ANGKASA), UKM, Malaysia, had"}, {"category_id": 15, "poly": [89.0, 1669.0, 1523.0, 1669.0, 1523.0, 1701.0, 89.0, 1701.0], "score": 0.97, "text": "been ilustrated in Figure 8. The dimensions ofthis anechoic chamber are 5.5 \u00d7 4.5 \u00d7 3.5 m?'. A reference antenna, a horn antenna, was used in this analysis that was"}, {"category_id": 15, "poly": [89.0, 1844.0, 760.0, 1844.0, 760.0, 1879.0, 89.0, 1879.0], "score": 0.99, "text": "Figure 8: The photograph of anechoic chamber for prototype measurement."}, {"category_id": 15, "poly": [89.0, 1896.0, 1513.0, 1896.0, 1513.0, 1927.0, 89.0, 1927.0], "score": 0.97, "text": "The return loss with measurement and simulation of the proposed microstrip antenna has been demonstrated in Figure 9. The -10 dB bandwidths of2.12 GHz from"}, {"category_id": 15, "poly": [91.0, 1925.0, 1540.0, 1925.0, 1540.0, 1959.0, 91.0, 1959.0], "score": 0.99, "text": "10.92 GHz to 13.04 GHz and 1.08 GHz from 14.40 GHz to 15.48 GHz have been achieved from the measurements which show that at the lower band the resonance"}, {"category_id": 15, "poly": [91.0, 1954.0, 1560.0, 1954.0, 1560.0, 1988.0, 91.0, 1988.0], "score": 0.96, "text": "shifted from 11.32 GHz to 11.44 GHz and the bandwidth slightly decreased. Moreover, at the upper band the resonant frequency shifted from 14.96 GHz to 14.84 GHz"}, {"category_id": 15, "poly": [91.0, 2071.0, 844.0, 2071.0, 844.0, 2103.0, 91.0, 2103.0], "score": 0.99, "text": "Figure 9: Comparisons between simulated and measured return loss on FR4 material."}, {"category_id": 15, "poly": [91.0, 2122.0, 1520.0, 2122.0, 1520.0, 2156.0, 91.0, 2156.0], "score": 0.97, "text": "Gain ofthe proposed antenna has been shown in Figure 10. Figure 10 has ilustrated that 7.82 dBi achieved at the frst resonance for 11.44 GHz and 5.66 dBiat the"}, {"category_id": 15, "poly": [91.0, 2151.0, 1547.0, 2151.0, 1547.0, 2186.0, 91.0, 2186.0], "score": 0.97, "text": "second resonance 14.84 GHz In adition, the gain for the upper band is less than that for the lower band. Figure 11 shows VSWR ofthe proposed antenna. The value"}, {"category_id": 15, "poly": [91.0, 2181.0, 1552.0, 2181.0, 1552.0, 2215.0, 91.0, 2215.0], "score": 0.97, "text": "of VSWR is less than 2 that is found fromthe graph apparently. It is a desired value. Figure 12 has shown the radiation effciency of the proposed antenna. In Figure 11,"}, {"category_id": 15, "poly": [91.0, 2210.0, 1537.0, 2210.0, 1537.0, 2242.0, 91.0, 2242.0], "score": 0.98, "text": "the average lower band efficiency is 79.76% whereas 80.36% is the higher band efficiency. It can also be observed that lower band radiation efficiency is smaller than"}, {"category_id": 15, "poly": [89.0, 1230.0, 702.0, 1230.0, 702.0, 1262.0, 89.0, 1262.0], "score": 0.99, "text": "better coupling has been acquired at the upper band using the value of"}, {"category_id": 15, "poly": [89.0, 1318.0, 570.0, 1318.0, 570.0, 1350.0, 89.0, 1350.0], "score": 0.98, "text": "Figure 6: Reflection coefficient with different values off"}, {"category_id": 15, "poly": [605.0, 1318.0, 612.0, 1318.0, 612.0, 1350.0, 605.0, 1350.0], "score": 0.54, "text": "\uff1a"}, {"category_id": 15, "poly": [89.0, 1087.0, 168.0, 1087.0, 168.0, 1121.0, 89.0, 1121.0], "score": 0.92, "text": "By using"}, {"category_id": 15, "poly": [89.0, 1170.0, 497.0, 1170.0, 497.0, 1201.0, 89.0, 1201.0], "score": 0.97, "text": "The reflection coeficient for different values of"}, {"category_id": 15, "poly": [91.0, 1430.0, 1134.0, 1430.0, 1134.0, 1464.0, 91.0, 1464.0], "score": 0.98, "text": "microstrip line has greater significance to the coupling at the entire frequency bands. The coupling can be achieved when"}, {"category_id": 15, "poly": [89.0, 1369.0, 629.0, 1369.0, 629.0, 1401.0, 89.0, 1401.0], "score": 0.98, "text": "Figure Z shows the reflection coefficient for different values of"}, {"category_id": 15, "poly": [86.0, 1520.0, 570.0, 1515.0, 570.0, 1547.0, 86.0, 1552.0], "score": 0.96, "text": "Figure 7: Reflection coeficient with different values of"}, {"category_id": 15, "poly": [1215.0, 1199.0, 1562.0, 1199.0, 1562.0, 1233.0, 1215.0, 1233.0], "score": 0.98, "text": ". It was seen from the graph clearly that"}, {"category_id": 15, "poly": [89.0, 887.0, 148.0, 887.0, 148.0, 919.0, 89.0, 919.0], "score": 1.0, "text": "Figure"}, {"category_id": 15, "poly": [1169.0, 1430.0, 1190.0, 1430.0, 1190.0, 1464.0, 1169.0, 1464.0], "score": 0.81, "text": ".s"}, {"category_id": 15, "poly": [737.0, 1230.0, 764.0, 1230.0, 764.0, 1262.0, 737.0, 1262.0], "score": 1.0, "text": "as"}, {"category_id": 15, "poly": [822.0, 1230.0, 1121.0, 1230.0, 1121.0, 1262.0, 822.0, 1262.0], "score": 0.97, "text": " That is why the optimized value is"}, {"category_id": 15, "poly": [533.0, 1170.0, 720.0, 1170.0, 720.0, 1201.0, 533.0, 1201.0], "score": 0.98, "text": " is as shown in Figure"}, {"category_id": 15, "poly": [1171.0, 1399.0, 1503.0, 1399.0, 1503.0, 1433.0, 1171.0, 1433.0], "score": 0.99, "text": ". It was observed that the width of the"}, {"category_id": 15, "poly": [1158.0, 916.0, 1537.0, 914.0, 1537.0, 948.0, 1158.0, 950.0], "score": 0.97, "text": " It was shown that resonances were shifted"}, {"category_id": 15, "poly": [89.0, 1107.0, 106.0, 1117.0, 106.0, 1154.0, 85.0, 1144.0], "score": 0.59, "text": ".5"}, {"category_id": 15, "poly": [939.0, 1399.0, 979.0, 1399.0, 979.0, 1433.0, 939.0, 1433.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [206.0, 1399.0, 463.0, 1399.0, 463.0, 1433.0, 206.0, 1433.0], "score": 0.96, "text": "W2 = 7 mm, Lg = 40 mm,"}, {"category_id": 15, "poly": [89.0, 1698.0, 1051.0, 1698.0, 1051.0, 1732.0, 89.0, 1732.0], "score": 0.97, "text": "double ridge guided. Pyramidal shaped absorbers have been used on the walls, ceiling, and floor with less than"}, {"category_id": 15, "poly": [1095.0, 1698.0, 1500.0, 1698.0, 1500.0, 1732.0, 1095.0, 1732.0], "score": 0.98, "text": "dB reflectivity. The diameter of the turntable is"}, {"category_id": 15, "poly": [112.0, 1757.0, 886.0, 1757.0, 886.0, 1788.0, 112.0, 1788.0], "score": 0.99, "text": "shaped proposed antenna has been measured in a standard far-field testing environment."}, {"category_id": 15, "poly": [939.0, 916.0, 979.0, 914.0, 979.0, 948.0, 939.0, 950.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [89.0, 587.0, 776.0, 587.0, 776.0, 621.0, 89.0, 621.0], "score": 0.98, "text": "at 14.32 GHz centre frequency on the upper band where -10 dB bandwidth is"}, {"category_id": 15, "poly": [1158.0, 446.0, 1540.0, 443.0, 1540.0, 478.0, 1158.0, 480.0], "score": 0.98, "text": "bandwidth is 1.68 GHz (14.64 GHz-13.96"}, {"category_id": 15, "poly": [91.0, 1725.0, 1198.0, 1725.0, 1198.0, 1759.0, 91.0, 1759.0], "score": 0.99, "text": "A vector network analyzer (VNA) (model mumber: Agilent E8362C) has been used for the measurements with a range ofup to"}, {"category_id": 15, "poly": [1277.0, 1725.0, 1560.0, 1725.0, 1560.0, 1759.0, 1277.0, 1759.0], "score": 0.98, "text": "In this way, the prototype of the"}, {"category_id": 15, "poly": [86.0, 446.0, 607.0, 443.0, 607.0, 478.0, 86.0, 480.0], "score": 1.0, "text": "resonance on the lower band. But, resonance was found at"}, {"category_id": 15, "poly": [710.0, 446.0, 1083.0, 443.0, 1083.0, 478.0, 710.0, 480.0], "score": 0.99, "text": "center frequency on the upper band where"}, {"category_id": 15, "poly": [740.0, 1170.0, 836.0, 1170.0, 836.0, 1201.0, 740.0, 1201.0], "score": 0.92, "text": " It includes"}, {"category_id": 15, "poly": [167.0, 887.0, 633.0, 887.0, 633.0, 919.0, 167.0, 919.0], "score": 0.98, "text": "shows the reflection coeffcient for different values of"}, {"category_id": 15, "poly": [654.0, 887.0, 755.0, 887.0, 755.0, 919.0, 654.0, 919.0], "score": 0.96, "text": " It includes"}, {"category_id": 15, "poly": [850.0, 334.0, 966.0, 334.0, 966.0, 368.0, 850.0, 368.0], "score": 1.0, "text": "bandwidth is"}, {"category_id": 15, "poly": [597.0, 1199.0, 606.0, 1199.0, 606.0, 1233.0, 597.0, 1233.0], "score": 0.91, "text": "\uff0c"}, {"category_id": 15, "poly": [91.0, 334.0, 301.0, 334.0, 301.0, 368.0, 91.0, 368.0], "score": 0.99, "text": "resonance was found at"}, {"category_id": 15, "poly": [404.0, 334.0, 775.0, 334.0, 775.0, 368.0, 404.0, 368.0], "score": 1.0, "text": "center frequency on the lower band where"}, {"category_id": 15, "poly": [982.0, 1199.0, 1022.0, 1199.0, 1022.0, 1233.0, 982.0, 1233.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [91.0, 1199.0, 131.0, 1199.0, 131.0, 1233.0, 91.0, 1233.0], "score": 0.92, "text": "mm,"}, {"category_id": 15, "poly": [86.0, 1976.0, 511.0, 1981.0, 511.0, 2022.0, 86.0, 2017.0], "score": 0.95, "text": "and the bandwidth decreased fom 1.16 GHzto"}, {"category_id": 15, "poly": [605.0, 1976.0, 1144.0, 1981.0, 1144.0, 2022.0, 605.0, 2017.0], "score": 0.91, "text": "whie the retum los ale decreased at resonancefequency."}, {"category_id": 15, "poly": [664.0, 1369.0, 766.0, 1369.0, 766.0, 1401.0, 664.0, 1401.0], "score": 0.97, "text": ". It includes"}, {"category_id": 15, "poly": [89.0, 1035.0, 633.0, 1035.0, 633.0, 1067.0, 89.0, 1067.0], "score": 0.98, "text": "Figure 5: Reflection coefficient with different values of radius,"}, {"category_id": 15, "poly": [86.0, 105.0, 565.0, 107.0, 565.0, 149.0, 86.0, 146.0], "score": 0.98, "text": "14.68 GHz center fequency on the upper band where"}, {"category_id": 15, "poly": [608.0, 105.0, 755.0, 107.0, 755.0, 149.0, 608.0, 146.0], "score": 0.97, "text": "dB bandwidth is"}, {"category_id": 15, "poly": [86.0, 945.0, 660.0, 948.0, 660.0, 982.0, 86.0, 979.0], "score": 0.97, "text": " on both ofthe lower and upper bands using the value ofradius as"}, {"category_id": 15, "poly": [717.0, 945.0, 753.0, 948.0, 753.0, 982.0, 717.0, 979.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [273.0, 1087.0, 1527.0, 1087.0, 1527.0, 1121.0, 273.0, 1121.0], "score": 0.98, "text": " desired dual band has been obtained with improved bandwidth on both ofthe lower and upper bands. 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"14.96\\,\\mathrm{GHz}"}, {"category_id": 13, "poly": [990, 2055, 1003, 2055, 1003, 2070, 990, 2070], "score": 0.33, "latex": "\\cdot"}, {"category_id": 13, "poly": [463, 1433, 553, 1433, 553, 1460, 463, 1460], "score": 0.33, "latex": "1.08\\,\\mathrm{GHz}"}, {"category_id": 13, "poly": [605, 1996, 619, 1996, 619, 2012, 605, 2012], "score": 0.27, "latex": "\\cdot"}, {"category_id": 13, "poly": [760, 2054, 774, 2054, 774, 2069, 760, 2069], "score": 0.26, "latex": "\\cdot"}, {"category_id": 13, "poly": [1035, 542, 1138, 542, 1138, 571, 1035, 571], "score": 0.25, "latex": "11.32\\,\\mathrm{GHz}"}, {"category_id": 13, "poly": [364, 572, 466, 572, 466, 599, 364, 599], "score": 0.25, "latex": "14.96\\,\\mathrm{GHz}"}, {"category_id": 15, "poly": [89.0, 168.0, 460.0, 168.0, 460.0, 202.0, 89.0, 202.0], "score": 0.98, "text": "Figure 10: Gain of the proposed antenna."}, {"category_id": 15, "poly": [91.0, 234.0, 482.0, 234.0, 482.0, 266.0, 91.0, 266.0], "score": 0.99, "text": "Figure 11: VSWR of the proposed antenna"}, {"category_id": 15, "poly": [89.0, 297.0, 588.0, 297.0, 588.0, 331.0, 89.0, 331.0], "score": 0.98, "text": "Figure 12: Radiation efficiency of the proposed antenna."}, {"category_id": 15, "poly": [89.0, 348.0, 1545.0, 348.0, 1545.0, 383.0, 89.0, 383.0], "score": 0.97, "text": "Figure 13 has been shown the current distribution ofthe proposed antenna for (a) 11.32 GHz and (b) 14.96 GHz It can be seen that a large amount of current flows at"}, {"category_id": 15, "poly": [91.0, 378.0, 1560.0, 378.0, 1560.0, 409.0, 91.0, 409.0], "score": 0.98, "text": "feeding line. Electric field has been created much in this point. Current distribution is more stable in lower band than in upper band. The creation ofelectric field near slots"}, {"category_id": 15, "poly": [89.0, 407.0, 1156.0, 407.0, 1156.0, 439.0, 89.0, 439.0], "score": 0.98, "text": "is reasonable. As a result, excitation is strong in the entire parts of the antenna on both the lower band and the upper band."}, {"category_id": 15, "poly": [86.0, 492.0, 699.0, 490.0, 699.0, 524.0, 86.0, 526.0], "score": 0.97, "text": "Figure 13: Current distribution at (a) 11.32 GHz and (b) 14.96 GHz."}, {"category_id": 15, "poly": [89.0, 604.0, 1518.0, 604.0, 1518.0, 638.0, 89.0, 638.0], "score": 0.98, "text": "polarization in radiation pattern is lowermicrostrp antenna. The cross-polarization effect is higher in the H-plane for both resonances. When frequency increases, the"}, {"category_id": 15, "poly": [89.0, 633.0, 1515.0, 633.0, 1515.0, 668.0, 89.0, 668.0], "score": 0.96, "text": "effect increases interpreting from the radiation pattern simply. Moreover, almost omnidirectional and symmetrical radiation patterns have been attained along both E"}, {"category_id": 15, "poly": [91.0, 665.0, 258.0, 665.0, 258.0, 692.0, 91.0, 692.0], "score": 1.0, "text": "plane and H-plane."}, {"category_id": 15, "poly": [89.0, 748.0, 1518.0, 748.0, 1518.0, 782.0, 89.0, 782.0], "score": 0.99, "text": "Figure 14: Radiation patterns of the proposed antenna, (a) 11.32 GHz at E-plane, (b) 11.32 at H-plane, (c) 14.96 GHz at E-plane, and (d) 14.96 GHz at H-plane."}, {"category_id": 15, "poly": [86.0, 797.0, 1542.0, 799.0, 1542.0, 833.0, 86.0, 831.0], "score": 0.97, "text": "It has been observed that the same radiation pattern exists over the X- and Ku-bands. The obtained radiation patterns denote that the proposed antenna delivers linear"}, {"category_id": 15, "poly": [89.0, 831.0, 1537.0, 831.0, 1537.0, 863.0, 89.0, 863.0], "score": 0.98, "text": "polarization where the level of cross-polarization is lower than that of copolarization in allof the simulated radiation patterns. When the radiation pattern ofa microstrip"}, {"category_id": 15, "poly": [89.0, 858.0, 1537.0, 855.0, 1537.0, 889.0, 89.0, 892.0], "score": 0.97, "text": "antenna is symmetric and ommidirectional, it faces some reasonable benefits. One is that resonance would never be shifed at diferent directions and a large amount of"}, {"category_id": 15, "poly": [89.0, 887.0, 1444.0, 884.0, 1444.0, 919.0, 89.0, 921.0], "score": 0.98, "text": "stable power would be at the direction of broadside beam Another advantage is that the radiation patterm would be more durable on the operational bands."}, {"category_id": 15, "poly": [91.0, 938.0, 1532.0, 938.0, 1532.0, 972.0, 91.0, 972.0], "score": 0.97, "text": "The phase variation ofthe proposed antenna is plotted in Figure 15. It is realized from the graph that the proposed antenna has the phase variation that is Inear across"}, {"category_id": 15, "poly": [91.0, 970.0, 1560.0, 970.0, 1560.0, 1004.0, 91.0, 1004.0], "score": 0.97, "text": "both the upper and the lower operating frequency bands. This phase variation indicates that all the frequency components of the signal have the same pulse distortion due"}, {"category_id": 15, "poly": [86.0, 994.0, 1122.0, 994.0, 1122.0, 1035.0, 86.0, 1035.0], "score": 0.96, "text": "to the same propagation delay. Comparisons between existing and proposed antenas have been tabulated in Table 2."}, {"category_id": 15, "poly": [91.0, 1087.0, 657.0, 1087.0, 657.0, 1118.0, 91.0, 1118.0], "score": 0.99, "text": "Table 2: Comparisons between existing and proposed antennas."}, {"category_id": 15, "poly": [89.0, 1150.0, 521.0, 1150.0, 521.0, 1182.0, 89.0, 1182.0], "score": 0.98, "text": "Figure 15: Phase value of the proposed antenna."}, {"category_id": 15, "poly": [89.0, 1201.0, 1510.0, 1201.0, 1510.0, 1233.0, 89.0, 1233.0], "score": 0.98, "text": "The Smith chart ofthe proposed antenna is shown in Figure 16. Two resonances ml and m2 are identified clearly from this chart which has validated the evidences."}, {"category_id": 15, "poly": [89.0, 1289.0, 517.0, 1289.0, 517.0, 1323.0, 89.0, 1323.0], "score": 1.0, "text": "Figure 16: Smith chart of the proposed antenna."}, {"category_id": 15, "poly": [91.0, 1350.0, 224.0, 1350.0, 224.0, 1377.0, 91.0, 1377.0], "score": 1.0, "text": "4. Conclusion"}, {"category_id": 15, "poly": [89.0, 1494.0, 1503.0, 1494.0, 1503.0, 1528.0, 89.0, 1528.0], "score": 0.96, "text": "for the proposed antenna for dual frequency operation has also been improved than conventional It is realized that a good combination has been focused between"}, {"category_id": 15, "poly": [89.0, 1523.0, 1508.0, 1523.0, 1508.0, 1554.0, 89.0, 1554.0], "score": 0.99, "text": "measurements and simulations that validate our proposed double F-shaped design concept. The patch resonator, compact size, stable radiation patterns, low cross"}, {"category_id": 15, "poly": [89.0, 1550.0, 1545.0, 1550.0, 1545.0, 1581.0, 89.0, 1581.0], "score": 0.98, "text": "polarization, efficiency with improved bandwidth, and higher gain have made the proposed double F-shaped antenna compatible for X-band and Ku-band applications."}, {"category_id": 15, "poly": [91.0, 1615.0, 285.0, 1615.0, 285.0, 1640.0, 91.0, 1640.0], "score": 0.98, "text": "Conflict of Interests"}, {"category_id": 15, "poly": [89.0, 1671.0, 903.0, 1671.0, 903.0, 1703.0, 89.0, 1703.0], "score": 0.97, "text": "The authors declare that there is no conflict of interests regarding the publication of this paper."}, {"category_id": 15, "poly": [91.0, 1732.0, 204.0, 1732.0, 204.0, 1759.0, 91.0, 1759.0], "score": 1.0, "text": "References"}, {"category_id": 15, "poly": [113.0, 1788.0, 1537.0, 1788.0, 1537.0, 1822.0, 113.0, 1822.0], "score": 0.97, "text": "1. Y.-C. Lu and Y.-C. Lin, \u201cA mode-based design method for dual-band and self-diplexing antennas using double T-stubs loaded aperture,\u201d\" IEEE Transactions"}, {"category_id": 15, "poly": [145.0, 1818.0, 1004.0, 1818.0, 1004.0, 1852.0, 145.0, 1852.0], "score": 0.98, "text": "on Antennas and Propagation, vol. 60, no. 12, pp. 5596-5603, 2012. View at Google Scholar"}, {"category_id": 15, "poly": [111.0, 1842.0, 1530.0, 1847.0, 1530.0, 1881.0, 111.0, 1876.0], "score": 0.98, "text": " 2. M. R. I. Faruque, M. T. Islam, and N. Misran, \u201cEvaluation of specific absorption rate (SAR) reduction for PIFA antenna using metamaterials,\u201d Frequenz, vol."}, {"category_id": 15, "poly": [143.0, 1874.0, 819.0, 1874.0, 819.0, 1908.0, 143.0, 1908.0], "score": 0.96, "text": " 64, no. 7-8, pp. 144-149, 2010. View at Google Scholar \u00b7 View at Scopus"}, {"category_id": 15, "poly": [111.0, 1900.0, 1542.0, 1903.0, 1542.0, 1937.0, 111.0, 1935.0], "score": 0.97, "text": " 3. J-S. Row and S.- W. Wu, \u201cCircularly-polarized wide slot antenna loaded with a parasitic patch, IEEE Transactions on Antennas and Propagation, vol. 56,"}, {"category_id": 15, "poly": [145.0, 1932.0, 957.0, 1932.0, 957.0, 1966.0, 145.0, 1966.0], "score": 0.98, "text": "no. 9, pp. 2826-2832, 2008. View at Publisher : View at Go0gle Scholar View at Scopus"}, {"category_id": 15, "poly": [111.0, 1959.0, 1542.0, 1961.0, 1542.0, 1995.0, 111.0, 1993.0], "score": 0.97, "text": "4. C.-C. Yu and X.-C. Lin, \u201cA wideband single chip inductor-loaded CPW-fed inductive slot antenna,\" IEEE Transactions on Antennas and Propagation, vol."}, {"category_id": 15, "poly": [111.0, 2017.0, 1486.0, 2020.0, 1486.0, 2054.0, 111.0, 2051.0], "score": 0.97, "text": " 5. S. I. Latif L. Shafai, and S. K. Sharma, \u201cBandwidth enhancement and size reduction ofmicrostrip slot antemnas,\u2032 IEEE Transactions on Antennas and"}, {"category_id": 15, "poly": [113.0, 2078.0, 1552.0, 2078.0, 1552.0, 2112.0, 113.0, 2112.0], "score": 0.97, "text": " 6. W. S. T. Rowe and R. B. Waterhouse, \u201cInvestigation of proximity coupled patch antennas suitable for MMIC integration,\u201d\" in IEEE Antennas and Propagation"}, {"category_id": 15, "poly": [148.0, 2108.0, 802.0, 2108.0, 802.0, 2142.0, 148.0, 2142.0], "score": 0.99, "text": "Society Symposium Digest, pp. 1591-1594, June 2004. View at Scopus"}, {"category_id": 15, "poly": [113.0, 2137.0, 1537.0, 2137.0, 1537.0, 2171.0, 113.0, 2171.0], "score": 0.97, "text": " 7. S.-C. Gao, L.-W. Li, T.-S. Yeo, and M-S. Leong \u201cFDTD analysis ofa slot-loaded meandered rectangular patch antenna for dual-frequency operation,\" IEE"}, {"category_id": 15, "poly": [140.0, 2159.0, 1515.0, 2161.0, 1515.0, 2203.0, 140.0, 2200.0], "score": 0.96, "text": "Proceedings: Microwaves, Antennas and Propagation, vol. 148, no. 1, p. 65-71, 2001. View at Publisher View at Google Schoar View at Scopus"}, {"category_id": 15, "poly": [113.0, 2195.0, 1459.0, 2195.0, 1459.0, 2227.0, 113.0, 2227.0], "score": 0.97, "text": " 8. J.-W. Wu, H-M. Hsiao, J.-H. Lu, and S.-H. Chang, \u201cDual broadband design of rectangular slot antenna for 2.4 and 5 GHz wireless communication,\""}, {"category_id": 15, "poly": [145.0, 2220.0, 1230.0, 2225.0, 1230.0, 2259.0, 145.0, 2254.0], "score": 0.96, "text": "Electronics Letters, vol. 40, no. 23, pp. 1461-1463, 2004. View at Publisher \u00b7 View at Go0gle Scholar \u00b7 View at Scopus"}, {"category_id": 15, "poly": [710.0, 575.0, 1488.0, 575.0, 1488.0, 607.0, 710.0, 607.0], "score": 0.99, "text": "fields indicate the cross-polar and copolar components, respectively. The effect of cross-"}, {"category_id": 15, "poly": [165.0, 1464.0, 202.0, 1464.0, 202.0, 1496.0, 165.0, 1496.0], "score": 1.0, "text": "and"}, {"category_id": 15, "poly": [281.0, 1464.0, 1542.0, 1464.0, 1542.0, 1496.0, 281.0, 1496.0], "score": 0.98, "text": " Since the antenna layout is simple and straightforward, fabrication and measurement are comparatively easier. The generalized design procedure"}, {"category_id": 15, "poly": [86.0, 1399.0, 100.0, 1401.0, 100.0, 1442.0, 86.0, 1440.0], "score": 0.92, "text": "A"}, {"category_id": 15, "poly": [260.0, 1399.0, 1496.0, 1401.0, 1496.0, 1442.0, 260.0, 1440.0], "score": 0.92, "text": "double invrted F-shae patch antea has beendiscussed in this paper for dual band-operation. The measured impedance bandwidths (2 : 1"}, {"category_id": 15, "poly": [1004.0, 2049.0, 1146.0, 2049.0, 1146.0, 2083.0, 1004.0, 2083.0], "score": 0.96, "text": "View at Scopus"}, {"category_id": 15, "poly": [91.0, 1433.0, 462.0, 1433.0, 462.0, 1467.0, 91.0, 1467.0], "score": 1.0, "text": "VSWR) 2.12 GHz on the upper band and"}, {"category_id": 15, "poly": [554.0, 1433.0, 1560.0, 1433.0, 1560.0, 1467.0, 554.0, 1467.0], "score": 0.98, "text": "on the lower band with average gains of 7.82 dBi and 5.66 dBi have been achieved belonging to radiation eficiency"}, {"category_id": 15, "poly": [143.0, 1991.0, 604.0, 1991.0, 604.0, 2025.0, 143.0, 2025.0], "score": 0.99, "text": " 56, no. 5, pp. 1498-1501, 2008. View at Publisher"}, {"category_id": 15, "poly": [620.0, 1991.0, 994.0, 1991.0, 994.0, 2025.0, 620.0, 2025.0], "score": 0.98, "text": "View at Google Scholar : View at Scopus"}, {"category_id": 15, "poly": [145.0, 2049.0, 759.0, 2049.0, 759.0, 2083.0, 145.0, 2083.0], "score": 0.99, "text": "Propagation, vol. 53, no. 3, pp. 994-1003, 2005. View at Publisher"}, {"category_id": 15, "poly": [775.0, 2049.0, 989.0, 2049.0, 989.0, 2083.0, 775.0, 2083.0], "score": 0.99, "text": "View at Google Scholar"}, {"category_id": 15, "poly": [89.0, 546.0, 1034.0, 546.0, 1034.0, 577.0, 89.0, 577.0], "score": 0.99, "text": "The radiation pattern of the proposed antenna with measurement has been demonstrated in Figure 14 for (a)"}, {"category_id": 15, "poly": [1139.0, 546.0, 1510.0, 546.0, 1510.0, 577.0, 1139.0, 577.0], "score": 1.0, "text": "at E-plane, (b) 11.32 GHz at H-plane, (c)"}, {"category_id": 15, "poly": [192.0, 575.0, 363.0, 575.0, 363.0, 607.0, 192.0, 607.0], "score": 1.0, "text": "at E-plane, and (d)"}, {"category_id": 15, "poly": [467.0, 575.0, 681.0, 575.0, 681.0, 607.0, 467.0, 607.0], "score": 1.0, "text": "at H-plane. The Ep and"}], "page_info": {"page_no": 3, "height": 2339, "width": 1653}}, {"layout_dets": [{"category_id": 1, "poly": [104.66098022460938, 73.2553939819336, 1560.8763427734375, 73.2553939819336, 1560.8763427734375, 724.37109375, 104.66098022460938, 724.37109375], "score": 0.9987766146659851}, {"category_id": 13, "poly": [845, 115, 859, 115, 859, 131, 845, 131], "score": 0.4, "latex": "\\cdot"}, {"category_id": 13, "poly": [1134, 694, 1149, 694, 1149, 710, 1134, 710], "score": 0.38, "latex": "\\cdot"}, {"category_id": 13, "poly": [905, 695, 919, 695, 919, 710, 905, 710], "score": 0.37, "latex": "\\cdot"}, {"category_id": 13, "poly": [1001, 346, 1014, 346, 1014, 362, 1001, 362], "score": 0.3, "latex": "\\cdot"}, {"category_id": 13, "poly": [479, 173, 493, 173, 493, 188, 479, 188], "score": 0.3, "latex": "\\cdot"}, {"category_id": 13, "poly": [421, 289, 435, 289, 435, 304, 421, 304], "score": 0.25, "latex": "\\cdot"}, {"category_id": 15, "poly": [111.0, 76.0, 1542.0, 78.0, 1542.0, 112.0, 111.0, 110.0], "score": 0.98, "text": " 9. C.-M. Su, H-T. Chen, and K.-L. Wong, \u201cPrinted dual-band dipole antenna with U-slotted arms for 2.4/5.2 GHz WLAN operation,\u201d\" Electronics Letters, vol."}, {"category_id": 15, "poly": [101.0, 134.0, 1564.0, 139.0, 1564.0, 173.0, 101.0, 168.0], "score": 0.97, "text": "10. Y.-H. Suh and K. Chang, \u201cLow cost microstrip-fed dual frequency printed dipole antenna for wireless commumications,\u201d Electronics Letters, vol 36, no. 14, pp."}, {"category_id": 15, "poly": [101.0, 192.0, 1525.0, 195.0, 1525.0, 229.0, 101.0, 227.0], "score": 0.97, "text": "11. D. Nashat, H. A. Elsadek, and H. Ghal, \u201cDual-band reduced size PIFA antemna with U-slot for Bluetooth and WLAN applications,\u201d in Proceedings of the"}, {"category_id": 15, "poly": [143.0, 222.0, 1427.0, 224.0, 1427.0, 258.0, 143.0, 256.0], "score": 0.98, "text": " IEEE Antennas and Propagation Society International symposium, vol. 2, pp. 962-967, Columbus, Ohio, USA, June 2003. View at Scopus "}, {"category_id": 15, "poly": [106.0, 256.0, 153.0, 256.0, 153.0, 283.0, 106.0, 283.0], "score": 1.0, "text": "12."}, {"category_id": 15, "poly": [140.0, 253.0, 1542.0, 253.0, 1542.0, 288.0, 140.0, 288.0], "score": 0.97, "text": "C.-C. Lin, G.-Y. Lee, and K.-L. Wong, \u201cSurface-mount dual-loop antenna for 2.4/5 GHz WLAN operation, Electronics Letters, vol 39, no. 18, pp. 1302-"}, {"category_id": 15, "poly": [103.0, 312.0, 1505.0, 312.0, 1505.0, 344.0, 103.0, 344.0], "score": 0.98, "text": "13. Y.-L. Kuo and K.-L. Wong, \u201cPrinted double-T monopole antenna for 2.4/5.2 GHz dual band WLAN operations,\" IEEE Transactions on Antennas and"}, {"category_id": 15, "poly": [103.0, 370.0, 1532.0, 370.0, 1532.0, 404.0, 103.0, 404.0], "score": 0.97, "text": "14. R. Azim, M. T. Islam, and N. Misran,\u03bcDual polarized microstrip patch antenna for Ku-band application,\u201d\" Informacije MIDEM, vol. 41, no. 2, pp. 114-117,"}, {"category_id": 15, "poly": [140.0, 395.0, 581.0, 397.0, 580.0, 431.0, 140.0, 429.0], "score": 0.98, "text": " 2011. View at Google Scholar \u00b7 View at Scopus"}, {"category_id": 15, "poly": [101.0, 424.0, 1505.0, 426.0, 1505.0, 461.0, 101.0, 458.0], "score": 0.97, "text": "15. L. Liu, S. W. Cheung, R. Azim, and M. T. Islam \u201cA compact circular-ring antenna for ulra-wideband applications,\u201d Microwave and Optical Technology"}, {"category_id": 15, "poly": [145.0, 456.0, 1124.0, 456.0, 1124.0, 490.0, 145.0, 490.0], "score": 0.97, "text": "Letters, vol. 53, no. 10, pp. 2283-2288, 2011. View at Publisher : View at Google Scholar \u00b7 View at Scopus "}, {"category_id": 15, "poly": [98.0, 480.0, 1550.0, 482.0, 1550.0, 524.0, 98.0, 521.0], "score": 0.95, "text": "16. R. Azim, M T. Islam and N. 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Kiang, \u201cDual-band circularly polarized cavity-backed anular slot antenma for GPS receiver, IEEE Transactions on"}, {"category_id": 15, "poly": [145.0, 107.0, 844.0, 107.0, 844.0, 141.0, 145.0, 141.0], "score": 0.98, "text": "38, no. 22, pp. 1308-1309, 2002. View at Publisher : View at Go0gle Scholar"}, {"category_id": 15, "poly": [860.0, 107.0, 1009.0, 107.0, 1009.0, 141.0, 860.0, 141.0], "score": 1.0, "text": "View at Scopus"}, {"category_id": 15, "poly": [1150.0, 690.0, 1296.0, 690.0, 1296.0, 724.0, 1150.0, 724.0], "score": 0.99, "text": "View at Scopus"}, {"category_id": 15, "poly": [145.0, 690.0, 904.0, 690.0, 904.0, 724.0, 145.0, 724.0], "score": 0.98, "text": "Antennas and Propagation, vol. 60, no. 4, pp. 2076-2080, 2012. 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+                "text": "的每一时刻，若存在一个白格至少与两个黑格相邻，则可将它也染成黑色.求最初"
+            },
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+                "score": 0.98,
+                "text": "至少要染多少个黑色格才能在某一时刻染黑整个棋盘？"
+            },
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+                "poly": [
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+                "text": "白棋盘上先将一些格染成黑色.在之后"
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+                "text": "第三题.给定正整数"
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+                "score": 0.94,
+                "text": "，考虑在"
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+                "score": 0.98,
+                "text": "(湖北武钢三中学生 王逸轩，上海大学冷岗松 供题)"
+            },
+            {
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+                "poly": [
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+                "score": 1.0,
+                "text": "与"
+            },
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+                "score": 0.99,
+                "text": "点，"
+            },
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+                "text": "．记"
+            },
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+                "text": "与"
+            },
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+                "score": 1.0,
+                "text": "外的另一条外公切线交"
+            },
+            {
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+                "score": 1.0,
+                "text": "于"
+            },
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+                "score": 0.97,
+                "text": "．设直线"
+            },
+            {
+                "category_id": 15,
+                "poly": [
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+                "score": 1.0,
+                "text": "与圆"
+            },
+            {
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+                "text": "第二题.如图，"
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+            },
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+                "score": 1.0,
+                "text": "，而直线"
+            },
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+                "text": "．证明："
+            },
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+                "text": "上一"
+            },
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+            },
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+                "score": 0.99,
+                "text": "的外心与内心．圆"
+            },
+            {
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+                "text": "为"
+            },
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+                "score": 0.97,
+                "text": "的外心与内心，"
+            },
+            {
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+                "poly": [
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+                "score": 1.0,
+                "text": "是正三角形"
+            },
+            {
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+                "score": 1.0,
+                "text": "的边"
+            },
+            {
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+                "poly": [
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+                "text": "第一题.设"
+            },
+            {
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+                "poly": [
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+                "score": 1.0,
+                "text": "是单位复数．证明存在单位复数"
+            },
+            {
+                "category_id": 15,
+                "poly": [
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+                "score": 0.98,
+                "text": "使得："
+            },
+            {
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+                "poly": [
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+                "score": 0.95,
+                "text": "(广西钦州 卢圣 供题)"
+            },
+            {
+                "category_id": 15,
+                "poly": [
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+                    1596.0,
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+                "score": 0.96,
+                "text": "(哈佛大学 牟晓生 供题)"
+            },
+            {
+                "category_id": 15,
+                "poly": [
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+                    278.0,
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+                "score": 1.0,
+                "text": "数学新星问题征解"
+            },
+            {
+                "category_id": 15,
+                "poly": [
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+                "score": 1.0,
+                "text": "分别是"
+            },
+            {
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+                "poly": [
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+                "score": 1.0,
+                "text": "上的点。证明"
+            },
+            {
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+                "score": 1.0,
+                "text": "周长的最小值"
+            },
+            {
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+                "score": 1.0,
+                "text": "的周长不小于"
+            },
+            {
+                "category_id": 15,
+                "poly": [
+                    251.0,
+                    1745.0,
+                    378.0,
+                    1745.0,
+                    378.0,
+                    1786.0,
+                    251.0,
+                    1786.0
+                ],
+                "score": 0.97,
+                "text": "第四题."
+            },
+            {
+                "category_id": 15,
+                "poly": [
+                    464.0,
+                    1745.0,
+                    753.0,
+                    1745.0,
+                    753.0,
+                    1786.0,
+                    464.0,
+                    1786.0
+                ],
+                "score": 1.0,
+                "text": "是一个三角形，而"
+            },
+            {
+                "category_id": 15,
+                "poly": [
+                    729.0,
+                    465.0,
+                    923.0,
+                    465.0,
+                    923.0,
+                    509.0,
+                    729.0,
+                    509.0
+                ],
+                "score": 1.0,
+                "text": "主持：牟晓生"
+            },
+            {
+                "category_id": 15,
+                "poly": [
+                    672.0,
+                    404.0,
+                    982.0,
+                    404.0,
+                    982.0,
+                    453.0,
+                    672.0,
+                    453.0
+                ],
+                "score": 1.0,
+                "text": "第十五期 (2016.06)"
+            },
+            {
+                "category_id": 15,
+                "poly": [
+                    1049.0,
+                    1856.0,
+                    1408.0,
+                    1862.0,
+                    1407.0,
+                    1910.0,
+                    1048.0,
+                    1905.0
+                ],
+                "score": 0.97,
+                "text": "(哈佛大学 牟晓生 供题)"
+            }
+        ],
+        "page_info": {
+            "page_no": 0,
+            "height": 2339,
+            "width": 1654
+        }
+    }
+]
\ No newline at end of file

tests/test_cli/pdf_dev/p3_图文混排84.json

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+                    963,
+                    1296,
+                    963,
+                    1296,
+                    991,
+                    1254,
+                    991
+                ],
+                "score": 0.86,
+                "latex": "5\\%"
+            },
+            {
+                "category_id": 13,
+                "poly": [
+                    1373,
+                    1003,
+                    1428,
+                    1003,
+                    1428,
+                    1032,
+                    1373,
+                    1032
+                ],
+                "score": 0.86,
+                "latex": "10\\%"
+            },
+            {
+                "category_id": 13,
+                "poly": [
+                    1332,
+                    1047,
+                    1388,
+                    1047,
+                    1388,
+                    1076,
+                    1332,
+                    1076
+                ],
+                "score": 0.86,
+                "latex": "\\cdot10\\%"
+            },
+            {
+                "category_id": 13,
+                "poly": [
+                    1373,
+                    1112,
+                    1428,
+                    1112,
+                    1428,
+                    1141,
+                    1373,
+                    1141
+                ],
+                "score": 0.85,
+                "latex": "10\\%"
+            },
+            {
+                "category_id": 13,
+                "poly": [
+                    1248,
+                    854,
+                    1302,
+                    854,
+                    1302,
+                    880,
+                    1248,
+                    880
+                ],
+                "score": 0.85,
+                "latex": "z0\\%"
+            }
+        ],
+        "page_info": {
+            "page_no": 3,
+            "height": 2339,
+            "width": 1654
+        }
+    }
+]
\ No newline at end of file

tests/test_cli/test_bench.py

+"""
+bench
+"""
+import os
+import shutil
+import json
+from lib import calculate_score
+import pytest
+from conf import conf
+
+code_path = os.environ.get('GITHUB_WORKSPACE')
+pdf_dev_path = conf.conf["pdf_dev_path"]
+pdf_res_path = conf.conf["pdf_res_path"]
+last_simscore = 0
+last_editdistance = 0
+last_bleu = 0
+
+class TestBench():
+    """
+    test bench
+    """
+    def test_ci_ben(self):
+        """
+        ci benchmark
+        """
+        try:
+            fr = open(os.path.join(pdf_dev_path, "result.json"), "r", encoding="utf-8")
+            lines = fr.readlines()
+            last_line = lines[-1].strip()
+            last_score = json.loads(last_line)
+            last_simscore = last_score["average_sim_score"]
+            last_editdistance = last_score["average_edit_distance"]
+            last_bleu = last_score["average_bleu_score"]
+        except IOError:
+            print ("result.json not exist")
+        os.system(f"python tests/test_cli/lib/pre_clean.py --tool_name mineru --download_dir {pdf_dev_path}")
+        now_score = get_score()
+        print ("now_score:", now_score)
+        if not os.path.exists(os.path.join(pdf_dev_path, "ci")):
+            os.makedirs(os.path.join(pdf_dev_path, "ci"), exist_ok=True)
+        fw = open(os.path.join(pdf_dev_path, "ci", "result.json"), "w+", encoding="utf-8")
+        fw.write(json.dumps(now_score) + "\n")
+        now_simscore = now_score["average_sim_score"]
+        now_editdistance = now_score["average_edit_distance"]
+        now_bleu = now_score["average_bleu_score"]
+        assert last_simscore <= now_simscore
+        assert last_editdistance <= now_editdistance
+        assert last_bleu <= now_bleu
+
+
+def get_score():
+    """
+    get score
+    """
+    score = calculate_score.Scoring(os.path.join(pdf_dev_path, "result.json"))
+    score.calculate_similarity_total("mineru", pdf_dev_path)
+    res = score.summary_scores()
+    return res
+
+

tests/test_cli/test_cli.py

 import pytest
 import os
 from conf import conf
-import subprocess
+import os
+import json
+from magic_pdf.pipe.UNIPipe import UNIPipe
+from magic_pdf.rw.DiskReaderWriter import DiskReaderWriter
 from lib import common
-import logging
+
 pdf_res_path = conf.conf["pdf_res_path"]
 code_path = conf.conf["code_path"]
 pdf_dev_path = conf.conf["pdf_dev_path"]
 class TestCli:
-   
-    def test_pdf_specify_dir(self):
+    """
+    test cli
+    """
+    def test_pdf_sdk(self):
         """
-        输入pdf和指定目录的模型结果
+        pdf sdk 方式解析
         """
-        cmd = 'cd %s && export PYTHONPATH=. && find %s -type f -name "*.pdf" | xargs -I{} python magic_pdf/cli/magicpdf.py  pdf-command  --pdf {}' % (code_path, pdf_dev_path)
-        logging.info(cmd)
-        common.check_shell(cmd)
-        #common.count_folders_and_check_contents(pdf_res_path)      
-   
+        demo_names = list()
+        pdf_path = os.path.join(pdf_dev_path, "pdf")
+        for pdf_file in os.listdir(pdf_path):
+            if pdf_file.endswith('.pdf'):
+                demo_names.append(pdf_file.split('.')[0])
+        for demo_name in demo_names:
+            model_path = os.path.join(pdf_dev_path, f"{demo_name}_model.json")
+            pdf_path = os.path.join(pdf_dev_path, "pdf", f"{demo_name}.pdf")
+            pdf_bytes = open(pdf_path, "rb").read()
+            model_json = json.loads(open(model_path, "r", encoding="utf-8").read())
+            image_writer = DiskReaderWriter(pdf_dev_path)
+            image_dir = str(os.path.basename(pdf_dev_path))
+            jso_useful_key = {"_pdf_type": "", "model_list": model_json}
+            pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
+            pipe.pipe_classify()
+            pipe.pipe_parse()
+            md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
+            dir_path = os.path.join(pdf_dev_path, "mineru")
+            if not os.path.exists(dir_path):
+                os.makedirs(dir_path, exist_ok=True)
+            res_path = os.path.join(dir_path, f"{demo_name}.md")
+            with open(res_path, "w+", encoding="utf-8") as f:
+                f.write(md_content)
+            common.count_folders_and_check_contents(res_path)
+        
     # def test_pdf_specify_jsonl(self):
     #     """
     #     输入jsonl, 默认方式解析