bluuebunny/crossref_metadata_2025_split huggingface dataset:
Lightweight Human Motion Recognition Method with Multiscale Temporal Features
Authors: Liu Ying , Luan Tian 
Published: November 2023

Abstract
To address the problems of the large size of human motion recognition models based on deep learning and insufficient mining of data features and thus low recognition accuracy, a lightweight human motion recognition algorithm based on multi-scale temporal features is proposed, the algorithm automatically extracted features through a multiscale feature fusion model. After, the integrated features are modeled by an improved time convolution network (TCN). In the TCN network structure, In the TCN network structure, the depthwise separable convolution is used instead of the normal convolutional layer to reduce the computational complexity, and the Leaky ReLU activation function is used instead of the ReLU function to improve the training efficiency. The experiments are based on the WISDM public dataset. finally achieve fast real-time recognition of actions, and structural and parametric optimization is performed through experiments to effectively improve the accuracy of results, and the final accuracy rate reaches 99.06%. In comparison with other methods, this method can reduce the model volume while maintaining a high accuracy rate.
Cooperative Spectrum Sensing Algorithm Based on Eigenvalue Fusion
Authors: Guo Qianrui , Guo Bin , Li Xiangkun , Ma Weijiao 
Published: November 2023

Abstract
A novel algorithm is introduced to improve collaborative spectrum sensing under low cognitive capabilities and insufficient signal-to-noise ratio. The algorithm is based on the difference of random matrix eigenvalues and uses the theory of random eigenvalues and the extreme distribution of the minimum eigenvalue. It makes use of the average, both arithmetic and geometric, as well as the minimum and maximum values of eigenvalues as the detection metric. It calculates the fusion power parameter through local energy spectrum sensing. Simulation results demonstrate that the algorithm outperforms the DMM algorithm and the NMME algorithm under users with low cognitive capabilities and Insufficient signal-to-noise ratio, making it more suitable for low signal-to-noise ratio environments.
Ultrasound Image Classification of Thyroid Nodules Based on Attention Mechanism
Authors: Yu Lintao , Qu Shuoou , Cong Zhibin , An Xiaofeng 
Published: November 2023

Abstract
Ultrasound imaging of the thyroid gland is currently the most common diagnostic technique, used for pre diagnosis of benign and malignant thyroid nodules. During the clinical diagnosis, the condition judgment requires the high clinical experience of physicians. The low level of medical care in remote areas has led to an increased rate of misdiagnosis of benign and malignant nodules. To improve the accuracy of diagnosis and treatment effectiveness of doctors, this work proposes a benign and malignant thyroid classification model based on mechanisms of attention. First, this paper performs data cleaning on the collected data and performs pre-processing operations such as data enhancement to construct a new dataset. Then, a network model based on Lightweight Global Attention Module (LGAM) is constructed for the benign and malignant classification of nodules. Finally, it is shown by experimental analysis and comparison that the improved LGAM-based thyroid node classification model has achieved better experimental results on the dataset and can achieve the optimal classification accuracy of 95.16% for the network model proposed in this work.
Regional Link Routing Algorithm for Wireless Sensor Networks based on Improved Grey Wolf Optimization Algorithm
Authors: Ding Jiaqian , Jia Xinyong , Chen Jiaqi 
Published: November 2023

Abstract
In order to improve the survival time, connectivity and the active number of nodes in the wireless sensor network, this paper proposes a regional chain wireless sensor network routing algorithm (RWA) based on improved grey Wolf optimization algorithm. The algorithm uses the method of dividing regions into chains, and in each region, chains are formed to select cluster heads before communication. The simulation results show that RWA has an improvement in avoiding too long chain, balancing energy consumption and prolonging the network life cycle.
Gesture Recognition of sEMG Signals Based on CNN-GRU Network
Authors: Bai Duanyuan , Zhang Dong , Zhang Yongheng , Shi Yingjie , Wu Tingyi 
Published: November 2023

Abstract
To improve the accuracy of surface electromyogram signal (sEMG) gesture recognition algorithm and solve the problem of manually extracting many features, this paper proposes a deep neural network-based gesture recognition method. A neural network integrating CNN and GRU was designed. The 8-channel sEMG data collected by the MYO armband is input to the CNN for feature extraction, and then the obtained feature sequence is input to the GRU network for gesture classification, and finally the recognition result of the gesture category is output. The experimental findings that the proposed technology reaches 76.41% recognition accuracy on the MyoUP dataset. This demonstrates the practicality of the suggested plan.
Multi-level Salient Feature Mining Network for Person Re-identification
Authors: Wang Dingyi , Du Haishun 
Published: November 2023

Abstract
Person re-identification (Re-ID) algorithms can retrieve the same pedestrian’s images from an image gallery captured by multiple cameras when given a pedestrian image. Due to changes in pedestrian postures, illuminations, and perspectives, it remains a significant challenge to improve the accuracy of person re-identification. Although the attention mechanism can alleviate some of these issues, it causes attention-based methods to pay excessive attention to features in the most salient areas of images while ignoring discriminant features outside the most salient areas, resulting in the insufficient discriminability of features extracted by attention-based methods. For this purpose, we propose a Multi-level Salient Feature Mining Network (MSFM-Net). First, by embedding attention modules in ResNet50, the model extract the most salient pedestrian feature maps. Second, the model uses two sub-salient feature mining branches to extract the second-level and third-level salient feature maps (collectively referred to as sub-salient feature maps). Third, the model uses the feature maps fusion module to combine the most salient feature maps with sub-salient feature maps to obtain the fused salient feature maps. Finally, the model pools the fused salient feature maps to produce more discriminant pedestrian representations. The results of two benchmark datasets demonstrate that MSFM-Nets performance reaches the current advanced level.
Remote-sensing Small-target Detection Based on Feature-dense Connection
Authors: Bao Wenshu 
Published: November 2023

Abstract
To address the problem that a large number of small targets exist in remote-sensing images but are difficult to detect, in this paper, a DenseYOLOv5 detection model is proposed for practical applications. DenseYOLOv5 is based on YOLOv5s and the small target detection head P2 and its feature fusion part are added to improve the detection performance of small targets. To address the problem of semantic information loss of small targets due to continuous upsampling in YOLOv5s, DenseYOLOv5 reconstructs the feature fusion pyramid (FPN) structure and incorporates dense connections. In addition, DenseYOLOv5 also uses transposed convolution as the upsampling method to further improve the small target detection capability. DenseYOLOv5 can achieve better detection results with less memory and computational overhead and thus has better usability.
Airworthiness Compliance Verification of Bird Strike on Civil Aircraft Windshield Frame
Authors: Hai Lan Xing 
Published: November 2023

Abstract
With the frequent occurrence of bird strikes, minimizing the impact damage effect of birdstrike on flight always is the goal of airworthiness certification. As a large windward component in front of the aircraft, the windshield, and frame have more probability to be suffered from birdstrike. This paper introduces the common connection forms of the windshield frame structure of civil aircraft, and through the interpretation of the airworthiness requirements related to windshield in CCAR-25, puts forward the process and method of compliance verification of windshield frame birdstrike. Based on the SPH method, the dynamic analysis of bird impact on windshield window frame is carried out, the test is carried out to verify that the analysis method is reasonable and conservative, and the results show that the bird impact performance of the windshield frame structure meets the airworthiness requirements.
Anti-vibration Design of the Civil Aircraft’s Horizontal Tail Trailing Edge Compartment
Authors: Lyu Yuanzhou , Yu Fengli , Chen Yin 
Published: November 2023

Abstract
The horizontal tail trailing edge compartment is a vital component of civil aircraft, situated between the horizontal tail beam and elevator. Its primary function is to connect the elevator and meet the aerodynamic sealing requirements of the unflapped elevator, and to transfer the aerodynamic and inertial load of the elevator to the outboard wing box of the horizontal tail in concentrated form. Under aerodynamic or other excitation, the horizontal tail trailing edge compartment may experience vibration, increasing the risk of structural damage. This study aims to enhance the anti-vibration capability of the horizontal tail trailing edge compartment via parametric finite element analysis. This analysis examines various design elements of the horizontal tail end cabin and compares the first-order modal frequency to deduce the influence of factors on the stiffness. The results provide a reference for the anti-vibration design of the horizontal tail trailing edge compartment in civil aircraft.
Random Error Analysis of Launch and Injection Positions for Distributed Multi Micro-nano Satellite System
Authors: Wu Huanqin , Wang Maocai , Song Zhiming , Chen Xiaoyu , Dai Guangming , Zheng Wei , Zhou Qingrui 
Published: November 2023

Abstract
Accurate satellite orbit calculation has always been a focus in the aerospace field. Especially, the error distribution of the orbit involving various internal and external factors in the satellite launch process has an important influence on the effectiveness of the satellite cluster. To qualitatively and quantitatively analyze the cooperative efficiency of multi-satellite clusters involving various random errors, this paper analyzes the random error sources in the launch process of micro-nano satellites and studies the distribution law of these satellite orbit position errors. Based on the parameter estimation and the central limit theorem of probability theory, the expression of the error function for the satellite’s orbit position is also constructed. In addition, the probability distribution model of the satellites in a certain range is designed, and the relationship between the number of satellites and the overall errors is proposed. Finally, the result is verified by the simulation experiment, which provides the rationale behind the position and altitude adjustment and constellation optimization of micro-nano satellites.
Design, construction and commissioning of a technological prototype of a highly granular SiPM-on-tile scintillator-steel hadronic calorimeter
Authors: White A. , Yu J. , Eigen G. , Zalieckas J. , Dannheim D. , Elsener K. , Grefe C. , Klempt W. , Linssen L. , Sailer A. , Sicking E. , Lai S. , Utehs J. , Wilhahn A. , Bach O. , Brianne E. , Ebrahimi A. , Gadow K. , Göttlicher P. , Hartbrich O. , Heuchel D. , Irles A. , Krüger K. , Laudrain A. , Lu S. , Neubüser C. , Provenza A. , Reinecke M. , Sefkow F. , Schuwalow S. , De Silva M. , Sudo Y. , Tran H.L. , Buhmann E. , Buhmann P. , Garutti E. , Huck S. , Laurien S. , Lomidze D. , Martens S. , Matysek M. , Rolph J. , Briggl K. , Eckert P. , Munwes Y. , Schultz-Coulon H.-Ch. , Shen W. , Stamen R. , Kawagoe K. , Kuhara M. , Onoe T. , Suehara T. , Tsumura S. , Yoshioka T. , Wing M. , Grenier G. , Büscher V. , Chau P. , Krause S. , Liu Y. , Masetti L. , Schäfer U. , Tapprogge S. , Wanke R. , Weitzel Q. , Corriveau F. , Freund B. , Emberger L. , Gabriel M. , Graf C. , Hummer F. , Sasikumar K.S. , Simon F. , Wagner M. , Windel H. , Bilokin S. , Bonis J. , Breton D. , Cornebise P. , Gallas A. , Jeglot J. , Maalmi J. , Pöschl R. , Thiebault A. , Richard F. , Zerwas D. , Callier S. , Dulucq F. , de la Taille Ch. , Martin-Chassard G. , Raux L. , Seguin-Moreau N. , Cvach J. , Janata M. , Kovalcuk M. , Kvasnicka J. , Polak I. , Smolik J. , Vrba V. , Zalesak J. , Zuklin J. , Fujita Y. , Itoh H. , Kanzaki I. , Kotera K. , Takeshita T. , Terada R. , Ootani W. , Liu L. , Masuda R. , Mori T. , Murata T. , Torimaru T. , Tsuji N. , Ueda Y. , Elkhalii A. , Götze M. , Zeitnitz C. , Chadeeva M. , Danilov M. , Popova E. , Rusinov V. , Tarkovsky E. ,   The CALICE Collaboration
Published: November 2023

Abstract
The CALICE collaboration is developing highly granular
electromagnetic and hadronic calorimeters for detectors at future
energy frontier electron-positron colliders. After successful tests
of a physics prototype, a technological prototype of the Analog
Hadron Calorimeter has been built, based on a design and
construction techniques scalable to a collider detector. The
prototype consists of a steel absorber structure and active layers
of small scintillator tiles that are individually read out by
directly coupled SiPMs. Each layer has an active area of
72 × 72 cm^2 and a tile size of
3 × 3 cm^2. With 38 active layers, the prototype
has nearly 22,000 readout channels, and its total thickness
amounts to 4.4 nuclear interaction lengths. The dedicated readout
electronics provide time stamping of each hit with an expected
resolution of about 1 ns. The prototype was constructed in
2017 and commissioned in beam tests at DESY. It recorded muons,
hadron showers and electron showers at different energies in test
beams at CERN in 2018. In this paper, the design of the prototype,
its construction and commissioning are described. The methods used
to calibrate the detector are detailed, and the performance achieved
in terms of uniformity and stability is presented.
Multi-scale analysis of urbanization and gross primary productivity during 2000–2018 in Beijing, China
Authors: Liu Xiaoyan , Cui Yaoping , Xiao Xiangming , Shi Zhifang , Li Mengdi , Li Nan , Dong Jinwei 
Published: November 2023

Abstract
Urban vegetation experiences multiple natural and human impacts during urbanization, including land conversion, local environmental factors, and human management, which may bring positive or negative impacts on vegetation gross primary productivity (GPP) at multiple scales. In this study, we analyzed the spatial-temporal changes of GPP and three urbanization factors: land urbanization (impervious surface coverage), population urbanization (Population), and economic urbanization Gross domestic product (GDP) at city-district-grid scales in Beijing during 2000–2018. Overall, both GPP and three urbanization factors showed an increased trend. The relationships between GPP and urbanization factors exhibit diverse characteristics at multiple scales: unlike the linear relationship observed at city scale, the relationships at district and grid scales all demonstrated nonlinear relationship, even a U shape between GPP and population/GDP. Furthermore, the positive impact of urbanization on GPP increased and offset the negative impact of land conversion from 9.9% in 2000 to 35% in 2018, indicating that urban management and climate during urbanization effectively promote vegetation photosynthesis and neutralize the negative impact of urban area expansion. Our findings highlight the increased growth offset by urbanization on vegetation and the importance of analysis at a finer scale. Understanding these urbanization types’ impact on vegetation is pivotal in formulating comprehensive strategies that foster sustainable urban development and preserve ecological balance.
The sharing economy is not always greener: a review and consolidation of empirical evidence
Authors: Meshulam Tamar , Goldberg Sarah , Ivanova Diana , Makov Tamar 
Published: November 2023

Abstract
The digital sharing economy is commonly seen as a promising circular consumption model that could potentially deliver environmental benefits through more efficient use of existing product stocks. Yet whether sharing is indeed more environmentally benign than prevalent consumption models and what features shape platforms’ sustainability remains unclear. To address this knowledge gap, we conduct a systematic literature review of empirical peer reviewed and conference proceeding publications. We screen over 2200 papers and compile a dataset of 155 empirical papers, and consolidate reported results on the environmental impacts of the sharing economy. We find that sharing is not inherently better from an environmental perspective. The type of resource shared, logistic operations, and the ways in which sharing influences users’ consumption more broadly affect environmental outcomes. Sharing goods is generally associated with better environmental outcomes compared to shared accommodations or mobility. Within mobility, shared scooters and ride-hailing emerge as particularly prone to negative environmental outcomes. Contrary to previous suggestions, peer-to-peer sharing (vs. centralized ownership) does not seem to be a good proxy for environmental performance. As sharing becomes intertwined with urbanization, efforts to steer digital sharing towards environmental sustainability should consider system levels effects and take into account platform operations as well as potential changes in consumer behavior.
Quantum gravity through geometric algebra
Authors: Wolk Brian Jonathan 
Published: November 2023

Abstract
Geometric Algebra is used to construct a model of quantum gravity in which the 4D spacetime manifold is built from qubitic quantum entangled spacetime elements of geometric algebraic nature. This is achieved by first investigating the spontaneous decomposition of the spacetime continuum’s metric structure at Planck distances as one approaches what otherwise would become the Universe’s initial singularity. Considering this metric deconstruction within the model one finds that the reverse induced emergence of the spacetime continuum is generated by a coupling action triggered by the embedding action between distinct pre-spacetime manifolds. Further uncovered in this re-engineering of spacetime is a Poincaré-constrained quantum foam existing at infra-Planck scales.
Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids
Authors: Wang Yu Mei , Sun Yuzhi , Wang Beiying , Wu Zhiping , He Xiao Ying , Zhao Yuansong 
Published: November 2023

Abstract
Due to the high dimensionality and sparsity of the gene expression matrix in single-cell RNA-sequencing (scRNA-seq) data, coupled with significant noise generated by shallow sequencing, it poses a great challenge for cell clustering methods. While numerous computational methods have been proposed, the majority of existing approaches center on processing the target dataset itself. This approach disregards the wealth of knowledge present within other species and batches of scRNA-seq data. In light of this, our paper proposes a novel method named graph-based deep embedding clustering (GDEC) that leverages transfer learning across species and batches. GDEC integrates graph convolutional networks, effectively overcoming the challenges posed by sparse gene expression matrices. Additionally, the incorporation of DEC in GDEC enables the partitioning of cell clusters within a lower-dimensional space, thereby mitigating the adverse effects of noise on clustering outcomes. GDEC constructs a model based on existing scRNA-seq datasets and then applying transfer learning techniques to fine-tune the model using a limited amount of prior knowledge gleaned from the target dataset. This empowers GDEC to adeptly cluster scRNA-seq data cross different species and batches. Through cross-species and cross-batch clustering experiments, we conducted a comparative analysis between GDEC and conventional packages. Furthermore, we implemented GDEC on the scRNA-seq data of uterine fibroids. Compared results obtained from the Seurat package, GDEC unveiled a novel cell type (epithelial cells) and identified a notable number of new pathways among various cell types, thus underscoring the enhanced analytical capabilities of GDEC.
Availability and implementation: https://github.com/YuzhiSun/GDEC/tree/main
DeepFold: enhancing protein structure prediction through optimized loss functions, improved template features, and re-optimized energy function
Authors: Lee Jae-Won , Won Jong-Hyun , Jeon Seonggwang , Choo Yujin , Yeon Yubin , Oh Jin-Seon , Kim Minsoo , Kim SeonHwa , Joung InSuk , Jang Cheongjae , Lee Sung Jong , Kim Tae Hyun , Jin Kyong Hwan , Song Giltae , Kim Eun-Sol , Yoo Jejoong , Paek Eunok , Noh Yung-Kyun , Joo Keehyoung 
Published: November 2023

Abstract

Motivation
Predicting protein structures with high accuracy is a critical challenge for the broad community of life sciences and industry. Despite progress made by deep neural networks like AlphaFold2, there is a need for further improvements in the quality of detailed structures, such as side-chains, along with protein backbone structures.

Results
Building upon the successes of AlphaFold2, the modifications we made include changing the losses of side-chain torsion angles and frame aligned point error, adding loss functions for side chain confidence and secondary structure prediction, and replacing template feature generation with a new alignment method based on conditional random fields. We also performed re-optimization by conformational space annealing using a molecular mechanics energy function which integrates the potential energies obtained from distogram and side-chain prediction. In the CASP15 blind test for single protein and domain modeling (109 domains), DeepFold ranked fourth among 132 groups with improvements in the details of the structure in terms of backbone, side-chain, and Molprobity. In terms of protein backbone accuracy, DeepFold achieved a median GDT-TS score of 88.64 compared with 85.88 of AlphaFold2. For TBM-easy/hard targets, DeepFold ranked at the top based on Z-scores for GDT-TS. This shows its practical value to the structural biology community, which demands highly accurate structures. In addition, a thorough analysis of 55 domains from 39 targets with publicly available structures indicates that DeepFold shows superior side-chain accuracy and Molprobity scores among the top-performing groups.

Availability and implementation
DeepFold tools are open-source software available at https://github.com/newtonjoo/deepfold.