esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
The \(2m+1\) (\(m=1\), \(2\), \(3\), \(\ldots\)) deformed subsets of the image series captured in the period of [\(t_{i-m}\), \(t_{i+m}\)] were averaged [50; 51]. This changes Equation (1) to  
\[f(\mathbf{X})=G_{i}^{(m)}(\mathbf{X}+\mathbf{U}(\mathbf{X},t))=\frac{1}{2m+1} \int_{t_{i-m}}^{t_{i+m}}g_{t}(\mathbf{X}+\mathbf{U}(\mathbf{X},t))dt \tag{3}\]  
where \(G_{i}^{m}(\mathbf{X}+\mathbf{U}(\mathbf{X},t))\) is the spatial-temporal subset, which can describe the displacement of the strongly heterogeneous deformation field well. The spatial-temporal subset in Equation (3) can improve the quality of the speckle images in certain circumstances as compared to the traditional spatial subset for the subset-based DIC [50]. According to the characteristics of rock deformation, the displacement of the spatial-temporal subset can be suitably expressed by a second-order expression in spatial and a short period, as shown in Figure 1b. Thus, the shape function of the second-order spatial-temporal subset can be denoted as follows:  
Figure 1: Schematic of DIC: (\(\mathbf{a}\)) traditional subset-based DIC and (\(\mathbf{b}\)) second-order STS-DIC. \[\mathbf{U}(\mathbf{X},t)=\mathbf{U}(\mathbf{X},t;\mathbf{b})=\left[ \begin{matrix}u\\ v\end{matrix} \right]\left| \begin{array}{c}t=t_{i}\\ \mathbf{x}=\mathbf{x}_{c}\end{array} \right. \tag{4}\]  
where \(\Delta t=t-t_{i}\) is the time interval between two images and \(\mathbf{b}\) is the unknown matrix to be solved. In practice, the number of pixels in the spatial-temporal subset is much larger than the number of unknowns \(\mathbf{b}\). Thus, Equation (3) is overdetermined and can be solved with an optimization method. The optimization objective function is given by:  
\[\underset{\mathbf{b}}{\min}C\Big{(}f(\mathbf{X}),G_{i}^{(m)}(\mathbf{X}+ \mathbf{U}(\mathbf{X},t;\mathbf{b}))\Big{)} \tag{5}\]  
where \(C\) is the correlation coefficient and can be defined in different ways. The Newton-Raphson method was used to solve this optimisation problem iteratively:  
\[\
abla\
abla C\Big{(}\mathbf{b}^{(l)}\Big{)}\Big{(}\mathbf{b}^{(l+1)}-\mathbf{ b}^{(l)}\Big{)}=-\
abla C\Big{(}\mathbf{b}^{(l)}\Big{)}\;\;l=0,1,2,\ldots \tag{6}\]  
where \(\mathbf{b}^{(l)}\) is the solution to \(\mathbf{b}\) in the \(l\)-th iteration. Compared with the traditional subset-based DIC, the second-order STS-DIC algorithm is more suitable for measuring the strongly heterogeneous deformation field of rock during the deformation localization stage. In the following, the second-order STS-DIC was used to analyze the deformed images captured in the uniaxial compression experiments to obtain a more detailed deformation field.
# Double-Layer Compressive Sensing Based Efficient DOA Estimation in WSAN with Block Data Loss
## 5 Conclusions  
In this paper, we investigate the adverse effects of block data loss on DOA estimation in WSAN. To eliminate the hazards of block data loss and realize high-accuracy and efficient DOA estimation, we propose a double-layer compressive sensing framework, where the random packet loss during transmission is modeled as a passive CS process, while the dimension-reduced random projection at each array sensor is modeled as an active CS process. Under the double-layer CS framework, DOA estimation can be implemented using two techniques, DCS-DOA and DCS-DDOA. In DCS-DOA, the FC will first recover the raw sparse vectors and then perform DOA estimation based on them. To avoid the error propagation from signal recovery to DOA estimation, DCS-DDOA is proposed. Leveraging a joint frequency and spatial domain sparse representation of the sensor array data, the FC can directly obtain the DOA estimation results according to the received data, skipping the phase of signal recovery. Extensive simulations demonstrate that the double-layer CS framework can be immune to block data loss and yield a satisfactory DOA estimation performance in WSAN. Furthermore, a comparison is made between DCS-DOA and DCS-DDOA to show their merits and deficiencies.  
This work was supported in part by the National Natural Science Foundation of China (No. NSFC61273079), in part by the National Natural Science Foundation of China Key Projects (U1509215), in part by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA06020201) and in part by the Open Research Project of the State Key Laboratory of Industrial Control Technology, Zhejiang University (No. ICT170319, No. ICT170320, No. ICT170342). [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] contributed analysis tools; and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 3 Results  
To better facilitate information storage for comprehensive management and retrieval using models, the underground pipeline scene is categorized into geometric, semantic, and temporal aspects. Through detailed research on the geometric, semantic, and temporal aspects of the data model, a spatial data fusion model for underground pipelines has been established, which integrates geometric, semantic, and temporal considerations. This model provides semantic constraints for all classes and objects within the underground pipeline network. The geometric data model accurately integrates underground pipeline objects with other entities in the spatial scene, facilitating future expansions for three-dimensional scene representation. The semantic-temporal parameters are designed based on the semantic and temporal components of the three-dimensional spatial data fusion model of the underground pipeline network mentioned above. The purpose is to reflect the different task contents of various parts of the underground pipeline system at different time points or periods, including pipeline network temporal states, engineering planning temporal states, pipeline construction temporal states, online monitoring temporal states, and facility decommissioning temporal states. These significant time points also encompass key milestones in the lifecycle of the pipeline network, such as construction dates, completion dates, maintenance periods, etc.  
The rule file can be created by using the constructed three-dimensional spatial data fusion model of the underground pipeline network. The rule file can be dragged into the selected layer in CityEngine to enable the model to automatically generate [30]. Blue pipes represent water pipes, red pipes represent heating pipes, and gray pipes represent other pipes. This paper applies the pipeline model to develop a three-dimensional visualization management platform for underground pipelines. Based on the design of the pipeline spatial data fusion model, data accuracy and completeness have been enhanced, and efficient sharing of pipeline data information has been achieved, resulting in clearer and smoother browsing of the pipeline network. The spatial relationships between the control points, pipelines, and attachments in the constructed pipeline model are very apparent, and the model parameters can be modified at any time. The specific effects are illustrated in Figure 5. The method centers on underground pipeline objects, integrating geometric, temporal, and semantic characteristics by mapping and transforming these characteristics. Compared to other existing methods [12; 31; 32], it effectively integrates multi-feature information, enabling efficient sharing of pipeline data and enhancing data accuracy, completeness, and information content. It improves work efficiency in the rapid expansion of new scenarios and model maintenance, achieving rapid and refined modeling of underground pipelines.  
Figure 5: Visualization of underground pipe networks.
## 2 Theory and Methods
### Model Improvements
#### 2.2.1 Lawds  
The task of small object detection requires high sensitivity to the details of target information. The original backbone network utilizes conventional 2 \(\times\) 2 convolution; however, this standard convolution operation can lead to the loss and omission of crucial information, ultimately degrading target detection accuracy. To address this issue, we introduce the lightweight adaptive weight downsampling module (LAWDS), which mitigates the information loss inherent in conventional convolution and downsampling operations while achieving a lightweight network architecture.  
The LAWDS (Light Adaptive Weight DownSampling) module is an adaptive downsampling method based on an attentional mechanism, designed to enhance downsampling efficiency. It introduces an adaptive attention mechanism that calculates attentional weights for local regions of the feature map, allowing the module to preserve important features while reducing spatial resolution. This approach effectively improves the downsampling performance of the model while minimizing computational complexity. As shown in Figure 2, the structure of LAWDS consists of an average pooling layer and a 1 \(\times\) 1 convolutional layer, which generate spatial attentional weights that are normalized using the Softmax function. The module down-samples the input using grouped convolution, expanding the number of channels to four times the original count, and aligns the result with the attention weights through a dimensional rearrangement operation. The down-sampled feature maps are then multiplied element-wise with the attention weights and summed across the spatial dimensions to yield the weighted down-sampled results. This mechanism allows the model to retain essential feature information while effectively reducing spatial resolution.  
Figure 2: LAWDS structure.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 7 Conclusions and Future Work  
This study presented MLKD-Net, which is a lightweight and efficient deep learning model tailored for single image dehazing. Central to the proposed architecture is the Multi-Head Large Kernel Attention (MLKA) module, which successfully combines the multi-head attention mechanism and large effective receptive fields--inspired by Vision Transformers--within a CNN-based framework. This hybrid design enables robust spatial feature modeling while preserving computational efficiency.  
Extensive experiments demonstrate that MLKD-Net-S achieved competitive performance across both quantitative and qualitative evaluations, incurring only a 1.9% PSNR drop compared to state-of-the-art Transformer-based models, while reducing the number of parameters by over 90%. Furthermore, MLKD-Net-S achieved real-time inference at 55.24 ms per image (18.2 FPS) on the NVIDIA Jetson Orin Nano using TensorRT-INT8, highlighting its strong potential for deployment in embedded and resource-constrained environments. These results suggest that the proposed model can be effectively integrated into real-world systems such as autonomous vehicles, robotic platforms, and mobile devices, where both efficiency and low latency are essential.  
While MLKD-Net effectively removes haze in diverse outdoor conditions, it does not explicitly model haze thickness, which may affect object visibility, color consistency, and edge sharpness. This limitation indicates a key avenue for future research: integrating dynamic haze modeling, such as scene-adaptive thickness estimation and uncertainty-aware learning, could further enhance dehazing accuracy in complex real-world scenarios. In summary, MLKD-Net offers a practical, scalable, and high-performing solution for single-image dehazing and lays a solid foundation for next-generation dehazing networks optimized for both performance and efficiency.  
Methodology, J.M.; Software, J.M.; Writing--review & editing, J.P.; Project administration, J.P. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Basic Research Laboratory through the National Research Foundation of Korea (NRF) (No. RS-2023-00221365) and by the Content Technology RD through the Korea Creative Content Agency (KOCCA) (No. RS-2024-00340342, Contribution Rate: 50%).  
All data used in this study are from the publicly available RESIDE dataset, which can be accessed at [[https://sites.google.com/view/reside-dehaze-datasets/reside-v0](https://sites.google.com/view/reside-dehaze-datasets/reside-v0)]([https://sites.google.com/view/reside-dehaze-datasets/reside-v0](https://sites.google.com/view/reside-dehaze-datasets/reside-v0)) (accessed on 20 January 2025).  
The authors declare no conflicts of interest.
## 4 Results  
### Experimental Setup  
In the experiment, three synthetic images and three real images are used to test six methods, namely Syn1, Syn2, Syn3, SAR1, SAR2, and SAR3. In the proposed classification framework MAKC, the numbers of original superpixels are set to 2000, 2800, 4300, 10,500, 64,500, and 4500, respectively. The AP uses four attributes to generate a twenty-five dimensional data set as [50 625 1849], [0.05 0.1 0.15], [58 248 328], [0.5 2 3.5]; the window size of GLCM is \(9\times 9\), the direction step of GLCM is 2, and the window size of WL is \(9\times 9\) respectively according to the literature [2,4,5]. The radial basis function kernel parameter \(\sigma\) in (15)-(17) is set to 1 in our method, and the parameters of the SVM classifier in all methods are selected by a fivefold cross-validation on the training set. Fifty training samples are randomly selected for each class to train the classifiers, and the remaining samples are used as testing data. The parameters of the other test methods are set as the defaulted values reported in [9,10,17,34,35], respectively. The experiments were conducted on a laptop computer with an Intel Core i5 2.6-GHz CPU and 16-GB memory; the algorithms were implemented in MATLAB 2016b.  
The classification results of the MAKC method are compared with the below methods visually and quantitatively: SVM [34], clone kernel spatial fuzzy c-means clustering (CKS-FCM) [35], composite kernel feature fusion (CKFF) [10], SVM-composite kernel (SVM-CK) [9], and multi-feature weighted sparse graph (MWSG) [17]. The MAKC uses the multi-feature texture information, context information, and generalized spatial information, constructs an adaptive composite kernel for classification with dynamic weights, and performs local consistency optimization to integrate multi-feature information. The SVM classifies images by statistical features without considering spatial information, and represents the classical basic SVM method. For the CKS-FCM method, spatial information is incorporated into the objective function of FCM, and a non-Euclidean distance based on a kernels metric is used in the kernel function. For the CKFF method, the spatial information is exploited by the morphological feature and stacked feature fusion with a composite kernel by manual weights on the SAR image. The SVM-CK method is used to extract context information for spatial information expression, the composite kernel is constructedby manual weights, and multi-feature fusion is not involved. In the MWSG method, multi-feature samples distance is expressed by Gaussian kernel distance as weights integrated sparse representation, which constructs an adjacent matrix of a graph framework for SAR image classification; it expresses the overall spatial information. To objectively evaluate the classification results, four metrics of overall accuracy (OA), class-special accuracy (CA), average accuracy (AA), and Kappa coefficient (Kappa) are adopted on real SAR images.
# Relationships between Anthropometric and Strength Profiles of Streetlifting Athletes
## 1 Introduction  
Streetlifting is a recent strength and power sport in which athletes compete in specific weight categories in the attempt to lift as much external loads. The sum of the best attempts on each lift determines the final ranking. In particular, male streetlifting competitions include loaded chin-ups, dips, muscle-ups and barbell back squats, while female streetlifting programs until 2024 did not include loaded muscle-ups. Streetlifting competitions derivate by the evolution of street-workout training, in which practitioners were traditionally engaged in free-body exercises [1]. To date, streetlifting has been regulated worldwide by the International Streetlifting Federation, which determines the international technical rules and organizes competitions [2]. Eighteen national federations are currently recognized by the International Streetlifting Federation [2].  
Anthropometric and body composition characteristics have been previously investigated in athletes competing in sports similar to streetlifting. In particular, these parameters were considered crucial for the performance in strength and power disciplines [3; 4; 5] and possibly useful for talent identification in powerlifting, gymnastics and weightlifting [6; 7; 8]. [PERSON] et al. [4], indeed, indicated that heavyweight powerlifters tend to be significantly taller and heavier and have a higher body fat percentage, larger girths and more endo-mesomorphy than their lighter counterparts. For that reason, body girths and body fat correlates with their final strength performance, parameters that can be predicted by specific equations [3]. In weightlifting, a relatively high body-mass-to-height ratio is considered positive for facilitating proper lifting technique and promoting large muscle cross-sectionalareas to body mass ratios [5]. In gymnastics, a short stature and a low body mass may be considered positive because an elevated endomorphic component and fat mass may negatively impact performance [7]. Therefore, investigating the anthropometric profile of athletes competing in different sport disciplines, including streetlifting, may represent the key to understanding athletes' needs and to improve their performance [9].  
To date, no research has investigated the anthropometric profile of streetlifting athletes included in the different body weight categories and the influence of different physical characteristics on specific types of strength performance. Thus, the aims of this study were to identify the anthropometric characteristics of streetlifting athletes of the different body weight categories and to investigate the relationships between anthropometric characteristics and strength performance. Another aim of the present study was to develop equations to predict the athlete's performance in each competitive exercise based on the most important components of each discipline included in the streetlifting competitive program.
# Intrinsic Interference Cancellation Scheme for FBMC-OQAM Systems Based on Power Multiplexing
## 5 Conclusions  
This article proposed a new method that eliminates intrinsic interference to pilot symbols of FBMC without iterations or sacrificing data symbols. Thanks to the power multiplexing in the transmitter and successive interference cancellation schemes in the receiver, the proposed method has a higher spectral efficiency compared with previous methods. We used numerical results to prove that the proposed method has a similar power penalty for intrinsic interference cancellation compared with the CAP method. The simulation results show that suboptimal results have similar BER performances compared with the optimal one, and they are all without a notable decrease in BER performance compared with the CAP method. Note that both the CAP and proposed methods need improvements in PAPR performance, and many studies about the PAPR reduction in FBMC have been proposed but further research is still needed [27; 28].  
Figure 10: The values of \(c\) when the suboptimal BER performance is obtained.  
Figure 9: BER performance when channel state information is known perfectly and \(E_{b}/N_{0}=30\) dB. Note that \(K\) represents the number of power multiplexed symbols and \(c\) stands for the power multiplexing level.  
**Author Contributions:** Conceptualization, J.L.; methodology, J.L., S.L. and H.D.; software, J.L., S.L. and H.D.; validation, J.L., S.L., H.D. and Z.L.; formal analysis, J.L. and Z.L.; investigation, J.L.; resources, J.L. and Z.L.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, S.L., H.D. and Z.L.; visualization, S.L.; supervision, Z.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 System Model and Identification
### Mechanical Coupling Model and System Identification  
A linear motor is set up on the X-axis of the gantry platform, while the Y-axis consists of two parallel linear motors. Thus, mechanical coupling is inevitable. The displacements on the X- and Y-axis are feedbacked by optical encoders. Figure 2 shows the experimental setup of the gantry system, and the block diagram of the gantry platform is shown in Figure 3. To address the coupling effect, here we adopt the coupled parallel linear servo scheme [9] to build the Y-axis model. The transfer function \(T_{X}(\mathrm{s})\) represents the X-axis PMLSM, while \(T_{M\_M}(\mathrm{s})\), \(T_{S\_S}(\mathrm{s})\), \(T_{M\_S}(\mathrm{s})\), and \(T_{S\_M}(\mathrm{s})\) model the Y-axis structure. \(T_{M\_M}(\mathrm{s})\) and \(T_{S\_S}(\mathrm{s})\) are the respective master and slave PMLSM models, and \(T_{M\_S}(\mathrm{s})\) and \(T_{S\_M}(\mathrm{s})\) describe the models of the coupling effects \(Y_{S}/V_{M}\) and \(Y_{M}/V_{S}\).  
To identify the Y-axis coupling, first, a unit step voltage input \(V_{M}\) is given to the master axis linear motor to drive the coupling system, while the slave axis retains its movement freely. Therefore, the slave axis is moved passively by the master axis, revealing the effect of coupling. From the measured displacements of the slides \(Y_{M}\) and \(Y_{S}\), it is possible to build approximations of the transfer functions \(T_{M\_M}(\mathrm{s})\) and \(T_{M\_S}(\mathrm{s})\), which are composed of the form shown in (7). The same process can be used to identify \(T_{S\_S}(\mathrm{s})\) and \(T_{S\_M}(\mathrm{s})\) by giving a unit step input \(V_{S}\) to the slave motor and measuring both the position outputs of the slides \(Y_{M}\) and \(Y_{S}\). Since the responses are presumed to be third-degree models, as in (7), from the collection of the output position and input voltage data, the model coefficients can be identified by using the MATLAB Toolbox. The third-order transfer functions describe the gantry platform with coupling on the Y-axis, and are described by  
\[T_{X}(\mathrm{s})=\frac{1.41\times 10^{7}}{s^{3}+76.1\mathrm{s}^{2}+144.73 \mathrm{s}} \tag{8}\]  
\[T_{M\_M}(\mathrm{s})=\frac{1.032\times 10^{9}}{s^{3}+676.71s^{2}+1.0681\times 10^{5 }\mathrm{s}} \tag{9}\]  
\[T_{M\_S}(\mathrm{s})=\frac{5.541\times 10^{9}}{s^{3}+1565.4s^{2}+5.6787\times 10^{5 }\mathrm{s}} \tag{10}\]  
\[T_{S\_S}(\mathrm{s})=\frac{1.804\times 10^{9}}{s^{3}+862.93s^{2}+1.862\times 10^{5 }\mathrm{s}} \tag{11}\]  
\[T_{S\_M}(\mathrm{s})=\frac{6.957\times 10^{9}}{s^{3}+1735.1s^{2}+7.156\times 10^{5 }\mathrm{s}} \tag{12}\]  
where \(T_{X}(\mathrm{s})\) is the X-axis dynamic model, and \(T_{M\_M}(\mathrm{s})\), \(T_{M\_S}(\mathrm{s})\), \(T_{S\_S}(\mathrm{s})\), and \(T_{S\_M}(\mathrm{s})\) represent the model for the Y-axis.  
Figure 1: The PMLSM equivalent model.  
Figure 3: Block diagram of the gantry platform.  
Figure 2: The two-axis gantry platform.