esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Effective Design Methods of Core Beam of Full Bridge Aeroelastic Model
## 2 Design of Reduced-Scale Aeroelastic Models  
Reduced-scale aeroelastic models are designed and produced (aeroelastic modeling), which aims to reproduce the exact structural dynamic response of the prototype bridge when tested in a wind tunnel. There are four main steps from the actual bridge to the scaled aeroelastic model, as shown in Figure 1. For more details on the design process of reduced-scale aeroelastic models, readers may refer to Ref. [30].  
Step 1. A prototype to the actual bridge is modeled and the dynamic characteristics of the prototype are determined. This prototype has to reproduce the stiffness of the actual bridge in the six degrees of freedom, or at least represent the stiffness in the most representative degree of freedom (the prototype shown as Figure 1).  
Step 2. Based on the fundamental relationships between the limiting dimension of the wind tunnel test section and the dimensions of the prototype, the length scale factor \(\lambda_{L}\) is defined, and the similarity requirements can be determined. Consequently, an ideal reduced-scale model can be defined by employing the scale factors and the similarity requirements.  
Figure 1: The design process of reduced-scale aeroelastic models.  
Step 3. Due to the limitation of model construction, it is difficult to realize the continuous characteristics of the ideal model in the length direction. So, the discretization of continuous properties and simplification of geometric details is necessary. Based on these adjustments, the real reduced model is created and targeted inertial moments are determined, which should be as close as possible to the ideal reduced model.  
Step 4. The model production is conducted according to the real reduced model.  
In model production, the section parameters of the core beam are determined and the inertial moments calculated by the section parameter should be as close as possible to the targeted ones. A detailed optimization process and numerical solution of the torsion constant are described in detail next.
## 4 Results and Analyses
### LSM of LR
Based on the regression coefficients, we can explain the statistical relationship between each control factor and the occurrence of landslides. Figure 10a shows that the highest LR coefficients are in the  
Figure 10: LR Coefficients for the ten controlling factors and the area of each classification variable. (**a**) Elevation; (**b**) slope angle; (**c**) aspect; (**d**) curvature; (**e**) distances to fault; (**f**) distances to epicenter; (**g**) PGA; (**h**) distance to rivers; (**i**) distances to roads; and (**j**) lithology. range of elevations 100-200 m and 200-300 m (Figure 10a). Figure 10b shows that the LR coefficients of slope angel change little within 0-40\({}^{\circ}\), then reach the highest value in \(>\)40\({}^{\circ}\) interval. Figure 10c shows that the east and southeast aspects are easier to produce landslides. The coefficient curve of the parallel seismogenic fault shows that the LR coefficients decrease with the increase of fault distances as a whole (Figure 10e). Figure 10g shows that the areas with 0.6-0.7g are more prone to landslides. The lithology of Late Cretaceous marine sedimentary rocks (K2 sm), Middle to Late Miocene marine and non-marine sediments (N2 sn) have the highest LR coefficients (Figure 10j). The corresponding weight values were given to each controlling factor according to the LR coefficients (Table 2). Based on Formula (1), the Z values and the weighted linear combination of the independent variables were calculated based on GIS platform and the superposition operations of each controlling factor layer (Figure 11a). By virtue of Formula (2) and Z and P values, the probability of landslide occurrence, was calculated subsequently. Based on the commonly used five-class interval method, the landslide susceptibility index was ranked into five classes: (1) Very low (0 to 0.2), (2) Low (0.2 to 0.4),  
As shown in Figure 12 for the LR, the area of very high susceptibility class is 98.60 km\({}^{2}\), which accounts for 16% of the entire study area; the number of landslides in this class is 4329, accounting for 64.78% of the total landslides, and the LAD and LND are 10.37% and 43.9/km\({}^{2}\), respectively. 51% of the study area is designated as the very low susceptibility classes, where the number of landslides is 209, accounting for 3.12% of the total number of landslides, and the LAD and LND are 0.18% and 0.68/km\({}^{2}\), respectively. On the whole, most landslides are concentrated in the very low and very high susceptibility classes.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 2 Background Research
### Rotational Invariance in Object Detection  
Rotational invariance poses a fundamental challenge in oriented object detection [14]. Conventional convolutional neural networks (CNNs), though highly effective in general object detection, exhibit limitations when addressing objects at arbitrary angles in aerial images [15; 16]. To mitigate this, approaches such as RoI Transformer [17] and Oriented R-CNN [18] have been developed, introducing rotation-specific enhancements, including rotated bounding boxes, to improve detection accuracy. However, these methods often rely on fixed rotational anchors or rigid transformations, which constrain their performance when dealing with diverse object orientations.  
Dynamic rotational mechanisms, such as ARC, have been proposed to better align features for rotated objects. While these methods offer improved feature extraction, they are typically associated with significant computational costs and limited scalability for large-scale datasets. RASST builds upon these advancements by incorporating rotationally aware patch embeddings and adaptive rotational convolutions within a transformer framework. This integration achieves robust rotational invariance while maintaining computational efficiency.
# Efficient Model-Based Object Pose Estimation Based on Multi-Template Tracking and PnP Algorithms
## 5 Experimental Results
### Multi-Object Pose Tracking  
The proposed method can be extended to track multiple objects. Figure 6 shows the experimental results of the proposed method to track three different objects simultaneously. From Figure 6, it is clear that the proposed method not only performs 3D pose tracking of multiple objects with a frame rate higher than 30 frames per second (FPS), but also overcomes the partial occlusion of the object No. 3 in the tracking process. Moreover, the pose estimation results are also accurate when the objects No. 1  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Estimation Error**} \\ \hline
**Unit** & \multicolumn{2}{c}{**cm**} & \multicolumn{3}{c}{**Degree**} \\ \hline  & \(\mathbf{e_{Tx}}\) & \(\mathbf{e_{Ty}}\) & \(\mathbf{e_{Tx}}\) & \(\mathbf{e_{Rx}}\) & \(\mathbf{e_{Ry}}\) & \(\mathbf{e_{Rx}}\) \\ \hline (a) & 0.0121 & 0.3721 & 0.0144 & 2.1025 & 2.1609 & 0.0100 \\ (b) & 0.0961 & 0.0009 & 0.0625 & 2.9241 & 3.5344 & 0.3844 \\ (c) & 0.2916 & 0.0081 & 0.1681 & 0.0676 & 2.1609 & 0.0225 \\ (d) & 0.4900 & 0.1521 & 0.1225 & 0.2401 & 0.0961 & 0.0144 \\ (e) & 0.0196 & 0.1296 & 0.2116 & 0.0841 & 0.5476 & 0.0729 \\ (f) & 0.0009 & 0.4356 & 0.2500 & 0.1849 & 2.2801 & 0.0729 \\ (g) & 0.0484 & 0.4096 & 0.2809 & 0.4225 & 1.3225 & 0.7744 \\ (h) & 0.1156 & 1.0816 & 0.3249 & 0.4900 & 1.0000 & 0.4225 \\
**Average** & 0.1343 & 0.3237 & 0.1794 & 0.8145 & 1.6378 & 0.2218 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Estimation errors of the proposed algorithm shown in Figures 4 and 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Stage** & **Object Recognition** & **Template Tracking** & **3D Pose Estimation** \\ \hline
**Process** & \begin{tabular}{c} Keypoint extraction \\ 65 ms \\ \end{tabular} & \begin{tabular}{c} Keypoint matching \\ 53 ms \\ \end{tabular} & \begin{tabular}{c} Tracker initialization \\ 3.85 ms \\ \end{tabular} & \
\begin{tabular}{c} Template tracking \\ 3.03 ms \\ \end{tabular} &
\begin{tabular}{c} Initial Pose Solver and \\ PnP Solver \\ 0.70 ms \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average processing times in each stage of the proposed algorithm.  
and No. 2 change their poses. Therefore, these experimental results validate the tracking performance and tracking accuracy of the proposed pose estimation algorithm. Two video clips of the experiment can be accessed through the webpages of [32; 33].  
Remark: From the above experimental results, we conclude that the advantage of the proposed method includes the real-time performance in tracking multiple targets and the accuracy of the pose estimation results. However, there are still some limitations of the proposed method; for example, the proposed method is not able to track textureless objects or planar objects with homogeneous surfaces. Furthermore, an unfavorable lighting condition may significantly reduce the tracking performance of the proposed method.
# Machine-Learning-Based Android Malware Family Classification Using Built-In and Custom Permissions
## 5 Evaluation  
### Feature Sets  
We used the AAPT to extract the permissions requested by malicious apps belonging to the top 20 malware families listed in Table 1. As mentioned in Section 4.1, the number of distinct permissions requested by at least one malicious app is 96. They consist of 87 built-in permissions and 9 custom ones.  
We took advantage of the feature importance score of the LightGBM classifier, which represents how many times the feature is used to split the data while constructing trees. We calculated the feature importance score of the 96 permissions and normalized it to the sum of the importance scores of all features. Figure 3 shows the feature importance of the top 10 among the 96 permissions. Permissions related to the network, SMS, or external storage were highly ranked. This implies that such permissions play an important role in malware family classification. On the other hand, there were some permissions whose feature importance was 0. They were 31 built-in permissions and 1 custom permission. These permissions hardly contributed to the malware family classification. Notice that, however, they might be needed by malware to conduct malicious behavior.
# Time-Sensitive Networking Mechanism Aided by Multilevel Cyclic Queues in LEO Satellite Networks
## 4 Time-Sensitive Networking Technique Aided by Multilevel Cyclic Queues
### Priority-Based Multilevel Cyclic-Queue Mechanism
In the traditional CQF mechanism, we cannot increase the time-sensitive forwarding capacity by increasing the size of the time-sensitive queue because the time-sensitive queue  
Figure 4: A control mechanism of TSN-MCQ. must guarantee that all packets are forwarded within that time slot. At the same time, it is difficult for the CQF to differentiate between services with different time-sensitive levels. Our proposed TSN-MCQ could remedy these shortcomings. We added multiple queues without increasing the latency of high-level TSFs. At the same time, we could perform differentiated forwarding for services with different time-sensitive levels and increase the forwarding capability for TSFs.
# A Zero-Reference Low-Light Image-Enhancement Approach Based on Noise Estimation
## 1 Introduction
[13], we propose an innovative zero-reference low-light image-enhancement approach founded on noise estimation. This approach achieves satisfactory reference-free low-light image enhancement by estimating the luminance curves of the low-light fused noisy images with a lightweight convolutional neural network. In contrast to preceding curve-fitting-based methods [12; 13], our method integrates the noise map with the original input image to augment image features, resulting in superior quality and visually appealing low-light image enhancement. This improvement is facilitated by the incorporation of a specifically designed noise loss function. During the noise-fusion process, our objective was to estimate the noise map of the low-light images, which serves as a representation of the noise distribution within each low-light image. As depicted in Figure 1, regions in the original input image exhibiting significant color value disparities (i.e., content-rich regions) correspond to sparser noise distribution in the enhanced image, and conversely, regions correspond to dense noise distribution. Thus, the noise distribution of images can be estimated by analyzing the color map of the image. Subsequently, the noise map is amalgamated with the input image to enhance its features, with a lightweight convolutional neural network employed to derive the parameter matrix for the image-enhancement curve. Ultimately, the designed loss function facilitates the realization of zero-reference image enhancement and noise reduction. Overall, the contributions of the proposed research are as follows:  
1. Proposing an improved high-order curve expression for mapping low-light images to enhanced images, which involves adding a noise term to reduce the noise in the enhanced image;
2. Proposing a color-map-based noise-estimation method, fusing noise maps with low-light images to enhance the input features of the depth curve estimation network;
3. Designing a reference-free noise loss function and adjusting the depth curve estimation network structure to achieve noise reduction in reference-free enhanced images while reducing color distortion;
4. Achieving SOAT on many reference-free low-light datasets and successfully applying to real coal mine image enhancement.
# Multi-Modal 3D Shape Clustering with Dual Contrastive Learning
## 1 Introduction
Meanwhile, point cloud and multi-view modalities mainly focus on the geometric and visual information about 3D shapes, respectively. Simultaneously exploring the cross-view consistent representations of different views, as well as the cross-modal consistent representations of point cloud and multi-view modalities, contributes to obtaining more discriminative 3D shape descriptions. Secondly, different views within the multi-view modality and the corresponding point cloud of the same 3D shape all share consistent semantics. In addition to learning consistent representations, exploring the cross-view and cross-modal consistent clustering assignments is beneficial to boosting the robustness of the 3D shape features for clustering, thus further enhancing the compactness of clustering partitions. Therefore, by simultaneously performing cross-view contrastive learning within multi-view modality and cross-modal contrastive learning between point cloud and multi-view modalities at both the representation and clustering assignment levels, a representation-level dual contrastive learning module and an assignment-level dual contrastive learning module were developed in the proposed method. The key contributions of this paper are as follows:  
1. A dual contrastive learning network for multi-modal 3D shape clustering is proposed to discover the underlying clustering partitions of unlabeled 3D shapes. To the best of our knowledge, this is the first deep multi-modal 3D shape clustering method;
2. By simultaneously ensuring the representation consistency within multi-view modality and between point cloud and multi-view modalities, a representation-level dual contrastive learning module is proposed to capture discriminative 3D shape features for clustering;
3. To further boost the compactness of clustering partitions, an assignment-level dual contrastive learning module is proposed to simultaneously capture consistent clustering assignments within multi-view modality and between point cloud and multi-view modalities;
4. Experimental results on two widely used 3D shape benchmark datasets are presented to demonstrate the superior clustering performance of the proposed DCL-Net. The remaining of this paper is organized as follows. Section 2 briefly describes the three aspects of current research that are the most relevant to the proposed method. Section 3 introduces the proposed DCL-Net in detail. Section 4 presents a series of experimental results and analyses. In Section 5, the conclusion is summarized.
# AMR-Assisted Order Picking: Models for Picker-to-Parts Systems in a Two-Blocks Warehouse
## 3 The Two-Blocks Warehouse Case Study  
The real-world context under study is that of a German company operating in the large-scale retail and tourism sectors. Products (food, drugs, clothing, housewares, cosmetics, consumer electronics, etc.) are stored in several warehouses. The warehouses are served by operators walking between aisles, carrying a cart in which they temporarily place the items collected. An order consists of the set of items requested by a supermarket. The Company is interested in the impact of the implementation of an AMR-assisted Picker-to-Parts system to a real-world warehouse characterized by two blocks, as depicted in Figure 2.  
In the context of the application we consider, the problem of Order Batching is solved in an earlier stage, conversely in the case study illustrated in [21], where the assignment of customer orders to different batches is part of the optimization problem. Consequently, also the restrictions made in [21] about the relations between orders and batches are drop and the model can be simplified consequently, by reducing the number of variables and constraints. However, note that the problem of Batch Sequencing remains part of the optimization problem: knowing the number of AMR and the batches to be processed, we search for the optimal assignment and sequencing of the batches to the carts. The rest of the settings remain the same.  
The AMRs begin their journey from the depot and visit the handover locations along the central corridor associated with the aisles containing batch items that the cart itself is processing, eventually turning back when the right-most aisle containing an order is reached. The depot is in front of the left-most corridor. Note that other locations are possible for depot, nut changing the location has a negligible influence on the average travel time [25]. The picker of each corridor, once returned to the respective handover location with the collected items, can load the cart, which, once the collection of all the items is finished, returns to the depot. The picker cannot start loading items until the cart has reached the handover location of its corridor. As soon as it is returned to the starting  
Figure 2: Warehouse with a two-blocks layout.  
point, the cart is downloaded from all the items in the batch, which can then be considered completed; at this point the AMR can begin the collection of the next batch.  
Previous studies in the literature have shown that the presence of a central corridor in the warehouse may lead to a decrease of the travel time required by the operator [28]. In the case study considered in this paper, we have each human operator retrieving the items of a specific sub-aisle.
# Linewidth Measurement of a Narrow-Linewidth Laser: Principles, Methods, and Systems
## 2 Basic Principles
### Delayed Self-Heterodyne Method
#### 2.3.4 Beat Frequency Power Spectrum Analysis  
Regarding the impact of \(1/f\) noise on linewidth measurements, [PERSON] et al. [49; 50] proposed a Voigt fitting method to separate the Lorentz components in the beat frequency power spectrum. The beat frequency power spectrum based on the self-heterodyne method is the Voigt spectrum curve, which is a convolution of the Lorentz spectrum and the approximate Gaussian spectrum, as shown in Figure 3. Two types of spectral lines are related to white noise and \(1/f\) noise, while \(1/f\) noise can cause Gaussian broadening of the spectral lines [51]. A Voigt contour can be expressed as [52]:  
\[\mathrm{V}(v_{v})=\int_{-\infty}^{+\infty}G(v_{v})\mathrm{L}(v_{v}-v_{G})dv \tag{14}\]  
where \(v_{v}\) is the frequency of the Voigt spectrum, \(v_{G}\) is the Gaussian spectral frequency, \(G(v)\) is the normalized Gaussian line shape with a center of \(v=v_{0}\), \(L(v)\) is the normalized Lorentz line shape with a center of \(v=v_{0}\), and \(v_{0}\) is the frequency corresponding to the peak of the Lorentz spectral line:  
\[G(v_{v})=\frac{2\sqrt{ln2}}{\sqrt{\pi}\Delta v_{G}}exp\Big{[}-4 ln2(v_{v}-v_{0 })^{2}/(\Delta v_{G})^{2}\Big{]} \tag{15}\]  
\[L(v_{v})=\frac{\Delta v_{L}}{2\pi}\frac{1}{\left(v_{v}-v_{0}\right)^{2}+ \Delta v_{L}{}^{2}/4} \tag{16}\]  
where \(\Delta v_{G}\) is the full width at half maximum (FWHM) of a Gaussian line shape and \(\Delta v_{L}\) is the FWHM of the Lorentz line shape. The relationships between the linewidths of the Voigt spectrum (\(\Delta v_{v}\)), Lorentz spectrum, and Gaussian spectrum can be approximated as:  
\[\Delta v_{V}=\frac{1}{2}\bigg{[}1.0692\Delta v_{L}+\sqrt{0.866639(\Delta v_{ L})^{2}+4(\Delta v_{G})^{2}}\bigg{]} \tag{17}\]The Gaussian component has a strong influence on the 3 dB spectral width based on the broadening effect of \(1/f\) noise, while the 20 dB spectral width is mainly affected by the contribution of the Lorentz component. The Lorentz linewidth can be obtained by dividing the 20 dB spectral width by 2\(\surd\)99. The initial values of the Gaussian and Lorentzian linewidths of 3 dB and 20 dB are substituted into Equations (14)-(17) to obtain the Voigt contour curve, which is compared with the measured spectral lines. A calculated value that equates the 20 dB width of the Voigt spectrum with the measured spectral width through iteration is obtained as a new estimated value. The above process is repeated until the estimated value converges, and this process is used as the Lorentz linewidth.