esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Parameter Definition** \\ \hline TVMD & tuned viscous mass damper \\ SDOF & single-degree-of-freedom \\ MDOF & multiple-degree-of-freedom \\ TID & tuned inerter damper \\ CBIS & cable-bracing inerter system \\ CICBIS & cross-layer installed CBIS \\ IICBIS & inter-layer installed CBIS \\ RMS & root mean square \\ FRF & frequency response function \\ \(F_{\text{in}}\) & resisting force of the inerter \\ \(u_{1}\), \(u_{2}\) & displacement of the inerter two ends \\ \(m_{\text{in}}\), \(c_{\text{in}}\), \(k_{\text{in}}\) & apparent mass, damping coefficient, and tuning spring stiffness of the TVMD \\ \(F_{\text{is}}\) & resisting force of the TVMD \\ \(u_{\text{is}}\), \(u_{\text{d}}\) & displacement differences at two ends of the TVMD and the inerter \\ \(k_{\text{c}}\) & cable stiffness \\ \(E_{\text{c}}\), \(A_{\text{c}}\) & elastic modulus and cross-section area of the cable \\ \(l_{\text{c}}\) & cable equivalent length \\ \(l_{\text{c}1}\), \(l_{\text{c}2}\) & one-side diagonal and horizontal cable length \\ \(\alpha\) & cable installation angle \\ \(\eta\) & lever arm ratio \\ \(\gamma\) & seesaw angle \\ \(k_{\text{s}}\) & stiffness of the CBIS \\ \(\beta\) & displacement transition ratio \\ \(m\), \(k\), \(c\) & mass, stiffness, and damping coefficient of the SDOF structure \\ \(F_{\text{control}}\) & control force applied to the structure \\ \(u_{0}\) & ground motion displacement \\ \(u\) & displacement of the structure relative to the ground \\ \(u_{\text{e}}\) & equivalent inerter displacement \\ \(m_{\text{e}}\), \(c_{\text{e}}\), \(k_{\text{e}}\) & equivalent apparent mass, damping coefficient, and stiffness of the CBIS \\ \(x_{\text{p,i}}\) & relative displacement of the \(i\)th layer of the primary structure \\ \(m_{\text{p,i}}\), \(k_{\text{p,i}}\), \(c_{\text{p,i}}\) & mass, stiffness, and damping coefficient of the \(i\)th layer of the primary structure \\ \(n\) & number of MDOF structure layers \\ \(m_{\text{e,i}}\), \(k_{\text{e,i}}\), \(c_{\text{e,i}}\) & equivalent apparent mass, stiffness, and of damping coefficient the \(i\)th CBIS \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nomenclature list.
## 2 Theoretical Derivation of Methods
### Determination of the Rotation and Translation Matrices  
This paper considers a high-speed camera with a variable focal length. To facilitate repeated experiments, a DLT [32] camera calibration method providing simple operation and high calculation efficiency is adopted to obtain the camera rotation and translation matrices. The DLT method does not require an approximate initial value of the internal orientation element (i.e., it does not require the manual calculation of the initial value in contrast with other space rendezvous algorithms) and is suitable for the calibration processing of non-metric digital cameras.  
As shown in Figure 1, \(N\) (\(N\geq 6\)) control points are arranged on each of the B and W series. Taking the control points on the body rigid body as an example, if the spatial coordinates are, the corresponding pixel coordinates are. It follows from the collinearity equation of the DLT method that  
\[\begin{cases}u_{i}=\frac{L_{1}X_{i}+L_{2}Y_{i}+L_{3}Z_{i}+L_{4}}{L_{4}X_{i}+L_{1 0}Y_{i}+L_{12}Z_{i}+1}\\ v_{i}=\frac{L_{5}X_{i}+L_{4}Y_{i}+L_{2}Z_{i}+L_{6}}{L_{9}X_{i}+L_{10}Y_{i}+L_{1 1}Z_{i}+1}\end{cases} \tag{7}\]  
where \(L_{i}(i=1,2,\cdots,11)\) denotes coefficients representing the camera's internal and external parameters.  
Equation (7) shows that for the 11 unknowns, at least 6 points are required to determine the 11 values. As the number of equations is greater than the number of unknowns, the least-squares method is used to solve for L and thus obtain the external parameters of the camera.  
The constraint conditions of the rotation matrix are not fully considered in obtaining the results using the proposed method, and the error in the results is thus bound to be large. To improve the accuracy, this paper uses the Gauss-Newton iterative method to  
Figure 2: Flow chart of the method of measuring the rigid-body pose.  
solve the 11 \(L\) equations and 6 constraint equations of the rotation matrix iteratively and then applies singular value decomposition to the obtained rotation matrix. If \(R=UDV^{T}\), then \(R^{B}_{\mathbb{C}}=UV^{T}\). Similarly, \(R^{W}_{\mathbb{C}}\) and \(T^{W}_{\mathbb{C}}\) can be determined. After this processing, the estimated attitude matrix strictly meets the inherent constraints of the rotation matrix, and the estimation error is effectively reduced.
# Dual-Energy CT Applications in Urological Diseases
## 3. DECT Benefits
### Virtual Monochromatic Images  
VMIs are CT images that simulate images obtained with monochromatic X-rays of arbitrary energy levels. VMIs at around 65-70 keV are commonly selected as standard images, as they have CT attenuation numbers equivalent to those obtained from single-energy CT scans acquired at 120 kVp [12]. VMIs in this energy range also tend to have lower image noise [13]. Like single-energy CT scans performed at low-tube voltage, VMIs obtained at lower energy levels (e.g., below 60 keV) show increased iodine contrast, which improves the visualization of contrast-enhanced lesions (Figure 1). This characteristic allows for a reduction in the contrast medium dose of 40-60% when using VMIs at 40-50 keV generated from dual-energy CT scans, which can be particularly beneficial in patients with renal insufficiency [14].  
Moreover, as a lower energy level results in higher X-ray absorption of iodine from energies close to its k-edge (33.2 keV), the reconstruction of monochromatic images at low keV enhances the conspicuity of hypervascular lesions, such as primitive renal-cell carcinomas and their secondary localizations [4; 15; 16; 17].  
Figure 1. Images of a patient affected by renal clear cell carcinoma. (**a**) Synthetic monochromatic reconstruction at 50 keV; (**b**) reconstruction at 75 keV. Note that hypervascular hepatic lesions are more easily identifiable on (**a**).
# Application of Thermochemical Method to Determine the Crystallinity Degree of Cellulose Materials
## 2 Materials and Methods
### Methods
#### 2.2.4 Enthalpy of Wetting  
The enthalpy of cellulose wetting with water (\(\Delta\)H\({}_{\text{w}}\)) was studied at 298 K using a TAM Precision Solution Calorimeter [29]. Small cellulose samples were used in the form of pieces, fibers, or powders. Prior to starting the experiments, the air-dry sample was weighed into a special glass ampoule and dried in a vacuum at 378 K to a constant weight. The glass ampoule containing the dry sample was sealed and introduced into the calorimetric cell filled with distilled water. The calorimeter was thermostated at 298 K to achieve an equilibrium state. Thereafter, the sealed ampoule with the dry sample was broken to ensure that the cellulose sample was wetted with water. The released exothermic heat effect was measured with accuracy \(\pm\)0.01 J. Three of the same sample were tested to calculate an average enthalpy value and standard deviation.
# YOLO-SWD--An Improved Ship Recognition Algorithm for Feature Occlusion Scenarios
## 3 Methods
### Overall Pipeline  
The YOLO series is a classic single-stage object detection algorithm that has garnered significant attention since its introduction. YOLOv11 achieves remarkable performance improvements over its predecessors in object detection, primarily through three aspects: model architecture optimization, multi-scale feature fusion mechanisms, and enhanced training strategies [33]. First, YOLOv11 adopts a novel backbone network architecture with lightweight design, balancing model complexity and feature extraction capability. This enables efficient capture of local details and global contextual information while reducing computational overhead. Second, the multi-scale feature fusion mechanism is further optimized through cross-stage partial connections (CSP) and enhanced feature pyramid  
Figure 2: Examples of internationally common ship datasets and images from the proposed dataset.  
networks (FPNs), improving detection capabilities for small targets and robustness for multi-scale targets in complex scenes.  
Additionally, YOLOv11 employs dynamic label assignment and mosaic augmentation during training, allowing the model to more effectively learn feature distributions of challenging samples and improve adaptability to occlusion and dense target scenarios. Combined with an optimized loss function, YOLOv11 achieves a better balance between accuracy and speed, particularly excelling in edge device deployment. Overall, these advancements establish YOLOv11 as a mainstream method in object detection tasks, offering high efficiency and precision. Given its superior detection accuracy and robustness, we select YOLOv11 as the foundation to further address challenges in ship detection, such as small targets, crowding, and occlusion.  
In this study, to tailor YOLOv11 for ship recognition tasks, we enhance the network by incorporating three key modules: the spatial pooling convolution block (CKSP), wavelet convolution detection head (WTHead), and dynamic large-kernel attention (DLKA). These additions enrich target feature extraction and improve the detection of occluded ships. The enhanced network is referred to as \"YOLO-SWD\", which signifies three core innovations integrated into the YOLO framework:  
S (strip pooling): Enhances spatial context modeling by aggregating long-range dependencies in horizontal and vertical directions.  
W (wavelet convolution): Combines wavelet transforms with convolutional layers to preserve high-frequency details.  
D (DLKA): Employs dynamic local-kernel attention to adaptively focus on critical regions while suppressing background noise.  
The full network architecture is illustrated in Figure 3.
## 2 Related Work
### Light Modulation for OST-HMDs  
In order to make arriving light subtraction possible, [PERSON] et al. [3] developed two prototypes to determine the capability of light modulation using a partially transparent LCD panel. One of the prototypes used an extra optical lenses to get the LCD in focus, and a beam-splitter was set between the LCD and the scene to get an on-axis image so that pixel precision could be ensured. The other prototype let user see directly through the LCD, using an off-axis camera for the scene image. However, the resulting image may suffer from the problems of blur and diffraction.  
[PERSON] et al. [17] also introduced a partially transparent LCD panel in their brightness adaptation study. In [17], the beam-splitter was set to induce the half-light of a scene to the scene camera for overexposure and underexposure detection. The LCD panel was positioned between the beam-splitter and the OST-HMD to stop a portion of the light from getting through the OST-HMD. They achieved the suppression of light in the overexposed area by utilizing the LCD and the compensation of the underexposed area with the OST-HMD. However, their system processes the three color channels (R,G,B) uniformly and gives no control over the individual color channels.  
[PERSON] et al. [24] introduced an approach for light subtraction based on a spatial light modulator (SLM) with a system that also used an additional optical lens and beam-splitter and provided pixel-level color subtraction. But because their approach also relied on the resolution of the SLM, it also suffered from diffraction because of the pixel grid.  
The construction of this paper's proposed prototype is similar to [17]. But, a new algorithm was developed for computing the images to be displayed on an OST-HMD and an LCD to achieve light chroma control, including light subtraction for individual color channels. By achieving final pixel-wise light chroma control with an OST-HMD while using an LCD to subtract an overall amount of light, the blur and diffraction artifacts can be alleviated without requiring getting the LCD in focus, which requires using extra devices, like the optical lenses in [3]. Furthermore, because reproducing color precisely for each pixel is particularly important for Daltonization, the proposed prototype also addresses color correction taking into consideration color distortions resulting from the transmission of light through an OST-HMD and LCD as well as the rendering on an OST-HMD.
###### Abstract  
In this paper, a simplified method is proposed to estimate the peak inter-story drift ratios of steel frames with hysteretic dampers. The simplified method involved the following: (1) the inelastic spectral displacement is estimated using a single-degree-of-freedom (SDOF) system with multi-springs, which is equivalent to a steel frame with dampers and in which multi-springs represent the hysteretic behavior of dampers; (2) the first inelastic mode vector is estimated using a pattern of story drifts obtained from nonlinear static pushover analysis; and (3) the effects of modes higher than the first mode are estimated by using the \(i\)th modal period, \(j\)th mode vector, and \(j\)th modal damping ratio obtained from eigenvalue analysis. The accuracy of the simplified method is estimated using the results of nonlinear time history analysis (NTHA) on a series of three-story, six-story, and twelve-story steel moment resisting frames with steel hysteretic dampers. Based on the results of a comparison of the peak inter-story drift ratios estimated by the simplified method and that computed via NTHA using an elaborate analytical model, the accuracy of the simplified method is sufficient for evaluating seismic demands.  
Keywords:simplified estimation method; steel hysteretic damper; peak inter-story drift ratio; inelastic mode vector; equivalent SDOF system +
Footnote †: journal: Applied
# Assessment Analysis of Flood Susceptibility in Tropical Desert Area: A Case Study of Yemen
## 2 Methods and Data
### Methods  
#### 2.6.1 Random Forest (RF)  
RF is an ML classification algorithm that improves the classification tree's flexibility and accuracy [71]. It is a variant of bagged decision trees created from many de-correlated trees and only requires tuning a few parameters [72]. The number of a split attribute (\(M_{\text{trp}}\)), which sets the number of parameters to divide at each tree node, is the most critical parameter to tune in RF. For classification and regression, RF is a highly efficient method. It can deal with multidimensional, categorical, and continuous data. RF does not necessitate any assumptions regarding the data's statistical distribution, and it is resistant to changes in the dataset's composition. One of the advantages of the RF is that it has a quick training speed and can detect the mutual influence of characteristics. RF can balance faults in uneven datasets, and even if a large portion of the features is missing, accuracy can still be maintained [73; 74]. These characteristics come in handy when working with nonlinear mutual relationship variables [74]. It consists of \(K\) integrated decision trees composed of a set of unrelated regression decision trees \(\{h(x,\theta_{k}),k\epsilon 1,2,\cdots K\}\).  
\[h(x)=\frac{1}{N}\sum h(x,\theta_{k}) \tag{3}\]  
where \(x\) is the conditioning factor of flood and \(k\) are the numbers of the decision tree, \(\theta_{k}\) is an independent, identically distributed random variable. \(N\) is the total number of decision trees generated by the model.  
\[I(x_{i})=\sum_{k=1}^{K}\frac{I_{k}(x_{i})}{K} \tag{4}\]  
where \(I_{k}\) is the importance of factor \(x_{i}\), and \(I\) is the importance of factor \(x\) in all random forests.  
In contrast, RF is complicated and could overfit the training data when dealing with noisy regression or classification problems. RF outcomes will be influenced more by attributes with more values. In general, RF is an effective integrated learning method [73].
## 3 Results and Discussion
### Dynamic Strain Sensing Properties
#### 3.4.3 Mechanism  
The changes in the surface morphology of the single- and hybrid-structured composites during tensile strain are shown in Figure 8. Unlike the ideal model, in the case of a composite manufactured through a heat-pressing process, surface roughness was observed on the surface to a minimal extent. That is, when the single composite was stretched, some defects that had formed on the surface were expanded by tensile stress. This expansion of surface defects can be predicted to lower the strain-sensing range, compared to the results of the ideal model. In addition, the continuous diffusion of surface cracks during repeated strain sensing can further increase the relative resistance (Figure 6). However, in the case of the hybrid composite, the elastomer layer covered the surface of the single composite to fill fine cracks. The elastomer layer can prevent the diffusion of cracks during tensile strength testing, owing to the excellent adhesive property between the interfaces. In addition, because the elastomer layer has a higher elongation than the MWNT composite layer, the adhesive force can be stably maintained even at a high enough strain to break the single layer. The prevention of crack diffusion increases the maximum elongation, and can result in a larger measurable strain range than a single composite. Therefore, the hybrid composite could operate as a wide-range sensor (Figure 7).  
Figure 8: Schematic of the behavior of the surface crack in single- and hybrid-structured MWNT/NR nanocomposite samples under stretching.  
Figure 7: Dynamic strain-sensing curve for (**a**) 5 wt.% hybrid composite strain at 130% under 100 cycles and (**b**) strain-sensing curve from 50 th cycle to 56 th cycle. (For (**a**,**b**), 20 pre-cycles were conducted before the cyclic test.)
# Research on Routing Algorithm of Construction Robot Cluster Enhanced Ad Hoc Network
## 1 Introduction  
Construction robots have thrived in the intelligent upgrading and transformation of the construction industry [1]; however, the complex construction site environment makes it difficult for traditional communication methods to guarantee the high-reliability interconnection communication needed for the coordinated control and scheduling of construction robots. There is an urgent need for a high-efficiency, high-quality, and low-cost communication network connection method. Wireless ad hoc networks are favored for their characteristics of no fixed infrastructure support and distributed communication [2].  
Because of their relatively strong mobility and limited energy, the nodes of construction robots have high requirements for the effectiveness of routing protocols. At present, the design and optimization of routing protocols are mainly divided into four categories [3, 4, 5]: (1) Reducing node energy consumption; (2) Reducing node load; (3) Maintaining link stability; (4) Dynamic maintenance [6, 7]. Building a high-reliability, large-bandwidth, and low-latency communication network required for coordinated control and scheduling for construction robot clusters [8, 9] expounded the construction and spatial modeling of the ground robot cluster communication network and integrated positioning and communication into the robot network, but ignored the complex secondary, coupling superposition characteristics of the industrial site. In [10], potential fields and graph theory were introduced to optimize the link capacity of the self-assembled network of clustered robots. However, the network's connectivity could not be guaranteed when there were more nodes and convergence was not fully achieved, leading to limitations in the scalability of the network.
## 3 Proposed Hybrid Optimization Approach
### Interface Connection  
To link the interfaces of the two applications, file compatibility between the two programs must be considered. In this sense, the Excel software is used as an intermediate stage to ensure an effective exchange of information between MATLAB and GAMS, so the linking of the interfaces is performed through files in _.xlsx_ format, in which the data necessary for the evaluation of the MINLP mathematical model defined in Section 2 are sent and received. However, as shown in Figure 1, it is necessary to use a function that allows the reading and writing of these files in the software.  
The GAMS software uses the GDXXRW feature, which allows reading and writing Excel spreadsheets [45], as well as a \(.gdx\) file extension to read multiple data ranges in \(.xslx\) format. Conversely, this function allows writing these data in \(.xslx\) format from the GAMS software for further processing in Excel. With MATLAB, the xlswrite and xlsread functions are used. The former allows writing data with the \(.m\) extension in the Excel files used to write the D-STATCOM locations. The latter function allows reading the \(.xlsx\) file in MATLAB. This function is used to read the value of the objective function resulting from the optimization carried out in GAMS [45]. One of the advantages of this information exchange is that the connection between the two interfaces allows \(.xlsx\) and \(.csv\) files to be executed so that large amounts of data can be processed in reduced computation times. However, it should be noted that the GDXXRW function is only available for devices running the Windows operating system [45].  
Figure 1 summarizes the methodology used in this research, depicting the duty cycle based on the implemented methodology. The MATLAB computational tool performs the function of evaluating the objective function associated with the total costs, as described in Equation (3) within the DSCA and, as a result, it generates the initial locations of the D-STATCOMs, which are sent to the Excel tool to be later read by GAMS. Afterward, the locations are evaluated, taking into account the constraints in terms of optimal power flow, power balance, and reactive power injected by the D-STATCOMs and the new value of the objective function, which is then sent to an Excel file to be read by MATLAB, which restarts the process and evaluates the number of iterations.