esa-sceva/satcom-chunk-collection huggingface dataset:
# Form Finding of Asymmetric Cable Network Reflector with Flexible Ring Truss
## 3 Form-Finding Method Considering the Deformation of Flexible Ring Truss  
In most of the existing literature, the connecting points between the cable networks and flexible ring truss were assumed to be fixed during the form finding for the cable network reflector. Therefore, the deformations of the flexible ring truss due to tensions in the cable networks were not considered. In this section, a novel design method that considers the deformation of the ring truss is proposed. Since the ring truss is deformed, the coordinates of the fixed nodes are updated to achieve the objective shapes of the reflector. By modifying the coordinates of the fixed nodes, a new series of force density values are calculated to decrease the influence of the deformation on the accuracy of the reflector. The procedure of the design method is given below:  
Step 1: Input basic parameters for the cable network reflector.  
Step 2: A set of suitable force density values should be obtained, using the MATLAB program to design an objective shape of the cable network structure.  
Step 3: Calculate the deformations of the ring truss in the ANSYS software to update the boundary coordinates. From Equation (12), the reaction force vector provided by the boundary nodes can be obtained as:  
\[p_{xf}=C_{f}^{T}\cdot Q\cdot C\cdot x+C_{f}^{T}\cdot Q\cdot C_{f} \cdot x_{f} \tag{26}\] \[p_{yf}=C_{f}^{T}\cdot Q\cdot C\cdot y+C_{f}^{T}\cdot Q\cdot C_{f} \cdot y_{f}\] \[p_{zf}=C_{f}^{T}\cdot Q\cdot C\cdot z+C_{f}^{T}\cdot Q\cdot C_{f }\cdot z_{f}\]  
The deformations in the \(n\)th iteration step are obtained from the following equation:  
\[\left(\begin{array}{c}\Delta x_{f,n}\\ \Delta y_{f,n}\\ \Delta z_{f,n}\end{array}\right)=-K_{f}\left(\begin{array}{c}p_{xf}\\ p_{yf}\\ p_{zf}\end{array}\right) \tag{27}\]Next, the coordinates of the fixed nodes are updated using:  
\[\left(\begin{array}{c}x_{f,n}\\ y_{f,n}\\ z_{f,n}\end{array}\right)=\left(\begin{array}{c}x_{f}\\ y_{f}\\ z_{f}\end{array}\right)+\left(\begin{array}{c}\Delta x_{f,n}\\ \Delta y_{f,n}\\ \Delta z_{f,n}\end{array}\right) \tag{28}\]  
Step 4: The surface root-sum-of-squares (RSS) error \(w_{n}\) during the \(n\)th iteration is used as a performance metric of the obtained mesh reflector. The RSS error can be expressed as  
\[w_{n}=\sqrt{(x_{n}-x_{0,n})^{2}+(y_{n}-y_{0,n})^{2}+(z_{n}-z_{0,n})^{2}} \tag{29}\]  
The termination condition for the algorithm is specified as:  
\[w_{n}\geq w_{n-1} \tag{30}\]  
where \(x_{0,n}\), \(y_{0,n}\), \(z_{0,n}\) are the coordinates of the free nodes when the boundary nodes are fixed, and \(x_{n}\), \(y_{n}\), \(z_{n}\) are the coordinates of the free nodes if the ring truss has a deformation in the \(n\)th iteration step.  
Step 5: Generate the desired mesh shape with the obtained \(x_{0,n}\), \(y_{0,n}\), \(z_{0,n}\) and \(x_{f,n}\), \(y_{f,n}\), \(z_{f,n}\).  
The design flow is illustrated using the flowchart shown in Figure 8.
## 4 System Model
### Mobile Topology  
As mobility act as a double edge sword in networking. It can enhance the network performance by bringing the requested contents closer to the requester node, thus, decreasing the average latency. It also has an adverse effect on the system performance which means if a specific node moves away, the requester node starts searching for it. In this work, a wireless mobile network consists of \(\mathcal{N}\) mobile ICN nodes interconnected to each other and deployed in a random position of probability-p as shown in Figure 5. The server is  
equipped with content catalogue \(\mathcal{C}\) is placed at the centre and is connected with nodes in a multi-hop manner.
# Characteristics of Changes in Contractile Properties of Muscle after Anaerobic Exercise
## 4 Discussion  
The research conducted focused on evaluating changes in muscle contractile properties in response to anaerobic exercise in the form of the Wingate test. In addition, the relationships occurring between the level of changes in TMG parameters and Wingate test parameters were characterized. The main findings of the present study reveal that anaerobic effort led to statistically significant alterations in TMG in almost all examined muscles of the lower limb. Changes in the muscle response concerned the parameters Tc, Dm, Td, Ts, and Tr. The baseline values of the TMG variables of this study were compared with data reported for other sports [15,17,30]. The results of the present study show that the Tc parameter had similar values to the group of football players [17], but different in relation to road cyclists [30]. Cyclists presented higher values of the Tc in both the preparatory and starting periods. Interestingly, in women practicing rugby [15], the Tc parameter had similar values both in relation to the study group and the group of football players. The obtained results can provide reference data for future research. The study showed statistically significant changes before and after anaerobic exercise in parameters measured using TMG. In addition, significant correlations were shown between these changes and selected parameters of the Wingate anaerobic test. A study by [PERSON] et al. [15] showed a relationship between the contractile characteristics of the vastus lateralis muscle and the peak power achieved in the Wingate test. In contrast, a study by [PERSON] et al. [24] found that TMG is able to identify significant muscle changes immediately after anaerobic exercise. In addition, the correlation found in our study between Wingate test parameters and changes in TMG parameter values for almost all muscles tested suggests that TMG can be used as a tool for measuring fatigue and performance
## 3 Methodology
### Shadow Direction Acquisition
#### 3.2.2 Estimating the Shadow Angle  
Because the high-rise building shadows most likely exhibit shadow angles with long straight lines, here, two typical and effective algorithms were introduced to find these line features: the Canny edge detector [36] and random sample consensus (RANSAC) [37]. We detected shadow edges on SH\({}_{\text{c}}\) with the Canny edge detector, and the result was recorded as EG. After Canny edge detection, each connected domain in EG was set as a unit. We used RANSAC to detect inliers in every unit. The inliers are the points that can be fitted to the line, and the outliers are the points not on the line. Next, we set the length thresholds of the line. Empirically, we set two length thresholds according to the resolution and shadow size, recorded as L1 and L2. Figure 3 shows the results of different area thresholds and different lengths of lines. At last, we count the number of lines at each angle obtained by RANSAC for different area thresholds and for different lengths of lines, as shown in the histograms in Figure 4. We sum up the proportions in every case; the angle that has the largest value is the shadow angle \(\theta\).  
Figure 3 illustrates the process of shadow angle detection; the corresponding original image is shown in Figure 2a. In this example, the morphological structure is the disk for the shadow detection result, and the open operation size for SE is 7. The remaining shadow results are shown in the second column in Figure 3; the area thresholds were 1000, 2000, and 3000 pixels respectively. The last two columns display the RANSAC line detection results in Figure 3. The corresponding RANSAC line detection results in Figure 3 are shown in Figure 3.  
results with different length thresholds. The angles of the lines in different cases are seen in Figure 4. The original scenes in the green boxes A and B in Figure 3c correspond to the partial original images A and B in Figure 4. When comparing the shadow angle value and the true shadow angle in Figure 4, the proposed method correctly obtained the shadow angle. Combining Figures 3 and 4, we found that the larger the area and length threshold chosen, the fewer lines remained, and the larger the proportion of the true shadow angle. If a is set as 500 pixels, or the A2 is set as 1500 pixels, the correct shadow angle can still be determined. Hence, this method is robust for shadow angles.
# Damage Detection Using Modal Rotational Mode Shapes Obtained with a Uniform Rate CSLDV Measurement
## 3 Numerical Investigation  
### Finite Element Analysis  
The finite element model of a plate is used to perform the demonstration of damage detection with the modal rotational mode shapes. The length of the plate along the \(x\)-axis is 180.0 mm and width along the \(y\)-axis is 80.0 mm. The thickness of the plate is 2.0 mm. Material property of the plate is assumed to be the same as the steel with [PERSON]'s modulus \(E\ =\ 200\)GPa, [PERSON]'s rate \(v\ =\ 0.3\), and density \(\rho\ =\ 7800\)Kg/m\({}^{3}\). The plate has clamped-free-free-free boundary conditions and is fixed at the left edge. It is divided into \(180\times 80\) quadrilateral shell elements of size 1 mm. There are two different length cracks located at the plate and span along the y direction. The first crack span length is 15 mm, approximately equal to 18.75% of the width of the plate and the second is 25 mm, which equals 31.25% of the width, as shown in Figure 2. Both cracks are called Crack 1 and Crack 2 hereafter, respectively. In order to demonstrate the impact of damage, the thickness at the damage area is reduced to 0.1 mm.  
Normal modal analysis of the damaged plate is performed and the first four displacement mode shapes are extracted and shown in Figure 3. The first and third modes are pure bending modes, while the second and fourth modes are torsional modes. For the first mode, the discontinuous of the displacement mode shape caused by the damage is not obvious and hardly reflects the location of the crack. Only Crack 2 can be reflected through the third mode shape and is shown in Figure 3c. On the country to the bending modes, the discontinuous of mode shapes of torsional modes at the crack location are more significant, as the second and fourth modes are shown in Figure 3b,d.  
_Appl. Sci._**2019**, \(9\), 4982  
Figure 3: The first four mode shapes of normal translation degree-of-freedoms (DOFs) of the damaged plate model. (**a**) The first mode. (**b**) The second mode. (**c**) The third mode. (**d**) The fourth mode.  
Figure 2: The geometry of a damaged plate structure.
# Design of a Dual-Path Speech Enhancement Model
## 3 Proposed Multi-Branch SE Model
### Loss Function  
We use a joint loss function to optimize our proposed model, composed of magnitude loss (\(\mathcal{L}_{m}\)) and complex spectrogram loss (\(L_{c}\)), given by  
\[\mathcal{L}_{m} =\sum_{k,l}||\hat{\mathbf{X}}(k,l)|-|\mathbf{X}(k,l)||||_{1}, \tag{1}\] \[\mathcal{L}_{c} =\sum_{k,l}||\hat{\mathbf{X}}^{\text{R}}(k,l)-\mathbf{X}^{\text{ R}}(k,l)||_{1}+\sum_{k,l}||\hat{\mathbf{X}}^{\text{I}}(k,l)-\mathbf{X}^{ \text{I}}(k,l)||_{1},\] (2) \[\mathcal{L} =\lambda_{1}L_{m}+\lambda_{2}\mathcal{L}_{c}, \tag{3}\]  
where \(|\hat{\mathbf{X}}(k,l)|\) and \(|\mathbf{X}(k,l)|\) are the magnitude spectra of the enhanced and target speech signal in the time-frequency domain, respectively. \(\mathbf{X}^{\text{R}}(k,l)\) and \(\mathbf{X}^{\text{I}}(k,l)\) denote the real and imaginary components of \(\mathbf{X}(k,l)\), and \(||\cdot||_{1}\) denote the \(l\)1 norm. \(\lambda_{1}\) and \(\lambda_{2}\) are the coupling coefficients determined considering the dynamic range of each term. We use 0.9 and 0.1 for \(\lambda_{1}\) and \(\lambda_{2}\), respectively, and these values were determined empirically [13].  
The magnitude loss is widely used in SE tasks as it encourages the model to approximate the spectral envelope of clean speech, which strongly correlates with perceptual quality. Meanwhile, the complex spectrogram loss complements this by accounting for both magnitude and phase components, thereby supporting the reconstruction of natural-sounding and intelligible speech.  
To improve training stability and emphasize perceptually important low-frequency components, we apply a power compression with a compression factor of 0.2 to both input and target magnitude spectrograms. This operation effectively reduces the dynamic range of the spectral values, allowing the model to focus more on low-energy but informative regions such as harmonic structures in the lower frequency bands [25]. Additionally, the compression reduces the risk of exploding gradients from high-magnitude regions and promotes more balanced loss contributions across the frequency spectrum [25].  
By jointly optimizing the compressed magnitude loss and the complex spectrogram loss, the model benefits from enhanced learning stability and improved fidelity in both the energy distribution and fine spectral details.
###### Abstract  
When considering perceptions, the observation scale and resolution are closely related properties. There is consensus on considering resolution as the density of the elementary pieces of information in a specified information space. On the other hand, with the concept of scale, several conceptions compete for a consistent meaning. Scale is typically regarded as a way to indicate the degree of detail in which an observation is performed. Surprisingly, there is not a unified definition of scale as a description's property. This paper offers a precise definition of scale and a method to quantify it as a property associated with the interpretation of a description. To complete the parameters needed to describe the perception of a description, the concepts of scope and resolution are also revealed with an exact meaning. A model describing a recursive process of interpretation, based on evolving steps of scale, scope and resolution, is introduced. The model relies on the conception of observation scale and its association to the selection of symbols. Five experiments illustrate the application of these concepts, showing that resolution, scale and scope integrate the set of properties to define any point of view from which an observation is performed and interpreted. The results obtained for descriptions expressed in one and two dimensions, are the basis for a comparison of the perceivable symbolic information from different interpretations of the same descriptions. In conclusion, this study provides a framework for building models of our interpretation process and suggests ways to understand some mechanisms in the formation of information from initially meaningless symbols.  
interpretation; scale; fundamental scale; scope; resolution; information Article
## 2 Modeling of Containment Thermal Hydraulics
### Modeling for Wall Condensation and Heat Transfer  
The wall condensation of water vapor mixed with a non-condensable gas such as air is limited by its mass diffusion rate. So, the condensation mass flux is expressed as the mass diffusion rate of water vapor through the non-condensable gas. Reference [23] describes the equation of the water vapor diffusion rate in a discretized form as shown in Equation (8).  
\[\dot{m}_{v}^{\prime\prime}=\rho\frac{D_{w}}{\delta}\frac{(Y_{vi}-Y_{vw})}{(1- Y_{vw})}=\rho h_{m}\frac{(Y_{vi}-Y_{vw})}{(1-Y_{vw})} \tag{8}\]  
The mass flux of water vapor removed by condensation on the wall is determined by the water vapor diffusion coefficient on the wall, and therefore condensation models are currently implemented by how to determine \(D_{w}\) or \(h_{m}\).  
One of the mechanistic models for wall condensation is to use a turbulence model and a wall function. The condensation mass flux of water vapor on the wall is obtained using the wall heat transfer coefficient calculated from the wall function and Chilton-Colburn analogy [26]. First, \(T^{+}\) is obtained from the wall function [27].  
\[if\ y^{+}<y_{T}^{+},\quad T^{+}=Pry^{+}+\frac{1}{2}\rho\frac{u_{\tau}U^{2}}{q_ {w}}Pr \tag{9}\]  
\[if\ y^{+}\geq y_{T}^{+},\quad T^{+}=Pr_{t}\big{(}U^{+}+P\big{)}+\frac{1}{2} \rho\frac{u_{\tau}U^{2}}{q_{w}}\Big{[}Pr_{t}U^{2}+(Pr-Pr_{t})U_{c}^{2}\Big{]} \tag{10}\]  
The wall heat transfer coefficient \(h\) can be obtained from the relationship between \(T^{+}\) and the wall heat flux.  
\[h=\frac{k_{w}}{\delta}=\frac{q_{w}}{\big{(}T_{w}-T_{p}\big{)}}=\frac{\rho C_{ p}u_{\tau}\big{(}T_{w}-T_{p}\big{)}}{T^{+}}\frac{1}{\big{(}T_{w}-T_{p}\big{)}}= \frac{\rho C_{p}u_{\tau}}{T^{+}} \tag{11}\]  
Here, using the Chilton-Colburn analogy, the mass diffusion coefficient can be obtained from the heat transfer coefficient \(h\).  
\[h_{m}=\frac{h}{\rho C_{p}}Pr^{2/3}Sc^{-2/3} \tag{12}\]  
The heat released from the water vapor condensation is transferred to the wall contacting with the condensate film. In general, the wall heat conduction can be considered by a conjugate heat transfer (CHT) approach, which needs a solid heat conduction solver coupled with a fluid solver. In addition to the CHT multi-region, a one-dimensional heat conduction model is implemented in the contain3D code for a fast simulation of contain ment thermal hydraulics. It solves the 1D unsteady heat conduction equation coupled with fluid boundary, as shown in Figure 1.
# Improved Image Denoising Algorithm Based on Superpixel Clustering and Sparse Representation
## 2 Basics of Superpixel Clustering and Sparse Representation (SC-SR) Algorithm
### Sparse Representation Based Image Denoising  
In general, sparse representation works in a patch-based framework, where an image is represented by sparse coefficients of its overlapping patches. Next, the recovery of image is achieved by averaging the sparse representation of all overlapping patches.  
Given an image \(\mathbf{Y}\) with noise, the column vector of \(b\times b\) patch at the location of \(i\), is denoted as \(\mathbf{y_{i}}=\mathbf{R_{i}}\mathbf{Y}\), where \(R_{i}\) is a binary matrix aiming to extract an overlapping patch and convert it to a column vector. The sparse coefficient \(\mathbf{\hat{\kappa_{i}}}\) of a patch is realized by the optimal solution to the following equation:  
\[\mathbf{\hat{\kappa_{i}}}=\underset{\mathbf{\alpha_{i}}}{argmin}\frac{1}{2}||\mathbf{y_{i} }-\mathbf{D\alpha_{i}}||_{2}^{2}+\zeta||\mathbf{\alpha_{i}}||_{p}, \tag{7}\]  
where \(p=0,1,\mathbf{D}\) is known as dictionary, and \(\zeta\) is a regularization parameter to keep a balance between sparsity and reconstruction error. Once \(\mathbf{\hat{\kappa_{i}}}\) is found, the denoising result \(\widetilde{\mathbf{y_{i}}}\) of the image patch \(\mathbf{y_{i}}\) can be computed by \(\widetilde{\mathbf{y_{i}}}=\mathbf{D\alpha_{i}}\). A challenging issue in finding the sparse coefficient \(\mathbf{\hat{\kappa_{i}}}\) is to choose the dictionary. In brief, a dictionary is a matrix, which is usually obtained by a specific transformation or learned from a large set of clean patches or noisy patches. When the dictionary and sparse coefficients are obtained, the denoised image \(\widetilde{\mathbf{Y}}\) can be reconstructed by aggregating all of the sparse representation of patches as follows [9]:  
\[\widetilde{\mathbf{Y}}=\left(\mathbf{R_{i}^{T}}\mathbf{R_{i}}\right)^{-1}\left(\sum_{i} \mathbf{R_{i}^{T}}\mathbf{D\alpha_{i}}\right), \tag{8}\]  
Nonetheless, only the local sparse representation model is employed in denoising problem, which may not lead to a preferable enough solution. Herein, this paper combines good image priors from the noise-corrupted image with sparse representation for a better solution to image denoising problem. The NSS prior in the spatial domain was learned by superpixel clustering, which also generated irregular regions consisting of similar superpixels. By regarding these regions as searching windows, similar patches to a local patch within each region were found. The NSS prior in sparse transform domain utilized a constraint that the weighted average of sparse coefficients for the acquired similar patches of each local patch constrains the sparse coefficient of the local patch to an optimal solution. The NSS priors in both the spatial domain and sparse transform domain were added into the sparse representation model to enhance the image denoising performance of our proposed algorithm. Further details of the proposed algorithm are explained in the next section.
# Pathogenic Roles of RNA-Binding Proteins in Sarcomas
## 3 Role of RBPs in Sarcomas
### Rhabdomyosarcoma  
Rhabdomyosarcoma (RMS) is the most common soft-tissue sarcoma in children and rarely affects adults. Pathologically, RMS is a high-grade mesenchymal tumor showing aberrant myogenic differentiation. The 5 year survival rates are approximately 60% in children and 30% in adults [76]. A multidisciplinary therapy combining local surgery and cytotoxic agents was established in 2009 as the standard of care [77]. Molecular targeted therapy is under investigation based on novel molecular findings. The embryonal and alveolar types of RMS are the two major pathologically distinct subtypes. Alveolar RMS usually contains a balanced chromosomal translocation of t(2;13)(q35;q14) or a variant t(1;13)(p36;q14), both of which generate oncogenic fusion proteins. On the other hand, such an oncogenic fusion is absent in embryonal RMS. Embryonal RMS and fusion-negative alveolar RMS have overlapped expression profiles, while fusion-positive alveolar RMS harbors a distinct molecular profile [76].  
Targeting the insulin growth factor (IGF) pathway is considered an attractive therapeutic target for RMS [78; 79]. RMS has a survival advantage by switching isoforms of  
Figure 2: Interaction and stabilization by the EWS/FLI1 fusion protein. EWS/FLI, together with the SWI/SNF complex, upregulates the expression of Ewing sarcoma-specific target genes via multiple mechanisms. An alternative splicing event in ARID1A in cells with EWS/FLI1 fusion enhances direct binding between SWI/SNF complex and EWS/FLI1, which promotes the growth of Ewing sarcoma cells. On the other hand, RBPs including LIN28B and IGF2 BP are activated in EWS/FLI1-positive cells and may play essential roles in stabilizing mRNA.  
IR, the receptor for insulin growth factor. IR-A, which lacks exon 11 of IR-B, has a greater affinity for the ligand than IR-B and is predominantly expressed in RMS tissue [80]. The mechanism of this isoform switching remains unknown.  
According to the DepMap database, Quaking (QKI) was identified as a candidate of the therapeutic target of RMS (Table 1). Although the mechanism is unknown, inhibition of QKI expression greatly suppressed the growth of RMS cell lines. QKI is an RBP which belongs to the signal transduction and activation RNA (STAR) family [61]. There are three major alternatively spliced isoforms, QKI-5, QKI-6, and QKI-7, which have differential carboxy-terminal domains. Although these isoforms share a common RNA-binding property, each isoform uniquely regulates pre-mRNA splicing, transportation, and stability in a cell type-specific manner. QKI-5, the most abundant splicing isoform of the human QKI gene, has been identified as a functional target of miR-221. [PERSON] et al. identified 2014 variable triplets, including 1101 modulators and 187 splicing events. Pathway enrichment analysis showed that QKI splicing targets were enriched in tightly connected pathways, i.e., the endocytosis and MAPK signaling pathways, on which a specific proportion of RMS cells are dependent [81].