esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Assessment of Accuracy  
Each sample model comprised 7 spheres and 21 vector measurements extending between the hypothetical sphere centres. Five models (\(n\) = 5) were printed for each printer to assess the dimensional (\(n\) = 35) and full-arch accuracy (\(n\) = 105). The printed models were measured using a coordinate measuring machine (CMM, Absolute Arm 7-Axis, Hexagon, Cobham, UK) with a 50 mm long probe and a 3 mm ruby tip within two weeks (T\({}_{1}\)) of printing. The Absolute Arm 7-Axis was calibrated according to ISO 10360-12 with a confirmed error of 0.005 mm. The CMM measurements began by outlining the base of the model to establish an area in space on PolyWorks Inspector (Innovmetric, QC, Canada) using six points circumferentially around the base of the model. The dimensions of the spheres were then measured using nine points with four points circumferentially below the equator, four points circumferentially above the equator, and one point at the top-centre of the sphere (Figure 1). The sequence of measurements was from S\({}_{1}\) through to S\({}_{7}\). The S\({}_{1}\) location was then combined with the line vector from S\({}_{1}\) to S\({}_{7}\) to create a cartesian axis located in the measured centre of S\({}_{1}\). The diameter of the spheres was then calculated using the sphere function on PolyWorks Inspector. The hypothetical centre of each sphere was then used to measure the 21 vectors listed in Table 2 on PolyWorks. All models were stored and measured in a temperature-controlled room (24 \({}^{\circ}\)C, 1013 hPa), with the same operator completing all measurements. The 3D printed models were stored in the same conditions in a dark storage compartment devoid of light, then measured again six weeks (T\({}_{2}\)) after printing to assess the dimensional stability of 3D printed models.  
The diameter of the spheres was used to assess the dimensional accuracy of the printed models. The full-arch accuracy was assessed based on the combination of the 21 vectors that extended between the hypothetical sphere centres. Specific arch segments were also assessed to identify the pattern of changes through a combination of different cross-arch vectors: left posterior (V\({}_{1}\), V\({}_{2}\), V\({}_{7}\)), right posterior (V\({}_{19}\), V\({}_{20}\), V\({}_{21}\)), posterior (V\({}_{5}\), V\({}_{6}\), V\({}_{10}\), V\({}_{11}\)), anterior (V\({}_{12}\), V\({}_{13}\), V\({}_{16}\)), and anteroposterior (V\({}_{3}\), V\({}_{18}\)).
# Time Delay Optimization of Compressing Shipborne Radar Digital Video Based on Deep Learning
## 2 Radar Digital Video Coding Depth Division Algorithm Based on Deep Learning
### Modeling of CTU Division Depth Interval
According to the predicted CU search depth interval, the RD-cost outside the calculation interval is skipped or terminated, which limits the exhaustive search times during CTU division and reduces the computational redundancy caused by the traversal RD-cost to some extent. \[\left\{ \begin{array}{l}Range_{min}=Depth_{min}(x,y,z)\\ Range_{max}=Depth_{max}(x,y,z)\\ \end{array} \right. \tag{1}\]  
\[Range_{Dep}=[Range_{min},Range_{max}] \tag{2}\]  
where \(x,y,z\in(0,1,2,3)\), \(Range_{Dep}\) represents the division depth interval of the current CTU, \(Depth_{min}(x,y,z)\) denotes the minimum depth of the interval, and \(Depth_{max}(x,y,z)\) indicates the maximum depth of the interval. If the two are equal, the same value is taken.
## 2 Composite Signal's Distribution and the Dataset
### Composite Signal  
The composite signal is generated by adding multiple component signals with varying modulation orders, data rates, power levels, and normalized frequencies. The number of component signals is chosen randomly between two and ten, inclusive. The component MQAM signal has the following form:  
\[s_{m}(t)=A_{i,m}g(t)cos(2\pi  
frequency relative to the center of the radio frequency band of interest and distributed in increments of 1 kHz. Figure 4 shows the power spectrum of a representative composite signal generated by adding ten component signals.
# Semantic-Aware Low-Light Image Enhancement by Learning from Multiple Color Spaces
## 4 Model Details
### Semantic-Guided Color Histogram (SCH) Loss
#### 4.3.1 Instance-Level Color Histogram Calculation  
To perform instance-level color histogram calculation, the image is first segmented into different instance regions using the semantic map obtained from the semantic segmentation network. The specific calculation is as follows:  
\[p^{c}=I\cdot I_{seg}^{c},P=p^{0},p^{1},\ldots,p^{n} \tag{8}\]  
where I is the enhanced image, \(I_{\text{seg}}^{c}\) is the semantic segmentation mask for class c, and \(\cdot\) denotes element-wise multiplication. \(p^{c}\) represents the image instance region of class c, and P is the set of all instance regions. This method allows for the calculation of color histograms for each semantic instance separately, thus enabling more precise modeling of the color distribution for different instances.
## 4 Experimental Results and Analysis
### Discussion of Mask-Gating Attention Mechanism  
The mask-gating attention mechanism in our model aims to dynamically emphasize informative elements. To evaluate its performance, we conducted a series of experiments with the embedding rate set to 100%. The experimental results are presented in Table 2, which reports the performance metrics obtained by our model with and without the masking attention mechanism. Specifically, the notation \(Ours_{w}\) represents our method with the mask-gating attention mechanism, while \(Ours_{w/o}\) represents our method without it. By comparing these two settings, we can assess the impact of the mask-gating attention mechanism on the overall performance of our model. Moreover, we can assess the impact of the mask-gating attention mechanism on the overall performance of existing frame-wise steganalysis methods.  
According to the results presented in Table 2, performance significantly decreased across the three steganography datasets when the mask-gating attention mechanism was removed from our model. This indicates that the mask-gating attention mechanism plays a crucial role in modeling higher-order feature interactions. Furthermore, it is worth noting that the mask-gating attention mechanism is also critical for other models, such as DMSM [31], FSMDP [33], and Stegformer [34]. Its inclusion in these models led to improved performance, suggesting that the mask-gating attention mechanism has broader applicability and effectiveness across different steganography tasks and models. Overall, these findings highlight the importance of the mask-gating attention mechanism in capturing and leveraging global information and informative elements, ultimately enhancing the performance of both our proposed method and other existing steganography models.
# Sequential Polar Decoding with Cost Metric Threshold
## 2 Preliminaries
### Creeper Decoding Concepts  
The Convolutional Creeper approach merges aspects of stack-based decoding with the Fano algorithm. Traditionally applied to convolutional codes, the Fano algorithm employs a sequential decoding method within a code tree structure. During each decoding step, it navigates the tree, opting for either the parent predecessor node or a designated child node. This hybrid approach, integrating stack-based techniques and the Fano algorithm's sequential strategy, offers a nuanced decoding process for efficient error correction and data retrieval in communication systems.  
The key components of Fano decoding include two constraints: the step size \(\Delta\) and the dynamic threshold \(T\). The search process continues along a code path as long as the Fano metric [13] increases.  
Let us assume that for each \(i\)-th virtual channel, there is a channel error probability value \(p_{i}\). SC-Fano employs a path metric called the Fano metric (PMF). It starts from \(PMF_{-1}=0\) and calculates iteratively as follows:  
\[PMF_{i}=PMF_{i-1}+\log_{2}\frac{\Pr(u_{i}|y_{0}^{N-1},\dot{\mathbf{u}}_{0}^{i-1} )}{(1-p_{i})}, \tag{2}\]  
It measures the discrepancy between the likelihood of the current received symbol being associated with each possible codeword and the likelihood of obtaining the correct codeword. A smaller Fano metric indicates [14] a better match between the received symbol and the corresponding codeword. This metric is also applicable for code tree path pruning, which provides a more flexible decoding process.  
It is worth noting that the choice of \(\Delta\) significantly impacts SC-Fano's ability to move backward through the tree. A large \(\Delta\) may limit backward movement, resembling basic SC decoding, while a very small \(\Delta\) may improve the performance but hinder backward traversal.  
Assuming that the current node is \(v_{i}^{(t)}\), where \(t\) represents the level, SC-Creeper stores only the current node (address and value), its children and its ancestors only.  
The stack \(\mathcal{N}\) in SC-Creeper uses two mappings, \(V:\ \mathcal{N}\to v\) and \(Pos:\ \mathcal{N}\to p_{v}\), to obtain the header node's element and the position of node \(v\), respectively. When the PMF metric for a node falls below the threshold \(T\), this node is labeled as \"excluded\". Conversely, if the PMF metric surpasses \(T\), it is referred to as a \"valid node\". All of the actual valid nodes are stored in the \(\mathcal{T}\) stack. In cases where both children of a node are valid, and the PMF metric of one child exceeds any other metric calculated thus far, this particular node is designated as a T-node. To manage these T-nodes, SC-Creeper employs a dedicated second stack.
## 3 Results
### Effect of F2 on Visceral Organ Weights and Histological Observation  
The epididymal WAT, liver, kidney, and spleen were isolated and weights were measured. Moreover, the histological analysis of WAT and liver were performed by H&E staining. The weights of the WAT and liver were increased significantly in the HFD control group compared with that in the normal group. However, the weights of these organs were markedly decreased by F2 and GC (Table 6). Analyzing histological observation of the liver tissue, consumption of an HFD for 12 weeks induced the accumulation of lipid droplets in the liver, indicating the development of liver steatosis (Figure 6). However, F2 significantly ameliorated the lipid accumulation in the liver. Also, the sizes of the adipocytes in the WAT were observed to be enlarged in the HFD control group. While the adipocyte size was significantly reduced by F2 in comparison to the HFD control (Figures 6 and 7).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Groups** & **WAT (g)** & **Liver (g)** & **Kidney (g)** & **Spleen (mg)** \\ \hline Normal & 0.39 \(\pm\) 0.07 *** & 0.93 \(\pm\) 0.05 *** & 0.29 \(\pm\) 0.02 & 60.42 \(\pm\) 6.50 \\ HFD & 2.07 \(\pm\) 0.12 & 1.06 \(\pm\) 0.04 & 0.31 \(\pm\) 0.02 & 62.64 \(\pm\) 6.84 \\ F2-46 & 1.63 \(\pm\) 0.29 *** & 0.92 \(\pm\) 0.04 *** & 0.31 \(\pm\) 0.02 & 61.70 \(\pm\) 6.78 \\ GC & 1.32 \(\pm\) 0.26 *** & 0.82 \(\pm\) 0.08 *** & 0.29 \(\pm\) 0.01 * & 58.03 \(\pm\) 9.34 \\ \hline \hline \end{tabular} Values are expressed as the mean \(\pm\) SD (n = 5 to 6). A one-way ANOVA was applied to calculate statistical significance. * \(p\) \(<\) 0.05; ** \(p\) \(<\) 0.01; and *** \(p\) \(<\) 0.001 vs. HFD control. Normal = standard diet; HFD = high-fat diet control; F2-46 = F2 (46 mg/kg); GC = _G. camlogia_ (200 mg/kg).  
\end{table}
Table 6: Effect of formulation F2 on organ weights.  
Figure 5: Effect of F2 on the mRNA expression levels of PPAR, C/EBPx, SREBPP-1c, aP2, LPL, adiponectin, leptin, and IL-6 in white adipose tissue. \(\beta\)-actin was used as a reference gene. Values are expressed as the mean \(\pm\) SEM (n = 4). A one-way ANOVA was applied to calculate statistical significance. * \(p\) \(<\) 0.05 vs. HFD control. HFD = high-fat diet control; F2-46 = F2 (46 mg/kg).
# Study on Wind Resistance Performance of Transmission Tower Using Fixture-Type Reinforcement Device
###### Abstract
conducted experiments on the retrofitted diagonal braces to derive their failure mode and the enhancement of the loading-carrying capacity, and the results show that the most vulnerable member in the retrofitted model is the leg member instead of the diagonal braces in the original model [21; 22]. To increase the strength and the stiffness of the aged transmission towers, [PERSON] et al. proposed to use the bolted cruciform and splice connectors to retrofit the tower leg and the effectiveness of this reinforcing scheme was verified by the cyclic experiment [23]. [PERSON] et al. [24] investigated the risk and losses of the steel lattice telecommunication towers under extreme wind load and studied the influence of different rehabilitation schemes. In this paper, the fixture-type reinforcement device (FRD) is used to enhance the wind resistance performance of the transmission tower, and the effectiveness is checked by numerical analysis. At first, the configuration of the device is introduced, and the finite element analysis of the main angled steel component is conducted. Secondly, the analytical model of the transmission tower is established, and the wind load is applied in two directions. Lastly, the reinforcing effect of the FRD is verified through the simulation of the tower model under different cases.
###### Abstract  
In the past few years, elastography has gained ground as a complementary method to ultrasonography in noninvasive breast cancer screening. Despite positive outcomes, there is a further need to refine the method, especially regarding BIRADS scores 3 and 4A, where the distinction between benignancy and malignancy is established. The aim of the present study was to evaluate the best risk-stratification system using both qualitative and semiquantitative elastographic methods for solid breast nodules. A total of 1405 solid nodules, described in 657 female patients, were examined in our endocrine unit between January 2018 and December 2019. The inclusion criterion for our retrospective study was the presence of any solid breast mass in women of all ages (mean, 40.85 \(\pm\) SD 27.11), detected during ultrasound examination using a HITACHI PREIRUS machine (Hitachi Medical Corporation, Tokyo, Japan). The Breast Imaging Reporting and Data System (BIRADS)-US criteria were used in the assessment of each nodule by conventional US (gray-scale mode) and Doppler evaluation. The Ueno score and strain ratio were also measured for all the described lesions. We considered multiple algorithms for the risk reassessment of solid breast nodules: classical BIRADS-US, EFSUMB BIRADS, worst-case scenario BIRADS and BIRADS TM. There were 93 malignant nodules out of 1405. The diagnosis was based on histopathological results for all the malignant lesions. Benign lesions were diagnosed based on histopathological results, Tru-Cut biopsy, mammography and MRI. The Sensitivity (Se), Specificity (Sp), Positive Predictive Value (PPV), Negative Predictive Value (NPV) and Accuracy (Acc) were obtained for all the proposed risk-stratification reporting systems: conventional BIRADS-US (Se, 74.23%; Sp, 63.95%; PPV, 13.53%; NPV, 97.79%; Acc, 65%); EFSUMB BIRADS (Se, 71.23%; Sp, 81.55%; PPV, 22.68%; NPV, 97.99%; Acc, 81%); worst-case scenario BIRADS (Se, 84.23%; Sp, 58.23%; PPV, 13.29%; NPV, 98.84%; Acc, 60%); BIRADS TM (Se, 81.23%; Sp, 75.84%; PPV, 20.35%; NPV, 98.81%; Acc, 77%). We found that the most efficient risk-stratification reporting system was the proposed one, BIRADS TM, which considers both upgrading and downgrading the conventional BIRADS-US, followed by the worst-case scenario BIRADS and EFSUMB BIRADS.  
risk-assessment algorithms; strain elastography; breast ultrasonography; breast cancer risk prediction
## 6 Discussion
### Specific Discussion on the YAPS  
At the heart of the BIM2 BEM-Flow toolchain lies the YAPS Parameter Server--a highly flexible, user-driven platform for managing exchange parameters. One of YAPS's most notable strengths is its flexible structure, which allows users to freely create and organize libraries. This design freedom transforms YAPS from a mere parameter repository into a dynamic hub for knowledge sharing and customization. Because of its flexibility, YAPS can serve multiple roles:
* Public Libraries and Standardization: YAPS supports the creation of public libraries that comply with industry standards. These libraries can be accessible for projects and organizations, providing a consistent and validated set of parameters for energy simulation and building performance assessment. In this model, simulation software companies can contribute to validated libraries that are pre-mapped for tool-specific imports. This not only reinforces trust in the simulation process but also streamlines the integration of BIM data into BEM tools.
* Private Custom Libraries: Beyond public standard libraries, YAPS empowers individual companies and project teams to develop private, fully individualized libraries. These libraries can be tailored to reflect unique project requirements, specific company standards, or proprietary simulation processes. This adaptability ensures that, while there is a push towards standardization, there is also room for innovation and customization where needed.
* Flexibility in Data Management and Mapping: The inherent flexibility of YAPS is further enhanced by its support for mapping functions that link standardized parameters with tool-specific requirements. Whether parameters are intended for a public standard library or a custom, private collection, the YAPS framework ensures that data remain interoperable across various BIM and BEM tools.  
In summary, the YAPS Parameter Server is not just a static database but a versatile platform that can be used to address both industry-wide standardization and individual user needs. It enables a dual approach: on one side, promoting a shared, standardized basis that benefits the broader community; and on the other, offering the freedom for tailored, private libraries that meet the specific demands of companies and projects. This dual capability is essential for fostering both collaboration and innovation in the evolving landscape of BIM-to-BEM integration.
## 5 Experimental Results and Discussions
### Discussions  
As a part of indoor scene recognition, we have proposed a hybrid model that combines the features of deep learning and fuzzy logic, segmentation, and feature selection to predict and recognise indoor scenes in the NYU depth dataset with high accuracy. Here, we have experimented on 20 different classes of the NYU depth dataset that are considered vital in assisting visually handicapped and elderly people in their indoor navigation. The image dataset is preprocessed using fuzzy colour stacking to remove the noise from the background and enhance the foreground details. These preprocessed images are then segmented to preserve the semantic cues and localization. Then, we used a pretrained DenseNet201 deep model for feature extraction. The extracted features were filtered, and only the predominant features were selected using the World Cup optimisation algorithm. The removal of irrelevant features improves the robust nature of the model. A deep liquid state machine trained on these selected features predicts our indoor scene classes with good accuracy. Our model showcases a specificity of 96%, a sensitivity of 100%, a precision of 94%, and an F1-score of 95%. By evaluating the performance matrices, we can conclude that, for the given indoor dataset, our DenseNet-LSM model obtained an accuracy of 96%.
## 5 Simulations and Results  
In this section, in order to test the validity of the proposed protocol, MATLAB simulations are carried out with a random network partially shown in Figure 2. In our proposed collaborative communication system, sensor nodes transmit the same data towards the base station. We assume that there are two types of sensors employed in the application scenario. More specifically, they are static sensors and mobile sensors, respectively. It is further assumed that all the static sensors are fixed on the pump-jacks, and sense the payload parameters of the pump-jacks at a fixed rate, while having data sent to the base station periodically. On the other hand, mobile sensors can move along the oil pipelines for temporary use (e.g., pipeline inspection [47] and leakage monitoring [48]). Simulations are carried out at different node deployment scenarios. Table 1 gives some important parameters used in our simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No. Item** & **Parameter Description** & **Value** \\ \hline
1 & Communication range & 100 \(\times\) 100 \\
2 & Location of the base station & (150, 50) \\
3 & Node number & 100 \\
4 & Initial energy range & [5,10] Joule \\
5 & Work time period & 0.1 s \\
6 & Packet size & 500 bytes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some important simulation parameters.
## 3 Algorithm Design
### Algorithm  
We now give a detailed Algorithm 1 based on the analysis from the Section 3.1 to Section 3.3.  
```
(a)\(SU_{i}\) want to lease spectra; (b) DC obtains the detection capability of \(SU_{i}\), which is denoted by \(w_{i}\); (c) If detection capability of \(SU_{i}\)\(w_{i}=1\), then use (8) to set the lowest price for \(SU_{i}\); Else use (8) to set the price based on the detection capability; (d) Use (6) and (8) to substitute the profit function of \(SU_{i}\); (e) Use (9) to calculate the profit of \(SU_{i}\); (f) Use (12) to allocate spectrum for \(SU_{i}\).
```  
**Algorithm 1** Given that there is one PU system (including one DC and some PUs) and some SUs (\(SU_{i}\), \(i=1\) to \(N\)) in a region grid of the MCRCS. If PUs have free spectra. For \(i=1\) to \(N\), do steps (a)-(f).  
The algorithm is executed at the DC in the MCRCS. In this algorithm, we have developed the new revenue function (6) and price function (8). Furthermore, we use these two functions to develop the profit function (9) and allocate the free spectra within the region effectively.
# A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging
## 3 Experimental Results
### Preprocess  
In order to demonstrate the whole process of super-resolution imaging more clearly, we show the results of each step from the start in Figure 3. Figure 3a shows the imaging result after the DAS. As seen, the echoes of the background tissues are strong, and the microbubbles cannot be observed. After the wall filtering, the signal of the contrast agents has been highlighted in Figure 3b, but the structure of the microvessels still cannot be seen at this time. After super-resolution reconstruction using the Gaussian PSF deconvolution and scatter plot is taken, the microvascular structure is finally clearly displayed, as shown in Figure 3c.  
Figure 3: The imaging results of rabbit kidney using: (**a**) DAS, (**b**) DAS + wall filter, and (**c**) DAS + wall filter + Gaussian PSF deconvolution + scatter plot respectively. All images are shown with a dynamic range of 50 dB.