esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The results of this study indicated that stone casts generated using conventional impression and pouring techniques have a higher linear dimensional accuracy than 3D-printed casts, which contradicts the findings reported by recent studies [27,28]. A recent systematic review evaluated the accuracy of 3D-printed models generated by different printing technologies and reported that dimensional variation might exceed 500 \(\upmu\)m, especially for stereolithographic technology printers [29]. The 3D printer used in the current study is reported to have a mean discrepancy in the range of 68.27 \(\pm\) 43.53 \(\upmu\)m, which is comparable to few other commonly used printers [30]. Such significant variability may be acceptable for diagnostic purposes but not for prosthodontic procedures requiring high accuracy and precision. In the present study, the accuracy of the die stone casts obtained using AA was comparable to that obtained using PVS-2. This is consistent with many studies that compared the accuracy of AA to elastomeric impression materials and found comparable accuracy due to the fillers that are present. However, they do not have the same stability [21,23]. Although AA was introduced to the dental practice more than two decades ago, it has not been studied extensively in prosthodontic literature [31,32]. The results of this study are in agreement with [PERSON] et al. [33], in which AA was found to be more accurate than extended pour ALG in terms of surface details reproduction and dimensional stability. In addition, [PERSON] and their colleagues [23] found that casts generated from addition silicone were more accurate than ALG impression materials. However, a direct comparison between AA, PVS, and 3D-printed casts has not been conducted to date. The results of this study indicated that the accuracy of AA impression-based casts is higher than extended pour ALG, 3D printing, and slightly lower than conventional addition silicone impression material using the putty-light body technique. Therefore, based on the results of this study, AA might be indicated for the fabrication of accurate long-span computer-aided design/computer-aided manufacturing (CAD/CAM) provisional fixed prosthesis and interim partial denture with benefits of the impression's ease of handling and predictable dimensional accuracy, while the putty-light body technique remains the golden standard for accurate fabrication of long-span definitive prosthesis up to the quadrant dimension. Although the test embodied in the ADA specifications provides good baseline data to compare materials, the testing conditions differ from those encountered during clinical practice. Therefore, future in vivo studies are recommended. Hence, future studies to compare the studied materials and technologies in a clinical setting are indicated. Clinical significance: for the quadrant dimension, a dentist may consider 2-step putty-wash polyvinyl siloxane because it had the highest accuracy compared to the studied conventional and digital impressions.
## 5 Conclusions  
The vertical variation of IOPs characterized by Chla(z) vertical profiles with maximum value on the water surface (shifted Gaussian function) frequently occurs in shallow eutrophic waters. However, current remote sensing reflectance algorithms typically do not consider the vertical structure of Chla(z) profiles. Radiative transfer simulations of underwater light field with different vertical profiles of Chla(z) indicated that vertical variations of phytoplankton considerably affected AOPs, e.g., \(r_{rs}(\lambda,z)\) and \(K_{x}(\lambda,z)\), which are key parameters to describe the underwater light field. The vertical distribution of Chla(z) profiles changed the spectrum shape and magnitude of \(r_{rs}(\lambda,z)\) and \(K_{x}(\lambda,z)\). The substantial vertical variation of the underwater light field occurs at water depths with large vertical variations of Chla(z) profiles. Differences in \(r_{rs}(\lambda)\) between the vertical non-uniform and asymptotic values can reach 78%, particularly when the Chla(z) profiles decreases dramatically with low \(\sigma\) or high h. In addition, the contribution of the first three layers to \(r_{rs}(\lambda,0^{-})\) was approximately 90%, but the contribution of each layer varied with wavelength, the structure parameters of Chla(z) profiles, SPIM, and CDOM. As an implication, a stratified remote sensing reflectance model was developed based on the contribution of each layer to \(r_{rs}(\lambda,0^{-})\) to understand the relationship between \(r_{rs}(\lambda,0^{-})\) and \(IOP^{\prime}(\lambda,z)\). To apply the model to other types of waters, accurate weighting average functions and validation based on large radiative transfer simulation dataset and field-measured data are necessary. Nevertheless, the proposed remote sensing reflectance model has the potential to be used in developing water color remote sensing models in inhomogeneous waters.  
Conceptualization, K.X. and R.M.; formal analysis, K.X.; funding acquisition, R.M.; methodology, K.X.; writing--original draft, K.X.; writing--review and editing, K.X. and R.M.  
This research was funded by State Key Program of National Natural Science of China (No. 41431176), National Natural Science Foundation of China (Nos. 41701416, 41771366), the Provincial Natural Science Foundation of Jiangsu of China (No. BK20181509), and the funding of NIGLAS (No. NIGLAS2017 GH03).  
**Acknowledgments:** The authors thank the colleagues from NIGLAS ([PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]) for their help with field measurements and data collections. Acknowledgement for the data support from \"Lake-Watershed Science Data Center, National Earth System Science Data Sharing Infrastructure, National Science & Technology Infrastructure of China. ([[http://lake.geodata.cn](http://lake.geodata.cn)]([http://lake.geodata.cn](http://lake.geodata.cn)))\".  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design and Analysis of an Inductive Coupling System for the Early Detection of Heart Failure
## 4 Experimental Measurements
Therefore it is clear from Figure 9c that S33 demonstrated shifts in the distance between the implanted coils, and this could be easily supervised by placing the transceiver coil just above chest in order to examine the expansion and contraction of the heart, as the reflection coefficient varies depending on the distance between the left and right ventricular chambers. Figure 8: \(S_{33}\) measured at different distances in the tissue medium. Figure 9: Comparison between the simulated and the measured results in the tissue medium: (**a**) \(S_{11}\), (**b**) \(S_{22}\), (**c**) \(S_{33}\), (**d**) \(S_{21}\).
# A Scalable Moving Boundary Treatment in the Lattice Boltzmann Method
## 3 The Proposed Solid Boundary Condition
Based on Formula (7) and considering the efficiency in parallel computing, the data transfer process between adjacent processes should be as simplified as possible. Before the grid point type changes, the solid point velocity is interpolated by the fluid boundary point velocity in a specific direction. The pressure is the pressure corresponding to the fluid boundary point. The equilibrium distribution function calculated by the macroscopic quantity of this point is used as the initial value of the new fluid boundary point, as shown in Formula (8). \[\left\{ \begin{array}{l}\mathbf{u}_{b}=\big{[}\,\mathbf{u}_{w}+(\Delta-\mathbf{1})\mathbf{u}_ {f}\big{]}\,/\,\Delta\\ \mathbf{p}_{b}=\mathbf{p}_{f}\\ \mathbf{f}(\mathbf{x}_{b})=\mathbf{f}^{\mathbf{\alpha}}(\mathbf{p}_{b},\mathbf{u}_{b})\end{array} \right. \tag{8}\]  
The specific direction is the discrete velocity direction corresponding to when \(\mathbf{n}\cdot\mathbf{e}_{\mathbf{\alpha}}\) is maximized. In order to show the effect of this correction, there is a two-dimensional cylinder moving horizontally and at a constant speed in the rectangular calculation domain with an open boundary to observe the pressure field distribution around the cylinder in the early development stage. Due to the horizontal movement to the right, only the solid grid points at the left end of the cylinder become fluid grid points. In the early stage, there is only a pressure gradient around the cylinder. Observe the local pressure field distribution around the cylinder, as shown in Figure 2. It can be seen from Figure 2a that compared with the left end of the cylinder, the pressure graph at the left end of the cylinder has a jagged feeling. After correction, compared with Figure 2a, the jaggedness of the flow field in the red box in Figure 2b is significantly reduced, indicating that this correction can alleviate the problem of the missing distribution function of the new fluid grid points. This article only adds a new fluid point initialization method based on the original boundary format. The overall method is relatively concise, it is easy to program, and it can also meet the needs of moving boundary problem simulation. Since this article directly expands the existing format, the accuracy is only affected by the grid size.
# Study on Preparation and Properties of Sintered Brick from Multi-Source Solid Waste
## 2 Materials and Methods
### Preparation Procedures and Design
The results of the three-factor ANOVA were correlated with the results of the ANOVA, and the sum of squares confirmed that the amount of construction waste > the amount of Yellow River sedimentary sand blending > the amount of glass slag blending, and that the degree of influence of the three factors on the compressive strength of sintered brick was, in order, the amount of construction waste > the amount of Yellow River sedimentary sand blending > the amount of glass slag blending. Combined with the experimental study, the main reasons for the three-factor ANOVA results and the extreme difference analysis results were analyzed as follows. (1) Clay particles play a vital role in the sintered brick forming process, and when the amount of construction waste is increased from 45% to 50%, the content of clay particles increases, and the bricks possess a certain wet strength. (2) Yellow River sedimentary sand has a substitution rate of 16% when the billet body forms a complete skeletal structure, but when the substitution rate continues to increase, it will lead to sintered brick strength reduction or even structural damage. This is due to the free water present around the saturated soil particles in the soil material at this time; \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source of Variance** & **Square Sum** & **Df** & **Sean Square** & **F** & \(p\) \\ \hline Intercept & 3324.193 & 1 & 3324.193 & 1207.121 & 0.000 \\ Content of construction waste & & & & & \\ (\%) (A) & 349.561 & 2 & 174.781 & 63.468 & 0.001 \\ Glass slag content (\%) (B) & 13.543 & 4 & 3.386 & 1.230 & 0.423 \\ Content of the Yellow River & & & & & \\ sand (\%) (C) & 78.470 & 4 & 19.618 & 7.124 & 0.042 \\ Residue & 11.015 & 4 & 2.754 & – & – \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results of three-factor variance analysis. according to the gravitational effect of the electric field to encapsulate the strong binding water film, the aged material reflects a strong viscosity. In addition, the liquid phase in the mixture wraps and bonds the grains to enhance the densification of the waste sintered brick, thus making the waste sintered brick have higher strength and lower water absorption. When the substitution rate is greater than 20%, the content of the viscous particles decreases, and the structural stability of the waste sintered brick is damaged, resulting in a large number of cracks, which reduces the strength and increases the water absorption. (3) A total of 10% of glass slag is incorporated to effectively reduce the firing temperature of sintered brick, play the effect of flux, and enhance the strength of sintered brick. In summary, this paper adopts 50% of construction waste, 10% of glass slag admixture, and 16% of Yellow River sedimentary sand replacement rate as the matching ratio for subsequent research.
# Development of the First Portuguese Radar Tracking Sensor for Space Debris
## 4 Waveform Design
### Advanced Waveform Design  
In the particular scenario where certain selected man-made targets are of interest, the transmitted waveform can be further optimized [44]. The basic idea is to change the signal used as reference for detection in such a way that it is adapted to the predominant scatterers of the targets, resulting in a better matched filtering which ultimately leads to better SNR and pulse compression.  
ATLAS also permits the testing of waveforms typically used on Noise Radar Technology [45], being able to transmit a virtually infinite set of waveforms produced via a set of random realizations, with the real-time configuration of the correspondent matched filter. This enables the use of the radar in critical environments where interception and jamming robustness is a requisite. Additionally, mutual interference between two radars that occupy the same transmit spectral band can be made negligible [45, 46].  
It is also possible to implement pulse-to-pulse diversity in ATLAS by loading a different waveform during the idle time of the previous pulse. Trains of diverse pulses can lead to reduction of the range sidelobes of the autocorrelation function. The Golay complementary sequences, for example, can be used to phase-modulate the carrier wave leading to trains of complementary pulses. The sum of the autocorrelation of complementary pulses is zero except for the zero shift, leading to an impulse like response [47].  
Since the system can be used in passive bistatic mode, it will also be used to test techniques for improved detection by illumination matching upon receiving such as the one published in [48].
## 4 Applications  
### Concrete Fatigue in Axial Compression  
While the proposed fatigue damage evolution equation was constructed based on fatigue experiments conducted in the literature [31], it could reasonably be applied to reflect the fatigue performance of concrete specimens tested under similar loading conditions. The cylindrical specimens F1 and F3, with a diameter of 100 mm and a height of 250 mm under axial compression from another literature [42], were selected as an application example. Numerical simulation was performed using the finite element software Abaqus 2019, incorporating the proposed equivalent fatigue damage constitution and fatigue damage evolution equation parameters from Table 2.
## 3 Experiments and Results
### Experimental Results for Comparisons  
In this experiment, this study compared the trajectories estimated by three well-known methods: RTAB-Map, ORB-SLAM, and Manhattan-SLAM. The trajectories were compared in both 3D space (Figure 8a, x-z projection plane Figure 8b) and three axes (Figure 9) in multiple dimensions. The results show that the trajectory estimated by RTAB-Map approximates a straight line, which aligns with the UAV's motion process in the pipeline. On the other hand, Manhattan-SLAM starts to drift at the end of the trajectory, while ORB-SLAM2 gradually drifts in the middle of the trajectory. These comparison results are consistent with the differences in the effectiveness of the three methods for 3D reconstruction of pipelines. In conclusion, RTAB-Map demonstrates greater robustness than the other two methods for localization and mapping in underground pipeline environments.  
Furthermore, three well-known visual SLAM algorithms, namely RTAB-Map, ORB-SLAM2, and Manhattan-SLAM, were also selected for benchmarking. To validate the comparison, the sparse point cloud generated by Manhattan-SLAM was passed through the PMVS method to complete the dense reconstruction. Figure 10a displays the 3D point cloud of the pipe generated by RTAB-Map, while Figure 10b shows the 3D point cloud generated by ORB-SLAM2. Figure 10c presents the dense point cloud of the pipeline obtained by Manhattan-SLAM with PMVS. These results indicate that RTAB-Map successfully reconstructs the internal information of the pipeline without noticeable geometric deformations. On the other hand, ORB-SLAM2 disperses the point cloud in all directions after running  
Figure 8: Trajectory comparison results derived by RTAB-Map, ORB-SLAM, and Manhattan-SLAM: (**a**) trajectories in 3D space; (**b**) trajectory after x-z projection.  
Figure 7: The 3D reconstruction results before and after treatments: (**a**) pipeline in-wall textures before treatment; (**b**) pipeline in-wall textures after treatment.  
for a certain distance, and Manhattan-SLAM reconstructs the pipeline's contours better but loses most of the features. In the pipeline environment, RTAB-Map demonstrates higher reconstruction accuracy.
## 1 Introduction  
Heptocellular carcinoma (HCC) is the fifth most common malignant tumor and is responsible for a half million deaths annually worldwide [1]. Curative treatment options such as surgical resection, local ablation and liver transplantation have been the most effective treatments for HCC. However, less than 20% of HCC patients are treated surgically, mainly because of associated cirrhosis and advanced stage of cancer at diagnosis [2, 3]. Based on the survival benefits with increasing evidence [4, 5], transarterial chemoembolization (TACE) is the widely-used, standard treatment modality for intermediate-stage HCC.  
Multi-detector computed tomography (CT) is the most widely used imaging tool for assessing therapeutic response after TACE [6]. However, previous studies have reported that the detection of viable nodules, using lipidoid, is usually ambiguous since hepatic lesions are frequently hyper attenuated in arterial phase images, such as arterio-portalshunts [7, 8]. Manual assessment, which is observer-dependent, is based on the tumor diameter measured in only one axial plane rather than its full three-dimensional nature [9]. Certainly, these methods are not always applicable considering the various conditions that HCC tumors can present with after TACE treatment, including those with irregular shapes, ill-defined margins, or heterogeneous necrosis [10]. Previous studies have shown that overall response rates range from 15% to 85% after TACE and cumulative local tumor progression rates at 1 and 5 years are 33% and 73%, respectively [11, 12]. Therefore, it is important to assess tumor response to TACE treatment, which can help guide subsequent treatment strategies.  
Recent emerging technologies in quantitative computational image analysis offer promising opportunities. Bioinformatic analysis transforms images into mineable data, enabling the characterization of lesions beyond visual recognition. Radiomics, a high-dimensional quantitative analysis approach, can compute a set of features that uniquely characterize a tumor [13, 14]. Radiomics has been successfully applied to predict outcomes through tumor imaging for various cancers [15, 16]. However, using radiomics to predict survival in HCC has not been explored.  
Recently, deep convolutional neural networks have been gaining recognition in imaging research [17]. A neural network is a modality used for artificial intelligence (AI), and convolutional layers are an effective tool for imaging pattern recognition. Previous studies demonstrated that deep learning with convolutional neural networks has achieved good performance in imaging pattern recognition [18, 19]. Therefore, further studies are needed to support the robustness of radiomics approaches for predicting recurrence in HCC patients after HCC treatment.  
The aim of this study was to evaluate an AI-based radiomics model applying CT studies after TACE that could predict HCC recurrence and be a prognostic biomarker for the survival of HCC patients.
## Materials and Methods  
### Preparation of Reference MgO and MgO/AC  
Reference bulk MgO powder was obtained starting from Mg (NO\({}_{3}\))\({}_{2}\) 6H\({}_{2}\)O (supplied by Sigma-Aldrich) aqueous solution by addition of ammonia solution with 5:1 NH\({}_{3}\):Mg ratio. The precipitate was stirred at 60 \({}^{\circ}\)C for 6 h in a closed bottle and, after filtration, dried at 120 \({}^{\circ}\)C overnight and calcined at 450 \({}^{\circ}\)C for 2 h, as described in Reference [14].  
Aqueous solutions of Mg(NO\({}_{3}\))\({}_{2}\) 6H\({}_{2}\)O were also used for the incipient wetness impregnation of acid washed granules of Darco G40 activated carbon (AC) (20 \(\times\) 40 mesh, supplied by Cabot Norit Italia SpA) in order to obtain 10, 20, and 30 wt% MgO loadings, respectively. After drying at 120 \({}^{\circ}\)C in air, the catalysts were eventually calcined for 2 h at 600 \({}^{\circ}\)C under N\({}_{2}\).  
A mechanical mixture consisting of 20 wt% MgO powder and 80 wt%. AC was also prepared to verify the effect of MgO dispersion onto the support. Finally, a Ru/MgO/\(\gamma\)-Al\({}_{2}\)O\({}_{3}\) pellet catalyst prepared as described in Reference [18] was used as reference system for catalytic tests under both dry and wet feed conditions.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 3 Designed Channel Code-Book MappingNet Based on Vector Quantization
### Training Algorithm  
To establish the channel parameter mapping relationship under different states of the SR channel, we first record different channel parameters. The channel parameters consist of four parts: \(b_{0}\), \(m\), \(\Omega\), and SNR. Then, we train the corresponding denoising parameters through the recorded channel parameters. In this step, the goal of this process is to minimize the difference between \(\mathbf{x}^{\prime\prime}\) and \(\mathbf{x}\), that is, \((\theta^{*})=\arg\min_{\theta}[d(\mathbf{x},\mathbf{x}^{\prime\prime})]\). By changing the parameters of the current training channel model, the CCB MappingNet can obtain and store the channel parameters of different environments and the denoising model parameters mapped to them. The training procedure is outlined in Algorithm 2.  
By this method, image transmission in different channel environments can be realized through a set of systems in the satellite-ground scene, which can greatly reduce the resource occupation of the system under the premise of ensuring the quality of data recovery.