esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To assure seismic isolation performance against design and beyond design basis earthquakes in the nuclear facility components, the lead inserted small-sized laminated rubber bearings (LRB), which has a 10 kN vertical design load, have been designed and quasi-statically tested to validate their design mechanical properties in previous studies. Following this study, the seismic shaking tests of these full-scale LRBs are performed and discussed in this paper with the dummy mass system to investigate actual seismic isolation performance, dynamic characteristics of LRBs, consistency of the LRB's quality, and so on. To study the seismic isolation performance, three beam structures (S1-S3) with different natural frequencies were installed both on the shaking table and the dummy mass supported by four LRBs: (1) S1: structure close to seismic isolation frequency; (2) S2: structure close to peak input spectral frequency; (3) S3: structure in the high-frequency region. The test results are described in various seismic levels of OBE (Operating Basis Earthquake), SSE (Safe Shutdown Earthquake), and BDBE (Beyond Design Basis Earthquake), and are compared with the analysis results to assure the seismic isolation performance and the LRB's design parameters. From the results of the shaking table tests, it is confirmed that the lead inserted small-sized LRBs reveal an adequate seismic isolation performance and their dynamic characteristics as intended in the LRB design.  
seismic isolation; laminated rubber bearing; lead rubber bearing; shaking table test; nuclear facility component; beyond design basis earthquake; LRB design; seismic performance; seismic isolation frequency
## 6 Discussion
### Practical Investment Applications  
The framework offers significant value for financial market participants. With 87% accuracy and 65% latency reduction, it enables real-time policy analysis for high-frequency trading and risk management. Institutional investors can leverage the 86.2% macro F1 performance for asset allocation decisions, while fixed-income managers can use precise interest rate scenario classification for duration management. The cross-institutional generalization enables global macro strategies across different central bank jurisdictions.  
These findings carry certain policy and market implications. The model demonstrates the potential to derive early signals regarding the short-term direction of future interest rate movements through in-depth analysis of central bank communication texts. If validated and optimized for practical application, this near real-time monitoring capability might offer financial market participants more timely informational input than traditional economic data releases.
## 3 Results and Discussions
### Load-Settlement Analysis  
Figure 4a-c shows the load-settlement behaviour of unreinforced and reinforced soft clay with columns made up of stone with diameters of 50, 75, and 100 mm and various methods of column reinforcement for VESCs and Figure 5a-c for HRSCs. OSCs, HRSCs, and VESCs were found to boost the soft soil's ultimate load-carrying capability. The ultimate carrying capacities of the three types of columns (OSCs, HRSCs, and VESCs) were raised by raising the a\({}_{\rm s}\) from 6.25 to 25%. Furthermore, their ultimate capacity rose when stone columns were strengthened by the geosynthetic material for vertical encasement or horizontal reinforcement. The vertical encasement for VESCs was studied for various lengths of reinforcement, i.e., L\({}_{\rm r}\) = L, L\({}_{\rm r}\) = 0.75 L, L\({}_{\rm r}\) = 0.5 L and L\({}_{\rm r}\) = 0.25 L. VESC data for two different geotextile materials with varying strengths are also displayed. Vertical encasement of the stone columns increases capacity and rigidity. Also, it was evident that the full length of the encasement, i.e., L\({}_{\rm r}\) = L, provides a higher capacity and stiffness than the other three L\({}_{\rm r}\) used. Furthermore, compared to OSCs, the ultimate carrying capacity of VESCs improves as the ultimate tensile strength of the encasement material increases. Prior researchers noticed a similar tendency (2013, 2013, 2013, 2014).  
Figure 4: Sketch represents the various failure modes for 100 mm diameter column (**a**) OSC (**b**) VESC (**c**) HRSC.  
Figure 5: _Cont._  
In comparison to OSC, certain tests were conducted to see how effective horizontal reinforcement is at enhancing the ultimate carrying capacity of HRSC. The outcome of experiments on HRSC with diameters of 50, 75, and 100 mm are shown in Figure 5a-c. The HRSCs were used at equal intervals throughout the length of the column (4 strips @ 0.1 m spacing), also at half of the column from the column head (2 strips @ 0.1 m spacing till 0.5 L from column head) and the bottom half of the column (2 strips @ 0.1 m spacing starting from the centre of the column along its length till the end). Because the horizontal reinforcement sheets constrain the materials of columns between the horizontal reinforcement layers and offer extra radial confinement due to the shear stresses generated between reinforcing sheets and granular materials of stone, the ultimate bearing capacity of columns improves as indicated. Also, it was found that providing HRSC throughout the column length was more effective [21] than the other two methods. However, the HRSC at the top half was more effective than it was when providing it at the bottom half as the initial load can be controlled effectively. As a result, the lateral bulging was reduced at an early stage. Furthermore, when the ultimate tensile strength of the horizontal reinforcement sheets is raised, the ultimate carrying capacity of HRSCs improves compared to OSCs.  
Figure 5: Load-Settlement variation of VESC for various L\({}_{\tau}\) on single stone column with diameters (**a**) 50 mm, (**b**) 75 mm, and (**c**) 100 mm.
## 1 Introduction  
Metal laser additive manufacturing (AM) is an important technology in the field of three-dimensional (3D) rapid prototyping [1]. It can directly convert a computer-generated 3D model into a physical model and has the advantages of high degrees of freedom [2] and high density [3]. At present, there are many mature metal laser additive manufacturing technologies such as selective laser melting (SLM) [4], selective laser sintering (SLS) [5], and direct energy disposition (DED) [6]. These technologies are based on continuous laser melting and point scanning mode, where the scanning point size is in the order of microns. However, when building a large sample, the processing speed is lower than that of traditional manufacturing technologies.  
The manufacturing accuracy of metal powder laser AM is in the range of approximately 80-250 \(\upmu\)m, which is lower than that of stereolithography [7, 8, 6]. Therefore, improving the printing accuracy is a crucial research area. It is well known that the thermal diffusion length of a laser is an important factor affecting the forming accuracy. The affected areas are typically composed of a fusion and a heat-affected zone [9, 10]. The time scale of the continuous laser is large, hence, it has a higher temperature and longer diffusion length compared to the pulsed laser, resulting in a larger heat-affected zone [11], which has a strong effect on the resolution. Therefore, a technology of surface exposure (SE) additive manufacturing of metal powder with short pulses is expected to improve the construction rate and resolution. [PERSON] reported a new AM method based on diodes and layer-by-layer printing by coupling the light valve with a diode and pulse light [12].  
Previously, our research group had developed a light-emitting addressable liquid crystal spatial light modulator with a high damage threshold, which has been used in several laser systems to realize beam pre-shielding [13, 14], homogenization, and other functions [13]. Recently, the damage threshold of this device can reach 521 mJ/cm\({}^{2}\). Based on this, we designed a 3D additive manufacturing scheme for SE technology and verified the feasibility of the scheme using tin material and a pulse light source with a pulse full width at half maximum of 8 ns. The resolution of the formed metal was also analyzed.
## 3 Data Collection and Its Characteristics
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Elements** & \(d\) **(mm)** & \(P\) **(m/min)** & \(W_{tn}\) **(\%)** & \(S\) **(\%)** \\ \hline Min. & 200.0 & 0.1600 & 0.29 & 15.20 \\
1 st Qua. & 200.0 & 0.2100 & 7.84 & 24.70 \\ Median & 230.0 & 0.2500 & 11.38 & 27.60 \\ Mean & 227.3 & 0.2558 & 11.57 & 27.58 \\
3 rd Qua. & 250.0 & 0.2900 & 15.39 & 30.10 \\ Max. & 250.0 & 0.4100 & 28.12 & 39.20 \\ \hline
**Elements** & \(\rho\) **(gm/cm\({}^{3}\))** & \(\sigma_{c}\) **(MPa)** & **R (m)** & **PM\({}_{10}\) (gm/s)** \\ \hline Min. & 1.220 & 13.00 & 16.00 & 0.148 \\
1 st Qua. & 1.230 & 15.00 & 20.00 & 0.329 \\ Median & 1.240 & 16.00 & 22.00 & 0.474 \\ Mean & 1.243 & 15.98 & 21.63 & 0.496 \\
3 rd Qua. & 1.260 & 17.00 & 23.00 & 0.646 \\ Max. & 1.270 & 19.00 & 27.00 & 1.306 \\ \hline \hline \end{tabular} Note: drilling diameter/diameter of borehole (\(d\)), penetration rate of the drill (\(P\)), moisture content (\(W_{tn}\)), silt content (\(S\)), density of rock mass (\(\rho\)), compressive strength (\(\sigma_{c}\)), rebound hardness number (\(R\)), particulate matter 10 micrometers (PM\({}_{10}\)).  
\end{table} 
Table 2: Characteristics of the data used. In Figure 5, the spread of the points denotes the relationship between the variables. Each sub-figure means the relationship between two variables. Note that the matrix is symmetrical. The correlated attributes showed good structure in their relationship, such as \(\rho\), \(W_{tn}\), and \(P\). They are not linear, but good predictable curved relationships. Also, Figure 5 shows that the correlation of most of the input variables was not high. It is a right candidate for AI techniques in finding out the association between the input and output variables. Figure 4: Data collection at the Coc Sau open-pit coal mine for this study. Figure 5: Relationship between input and output variables used in this study.
## 3 Results and Discussion
### Air Quality Index Prediction of Beijing  
In this experiment, we took the AQI of Beijing as the regression target. The data from the first four years of the dataset were used to train the model, and the data from the last year were used for the model testing.  
For the SVR-based model training, radial basis function (RBF) was chosen as the kernel function. The kernel parameter gamma (\(\gamma\)) and the penalty parameter (\(C\)) were selected by a grid search method. The experimental results showed that the best combination for the SVR model was \(C=10\) and \(\gamma=0.1\). Table 1 summarizes the statistical parameters of the SVR regression model for AQI prediction. The statistical parameters for the testing set were as follows: \(r=0.9887\), \(R^{2}=0.9776\), and RMSE = \(7.666\). These results demonstrate that the SVR-based method was a good substitute for analyzing the regression on the AQI.  
In this experiment, for the RF-based model, 100 regression trees were used to build the regression model; \(N_{F}\) was defined using the empirical formula (\(N_{F}=\sqrt{M}\)) mentioned earlier. The statistical parameters (\(r=0.9823\), \(R^{2}=0.9633\), and RMSE = \(9.602\)) of the RFR-based model are also shown in Table 1.  
As shown in Figure 4, the AQI of the testing samples was estimated using SVR and RFR, with the \(x\)-axis denoting the sequence number of the testing samples and the \(y\)-axis (target value) representing the AQI of Beijing. Subplots 4a and 4b, the line chart of the actual values, and the values predicted using different regression models, all reflect the regression of the AQI very well. In particular, the SVR-based model shows better performance.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Result of AQI Prediction**} & \multicolumn{3}{c}{**Result of NO\({}_{\mathbf{X}}\) Prediction**} \\ \cline{2-7}  & \(r\) & \(R^{2}\) & **RMSE** & \(r\) & \(R^{2}\) & **RMSE** \\ \hline SVR & 0.9887 & 0.9766 & 7.666 & 0.8923 & 0.7960 & 94.4918 \\ RFR & 0.9823 & 0.9633 & 9.602 & 0.9180 & 0.8401 & 83.6716 \\ \hline \end{tabular}
\end{table}
Table 1: The statistical parameters of the experiments for the Air Quality Index (AQI) and total nitrogen oxide (NO\(\chi\)) prediction.  
Figure 3: The matrix of correlation coefficients for air pollution indicators of (**a**) Beijing and (**b**) an Italy city.
# Tumor Volume Distributions Based on Weibull Distributions of Maximum Tumor Diameters
###### Abstract  
(1) Background: The distribution of tumor volumes is important for various aspects of cancer research. Unfortunately, tumor volume is rarely documented in tumor registries; usually only maximum tumor diameter is. This paper presents a method to derive tumor volume distributions from tumor diameter distributions. (2) Methods: The hypothesis is made that tumor maximum diameters \(d\) are Weibull distributed, and tumor volume is proportional to \(d^{k}\), where \(k\) is a parameter from the Weibull distribution of \(d\). The assumption is tested by using a test dataset of 176 segmented tumor volumes and comparing the \(k\) obtained by fitting the Weibull distribution of \(d\) and from a direct fit of the volumes. Finally, tumor volume distributions are calculated from the maximum diameters of the SEER database for breast, NSCLC and liver. (3) Results: For the test dataset, the \(k\) values obtained from the two separate methods were found to be \(k\) = 2.14 \(\pm\) 0.36 (from Weibull distribution of \(d\)) and 2.21 \(\pm\) 0.25 (from tumor volume). The tumor diameter data from the SEER database were fitted to a Weibull distribution, and the resulting parameters were used to calculate the corresponding exponential tumor volume distributions with an average volume obtained from the diameter fit. (4) Conclusions: The agreement of the fitted \(k\) using independent data supports the presented methodology to obtain tumor volume distributions. The method can be used to obtain tumor volume distributions when only maximum tumor diameters are available.  
tumor volume; tumor diameter; distribution of tumor volumes; tumor size Article
## 6 Performance Evaluation
### Evaluation for the Analytical Model
#### 6.2.3 One-Hop Transmission Distance for the Intra-Lane Scenario  
With the identical set of parameters, Figure 7 depicts the one-hop transmission distance's PMF versus different vehicular traffic density. Similar to the results in Figure 6, we can see that the analysis results are consistent with the simulation results. The PMF values show an upward tendency with  
Figure 6: The PMF of one-hop backoff timer delay for the intra-lane scenario with different vehicular traffic density. (**a**) \(\rho_{1}=10\) vehs/km; (**b**) \(\rho_{1}=50\) vehs/km; (**c**) \(\rho_{1}=100\) vehs/km.  
the increase of the one-hop distance, which is motivated by the fact that a longer relaying distance is preferred for the intra-lane scenario. Again, with the rise of vehicular traffic density and corresponding more neighboring vehicles, the larger one-hop transmission distance can be obtained.
## 4 Autonomous Shovel Technology
### Rock Pile Identification and Modeling Technology
#### 4.1.1 Image Sensor-Based Rock Pile Identification  
The image sensor is the core component in cameras. It converts an optical image on the photosensitive surface to electrical signals through photoelectric cells and obtains information consistent with human perception under good illumination conditions [37]. According to the number of cameras, the vehicle camera system can be divided into monocular camera, binocular camera, depth cameras (RGBD) and panoramic camera [38]. The utilization of monocular camera faces contradictions in ranging and distance. The wider the perspective view of the camera, the shorter the length of the accurate distance that can be detected and the narrower the angle of view, the longer the distance detected. As a result, when it is used on the scraper to perceive the ore pile, it can only obtain its information from a limited certain angle, which makes it difficult to construct the 3D model for the ore pile. The binocular camera system can cover different ranges of scenes through different cameras [39] and obtain comprehensive information about the ore pile. However, the process for comprehensively extracting information of the ore pile from multiple anglesand multiple pictures is always extremely complicated to calculate, with high system requirements, which makes the 3D modeling construction quite difficult and slow.  
The University of Southern Queensland produced an LHD model with a ratio of 1:5 to the physical machine. It is constituted with a CCD camera, a PC with image acquisition hardware and structured lighting form a vision system together and was successfully applied on 3D model establishment for rock piles [40]. The Western Mining Resource Center in the Colorado School of Mines in the United States conducted a project about LHD automation research. They collected mine production images with digital cameras, then filtered and interpolated the images and established a 3D curved surface model finally. This is helpful for the enhancement of the LHD's autonomous controlling and running capability as feedback. According to the experimental results, 3D models could be built even in a dark environment, but failed to be updated in real-time. An example to illustrate the image-sensor-based rock pile identification and 3D modeling process is shown in Figure 4. It proved that even for images obtained from pretty dark sites (Figure 4a), the 3D model was established successfully (Figure 4b) [41].  
The advantage of a 3D ore pile model based on an image sensor is that, after obtaining the information of the ore pile through the image sensor as images, it can quickly build a 3D model through an image processing algorithm and update the model in real time. However, when the light is insufficient or the camera is blocked by dust, the image obtained would be not clear enough, possibly resulting in failure of the 3D mine pile model establishment through a single view [5].
## 2 Simulation Setup
### Computational Domain and Boundary Conditions  
For an airfoil, a sufficiently large C-shaped computational domain can effectively mitigate the adverse impact of boundary conditions on the flow [43]. Therefore, the computational domain was established as depicted in Figure 4, with the airfoil positioned at the center of the domain. The shortest distance from the airfoil trailing edge to each boundary of the computational domain was 15 times the chord length, fully satisfying the computational requirements [44]. The size of the fixed wing MAV is relatively small,and we consider that its airfoil has little variation in spanwise direction. Therefore, a two-dimensional grid was used, and the calculation was also two-dimensional.  
In Figure 4, the inlet velocity was set at 15 m/s, the outlet pressure was maintained at 0 Pa (gauge pressure), and the airfoil surfaces were treated as no-slip wall boundaries. Structured quadrilateral grids were generated using the commercial software ICEM 18.2, with a first-layer grid height of 0.001 mm (the \(y^{+}\) value less than 1), aiming to fulfill the computational requirements of the k-omega SST model while accurately capturing geometric variations in the normal direction of grooves. Additionally, uniform grid refinement was implemented in the streamwise direction of the grooves to ensure the accuracy of the flow simulation near the grooves. Ultimately, the mesh achieved a minimum angle of 43.3 degrees and a Jacobian Ratio minimum value of 0.876, indicating that the mesh exhibits favorable orthogonality and possesses overall high quality. This meticulously crafted mesh was employed for pressure-based implicit computations using the RANS method within the commercial software Fluent 18.2. Due to the selected cruising state for the calculation conditions and stable flow, we adopted a steady state calculation.
### Research Approach  
In this paper, we introduce an inter-floor noise type/position classification algorithm based on supervised learning. This method classifies a given inter-floor noise into a type and position. An intuitive explanation of the method is as follows: (1) inter-floor noises are generated and recorded with a single microphone, (2) the dataset is labeled and converted to time-frequency (TF) patches, (3) a convolutional neural network (CNN)-based model is trained on the TF patches, and (4) a new inter-floor noise is classified into a noise type/position with the trained model.  
One unique feature of the method is that inter-floor noise measured with a single microphone is used to determine the position of the noise source rather than using signals over accelerometers or geophones with multiple channels. Instead of estimating the TDOA between multiple accelerometers or geophones, a signal over a single microphone with a sufficient duration is converted to a TF-patch and used as an input. The TF-patch might encode the dispersive nature of the plate wave, e.g., a change in propagation velocity or other unidentified features. At this point, the method can be considered to learn the response of a building observed with a single sensor for a given excitation [8].  
Because this approach determines the type/position of a given inter-floor noise using a signal over a single microphone via supervised learning, there are some inherent limitations. Although sub-meter localization accuracy shown in the literature might not be necessary for inter-floor noise type/position classification, using a single microphone might provide an estimated position of a noise source with lower accuracy than using multiple densely installed sensors. The machine learning approach also requires collecting data from every building structure.  
Despite these limitations, our method has some clear advantages. (1) A microphone embedded in a smartphone can be used for classifications. If the method is included in a mobile application, many people can use it to identify noises. (2) In the literature [8; 9; 10], only footstep-induced vibration is considered herein, whereas this method can be used to identify the type of noise, as shown in Sections 3.4 and 4.1. (3) Management and implementation of a single sensor is relatively easier than using multiple sensors.
## 2 Material and Method
### HPLC of the Ethyl Acetate Fraction of C. bruguieriana and Chromatographic Conditions  
The biomarkers, luteolin 7-O-glucoside, chlorogenic acid, kaempfer and isorham-netin were purchased from Phytolab (Vestenbergsgreuth, Germany). The analysis was performed by HPLC with UV detection (Alliance 2695 Separations Module, Waters Instruments, Inc., Milford, MA, USA) using a reverse-phase C18 column (Pinnacle C18 column, 250 \(\times\) 4.6 mm, 5 \(\upmu\)m, Shimadzu, Kyoto, Japan). The mobile phase was composed of different proportions of (A) 0.5% acetic acid in ultra-pure water (acidified water), and (B) acetonitrile and methanol (60:40, \(v/v\)) using a flow rate of 1 mL/min. The optimized gradient program was as follows: 0-5 min (0-55% B), 5-10 min (55-65% B), 10-20 min (65-90% B), 20-30 min (90-100% B), and 30-35 min (0% B). Samples were injected into the system at 20 \(\upmu\)L. The detection was performed at a single wavelength (280 nm) and processed using EMPOWER software, version 2.